LLMs for Access to justice Data
Full Index of Sources
This section displays the complete list of sources from Google Scholar, HeinOnline, IEEE Xplore, Scopus, and Web of Science.
Google Scholar
| title | result_id | link | num_citations | pdf_link |
|---|---|---|---|---|
| How generative AI can help address the access to justice gap through the courts | lNX-6qdZr2wJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4683309 | 11.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4683309 |
| Generative AI and legal aid: Results from a field study and 100 use cases to bridge the access to justice gap | P_QMzDF2YcAJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/lla57§ion=28 | 12.0 | https://digitalcommons.lmu.edu/cgi/viewcontent.cgi?article=3210&context=llr |
| AI and Tools for Expanding Access to Justice | k1-G1sD5mA0J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4876633 | 1.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4876633 |
| Using artificial intelligence to increase access to justice | 309XxeqZV9EJ | https://papyrus.bib.umontreal.ca/xmlui/handle/1866/32168 | 3.0 | https://dam-oclc.bac-lac.gc.ca/download?is_thesis=1&oclc_number=1422529643&id=1c0f22cc-24de-4db6-b615-629e921ca1d9&fileName=Westermann_Hannes_2023_these.pdf |
| Legal technology in the service of access to justice | biypHANDqYwJ | https://akjournals.com/view/journals/2052/64/3/article-p323.xml | NaN | NaN |
| AI and access to justice: How AI legal advisors can reduce economic and shame-based barriers to justice | ck8Ac0neujYJ | https://www.tatup.de/index.php/tatup/article/view/7098 | NaN | https://www.tatup.de/index.php/tatup/article/download/7098/11908 |
| Generative AI and access to justice in Canada: The case of Self-Represented Litigants | l4r5s2gwfukJ | https://www.erudit.org/en/journals/wyaj/2024-v40-wyaj09654/1115371ar/abstract/ | NaN | https://www.erudit.org/en/journals/wyaj/2024-v40-wyaj09654/1115371ar.pdf |
| Experimenting with Legal AI Solutions: The Case of Question-Answering for Access to Justice | vbPLjHykthcJ | https://arxiv.org/abs/2409.07713 | 2.0 | https://arxiv.org/pdf/2409.07713? |
| Digital transformation of legal services and access to justice: Challenges and possibilities | feE2u5tQrLYJ | https://sciendo.com/pdf/10.2478/bjlp-2022-0007 | 11.0 | https://sciendo.com/pdf/10.2478/bjlp-2022-0007 |
| ChatGPT, I Have a Legal Question? The Impact of Gen AI Tools on Law Clinics and Access to Justice | uTqP15w03YEJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/clled31§ion=8 | 1.0 | https://www.northumbriajournals.co.uk/index.php/ijcle/article/download/1401/1789 |
| REVOLUTIONIZING ACCESS TO JUSTICE: THE ROLE OF AI-POWERED CHATBOTS AND RETRIEVALAUGMENTED GENERATION IN LEGAL SELF-HELP. | _XbF4r9GcY0J | https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=02730995&asa=N&AN=179153092&h=Bcw%2FUbUn3zYxcSKX2IDDAft0bkHVrvy2Sgs3G1J6LCvZECqVshvJ3%2FfCsz%2FeN4eTq5Pzbt9Mnatq0lF24diLKQ%3D%3D&crl=c | 1.0 | NaN |
| Large language models and community legal centres: Could chatbots help reduce Australia's justice gap? | GKWekWgMka0J | https://journals.sagepub.com/doi/abs/10.1177/1037969X241269079 | NaN | https://journals.sagepub.com/doi/pdf/10.1177/1037969X241269079 |
| Analyzing Images of Legal Documents: Toward Multi-Modal LLMs for Access to Justice | gHXfe3cys0IJ | https://arxiv.org/abs/2412.15260 | NaN | https://arxiv.org/pdf/2412.15260 |
| Large language models and their possible uses in law | Y167Kf-vw-gJ | https://akjournals.com/view/journals/2052/64/3/article-p435.xml | 13.0 | https://akjournals.com/downloadpdf/view/journals/2052/64/3/article-p435.pdf |
| AI, UPL, & A2J—Generative AI's Disruptions in the Delivery of Legal Services to Low-Income Individuals | UsGFKAW4Sj4J | https://journals.library.wustl.edu/lawpolicy/article/id/9025/ | 1.0 | https://journals.library.wustl.edu/lawpolicy/article/id/9025/download/pdf/ |
| Large Language Models (LLMs) for Legal Advice: A Scoping Review | ZRf7TqNsvaYJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4976189 | 1.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4976189 |
| Regulation of the Use of Generative Artificial Intelligence Tools in the Delivery of Legal Services: Verification and Accountability | L8CImat85ScJ | https://journals.library.wustl.edu/lawpolicy/article/id/9026/ | 1.0 | https://journals.library.wustl.edu/lawpolicy/article/id/9026/download/pdf/ |
| Lawyer GPT: A legal large language model with enhanced domain knowledge and reasoning capabilities | DLOuyvylDt8J | https://dl.acm.org/doi/abs/10.1145/3689299.3689319 | 7.0 | NaN |
| Access to Justice: The Role of Legal Aid in Society | rzMD0sznpJsJ | http://193.36.85.187:8089/index.php/isslp/article/view/12 | 2.0 | http://193.36.85.187:8089/index.php/isslp/article/download/12/12 |
| The Cost of Justice at the Dawn of AI | assyXFv39zkJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4543803 | 1.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4543803 |
| Legal Assistance Redefined: Transforming Legal Access with AI-Powered LegalLink | RRFM2ZJcLmcJ | https://ieeexplore.ieee.org/abstract/document/10779909/ | NaN | NaN |
| Access to Civil Justice in the Age of AI: Mindsets & Pathways to New Practices | xJJvD-ECVPIJ | https://digitalcommons.onu.edu/cgi/viewcontent.cgi?article=1370&context=onu_law_review | NaN | https://digitalcommons.onu.edu/cgi/viewcontent.cgi?article=1370&context=onu_law_review |
| The Multifaceted Impact of Generative AI on Lawyers and Legal Services | Bc4bl2iDNI4J | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/jchlet14§ion=18 | NaN | NaN |
| How to Harness AI for Justice: A Preliminary Agenda for Using Generative AI to Improve Access to Justice | EDvM8i040AgJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4887933 | NaN | NaN |
| Generative ai and finding the law | dPkZcjcHFQsJ | https://irlaw.umkc.edu/faculty_works/911/ | 5.0 | https://irlaw.umkc.edu/cgi/viewcontent.cgi?article=1902&context=faculty_works |
| “This Verdict was Created with the Help of Generative AI...?” On the Use of Large Language Models by Judges | YDJQ1oWzHHAJ | https://dl.acm.org/doi/abs/10.1145/3696319 | 1.0 | https://dl.acm.org/doi/pdf/10.1145/3696319 |
| Lawyers should not trust ai: A call for an open-source legal language model | 4IGMF7HagUQJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4587092 | 7.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4587092 |
| Legal AI: Enhancing Justice through Technology, Practical Considerations | GV6mowVAwRsJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4976734 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4976734 |
| ChatGPT, Esq.: Recasting Unauthorized Practice of Law in the Era of Generative AI | kIhJxHbh8BgJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5152523 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5152523 |
| Chatgpt, esq.: Recasting unauthorized practice of law in the era of generative ai | meIFFFgdLAMJ | https://repository.law.miami.edu/fac_articles/1257/ | 17.0 | https://repository.law.miami.edu/cgi/viewcontent.cgi?article=2252&context=fac_articles |
| Access to Civil Justice in the Age of AI: Mindsets & Pathways to New Practices | EIdbh0dHc-wJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/onulr50§ion=30 | 1.0 | NaN |
| From text to structure: Using large language models to support the development of legal expert systems | BO49BB8AYbkJ | https://ebooks.iospress.nl/volumearticle/65584 | 18.0 | https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA230962 |
| AI-Powered Platforms for Access to Justice: The Case of Hear Me Out | qOSNB97orXcJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5213638 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5213638 |
| The implications of ChatGPT for legal services and society | 0b5FFMvGIoYJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/mttlr30§ion=6 | 68.0 | https://repository.law.umich.edu/cgi/viewcontent.cgi?article=1058&context=mtlr |
| How to Harness AI for Justice | R1ytEzxxpXkJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/judica108§ion=12 | 1.0 | NaN |
| Empowering Justice: Blockchain and Legal Chatbots as Catalysts for Access to Legal Aid | ieWkuwRsfCYJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/ijlet2024§ion=34 | NaN | https://www.ijlet.org/wp-content/uploads/2025/01/IJLET-4.4.4.pdf |
| Openjustice. ai: A global open-source legal language model | mIXnP9q0bRsJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4624814 | 2.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4624814 |
| Certifying Legal AI Assistants for Unrepresented Litigants: A Global Survey of Access to Civil Justice, Unauthorized Practice of Law, and AI | oiF84vWI26YJ | https://journals.library.columbia.edu/index.php/stlr/article/view/13336 | NaN | https://journals.library.columbia.edu/index.php/stlr/article/download/13336/6540 |
| Large language models as tax attorneys: a case study in legal capabilities emergence | fIfkQQ1X9twJ | https://royalsocietypublishing.org/doi/abs/10.1098/rsta.2023.0159 | 31.0 | https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2023.0159 |
| Exploring Possibilities of AI-Powered Legal Assistance in Bangladesh through Large Language Modeling | p4PiylqM104J | https://arxiv.org/abs/2410.17210 | NaN | https://arxiv.org/pdf/2410.17210? |
| Navigating the legal landscape: large language models and the hesitancy of legal professionals | ZkqSovoU9hcJ | https://www.tandfonline.com/doi/abs/10.1080/09695958.2024.2379794 | 2.0 | NaN |
| Intention and context elicitation with large language models in the legal aid intake process | _Q1r5ohGDz8J | https://arxiv.org/abs/2311.13281 | 4.0 | https://arxiv.org/pdf/2311.13281 |
| Interoperable Legal AI for Access to Justice | 3-4xM4xi2w4J | https://www.yalelawjournal.org/pdf/SimshawYLJForumEssay_omw1vdsn.pdf | NaN | https://www.yalelawjournal.org/pdf/SimshawYLJForumEssay_omw1vdsn.pdf |
| LLMs & Legal Aid: Understanding Legal Needs Exhibited Through User Queries | z68SfimV9U0J | https://arxiv.org/abs/2501.01711 | 1.0 | https://arxiv.org/pdf/2501.01711 |
| Equitable Access to Justice: Logical LLMs Show Promise | HjuAmkWUb1QJ | https://arxiv.org/abs/2410.09904 | 1.0 | https://arxiv.org/pdf/2410.09904 |
| The Impact of Artificial Intelligence on Access to Justice: Predictive Analytics and the Legal Services Market | 5optiAllNawJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4930144 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4930144 |
| Better call gpt, comparing large language models against lawyers | Y4rTcW-hKRcJ | https://arxiv.org/abs/2401.16212 | 33.0 | https://arxiv.org/pdf/2401.16212 |
| Who Wants a Robo-Lawyer Now?: On AI Chatbots in China's Public Legal Services Sector | cpMmdLuTaPkJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/yjolt26§ion=11 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4895906 |
| Enhancing legal assistance with AI: a comprehensive approach to intent classification and domain specific model tuning | u6dT2sjC_MwJ | https://link.springer.com/article/10.1007/s10506-025-09441-1 | NaN | NaN |
| Large language models in cryptocurrency securities cases: can a GPT model meaningfully assist lawyers? | 18FrJ_f-ToAJ | https://link.springer.com/article/10.1007/s10506-024-09399-6 | 19.0 | https://link.springer.com/content/pdf/10.1007/s10506-024-09399-6.pdf |
| Getting in the Door: Streamlining Intake in Civil Legal Services with Large Language Models | mDOOmREBPQoJ | https://arxiv.org/abs/2410.03762 | NaN | https://arxiv.org/pdf/2410.03762 |
| TRANSFORMING LEGAL PRACTICE: THE RISE OF AI FOR EFFICIENCY AND ACCESS TO JUSTICE | nexxQ6MBzmYJ | https://ijlr.iledu.in/wp-content/uploads/2024/10/V4I387.pdf | NaN | https://ijlr.iledu.in/wp-content/uploads/2024/10/V4I387.pdf |
| Regenerating Justice: ChatGPT and the Legal Minefield of Generative AI | BuN0HcT9T0sJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4976738 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4976738 |
| Revolutionizing Justice: Unleashing the Power of Artificial Intelligence | DOSrEgjcnAoJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/comlrtj26§ion=15 | 20.0 | https://scholar.smu.edu/cgi/viewcontent.cgi?article=1358&context=scitech |
| Critical appraisal of large language models in judicial decision-making | vN8QaaJG6oMJ | https://www.elgaronline.com/edcollchap/book/9781803922171/book-part-9781803922171-33.xml | 7.0 | NaN |
| Generative AI systems in legal practice offering quality legal services while upholding legal ethics | av1Arye_3y4J | https://www.cambridge.org/core/journals/international-journal-of-law-in-context/article/generative-ai-systems-in-legal-practice-offering-quality-legal-services-while-upholding-legal-ethics/34011A84AA58A2BAB556A406A4653A8D | NaN | https://www.cambridge.org/core/services/aop-cambridge-core/content/view/34011A84AA58A2BAB556A406A4653A8D/S1744552325000047a.pdf/generative_ai_systems_in_legal_practice_offering_quality_legal_services_while_upholding_legal_ethics.pdf |
| Bettercall: AI based legal assistant | q-vQZAopKDMJ | https://ieeexplore.ieee.org/abstract/document/10660894/ | 1.0 | NaN |
| Good AI Legal Help, Bad AI Legal Help: Establishing quality standards for responses to people's legal problem stories | -1ZohptBDsIJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4696936 | 2.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4696936 |
| Chatlaw: A multi-agent collaborative legal assistant with knowledge graph enhanced mixture-of-experts large language model | pf5cO5kK6_QJ | https://arxiv.org/abs/2306.16092 | 38.0 | https://arxiv.org/pdf/2306.16092 |
| Are Robot Lawyers the Future of Increasing Access to Justice? | EA5UKSipTqkJ | https://ueaeprints.uea.ac.uk/id/document/180999#page=70 | NaN | https://ueaeprints.uea.ac.uk/id/document/180999#page=70 |
| Why lawyers must responsibly embrace generative AI | atliUSlYSC0J | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/berkbusj21§ion=14 | 5.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4477704 |
| Generative AI for the legal profession: Facing the implications of the use of ChatGPT through an intradisciplinary approach | UEryaxmiBzIJ | https://orbilu.uni.lu/bitstream/10993/58939/1/Generative%20AI%20for%20the%20Legal%20Profession%3A%20Facing%20the%20Implications%20of%20the%20Use%20of%20ChatGPT%20through%20an%20Intradisciplinary%20Approach.pdf | 3.0 | https://orbilu.uni.lu/bitstream/10993/58939/1/Generative%20AI%20for%20the%20Legal%20Profession%3A%20Facing%20the%20Implications%20of%20the%20Use%20of%20ChatGPT%20through%20an%20Intradisciplinary%20Approach.pdf |
| Access to AI justice: Avoiding an inequitable two-tiered system of legal services | jUunUV6T7O0J | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/yjolt24§ion=5 | 48.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4090984 |
| Expert Q&A on ChatGPT, Generative Al, and LLMs for Litigators | z1izrqQPYVYJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/tortso25§ion=28 | NaN | NaN |
| Ai-powered lawyering: Ai reasoning models, retrieval augmented generation, and the future of legal practice | -miNsCrBVBwJ | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5162111 | 4.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5162111 |
| Bridging the Gap: Mapping Layperson Narratives to Legal Issues with Language Models. | Ey5B4UxN4Q8J | https://ceur-ws.org/Vol-3441/paper5.pdf?trk=public_post_comment-text | 17.0 | https://ceur-ws.org/Vol-3441/paper5.pdf?trk=public_post_comment-text |
| The Future of Advocacy | HkNIuWlMdoIJ | https://www.baronedefensefirm.com/blog/wp-content/uploads/2024/12/Champion-Future-of-Advocacy-AI-Barone-August-2024-p16-18.pdf | NaN | https://www.baronedefensefirm.com/blog/wp-content/uploads/2024/12/Champion-Future-of-Advocacy-AI-Barone-August-2024-p16-18.pdf |
| Interactive Legal Assistance System using Large Language Models | QrHo-tD2180J | https://ieeexplore.ieee.org/abstract/document/10714868/ | NaN | NaN |
| Chatgpt as an artificial lawyer? | Mc-1PNuCNIsJ | https://ceur-ws.org/Vol-3435/short2.pdf?utm_source=sasktoday.ca&utm_campaign=sasktoday.ca%3A%20outbound&utm_medium=referral | 52.0 | https://ceur-ws.org/Vol-3435/short2.pdf?utm_source=sasktoday.ca&utm_campaign=sasktoday.ca%3A%20outbound&utm_medium=referral |
| Legal Validity with Artificial Intelligence Technology on Gpt Chat as Legal Aid | l1iBcAN9nccJ | https://dinastires.org/JLPH/article/view/891 | NaN | https://dinastires.org/JLPH/article/download/891/773 |
| Large Language Scholarship: Generative AI in the Legal Academy | AE9Y5NtXFKEJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5200768 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5200768 |
| Towards human-centred standards for legal help AI | YimleaMoY5QJ | https://royalsocietypublishing.org/doi/abs/10.1098/rsta.2023.0157 | 5.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4582745 |
| Using generative ai to identify arguments in judges' reasons: Accuracy and benefits for students | SPnBIdGJ9doJ | https://search.informit.org/doi/abs/10.3316/informit.T2025011900000101519001919 | 3.0 | https://search.informit.org/doi/pdf/10.3316/informit.T2025011900000101519001919 |
| Chat Kanoon: A Novel Approach to Legal Assistance in India | XurNiV9wTRQJ | https://pdfs.semanticscholar.org/a0b2/ef25cb7adcc998381d4fbe650ac1b884b134.pdf | NaN | https://pdfs.semanticscholar.org/a0b2/ef25cb7adcc998381d4fbe650ac1b884b134.pdf |
| Generative AI and the Rule of Law. | vFkrzAPX5eMJ | https://optimai.eu/wp-content/uploads/2024/01/LPaper_03.pdf | 4.0 | https://optimai.eu/wp-content/uploads/2024/01/LPaper_03.pdf |
| Generative AI as tax attorneys: exploring legal understanding through experiments | 6kXvecJ69wAJ | https://www.um.edu.mt/library/oar/handle/123456789/131176 | NaN | https://www.um.edu.mt/library/oar/bitstream/123456789/131176/1/ERSJ27%28B%29A72.pdf |
| Sentimental Analysis of Legal Aid Services: A Machine Learning Approach | h_pgclBU5VYJ | http://bright-journal.org/Journal/index.php/JADS/article/view/521 | NaN | https://bright-journal.org/Journal/index.php/JADS/article/download/521/362 |
| The role of a future lawyer in an Artificial intelligence environment | JSCdBCsltFMJ | https://jst.bdu.edu.vn/index.php/jst/article/download/175/134 | NaN | https://jst.bdu.edu.vn/index.php/jst/article/download/175/134 |
| Generative Artificial Intelligence and revolution of market for legal services | SafrZAuaSrMJ | https://laweconcenter.org/wp-content/uploads/2025/01/GREDEG-WP-2025-01.pdf | NaN | https://laweconcenter.org/wp-content/uploads/2025/01/GREDEG-WP-2025-01.pdf |
| Judge AI: Assessing Large Language Models in Judicial Decision-Making | qsyjdYBfGrUJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5098708 | 1.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5098708 |
| LawPal: A Retrieval Augmented Generation Based System for Enhanced Legal Accessibility in India | Pnl5_5sEE-gJ | https://arxiv.org/abs/2502.16573 | NaN | https://arxiv.org/pdf/2502.16573? |
| Generative Artificial Intelligence in Legal Drafting | V49w84gGLn4J | https://ieeexplore.ieee.org/abstract/document/10717541/ | NaN | NaN |
| Large language models as tax attorneys: A case study in legal capabilities emergence | 62PlXWw-qiYJ | https://arxiv.org/abs/2306.07075 | 35.0 | https://arxiv.org/pdf/2306.07075 |
| Artificial intelligence at the bench: Legal and ethical challenges of informing—or misinforming—judicial decision-making through generative AI | Z5qcyozSxVQJ | https://www.cambridge.org/core/journals/data-and-policy/article/artificial-intelligence-at-the-bench-legal-and-ethical-challenges-of-informingor-misinformingjudicial-decisionmaking-through-generative-ai/D1989AC5C81FB67A5FABB552D3831E46 | 3.0 | https://www.cambridge.org/core/services/aop-cambridge-core/content/view/D1989AC5C81FB67A5FABB552D3831E46/S2632324924000531a.pdf/artificial_intelligence_at_the_bench_legal_and_ethical_challenges_of_informingor_misinformingjudicial_decisionmaking_through_generative_ai.pdf |
| LegalGuardian: A Privacy-Preserving Framework for Secure Integration of Large Language Models in Legal Practice | jhu4mHJ3DpUJ | https://arxiv.org/abs/2501.10915 | NaN | https://arxiv.org/pdf/2501.10915 |
| A brief report on lawgpt 1.0: A virtual legal assistant based on gpt-3 | Y81SvrL3LE0J | https://arxiv.org/abs/2302.05729 | 58.0 | https://arxiv.org/pdf/2302.05729 |
| Ai assistance in legal analysis: An empirical study | PiELflBCXh8J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4539836 | 83.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4539836 |
| Robots vs. Predators: Can Generative Artificial Intelligence Help to Address the Justice Gap in Consumer Debt Litigation? | kLpjOdGODhMJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/frdurb51§ion=49 | 3.0 | https://ir.lawnet.fordham.edu/cgi/viewcontent.cgi?article=2983&context=ulj |
| Caveat lector: Large language models in legal practice | DbfZd4p_mVUJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/rutgblaj19§ion=11 | 11.0 | https://arxiv.org/pdf/2403.09163 |
| Unlocking the Potential of Large Language Models in Legal Discourse: Challenges, Solutions, and Future Directions | udAy1Hl-hDgJ | https://ieeexplore.ieee.org/abstract/document/10756345/ | NaN | NaN |
| Large language models for automated q&a involving legal documents: a survey on algorithms, frameworks and applications | JkPuzLwZ4YYJ | https://www.emerald.com/insight/content/doi/10.1108/IJWIS-12-2023-0256 | 27.0 | NaN |
| Justice AI: Legal Case Retrieval Using Dense Passage Retrieval | BvKpZPqOdvsJ | https://ieeexplore.ieee.org/abstract/document/10930108/ | NaN | NaN |
| An Analysis on Integrating Advanced Conversational AI in Legal Summarization and Information Retrieval | j39EDnQ2Y-0J | https://ieeexplore.ieee.org/abstract/document/10675619/ | NaN | NaN |
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| Iraqi Legal GPT | U31BPYa4NiMJ | https://ieeexplore.ieee.org/abstract/document/10548909/ | 1.0 | NaN |
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| The GPTJudge: justice in a generative AI world | 1PICXeaunP8J | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/dltr23§ion=2 | 44.0 | https://scholarship.law.duke.edu/cgi/viewcontent.cgi?article=1378&context=dltr |
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| Large Language Model Agent as Insurance Law Assistant | -CZiMBSVZrgJ | https://aaltodoc.aalto.fi/items/4b5b6907-caa7-4a21-89fa-7222bdee695e | NaN | https://aaltodoc.aalto.fi/bitstreams/a44ccb07-e09a-491a-8452-7b75501db27e/download |
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| Internlm-law: An open source chinese legal large language model | HsqxFTOulbAJ | https://arxiv.org/abs/2406.14887 | 4.0 | https://arxiv.org/pdf/2406.14887 |
| Leveraging Large Language Models for Legal Document Understanding and Software System Analysis: Addressing Key Challenges | d1iBe9_FjXMJ | https://search.proquest.com/openview/008ba9ac0834da09ebe204040efc11c9/1?pq-origsite=gscholar&cbl=18750&diss=y | 1.0 | NaN |
| Design and Application of a Multi-task Legal Large Language Model | 72_cTI30KBIJ | https://ieeexplore.ieee.org/abstract/document/10911477/ | NaN | NaN |
| Interpretable long-form legal question answering with retrieval-augmented large language models | AiNuo2C-gn4J | https://ojs.aaai.org/index.php/AAAI/article/view/30232 | 77.0 | https://ojs.aaai.org/index.php/AAAI/article/view/30232/32192 |
| Expanding Access to Justice Through Regulatory Reform and Innovation: Arizona Lessons from the Past, Present and Future | 5TdSakvWXuwJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4963150 | 1.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4963150 |
| Using Large Language Models to Support Thematic Analysis in Empirical Legal Studies. | Os_WQAcXUMAJ | https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA230965 | 32.0 | https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA230965 |
| Toward National Regulation of Legal Technology: A Path Forward for Access to Justice | 3Kw3imwyDSMJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/flr92§ion=5 | 20.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4565341 |
| Towards robust legal reasoning: Harnessing logical llms in law | vKMGIQ_M6YUJ | https://arxiv.org/abs/2502.17638 | 1.0 | https://arxiv.org/pdf/2502.17638? |
| Answering legal questions from laymen in german civil law system | FLt-qPRZA6YJ | https://aclanthology.org/2024.eacl-long.122/ | 5.0 | https://aclanthology.org/2024.eacl-long.122.pdf |
| From gibberish to clarity: combining plain language and legal design for better communication and greater impact | CAGLPdPpwYMJ | https://researchportal.tuni.fi/files/135931407/From_gibberish_to_clarity_Haapio_Toivonen_Ketola.pdf | NaN | https://researchportal.tuni.fi/files/135931407/From_gibberish_to_clarity_Haapio_Toivonen_Ketola.pdf |
| IA Generativa e acesso à Justiça: sexta onda e os riscos dos LLMs no Judiciário: sixth wave and the risks of LLMs in the Judiciary | 6dCMgNYlD1wJ | https://revistajuridica.presidencia.gov.br/index.php/saj/article/view/3218 | NaN | https://revistajuridica.presidencia.gov.br/index.php/saj/article/download/3218/1517 |
| A short survey of viewing large language models in legal aspect | kN0VpM62IsIJ | https://arxiv.org/abs/2303.09136 | 84.0 | https://arxiv.org/pdf/2303.09136 |
| Generative vs Intent-based Chatbot for Judicial Advice | cieAxJTBHN8J | https://ieeexplore.ieee.org/abstract/document/10502550/ | 4.0 | NaN |
| Prompts for generative artificial intelligence in legal discourse | aRJ0E_41Vj4J | https://journals.rudn.ru/law/article/view/41937 | NaN | https://journals.rudn.ru/law/article/download/41937/24184 |
| Computational legal studies comes of age | 6aFMjU4sNkIJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4826705 | 4.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4826144 |
| Legal evalutions and challenges of large language models | oWv69BqZmVMJ | https://arxiv.org/abs/2411.10137 | 4.0 | https://arxiv.org/pdf/2411.10137? |
| Natural language processing in legal tech | wOJSYY9rFZIJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4027030 | 26.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4027030 |
| How ChatGPT and generative AI systems will revolutionize legal services and the legal profession | DkyUIApE_CAJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4366749 | 20.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4366749 |
| New Rules for a New Era: Regulating Artificial Intelligence in the Legal Field | QWltlnjUlekJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/caswestres15§ion=4 | 5.0 | https://scholarlycommons.law.case.edu/cgi/viewcontent.cgi?article=1154&context=jolti |
| Topic classification of case law using a large language model and a new taxonomy for UK law: AI insights into summary judgment | 2dTgL-HM2fkJ | https://link.springer.com/article/10.1007/s10506-025-09434-0 | NaN | https://link.springer.com/content/pdf/10.1007/s10506-025-09434-0.pdf |
| Leveraging large language models for learning complex legal concepts through storytelling | h4InHnlnqGoJ | https://arxiv.org/abs/2402.17019 | 13.0 | https://arxiv.org/pdf/2402.17019 |
| Generative AI, Cybersecurity And Cybercrime For Lawyers: Myths, Risks And Benefits | fyLCMIyr3Q4J | https://platt.law/Generative-AI-Cybersecurity-and-Cybercrime-for-Lawyers.pdf | 1.0 | https://platt.law/Generative-AI-Cybersecurity-and-Cybercrime-for-Lawyers.pdf |
| Generative AI's Role in Reducing Transaction Costs in Finnish Legal Markets-An Analysis of Litigation Process Participants | cKasUkcMinkJ | https://aaltodoc.aalto.fi/items/1397cf9d-4753-4261-8548-c6c8aadd5fc3 | NaN | https://aaltodoc.aalto.fi/bitstreams/80676499-f25c-460c-90be-1773c5e78fc6/download |
| Law, Technology and Humans | N700Fv8PtfMJ | https://www.austlii.edu.au/cgi-bin/viewdoc/au/journals/LawTechHum/2024/16.html | 3.0 | NaN |
| Aspects of artificial intelligence on e-justice and personal data limitations | zi720rtNCuEJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/jnlolletl26§ion=17 | 2.0 | NaN |
| Luck of the Draw III: Using AI to Examine Decision‐Making in Federal Court Stays of Removal | m0OdIkZgr7MJ | https://digitalcommons.osgoode.yorku.ca/all_papers/352/ | 2.0 | https://digitalcommons.osgoode.yorku.ca/cgi/viewcontent.cgi?article=1359&context=all_papers |
| AI at the Bench: Legal and Ethical Challenges of Informing-or Misinforming-Judicial Decision-Making Through Generative AI | A_hlzFppnd4J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4860853 | NaN | NaN |
| Explaining legal concepts with augmented large language models (gpt-4) | 5NF0TDdTxRgJ | https://arxiv.org/abs/2306.09525 | 57.0 | https://arxiv.org/pdf/2306.09525 |
| Professor GPT: Having a large language model write a commentary on freedom of assembly | pjmf6r2ahe8J | https://pure.mpg.de/pubman/faces/ViewItemOverviewPage.jsp?itemId=item_3617210 | 1.0 | https://pure.mpg.de/rest/items/item_3617210/component/file_3635105/content |
| Opening the Virtual Window: How on-Line Processes Could Increase Access to Justice in the Criminal Legal System | UmP53qugiQoJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/cardcore25§ion=12 | NaN | https://scholarship.law.tamu.edu/cgi/viewcontent.cgi?article=3073&context=facscholar |
| Efficiency, ethics, and algorithms: The implications of ai on the legal profession and the aba model rules | rM1eMjqvg9cJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4461276 | 1.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4461276 |
| Re-Regulating UPL in an Age of AI | bByRJ9_vmPAJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/gtltr8§ion=16 | 4.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4822750 |
| The future of court's procurators with the advent of artificial intelligence technologies | 7i_a2xpSHxAJ | https://opo.iisj.net/index.php/osls/article/view/1907 | NaN | https://opo.iisj.net/index.php/osls/article/download/1907/2367 |
| LEEC for Judicial Fairness: A Legal Element Extraction Dataset with Extensive Extra-Legal Labels | 231FJFX2Ms8J | https://www.ijcai.org/proceedings/2024/0833.pdf | 2.0 | https://www.ijcai.org/proceedings/2024/0833.pdf |
| The Escalation of ChatGPT: How ChatGPT Will Exert Influence on the Legal Profession? | _1CI5O7O0JcJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/juscrp3§ion=491 | 1.0 | NaN |
| Topic Modelling Case Law Using a Large Language Model and a New Taxonomy for UK Law: AI Insights into Summary Judgment | ib5lJKhwk9AJ | https://arxiv.org/abs/2405.12910 | 3.0 | https://arxiv.org/pdf/2405.12910 |
| NyayaAnumana & INLegalLlama: The Largest Indian Legal Judgment Prediction Dataset and Specialized Language Model for Enhanced Decision Analysis | DrMmT3gajroJ | https://arxiv.org/abs/2412.08385 | 1.0 | https://arxiv.org/pdf/2412.08385 |
| Integrating Generative AI into Legal Education: Form Casebooks to Code, Opportunities and Challenges | bEqaaktWOKwJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/lwtchmn6§ion=23 | 1.0 | https://lthj.qut.edu.au/article/download/3640/1542 |
| Decoding Legalese Without Borders: Multilingual Evaluation of Language Models on Long Legal Texts | PO2gt4t0fl4J | https://core.ac.uk/download/pdf/604519313.pdf | NaN | https://core.ac.uk/download/pdf/604519313.pdf |
| Generative AI, fake law and professional guidance | _UgzRabzPPEJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5005967 | 2.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5005967 |
| Llmediator: Gpt-4 assisted online dispute resolution | _3PICPHoZiIJ | https://arxiv.org/abs/2307.16732 | 25.0 | https://arxiv.org/pdf/2307.16732 |
| Generative artificial intelligence prompt-kit for enhanced legal learning and analysis/Assoc. Professor Dr Hartini Saripan... | 98-JoACmT0MJ | https://ir.uitm.edu.my/id/eprint/83402/ | NaN | https://ir.uitm.edu.my/id/eprint/83402/1/83402.pdf |
| Integrating Generative AI into Legal Education: From Casebooks to Code, Opportunities and Challenges | 7WtIL9mLIEQJ | https://lthj.qut.edu.au/article/download/3640/1542/14088 | 1.0 | https://lthj.qut.edu.au/article/download/3640/1542/14088 |
| Legal lens: Exploring NLP for Document Analysis in Law | Qm2Aqa2CyLEJ | https://ieeexplore.ieee.org/abstract/document/10837237/ | NaN | NaN |
| Let's Chat About ChatGPT: A Practical Guide to Risks in Attorney Use of Generative AI | cauuCB_XXSkJ | https://ideaexchange.uakron.edu/akronlawreview/vol57/iss3/1/ | NaN | https://ideaexchange.uakron.edu/cgi/viewcontent.cgi?article=2588&context=akronlawreview |
| Professionals beware: The opportunities and risks of generative ai in legal practice | fXjnV8ksgc0J | https://search.informit.org/doi/abs/10.3316/informit.T2024072700017391138235201 | NaN | https://opus.lib.uts.edu.au/bitstream/10453/176325/3/Professionals%20Beware%20The%20Opportunities%20and%20Risks%20of%20Generative%20AI%20in%20Legal%20Practice.pdf |
| Artificial Intelligence in Legal Practice: Opportunities, Challenges, and Future Directions | wwHH_NCTLVgJ | https://www.researchgate.net/profile/Abiola-Ajayi-12/publication/390238094_Artificial_Intelligence_in_Legal_Practice_Opportunities_Challenges_and_Future_Directions/links/67e57c7e920b736ca9b11c70/Artificial-Intelligence-in-Legal-Practice-Opportunities-Challenges-and-Future-Directions.pdf | NaN | https://www.researchgate.net/profile/Abiola-Ajayi-12/publication/390238094_Artificial_Intelligence_in_Legal_Practice_Opportunities_Challenges_and_Future_Directions/links/67e57c7e920b736ca9b11c70/Artificial-Intelligence-in-Legal-Practice-Opportunities-Challenges-and-Future-Directions.pdf |
| Artificial Intelligence as an Innovative Element of Support in Policing | O9Gtd71-g1MJ | https://www.sciencedirect.com/science/article/pii/S1877050924011177 | 3.0 | https://www.sciencedirect.com/science/article/pii/S1877050924011177/pdf?md5=c45bb207abda0e1dacc5246ce388fece&pid=1-s2.0-S1877050924011177-main.pdf |
| Generative AI–Uses and Abuses in Litigation | 8Yv6l4FgOwUJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5161575 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5161575 |
| AI Diversity and the Future of" Fair" Legal AI | HmTfEfhHkRMJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/gslr40§ion=43 | 1.0 | https://readingroom.law.gsu.edu/cgi/viewcontent.cgi?article=3273&context=gsulr |
| Integrating Generative AI into Legal Education: From Casebooks to Code, Opportunities and Challenges | ecxnpROAuQAJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5037609 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5037609 |
| Navigating the Challenges of Generative AI | bY8xyMfAAK0J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5004973 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5004973 |
| Generative Pre-Trained Transformers and the Department of Defense's Own Generative Artificial Intelligence Large Language Model | 1SMBKUKA2lUJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/armylaw2024§ion=16 | NaN | NaN |
| Attributed Question Answering for Preconditions in the Dutch Law | -OR_MJVKvsoJ | https://aclanthology.org/2024.nllp-1.12/ | 1.0 | https://aclanthology.org/2024.nllp-1.12.pdf |
| The Duty of Efficiency & Generative AI Pedagogy | 6RYeLVaZ8VgJ | https://journals.library.wustl.edu/lawpolicy/article/id/9024/ | 1.0 | https://journals.library.wustl.edu/lawpolicy/article/id/9024/download/pdf/ |
| LeDQA: A Chinese Legal Case Document-based Question Answering Dataset | jGMTTigSk60J | https://dl.acm.org/doi/abs/10.1145/3627673.3679154 | 3.0 | https://dl.acm.org/doi/pdf/10.1145/3627673.3679154 |
| Economic and Financial Analysis of Artificial Intelligence's Impact on Law and Legal Profession | MCZA3RN2BbcJ | https://www.zf.uni-lj.si/images/zalozba/Sokratska_10_I/27_Kocjan%C4%8Di%C4%8D_Rok.pdf | 1.0 | https://www.zf.uni-lj.si/images/zalozba/Sokratska_10_I/27_Kocjan%C4%8Di%C4%8D_Rok.pdf |
| How to Retain Being a Human Lawyer While Using Generative AI | xKbxhnWSeokJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/cwlr61§ion=16 | NaN | NaN |
| The Truth (s) About AI and Legal Education: A Discourse Analysis of the Conflicting Narratives Regarding the Implications of Generative AI for the Teaching of Law | Qthw3dIjQSQJ | https://research.bond.edu.au/en/publications/the-truths-about-ai-and-legal-education-a-discourse-analysis-of-t-2 | NaN | https://research.bond.edu.au/files/243318036/The_Truth_s_About_AI_and_Legal_Education_A_Discourse_Analysis_of_the_Conflicting_Narratives_Regarding_the_Implications_of_Generative_AI_for_the_Teaching_of_Law.pdf |
| Preparing Future Lawyers to Draft Contracts and Communicate with Clients in the Era of Generative AI | 8tXLl2NSk6sJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/transac25§ion=52 | NaN | NaN |
| Access to technology, access to justice: China's artificial intelligence application in criminal proceedings | b899k7DM3OkJ | https://www.sciencedirect.com/science/article/pii/S1756061625000175 | NaN | NaN |
| Artificial Lawyering: A Jekyll and Hyde Story | Qs9Hxl2Iir4J | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/aalwjloeg7§ion=4 | 1.0 | https://journals.librarypublishing.arizona.edu/azlawjet/article/6396/galley/5950/download/ |
| The law and NLP: Bridging disciplinary disconnects | XLMN4NL-8-wJ | https://arxiv.org/abs/2310.14346 | 6.0 | https://arxiv.org/pdf/2310.14346 |
| Bracing for Impact: Revising Legal Writing Assessments Ahead of the Collision of Generative AI and the NextGen Bar Exam | is0vd6i0r_kJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/jlwriins28§ion=3 | 10.0 | NaN |
| Nirnayaak: Revolutionizing Legal Research | bmtkOgRiBHIJ | https://ieeexplore.ieee.org/abstract/document/10829440/ | NaN | NaN |
| More than Machines: The Ethical and Human Implications of Generative AI and Lawyering | J-w0NyEAoUwJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/jchlet14§ion=19 | NaN | NaN |
| Artificial Intelligence and the Future of Law and Justice in Nigeria | Cnozql-PhfgJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4566440 | 1.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4566440 |
| Chatlaw: Open-source legal large language model with integrated external knowledge bases | fzUdzLWIeIwJ | https://openreview.net/forum?id=Cjas49BCAf | 358.0 | NaN |
| Legal Practices Redefined: Transforming Law Firm Operations and Management with AI | a1EG4Mo9tUYJ | https://www.taylorfrancis.com/chapters/edit/10.1201/9781003469551-6/legal-practices-redefined-siok-khoon-lim | NaN | NaN |
| Assessing Information Literacy in the Age of Generative AI: A Call to the National Conference of Bar Examiners | RiRt0XNLpwEJ | https://www.tandfonline.com/doi/abs/10.1080/0270319X.2025.2452717 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5073718 |
| Revolutionizing family courts: Catalysts for reform and the transformative role of technology | Aqjokc1izuoJ | https://onlinelibrary.wiley.com/doi/abs/10.1111/fcre.12783 | NaN | NaN |
| LawLLM: Intelligent Legal System with Legal Reasoning and Verifiable Retrieval | uBHZkwvRvS0J | https://link.springer.com/chapter/10.1007/978-981-97-5569-1_19 | 6.0 | https://charlie-xiao.github.io/assets/pdf/projects/disc-lawllm.pdf |
| Gpt-4 passes the bar exam | 2nA3TxColtsJ | https://royalsocietypublishing.org/doi/abs/10.1098/rsta.2023.0254 | 545.0 | https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2023.0254 |
| Large legal fictions: Profiling legal hallucinations in large language models | NUQXoQVtADQJ | https://academic.oup.com/jla/article-abstract/16/1/64/7699227 | 142.0 | https://academic.oup.com/jla/article-pdf/16/1/64/58336922/laae003.pdf |
| Technologically Competent Reprised: Ethical Practice in an AI Age and Considerations for Our Courts in a Burgeoning AI Era | pTUY-puzpdkJ | https://huskiecommons.lib.niu.edu/clglaw/86/ | NaN | https://huskiecommons.lib.niu.edu/cgi/viewcontent.cgi?article=1085&context=clglaw |
| Access to justice and the legal profession: Three questions | lnLVibnxH7AJ | https://digitalcommons.osgoode.yorku.ca/scholarly_works/3143/ | NaN | https://digitalcommons.osgoode.yorku.ca/cgi/viewcontent.cgi?article=4139&context=scholarly_works |
| If You Give an LLM a Legal Practice Guide | nMYAEiY8Io4J | https://blog.genlaw.org/pdfs/genlaw_icml2024/97.pdf | NaN | https://blog.genlaw.org/pdfs/genlaw_icml2024/97.pdf |
| " Reasoning before Responding": Towards Legal Long-form Question Answering with Interpretability | UwvBINe7mxkJ | https://dl.acm.org/doi/abs/10.1145/3627673.3680082 | 3.0 | NaN |
| How to Retain Being a Human Lawyer While Using Generative AI | S4wC53qpRXQJ | https://scholarlycommons.law.cwsl.edu/cgi/viewcontent.cgi?article=1789&context=cwlr | NaN | https://scholarlycommons.law.cwsl.edu/cgi/viewcontent.cgi?article=1789&context=cwlr |
| LexSage: Multi-Task Optimization in Legal Large Language Model Applications | uOXsjHIJneUJ | https://ieeexplore.ieee.org/abstract/document/10900222/ | NaN | NaN |
| Generative Contracts | --chwZiMxA0J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4582753 | 3.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4582753 |
| AI-Law Firms of the future. The integration of artificial intelligence and other cutting-edge technologies for value creation | 5e5a2UdgaxUJ | https://www.researchgate.net/profile/Breno-Niero/publication/370716693_AI-Law_Firms_of_the_future_The_integration_of_artificial_intelligence_and_other_cutting-edge_technologies_for_value_creation/links/64ab99718de7ed28ba885c0a/AI-Law-Firms-of-the-future-The-integration-of-artificial-intelligence-and-other-cutting-edge-technologies-for-value-creation.pdf | NaN | https://www.researchgate.net/profile/Breno-Niero/publication/370716693_AI-Law_Firms_of_the_future_The_integration_of_artificial_intelligence_and_other_cutting-edge_technologies_for_value_creation/links/64ab99718de7ed28ba885c0a/AI-Law-Firms-of-the-future-The-integration-of-artificial-intelligence-and-other-cutting-edge-technologies-for-value-creation.pdf |
| From Knowledge Management to Intelligence Engineering-A practical approach to building AI inside the law-firm using open-source Large Language Models | LX4k-4hBp0EJ | https://ceur-ws.org/Vol-3423/paper5.pdf?ref=fringelegal.com | 3.0 | https://ceur-ws.org/Vol-3423/paper5.pdf?ref=fringelegal.com |
| Empirical legal analysis simplified: reducing complexity through automatic identification and evaluation of legally relevant factors | fxdudTUHWjMJ | https://royalsocietypublishing.org/doi/abs/10.1098/rsta.2023.0155 | 13.0 | NaN |
| LeXFiles and LegalLAMA: Facilitating English multinational legal language model development | Hzv8CB3O47YJ | https://arxiv.org/abs/2305.07507 | 57.0 | https://arxiv.org/pdf/2305.07507 |
| The role of Language Prediction Models in contractual interpretation: The challenges and future prospects of GPT-3 | Yn0VAFFQdukJ | https://www.taylorfrancis.com/chapters/edit/10.1201/9781003215998-5/role-language-prediction-models-contractual-interpretation-malcolm-katrak | 9.0 | NaN |
| The Adoption of Artificial Intelligence in Family Law–Brand New or Well-known Idea? | E6W_Q2hG56EJ | https://www.researchgate.net/profile/Michal-Piegzik-2/publication/388188589_The_Adoption_of_Artificial_Intelligence_in_Family_Law_-_Brand_New_or_Well-known_Idea/links/678e40bb1ec9f9589f51b557/The-Adoption-of-Artificial-Intelligence-in-Family-Law-Brand-New-or-Well-known-Idea.pdf | NaN | https://www.researchgate.net/profile/Michal-Piegzik-2/publication/388188589_The_Adoption_of_Artificial_Intelligence_in_Family_Law_-_Brand_New_or_Well-known_Idea/links/678e40bb1ec9f9589f51b557/The-Adoption-of-Artificial-Intelligence-in-Family-Law-Brand-New-or-Well-known-Idea.pdf |
| ChatGPT and GPT-4: utilities in the legal sector | TM1elPIE7dsJ | https://core.ac.uk/download/pdf/604042742.pdf | NaN | https://core.ac.uk/download/pdf/604042742.pdf |
| LegalMind System and the LLM-based Legal Judgment Query System | 09WioUcWFAsJ | https://ieeexplore.ieee.org/abstract/document/10545179/ | NaN | NaN |
| Structured Legal Argumentation with LLMs: A Study in Landlord-Tenant Law | 1WKST3FL64cJ | https://ebooks.iospress.nl/doi/10.3233/FAIA241272 | NaN | https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA241272 |
| New Frontiers in Attorney Regulation: Introduction to Volume II of II | Y4hGJX6FeicJ | https://journals.library.wustl.edu/lawpolicy/article/id/9021/download/pdf/ | NaN | https://journals.library.wustl.edu/lawpolicy/article/id/9021/download/pdf/ |
| Towards the exploitation of LLM-based chatbot for providing legal support to Palestinian cooperatives | 1hDJ716g7tIJ | https://arxiv.org/abs/2306.05827 | 11.0 | https://arxiv.org/pdf/2306.05827 |
| Do Robot Lawyers Dream of Electric Clients | OLZjlJlYtzIJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4583345 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4583345 |
| Legal Framework for Digital India | bKw8btfHNi4J | https://ieeexplore.ieee.org/abstract/document/10743727/ | NaN | NaN |
| LAWBOTS: Utilization of AI Chatbots for Legal Advising in the Philippines | lsblFGSMytEJ | https://ieeexplore.ieee.org/abstract/document/10761649/ | NaN | NaN |
| ChatGPT: What Lawyers Need to Know Before Using AI. | sIKQ26gqonMJ | https://go.gale.com/ps/i.do?id=GALE%7CA759873378&sid=googleScholar&v=2.1&it=r&linkaccess=abs&issn=27672476&p=AONE&sw=w | NaN | NaN |
| Navigating Legal Advice through AI Chatbots | mu7Gzftbb-EJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/juscrp4§ion=276 | NaN | NaN |
| Generative Artificial Intelligence and Article 6 of the European Convention on Human Rights: The Right to a Human Judge? | gzrmVfqby74J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5040351 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5040351 |
| A survey on large language models for critical societal domains: Finance, healthcare, and law | lm9K0vSCKEcJ | https://arxiv.org/abs/2405.01769 | 39.0 | https://arxiv.org/pdf/2405.01769 |
| Joint Representation in Auto Litigation-Can This Marriage Be Saved? | -9oFcj-GuMkJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/tortso25§ion=24 | NaN | NaN |
| Transforming SEO in the Era of Generative AI: Challenges, Opportunities, and Future Prospects | 6kh8GTedeUAJ | https://www.taylorfrancis.com/chapters/edit/10.4324/9781032688305-6/transforming-seo-era-generative-ai-challenges-opportunities-future-prospects-vajratiya-vajrobol-nitisha-aggarwal-geetika-jain-saxena-sanjeev-singh-amit-pundir | 1.0 | NaN |
| Sizing the potential value of Generative AI for legal services1 | CTpjRDCv-60J | https://www.researchgate.net/profile/Markus-Lips/publication/373717592_Sizing_the_potential_value_of_Generative_AI_for_legal_services/links/64f967db4c72a2514e5943c6/Sizing-the-potential-value-of-Generative-AI-for-legal-services.pdf | NaN | https://www.researchgate.net/profile/Markus-Lips/publication/373717592_Sizing_the_potential_value_of_Generative_AI_for_legal_services/links/64f967db4c72a2514e5943c6/Sizing-the-potential-value-of-Generative-AI-for-legal-services.pdf |
| Through the AI-Looking Glass and What Consumers Find There | 7PttF-rL6z8J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4722695 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4722695 |
| Artificial Intelligence (AI) and the Practice of Law in Texas | 1kPOH0YAEmMJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/stexlr63§ion=5 | 5.0 | NaN |
| Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language Models | wu2VSu_u2G0J | https://arxiv.org/abs/2410.08731 | 2.0 | https://arxiv.org/pdf/2410.08731 |
| AI Justice: Harnessing Generative AI in Legal Services | 6GY6AULfDx8J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4864236 | NaN | NaN |
| LEGAL PROCEDURE BOT | XVXXHioJe_wJ | https://alochana.org/wp-content/uploads/34_AJ2702.pdf | NaN | https://alochana.org/wp-content/uploads/34_AJ2702.pdf |
| Interrogating new methods in socio-legal studies: Content analysis, case law and artificial intelligence | ehn7A_VfWmIJ | https://journals.sagepub.com/doi/abs/10.1177/1037969X251325869 | NaN | https://journals.sagepub.com/doi/pdf/10.1177/1037969X251325869 |
| Making a computational attorney | l43BwBgxvt0J | https://epubs.siam.org/doi/abs/10.1137/1.9781611977653.ch111 | 5.0 | https://epubs.siam.org/doi/pdf/10.1137/1.9781611977653.ch111 |
| GPT takes the bar exam | BqZr04cxhiwJ | https://arxiv.org/abs/2212.14402 | 141.0 | https://arxiv.org/pdf/2212.14402 |
| LexGPT 0.1: pre-trained GPT-J models with Pile of Law | lRhwzB6FxjgJ | https://arxiv.org/abs/2306.05431 | 12.0 | https://arxiv.org/pdf/2306.05431 |
| A PROPOSAL FOR THE JOINT DEVELOPMENT OF GENERATIVE AI FOR THE DISPUTE RESOLUTION PROFESSION | Sg6cPoNeAzoJ | https://scholarship.law.missouri.edu/fac_blogs/70/ | NaN | https://scholarship.law.missouri.edu/cgi/viewcontent.cgi?article=1070&context=fac_blogs |
| Oñati Socio-Legal Series (ISSN: 2079-5971) | HXDmOtw5Oj0J | https://pdfs.semanticscholar.org/2b9a/db4af7e6942288e684f3f35ac19fc1032d77.pdf | NaN | https://pdfs.semanticscholar.org/2b9a/db4af7e6942288e684f3f35ac19fc1032d77.pdf |
| Lawma: The power of specialization for legal tasks | QH89sWPQoGIJ | https://arxiv.org/abs/2407.16615 | 5.0 | https://arxiv.org/pdf/2407.16615? |
| Generative AI in Legal Practice: A Survey of Professional and Ethical Challenges. | 4ABjcQjsphsJ | https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=05154987&asa=N&AN=183686392&h=52aIIPGPc61zuulGQTm2V1AHognjWNx0fxHhJcGNHP6P9Z8zr9%2BWHf0sz3elGDpPNkTtXMpf%2FWeXDCaI6im3pQ%3D%3D&crl=c | NaN | NaN |
| The Impact of Empathy Display in Language of Conversational AI: A Controlled Experiment with a Legal Chatbo | h-3gmbIrAeQJ | https://aisel.aisnet.org/hicss-57/cl/ethics/2/ | 2.0 | NaN |
| ARTIFICIAL REASON AND ARTIFICIAL INTELLIGENCE: THE LEGAL REASONING CAPABILITIES OF GPT-4. | GocnXfuRjPsJ | https://www.ceeol.com/search/article-detail?id=1287965 | NaN | https://anali.rs/xml/202-/2024c/2024-3c/Anali_2024-3c-03.pdf |
| A Question-Answering Approach to Evaluating Legal Summaries | Wx_p4tUveXoJ | https://ebooks.iospress.nl/doi/10.3233/FAIA230977 | 2.0 | https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA230977 |
| Is generative AI (artificial intelligence) the next advent in the evolution of finance and navigating financial crime and regulation? | vofk2iXMNq8J | https://www.emerald.com/insight/content/doi/10.1108/jfc-07-2024-0232/full/html | 1.0 | NaN |
| Trisurya: A Local Language-Powered Omnichannel Chatbot as a Solution for the Enhancement of E-Govemment Quality and Accessibility of Public Services in … | DKrKC8fCew4J | https://ieeexplore.ieee.org/abstract/document/10956359/ | NaN | NaN |
| Hallucinating law: Legal mistakes with large language models are pervasive | JQl5IoVQjuAJ | https://nacmnet.org/wp-content/uploads/Stanford-HAI-Dahl-et-al.-Hallucinating-Law-Legal-Mistakes-with-Large-Language-Models-are-Pervasive-2024JAN11-6pp.pdf | 12.0 | https://nacmnet.org/wp-content/uploads/Stanford-HAI-Dahl-et-al.-Hallucinating-Law-Legal-Mistakes-with-Large-Language-Models-are-Pervasive-2024JAN11-6pp.pdf |
| Masterclass: On boosting access to justice for renters | lwF1Laye0ZUJ | https://search.informit.org/doi/abs/10.3316/informit.352692159192651 | NaN | NaN |
| Striking the right balance: approaching disclosure of generative AI-assisted work product in international arbitration | ivkOlNvtw2wJ | https://kluwerlawonline.com/journalarticle/b-Arbitra+%7C+Belgian+Review+of+Arbitration/2024.1/BARBIT2024028 | NaN | NaN |
| Lawyers' Ethics and the Use of Artificial Intelligence in legal services | geEhL5-90NcJ | https://search.informit.org/doi/abs/10.3316/informit.257533574977024 | NaN | NaN |
| An assessment of compliance of large language models through automated information retrieval and answer generation | ahQVWe_3I3kJ | https://www.techrxiv.org/doi/full/10.36227/techrxiv.172668489.92285234 | 51.0 | https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.172668489.92285234 |
| Evolving Norms Governing AI Engagement in Legal Practice and the Prospective Alignment of Law School Curriculum | QWScjUiMBQwJ | https://ojs.ucp.edu.pk/index.php/ucpjlle/article/view/349 | NaN | https://ojs.ucp.edu.pk/index.php/ucpjlle/article/download/349/148 |
| Large language model (LLM) with retrieve-augmented generation (RAG) for legal case research | UuUZcclDgs0J | https://dr.ntu.edu.sg/handle/10356/176464 | NaN | NaN |
| Enhancing privacy and security in large-language models: a zero-knowledge proof approach | nPlqBz7DawEJ | https://pdfs.semanticscholar.org/772f/d3f8d8838bdb0b6e4fa57bdd7fd69dd1c958.pdf | 8.0 | https://pdfs.semanticscholar.org/772f/d3f8d8838bdb0b6e4fa57bdd7fd69dd1c958.pdf |
| Can gpt-4 support analysis of textual data in tasks requiring highly specialized domain expertise? | ys2Ue4MeS4MJ | https://arxiv.org/abs/2306.13906 | 63.0 | https://arxiv.org/pdf/2306.13906 |
| GeoTool-GPT: a trainable method for facilitating Large Language Models to master GIS tools | RPxd103IWsoJ | https://www.tandfonline.com/doi/abs/10.1080/13658816.2024.2438937 | 5.0 | NaN |
| Fine-tuning a Large Language Model for the Indian Legal System | dRqtfbwcigIJ | https://ieeexplore.ieee.org/abstract/document/10959207/ | NaN | NaN |
| Chatgpt & generative ai systems as quasi-expert legal advice lawyers-case study considering potential appeal against conviction of tom hayes | CQsEueGL1vcJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4342686 | 18.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4342686 |
| Generative AI or the Doom of Translation as we Know it? | NicsAI9dKjAJ | https://asjp.cerist.dz/index.php/en/article/245812 | 1.0 | NaN |
| The Generative AI Revolution | EUlupy7HOaEJ | https://www.ukonward.com/wp-content/uploads/2023/05/Generative-AI-Revolution-Final.pdf | NaN | https://www.ukonward.com/wp-content/uploads/2023/05/Generative-AI-Revolution-Final.pdf |
| Generative AI and the Purpose of Legal Scholarship | pbtKJPybqNYJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5081325 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5081325 |
| Applications of AI chatbots based on generative AI, large language models and large multimodal models | qd-RPAfGUN8J | https://link.springer.com/chapter/10.1007/978-3-031-74443-3_39 | 14.0 | https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.173121409.91676949 |
| Fighting the Knowledge Representation Bottleneck with Large Language Models | 4Nbz7njEtzoJ | https://ebooks.iospress.nl/doi/10.3233/FAIA241230 | NaN | https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA241230 |
| Is ChatGPT leading generative AI? What is beyond expectations? | gchkptrNhsIJ | https://dergipark.org.tr/en/pub/apjess/article/1293702 | 330.0 | https://dergipark.org.tr/en/download/article-file/3127764 |
| Generative AI: Can The Stakes Get Higher? | N1XoDxE0uJwJ | https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=00153915&asa=N&AN=173421199&h=5myMsQgH%2FbAp6JaTPhOKtkJ5GvWBBt5x%2BMlCKYJ2MDYij8yW1CnXb5wZFAuXNsSsjlpKLMqY4YL9rqe8k5QTnw%3D%3D&crl=c | NaN | NaN |
| Can generative AI serve as the modern-day white-collar knowledge laborer? | 5WpFERnxZl8J | https://www.emerald.com/insight/content/doi/10.1108/978-1-83753-750-120251010 | NaN | NaN |
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| Generative AI considered harmful | mMl9vQtSY1YJ | https://dl.acm.org/doi/abs/10.1145/3571884.3603756 | 51.0 | https://people.cs.nott.ac.uk/pszjf1/papers/Fischer_CUI23.pdf |
| Scaling Up: Analyzing Classification Outcomes in Large Korean Legal Document Datasets | ifdNhsw2kgsJ | https://hrcak.srce.hr/ojs/index.php/jahr/article/download/32515/17400/149836 | NaN | https://hrcak.srce.hr/ojs/index.php/jahr/article/download/32515/17400/149836 |
| Employing label models on ChatGPT answers improves legal text entailment performance | 5VFMMdneX9MJ | https://arxiv.org/abs/2401.17897 | 4.0 | https://arxiv.org/pdf/2401.17897 |
| Ethics guidance for generative AI use. | pEztotuCUbAJ | https://lprb.mncourts.gov/Pages/bba0924.pdf | NaN | https://lprb.mncourts.gov/Pages/bba0924.pdf |
| AI, UPL, AND THE JUSTICE GAP: A PROFESSION AT THE CROSSROADS. | 8HUjCV-A5ysJ | https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=02761505&asa=N&AN=176487959&h=%2FFxuw9uyQMQ%2FX5B0lHsa8HWGfr%2FvokUft%2FVPf2rouVRJ07h5Zbqq3p5qjjtl57GzUwbHBxvFABWE7espUxvkVA%3D%3D&crl=c | NaN | NaN |
| Judicial Economy in the Age of AI | LHiwGkmrSvcJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/ucollr96§ion=16 | 2.0 | https://lawreview.colorado.edu/wp-content/uploads/2025/03/96.2.5-Arbel.pdf |
| Analyzing the justification for using generative AI technology to generate judgments based on the virtue jurisprudence theory | YcVaEHSvuaoJ | https://www.tandfonline.com/doi/abs/10.1080/12460125.2024.2428999 | NaN | https://www.tandfonline.com/doi/pdf/10.1080/12460125.2024.2428999 |
| Black-box analysis: GPTs across time in legal textual entailment task | sC5dwrTUpGUJ | https://arxiv.org/abs/2309.05501 | 6.0 | https://arxiv.org/pdf/2309.05501 |
| Generative AI and the Future of Legal Scholarship | eWEN5-3w78IJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5072765 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5072765 |
| Gpt-4 technical report | yMRuEJga_zIJ | https://arxiv.org/abs/2303.08774 | 11027.0 | https://arxiv.org/pdf/2303.08774 |
| Generative AI in Practice: Pipeline Design, Implementation, and Ethical Considerations | jF11Enuhmo8J | https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.174495064.44742209 | NaN | https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.174495064.44742209 |
| Generative AI and the digital commons | LDrTM_lyDkQJ | https://arxiv.org/abs/2303.11074 | 88.0 | https://arxiv.org/pdf/2303.11074 |
| Privacy-Preserving Techniques in Generative AI and Large Language Models: A Narrative Review | l5SCDqV8nkgJ | https://www.mdpi.com/2078-2489/15/11/697 | 26.0 | NaN |
| A Present Look at ChatGPT in Your Future. | L0qLXFGYpq4J | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/lwpra49§ion=90 | NaN | NaN |
| Incorporating Generative Artificial Intelligence into the Practice of Law: Utilizing Generative AI within the Framework of the California Rules of Professional … | 3nlNDsmskIAJ | https://scholarlycommons.law.cwsl.edu/cgi/viewcontent.cgi?article=1781&context=cwlr | NaN | https://scholarlycommons.law.cwsl.edu/cgi/viewcontent.cgi?article=1781&context=cwlr |
| Analysis of GPT Sentiments Using Blog Mining | JZK1o37C-isJ | https://search.proquest.com/openview/ce0125811b61b209f329b60af7c0d1d8/1?pq-origsite=gscholar&cbl=2029993 | NaN | NaN |
| Exploring the transformative impact of generative AI on higher education | 6W8XS53I71QJ | https://link.springer.com/chapter/10.1007/978-3-031-50040-4_6 | 27.0 | NaN |
| Enhancing Legal Argument Retrieval with Optimized Language Model Techniques | ZATBLQhxPzQJ | https://link.springer.com/chapter/10.1007/978-981-97-3076-6_7 | 1.0 | NaN |
| Copyright protection in generative ai: A technical perspective | Cr-3p3D0J4gJ | https://arxiv.org/abs/2402.02333 | 45.0 | https://arxiv.org/pdf/2402.02333 |
| Enhancing semantic validity in large language model tasks through automated grammar checking | H-SXQ38r3nMJ | https://files.osf.io/v1/resources/7xp6s/providers/osfstorage/66931912e9fa1302e256afcd?action=download&direct&version=1 | 48.0 | https://files.osf.io/v1/resources/7xp6s/providers/osfstorage/66931912e9fa1302e256afcd?action=download&direct&version=1 |
| The Legal Ethics of Generative AI | mdjWtUfQe6AJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4735389 | 5.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4735389 |
| Inadequacies of large language model benchmarks in the era of generative artificial intelligence | i1mVllezProJ | https://arxiv.org/abs/2402.09880 | 135.0 | https://arxiv.org/pdf/2402.09880 |
| Winners and losers of generative AI: Early Evidence of Shifts in Freelancer Demand | 8p3JZifl-3oJ | https://www.sciencedirect.com/science/article/pii/S0167268124004591 | 1.0 | NaN |
| Artificial Intelligence and the Sustainable Development Goals: GPT-3s Reflections on the Society Domain | bWkbsgfjIKIJ | https://www.preprints.org/manuscript/202303.0025/download/final_file | 5.0 | https://www.preprints.org/manuscript/202303.0025/download/final_file |
| Exploring the impact of generative AI-based technologies on learning performance through self-efficacy, fairness & ethics, creativity, and trust in higher education | YiYLsW-40h4J | https://link.springer.com/article/10.1007/s10639-024-12949-9 | 38.0 | NaN |
| GPT, ontology, and CAABAC: A tripartite personalized access control model anchored by compliance, context and attribute | e3vCn9f3qxcJ | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0310553 | 5.0 | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0310553&type=printable |
| ChatGPT and the Future of Legal Services | eL1CqE7Zs-EJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4384000 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4384000 |
| The Courtrooms Strikes Back: Generative AI's Force in Courts | E7b4JLhQct8J | https://lirias.kuleuven.be/retrieve/759453 | NaN | https://lirias.kuleuven.be/retrieve/759453 |
| The case for nurturing AI literacy in Law schools | iCexYhacUiUJ | https://journals.sagepub.com/doi/abs/10.1177/23220058241265613 | 7.0 | NaN |
| Bias transmission in large language models: evidence from gender-occupation bias in GPT-4 | F_ecQdtwRv4J | https://openreview.net/forum?id=Fg6qZ28Jym | 4.0 | https://openreview.net/pdf?id=Fg6qZ28Jym |
| How we learned to stop worrying and love ai: Analyzing the rapid evolution of generative pre-trained transformer (gpt) and its impacts on law, business, and society | 4tKvGBLOdNEJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4516154 | 27.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4516154 |
| Building trust with generative AI chatbots: Exploring explainability, privacy, and user acceptance | IKls0XJ6WLIJ | https://www.researchgate.net/profile/Muhammad-Ashraf-Faheem/publication/386330933_Building_Trust_with_Generative_AI_Chatbots_Exploring_Explainability_Privacy_and_User_Acceptance/links/674d7838a7fbc259f1a5c5b9/Building-Trust-with-Generative-AI-Chatbots-Exploring-Explainability-Privacy-and-User-Acceptance.pdf | 77.0 | https://www.researchgate.net/profile/Muhammad-Ashraf-Faheem/publication/386330933_Building_Trust_with_Generative_AI_Chatbots_Exploring_Explainability_Privacy_and_User_Acceptance/links/674d7838a7fbc259f1a5c5b9/Building-Trust-with-Generative-AI-Chatbots-Exploring-Explainability-Privacy-and-User-Acceptance.pdf |
| Rule 11 is no match for generative AI | L8yvUKWzscwJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/stantlr27§ion=10 | 8.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4769448 |
| LegiLM: A Fine-Tuned Legal Language Model for Data Compliance | ti1sOnOBim4J | https://arxiv.org/abs/2409.13721 | 3.0 | https://arxiv.org/pdf/2409.13721 |
| BB-GeoGPT: A framework for learning a large language model for geographic information science | psYEIYB44BgJ | https://www.sciencedirect.com/science/article/pii/S0306457324001675 | 28.0 | NaN |
| Generative AI and LLMs in Industry: A text-mining Analysis and Critical Evaluation of Guidelines and Policy Statements Across Fourteen Industrial Sectors | 5ofcHggE0bkJ | https://arxiv.org/abs/2501.00957 | 2.0 | https://arxiv.org/pdf/2501.00957 |
| Generative AI in education from the perspective of students, educators, and administrators | AKc59QWPmfEJ | https://digitalcommons.usu.edu/etd2023/124/ | 8.0 | https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1119&context=etd2023 |
| Artificial Intelligence and Law–An Overview of Recent Technological Changes in Large Language Models and Law | VK3sHji0IeoJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5135305 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5135305 |
| Generative AI and Tax Professionals: Current PR Guidance 2024 Gem State Tax Symposium | X_xiJkMBc48J | https://digitalcommons.law.uw.edu/cgi/viewcontent.cgi?article=1093&context=faculty-presentations | NaN | https://digitalcommons.law.uw.edu/cgi/viewcontent.cgi?article=1093&context=faculty-presentations |
| How ChatGPT and Other AI Platforms Could Dramatically Reshape the Legal Industry | Mb8K7cKad9oJ | https://www.abajournal.com/magazine/article/how-chatgpt-and-other-ai-platforms-could-dramatically-reshape-the-legal-industry?utm_source=sfmc&utm_medium=email&utm_campaign=5DAY_JRNL_MTH_EML_NONMEM&utm_term=%%%3DRedirectTo(%40URL)%3D%%&utm_id=677476&sfmc_id=50756999 | 3.0 | NaN |
| How knowledge workers think generative ai will (not) transform their industries | ANu2hSCJxXgJ | https://dl.acm.org/doi/abs/10.1145/3613904.3642700 | 52.0 | https://dl.acm.org/doi/pdf/10.1145/3613904.3642700 |
| Semantic interlinking of immigration data using LLMs for knowledge graph construction | OvD3Si0lqVYJ | https://dl.acm.org/doi/abs/10.1145/3589335.3651557 | 8.0 | https://dl.acm.org/doi/pdf/10.1145/3589335.3651557 |
| The Impact of Generative AI on Business Consulting Engagements: A New Paradigm for Client Interaction and Value Creation | A4GYinhxFdIJ | https://www.researchgate.net/profile/Suprit-Kumar-Pattanayak/publication/385859257_The_Impact_of_Generative_AI_on_Business_Consulting_Engagements_A_New_Paradigm_for_Client_Interaction_and_Value_Creation/links/6737b01837496239b2c19715/The-Impact-of-Generative-AI-on-Business-Consulting-Engagements-A-New-Paradigm-for-Client-Interaction-and-Value-Creation.pdf | 15.0 | https://www.researchgate.net/profile/Suprit-Kumar-Pattanayak/publication/385859257_The_Impact_of_Generative_AI_on_Business_Consulting_Engagements_A_New_Paradigm_for_Client_Interaction_and_Value_Creation/links/6737b01837496239b2c19715/The-Impact-of-Generative-AI-on-Business-Consulting-Engagements-A-New-Paradigm-for-Client-Interaction-and-Value-Creation.pdf |
| Generative AI as a new innovation platform | ILv4ul6iMEkJ | https://dl.acm.org/doi/fullHtml/10.1145/3615859 | 15.0 | https://dl.acm.org/doi/pdf/10.1145/3615859 |
| Fairness and fair use in generative AI | fYmtydY0ZpUJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/flr92§ion=70 | 42.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4654875 |
| Mixed-domain language modeling for processing long legal documents | gre9EWR6YS0J | https://aclanthology.org/2023.nllp-1.7/ | 4.0 | https://aclanthology.org/2023.nllp-1.7.pdf |
| Beyond Readability with RateMyPDF: A Combined Rule-based and Machine Learning Approach to Improving Court Forms | 9iFAqWoo1T0J | https://dl.acm.org/doi/abs/10.1145/3594536.3595146 | 4.0 | NaN |
| What will ChatGPT revolutionize in the financial industry? | 21M7EwsP0fIJ | https://cejsh.icm.edu.pl/cejsh/element/bwmeta1.element.ojs-doi-10_61351_mf_v1i1_67 | 77.0 | https://cejsh.icm.edu.pl/cejsh/element/bwmeta1.element.ojs-doi-10_61351_mf_v1i1_67/c/articles-23942837.pdf.pdf |
| Introduction to generative AI | UfJvWH4Y884J | https://books.google.com/books?hl=en&lr=&id=yxXwEAAAQBAJ&oi=fnd&pg=PR1&dq=(%22access+to+justice%22+OR+%22legal+aid%22+OR+%22legal+services%22)+AND+(%22language+model%22+OR+%22GPT%22+OR+%22generative+AI%22)&ots=j_5zLkC_0P&sig=bypKUu6sBSIX4u4IhQBnj67UyOU | 26.0 | NaN |
| Automated user story generation with test case specification using large language model | VzUbPp4kve8J | https://arxiv.org/abs/2404.01558 | 13.0 | https://arxiv.org/pdf/2404.01558 |
| Technology Tools, Access to Justice, and the Joint Technology Committee: The Time Is Right. | GuyrTxFB_KIJ | https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=00472972&asa=N&AN=175686309&h=OOXKjLyYuF%2FEwO6COkeK1R4U8bZJ2JNfYjiEL0CQPZBwX4ziE%2FpipyMVBhXD0Y9yddtvMrlRVqeJwvZt44lgzA%3D%3D&crl=c | NaN | NaN |
| The Future of Generative AI | NJ6G5I09XHcJ | https://link.springer.com/chapter/10.1007/979-8-8688-0885-2_11 | NaN | NaN |
| A negation detection assessment of GPTs: analysis with the xNot360 dataset | P6imolD5eSQJ | https://arxiv.org/abs/2306.16638 | 9.0 | https://arxiv.org/pdf/2306.16638 |
| Lawyering in the age of artificial intelligence | ldw0ALaiFLgJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/mnlr109§ion=5 | 39.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4626276 |
| Data-Driven Justice: Effective Data Governance to achieve SDG 16 | 4kOtMViO_DAJ | https://pgcl.ac.in/wp-content/uploads/2025/03/Book-National-Conference-on-Sustainavle-Development-Good-Governance-and-Rule-of-Law-2024.pdf#page=42 | NaN | https://pgcl.ac.in/wp-content/uploads/2025/03/Book-National-Conference-on-Sustainavle-Development-Good-Governance-and-Rule-of-Law-2024.pdf#page=42 |
| Generative AI in the wild: prospects, challenges, and strategies | 1yDtTGB00T8J | https://dl.acm.org/doi/abs/10.1145/3613904.3642160 | 26.0 | https://arxiv.org/pdf/2404.04101 |
| ChatGPT for legal and tax professionals | vsJBIMMWpjwJ | http://kostelanetz.com/wp-content/uploads/2023/09/CPAJ-July-Aug2023_Brodeur.pdf | 5.0 | http://kostelanetz.com/wp-content/uploads/2023/09/CPAJ-July-Aug2023_Brodeur.pdf |
| How generative AI Is shaping the future of marketing | 37KsD-fAzisJ | https://link.springer.com/article/10.1007/s11747-024-01064-3 | 12.0 | https://link.springer.com/content/pdf/10.1007/s11747-024-01064-3.pdf |
| Generative AI as a new platform for applications development | j1E-2Ix1kmwJ | https://mit-genai.pubpub.org/pub/r8xcl5ol | 3.0 | NaN |
| Asking GPT for the ordinary meaning of statutory terms | 5ZkDnCaTzcUJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/jltp2024§ion=12 | 4.0 | https://pure.mpg.de/rest/items/item_3617884/component/file_3617889/content |
| Hacking generative AI | SP6gNobGvBUJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4788909 | 7.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4788909 |
| NAVIGATING HALLUCINATIONS IN GENERATIVE AI FOR EDUCATION: A CASE STUDY IN LEGAL TEACHING AND LEARNING | yWrGxDTrsfAJ | https://library.iated.org/view/CHIANG2024NAV | NaN | NaN |
| Private Ordering and Generative AI What Can We Learn from Model Terms and Conditions? | lIQ28MAsj1IJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5026677 | 5.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5026677 |
| Consumer Protection Law in Australia | VoA340C34i4J | https://testcdn.kluwerlawonline.com/api/Product/CitationPDFURL?file=Journals\EuCML\EuCML2024021.pdf | NaN | NaN |
| Generative AI-driven storytelling: A new era for marketing | dGh5QjYACUIJ | https://arxiv.org/abs/2309.09048 | 16.0 | https://arxiv.org/pdf/2309.09048 |
| Dark echoes: the exploitative potential of generative AI in online harassment | iMzLClFRCoAJ | https://www.taylorfrancis.com/chapters/edit/10.1201/9781032664859-5/dark-echoes-adrian-wood | 3.0 | NaN |
| Global insights and the impact of generative AI-ChatGPT on multidisciplinary: a systematic review and bibliometric analysis | 0u2H8lfkoYwJ | https://www.tandfonline.com/doi/abs/10.1080/09540091.2024.2353630 | 27.0 | https://www.tandfonline.com/doi/pdf/10.1080/09540091.2024.2353630 |
| A question and answering service of typhoon disasters based on the t5 large language model | oS44t3l-DBgJ | https://www.mdpi.com/2220-9964/13/5/165 | 7.0 | https://www.mdpi.com/2220-9964/13/5/165/pdf |
| From distributional to overton pluralism: Investigating large language model alignment | 0NeSdgcY4UUJ | https://arxiv.org/abs/2406.17692 | 6.0 | https://arxiv.org/pdf/2406.17692 |
| Generative AI raises bias, privacy concerns. | dR-Lq-cvXdMJ | https://go.gale.com/ps/i.do?p=AONE&sw=w&issn=&v=2.1&it=r&id=GALE%7CA772241206&sid=googleScholar&linkaccess=abs | NaN | NaN |
| Large language model for causal decision making | deDSwE3z9PMJ | https://arxiv.org/abs/2312.17122 | 11.0 | https://arxiv.org/pdf/2312.17122 |
| 생성형 AI 를 활용한 법률서비스의 쟁점과 과제 | pR2rGSWlK8oJ | https://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE11913368 | NaN | NaN |
| 7th Annual Innovation and Technology Law Conference: Generative AI: Infringement or Innovation? | CXzDSayL0SEJ | https://digitalcommons.law.seattleu.edu/cgi/viewcontent.cgi?article=1028&context=sitie_symposium | NaN | https://digitalcommons.law.seattleu.edu/cgi/viewcontent.cgi?article=1028&context=sitie_symposium |
| Law and Economics of Language Model Development: Empirical Examination of Corporate Strategies and Vaporware Claims | tC9KkckGsnoJ | https://www.degruyter.com/document/doi/10.1515/ajle-2023-0118/html | 1.0 | https://www.degruyter.com/document/doi/10.1515/ajle-2023-0118/pdf |
| Disc-finllm: A chinese financial large language model based on multiple experts fine-tuning | UQg4PX163KgJ | https://arxiv.org/abs/2310.15205 | 20.0 | https://arxiv.org/pdf/2310.15205 |
| Hallucination‐Free? Assessing the Reliability of Leading AI Legal Research Tools | ycQECP3fDP4J | https://onlinelibrary.wiley.com/doi/abs/10.1111/jels.12413 | 70.0 | https://onlinelibrary.wiley.com/doi/pdf/10.1111/jels.12413 |
| The rapid rise of generative ai: Assessing risks to safety and security | WB8suT_r-MIJ | https://cetas.turing.ac.uk/sites/default/files/2023-12/cetas_research_report_-_the_rapid_rise_of_generative_ai_-_2023.pdf | 10.0 | https://cetas.turing.ac.uk/sites/default/files/2023-12/cetas_research_report_-_the_rapid_rise_of_generative_ai_-_2023.pdf |
| Conflict, creativity--and legal'hallucinations'. | FzcUoV9GuWkJ | https://go.gale.com/ps/i.do?id=GALE%7CA767510362&sid=googleScholar&v=2.1&it=r&linkaccess=abs&issn=03071766&p=AONE&sw=w | NaN | NaN |
| Scale: Scaling up the complexity for advanced language model evaluation | FEWa_jU34vsJ | https://www.academia.edu/download/106491596/2306.09237.pdf | 10.0 | https://www.academia.edu/download/106491596/2306.09237.pdf |
| Chatgpt, professor of law | RAoTkOxbBj0J | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/jltp2023§ion=8 | 44.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4347630 |
| Seeking the golden thread in the black box: Artificial intelligence and personal injury law | Vfek1SN93ncJ | https://search.informit.org/doi/abs/10.3316/informit.T2024082200016591870913932 | NaN | NaN |
| Leveraging automated technologies for law-making in Italy: Generative AI and constitutional challenges | Q3u3v6jyynMJ | https://academic.oup.com/pa/advance-article-abstract/doi/10.1093/pa/gsae040/7907222 | NaN | NaN |
| On evaluating legal-reasoning capabilities of generative ai | G4OomxoYXtoJ | https://ceur-ws.org/Vol-3769/paper12.pdf | 4.0 | https://ceur-ws.org/Vol-3769/paper12.pdf |
| Using generative AI in human resource development: an applied research study | yDiLMyYucmwJ | https://www.tandfonline.com/doi/abs/10.1080/13678868.2024.2337964 | 8.0 | https://www.researchgate.net/profile/Sami-Jabarkhail/publication/380437578_Using_generative_AI_in_human_resource_development_an_applied_research_study/links/665095dd0b0d28457454acce/Using-generative-AI-in-human-resource-development-an-applied-research-study.pdf |
| Access to Justice and Public Confidence in Courts: Whose Law Is It Anyway? | OWYoZcEYvLwJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/catoscrev23§ion=4 | NaN | NaN |
| Implications of ChatGPT Technology on Criminal Law | ELENLaCk7RAJ | https://www.ceeol.com/search/chapter-detail?id=1242801 | NaN | NaN |
| Generative AI for Software Test Modelling with a focus on ERP Software | MuHu-Fx25cMJ | https://ieeexplore.ieee.org/abstract/document/10466102/ | 4.0 | NaN |
| The Disruption We Needed: Accelerated Innovation in Courts and Access to Justice | 6BhLRkmvmP8J | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/nylr99§ion=5 | 2.0 | NaN |
| Generative AI and LLMs in Industry: A text-mining Analysis and Critical Evaluation of Guidelines and Policy Statements Across Fourteen Industrial Sectors | N0wKgsc8LgUJ | https://www.researchgate.net/profile/Saleh-Afroogh/publication/387671573_Generative_AI_and_LLMs_in_Industry_A_text-mining_Analysis_and_Critical_Evaluation_of_Guidelines_and_Policy_Statements_Across_Fourteen_Industrial_Sectors/links/67a0d3f852b58d39f268670e/Generative-AI-and-LLMs-in-Industry-A-text-mining-Analysis-and-Critical-Evaluation-of-Guidelines-and-Policy-Statements-Across-Fourteen-Industrial-Sectors.pdf | NaN | https://www.researchgate.net/profile/Saleh-Afroogh/publication/387671573_Generative_AI_and_LLMs_in_Industry_A_text-mining_Analysis_and_Critical_Evaluation_of_Guidelines_and_Policy_Statements_Across_Fourteen_Industrial_Sectors/links/67a0d3f852b58d39f268670e/Generative-AI-and-LLMs-in-Industry-A-text-mining-Analysis-and-Critical-Evaluation-of-Guidelines-and-Policy-Statements-Across-Fourteen-Industrial-Sectors.pdf |
| PanGu-: Enhancing Language Model Architectures via Nonlinearity Compensation | n0Hs2VDVw4QJ | https://arxiv.org/abs/2312.17276 | 21.0 | https://arxiv.org/pdf/2312.17276 |
| From Data to Verdict: Navigating AI's Growth & Blemish in the Legal System | gdOpGn0QV3kJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/ijlmhs26§ion=30 | NaN | NaN |
| The Rise of Intelligent Machines: An Introduction to Artificial Intelligence | yDluiVwADsMJ | https://onlinelibrary.wiley.com/doi/abs/10.1002/9781394234196.ch1 | 4.0 | NaN |
| ChatGPT as a Copilot for Investigating Digital Evidence. | LMgkDC4nxD0J | https://ceur-ws.org/Vol-3423/paper6.pdf | 19.0 | https://ceur-ws.org/Vol-3423/paper6.pdf |
| Comments on “Guide on the use of Generative AI” | XKSKlgFLZNUJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4624621 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4624621 |
| Transformative Applications of ChatGPT: A Comprehensive Review of Its Impact across Industries | NX2I9Mud70EJ | https://www.researchgate.net/profile/Ahmad-Jamal-16/publication/383127794_Transformative_Applications_of_ChatGPT_A_Comprehensive_Review_of_Its_Impact_across_Industries/links/66be0a34311cbb09493ceb4c/Transformative-Applications-of-ChatGPT-A-Comprehensive-Review-of-Its-Impact-across-Industries.pdf | 18.0 | https://www.researchgate.net/profile/Ahmad-Jamal-16/publication/383127794_Transformative_Applications_of_ChatGPT_A_Comprehensive_Review_of_Its_Impact_across_Industries/links/66be0a34311cbb09493ceb4c/Transformative-Applications-of-ChatGPT-A-Comprehensive-Review-of-Its-Impact-across-Industries.pdf |
| Automatic linking of judgements to UK Supreme Court hearings | OTFgKz00ph8J | https://aclanthology.org/2023.emnlp-industry.47/ | 1.0 | https://aclanthology.org/2023.emnlp-industry.47.pdf |
| Healthcare: A Growing Role for Large Language Models and Generative AI | uXrqwmlsRoEJ | https://www.researchgate.net/profile/Saurabh-Pahune-2/publication/373634933_Healthcare_A_Growing_Role_for_Large_Language_Models_and_Generative_AI/links/64f48922827074313ff5af90/Healthcare-A-Growing-Role-for-Large-Language-Models-and-Generative-AI.pdf | 4.0 | https://www.researchgate.net/profile/Saurabh-Pahune-2/publication/373634933_Healthcare_A_Growing_Role_for_Large_Language_Models_and_Generative_AI/links/64f48922827074313ff5af90/Healthcare-A-Growing-Role-for-Large-Language-Models-and-Generative-AI.pdf |
| GeoLLM: A specialized large language model framework for intelligent geotechnical design | kzkt1kFidbcJ | https://www.sciencedirect.com/science/article/pii/S0266352X24007882 | 7.0 | NaN |
| ChatGPT-The Blurst of Times | GZT4ftQyGR0J | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/auslwlib31§ion=11 | NaN | NaN |
| The Impact of Generative AI on Tenant-Driven Commercial Real Estate Valuation | m5lEJ--ziNcJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4885867 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4885867 |
| Private ordering, generative AI and the 'platformisation paradigm': What can we learn from comparative analysis of models terms and conditions? | JOrIKdo7d-kJ | https://www.cambridge.org/core/journals/cambridge-forum-on-ai-law-and-governance/article/private-ordering-generative-ai-and-the-platformisation-paradigm-what-can-we-learn-from-comparative-analysis-of-models-terms-and-conditions/92790919A0203140ED012BF8A4BA8A0F | NaN | https://www.cambridge.org/core/services/aop-cambridge-core/content/view/92790919A0203140ED012BF8A4BA8A0F/S3033373324000115a.pdf/private-ordering-generative-ai-and-the-platformisation-paradigm-what-can-we-learn-from-comparative-analysis-of-models-terms-and-conditions.pdf |
| Chatmap: Large Language Model Interaction with Cartographic Data | Yyq2ZqBnxDMJ | https://arxiv.org/abs/2310.01429 | 4.0 | https://arxiv.org/pdf/2310.01429 |
| Language Model Fine-Tuning | kcm5NP6MOecJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5044165 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5044165 |
| How to Augment Language Skills: Generative AI and Machine Translation in Language Learning and Translator Training | _IKmSkYdY2AJ | https://books.google.com/books?hl=en&lr=&id=hjwTEQAAQBAJ&oi=fnd&pg=PP1&dq=(%22access+to+justice%22+OR+%22legal+aid%22+OR+%22legal+services%22)+AND+(%22language+model%22+OR+%22GPT%22+OR+%22generative+AI%22)&ots=J6uTtsnA3W&sig=pMp2c7yeWm4ZZ-o9xZ1iD0Nzemc | 11.0 | NaN |
| Analysis of barriers and proposals for inclusive access to justice for vulnerable groups | K7hkE1GDNtkJ | https://journal.publiseditorial.com/index.php/jles/article/view/69 | NaN | https://journal.publiseditorial.com/index.php/jles/article/download/69/233 |
| Utilising Generative AI in Businesses: Risks and Best Practices | 9N9wylmY7dQJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/blawintnl24§ion=26 | 1.0 | NaN |
| DO USO DE IA GENERATIVA NOS TRIBUNAIS A UMA JUSTIÇA DEGENERATIVA: QUANDO A TECNOLOGIA ALUCINA (From the Use of Generative Ai in Courts to … | HIO1C6XNEh8J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4904844 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4904844 |
| A Knowledge Graph Modeling Approach for Augmenting Language Model-Based Contract Risk Identification | idl5o7BDS4YJ | https://ec-3.org/publications/conference/paper/?id=EC32024_178 | NaN | https://ec-3.org/publications/conferences/EC32024/papers/EC32024_178.pdf |
| Why Are Lawyers Afraid of AI? | H2zmHbKLRC0J | https://dl.acm.org/doi/abs/10.1145/3631935 | 5.0 | https://dl.acm.org/doi/pdf/10.1145/3631935 |
| AI and law | zbCs_xPzI6IJ | https://www.scienceinparliament.org.uk/wp-content/uploads/2025/04/Atkinson.pdf | NaN | https://www.scienceinparliament.org.uk/wp-content/uploads/2025/04/Atkinson.pdf |
| Mapping the challenges of HCI: An application and evaluation of ChatGPT and GPT-4 for mining insights at scale | gkdm8RV9wjYJ | https://arxiv.org/abs/2306.05036 | 7.0 | https://arxiv.org/pdf/2306.05036 |
| The CLC-UKET Dataset: Benchmarking Case Outcome Prediction for the UK Employment Tribunal | oO6c-Wwoy2sJ | https://arxiv.org/abs/2409.08098 | 4.0 | https://arxiv.org/pdf/2409.08098 |
| Beyond Readability with RateMyPDF | 6hCPZ8Fr_aMJ | https://assemblyline.suffolklitlab.org/assets/files/Beyond%20Readability%20with%20RateMyPDF-306d8c3ae5c5cb2ff33d436a859d1581.pdf | NaN | https://assemblyline.suffolklitlab.org/assets/files/Beyond%20Readability%20with%20RateMyPDF-306d8c3ae5c5cb2ff33d436a859d1581.pdf |
| Annual Donahue Lecture: Diverse Student Bodies Post-Students for Fair Admissions-A Path Forward | naR_jC8HkM0J | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/sufflr57§ion=19 | NaN | NaN |
| Somelvlm: A large vision language model for social media processing | zUG2dOzRSrAJ | https://arxiv.org/abs/2402.13022 | 7.0 | https://arxiv.org/pdf/2402.13022 |
| Enhancing Trust in Generative AI: Investigating Explainability of LLMs to Analyse Confusion in MOOC Discussions. | 4-9vj5mx-4EJ | https://www.researchgate.net/profile/Yuanyuan-Hu-9/publication/379261874_Enhancing_Trust_in_Generative_AI_Investigating_Explainability_of_LLMs_to_Analyse_Confusion_in_MOOC_Discussions/links/6603357ba6d9fc55fd9921b2/Enhancing-Trust-in-Generative-AI-Investigating-Explainability-of-LLMs-to-Analyse-Confusion-in-MOOC-Discussions.pdf | 1.0 | https://www.researchgate.net/profile/Yuanyuan-Hu-9/publication/379261874_Enhancing_Trust_in_Generative_AI_Investigating_Explainability_of_LLMs_to_Analyse_Confusion_in_MOOC_Discussions/links/6603357ba6d9fc55fd9921b2/Enhancing-Trust-in-Generative-AI-Investigating-Explainability-of-LLMs-to-Analyse-Confusion-in-MOOC-Discussions.pdf |
| Judicial Reforms and Access to Justice: A Comparative Analysis of E-Courts and Technological Integration in India and Singapore | 27k8KQKHkVAJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/ijlmhs31§ion=86 | NaN | NaN |
| Digital Saviour... Or Just Another Problem to Deal With: A Discourse Analysis of the Conflicting Narratives Regarding the Implications of Generative AI for the Teaching … | kgj0XM0lXrYJ | https://research.bond.edu.au/en/publications/digital-saviour-or-just-another-problem-to-deal-with-a-discourse- | NaN | https://research.bond.edu.au/files/243282020/LERC_Book_of_abstracts_website.pdf |
| Introduction to the Minitrack on ICT and Criminal Justice | pRL5Y0ANm8AJ | https://scholarspace.manoa.hawaii.edu/bitstreams/42dea31a-edeb-41a5-8d72-c5642e416f86/download | NaN | NaN |
| The Expanding Function of Generative AI and Large Language Models in Healthcare | Ezi7O1rqMCQJ | https://www.researchgate.net/profile/Saurabh-Pahune-2/publication/386253097_The_Expanding_Function_of_Generative_AI_and_Large_Language_Models_in_Healthcare/links/674a5933a7fbc259f19e67eb/The-Expanding-Function-of-Generative-AI-and-Large-Language-Models-in-Healthcare.pdf | NaN | https://www.researchgate.net/profile/Saurabh-Pahune-2/publication/386253097_The_Expanding_Function_of_Generative_AI_and_Large_Language_Models_in_Healthcare/links/674a5933a7fbc259f19e67eb/The-Expanding-Function-of-Generative-AI-and-Large-Language-Models-in-Healthcare.pdf |
| Persuading across Diverse Domains: a Dataset and Persuasion Large Language Model | dDbdmiHfrYoJ | https://aclanthology.org/2024.acl-long.92/ | 8.0 | https://aclanthology.org/2024.acl-long.92.pdf |
| Exploring a GPT-based large language model for variable autonomy in a VR-based human-robot teaming simulation | nI4pc9EGbUoJ | https://www.frontiersin.org/articles/10.3389/frobt.2024.1347538/full | 3.0 | https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2024.1347538/pdf |
| Generative AI-Powered Solutions for Sustainable Financial Ecosystems: A Neural Network Approach to Driving Social and Environmental Impact | 0JVdOYKr-5YJ | https://www.academia.edu/download/121344922/2956_Article_Text_5128_1_10_20250213.pdf | 7.0 | https://www.academia.edu/download/121344922/2956_Article_Text_5128_1_10_20250213.pdf |
| Fine-tuned large language model for visualization system: A study on self-regulated learning in education | qj8laKSbW8gJ | https://ieeexplore.ieee.org/abstract/document/10670435/ | 12.0 | https://arxiv.org/pdf/2407.20570 |
| Enhancing generative AI reliability via agentic AI in 6G-enabled edge computing | oG3kEddoP88J | https://www.nature.com/articles/s44287-025-00169-3 | NaN | https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.173931080.06536297 |
| Replacing This Old House: Certifying and Regulating New Legal Services Providers | 2CGGojIZFmYJ | https://journals.library.wustl.edu/lawpolicy/article/id/8977/ | 1.0 | https://journals.library.wustl.edu/lawpolicy/article/id/8977/download/pdf/ |
| Cahiers de Traduction | jpcoqVYi6QcJ | https://asjp.cerist.dz/index.php/en/downArticle/224/30/1/245812 | NaN | https://asjp.cerist.dz/index.php/en/downArticle/224/30/1/245812 |
| Better transcription of uk supreme court hearings | NJWO2V-bSKAJ | https://arxiv.org/abs/2211.17094 | 9.0 | https://arxiv.org/pdf/2211.17094 |
| Mapping the Potentials and Limitations of Using Generative AI Technologies to Address Socio-Economic Challenges in LMICs | 3xpB1xoOKekJ | https://verixiv.org/articles/2-57 | NaN | https://verixiv.org/articles/2-57/pdf |
| Bekenbey AI: Innovative Solutions at the Intersection of Deep Learning and Law | ju14jCLQ_TMJ | https://dergipark.org.tr/en/pub/cumfad/issue/88347/1590764 | NaN | https://dergipark.org.tr/en/download/article-file/4391255 |
| Argumentative segmentation enhancement for legal summarization | 21TvEm4C_3QJ | https://arxiv.org/abs/2307.05081 | 10.0 | https://arxiv.org/pdf/2307.05081 |
| Generative AI: Language models and multimodal foundation models | 9ocGP8hgKUoJ | https://atse.org.au/media/g4tfvqhu/rapid-response-information-report-generative-ai-220602.pdf | 1.0 | https://atse.org.au/media/g4tfvqhu/rapid-response-information-report-generative-ai-220602.pdf |
| Automatic Text Simplification for the Legal Domain in Brazilian Portuguese | 0OF0hAJP9VAJ | https://link.springer.com/chapter/10.1007/978-3-031-79038-6_3 | NaN | NaN |
| Addressing Technical Challenges in Large Language Model-Driven Educational Software System | 5WvELCt1arQJ | https://ieeexplore.ieee.org/abstract/document/10845786/ | NaN | https://ieeexplore.ieee.org/iel8/6287639/6514899/10845786.pdf |
| Private Ordering, Generative AI and the 'Platformisation Paradigm': What Can We Learn From Comparative Analysis of Models Terms and Conditions? | FwohY2KPsOQJ | https://eprints.whiterose.ac.uk/219330/1/CFL-2024-0014.R1_Proof_hi%20%281%29.pdf | NaN | https://eprints.whiterose.ac.uk/219330/1/CFL-2024-0014.R1_Proof_hi%20%281%29.pdf |
| PR Council guidelines on generative AI | Cozd6dhwLwwJ | https://ctpr.com/wp-content/uploads/2023/10/Packet-Final.pdf | 3.0 | https://ctpr.com/wp-content/uploads/2023/10/Packet-Final.pdf |
| Creative and Strategic Capabilities of Generative AI: Evidence from Large-Scale Experiments | Q_89nrnh9yYJ | https://www.econstor.eu/handle/10419/305744 | 3.0 | https://www.econstor.eu/bitstream/10419/305744/1/dp17302.pdf |
| BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation | oKOMNS2NmFsJ | https://arxiv.org/abs/2406.13555 | 3.0 | https://arxiv.org/pdf/2406.13555 |
| Analysis of the Digital Transformation of Legal Services and the Role of Policy Brokers in KOREA through the Advocacy Coalition Framework | 1B6lhnMG9xwJ | http://journal.iapa.or.id/pgr/article/view/1171 | NaN | http://journal.iapa.or.id/pgr/article/download/1171/594 |
| Free LLMs Hallucinate and Rarely Signal Their Limitations in Solving Legal Problems | aH2ZKJ7gUmYJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5188654 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5188654 |
| Let's Talk, ChatGPT: What Will the Judiciary's Future Look Like? | tcr1n9PQGaIJ | https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=00153915&asa=N&AN=163343935&h=62KsOv1zlVbXmFBwvaei%2FkRBiMvk7uhhuB7P8HztvrfQUx3P5bLvv3fYr9kYRVPimlyY07lUg%2BRdiZQKngQYOA%3D%3D&crl=c | 6.0 | NaN |
| Transdisciplinary research as a way forward in AI & Law | YT8QBByRvOoJ | https://journalcrcl.org/crcl/article/view/61 | 1.0 | https://journalcrcl.org/crcl/article/download/61/30 |
| Too Legal; Didn't Read (TLDR): Summarization of Court Opinions | BUSGAZ98jukJ | https://ieeexplore.ieee.org/abstract/document/10152119/ | 3.0 | NaN |
| Generative AI Impact on Labor Market: Analyzing ChatGPT's Demand in Job Advertisements | F0YMldnt1UoJ | https://arxiv.org/abs/2412.07042 | NaN | https://arxiv.org/pdf/2412.07042 |
| NATURALIZING LEGAL INTERPRETATION AFTER GENERATIVE AI | 8JE3jvLmRJIJ | https://am.aals.org/app/uploads/sites/4/2024/12/naturalizing_jurisprudence__after_ai_distrib.cleaned.pdf | NaN | https://am.aals.org/app/uploads/sites/4/2024/12/naturalizing_jurisprudence__after_ai_distrib.cleaned.pdf |
| A Knight in Shining Nascency: Under-the-Radar Platforms as a Solution to Access to Justice for Incarcerated Litigants | 21Up8qsNj6cJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4919943 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4919943 |
| We need to talk about ChatGPT: A lawyer's introduction to the exploding field of AI and large language models. | b2NaPMQ6fYoJ | https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=02761505&asa=N&AN=163906449&h=rgfBHBbG3XuUxY%2BCzbDz2DZtKvVzERyhjjxwnNW1%2BBD8n00ZB%2Ft55bxNAgnKHxlyxEXUtI4%2B7ev8RaKMAtgOnw%3D%3D&crl=c | NaN | NaN |
| Proposal for enhancing legal advisory services in the montenegrin banking sector with artificial intelligence | ncxqGpKb3PoJ | https://ieeexplore.ieee.org/abstract/document/10475735/ | 3.0 | NaN |
| Use of Generative AI by Higher Education Students | MIpaOZFeIsQJ | https://www.mdpi.com/2079-9292/14/7/1258 | NaN | NaN |
| The Use of Artificial Intelligence and the Professional Duties of German Lawyers | 9vDU08JcYqsJ | https://pressto.amu.edu.pl/index.php/spp/article/view/46816 | NaN | https://pressto.amu.edu.pl/index.php/spp/article/download/46816/38275 |
| The Early-Career Professional's Guide to Generative AI | yR_7wumqljMJ | https://link.springer.com/content/pdf/10.1007/979-8-8688-0456-4.pdf | 1.0 | NaN |
| Artificial Intelligence and Legal Transparency: A Comparative Analysis between Public and Private Law | r0sO2A6Lo0UJ | http://posthumanism.co.uk/jp/article/view/685 | NaN | https://posthumanism.co.uk/jp/article/download/685/357 |
| Roles and challenges of ChatGPT and similar generative artificial intelligence for achieving the sustainable development goals (SDGs) | flw_DqScUF4J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4603244 | 66.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4603244 |
| Enhancing Vision-Language Model Safety through Progressive Concept-Bottleneck-Driven Alignment | 0epoEcgwBXoJ | https://arxiv.org/abs/2411.11543 | NaN | https://arxiv.org/pdf/2411.11543 |
| Stepping Above the Generative Ai Ethical Floor: The Sky's the Limit | -1qPa25dIGUJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4746753 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4746753 |
| The Law Firm Playbook: Reinventing Legal Practice for the Modern Era | R_zh4u87s9YJ | https://books.google.com/books?hl=en&lr=&id=1OhXEQAAQBAJ&oi=fnd&pg=PT6&dq=(%22access+to+justice%22+OR+%22legal+aid%22+OR+%22legal+services%22)+AND+(%22language+model%22+OR+%22GPT%22+OR+%22generative+AI%22)&ots=kMiNMVbVCj&sig=V29J9X8YIG-_wVx893drijAFmwo | NaN | NaN |
| Unmoderated Usability Studies Evolved: Can GPT Ask Useful Follow-up Questions? | 3Ks5l75Jqo4J | https://www.tandfonline.com/doi/abs/10.1080/10447318.2024.2427978 | 1.0 | https://www.tandfonline.com/doi/pdf/10.1080/10447318.2024.2427978 |
| Man or machine? An exploratory study of the performance of chat GPT 3.5 in the CFC sufficiency exam | 5U2GMFXt3YIJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4560434 | 6.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4560434 |
| ChatGPT in a Nutshell | r9O1jy8d334J | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/colaworl37§ion=38 | NaN | NaN |
| A framework for LLM-assisted smart policing system | QwvSH7T8Z0EJ | https://ieeexplore.ieee.org/abstract/document/10538107/ | 12.0 | https://ieeexplore.ieee.org/iel7/6287639/6514899/10538107.pdf |
| AI in the nonprofit human services: Distinguishing between hype, harm, and hope | L4gmHMOQDUEJ | https://www.tandfonline.com/doi/abs/10.1080/23303131.2024.2427459 | 2.0 | https://www.tandfonline.com/doi/pdf/10.1080/23303131.2024.2427459 |
| Large language models and generative ai's expanding role in healthcare | uafOrG00ClgJ | https://www.researchgate.net/profile/Saurabh-Pahune-2/publication/377217911_Large_Language_Models_and_Generative_AI's_Expanding_Role_in_Healthcare/links/659aad286f6e450f19d3f129/Large-Language-Models-and-Generative-AIs-Expanding-Role-in-Healthcare.pdf | 12.0 | https://www.researchgate.net/profile/Saurabh-Pahune-2/publication/377217911_Large_Language_Models_and_Generative_AI's_Expanding_Role_in_Healthcare/links/659aad286f6e450f19d3f129/Large-Language-Models-and-Generative-AIs-Expanding-Role-in-Healthcare.pdf |
| LLM4Causal: Democratized Causal Tools for Everyone via Large Language Model | _C_qVU_R_oMJ | https://openreview.net/forum?id=H1Edd5d2JP | 2.0 | https://openreview.net/pdf?id=H1Edd5d2JP |
| Large Language Model Evaluation Criteria Framework in Healthcare: Fuzzy MCDM Approach | FtAPs-_jYOIJ | https://link.springer.com/article/10.1007/s42979-024-03533-6 | 1.0 | https://www.researchgate.net/profile/Hamzeh-Alabool/publication/387671509_Large_Language_Model_Evaluation_Criteria_Framework_in_Healthcare_Fuzzy_MCDM_Approach/links/679883284c479b26c9bd167a/Large-Language-Model-Evaluation-Criteria-Framework-in-Healthcare-Fuzzy-MCDM-Approach.pdf |
| LegalRelectra: Mixed-domain language modeling for long-range legal text comprehension | 11KlEG9Q9e8J | https://arxiv.org/abs/2212.08204 | 7.0 | https://arxiv.org/pdf/2212.08204 |
| 생성형 인공지능과 ADR―대체적 분쟁해결을 위한 생성형 인공지능 활용에 대한 소고― | 3_JLFL-U4bUJ | https://kiss.kstudy.com/Detail/Ar?key=4138522 | NaN | NaN |
| Do Large Language Model Benchmarks Test Reliability? | yU764-jHuYIJ | https://arxiv.org/abs/2502.03461 | 5.0 | https://arxiv.org/pdf/2502.03461? |
| The rise of generative AI: modelling exposure, substitution, and inequality effects on the US labour market | A74EjRLf2AgJ | https://www.econstor.eu/handle/10419/307340 | 1.0 | https://www.econstor.eu/bitstream/10419/307340/1/cesifo1_wp11410.pdf |
| Student Scholars: Access-to-Justice Research in the Law School Direct Representation Clinic | i3AWry70BicJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5193310 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5193310 |
| Artificial Intelligence (AI) Vs Academic Integrity (AI) in Law and Society | 573DLIdsWUYJ | http://bjbio.bioethics.org.bd/index.php/BJBio/article/view/124 | NaN | https://bjbio.bioethics.org.bd/index.php/BJBio/article/download/124/124 |
| Have the fundamentals really changed since maister? | EbJYkieLblQJ | https://search.informit.org/doi/pdf/10.3316/informit.T2024103000004191224291978 | NaN | NaN |
| Design at the Center for Future Strategy Making: Generative AI as an Affordance Catalyst | uySo4wqTXNAJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4994466 | 1.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4994466 |
| Law as data, data as law | 48mXc0v91WcJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/lwtchmn6§ion=19 | NaN | https://search.informit.org/doi/pdf/10.3316/informit.T2025011900000390025191863 |
| Knowledge-infused legal wisdom: Navigating llm consultation through the lens of diagnostics and positive-unlabeled reinforcement learning | sdjBd5vNE04J | https://arxiv.org/abs/2406.03600 | 7.0 | https://arxiv.org/pdf/2406.03600 |
| AI and the Legal Profession | hZEhVgHbzWEJ | https://globelawonline.com/pdf/book/301/ai-and-the-legal-profession.pdf | NaN | NaN |
| Linking Judgement Text to Court Hearing Videos: UK Supreme Court as a Case Study | hfKbdgn8f08J | https://aclanthology.org/2024.lrec-main.927/ | NaN | https://aclanthology.org/2024.lrec-main.927.pdf |
| A cross-lingual syntactic investigation of gender bias and stereotyping in GPT-4o: English vs Hindi | lVLwoMS_9fcJ | https://link.springer.com/article/10.1007/s43681-024-00565-9 | NaN | NaN |
| 基于生成式人工智能法律服务的数智化发展逻辑与建构路径 | 6ECXeNeROUgJ | https://xb.szu.edu.cn/CN/article/downloadArticleFile.do?attachType=PDF&id=1113 | NaN | https://xb.szu.edu.cn/CN/article/downloadArticleFile.do?attachType=PDF&id=1113 |
| Large Vision-Language Model Security: A Survey | 4gUeSLlHb8MJ | https://link.springer.com/chapter/10.1007/978-981-96-0151-6_1 | 1.0 | https://scholar.xjtlu.edu.cn/files/51213217/978-981-96-0151-6_1_6_.pdf |
| Artificial intelligence and the sustainable development goals: an exploratory study in the context of the society domain | Hb7vkjE6mpUJ | https://www.scirp.org/journal/paperinformation?paperid=124905 | 18.0 | https://www.scirp.org/pdf/jsea_2023051516234211.pdf |
| ChatGPT: limitations, challenges and potential applications | SKfsqhktqhMJ | https://periodicos.cerradopub.com.br/bjs/article/view/427 | 3.0 | https://periodicos.cerradopub.com.br/bjs/article/download/427/268 |
| Confirmation Bias in Generative AI Chatbots: Mechanisms, Risks, Mitigation Strategies, and Future Research Directions | mI1P374fIKEJ | https://arxiv.org/abs/2504.09343 | NaN | https://arxiv.org/pdf/2504.09343 |
| An Empirical Study of Production Incidents in Generative AI Cloud Services | _OXxj01xIJYJ | https://arxiv.org/abs/2504.08865 | NaN | https://arxiv.org/pdf/2504.08865 |
| Learning-Retrieval-Revision For Large Language Model Domain Adaptation | 6C9WMJbxT4oJ | https://openreview.net/forum?id=3GunDQNKFJ | NaN | https://openreview.net/pdf?id=3GunDQNKFJ |
| Generative AI in the Law | jcDJsdOzy-4J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5040429 | 1.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5040429 |
| Artificial Intelligence and Quality of Composition Verdicts in Indonesia: Lessons from New Zealand | H75nWp8KzkAJ | https://eprints.umm.ac.id/id/eprint/4583/ | 5.0 | https://eprints.umm.ac.id/id/eprint/4583/20/Similarity%20-%20Hidayah%20Wicaksono%20Aditya%20Munarko%20-%20Artificial%20Intelligence%20Structure%20Verdict.pdf |
| BianCang: A Traditional Chinese Medicine Large Language Model | mfP-piOctqgJ | https://arxiv.org/abs/2411.11027 | 5.0 | https://arxiv.org/pdf/2411.11027 |
| Gen-SynDi: Leveraging Knowledge-Guided Generative AI for Dual Education of Syndrome Differentiation and Disease Diagnosis | XHNmXNv_vJgJ | https://www.mdpi.com/2076-3417/15/9/4862 | NaN | NaN |
| ChatGPT and Generative AI Systems as Military Ethics Advisors | G2vdU-5fzE4J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4413206 | 7.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4413206 |
| Opportunities and threats of generative AI technology | kjebtrl8T0IJ | https://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE11408659 | 2.0 | NaN |
| Creative and Critical Engagement: An Ongoing Reflection on AI Pedagogy in Legal Practice | QmqyWgHi4t4J | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/callb49§ion=26 | NaN | NaN |
| Dynamic Universally Adaptive Language Model–A New Approach to Natural Language Processing in Machine Learning | bTnHSAeWCCsJ | https://www.researchgate.net/profile/Neerav-Sood/publication/379568207_Dynamic_Universally_Adaptive_Language_Model_-A_New_Approach_to_Natural_Language_Processing_in_Machine_Learning/links/6616057843f8df018deaf977/Dynamic-Universally-Adaptive-Language-Model-A-New-Approach-to-Natural-Language-Processing-in-Machine-Learning.pdf | NaN | https://www.researchgate.net/profile/Neerav-Sood/publication/379568207_Dynamic_Universally_Adaptive_Language_Model_-A_New_Approach_to_Natural_Language_Processing_in_Machine_Learning/links/6616057843f8df018deaf977/Dynamic-Universally-Adaptive-Language-Model-A-New-Approach-to-Natural-Language-Processing-in-Machine-Learning.pdf |
| Large Language Model and Application | 6DtaTg6M_doJ | https://books.google.com/books?hl=en&lr=&id=D7wyEQAAQBAJ&oi=fnd&pg=PA194&dq=(%22access+to+justice%22+OR+%22legal+aid%22+OR+%22legal+services%22)+AND+(%22language+model%22+OR+%22GPT%22+OR+%22generative+AI%22)&ots=r4ceYV0TUq&sig=uq_N5PtcLQXnfYgr5869x1CW-Bo | NaN | NaN |
| Guarding The News Media's Intellectual Property in the Age of Generative AI | pMuzPPoMHigJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4957170 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4957170 |
| Prompting Minds: Evaluating how Students Perceive Generative AI's Critical Thinking Dispositions | F5YEH5n2YDoJ | https://academic-publishing.org/index.php/ejel/article/view/3986 | NaN | https://academic-publishing.org/index.php/ejel/article/download/3986/2371 |
| A Survey of Generative AI in Finance | KthtaKV79LAJ | https://hal.science/hal-05020829/ | NaN | https://hal.science/hal-05020829v1/file/A%20Survey%20of%20Generative%20AI%20in%20Finance.pdf |
| How can we manage the risks and liabilities associated with legal translation in the age of machine translation and generative AI? | l4PMeM8YyF0J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4707819 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4707819 |
| Generative AI and Entrepreneurial Entry | b496aCfAJecJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5167018 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5167018 |
| Developing a Generative AI Model to Enhance Sentiment Analysis for the Saudi Dialect | JjKy892udNQJ | https://ttu-ir.tdl.org/items/a36dc4b8-82b0-4057-9bd4-ec059b3e1c48 | NaN | https://ttu-ir.tdl.org/bitstreams/36089ce3-e2a8-4e29-9bf8-55b63e93e8f0/download |
| Breaking the Loop: Detecting and Mitigating Denial-of-Service Vulnerabilities in Large Language Models | B-J7tTJ2YCMJ | https://arxiv.org/abs/2503.00416 | NaN | https://arxiv.org/pdf/2503.00416 |
| Copyright in the Age of Generative Artificial Intelligence: Comments in Response to the Government of Canada's Consultation Questionnaire | FCFL8LhLaeMJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4718941 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4718941 |
| Mindllm: Pre-training lightweight large language model from scratch, evaluations and domain applications | VniR1rNFOEwJ | https://arxiv.org/abs/2310.15777 | 14.0 | https://arxiv.org/pdf/2310.15777 |
| Discussion on the reform of higher legal education in China based on the application and limitation of artificial intelligence in law represented by ChatGPT | jdzBM0w9BAQJ | https://pdfs.semanticscholar.org/ecd1/c5ec9c8aca260509dfc0d20dcb2cc73f68be.pdf | 7.0 | https://pdfs.semanticscholar.org/ecd1/c5ec9c8aca260509dfc0d20dcb2cc73f68be.pdf |
| The Role of ChatGPT and Emojis in Modern Legal Interpretation | eDVjUUg-P18J | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/juscrp4§ion=135 | NaN | NaN |
| Developing Fictitious Country Maps through Generative AI Techniques | DlbOhgReUPsJ | https://run.unl.pt/bitstream/10362/180721/1/TGEO298_F.pdf | NaN | https://run.unl.pt/bitstream/10362/180721/1/TGEO298_F.pdf |
| Hallucinations in GPT-2 Trained Model. | Q--IJLBk6xkJ | https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=16331311&AN=182588965&h=5KB6mLypPjTdAOIjXGmCn8J14NSk%2FI2ZhI92NzYHdBMoy%2F8kSuG0HN5sHoaWC2TgeRQqzlM40z5TrSnHld4UgA%3D%3D&crl=c | NaN | NaN |
| Ethical Foresight: Confronting Misinformation, Representation and Toxicity in Generative AI | hCFGY1A7TzoJ | https://dspace.cuni.cz/bitstream/handle/20.500.11956/197727/120486728.pdf?sequence=1 | NaN | https://dspace.cuni.cz/bitstream/handle/20.500.11956/197727/120486728.pdf?sequence=1 |
| Mitigating Translationese with GPT-4: Strategies and Performance | o1rPy5FGPjIJ | https://aclanthology.org/2024.eamt-1.35/ | 1.0 | https://aclanthology.org/2024.eamt-1.35.pdf |
| Inspirepat: An Approach for Patent Recommendation Based on Siamese Ernie Model and Large Language Model | TCfDBG8LDZYJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5043639 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5043639 |
| The Legal AId: Justifying Justice | DoIEmP47jgoJ | https://ir.iimcal.ac.in:8443/jspui/handle/123456789/4082 | NaN | https://ir.iimcal.ac.in:8443/jspui/bitstream/123456789/4082/1/Legal%20Aid%20Justifying%20Justice.pdf |
| From Flowchart to Questionnaire: Increasing Access to Justice via Visualization | g4WP7TIImlgJ | https://ieeexplore.ieee.org/abstract/document/10339885/ | NaN | http://www.sci.utah.edu/~beiwang/publications/VIS4Good_BeiWang_2023.pdf |
| AI Lawyering Skills Trainers: Transforming Legal Education with Generative AI | le3MEvufjeIJ | https://scholarlycommons.law.case.edu/jolti/vol16/iss1/4/ | 1.0 | https://scholarlycommons.law.case.edu/cgi/viewcontent.cgi?article=1168&context=jolti |
| Factors Associated with the Low Uptake of Quality Medico-Legal Services at Secured Diagnostic Crime Scene, Western Kenya | iYt6Lm5ISPUJ | https://www.researchgate.net/profile/Maurice-Silali/publication/388063432_Factors_Associated_with_the_Low_Uptake_of_Quality_Medico-Legal_Services_at_Secured_Diagnostic_Crime_Scene/links/6788e5692be36743a5da1c0c/Factors-Associated-with-the-Low-Uptake-of-Quality-Medico-Legal-Services-at-Secured-Diagnostic-Crime-Scene.pdf | NaN | https://www.researchgate.net/profile/Maurice-Silali/publication/388063432_Factors_Associated_with_the_Low_Uptake_of_Quality_Medico-Legal_Services_at_Secured_Diagnostic_Crime_Scene/links/6788e5692be36743a5da1c0c/Factors-Associated-with-the-Low-Uptake-of-Quality-Medico-Legal-Services-at-Secured-Diagnostic-Crime-Scene.pdf |
| Enhancing Conversational Agents with Generative AI: A Framework for Creating More Adaptive and Context-aware chatbots | EUg9ZyvbZOkJ | https://www.researchgate.net/profile/Manoj-Bhoyar-2/publication/391366390_Enhancing_Conversational_Agents_with_Generative_AI_A_Framework_for_Creating_More_Adaptive_and_Context-aware_chatbots/links/6813def1d1054b0207e6e0b3/Enhancing-Conversational-Agents-with-Generative-AI-A-Framework-for-Creating-More-Adaptive-and-Context-aware-chatbots.pdf | NaN | https://www.researchgate.net/profile/Manoj-Bhoyar-2/publication/391366390_Enhancing_Conversational_Agents_with_Generative_AI_A_Framework_for_Creating_More_Adaptive_and_Context-aware_chatbots/links/6813def1d1054b0207e6e0b3/Enhancing-Conversational-Agents-with-Generative-AI-A-Framework-for-Creating-More-Adaptive-and-Context-aware-chatbots.pdf |
| ChatGPT for marketers: Limitations and mitigations | ORNwdjHgAR8J | https://www.ingentaconnect.com/content/hsp/jdsmm/2024/00000011/00000004/art00002 | 5.0 | NaN |
| The Impact of Generative AI on Education | 3v6EqjxTPO0J | https://www.igi-global.com/viewtitle.aspx?titleid=375506 | NaN | NaN |
| Advances in Generative AI and Platform Moderation: Implications for Online Knowledge Sharing | 7kzQWOd65PQJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4867815 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4867815 |
| Development of AI Prototype for Generating Construction Safety Guidelines Through Fine-Tuning of Large-Scale Language Model | moqz6a30twoJ | https://koreascience.kr/article/JAKO202510561208293.page | NaN | https://koreascience.kr/article/JAKO202510561208293.pdf |
| ACCESS TO JUSTICE FOR SELF-REPRESENTED LITIGANTS THROUGH THE NEW HAMPSHIRE CIRCUIT COURT NAVIGATOR PROGRAM: A PATH … | EX0pygEbHrgJ | https://rockefeller.dartmouth.edu/sites/rockefeller.prod/files/prs_2323_04_court_navigators_final.pdf | NaN | https://rockefeller.dartmouth.edu/sites/rockefeller.prod/files/prs_2323_04_court_navigators_final.pdf |
| From Competence to Excellence: How Utah's Law Schools Are Training Tomorrow's AI-Savvy Lawyers. | fc9QurqIOZgJ | https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=00919691&asa=N&AN=178706363&h=dsnsPGpcro4oVPa7lfQ5MJqNM9jp6WDWi5%2FjRSPFxn81dbAEC4OjvblmxAXQT3tZoyEfgMxCqF7Oet1ZsQYdbg%3D%3D&crl=c | NaN | NaN |
| Ten Thousand AI Systems Typing on Keyboards: Generative AI in Patent Applications and Preemptive Prior Art | f4RWySu8iFcJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/vanep26§ion=16 | 1.0 | https://cdn.vanderbilt.edu/vu-URL/wp-content/uploads/sites/356/2024/05/07175402/Villasenor_FINAL.pdf |
| Retrieval-based evaluation for LLMs: a case study in Korean legal QA | OGl1CpY-kTkJ | https://aclanthology.org/2023.nllp-1.13/ | 23.0 | https://aclanthology.org/2023.nllp-1.13.pdf |
| Silicon Valley start-ups explore sales as funds run dry and buyers return. Flurry of AI deals raises hopes for wave of consolidation involving cash-strapped tech … | HyuG1VbtALAJ | https://go.gale.com/ps/i.do?id=GALE%7CA757147039&sid=googleScholar&v=2.1&it=r&linkaccess=abs&issn=03071766&p=AONE&sw=w | NaN | NaN |
| Ethical Pitfalls When Lawyers are Using Artificial Intelligence | Cccdwh7GboYJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4457790 | 2.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4457790 |
| Deep Fake Detection using Generative AI Techniques | CYTtXANcRR4J | https://www.academia.edu/download/115593007/Conference_Proceedings_IRSD_2024.pdf#page=247 | 1.0 | https://www.academia.edu/download/115593007/Conference_Proceedings_IRSD_2024.pdf#page=247 |
| ChatGPT: Vision and challenges | a9a78JWo6rIJ | https://www.sciencedirect.com/science/article/pii/S2667345223000317 | 266.0 | NaN |
| Exploring the factors influencing the actual usage of generative AI in academic research | AwmwqPYg6eIJ | https://oulurepo.oulu.fi/handle/10024/53153 | NaN | https://oulurepo.oulu.fi/bitstream/handle/10024/53153/nbnfioulu-202412117185.pdf?sequence=1 |
| Application of Generative AI to the business context: analysis and assessment | VXGVZ8xwopIJ | https://thesis.unipd.it/handle/20.500.12608/77802 | NaN | https://thesis.unipd.it/bitstream/20.500.12608/77802/1/BERDIBEK%20S.%20Application%20of%20GenAI%20to%20the%20business%20context%20pdfA.pdf |
| Predicting consumer contracts | gScUXpSxSxgJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/berktech37§ion=6 | 45.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3844988 |
| Competitive Advantage in B2B Marketing and Sales Through Generative AI | JxMDLxGZzF4J | https://www.diva-portal.org/smash/record.jsf?pid=diva2:1873122 | 1.0 | https://www.diva-portal.org/smash/get/diva2:1873122/FULLTEXT01.pdf |
| Better than a bot–instilling ethical judgement into the lawyers of the future in the age of AI | lgzLmIUsptsJ | https://www.tandfonline.com/doi/abs/10.1080/10383441.2025.2493493 | NaN | https://www.tandfonline.com/doi/pdf/10.1080/10383441.2025.2493493 |
| ChatGPT and digital capitalism: need for an antidote of Competition Law | uNIX4MJ1rq8J | https://link.springer.com/article/10.1007/s00146-023-01822-x | 3.0 | https://link.springer.com/content/pdf/10.1007/s00146-023-01822-x.pdf |
| PhiloGPT: A Philology-Oriented Large Language Model for Ancient Chinese Manuscripts with Dunhuang as Case Study | NYeeny7dKncJ | https://aclanthology.org/2024.emnlp-main.163/ | 1.0 | https://aclanthology.org/2024.emnlp-main.163.pdf |
| Continual Pre-Training is (not) What You Need in Domain Adaption | feikXgtDjy8J | https://arxiv.org/abs/2504.13603 | 1.0 | https://arxiv.org/pdf/2504.13603 |
| Trustworthy AI: Securing sensitive data in large language models | Q-83yhRN5qwJ | https://www.mdpi.com/2673-2688/5/4/134 | 19.0 | NaN |
| Legal Profession in an Age of Generative Artificial Intelligence | -ELgNky2g2UJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/iihcj16§ion=66 | NaN | NaN |
| Prompt packer: Deceiving llms through compositional instruction with hidden attacks | bIi8Ve6d_s8J | https://arxiv.org/abs/2310.10077 | 27.0 | https://arxiv.org/pdf/2310.10077 |
| SynBioGPT: A Retrieval-Augmented Large Language Model Platform for AI-Guided Microbial Strain Development | 6mcJP0lvxysJ | https://www.biorxiv.org/content/10.1101/2025.03.23.644789.abstract | NaN | https://www.biorxiv.org/content/biorxiv/early/2025/03/24/2025.03.23.644789.full.pdf |
| Diseño de un asistente virtual orientado a digitalizar la atención al usuario en los consultorios jurídicos universitarios regulados por el ministerio de justicia y del … | ATpQwrG0OewJ | https://repositorio.cuc.edu.co/entities/publication/c97f5866-733f-4716-9957-9680eb787dce | NaN | NaN |
| Position: Stop Acting Like Language Model Agents Are Normal Agents | r84CfYBTdlEJ | https://arxiv.org/abs/2502.10420 | NaN | https://arxiv.org/pdf/2502.10420 |
| Cultural fidelity in large-language models: An evaluation of online language resources as a driver of model performance in value representation | KS2K506sQ5sJ | https://arxiv.org/abs/2410.10489 | 1.0 | https://arxiv.org/pdf/2410.10489 |
| What are Models Thinking about? Understanding Large Language Model Hallucinations" Psychology" through Model Inner State Analysis | PHjZDne92UkJ | https://arxiv.org/abs/2502.13490 | NaN | https://arxiv.org/pdf/2502.13490? |
| Artificial Intelligence & the Future of Law Libraries: Mid-Atlantic Roundtable Report | Wgt3m-_XVr8J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4955870 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4955870 |
| Large Language Model and Application for Railway Track Management Based on Domain Specialization | OvAgH9GFsPMJ | https://link.springer.com/chapter/10.1007/978-981-97-9644-1_21 | NaN | NaN |
| Hallucinations and Truth: A Comprehensive Accuracy Evaluation of RAG, LoRA and DoRA | xI22v_VkAogJ | https://arxiv.org/abs/2502.10497 | NaN | https://arxiv.org/pdf/2502.10497 |
| Generative AI with SAP and Amazon Bedrock | cRt9ajQWv_4J | https://link.springer.com/content/pdf/10.1007/979-8-8688-0968-2.pdf | NaN | NaN |
| Shariah Governance Standard on Generative AI for Islamic Financial Institutions | -ajvsbsAALoJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5143165 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5143165 |
| News GPT: A Large Language Model for Reliable and Hallucination-Controlled News Generation | VCvjJcmVWYkJ | https://dl.acm.org/doi/abs/10.1145/3689299.3689320 | NaN | NaN |
| Compassionate AI Democracy: Eliminating Legal Gaps Between the Poor and Wealthy | i9MUKt9HIPwJ | https://amitray.com/compassionate-ai-democracy-eliminating-legal-gaps-between-the-poor-and-wealthy/ | 9.0 | NaN |
| Some Emerging Hypotheses about Using Generative AI in Public Sector Operations | o6B30NBG7GIJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4544943 | 2.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4544943 |
| The technological developments in Al that will supposedly drive the move to the legal singularity are outlined in Chapters 2 and 3 of the book and cul | cNvHFX4lEgEJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/modlr87§ion=58 | NaN | NaN |
| WenyanGPT: A Large Language Model for Classical Chinese Tasks | w_7u2VP-ra8J | https://arxiv.org/abs/2504.20609 | NaN | https://arxiv.org/pdf/2504.20609 |
| AI for Data Science: A Benchmark for Differentially Private Text Dataset Generators | YfVyFeUpCzoJ | https://openreview.net/pdf?id=VUXt0E94gv | NaN | https://openreview.net/pdf?id=VUXt0E94gv |
| LePaRD: A Large-Scale Dataset of Judicial Citations to Precedent | k2JpemYUV94J | https://www.research-collection.ethz.ch/handle/20.500.11850/717967 | NaN | https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/717967/3/2024.acl-long.532.pdf |
| An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges | lCC16OQl0NMJ | https://www.sciencedirect.com/science/article/pii/S2772485923000066 | 797.0 | NaN |
| ChatGPT for good? Taking 'beneficence'seriously in the regulation of generative artificial intelligence | g94JtUW0IccJ | https://www.tandfonline.com/doi/abs/10.1080/13600869.2024.2364989 | 2.0 | NaN |
| Warhol, Drake, and Deepfakes: Monetizing the Right of Publicity in the Generative AI Era | Ksz1ZJFlB00J | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/gslr40§ion=47 | 2.0 | https://readingroom.law.gsu.edu/cgi/viewcontent.cgi?article=3277&context=gsulr |
| Exploring the feasibility of developing an education tool for pattern identification using a large language model: focusing on the case of a simulated patient with fatigue … | PEdAAU7Q1McJ | https://koreascience.kr/article/JAKO202410348414036.page | 1.0 | https://koreascience.kr/article/JAKO202410348414036.pdf |
| Reducing hallucinations in large language models through contextual position encoding | O-v-HRHTS3wJ | https://files.osf.io/v1/resources/exjqb/providers/osfstorage/665921bd65e1de48d5893f4d?action=download&direct&version=2 | 64.0 | https://files.osf.io/v1/resources/exjqb/providers/osfstorage/665921bd65e1de48d5893f4d?action=download&direct&version=2 |
| Exploring Generative AI as Personally Effective Decision-Making Tools: A Thought Experiment | 3mgKj_0vfpQJ | https://www.igi-global.com/chapter/exploring-generative-ai-as-personally-effective-decision-making-tools/364308 | NaN | NaN |
| Legal Knowledge and Information Systems: JURIX 2023: The Thirty-sixth Annual Conference, Maastricht, the Netherlands, 18-20 December 2023 | e1k4BdHiiXkJ | https://books.google.com/books?hl=en&lr=&id=B4LyEAAAQBAJ&oi=fnd&pg=PR1&dq=(%22access+to+justice%22+OR+%22legal+aid%22+OR+%22legal+services%22)+AND+(%22language+model%22+OR+%22GPT%22+OR+%22generative+AI%22)&ots=94ftekjds9&sig=fgGkSV-bPjQdQMwvYEM7tIDCi3Y | 2.0 | NaN |
| Legal large language models (LLMs): legal dynamos or “fancifully packaged ChatGPT”? | dIjEiCOLmbgJ | https://link.springer.com/article/10.1007/s44163-024-00167-8 | NaN | https://link.springer.com/content/pdf/10.1007/s44163-024-00167-8.pdf |
| ChatGPT y GPT-4: utilidades en el sector jurídico, funcionamiento, limitaciones y riesgos de los modelos fundacionales | M75_cXLMCPYJ | https://www.tecnologia-ciencia-educacion.com/index.php/TCE/article/view/19081 | 2.0 | https://www.tecnologia-ciencia-educacion.com/index.php/TCE/article/download/19081/22121 |
| Treu und Glauben: Frag GPT | DHTZl_KKZDkJ | https://www.econstor.eu/handle/10419/283133 | 1.0 | https://www.econstor.eu/bitstream/10419/283133/1/2023-10online.pdf |
| Pile of law: Learning responsible data filtering from the law and a 256gb open-source legal dataset | OO-3hRkHauEJ | https://proceedings.neurips.cc/paper_files/paper/2022/hash/bc218a0c656e49d4b086975a9c785f47-Abstract-Datasets_and_Benchmarks.html | 114.0 | https://proceedings.neurips.cc/paper_files/paper/2022/file/bc218a0c656e49d4b086975a9c785f47-Paper-Datasets_and_Benchmarks.pdf |
| Enhancing Generative AI Usage for Employees: Key Drivers and Barriers | CZsvZ6XE5O8J | https://jitm.ut.ac.ir/article_100696.html | NaN | https://jitm.ut.ac.ir/article_100696_9e30af20a9cbcd25edaec132e4806949.pdf |
| Authors in the Age of Language-generation AI: To be or not to be, is that Really the Question? | o_Le5Ov5hIAJ | https://www.sciencedirect.com/science/article/pii/S0188440923000395 | 11.0 | NaN |
| BB-GeoGPT: A Framework for Learning a Large Language Model for Geographic Information Science | 6jkS60UEb6sJ | https://www.researchgate.net/profile/Wenhao-Yu-4/publication/381630694_BB-GeoGPT_A_framework_for_learning_a_large_language_model_for_geographic_information_science/links/6676daa21846ca33b8453b73/BB-GeoGPT-A-framework-for-learning-a-large-language-model-for-geographic-information-science.pdf | NaN | https://www.researchgate.net/profile/Wenhao-Yu-4/publication/381630694_BB-GeoGPT_A_framework_for_learning_a_large_language_model_for_geographic_information_science/links/6676daa21846ca33b8453b73/BB-GeoGPT-A-framework-for-learning-a-large-language-model-for-geographic-information-science.pdf |
| Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise: A Case Study on Chinese Legal Domain | n-8yKQ3b_hkJ | https://arxiv.org/abs/2310.03328 | 3.0 | https://arxiv.org/pdf/2310.03328 |
| Harmonizing Innovation and Ethics: The Complex Landscape of Artificial Intelligence in Legal Practice | r57zQs5yHEMJ | http://thecrsss.com/index.php/Journal/article/view/345 | NaN | https://thecrsss.com/index.php/Journal/article/download/345/400 |
| Open the UPL gates and let the robot-lawyers walk in | hu05NWxRVEIJ | https://journals.sagepub.com/doi/abs/10.1177/23220058241305228 | NaN | NaN |
| Artificial intelligence & creativity: A manifesto for collaboration | NyCB4O1H164J | https://onlinelibrary.wiley.com/doi/abs/10.1002/jocb.597 | 163.0 | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/jocb.597 |
| A Review: Knowledge Inference Integrating Large Language Model with Knowledge Graph | S5yX8D1-i70J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5097517 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5097517 |
| By Senior Judge Stephanie Domitrovich and Judge Herbert B. Dixon Jr. | WPdBt6GYnpgJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/judgej63§ion=3 | NaN | NaN |
| GoalAct: A Globally Adaptive Dynamic Legal Multi-agent Collaboration System | Iv6wOJNR-lkJ | https://openreview.net/forum?id=0r81vMYJcz | NaN | https://openreview.net/pdf?id=0r81vMYJcz |
| The future of cybercrime: AI and emerging technologies are creating a cybercrime tsunami | _FX_fECYb00J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4507244 | 12.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4507244 |
| Large Language Models in Politics and Democracy: A Comprehensive Survey | cQHRZiimZz0J | https://arxiv.org/abs/2412.04498 | 2.0 | https://arxiv.org/pdf/2412.04498 |
| The Influence of Reinforcement Learning From Human Feedback on Large Language Model Biases | JPoCuOhdeiUJ | https://search.proquest.com/openview/97e69e4f9059aec6e395483d7a616cc8/1?pq-origsite=gscholar&cbl=18750&diss=y | NaN | NaN |
| Large language models and political science | 0RI6qBV7pvcJ | https://www.frontiersin.org/articles/10.3389/fpos.2023.1257092/full | 43.0 | https://www.frontiersin.org/journals/political-science/articles/10.3389/fpos.2023.1257092/pdf |
| Large Language Models (LLM) in Industry: A Survey of Applications, Challenges, and Trends | LgVEDe98SZAJ | https://ieeexplore.ieee.org/abstract/document/10822885/ | 8.0 | NaN |
| Integrating ChatGPT, Bard, and leading-edge generative artificial intelligence in building and construction industry: applications, framework, challenges, and future … | 7j3b1GMYe48J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4645597 | 17.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4645597 |
| Capturing value from artificial intelligence | x8wiCT_UuY0J | https://journals.aom.org/doi/abs/10.5465/amd.2023.0106 | 96.0 | https://justinmberg.com/wp-content/uploads/Berg-et-al_2023_AMD.pdf |
| The rise of the robotic tax analyst | F6cddn0EKiIJ | https://utoronto.scholaris.ca/items/a80e45e1-bc58-4363-ac1e-67a4ad447d7f | 4.0 | https://utoronto.scholaris.ca/bitstreams/e19791dc-12ce-4dd1-b77f-57fc5e237b12/download |
| Attributing AI Authorship: Towards a System of Icons for Legal and Ethical Disclosure | AT2VUVl9UdYJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5152508 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5152508 |
| Determinants of Socially Responsible AI Governance | RTRKZVYlBPYJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/dltr25§ion=6 | NaN | https://scholarship.law.duke.edu/cgi/viewcontent.cgi?article=1393&context=dltr |
| Legal-Emotional BATNA: AI Chatbot Addressing Divorce Legalities and Emotional Complexities, and Research of Social Implementation in Japan | eZOdC8SrDBkJ | https://ebooks.iospress.nl/doi/10.3233/FAIA241264 | NaN | https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA241264 |
| The potential legal risks of artificial intelligence | 9qQTNonUeVYJ | https://dl.acm.org/doi/abs/10.1145/3691422.3691471 | NaN | NaN |
| Mini-Carbongpt: A Domain-Specific Large Language Model for Carbon Neutrality | 0FONfFqRRU4J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5170350 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5170350 |
| AI and the Legal Profession: transforming the future of law. Edited by Alex Davies 2023, published by Globe Law and Business | SLf0lw61na8J | https://www.cambridge.org/core/journals/legal-information-management/article/ai-and-the-legal-profession-transforming-the-future-of-law-edited-by-alex-davies-2023-published-by-globe-law-and-business/2C94B084D0B1A1D6C0BEFF107A8B0672 | 2.0 | NaN |
| Evaluation of domain-specific prompt engineering attacks on large language models | 4TYD9xDBosYJ | https://www.authorea.com/doi/full/10.22541/au.172252453.36267312 | 52.0 | https://www.authorea.com/doi/pdf/10.22541/au.172252453.36267312 |
| AHOW ARTIFICIAL INTELLIGENCE CAN HELP TO RESHAPE LEGAL PROFESSION THROUGHOUT THE WORLD | FET1CXtySgkJ | https://journal.imras.org/index.php/sps/article/view/1490 | NaN | https://journal.imras.org/index.php/sps/article/download/1490/1979 |
| From gavels to algorithms: The Vidhii Partners GenAI evolution | NLb1_WqQEkwJ | https://www.emerald.com/insight/content/doi/10.1108/eemcs-04-2024-0160/full/html | 1.0 | NaN |
| Building GenAI Benchmarks: A Case Study in Legal Applications | pqvZKAEc7eIJ | https://neelguha.github.io/assets/pdf/building_genai_benchmarks_for_law_oxford_chapter.pdf | NaN | https://neelguha.github.io/assets/pdf/building_genai_benchmarks_for_law_oxford_chapter.pdf |
| DOL-LLM-Optimizing Large Language Model Inference with Domain-Specific Adaptations and Efficiency Techniques via Quantization, Pruning, and Distillation | lqYsZJkmc00J | http://www.techrxiv.org/doi/full/10.36227/techrxiv.174286524.45842767/v1 | NaN | https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.174286524.45842767 |
| GenAI avatar judges and virtuous adjudication | B63auF-5cIEJ | https://www.researchgate.net/profile/Mihaela-Constantinescu-5/publication/389026924_GenAI_avatar_judges_and_virtuous_adjudication/links/67b0b9d3461fb56424dc0179/GenAI-avatar-judges-and-virtuous-adjudication.pdf | 1.0 | https://www.researchgate.net/profile/Mihaela-Constantinescu-5/publication/389026924_GenAI_avatar_judges_and_virtuous_adjudication/links/67b0b9d3461fb56424dc0179/GenAI-avatar-judges-and-virtuous-adjudication.pdf |
| Generative AI in Business: Visual Illustrations of Applications and Insights from Q1 2025 | K32P7Y_q8boJ | https://philpapers.org/rec/JOSGAI | NaN | https://philarchive.org/archive/JOSGAI |
| Computational Attorneys: Opportunities and Challenges | MJyQA761ATkJ | https://www.siam.org/publications/siam-news/articles/computational-attorneys-opportunities-and-challenges | NaN | NaN |
| Summary of young-OGEMID symposium No. 17:" The role of artificial intelligence in shaping ADR practices" | XZX5nvn_88QJ | https://orca.cardiff.ac.uk/id/eprint/165682/ | NaN | https://orca.cardiff.ac.uk/id/eprint/165682/1/TDM-YO-Draft-AI-ADR_SN_16.01.24.pdf |
| Latest technology trends and their cybersecurity implications | 66qIQXXIL1UJ | https://link.springer.com/article/10.1365/s43439-023-00091-0 | 10.0 | NaN |
| The impact of artificial intelligence on the evolution of digital education: A comparative study of openAI text generation tools including ChatGPT, Bing Chat, Bard, and … | x_J2nC_89RsJ | https://arxiv.org/abs/2309.02029 | 90.0 | https://arxiv.org/pdf/2309.02029 |
| Llm vs. lawyers: Identifying a subset of summary judgments in a large uk case law dataset | itbYunRMpiQJ | https://arxiv.org/abs/2403.04791 | 6.0 | https://arxiv.org/pdf/2403.04791 |
| Hallucination is the last thing you need | qHJ-ypmosE4J | https://arxiv.org/abs/2306.11520 | 14.0 | https://arxiv.org/pdf/2306.11520 |
| Generative AI in Business: Visual Illustrations of Applications and Insights | s1yPEHov7IcJ | https://www.preprints.org/manuscript/202504.1660 | NaN | https://www.preprints.org/frontend/manuscript/72dfb4834ab1b44dd5a8b39e5970e22b/download_pub |
| Regulatory Framework for Artificial Intelligence in the Legal System of Pakistan | yV9JSc0E-YYJ | http://thecrsss.com/index.php/Journal/article/view/90 | 2.0 | https://thecrsss.com/index.php/Journal/article/download/90/101 |
| Leveraging the use of ChatGPT: exploring its real-world applications including their related ethical and regulatory considerations | gNVuA0DSimMJ | https://link.springer.com/chapter/10.1007/978-3-031-74443-3_38 | 12.0 | https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.173121400.09618775 |
| Technologies versus justice: Challenges of AI regulation in the judicial system | L1-b91kUqaIJ | https://cyberleninka.ru/article/n/technologies-versus-justice-challenges-of-ai-regulation-in-the-judicial-system | 2.0 | NaN |
| Legalbench: Prototyping a collaborative benchmark for legal reasoning | xxzftjRKRFAJ | https://arxiv.org/abs/2209.06120 | 22.0 | https://arxiv.org/pdf/2209.06120 |
| Handbook of Legal Tech edited by Colin S Levy, 2023 published by Globe Law and Business | 9GwGi7t5TYoJ | https://search.proquest.com/openview/35cb7db8cebdc80c402ea4f9c0ef1f21/1?pq-origsite=gscholar&cbl=29062 | 1.0 | NaN |
| Basis is also explanation: Interpretable Legal Judgment Reasoning prompted by multi-source knowledge | c8H2544Mr-MJ | https://www.sciencedirect.com/science/article/pii/S0306457324003558 | NaN | NaN |
| RAG-Based LLM Chatbot Using Llama-2 | oM-cDtdH33kJ | https://ieeexplore.ieee.org/abstract/document/10561020/ | 12.0 | NaN |
| Can ChatGPT-like AI Function as ODR Fourth Party for Handling School-Related Disputes in China? | 9XAU1incic8J | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/ijodr9§ion=23 | NaN | https://www.elevenjournals.com/tijdschrift/ijodr/2022/2/IJODR_2352-5002_2022_009_002_008.pdf |
| … . Ein empirischer Test mit der Hilfe eines Sprachmodells (German text) | 2rvZcTZWe3oJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4763210 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4763210 |
| Continuing legal education in Germany–Digitalization | K27PL870OFwJ | https://ink.library.smu.edu.sg/sol_research/4533/ | NaN | https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6491&context=sol_research |
| Research on confusing responses based on ChatGPT | cmEVvwOLi6sJ | https://www.ewadirect.com/proceedings/ace/article/view/11813 | NaN | NaN |
| Llm app store analysis: A vision and roadmap | RrjGKSUwK2IJ | https://dl.acm.org/doi/abs/10.1145/3708530 | 12.0 | https://dl.acm.org/doi/pdf/10.1145/3708530 |
| IMPACT OF DİGİTAL TRANSFORMATİON ON ADMİNİSTRATİVE LAW İN THE FİELD OF LEGAL SERVİCES | R9Rm5fVzOc0J | https://westerneuropeanstudies.com/index.php/4/article/view/1850 | NaN | https://westerneuropeanstudies.com/index.php/4/article/download/1850/1261 |
| The Role of Generative AI in developing new Supply Chain Strategies-Future Trends and Innovations | x4foJ0wSk_kJ | https://www.allmultidisciplinaryjournal.com/uploads/archives/20250215133922_MGE-2025-1-302.1.pdf | NaN | https://www.allmultidisciplinaryjournal.com/uploads/archives/20250215133922_MGE-2025-1-302.1.pdf |
| Leveraging AI for enhanced alignment of national biodiversity targets with the global biodiversity goals | 4SkMmB5fM-wJ | https://www.sciencedirect.com/science/article/pii/S2772411524000892 | 1.0 | NaN |
| Ethics 3.0-Attorney Responsibility in the Age of Generative Al | Szk2wzoXOGwJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/busl79§ion=15 | 5.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4746102 |
| Beyond Text: Analyzing Artificial Intelligence Models through Prompt Engineering | 8KgidaYD8GoJ | https://www.taylorfrancis.com/chapters/edit/10.1201/9781032715957-8/beyond-text-kishor-kumar-reddy-pellate-anoushka-akhil-draksharapu-srinath-doss | 1.0 | NaN |
| AI In Law: Adversary or Ally? Addressing the Possible Implications of AI Technology in Law and the Necessity of Regulation | LHYfQYjVfOUJ | https://ojs.stanford.edu/ojs/index.php/grace/article/view/3329 | NaN | https://ojs.stanford.edu/ojs/index.php/grace/article/download/3329/1755 |
| Casegen: A benchmark for multi-stage legal case documents generation | YOXCYWgzfXYJ | https://arxiv.org/abs/2502.17943 | 2.0 | https://arxiv.org/pdf/2502.17943 |
| Artificial Intelligence Cannibalism and the Law | lxdPlzm8HF8J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4622769 | 1.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4622769 |
| 律師行業數位轉型之研究-以生成式 AI 應用為例 | BLg7JM6so_EJ | https://tdr.lib.ntu.edu.tw/handle/123456789/94552 | NaN | NaN |
| Judicialtech supporting justice | Vl9jvdYVpp4J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4597917 | 2.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4597917 |
| Legal Tech Abolition: Using legal technology to free them all | cepniqfn3kwJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5133606 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5133606 |
| LLM-Based AI Assistant for Codes and Standards in Civil Engineering | Ud9JaGm2X2QJ | https://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE11627581 | 2.0 | NaN |
| OBJECTION! USE OF AI!: Evaluating the Role of Generative Artificial Intelligence in Litigation: Risks and Regulations | h2e8YjKeFzcJ | https://openjournals.maastrichtuniversity.nl/MJLA/article/view/981 | NaN | https://openjournals.maastrichtuniversity.nl/MJLA/article/view/981/570 |
| AI, Justice, and the Ecosystem Approach–Notes from the OpenNyAI Mission | tags21nqSE4J | https://socialinnovationsjournal.com/index.php/sij/article/view/7121 | NaN | https://socialinnovationsjournal.com/index.php/sij/article/download/7121/5944 |
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| AI-assisted German Employment Contract Review: A Benchmark Dataset | dkUIaaWEdX8J | https://arxiv.org/abs/2501.17194 | 1.0 | https://arxiv.org/pdf/2501.17194 |
| A CIA Triad-Based Taxonomy of Prompt Attacks on Large Language Models | 8Wztx4vuDxcJ | https://www.mdpi.com/1999-5903/17/3/113 | NaN | NaN |
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| Will AI Replace Tax Practitioners? | R0gkfcmKmPwJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4631285 | 1.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4631285 |
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| Book Review—Shaping the Bar: The Future of Attorney Licensing | KsIty3_cK1AJ | https://ideas.dickinsonlaw.psu.edu/cgi/viewcontent.cgi?article=1201&context=dlr | NaN | https://ideas.dickinsonlaw.psu.edu/cgi/viewcontent.cgi?article=1201&context=dlr |
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| EMPOWER-KARE: Deep Prompt Learning for Knowledge-aware Response Generation in Clinical Counseling and Legal Support Conversations | a_okOOf3aysJ | https://ieeexplore.ieee.org/abstract/document/10910093/ | NaN | NaN |
| The Impact of Artificial Intelligence Technologies on the Justice Administration and on the Judicial Office Personnel | g8yPDVQinAIJ | https://www.degruyterbrill.com/document/doi/10.1515/zfrs-2025-2008/html | NaN | https://www.degruyterbrill.com/document/doi/10.1515/zfrs-2025-2008/pdf |
| JurisCTC: Enhancing Legal Judgment Prediction via Cross-Domain Transfer and Contrastive Learning | mIAZXGRNg7QJ | https://arxiv.org/abs/2504.17264 | NaN | https://arxiv.org/pdf/2504.17264 |
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| You Just Can't Beat the Machine: A Lawyer's Duty to Adapt in the Age of Artificial Intelligence | ghPzKgistSIJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5211160 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5211160 |
| The Keynote Address to Georgia State University College of Law's 29th Annual Law Review Symposium-Access to AI Justice: A Global Response to a Global Crisis | MclRsgjrGSEJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4926335 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4926335 |
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| Balancing Innovation and Copyrights: The Legal Framework for AI Training in the European Union | 8fuknjhzvVkJ | http://arno.uvt.nl/show.cgi?fid=176939 | NaN | http://arno.uvt.nl/show.cgi?fid=176939 |
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| AI-Based Contract & Legal Document Generator using Machine Learning | t8zFmiNLlnkJ | https://jjem.jnnce.ac.in/journals/SP-2/JJEMSP0249.pdf | NaN | https://jjem.jnnce.ac.in/journals/SP-2/JJEMSP0249.pdf |
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| What should ChatGPT mean for bioethics? | YMpBXSigfgQJ | https://www.tandfonline.com/doi/abs/10.1080/15265161.2023.2233357 | 82.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4430100 |
| “Chat‐Up”: The role of competition in street‐level bureaucrats' willingness to break technological rules and use generative pre‐trained transformers (GPTs) | TGmJx6Olb_MJ | https://onlinelibrary.wiley.com/doi/abs/10.1111/puar.13824 | 2.0 | NaN |
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| Robots in the Middle: Evaluating LLMs in Dispute Resolution | rxTZXXLaMTcJ | https://ebooks.iospress.nl/doi/10.3233/FAIA241243 | 2.0 | https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA241243 |
| Retraining us workforce in the age of agentic gen ai: Role of prompt engineering and up-skilling initiatives | eTbHK0NwYWcJ | https://www.researchgate.net/profile/Satyadhar-Joshi-2/publication/389055487_Retraining_US_Workforce_in_the_Age_of_Agentic_Gen_AI_Role_of_Prompt_Engineering_and_Up-_Skilling_Initiatives/links/67b34417645ef274a4852fbf/Retraining-US-Workforce-in-the-Age-of-Agentic-Gen-AI-Role-of-Prompt-Engineering-and-Up-Skilling-Initiatives.pdf | 9.0 | https://www.researchgate.net/profile/Satyadhar-Joshi-2/publication/389055487_Retraining_US_Workforce_in_the_Age_of_Agentic_Gen_AI_Role_of_Prompt_Engineering_and_Up-_Skilling_Initiatives/links/67b34417645ef274a4852fbf/Retraining-US-Workforce-in-the-Age-of-Agentic-Gen-AI-Role-of-Prompt-Engineering-and-Up-Skilling-Initiatives.pdf |
| How effectively can ChatGPT-4 draft data transfer agreements for health research? | VxfIMJROhukJ | https://www.nature.com/articles/s41599-025-04643-z | NaN | https://www.nature.com/articles/s41599-025-04643-z.pdf |
| How Privacy-Savvy Are Large Language Models? A Case Study on Compliance and Privacy Technical Review | FbgEwaRT2gcJ | https://arxiv.org/abs/2409.02375 | NaN | https://arxiv.org/pdf/2409.02375 |
| How to Turn Professional Services Into Products | DQmzdKn6QOwJ | https://search.proquest.com/openview/6983fb1b7b2145fc0cae641f0f0892dd/1?pq-origsite=gscholar&cbl=26142 | NaN | NaN |
| Chat GPT And Academic Integrity: Analyzing Its Influence On College Students' Study Practices And Performance. | rrHCzX44UCYJ | https://www.researchgate.net/profile/Amarnath-Paswan/publication/384661685_Chat_GPT_And_Academic_Integrity_Analyzing_Its_Influence_On_College_Students'_Study_Practices_And_Performance/links/670178d6f599e0392fbc19f9/Chat-GPT-And-Academic-Integrity-Analyzing-Its-Influence-On-College-Students-Study-Practices-And-Performance.pdf | NaN | https://www.researchgate.net/profile/Amarnath-Paswan/publication/384661685_Chat_GPT_And_Academic_Integrity_Analyzing_Its_Influence_On_College_Students'_Study_Practices_And_Performance/links/670178d6f599e0392fbc19f9/Chat-GPT-And-Academic-Integrity-Analyzing-Its-Influence-On-College-Students-Study-Practices-And-Performance.pdf |
| Impartial or Biased? The Effect of Race, Gender, and Priming on AI's Conviction Predictions | nPNWRlE8MbcJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4779332 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4779332 |
| AI-Powered Indian Courtroom: ChatGPT a Boon or a Bane? | xxA8YbLopDIJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/juscrp4§ion=59 | 1.0 | NaN |
| ALLA Conference 2024: Take the leap | f1HyfvDH-IEJ | https://search.informit.org/doi/abs/10.3316/informit.T2024121000001400747097470 | NaN | https://search.informit.org/doi/pdf/10.3316/informit.T2024121000001400747097470 |
| Chatgpt Accuracy Analysis for Legal Field and Anticipation of Potential Problems | Y8UaRGVOxgoJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5111518 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5111518 |
| Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language Models | BthWmIW7q08J | https://aclanthology.org/2024.findings-emnlp.319/ | NaN | https://aclanthology.org/2024.findings-emnlp.319.pdf |
| OPERATIONALISING ORGANISATIONAL WISDOM IN THE INDIAN LAW AND GOVERNANCE: A DIGITISATION, DIGITALISATION AND DIGITAL … | AIoPYeNkVewJ | http://www.iipabiharbranch.org/BJPA_XIX%20No%202%20S%20(supp_Law%20&%20Gov)%20July-Dec%202022.pdf#page=196 | NaN | http://www.iipabiharbranch.org/BJPA_XIX%20No%202%20S%20(supp_Law%20&%20Gov)%20July-Dec%202022.pdf#page=196 |
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| AI Cannibalism and the Law | dhPMuPLW7IIJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/jtelhtel22§ion=19 | 4.0 | https://scholar.law.colorado.edu/cgi/viewcontent.cgi?article=1015&context=ctlj |
| Llms-as-judges: a comprehensive survey on llm-based evaluation methods | T_UxWrCFaRQJ | https://arxiv.org/abs/2412.05579 | 9.0 | https://arxiv.org/pdf/2412.05579 |
| … ДАННЫХ NER НА КАЗАХСКОМ ЯЗЫКЕ ДЛЯ МУЛЬТИКЛАССИФИКАЦИИ В ПРАВОВОЙ СФЕРЕ: СРАВНИТЕЛЬНОЕ ИССЛЕДОВАНИЕ МОДЕЛЕЙ BERT, GPT … | VWi01BsHzJwJ | https://journals.nauka-nanrk.kz/physics-mathematics/article/view/7093 | NaN | https://journals.nauka-nanrk.kz/physics-mathematics/article/download/7093/4886 |
| Digitalisation of the Slovenian Justice System and Its Discontents1 | zrpW9rvsnukJ | https://www.policija.si/images/stories/Publikacije/RKK/PDF/2024/04/RKK2024-04_AlesZavrsnik_DigitalisationOfTheSLOJusticeSystem.pdf | NaN | https://www.policija.si/images/stories/Publikacije/RKK/PDF/2024/04/RKK2024-04_AlesZavrsnik_DigitalisationOfTheSLOJusticeSystem.pdf |
| From Assimilation to Autonomy: Rethinking Data Sovereignty in the Age of Large Language Models | 47aazd3L9TkJ | https://www.tandfonline.com/doi/abs/10.1080/10572252.2025.2490503 | NaN | NaN |
| Judicial Administration 4.0: strengths, challenges and opportunities of a proactive approach to AI regulation in Spain. | hUHRaiZY4LYJ | https://sciendo.com/pdf/10.2478/bjes-2025-0007 | NaN | https://sciendo.com/pdf/10.2478/bjes-2025-0007 |
| Human Law, Human Lawyers and the Emerging AI Faith | VIrPJN95W2sJ | https://repository.essex.ac.uk/39347/ | NaN | https://repository.essex.ac.uk/39347/1/6728a529ae14c.pdf |
| Using deep learning to predict outcomes of legal appeals better than human experts: A study with data from Brazilian federal courts | yytdIHOdBqkJ | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0272287 | 28.0 | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0272287&type=printable |
| THE KEYNOTE ADDRESS TO GEORGIA STATE UNIVERSITY COLLEGE OF LAW'S 29TH ANNUAL LAW REVIEW SYMPOSIUM AI & THE LAW: PRACTICE, ETHICS … | -C3nOE6rI8QJ | https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=87556847&asa=N&AN=178165277&h=jsNsNXmn%2BU2nnu0%2B13ZgVt%2Bbz2Pgt8aftxHPHBKUc5OyX38XTVm1eVk730S16gz6G2ZEHWSxkl57Ieulc6Bflg%3D%3D&crl=c | NaN | NaN |
| The Business Opportunities of Today | nwfQO4U-tN4J | https://link.springer.com/chapter/10.1007/979-8-8688-0456-4_5 | NaN | NaN |
| Artificial intelligence and large language models: A practical insight for today's barrister | dYi2G2PJI-YJ | https://search.informit.org/doi/abs/10.3316/informit.T2024032300005791388396256 | NaN | NaN |
| LegalTech in the Light of the Upcoming Artificial Intelligence Act | T6NjEju5IEEJ | https://www.dirittoequestionipubbliche.org/page/2023_dq-recognise/j-02-Navas.pdf | NaN | https://www.dirittoequestionipubbliche.org/page/2023_dq-recognise/j-02-Navas.pdf |
| The Disrupting Influence of AI and the Potential Impact of ChatGPT on Maritime Law and Practice | H5HwzgGHq2wJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/jnltllap90§ion=8 | NaN | https://scmresearchandtraining.com/wp-content/uploads/2023/11/AI.pdf |
| Board reviews guidelines for the use of AI in the practice of law. | CuhYoEtaJhYJ | https://go.gale.com/ps/i.do?id=GALE%7CA780546773&sid=googleScholar&v=2.1&it=r&linkaccess=abs&issn=03600114&p=HRCA&sw=w | NaN | NaN |
| Traditional and Computational Canons | PxXf3m9gRGsJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5155444 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5155444 |
| Addressing the Failures of the US Civil Legal System | vmJp9pKwcFwJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/rwulr28§ion=12 | 1.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4660923 |
| A Few-Shot Entity Relation Extraction Method in the Legal Domain Based on Large Language Models | ZrapqJJDlX8J | https://dl.acm.org/doi/abs/10.1145/3675417.3675513 | 1.0 | NaN |
| Robustness of structured data extraction from in-plane rotated documents using multi-modal large language models (LLM) | SulcX-It8GoJ | https://arxiv.org/abs/2406.10295 | 17.0 | https://arxiv.org/pdf/2406.10295 |
| Ai tools for lawyers: a practical guide | yNavCh3Cl8gJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/headnotpan108§ion=3 | 25.0 | https://scholarship.law.umn.edu/cgi/viewcontent.cgi?article=2059&context=faculty_articles |
| Shaping the Bar: The Future of Attorney Licensing | N0yfwyKR9jwJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/dknslr128§ion=20 | NaN | NaN |
| Measuring and mitigating gender bias in legal contextualized language models | FCddXdQSrNEJ | https://dl.acm.org/doi/abs/10.1145/3628602 | 15.0 | https://dl.acm.org/doi/pdf/10.1145/3628602 |
| LegalBench. PT: A Benchmark for Portuguese Law | h6yAuPmgJMMJ | https://arxiv.org/abs/2502.16357 | NaN | https://arxiv.org/pdf/2502.16357 |
| Exploring the influence, implications and challenges of integrating generative artificial intelligence into organizational learning and development | uSS0SsDihNAJ | https://www.emerald.com/insight/content/doi/10.1108/CR-06-2024-0121/full/html | NaN | NaN |
| Artificial Intelligence in Accounting, Medicine, and Law with Potential Implications for Financial Planning: A Review of Literature | EeMzvDhc2e8J | https://openjournals.libs.uga.edu/fsr/article/view/4017 | NaN | https://openjournals.libs.uga.edu/fsr/article/download/4017/3449 |
| Justice Link: Tech-Driven Solutions for Undertrial Prisoner | KzGuuCj9bCMJ | https://philpapers.org/rec/SREJLT | NaN | https://philarchive.org/archive/SREJLT |
| Between human and AI: assessing the reliability of AI text detection tools | VVMlWAUIWwIJ | https://www.tandfonline.com/doi/abs/10.1080/03007995.2024.2310086 | 28.0 | NaN |
| A topic discovery approach for unsupervised organization of legal document collections | ifiKlHJReWAJ | https://link.springer.com/article/10.1007/s10506-023-09371-w | 10.0 | NaN |
| THE DISRUPTING INFLUENCE OF AI AND THE POTENTIAL IMPACT OF CHATGPT ON MARITIME LAW AND PRACTICE | ww2Omf-qwKkJ | https://search.proquest.com/openview/d7540f86e23a9e02c1d2219c2f547de8/1?pq-origsite=gscholar&cbl=45932 | NaN | NaN |
| LLMs Provide Unstable Answers to Legal Questions | HZyLbSmv6fAJ | https://arxiv.org/abs/2502.05196 | 1.0 | https://arxiv.org/pdf/2502.05196 |
| KRAG Framework for Enhancing LLMs in the Legal Domain | 9I7B1zrXEvgJ | https://arxiv.org/abs/2410.07551 | 1.0 | https://arxiv.org/pdf/2410.07551 |
| Empowering women through AI: A comprehensive chatbot for domestic violence awareness and legal support in India | QiGj-g0Tb28J | https://link.springer.com/chapter/10.1007/978-3-031-83520-9_19 | 1.0 | NaN |
| Effective Practices in AI Literacy Education: A Legal Education Perspective | PQ86olCEEuoJ | https://www.emerald.com/insight/content/doi/10.1108/978-1-83608-852-320241014/full/html | NaN | NaN |
| Predicting Judgement Outcomes from Legal Case File Summaries with Explainable Approach | W65w29RUoQkJ | https://link.springer.com/chapter/10.1007/978-3-031-78107-0_11 | NaN | NaN |
| The CLC-UKET Dataset: Benchmarking Case Outcome Prediction for the UK Employment Tribunal | hlypjeZ5b94J | https://www.repository.cam.ac.uk/items/42f8f272-5063-4c32-963f-7ac02ad75c0d | NaN | NaN |
| RE: Literature Review Memo: Artificial Intelligence in Law | SYHocniYWCEJ | https://www.cyberjustice.ca/files/sites/102/FINAL-AI-LAW-lit-review19.pdf | NaN | https://www.cyberjustice.ca/files/sites/102/FINAL-AI-LAW-lit-review19.pdf |
| LawNet-Viz: A Web-based System to Visually Explore Networks of Law Article References | Nkd_Exsr9oQJ | https://dl.acm.org/doi/abs/10.1145/3477495.3531668 | 5.0 | https://dl.acm.org/doi/pdf/10.1145/3477495.3531668 |
| A Cross-Lingual Statutory Article Retrieval Dataset for Taiwan Legal Studies | SoCFwEeEKWUJ | https://arxiv.org/abs/2410.11450 | NaN | https://arxiv.org/pdf/2410.11450 |
| Generative Artificial Intelligence: Legal Profession Disrupted? | KF1W2-DIRVYJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/iihcj16§ion=72 | NaN | NaN |
| Multi-level Correlation Matching for Legal Text Similarity Modeling with Multiple Examples | M7232qUq6e0J | https://link.springer.com/chapter/10.1007/978-981-99-7254-8_48 | NaN | NaN |
| Construction of a japanese financial benchmark for large language models | ysPWbU8zbbEJ | https://arxiv.org/abs/2403.15062 | 16.0 | https://arxiv.org/pdf/2403.15062 |
| A Framework for Data-Driven Legal Regulatory Reform | S4tkmznxx80J | https://digitalcommons.law.seattleu.edu/sjteil/vol14/iss2/2/ | NaN | https://digitalcommons.law.seattleu.edu/cgi/viewcontent.cgi?article=1072&context=sjteil |
| A legal multi-choice question answering model based on BERT and attention | oBON2vnTx8QJ | https://link.springer.com/chapter/10.1007/978-3-031-40292-0_21 | 10.0 | NaN |
| Judicial training to prepare criminal justice professionals for# digitalisation and# artificialintelligence | zYPa4rOtSP0J | https://link.springer.com/article/10.1007/s12027-024-00788-7 | NaN | https://link.springer.com/content/pdf/10.1007/s12027-024-00788-7.pdf |
| Generative Artificial intelligence Applications in Banking and Finance sector | A3Jd36joQBcJ | https://www.researchgate.net/profile/Praneeth-Reddy-Amudala-Puchakayala/publication/387519004_Generative_Artificial_intelligence_Applications_in_Banking_and_Finance_sector/links/67f5483649e91c0feaea0470/Generative-Artificial-intelligence-Applications-in-Banking-and-Finance-sector.pdf | NaN | https://www.researchgate.net/profile/Praneeth-Reddy-Amudala-Puchakayala/publication/387519004_Generative_Artificial_intelligence_Applications_in_Banking_and_Finance_sector/links/67f5483649e91c0feaea0470/Generative-Artificial-intelligence-Applications-in-Banking-and-Finance-sector.pdf |
| Integrating Content Moderation Systems with Large Language Models | K5t75akao_cJ | https://dl.acm.org/doi/abs/10.1145/3700789 | 1.0 | https://dl.acm.org/doi/pdf/10.1145/3700789 |
| Can AI Help Indian Judiciary to Reduce Its Burden of Cases? Exploring the Potential of AI in Judicial Decision-Making Process | sjRayaTR9fkJ | https://link.springer.com/chapter/10.1007/978-981-97-8457-8_29 | NaN | NaN |
| War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education | _rV9oWYXkzoJ | https://journals.sfu.ca/jalt/index.php/jalt/article/download/771/577/3333 | 473.0 | https://journals.sfu.ca/jalt/index.php/jalt/article/download/771/577/3333 |
| Beyond words: A controlled experiment on the role of linguistic empathy for trust in conversational ai | SGGW0H1kypUJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4924883 | 1.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4924883 |
| A text intelligence-based approach for automatic generation of fault trees in nuclear power plants | rvLCnh_zjw0J | https://asmedigitalcollection.asme.org/ICONE/proceedings-abstract/ICONE31/1208397 | 3.0 | https://www.researchgate.net/profile/Xingyu-Xiao-4/publication/385473861_A_Text_Intelligence-Based_Approach_for_Automatic_Generation_of_Fault_Trees_in_Nuclear_Power_Plants/links/67335a034a70511f071bf590/A-Text-Intelligence-Based-Approach-for-Automatic-Generation-of-Fault-Trees-in-Nuclear-Power-Plants.pdf |
| Unleashing AI in Education: A Pre-Trained LLMs for Accurate and Efficient Question-Answering Systems | soIUWkbfLOQJ | https://ieeexplore.ieee.org/abstract/document/10837606/ | NaN | NaN |
| Privacy Perceptions of Custom GPTs by Users and Creators | xRxqdz9A6c8J | https://research.aalto.fi/en/publications/privacy-perceptions-of-custom-gpts-by-users-and-creators | NaN | https://research.aalto.fi/files/178039903/TAPs_final_CHI.pdf |
| Unlocking legal knowledge with multi-layered embedding-based retrieval | uVSVKWt3LiMJ | https://arxiv.org/abs/2411.07739 | 3.0 | https://arxiv.org/pdf/2411.07739 |
| Arablegaleval: A multitask benchmark for assessing arabic legal knowledge in large language models | zWAOn2V0xsMJ | https://arxiv.org/abs/2408.07983 | 3.0 | https://arxiv.org/pdf/2408.07983 |
| Artificial Intelligence, Large Language Models, and the Colonialization of Data: Implications for the Rhetoric of Human Rights | N5RK76xsj1QJ | https://ecommons.udayton.edu/human_rights/2023/concurrent5e/3/ | NaN | NaN |
| Introduction to the Minitrack on Conversational AI and Ethical Issues | fFEfZP8uY98J | https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1044&context=hicss-57 | NaN | NaN |
| Submission to government on the safe and responsible use of AI in Australia | Za5J0lAUgr8J | https://research.bond.edu.au/en/publications/submission-to-government-on-the-safe-and-responsible-use-of-ai-in | NaN | https://research.bond.edu.au/files/225189665/Safe_and_Responsible_AI_in_Australia_-_Submission_-_Dr_Francina_Cantatore.489fe100215e6.pdf |
| ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope | ofPucgkSvisJ | https://www.sciencedirect.com/science/article/pii/S266734522300024X | 2534.0 | NaN |
| Beyond Human Discretion: Reconciling AI Systems With Traditional Legal Frameworks | ilFk-RDHRnYJ | https://digitalcommons.law.udc.edu/udclr/vol28/iss1/17/ | NaN | https://digitalcommons.law.udc.edu/cgi/viewcontent.cgi?article=1334&context=udclr |
| The death of the short-form physics essay in the coming AI revolution | 2dYvvWFd0UIJ | https://iopscience.iop.org/article/10.1088/1361-6552/acc5cf/meta | 208.0 | https://iopscience.iop.org/article/10.1088/1361-6552/acc5cf/pdf |
| How well do SOTA legal reasoning models support abductive reasoning? | RtbC1q5BGA0J | https://arxiv.org/abs/2304.06912 | 15.0 | https://arxiv.org/pdf/2304.06912 |
| Exploring Large Language Models to Facilitate Variable Autonomy for Human-Robot Teaming | -e-mbKxBPzcJ | https://arxiv.org/abs/2312.07214 | 2.0 | https://arxiv.org/pdf/2312.07214 |
| Advancing Risk and Quality Assurance: A RAG Chatbot for Improved Regulatory Compliance | 3rGSVc_uP-cJ | https://ieeexplore.ieee.org/abstract/document/10825431/ | NaN | NaN |
| A Hybrid Transformer-based Framework for Multi-Document Summarization of Turkish Legal Documents | kkrCWNk7zEoJ | https://ieeexplore.ieee.org/abstract/document/10902382/ | NaN | https://ieeexplore.ieee.org/iel8/6287639/6514899/10902382.pdf |
| Future Prospects for the Application of Artificial Intelligence in Judicial Management | bBYvbhf2w6QJ | https://ecohumanism.co.uk/joe/ecohumanism/article/view/4450 | NaN | https://ecohumanism.co.uk/joe/ecohumanism/article/download/4450/3953 |
| The Contribution and Challenges of Artificial Intelligence (AI)-based techniques for achieving Sustainable Development Goals | FkQHMGrxLEMJ | https://www.researchgate.net/profile/Gaurav-Gomase/publication/381669561_The_Contribution_and_Challenges_of_Artificial_Intelligence_AI-based_techniques_for_achieving_Sustainable_Development_Goals/links/667a6694d21e220d89cebd18/The-Contribution-and-Challenges-of-Artificial-Intelligence-AI-based-techniques-for-achieving-Sustainable-Development-Goals | 1.0 | NaN |
| Tinkering With ChatGPT, Workers Wonder: Will This Take My Job? | UStn6P59dg8J | https://go.gale.com/ps/i.do?id=GALE%7CA743464753&sid=googleScholar&v=2.1&it=r&linkaccess=abs&issn=22699740&p=AONE&sw=w | 11.0 | NaN |
| Detecting llm hallucinations using monte carlo simulations on token probabilities | toOS7yq810MJ | https://www.techrxiv.org/doi/full/10.36227/techrxiv.171822396.61518693 | 61.0 | https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.171822396.61518693 |
| A inteligência artificial na análise e triagem de processos criminais: implicações à celeridade e ao acesso à justiça | oCY_5uUnGtIJ | http://revistajrg.com/index.php/jrg/article/view/1609 | NaN | http://revistajrg.com/index.php/jrg/article/download/1609/1333 |
| Artificial Intelligence and Law | jzgO206yX6YJ | https://books.google.com/books?hl=en&lr=&id=Na1OEQAAQBAJ&oi=fnd&pg=PR9&dq=(%22access+to+justice%22+OR+%22legal+aid%22+OR+%22legal+services%22)+AND+(%22language+model%22+OR+%22GPT%22+OR+%22generative+AI%22)&ots=-W5N1YvknI&sig=l4Fuu6y4KJ2WXzBYJc5niBxQPTg | 3.0 | NaN |
| Authors in the age of language-generation AI: to be or not to be, that is… the question? | reBnNJjAlrgJ | https://osf.io/preprints/7uy63/ | 1.0 | https://osf.io/7uy63/download |
| Beyond a reasonable doubt? Audiovisual evidence, AI manipulation, deepfakes, and the law | xRgiPUMYHLAJ | https://ieeexplore.ieee.org/abstract/document/10632877/ | 6.0 | NaN |
| Artificial Intelligence in Civil Justice Systems: An Empirical and Interdisciplinary Analysis and Proposal for Moving Forward | nvJ-YKrRcQAJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5239069 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5239069 |
| Let's have a chat! A Conversation with ChatGPT: Technology, Applications, and Limitations | v19LnyREUAsJ | https://arxiv.org/abs/2302.13817 | 209.0 | https://arxiv.org/pdf/2302.13817 |
| Quebec Automobile Insurance Question-Answering With Retrieval-Augmented Generation | _xt52fZFqmoJ | https://arxiv.org/abs/2410.09623 | 2.0 | https://arxiv.org/pdf/2410.09623 |
| Measuring Political Preferences in AI Systems: An Integrative Approach | l9PBmsLmLYwJ | https://arxiv.org/abs/2503.10649 | NaN | https://arxiv.org/pdf/2503.10649 |
| From bytes to borsch: Fine-tuning gemma and mistral for the Ukrainian language representation | wNT2cfBmGiQJ | https://arxiv.org/abs/2404.09138 | 6.0 | https://arxiv.org/pdf/2404.09138 |
| Legal considerations in machine-assisted decision-making: Planning and building as a case study | JIrkB5Ps8MEJ | https://search.informit.org/doi/abs/10.3316/informit.T2024052700010091150055676 | 4.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4564233 |
| Multidisciplinary collaboration: key players in successful implementation of ChatGPT and similar generative artificial intelligence in manufacturing, finance, retail … | BGBNDfe58egJ | https://osf.io/preprints/npm3d/ | 52.0 | https://osf.io/npm3d/download |
| Parameter-efficient legal domain adaptation | Ugs9Xq5VlCIJ | https://arxiv.org/abs/2210.13712 | 11.0 | https://arxiv.org/pdf/2210.13712 |
| Bridging law and data: Augmenting reasoning via a semi-structured dataset with irac methodology | MSfmdl3ZpvMJ | https://arxiv.org/abs/2406.13217 | 2.0 | https://arxiv.org/pdf/2406.13217 |
| The Impact of Generative | 1Q0Vrd94pnQJ | https://books.google.com/books?hl=en&lr=&id=EPJXEQAAQBAJ&oi=fnd&pg=PA29&dq=(%22access+to+justice%22+OR+%22legal+aid%22+OR+%22legal+services%22)+AND+(%22language+model%22+OR+%22GPT%22+OR+%22generative+AI%22)&ots=wbA2ZABm8a&sig=Xvjh6JIQ58rbnt2__CPm6UK2x-E | NaN | NaN |
| Rethinking machine learning benchmarks in the context of professional codes of conduct | sNDFv3vLQ5kJ | https://dl.acm.org/doi/abs/10.1145/3614407.3643708 | 5.0 | https://dl.acm.org/doi/pdf/10.1145/3614407.3643708 |
| Preparing Students for the Artificial Intelligence Era: The Crucial Role of Critical Thinking Skills | uQbnPMyYpagJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5193298 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5193298 |
| AI Takes the Gavel: Contract Laws' New Sidekick in Automated Decision-Making | PLFHrc0U1FoJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4786945 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4786945 |
| Ushering In a New Era of User Rights | pXk4HgLmMQMJ | https://files.osf.io/v1/resources/29sbh/providers/osfstorage/64fc2174f3dcd142b3ddd32e?action=download&direct&version=1 | NaN | https://files.osf.io/v1/resources/29sbh/providers/osfstorage/64fc2174f3dcd142b3ddd32e?action=download&direct&version=1 |
| A Debate-Driven Experiment on LLM Hallucinations and Accuracy | 6G3ocYCwgrcJ | https://arxiv.org/abs/2410.19485 | 1.0 | https://arxiv.org/pdf/2410.19485 |
| Bringing AI Tools to the Workplace Requires a Delicate Balance. | lGG42t4MpX8J | https://go.gale.com/ps/i.do?id=GALE%7CA748740046&sid=googleScholar&v=2.1&it=r&linkaccess=abs&issn=22699740&p=AONE&sw=w | 1.0 | NaN |
| Uncovering the Fairness of AI: Exploring Focal Point, Inequality Aversion, and Altruism in ChatGPT's Dictator Game Decisions | -FEDgvjRcnIJ | http://195.220.190.85/GREDEG-WP-2025-09.pdf | NaN | http://195.220.190.85/GREDEG-WP-2025-09.pdf |
| Towards effective governance of justice data | q-iUhUhxMnsJ | https://search.proquest.com/openview/67bddd22df11628efff95bf2f79c5444/1?pq-origsite=gscholar&cbl=54503 | NaN | NaN |
| Ai luddites: Consumers penalize creative work output generated by artificial intelligence | K9RrIhC9DNcJ | https://www.researchsquare.com/article/rs-3444321/latest | 3.0 | https://www.researchsquare.com/article/rs-3444321/latest.pdf |
| Eliciting the priors of large language models using iterated in-context learning | 17mwWRXSIXUJ | https://arxiv.org/abs/2406.01860 | 7.0 | https://arxiv.org/pdf/2406.01860 |
| Text Mining Legal Documents for Clause Extraction | VhD8GBNm_7QJ | https://ieeexplore.ieee.org/abstract/document/10487546/ | NaN | https://sure.sunderland.ac.uk/id/eprint/16508/1/CSCE23-vidler%20v4.pdf |
| DECISION SCIENCES INSTITUTE | uNE_TxZM_g0J | https://matthewalanham.com/Students/2025/2025_MWDSI_Enhancing_Judicial_Efficiency_and_Access_to_Justice_Using_AI.pdf | NaN | https://matthewalanham.com/Students/2025/2025_MWDSI_Enhancing_Judicial_Efficiency_and_Access_to_Justice_Using_AI.pdf |
| AI in 2024: A Year of Crossroads and Decisions | 5CMZjvbLt4oJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/gslr40§ion=42 | NaN | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/gslr40§ion=42 |
| A computational intelligence model for legal prediction and decision support | eqZ7gaF1c_MJ | https://onlinelibrary.wiley.com/doi/abs/10.1155/2022/5795189 | 20.0 | https://onlinelibrary.wiley.com/doi/pdf/10.1155/2022/5795189 |
| SwiLTra-Bench: The Swiss Legal Translation Benchmark | jVRZDuKmAFkJ | https://arxiv.org/abs/2503.01372 | 1.0 | https://arxiv.org/pdf/2503.01372 |
| Use cases of ChatGPT and other AI tools with security concerns | Qlw_pKolkoIJ | https://www.igi-global.com/chapter/use-cases-of-chatgpt-and-other-ai-tools-with-security-concerns/348321 | 8.0 | NaN |
| Analyzing the use of large language models for content moderation with chatgpt examples | Oltk20_EZBYJ | https://dl.acm.org/doi/abs/10.1145/3599696.3612895 | 28.0 | https://dl.acm.org/doi/pdf/10.1145/3599696.3612895 |
| MAINDZ at SemEval-2024 Task 5: CLUEDO-Choosing Legal oUtcome by Explaining Decision through Oversight | 4oxIKlQvBHAJ | https://aclanthology.org/2024.semeval-1.144/ | 1.0 | https://aclanthology.org/2024.semeval-1.144.pdf |
| Enhancements for Developing a Comprehensive AI Fairness Assessment Standard | ACmFBJB5spsJ | https://ieeexplore.ieee.org/abstract/document/10885551/ | NaN | https://arxiv.org/pdf/2504.07516 |
| Artificial Intelligence: An Analysis in the Legal Field | ldtUP-2Bg5EJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/ijlmhs28§ion=14 | NaN | NaN |
| Expert Thinking With Generative Chatbots | VGLs0EXZs7sJ | https://psycnet.apa.org/fulltext/2025-57961-001.pdf | NaN | NaN |
| By Gary Rhoades | MXCb_oO4yUEJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/huri49§ion=56 | NaN | NaN |
| 법률분야에서 Chat GPT | xv5BCsGSIl4J | https://scholar.kyobobook.co.kr/article/detail/4010053329793 | 1.0 | NaN |
| The Regulation of Legal Practice in Nigeria: Charting the Way Forward | leGsUSWRVMYJ | https://www.academia.edu/download/110258092/The_regulation_of_legal_practice_in_Nigeria_Article_Edited.pdf | NaN | https://www.academia.edu/download/110258092/The_regulation_of_legal_practice_in_Nigeria_Article_Edited.pdf |
| Scenario-based Sociotechnical Envisioning (SSE) | oLraehfrATYJ | https://osf.io/j5ske/download | NaN | https://osf.io/j5ske/download |
| Exploring the Impact of Attention Mechanisms in Big Data Analysis and Large Language Models | O3Q_xyM4nOAJ | https://hal.science/hal-04983939/ | NaN | https://hal.science/hal-04983939/document |
| Working Smarter: A Quantitative Investigation Into Higher Education Faculty's Perceptions, Adoption, and Use of Generative Artificial Intelligence (AI) in Alignment With … | zho6ctN2dzQJ | https://search.proquest.com/openview/50ba01688ccd26d8d913296cb226f218/1?pq-origsite=gscholar&cbl=18750&diss=y | NaN | https://researchdiscovery.drexel.edu/esploro/fulltext/doctoral/Working-Smarter/991021890314304721?repId=12548551190004721&mId=13548551180004721&institution=01DRXU_INST |
| Unveiling Retail Insights with Generative AI | tuhct2ee6vgJ | https://repositorio-aberto.up.pt/bitstream/10216/162357/2/694015.pdf | NaN | https://repositorio-aberto.up.pt/bitstream/10216/162357/2/694015.pdf |
| WANLI: Worker and AI collaboration for natural language inference dataset creation | T2yiP3KjaMUJ | https://arxiv.org/abs/2201.05955 | 231.0 | https://arxiv.org/pdf/2201.05955 |
| Breaking barriers to creative expression: Co-designing and implementing an accessible text-to-image interface | EAq8gE4cA44J | https://arxiv.org/abs/2309.02402 | 4.0 | https://arxiv.org/pdf/2309.02402 |
| ChatGPT: Literate or intelligent about UN sustainable development goals? | x2Rhas8fUBAJ | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0297521 | 21.0 | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0297521&type=printable |
| The Long Shadow of Vinuya in the Time of Artificial Intelligence: Reflections on Ethical Issues in Legal Research | 3Ix_R8FlESUJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/philplj96§ion=41 | NaN | NaN |
| NOWJ1@ ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models | Mr3hcqPrRuYJ | https://arxiv.org/abs/2309.09070 | 2.0 | https://arxiv.org/pdf/2309.09070 |
| Research on the Application of Mediation Model Based on Deep Learning in Dispute Resolution | eQyp9yPeJDYJ | https://ieeexplore.ieee.org/abstract/document/10685376/ | 1.0 | https://ieeexplore.ieee.org/iel8/6287639/6514899/10685376.pdf |
| Incorporating AI impacts in BLS employment projections: occupational case studies | PoJ8D2VsNwoJ | https://fraser.stlouisfed.org/files/docs/publications/bls_mlr/bls_mlr_20250210.pdf | NaN | https://fraser.stlouisfed.org/files/docs/publications/bls_mlr/bls_mlr_20250210.pdf |
| Generative artificial intelligence: What everyone needs to know | xfUxKtQDkmQJ | https://books.google.com/books?hl=en&lr=&id=GJXsEAAAQBAJ&oi=fnd&pg=PP1&dq=(%22access+to+justice%22+OR+%22legal+aid%22+OR+%22legal+services%22)+AND+(%22language+model%22+OR+%22GPT%22+OR+%22generative+AI%22)&ots=4nPoPNb1ey&sig=a-fPmr1x1q15jy0xxmlFdwSI6uw | 21.0 | NaN |
| Lawful grounds to share justice data for lawtech innovation in the UK | XKWZNKbyZE0J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4744467 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4744467 |
| Rethinking jurisdictional barriers to practising law abroad: A soft technological deterministic approach | AkphJGe4rDEJ | https://search.informit.org/doi/abs/10.3316/informit.T2024051500023791292031947 | 1.0 | https://search.informit.org/doi/pdf/10.3316/informit.T2024051500023791292031947 |
| … ? Um estudo exploratório do desempenho do Chat GPT 3.5 no Exame de Suficiência do CFC Man or machine? An exploratory study of GPT 3.5 chat performance in … | NjIR43j67MAJ | https://revistas.unicentro.br/index.php/capitalcientifico/article/view/7609 | NaN | NaN |
| A Pattern Language for Persona-based Interactions with LLMs | kkd5gfg1ZFcJ | https://www.dre.vanderbilt.edu/~schmidt/PDF/Persona-Pattern-Language.pdf | 1.0 | https://www.dre.vanderbilt.edu/~schmidt/PDF/Persona-Pattern-Language.pdf |
| Shaping the emerging norms of using large language models in social computing research | hNrUNzL_HcgJ | https://dl.acm.org/doi/abs/10.1145/3584931.3606955 | 39.0 | https://dl.acm.org/doi/pdf/10.1145/3584931.3606955 |
| ChatGPT Creates a Review Article: State of the Art in the Most-Cited Articles on ChatGPT in Health Science, Computer Science, Communication, and Culture … | taH8uxiVYoAJ | https://arxiv.org/abs/2307.02488 | 1.0 | https://arxiv.org/pdf/2307.02488 |
| Empowering Air Travelers: A Chatbot for Canadian Air Passenger Rights | W7292Ow-LfoJ | https://arxiv.org/abs/2403.12678 | NaN | https://arxiv.org/pdf/2403.12678 |
| TOWARDS HIGH-QUALITY, PRIVACY-FOCUSED BLOG GENERATION: AN OPEN-SOURCE APPROACH USING LLAMA-2 | IDsoJMvT-Q8J | https://ijetrm.com/issues/files/Nov-2024-11-1731336387-NOV016.pdf | NaN | https://ijetrm.com/issues/files/Nov-2024-11-1731336387-NOV016.pdf |
| At your service | Fg7oPaJTV9cJ | https://search.informit.org/doi/pdf/10.3316/informit.181622850071117 | NaN | NaN |
| Assessing ChatGPT as a Power Analysis Tool: An Empirical Investigation | KRoJJ5fKn0IJ | https://osf.io/32mkv/download | NaN | https://osf.io/32mkv/download |
| The Impact of ChatGPT Technological Innovation on Civil Law Practices: Challenges, Opportunities, and Implications of Article 1338 of the Civil Code | MiabMAWShnEJ | https://www.jurnal-id.com/index.php/jupin/article/view/383 | NaN | https://www.jurnal-id.com/index.php/jupin/article/download/383/242 |
| Transforming Education with Large Language Models: Opportunities, Challenges, and Ethical Considerations | zwv1YKELvKwJ | https://www.researchgate.net/profile/Hao-Qin-52/publication/382825702_Transforming_Education_with_Large_Language_Models_Opportunities_Challenges_and_Ethical_Considerations/links/66ad60ca51aa0775f264c5e0/Transforming-Education-with-Large-Language-Models-Opportunities-Challenges-and-Ethical-Considerations.pdf | 2.0 | https://www.researchgate.net/profile/Hao-Qin-52/publication/382825702_Transforming_Education_with_Large_Language_Models_Opportunities_Challenges_and_Ethical_Considerations/links/66ad60ca51aa0775f264c5e0/Transforming-Education-with-Large-Language-Models-Opportunities-Challenges-and-Ethical-Considerations.pdf |
| Improve the accuracy and efficiency of large language models via dynamic token compression and adaptive layer pruning | JE-BF1UgX3sJ | https://www.techrxiv.org/doi/full/10.36227/techrxiv.173030653.35636983 | 20.0 | https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.173030653.35636983 |
| Reading Law with ChatGPT (With Special Emphasis on Contextual Canons) | _y_E2nhQTJwJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4830669 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4830669 |
| Artificial Intelligence and the Crises of Judicial Power:(Not) Cutting the Gordian Knot? | o-QdXzv2krIJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4731231 | 4.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4731231 |
| Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects | FxLxW3653oAJ | https://www.researchgate.net/profile/Muhammad-Shaikh-9/publication/383818024_Large_Language_Models_A_Comprehensive_Survey_of_its_Applications_Challenges_Limitations_and_Future_Prospects/links/66dffb06b1606e24c21d8936/Large-Language-Models-A-Comprehensive-Survey-of-its-Applications-Challenges-Limitations-and-Future-Prospects.pdf | 366.0 | https://www.researchgate.net/profile/Muhammad-Shaikh-9/publication/383818024_Large_Language_Models_A_Comprehensive_Survey_of_its_Applications_Challenges_Limitations_and_Future_Prospects/links/66dffb06b1606e24c21d8936/Large-Language-Models-A-Comprehensive-Survey-of-its-Applications-Challenges-Limitations-and-Future-Prospects.pdf |
| The Role of Artificial Intelligence (AI) in the Academic Paper Writing and Its Prospective Application as a Co-Author: A Letter to the Editor | DaI7QWpvj28J | https://eurjther.com/index.php/home/article/view/1808 | 5.0 | https://eurjther.com/index.php/home/article/download/1808/1561 |
| Requirements for “Legal Tech AI Systems”—Reflections on the negotiated AI Act with regard to Legal Technology using AI | tw2I5qfpqm0J | https://www.degruyter.com/document/doi/10.9785/cri-2023-240402/html | 1.0 | NaN |
| From evolutionary to revolutionary: Lessons learned from the previous technology transformation | Cb0aDPXiZJ4J | https://search.informit.org/doi/abs/10.3316/informit.T2024082200015891302987543 | NaN | NaN |
| AI-transforming the work of lawyers and judges | _b2PFRcCuycJ | https://search.informit.org/doi/abs/10.3316/informit.T2024052400018690060600341 | NaN | NaN |
| Psycollm: Enhancing llm for psychological understanding and evaluation | SQQr7rMNtGMJ | https://ieeexplore.ieee.org/abstract/document/10772313/ | 12.0 | https://arxiv.org/pdf/2407.05721 |
| Educational Exposure to Generative Artificial Intelligence | Qx8TVWpjYbEJ | https://www.federalreserve.gov/econres/notes/feds-notes/educational-exposure-to-generative-artificial-intelligence-20250226.html | NaN | NaN |
| ChatGPT and the Future of Work” Banking Industry Use Cases “ | gOypQXV-mAMJ | https://masrafeyoun.ebi.gov.eg/wp-content/uploads/2024/01/ChatGPT-and-the-Future-of-Work-.pdf | NaN | https://masrafeyoun.ebi.gov.eg/wp-content/uploads/2024/01/ChatGPT-and-the-Future-of-Work-.pdf |
| Homem ou Máquina? Um estudo exploratório do desempenho do Chat GPT 3.5 no Exame de Suficiência do CFC. | tGOBoCH3K1wJ | https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=16791991&AN=177542883&h=9P%2FR%2B9PZ53M6FS7WNz7sTw4IHKNTaBN5qUSo34WP3PJn4UDNul9pSrzWlghJdT%2FhNUIg5w%2FxJo%2B5JxiacNFAIw%3D%3D&crl=c | NaN | NaN |
| Human resource analytics in the era of artificial intelligence: leveraging knowledge towards organizational success in Pakistan | vkejhE-Ze-oJ | https://www.wsp-publishing.com/rc-pub/front/front-article/download/59290803/lowqualitypdf/Human%20Resource%20Analytics%20in%20the%20Era%20of%20Artificial%20Intelligence:%20Leveraging%20Knowledge%20towards%20Organizational%20Success%20in%20Pakistan.pdf | 13.0 | https://www.wsp-publishing.com/rc-pub/front/front-article/download/59290803/lowqualitypdf/Human%20Resource%20Analytics%20in%20the%20Era%20of%20Artificial%20Intelligence:%20Leveraging%20Knowledge%20towards%20Organizational%20Success%20in%20Pakistan.pdf |
| Design Novel Effective Method for Large Language Model Compression: BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation | pCYBrSdeFH8J | https://www.diva-portal.org/smash/record.jsf?pid=diva2:1887985 | NaN | https://www.diva-portal.org/smash/get/diva2:1887985/FULLTEXT01.pdf |
| Adapting to “AI” | xKwqTL_AGokJ | https://www.academia.edu/download/111257289/Adapting_to_AI_101_Joseph_Cohen.pdf | NaN | https://www.academia.edu/download/111257289/Adapting_to_AI_101_Joseph_Cohen.pdf |
| Business Plan Development for an AI Based Legal Chatbot Startup-Marketing Mix | kb1e_e1dYqMJ | https://search.proquest.com/openview/c68ce696933deed106a47c84247964eb/1?pq-origsite=gscholar&cbl=2026366&diss=y | NaN | NaN |
| Amusing Inventions Not to Be Thrown Away: ChatGPT and the Future of Tax. | U5ILlHRdNAkJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/jtaxpp25§ion=20 | NaN | https://www.crowell.com/a/web/3U1R25jhM6xZ17mZekGMkx/82ZT42/jtpp_25-02_federico-thompson.pdf |
| ChatGPT and service: opportunities, challenges, and research directions | 7VLS4kLM-rYJ | https://www.emerald.com/insight/content/doi/10.1108/jstp-11-2023-0292/full/html | 13.0 | https://acuresearchbank.acu.edu.au/download/7768bcd68dca0c5962a8da8661318b7cf573e273f62ccee392b38b8bda0ef3ae/228417/Letheren_2024_ChatGPT_and_service_opportunities_challenges_and.pdf |
| Generative adversarial training with perturbed token detection for model robustness | -bjvGA-RheQJ | https://aclanthology.org/2023.emnlp-main.804/ | 4.0 | https://aclanthology.org/2023.emnlp-main.804.pdf |
| Research status and application of artificial intelligence large models in the oil and gas industry | N7vq-A9c2M4J | https://www.sciencedirect.com/science/article/pii/S1876380424605240 | 5.0 | https://www.sciencedirect.com/science/article/pii/S1876380424605240/pdf?md5=67673ee303c5d6a22217fe5c695df5bb&pid=1-s2.0-S1876380424605240-main.pdf |
| Strengthening Legal Mechanisms for Consumer Protection in the Digital Marketplace | W4582WozKXQJ | http://193.36.85.187:8089/index.php/isslp/article/view/35 | NaN | http://193.36.85.187:8089/index.php/isslp/article/download/35/31 |
| Artificial intelligence (AI) technology in OpenAI ChatGPT application: A review of ChatGPT in writing English essay | afRxufBh6fkJ | https://journal.unnes.ac.id/sju/elt/article/view/64069 | 369.0 | https://journal.unnes.ac.id/sju/elt/article/download/64069/24008 |
| Artificial Intelligence & Criminal Justice: A Primer | QE9gSMM7MkYJ | https://commons.allard.ubc.ca/fac_pubs/2791/ | NaN | https://commons.allard.ubc.ca/cgi/viewcontent.cgi?article=3799&context=fac_pubs |
| A Look at the Recursive Potential of Generative Artificial Intelligence in the Financial Technology Sector | 7FHm5UTr-qcJ | https://www.emerald.com/insight/content/doi/10.1108/978-1-83608-432-720251005/full/html | NaN | NaN |
| Human realignment: An empirical study of LLMs as legal decision-aids in moral dilemmas | bpVcEyHR4cQJ | https://pure.mpg.de/pubman/faces/ViewItemOverviewPage.jsp?itemId=item_3645009 | NaN | https://pure.mpg.de/rest/items/item_3645009/component/file_3645010/content |
| Artificial Intelligence-Based Prediction Modeling of Audit Dispositions in Korean Public Institutions | v4TDTh77l8oJ | https://dl.acm.org/doi/abs/10.1145/3711542.3711563 | NaN | NaN |
| Highlights from the 2024 Utah Legislative General Session. | TLm-VkgthqgJ | https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=00919691&asa=N&AN=178706359&h=aYpcy94dauG17lkjacZId8htjFZcRXwUT1RMGDyLxXibqQ3kCmCSURyXJkT%2BZZnvEd%2FtGeph8%2F%2BFOlM61PvEyg%3D%3D&crl=c | NaN | NaN |
| Game Theory Approach to Identifying Deception in Large Language Models | fKnhXnS7NQYJ | https://www.techrxiv.org/doi/full/10.36227/techrxiv.171822179.99413216 | NaN | https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.171822179.99413216 |
| “Is this site legit?”: LLMs for scam website detection | mBoJl_DWI7gJ | https://link.springer.com/chapter/10.1007/978-981-96-0573-6_17 | 2.0 | NaN |
| Investigating code generation performance of ChatGPT with crowdsourcing social data | Vp2PeZVWrfAJ | https://ieeexplore.ieee.org/abstract/document/10196869/ | 147.0 | https://yunhefeng.me/material/COMPSAC___SETA_23.pdf |
| WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling | 1FuR9e3J6qUJ | https://arxiv.org/abs/2410.05970 | 6.0 | https://arxiv.org/pdf/2410.05970 |
| Sok: Prompt hacking of large language models | AMAOheVRUmwJ | https://ieeexplore.ieee.org/abstract/document/10825103/ | 3.0 | https://arxiv.org/pdf/2410.13901 |
| Risk analysis and control of large artificial intelligence models | mcocs4sPwhUJ | https://dl.acm.org/doi/abs/10.1145/3695080.3695149 | NaN | NaN |
| A quantitative study on the negative and positive impacts of using artificial intelligence (AI) in the information technology field. | P1n84Z_tYPUJ | https://iacis.org/iis/2024/1_iis_2024_341-351.pdf | NaN | https://iacis.org/iis/2024/1_iis_2024_341-351.pdf |
| Artificial Intelligence and the Lawyer's Duty of Confidentiality. | CMyIFsUHV_sJ | https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=00919691&asa=N&AN=182371971&h=YeA%2FEhwQ2MrA5p4v4Ez6o5dByt%2FJvaLhKK4bhaHBQr4GEe5wcyOrHEC3kkIVED7%2Bq3hQq8lJQoNCdJbbxL7XIg%3D%3D&crl=c | NaN | NaN |
| Dynamic LLM Routing and Selection based on User Preferences: Balancing Performance, Cost, and Ethics | 7hNr4uL27jwJ | https://arxiv.org/abs/2502.16696 | 1.0 | https://arxiv.org/pdf/2502.16696? |
| Conference Schedule | TYEifIONNHsJ | https://glsaonline.org/wp-content/uploads/2025/02/GLSA-Annual-Conference-2025-Brochure-2025.pdf | NaN | https://glsaonline.org/wp-content/uploads/2025/02/GLSA-Annual-Conference-2025-Brochure-2025.pdf |
| Modern innovative machine linguistics | ukGcRPmEsjkJ | https://er.knutd.edu.ua/bitstream/123456789/27794/3/%D0%91%D0%B5%D1%80%D0%BB%D0%B8%D0%BD_18%D0%96%D0%BE%D0%B2_%D1%82%D0%B8%D1%82%D1%83%D0%BB.pdf | 2.0 | https://er.knutd.edu.ua/bitstream/123456789/27794/2/%D0%91%D0%B5%D1%80%D0%BB%D0%B8%D0%BD_18%D0%96%D0%BE%D0%B2_%D1%81%D1%82%D0%BE%D1%80.118-122.pdf |
| Enhancing Plant Protection Knowledge with Large Language Models: A Fine-Tuned Question-Answering System Using LoRA | rL65ZW6D_yAJ | https://www.mdpi.com/2076-3417/15/7/3850 | NaN | NaN |
| Artificial Intelligence May Assist, but Can Never Replace, the Judicial Decision-Making Process of Human Judges. | jIZ2tigjzjsJ | https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=00153915&asa=N&AN=180320980&h=i4wDxP6sSdWkwdFhJAzv2g0vvJmXCLQqFwk%2FhLJirN%2BgloPRmWsbMxUrGS6dq4MqAyRWa5r3WiShoSyXLdnLDQ%3D%3D&crl=c | NaN | NaN |
| AI White Paper Consultation Response | crj8G8qyKYEJ | https://research.gold.ac.uk/id/eprint/33785/ | NaN | https://research.gold.ac.uk/id/eprint/33785/3/AI%20regulation%20white%20paper%20consultation%2C%20BILETA%20response%2C%20final%20%281%29.pdf |
| Modernizing Data Management by Developing a Data Mesh Knowledge Layer with OSDU | LijZCSLVhJUJ | https://onepetro.org/SPEATCE/proceedings-abstract/23ATCE/23ATCE/535538 | 2.0 | NaN |
| Governing Data and AI to Protect Inner Freedoms Includes a Role for IP | Ra4RfztIjSYJ | https://www.cigionline.org/documents/2672/FoT_PB_no.7.pdf | NaN | https://www.cigionline.org/documents/2672/FoT_PB_no.7.pdf |
| Citation-Enhanced Generation for LLM-based Chatbots | FuGSzl4d1IIJ | https://arxiv.org/abs/2402.16063 | 25.0 | https://arxiv.org/pdf/2402.16063 |
| Exploring the Landscape of Large and Small Language Models: Advancements, Trade-offs, and Future Directions | N4XvXhSJkIMJ | https://www.preprints.org/frontend/manuscript/c87907bf54d8f94de18a55180792873f/download_pub | NaN | https://www.preprints.org/frontend/manuscript/c87907bf54d8f94de18a55180792873f/download_pub |
| Artificial Intelligence and machine learning in drug design and development | MmxfAS1F1wgJ | https://books.google.com/books?hl=en&lr=&id=DWQQEQAAQBAJ&oi=fnd&pg=PP1&dq=(%22access+to+justice%22+OR+%22legal+aid%22+OR+%22legal+services%22)+AND+(%22language+model%22+OR+%22GPT%22+OR+%22generative+AI%22)&ots=-ULIG5BQIo&sig=CBZuZMszmzwI0B0zM3v8gLgHnLc | 5.0 | NaN |
| Researching Law in the Metaverse | KfQ7GRrik34J | https://www.degruyter.com/document/doi/10.1515/9781399515092-016/pdf?licenseType=restricted | NaN | NaN |
| ChatGPT Practices: Finance and Banking Domain | _J-yDG-ZRmwJ | https://bctjournal.com/article_402.html | NaN | https://bctjournal.com/article_402_fbe691509f4dd5426d010b4b2284b2ee.pdf |
| Comprehensibility and Automation: Plain Language in the Era of Digitalization. | B09Gn6auhTQJ | https://sciendo.com/pdf/10.2478/bjes-2022-0012 | 5.0 | https://sciendo.com/pdf/10.2478/bjes-2022-0012 |
| The use of artificial intelligence in corporate decision-making at board level: A preliminary legal analysis | pDsTcmAOZK4J | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4339413 | 8.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4339413 |
| LLM-Datasets: An Open Framework for Pretraining Datasets of Large Language Models | jxLBw6Jkp30J | https://openreview.net/forum?id=5RdIMlGLXL | 4.0 | https://openreview.net/pdf?id=5RdIMlGLXL |
| Legal Market Decartelization | xqPlbTspskYJ | https://scholarship.law.tamu.edu/facscholar/2204/ | NaN | https://scholarship.law.tamu.edu/cgi/viewcontent.cgi?article=3191&context=facscholar |
| Potential Role of ChatGPT in Healthcare in the Prevention and Management of Non-communicable Diseases | eTdFrn9U1FEJ | https://link.springer.com/chapter/10.1007/978-3-031-34045-1_34 | 6.0 | NaN |
| Key Regulatory Principles and Current Regulatory Approaches | n9pVdjGftqYJ | https://link.springer.com/chapter/10.1007/978-3-031-65514-2_6 | NaN | NaN |
| Efficient Prompt Engineering: Techniques and Trends for Maximizing LLM Output | FAY4biWRjJsJ | https://www.researchgate.net/profile/Aqsa-Aqsa-4/publication/390877305_Efficient_Prompt_Engineering_Techniques_and_Trends_for_Maximizing_LLM_Output/links/68014a38bd3f1930dd5fba8e/Efficient-Prompt-Engineering-Techniques-and-Trends-for-Maximizing-LLM-Output.pdf | NaN | https://www.researchgate.net/profile/Aqsa-Aqsa-4/publication/390877305_Efficient_Prompt_Engineering_Techniques_and_Trends_for_Maximizing_LLM_Output/links/68014a38bd3f1930dd5fba8e/Efficient-Prompt-Engineering-Techniques-and-Trends-for-Maximizing-LLM-Output.pdf |
| Embracing the evolution: Artificial intelligence in legal practice | D8ewtSDrS8oJ | https://search.informit.org/doi/abs/10.3316/informit.T2024082200016200528865278 | NaN | NaN |
| Artificial Intelligence and Copyright: Comments on a Notice requested by the US Copyright Office | 6Lu9Wgf2rMcJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4619322 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4619322 |
| Co-authoring with an AI? Ethical dilemmas and artificial intelligence | bUM57XdgCiAJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/arzjl56§ion=8 | 37.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4303959 |
| A servant of two masters: How Academic Fears about Artificial Intelligence map to Employer Engagement | m75AeQpYYMIJ | https://pure.northampton.ac.uk/en/publications/a-servant-of-two-masters-how-academic-fears-about-artificial-inte | NaN | https://pure.northampton.ac.uk/files/75837610/Sneddon_et_al_2024_A_servant_of_two_masters_How_Academic_Fears_about_Artificial_Intelligence_map_to_Employer_Engagement.pdf |
| The legal team of the future: by Adam Curphey, London, UK, London Publishing Partnership, 2022, 288 pp.,£ 25 (paperback), ISBN 1913019640 | L5OMaCCYQTQJ | https://www.tandfonline.com/doi/full/10.1080/03069400.2023.2238473 | NaN | NaN |
| AI vs. Human Translators: Navigating the Complex World of Religious Texts and Cultural Sensitivity. | CMs1hDgYQYEJ | https://pdfs.semanticscholar.org/65cc/8c00b2a53cafba7b76f7aa17ed35f5ed2116.pdf | 9.0 | https://pdfs.semanticscholar.org/65cc/8c00b2a53cafba7b76f7aa17ed35f5ed2116.pdf |
| State Bar of California | RBzOwCxynRAJ | https://digital.sandiego.edu/cgi/viewcontent.cgi?article=3221&context=crlr | NaN | https://digital.sandiego.edu/cgi/viewcontent.cgi?article=3221&context=crlr |
| GeoGPT: An assistant for understanding and processing geospatial tasks | Qes4fbIB214J | https://www.sciencedirect.com/science/article/pii/S1569843224003303 | 35.0 | NaN |
| ChatGPT: reflections from the UK higher education institutions, accountancy bodies and BIG4s | Th0n7V2jFmsJ | https://www.emerald.com/insight/content/doi/10.1108/arj-07-2023-0184/full/html | 2.0 | NaN |
| Komodo: A Linguistic Expedition into Indonesia's Regional Languages | aKgI4nl8ulwJ | https://arxiv.org/abs/2403.09362 | 9.0 | https://arxiv.org/pdf/2403.09362 |
| The First Hardware Circuit Emulating Italian Road Homicides Legal Logic, DAJE! | AXew9YzxxOgJ | https://hal.science/hal-04990194/ | 2.0 | https://hal.science/hal-04990194v1/file/IS_ES_2025_ggarzo.pdf |
| Assessing the benefits of ChatGPT for business: an empirical study on organizational performance | mCy6nJpL7mgJ | https://ieeexplore.ieee.org/abstract/document/10189342/ | 51.0 | https://ieeexplore.ieee.org/iel7/6287639/6514899/10189342.pdf |
| Cyberattacks on Large Language Models-Attack Detection and Architecture Adaptability | UkGB2qRqcFcJ | https://ieeexplore.ieee.org/abstract/document/10971722/ | NaN | NaN |
| VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs | vgbfX5Ex6JoJ | https://arxiv.org/abs/2406.10326 | 1.0 | https://arxiv.org/pdf/2406.10326 |
| Evaluating the performance of chatgpt in the automation of maintenance recommendations for prognostics and health management | GIrhz2GUiNgJ | https://scholar.archive.org/work/fq7blfn7pzdqlm7f625zbdpr4q/access/wayback/https://papers.phmsociety.org/index.php/phmconf/article/download/3487/phmc_23_3487 | 8.0 | https://scholar.archive.org/work/fq7blfn7pzdqlm7f625zbdpr4q/access/wayback/https://papers.phmsociety.org/index.php/phmconf/article/download/3487/phmc_23_3487 |
| Artificial intelligence and employment: a look into the crystal ball | Ub_Ju_UT7V4J | https://www.econstor.eu/handle/10419/278106 | 16.0 | https://www.econstor.eu/bitstream/10419/278106/1/GLO-DP-1333.pdf |
| Concentrating intelligence: scaling and market structure in artificial intelligence | TUZpZNjejiYJ | https://academic.oup.com/economicpolicy/article-abstract/40/121/225/7905140 | 12.0 | https://www.nber.org/system/files/working_papers/w33139/w33139.pdf |
| Unleashing the potential of prompt engineering in large language models: a comprehensive review | hdPHnUO15h0J | https://arxiv.org/abs/2310.14735 | 339.0 | https://arxiv.org/pdf/2310.14735 |
| GUIDEQ: Framework for Guided Questioning for progressive informational collection and classification | XcEkh7h2e9kJ | https://arxiv.org/abs/2411.05991 | NaN | https://arxiv.org/pdf/2411.05991 |
| RECENT ADVANCES IN NATURAL LANGUAGE PROCESSING SYSTEMS: A TECHNICAL OVERVIEW | CZHdjYUYW2UJ | https://www.researchgate.net/profile/Venkatesh-Sriram-2/publication/389712327_Recent_Advances_in_Natural_Language_Processing_Systems_A_Technical_Overview/links/67cf5a82d759700065077b55/Recent-Advances-in-Natural-Language-Processing-Systems-A-Technical-Overview.pdf | NaN | https://www.researchgate.net/profile/Venkatesh-Sriram-2/publication/389712327_Recent_Advances_in_Natural_Language_Processing_Systems_A_Technical_Overview/links/67cf5a82d759700065077b55/Recent-Advances-in-Natural-Language-Processing-Systems-A-Technical-Overview.pdf |
| Sustainable Topic Modeling for Legal Moroccan Arabic Language: A Challenging Study on BERTopic Technique | y9CAx5HY2yIJ | https://www.sciencedirect.com/science/article/pii/S1877050924010858 | 2.0 | https://www.sciencedirect.com/science/article/pii/S1877050924010858/pdf?md5=b7baccee582f930f8469789cb7339050&pid=1-s2.0-S1877050924010858-main.pdf |
| Uncovering the influence of ChatGPT's prompts on scientific writings using machine learning-based text mining approaches | Viuh1_Du-4oJ | https://www.researchgate.net/profile/Boniphace-Kutela/publication/369272116_Uncovering_the_Influence_of_ChatGPT's_Prompts_on_Scientific_Writings_using_Machine_Learning-Based_Text_Mining_Approaches/links/6415c6c2315dfb4cce8d779e/Uncovering-the-Influence-of-ChatGPTs-Prompts-on-Scientific-Writings-using-Machine-Learning-Based-Text-Mining-Approaches.pdf | 6.0 | https://www.researchgate.net/profile/Boniphace-Kutela/publication/369272116_Uncovering_the_Influence_of_ChatGPT's_Prompts_on_Scientific_Writings_using_Machine_Learning-Based_Text_Mining_Approaches/links/6415c6c2315dfb4cce8d779e/Uncovering-the-Influence-of-ChatGPTs-Prompts-on-Scientific-Writings-using-Machine-Learning-Based-Text-Mining-Approaches.pdf |
| Understanding the Dynamics in Deploying AI-Based Content Creation Support Tools in Broadcasting Systems-Benefits, Challenges, and Directions | Yv2j5niguOwJ | https://dl.acm.org/doi/abs/10.1145/3706598.3713532 | NaN | NaN |
| Continuous training and fine-tuning for domain-specific language models in medical question answering | TGKE4_F0dVoJ | https://arxiv.org/abs/2311.00204 | 5.0 | https://arxiv.org/pdf/2311.00204 |
| REGULATING ARTIFICIAL INTELLIGENCE AS A PERPETRATOR OF DEEPFAKE CRIMES IN INDONESIA | vflh02DRLncJ | https://asasijournal.com/index.php/icuw2023/article/view/21 | NaN | https://asasijournal.com/index.php/icuw2023/article/download/21/15 |
| AI Revolution: Mastering AI for Personal and Organizational Growth | DwdyiV05SdUJ | https://books.google.com/books?hl=en&lr=&id=nvokEQAAQBAJ&oi=fnd&pg=PP8&dq=(%22access+to+justice%22+OR+%22legal+aid%22+OR+%22legal+services%22)+AND+(%22language+model%22+OR+%22GPT%22+OR+%22generative+AI%22)&ots=LMG4Q-HOBb&sig=lq9a_tyXn-rkVp8OAbLbb8ySLCc | 1.0 | NaN |
| Natural Language Understanding in Big Data: AI-Driven Approaches for Automated Insights | R_-NZ2F_XtYJ | https://www.researchgate.net/profile/Muhammad-Sani-48/publication/388526574_Natural_Language_Understanding_in_Big_Data_AI-Driven_Approaches_for_Automated_Insights/links/679bdef94c479b26c9c2ec1c/Natural-Language-Understanding-in-Big-Data-AI-Driven-Approaches-for-Automated-Insights.pdf | NaN | https://www.researchgate.net/profile/Muhammad-Sani-48/publication/388526574_Natural_Language_Understanding_in_Big_Data_AI-Driven_Approaches_for_Automated_Insights/links/679bdef94c479b26c9c2ec1c/Natural-Language-Understanding-in-Big-Data-AI-Driven-Approaches-for-Automated-Insights.pdf |
| The Path of Tax Law: Toward Legal Singularity | N0eYrm4EzjUJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4582653 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4582653 |
| Legal Ethics, Artificial Intelligence, and Mindfulness, Oh My! | xIDsCy5DQfsJ | https://themindfullawstudent.org/resources/Jan-Writings/Jan-L.-Jacobowitz-AI-article-LBW.pdf | NaN | https://themindfullawstudent.org/resources/Jan-Writings/Jan-L.-Jacobowitz-AI-article-LBW.pdf |
| The Continued Rise of Artificial Intelligence in Higher Education | ftds8EOUbrIJ | https://hr.unc.edu/wp-content/uploads/sites/222/2023/09/The-Continued-Rise-of-Artificial-Intelligence-in-Higher-Education.pdf | 2.0 | https://hr.unc.edu/wp-content/uploads/sites/222/2023/09/The-Continued-Rise-of-Artificial-Intelligence-in-Higher-Education.pdf |
| The Unexpected Evolution of AI | IWHLXo0R1fQJ | https://link.springer.com/chapter/10.1007/979-8-8688-0456-4_4 | NaN | NaN |
| BILETA Response to White Paper AI Regulation: A Pro-innovation Approach | cMPinDF2OpkJ | https://researchprofiles.herts.ac.uk/en/publications/bileta-response-to-white-paper-ai-regulation-a-pro-innovation-app | NaN | https://researchprofiles.herts.ac.uk/files/46728996/BILETA_Response_to_White_Paper_AI_Regulation_A_Proinnovation_Approach.pdf |
| Investigating the effectiveness of chatgpt in mathematical reasoning and problem solving: Evidence from the vietnamese national high school graduation examination | KYZmCs74QmQJ | https://arxiv.org/abs/2306.06331 | 87.0 | https://arxiv.org/pdf/2306.06331 |
| Conversational Factor Information Retrieval Model (ConFIRM) | Pfjjr1-EIwwJ | https://arxiv.org/abs/2310.13001 | NaN | https://arxiv.org/pdf/2310.13001 |
| Seyahat danışmanı olarak Chatgpt'nin yeteneklerini keşfetmek: Turizm pazarlamasında üretken yapay zekâ üzerine bir araştırma | QlA7C_8Et-MJ | https://dergipark.org.tr/en/pub/ijctr/article/1325428 | 12.0 | https://dergipark.org.tr/en/download/article-file/3255592 |
| Artificial intelligence and K-12 education: Possibilities, pedagogies and risks | KZFco1VIAywJ | https://www.tandfonline.com/doi/abs/10.1080/07380569.2023.2279870 | 30.0 | https://www.tandfonline.com/doi/pdf/10.1080/07380569.2023.2279870 |
| Establishing a Robust LLMOps Framework for Intelligent Automation: Strategies and Best Practices | vEZXefSk20AJ | https://ieeexplore.ieee.org/abstract/document/10961869/ | NaN | NaN |
| THE USE OF AL IN TODAY'S TECHNOLOGY DEVICES | 9VZt6DyvzXsJ | https://www.thestanfordjournal.com/images/new_paper/08paper%2057-68.pdf | NaN | https://www.thestanfordjournal.com/images/new_paper/08paper%2057-68.pdf |
| Evaluating human resources management literacy: A performance analysis of ChatGPT and bard | Nfp1nMXNUI4J | https://www.cell.com/heliyon/fulltext/S2405-8440(24)03057-3 | 15.0 | https://www.cell.com/heliyon/pdf/S2405-8440(24)03057-3.pdf |
| Sociological Phenomenology: Understanding Neighborhood Development and Local Culture | EABTT6lmmYYJ | https://hal.science/hal-04984395/ | NaN | https://hal.science/hal-04984395/document |
| When to use ChatGPT: An Exploratory Development of a 2x2 Matrix Framework. | en8lmYXxtFwJ | https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=1545679X&AN=184005674&h=jPCJ0Ol4SnHu180ziuYz74BpG7%2FD0l0GU1%2Bt6bzMMVsguaRDyDZ0IeGUSKzrUGe6NFXgQ1rlu4XT6RNBnFxdRQ%3D%3D&crl=c | NaN | NaN |
| GenAI in the Spotlight | ZCFPd-rvhmwJ | https://link.springer.com/chapter/10.1007/979-8-8688-0968-2_2 | NaN | NaN |
| Evaluating Errors and Improving Performance of ChatGPT: A Research Paper | H5rHkJZPK4QJ | https://clinicsearchonline.org/uploads/articles/1693389432CRS-RA-19-Galley_Proof.pdf | 2.0 | https://clinicsearchonline.org/uploads/articles/1693389432CRS-RA-19-Galley_Proof.pdf |
| AI Catalyst: Cracking the code for MSME productivity | L4nL0nO_ZeIJ | https://figshare.manchester.ac.uk/articles/report/AI_Catalyst_Cracking_the_code_for_MSME_productivity/27980510/1/files/51028634.pdf | NaN | https://figshare.manchester.ac.uk/articles/report/AI_Catalyst_Cracking_the_code_for_MSME_productivity/27980510/1/files/51028634.pdf |
| Megan-A Sports Chatbot using OpenAI APIs and Django Framework with Python | zW8vRu5T3m8J | https://ieeexplore.ieee.org/abstract/document/10543499/ | NaN | NaN |
| Generalizing Trust: Weak-to-Strong Trustworthiness in Language Models | M0yH6UAgsI0J | https://arxiv.org/abs/2501.00418 | 1.0 | https://arxiv.org/pdf/2501.00418 |
| Navigating Stormy Seas: Young People Steering the Future of Justice Through Troubling Tides. | JnGYWQdiNR8J | https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=10276084&AN=182468497&h=2q%2Bnr259sWt%2B%2FTf0tUM4B0ku0IobqSrG9cfWrsuGugT0BMdb9N%2FwGBCUAhGNyiy44%2BBnHEjYKeo7gPeOsYhuCw%3D%3D&crl=c | NaN | NaN |
| Benchmarking Medical LLMs on Anesthesiology: A Comprehensive Dataset in Chinese | MWjB6j1Py8kJ | https://ieeexplore.ieee.org/abstract/document/10840322/ | 1.0 | NaN |
| The use of artificial intelligence in academic publishing: Preliminary remarks and perspectives | JDIxmlFdiQYJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/ajee2024§ion=73 | 7.0 | https://ajee-journal.com/upload/attaches/att_1724713178.pdf |
| Algorithmic theatre and AI:“There's no contest; people are better.” An interview with Annie Dorsen | V5_xjTD6z9YJ | https://www.idunn.no/doi/full/10.18261/drama.61.2.7 | NaN | NaN |
| Empirical study on fine-tuning pre-trained large language models for fault diagnosis of complex systems | f9dcsDpjx4IJ | https://www.sciencedirect.com/science/article/pii/S095183202400454X | 17.0 | NaN |
| We have no idea what we are walking into: AI and ethical considerations | DRB-z5OGLaYJ | https://nyaspubs.onlinelibrary.wiley.com/doi/abs/10.1111/nyas.15133 | 2.0 | NaN |
| Exploring the Comprehension of ChatGPT in Traditional Chinese Medicine Knowledge | EM0LbiC9NWUJ | https://arxiv.org/abs/2403.09164 | 5.0 | https://arxiv.org/pdf/2403.09164 |
| A survey on symbolic knowledge distillation of large language models | 32IjpX6kRLwJ | https://ieeexplore.ieee.org/abstract/document/10597596/ | 10.0 | https://arxiv.org/pdf/2408.10210 |
| Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-Generation | kbfw6Fsq-qAJ | https://arxiv.org/abs/2504.03165 | NaN | https://arxiv.org/pdf/2504.03165? |
| The interplay between machine learning and data minimization under the GDPR: the case of Google's topics API | 45RtolUBQu0J | https://academic.oup.com/idpl/article-pdf/doi/10.1093/idpl/ipad020/56581396/ipad020.pdf | 2.0 | NaN |
| COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis | HZ-4I9MfOCgJ | https://aclanthology.org/2025.findings-naacl.139/ | NaN | https://aclanthology.org/2025.findings-naacl.139.pdf |
| Create an account | h8qHkGvg_HcJ | https://worldfinancialreview.com/page/3/?p=l | NaN | NaN |
| Harnessing Artificial Intelligence in Nursing—Insights From the Third International Workshop of Artificial Intelligence in Nursing (AINurse2024) | 1tOEkS6QY84J | https://journals.lww.com/cinjournal/fulltext/2024/10000/harnessing_artificial_intelligence_in.2.aspx | NaN | NaN |
| The Consequences of Implementing Artificial Intelligence Technology in the Digital Economy from the Perspective of Generation Z | BRrBu4S54Q0J | https://ersj.eu/journal/3764/download/The+Consequences+of+Implementing+Artificial+Intelligence+Technology+in+the+Digital+Economy+from+the+Perspective+of+Generation+Z.pdf | NaN | https://ersj.eu/journal/3764/download/The+Consequences+of+Implementing+Artificial+Intelligence+Technology+in+the+Digital+Economy+from+the+Perspective+of+Generation+Z.pdf |
| Knowledge Pyramid Construction for Multi-Level Retrieval-Augmented Generation | UMyqIKz4N7YJ | https://arxiv.org/abs/2407.21276 | 1.0 | https://arxiv.org/pdf/2407.21276 |
| TokenOps: A Compiler-Style Architecture for Token Optimization in LLM API Workflows | W_JZQ2ES964J | https://www.researchgate.net/profile/Nitin-Lodha-2/publication/391063956_TokenOps_Reducing_Cost_Latency_and_Carbon_in_LLM_Workflows_through_Token-Aware_Middleware/links/6809eb7060241d51401826a1/TokenOps-Reducing-Cost-Latency-and-Carbon-in-LLM-Workflows-through-Token-Aware-Middleware.pdf | NaN | https://www.researchgate.net/profile/Nitin-Lodha-2/publication/391063956_TokenOps_Reducing_Cost_Latency_and_Carbon_in_LLM_Workflows_through_Token-Aware_Middleware/links/6809eb7060241d51401826a1/TokenOps-Reducing-Cost-Latency-and-Carbon-in-LLM-Workflows-through-Token-Aware-Middleware.pdf |
| Artificial Intelligence & Criminal Justice: Cases and Commentary | 3NLzN6i5MaIJ | https://commons.allard.ubc.ca/fac_pubs/2793/ | NaN | https://commons.allard.ubc.ca/cgi/viewcontent.cgi?article=3801&context=fac_pubs |
| ChatGPT Empowers Higher Education: —Research Topics Hotspots and Quantitative Visual Analysis | Ff0gMFpn4bgJ | https://dl.acm.org/doi/abs/10.1145/3722237.3722245 | NaN | NaN |
| Emerging Artificial Intelligence Risk Management Considerations for Law Firms | dwaKJ69S2wEJ | https://www.iadclaw.org/assets/1/17/Emerging_AI_Risk_Management_Considerations_for_Law_Firms.pdf | NaN | https://www.iadclaw.org/assets/1/17/Emerging_AI_Risk_Management_Considerations_for_Law_Firms.pdf |
| Tech-Business Analytics–A Review Based New Model to Improve the Performances of Various Industry Sectors | 2hMq09zdNrgJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4400253 | 28.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4400253 |
| The ChatBots Are Coming! | 0jP5kd85LOYJ | https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/jotwell2023§ion=97 | 1.0 | NaN |
| Ketergantungan mahasiswa Universitas Jember terhadap artificial intelligence (AI) | nN70shbEoP0J | https://ejurnalqarnain.stisnq.ac.id/index.php/ALADALAH/article/view/608 | 13.0 | https://ejurnalqarnain.stisnq.ac.id/index.php/ALADALAH/article/download/608/616 |
| HF01-11 THE HISTORY OF THE HIJRA: THE THIRD GENDER IN THE INDIAN SUBCONTINENT | NPeRE3GH_4oJ | https://www.auajournals.org/doi/abs/10.1097/01.JU.0001008828.35887.de.11 | NaN | NaN |
| The Routledge Handbook of Ethics in Technical and Professional Communication | r6zWriLJBGAJ | https://books.google.com/books?hl=en&lr=&id=wss-EQAAQBAJ&oi=fnd&pg=PT22&dq=(%22access+to+justice%22+OR+%22legal+aid%22+OR+%22legal+services%22)+AND+(%22language+model%22+OR+%22GPT%22+OR+%22generative+AI%22)&ots=-cBVJKhPie&sig=bZ1yGebiJhG2POmZ2wulnCXnLQ0 | NaN | NaN |
| פרטיות ואוטונומיה בהליכי גישור דיגיטליים: מיפוי הסיכונים והצעות לפתרון Privacy and Autonomy in Online Mediation: Mapping the Risks and Proposals for a Solution | NrBD0NLvApwJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5000657 | NaN | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5000657 |
| Understanding RAG: The Power of Retrieval-Augmented Generation in AI | n_aBO8dObAoJ | https://www.authorea.com/doi/pdf/10.22541/au.174345452.22175926 | NaN | NaN |
| Navigating the Digital Dispute Resolution Landscape: Challenges and Opportunities | Ll274-1nqZsJ | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4802590 | 1.0 | https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4802590 |
| cally.| | IY7gvUsCP3cJ | https://www.jstor.org/stable/pdf/community.28034239.pdf | NaN | NaN |
| Higher Education After Artificial Intelligence | ZqGazwM9LhwJ | https://www.acenet.edu/Documents/Higher-Education-After-AI.pdf | NaN | https://www.acenet.edu/Documents/Higher-Education-After-AI.pdf |
| To Learn or Not to Learn: Replaced Token Detection for Learning the Meaning of Negation | 26yOzn8f_vkJ | https://aclanthology.org/2024.lrec-main.1411/ | NaN | https://aclanthology.org/2024.lrec-main.1411.pdf |
| Andrew Phang (Gen. Ed.), Pioneer, polymath and mentor: The life and legacy of Yong Pung How | hBRxJO9petYJ | https://ink.library.smu.edu.sg/sol_research/4579/ | NaN | https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6537&context=sol_research |
| Don't Trust ChatGPT: A Case Study of a Defective Research Tool | _QOx27nzCxgJ | https://www.researchgate.net/profile/Robert-Mcgee-5/publication/375792408_Don't_Trust_ChatGPT_A_Case_Study_of_a_Defective_Research_Tool/links/655cb4c6b86a1d521bfcdf61/Dont-Trust-ChatGPT-A-Case-Study-of-a-Defective-Research-Tool.pdf | 27.0 | https://www.researchgate.net/profile/Robert-Mcgee-5/publication/375792408_Don't_Trust_ChatGPT_A_Case_Study_of_a_Defective_Research_Tool/links/655cb4c6b86a1d521bfcdf61/Dont-Trust-ChatGPT-A-Case-Study-of-a-Defective-Research-Tool.pdf |
| Yapay zekâ sohbet robotu chatgpt ve Türkiye internet gündeminde oluşturduğu temalar | rpfKPBonepgJ | https://dergipark.org.tr/en/pub/ejnm/issue/77129/1266798 | 35.0 | https://dergipark.org.tr/en/download/article-file/3017587 |
| When Should Algorithms Resign? A Proposal for AI Governance | f2yKZZWwhKkJ | https://ieeexplore.ieee.org/abstract/document/10687308/ | 1.0 | https://ieeexplore.ieee.org/iel8/2/10687304/10687308.pdf |
| ADR and workplace conflicts: a British Perspective with Professor Susan Blake | z7tNankivAsJ | https://research.brighton.ac.uk/en/publications/adr-and-workplace-conflicts-a-british-perspective-with-professor- | NaN | NaN |
| 'Hey ChatGPT, Finish This Building…': A Worker‐Led AI Agenda for the Construction Industry | WoeKQex2sKUJ | https://onlinelibrary.wiley.com/doi/abs/10.1002/ad.3053 | NaN | NaN |
| Managing and Addressing AI Compliance | 00ENZHvqEi8J | https://link.springer.com/chapter/10.1007/979-8-8688-0983-5_8 | NaN | NaN |
| NYLS Earns Accolades in preLaw's 2024 Back to School Issue | 2hFqi_KCZhIJ | https://digitalcommons.nyls.edu/cgi/viewcontent.cgi?article=1105&context=community_news | NaN | NaN |
| Weighing AI ethics? Cybersecurity is not optional: How to do your best in a time of intense change. | aIa753yeZI0J | https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=02761505&asa=N&AN=182004633&h=ilcErWzTx0eMgKWt6OCc3Pr6HjvtvCS3Oct8d2zd7XKtlkb3kLb0tbHdjEeTvlL8GEgBJw%2F9Y3t7onPWJGXG2Q%3D%3D&crl=c | NaN | NaN |
| THE EVOLVING LANDSCAPE OF REGULATION. | 0yhdup-UBaEJ | https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=02761505&asa=N&AN=183669131&h=%2F5KF6h9FVK6LcTuc7B96U4T8LqEvI0ABANbMA2y9wCSI7glFymA%2BrsB%2Fzp8uO4YYSF2T9b44rspGznbW4%2BEFLw%3D%3D&crl=c | NaN | NaN |
| Generative AI's Legal Leap. Navigating Risk While Fostering Innovation in Litigation | V2Q6vPcmxXAJ | https://unsworks.unsw.edu.au/entities/publication/fca8612c-7ae0-40c4-8839-ffe1c4151802 | NaN | NaN |
| Committing to Foundational Principles: An Update on Our Progress. | aMLsYgFDlhYJ | https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=00153915&asa=N&AN=175890806&h=E3xX8S8YIKvQVKr%2B4t11LHOKcT%2BKwRHVLWNHJHC5qMvpLkdQ7Q%2Br3W41tQZat%2B%2F8pKlKEAXuDSG%2BZNXZy%2FyFOQ%3D%3D&crl=c | NaN | NaN |
| Generative AI-friend or foe? | T0k9ZapUb1oJ | https://search.informit.org/doi/abs/10.3316/informit.322711708438207 | NaN | NaN |
| An Introduction to A Roadmap for Law School Modernity: Teaching Tech Competence | yRTzQcw3sdsJ | https://researchonline.stthomas.edu/esploro/fulltext/journalArticle/An-Introduction-to-A-Roadmap-for/991015177199503691?repId=12446030220003691&mId=13446030210003691&institution=01CLIC_STTHOMAS | NaN | https://researchonline.stthomas.edu/esploro/fulltext/journalArticle/An-Introduction-to-A-Roadmap-for/991015177199503691?repId=12446030220003691&mId=13446030210003691&institution=01CLIC_STTHOMAS |
| Artificial Intelligence, Ethics and Speed Processing in the Law System | I6Ful7p1yP0J | https://www.corruptionreview.org/revista/article/view/84 | NaN | https://www.corruptionreview.org/revista/article/download/84/39 |
| Information and legal support for balancing the interests of justice and human rights protection | 7dlcyVpmL_gJ | https://repo.btu.kharkov.ua/bitstream/123456789/56984/1/Prava_lyudyny_v_hlobalizovanomu_sviti_2024_484-487.pdf | NaN | https://repo.btu.kharkov.ua/bitstream/123456789/56984/1/Prava_lyudyny_v_hlobalizovanomu_sviti_2024_484-487.pdf |
| Ginger & Rosa Full Credit List | fwYJMu-vDLMJ | https://www.cambridge.org/core/services/aop-cambridge-core/content/view/AB8E2932574A1A28725CE197980061EF/stamped-9780748693597mem2_p219-224_CBO.pdf/ginger_rosa_full_credit_list.pdf | NaN | NaN |
| Intelligence in the Workforce | iCJapnvrHUoJ | https://wfsolutions.org/images/workforce/GeneralWebsite/Content/HowWeHelp/EmployerServices/Industry%20Sector%20Partnerships/Information%20Technology/AI%20Updates%20Articles/AI%20in%20the%20Workforce%20-%20Research%20Paper.pdf | NaN | https://wfsolutions.org/images/workforce/GeneralWebsite/Content/HowWeHelp/EmployerServices/Industry%20Sector%20Partnerships/Information%20Technology/AI%20Updates%20Articles/AI%20in%20the%20Workforce%20-%20Research%20Paper.pdf |
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| 법률상담 도메인의 자연어이해 모델 학습을 위한언어자원 구축 방법론 | puRbDQ51Ct8J | https://hal.science/hal-03996144/ | NaN | NaN |
| 법률상담 도메인의 자연어이해 모델 학습을 위한언어자원 구축 방법론 | 6bO5F_IhKDwJ | https://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE11247694 | NaN | NaN |
| Panel on AI, Digital and Sustainable Futures, University of Manchester, 19/05/2025. A Regulatory Theory for the Economic Problems Arising in Corporate … | oZsXj4Vs990J | https://www.law.cam.ac.uk/sites/www.law.cam.ac.uk/files/images/www.law.cam.ac.uk/documents/presentations_and_convenorship_steffek_2024-11-30.pdf | NaN | https://www.law.cam.ac.uk/sites/www.law.cam.ac.uk/files/images/www.law.cam.ac.uk/documents/presentations_and_convenorship_steffek_2024-11-30.pdf |
| 법률플랫폼이 변호사업에 미치는 영향과 과제-사건 수임과 법률사무의 처리를 중심으로 | Hxpp8LHik9AJ | https://scholar.kyobobook.co.kr/article/detail/4010070343518 | NaN | NaN |
| ChatGPT and navigating AI. | sUNOl0hz2igJ | https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=02761505&asa=N&AN=163906444&h=zjBHaSlo%2FK2CkAk1h2dt6%2F93CLhdae6q3f6%2FN3A4PUemOm74641pFlfatY41U9uITcnKPrfwY3gXNi0SSofPwQ%3D%3D&crl=c | NaN | NaN |
HeinOnline
| Title | Link | Journal | Download Link |
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| Generative AI and Access to Justice in Canada: The Case of Self-Represented Litigants [SRLs] [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/windyrbaj40&div=12&start_page=211&collection=usjournals&set_as_cursor=0&men_tab=srchresults | Windsor Yearbook of Access to Justice | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/windyrbaj40&div=12&start_page=211&collection=usjournals&set_as_cursor=0&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Generative Contracts [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/arzjl56&div=35&start_page=1503&collection=usjournals&set_as_cursor=1&men_tab=srchresults | Arizona State Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/arzjl56&div=35&start_page=1503&collection=usjournals&set_as_cursor=1&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Words with Bots: How ChatGPT and Other AI Platforms Could Dramatically Reshape the Legal Industry [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/abaj109&div=69&start_page=34&collection=usjournals&set_as_cursor=2&men_tab=srchresults | ABA Journal | NaN |
| Generative AI and Legal Aid: Results from a Field Study and 100 Use Cases to Bridge the Access to Justice Gap [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/lla57&div=28&start_page=903&collection=usjournals&set_as_cursor=4&men_tab=srchresults | Loyola of Los Angeles Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/lla57&div=28&start_page=903&collection=usjournals&set_as_cursor=4&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Access to Civil Justice in the Age of AI: Mindsets & Pathways to New Practices [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/onulr50&div=30&start_page=533&collection=usjournals&set_as_cursor=5&men_tab=srchresults | Ohio Northern University Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/onulr50&div=30&start_page=533&collection=usjournals&set_as_cursor=5&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Unveiling the Impact of ChatGPT on Legal Services [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ijlmhs26&div=10&start_page=52&collection=journals&set_as_cursor=6&men_tab=srchresults | International Journal of Law Management & Humanities | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ijlmhs26&div=10&start_page=52&collection=journals&set_as_cursor=6&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Who Wants a Robo-Lawyer Now?: On AI Chatbots in China's Public Legal Services Sector [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/yjolt26&div=11&start_page=527&collection=usjournals&set_as_cursor=7&men_tab=srchresults | Yale Journal of Law and Technology | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/yjolt26&div=11&start_page=527&collection=usjournals&set_as_cursor=7&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Legal Profession in an Age of Generative Artificial Intelligence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/iihcj16&div=66&start_page=8693&collection=journals&set_as_cursor=8&men_tab=srchresults | International In-House Counsel Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/iihcj16&div=66&start_page=8693&collection=journals&set_as_cursor=8&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Game Changing: This Year's Rebels Are Generative AI Tools like ChatGPT, Which Have Upended the Legal Industry [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/abaj110&div=19&start_page=36&collection=usjournals&set_as_cursor=9&men_tab=srchresults | ABA Journal | NaN |
| Generative Artificial Intelligence: Legal Profession Disrupted? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/iihcj16&div=72&start_page=8757&collection=journals&set_as_cursor=10&men_tab=srchresults | International In-House Counsel Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/iihcj16&div=72&start_page=8757&collection=journals&set_as_cursor=10&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Teaching Law in the Age of Generative AI [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/juraba64&div=9&start_page=111&collection=usjournals&set_as_cursor=11&men_tab=srchresults | Jurimetrics | NaN |
| Revolutionizing Justice: Unleashing the Power of Artificial Intelligence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/comlrtj26&div=15&start_page=217&collection=usjournals&set_as_cursor=13&men_tab=srchresults | SMU Science and Technology Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/comlrtj26&div=15&start_page=217&collection=usjournals&set_as_cursor=13&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Generative AI in American and Canadian Courts: A 'Training' Approach to Regulation [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/linovte16&div=28&start_page=715&collection=journals&set_as_cursor=14&men_tab=srchresults | Law, Innovation and Technology | NaN |
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| Beyond ChatGPT: Transforming Government with Augmented LLMs [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/tenn92&div=6&start_page=87&collection=usjournals&set_as_cursor=17&men_tab=srchresults | Tennessee Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/tenn92&div=6&start_page=87&collection=usjournals&set_as_cursor=17&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
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| Unboxing Generative AI for the Legal Professions: Functions, Impacts and Governance [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ijca15&div=11&start_page=1&collection=usjournals&set_as_cursor=19&men_tab=srchresults | International Journal for Court Administration | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ijca15&div=11&start_page=1&collection=usjournals&set_as_cursor=19&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Judicial Economy in the Age of AI [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ucollr96&div=16&start_page=549&collection=usjournals&set_as_cursor=20&men_tab=srchresults | University of Colorado Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ucollr96&div=16&start_page=549&collection=usjournals&set_as_cursor=20&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Generative Artificial Intelligence and the Practice of Law: Impact, Opportunities, and Risks [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/mipr25&div=22&start_page=25&collection=usjournals&set_as_cursor=21&men_tab=srchresults | Minnesota Journal of Law, Science and Technology | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/mipr25&div=22&start_page=25&collection=usjournals&set_as_cursor=21&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| From Briefs to Bytes: How Generative AI Is Transforming Legal Writing and Practice [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/tlj59&div=12&start_page=193&collection=usjournals&set_as_cursor=24&men_tab=srchresults | Tulsa Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/tlj59&div=12&start_page=193&collection=usjournals&set_as_cursor=24&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
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| Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jlegan16&div=5&start_page=64&collection=usjournals&set_as_cursor=27&men_tab=srchresults | Journal of Legal Analysis | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jlegan16&div=5&start_page=64&collection=usjournals&set_as_cursor=27&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
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| New Rules for a New Era: Regulating Artificial Intelligence in the Legal Field [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/caswestres15&div=4&start_page=1&collection=usjournals&set_as_cursor=30&men_tab=srchresults | Case Western Reserve Journal of Law, Technology and the Internet | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/caswestres15&div=4&start_page=1&collection=usjournals&set_as_cursor=30&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Revolutionizing Access to Justice: The Role of AI-Powered Chatbots and Retrieval-Augmented Generation in Legal Self-Help [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/tbrief53&div=33&start_page=10&collection=usjournals&set_as_cursor=31&men_tab=srchresults | Brief | NaN |
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| Utilising Generative AI in Businesses: Risks and Best Practices [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/blawintnl24&div=26&start_page=215&collection=journals&set_as_cursor=35&men_tab=srchresults | Business Law International | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/blawintnl24&div=26&start_page=215&collection=journals&set_as_cursor=35&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Addressing the Failures of the U.S. Civil Legal System [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/rwulr28&div=12&start_page=118&collection=usjournals&set_as_cursor=36&men_tab=srchresults | Roger Williams University Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/rwulr28&div=12&start_page=118&collection=usjournals&set_as_cursor=36&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Judge, the AI, and the Crown: A Collusive Network [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/infctel33&div=21&start_page=330&collection=journals&set_as_cursor=37&men_tab=srchresults | Information & Communications Technology Law | NaN |
| AI Legal Innovations: The Benefits and Drawbacks of Chat-GPT and Generative AI in the Legal Industry [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/onulr50&div=29&start_page=513&collection=usjournals&set_as_cursor=38&men_tab=srchresults | Ohio Northern University Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/onulr50&div=29&start_page=513&collection=usjournals&set_as_cursor=38&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| What Should ChatGPT Mean for Bioethics? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ajbio23&div=300&start_page=8&collection=journals&set_as_cursor=41&men_tab=srchresults | American Journal of Bioethics | NaN |
| Artificial Intelligence (AI) and the Practice of Law in Texas [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/stexlr63&div=5&start_page=1&collection=usjournals&set_as_cursor=42&men_tab=srchresults | South Texas Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/stexlr63&div=5&start_page=1&collection=usjournals&set_as_cursor=42&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Navigating the Ethical and Technical Challenges of ChatGPT [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/nysbaj0095&div=60&start_page=26&collection=barjournals&set_as_cursor=43&men_tab=srchresults | New York State Bar Association Journal | NaN |
| Where's the Liability in harmful AI Speech? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jfspl3&div=33&start_page=589&collection=usjournals&set_as_cursor=44&men_tab=srchresults | Journal of Free Speech Law | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jfspl3&div=33&start_page=589&collection=usjournals&set_as_cursor=44&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Integrating Generative AI into Legal Education: Form Casebooks to Code, Opportunities and Challenges [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/lwtchmn6&div=23&start_page=60&collection=journals&set_as_cursor=45&men_tab=srchresults | Law, Technology and Humans | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/lwtchmn6&div=23&start_page=60&collection=journals&set_as_cursor=45&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| How to Harness AI for Justice [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/judica108&div=12&start_page=42&collection=usjournals&set_as_cursor=46&men_tab=srchresults | Judicature | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/judica108&div=12&start_page=42&collection=usjournals&set_as_cursor=46&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Fairness and Fair Use in Generative AI [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/flr92&div=70&start_page=1887&collection=usjournals&set_as_cursor=47&men_tab=srchresults | Fordham Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/flr92&div=70&start_page=1887&collection=usjournals&set_as_cursor=47&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Lex Ex Machina: Forging a New Ethical Framework for AI and Technology in the Law [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/cumlr55&div=7&start_page=53&collection=usjournals&set_as_cursor=48&men_tab=srchresults | Cumberland Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/cumlr55&div=7&start_page=53&collection=usjournals&set_as_cursor=48&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Large Language Models: AI's Legal Revolution [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/pace44&div=5&start_page=91&collection=usjournals&set_as_cursor=49&men_tab=srchresults | Pace Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/pace44&div=5&start_page=91&collection=usjournals&set_as_cursor=49&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| AI Now [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/temple97&div=13&start_page=227&collection=usjournals&set_as_cursor=51&men_tab=srchresults | Temple Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/temple97&div=13&start_page=227&collection=usjournals&set_as_cursor=51&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The GPTJudge: Justice in a Generative AI World [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/dltr23&div=2&start_page=1&collection=usjournals&set_as_cursor=52&men_tab=srchresults | Duke Law & Technology Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/dltr23&div=2&start_page=1&collection=usjournals&set_as_cursor=52&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Artificial Lawyering: A Jekyll and Hyde Story [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/aalwjloeg7&div=4&start_page=i&collection=usjournals&set_as_cursor=53&men_tab=srchresults | Arizona Law Journal of Emerging Technologies | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/aalwjloeg7&div=4&start_page=i&collection=usjournals&set_as_cursor=53&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Use It like a Lawyer: Why Generative AI Presents Both Risks and Small Steps Forward for Efficient and Ethical Lawyering [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/jkabr0092&div=89&start_page=51&collection=barjournals&set_as_cursor=54&men_tab=srchresults | Kansas Bar Journal | NaN |
| A(I)ccess to Justice: How AI and Ethics Opinions Approving Limited Scope Representation Support Legal Market Consolidation [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/gslr40&div=46&start_page=957&collection=usjournals&set_as_cursor=56&men_tab=srchresults | Georgia State University Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/gslr40&div=46&start_page=957&collection=usjournals&set_as_cursor=56&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Legal Profession in an Age of Generative Artificial Intelligence - An Update [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/iihcj17&div=53&start_page=9211&collection=journals&set_as_cursor=57&men_tab=srchresults | International In-House Counsel Journal | NaN |
| The Current State of US Regulation of the Use of AI in Dispute Resolution [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/disreint18&div=17&start_page=105&collection=journals&set_as_cursor=59&men_tab=srchresults | Dispute Resolution International | NaN |
| AI Lawyering Skills Trainers: Transforming Legal Education with Generative AI [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/caswestres16&div=6&start_page=74&collection=usjournals&set_as_cursor=61&men_tab=srchresults | Case Western Reserve Journal of Law, Technology and the Internet | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/caswestres16&div=6&start_page=74&collection=usjournals&set_as_cursor=61&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Algorithmic Family: How AI Is Rewriting the Rules of Family Law [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/cololaw0053&div=81&start_page=44&collection=barjournals&set_as_cursor=62&men_tab=srchresults | Colorado Lawyer | NaN |
| Generative AI in the Attorney-Client Relationship: An Exercise in Critical Revision and Client Management [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/comlrtj27&div=16&start_page=275&collection=usjournals&set_as_cursor=63&men_tab=srchresults | SMU Science and Technology Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/comlrtj27&div=16&start_page=275&collection=usjournals&set_as_cursor=63&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/thetmr114&div=39&start_page=880&collection=usjournals&set_as_cursor=64&men_tab=srchresults | Trademark Reporter | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/thetmr114&div=39&start_page=880&collection=usjournals&set_as_cursor=64&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| ChatGPT: A Lawyer's Friend or Ethical Time Bomb? Professional Responsibility in the Age of Generative AI [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/arklwr0058&div=48&start_page=24&collection=barjournals&set_as_cursor=65&men_tab=srchresults | Arkansas Lawyer | NaN |
| AI in Courtroom: The Boundaries of RoboLawyers and RoboJudges [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/frdipm35&div=11&start_page=286&collection=usjournals&set_as_cursor=66&men_tab=srchresults | Fordham Intellectual Property, Media & Entertainment Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/frdipm35&div=11&start_page=286&collection=usjournals&set_as_cursor=66&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Navigating the Legal Landscape: Large Language Models and the Hesitancy of Legal Professionals [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/injlepro31&div=22&start_page=311&collection=journals&set_as_cursor=67&men_tab=srchresults | International Journal of the Legal Profession | NaN |
| The Implications of ChatGPT for Legal Services and Society [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/mttlr30&div=6&start_page=1&collection=usjournals&set_as_cursor=68&men_tab=srchresults | Michigan Technology Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/mttlr30&div=6&start_page=1&collection=usjournals&set_as_cursor=68&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| AI Diversity and the Future of "Fair" Legal AI [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/gslr40&div=43&start_page=863&collection=usjournals&set_as_cursor=69&men_tab=srchresults | Georgia State University Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/gslr40&div=43&start_page=863&collection=usjournals&set_as_cursor=69&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Analyzing the Primary and Attendant Risks of GAI-Based Natural Language Processing Models in Legal Research [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/sjost39&div=5&start_page=15&collection=usjournals&set_as_cursor=72&men_tab=srchresults | Syracuse Journal of Science and Technology Law (JOST) | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/sjost39&div=5&start_page=15&collection=usjournals&set_as_cursor=72&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Using Generative AI to Identify Arguments in Judges' Reasons: Accuracy and Benefits for Students [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/lwtchmn6&div=20&start_page=5&collection=journals&set_as_cursor=73&men_tab=srchresults | Law, Technology and Humans | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/lwtchmn6&div=20&start_page=5&collection=journals&set_as_cursor=73&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Artificial Intelligence and Legal Malpractice Liability [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/smjmale14&div=7&start_page=55&collection=usjournals&set_as_cursor=75&men_tab=srchresults | St. Mary's Journal on Legal Malpractice and Ethics | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/smjmale14&div=7&start_page=55&collection=usjournals&set_as_cursor=75&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Harnessing Artificial Intelligence in International Arbitration Practice [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/caaj16&div=13&start_page=263&collection=journals&set_as_cursor=76&men_tab=srchresults | Contemporary Asia Arbitration Journal (CAA Journal) | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/caaj16&div=13&start_page=263&collection=journals&set_as_cursor=76&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| On Adult A.I. Interactions with Artificial Intelligence in the Shadows of Regulation, Antitrust, and Family Law [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/albnyst34&div=4&start_page=27&collection=usjournals&set_as_cursor=77&men_tab=srchresults | Albany Law Journal of Science & Technology | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/albnyst34&div=4&start_page=27&collection=usjournals&set_as_cursor=77&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Bracing for Impact: Revising Legal Writing Assessments ahead of the Collision of Generative AI and the NextGen Bar Exam [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jlwriins28&div=3&start_page=1&collection=usjournals&set_as_cursor=81&men_tab=srchresults | Legal Writing: The Journal of the Legal Writing Institute | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jlwriins28&div=3&start_page=1&collection=usjournals&set_as_cursor=81&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Generative AI before the Court: Is Disclosure and Certification of Generative-AI Use Really Necessary? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/ibarq0055&div=6&start_page=53&collection=barjournals&set_as_cursor=83&men_tab=srchresults | International Society of Barristers Quarterly | NaN |
| Amusing Inventions Not to Be Thrown Away: ChatGPT and the Future of Tax [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jtaxpp25&div=20&start_page=21&collection=usjournals&set_as_cursor=85&men_tab=srchresults | Journal of Tax Practice & Procedure | NaN |
| AI and KM: Two Great Tools That Work Great Together [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/lwpra50&div=5&start_page=[18]&collection=usjournals&set_as_cursor=86&men_tab=srchresults | Law Practice | NaN |
| Making the Justice Leap: Using Generative AI to Bridge the Literacy, Equity, Access, and Privilege Gaps for Self-Represented Litigants [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.aallar/spectrum0028&div=104&start_page=10&collection=usjournals&set_as_cursor=88&men_tab=srchresults | AALL Spectrum | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.aallar/spectrum0028&div=104&start_page=10&collection=usjournals&set_as_cursor=88&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Co-Authoring with an AI? Ethical Dilemmas and Artificial Intelligence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/arzjl56&div=8&start_page=187&collection=usjournals&set_as_cursor=89&men_tab=srchresults | Arizona State Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/arzjl56&div=8&start_page=187&collection=usjournals&set_as_cursor=89&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| AI-Ready Attorneys: Ethical Obligations and Privacy Considerations in the Age of Artificial Intelligence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ukalr72&div=19&start_page=313&collection=usjournals&set_as_cursor=90&men_tab=srchresults | University of Kansas Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ukalr72&div=19&start_page=313&collection=usjournals&set_as_cursor=90&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| From Coding to Code of Conduct: Understanding the Ethical Dimensions of Lawyers' Use of Generative AI [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/adsbate0109&div=7&start_page=8&collection=barjournals&set_as_cursor=91&men_tab=srchresults | Advocate | NaN |
| A Framework for Applying Copyright Law to the Training of Textual Generative Artificial Intelligence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/tipj32&div=16&start_page=225&collection=usjournals&set_as_cursor=92&men_tab=srchresults | Texas Intellectual Property Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/tipj32&div=16&start_page=225&collection=usjournals&set_as_cursor=92&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Law and Economics of Language Model Development: Empirical Examination of Corporate Strategies and Vaporware Claims [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ajle15&div=5&start_page=31&collection=journals&set_as_cursor=93&men_tab=srchresults | Asian Journal of Law and Economics | NaN |
| Demystifying Generative AI [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/judgej63&div=8&start_page=13&collection=usjournals&set_as_cursor=94&men_tab=srchresults | Judges' Journal | NaN |
| Robot Lawyers Don't Have Disciplinary Hearings - Real Lawyers Do: The Ethical Risks and Responses in Using Generative Artificial Intelligence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/gslr40&div=45&start_page=917&collection=usjournals&set_as_cursor=95&men_tab=srchresults | Georgia State University Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/gslr40&div=45&start_page=917&collection=usjournals&set_as_cursor=95&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| AI and Access to Justice: A Roundtable Discussion [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/lwpra50&div=4&start_page=[6]&collection=usjournals&set_as_cursor=96&men_tab=srchresults | Law Practice | NaN |
| Systemic Regulation of Artificial Intelligence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/arzjl56&div=17&start_page=545&collection=usjournals&set_as_cursor=99&men_tab=srchresults | Arizona State Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/arzjl56&div=17&start_page=545&collection=usjournals&set_as_cursor=99&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Teaching Critical Use of Legal Research Technology [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jlwriins28&div=5&start_page=123&collection=usjournals&set_as_cursor=100&men_tab=srchresults | Legal Writing: The Journal of the Legal Writing Institute | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jlwriins28&div=5&start_page=123&collection=usjournals&set_as_cursor=100&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Why Can't I Have a Robot Lawyer? Limits on the Right to Appear Pro Se [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/tulr98&div=19&start_page=363&collection=usjournals&set_as_cursor=101&men_tab=srchresults | Tulane Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/tulr98&div=19&start_page=363&collection=usjournals&set_as_cursor=101&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Asking GPT for the Ordinary Meaning of Statutory Terms [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jltp2024&div=12&start_page=235&collection=usjournals&set_as_cursor=102&men_tab=srchresults | University of Illinois Journal of Law, Technology & Policy | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jltp2024&div=12&start_page=235&collection=usjournals&set_as_cursor=102&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Case for Nurturing AI Literacy in Law Schools [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/asjledu12&div=3&start_page=7&collection=journals&set_as_cursor=105&men_tab=srchresults | Asian Journal of Legal Education | NaN |
| The Interaction between AI (Artificial Intelligence) and IA (International Arbitration): Technology as the New Partner of Arbitration [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/romabj18&div=7&start_page=42&collection=journals&set_as_cursor=106&men_tab=srchresults | Romanian Arbitration Journal / Revista Romana de Arbitraj | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/romabj18&div=7&start_page=42&collection=journals&set_as_cursor=106&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Predicting Consumer Contracts [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/berktech37&div=6&start_page=71&collection=usjournals&set_as_cursor=107&men_tab=srchresults | Berkeley Technology Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/berktech37&div=6&start_page=71&collection=usjournals&set_as_cursor=107&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Navigating AI's Impact on Intellectual Property Law: A Checklist for Negotiating AI Transactions [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/neblwr0027&div=45&start_page=27&collection=barjournals&set_as_cursor=108&men_tab=srchresults | Nebraska Lawyer | NaN |
| Prompts and Large Language Models: A New Tool for Drafting, Reviewing and Interpreting Contracts? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/lwtchmn6&div=16&start_page=88&collection=journals&set_as_cursor=109&men_tab=srchresults | Law, Technology and Humans | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/lwtchmn6&div=16&start_page=88&collection=journals&set_as_cursor=109&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Determinants of Socially Responsible AI Governance [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/dltr25&div=6&start_page=183&collection=usjournals&set_as_cursor=110&men_tab=srchresults | Duke Law & Technology Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/dltr25&div=6&start_page=183&collection=usjournals&set_as_cursor=110&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| ChatGPT - The Blurst of Times [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/auslwlib31&div=11&start_page=19&collection=journals&set_as_cursor=111&men_tab=srchresults | Australian Law Librarian | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/auslwlib31&div=11&start_page=19&collection=journals&set_as_cursor=111&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Leveraging Artificial Intelligence in eDiscovery: Enhancing Efficiency, Accuracy, and Ethical Considerations [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/rgllr2024&div=18&start_page=179&collection=journals&set_as_cursor=112&men_tab=srchresults | Regional Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/rgllr2024&div=18&start_page=179&collection=journals&set_as_cursor=112&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Emerging Artificial Intelligence Risk Management Considerations for Law Firms [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/defcon91&div=27&start_page=1&collection=usjournals&set_as_cursor=113&men_tab=srchresults | Defense Counsel Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/defcon91&div=27&start_page=1&collection=usjournals&set_as_cursor=113&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Bridging the Divide: Does the EU's AI Act Offer Code for Regulating Emergent Technologies in America? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/molr89&div=31&start_page=847&collection=usjournals&set_as_cursor=114&men_tab=srchresults | Missouri Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/molr89&div=31&start_page=847&collection=usjournals&set_as_cursor=114&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| AI: Increasing Alternatives in Alternative Dispute Resolution Resolved: Journal of Alternative Dispute Resolution [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/resolvjo12&div=5&start_page=21&collection=usjournals&set_as_cursor=115&men_tab=srchresults | Resolved: Journal of Alternative Dispute Resolution | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/resolvjo12&div=5&start_page=21&collection=usjournals&set_as_cursor=115&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Bringing Legal Knowledge to the Public by Constructing a Legal Question Bank Using Large-Scale Pre-Trained Language Model [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/artinl32&div=29&start_page=769&collection=journals&set_as_cursor=116&men_tab=srchresults | Artificial Intelligence and Law | NaN |
| Establishing a Future-Proof Framework for AI Regulation: Balancing Ethics, Transparency, and Innovation [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/transac25&div=21&start_page=253&collection=usjournals&set_as_cursor=117&men_tab=srchresults | Transactions: The Tennessee Journal of Business Law | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/transac25&div=21&start_page=253&collection=usjournals&set_as_cursor=117&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Research on Generative Artificial Intelligence Legal Profession Substitution [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/mdnlwrsch4&div=24&start_page=32&collection=journals&set_as_cursor=118&men_tab=srchresults | Modern Law Research | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/mdnlwrsch4&div=24&start_page=32&collection=journals&set_as_cursor=118&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Certifying Legal AI Assistants for Unrepresented Litigants: A Global Survey of Access to Civil Justice, Unauthorized Practice of Law, and AI [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/cstlr26&div=4&start_page=34&collection=usjournals&set_as_cursor=119&men_tab=srchresults | Columbia Science and Technology Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/cstlr26&div=4&start_page=34&collection=usjournals&set_as_cursor=119&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Toward an Ethical Human-Computer Division of Labor in Law Practice [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/flr92&div=65&start_page=1797&collection=usjournals&set_as_cursor=120&men_tab=srchresults | Fordham Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/flr92&div=65&start_page=1797&collection=usjournals&set_as_cursor=120&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Disrupting Influence of AI and the Potential Impact of ChatGPT on Maritime Law and Practice [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jnltllap90&div=8&start_page=163&collection=usjournals&set_as_cursor=121&men_tab=srchresults | Journal of Transportation Law, Logistics and Policy | NaN |
| Artificial Intelligence and Professional Conduct: Considering the Ethical Implications of Using Electronic Legal Assistants [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/cololaw0053&div=9&start_page=20&collection=barjournals&set_as_cursor=124&men_tab=srchresults | Colorado Lawyer | NaN |
| Ten Thousand AI Systems Typing on Keyboards: Generative AI in Patent Applications and Preemptive Prior Art [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/vanep26&div=17&start_page=375&collection=usjournals&set_as_cursor=125&men_tab=srchresults | Vanderbilt Journal of Entertainment & Technology Law | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/vanep26&div=17&start_page=375&collection=usjournals&set_as_cursor=125&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| We Need to Talk about ChatGPT: A Lawyer's Introduction to the Exploding Field of AI and Large Language Models [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/benchnbar0080&div=49&start_page=26&collection=barjournals&set_as_cursor=126&men_tab=srchresults | Bench & Bar of Minnesota | NaN |
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| Use and Regulation of AI in Dispute Resolution: Focus on the United Kingdom, Singapore and India [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/disreint18&div=7&start_page=5&collection=journals&set_as_cursor=129&men_tab=srchresults | Dispute Resolution International | NaN |
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| Regulating Chatbot Output via Inter-Informational Competition [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/nwteintp22&div=5&start_page=109&collection=usjournals&set_as_cursor=132&men_tab=srchresults | Northwestern Journal of Technology and Intellectual Property | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/nwteintp22&div=5&start_page=109&collection=usjournals&set_as_cursor=132&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Large Language Models and Their Possible Uses in Law [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ajur64&div=26&start_page=435&collection=journals&set_as_cursor=133&men_tab=srchresults | Hungarian Journal of Legal Studies | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ajur64&div=26&start_page=435&collection=journals&set_as_cursor=133&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Sam Altman, OpenAI, and the Importance of Corporate Governance [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/caswestres16&div=7&start_page=133&collection=usjournals&set_as_cursor=135&men_tab=srchresults | Case Western Reserve Journal of Law, Technology and the Internet | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/caswestres16&div=7&start_page=133&collection=usjournals&set_as_cursor=135&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Robots vs. Predators: Can Generative Artificial Intelligence Help to Address the Justice Gap in Consumer Debt Litigation? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/frdurb51&div=49&start_page=1553&collection=usjournals&set_as_cursor=136&men_tab=srchresults | Fordham Urban Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/frdurb51&div=49&start_page=1553&collection=usjournals&set_as_cursor=136&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Empowering Justice: Blockchain and Legal Chatbots as Catalysts for Access to Legal Aid [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ijlet2024&div=34&start_page=106&collection=journals&set_as_cursor=137&men_tab=srchresults | International Journal of Law, Ethics, and Technology (IJLET) | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ijlet2024&div=34&start_page=106&collection=journals&set_as_cursor=137&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Legal Imitation Game: Generative AI's Incompatibility with Clinical Legal Education [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/flr92&div=69&start_page=1867&collection=usjournals&set_as_cursor=138&men_tab=srchresults | Fordham Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/flr92&div=69&start_page=1867&collection=usjournals&set_as_cursor=138&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Limits of Using Artificial Intelligence and GPT-3 in Patent Prosecution [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/text54&div=20&start_page=255&collection=usjournals&set_as_cursor=139&men_tab=srchresults | Texas Tech Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/text54&div=20&start_page=255&collection=usjournals&set_as_cursor=139&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Large Language Models and Community Legal Centres: Could Chatbots Help Reduce Australia's Justice Gap? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/alterlj49&div=38&start_page=181&collection=journals&set_as_cursor=140&men_tab=srchresults | Alternative Law Journal | NaN |
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| Can AI Make a Case? AI vs. Lawyer in the Dutch Legal Context [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ijlet2024&div=20&start_page=1&collection=journals&set_as_cursor=142&men_tab=srchresults | International Journal of Law, Ethics, and Technology (IJLET) | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ijlet2024&div=20&start_page=1&collection=journals&set_as_cursor=142&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Prometheus' Digital Fire: The Civic Responsibilities of Artificial Intelligence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/isjlpsoc20&div=11&start_page=225&collection=usjournals&set_as_cursor=143&men_tab=srchresults | Ohio State Technology Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/isjlpsoc20&div=11&start_page=225&collection=usjournals&set_as_cursor=143&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Let's Talk, ChatGPT: What Will the Judiciary's Future Look like? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/florbarj0097&div=33&start_page=26&collection=barjournals&set_as_cursor=144&men_tab=srchresults | Florida Bar Journal | NaN |
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| Persuasive Legal Writing Using Large Language Models [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/legedr34&div=10&start_page=183&collection=journals&set_as_cursor=147&men_tab=srchresults | Legal Education Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/legedr34&div=10&start_page=183&collection=journals&set_as_cursor=147&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The New Kid on the Block - The Use of Artificial Intelligence in Alternative Dispute Resolution [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/albnyst34&div=3&start_page=1&collection=usjournals&set_as_cursor=148&men_tab=srchresults | Albany Law Journal of Science & Technology | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/albnyst34&div=3&start_page=1&collection=usjournals&set_as_cursor=148&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
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| ChatGPT, I Have a Legal Question? The Impact of Gen AI Tools on Law Clinics and Access to Justice [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/clled31&div=8&start_page=166&collection=journals&set_as_cursor=155&men_tab=srchresults | International Journal of Clinical Legal Education | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/clled31&div=8&start_page=166&collection=journals&set_as_cursor=155&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Digital Transformation of Legal Services and Access to Justice: Challenges and Possibilities [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/bjlp15&div=9&start_page=141&collection=journals&set_as_cursor=156&men_tab=srchresults | Baltic Journal of Law and Politics | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/bjlp15&div=9&start_page=141&collection=journals&set_as_cursor=156&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Caveat Lector: Large Language Models in Legal Practice [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/rutgblaj19&div=11&start_page=70&collection=usjournals&set_as_cursor=157&men_tab=srchresults | Rutgers Business Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/rutgblaj19&div=11&start_page=70&collection=usjournals&set_as_cursor=157&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Artificial Intelligence and the Practice of Law in the 21st Century [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/nysbaj0095&div=59&start_page=23&collection=barjournals&set_as_cursor=159&men_tab=srchresults | New York State Bar Association Journal | NaN |
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| Use Artificial Intelligence Intelligently: Avoid Sanctions, Ditch the Billable Hour, and Become the Lawyer of the Future [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/gpsolo40&div=114&start_page=50&collection=usjournals&set_as_cursor=163&men_tab=srchresults | GP Solo | NaN |
| Attributing AI Authorship: Towards a System of Icons for Legal and Ethical Disclosure [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/nwteintp22&div=3&start_page=1&collection=usjournals&set_as_cursor=164&men_tab=srchresults | Northwestern Journal of Technology and Intellectual Property | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/nwteintp22&div=3&start_page=1&collection=usjournals&set_as_cursor=164&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Legal Considerations in Machine-Assisted Decision-Making: Planning and Building as a Case Study [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/bondlr35&div=7&start_page=143&collection=journals&set_as_cursor=165&men_tab=srchresults | Bond Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/bondlr35&div=7&start_page=143&collection=journals&set_as_cursor=165&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Luck of the Draw III: Using AI to Extract Data about Decision-Making in Federal Court Stays of Removal [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/queen49&div=10&start_page=73&collection=journals&set_as_cursor=166&men_tab=srchresults | Queen's Law Journal (QLJ) | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/queen49&div=10&start_page=73&collection=journals&set_as_cursor=166&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Escalation of ChatGPT: How ChatGPT Will Exert Influence on the Legal Profession? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/juscrp3&div=491&start_page=106&collection=journals&set_as_cursor=167&men_tab=srchresults | Jus Corpus Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/juscrp3&div=491&start_page=106&collection=journals&set_as_cursor=167&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
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| The Multifaceted Impact of Generative AI on Lawyers and Legal Services [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jchlet14&div=18&start_page=8&collection=usjournals&set_as_cursor=170&men_tab=srchresults | Journal of Christian Legal Thought | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jchlet14&div=18&start_page=8&collection=usjournals&set_as_cursor=170&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Don't Kill the Baby! The Case for AI in Arbitration [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/nyujolbu21&div=7&start_page=119&collection=usjournals&set_as_cursor=171&men_tab=srchresults | New York University Journal of Law and Business | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/nyujolbu21&div=7&start_page=119&collection=usjournals&set_as_cursor=171&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| An Empirical Study of Lawyers' Capability to Adapt to Disruption in Queensland, Australia [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/injlepro31&div=7&start_page=83&collection=journals&set_as_cursor=172&men_tab=srchresults | International Journal of the Legal Profession | NaN |
| The Role of ChatGPT and Emojis in Modern Legal Interpretation [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/juscrp4&div=135&start_page=228&collection=journals&set_as_cursor=173&men_tab=srchresults | Jus Corpus Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/juscrp4&div=135&start_page=228&collection=journals&set_as_cursor=173&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Who Cares about Pro Bono? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/valaw0073&div=49&start_page=18&collection=barjournals&set_as_cursor=174&men_tab=srchresults | Virginia Lawyer | NaN |
| Tribes and AI: Possibilities for Tribal Sovereignty [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/dltr25&div=2&start_page=1&collection=usjournals&set_as_cursor=175&men_tab=srchresults | Duke Law & Technology Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/dltr25&div=2&start_page=1&collection=usjournals&set_as_cursor=175&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Generative Interpretation [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/nylr99&div=15&start_page=451&collection=usjournals&set_as_cursor=177&men_tab=srchresults | New York University Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/nylr99&div=15&start_page=451&collection=usjournals&set_as_cursor=177&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Technology Competence Instruction and Assessment under the Principles and Standards of Legal Research Competency [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/lgrefsq42&div=10&start_page=56&collection=usjournals&set_as_cursor=179&men_tab=srchresults | Legal Reference Services Quarterly | NaN |
| Justice Is Mechanized: Ethical Implications of AI in Law [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ijlmhs31&div=50&start_page=651&collection=journals&set_as_cursor=180&men_tab=srchresults | International Journal of Law Management & Humanities | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ijlmhs31&div=50&start_page=651&collection=journals&set_as_cursor=180&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Contracts in the Age of Smart Readers [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/gwlr90&div=5&start_page=83&collection=usjournals&set_as_cursor=181&men_tab=srchresults | George Washington Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/gwlr90&div=5&start_page=83&collection=usjournals&set_as_cursor=181&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Legal Ethics of Generative AI [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/sufflr57&div=20&start_page=345&collection=usjournals&set_as_cursor=183&men_tab=srchresults | Suffolk University Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/sufflr57&div=20&start_page=345&collection=usjournals&set_as_cursor=183&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| When Art Becomes a Lemon: The Economics of Machine-Enabled Artworks and the Need for a Rule of Origin [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/lwtchmn5&div=5&start_page=24&collection=journals&set_as_cursor=184&men_tab=srchresults | Law, Technology and Humans | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/lwtchmn5&div=5&start_page=24&collection=journals&set_as_cursor=184&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Can ChatGPT-like AI Function as ODR Fourth Party for Handling School-Related Disputes in China? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ijodr9&div=23&start_page=177&collection=journals&set_as_cursor=185&men_tab=srchresults | International Journal of Online Dispute Resolution | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ijodr9&div=23&start_page=177&collection=journals&set_as_cursor=185&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Catalyst for Common Law Evolution: Experiment with ChatCPT and a Hypothetical Common Law Jurisdiction [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ajle15&div=6&start_page=55&collection=journals&set_as_cursor=186&men_tab=srchresults | Asian Journal of Law and Economics | NaN |
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| Chief Justice: Navigating Public Trust, AI in Legal Profession [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/jrmobar0080&div=94&start_page=238&collection=barjournals&set_as_cursor=189&men_tab=srchresults | Journal of the Missouri Bar | NaN |
| Artificial Intelligence Presents Opportunities and Challenges for the Legal Ecosystem [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/nysbaj0095&div=38&start_page=13&collection=barjournals&set_as_cursor=190&men_tab=srchresults | New York State Bar Association Journal | NaN |
| Artificial Intelligence and Ethics [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/rutcomt50&div=13&start_page=283&collection=usjournals&set_as_cursor=191&men_tab=srchresults | Rutgers Computer and Technology Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/rutcomt50&div=13&start_page=283&collection=usjournals&set_as_cursor=191&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Artificial Intelligence vs. Judicial Discretion: Prospects and Risks of Judicial Practice Automation [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/lglisited4&div=25&start_page=59&collection=journals&set_as_cursor=192&men_tab=srchresults | Legal Issues in the Digital Age | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/lglisited4&div=25&start_page=59&collection=journals&set_as_cursor=192&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| "Alexa, Write a Memo": The Promise and Challenges of AI and Legal Writing [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jlwriins26&div=21&start_page=329&collection=usjournals&set_as_cursor=193&men_tab=srchresults | Legal Writing: The Journal of the Legal Writing Institute | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jlwriins26&div=21&start_page=329&collection=usjournals&set_as_cursor=193&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Former Michigan Chief Justice Bridget Mary McCormack on the Impact of AI [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/nysbaj0096&div=27&start_page=13&collection=barjournals&set_as_cursor=194&men_tab=srchresults | New York State Bar Association Journal | NaN |
| The New Judicial Governance: Courts, Data, and the Future of Civil Justice [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/deplr72&div=13&start_page=171&collection=usjournals&set_as_cursor=195&men_tab=srchresults | DePaul Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/deplr72&div=13&start_page=171&collection=usjournals&set_as_cursor=195&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| ChatGPT in a Nutshell [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/colaworl37&div=38&start_page=38&collection=usjournals&set_as_cursor=197&men_tab=srchresults | Commercial Law World | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/colaworl37&div=38&start_page=38&collection=usjournals&set_as_cursor=197&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Competition, Consumer Protection, and Artificial Intelligence (and the Future of Freedom of Thought) [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/wasbn0078&div=55&start_page=26&collection=barjournals&set_as_cursor=198&men_tab=srchresults | Washington State Bar News | NaN |
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| Access to A.I. Justice: Avoiding an Inequitable Two-Tiered System of Legal Services [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/yjolt24&div=5&start_page=150&collection=usjournals&set_as_cursor=206&men_tab=srchresults | Yale Journal of Law and Technology | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/yjolt24&div=5&start_page=150&collection=usjournals&set_as_cursor=206&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Error 404 or an Error in Judgment? An Ethical Framework for the Use of ChatGPT in the Legal Profession [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jsouafl2024&div=41&start_page=469&collection=journals&set_as_cursor=207&men_tab=srchresults | Journal of South African Law / Tydskrif vir die Suid-Afrikaanse Reg | NaN |
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| Artificial Intelligence Tools for Lawyers [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/gpsolo41&div=102&start_page=65&collection=usjournals&set_as_cursor=213&men_tab=srchresults | GP Solo | NaN |
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| Robots, Markets, and the Value of Deal Lawyers [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jcorl49&div=36&start_page=833&collection=usjournals&set_as_cursor=219&men_tab=srchresults | Journal of Corporation Law | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jcorl49&div=36&start_page=833&collection=usjournals&set_as_cursor=219&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| What Lawyers Are Talking about in 2024 [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/cbarc0038&div=133&start_page=20&collection=barjournals&set_as_cursor=221&men_tab=srchresults | CBA Record | NaN |
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| Untangling Unreliable Citations [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/geojlege37&div=15&start_page=415&collection=usjournals&set_as_cursor=223&men_tab=srchresults | Georgetown Journal of Legal Ethics | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/geojlege37&div=15&start_page=415&collection=usjournals&set_as_cursor=223&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Evaluating the Use of Artificial Intelligence for an Effective Justice System in Sri Lanka [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/kdulj4&div=18&start_page=21&collection=journals&set_as_cursor=224&men_tab=srchresults | KDU Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/kdulj4&div=18&start_page=21&collection=journals&set_as_cursor=224&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| ChatGPT, AI Large Language Models, and Law [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/flr92&div=72&start_page=1941&collection=usjournals&set_as_cursor=225&men_tab=srchresults | Fordham Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/flr92&div=72&start_page=1941&collection=usjournals&set_as_cursor=225&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Rules over Words: Regulation of Chatbots in the Legal Market and Ethical Considerations [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ajur64&div=28&start_page=472&collection=journals&set_as_cursor=226&men_tab=srchresults | Hungarian Journal of Legal Studies | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ajur64&div=28&start_page=472&collection=journals&set_as_cursor=226&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Judicial Regulation on the Use of AI [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/adsbate0109&div=9&start_page=17&collection=barjournals&set_as_cursor=227&men_tab=srchresults | Advocate | NaN |
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| Incorporating Generative Artificial Intelligence into the Practice of Law: Utilizing Generative AI within the Framework of the California Rules of Professional Conduct [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/cwlr61&div=7&start_page=71&collection=usjournals&set_as_cursor=229&men_tab=srchresults | California Western Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/cwlr61&div=7&start_page=71&collection=usjournals&set_as_cursor=229&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Future of Healthcare: A Case for Online Dispute Resolution [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ijodr10&div=11&start_page=76&collection=journals&set_as_cursor=231&men_tab=srchresults | International Journal of Online Dispute Resolution | NaN |
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| New Governance and New Technologies: Creating a Regulatory Regime for the Use of Generative Artificial Intelligence in the Courts [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ncjl26&div=5&start_page=1&collection=usjournals&set_as_cursor=237&men_tab=srchresults | North Carolina Journal of Law & Technology | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ncjl26&div=5&start_page=1&collection=usjournals&set_as_cursor=237&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Behind the Screens: Inside the Claims against DoNotPay's Joshua Browder and the "World's First Robot Lawyer' [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/abaj109&div=152&start_page=36&collection=usjournals&set_as_cursor=238&men_tab=srchresults | ABA Journal | NaN |
| The Utility of Artificial Intelligence in the Pursuit of Justice through Judicial Precedent in Nigeria [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/beijlar15&div=144&start_page=2445&collection=journals&set_as_cursor=241&men_tab=srchresults | Beijing Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/beijlar15&div=144&start_page=2445&collection=journals&set_as_cursor=241&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| "Chat-up": The Role of Competition in Street-Level Bureaucrats' Willingness to Break Technological Rules and Use Generative Pre-Trained Transformers (GPTs) [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/pbcamnstn85&div=44&start_page=468&collection=usjournals&set_as_cursor=242&men_tab=srchresults | Public Administration Review (PAR) | NaN |
| Expanding Access to Justice through Regulatory Reform and Innovation: Arizona Lessons from the Past, Present, and Future [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ucinlr93&div=17&start_page=408&collection=usjournals&set_as_cursor=244&men_tab=srchresults | University of Cincinnati Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ucinlr93&div=17&start_page=408&collection=usjournals&set_as_cursor=244&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| "Do Not Read" [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/sdlr70&div=13&start_page=117&collection=usjournals&set_as_cursor=248&men_tab=srchresults | South Dakota Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/sdlr70&div=13&start_page=117&collection=usjournals&set_as_cursor=248&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Comments on Artificial Intelligence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ijodr9&div=22&start_page=147&collection=journals&set_as_cursor=249&men_tab=srchresults | International Journal of Online Dispute Resolution | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ijodr9&div=22&start_page=147&collection=journals&set_as_cursor=249&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| AI Malpractice [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/deplr73&div=17&start_page=301&collection=usjournals&set_as_cursor=252&men_tab=srchresults | DePaul Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/deplr73&div=17&start_page=301&collection=usjournals&set_as_cursor=252&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Adopting Emerging Technology Responsibly [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ohiolawr38&div=13&start_page=22&collection=journals&set_as_cursor=253&men_tab=srchresults | Ohio Lawyer | NaN |
| AI, Law and beyond. A Transdisciplinary Ecosystem for the Future of AI & Law [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/artinl33&div=12&start_page=253&collection=journals&set_as_cursor=254&men_tab=srchresults | Artificial Intelligence and Law | NaN |
| Legal Analysis of EU Artificial Intelligence Act (2024): Insights from Personal Data Governance and Health Policy [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ajee2024&div=82&start_page=120&collection=journals&set_as_cursor=256&men_tab=srchresults | Access to Justice in Eastern Europe | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ajee2024&div=82&start_page=120&collection=journals&set_as_cursor=256&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| A Framework for Data-Driven Legal Regulatory Reform [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/sjel14&div=15&start_page=1&collection=usjournals&set_as_cursor=257&men_tab=srchresults | Seattle Journal of Technology, Environmental & Innovation Law (SJTEIL) | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/sjel14&div=15&start_page=1&collection=usjournals&set_as_cursor=257&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Governing Artificial Intelligence Responsibility in Low to Middle Income Countries: Enabling Pathways to Sustainable Development [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/calwi54&div=15&start_page=415&collection=journals&set_as_cursor=259&men_tab=srchresults | California Western International Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/calwi54&div=15&start_page=415&collection=journals&set_as_cursor=259&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Alternative Legal Service Providers: Should They Be "Alternative?" [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/sclwy0035&div=55&start_page=28&collection=barjournals&set_as_cursor=260&men_tab=srchresults | South Carolina Lawyer | NaN |
| Toward National Regulation of Legal Technology: A Path Forward for Access to Justice [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/flr92&div=5&start_page=1&collection=usjournals&set_as_cursor=261&men_tab=srchresults | Fordham Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/flr92&div=5&start_page=1&collection=usjournals&set_as_cursor=261&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| More than Machines: The Ethical and Human Implications of Generative AI and Lawyering [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jchlet14&div=19&start_page=16&collection=usjournals&set_as_cursor=263&men_tab=srchresults | Journal of Christian Legal Thought | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jchlet14&div=19&start_page=16&collection=usjournals&set_as_cursor=263&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Automation Paradox [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/tlj59&div=15&start_page=361&collection=usjournals&set_as_cursor=266&men_tab=srchresults | Tulsa Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/tlj59&div=15&start_page=361&collection=usjournals&set_as_cursor=266&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| AI Regulation for the AI Revolution [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/singclr2023&div=17&start_page=130&collection=journals&set_as_cursor=269&men_tab=srchresults | Singapore Comparative Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/singclr2023&div=17&start_page=130&collection=journals&set_as_cursor=269&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| AI in the Legal Sector - An Overview for Information Professionals [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/leginfom23&div=41&start_page=150&collection=journals&set_as_cursor=274&men_tab=srchresults | Legal Information Management | NaN |
| Harnessing Artificial Intelligence for a Cutting-Edge Courtroom Presentation [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.ali/praclaw0070&div=26&start_page=59&collection=ali&set_as_cursor=275&men_tab=srchresults | Practical Lawyer | NaN |
| The Impact of Digitalization on Global Trade Law [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/germlajo24&div=36&start_page=551&collection=journals&set_as_cursor=276&men_tab=srchresults | German Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/germlajo24&div=36&start_page=551&collection=journals&set_as_cursor=276&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Technology Tools, Access to Justice, and the Joint Technology Committee: The Time Is Right [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/judgej63&div=6&start_page=4&collection=usjournals&set_as_cursor=277&men_tab=srchresults | Judges' Journal | NaN |
| Is the Use of Artificial Intelligence in Alternative Dispute Resolution a Viable Option or Wishful Thinking? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/pepds24&div=7&start_page=91&collection=usjournals&set_as_cursor=278&men_tab=srchresults | Pepperdine Dispute Resolution Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/pepds24&div=7&start_page=91&collection=usjournals&set_as_cursor=278&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Unstructuring for Insight: The Legal Profession in an Age of AI and Social Change [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/lwtch58&div=7&start_page=74&collection=journals&set_as_cursor=280&men_tab=srchresults | Law Teacher | NaN |
| Preparing Future Lawyers to Draft Contracts and Communicate with Clients in the Era of Generative AI [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/transac25&div=52&start_page=819&collection=usjournals&set_as_cursor=282&men_tab=srchresults | Transactions: The Tennessee Journal of Business Law | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/transac25&div=52&start_page=819&collection=usjournals&set_as_cursor=282&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Improving Solutions to AI-Related Difficulties [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/rutcomt50&div=11&start_page=159&collection=usjournals&set_as_cursor=283&men_tab=srchresults | Rutgers Computer and Technology Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/rutcomt50&div=11&start_page=159&collection=usjournals&set_as_cursor=283&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Judicial Reforms and Access to Justice: A Comparative Analysis of E-Courts and Technological Integration in India and Singapore [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ijlmhs31&div=86&start_page=1147&collection=journals&set_as_cursor=285&men_tab=srchresults | International Journal of Law Management & Humanities | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ijlmhs31&div=86&start_page=1147&collection=journals&set_as_cursor=285&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Analog Privilege [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/nyulpp26&div=17&start_page=625&collection=usjournals&set_as_cursor=287&men_tab=srchresults | New York University Journal of Legislation and Public Policy | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/nyulpp26&div=17&start_page=625&collection=usjournals&set_as_cursor=287&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Reimagining the Successful Attorney Archetype [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/sdlr69&div=35&start_page=652&collection=usjournals&set_as_cursor=288&men_tab=srchresults | South Dakota Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/sdlr69&div=35&start_page=652&collection=usjournals&set_as_cursor=288&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Algorithmic Adjudication and Constitutional AI - The Promise of a Better AI Decision Making Future? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/comlrtj27&div=5&start_page=11&collection=usjournals&set_as_cursor=289&men_tab=srchresults | SMU Science and Technology Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/comlrtj27&div=5&start_page=11&collection=usjournals&set_as_cursor=289&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| How Should Legal Ethics Rules Apply When Artificial Intelligence Assists Pro Se Litigants? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/geojlege35&div=26&start_page=549&collection=usjournals&set_as_cursor=292&men_tab=srchresults | Georgetown Journal of Legal Ethics | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/geojlege35&div=26&start_page=549&collection=usjournals&set_as_cursor=292&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Keeping Our Porticos Accessible, Open, and Relevant: A Frank Discussion by a Retired Judge regarding the Remarkable Features of Nevada's Justice System and a Couple of Challenges That Lie Ahead [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/nevlyr0032&div=10&start_page=22&collection=barjournals&set_as_cursor=293&men_tab=srchresults | Nevada Lawyer | NaN |
| A Lawyer's Guide to Artificial Intelligence and Its Use in the Practice of Law [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/ubjss0037&div=25&start_page=16&collection=barjournals&set_as_cursor=295&men_tab=srchresults | Utah Bar Journal | NaN |
| International Commercial Arbitration & Technology: An Authors' Interview with Generative Artificial Intelligence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/itarev5&div=30&start_page=46&collection=usjournals&set_as_cursor=300&men_tab=srchresults | ITA in Review: The Journal of the Institute for Transnational Arbitration | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/itarev5&div=30&start_page=46&collection=usjournals&set_as_cursor=300&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| No "Robot Lawyers" Just Yet: The Role of Continuing Legal Education in Fulfilling the Duty of Technological Competence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jled72&div=30&start_page=577&collection=usjournals&set_as_cursor=302&men_tab=srchresults | Journal of Legal Education | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jled72&div=30&start_page=577&collection=usjournals&set_as_cursor=302&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Aspects of Artificial Intelligence on E-Justice and Personal Data Limitations [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jnlolletl26&div=17&start_page=1&collection=journals&set_as_cursor=303&men_tab=srchresults | Journal of Legal, Ethical and Regulatory Issues | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jnlolletl26&div=17&start_page=1&collection=journals&set_as_cursor=303&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Searching for Justice: Incorporating Critical Legal Research into Clinic Seminar [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/clinic30&div=15&start_page=227&collection=usjournals&set_as_cursor=307&men_tab=srchresults | Clinical Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/clinic30&div=15&start_page=227&collection=usjournals&set_as_cursor=307&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| A Perfect Storm for Legal Education: Privatization, Polarization, and Pedagogy [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/upitt85&div=15&start_page=331&collection=usjournals&set_as_cursor=309&men_tab=srchresults | University of Pittsburgh Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/upitt85&div=15&start_page=331&collection=usjournals&set_as_cursor=309&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Are A.I. Lawyers a Legal Product or Legal Service?: Why Current UPL Laws Are Not up to the Task of Regulating Autonomous A.I. Actors [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/hoflr53&div=15&start_page=391&collection=usjournals&set_as_cursor=310&men_tab=srchresults | Hofstra Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/hoflr53&div=15&start_page=391&collection=usjournals&set_as_cursor=310&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/nwteintp20&div=14&start_page=309&collection=usjournals&set_as_cursor=312&men_tab=srchresults | Northwestern Journal of Technology and Intellectual Property | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/nwteintp20&div=14&start_page=309&collection=usjournals&set_as_cursor=312&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Right to (Human) Counsel: Real Responsibility for Artificial Intelligence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/sclr74&div=35&start_page=823&collection=usjournals&set_as_cursor=313&men_tab=srchresults | South Carolina Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/sclr74&div=35&start_page=823&collection=usjournals&set_as_cursor=313&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Rage against the Machine: Who Is Responsible for Regulating Generative Artificial Intelligence in Domestic and Cross-Border Litigation? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/uilro2023&div=13&start_page=165&collection=usjournals&set_as_cursor=314&men_tab=srchresults | University of Illinois Law Review Online | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/uilro2023&div=13&start_page=165&collection=usjournals&set_as_cursor=314&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Discovering and Admitting AI Data in State and Federal Court: Part 1 [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/wilaw0097&div=199&start_page=10&collection=barjournals&set_as_cursor=316&men_tab=srchresults | Wisconsin Lawyer | NaN |
| Artificial Authorship and Judicial Opinions [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/gwlr92&div=46&start_page=1558&collection=usjournals&set_as_cursor=317&men_tab=srchresults | George Washington Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/gwlr92&div=46&start_page=1558&collection=usjournals&set_as_cursor=317&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Brace Yourself: Here Are Seven Legal Tech Trends That Are Transforming the Practice of Law [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/benchnbar0080&div=125&start_page=16&collection=barjournals&set_as_cursor=322&men_tab=srchresults | Bench & Bar of Minnesota | NaN |
| The Artificial Intelligence Act: Critical Overview [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jipitec16&div=6&start_page=2&collection=journals&set_as_cursor=327&men_tab=srchresults | Journal of Intellectual Property, Information Technology and Electronic Commerce Law | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jipitec16&div=6&start_page=2&collection=journals&set_as_cursor=327&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Places We'll Go [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ajle15&div=16&start_page=281&collection=journals&set_as_cursor=328&men_tab=srchresults | Asian Journal of Law and Economics | NaN |
| Beyond the Binary: AI, Ethics, and Liability in the Legal Landscape [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/txamrpl10&div=21&start_page=389&collection=usjournals&set_as_cursor=329&men_tab=srchresults | Texas A&M Journal of Property Law | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/txamrpl10&div=21&start_page=389&collection=usjournals&set_as_cursor=329&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Ethics of Generative Artificial Intelligence in Law Practice [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/alwyr0085&div=53&start_page=189&collection=barjournals&set_as_cursor=331&men_tab=srchresults | Alabama Lawyer | NaN |
| Forming Good Lawyers [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/wflr58&div=30&start_page=981&collection=usjournals&set_as_cursor=333&men_tab=srchresults | Wake Forest Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/wflr58&div=30&start_page=981&collection=usjournals&set_as_cursor=333&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Life beyond Zoom: The Promise of Emerging Virtual Court Alternatives [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/wasbur62&div=30&start_page=587&collection=usjournals&set_as_cursor=335&men_tab=srchresults | Washburn Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/wasbur62&div=30&start_page=587&collection=usjournals&set_as_cursor=335&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Questions to Ask Your Law Firms about Their Use of Artificial Intelligence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/rail7&div=54&start_page=357&collection=usjournals&set_as_cursor=341&men_tab=srchresults | RAIL: The Journal of Robotics, Artificial Intelligence & Law | NaN |
| Afraid to Ask? ChatGPT Won't Replace Attorneys, but It Just Might Be of Assistance [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/ilbj0111&div=41&start_page=24&collection=barjournals&set_as_cursor=343&men_tab=srchresults | Illinois Bar Journal | NaN |
| Trajectories of Legal Work in the Context of Machine Learning AI: Conceptualising Mediated Evolution [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/injlepro32&div=7&start_page=97&collection=journals&set_as_cursor=345&men_tab=srchresults | International Journal of the Legal Profession | NaN |
| Artificial Intelligence and Arbitration: Some Considerations on the Eve of a Global Regulation [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/romabj17&div=35&start_page=31&collection=journals&set_as_cursor=349&men_tab=srchresults | Romanian Arbitration Journal / Revista Romana de Arbitraj | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/romabj17&div=35&start_page=31&collection=journals&set_as_cursor=349&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Artificial Intelligence: An Analysis in the Legal Field [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ijlmhs28&div=14&start_page=92&collection=journals&set_as_cursor=350&men_tab=srchresults | International Journal of Law Management & Humanities | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ijlmhs28&div=14&start_page=92&collection=journals&set_as_cursor=350&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Unauthorized Practice or Untenable Prohibitions: Refining and Redefining UPL [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/smjmale13&div=15&start_page=283&collection=usjournals&set_as_cursor=352&men_tab=srchresults | St. Mary's Journal on Legal Malpractice and Ethics | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/smjmale13&div=15&start_page=283&collection=usjournals&set_as_cursor=352&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Computational Turn in International Law [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/nordic93&div=6&start_page=38&collection=journals&set_as_cursor=353&men_tab=srchresults | Nordic Journal of International Law | NaN |
| Lawmaps: Enabling Legal AI Development through Visualisation of the Implicit Structure of Legislation and Lawyerly Process [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/artinl31&div=9&start_page=169&collection=journals&set_as_cursor=354&men_tab=srchresults | Artificial Intelligence and Law | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/artinl31&div=9&start_page=169&collection=journals&set_as_cursor=354&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Influence of Technology and Artificial Intelligence Impacting the Growth of Legal Industry [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/injlolw11&div=375&start_page=1&collection=journals&set_as_cursor=356&men_tab=srchresults | Indian Journal of Law and Legal Research | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/injlolw11&div=375&start_page=1&collection=journals&set_as_cursor=356&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| A Practical Guide to Generative AI [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/legman0042&div=111&start_page=29&collection=barjournals&set_as_cursor=357&men_tab=srchresults | Legal Management | NaN |
| Opening the Virtual Window: How on-Line Processes Could Increase Access to Justice in the Criminal Legal System [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/cardcore25&div=12&start_page=177&collection=usjournals&set_as_cursor=358&men_tab=srchresults | Cardozo Journal of Conflict Resolution | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/cardcore25&div=12&start_page=177&collection=usjournals&set_as_cursor=358&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Subjects and Stages of AI Dataset Development: A Framework for Dataset Accountability [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/isjlpsoc19&div=9&start_page=171&collection=usjournals&set_as_cursor=359&men_tab=srchresults | Ohio State Technology Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/isjlpsoc19&div=9&start_page=171&collection=usjournals&set_as_cursor=359&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Bias and Fairness in Artificial Intelligence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/nysbaj0095&div=61&start_page=29&collection=barjournals&set_as_cursor=360&men_tab=srchresults | New York State Bar Association Journal | NaN |
| Rule 11 Is No Match for Generative AI [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/stantlr27&div=10&start_page=308&collection=usjournals&set_as_cursor=361&men_tab=srchresults | Stanford Technology Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/stantlr27&div=10&start_page=308&collection=usjournals&set_as_cursor=361&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Legal Arrangements of Artificial Intelligence in the European Union and the Republic of North Macedonia [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ejplt2024&div=10&start_page=79&collection=journals&set_as_cursor=362&men_tab=srchresults | European Journal of Privacy Law & Technologies (EJPLT) | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ejplt2024&div=10&start_page=79&collection=journals&set_as_cursor=362&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| An Economic Perspective on Costs in Australian Class Actions [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/mulr45&div=31&start_page=950&collection=journals&set_as_cursor=363&men_tab=srchresults | Melbourne University Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/mulr45&div=31&start_page=950&collection=journals&set_as_cursor=363&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Best Use or No Use? What Should Lawyers Do about AI Right Now? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/probpro39&div=11&start_page=38&collection=usjournals&set_as_cursor=364&men_tab=srchresults | Probate and Property | NaN |
| AI-Powered Indian Courtroom: ChatGPT a Boon or a Bane? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/juscrp4&div=59&start_page=[601]&collection=journals&set_as_cursor=366&men_tab=srchresults | Jus Corpus Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/juscrp4&div=59&start_page=[601]&collection=journals&set_as_cursor=366&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Artificial Intelligence and the HIPAA Privacy Rule: A Primer [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/hhpol24&div=4&start_page=77&collection=usjournals&set_as_cursor=368&men_tab=srchresults | Houston Journal of Health Law & Policy | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/hhpol24&div=4&start_page=77&collection=usjournals&set_as_cursor=368&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Digitalization of Litigation [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/emint38&div=29&start_page=819&collection=journals&set_as_cursor=370&men_tab=srchresults | Emory International Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/emint38&div=29&start_page=819&collection=journals&set_as_cursor=370&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Innovations of Artificial Intelligence in Light of the Applicable Copyright Law: Realistic Solutions and Future Prospects. A Comparative Study of UAE, Egyptian, and French Laws [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ajee2025&div=14&start_page=241&collection=journals&set_as_cursor=371&men_tab=srchresults | Access to Justice in Eastern Europe | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ajee2025&div=14&start_page=241&collection=journals&set_as_cursor=371&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Internet Frisking Jurors during Voir Dire: The Case for Imposing Judicial Limitations [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/lunlr18&div=21&start_page=705&collection=usjournals&set_as_cursor=372&men_tab=srchresults | Liberty University Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/lunlr18&div=21&start_page=705&collection=usjournals&set_as_cursor=372&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| How Technology Supports Open Justice and Transparency [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/leginfom24&div=40&start_page=165&collection=journals&set_as_cursor=373&men_tab=srchresults | Legal Information Management | NaN |
| Technically Speaking: How to Improve Technology CLEs to Meet the Needs of Lawyers and Get Them to Attend [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jled72&div=31&start_page=598&collection=usjournals&set_as_cursor=374&men_tab=srchresults | Journal of Legal Education | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jled72&div=31&start_page=598&collection=usjournals&set_as_cursor=374&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| How A.I. Will Revolutionize Our 20th Century Understanding of Administrative Law [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/aqrty55&div=20&start_page=318&collection=journals&set_as_cursor=376&men_tab=srchresults | Advocates' Quarterly | NaN |
| Georgia State Legal Technology Competency Model: A Framework for Examining and Evaluating What It Means to Be a Technologically Competent Lawyer [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/usthomlj20&div=7&start_page=53&collection=usjournals&set_as_cursor=378&men_tab=srchresults | University of St. Thomas Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/usthomlj20&div=7&start_page=53&collection=usjournals&set_as_cursor=378&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Evaluating ICT Adoption in the Indian Judiciary: Challenges, Opportunities, and the Impact of the E-Courts Project [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ijlj15&div=6&start_page=1&collection=journals&set_as_cursor=381&men_tab=srchresults | Indian Journal of Law and Justice | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ijlj15&div=6&start_page=1&collection=journals&set_as_cursor=381&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Arbitration and the Transnational System of Commercial Justice: Charting the Path Forward [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.kluwer/asiainta0020&div=11&start_page=67&collection=kluwer&set_as_cursor=383&men_tab=srchresults | Asian International Arbitration Journal | NaN |
| Ethical Implications of Using Generative AI [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/lwpra50&div=6&start_page=[26]&collection=usjournals&set_as_cursor=385&men_tab=srchresults | Law Practice | NaN |
| How Technology Can Support Open Justice and Transparency: A UK Perspective [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/auslwlib32&div=36&start_page=68&collection=journals&set_as_cursor=386&men_tab=srchresults | Australian Law Librarian | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/auslwlib32&div=36&start_page=68&collection=journals&set_as_cursor=386&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| From Court Automation to e-Justice and beyond in Europe [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ijca15&div=24&start_page=1&collection=usjournals&set_as_cursor=390&men_tab=srchresults | International Journal for Court Administration | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ijca15&div=24&start_page=1&collection=usjournals&set_as_cursor=390&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Human Impact on Arbitration in the Emerging Era of Artificial Intelligence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/caaj17&div=8&start_page=91&collection=journals&set_as_cursor=392&men_tab=srchresults | Contemporary Asia Arbitration Journal (CAA Journal) | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/caaj17&div=8&start_page=91&collection=journals&set_as_cursor=392&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| AI Ethics of Online Commercial Arbitration in Comparative Perspective [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jrnatila18&div=34&start_page=529&collection=journals&set_as_cursor=393&men_tab=srchresults | Journal of Comparative Law | NaN |
| OArb Enters the Age of Artificial Intelligence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/disput29&div=23&start_page=36&collection=usjournals&set_as_cursor=395&men_tab=srchresults | Dispute Resolution Magazine | NaN |
| Image-Based Sexual Abuse and EU Law: A Critical Analysis [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/germlajo25&div=98&start_page=1472&collection=journals&set_as_cursor=397&men_tab=srchresults | German Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/germlajo25&div=98&start_page=1472&collection=journals&set_as_cursor=397&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Artificial Intelligence May Assist, but Can Never Replace, the Judicial Decision-Making Process of Human Judges [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/florbarj0098&div=73&start_page=8&collection=barjournals&set_as_cursor=399&men_tab=srchresults | Florida Bar Journal | NaN |
| Exploring the Viability of AI as Judicial Replacements: A Cautionary Perspective [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jipitec14&div=55&start_page=619&collection=journals&set_as_cursor=401&men_tab=srchresults | Journal of Intellectual Property, Information Technology and Electronic Commerce Law | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jipitec14&div=55&start_page=619&collection=journals&set_as_cursor=401&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| What's a Lawyer for? Artificial Intelligence and Third-Wave Lawyering [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/flsulr51&div=18&start_page=543&collection=usjournals&set_as_cursor=404&men_tab=srchresults | Florida State University Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/flsulr51&div=18&start_page=543&collection=usjournals&set_as_cursor=404&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Interfering with Judicial Independence? The Legal Constraints in the Realm of AI-Powered Judicial Decision-Making [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jloailw1&div=27&start_page=199&collection=journals&set_as_cursor=406&men_tab=srchresults | Journal of AI Law and Regulation (AIRe) | NaN |
| Practice Guide: How to Integrate AI and Emerging Technology into Your Practice and Comply with Model Rule 3.1 [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/mipr25&div=24&start_page=67&collection=usjournals&set_as_cursor=407&men_tab=srchresults | Minnesota Journal of Law, Science and Technology | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/mipr25&div=24&start_page=67&collection=usjournals&set_as_cursor=407&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Integrating AI into Your Legal Practice: Practical Use Cases and How to Get Started [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/resgestae0068&div=62&start_page=12&collection=barjournals&set_as_cursor=408&men_tab=srchresults | Res Gestae | NaN |
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| Precedential Value of Judicial Decisions in Increasingly Hybridised Civil Law Systems: Chinese Choreographies at the WTO [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/asjcoml19&div=10&start_page=107&collection=journals&set_as_cursor=414&men_tab=srchresults | Asian Journal of Comparative Law | NaN |
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| A New Era of Maritime Arbitration: Ex Machina Determinations [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.kluwer/jia0040&div=34&start_page=521&collection=kluwer&set_as_cursor=418&men_tab=srchresults | Journal of International Arbitration | NaN |
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| A Liberal Theory of Legal Education [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/bamalr75&div=20&start_page=563&collection=usjournals&set_as_cursor=421&men_tab=srchresults | Alabama Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/bamalr75&div=20&start_page=563&collection=usjournals&set_as_cursor=421&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Law without Lawyers: Examining the Limitations of Consumer-Centric Legal Tech Services [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jillptc3&div=5&start_page=15&collection=journals&set_as_cursor=422&men_tab=srchresults | Journal of Intellectual Property and Information Technology Law | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jillptc3&div=5&start_page=15&collection=journals&set_as_cursor=422&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Framing the Future: The Foundation Series, Foundation Models and Framing AI [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/lwtchmn4&div=18&start_page=109&collection=journals&set_as_cursor=423&men_tab=srchresults | Law, Technology and Humans | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/lwtchmn4&div=18&start_page=109&collection=journals&set_as_cursor=423&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Digital Single Market: Consumer Protection Rules in the Digital Services Act [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ijlsod3&div=38&start_page=400&collection=journals&set_as_cursor=425&men_tab=srchresults | International Journal of Legal and Social Order | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ijlsod3&div=38&start_page=400&collection=journals&set_as_cursor=425&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Technologies versus Justice: Challenges of AI Regulation in the Judicial System [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/lglisited5&div=18&start_page=113&collection=journals&set_as_cursor=427&men_tab=srchresults | Legal Issues in the Digital Age | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/lglisited5&div=18&start_page=113&collection=journals&set_as_cursor=427&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| An Economic Perspective of the Justice Digitalisation Process: The Questions of Efficiency and Equity [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/atnsj9&div=39&start_page=509&collection=journals&set_as_cursor=428&men_tab=srchresults | Athens Journal of Law (AJL) | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/atnsj9&div=39&start_page=509&collection=journals&set_as_cursor=428&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Towards a Digitalised Criminal Justice System: Lessons from Poland [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/rbdpp10&div=21&start_page=1&collection=journals&set_as_cursor=430&men_tab=srchresults | Revista Brasileira de Direito Processual Penal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/rbdpp10&div=21&start_page=1&collection=journals&set_as_cursor=430&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Changing All the Time: AI's Impact on Humanity's Role in Common Law Development and Interpretation [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/bulr103&div=64&start_page=2215&collection=usjournals&set_as_cursor=431&men_tab=srchresults | Boston University Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/bulr103&div=64&start_page=2215&collection=usjournals&set_as_cursor=431&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Technology and Judges in Australia [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/aslnlwjunl197&div=120&start_page=636&collection=journals&set_as_cursor=432&men_tab=srchresults | Australian Law Journal | NaN |
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| The Implementation of AI Systems in the Colombian Justice: The Constitutional Court and the Council of State [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/reveurda11&div=9&start_page=1&collection=journals&set_as_cursor=434&men_tab=srchresults | Revista Eurolatinoamericana de Derecho Administrativo (Euro-Latin American Journal of Administrative Law) | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/reveurda11&div=9&start_page=1&collection=journals&set_as_cursor=434&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Teaching Law Subjects by Using Educational Robots: Does the Use of Robots Lead to the Development of Legal Skills among Law Students? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/asjledu11&div=17&start_page=188&collection=journals&set_as_cursor=435&men_tab=srchresults | Asian Journal of Legal Education | NaN |
| A Vision for Digitizing Judicial Processes and Integrating Artificial Intelligence in Pakistan's Judiciary: Enhancing Efficiency and Upholding Judicial Integrity [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ijlet2024&div=23&start_page=108&collection=journals&set_as_cursor=437&men_tab=srchresults | International Journal of Law, Ethics, and Technology (IJLET) | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ijlet2024&div=23&start_page=108&collection=journals&set_as_cursor=437&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Regulation of Judicial Analytics: Towards a New Research Agenda [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/lwtchmn6&div=15&start_page=69&collection=journals&set_as_cursor=438&men_tab=srchresults | Law, Technology and Humans | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/lwtchmn6&div=15&start_page=69&collection=journals&set_as_cursor=438&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Law, Technology, and Our Governance Dilemma [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/laws13&div=37&start_page=1&collection=journals&set_as_cursor=439&men_tab=srchresults | Laws | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/laws13&div=37&start_page=1&collection=journals&set_as_cursor=439&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Computing the Laws of War: Investigating the Relationship between War, International Law and Military Computer Technology [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/nordic93&div=30&start_page=537&collection=journals&set_as_cursor=442&men_tab=srchresults | Nordic Journal of International Law | NaN |
| The AI-Based Legal Paradise - A (Necessary!) Thought Experiment [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jolttx6&div=8&start_page=168&collection=usjournals&set_as_cursor=443&men_tab=srchresults | Journal of Law and Technology at Texas (JOLTT) | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jolttx6&div=8&start_page=168&collection=usjournals&set_as_cursor=443&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Lawyer's Duty of Competence in a Climate-Imperiled World [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/umkc92&div=46&start_page=859&collection=usjournals&set_as_cursor=447&men_tab=srchresults | UMKC Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/umkc92&div=46&start_page=859&collection=usjournals&set_as_cursor=447&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| After Reaching the Courthouse Door: Why Lack of Affirmative Assistance Post-Pleading Violates Prisoners' Access to Courts Right [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/collsp57&div=16&start_page=397&collection=usjournals&set_as_cursor=448&men_tab=srchresults | Columbia Journal of Law and Social Problems | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/collsp57&div=16&start_page=397&collection=usjournals&set_as_cursor=448&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Embracing the Inevitable: Integrating AI Technologies in Mediation [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/cololaw0053&div=44&start_page=24&collection=barjournals&set_as_cursor=449&men_tab=srchresults | Colorado Lawyer | NaN |
| Cybersymbiosis of Human Judges and Artificial Intelligence: Problems and Potential Solutions for Integration and for the Successful Modernization of the Judicial Systems of the BRICS Countries [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/tfm2024&div=23&start_page=249&collection=journals&set_as_cursor=450&men_tab=srchresults | Journal of Commercial and Intellectual Property Law | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/tfm2024&div=23&start_page=249&collection=journals&set_as_cursor=450&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Informational Sovereignty: A New Framework for AI Regulation [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ijodr10&div=10&start_page=60&collection=journals&set_as_cursor=451&men_tab=srchresults | International Journal of Online Dispute Resolution | NaN |
| The Language of the Law vs. the Language of the Computer: A Bilingual Model of Legal Education in the Age of Technology and Artificial Intelligence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/linovte16&div=23&start_page=558&collection=journals&set_as_cursor=452&men_tab=srchresults | Law, Innovation and Technology | NaN |
| Technological Challenges for Modern Law School Pedagogy: Preparing Graduates for the Modern Legal Workplace [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/lwtch58&div=5&start_page=32&collection=journals&set_as_cursor=453&men_tab=srchresults | Law Teacher | NaN |
| Gray Advice [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/dltr25&div=3&start_page=48&collection=usjournals&set_as_cursor=454&men_tab=srchresults | Duke Law & Technology Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/dltr25&div=3&start_page=48&collection=usjournals&set_as_cursor=454&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Framing Online Speech Governance as an Algorithmic Accountability Issue [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/spplmntinlj99&div=4&start_page=37&collection=usjournals&set_as_cursor=455&men_tab=srchresults | Indiana Law Journal Supplement | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/spplmntinlj99&div=4&start_page=37&collection=usjournals&set_as_cursor=455&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Using Aceh's Qanun to Expand Protection for Domestic Violence Victims [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ajal23&div=17&start_page=63&collection=journals&set_as_cursor=457&men_tab=srchresults | Australian Journal of Asian Law | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ajal23&div=17&start_page=63&collection=journals&set_as_cursor=457&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Integrating Sustainable Development Goals in the Law Curriculum: Legal Education for "People, Planet and Prosperity" [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/belmolre12&div=7&start_page=196&collection=usjournals&set_as_cursor=459&men_tab=srchresults | Belmont Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/belmolre12&div=7&start_page=196&collection=usjournals&set_as_cursor=459&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Hidden Contracts [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/byulr49&div=12&start_page=307&collection=usjournals&set_as_cursor=460&men_tab=srchresults | Brigham Young University Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/byulr49&div=12&start_page=307&collection=usjournals&set_as_cursor=460&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Building a Better Lawyer: Experimental Evidence That Artificial Intelligence Can Increase Legal Work Efficiency [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/emplest21&div=35&start_page=979&collection=usjournals&set_as_cursor=461&men_tab=srchresults | Journal of Empirical Legal Studies | NaN |
| Exploring Shortcomings in International Trade Law in Light of the Expansion of Artificial Intelligence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.kluwer/glotcuj0019&div=102&start_page=731&collection=kluwer&set_as_cursor=462&men_tab=srchresults | Global Trade and Customs Journal | NaN |
| AI Generated Art and the Gap in Copyright Law [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/inprobr15&div=11&start_page=23&collection=usjournals&set_as_cursor=464&men_tab=srchresults | American University Intellectual Property Brief | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/inprobr15&div=11&start_page=23&collection=usjournals&set_as_cursor=464&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Educating Deal Lawyers for the Digital Age [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/flr92&div=68&start_page=1855&collection=usjournals&set_as_cursor=465&men_tab=srchresults | Fordham Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/flr92&div=68&start_page=1855&collection=usjournals&set_as_cursor=465&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| An Information Flow Model of Online Mediation: Jeopardizing Privacy and Autonomy in the Shadow of Innovation [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/cardcore25&div=22&start_page=443&collection=usjournals&set_as_cursor=466&men_tab=srchresults | Cardozo Journal of Conflict Resolution | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/cardcore25&div=22&start_page=443&collection=usjournals&set_as_cursor=466&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Observing the Effects of Automating the Judicial System with Behavioral Equivalence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/sclr73&div=30&start_page=825&collection=usjournals&set_as_cursor=467&men_tab=srchresults | South Carolina Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/sclr73&div=30&start_page=825&collection=usjournals&set_as_cursor=467&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Client Confidentiality as Data Security [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/washlr99&div=23&start_page=781&collection=usjournals&set_as_cursor=468&men_tab=srchresults | Washington Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/washlr99&div=23&start_page=781&collection=usjournals&set_as_cursor=468&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Artificial Intelligence and the Future of International Trade Law and Dispute Settlement [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/caaj17&div=6&start_page=35&collection=journals&set_as_cursor=470&men_tab=srchresults | Contemporary Asia Arbitration Journal (CAA Journal) | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/caaj17&div=6&start_page=35&collection=journals&set_as_cursor=470&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Digital "To Kill a Mockingbird": Artificial Intelligence Biases in Courts [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/calwi54&div=16&start_page=459&collection=journals&set_as_cursor=471&men_tab=srchresults | California Western International Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/calwi54&div=16&start_page=459&collection=journals&set_as_cursor=471&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Navigating Ethical Concerns for Lawyers Using AI [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/mdbj2024&div=11&start_page=25&collection=barjournals&set_as_cursor=472&men_tab=srchresults | Maryland Bar Journal | NaN |
| AI Regulation: A Review of AI Usage in the Consumer Finance Industry and the Growing Federal and State Regulation of the Technology [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/cnsmrfinlw78&div=6&start_page=13&collection=usjournals&set_as_cursor=473&men_tab=srchresults | Consumer Finance Law Quarterly Report | NaN |
| Data Justice Readiness: An Abolitionist Framework for Tech Clinic Intake [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/clinic31&div=8&start_page=153&collection=usjournals&set_as_cursor=474&men_tab=srchresults | Clinical Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/clinic31&div=8&start_page=153&collection=usjournals&set_as_cursor=474&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Bias Notification Duty [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/caelj42&div=14&start_page=295&collection=usjournals&set_as_cursor=475&men_tab=srchresults | Cardozo Arts & Entertainment Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/caelj42&div=14&start_page=295&collection=usjournals&set_as_cursor=475&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| AI Systems' Impact on the Recognition of Foreign Judgements: The Case of Estonia [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jurdint32&div=13&start_page=107&collection=journals&set_as_cursor=476&men_tab=srchresults | Juridica International | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jurdint32&div=13&start_page=107&collection=journals&set_as_cursor=476&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| X-Raying the Legality of a Robot Lawyer in the Nigerian Courts [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ijlmhs23&div=268&start_page=3188&collection=journals&set_as_cursor=477&men_tab=srchresults | International Journal of Law Management & Humanities | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ijlmhs23&div=268&start_page=3188&collection=journals&set_as_cursor=477&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| AI Accountability in Judicial Proceedings: An Actor-Network Approach [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/laws13&div=80&start_page=1&collection=journals&set_as_cursor=478&men_tab=srchresults | Laws | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/laws13&div=80&start_page=1&collection=journals&set_as_cursor=478&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Role of Disruptive Artificial Intelligence Technology in Combating Crime in Indonesia [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/beijlar15&div=105&start_page=1668&collection=journals&set_as_cursor=479&men_tab=srchresults | Beijing Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/beijlar15&div=105&start_page=1668&collection=journals&set_as_cursor=479&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The General Data Protection Regulation of 2016 (GDPR) Meets Its Sibling the Artificial Intelligence Act of 2024: A Power Couple, or a Clash of Titans? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/actdaj2024&div=11&start_page=7&collection=journals&set_as_cursor=480&men_tab=srchresults | Acta Universitatis Danubius Juridica | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/actdaj2024&div=11&start_page=7&collection=journals&set_as_cursor=480&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Exploring Legal and Ethical Dimensions of Artificial Intelligence in Employment: Safeguarding Worker Rights and Ensuring Fair Practices [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ijlmhs27&div=211&start_page=2497&collection=journals&set_as_cursor=482&men_tab=srchresults | International Journal of Law Management & Humanities | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ijlmhs27&div=211&start_page=2497&collection=journals&set_as_cursor=482&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Ethical and Legal Aspects of the Development and Use of Robotics and Artificial Intelligence. Protection of Human Rights in the Era of Globalization and Digitisation [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jladsc19&div=6&start_page=20&collection=journals&set_as_cursor=483&men_tab=srchresults | Journal of Law and Administrative Sciences | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jladsc19&div=6&start_page=20&collection=journals&set_as_cursor=483&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Lawyers as Infrastructures: Mediations, Blockages, and New Possibilities in Grassroots Movements [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jlsocty50&div=22&start_page=231&collection=journals&set_as_cursor=484&men_tab=srchresults | Journal of Law and Society | NaN |
| Balancing Interests: AI, Business & Human Rights, and the Legal Landscape in an Era of Disruption [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/wvb127&div=4&start_page=1&collection=usjournals&set_as_cursor=485&men_tab=srchresults | West Virginia Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/wvb127&div=4&start_page=1&collection=usjournals&set_as_cursor=485&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Have Plain Language Laws Kept up with the AI Revolution? An Empirical Test [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/berkbusj22&div=8&start_page=108&collection=usjournals&set_as_cursor=487&men_tab=srchresults | Berkeley Business Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/berkbusj22&div=8&start_page=108&collection=usjournals&set_as_cursor=487&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Assessing the Impact of Artificial Intelligence on the Arbitration Process [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/caaj17&div=13&start_page=133&collection=journals&set_as_cursor=488&men_tab=srchresults | Contemporary Asia Arbitration Journal (CAA Journal) | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/caaj17&div=13&start_page=133&collection=journals&set_as_cursor=488&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Rise of Generative AI: Benefits and Dangers for Professionals [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/iihcj17&div=24&start_page=8977&collection=journals&set_as_cursor=489&men_tab=srchresults | International In-House Counsel Journal | NaN |
| The Ethics of ChatGPT [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/resgestae0066&div=106&start_page=12&collection=barjournals&set_as_cursor=491&men_tab=srchresults | Res Gestae | NaN |
| How Innovation Is Revolutionizing Client Service [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/legman0042&div=109&start_page=20&collection=barjournals&set_as_cursor=492&men_tab=srchresults | Legal Management | NaN |
| What's Really Wrong with ISDS? - A Critical Analysis of Phantom Issues and Real Issues Triggered by Practice and Technological Development [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/caaj17&div=5&start_page=1&collection=journals&set_as_cursor=493&men_tab=srchresults | Contemporary Asia Arbitration Journal (CAA Journal) | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/caaj17&div=5&start_page=1&collection=journals&set_as_cursor=493&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| What Does Relevant Mean to You? Creating a Choose-Your-Own-Adventure Technology Competency Framework [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/usthomlj20&div=12&start_page=190&collection=usjournals&set_as_cursor=494&men_tab=srchresults | University of St. Thomas Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/usthomlj20&div=12&start_page=190&collection=usjournals&set_as_cursor=494&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Big Data and Competition Law: Navigating Trade Practices in the Digital Age [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jlolwmkt2025&div=9&start_page=63&collection=journals&set_as_cursor=499&men_tab=srchresults | Journal of Law, Market & Innovation | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jlolwmkt2025&div=9&start_page=63&collection=journals&set_as_cursor=499&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Artificial Intelligence at the Crossroads between the European Union & the Council of Europe: Who Safeguards What & How? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/itajpul16&div=11&start_page=165&collection=journals&set_as_cursor=501&men_tab=srchresults | Italian Journal of Public Law | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/itajpul16&div=11&start_page=165&collection=journals&set_as_cursor=501&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Legal Challenges of Realistic and AI-Driven Child Sexual Abuse Material: Regulatory and Enforcement Perspectives in Europe [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/laws13&div=84&start_page=1&collection=journals&set_as_cursor=506&men_tab=srchresults | Laws | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/laws13&div=84&start_page=1&collection=journals&set_as_cursor=506&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The UK Online Safety Act, the EU Digital Services Act and Online Disinformation: Is the Right to Political Participation Adequately Protected? [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/joomaw16&div=26&start_page=440&collection=journals&set_as_cursor=507&men_tab=srchresults | Journal of Media Law | NaN |
| Making Sense of Interlinkages in EU Marine Environment Legislation: Unearthing Effectiveness [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/intjsemi37&div=125&start_page=2287&collection=journals&set_as_cursor=509&men_tab=srchresults | International Journal for the Semiotics of Law | NaN |
| Putting the Artificial Intelligence in Alternative Dispute Resolution: How AI Rules Will Become ADR Rules [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/amcraesii4&div=57&start_page=685&collection=journals&set_as_cursor=510&men_tab=srchresults | Amicus Curiae | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/amcraesii4&div=57&start_page=685&collection=journals&set_as_cursor=510&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Future of the Legal Profession (I) on Non-Lawyering: The British and American Perspectives; ChatGPT "Sins" in the Legal Profession [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jbcluj2024&div=7&start_page=27&collection=journals&set_as_cursor=511&men_tab=srchresults | Jurnalul Baroului Cluj | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jbcluj2024&div=7&start_page=27&collection=journals&set_as_cursor=511&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| AI Will Change How Law Is Practiced [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.barjournals/arklwr0060&div=10&start_page=18&collection=barjournals&set_as_cursor=513&men_tab=srchresults | Arkansas Lawyer | NaN |
| Impact of Artificial Intelligence (AI) on Legal Profession and Justice System [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ijlmhs22&div=111&start_page=1084&collection=journals&set_as_cursor=514&men_tab=srchresults | International Journal of Law Management & Humanities | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ijlmhs22&div=111&start_page=1084&collection=journals&set_as_cursor=514&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Legal Education and Artificial Intelligence: Vectors of Interaction [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/apel2024&div=48&start_page=804&collection=journals&set_as_cursor=515&men_tab=srchresults | Russian Journal of Economics and Law | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/apel2024&div=48&start_page=804&collection=journals&set_as_cursor=515&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The AI Regulatory Pyramid: A Taxonomy & Analysis of the Emerging Toolbox in the Global Race for the Regulation and Governance of Artificial Intelligence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/lla57&div=27&start_page=859&collection=usjournals&set_as_cursor=516&men_tab=srchresults | Loyola of Los Angeles Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/lla57&div=27&start_page=859&collection=usjournals&set_as_cursor=516&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Interplay between Machine Learning and Data Minimization under the GDPR: the Case of Google's Topics API [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/intldatpc13&div=24&start_page=284&collection=journals&set_as_cursor=519&men_tab=srchresults | International Data Privacy Law | NaN |
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| Breaking up with the Anti-Hero: How 303(b)(3) Can Help Law Schools Mitigate Their Perennial Devices, Prices, Vices, and Crises [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/maine77&div=7&start_page=69&collection=usjournals&set_as_cursor=524&men_tab=srchresults | Maine Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/maine77&div=7&start_page=69&collection=usjournals&set_as_cursor=524&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Ethical Obligations of the Lawyer-Client Relationship in Immigration Law [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/gpsolo41&div=77&start_page=33&collection=usjournals&set_as_cursor=525&men_tab=srchresults | GP Solo | NaN |
| Protest, Public Authorities, and Property Rights: An Evaluation of the Applicability of Article 1 of Protocol 1 to Public Authorities [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/judire29&div=27&start_page=238&collection=journals&set_as_cursor=526&men_tab=srchresults | Judicial Review | NaN |
| Hyperrealistic Jurisprudence: The Digital Age and the (Un) Certainty of Judge Analytics [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/intjsemi36&div=113&start_page=2261&collection=journals&set_as_cursor=527&men_tab=srchresults | International Journal for the Semiotics of Law | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/intjsemi36&div=113&start_page=2261&collection=journals&set_as_cursor=527&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Second-Wave DREAMers [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/yalpr42&div=6&start_page=107&collection=usjournals&set_as_cursor=528&men_tab=srchresults | Yale Law & Policy Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/yalpr42&div=6&start_page=107&collection=usjournals&set_as_cursor=528&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| A Topic Discovery Approach for Unsupervised Organization of Legal Document Collections [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/artinl32&div=35&start_page=1045&collection=journals&set_as_cursor=529&men_tab=srchresults | Artificial Intelligence and Law | NaN |
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| The Evolving Concept of Gender and Intersectional Stereotypes in International Norm Creation: Directions for a New CEDAW General Recommendation [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/penjuaf8&div=15&start_page=129&collection=usjournals&set_as_cursor=532&men_tab=srchresults | University of Pennsylvania Journal of Law & Public Affairs | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/penjuaf8&div=15&start_page=129&collection=usjournals&set_as_cursor=532&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| What Social Science Can Teach Us regarding Briefing [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/jlwriins28&div=9&start_page=305&collection=usjournals&set_as_cursor=533&men_tab=srchresults | Legal Writing: The Journal of the Legal Writing Institute | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/jlwriins28&div=9&start_page=305&collection=usjournals&set_as_cursor=533&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Major Reform with Minor Risk: Implementation of Change Initiatives as a Learning Challenge [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/plr22&div=13&start_page=151&collection=usjournals&set_as_cursor=534&men_tab=srchresults | University of New Hampshire Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/plr22&div=13&start_page=151&collection=usjournals&set_as_cursor=534&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Cruel Optimism of International Prison Regulation: Prison Ontologies and Carceral Harms [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/lsociq49&div=77&start_page=1683&collection=usjournals&set_as_cursor=535&men_tab=srchresults | Law & Social Inquiry | NaN |
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| Artificial Intelligence, Trade Secrets, and the Challenge of Transparency [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ncjl25&div=20&start_page=495&collection=usjournals&set_as_cursor=540&men_tab=srchresults | North Carolina Journal of Law & Technology | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ncjl25&div=20&start_page=495&collection=usjournals&set_as_cursor=540&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Pretrial Disparity and the Consequences of Money Bail [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/mllr81&div=33&start_page=557&collection=usjournals&set_as_cursor=541&men_tab=srchresults | Maryland Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/mllr81&div=33&start_page=557&collection=usjournals&set_as_cursor=541&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| A Justice as Fairness Framework for a Revised Efficiencies Defence [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/cacmplr36&div=17&start_page=88&collection=journals&set_as_cursor=543&men_tab=srchresults | Canadian Competition Law Review (CCLR) | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/cacmplr36&div=17&start_page=88&collection=journals&set_as_cursor=543&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Judicial Knowledge-Enhanced Magnitude-Aware Reasoning for Numerical Legal Judgment Prediction [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/artinl31&div=33&start_page=773&collection=journals&set_as_cursor=545&men_tab=srchresults | Artificial Intelligence and Law | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/artinl31&div=33&start_page=773&collection=journals&set_as_cursor=545&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Thirty Years of Artificial Intelligence and Law: The Third Decade [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/artinl30&div=26&start_page=561&collection=journals&set_as_cursor=546&men_tab=srchresults | Artificial Intelligence and Law | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/artinl30&div=26&start_page=561&collection=journals&set_as_cursor=546&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| "Trustworthy AI" Cannot Be Trusted: A Virtue Jurisprudence-Based Approach to Analyse Who Is Responsible for AI Errors [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ijlet2024&div=26&start_page=186&collection=journals&set_as_cursor=551&men_tab=srchresults | International Journal of Law, Ethics, and Technology (IJLET) | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ijlet2024&div=26&start_page=186&collection=journals&set_as_cursor=551&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Boycotting Chinese Genocide and the Duty to Prevent: Opportunities Lost in the 2019-2021 UK Trade Bill [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/iicl34&div=14&start_page=249&collection=journals&set_as_cursor=552&men_tab=srchresults | Indiana International & Comparative Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/iicl34&div=14&start_page=249&collection=journals&set_as_cursor=552&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Network Effects of International Crypto and DLT Regulation [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/vantl57&div=35&start_page=1285&collection=usjournals&set_as_cursor=553&men_tab=srchresults | Vanderbilt Journal of Transnational Law | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/vantl57&div=35&start_page=1285&collection=usjournals&set_as_cursor=553&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| AI, Equity, and the IP Gap [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/smulr75&div=38&start_page=815&collection=usjournals&set_as_cursor=555&men_tab=srchresults | SMU Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/smulr75&div=38&start_page=815&collection=usjournals&set_as_cursor=555&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Greening AI? The New Principle of Sustainable Digital Products and Services in the EU [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.kluwer/cmlr0061&div=76&start_page=1019&collection=kluwer&set_as_cursor=556&men_tab=srchresults | Common Market Law Review | NaN |
| "I Am Become Death, the Destroyer of Worlds": Applying Strict Liability to Artificial Intelligence as an Abnormally Dangerous Activity [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/temple96&div=18&start_page=349&collection=usjournals&set_as_cursor=559&men_tab=srchresults | Temple Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/temple96&div=18&start_page=349&collection=usjournals&set_as_cursor=559&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Non-Educator Stakeholders and Public-School Principals' Views on the Proposed Amendments to the South African Schools Act 84 of 1996 [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/per27&div=2&start_page=1&collection=journals&set_as_cursor=560&men_tab=srchresults | Potchefstroom Electronic Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/per27&div=2&start_page=1&collection=journals&set_as_cursor=560&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Protecting the Promise to the Families of Tuskegee: Banning the Use of Persuasive AI in Obtaining Informed Consent for Commercial Drug Trials [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/sanlr60&div=29&start_page=671&collection=usjournals&set_as_cursor=562&men_tab=srchresults | San Diego Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/sanlr60&div=29&start_page=671&collection=usjournals&set_as_cursor=562&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| We(ed) Hold These Truths to Be Self-Evident: All Things Cannabis Are Inequitable [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/sonengrs19&div=4&start_page=39&collection=usjournals&set_as_cursor=564&men_tab=srchresults | UMass Law Review / University of Massachusetts Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/sonengrs19&div=4&start_page=39&collection=usjournals&set_as_cursor=564&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Slavery.AI [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/walee30&div=16&start_page=1&collection=usjournals&set_as_cursor=566&men_tab=srchresults | Washington and Lee Journal of Civil Rights and Social Justice | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/walee30&div=16&start_page=1&collection=usjournals&set_as_cursor=566&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| From Pixels to Prescriptions: The Case for National Telehealth Licensing & AI-Enhanced Care [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/indilr57&div=29&start_page=581&collection=usjournals&set_as_cursor=567&men_tab=srchresults | Indiana Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/indilr57&div=29&start_page=581&collection=usjournals&set_as_cursor=567&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Bisexual Erasure, Marjorie Rowland, and the Evolution of LGBTQ Rights [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/hwlj46&div=10&start_page=265&collection=usjournals&set_as_cursor=568&men_tab=srchresults | Harvard Journal of Law and Gender | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/hwlj46&div=10&start_page=265&collection=usjournals&set_as_cursor=568&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Epistemic Injustice of Algorithmic Family Policing [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ucirvlre14&div=15&start_page=404&collection=usjournals&set_as_cursor=569&men_tab=srchresults | UC Irvine Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ucirvlre14&div=15&start_page=404&collection=usjournals&set_as_cursor=569&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Statutory Structure [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/ylr132&div=28&start_page=1528&collection=usjournals&set_as_cursor=570&men_tab=srchresults | Yale Law Journal | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/ylr132&div=28&start_page=1528&collection=usjournals&set_as_cursor=570&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| Who Watches the Watchmen? Using the Law Governing Lawyers to Identify the Applicant Duty Gap and Hold Bar Examiner Gatekeepers Accountable [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/mslr2023&div=12&start_page=377&collection=usjournals&set_as_cursor=571&men_tab=srchresults | Michigan State Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/mslr2023&div=12&start_page=377&collection=usjournals&set_as_cursor=571&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
| The Categorical Imperative: In Search of the Mythical Perfect Privilege Log So Devoutly to Be Wished [article] | https://heinonline.org/HOL/Page?public=true&handle=hein.journals/touro39&div=9&start_page=165&collection=usjournals&set_as_cursor=572&men_tab=srchresults | Touro Law Review | https://heinonline.org/HOL/PrintRequest?public=true&handle=hein.journals/touro39&div=9&start_page=165&collection=usjournals&set_as_cursor=572&men_tab=srchresults&print=section&format=PDFsearchable&submit=Print%2FDownload |
IEEE Xplore
| Document Title | Authors | Author Affiliations | Publication Title | Date Added To Xplore | Publication Year | Volume | Issue | Start Page | End Page | Abstract | ISSN | ISBNs | DOI | Funding Information | PDF Link | Author Keywords | IEEE Terms | Mesh_Terms | Article Citation Count | Patent Citation Count | Reference Count | License | Online Date | Issue Date | Meeting Date | Publisher | Document Identifier |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Transforming Legal Workflows: A Deep Dive into NLP Solutions for Legal Challenges | H. Irfan; S. Peerzada; M. Mansoor | Faculty of Computer Science & Engg., GIK Institute of Engg. Sciences & Tech., Khyber Pakhtunkhwa, Pakistan; Faculty of Computer Science & Engg., GIK Institute of Engg. Sciences & Tech., Khyber Pakhtunkhwa, Pakistan; Faculty of Computer Science & Engg., GIK Institute of Engg. Sciences & Tech., Khyber Pakhtunkhwa, Pakistan | 2024 19th International Conference on Emerging Technologies (ICET) | 26 Mar 2025 | 2024 | NaN | NaN | 1 | 6 | Tasks like summarization and evaluating similar cases present substantial challenges for the legal industry, for which there are currently no comprehensive solutions. We propose a novel framework utilizing a BERT-based model to address this gap. Our research explores how AI technologies can be integrated to enhance decision-making and streamline legal processes. Through rigorous testing and evaluation, our model achieved a loss of 0.5562 and an accuracy of 0.7179, demonstrating its effectiveness in improving access to legal services and promoting more efficient legal reasoning. Our results highlight the transformative potential of AI solutions tailored specifically for the legal industry, offering new insights into enhancing the precision, effectiveness, and thoroughness of legal work. This study bridges the gap between AI advancements and practical applications in the legal domain, paving the way for significant improvements in legal practice and service delivery. | 2994-5798 | 979-8-3503-7958-7 | 10.1109/ICET63392.2024.10935072 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10935072 | Transformers;BERT;Artificial Intelligence;Legal Industry | Industries;Technological innovation;Ethics;Law;Shape;Natural languages;Transformers;Artificial intelligence;Research and development;Testing | NaN | NaN | NaN | 6.0 | IEEE | 26 Mar 2025 | NaN | NaN | IEEE | IEEE Conferences |
| An Analysis on Integrating Advanced Conversational AI in Legal Summarization and Information Retrieval | J. S. Garlyal; B. Hariharan; A. K. Singh | Department of Computational Intelligence, SRM Institute of Science & Technology, Chennai, India; Department of Computational Intelligence, SRM Institute of Science & Technology, Chennai, India; Department of Computational Intelligence, SRM Institute of Science & Technology, Chennai, India | 2024 Second International Conference on Inventive Computing and Informatics (ICICI) | 18 Sep 2024 | 2024 | NaN | NaN | 43 | 46 | This research study explores the impact of advanced conversational AI, particularly LawGPT, on legal practice. Specializing in the Indian Penal Code (IPC) and integrated with the Legal Language Model (LLM), LawGPT offers precise interpretation of legal queries. By utilizing advanced pre-processing techniques, Dense Passage Retriever (DPR) for retrieval, and BART architecture for generation, LawGPT ensures contextually relevant responses. Through validation against human-generated responses, its efficacy and accuracy are affirmed, democratizing access to legal knowledge for professionals and laypersons. LawGPT's adoption promises to revolutionize legal research, enhancing efficiency and inclusivity in legal interactions. This study analyzes its widespread integration, recognizing its potential to shape a more accessible and efficient legal system, contributing to the ongoing research on exploring AI's role in legal practice. | NaN | 979-8-3503-7329-5 | 10.1109/ICICI62254.2024.00016 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10675619 | Legal Document Summarizer;LawGPT;RAG Architecture;LLM | Technological innovation;Codes;Law;Shape;Navigation;Collaboration;Computer architecture | NaN | NaN | NaN | 19.0 | IEEE | 18 Sep 2024 | NaN | NaN | IEEE | IEEE Conferences |
| Iraqi Legal GPT | M. S. Mustafa; M. B. Abdulfatah; H. A. Abdulkareem; A. M. Ashir | Dept. of Computer Engineering, Tishk International University, Erbil, Iraq; Dept. of Computer Engineering, Tishk International University, Erbil, Iraq; Dept. of Computer Engineering, Tishk International University, Erbil, Iraq; Dept. of Computer Engineering, Tishk International University, Erbil, Iraq | 2024 21st International Multi-Conference on Systems, Signals & Devices (SSD) | 12 Jun 2024 | 2024 | NaN | NaN | 545 | 551 | This paper proposes and develops a small language model artificial intelligence (AI) system or chatbots capable of providing competent legal information to users within the Iraqi legal jurisprudence. One of the major contributions of the papers is addressing critical challenges related to the current Large Language Model (LLM) like ChatGPT of their enormous size and lack of localized knowledge of internal processes and lifestyles. The proposed model provides low latency time with limited economic resources and the availability of efficient legal professionals for understanding the legal and rights requirements of individuals within the Iraqi region which can be run on a local computer without an internet connection. The research leverages the open source h2ogpt as a backbone language model and was trained with aggregated and curated legal documents from the Iraqi legal system. This approach allows users to input questions, and through the aid of Embedding algorithms and Vector Store algorithms, the data is stored and converted into a machine-readable format for subsequent comprehension and processing by the computer. The findings indicate a promising foundation for the project, featuring a user-friendly interface (UI) and an updateable back-end to accommodate future enhancements. The system achieved a noteworthy 1-minute response time with an accuracy rate of 80%. Notably, the development objectives aimed to establish a locally deployable system that is easily accessible and modifiable to address evolving challenges in the current computational era of information dissemination. | 2474-0446 | 979-8-3503-7413-1 | 10.1109/SSD61670.2024.10548909 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10548909 | Artificial Intelligence (AI);Website Interface;Legal Chatbot;Large Language Models (LLM);Local Files (LF);h2ogpt;Dataset;Local Chatbots;Vector Store chroma | Economics;Law;Computational modeling;Machine vision;Chatbots;Vectors;Supercomputers | NaN | NaN | NaN | 25.0 | IEEE | 12 Jun 2024 | NaN | NaN | IEEE | IEEE Conferences |
| The Significance of Cultivating High-Value Patents in the Development of AI | M. Jiang; X. Fang | School of Economics and Management, Shanghai Institute Of Technology, Shanghai, CHINA; School of Economics and Management, Shanghai Institute Of Technology, Shanghai, CHINA | 2024 9th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) | 17 Dec 2024 | 2024 | 9 | NaN | 648 | 651 | With the rapid development of generative artificial intelligence (AI) technology, high-value patents play an increasingly important role in protecting innovation, promoting technological progress, and enhancing market competitiveness. This paper aims to explore how to effectively cultivate high-value patents in the field of generative AI and analyze their significance for technological innovation and industrial development. First, we review the basic concepts and current technological status of generative AI, conduct an in-depth analysis of high-value patents, and propose various strategies and methods for cultivating such patents. Through detailed case studies of Transformer architectures, this paper demonstrates the cultivation process and key success factors of these high-value patents. The study shows that high-value patents not only protect innovation outcomes but also promote the widespread application and commercialization of generative AI technology, providing strong support for sustainable technological development. | 2189-8723 | 979-8-3503-6304-3 | 10.1109/ICIIBMS62405.2024.10792726 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10792726 | Generative AI;High-Value Patents;Patent Cultivation | Industries;Patents;Technological innovation;Generative AI;Reviews;Technology transfer;Companies;Transformers;Faces;Commercialization | NaN | NaN | NaN | 20.0 | IEEE | 17 Dec 2024 | NaN | NaN | IEEE | IEEE Conferences |
| Generative Artificial Intelligence in Legal Drafting | T. Y. Basha; B. Kalyani; Y. Sandeep | Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India; Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India; Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India | 2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST) | 23 Oct 2024 | 2024 | NaN | NaN | 1 | 6 | “Lexi” is a light of clarity in a world where legal complexity frequently makes comprehension difficult. It is a tool that uses the revolutionary potential of generative Artificial Intelligence (AI) to completely change the process of producing legal documents. To simplify legal language and improve the accessibility and comprehension of legal documents, this paper introduces Lexi, a revolutionary tool. Through the integration of cutting-edge AI technology, Lexi not only improves legal drafting productivity but also supports legal communications that are clear and understandable. This innovation marks a paradigm change in legal documentation by emphasizing readability and ease of use and opening the door to a more diverse legal environment. | NaN | 979-8-3503-8188-7 | 10.1109/ICCIGST60741.2024.10717541 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10717541 | Generative Artificial Intelligence;Legal Drafting;AI-powered Tools;Legal Jargon Simplification;Fine-Tuning;Prompt Engineering;NLP | Productivity;Technological innovation;Law;Generative AI;Green products;Web pages;Documentation;Complexity theory;Standards;Computational intelligence | NaN | NaN | NaN | 15.0 | IEEE | 23 Oct 2024 | NaN | NaN | IEEE | IEEE Conferences |
| LexSage: Multi-Task Optimization in Legal Large Language Model Applications | L. Han; Y. Zhang; W. Gao; X. Li | National Supercomputing Center in Zhengzhou, Zhengzhou, China; Zhengzhou University, Zhengzhou, China; National Supercomputing Center in Zhengzhou, Zhengzhou, China; Zhengzhou University, Zhengzhou, China | 2024 4th International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC) | 4 Mar 2025 | 2024 | NaN | NaN | 325 | 330 | With the increasing complexity of tasks in the legal domain, traditional methods struggle to meet the demands of multi-task scenarios and face significant bottlenecks in task accuracy and efficiency. To address this, we propose LexSage, a legal large language model that leverages the one-shot capability of large language models (LLMs). By utilizing different prompt templates to assist in data generation and applying data augmentation techniques to further expand the dataset, LexSage performs instruction fine-tuning on the Qwen2.5-7B model with a comprehensive legal dataset. Through knowledge sharing and structural optimization across tasks, LexSage achieves a synergistic effect during multi-task fine-tuning, effectively integrating and enhancing the knowledge and expressive power across different tasks. Evaluation on LawBench shows that LexSage exhibits significant performance improvement in multiple tasks, including 25.6% improvement in the case analysis task compared to the GPT4 model, and 15.6% improvement in the law recitation task compared to the hanfei legal large language model. The experimental results show that LexSage demonstrates a strong comprehensive processing capability in legal intelligence applications, providing new ideas and technical support for the intelligent development of the legal field. | NaN | 979-8-3315-3122-5 | 10.1109/ICAIRC64177.2024.10900222 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10900222 | legal large language model;instruction fine-tuning;multi-task;legal intelligence applications | Law;Large language models;Soft sensors;Retrieval augmented generation;Data collection;Multitasking;Data models;Optimization;Robots;Text processing | NaN | NaN | NaN | 21.0 | IEEE | 4 Mar 2025 | NaN | NaN | IEEE | IEEE Conferences |
| Fine-tuning a Large Language Model for the Indian Legal System | R. Ta; R. Salunke; R. R; R. Nv; S. R. Upadhyaya | Department of Computer Science, PES University, Bengaluru, India; Department of Computer Science, PES University, Bengaluru, India; Department of Computer Science, PES University, Bengaluru, India; Department of Computer Science, PES University, Bengaluru, India; Department of Computer Science, PES University, Bengaluru, India | 2025 24th International Symposium INFOTEH-JAHORINA (INFOTEH) | 14 Apr 2025 | 2025 | NaN | NaN | 1 | 6 | This paper introduces an application based on a large language model customized for the Indian legal system, leveraging the LLama 3.1 8B foundational model. The model is pre-trained on a diverse corpus of legal texts and subsequently fine-tuned with curated Indian legal data to enhance accuracy and contextual relevance in legal responses. Advanced techniques such as Low-Rank Adaptation and Quantized Low-Rank Adaptations are employed to optimize the model’s efficiency while minimizing computational costs during fine-tuning. Pruning, as a compression method is utilized to enhance the model’s performance further and enable its deployment in resource-constrained environments. Additionally, the Retrieval Augmented Generation module is strategically implemented for document-specific queries, ensuring contextually accurate responses when processing legal documents. This research work is backed up by extensive experiments measuring the effectiveness across many precision metrics. The application is also tested against HaluEval, a Hallucination Evaluation Benchmark for factual reliability, and has demonstrated significant improvements in the model’s effectiveness as a resource for the legal domain. This AI-driven tool is the first step in simplifying the legal advisory services and decision-support systems in the Indian judiciary. It also goes a long way in enhancing the legal services experience. | 2767-9470 | 979-8-3315-1579-9 | 10.1109/INFOTEH64129.2025.10959207 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10959207 | NaN | Measurement;Adaptation models;Accuracy;Law;Computational modeling;Large language models;Retrieval augmented generation;Benchmark testing;Reliability;Context modeling | NaN | NaN | NaN | 15.0 | IEEE | 14 Apr 2025 | NaN | NaN | IEEE | IEEE Conferences |
| LegalMind System and the LLM-based Legal Judgment Query System | A. S; A. Saxena; J. Mahajan; L. Panikulangara; S. Kulkarni; D. S. Bang | Christ University, India; Christ University, India; Christ University, India; Synechron, India; IBM, India; Christ University, India | 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies | 5 Sep 2024 | 2024 | NaN | NaN | 1 | 5 | LegalMind-GPT represents a notable advancement in legal technology, specifically tailored for the finance sector. This research paper introduces LegalMind-GPT, a system that integrates Large Language Models (LLMs) to develop a Legal Judgment Query System for financial legal contexts. The study focuses on the application of LLMs, particularly LLAMA-2, Claude AI, and FLAN-T5-Base, for interpreting and analysing complex legal documents in finance. The aim is to evaluate the system’s effectiveness in providing accurate legal judgments and insights. The comparative analysis of these LLMs shows that LegalMind-GPT, powered by these models, significantly improves the accuracy and efficiency of legal analysis in the finance domain. | NaN | 979-8-3503-8427-7 | 10.1109/TQCEBT59414.2024.10545179 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10545179 | LegalMind-GPT;Finance sector;Large Language Models (LLMs);Legal Judgment Query System;LLAMA-2;Claude AI;FLAN-T5-Base;Legal documents;Legal analysis;Comparative analysis;Financial legal contexts;Legal insights;Research paper;Technology integration;Natural language processing;Artificial intelligence;Evaluation | Analytical models;Text analysis;Quantum computing;Law;Finance;System integration;Market research | NaN | NaN | NaN | 20.0 | IEEE | 5 Sep 2024 | NaN | NaN | IEEE | IEEE Conferences |
| Justice AI: Legal Case Retrieval Using Dense Passage Retrieval | Y. -C. Park; H. Lee; J. Lee | Department of Artificial Intelligence, Chung-Ang University, Seoul, Republic of Korea; Department of Artificial Intelligence, Chung-Ang University, Seoul, Republic of Korea; Department of Artificial Intelligence, Chung-Ang University, Seoul, Republic of Korea | 2025 IEEE International Conference on Consumer Electronics (ICCE) | 26 Mar 2025 | 2025 | NaN | NaN | 1 | 6 | In contemporary society, resolving legal issues effectively requires precise knowledge and expert assistance, yet accessing and utilizing legal information remains challenging for many. Justice AI addresses this gap using advanced Dense Passage Retrieval (DPR) technology, implemented with the BERT-based KoBERT model and the GPT-based LCube model. KoBERT, optimized for Korean legal texts, ensures high relevance and comprehension of complex legal terms, while LCube enhances understanding and generates natural language responses for deeper contextual insight. The system achieved a cosine similarity score of 0.9002, demonstrating excellent relevance in retrieved results. Performance evaluation using Precision, Recall, and F1 Score yielded metrics of 0.42, 1.0, and 0.5915, respectively, for LCube. Although tailored for Korean legal texts, the underlying technology showcases strong adaptability for diverse legal systems, positioning Justice AI as a globally applicable tool with potential to transform legal research and services. | 2158-4001 | 979-8-3315-2116-5 | 10.1109/ICCE63647.2025.10930108 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10930108 | Dense Passage Retrieval;Legal Case Retrieval;Transformer Model | Performance evaluation;Accuracy;Law;Scalability;Natural languages;Transforms;Transformers;Reliability;Artificial intelligence;Consumer electronics | NaN | NaN | NaN | 18.0 | IEEE | 26 Mar 2025 | NaN | NaN | IEEE | IEEE Conferences |
| LAWBOTS: Utilization of AI Chatbots for Legal Advising in the Philippines | R. B. Sedilla; A. E. T. Despues; K. C. O. Ty; C. R. Ochoa; L. M. Wong; M. J. C. Samonte | School of Information Technology, Mapua University, Manila, Philippines; School of Industrial Engineering and Engineering Management, Mapua University, Manila, Philippines; School of Mechanical and Manufacturing Engineering, Mapua University, Manila, Philippines; School of Industrial Engineering and Engineering Management, Mapua University, Manila, Philippines; School of Civil, Environmental, and Geological Engineering, Mapua University, Manila, Philippines; School of Information Technology, Mapua University, Makati, Philippines | 2024 IEEE 12th International Conference on Information, Communication and Networks (ICICN) | 2 Dec 2024 | 2024 | NaN | NaN | 594 | 600 | Artificial intelligence is reshaping global industries, optimizing processes, and fostering innovation across healthcare, finance, and education sectors. Chatbots, a popular AI form, are widely embraced for their ability to deliver instant responses, streamline customer service, and provide 24/7 assistance in the digital era. With the benefits chatbots provide, chatbots for legal advising currently exist and are developing, aiming to provide efficient legal assistance and guidance for users to navigate legal complexities. This paper explores the potential use of AI chatbot technology for legal advising activities or “Lawbots” in the Philippines by exploring existing legal advising chatbots, their effectiveness and potential in shaping the legal advising industry, and the public perception regarding its innovation. This paper also examines Filipinos' perception of Lawbots regarding the technology's benefits, challenges, and impact on the Philippines, providing knowledge of its acceptance and potential implementation. | NaN | 979-8-3503-5580-2 | 10.1109/ICICN62625.2024.10761649 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10761649 | AI-integration;chatbot;legal advising;public perception;Philippines | Industries;Surveys;Technological innovation;Law;Navigation;Customer services;Education;Finance;Medical services;Chatbots | NaN | NaN | NaN | 29.0 | IEEE | 2 Dec 2024 | NaN | NaN | IEEE | IEEE Conferences |
| Ensemble Learning Methods for Legal Processing Tasks in ALQAC 2022 | H. N. Trung; S. N. Truong | VNU-HCM, Ho Chi Minh University of Science, Ho Chi Minh City, Vietnam; VNU-HCM, Ho Chi Minh University of Science, Ho Chi Minh City, Vietnam | 2022 14th International Conference on Knowledge and Systems Engineering (KSE) | 21 Nov 2022 | 2022 | NaN | NaN | 1 | 5 | Automated Legal Question Answering Competition is an annual competition to find the best solution to automatically answer legal questions based on well-known statute laws in the Vietnamese Language. In this paper, we will demonstrate how to solve the problems posed by ALQAC 2022, using BERT and its variants as a backbone network. In addition, we also study using tf-idf and BM-25 to rank the relevance of legal documents. At the same time, this publication also show how to enhance training data to solve the problem of limited training data. | 2694-4804 | 978-1-6654-5281-6 | 10.1109/KSE56063.2022.9953756 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9953756 | Deep Learning;Question Answering;Legal Text Processing | Knowledge engineering;Law;Bit error rate;Training data;Multitasking;Question answering (information retrieval);Reliability | NaN | 2.0 | NaN | 25.0 | IEEE | 21 Nov 2022 | NaN | NaN | IEEE | IEEE Conferences |
| LDAA: Legal Documents Automation and Assistance | Aditya; A. Tomar; P. Mishra; S. Pandey; D. Kamboj; K. K. Gola | KIET Group of Institutions, India; KIET Group of Institutions, India; KIET Group of Institutions, India; KIET Group of Institutions, India; KIET Group of Institutions, India; COER University, India | 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit) | 9 Apr 2025 | 2024 | NaN | NaN | 1224 | 1229 | The traditional legal documentation can be pretty complex and intimidating, requiring the services of lawyers and legal experts, which are expensive and mostly out of reach for many people. As part of that challenge, the proposed solution would, therefore, try to develop automation in the development of legal documents by finetuning pre-trained open-source Large Language Models like Llama3 or Gemma series into This groundbreaking “Legal Documents Automation and Assistance (LDAA)” has been designed to help illiterate, underprivileged rural people as it automates and makes accessible with simplified process of creating legal documents. It is user-friendly, efficient, and integrates AI technology with legal expertise to provide accurate and personalized legal guidance tailored to user specific needs. | NaN | 979-8-3503-7971-6 | 10.1109/GlobalAISummit62156.2024.10947941 | Department of Information Technology, KIET Group of Institutions; | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10947941 | LLMs;LDAA;Automation | Access control;Automation;Accuracy;Law;Large language models;Training data;Documentation;Encryption | NaN | NaN | NaN | 24.0 | IEEE | 9 Apr 2025 | NaN | NaN | IEEE | IEEE Conferences |
| AI Legal Assistant for IPC | A. Dutta; K. K. Sarma; Y. S. P. | Dept. of AI&ML, BIT, Bengaluru, India; Dept. of AI&ML, BIT, Bengaluru, India; Dept. of AI&ML, BIT, Bengaluru, India | 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS) | 1 Jan 2025 | 2024 | NaN | NaN | 1 | 5 | The legal framework is a critical component of societal structure, yet its complexity often leaves individuals struggling to understand their rights and obligations. This paper introduces an advanced NLP-based chatbot designed to enhance legal accessibility and comprehension, focusing on the Indian Penal Code (IPC). The proposed system integrates Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) techniques to provide precise and contextually relevant legal information. The chatbot utilizes NLP algorithms for interpreting legal texts and generating user-friendly responses, thus facilitating a better understanding of legal articles and statutes. Streamlit is employed to create an interactive user interface, ensuring a seamless experience for users seeking legal advice. The implementation of this system aims to bridge the gap between complex legal language and public understanding, improve the efficiency of accessing legal information, and support individuals and small businesses in navigating legal challenges. This approach is anticipated to significantly enhance legal awareness and accessibility, contributing to a more informed and legally savvy society. | 2767-1097 | 979-8-3315-0546-2 | 10.1109/CSITSS64042.2024.10817061 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10817061 | Natural Language Processing;Large Language Models;Retrieval-Augmented Generation;Legal Information Systems;Indian Penal Code;Chatbot Technology;Streamlit;NLP Algorithms | Codes;Law;Navigation;Large language models;Soft sensors;Retrieval augmented generation;User interfaces;Chatbots;Real-time systems;Business | NaN | NaN | NaN | 20.0 | IEEE | 1 Jan 2025 | NaN | NaN | IEEE | IEEE Conferences |
| Interactive Legal Assistance System using Large Language Models | K. K; P. T; O. V. G; D. J; S. S. S | Dept. of Computer Science and Engineering, Sri Venkateswara College of Engineering, Anna University, Chennai, India; Dept. of Computer Science and Engineering, Sri Venkateswara College of Engineering, Anna University, Chennai, India; Dept. of Computer Science and Engineering, Sri Venkateswara College of Engineering, Anna University, Chennai, India; Zoho Corporation Private Limited, Chennai, India; Dept. of Computer Science and Engineering, Sri Venkateswara College of Engineering, Anna University, Chennai, India | 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) | 23 Oct 2024 | 2024 | NaN | NaN | 931 | 937 | There are a vast number of legal documents pertaining to various sectors in India including India’s Food Safety Regulations which is governed by the Food Safety and Standards Authority of India (FSSAI). For the individuals without legal expertise, understanding the context of these documents would be challenging. To address this issue, a Retrieval Augmented Generation (RAG) chatbot specially tailored to assist the common people in understanding the information provided in Food Safety Documents is introduced. The proposed work is developed with the emerging advancements in Large Language Models (LLMs) like Generative Pre-trained Transformers (GPTs) and Llama3. The system provides a user - friendly interface enabling users to get clarifications either by raising query or by requesting a summary of a specific section in the document. Crucially, the chatbot operates in both Tamil and English, ensuring accessibility and ease of understanding for users preferring their native language. This bridges the gap between legal complexities and public comprehension while leveraging the importance of linguistic accessibility. | 2768-0673 | 979-8-3503-7642-5 | 10.1109/I-SMAC61858.2024.10714868 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10714868 | Retrieval Augmented Generation;Large Language Models;Legal Assistance;Generative AI;IndicTrans2 | Analytical models;Law;Large language models;Linguistics;Chatbots;Transformers;Regulation;Complexity theory;Standards | NaN | NaN | NaN | 15.0 | IEEE | 23 Oct 2024 | NaN | NaN | IEEE | IEEE Conferences |
| Bettercall: AI based legal assistant | M. Dhore; A. Vimal; A. Agrawal; R. Bajaj; R. Barde | Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, India; Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, India; Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, India; Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, India; Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, India | 2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN) | 9 Sep 2024 | 2024 | NaN | NaN | 248 | 256 | This research introduces an AI-based chat-bot designed to enhance access to legal and judicial information in India. The system leverages advanced natural language processing techniques to transform legal texts into vector embeddings, facilitating semantic search capabilities that improve the user experience in retrieving relevant legal information. The chat-bot aims to provide primary legal aid, support informed decision-making, offer instant access to legal rights and information, and promote legal awareness among users. The methodology includes rigorous data collection, cleaning, and pre-processing processes to ensure high accuracy and reliability. The research also navigates to a potential implementation of the solution and addresses the complexities, results and challenges faced by the authors. The research highlights the novelty of the approach and addresses existing gaps in scalability, multilingual support, and domain coverage, presenting a comprehensive system ready for real-world deployment. | NaN | 979-8-3503-6717-1 | 10.1109/ICIPCN63822.2024.00048 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10660894 | Vector Embeddings;Semantic Searching;Web Scrapping;Legal Informatics | Accuracy;Law;Navigation;Semantic search;Scalability;Transforms;Chatbots | NaN | 1.0 | NaN | 15.0 | IEEE | 9 Sep 2024 | NaN | NaN | IEEE | IEEE Conferences |
| CHRExpert: An AI-Driven Court of Human Rights Expert Assistant for Legal Practitioners Utilizing Transformer Models | Y. Al-Shareef | College of Law and Political Science, King Saud University, Riyadh, Saudi Arabia | IEEE Access | 7 Mar 2025 | 2025 | 13 | NaN | 41097 | 41110 | The growing complexity of human rights litigation, combined with the increasing volume of legal documents, presents significant challenges for legal professionals in efficiently managing case preparation and decision-making. This paper introduces CHRExpert, an Artificial Intelligence (AI)-driven legal assistant designed to support law practitioners in analyzing judicial decisions and predicting case outcomes. Utilizing a Pretrained Generative Transformer (GPT) with 6 billion parameters, fine-tuned on the European Court of Human Rights (ECHR) dataset, CHRExpert automates legal document analysis by identifying key legal provisions, performing statutory interpretation, and applying analogical reasoning to suggest legal strategies. The system is designed to support practitioners in aligning their legal arguments with judicial reasoning, particularly in post-litigation analysis. Evaluations based on final judgments demonstrated an accuracy of 83% in predicting outcomes for cases involving Articles 3, 6, and 8 of the European Convention on Human Rights, with an average AUC of 0.93. Additionally, CHRExpert reduced the time required for case preparation by 40%, streamlining legal workflows and improving efficiency. This paper demonstrates the potential practical application of AI to enhance legal processes by offering accurate predictions and experiential insights, making it a valuable tool for human rights litigation. | 2169-3536 | NaN | 10.1109/ACCESS.2025.3547763 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10909511 | AI legal assistant;human rights litigation;transformer models;GPT-J;case outcome prediction;legal document analysis | Law;Transformers;Artificial intelligence;Mathematical models;Europe;Decision making;Accuracy;Vectors;Encoding;Vocabulary | NaN | NaN | NaN | 46.0 | CCBY | 4 Mar 2025 | NaN | NaN | IEEE | IEEE Journals |
| Unlocking the Potential of Large Language Models in Legal Discourse: Challenges, Solutions, and Future Directions | M. Kaoutar; B. J. Chaima; B. Omar; B. Outmane | Computer and system engineering laboratory, FSTG, Cadi Ayyad University, Marrakesh, Morocco; Computer and system engineering laboratory, FSTG, Cadi Ayyad University, Marrakesh, Morocco; Computer and system engineering laboratory, FSTG, Cadi Ayyad University, Marrakesh, Morocco; Computer and system engineering laboratory, FSTG, Cadi Ayyad University, Marrakesh, Morocco | 2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS) | 25 Nov 2024 | 2024 | NaN | NaN | 1 | 7 | The rise of Large Language Models (LLMs) has been remarkable, especially exemplified by the achievements of systems such as ChatGPT and Google's Bard. Both specialized and general users are warmly embracing these potent tools, indicating their increasing integration into everyday life. Nevertheless, challenges persist in their widespread adoption, particularly within specialized fields where they necessitate meticulous fine-tuning and access to high-quality data. Additionally, their lack of interpretability further complicates matters, often relegating them to the status of “black boxes”. Within the legal domain, LLMs harbor transformative potential but encounter obstacles due to legal hallucinations. This research delves into these hallucinations through a distinct set of legal queries pertaining to Canadian tax law, drawing comparisons between state-of-the-art LLMs. Its objective is to illuminate their efficacy in legal discourse and specialized domains, capitalizing on their broad knowledge base. The research advocates for fine-tuning as a potential solution, stressing the significance of domain-specific LLMs and delineating methods for their development. This includes considerations such as dataset curation, preprocessing techniques, model selection, and adherence to regulatory requirements, encompassing the creation of domain-specific vocabularies. Practical implementation entails the generation of domain-specific LLMs tailored for legal tasks such as research, information retrieval, and question answering. Despite inherent limitations, the study proposes avenues for enhancement and underscores the significance of LLMs utilization in legal services. This contributes to the evolution of natural language processing technology within the legal realm. | NaN | 979-8-3503-5120-0 | 10.1109/ICDS62089.2024.10756345 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10756345 | Large Language Models;Juridical Questions;Legal Discourse;Natural Language Processing;Legal Education;AI in Law | Productivity;Vocabulary;Technological innovation;Law;Navigation;Large language models;Semantics;Knowledge based systems;Finance;Question answering (information retrieval) | NaN | 1.0 | NaN | 31.0 | IEEE | 25 Nov 2024 | NaN | NaN | IEEE | IEEE Conferences |
| Large Language Models (LLM) in Industry: A Survey of Applications, Challenges, and Trends | Z. Chkirbene; R. Hamila; A. Gouissem; U. Devrim | Electrical Engineering, Qatar University, Qatar; Electrical Engineering, Qatar University, Qatar; College of Computing and Information Technology, University of Doha for Science and Technology, Qatar; KINDI Center for Computing Research, College of Engineering, Qatar University, Qatar | 2024 IEEE 21st International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET) | 7 Jan 2025 | 2024 | NaN | NaN | 229 | 234 | Large Language Models (LLMs) are transforming industries by automating tasks such as text generation, data analysis, and customer interactions. Their impact spans various sectors, including finance, healthcare, legal services, and education’ where they streamline operations and enhance decision-making. Despite these advantages, the adoption of LLMs is hindered by challenges such as high computational costs, data privacy concerns, and the lack of explainability. Existing surveys on LLMs primarily focus on their capabilities and applications, emphasizing their role in generating human-like text, processing unstructured data, and supporting decision-making. However, these studies also highlight the significant limitations of LLMs, particularly around computational expense, privacy, and the “black box” nature of their outputs, which restrict their use in critical, regulated industries. This paper builds on prior work by exploring emerging solutions to address these challenges. It examines innovations such as domain-specific LLMs, LLM-as-a-Service (LLMaaS), and advancements in explainable AI (XAI) to enhance transparency and accessibility. The paper provides practical insights into how businesses can strategically adopt LLMs while mitigating risks, making them more viable for broader industry application. | 1949-4106 | 979-8-3503-7807-8 | 10.1109/HONET63146.2024.10822885 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10822885 | Large Language Models;automation;bias;data privacy;industry applications | Industries;Surveys;Technological innovation;Data privacy;Law;Large language models;Decision making;Finance;Medical services;Business | NaN | NaN | NaN | 16.0 | IEEE | 7 Jan 2025 | NaN | NaN | IEEE | IEEE Conferences |
| Too Legal; Didn't Read (TLDR): Summarization of Court Opinions | A. Ghimire; R. Shrestha; J. Edwards | Department of Computer Science, Utah State University, Logan, Utah; Department of Computer Science, Utah State University, Logan, Utah; Department of Computer Science, Utah State University, Logan, Utah | 2023 Intermountain Engineering, Technology and Computing (IETC) | 20 Jun 2023 | 2023 | NaN | NaN | 164 | 169 | Access to justice remains one of the fundamental principles of the rule of law. The original US constitution was four pages and a few thousand words long [1]. But with new additions to laws and bills every year, understanding legal texts or navigating through them in itself requires specialized training and skills. Most of the legal processes and arguments rely on precedents from the past and the previous interpretation of laws. Thus, having access to the last case documents is really important and convenient. Unfortunately, these case documents are often very long, and parsing through them is time-consuming. Case summaries are meant to be of help but are written by experienced professionals and are expensive and labor-intensive. In this article we propose (Natural Language Processing) NLP based legal text summarization approach that can help professionals in writing summaries quickly with a minimum effort or create summaries automatically | NaN | 979-8-3503-3590-3 | 10.1109/IETC57902.2023.10152119 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10152119 | Legal Text Summarization;Natural Language Processing;Court Opinion Summarization;Transformer Based Summarization | Training;Law;Navigation;Writing;Natural language processing;Internet;Data mining | NaN | 1.0 | NaN | 32.0 | IEEE | 20 Jun 2023 | NaN | NaN | IEEE | IEEE Conferences |
| Classifying European Court of Human Rights Cases Using Transformer-Based Techniques | A. S. Imran; H. Hodnefjeld; Z. Kastrati; N. Fatima; S. M. Daudpota; M. A. Wani | Department of Computer Science, Norwegian University of Science and Technology (NTNU), Gjøvik, Norway; Department of Computer Science, Norwegian University of Science and Technology (NTNU), Gjøvik, Norway; Department of Informatics, Linnaeus University, Växjö, Sweden; Department of Computer Science, Sukkur IBA University, Sukkur, Pakistan; Department of Computer Science, Sukkur IBA University, Sukkur, Pakistan; EIAS Data Science Laboratory, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia | IEEE Access | 8 Jun 2023 | 2023 | 11 | NaN | 55664 | 55676 | In the field of text classification, researchers have repeatedly shown the value of transformer-based models such as Bidirectional Encoder Representation from Transformers (BERT) and its variants. Nonetheless, these models are expensive in terms of memory and computational power but have not been utilized to classify long documents of several domains. In addition, transformer models are also often pre-trained on generalized languages, making them less effective in language-specific domains, such as legal documents. In the natural language processing (NLP) domain, there is a growing interest in creating newer models that can handle more complex input sequences and domain-specific languages. Keeping the power of NLP in mind, this study proposes a legal documentation classifier that classifies the legal document by using the sliding window approach to increase the maximum sequence length of the model. We used the ECHR (European Court of Human Rights) publicly available dataset which to a large extent is imbalanced. Therefore, to balance the dataset we have scrapped the case articles from the web and extracted the data. Then, we employed conventional machine learning techniques such as SVM, DT, NB, AdaBoost, and transformer-based neural networks models including BERT, Legal-BERT, RoBERTa, BigBird, ELECTRA, and XLNet for the classification task. The experimental findings show that RoBERTa outperformed all the mentioned BERT versions by obtaining precision, recall, and F1-score of 89.1%, 86.2%, and 86.7%, respectively. While from conventional machine learning techniques, AdaBoost outclasses SVM, DT, and NB by achieving scores of 81.9%, 81.5%, and 81.7% for precision, recall, and F1-score, respectively. | 2169-3536 | NaN | 10.1109/ACCESS.2023.3279034 | Department of Computer Science (IDI), Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU), Gjøvik, Norway; Direktoratet for Internasjonalisering og Kvalitetsutvikling i høyere utdanning (DIKU) through the Curricula Development and Capacity Building in Applied Computer Science for Pakistani Higher Education Institutions (CONNECT) Project(grant numbers:NORPART-2021/10502); | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130544 | Legal documents classification;European court of human rights (ECHR) dataset;natural language processing;transformers;BERT;BigBird;ELECTRA;XLNet;legal-BERT | Law;Transformers;Bit error rate;Natural language processing;Legal aspects;Europe;Mathematical models | NaN | 3.0 | NaN | 41.0 | CCBYNCND | 22 May 2023 | NaN | NaN | IEEE | IEEE Journals |
| EMPOWER-KARE: Deep Prompt Learning for Knowledge-aware Response Generation in Clinical Counseling and Legal Support Conversations | P. Priya; A. M. Tripathi; D. Varshney; M. Firdaus; A. Ekbal | Indian Institute of Technology Patna, Bihar, India; Indian Institute of Technology Patna, Bihar, India; Indian Institute of Technology Patna, Bihar, India; Indian Institute of Technology (Indian School of Mines) Dhanbad, Jharkhand, India; Indian Institute of Technology Patna, Bihar, India | IEEE Transactions on Artificial Intelligence | NaN | 2025 | PP | 99.0 | 1 | 10 | In this paper, we investigate the effect of external domain knowledge while generating responses in clinical counseling and legal support conversations for crime victims. To facilitate this task, we propose EMPOWER, a novel dual-tier dEep proMPt learning framework for knOWledge-aware rEsponse geneRation task. EMPOWER first learns the knowledge-attributed deep prompt to generate the relevant knowledge grounded on conversational context and, in the next step, it learns response-attributed deep prompt grounded on conversational context and the learned knowledge-attributed deep prompt to guide the knowledge-aware response generation. To develop EMPOWER, we introduce KARE, a novel dataset consisting of 5,000 Knowledge-grounded clinicAl counseling and legal suppoRt convErsations, specifically focused on crime victims. Experiments demonstrate that our proposed method significantly outperforms the state-of-the-art baseline approaches, achieving improvements of 11.50% in BLEU-4, 28.5% in Knowledge-F1, and 11.6% in BERTScore on the proposed dataset. Further analysis also shows the promising abilities of EMPOWER for knowledge-aware response generation task in clinical counseling and legal support conversations. | 2691-4581 | NaN | 10.1109/TAI.2025.3548628 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10910093 | Dialogue Systems;Clinical Counseling and Legal Support Conversations;Knowledge-awareness;Prompt Learning | Employee welfare;Law;Artificial intelligence;Oral communication;Depression;Natural language processing;Mental health;Training;Mood;Electronic mail | NaN | NaN | NaN | NaN | IEEE | 5 Mar 2025 | NaN | NaN | IEEE | IEEE Early Access Articles |
| Generative vs Intent-based Chatbot for Judicial Advice | M. Wyawahare; S. Roy; S. Zanwar | Dept. of Electronics and Telecommunication Engineering, Vishwakarma Institute of Technology, Pune, India; Dept. of Electronics and Telecommunication Engineering, Vishwakarma Institute of Technology, Pune, India; Dept. of Electronics and Telecommunication Engineering, Vishwakarma Institute of Technology, Pune, India | 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) | 24 Apr 2024 | 2024 | 2 | NaN | 1 | 6 | The advent of artificial intelligence has led to transformative changes in various industries, and the legal sector is no exception. One of the remarkable applications within this domain is the development of AI-powered chatbots tailored for providing judicial advice. The paper presents a generative chatbot and an Intent-based chatbot aimed at providing judicial advice to Indians and explore and compare the two approaches on the basis of various factors such as nature of responses, response quality, handling changing scenarios, training and data requirements and user experience in the context of offering judicial advice specific to Indian laws. The underlying technologies of the chatbots along with their advantages, limitations, and potential use cases have been discussed. For the Intent-based chatbot, 36 intents based on various Indian criminal and civil laws were created with appropriate chatbot responses. Conversely, in the case of the generative chatbot, a custom dataset of 100 conversations was curated. By analyzing their strengths and weaknesses, this paper seeks to shed light on the suitability of each approach for addressing the complexities of legal queries and assisting users in navigating the intricate landscape of legal matters. | NaN | 979-8-3503-6052-3 | 10.1109/IATMSI60426.2024.10502550 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10502550 | artificial intelligence;chatbot;Indian laws;judicial advice | Training;Industries;Technological innovation;Law;Navigation;Oral communication;Chatbots | NaN | NaN | NaN | 14.0 | IEEE | 24 Apr 2024 | NaN | NaN | IEEE | IEEE Conferences |
| Proposal for Enhancing Legal Advisory Services in the Montenegrin Banking Sector with Artificial Intelligence | I. Bošković; V. Tabaš | IT Advanced Services, Podgorica, Montenegro; Čikom, Podgorica, Montenegro | 2024 28th International Conference on Information Technology (IT) | 25 Mar 2024 | 2024 | NaN | NaN | 1 | 6 | This paper examines the integration of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) in improving legal advisory services within the Montenegrin banking industry. It explores the vectorization of regulatory documents using ADA-2 embedding model, the storage and management of these vectorized forms in Chroma DB, and the utilization of GPT-4 for processing relevant documents to generate user responses, providing insights into the use of artificial intelligence (AI) for legal advisement and financial education. | 2836-3744 | 979-8-3503-6961-8 | 10.1109/IT61232.2024.10475735 | NaN | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10475735 | NaN | Industries;Ethics;Law;Education;Banking;Proposals;Task analysis | NaN | 3.0 | NaN | 20.0 | IEEE | 25 Mar 2024 | NaN | NaN | IEEE | IEEE Conferences |
Scopus
| Authors | Author full names | Author(s) ID | Title | Year | Source title | Volume | Issue | Art. No. | Page start | Page end | Page count | Cited by | DOI | Link | Document Type | Publication Stage | Open Access | Source | EID |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Üveges I.; Vági R. | Üveges, István (57391635100); Vági, Renátó (57226807531) | 57391635100; 57226807531 | Laws Clearly: Large language models and plain language transformation | 2024 | Magyar Nyelvor | 148 | 5.0 | NaN | 696.0 | 716.0 | 20.0 | 0 | 10.38143/Nyr.2024.5.696 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204958128&doi=10.38143%2fNyr.2024.5.696&partnerID=40&md5=362c416412b9949cdcf25eeafcf1731a | Article | Final | All Open Access; Gold Open Access | Scopus | 2-s2.0-85204958128 |
| Trozze A.; Davies T.; Kleinberg B. | Trozze, Arianna (57383830700); Davies, Toby (55605786800); Kleinberg, Bennett (56518219900) | 57383830700; 55605786800; 56518219900 | Large language models in cryptocurrency securities cases: can a GPT model meaningfully assist lawyers? | 2024 | Artificial Intelligence and Law | NaN | NaN | NaN | NaN | NaN | NaN | 8 | 10.1007/s10506-024-09399-6 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189801902&doi=10.1007%2fs10506-024-09399-6&partnerID=40&md5=de547245c9d20ed36273fb12c7a05992 | Article | Article in press | All Open Access; Hybrid Gold Open Access | Scopus | 2-s2.0-85189801902 |
| Dahan S.; Bhambhoria R.; Liang D.; Zhu X. | Dahan, Samuel (55360390900); Bhambhoria, Rohan (57411317300); Liang, David (57218955688); Zhu, Xiaodan (55696698900) | 55360390900; 57411317300; 57218955688; 55696698900 | OpenJustice.ai: A Global Open-Source Legal Language Model | 2023 | Frontiers in Artificial Intelligence and Applications | 379 | NaN | NaN | 387.0 | 390.0 | 3.0 | 1 | 10.3233/FAIA230995 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181167476&doi=10.3233%2fFAIA230995&partnerID=40&md5=72c04b79b7780cbe00fffec85ea0b4cc | Conference paper | Final | All Open Access; Hybrid Gold Open Access | Scopus | 2-s2.0-85181167476 |
| NaN | NaN | NaN | Legal Knowledge and Information Systems - JURIX 2024: 37th Annual Conference | 2024 | Frontiers in Artificial Intelligence and Applications | 395 | NaN | NaN | NaN | NaN | 418.0 | 0 | NaN | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217178018&partnerID=40&md5=baead58c797596affd8b69767ab902bd | Conference review | Final | NaN | Scopus | 2-s2.0-85217178018 |
| NaN | NaN | NaN | AI4AJ 2023 - Proceedings of the ICAIL 2023 Workshop on Artificial Intelligence for Access to Justice, co-located with 19th International Conference on AI and Law, ICAIL 2023 | 2023 | CEUR Workshop Proceedings | 3435 | NaN | NaN | NaN | NaN | 66.0 | 0 | NaN | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167449052&partnerID=40&md5=152c44738cee576c34805fa9c5759c2b | Conference review | Final | NaN | Scopus | 2-s2.0-85167449052 |
| Tan J.; Westermann H.; Benyekhlef K. | Tan, Jinzhe (58529232000); Westermann, Hannes (57210697734); Benyekhlef, Karim (36711201100) | 58529232000; 57210697734; 36711201100 | ChatGPT as an Artificial Lawyer? | 2023 | CEUR Workshop Proceedings | 3435 | NaN | NaN | NaN | NaN | NaN | 2 | NaN | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167430056&partnerID=40&md5=3e1e0b6733ae83f70913564f74d41d7d | Conference paper | Final | NaN | Scopus | 2-s2.0-85167430056 |
| Steenhuis Q.; Westermann H. | Steenhuis, Quinten (57210697729); Westermann, Hannes (57210697734) | 57210697729; 57210697734 | Getting in the Door: Streamlining Intake in Civil Legal Services with Large Language Models | 2024 | Frontiers in Artificial Intelligence and Applications | 395 | NaN | NaN | 155.0 | 167.0 | 12.0 | 0 | 10.3233/FAIA241242 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217088251&doi=10.3233%2fFAIA241242&partnerID=40&md5=d0781282bf59d85a4931639dfe022978 | Conference paper | Final | All Open Access; Green Open Access | Scopus | 2-s2.0-85217088251 |
| Westermann H.; Savelka J.; Benyekhlef K. | Westermann, Hannes (57210697734); Savelka, Jaromir (54783405100); Benyekhlef, Karim (36711201100) | 57210697734; 54783405100; 36711201100 | LLMediator: GPT-4 Assisted Online Dispute Resolution | 2023 | CEUR Workshop Proceedings | 3435 | NaN | NaN | NaN | NaN | NaN | 0 | NaN | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167439331&partnerID=40&md5=036bf8b565838486f39a549a714691bc | Conference paper | Final | NaN | Scopus | 2-s2.0-85167439331 |
| Feretzakis G.; Verykios V.S. | Feretzakis, Georgios (57191371275); Verykios, Vassilios S. (6602452651) | 57191371275; 6602452651 | Trustworthy AI: Securing Sensitive Data in Large Language Models | 2024 | AI (Switzerland) | 5 | 4.0 | NaN | 2773.0 | 2800.0 | 27.0 | 7 | 10.3390/ai5040134 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213474320&doi=10.3390%2fai5040134&partnerID=40&md5=607440ea80f6a9afd62374118c826dbf | Review | Final | All Open Access; Gold Open Access; Green Open Access | Scopus | 2-s2.0-85213474320 |
| Arbel Y.A.; Becher S.I. | Arbel, Yonathan A. (57190963030); Becher, Shmuel I. (23088172200) | 57190963030; 23088172200 | Contracts in the Age of Smart Readers | 2022 | George Washington Law Review | 90 | 1.0 | NaN | 83.0 | 146.0 | 63.0 | 11 | NaN | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124960585&partnerID=40&md5=557f1a3a0014ff3d95ba186e5b1853fb | Article | Final | NaN | Scopus | 2-s2.0-85124960585 |
| Olimid A.P.; Georgescu C.M.; Olimid D.A. | Olimid, Anca Parmena (56023059100); Georgescu, Cătălina Maria (56601181900); Olimid, Daniel Alin (55507404700) | 56023059100; 56601181900; 55507404700 | LEGAL ANALYSIS OF EU ARTIFICIAL INTELLIGENCE ACT (2024): INSIGHTS FROM PERSONAL DATA GOVERNANCE AND HEALTH POLICY | 2024 | Access to Justice in Eastern Europe | 7 | 4.0 | NaN | 120.0 | 142.0 | 22.0 | 0 | 10.33327/AJEE-18-7.4-a000103 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210267603&doi=10.33327%2fAJEE-18-7.4-a000103&partnerID=40&md5=1fd26bdd239a975090d19dddf2c15980 | Article | Final | All Open Access; Gold Open Access | Scopus | 2-s2.0-85210267603 |
| Khoon Lim S. | Khoon Lim, Siok (59722956600) | 59722956600 | Legal Practices Redefined: Transforming Law Firm Operations and Management with AI | 2025 | The Rise of Intelligent Machines: A Multi-Disciplinary Perspective from Industry and Impact on Higher Education | NaN | NaN | NaN | 134.0 | 159.0 | 25.0 | 0 | 10.1201/9781003469551-6 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001782148&doi=10.1201%2f9781003469551-6&partnerID=40&md5=c1ad8b3a859864e34b253fd768183bad | Book chapter | Final | NaN | Scopus | 2-s2.0-105001782148 |
| Steenhuis Q.; Willey B.; Colarusso D. | Steenhuis, Quinten (57210697729); Willey, Bryce (58721961100); Colarusso, David (58722484100) | 57210697729; 58721961100; 58722484100 | Beyond Readability with RateMyPDF | 2023 | 19th International Conference on Artificial Intelligence and Law, ICAIL 2023 - Proceedings of the Conference | NaN | NaN | NaN | 287.0 | 296.0 | 9.0 | 2 | 10.1145/3594536.3595146 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177867545&doi=10.1145%2f3594536.3595146&partnerID=40&md5=5ec9bad155922f358a5de21fdce57084 | Conference paper | Final | NaN | Scopus | 2-s2.0-85177867545 |
| Louis A.; van Dijck G.; Spanakis G. | Louis, Antoine (57243706700); van Dijck, Gijs (39162293200); Spanakis, Gerasimos (35749092700) | 57243706700; 39162293200; 35749092700 | Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models | 2024 | Proceedings of the AAAI Conference on Artificial Intelligence | 38 | 20.0 | NaN | 22266.0 | 22275.0 | 9.0 | 40 | 10.1609/aaai.v38i20.30232 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189622802&doi=10.1609%2faaai.v38i20.30232&partnerID=40&md5=f35e03c11ff2071c6d50c516aac8beee | Conference paper | Final | All Open Access; Gold Open Access; Green Open Access | Scopus | 2-s2.0-85189622802 |
| NaN | NaN | NaN | 5th International Conference on Artificial Intelligence in HCI, AI-HCI 2024, held as part of the 26th International Conference on Human-Computer Interaction, HCI International 2024 | 2024 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 14736 LNAI | NaN | NaN | NaN | NaN | 479.0 | 0 | NaN | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195462297&partnerID=40&md5=99422dfcb8af2fd67ac8407aeb0f3633 | Conference review | Final | NaN | Scopus | 2-s2.0-85195462297 |
| Vajrobol V.; Aggarwal N.; Saxena G.J.; Singh S.; Pundir A. | Vajrobol, Vajratiya (58138624400); Aggarwal, Nitisha (58871677700); Saxena, Geetika Jain (55779860500); Singh, Sanjeev (57216140046); Pundir, Amit (57206726943) | 58138624400; 58871677700; 55779860500; 57216140046; 57206726943 | Transforming SEO in the Era of Generative AI: Challenges, Opportunities, and Future Prospects | 2024 | Revolutionizing the AI-Digital Landscape: A Guide to Sustainable Emerging Technologies for Marketing Professionals | NaN | NaN | NaN | 86.0 | 100.0 | 14.0 | 1 | 10.4324/9781032688305-6 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193370911&doi=10.4324%2f9781032688305-6&partnerID=40&md5=3f825e65fc0d432e8e33532b8de00fb0 | Book chapter | Final | NaN | Scopus | 2-s2.0-85193370911 |
| Shapiro J.; Lyakhovitsky A. | Shapiro, Jonathan (26021519500); Lyakhovitsky, Anna (6507721208) | 26021519500; 6507721208 | Revolutionizing teledermatology: Exploring the integration of artificial intelligence, including Generative Pre-trained Transformer chatbots for artificial intelligence-driven anamnesis, diagnosis, and treatment plans | 2024 | Clinics in Dermatology | 42 | 5.0 | NaN | 492.0 | 497.0 | 5.0 | 4 | 10.1016/j.clindermatol.2024.06.020 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201286858&doi=10.1016%2fj.clindermatol.2024.06.020&partnerID=40&md5=82e623385c9044a28d673d6804707e8b | Article | Final | NaN | Scopus | 2-s2.0-85201286858 |
| Hua W.; Zhang Y.; Chen Z.; Li J.; Weber M. | Hua, Wenyue (57311348800); Zhang, Yuchen (58657017800); Chen, Zhe (58034444600); Li, Josie (58034099600); Weber, Melanie (58034610500) | 57311348800; 58657017800; 58034444600; 58034099600; 58034610500 | Mixed-domain Language Modeling for Processing Long Legal Documents | 2023 | NLLP 2023 - Natural Legal Language Processing Workshop 2023, Proceedings of the Workshop | NaN | NaN | NaN | 51.0 | 61.0 | 10.0 | 2 | NaN | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184995828&partnerID=40&md5=8b05149c149861cc9ef6b4159b9fc5d4 | Conference paper | Final | NaN | Scopus | 2-s2.0-85184995828 |
| Nay J.J.; Karamardian D.; Lawsky S.B.; Tao W.; Bhat M.; Jain R.; Lee A.T.; Choi J.H.; Kasai J. | Nay, John J. (56117999100); Karamardian, David (58486215800); Lawsky, Sarah B. (6506207825); Tao, Wenting (58486429000); Bhat, Meghana (58486640500); Jain, Raghav (58485587200); Lee, Aaron Travis (58486640600); Choi, Jonathan H. (56115340500); Kasai, Jungo (57216610880) | 56117999100; 58486215800; 6506207825; 58486429000; 58486640500; 58485587200; 58486640600; 56115340500; 57216610880 | Large language models as tax attorneys: a case study in legal capabilities emergence | 2024 | Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences | 382 | 2270.0 | 20230159 | NaN | NaN | NaN | 16 | 10.1098/rsta.2023.0159 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186140789&doi=10.1098%2frsta.2023.0159&partnerID=40&md5=e43de89be2d93775cb39d4a6e45b3dbc | Article | Final | All Open Access; Green Open Access; Hybrid Gold Open Access | Scopus | 2-s2.0-85186140789 |
| Chkirbene Z.; Hamila R.; Gouissem A.; Devrim U. | Chkirbene, Zina (55844084200); Hamila, Ridha (6603562710); Gouissem, Ala (55546448000); Devrim, Unal (59527008600) | 55844084200; 6603562710; 55546448000; 59527008600 | Large Language Models (LLM) in Industry: A Survey of Applications, Challenges, and Trends | 2024 | 2024 IEEE 21st International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024 | NaN | NaN | NaN | 229.0 | 234.0 | 5.0 | 0 | 10.1109/HONET63146.2024.10822885 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217420069&doi=10.1109%2fHONET63146.2024.10822885&partnerID=40&md5=bbf3532eddde4731a80c250e503cb36a | Conference paper | Final | NaN | Scopus | 2-s2.0-85217420069 |
| Socol De La Osa D.U.; Remolina N. | Socol De La Osa, David Uriel (57218922012); Remolina, Nydia (57209246724) | 57218922012; 57209246724 | Artificial intelligence at the bench: Legal and ethical challenges of informing - Or misinforming - Judicial decision-making through generative AI | 2024 | Data and Policy | 6 | NaN | e59 | NaN | NaN | NaN | 2 | 10.1017/dap.2024.53 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210937688&doi=10.1017%2fdap.2024.53&partnerID=40&md5=2320267e143dcf1b1405460fddd6228b | Article | Final | All Open Access; Gold Open Access | Scopus | 2-s2.0-85210937688 |
| Katz D.M.; Bommarito M.J.; Gao S.; Arredondo P. | Katz, Daniel Martin (57214283634); Bommarito, Michael James (6507511406); Gao, Shang (59279436300); Arredondo, Pablo (58909398400) | 57214283634; 6507511406; 59279436300; 58909398400 | GPT-4 passes the bar exam | 2024 | Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences | 382 | 2270.0 | 20230254 | NaN | NaN | NaN | 90 | 10.1098/rsta.2023.0254 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186143430&doi=10.1098%2frsta.2023.0254&partnerID=40&md5=0b39f0303457f7345f006969b8bfd5de | Article | Final | All Open Access; Green Open Access; Hybrid Gold Open Access | Scopus | 2-s2.0-85186143430 |
| Henderson P.; Krass M.S.; Zheng L.; Manning N.G.C.D.; Jurafsky D.; Ho D.E. | Henderson, Peter (57202852272); Krass, Mark S. (57222489300); Zheng, Lucia (57223962948); Manning, Neel Guha Christopher D. (58367450500); Jurafsky, Dan (6602872553); Ho, Daniel E. (25629650100) | 57202852272; 57222489300; 57223962948; 58367450500; 6602872553; 25629650100 | Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset | 2022 | Advances in Neural Information Processing Systems | 35 | NaN | NaN | NaN | NaN | NaN | 43 | NaN | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163202492&partnerID=40&md5=0677e918592c09745006c1046ead92e9 | Conference paper | Final | NaN | Scopus | 2-s2.0-85163202492 |
| Pereira F.V.; Frazão A.; Moreira V.P. | Pereira, Francielle Vasconcellos (59660885200); Frazão, Ana (59660885300); Moreira, Viviane P. (15769964500) | 59660885200; 59660885300; 15769964500 | Automatic Text Simplification for the Legal Domain in Brazilian Portuguese | 2025 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 15415 LNAI | NaN | NaN | 31.0 | 45.0 | 14.0 | 0 | 10.1007/978-3-031-79038-6_3 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219174450&doi=10.1007%2f978-3-031-79038-6_3&partnerID=40&md5=79ec4334990e98c7cda30f9e54361a4e | Conference paper | Final | NaN | Scopus | 2-s2.0-85219174450 |
| Terzidou K. | Terzidou, Kalliopi (57221263403) | 57221263403 | Generative AI systems in legal practice offering quality legal services while upholding legal ethics | 2025 | International Journal of Law in Context | NaN | NaN | NaN | NaN | NaN | NaN | 0 | 10.1017/S1744552325000047 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001945435&doi=10.1017%2fS1744552325000047&partnerID=40&md5=101270f71776974d1af82cfbc0c5c3f2 | Article | Article in press | NaN | Scopus | 2-s2.0-105001945435 |
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| Saadany H.; Breslin C.; Orăsan C.; Walker S. | Saadany, Hadeel (57212509470); Breslin, Catherine (59265876500); Orăsan, Constantin (8678677900); Walker, Sophie (58528833500) | 57212509470; 59265876500; 8678677900; 58528833500 | Automatic Linking of Judgements to UK Supreme Court Hearings | 2023 | EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Industry Track | NaN | NaN | NaN | 492.0 | 500.0 | 8.0 | 2 | 10.18653/v1/2023.emnlp-industry.47 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184656078&doi=10.18653%2fv1%2f2023.emnlp-industry.47&partnerID=40&md5=49a51cd9955bf86dd6ff120b50bd4ebf | Conference paper | Final | All Open Access; Hybrid Gold Open Access | Scopus | 2-s2.0-85184656078 |
| Burgess P.; Williams I.; Qu L.; Wang W. | Burgess, Paul (57191523722); Williams, Iwan (57823947800); Qu, Lizhen (57196124952); Wang, Weiqing (56336058500) | 57191523722; 57823947800; 57196124952; 56336058500 | Using Generative AI to Identify Arguments in Judges’ Reasons: Accuracy and Benefits for Students | 2024 | Law, Technology and Humans | 6 | 3.0 | NaN | 5.0 | 22.0 | 17.0 | 2 | 10.5204/lthj.3637 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210959791&doi=10.5204%2flthj.3637&partnerID=40&md5=f8bf42594fb5fc01b95a1d24e1a0ccd3 | Article | Final | All Open Access; Gold Open Access | Scopus | 2-s2.0-85210959791 |
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| Park S.H. | Park, Seong Ho (57049729900) | 57049729900 | Use of Generative Artificial Intelligence, Including Large Language Models Such as ChatGPT, in Scientific Publications: Policies of KJR and Prominent Authorities | 2023 | Korean Journal of Radiology | 24 | 8.0 | NaN | 715.0 | 718.0 | 3.0 | 23 | 10.3348/kjr.2023.0643 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165868103&doi=10.3348%2fkjr.2023.0643&partnerID=40&md5=1c55ec9efd164495ece7b6b28311cba7 | Editorial | Final | All Open Access; Green Open Access | Scopus | 2-s2.0-85165868103 |
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| Saadany H.; Breslin C.; Orăsan C.; Walker S. | Saadany, Hadeel (57212509470); Breslin, Catherine (59265876500); Orăsan, Constantin (8678677900); Walker, Sophie (58528833500) | 57212509470; 59265876500; 8678677900; 58528833500 | Better Transcription of UK Supreme Court Hearings | 2023 | CEUR Workshop Proceedings | 3435 | NaN | NaN | NaN | NaN | NaN | 0 | NaN | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167423213&partnerID=40&md5=4409c60158581a9037cfec90089c846b | Conference paper | Final | NaN | Scopus | 2-s2.0-85167423213 |
| Yuan M.; Kao B.; Wu T.-H.; Cheung M.M.K.; Chan H.W.H.; Cheung A.S.Y.; Chan F.W.H.; Chen Y. | Yuan, Mingruo (58476334900); Kao, Ben (35221592600); Wu, Tien-Hsuan (57204946775); Cheung, Michael M. K. (57221271908); Chan, Henry W. H. (58075507400); Cheung, Anne S. Y. (54400708000); Chan, Felix W. H. (16174337300); Chen, Yongxi (57202956714) | 58476334900; 35221592600; 57204946775; 57221271908; 58075507400; 54400708000; 16174337300; 57202956714 | Bringing legal knowledge to the public by constructing a legal question bank using large-scale pre-trained language model | 2024 | Artificial Intelligence and Law | 32 | 3.0 | NaN | 769.0 | 805.0 | 36.0 | 2 | 10.1007/s10506-023-09367-6 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164200862&doi=10.1007%2fs10506-023-09367-6&partnerID=40&md5=4ac8cc75913ec1acfd079c870a9f211a | Article | Final | NaN | Scopus | 2-s2.0-85164200862 |
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| Draper C.; Gillibrand N. | Draper, Chris (58529232900); Gillibrand, Nicky (58529233000) | 58529232900; 58529233000 | The Potential for Jurisdictional Challenges to AI or LLM Training Datasets | 2023 | CEUR Workshop Proceedings | 3435 | NaN | NaN | NaN | NaN | NaN | 0 | NaN | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167400514&partnerID=40&md5=e475972f7373afb58c7dce294fa8a916 | Conference paper | Final | NaN | Scopus | 2-s2.0-85167400514 |
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| Fournier G.; Linna D.W., Jr. | Fournier, Gregoire (57979368500); Linna, Daniel W. (57384845800) | 57979368500; 57384845800 | Structured Legal Argumentation with LLMs: A Study in Landlord-Tenant Law | 2024 | Frontiers in Artificial Intelligence and Applications | 395 | NaN | NaN | 369.0 | 371.0 | 2.0 | 0 | 10.3233/FAIA241272 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217079667&doi=10.3233%2fFAIA241272&partnerID=40&md5=cf207bacee5108caae82d5903e13688a | Conference paper | Final | All Open Access; Hybrid Gold Open Access | Scopus | 2-s2.0-85217079667 |
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| Saadany H.; Orăsan C.; Barczentewicz M.; Breslin C.; Walker S. | Saadany, Hadeel (57212509470); Orăsan, Constantin (8678677900); Barczentewicz, Mikolaj (57204436875); Breslin, Catherine (59265876500); Walker, Sophie (58528833500) | 57212509470; 8678677900; 57204436875; 59265876500; 58528833500 | Linking Judgement Text to Court Hearing Videos: UK Supreme Court as a Case Study | 2024 | 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings | NaN | NaN | NaN | 10598.0 | 10609.0 | 11.0 | 0 | NaN | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195945529&partnerID=40&md5=23797a66c9b552a3f6f48679ef3edfa4 | Conference paper | Final | NaN | Scopus | 2-s2.0-85195945529 |
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| Publication Type | Authors | Book Authors | Book Editors | Book Group Authors | Author Full Names | Book Author Full Names | Group Authors | Article Title | Source Title | Book Series Title | Book Series Subtitle | Language | Document Type | Conference Title | Conference Date | Conference Location | Conference Sponsor | Conference Host | Author Keywords | Keywords Plus | Abstract | Addresses | Affiliations | Reprint Addresses | Email Addresses | Researcher Ids | ORCIDs | Funding Orgs | Funding Name Preferred | Funding Text | Cited References | Cited Reference Count | Times Cited, WoS Core | Times Cited, All Databases | 180 Day Usage Count | Since 2013 Usage Count | Publisher | Publisher City | Publisher Address | ISSN | eISSN | ISBN | Journal Abbreviation | Journal ISO Abbreviation | Publication Date | Publication Year | Volume | Issue | Part Number | Supplement | Special Issue | Meeting Abstract | Start Page | End Page | Article Number | DOI | DOI Link | Book DOI | Early Access Date | Number of Pages | WoS Categories | Web of Science Index | Research Areas | IDS Number | Pubmed Id | Open Access Designations | Highly Cited Status | Hot Paper Status | Date of Export | UT (Unique WOS ID) | Web of Science Record |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| J | Terzidou, K | NaN | NaN | NaN | Terzidou, Kalliopi | NaN | NaN | Generative AI systems in legal practice offering quality legal services while upholding legal ethics | INTERNATIONAL JOURNAL OF LAW IN CONTEXT | NaN | NaN | English | Article; Early Access | NaN | NaN | NaN | NaN | NaN | ChatGPT; client-centricity; competence; confidentiality; generative AI; large language models | NaN | Generative artificial intelligence (AI) systems, notably ChatGPT, have emerged in legal practice, facilitating the completion of tasks, ranging from electronic communications to the drafting of documents. The generative capabilities of these systems underscore the duty of lawyers to competently represent their clients by keeping abreast of technological developments that can enhance the efficiency and effectiveness of their work. At the same time, the processing of clients' information through generative AI systems threatens to compromise their confidentiality if disclosed to third parties, including the systems' providers. The present paper aims to determine the impact of the use of generative AI systems by lawyers on the duties of competence and confidentiality. The findings derive from the application of doctrinal and empirical research on the legal practice and its digitalisation in Luxembourg. The paper finally reflects on the integration of generative AI systems in legal practice to raise the quality of legal services for clients. | [Terzidou, Kalliopi] Univ Luxembourg, Fac Law Econ & Finance, Dept Law, Luxembourg, Luxembourg | University of Luxembourg | Terzidou, K (corresponding author), Univ Luxembourg, Fac Law Econ & Finance, Dept Law, Luxembourg, Luxembourg. | kalliopi.terzidou@uni.lu | NaN | NaN | Luxembourg National Research Fund (Fonds National de la Recherche) under the PRIDE programme [PRIDE 19/14268506]; Bar Association of Luxembourg (Barreau de Luxembourg) | Luxembourg National Research Fund (Fonds National de la Recherche) under the PRIDE programme; Bar Association of Luxembourg (Barreau de Luxembourg) | The present paper has been written in the context of the author's doctoral research, funded by the Luxembourg National Research Fund (Fonds National de la Recherche) under the PRIDE programme (PRIDE 19/14268506). The paper was first presented at the International Legal Ethics Conference 2024 (ILEC 10) at Amsterdam Law School (University of Amsterdam). The author is grateful to the Bar Association of Luxembourg (Barreau de Luxembourg) for circulating the survey among its members and to all the members that participated in the survey. The author would also like to thank the representatives of the law firms and the legal tech companies that shared their insights and opinions during the interviews. | NaN | 21 | 0 | 0 | 4 | 4 | CAMBRIDGE UNIV PRESS | CAMBRIDGE | EDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND | 1744-5523 | 1744-5531 | NaN | INT J LAW CONTEXT | Int. J. Law Context | 2025 MAR 27 | 2025 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 10.1017/S1744552325000047 | http://dx.doi.org/10.1017/S1744552325000047 | NaN | MAR 2025 | 22 | Law | Social Science Citation Index (SSCI) | Government & Law | 0SE8X | NaN | NaN | NaN | NaN | 2025-04-30 | WOS:001454760300001 | View Full Record in Web of Science |
| J | Harasta, J; Novotná, T; Savelka, J | NaN | NaN | NaN | Harasta, Jakub; Novotna, Tereza; Savelka, Jaromir | NaN | NaN | It cannot be right if it was written by AI: on lawyers' preferences of documents perceived as authored by an LLM vs a human | ARTIFICIAL INTELLIGENCE AND LAW | NaN | NaN | English | Article; Early Access | NaN | NaN | NaN | NaN | NaN | Generative AI; GenAI; Large Language Model; LLM; Automatic Text Generation; Legal Document; Perception of AI-generated Content | PEOPLE | Large Language Models (LLMs) enable a future in which certain types of legal documents may be generated automatically. This has a great potential to streamline legal processes, lower the cost of legal services, and dramatically increase access to justice. While many researchers focus on proposing and evaluating LLM-based applications supporting tasks in the legal domain, there is a notable lack of investigations into how legal professionals perceive content if they believe an LLM has generated it. Yet, this is a critical point as over-reliance or unfounded scepticism may influence whether such documents bring about appropriate legal consequences. This study is the necessary analysis of the ongoing transition towards mature generative AI systems. Specifically, we examined whether the perception of legal documents' by lawyers and law students (n = 75) varies based on their assumed origin (human-crafted vs AI-generated). The participants evaluated the documents, focusing on their correctness and language quality. Our analysis revealed a clear preference for documents perceived as crafted by a human over those believed to be generated by AI. At the same time, most participants expect the future in which documents will be generated automatically. These findings could be leveraged by legal practitioners, policymakers, and legislators to implement and adopt legal document generation technology responsibly and to fuel the necessary discussions on how legal processes should be updated to reflect recent technological developments. | [Harasta, Jakub; Novotna, Tereza] Masaryk Univ, Fac Law, Veveri 70, Brno 61180, Czech Republic; [Savelka, Jaromir] Carnegie Mellon Univ, Sch Comp Sci, 5000 Forbes Ave, Pittsburgh, PA 15213 USA | Masaryk University Brno; Carnegie Mellon University | Harasta, J (corresponding author), Masaryk Univ, Fac Law, Veveri 70, Brno 61180, Czech Republic. | jakub.harasta@law.muni.cz; tereza.novotna@law.muni.cz; jsavelka@cs.cmu.edu | Savelka, Jaromir/GOK-0488-2022; Harašta, Jakub/AAB-7815-2022; Novotná, Tereza/JNR-9159-2023 | Harasta, Jakub/0000-0002-5722-0325; Novotna, Tereza/0000-0002-1426-4547 | Masarykova Univerzita | Masarykova Univerzita | We thank Bettina Bacher, and the two anonymous reviewers, for their helpful comments on previous versions of this paper. | NaN | 104 | 2 | 2 | 15 | 15 | SPRINGER | DORDRECHT | VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS | 0924-8463 | 1572-8382 | NaN | ARTIF INTELL LAW | Artif. Intell. Law | 2024 DEC 3 | 2024 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 10.1007/s10506-024-09422-w | http://dx.doi.org/10.1007/s10506-024-09422-w | NaN | DEC 2024 | 38 | Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Law | Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) | Computer Science; Government & Law | O1M4N | NaN | NaN | NaN | NaN | 2025-04-30 | WOS:001368851900001 | View Full Record in Web of Science |
| J | Katz, DM; Bommarito, MJ; Gao, S; Arredondo, P | NaN | NaN | NaN | Katz, Daniel Martin; Bommarito, Michael James; Gao, Shang; Arredondo, Pablo | NaN | NaN | GPT-4 passes the bar exam | PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES | NaN | NaN | English | Article | NaN | NaN | NaN | NaN | NaN | large language models; Bar Exam; GPT-4; legal services; legal complexity; legal language | LAW | In this paper, we experimentally evaluate the zero-shot performance of GPT-4 against prior generations of GPT on the entire uniform bar examination (UBE), including not only the multiple-choice multistate bar examination (MBE), but also the open-ended multistate essay exam (MEE) and multistate performance test (MPT) components. On the MBE, GPT-4 significantly outperforms both human test-takers and prior models, demonstrating a 26% increase over ChatGPT and beating humans in five of seven subject areas. On the MEE and MPT, which have not previously been evaluated by scholars, GPT-4 scores an average of 4.2/6.0 when compared with much lower scores for ChatGPT. Graded across the UBE components, in the manner in which a human test-taker would be, GPT-4 scores approximately 297 points, significantly in excess of the passing threshold for all UBE jurisdictions. These findings document not just the rapid and remarkable advance of large language model performance generally, but also the potential for such models to support the delivery of legal services in society.This article is part of the theme issue 'A complexity science approach to law and governance'. | [Katz, Daniel Martin; Bommarito, Michael James] Chicago Kent Coll Law, Illinois Tech, Chicago, IL 60661 USA; [Katz, Daniel Martin; Bommarito, Michael James; Arredondo, Pablo] Stanford Ctr Legal Informat, CodeX, Stanford, CA USA; [Katz, Daniel Martin; Bommarito, Michael James] Bucerius Law Sch, Hamburg, Germany; [Katz, Daniel Martin; Bommarito, Michael James] 273 Ventures LLC, Woburn, MA USA; [Gao, Shang; Arredondo, Pablo] Casetext Inc, Herndon, VA USA | NaN | Katz, DM (corresponding author), Chicago Kent Coll Law, Illinois Tech, Chicago, IL 60661 USA. | dkatz3@kentlaw.iit.edu | NaN | NaN | NaN | NaN | NaN | NaN | 85 | 96 | 98 | 24 | 59 | ROYAL SOC | LONDON | 6-9 CARLTON HOUSE TERRACE, LONDON SW1Y 5AG, ENGLAND | 1364-503X | 1471-2962 | NaN | PHILOS T R SOC A | Philos. Trans. R. Soc. A-Math. Phys. Eng. Sci. | APR 15 | 2024 | 382.0 | 2270.0 | NaN | NaN | NaN | NaN | NaN | NaN | 20230254 | 10.1098/rsta.2023.0254 | http://dx.doi.org/10.1098/rsta.2023.0254 | NaN | NaN | 17 | Multidisciplinary Sciences | Science Citation Index Expanded (SCI-EXPANDED) | Science & Technology - Other Topics | JV6Z5 | 38403056.0 | hybrid, Green Published | NaN | NaN | 2025-04-30 | WOS:001175989800012 | View Full Record in Web of Science |
| J | de la Osa, DUS; Remolina, N | NaN | NaN | NaN | de la Osa, David Uriel Socol; Remolina, Nydia | NaN | NaN | Artificial intelligence at the bench: Legal and ethical challenges of informing-or misinforming-judicial decision-making through generative AI | DATA & POLICY | NaN | NaN | English | Article | NaN | NaN | NaN | NaN | NaN | access to justice; accountability; AI ethical principles; AI regulation; artificial intelligence; fairness; generative AI; judicial decision-making; transparency; AI in the courtroom | NaN | Generative artificial intelligence (GenAI) has gained significant popularity in recent years. It is being integrated into a variety of sectors for its abilities in content creation, design, research, and many other functionalities. The capacity of GenAI to create new content-ranging from realistic images and videos to text and even computer code-has caught the attention of both the industry and the general public. The rise of publicly available platforms that offer these services has also made GenAI systems widely accessible, contributing to their mainstream appeal and dissemination. This article delves into the transformative potential and inherent challenges of incorporating GenAI into the domain of judicial decision-making. The article provides a critical examination of the legal and ethical implications that arise when GenAI is used in judicial rulings and their underlying rationale. While the adoption of this technology holds the promise of increased efficiency in the courtroom and expanded access to justice, it also introduces concerns regarding bias, interpretability, and accountability, thereby potentially undermining judicial discretion, the rule of law, and the safeguarding of rights. Around the world, judiciaries in different jurisdictions are taking different approaches to the use of GenAI in the courtroom. Through case studies of GenAI use by judges in jurisdictions including Colombia, Mexico, Peru, and India, this article maps out the challenges presented by integrating the technology in judicial determinations, and the risks of embracing it without proper guidelines for mitigating potential harms. Finally, this article develops a framework that promotes a more responsible and equitable use of GenAI in the judiciary, ensuring that the technology serves as a tool to protect rights, reduce risks, and ultimately, augment judicial reasoning and access to justice. | [de la Osa, David Uriel Socol] Hitotsubashi Univ, Hitotsubashi Inst Adv Study, Grad Sch Law, Tokyo, Japan; [Remolina, Nydia] Singapore Management Univ, Yong Pung Sch Law, Singapore, Singapore; [Remolina, Nydia] SMU, Ctr AI & Data Governance, Singapore, Singapore | Hitotsubashi University; Singapore Management University; Singapore Management University | Remolina, N (corresponding author), Singapore Management Univ, Yong Pung Sch Law, Singapore, Singapore.;Remolina, N (corresponding author), SMU, Ctr AI & Data Governance, Singapore, Singapore. | nydiarl@smu.edu.sg | Remolina, Nydia/GZL-5121-2022 | Remolina, Nydia/0000-0003-3356-6089 | NaN | NaN | NaN | NaN | 70 | 0 | 0 | 10 | 10 | CAMBRIDGE UNIV PRESS | CAMBRIDGE | EDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND | NaN | 2632-3249 | NaN | DATA POLICY | Data Policy | NaN | 2024 | 6.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | e59 | 10.1017/dap.2024.53 | http://dx.doi.org/10.1017/dap.2024.53 | NaN | NaN | 30 | Public Administration | Emerging Sources Citation Index (ESCI) | Public Administration | P8S1V | NaN | gold | NaN | NaN | 2025-04-30 | WOS:001380529700004 | View Full Record in Web of Science |
| C | Singh, S | NaN | NaN | ACIL | Singh, Shridhar | NaN | NaN | Enhancing Privacy and Security in Large-Language Models: A Zero-Knowledge Proof Approach | PROCEEDINGS OF THE 19TH INTERNATIONAL CONFERENCE ON CYBER WARFARE AND SECURITY, ICCWS 2024 | International Conference on Cyber Warfare and Security | NaN | English | Proceedings Paper | 19th International Conference on Cyber Warfare and Security | MAR 26-27, 2024 | University of Johannesburg, Johannesburg, SOUTH AFRICA | NaN | University of Johannesburg | Zero-Knowledge Proof (ZKP); Succinct Non-interactive Argument of Knowledge (SNARK); Large-Language Model (LLM); Generative Pre-trained Transformer (GPT) | NaN | The explosive growth of Large-Language Models (LLMs), particularly Generative Pre-trained Transformer (GPT) models, has revolutionised fields ranging from natural language processing to creative writing. Yet, their reliance on vast, often unverified data sources introduces a critical vulnerability: unreliability and security concerns. Traditional GPT models, while impressive in their capabilities, struggle with limited factual accuracy and susceptibility to manipulation by biased or malicious data. This poses a significant risk in professional and personal environments where sensitive or mission-critical data is paramount. This work tackles this challenge head-on by proposing a novel approach to enhance GPT security and reliability: leveraging Zero-Knowledge Proofs (ZKPs). Unlike traditional cryptographic methods that require sensitive data exchange, ZKPs allow one party to convincingly prove the truth of a statement, without revealing the underlying information. In the context of GPTs, ZKPs can validate the legitimacy and quality of data sources used in GPT computations, combating data manipulation and misinformation. This ensures trustworthy outputs, even when incorporating third-party data (TPD). ZKPs can securely verify user identities and access privileges, preventing unauthorised access to sensitive data and functionality. This protects critical information and promotes responsible LLM usage. ZKPs can identify and filter out manipulative prompts designed to elicit harmful or biased responses from GPTs. This safeguards against malicious actors and promotes ethical LLM development. ZKPs facilitate training specialised GPT models on targeted datasets, resulting in deeper understanding and more accurate outputs within specific domains. This allows the creation of 'expert-GPT' applications in specialised fields like healthcare, finance, and legal services. The integration of ZKPs into GPT models represents a crucial step towards overcoming trust and security barriers. Our research demonstrates the viability and efficacy of this approach, with our ZKP-based authentication system achieving promising results in data verification, user control, and malicious prompt detection. These findings lay the groundwork for a future where GPTs, empowered by ZKPs, operate with unwavering integrity, fostering trust and accelerating ethical AI development across diverse domains. | [Singh, Shridhar] Univ KwaZulu Natal, Westville, South Africa | University of Kwazulu Natal | Singh, S (corresponding author), Univ KwaZulu Natal, Westville, South Africa. | 217008024@stu.ukzn.ac.za | NaN | NaN | NaN | NaN | NaN | NaN | 20 | 1 | 1 | 0 | 0 | ACAD CONFERENCES LTD | NR READING | CURTIS FARM, KIDMORE END, NR READING, RG4 9AY, ENGLAND | 2048-9870 | 2048-9889 | 978-1-914587-96-2; 978-1-914587-97-9 | INT C CYBER WARFARE | NaN | NaN | 2024 | 19.0 | NaN | NaN | NaN | NaN | NaN | 574.0 | 582.0 | NaN | NaN | NaN | NaN | NaN | 9 | Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods | Conference Proceedings Citation Index - Science (CPCI-S) | Computer Science | BY3JK | NaN | NaN | NaN | NaN | 2025-04-30 | WOS:001418421600065 | View Full Record in Web of Science |
| J | Chan, KV | NaN | NaN | NaN | Chan, Kevin | NaN | NaN | A New Era of Maritime Arbitration: Ex Machina Determinations | JOURNAL OF INTERNATIONAL ARBITRATION | NaN | NaN | English | Article | NaN | NaN | NaN | NaN | NaN | maritime; shipping; arbitration; artificial intelligence; ChatGPT; tech; ex aequo et bono; access to justice; expedite; replacement of arbitrator | NaN | The development and release of Large Language Models (LLM) (most famously, Chat(GPT) in the past year have sparked new conversations about the limits of Artificial Intelligence (AI). This article explores the exciting possibilities of using AI as arbitrators in maritime disputes, including an examination of the benefits, challenges, and areas for further development to facilitate its use. | [Chan, Kevin] Kennedys Legal Solut, Singapore, Singapore | NaN | Chan, KV (corresponding author), Kennedys Legal Solut, Singapore, Singapore. | kevin_jchan@hotmail.com | Chan, Kevin/JQI-6002-2023 | NaN | NaN | NaN | NaN | NaN | 15 | 1 | 1 | 2 | 3 | KLUWER LAW INT | ALPHEN AAN DEN RIJN | ZUIDPOOLSINGEL 2, PO BOX 316, 2400 AH ALPHEN AAN DEN RIJN, NETHERLANDS | 0255-8106 | 2212-182X | NaN | J INT ARBITR | J. Int. Arbitr. | OCT | 2023 | 40.0 | 5.0 | NaN | NaN | NaN | NaN | 521.0 | 550.0 | NaN | NaN | NaN | NaN | NaN | 30 | Law | Emerging Sources Citation Index (ESCI) | Government & Law | Z0MV5 | NaN | NaN | NaN | NaN | 2025-04-30 | WOS:001109120000001 | View Full Record in Web of Science |
| J | Burgess, P; Williams, I; Qu, LZ; Wang, WQ | NaN | NaN | NaN | Burgess, Paul; Williams, Iwan; Qu, Lizhen; Wang, Weiqing | NaN | NaN | Using Generative AI to Identify Arguments in Judges' Reasons: Accuracy and Benefits for Students | LAW TECHNOLOGY AND HUMANS | NaN | NaN | English | Article | NaN | NaN | NaN | NaN | NaN | generative AI; Large Language Models; arguments; education; judges' reasons | ARTIFICIAL-INTELLIGENCE | This study evaluates the effectiveness of generative artificial intelligence (GAI) in identifying and reconstructing legal arguments from judges' reasons in court cases, focusing on the practical implications for law students and legal educators. By examining the performance of two versions of popular Large Language Models - ChatGPT and Claude - across five recent High Court of Australia decisions, the study makes a preliminary assessment of the accuracy of LLM systems in replicating a skill essential for l awyers: identification of arguments and argument chains in judges' reasons. The methodology involves marking LLM-generated outputs with reference to both a sample answer and a detailed rubric. Key findings reveal a significant variance in the accuracy of different LLMs, with Claude 3.5 markedly outperforming all others, achieving average grades up to 90 per cent. In contrast, ChatGPT versions demonstrated lower accuracy, with average marks not exceeding 50 per cent. These results highlight the critical importance of selecting the right GAI system for legal applications, as well as the necessity for users to critically engage with AI outputs rather than relying solely on automated tools. The study concludes that while LLMs hold potential benefits for the legal profession, including increased efficiency and enhanced access to justice, for GAI use that may be carried out by a law student, the technology cannot yet replace the nuanced human skill of legal argument analysis. | [Burgess, Paul; Williams, Iwan; Qu, Lizhen; Wang, Weiqing] Monash Univ, Monash, Australia | Monash University | Burgess, P (corresponding author), Monash Univ, Monash, Australia. | NaN | NaN | Qu, Lizhen/0000-0002-7764-431X; Williams, Iwan/0000-0003-0582-0983 | NaN | NaN | NaN | NaN | 51 | 1 | 1 | 4 | 4 | QUEENSLAND UNIV TECHNOLOGY | BRISBANE | GPO BOX 2434, BRISBANE, QLD 4001, AUSTRALIA | NaN | 2652-4074 | NaN | LAW TECHNOL HUMANS | Law Technol. Humans | NaN | 2024 | 6.0 | 3.0 | NaN | NaN | NaN | NaN | 5.0 | 22.0 | NaN | 10.5204/lthj.3637 | http://dx.doi.org/10.5204/lthj.3637 | NaN | NaN | 18 | Law; Social Sciences, Interdisciplinary | Emerging Sources Citation Index (ESCI) | Government & Law; Social Sciences - Other Topics | O0M0E | NaN | gold | NaN | NaN | 2025-04-30 | WOS:001368158500001 | View Full Record in Web of Science |
| J | Trozze, A; Davies, T; Kleinberg, B | NaN | NaN | NaN | Trozze, Arianna; Davies, Toby; Kleinberg, Bennett | NaN | NaN | Large language models in cryptocurrency securities cases: can a GPT model meaningfully assist lawyers? | ARTIFICIAL INTELLIGENCE AND LAW | NaN | NaN | English | Article; Early Access | NaN | NaN | NaN | NaN | NaN | Cryptocurrency; Securities law; Artificial intelligence (AI); Large language models (LLMs); ChatGPT | NaN | Large Language Models (LLMs) could be a useful tool for lawyers. However, empirical research on their effectiveness in conducting legal tasks is scant. We study securities cases involving cryptocurrencies as one of numerous contexts where AI could support the legal process, studying GPT-3.5's legal reasoning and ChatGPT's legal drafting capabilities. We examine whether a) GPT-3.5 can accurately determine which laws are potentially being violated from a fact pattern, and b) whether there is a difference in juror decision-making based on complaints written by a lawyer compared to ChatGPT. We feed fact patterns from real-life cases to GPT-3.5 and evaluate its ability to determine correct potential violations from the scenario and exclude spurious violations. Second, we had mock jurors assess complaints written by ChatGPT and lawyers. GPT-3.5's legal reasoning skills proved weak, though we expect improvement in future models, particularly given the violations it suggested tended to be correct (it merely missed additional, correct violations). ChatGPT performed better at legal drafting, and jurors' decisions were not statistically significantly associated with the author of the document upon which they based their decisions. Because GPT-3.5 cannot satisfactorily conduct legal reasoning tasks, it would be unlikely to be able to help lawyers in a meaningful way at this stage. However, ChatGPT's drafting skills (though, perhaps, still inferior to lawyers) could assist lawyers in providing legal services. Our research is the first to systematically study an LLM's legal drafting and reasoning capabilities in litigation, as well as in securities law and cryptocurrency-related misconduct. | [Trozze, Arianna] UCL, Dept Comp Sci, Gower St, London WC1E 6EA, England; [Trozze, Arianna; Davies, Toby; Kleinberg, Bennett] UCL, Dept Secur & Crime Sci, 35 Tavistock Sq, London WC1H 9EZ, England; [Davies, Toby] Univ Leeds, Sch Law, Liberty Bldg, Leeds LS2 9JT, England; [Kleinberg, Bennett] Tilburg Univ, Dept Methodol & Stat, Warandelaan 2, NL-5037 AB Tilburg, Netherlands | University of London; University College London; University of London; University College London; University of Leeds; Tilburg University | Trozze, A (corresponding author), UCL, Dept Comp Sci, Gower St, London WC1E 6EA, England.;Trozze, A (corresponding author), UCL, Dept Secur & Crime Sci, 35 Tavistock Sq, London WC1H 9EZ, England. | arianna.trozze@ucl.ac.uk | Trozze, Arianna/GRX-5066-2022 | Davies, Toby/0000-0002-9677-2579 | Engineering and Physical Sciences Research Council | Engineering and Physical Sciences Research Council(UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC)) | The authors would like to thank Antonis Papasavva for participating in the pilot phase of our study and his valuable feedback. We would also like to thank Anne Coventry for her assistance in conceptualizing our study and review of the legal concepts presented in this paper. | NaN | 78 | 2 | 2 | 10 | 35 | SPRINGER | DORDRECHT | VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS | 0924-8463 | 1572-8382 | NaN | ARTIF INTELL LAW | Artif. Intell. Law | 2024 APR 8 | 2024 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 10.1007/s10506-024-09399-6 | http://dx.doi.org/10.1007/s10506-024-09399-6 | NaN | APR 2024 | 47 | Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Law | Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) | Computer Science; Government & Law | ND6D5 | NaN | hybrid | NaN | NaN | 2025-04-30 | WOS:001198543600001 | View Full Record in Web of Science |
| C | Steenhuis, Q; Willey, B; Colarusso, D | NaN | NaN | ACM | Steenhuis, Quinten; Willey, Bryce; Colarusso, David | NaN | NaN | Beyond Readability with RateMyPDF | PROCEEDINGS OF THE 19TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND LAW, ICAIL 2023 | NaN | NaN | English | Proceedings Paper | 19th International Conference on Artificial Intelligence and Law (ICAIL) | JUN 19-23, 2023 | Univ Minho Law Sch, Braga, PORTUGAL | Int Assoc Artificial Intelligence & Law,Univ Minho Informat Dept Engn Sch,JUSGOV Res Ctr Justice & Governance,Centro Algoritmi,Intelligent Syst Associated Lab,Thomson Reuters,Centro Juridico Minho,Antas da Cunha ECIJA Soc Advogados,Visionware,Simplexico,Assoc Advancement Artificial Intelligence,ACM SIGAI | Univ Minho Law Sch | Accessibility; Law; Administrative Burden; Readability; Court Forms; Automated Analysis | TEXTS | In this paper, we describe RateMyPDF, a web application that helps authors measure and improve the usability of court forms. It offers a score together with automated suggestions to improve the form drawn from both traditional machine learning approaches and the general purpose GPT-3 large language model. We worked with form authors and usability experts to determine the set of features we measure and validated them by gathering a dataset of approximately 24,000 PDF forms from 46 U.S. States and the District of Columbia. Our tool and automated measures allow a form author or court tasked with improving a large library of forms to work at scale. This paper describes the features that we find improve form usability, the results from our analysis of the large form dataset, details of the tool, and the implications of our tool on access to justice for self-represented litigants. We found that the RateMyPDF score significantly correlates to the score of expert reviewers. While the current version of the tool allows automated analysis of Microsoft Word and PDF court forms, the findings of our research apply equally to the growing number of automated wizard-driven interactive legal applications that replace paper forms with interactive websites. | [Steenhuis, Quinten; Willey, Bryce; Colarusso, David] Suffolk Univ, Law Sch, Boston, MA 02114 USA | Suffolk University | Steenhuis, Q (corresponding author), Suffolk Univ, Law Sch, Boston, MA 02114 USA. | qsteenhuis@suffolk.edu; bwilley@suffolk.edu; dcolarusso@suffolk.edu | NaN | Steenhuis, Quinten/0009-0001-0110-064X | NaN | NaN | NaN | NaN | 42 | 1 | 1 | 0 | 0 | ASSOC COMPUTING MACHINERY | NEW YORK | 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES | NaN | NaN | 979-8-4007-0197-9 | NaN | NaN | NaN | 2023 | NaN | NaN | NaN | NaN | NaN | NaN | 287.0 | 296.0 | NaN | 10.1145/3594536.3595146 | http://dx.doi.org/10.1145/3594536.3595146 | NaN | NaN | 10 | Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Law | Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) | Computer Science; Government & Law | BW3KK | NaN | NaN | NaN | NaN | 2025-04-30 | WOS:001139079400030 | View Full Record in Web of Science |
| J | Yuan, MR; Kao, B; Wu, TH; Cheung, MMK; Chan, HWH; Cheung, ASY; Chan, FWH; Chen, YX | NaN | NaN | NaN | Yuan, Mingruo; Kao, Ben; Wu, Tien-Hsuan; Cheung, Michael M. K.; Chan, Henry W. H.; Cheung, Anne S. Y.; Chan, Felix W. H.; Chen, Yongxi | NaN | NaN | Bringing legal knowledge to the public by constructing a legal question bank using large-scale pre-trained language model | ARTIFICIAL INTELLIGENCE AND LAW | NaN | NaN | English | Article | NaN | NaN | NaN | NaN | NaN | Legal knowledge dissemination; Navigability and comprehensibility of legal information; Machine question generation; Pre-trained language model | READABILITY | Access to legal information is fundamental to access to justice. Yet accessibility refers not only to making legal documents available to the public, but also rendering legal information comprehensible to them. A vexing problem in bringing legal information to the public is how to turn formal legal documents such as legislation and judgments, which are often highly technical, to easily navigable and comprehensible knowledge to those without legal education. In this study, we formulate a three-step approach for bringing legal knowledge to laypersons, tackling the issues of navigability and comprehensibility. First, we translate selected sections of the law into snippets (called CLIC-pages), each being a small piece of article that focuses on explaining certain technical legal concept in layperson's terms. Second, we construct a Legal Question Bank, which is a collection of legal questions whose answers can be found in the CLIC-pages. Third, we design an interactive CLIC Recommender. Given a user's verbal description of a legal situation that requires a legal solution, CRec interprets the user's input and shortlists questions from the question bank that are most likely relevant to the given legal situation and recommends their corresponding CLIC pages where relevant legal knowledge can be found. In this paper we focus on the technical aspects of creating an LQB. We show how large-scale pre-trained language models, such as GPT-3, can be used to generate legal questions. We compare machine-generated questions against human-composed questions and find that MGQs are more scalable, cost-effective, and more diversified, while HCQs are more precise. We also show a prototype of CRec and illustrate through an example how our 3-step approach effectively brings relevant legal knowledge to the public. | [Yuan, Mingruo; Kao, Ben; Wu, Tien-Hsuan] Univ Hong Kong, Dept Comp Sci, Pokfulam, Hong Kong, Peoples R China; [Cheung, Michael M. K.; Chan, Henry W. H.; Cheung, Anne S. Y.; Chan, Felix W. H.] Univ Hong Kong, Fac Law, Pokfulam, Hong Kong, Peoples R China; [Chen, Yongxi] Australian Natl Univ, Coll Law, Canberra, ACT 2601, Australia | University of Hong Kong; University of Hong Kong; Australian National University | Yuan, MR (corresponding author), Univ Hong Kong, Dept Comp Sci, Pokfulam, Hong Kong, Peoples R China. | mryuan@cs.hku.hk; kao@cs.hku.hk; thwu@cs.hku.hk; michaelmkcheung@hku.hk; hwhchan@hku.hk; anne.cheung@hku.hk; fwhchan@hku.hk; yongxi.chen@anu.edu.au | Chen, Clement/AAM-9236-2021; Chan, Felix/G-1669-2010; Wu, Tien-Hsuan/IAQ-8586-2023 | Yuan, Mingruo/0000-0001-7834-9737 | Innovation and Technology Fund [ITS/234/20]; WYNG Foundation [HKU KG210018] | Innovation and Technology Fund; WYNG Foundation(ACEV Foundation) | & nbsp;This project is supported by Innovation and Technology Fund (ITS/234/20) and the WYNG Foundation (HKU KG210018). | NaN | 24 | 2 | 2 | 8 | 21 | SPRINGER | DORDRECHT | VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS | 0924-8463 | 1572-8382 | NaN | ARTIF INTELL LAW | Artif. Intell. Law | SEP | 2024 | 32.0 | 3.0 | NaN | NaN | NaN | NaN | 769.0 | 805.0 | NaN | 10.1007/s10506-023-09367-6 | http://dx.doi.org/10.1007/s10506-023-09367-6 | NaN | JUL 2023 | 37 | Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Law | Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) | Computer Science; Government & Law | A3L4V | NaN | NaN | NaN | NaN | 2025-04-30 | WOS:001023368400001 | View Full Record in Web of Science |
| C | Brunswicker, S; Zhang, Y; Rashidian, CE; Linna, DW | NaN | Bui, TX | NaN | Brunswicker, Sabine; Zhang, Yifan; Rashidian, Christopher E.; Linna, Dan W., Jr. | NaN | NaN | The Impact of Empathy in Conversational AI: A Controlled Experiment with a Legal Chatbot | PROCEEDINGS OF THE 57TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES | Hawaii International Conference on System Sciences | NaN | English | Proceedings Paper | 57th Hawaii International Conference on System Sciences (HICSS) | JAN 03-06, 2024 | Honolulu, HI | NaN | NaN | Conversational AI; Social Intelligence; Empathy in Dialogue; Linguistics; human-AI teaming | NaN | The rise of ChatGPT has revealed the potential of chatbots and other conversational AI tools to assist humans in fields such as law and healthcare, where the best human experts can engage in empathetic conversations. The belief is that if chatbots can connect with humans on a social and emotional level, they can reduce the cognitive effort required by humans to solve their problems, while increasing user satisfaction and trust. Although existing research has shown that empathy is crucial for designing human-AI conversations and their outcomes (effort, helpfulness, trust), it fails to separate the impact of empathy in language display from the AI's underlying cognitive abilities, like logical reasoning. To address this gap, this research aims to develop and empirically test a theory of empathy in the language displayed by conversational AI, explaining the relational outcomes of human-AI conversations in terms of cognitive effort, helpfulness, and trustworthiness. Using this theory, a chatbot is designed using syntactic and rhetorical linguistic elements that evoke empathy when providing legal services to tenants renting property. Through a randomized controlled experiment with a 2 by 3 factorial design, the effects of this empathetic chatbot on three relational outcomes in human-AI conversations are examined and compared to a non-empathetic chatbot that maintains the same logic. A baseline model utilizing non-conversational access to legal services via frequently asked questions (FAQs) is also implemented, and the subjects' emotional state (anger) is manipulated as a moderating factor. The study involves 277 participants randomly assigned to one of six groups. The findings demonstrate the significance of both main and interaction effects on trustworthiness, usefulness, and cognitive effort. The results indicate that subtle changes in language syntax and style can have substantial implications for the outcomes of human-AI conversations. These findings contribute to the growing literature on conversational AI and have practical implications for the design of conversational and generative AI. | [Brunswicker, Sabine; Rashidian, Christopher E.] Purdue Univ, W Lafayette, IN 47907 USA; [Zhang, Yifan; Linna, Dan W., Jr.] Northwestern Univ, Evanston, IL 60208 USA | Purdue University System; Purdue University; Northwestern University | Brunswicker, S (corresponding author), Purdue Univ, W Lafayette, IN 47907 USA. | sbrunswi@purdue.edu; yifanzhang2024@u.northwestern.edu; crashid@purdue.edu | NaN | NaN | NaN | NaN | NaN | NaN | 0 | 1 | 1 | 1 | 1 | HICSS | Honolulu | Dept IT Mgmt, Shidler College of Business, Univ Hawaii at Manoa 2404 Maile Way D307, Honolulu, Hawaii, UNITED STATES | NaN | NaN | 978-0-9981331-7-1 | Hawaii Int Con Sys S | NaN | NaN | 2024 | NaN | NaN | NaN | NaN | NaN | NaN | 455.0 | 466.0 | NaN | NaN | NaN | NaN | NaN | 12 | Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering | Conference Proceedings Citation Index - Science (CPCI-S) | Computer Science | BX5OE | NaN | NaN | NaN | NaN | 2025-04-30 | WOS:001301787500054 | View Full Record in Web of Science |
| C | Dahan, S; Bhambhoria, R; Liang, D; Zhu, XD | NaN | Spanakis, J; VanDijck, G; Sileno, G | NaN | Dahan, Samuel; Bhambhoria, Rohan; Liang, David; Zhu, Xiaodan | NaN | NaN | OpenJustice.ai: A Global Open-Source Legal Language Model | LEGAL KNOWLEDGE AND INFORMATION SYSTEMS | Frontiers in Artificial Intelligence and Applications | NaN | English | Proceedings Paper | 36th Annual International Conference on Legal Knowledge and Information Systems (JURIX) | DEC 18-20, 2023 | Maastricht Univ, Maastricht, NETHERLANDS | JURIX Fdn Legal Knowledge Based Syst | Maastricht Univ | Legal AI; Open source; Decentralized and Distributed Learning; Feedback; Legal profession | NaN | Generalized AI like ChatGPT cannot and should not be used for legal tasks. It presents significant risks for both the legal professions as well as litigants. However, domain-specific AI should not be ruled out. It has the potential for legal research as well as access to justice. In this paper, we call for the development of an open-source and distributed legal AI accessible to the entire legal community. We believe it has the potential to address some of the limitations related to the use of general AI for legal problems and resolving disputes - shortcomings that include legal misinformation or hallucinations, lack of transparency and precision, and inability to offer diverse and multiple narratives. | [Dahan, Samuel] Cornell Law Sch, Conflict Analyt Lab, Queens Law, Ithaca, NY 14853 USA; [Bhambhoria, Rohan; Zhu, Xiaodan] Queens Univ, Ingenu Labs Res Inst, Dept Elect & Comp Engn, Conflict Analyt Lab, Kingston, ON, Canada; [Liang, David] Queens Univ, Smith Sch Business, Conflict Analyt Lab, Queens Law, Kingston, ON, Canada | Cornell University; Ingenuity Labs Research Institute; Queens University - Canada; Queens University - Canada | Dahan, S (corresponding author), Cornell Law Sch, Conflict Analyt Lab, Queens Law, Ithaca, NY 14853 USA. | samuel.dahan@queensu.ca | NaN | Dahan, Samuel/0000-0002-1079-8998 | NaN | NaN | NaN | NaN | 11 | 0 | 0 | 3 | 5 | IOS PRESS | AMSTERDAM | NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS | 0922-6389 | 1879-8314 | 978-1-64368-472-7; 978-1-64368-473-4 | FRONT ARTIF INTEL AP | NaN | NaN | 2023 | 379.0 | NaN | NaN | NaN | NaN | NaN | 387.0 | 390.0 | NaN | 10.3233/FAIA230995 | http://dx.doi.org/10.3233/FAIA230995 | NaN | NaN | 4 | Computer Science, Artificial Intelligence; Computer Science, Information Systems; Information Science & Library Science; Law | Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) | Computer Science; Information Science & Library Science; Government & Law | BW6JJ | NaN | hybrid | NaN | NaN | 2025-04-30 | WOS:001175464100054 | View Full Record in Web of Science |
| J | Long, B; Palmer, A | NaN | NaN | NaN | Long, Brandon; Palmer, Amitabha | NaN | NaN | AI and access to justice: How AI legal advisors can reduce economic and shame-based barriers to justice | ZEITSCHRIFT FUER TECHNIKFOLGENABSCHAETZUNG IN THEORIE UND PRAXIS - TATUP | NaN | NaN | English | Article | NaN | NaN | NaN | NaN | NaN | artificial intelligence; shame; barriers to justice; philosophy of technology; law | NaN | center dot ChatGPT - a large language model - recently passed the U.S. bar exam. The startling rise and power of generative artificial intelligence (AI) systems such as ChatGPT lead us to consider whether and how more specialized systems could be used to overcome existing barriers to the legal system. Such systems could be employed in either of the two major stages of the pursuit of justice: preliminary information gathering and formal engagement with the state's legal institutions and professionals. We focus on the former and argue that developing and deploying publicly funded AI legal advisors can reduce economic and shame-based cultural barriers to the information-gathering stage of pursuing justice. | [Long, Brandon] Bowling Green State Univ, Dept Philosophy, Bowling Green, OH 43403 USA; [Palmer, Amitabha] Univ Texas MD Anderson Canc Ctr, Houston, TX USA | University System of Ohio; Bowling Green State University; University of Texas System; UTMD Anderson Cancer Center | Long, B (corresponding author), Bowling Green State Univ, Dept Philosophy, Bowling Green, OH 43403 USA. | brlong@bgsu.edu | NaN | Long, Brandon/0000-0002-7153-4267; Palmer, Amitabha/0000-0001-6362-4935 | NaN | NaN | NaN | NaN | 42 | 0 | 0 | 5 | 5 | OEKOM VERLAG GMBH | MUNICH | WALTHERSTR 29, MUNICH, 80337, GERMANY | 2568-020X | 2567-8833 | NaN | Z TECHN THEOR PRAX | Z. Tech. Theor. Prax.-TATuP | NaN | 2024 | 33.0 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 10.14512/tatup.33.1.21 | http://dx.doi.org/10.14512/tatup.33.1.21 | NaN | NaN | 80 | Social Sciences, Interdisciplinary | Emerging Sources Citation Index (ESCI) | Social Sciences - Other Topics | Q6V0E | NaN | Green Published, gold | NaN | NaN | 2025-04-30 | WOS:001386016800004 | View Full Record in Web of Science |
| C | Pereira, FV; Frazao, A; Moreira, VP | NaN | Paes, A; Verri, FAN | NaN | Pereira, Francielle Vasconcellos; Frazao, Ana; Moreira, Viviane P. | NaN | NaN | Automatic Text Simplification for the Legal Domain in Brazilian Portuguese | INTELLIGENT SYSTEMS, BRACIS 2024, PT IV | Lecture Notes in Artificial Intelligence | NaN | English | Proceedings Paper | 34th Brazilian Conference on Intelligent Systems | NOV 17-21, 2024 | Universidade Federal do Para, Belem, BRAZIL | NaN | Universidade Federal do Para | Automatic Text Simplification; Legal Texts; Natural Language Processing; Plain Language | NaN | Legal and juridical documents such as rulings, laws, agreements, and contracts contain domain-specific terms and jargon, long and complex sentences that may be difficult to understand for laypeople without domain expertise, reading issues, or with a low education level. The simplification of these documents has been a concern for several years, aiming to democratize access to justice. Courts are already adopting simpler language, especially in documents aimed at laypeople, such as warrants and notifications, to enhance inclusion and clarity. Automatic textual simplification, a subfield of Natural Language Processing, seeks to make complex texts more accessible. This paper explores the task of automatic text simplification in Portuguese for the legal domain. The main challenge here is the lack of datasets containing complex sentences and their simplified versions. This work investigates how existing datasets, methods, and metrics used for text simplification perform applied to legal texts in Portuguese. We present qualitative and quantitative analyses using five models. The results show that GPT-based models have the best results, but fine-tuning with domain data is a viable open-source alternative. | [Pereira, Francielle Vasconcellos; Moreira, Viviane P.] UFRGS Porto Alegre, Inst Informat, Porto Alegre, RS, Brazil; [Frazao, Ana] USP Sao Paulo, Sao Paulo, Brazil | Universidade Federal do Rio Grande do Sul | Moreira, VP (corresponding author), UFRGS Porto Alegre, Inst Informat, Porto Alegre, RS, Brazil. | fvpereira@inf.ufrgs.br; anarosapaiva@usp.br; viviane@inf.ufrgs.br | NaN | NaN | CNPq-Brazil; Capes [001] | CNPq-Brazil(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)); Capes(Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)) | This work has been partially funded by CNPq-Brazil, and Capes Finance Code 001. The authors are thankful to Edleno Silva de Moura for sharing the legal case updates created by JusBrasil and to INOVAJUS for sharing examples of simplification. | NaN | 27 | 0 | 0 | 0 | 0 | SPRINGER INTERNATIONAL PUBLISHING AG | CHAM | GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND | 2945-9133 | 1611-3349 | 978-3-031-79037-9; 978-3-031-79038-6 | LECT NOTES ARTIF INT | NaN | NaN | 2025 | 15415.0 | NaN | NaN | NaN | NaN | NaN | 31.0 | 45.0 | NaN | 10.1007/978-3-031-79038-6_3 | http://dx.doi.org/10.1007/978-3-031-79038-6_3 | NaN | NaN | 15 | Computer Science, Artificial Intelligence | Conference Proceedings Citation Index - Science (CPCI-S) | Computer Science | BY4OY | NaN | NaN | NaN | NaN | 2025-04-30 | WOS:001447188600003 | View Full Record in Web of Science |
| J | Jones, N; Whaiduzzaman, M; Jan, T; Adel, A; Alazab, A; Alkreisat, A | NaN | NaN | NaN | Jones, Nicholas; Whaiduzzaman, Md; Jan, Tony; Adel, Amr; Alazab, Ammar; Alkreisat, Afnan | NaN | NaN | A CIA Triad-Based Taxonomy of Prompt Attacks on Large Language Models | FUTURE INTERNET | NaN | NaN | English | Article | NaN | NaN | NaN | NaN | NaN | large language model; prompt security engineering; prompt attack; CIA triad; taxonomy; mitigation protocols | NaN | The rapid proliferation of Large Language Models (LLMs) across industries such as healthcare, finance, and legal services has revolutionized modern applications. However, their increasing adoption exposes critical vulnerabilities, particularly through adversarial prompt attacks that compromise LLM security. These prompt-based attacks exploit weaknesses in LLMs to manipulate outputs, leading to breaches of confidentiality, corruption of integrity, and disruption of availability. Despite their significance, existing research lacks a comprehensive framework to systematically understand and mitigate these threats. This paper addresses this gap by introducing a taxonomy of prompt attacks based on the Confidentiality, Integrity, and Availability (CIA) triad, an important cornerstone of cybersecurity. This structured taxonomy lays the foundation for a unique framework of prompt security engineering, which is essential for identifying risks, understanding their mechanisms, and devising targeted security protocols. By bridging this critical knowledge gap, the present study provides actionable insights that can enhance the resilience of LLM to ensure their secure deployment in high-stakes and real-world environments. | [Jones, Nicholas; Whaiduzzaman, Md; Jan, Tony; Adel, Amr; Alazab, Ammar] Torrens Univ Australia, Ctr Artificial Intelligence Res & Optimizat AIRO D, Ultimo, NSW 2007, Australia; [Alkreisat, Afnan] CyberNex, Somerton, Vic 3062, Australia | Torrens University Australia | Jan, T; Alazab, A (corresponding author), Torrens Univ Australia, Ctr Artificial Intelligence Res & Optimizat AIRO D, Ultimo, NSW 2007, Australia. | md.whaiduzzaman@torrens.edu.au; tony.jan@torrens.edu.au; ammar.alazab@torrens.edu.au; afnan@cybernex.org | Jan, Tony/JTT-5263-2023; alazab, ammar/E-2615-2012; Adel, Amr/IAR-2773-2023 | Jan, Tony/0000-0002-3114-8978; Adel, Amr/0000-0002-0632-0940 | NaN | NaN | NaN | NaN | 75 | 0 | 0 | 1 | 1 | MDPI | BASEL | MDPI AG, Grosspeteranlage 5, CH-4052 BASEL, SWITZERLAND | 1999-5903 | NaN | NaN | FUTURE INTERNET | Future Internet | MAR 3 | 2025 | 17.0 | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | 113 | 10.3390/fi17030113 | http://dx.doi.org/10.3390/fi17030113 | NaN | NaN | 28 | Computer Science, Information Systems | Emerging Sources Citation Index (ESCI) | Computer Science | 0OO5R | NaN | gold | NaN | NaN | 2025-04-30 | WOS:001452297600001 | View Full Record in Web of Science |
| J | Nay, JJ; Karamardian, D; Lawsky, SB; Tao, WT; Bhat, M; Jain, R; Lee, AT; Choi, JH; Kasai, J | NaN | NaN | NaN | Nay, John J.; Karamardian, David; Lawsky, Sarah B.; Tao, Wenting; Bhat, Meghana; Jain, Raghav; Lee, Aaron Travis; Choi, Jonathan H.; Kasai, Jungo | NaN | NaN | Large language models as tax attorneys: a case study in legal capabilities emergence | PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES | NaN | NaN | English | Article | NaN | NaN | NaN | NaN | NaN | artificial intelligence; large language models; machine learning; computational law; law-informed AI; law informs code | NaN | Better understanding of Large Language Models' (LLMs) legal analysis abilities can contribute to improving the efficiency of legal services, governing artificial intelligence and leveraging LLMs to identify inconsistencies in law. This paper explores LLM capabilities in applying tax law. We choose this area of law because it has a structure that allows us to set up automated validation pipelines across thousands of examples, requires logical reasoning and maths skills, and enables us to test LLM capabilities in a manner relevant to real-world economic lives of citizens and companies. Our experiments demonstrate emerging legal understanding capabilities, with improved performance in each subsequent OpenAI model release. We experiment with retrieving and using the relevant legal authority to assess the impact of providing additional legal context to LLMs. Few-shot prompting, presenting examples of question-answer pairs, is also found to significantly enhance the performance of the most advanced model, GPT-4. The findings indicate that LLMs, particularly when combined with prompting enhancements and the correct legal texts, can perform at high levels of accuracy but not yet at expert tax lawyer levels. As LLMs continue to advance, their ability to reason about law autonomously could have significant implications for the legal profession and AI governance.This article is part of the theme issue 'A complexity science approach to law and governance'. | [Nay, John J.] Stanford Univ, Ctr Legal Informat, CodeX, Stanford, CA 94305 USA; [Karamardian, David; Tao, Wenting] Stanford Univ, Stanford, CA USA; [Lawsky, Sarah B.] Northwestern Pritzker Sch Law, Chicago, IL USA; [Bhat, Meghana] Univ Michigan, Engn, Ann Arbor, MI USA; [Jain, Raghav] SimPPL, Faridabad, India; [Lee, Aaron Travis; Choi, Jonathan H.] Univ Southern Calif, Sch Law, Los Angeles, CA USA; [Kasai, Jungo] Univ Washington, Dept Comp Sci, Seattle, WA USA | Stanford University; Stanford University; Northwestern University; University of Michigan System; University of Michigan; University of Southern California; University of Washington; University of Washington Seattle | Nay, JJ (corresponding author), Stanford Univ, Ctr Legal Informat, CodeX, Stanford, CA 94305 USA. | john.j.nay@gmail.com | NaN | NaN | Mercatus Center at George Mason University | Mercatus Center at George Mason University | The Mercatus Center at George Mason University funded Meghana Bhat's work related to this research, and some of the computing costs for running the experiments. | NaN | 60 | 13 | 14 | 19 | 51 | ROYAL SOC | LONDON | 6-9 CARLTON HOUSE TERRACE, LONDON SW1Y 5AG, ENGLAND | 1364-503X | 1471-2962 | NaN | PHILOS T R SOC A | Philos. Trans. R. Soc. A-Math. Phys. Eng. Sci. | APR 15 | 2024 | 382.0 | 2270.0 | NaN | NaN | NaN | NaN | NaN | NaN | 20230159 | 10.1098/rsta.2023.0159 | http://dx.doi.org/10.1098/rsta.2023.0159 | NaN | NaN | 15 | Multidisciplinary Sciences | Science Citation Index Expanded (SCI-EXPANDED) | Science & Technology - Other Topics | JV6Z5 | 38403061.0 | Green Published, hybrid, Green Submitted | NaN | NaN | 2025-04-30 | WOS:001175989800007 | View Full Record in Web of Science |
| J | Olimid, AP; Georgescu, CM; Olimid, DA | NaN | NaN | NaN | Olimid, Anca Parmena; Georgescu, Catalina Maria; Olimid, Daniel Alin | NaN | NaN | LEGAL ANALYSIS OF EU ARTIFICIAL INTELLIGENCE ACT (2024): INSIGHTS FROM PERSONAL DATA GOVERNANCE AND HEALTH POLICY | ACCESS TO JUSTICE IN EASTERN EUROPE | NaN | NaN | English | Article | NaN | NaN | NaN | NaN | NaN | artificial intelligence; AI; ethical AI; EU legislation; generative AI; medical data | AI | Background: This study correlates the up-to-date ethical, functional and legal evaluations related to the management and governance of artificial intelligence (AI) under European Union (EU) law, particularly impacting the health data sector and medical standards as provided by the Artificial Intelligence Act within the Regulation adopted by the European Council in May 2024. The initial proposal for the management and governance of the AI sector was submitted in April 2021. Three years later, on 13 March 2024, the European Union Artificial Intelligence Act (EU AIA) was adopted by the European Parliament. Subsequently, on 21 May 2024, the Council adopted an innovative legislative framework that harmonises the standards and rules for AI regulation. This framework is set to take effect in May 2026, with the central objective of stimulating and motivating a fair, safe, legal single market that respects the principles of ethics and the fundamental rights of the human person. Methods: The current legal analysis focuses on the European Unions' new institutional governance involving a multistage approach to managing health data, ethical artificial intelligence, generative artificial intelligence and classification of types of AI by considering the degree of risk (e.g. artificial intelligence systems with limited risk and systems with high risk) and medical devices. It outlines the legal framework for AI regulation and governance in the EU by focusing on compliance with the previously adopted legislation in the Medical Devices Regulation (2017) and the In-Vitro Diagnostic Regulation (2017). The paper also examines the application of the newly adopted EU Artificial Intelligence Act in relation to national justice systems, previous EU regulations on medical devices and personal data protection regulation, and its correlation with the European Court of Human Rights jurisprudence. This opens up complex discussions related to judicial reform and access to justice. For this purpose, as a research objective, the legal analysis includes an innovative perspective following an integrative discussion on the latest legal reforms and regulations of the AI sector in Eastern Europe launched in 2024 with a special focus on the latest developments in the EU Candidate Countries namely Ukraine and the Republic of Moldova. Results and conclusions: The present research facilitates the exploration of the real benefits of managing innovative AI systems for medical data, research, and development, as well as within the medical technology industry. | [Olimid, Anca Parmena; Georgescu, Catalina Maria] Univ Craiova, Fac Social Sci, Dept Hist Polit Sci & Int Relat, Craiova, Romania; [Olimid, Daniel Alin] Univ Craiova, Dept Biol & Environm Engn, Craiova, Romania | University of Craiova; University of Craiova | Olimid, AP (corresponding author), Univ Craiova, Fac Social Sci, Dept Hist Polit Sci & Int Relat, Craiova, Romania. | anca.olimid@edu.ucv.ro; catalina.georgescu@edu.ucv.ro; daniel.olimid@edu.ucv.ro | anca, olimid/ABC-9367-2020; Olimid, Daniel/ABE-5610-2020; Georgescu, Catalina/GZL-2671-2022 | Georgescu, Catalina Maria/0000-0002-4462-4689; Olimid, Anca Parmena/0000-0002-7546-9845 | NaN | NaN | NaN | NaN | 27 | 1 | 1 | 29 | 37 | EAST EUROPEAN LAW RESEARCH CENTER | KYIV | BANDERY STEPANA STR., 20A, KYIV, 04655, UKRAINE | 2663-0575 | 2663-0583 | NaN | ACCESS JUSTICE E EUR | Access Justice East Eur. | NOV | 2024 | 7.0 | 4.0 | NaN | NaN | NaN | NaN | 120.0 | 142.0 | NaN | 10.33327/AJEE-18-7.4-a000103 | http://dx.doi.org/10.33327/AJEE-18-7.4-a000103 | NaN | SEP 2024 | 23 | Law | Emerging Sources Citation Index (ESCI) | Government & Law | O1Q6X | NaN | gold | NaN | NaN | 2025-04-30 | WOS:001304269100001 | View Full Record in Web of Science |
Raw Annotations
This section shows the initial LLM-generated annotations before any processing or cleaning.
Raw Annotations
| filename | source | title | summary | is_english | audience_legal_access | llm_use | paper_type | sentiment | technique | testing | results | obstacles | solutions | topics | community | legal_field | jurisdiction | training_data | design_methodologies | deployment | claimed_availability | claimed_open_availability | which_claimed_availability | gaps | challenges | risks |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| UsinggenerativeAIinhumanresourcedevelopmentanappliedresearchstudy.pdf | Google_Scholar | Using generative AI in human resource development: an applied research study | This paper reports an experimental study investigating the use of ChatGPT for designing Human Resource Development (HRD) interventions with doctoral students. It identifies benefits like accelerated idea generation and drawbacks such as generic outputs and lack of contextual understanding, emphasizing the crucial role of human expertise in refining AI suggestions. | True | NaN | True | 2.0 | NaN | ChatGPT | Experimental study where 16 HRD doctoral students from two US universities used ChatGPT to develop an HRD intervention plan based on a provided organizational scenario. Data collected included intervention plans, prompts used, ChatGPT outputs, and participant responses to open-ended questions. | ChatGPT significantly reduced the time needed for the task (estimated three times faster) and was valuable for initial brainstorming and idea generation. However, its outputs were often generic, lacked contextual understanding specific to HRD situations, sometimes returned the same text despite refinement prompts, lacked source disclosure, and required substantial revision and validation by users with HRD expertise. | NaN | NaN | NaN | NaN | Human Resource Development | USA | NaN | NaN | NaN | True | True | ChatGPT is publicly accessible online through OpenAI, offering free usage tiers. | NaN | Challenges in using ChatGPT included its tendency to produce generic, non-contextualized outputs; difficulty in refining outputs through prompts; lack of source disclosure and accountability; potential security and confidentiality issues with proprietary data; the need for significant user expertise to evaluate and refine outputs; and the risk of over-reliance or accepting incorrect information ('black boxing'). | Risks identified include generating inaccurate or biased information, deskilling of HRD professionals, loss of human learning opportunities (especially for novices), potential for cognitive overload, security/confidentiality breaches when using company data, ethical concerns (e.g., inappropriate AI-driven decisions impacting individuals), and the danger of users uncritically accepting AI output leading to poor problem-solving. |
| KzGuuCj9bCMJ.pdf | Google_Scholar | Justice Link: Tech-Driven Solutions for Undertrial Prisoner | This paper proposes "Justice Link", a web-based platform designed to address systemic challenges faced by undertrial prisoners in India, such as prolonged detention and limited legal access. Pilot testing showed improved communication and reduced case processing times, highlighting technology's potential to enhance efficiency and access to justice despite infrastructure challenges. | True | Idealistic | False | 1.0 | Positive | Justice Link: A web-based platform with features like a centralized digital dashboard, real-time case request system, automated notifications, secure login, remote legal consultations, rehabilitation program integration, and database management. | Pilot implementation in selected prisons. Evaluation focused on system performance metrics (response time, query efficiency, data accuracy) and impact metrics (reduction in case processing times, improvement in lawyer-prisoner communication). | Pilot projects showed a 30% reduction in case-processing times and a 40% improvement in lawyer-prisoner communication. System performance testing showed response time < 3 seconds (observed 2.8s), query efficiency < 200ms (observed 185ms), and data accuracy of 99.8%. | Systemic delays in judicial proceedings, overcrowded prisons, psychological impact and social stigma, insufficient rehabilitation opportunities, poor health and hygiene conditions, limited legal aid access, lack of efficient case management, communication barriers between prisoners and legal representatives. | Developing and deploying a web-based platform ("Justice Link") to digitize case management, facilitate secure communication between prisoners and lawyers, provide access to rehabilitation programs, automate notifications, and streamline legal aid requests. | Access to legal aid, case management efficiency, prisoner-lawyer communication, rehabilitation access, reducing pre-trial detention time, ensuring speedy trials. | Undertrial prisoners in India. | Criminal Justice, Criminal Procedure | India | NaN | Literature review, primary data collection (interviews, surveys), secondary data analysis (official reports, pilot projects), case study analysis, qualitative analysis, quantitative analysis, field visits, pilot implementation and evaluation. | Pilot implementation in selected prisons. | False | False | NaN | Need for improved digital infrastructure in prisons, staff training, system scalability, integration with national databases and court management systems, multilingual support. | Implementing technology within existing prison infrastructure limitations, ensuring usability for prisoners and staff, need for staff training, ensuring data accuracy and system reliability for sensitive information. | Privacy and security risks associated with handling sensitive prisoner and case data (mitigated by secure login/authentication). |
| zMxRuhVaiw8J.pdf | Google_Scholar | Bridging the Legal Literacy Gap: A Survey on \nAI-Driven Document Simplification and Generation | This paper surveys AI-driven legal document simplification and generation, proposing an AI-powered legal documentation assistant for India. The system, using NLP and ML, aims to offer bilingual (English/Hindi) document drafting, simplification, and compliance checks to improve legal literacy and access to justice. | True | Idealistic | True | 1.0 | Positive | AI-powered legal documentation assistant utilizing NLP (tokenization, POS tagging, NER, dependency parsing, sentiment analysis) and ML techniques (supervised, unsupervised, transfer learning), including transformer models (BERT, GPT, Seq2Seq with attention). It provides bilingual (English/Hindi) document simplification, generation, and rule-based compliance checking. | NaN | NaN | Legal literacy gap; high cost and complexity of legal services hindering access to justice, particularly for underprivileged individuals and small businesses in India. | Development of an AI-powered legal documentation assistant for document simplification and generation, bilingual (English/Hindi) support, automated compliance monitoring, and an option to seek expert legal advice, aimed at democratizing legal information and reducing costs. | Legal document simplification, legal document generation, legal literacy, access to legal information. | Individuals, small businesses, and underprivileged populations in India. | General legal matters, routine legal documents. | India | Proposed collection of diverse Indian legal documents, including original texts paired with simplified versions, and a parallel corpus of English and Hindi legal terms/phrases. Data is unstructured; public/proprietary status and specific sources are not detailed. | Iterative design process including: data collection & preprocessing, training data preparation (paired texts, parallel corpus), transformer-based model architecture design (BERT, GPT), model training (fine-tuning, transfer learning), web-based user interface development, template-based & AI-generated content pipeline for document creation, rule-based compliance checking, and planned testing (unit, integration, user acceptance). | Proposed deployment via a web-based interface. No further deployment or diffusion strategies are detailed. | False | False | NaN | Accuracy and reliability of AI in handling legal nuances; lack of contextual understanding in AI; limited customization of AI tools for specific legal areas/jurisdictions; lack of transparency/explainability in AI models; perpetuation of biases from historical data; security/privacy concerns with sensitive legal data; limited scope of document types handled by AI; scarcity of large-scale Indian legal datasets for AI training; limited research focus on the Indian legal context. | Ensuring accuracy and reliability with complex legal language; achieving deep contextual understanding; providing sufficient customization for diverse legal needs; improving transparency and explainability of AI models; mitigating bias from training data; addressing security and privacy of legal information; handling a wide range of document types; overcoming scarcity of domain-specific (Indian legal) datasets; adapting models for multilingual contexts; high computational resource requirements for advanced models. | Misinterpretations or inaccuracies in AI-generated legal documents leading to serious legal consequences; perpetuation of existing biases present in historical legal data by AI systems; data privacy and confidentiality breaches of sensitive legal information. |
| DHTZl_KKZDkJ.pdf | Google_Scholar | Treu und Glauben: Frag GPT | The paper interprets the German legal principle of "Treu und Glauben" (good faith/fairness) through the lens of behavioral economics, focusing on fairness norms. It proposes using Large Language Models (LLMs) like GPT as an empirical tool to probe fairness perceptions in specific legal case variations involving this principle. | False | Idealistic | True | 1.0 | Positive | Using LLM (GPT-3.5 Turbo) via API calls to repeatedly assess fairness judgments (binary yes/no answers) on specific, detailed legal case vignettes involving the principle of 'Treu und Glauben'. | Three case studies (contractual adjustment due to unforeseen costs, withdrawal of administrative benefits, refinements of the contractual adjustment case) with variations were presented to GPT-3.5 Turbo 100 times each via API. The distribution of 'yes'/'no' answers regarding the fairness/appropriateness of a specific outcome was analyzed. | GPT's fairness assessments varied significantly and meaningfully across different case variations (e.g., justifying price increases more for external raw material cost shocks (71% yes) than internal personnel issues (36% yes)), suggesting sensitivity to context. Adding information about explicit reliance or prior negotiation details also strongly influenced the model's assessment. | The difficulty for legal practitioners to apply open-ended normative standards like 'Treu und Glauben' objectively, as it involves complex, context-dependent fairness assessments where professional legal training alone may not suffice. | Employing LLMs as an empirical method to gather data on fairness intuitions regarding specific case details, thereby providing judges and lawyers with broader, potentially more objective input for their discretionary judgments. | Improving judicial/administrative decision-making quality and consistency in cases requiring fairness assessments under 'Treu und Glauben'. | NaN | German Civil Law (Contract Law), German Administrative Law | Germany | The technique uses GPT-3.5 Turbo, which is trained on a large, proprietary dataset of general text and code by OpenAI. | Experimental design using structured prompts presented via API to an LLM, prompt engineering to elicit binary responses, repetition for statistical analysis, systematic variation of case details. | NaN | False | False | NaN | The need for further research to validate how accurately LLM responses reflect nuanced human fairness judgments and cognitive biases before such methods can be reliably used in legal practice. | Designing effective prompts ('prompt engineering') to elicit specific, quantifiable fairness judgments (yes/no) from the LLM; ensuring reproducibility and control over the interaction (addressed via API usage). | The risk of relying on insufficiently validated AI assessments for legal judgments; potential discrepancies between LLM outputs and genuine societal fairness norms. |
| zUG2dOzRSrAJ.pdf | Google_Scholar | SoMeLVLM: A Large Vision Language Model for Social Media Processing | The paper introduces SoMeLVLM, a large vision-language model specifically designed for social media tasks, leveraging a custom cognitive framework and a large multimodal dataset for instruction tuning. SoMeLVLM demonstrates state-of-the-art performance on various social media analysis benchmarks, overcoming limitations of general models in understanding informal language and multimodal context. | True | NaN | True | 1.0 | NaN | SoMeLVLM: A Large Vision Language Model (based on Vicuna-7b-v1.1 and Blip2 architecture) fine-tuned using instruction tuning (QLoRA for LLM) on a custom multimodal social media dataset based on a five-level cognitive framework (Knowledge & Comprehension, Application, Analysis, Evaluation, Creation). | Evaluated on 14 multimodal datasets and 12 plain text datasets covering social media tasks (emotion, humor, hate speech, misinformation, ideology, etc.) using zero-shot settings. Comparison against baseline LLMs and LVLMs using Accuracy (overall Acc and instruction-following Acc*), BLEU-L, ROUGE-L, and GPT-4 scoring. | Achieved state-of-the-art zero-shot performance on various social media classification and generation tasks (both text-only and multimodal), significantly outperforming baseline LLMs and LVLMs across different cognitive levels. For instance, achieved 72.57% overall accuracy (Acc) on multimodal hate speech classification (Table 2). | NaN | NaN | NaN | NaN | NaN | International | A custom 654k multimodal social media instruction-tuning dataset, combining existing open-source benchmarks (e.g., Sentiment140, FakeNewsNet, jigsaw, MVSA, listed in Appendix A.1.1) and self-collected social media data (text and text-image pairs from unspecified platforms). Some labels/explanations for generation tasks were produced using GPT-4/GPT-4V. | Instruction tuning (QLoRA for LLM, standard fine-tuning for vision connection module) of a base LVLM architecture (Vicuna-7b-v1.1 + vision components). Development of a cognitive framework (based on Bloom's Taxonomy) to guide task selection and dataset construction. Manual prompt engineering for instruction formatting. | NaN | False | False | NaN | The model primarily focuses on English and may not generalize well to other languages. It may exhibit interpretive biases towards neologisms and culturally specific terms, especially without sufficient context. | Curating a large-scale, high-quality multimodal instruction dataset specific to social media, covering diverse tasks and cognitive levels. Designing an effective cognitive framework to structure the tasks and model capabilities. Overcoming limitations of general-purpose models in understanding informal language, multimodal nuances, and complex cognitive requirements of social media tasks. Addressing the observed degradation in instruction-following ability when adapting LLMs into LVLMs. | Potential for interpretive biases towards neologisms, slang, or culturally specific language. Privacy risks associated with the collection and use of real user data from social media platforms (though the authors state intent to mitigate this before dataset release). |
| F0YMldnt1UoJ.pdf | Google_Scholar | GENERATIVE AI IMPACT ON LABOR MARKET: ANALYZING CHATGPT’S DEMAND IN JOB ADVERTISEMENTS | This study examines the demand for ChatGPT-related skills in the U.S. labor market by analyzing job advertisements collected between May and December 2023. Using text mining and topic modeling, it identifies five key ChatGPT-related skill sets and details associated job attributes, highlighting Gen AI's increasing integration and evolving skill requirements. | True | Market | True | 2.0 | NaN | Data-driven analysis of job advertisements using text mining, NLP (TF-IDF, cosine similarity, fuzzy string matching), machine learning classification (SVM), and topic modeling (LDA) to identify trends in demand for Generative AI skills. | SVM model for job title classification: 5-fold cross-validation, evaluated for false positive rate (0.5%), precision (99%), and accuracy (99%). LDA model for topic modeling: evaluated using coherence and perplexity metrics, and manual examination of topic interpretability. | SVM for job title classification achieved 99% precision and accuracy. LDA identified five distinct ChatGPT-related skill sets (General Familiarity, Creative Content Generation, Marketing, Advanced Functionalities, AI Product Development) with varying prevalence and centrality across occupation families. | NaN | NaN | NaN | NaN | Legal services | United States | A proprietary dataset of 1128 unique U.S. job postings collected from Indeed, LinkedIn, and ZipRecruiter (May-Dec 2023). O*NET database was used as a reference for job titles. A subset of this data, with O*NET titles as labels, was used to train an SVM for job title classification. | A multi-step research methodology involving: 1) Web scraping of job advertisements, data cleaning (removing irrelevant and duplicate postings), and de-duplication. 2) Job title identification and standardization using TF-IDF, cosine similarity, fuzzy string matching, and SVM classification against the O*NET database. 3) Topic modeling (LDA) on job description segments related to ChatGPT to identify skill clusters. | NaN | True | True | The dataset of 1128 unique job postings is stated to be provided in Supplementary Materials. The analytical techniques (text mining, SVM, LDA) are standard and can be implemented with open-source tools. | NaN | Data quality issues (irrelevant and duplicate job postings), complexity in standardizing job titles from natural language descriptions, ensuring interpretability of topic models. | Job displacement, widening of skills gaps, wealth disparity; ethical concerns regarding biases in AI-generated legal advice. |
| S4tkmznxx80J.pdf | Google_Scholar | A Framework for Data-Driven Legal Regulatory Reform | This paper proposes a framework based on the scientific method for data-driven legal regulatory reform in Washington State, aiming to make the process faster, more evidence-based, and focused on access-to-justice impact. The framework incorporates risk assessment (present and future) and suggests methods for measuring benefits, particularly concerning the access-to-justice gap. | True | Idealistic | False | 1.0 | Positive | A conceptual three-dimensional framework for data-driven legal regulatory reform, incorporating risk assessment (current and future) and access-to-justice impact evaluation, potentially implemented within a regulatory lab/sandbox. | The paper describes the conceptual framework and its components (e.g., risk matrix, reference to NCSC A2J assessment tool) but does not report on specific testing or empirical evaluation of the framework itself in practice. | NaN | Slow pace of traditional legal regulatory reform; bespoke nature of reform efforts; lack of data collection and evaluation of reform impacts; difficulty measuring access-to-justice improvements; cost and perceived difficulty of data collection; concerns about client confidentiality (RPC 1.6); passive public involvement in reform processes; professional conservatism. | Adopt a data-driven framework using the scientific method for legal regulatory reform; use hypotheses (proposed reforms) and test them in safe environments (labs/sandboxes); systematically collect data on risks (present and future) and benefits (especially A2J impact); use tools like risk matrices and A2J assessment methodologies; make reform processes more timely and evidence-based. | Legal regulatory reform process; Access to justice gap measurement and reduction; Innovation in legal services delivery; Regulation of legal service providers (including alternative providers and online services). | General public facing the access-to-justice gap in Washington State. | General / Regulatory | Washington State (USA), with references to Utah and Arizona. | NaN | Scientific method principles; Iterative development (multiple blueprint versions); Conceptual modeling (3D framework); Incorporation of existing risk assessment matrices and access-to-justice evaluation tools (e.g., NCSC tool). | The framework was developed by the Washington State Practice of Law Board (POLB) and described in this publication. The paper expresses hope that others will adopt and adapt it. | False | False | NaN | Need for robust methodologies to measure access-to-justice impact; Overcoming challenges in collecting meaningful data while respecting confidentiality; Addressing long-term/future risks of legal innovations; Mitigating bias in risk assessment; Gaining wider adoption of data-driven approaches within the legal profession. | Dealing with scarce data in the legal services market; Quantifying the benefits (access-to-justice impact) of regulatory reforms; Accurately assessing and mitigating both current and future risks; Overcoming stakeholder resistance or skepticism (e.g., framing of 'sandbox' vs 'lab'); Balancing innovation with core professional duties (competence, confidentiality, conflicts, communication, safeguarding property). | Consumer harm from ineffective or flawed reforms (inaccurate results, failure to exercise rights, buying unnecessary services); Breach of client confidentiality; Conflicts of interest; Incompetent service provision; Poor communication; Mishandling client funds/property; Bias in risk assessment favoring developers over public good/justice; Unforeseen negative consequences ('unknown unknowns') of reforms, especially longer-term ones. |
| n-8yKQ3b_hkJ.pdf | Google_Scholar | Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise: A Case Study on Chinese Legal Domain | This paper proposes an 'adapt-retrieve-revise' framework to improve the performance of Large Language Models (LLMs) like GPT-4 in specialized domains, specifically Chinese law, by mitigating hallucinations. The method involves adapting a smaller 7B LLM to the domain, using its output to retrieve evidence, and then having GPT-4 revise the draft answer based on this evidence. | True | Market | True | 1.0 | NaN | Adapt-retrieve-revise framework for LLM domain adaptation. | Evaluated in a zero-shot setting on four Chinese legal tasks (Law Clause Recommendation, Criminal Prediction, LegalQA, JEC-QA) and a Similar Case Retrieval task. Metrics included F1, Recall, Accuracy, Precision@k, and MAP. Human evaluation was used for JEC-QA. | The proposed method (7B legal LLM for draft, answer-based retrieval, GPT-4 for revision) achieved an average score of 78.7 across four tasks, improving by +33.6 points over GPT-4 direct generation, and outperforming two stronger retrieval-based baselines by +17.0 and +23.5 points. | NaN | NaN | NaN | NaN | General Chinese law, potentially covering civil, criminal, administrative, and enforcement law based on training data sources and tasks like criminal prediction and legal QA. | Chinese legal domain | For domain adaptation of the 7B LLM (Baichuan-7B): continual learning on over 50B tokens from 'Chinese Law Clauses' (publicly available from flk.npc.gov.cn) and 'Chinese Judgments Online' (publicly available from wenshu.court.gov.cn). For supervised fine-tuning: 70K instruction examples (52K GPT-4 self-instruct Chinese data and 18K human-expert created legal instructions). | The proposed adapt-retrieve-revise framework consists of: 1) Domain adaptation of a 7B LLM (Baichuan-7B) through continual pre-training on Chinese legal corpora followed by supervised fine-tuning on legal instruction data. 2) Answer-based evidence retrieval, where the draft answer from the adapted 7B LLM is used to query an external knowledge base (e.g., Chinese law clauses, legal textbooks) via a sentence embedding model (Multilingual-E5-large) and kNN. 3) Revision by GPT-4, which takes the original query, the draft answer, and the retrieved evidence as input to produce the final answer. | The primary diffusion strategy is the release of the training code for their domain-adapted 7B LLM on GitHub. | False | False | NaN | NaN | Key challenges include LLM hallucinations in specialized domains like Chinese law, the high cost and impracticality of continually training very large models (e.g., GPT-4 scale) on domain-specific data, limitations of standard query-based retrieval systems, residual inaccuracies and hallucinations in smaller domain-adapted LLMs (7B scale) due to limited capacity, difficulties in automatic evaluation of generative outputs requiring human evaluation, and the expense of GPT-4 API access and human evaluation limiting the scale of experiments. | The primary risk discussed and addressed is LLM hallucination: the tendency of LLMs to generate non-logical content, factual mistakes, and fail to refer to correct legal provisions, especially when applied to specialized domains like law where accuracy is critical. |
| oWv69BqZmVMJ.pdf | Google_Scholar | Legal Evalutions and Challenges of Large Language Models | This paper reviews and evaluates the performance of various Large Language Models (LLMs), including general-purpose and legal-specific ones, on legal case judgment tasks using Chinese and English datasets. It discusses LLM capabilities, highlights key challenges like data privacy, liability, ethics, and technical limitations, and assesses their potential in the legal field. | True | Market | True | 2.0 | Neutral | Evaluation of various LLMs (e.g., GPT-4o, O1-preview, Qwen2, Gemma2, GLM-4, LawGPT_zh, lawyer-llama, llama3.2, Mistral, Phi-3.5) on legal case judgment tasks. | Evaluation on 26 legal cases (13 Chinese, 13 US from Court Listener and Chinese Judgments Online) covering civil, criminal, and administrative law. Metrics used: ROUGE-1, ROUGE-2, ROUGE-L, BLEU, and human evaluation scores (1-5 scale by law students). | O1-preview achieved the highest overall human evaluation score (3.96). Automated metrics (ROUGE) showed different top performers (e.g., Phi-3.5-mini-instruct, lawyer-llama-13b-v2), highlighting a discrepancy between lexical overlap and perceived judgment quality. | NaN | NaN | NaN | NaN | Civil law, criminal law, administrative law, immigration law | China, United States | NaN | NaN | NaN | False | False | NaN | Need for improvements in LLM training methodologies, domain-specific legal knowledge integration, reasoning capabilities, interpretability, legal frameworks for liability, and ethical review mechanisms. | Data privacy concerns with sensitive case information; unclear definition of legal liability for AI outputs; ethical issues including bias and lack of transparency; technical limitations in understanding legal nuances and interpretability; complexities due to legislative differences across jurisdictions. | Data leakage (privacy violation), generation of biased/unfair outputs, incorrect legal advice/analysis leading to undesirable outcomes and liability issues, undermining reliability of legal practice due to technical limitations/lack of interpretability, compliance risks due to legislative differences. |
| BILETA_Response_to_White_Paper_AI_Regulation_A_Proinnovation_Approach.pdf | Google_Scholar | BILETA Response to White Paper AI Regulation: A Pro-innovation Approach | This paper is a formal response by the British Irish Law, Education and Technology Association (BILETA) to the UK government's white paper on AI regulation. BILETA critiques the proposed non-statutory, principles-based approach, advocating for mandatory statutory regulation and clearer, more accessible redress mechanisms to effectively address AI risks and protect user rights. | True | Idealistic | False | 3.0 | Negative | NaN | NaN | NaN | Inadequate, unclear, expensive, and inaccessible redress mechanisms for AI-related harms; a proposed voluntary and principles-based regulatory system is deemed insufficient to protect user rights or prevent AI abuse, potentially leading to arbitrary enforcement and adverse impacts. | Implementation of mandatory, statutory AI regulation (akin to the EU AI Act) to provide clarity and enforcement; establishment of clear, accessible, and effective redress mechanisms for individuals and groups, including class actions and judicial review, potentially overseen by a single or coordinated independent regulatory body. | Adequacy and accessibility of redress mechanisms for AI-related harms; contestability of AI decisions; fairness, accountability, and governance in AI systems; protection of user rights against AI-driven harms. | General users and consumers of AI systems; marginalized populations and communities (specifically mentioned in the context of risks from Large Language Models). | AI regulation, Technology Law, Administrative Law, Human Rights Law | UK | NaN | NaN | NaN | False | False | NaN | The lack of mandatory and statutory regulation, leading to a potentially voluntary and arbitrary system; insufficient clarity and strength in the proposed non-statutory principles to ensure fairness, accountability, and redress; inadequate mechanisms for user contestation and appeals for AI-related harms; difficulty in allocating legal responsibility, especially for foundation models. | NaN | Difficulties in obtaining redress for AI-related harms; potential for AI abuse and adverse impacts on user rights and interests due to weak regulation; specific risks from LLMs such as 'hallucinations', propagation of bias against marginalized communities, and negative impacts on the workforce, economy, and fundamental rights like free elections and non-discrimination. |
| EeMzvDhc2e8J.pdf | Google_Scholar | Artificial Intelligence in Accounting, Medicine, and Law with Potential Implications for Financial Planning: A Review of Literature | This paper reviews the impact of generative Artificial Intelligence (AI) on the professions of accounting, medicine, and law, drawing parallels and discussing potential implications for financial planning. It highlights AI's capacity to automate tasks and improve efficiency while emphasizing the ongoing necessity of human skills, judgment, and ethical considerations in these fields. | True | Idealistic | True | 3.0 | Positive | DoNotPay ("World's First Robot Lawyer") | The paper reports that DoNotPay's CEO claims over 2 million successfully resolved cases through AI. The paper does not conduct its own evaluation of DoNotPay. | The paper reports that DoNotPay's CEO claims over 2 million successfully resolved cases. | The financial prohibitiveness of hiring a lawyer for low-income individuals, with 80% reportedly unable to afford legal representation. | AI-powered tools like DoNotPay to bridge the justice gap and expand access to legal counsel for low-economic communities. | Access to legal counsel, resolving common legal disputes (e.g., related to medical bills). | Individuals from low-economic communities, low-income individuals. | Legal conflicts related to medical bills (specifically for DoNotPay). The paper also broadly mentions AI for developing wills, trusts, and other legal documents. | USA | NaN | NaN | Available as a subscription-based online service/app (DoNotPay). | True | False | DoNotPay is described as having active subscribers and a website (donotpay.com). | The need for human lawyer involvement for complex legal issues and situations requiring nuanced legal strategy, decision-making, and ethical considerations, which AI tools like DoNotPay may not fully address for underserved communities. | Potential for AI (like the type DoNotPay might use, e.g., generative AI) to 'hallucinate' or produce fictitious information, lack of nuanced legal reasoning, and the challenge of ensuring authenticity and reliability of AI-generated legal content or advice. | Over-reliance on AI for tasks requiring human judgment, leading to serious repercussions such as the submission of fictitious case law generated by AI (as in Mata v. Avianca), resulting in legal penalties and undermining the legal process. |
| uBHZkwvRvS0J.pdf | Google_Scholar | LawLLM: Intelligent Legal System with Legal Reasoning and Verifiable Retrieval | This paper introduces LawLLM, an intelligent legal system powered by LLMs, designed to offer versatile legal services. LawLLM is enhanced with legal reasoning through syllogism-based fine-tuning and verifiable retrieval capabilities to ensure accurate and reliable outputs based on external knowledge. | True | Idealistic | True | 1.0 | Positive | LawLLM: An LLM (Baichuan-13B-Base) fine-tuned using a custom supervised dataset (Law-SFT) incorporating legal syllogism prompting for enhanced legal reasoning and a triplet instruction format for verifiable knowledge retrieval. | Evaluated using the custom Law-Eval benchmark (objective and subjective assessments, including Chinese legal examinations and GPT-3.5 as a subjective referee) and the LawBench benchmark (20 legal tasks). | On the Law-Eval objective evaluation, LawLLM (13B) achieved an average total score of 37.11, outperforming other LLMs including GPT-3.5-turbo (34.10). On LawBench, LawLLM outperformed GPT-3.5-turbo on average performance (zero-shot). | Limited accessibility to reliable and versatile intelligent legal systems for the general public due to the task-specific focus of prior work. Unreliability of LLMs stemming from issues like hallucinations, difficulty with long-tail knowledge, and inconsistent or unverified use of retrieved information. | Development of multi-task LLMs like LawLLM with features such as: 1) Versatile services through multi-task capabilities, 2) Enhanced legal reasoning fine-tuned with legal syllogism prompting, and 3) Verifiable retrieval to distinguish, incorporate, and validate external knowledge, thereby improving reliability and accessibility. | Legal consultation, legal question answering, improving understanding and accessibility of legal information and services for the general public. | General population, students (as part of a broader set of users that also includes legal professionals). | General Chinese Law (covering areas tested in National Judicial Examination, Patent Agent Examination, CPA examination, etc., e.g., civil law, bidding law). | China | Law-SFT dataset, a high-quality supervised fine-tuning dataset, constructed from: 1) Public NLP legal task datasets (e.g., LEVEN, JEC-QA, CAIL2018), 2) Crawled legal raw text (e.g., judicial advisory websites, Chinese laws and regulations, typical cases, judicial verdicts), 3) Open-source instruction datasets (e.g., Lawyer-LLaMa, LawGPT-zh). Data is primarily unstructured text processed into instruction pairs and triplets. | Supervised fine-tuning (SFT) of a pre-trained LLM (Baichuan-13B-Base). Creation of the Law-SFT dataset involved: Pair Instruction Generation (rule-based cleaning, LLM-assisted Behavior Shaping with legal syllogism prompting, Thinking Development with Law-specific Chain of Thought) and Triplet Instruction Generation (for verifiable retrieval, including addition of distractors). A two-step fine-tuning process: legal reasoning fine-tuning and retrieval augmentation fine-tuning. | NaN | True | True | Detailed resources (model, code, and/or data) are available on GitHub: https://github.com/FudanDISC/DISC-LawLLM. | NaN | Developing advanced legal reasoning capabilities in LLMs that align with established legal frameworks. Ensuring robust, faithful, and verifiable utilization of external legal knowledge, including the ability to distinguish relevant information from distractors and mitigate model hallucinations. | Model hallucinations and generation of unreliable outputs in legal scenarios, particularly if external knowledge is not correctly distinguished, incorporated, and verified. |
| TCfDBG8LDZYJ.pdf | Google_Scholar | InspirePat: An approach for patent recommendation based on Siamese ERNIE model and Large Language Model | This paper introduces InspirePat, a novel framework for patent recommendation that utilizes Large Language Models (LLMs) for technical problem extraction and expansion, and a Siamese ERNIE (SERNIE) model for information retrieval. The system aims to help engineers find innovative solutions from patents by improving upon existing keyword matching and BERT-based methods. | True | Market | True | 1.0 | NaN | InspirePat framework, which includes LLMs (GPT-3.5 Turbo) for technical problem extraction and generation, a technical problem database constructed using BART summarization, a Siamese ERNIE (SERNIE) model for patent retrieval, and a HyDE-inspired filtering mechanism. | The SERNIE model component was evaluated on the SICK dataset (Pearson and Spearman correlations) and a labeled patent sentence pair dataset (F1 score, Precision, Recall, Accuracy). The overall InspirePat framework was demonstrated through case studies. | On a labeled patent sentence pair dataset, the SERNIE model (NO. =7 configuration: epoch=4, learning rate=2e-5, batch size=8) achieved an F1 score of 0.893. This outperformed BERT (F1=0.8412) by 5.2% and ERNIE (F1=0.8703) by 3.2%. | NaN | NaN | NaN | NaN | Patent law | United States | SERNIE model: Sentences Involving Compositional Knowledge (SICK) dataset, a public benchmark dataset. Technical problem database: Constructed from full-text XML data of US patents (2010-2020) from USPTO, specifically using a 'technical problems' dataset of over 89,000 entries, with descriptions summarized using a BART model. | Framework development involving: 1) LLM (GPT-3.5 Turbo) for technical problem extraction from patent IDs and generation of relevant problems using prompt engineering. 2) Construction of a technical problem database from USPTO patent data, summarized using a BART model. 3) Training a Siamese ERNIE (SERNIE) model for similarity assessment. 4) A HyDE-inspired filtering mechanism using LLM-generated hypothetical answers. 5) Text segmentation for patent Q&A. | A prototype system was built to demonstrate the InspirePat framework. | False | False | NaN | NaN | Key challenges included: 1) Overcoming LLM hallucination and leveraging their generative capabilities effectively. 2) Processing full patent text despite model input length limitations. 3) Constructing a high-quality, concise technical problem database from lengthy patent texts without losing critical information. 4) The scarcity of large-scale, labeled patent-specific datasets for training retrieval models. 5) Improving retrieval accuracy beyond simple cosine similarity in high-dimensional spaces. | LLM hallucination leading to misunderstandings and inaccuracies in responses. |
| feE2u5tQrLYJ.pdf | Google_Scholar | DIGITAL TRANSFORMATION OF LEGAL SERVICES AND ACCESS TO JUSTICE: CHALLENGES AND POSSIBILITIES | This paper examines the potential and challenges of using digital technologies, particularly AI and Human Language Technologies (HLT), to improve access to justice in the post-pandemic era. It discusses technical, legal, and ethical hurdles, using the Lithuanian language and the Semantika-2 project as a case study to illustrate difficulties, especially for under-resourced languages. | True | Idealistic | False | 3.0 | Neutral | Semantika-2 project tools: automatic speech-to-text transcription, automatic document summarisation, semantic analysis (NER, aspect-based sentiment analysis), automatic spell checking, linguistic analysis tools for Lithuanian. | Qualitative discussion of challenges and outcomes for the Semantika-2 project (e.g., need for hybrid methods, 96% accuracy for morphological tagging) without formal benchmark testing reported. | Achieved 96% accuracy for morphological tagging using a custom adjusted Hunspell-based tagger. Highlighted significant challenges and limitations for other NLP tasks (e.g., NER, context handling) due to data scarcity and linguistic peculiarities of Lithuanian legal text, necessitating hybrid rule-based/neural approaches. | Lack of access to legal services for vulnerable groups; high litigation costs; lack of transparency; inequality of arms due to digital divide and technological illiteracy; complexity of legal language; lack of public legal knowledge. | Digitalisation (e-filing, online hearings); AI/HLT for document automation, legal research, tech-assisted review, legal advice, outcome prediction; development of language-specific tools (e.g., Semantika-2); ethical frameworks; enhancing AI trustworthiness; citizen empowerment. | Access to legal services, procedural justice (cost, transparency, equality of arms), e-justice, legal information access, language barriers in law, legal tech adoption. | Socially vulnerable groups (women, elderly, minorities, disabled, refugees, low-income, linguistically diverse populations); Lithuanian language users (as case study). | General / Multiple Fields (including civil and criminal procedure) | Lithuania (case study), EU, International | Semantika-2 used a created corpus of Lithuanian legal texts: publicly available legal acts (e-tar.lt) and court decisions (LITEKO), plus a proprietary dataset of 1,500 anonymised, synthetically augmented contracts. Primarily unstructured text. | Applied AI, machine learning (neural networks), rule-based methods, and hybrid approaches. Included linguistic analysis (lexical, syntactic, semantic) and adaptation of NLP techniques for the specifics of Lithuanian legal language. | Results stated as free for public use due to public funding. A project website (www.semantika.lt) is mentioned. | True | False | Semantika-2 project results are free for public use, funded by public sources, accessible via www.semantika.lt. | Technical: AI/NLP limitations (especially for non-English/morphologically rich languages), data scarcity, long-document processing, context handling, AI opacity, bias. Societal/Systemic: Digital divide, lack of trust, need for ethical/legal frameworks, cultural resistance, legal language complexity, ensuring fundamental rights, slow tech adoption. | For Semantika-2: Insufficient Lithuanian legal training data; linguistic peculiarities (capitalization, syntax, vocabulary context); long document context handling; need for custom tools (tagger, NER) and hybrid methods; data anonymization hindering linking; lack of suitable pre-trained models; computational costs. | AI misuse (manipulation, control); biased/discriminatory outcomes; opacity hindering accountability; system inaccuracy/unreliability; infringement of fundamental rights (fair trial, privacy); dehumanisation of justice; projecting past biases via predictive justice. |
| x8wiCT_UuY0J.pdf | Google_Scholar | GUIDEPOST CAPTURING VALUE FROM ARTIFICIAL INTELLIGENCE | This essay discusses how organizations can capture value from rapidly evolving AI, particularly generative AI like ChatGPT. It emphasizes the critical role of developing and managing complementary assets like talent and data amidst technological uncertainty, outlining key questions for future management research. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal services (mentioned as highly exposed industry) | International | NaN | NaN | NaN | False | False | NaN | Understanding required complementary assets for different AI types (generative vs. discriminative); Managing rapid AI evolution and updating complementary assets ('dynamic complementary assets'); Navigating human capital challenges and emerging AI skillsets, especially in creative industries. | Rapid pace of AI development leading to investment uncertainty; Identifying, developing, and updating necessary complementary assets; Managing changes in human capital requirements and the nature of work; Adapting organizational strategies and capabilities. | Potential job displacement due to AI substitution; Risk of technology investments becoming quickly outdated; Challenges for education systems (e.g., cheating). |
| -OR_MJVKvsoJ.pdf | Google_Scholar | Attributed Question Answering for Preconditions in the Dutch Law | This paper proposes and evaluates a Retrieval Augmented Generation (RAG) pipeline designed to answer questions about legal preconditions in Dutch law, providing answers attributed with specific law article references. A new Dutch legal QA dataset with attributions was created for evaluation, showing promising results for generating verifiable legal information for laypeople. | True | Idealistic | True | 1.0 | Positive | Retrieval Augmented Generation (RAG) pipeline for attributed legal question answering focusing on preconditions. | Evaluation used a custom dataset of 102 Dutch legal QA pairs with ground-truth attributions. Metrics included adapted versions of ALCE and G-EVAL, measuring fluency (Coherence, Fluency), correctness (ROUGE-L, METEOR, Consistency, Relevance), and citation quality (Precision, Recall, HitRate@k). Various retrievers (BM25, SBERT, E5, DRAGON, SPLADE) and LLM generators (GPT-3.5, GPT-4O, GEITje, Llama-3-dutch, Fietje) were tested. | The best results were achieved using the E5-multilingual-LARGE retriever and the GPT-4O generator, attaining high scores across metrics, including an 83.0% Hitrate@3 for citation quality. GPT models generally outperformed the tested open-source models. | Costs of legal assistance, lack of public awareness about legal rights and options, and the complexity/specificity of national legal frameworks hindering the development of universal digital legal aid. | Developing automated, language-specific legal Question Answering (QA) systems, particularly Attributed QA using RAG, to provide affordable, accessible, and verifiable legal information tied to primary sources. | Accessing legal information, understanding legal preconditions and rights. | Laypeople encountering civil justice problems, particularly those lacking legal knowledge or facing cost barriers. | Civil Law (based on examples and corpus filtering) | The Netherlands | The RAG system retrieves from a knowledge corpus created from publicly available Dutch law texts (XML from wetten.overheid.nl, parsed into chunks). The LLM generators used were pre-trained models, some with specific Dutch fine-tuning. The evaluation dataset consists of 102 manually created QA pairs with expert verification. | Standard RAG architecture implementation, corpus creation from legal texts, manual creation and expert validation of a QA evaluation dataset, experimentation with various off-the-shelf retrieval and generation components, adaptation of existing evaluation frameworks (ALCE, G-EVAL). | NaN | False | True | Code and dataset are publicly released on GitLab. | Need for validation of layperson understandability and inter-expert agreement, expansion of dataset (e.g., jurisdictions), testing more advanced retrievers (e.g., multilingual hybrid), potential retrieval bias towards specific linguistic patterns (conditional phrases). | Need for language-specific solutions, ensuring output format consistency from LLMs (especially open-source), creating high-quality expert-verified legal datasets, potential loss of meaning when chunking long legal articles. | Implicit risks include generating incorrect or hallucinatory legal information (addressed by attribution) and potential retrieval bias leading to incomplete answers. |
| XR0M6OXV57cJ.pdf | Google_Scholar | Weaving Pathways for Justice with GPT LLM-driven automated drafting of interactive legal applications | This paper investigates using LLMs like GPT-3 and GPT-4 turbo to automate the creation of guided interviews that complete court forms, aiming to assist self-represented litigants. It compares generative AI, constrained template-driven, and hybrid approaches, finding a hybrid model with human review, leveraging the Docassemble platform and Assembly Line Weaver tool, to be the most promising. | True | Idealistic | True | 1.0 | Positive | A hybrid approach using LLMs (GPT-3, GPT-4 turbo) for auto-labeling fields in Word documents and generating draft questions/interview flows from PDF forms, integrated with the Docassemble platform and Assembly Line Weaver tool, with human review points. | Qualitative evaluation of Word document auto-labeling (visual inspection of output); quantitative evaluation of PDF-to-interactive app generation on 12 name change forms (measuring field recognition rates, e.g., 62-69% average, 93% best, 27% worst; 28% checkbox pairing success). | For PDF app generation from 12 forms, 62-69% of fields were automatically processed (93% best, 27% worst); checkbox field to text pairing was successful 28% of the time. Word document field labeling showed promising qualitative results with a revised prompting strategy. | High cost and time (hundreds of hours per form) for manual creation of interactive legal applications (guided interviews) for court forms, hindering assistance for self-represented litigants and large-scale automation efforts. | A hybrid model using LLMs for automated drafting of guided interviews with human review, integrated with tools like Assembly Line Weaver and Docassemble, to significantly reduce the cost and time of form automation. | Automating court form completion via guided interviews for self-represented litigants. | Self-represented litigants | General civil litigation forms (complaints, answers, deeds, wills, demand letters); specifically tested on name change forms (family law). | USA (experiments focused on Massachusetts and name change forms from 12 US jurisdictions). | Pre-trained LLMs (GPT-3, GPT-4 turbo) prompted with text extracted from Word and PDF court forms. No fine-tuning described. | Prototyping, iterative prompt engineering, experimental comparison of three approaches (generative AI, constrained template-driven, hybrid), integration with existing open-source tools (Docassemble, Assembly Line Weaver). | NaN | True | True | Python notebooks on GitHub and Google Colab demonstrating experimental auto-labeling of Word documents and LLM-driven generation of interactive apps from PDF forms. | Improving checkbox field identification in PDFs (28% success); handling all field types; further integration of LLM capabilities with existing tools (Assembly Line Weaver); reducing need for extensive human review for complex forms; addressing form elements requiring external legal research. | For Word documents: Balancing automated field identification with document format preservation, managing LLM context window limitations, and enforcing specific variable naming conventions. For PDF documents: Accurately identifying and contextualizing all fields, especially small ones like checkboxes, due to PDF's stream-based format and reliance on OCR. For both: Designing effective input validation without being resource-intensive or error-prone; preventing error propagation from initial inaccuracies in field identification or question generation. | User annoyance or offense from overly rigid or conversational LLM-based validation; incorrect data processing due to LLM misclassification of field types (e.g., ZIP codes, phone numbers); potential for errors in legal documents if AI-generated content, especially for complex forms, is not thoroughly reviewed by humans. |
| 6dCMgNYlD1wJ.pdf | Google_Scholar | IA Generativa e acesso \nà Justiça: sexta onda e os riscos \ndos LLMs no Judiciário | This paper examines the potential use of Generative AI (LLMs) in the Brazilian Judiciary, framing it within the sixth wave of access to justice. It analyzes benefits like increased efficiency but primarily focuses on proposing a typology of risks—operational, interactional, and systemic—to guide its responsible adoption. | False | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Risks associated with using Generative AI in the judiciary, including: Opacity, lack of explainability and validation, potential for copyright infringement, inaccuracies/errors ('hallucinations'), misalignment with human values, difficulty translating legal concepts to code, automation bias leading to dependency and acriticality, loss of human decisional autonomy, threats to privacy/data protection/confidentiality, generation of disinformation/toxic content, cybersecurity threats, amplification of societal biases and discrimination, loss of cultural/legal particularities. | Proposes a risk typology (operational, interactional, systemic) for better management. Advocates for caution, robust mitigation strategies, human-centric governance, adherence to principles (transparency, accountability, impartiality, due process), human oversight and validation, compliance with regulations (e.g., CNJ Resolution 332/2020, LGPD), and potentially creating clearer, machine-readable laws. | Judicial efficiency and celerity, reduction of case backlog, administration of justice, quality of judicial services, technology's role in access to justice (Sixth Wave). | NaN | Judicial Administration, Constitutional Law, Data Protection Law | Brazil | General discussion: LLMs trained on large, unspecified datasets often scraped from the web; specific mention of desired training on Brazilian jurisprudence for legal-specific AI. | NaN | NaN | False | False | NaN | Lack of transparency and explainability in LLMs; difficulty aligning AI with human/legal values; underdeveloped legal frameworks for Generative AI governance; challenges in translating legal concepts into code; potential for bias amplification; insufficient human oversight mechanisms; concentration of AI development in specific private companies and regions leading to potential cultural homogenization. | Ensuring accuracy and avoiding 'hallucinations'; managing copyright issues with training data/outputs; preventing bias; ensuring privacy and confidentiality; maintaining human autonomy and critical thinking; translating complex legal norms into code; developing effective governance and regulation for Generative AI in the judicial context. | Operational risks (opacity, low explainability/validation, copyright issues, inaccuracies/errors, value misalignment, legal translation difficulties); Interactional risks (automation bias/dependency, loss of autonomy, privacy/data protection/confidentiality/honor violations); Systemic risks (disinformation/toxic content, cybersecurity threats/fraud, bias/discrimination amplification, loss of cultural/legal diversity). |
| Os_WQAcXUMAJ.pdf | Google_Scholar | Using Large Language Models to Support Thematic Analysis in Empirical Legal Studies | This paper proposes a framework using GPT-4 to assist legal experts with thematic analysis, focusing on generating initial codes and identifying themes from legal texts like criminal court opinions. The study demonstrates that the LLM can produce reasonable codes, improve with expert feedback, and effectively classify data and discover themes, supporting empirical legal studies. | True | NaN | True | 1.0 | NaN | A framework leveraging GPT-4 for supporting legal experts in thematic analysis of legal texts, specifically for generating initial codes (phase 2), searching for themes (phase 3), and initial data classification by themes (kick-starting phase 4). | The framework was evaluated using a dataset of 785 facts descriptions from Czech criminal court opinions on thefts. Evaluation included: 1) Manual assessment of LLM-generated initial codes for quality (addressing 'how' and 'what'). 2) Re-assessment after expert feedback. 3) Zero-shot classification performance (R@1, R@3) predicting human-expert themes. 4) Comparison of LLM-discovered themes against expert-identified themes. | After expert feedback, 88.8% of LLM-generated initial codes for facts descriptions were deemed reasonable (addressing how the theft happened and what was stolen). For zero-shot prediction of expert-defined themes, the system achieved an overall Recall@1 of .66 and Recall@3 of .82. | NaN | NaN | NaN | NaN | Criminal Law (specifically theft offenses), Empirical Legal Studies | Czechia | The technique uses OpenAI's GPT-4 model, which is pre-trained on a large, general corpus of text data (proprietary to OpenAI, not detailed in the paper). The framework operates in a zero-shot manner on the evaluation dataset (785 facts descriptions from Czech criminal court opinions) without task-specific fine-tuning. | The framework design involved: 1) Basing the process on the established thematic analysis methodology (Braun & Clarke). 2) An iterative approach for code and theme generation, incorporating an expert feedback loop. 3) Batch processing of input texts to manage LLM context limits. 4) Prompt engineering, including general instructions on thematic analysis and specific research questions/parameters for the given task. | NaN | True | False | The paper describes the framework methodology which can be implemented using OpenAI's GPT-4 API and standard Python libraries; no specific tool or code is directly provided for download. | NaN | Challenges included: initial LLM outputs not fully aligning with analytical needs (e.g., focus of codes), variability in zero-shot theme prediction accuracy, differences in granularity between LLM-discovered and expert-identified themes, ensuring adherence to negative instructions, the black-box nature of proprietary LLMs, and managing LLM context window limitations. | Potential risks include methodological implications of using LLMs for qualitative analysis, over-reliance on autonomous outputs without sufficient expert intervention, and lack of transparency and researcher agency due to the black-box nature of proprietary LLMs. |
| QlA7C_8Et-MJ.pdf | Google_Scholar | Exploring the Capabilities of Chatgpt as a Travel Advisor: A Study on the Use of Generatıve AI in Tourism Marketing | This paper evaluates the capabilities of ChatGPT Model 4 as a travel advisor by analyzing its holiday recommendations for different budgets. The study found that ChatGPT can provide personalized, budget-conscious travel suggestions, typically structured around accommodation, dining, and attractions, but lacks real-time information and transportation advice. | False | NaN | True | 2.0 | NaN | ChatGPT Model 4 | ChatGPT Model 4 was prompted to provide holiday destination recommendations for daily budgets ranging from $50 to $1000, without geographical restrictions. The first five suggestions for 6 different budget levels (30 responses in total) were analyzed using content analysis with MAXQDA, including word frequency, word cloud, and interactive word tree. | ChatGPT demonstrated an ability to offer personalized travel recommendations tailored to different budgets. Recommendations were generally made under the categories of accommodation, dining, and other attractions, with region-specific elements emphasized, and responses followed similar structural patterns. | NaN | NaN | NaN | NaN | NaN | International | The paper states that language models like ChatGPT are trained on an unlabeled dataset consisting of texts from various sources, primarily Wikipedia and various other websites. | NaN | NaN | True | False | ChatGPT Model 4 is available via OpenAI, typically through a subscription model. | NaN | Lack of real-time data access; reliance on pre-loaded information (making its performance on dynamic data like prices or current events questionable); inconsistency in responses to identical prompts; potential biases in output; and the need for human verification of generated content. | Privacy and security concerns, potential for biases in output, risk of misuse, and dissemination of misinformation. |
| ckBaHf32WokJ.pdf | Google_Scholar | UNDERSTANDING THE DUTY OF COMPETENCE FOR ATTORNEYS USING GENERATIVE AI | This paper argues that ethical duties, particularly the duty of competence, are the main regulatory mechanism for attorneys using generative AI (GAI). It posits that competence requires attorneys to make informed decisions about GAI tools and their application, coupled with diligent verification of outputs and retention of human judgment to avoid automation complacency. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal Ethics, Professional Responsibility, General Legal Practice | United States | NaN | NaN | NaN | False | False | NaN | NaN | Challenges for attorneys using GAI include understanding tool-specific limitations, managing output issues (hallucinations, inaccuracies, incompleteness, misgrounded information), the lack of explainability ('black box' problem), and overcoming tendencies towards automation bias and complacency. | Stated risks include attorneys making errors due to GAI hallucinations, inaccuracies, or biases; erosion of attorney skills and judgment through automation complacency and bias; violation of ethical duties (competence, confidentiality, candor); and the potential for GAI's 'black box' nature to obscure flawed reasoning or accountability. |
| XKSKlgFLZNUJ.pdf | Google_Scholar | Comments on “Guide on the use of Generative AI” | This paper provides feedback on the Canadian Treasury Board Secretariat's preliminary guidance on using generative AI (GAI) in federal institutions. It recommends strengthening the guide through new legislation, clearer ethical sourcing rules (copyright, worker protection), and enhanced environmental impact mitigation guidance. | True | NaN | True | 2.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | AI Policy/Regulation, Administrative Law, Copyright Law, Labour Law, Environmental Law, Procurement Law | Canada | NaN | NaN | NaN | False | False | NaN | NaN | Challenges identified regarding public sector GAI use and governance include: difficulty verifying legality and copyright status of GAI data/outputs due to opacity; assessing and mitigating biases in foundation models; ensuring ethical sourcing (especially worker protection in global supply chains); measuring and mitigating environmental impacts; lack of enforceable regulations and independent oversight. | Legal risks of copyright infringement; Perpetuation of biases against minorities due to foundation models; Harm (physical, psychological, socio-economic) to data workers in the GAI supply chain; Significant environmental impacts (water/resource consumption, emissions); Lack of accountability and public trust due to weak governance frameworks. |
| PIIS2405844024030573.pdf | Google_Scholar | Evaluating human resources management literacy: A performance analysis of ChatGPT and bard | This study compares the performance of ChatGPT and Bard on 134 Human Resources (HR) management certification questions, assessing accuracy, relevance, and clarity. ChatGPT slightly outperformed Bard in overall accuracy, suggesting potential suitability for transactional HR roles, while Bard showed more caution, possibly indicating different design safeguards. | True | Market | True | 2.0 | NaN | Comparative evaluation of ChatGPT (GPT-4) and Bard | Performance analysis using a dataset of 134 multiple-choice questions (MCQs) from the Society for Human Resource Management (SHRM) Certified Professional (SHRM-CP) certification. Responses evaluated for accuracy, relevance, and clarity by experts using a 5-point Likert scale, supplemented by statistical tests (t-tests), cosine similarity, readability scores (Flesch), and testing the impact of confirmation queries. | ChatGPT achieved slightly higher overall accuracy (84.3%) than Bard (82.8%). ChatGPT scored significantly higher on clarity. Confirmation queries did not improve accuracy for either model. Bard sometimes refused to answer or gave more cautious responses. | NaN | NaN | NaN | NaN | Human Resources Management | United States (based on SHRM certification) | NaN | NaN | NaN | True | False | The tools evaluated (ChatGPT via web interface, Bard via web interface) are publicly accessible online, though specific versions like GPT-4 may require payment. | NaN | Inconsistent accuracy, variable relevance and clarity between models; limitations in improving accuracy via confirmation queries; potential for AI hallucinations; differing approaches to sensitive/strategic questions; readability variations; lack of contextual understanding; influence of training data biases; need for human oversight. | Generating deceptive responses; reliance on outdated training data; lack of transparency; propagation of errors; potential misuse; ethical implications (data usage, privacy, consent, bias in HRM); legal problems; inherent biases from training data leading to discriminatory outcomes; erosion of diverse thinking; AI hallucinations; impact on employee wellbeing (job security anxiety); reduction of ethical standards in decision-making. |
| s42979-024-03533-6.pdf | Google_Scholar | Large Language Model Evaluation Criteria Framework in Healthcare: Fuzzy MCDM Approach | This paper proposes a framework using Fuzzy Analytical Hierarchy Process (FAHP) to help healthcare providers evaluate and select Large Language Models (LLMs). Based on expert interviews, it identifies and ranks nine key evaluation criteria and associated sub-criteria for LLMs in the healthcare domain. | True | Market | True | 1.0 | NaN | An LLM evaluation criteria framework for healthcare using Fuzzy Analytical Hierarchy Process (FAHP) based on expert judgment. | The framework was developed by interviewing 38 AI and healthcare experts using the Delphi technique to identify criteria, sub-criteria, and metrics. FAHP was used to calculate the weights and rank the criteria based on aggregated expert judgments. | Identified 9 main criteria and 12 sub-criteria with metrics. The main criteria ranking (highest to lowest weight) is: Reliability (0.200), Robustness (0.171), Bias and Fairness (0.126), Availability (0.121), Performance (0.092), Usability (0.090), Resilience (0.089), Predictability (0.063), Cost (0.047). | The primary challenge identified is the difficulty for healthcare providers in selecting suitable LLMs from diverse options due to a lack of standardized evaluation methods and technical expertise. | Proposes a structured evaluation framework with weighted criteria (derived via FAHP and expert input) to guide healthcare providers in selecting appropriate LLMs. | NaN | Healthcare providers | NaN | International | Expert judgments collected from 38 AI and healthcare specialists via interviews and questionnaires using a 9-point fuzzy scale, processed using FAHP. Not a traditional ML training dataset. | Literature review, Expert interviews (Delphi technique), Qualitative content analysis, Fuzzy Set Theory, Analytical Hierarchy Process (AHP), Fuzzy AHP (FAHP). | NaN | False | False | NaN | Existing LLM evaluation methods are fragmented, neglect user preferences, lack interpretability/customization, and don't capture the full picture. The proposed framework needs validation for broader model types and could be extended to non-quantifiable criteria (trust, security, privacy) and other sectors beyond healthcare. | Identifying comprehensive and relevant evaluation criteria for LLMs in healthcare; defining measurable metrics for qualitative aspects; managing subjectivity and uncertainty in expert judgments during the weighting process. | Risks of poor LLM selection in healthcare (inaccurate diagnoses, inappropriate treatment suggestions, safety risks, compromised decision-making, legal consequences). Inherent LLM risks including bias leading to unfair outcomes, lack of reliability (incomplete/inaccurate information), security vulnerabilities, data privacy violations, and model drift. |
| bBYvbhf2w6QJ.pdf | Google_Scholar | Future Prospects for the Application of Artificial Intelligence in Judicial Management | This paper explores the current state and future potential of artificial intelligence (AI) in judicial management across various jurisdictions, reviewing existing applications and challenges. It highlights significant hurdles such as the lack of legal frameworks, ethical concerns, and low trust, proposing a conceptual model for integrating AI governance within judicial systems. | True | Market | True | 3.0 | Neutral | Conceptual model for AI governance in judicial management | NaN | NaN | Lack of legal foundation, legitimacy, and regulation for AI in judicial settings; Lack of trust in AI decision-making; Ethical challenges (bias, privacy, transparency, responsibility); Algorithmic bias and the "black box" problem; Security risks; Need for human oversight; Potential dehumanization of justice. | Develop comprehensive legal and regulatory frameworks for AI in justice; Implement a conceptual model integrating legal basis (constitution), human factors (training, ethics), AI system design, and secure ICT infrastructure; Ensure human oversight and 'human-in-the-loop' approaches; Establish and adhere to ethical guidelines (fairness, non-discrimination, transparency); Foster collaboration and stakeholder engagement. | Access to legal information/advice; Judicial process efficiency; Judicial decision support; Automation of legal tasks. | NaN | General judicial administration, Criminal law, Constitutional law principles | International (with examples from Ecuador, USA, UK, Canada, Germany, Portugal, Spain, Saudi Arabia, India, China, Italy, Malaysia, Pakistan, Philippines, Bangladesh, Ukraine, EU) | NaN | Literature review, conceptual synthesis based on deductive method and exploratory research. | NaN | False | False | NaN | Lack of comprehensive legal frameworks and regulations globally; Need for improved trust, transparency, and explainability in judicial AI; Research and development in judicial AI lagging behind other fields; Need for effective human-AI collaboration models; Methods for addressing algorithmic bias and ensuring fairness; Standardized security measures for sensitive legal data; Ensuring AI upholds fundamental rights and democratic values. | AI misinterpretation of judicial decisions; Inability of AI to make nuanced value judgments; Algorithmic bias and data selectivity; Explainability issues ('black box' problem); Achieving public and professional acceptance of AI rulings; Managing security risks of implemented technologies; Ensuring ethical and responsible use; Integrating human expertise effectively ('human-in-the-loop'). | Algorithmic bias leading to discrimination; Lack of transparency hindering accountability; Security vulnerabilities exposing sensitive data; Erosion of fundamental human rights; Dehumanization of the judicial process; Potential for errors in AI analysis impacting case outcomes; Misuse of AI for illegal activities (e.g., deepfakes); Diminished public trust in the justice system; Loss of judicial autonomy. |
| DkyUIApE_CAJ.pdf | Google_Scholar | How ChatGPT and generative AI systems will revolutionize legal services and \nthe legal profession. | This paper presents predictions elicited from ChatGPT regarding the transformative impact of generative AI on legal services and the profession. It details ChatGPT's own views on areas of application, efficiency gains, benefits for access to justice, timelines, and the future for legal practitioners and students. | True | Idealistic | True | 2.0 | Positive | ChatGPT, a generative AI language model by OpenAI. | Eliciting detailed responses from a February 2023 version of ChatGPT (Free Research Preview) to a series of specific questions about its impact on the legal field. | ChatGPT predicts a seismic shock to the legal sector within 5-10 years, with reduced human-centric work, increased client self-help, and fundamental changes in pricing and manpower. | High cost of legal services, limited availability/accessibility of legal professionals, complexity of legal language, and slow legal processes. | AI-powered tools offering 24/7 access to cost-effective, simplified legal information, document preparation, research, and basic advice for ordinary people. | Access to legal advice, legal research, document preparation (contracts, wills), contract review, court filings, mediation and dispute resolution, and legal education for the public. | Ordinary people / General public | Multiple fields, including contract law, intellectual property, e-discovery, compliance, litigation support, and alternative dispute resolution. | International | Proprietary, large-scale, general textual data from diverse sources up to 2021, used by OpenAI to train ChatGPT. | NaN | Web-based access provided by OpenAI, initially as a free research preview. | True | False | ChatGPT was accessible via a free research preview on OpenAI's website. | AI's limitations in highly complex/novel legal reasoning; lack of established ethical and regulatory frameworks for legal AI; need for upskilling legal professionals. | Slow/varied adoption by legal professionals, evolving regulatory landscape, need for user training and education, integration with existing legal technology infrastructure, and addressing ethical considerations. | Significant job displacement for legal professionals (lawyers, support staff, potentially academics and judges), downward pressure on legal fees, and unaddressed ethical implications of AI in legal practice. |
| 3589335.3651557.pdf | Google_Scholar | Semantic interlinking of Immigration Data using LLMs for Knowledge Graph Construction | This paper proposes a framework using Large Language Models (LLMs) and Knowledge Graphs (KGs) to structure and analyze complex immigration data, specifically focusing on the US Adjustment of Status process. The framework aims to transform paper-based records into an interconnected knowledge network to improve data handling efficiency and decision-making for legal professionals. | True | Market | True | 1.0 | NaN | A framework combining LLMs (GPT-3.5 tested) and Knowledge Graphs (Neo4j) to extract entities and relationships from immigration forms (US Form I-485), generate structured summaries, create Cypher queries, and build a KG representing the immigration process. | Qualitative assessment based on constructing a KG using synthetic data derived from US Form I-485 populated by experts. Compared GPT-3.5 and GPT-4 performance for entity/relationship extraction (negligible difference found). Preliminary tests with local Mistral 8*7b mentioned. | Successfully constructed a knowledge graph from synthetic immigration data using GPT-3.5, demonstrating effective information aggregation and relationship mapping with minimal manual intervention. GPT-3.5 performance was comparable to GPT-4 but more cost-effective. | NaN | NaN | NaN | NaN | Immigration Law | United States | Synthetic data generated by experts based on US Form I-485 (Adjustment of Status) and its instructions. The LLMs used (GPT-3.5) are pre-trained on general large datasets. | PDF data extraction (PyMuPDF), key-value mapping strategy, transformation into summaries, prompt engineering, LLM-based JSON generation (entities, relationships), LLM-based Cypher query generation, manual query refinement, population into Neo4j graph database. | NaN | False | False | NaN | Technical challenges in PDF extraction (especially multiple-choice questions). LLM context window and output token limitations requiring workarounds and manual refinement. Need for expanded testing and inclusion of regulatory/legal texts. | Limitations of PDF form extraction tools (line-by-line processing, handling multi-column layouts and multiple-choice questions). LLM context window/output token limits requiring section-wise processing and subsequent manual merging/refinement of generated queries to ensure consistency. | General AI risks mentioned: data privacy issues with evolving technology, potential for algorithmic bias leading to unfair outcomes (though not evaluated for the proposed technique). |
| -bjvGA-RheQJ.pdf | Google_Scholar | Generative Adversarial Training with Perturbed Token Detection for Model Robustness | This paper proposes GenerAT, a novel generative adversarial training framework to enhance the robustness of language models against text-based adversarial attacks. The framework integrates gradient-based adversarial token generation and perturbed token detection, significantly outperforming existing methods and ChatGPT on the AdvGLUE benchmark. | True | NaN | False | 1.0 | NaN | Generative Adversarial Training (GenerAT) framework combining a generative adversarial attack (gradient-based discrete token generation using shared embeddings between classifier and generator) and an adversarial training process (integrating adversarial regularization via KL-divergence and perturbed token detection). | Evaluated on the five datasets (adversarial versions of SST-2, QQP, MNLI-m, QNLI, RTE) from the AdvGLUE benchmark. | GenerAT achieved state-of-the-art results on AdvGLUE, reaching an average accuracy of 80.1%, surpassing ChatGPT by 10%. | NaN | NaN | NaN | NaN | NaN | NaN | The technique fine-tunes a pre-trained discriminative language model (DeBERTa-v3-large). Training and evaluation uses the datasets from the AdvGLUE benchmark (adversarially modified versions of SST-2, QQP, MNLI, QNLI, RTE derived from GLUE). | The framework integrates several components: using a discriminative PLM (DeBERTa-v3) as the base, sharing embeddings between the classifier (discriminator) and a generator model, propagating adversarial gradients calculated on the classifier to guide the generator's perturbed token generation, and optimizing a combined loss function including task loss, perturbed token detection loss, and symmetric KL-divergence for adversarial regularization. | The authors state that the code is provided via a GitHub link. | True | True | Code is available on GitHub: https://github.com/Opdoop/GenerAT | NaN | Bridging the gap between continuous embedding perturbations used in traditional adversarial training and discrete token perturbations seen in real attacks. Reducing the high computational cost associated with adversarial augmentation methods. Balancing the contributions of task loss, perturbed token detection loss, and adversarial regularization loss during training. | The primary risk addressed is the vulnerability of language models to adversarial attacks. The ethics statement briefly notes the need to understand impacts of robustness enhancements on model bias and fairness. |
| uNIX4MJ1rq8J.pdf | Google_Scholar | ChatGPT and digital capitalism: need for an antidote of Competition Law | This paper analyzes generative AI like ChatGPT, highlighting risks to market competition, consumer welfare (privacy, misinformation), and academic integrity. It advocates for proactive competition law measures to address these threats. | True | Market | True | 2.0 | NaN | ChatGPT (Conversational Generative Pre-Training Transformer) | NaN | NaN | NaN | NaN | NaN | NaN | Competition Law, Privacy Law, Data Protection Law | International | NaN | NaN | Launched by OpenAI Inc., achieving rapid mass user adoption (over 100 million users in 2 months), and offering a premium subscription service (ChatGPT Plus). | True | False | Publicly accessible online service provided by OpenAI Inc., with a premium version (ChatGPT Plus) available for a monthly fee. | NaN | NaN | Distortion of market competition; harm to consumer welfare (e.g., reduced quality of information, privacy violations, misinformation); undermining academic integrity and intellectual growth; potential for anti-competitive practices by AI providers (e.g., abuse of dominance, tying arrangements, data misuse, algorithmic collusion). |
| 3628602.pdf | Google_Scholar | Measuring and Mitigating Gender Bias in Legal Contextualized Language Models | This paper proposes methods to measure and mitigate gender bias in legal contextualized language models like LegalBERT, introducing a new crime-based evaluation corpus (BEC-Cri) and a fine-tuning debiasing technique (LCD) using ECtHR data. Evaluations on the LexGLUE benchmark show the proposed LCD method effectively reduces bias with minimal impact on downstream task performance. | True | Idealistic | True | 1.0 | Positive | Proposes two techniques: 1) BEC-Cri: A template-based gender bias measurement method using MLM probabilities on a new corpus derived from FBI crime data. 2) Legal-Context-Debias (LCD): A fine-tuning debiasing method using a gender-balanced European Court of Human Rights (ECtHR) corpus for a gender classification task. | Bias was measured by comparing association scores (derived from MLM probabilities) for male/female targets using the proposed BEC-Cri and existing BEC-Pro datasets, before and after debiasing. Downstream performance was evaluated using µ-F1/m-F1 scores on six classification tasks from the LexGLUE benchmark. | The proposed LCD debiasing method significantly reduced measured gender bias scores towards zero on both BEC-Cri and BEC-Pro, outperforming baseline methods (GPD, GAP). LexGLUE benchmark performance showed only slight decreases after LCD debiasing, preserving overall semantic utility. A proposed bias-penalized performance metric showed LCD incurred the lowest penalty. | Inherent gender bias exists in legal language models, stemming from training data, which can lead to unfair outcomes in legal AI applications. Mitigating this bias without significantly degrading model performance on useful tasks is challenging. | Develop and apply domain-specific methods for bias measurement (BEC-Cri) and mitigation (LCD fine-tuning with balanced legal data). Evaluate models using a bias penalty framework alongside standard performance metrics. | Fairness in AI, Gender bias mitigation, Legal NLP | NaN | General legal NLP / Multiple (Human Rights Law, US Constitutional Law, EU Law, Contract Law, Criminal Law references) | Multiple (European Court of Human Rights, US, EU) | Debiasing (LCD) used a modified, gender-balanced subset (3,032 cases) of the publicly available European Court of Human Rights (ECtHR) corpus [30]. Bias measurement (BEC-Cri) used author-created templates populated with crime words from the public FBI database [79]. The base model (LegalBERT-Small) was pre-trained on various legal corpora. | Template-based bias measurement using MLM probabilities, Supervised fine-tuning for debiasing using a curated dataset and classification task, Comparative analysis against baseline methods, Evaluation on standard NLP benchmark (LexGLUE), Proposal of a bias-penalized evaluation framework. | NaN | True | True | Code and data stated to be available on GitHub (https://github.com/koc-lab/ContextLegalBias). | Focus limited to gender bias (other biases like race remain unaddressed for these models). Potential for catastrophic forgetting during fine-tuning requires careful monitoring. Need for potentially more sophisticated debiasing tasks or hyperparameter tuning. | Balancing bias mitigation with preservation of model performance on downstream legal tasks. Creating effective domain-specific datasets and methods for bias analysis and reduction in law. Computational costs associated with transformer models. | NLP models perpetuating or amplifying gender bias in legal applications, leading to unfair outcomes. Debiasing techniques potentially harming the model's general language understanding capabilities (catastrophic forgetting). |
| P_QMzDF2YcAJ.pdf | Google_Scholar | Gener ative AI and Legal Aid: Results fr om a Field Study and 100 Use Cases t o Bridge the Access t o Justice Gap | This paper reports on a field study where legal aid professionals used generative AI tools (ChatGPT-4, Gavel, CoCounsel), finding increased productivity and intent for continued use. It also releases 100 use cases and offers recommendations to bridge the access to justice gap, emphasizing equitable AI adoption and lawyer-AI collaboration. | True | Idealistic | True | 2.0 | Positive | A field study providing 91 legal aid professionals with free access to paid generative AI tools (ChatGPT-4, Gavel, CoCounsel) for up to two months. A randomized controlled trial component tested 'concierge' support (peer use cases, office hours, assistance) for a subset of participants. A companion database of 100 use cases was compiled from participant submissions. | Baseline and exit surveys administered to pilot participants (N=91, with 66 completing exit survey). Outcomes measured included self-reported productivity, satisfaction with AI, quality of output, frequency of use, changes in attitudes, and intentions to continue using AI tools. Comparison between control group and 'concierge' support group on these metrics. | 90% of participants reported increased productivity (25% medium/high increase); 75% intended to continue using generative AI. 'Concierge' services significantly improved outcomes (productivity, satisfaction, quality of output, frequency of use, attitudes, future paid use). Despite women being less likely to use AI tools pre-pilot, post-pilot outcomes were statistically indistinguishable by gender for most metrics. | The access to justice gap (92% of low-income Americans' civil legal needs unmet) due to knowledge and service gaps. Financial constraints for legal aid organizations to adopt AI. Regulatory hurdles like Unauthorized Practice of Law (UPL) rules stifling innovation. Risk of AI exacerbating inequities. | Augment legal aid lawyers with AI. Provide funding and supportive services (e.g., 'concierge' support, help desks) for AI adoption. Foster 'Tech + Legal Aid Lawyer' collaborations. Explore regulatory sandboxes and voluntary certification for legal aid AI tools. Develop lawyer-directed and consumer-facing AI solutions. | Increasing productivity of legal aid professionals, document summarization/analysis, legal research (preliminary/confirmatory), legal and non-legal writing/drafting, translation (plain language/other languages), client intake automation, grant writing, case management support. | Low-income Americans with unmet legal needs, clients of legal aid organizations. | Eviction defense/housing, expungement (criminal records), immigration, family law, employment/workers' rights, civil rights, consumer/economic justice, disability rights, domestic violence, elder law, health, income maintenance, veterans' rights. | United States | The study utilized existing pre-trained models: ChatGPT-4 (trained by OpenAI on diverse large-scale text/code) and CoCounsel (GPT-4 augmented with Casetext’s proprietary legal databases). Gavel.io uses rules-based AI and automation technologies. | For the field study: Randomized Controlled Trial (RCT) for the 'concierge services' component. Survey methodology (baseline and exit surveys) for quantitative and qualitative data collection. Use case compilation and analysis. | The paper releases a companion database of 100 use cases via a public URL (https://bit.ly/AIA2J). The AI tools studied (ChatGPT-4, Gavel, CoCounsel) are commercially available products. | True | False | The AI tools studied (ChatGPT-4, Gavel, CoCounsel) are commercially available through subscriptions. The companion database of 100 use cases is openly accessible at https://bit.ly/AIA2J. | Gender gap in organic AI tool uptake by legal professionals. Insufficient funding for AI in legal aid. Need for ongoing training, support structures, and quality control/certification for legal aid bots. Regulatory frameworks (UPL) hindering direct-to-consumer AI solutions. Technical limitations of AI (hallucinations, bias). | Managing AI risks (data privacy, confidentiality, hallucinations). Overcoming learning curves for AI tools. Ensuring equitable access and adoption, particularly addressing the gender gap. Securing funding for paid AI tools in resource-constrained legal aid settings. | AI hallucinations (e.g., fake case citations), data privacy and confidentiality breaches, inaccurate results, algorithmic bias (racial, gender, anti-consumer), consumer harm from unauthorized practice of law by AI, creation of a two-tiered justice system, dehumanizing the law. |
| M0yH6UAgsI0J.pdf | Google_Scholar | GENERALIZING TRUST : WEAK-TO-STRONG TRUSTWORTHINESS IN LANGUAGE MODELS | This paper investigates whether trustworthiness properties like fairness, robustness, and privacy can transfer from a smaller (weak) language model to a larger (strong) one when the strong model is trained on the weak model's outputs (weak-to-strong generalization). The authors propose and evaluate two fine-tuning strategies (Weak TFT and Weak+WTS TFT) incorporating trustworthiness regularization, finding that fairness and robustness can generalize and even improve, while privacy does not. | True | NaN | True | 1.0 | NaN | Proposes two fine-tuning strategies for weak-to-strong generalization focusing on trustworthiness: Weak Trustworthiness Finetuning (Weak TFT) and Weak and Weak-to-Strong Trustworthiness Finetuning (Weak+WTS TFT), which incorporate regularization for fairness, robustness, or privacy. | Evaluated using Pythia models (14M, 70M, 410M, 1B, 6.9B) on four real-world datasets: Adult (fairness), OOD Style Transfer (OOD robustness), AdvGLUE++ (adversarial robustness), and Enron Emails (privacy). Compared No TFT, Weak TFT, and Weak+WTS TFT strategies against each other and a 'strong ceiling' baseline, using metrics like DPD (fairness), Robust Accuracy (robustness), and Extraction Rate (privacy). Included sensitivity analysis on model size and regularization strength. | Weak+WTS TFT consistently showed weak-to-strong trustworthiness generalization for fairness, OOD robustness, and adversarial robustness, often enhancing the property compared to the weak model. Privacy did not exhibit weak-to-strong generalization in any tested setting. Tradeoffs between trustworthiness and task performance were minimal (<= 1.5% accuracy decrease for fairness/adversarial robustness). | NaN | NaN | NaN | NaN | NaN | International | Uses publicly available datasets: Adult (reconstructed US Census data), OOD Style Transfer (modified SST-2), AdvGLUE++ (multi-task NLP benchmark with adversarial examples), Enron Emails. Data is primarily unstructured text, except Adult (structured). | Proposed novel training strategies (Weak TFT, Weak+WTS TFT) adapting the weak-to-strong generalization framework with specific trustworthiness regularizers (based on Demographic Parity, adversarial training, embedding perturbations, DP-SGD). Employed empirical evaluation with multiple model sizes, datasets, and metrics, including comparative analysis and sensitivity analysis. | NaN | False | False | NaN | The paper identifies the difficulty in achieving weak-to-strong generalization for privacy as a remaining gap or limitation, contrasting with the successful generalization observed for fairness and robustness. The reasons why privacy behaves differently require further investigation. | Achieving successful weak-to-strong transfer of trustworthiness properties, particularly privacy. Tuning hyperparameters (regularization strength λ, auxiliary loss weight α) to balance trustworthiness and task performance. Understanding the impact of model size combinations on transfer dynamics (e.g., disruption of adversarial robustness trend for 70M weak models). | The paper implicitly addresses the risks of deploying LLMs that lack trustworthiness (unfairness, non-robustness, privacy leakage) in high-stakes domains. It highlights the specific risk that larger models may be inherently more prone to privacy leakage due to memorization, complicating weak-to-strong privacy transfer. |
| 2025.03.23.644789.full.pdf | Google_Scholar | SynBioGPT: A Retrieval-Augmented Large Language Model Platform for \nAI-Guided Microbial Strain Development | This paper introduces SynBioGPT v2.0, an LLM platform for synthetic biology, which enhances knowledge retrieval by decomposing queries and using keyword-based searches. Tested on a domain-specific benchmark, v2.0 achieved 98% accuracy, showing significant improvement over its predecessor by mitigating hallucination and improving contextual relevance in microbial strain development. | True | NaN | True | 1.0 | NaN | SynBioGPT v2.0, a Retrieval-Augmented Generation (RAG)-enhanced LLM platform. It uses LLM reasoning for query decomposition into sub-questions, followed by targeted keyword-based searches (BM25 index) on a curated knowledge base, and LLM synthesis of retrieved paragraphs into a coherent answer. | Tested on a custom 100-question synthetic biology benchmark (71 specific, fact-based questions and 29 open-ended, reasoning-based questions) covering gene mutation, overexpression, etc. Performance was compared with SynBioGPT v1.0 and different LLM backends (DeepSeek V3, Gemini-2.0-flash, Claude-3.7-sonnet). | SynBioGPT v2.0 with the Claude-3.7-sonnet backend achieved 98% accuracy on the 100-question benchmark, a 10% improvement over SynBioGPT v1.0 (88% with Llama3-8B-Instruct). | NaN | NaN | NaN | NaN | NaN | NaN | Knowledge base built from open-access PDF documents (peer-reviewed studies in synthetic biology, >51,777 for v1.0, expanded and updated monthly for v2.0 from sources like PubMed). Raw data stored as Hugging Face dataset, processed into Markdown using Docling and spaCy Layout for text and metadata extraction. | Iterative development from SynBioGPT v1.0. SynBioGPT v2.0 architecture: 1) Data acquisition and cleaning (PDFs to Markdown, metadata extraction via Docling, spaCy Layout). 2) Index creation (BM25 from TF/IDF on curated Markdown and Table of Contents, serialized using Python's pickle). 3) Service deployment (FastAPI backend, Streamlit frontend, SQLite for session/chat history, OAuth for SSO). Core RAG method involves LLM-driven query decomposition, keyword-based search, direct paragraph extraction, and LLM-based synthesis. | Deployed as a web platform with a FastAPI-based API server for backend and Streamlit for frontend user interaction. User authentication via BDC’s single sign-on (SSO) service using OAuth. | True | False | Available as a web platform at https://synbiogpt.biodesign.ac.cn. | NaN | Limitations of keyword search (dependency on exact term matching, potential to miss relevant documents if terminology differs). Ensuring high-quality sub-problem generation by the LLM for effective query decomposition. Significant impact of LLM inference backend choice on overall system performance. Need for continuous updates of the specialized knowledge base. | General LLM risks like reliance on outdated corpora and hallucination. For SynBioGPT v1.0 (vector search): retrieval of semantically similar but contextually irrelevant documents, leading to inaccuracies. For SynBioGPT v2.0 (keyword search): potential to overlook relevant documents due to terminology mismatch, and risk of poorly defined LLM-generated sub-problems leading to retrieval results that deviate from user intent. |
| xxzftjRKRFAJ.pdf | Google_Scholar | LegalBench : Prototyping a Collaborative Benchmark for Legal Reasoning | This paper introduces LegalBench, a collaborative benchmark designed to evaluate the legal reasoning capabilities of foundation models (FMs) using the IRAC framework. It presents an initial set of 44 tasks with preliminary FM performance results and calls for community contributions to expand the benchmark. | True | Idealistic | True | 1.0 | Positive | LegalBench, a collaborative benchmark for legal reasoning structured using the Issue, Rule, Application, Conclusion (IRAC) framework, comprising a seed set of 44 tasks. | Five different foundation models (GPT-3 davinci, GPT-3 curie, J1-Jumbo, J1-Grande, J1-Large) were evaluated on the 44 LegalBench tasks using zero-shot, few-shot, and chain-of-thought prompting. Performance was measured using F1 (macro) for classification/conclusion tasks and accuracy for others. | GPT-3 (davinci) with chain-of-thought prompting achieved the highest reported score, with an F1 (macro) of 0.92 on the PROA (Conclusion) task. Generally, larger models performed better, classification tasks were easier than application tasks, and chain-of-thought prompting improved performance. | The paper identifies the United States' "access-to-justice crisis" as a key challenge. It also implies that a lack of understanding of Foundation Models' capabilities and limitations in legal reasoning, alongside the high-risk nature and ethical concerns of AI tools in law, are hurdles to leveraging AI for access to justice. | The paper proposes LegalBench, an open and collaborative benchmark, to systematically assess the legal reasoning capabilities of Foundation Models. This evaluation aims to guide the safe, ethical, and effective development and use of AI tools, which could in turn improve the accessibility of legal services. | Improving accessibility of legal services; Evaluating AI capabilities in legal reasoning; Fostering safe and ethical use of AI in law. | Low-income Americans (mentioned via reference to "The Justice Gap" report). | Contract law (CUAD), Civil Procedure (Diversity Jurisdiction, Personal Jurisdiction), Evidence (Hearsay), Trademark Law (Abercrombie), Statutory Interpretation (PROA). | United States | The benchmark tasks use a variety of data: CUAD tasks use annotated contracts from the EDGAR database (publicly available); other tasks (Rule QA, Abercrombie, Hearsay, Personal Jurisdiction, PROA) use manually constructed or annotated datasets of legal questions, scenarios, product-mark-pairs, or statutes, typically with small numbers of samples (50-100). | The IRAC (Issue, Rule, Application, Conclusion) framework is used to categorize and structure legal reasoning tasks. A data-centric approach is adopted, with lightweight and accessible task construction to encourage collaboration. | LegalBench is presented as an ongoing, collaborative project hosted on GitHub. The authors call for community contributions of new tasks, and plan to run new FMs on the benchmark and release results. | True | True | The LegalBench project, including initial tasks, is available on GitHub: https://github.com/HazyResearch/legalbench. | A clear understanding of which types of legal reasoning Foundation Models can perform and what FM programming strategies are effective for legal tasks. Current FMs perform significantly worse on legal application tasks compared to classification or conclusion tasks. The need for law-specific prompting strategies and frameworks for safe and ethical usage. | Distinguishing between different types of IRAC tasks during benchmark design; fostering sustained interdisciplinary collaboration between computer science and legal communities; designing tasks that meaningfully measure legal reasoning while being accessible for contribution. | The paper mentions the "high risk nature" of computational legal tools and the need for evaluation to ensure "safe and ethical usage." It implicitly acknowledges the risk of misapplication if AI capabilities are not well understood, or if tools are used to replace human legal professionals inappropriately. |
| SKfsqhktqhMJ.pdf | Google_Scholar | ChatGPT: limitations, challenges and potential applications | This paper provides an overview of ChatGPT, an AI language model based on GPT-3.5 developed by OpenAI. It discusses the model's training, characteristics, potential applications across various sectors (including law), limitations (e.g., accuracy, bias), and ethical challenges. | True | NaN | True | 3.0 | Neutral | ChatGPT (based on GPT-3.5) | NaN | NaN | NaN | NaN | NaN | NaN | Law and legal services (mentioned as one potential application area) | International | Large-scale, diverse text and human conversation data (e.g., forums, chat, customer service), likely proprietary to OpenAI. | Based on GPT-3.5 architecture (Transformer, attention mechanism); trained using supervised learning, reinforcement learning, and autoregressive techniques. | NaN | True | False | Available via OpenAI's website (chat.openai.com). | NaN | Factual inaccuracy, sensitivity to input phrasing, bias mitigation, ensuring ethical responsibility and transparency, handling complex commands, preventing abuse. | Generating factually inaccurate information, amplifying bias, generating inappropriate responses, potential for abuse, privacy and security concerns. |
| 5VFMMdneX9MJ.pdf | Google_Scholar | Employing Label Models on ChatGPT Answers Improves Legal Text Entailment Performance | This paper proposes a method to enhance legal text entailment accuracy by using label models to aggregate multiple, potentially inconsistent answers generated by ChatGPT. Experimental results on the COLIEE 2022 dataset show this approach significantly outperforms existing state-of-the-art methods. | True | Idealistic | True | 1.0 | Positive | Employing label models (specifically a 'Generative model' label model) to integrate multiple provisional answers generated by ChatGPT (using a 'Reason-then-Answer' prompt and varying temperature settings) for legal text entailment decision. | The approach was evaluated on the COLIEE 2022 legal text entailment dataset. The authors tested different prompt types for ChatGPT, varied temperature settings to generate multiple answers, and applied several label models (Majority voting, FlyingSquid, Dawid-Skene, Hyper label model, FABLE, Generative model) to consolidate these answers, measuring accuracy. | The 'Generative model' label model, when applied to 10 provisional answers from ChatGPT (using the Reason-then-Answer prompt with temperature=0.5), achieved an accuracy of 76.15% on the COLIEE 2022 dataset. | The main obstacles identified are the reasoning errors of LLMs like ChatGPT, which hinder their reliable application. These include: 1) hallucinating facts, 2) incorrect deduction from correct premises, 3) difficulty with nuanced legal concepts like 'mutatis mutandis', and 4) issues arising from incomplete contextual information (e.g., missing relevant legal articles). | The paper proposes using label models to aggregate and refine multiple LLM-generated answers, thereby improving the robustness and accuracy of legal text entailment. The error analysis also implicitly suggests that providing more comprehensive and accurate contextual information to the LLM could mitigate some errors. | Improving the accuracy and reliability of legal text entailment, a foundational capability for developing advanced legal AI applications (e.g., legal chatbots, question-answering systems) aimed at enhancing access to legal information and services. | People who cannot afford expensive legal advice. | Civil Law (based on COLIEE competition context and examples like contract law provisions) | Japan (based on the COLIEE competition, which typically uses Japanese legal texts, and mentions of 'Japanese legal data' in related work). | The label models operate on provisional answers generated by ChatGPT for queries from the COLIEE 2022 dataset. These provisional answers (text including 'Yes'/'No' and reasoning) serve as the noisy labeled data for the label models. ChatGPT itself is pre-trained on a massive, general corpus. | Experimental evaluation comparing different ChatGPT prompting strategies ('Answer-only', 'Answer-then-Explain', 'Reason-then-Answer'), varying ChatGPT's temperature parameter to generate diverse outputs, and applying various established label models to consolidate these outputs. Includes a qualitative error analysis of ChatGPT's incorrect responses. | NaN | False | False | NaN | Remaining technical gaps include LLMs' deficiencies in complex legal reasoning, such as handling 'mutatis mutandis' clauses, consistent logical deduction, and avoiding factual inaccuracies (hallucinations). The lack of sufficient relevant articles provided as context for LLMs also poses a challenge to accurate entailment. | The primary challenges included managing the variability and potential inconsistency of ChatGPT's outputs (especially with non-zero temperature settings), identifying the most effective prompting technique for legal reasoning, and developing a robust method to integrate multiple, potentially noisy, LLM-generated answers into a single, more accurate consolidated answer. | The paper identifies concrete risks associated with ChatGPT's errors in legal text entailment: 1) incorrect provision of facts (hallucinations), 2) inability to draw correct conclusions from correct premises, 3) difficulties reasoning on 'mutatis mutandis' articles, and 4) incorrect responses or inability to conclude due to lack of relevant articles in the provided dataset. |
| G2vdU-5fzE4J.pdf | Google_Scholar | ChatGPT and Generative AI Systems as Military Ethics Advisors | This paper explores the potential of ChatGPT and similar generative AI systems to serve as military ethics advisors by testing ChatGPT's responses to a complex ethical dilemma scenario involving a potential strike on a hospital. The author suggests that AI could provide valuable, accessible ethical guidance to soldiers and commanders, potentially reducing war crimes and improving decision-making. | True | Idealistic | True | 2.0 | Positive | Using ChatGPT as a military ethics advisor by prompting it with specific scenarios. | ChatGPT (Feb 2023 version, trained on data up to end of 2021) was prompted with a detailed hypothetical military scenario involving a potential strike on a hospital suspected of hiding enemy artillery, followed by specific ethical and legal questions related to the scenario. | ChatGPT provided extensive, detailed, and ethically reasoned advice addressing principles like military necessity, proportionality, discrimination, the implications of attacking hospitals (treating civilians or enemy wounded), the use of human shields, the certainty required for intelligence, and the legality of following or refusing potentially unlawful orders. | Lack of readily accessible ethical guidance for soldiers in combat, complexity of formal Law of War manuals, stress of war leading to poor decisions, potential for immoral leadership, leading to ethical breaches and war crimes. | Deploying generative AI systems like ChatGPT, potentially integrated via voice interfaces, to provide real-time military ethics advice, automatic checking of orders, and decision support for both frontline soldiers and commanders. | Access to ethical guidance in conflict zones, interpretation of the Law of Armed Conflict / International Humanitarian Law, prevention of war crimes, ethical military decision-making. | Military personnel (specifically frontline soldiers and commanders) lacking immediate access to ethical/legal guidance. | Military Law, Law of Armed Conflict, International Humanitarian Law, Ethics | International | ChatGPT was predominantly trained on general data up to the end of 2021 (as per OpenAI FAQ cited in the paper). | NaN | The paper suggests potential future deployment (e.g., via voice interface) but does not describe current deployment strategies. | False | False | NaN | NaN | NaN | Implicit risks related to reliance on AI for high-stakes ethical decisions in warfare, potential for AI to provide incorrect or flawed advice leading to illegal or immoral actions, misuse of AI. |
| HXDmOtw5Oj0J.pdf | Google_Scholar | The future of court’s procurators with the advent of artificial intelligence technologies | This paper analyzes the impact of artificial intelligence on Spain's justice administration, with a particular focus on the court procurator profession. It argues that AI-driven automation of routine tasks, especially in procedural communication, threatens the traditional roles and future necessity of court procurators. | True | Market | False | 3.0 | Neutral | NaN | NaN | NaN | Lack of algorithmic transparency ("black box" issue), potential for bias and discrimination in AI systems, threatening due process and the right to defense. | Ensuring algorithmic transparency and non-discrimination in AI design and use; utilizing AI as a support tool rather than a replacement for human judgment, especially in judicial decision-making; comprehensive training for personnel. | Efficiency and speed of judicial processes, right to defense, due process, procedural representation, automation of legal tasks. | NaN | General (Civil Procedure, Criminal Law, Bankruptcy Law) | Spain | Existing legal data including case law (judicial decisions, sentences), legislation, and large text datasets for generative models. Data sources include historical case records and legal texts, used for training predictive and generative AI systems. | NaN | NaN | True | False | The paper discusses existing tools like Chat GPT which are publicly accessible as online services, and operational systems like Lexnet integrated into specific national justice infrastructures for authorized users. | Technical gaps include the need for fully transparent and unbiased AI, and AI with human-like emotional intelligence for complex legal tasks. Societal gaps include addressing widespread job displacement and potential increases in economic inequality due to AI. | NaN | Widespread job displacement in legal and administrative sectors, particularly for court procurators; violation of fundamental rights (due process, right to defense) through non-transparent or biased AI; perpetuation of discrimination through biased algorithms; increased socio-economic inequality. |
| MclRsgjrGSEJ.pdf | Google_Scholar | The K eynote Addr ess t o Geor gia State Univ ersity College of Law' s 29th Annual Law Re view Symposium - Access t o AI Justice: A Global Response t o a Global Crisis | This paper, a keynote address, argues that the narrative around AI in law should shift to focus on closing the justice gap and discusses how AI can serve the public interest. It calls for significant regulatory reforms, including a U.S. national legal regulatory sandbox and globally-informed approaches, to ensure AI's potential is realized without creating a two-tiered legal system. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | The significant justice gap due to unaffordable legal services, which AI could worsen by creating a two-tiered system. Systemic barriers include high costs and restrictive regulations (e.g., limiting non-lawyer investment), alongside AI-specific issues like bias, lack of transparency, and data privacy concerns. | Implement 'calibrated' AI for access to justice, focusing on consumer needs, specific legal issues, and tasks. Advocate for data-driven regulatory reform through a national U.S. legal regulatory sandbox and learning from international approaches to foster innovation and equitable access to legal services. | Closing the justice gap; democratizing access to legal information; regulatory reform of legal services; preventing a two-tiered system of legal services; nonlawyer ownership/investment in legal services; ethical use of AI in law. | Low-income Americans; individuals who cannot afford legal services. | General civil legal problems; Legal ethics and professional regulation. | United States (primarily for proposed reforms), with comparative discussion of international jurisdictions (e.g., Colombia, France, UK, Canada, Australia). | NaN | NaN | NaN | False | False | NaN | Lack of data-driven regulatory reform in the U.S. legal services industry; state-level resistance to regulatory experimentation, such as sandboxes; insufficient and outdated ethical guidance for new AI technologies; failure of the U.S. legal industry to systematically learn from international experiences in legal tech regulation; need for more interdisciplinary and collaborative reform efforts. | NaN | AI generating fictitious legal citations (hallucinations); creation of a two-tiered system of legal services disadvantaging certain populations; perpetuation of existing biases through data-driven conservatism; breaches of client confidentiality and data protection; stifling of lawyer creativity and critical thinking; potential for discriminatory outcomes from AI systems. |
| December2024Publication.pdf | Google_Scholar | Factors Associated with the Low Uptake of Quality Medico-Legal Services at Secured Diagnostic Crime Scene, Western Kenya | This paper investigates the reasons for the underutilization of quality medico-legal services at crime scenes in Western Kenya, identifying issues like evidence contamination, lack of trained personnel, and community interference. The study finds significant problems with evidence handling and staff training, recommending an integrated forensic system and improved capacity building to ensure evidence admissibility and access to justice. | True | Idealistic | False | 2.0 | NaN | NaN | NaN | NaN | Widespread evidence contamination at crime scenes (84%), often due to community participation or improper handling; lack of adequately trained forensic service providers (93% of mortuary staff lack formal training); limited understanding and use of witness grant immunity (85% unaware); poor maintenance of chain of custody; fragmented forensic services. | Promote an integrated forensic science ecosystem under unified management; enhance capacity building and training for forensic personnel; develop and implement policies for witness grant immunity; improve evidence reconstruction techniques and adherence to chain of custody procedures; foster public-private partnerships for training. | Quality and reliability of forensic evidence collection; medico-legal procedures at crime scenes; evidence admissibility; role of trained personnel in investigations; chain of custody. | General population affected by crime in Western Kenya. | Criminal Law, Forensic Law, Evidence Law | Kenya (Western Kenya) | NaN | NaN | NaN | False | False | NaN | Lack of integrated forensic management systems; insufficient training infrastructure/access for forensic personnel; absence of robust witness protection/immunity policies; under-documentation of regional medico-legal practices; need for improved community engagement that avoids contamination. | NaN | Inadmissibility of evidence leading to delayed or denied justice; harm to population health (due to unresolved crimes); undermining the rule of law through flawed investigations; continued evidence contamination; potential for wrongful convictions or acquittals. |
| 6C9WMJbxT4oJ.pdf | Google_Scholar | REFORMULATING DOMAIN ADAPTATION OF LARGE LANGUAGE MODELS AS ADAPT -RETRIEVE -REVISE | This paper introduces an 'adapt-retrieve-revise' framework to improve domain adaptation for large language models (LLMs) like GPT-4, specifically targeting hallucination issues in specialized domains such as Chinese law. The method uses a domain-adapted smaller LLM to generate a draft answer, retrieves evidence based on this draft, and then employs GPT-4 to revise the draft using the query and retrieved evidence, demonstrating significant accuracy improvements on Chinese legal tasks. | True | Market | True | 1.0 | NaN | Adapt-Retrieve-Revise framework: 1) A domain-adapted smaller LLM (Baichuan 7B) generates a draft answer to a query. 2) The draft answer is used to retrieve supporting evidence candidates from an external in-domain knowledge base using a sentence embedding model (Multilingual-E5-large). 3) GPT-4 assesses the retrieved evidence and revises the draft answer to generate the final answer, using a prompt that includes an instruction, the original query, the draft answer, and the retrieved evidence. | Evaluated in a zero-shot setting on four Chinese legal tasks: Law Clause Recommendation (LCR), Criminal Prediction (CP), LegalQA (filtered EUQALS), and JEC-QA (lawyer's certificate exam questions). Metrics used were recall for LCR, CP, and LegalQA, and accuracy for JEC-QA (human-evaluated). Retrieval was also evaluated on a Similar Case Retrieval task using precision@k and MAP. | The proposed adapt-retrieve-revise method (using the 7B legal LLM for draft generation/retrieval and GPT-4 as the revisor) achieved an average improvement of 33.3% in accuracy/recall compared to direct GPT-4 generation across the four Chinese legal tasks (LCR, CP, LegalQA, JEC-QA), reaching an average score of 80.7%. It also outperformed query-based retrieval baselines. | NaN | NaN | NaN | NaN | Chinese Law (general), covering tasks like law clause recommendation, criminal prediction, legal question answering from laws, and question answering for legal qualification exams (JEC-QA). | China | For the domain-adapted 7B LLM (Baichuan 7B): Continual pre-training on 50B tokens from Chinese law clauses (publicly available from flk.npc.gov.cn) and 100M Chinese judgments online (publicly available from wenshu.court.gov.cn). Supervised fine-tuning on 70K instruction examples, including 52K GPT-4 self-instruct Chinese data and 18K undisclosed human-expert annotated legal instructions (guideline to be released). | Continual learning (for domain adaptation of the 7B LLM), supervised fine-tuning (for instruction alignment), retrieval-augmented generation (using draft answers for similarity-based evidence retrieval and subsequent revision by a larger LLM). | The paper states that the code and the domain-adapted 7B LLM 'will be released', with an anonymous link provided for review purposes. | False | False | NaN | NaN | The infeasibility of continual training for very large LLMs (e.g., GPT-4 scale) on in-domain data due to cost and API limitations. Limitations of retrieval modules in mapping queries to evidence and susceptibility to domain issues. The limited capability of smaller 7B models to fully understand queries/evidence and assess evidence effectively, despite domain adaptation. The high cost of using GPT-4 API for experiments and evaluation. | Hallucination in LLM-generated content when applied to specific domains like Chinese law, manifesting as non-logical content, factual mistakes, and failure to refer to correct legal provisions. This is due to the absence of sufficient in-domain training data for general large models. |
| bUM57XdgCiAJ.pdf | Google_Scholar | CO-AUTHORING WITH AN AI? ETHICAL DILEMMAS AND ARTIFICIAL INTELLIGENCE | This paper explores the ethical dilemmas and practical challenges of using generative AI like ChatGPT and Bing Chat for legal academic writing. It evaluates the strengths and weaknesses of these tools through direct engagement and analyzes the divergent approaches of academic publishers and the notable lack of AI policies among law reviews. | True | NaN | True | 2.0 | Neutral | Use of Generative AI chatbots (ChatGPT, Bing Chat) for legal academic writing. | The authors posed questions about AI ethics to ChatGPT and Bing Chat, asking them to generate text (abstract, introduction, arguments, references, conclusion) for an academic paper. The outputs were analyzed for relevance, accuracy (especially of references), and completeness. | ChatGPT provided relevant text but hallucinated/inaccurate references and used outdated (pre-2021) knowledge. Bing Chat offered accurate, up-to-date sources (links) but they were less academically relevant (blogs, primary sources), and its answers were less focused. Both missed key recent regulatory developments. | For AI in academic writing: Hallucinations (fake references/cases), potential for bias, lack of up-to-date knowledge, lack of accountability, risk of plagiarism, difficulty enforcing AI bans, unclear disclosure requirements, lack of policies in law reviews. | For AI in academic writing: Transparency (disclosing AI use rather than banning), developing clear publisher/journal guidelines (especially for law reviews), evolving the scholar's role towards guiding AI and critical evaluation, maintaining human accountability for final work. | NaN | NaN | Legal Academia, Legal Research, Legal Ethics, Academic Publishing | International | Massive text corpora (including internet data) predating late 2021 for ChatGPT; Bing Chat uses a similar model connected to the live internet. | NaN | NaN | True | True | ChatGPT via OpenAI website (free tier available); Bing Chat integrated into Microsoft products (typically free). | Lack of clear, consistent AI policies among academic publishers, particularly law reviews. Need for better methods than outright bans or unreliable detection. Lack of clarity on disclosure standards. Need to understand AI's impact on scholarly roles and editorial practices. | Evaluating the ethical/practical implications of using rapidly evolving generative AI for academic legal writing; dealing with AI inaccuracies/hallucinations; ensuring proper attribution/avoiding plagiarism; navigating inconsistent publisher policies; assessing AI vs human contribution. | AI hallucination leading to misinformation/sanctions; plagiarism; bias amplification; misuse by authors (passing off AI work); misuse by editors (biased screening); lack of accountability; chilling effects from AI bans; erosion of trust in academic publishing. |
| XLMN4NL-8-wJ.pdf | Google_Scholar | The Law and NLP: Bridging Disciplinary Disconnects | This position paper argues that legal NLP research is often disconnected from the practical needs of the legal community, which impedes its potential to address the access to justice crisis. The authors call for a shift towards more needs-driven research, greater interdisciplinary collaboration, and the adoption of access to justice as a primary normative goal for the field. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | High cost and unequal access to legal services, particularly for low-income individuals and small businesses; a disconnect between the focus of legal NLP research and the practical needs of the legal community; slow adoption of technology by the legal profession due to factors like risk aversion and lack of expertise. | Adopting access to justice as a shared normative goal for legal NLP research; fostering closer interdisciplinary collaboration between NLP researchers and legal professionals; reorienting research towards practical applications like document generation/analysis, semantic search, legal language accessibility, and practice-oriented tools. | Addressing the access to justice gap; improving legal services for low-income individuals, public defenders, and small businesses; enhancing the accessibility of legal language and processes for non-lawyers; increasing the efficiency of legal professionals to potentially lower costs. | Low-income individuals, criminal defendants reliant on public defenders, small businesses, non-lawyers seeking to understand legal matters, and underserved communities globally. | General legal practice, including civil law, criminal law, contract law, statutory interpretation, and litigation. | United States; International | NaN | NaN | NaN | False | False | NaN | Societal/Systemic: Deep-rooted inequities in the justice system and resistance to technological adoption within the legal field. Research Focus: A misalignment between academic NLP research agendas and the practical requirements of legal work, leading to underexplored areas with high potential impact; insufficient interdisciplinary interaction. Ethical: Need for robust frameworks to manage bias, ensure accountability, and maintain trust in legal AI systems. | NaN | Poorly designed NLP tools may embed or amplify biases, reduce essential human oversight in legal decision-making, undermine public trust in the judicial system, lead to inaccurate or unfair automated judgments, and risk the leakage of sensitive or confidential legal information. |
| TransformingEducationwithLargeLanguageModels.pdf | Google_Scholar | Transforming Education with Large Language Models: Opportunities, Challenges, and Ethical Considerations | This paper examines the potential of Large Language Models (LLMs) like GPT-4 to enhance education through personalized learning, content creation, and tutoring. It also discusses significant challenges, including technology dependency, content accuracy, data privacy, bias, and ethical considerations, offering recommendations for integration. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | NaN | NaN | False | False | NaN | NaN | Challenges discussed include dependency on technology, potential degradation of critical thinking skills, access inequality exacerbating the digital divide, system failures disrupting learning, ensuring content accuracy and reliability, keeping information up-to-date, protecting student data privacy (compliance with regulations like GDPR/FERPA, obtaining consent, preventing breaches), and addressing bias in training data (representation and performance bias) to ensure fairness. | Specific risks mentioned include skill degradation due to over-reliance on AI, exacerbation of educational inequalities, learning disruptions from technical failures, students receiving incorrect or misleading information, violation of student privacy through data collection and potential breaches, and perpetuation of stereotypes or unfair outcomes due to algorithmic bias. |
| taH8uxiVYoAJ.pdf | Google_Scholar | ChatGPT Creates a Review Article: State of the Art in the Most-Cited Articles on ChatGPT in Health Science, Computer Science, Communication, and Culture, According to Altmetric in Dimensions.ai | This paper explores using ChatGPT to generate a review article summarizing the most influential preprints about ChatGPT across Health Science, Computer Science, Communication, and Culture, identified via Dimensions.ai and Altmetric. The authors prompted ChatGPT to analyze selected preprints and found the results promising for automating review article creation. | True | NaN | True | 2.0 | NaN | Using ChatGPT (GPT-4) to summarize and structure information from selected research preprints to generate a review article, based on specific prompts. | Qualitative evaluation. Abstracts from top preprints (selected via Dimensions.ai search filtered by Altmetric score) were fed to ChatGPT via prompts. The authors assessed the generated tabular summaries for coherence and utility in creating a review article. | ChatGPT successfully generated structured tables summarizing the preprints' design, applications, risks, conclusions, and sentiment, which the authors deemed 'surprisingly promising' for review article creation. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | False | NaN | The paper highlights the general difficulty and time-consuming nature of creating and updating traditional academic review articles. It also mentions limitations of ChatGPT identified in the reviewed literature, such as potential bias, inaccuracy, lack of transparency, and ethical concerns. | Effectively prompting ChatGPT to generate accurate and structured summaries from scientific abstracts. Selecting relevant and influential source material from a large volume of preprints using metrics like Altmetric. | Potential for generating harmful/biased content, lack of interpretability/transparency, potential job displacement, generation of fabricated data, inaccuracy (especially in complex domains or low-resource languages), decreased user trust for complex tasks (e.g., health advice). |
| EnhancingTrustinGenerativeAI_InvestigatingExplainabilityofLLMstoAnalyseConfusioninMOOCDiscussions.pdf | Google_Scholar | Enhancing Trust in Generative AI: Investigating Explainability of LLMs to Analyse Confusion in MOOC Discussions | This paper investigates using the Explainable AI (XAI) method Integrated Gradients to understand how a DistilBERT language model identifies learner confusion in MOOC discussion forums. The goal is to enhance trust in AI-generated feedback by making the model's reasoning transparent. | True | NaN | True | 2.0 | NaN | Application of the Integrated Gradients XAI method to interpret predictions from a fine-tuned DistilBERT model classifying learner confusion in MOOC discussion texts. | The DistilBERT model was trained and evaluated on the Stanford MOOC discussion datasets (split by domain: Education, Medicine, Humanities and combined) using weighted-averaged F1 scores. The Integrated Gradients XAI method was then applied, and its outputs (word-level attributions) were qualitatively analyzed and compared to findings from previous studies. | The fine-tuned DistilBERT model achieved high classification performance (weighted F1 scores up to 0.94 when excluding neutral messages). The Integrated Gradients method successfully identified word-level indicators of confusion (e.g., first-person pronouns, question stems) and non-confusion (e.g., second-person pronouns, academic writing expressions), aligning with prior research. | NaN | NaN | NaN | NaN | NaN | NaN | Publicly available Stanford MOOC discussion datasets: unstructured text messages from 11 courses (Education, Medicine, Humanities), annotated for confusion level by experts. | Machine learning pipeline (data preprocessing, model fine-tuning, evaluation), application of XAI technique (Integrated Gradients), qualitative interpretation, comparison with prior work. | NaN | False | False | NaN | NaN | Lack of trust in 'black-box' AI models in education. Potential impact of ambiguous (neutral) data points on model performance. Need for further model refinement for generalisability across different domains. Investigating direct applicability of XAI methods to generative models. | Risk of low adoption of AI tools in education due to lack of trust and transparency. General risks associated with GenAI mentioned (biases, reliability, ethics, safety, accountability, equality, eco-friendliness). |
| EAq8gE4cA44J.pdf | Google_Scholar | Breaking Barriers to Creative Expression: Co-Designing and Implementing an Accessible Text-to-Image Interface | This paper describes the co-design and development of PromptAssist, a prototype accessible interface for text-to-image (T2I) generation models. PromptAssist uses a large language model (LLM) to provide prompt suggestions, reducing typing effort for users, particularly those with motor disabilities. | True | Idealistic | True | 1.0 | Positive | PromptAssist: An accessible web-based interface for creating text-to-image prompts using LLM-generated suggestions via a wizard-based workflow, supporting keyboard and pointer input. | Iterative co-design and testing sessions within the research team, which included members with motor disabilities. Feedback was gathered through think-aloud protocols during prototype use. | Iterative testing led to UI improvements enhancing flexibility, navigation, suggestion quantity, and keyboard accessibility. Team members found the revised prototype easier to use and better supported their creative processes, demonstrating LLMs can improve T2I accessibility. | Difficulty for users with motor disabilities in typing the long and detailed text prompts required by standard text-to-image (T2I) interfaces. | An accessible interface (PromptAssist) that reduces typing effort by using an LLM to generate contextual prompt suggestions and supports multiple input methods (keyboard, pointer). | Accessibility of creative tools (Text-to-Image generation). | People with motor disabilities. | NaN | International | The technique uses an internal (Google) transformer-based LLM. The specific training data for the base LLM is not specified. The system is prompted using examples (provided in Appendix A) created by the authors to generate relevant suggestions for T2I prompts. | Iterative development, co-design involving researchers with motor disabilities, usability testing with think-aloud protocols within the team. | Developed as an internal prototype within Google; no broader deployment strategies mentioned. | False | False | NaN | Future work could include multimodal input (speech, body movements) and adjusting prompts based on previously generated images. Need for platforms for users to share, collaborate, and exchange ideas. | Balancing ease of use (provided by suggestions) with creative flexibility and user autonomy. Ensuring the interface supports varied creative workflows rather than enforcing a strict sequence. Optimizing the UI layout and interaction based on accessibility feedback. | Over-reliance on LLM suggestions might limit user creativity or diminish the user's sense of agency and independence, particularly for users with disabilities. |
| le3MEvufjeIJ.pdf | Google_Scholar | AI LAWYERING SKILLS TRAINERS: TRANSFORMING LEGAL EDUCATION WITH GENERATIVE AI | This paper advocates for integrating generative AI (GenAI) skills trainers into legal education to enhance advocacy skills through personalized, continuous coaching. It details the development and potential of MootMentorAI, a custom GenAI tool designed at UMKC School of Law to simulate courtroom scenarios and provide feedback to law students. | True | Market | True | 1.0 | Positive | MootMentorAI: A custom Generative AI (GenAI) tool built on OpenAI's GPT platform (specifically mentioning GPT-4o and GPT Builder) designed to act as an AI lawyering skills trainer, simulating a judge in moot court scenarios and providing personalized feedback. | Designer-led testing involved iterative simulations (30 mentioned) using the tool, systematic feedback collection, dialogue with the GPT builder, and intentionally introducing errors (misciting cases, confusing facts) to test the AI's ability to identify and correct them or redirect appropriately. Student-led testing was planned but pending at the time of writing. | Designer-led testing demonstrated the tool's ability to simulate courtroom interactions based on provided training data, identify user errors, provide feedback, and be iteratively refined based on performance. The process confirmed the feasibility of creating such a tool using platforms like GPT Builder. | NaN | NaN | NaN | NaN | Legal Education, Advocacy Skills Training | USA | Proprietary, domain-specific, unstructured text data from the University of Missouri-Kansas City (UMKC) School of Law's 1L Lawyering Skills II course, including bench briefs, sample questions for judges, the factual record, and assigning memos. A custom training guide developed by the author outlining AI behaviors and scenarios was also used within a 'closed knowledge universe'. | Agile methodology adapted for instructional design, involving phases: Align (identifying needs, compiling materials), Get Set (defining user experience, AI interaction strategy), Iterate & Implement (building, testing, iterative improvements), and Leverage & Evaluate (designer/student-led feedback). | Developed using OpenAI's GPT Builder platform (requiring a ChatGPT Plus subscription). Deployment involves sharing a link to the custom GPT. Planned deployment for student-led testing within UMKC Law. | False | False | NaN | NaN | The iterative refinement process of training the GPT, which can be time-consuming and requires patience ('akin to mentoring a teaching assistant'). Ensuring clear prompts and avoiding confusing information to prevent unexpected AI behavior. Initial consideration of platform cost (CustomGPT.ai). Potential need for institutional approval (IRB) for student testing. | AI 'hallucinations' (generating incorrect information), requiring expert oversight and mitigation techniques (like RAG or closed knowledge universes). Ethical considerations regarding AI use (need for guidelines, ABA Formal Opinion 512 mentioned). Data privacy concerns (FERPA) when used with identifiable student data. Potential for student misuse or unprofessional interaction with the tool. |
| bWkbsgfjIKIJ.pdf | Google_Scholar | Artificial Intelligence and the Sustainable Development Goals: \nGPT -3`s reflect ions on the Society Domain | This paper evaluates the large language model GPT-3's perspectives on how Artificial Intelligence (AI) can contribute to achieving the Sustainable Development Goals (SDGs) within the society domain. Through analyzing GPT-3's responses to queries about specific SDGs, the study identifies potential benefits, such as in education and health, alongside significant risks like bias and privacy concerns, ultimately stressing the need for robust regulation for responsible AI deployment. | True | Idealistic | True | 2.0 | Neutral | GPT-3 model (text-davinci-003) | The authors prompted GPT-3 (text-davinci-003 model) with queries related to nine societal SDGs and their 58 outcome targets. The prompts requested shortened target titles and 3-5 sentences outlining benefits and risks of AI's contribution to each target. The AI's textual responses were then descriptively analyzed for content, structure, word/sentence counts, and patterns of consistency or error. | GPT-3 identified numerous potential benefits of AI for societal SDGs, including poverty reduction, enhanced food security, improved healthcare diagnostics, personalized education, support for gender equality, better water management, optimized energy systems, sustainable urban planning, and enhanced access to justice. However, it consistently highlighted risks such as data bias leading to discrimination, privacy violations, exacerbation of existing inequalities, job displacement, and the necessity of human oversight. The model exhibited variability in response structure and an increase in errors (e.g., punctuation) with longer text generations. | For access to justice (primarily under SDG 16), identified hurdles include: AI systems potentially targeting specific populations or being biased against certain groups, leading to discriminatory outcomes; misinterpretation of data by AI leading to false accusations or unjust decisions; increased surveillance capabilities infringing on privacy rights critical for justice; and the risk of AI perpetuating or creating new forms of inequality in legal and institutional processes. | The paper emphasizes the critical need for proper regulation and oversight of AI development and deployment. It calls for establishing ethical guidelines, ensuring transparency and safety of AI systems, fostering a global, science-driven debate to develop shared principles and legislation, and promoting responsible AI use to mitigate risks and align AI with sustainable development. | Poverty eradication (SDG 1), zero hunger (SDG 2), good health and well-being (SDG 3), quality education (SDG 4), gender equality (SDG 5), clean water and sanitation (SDG 6), affordable and clean energy (SDG 7), sustainable cities and communities (SDG 11), and peace, justice, and strong institutions (SDG 16). Within SDG 16, specific topics include reducing violence, ending child abuse, promoting rule of law, reducing illicit financial flows, combating corruption, building effective institutions, inclusive decision-making, and ensuring legal identity and access to information. | Vulnerable populations, people living in poverty, communities in developing nations, women and girls (gender disparities), minority groups, children, and individuals at risk of discrimination. | Human rights law, criminal justice, anti-corruption law, data privacy law, administrative law, access to information law, environmental law (as it relates to social impacts of resource management). | International | The study used the GPT-3 model 'text-davinci-003', which was trained on data up to June 2021. The paper generally notes that such AI models are trained on vast amounts of internet text, which can include misinformed and biased content. | NaN | NaN | True | False | The authors interacted with GPT-3 via the OpenAI playground, implying availability through OpenAI's platform (API and playground). | Technical gaps include the unreliability and error-proneness of current LLMs like GPT-3, inconsistencies in output, and the need for improved natural language processing skills to avoid mimicking human writing flaws. Societal and ethical gaps include the lack of adequate regulation for AI, the potential for AI to exacerbate existing inequalities, pervasive data biases, significant privacy concerns, and the challenge of differentiating AI-generated content from human-written text, necessitating a global consensus on ethical AI principles and legislation. | The authors encountered challenges in obtaining consistent and accurate outputs from GPT-3, including variability in answering patterns and adherence to formatting instructions. They also noted an increase in punctuation mistakes in longer AI-generated texts and instances where the system failed to produce results without error messages, possibly due to beta tier limitations or capacity issues. | Bias in AI algorithms leading to discrimination and unfair decisions; privacy violations from data collection and analysis (e.g., health, financial, personal information); exacerbation of existing social and economic inequalities; job displacement due to automation; over-reliance on AI leading to reduced human oversight and accountability; potential for misuse of AI for manipulative purposes (e.g., targeting specific populations, citizen scores); and infringement on human rights and fundamental freedoms (e.g., through surveillance, profiling). |
| h4InHnlnqGoJ.pdf | Google_Scholar | Leveraging Large Language Models for Learning Complex Legal Concepts\nthrough Storytelling | This paper presents a novel application of LLMs to generate stories and multiple-choice questions for explaining complex legal concepts to non-experts, and introduces the LEGAL STORIES dataset. Through RCTs, it demonstrates that LLM-generated stories, using an expert-in-the-loop process, can enhance legal comprehension, interest, and knowledge retention, especially for non-native English speakers. | True | Idealistic | True | 1.0 | Positive | Using LLMs (LLaMA 2, GPT-3.5, GPT-4) to generate explanatory legal stories and multiple-choice questions from legal doctrine definitions, with an expert-in-the-loop process for question refinement, to create the LEGAL STORIES dataset. | Human evaluation of story quality (Prolific workers, automatic complexity metrics) and question quality (Prolific workers, legal expert critiques). Randomized Controlled Trials (RCTs) with legal novices (native and non-native English speakers) comparing learning with definition vs. definition + story, assessed by comprehension questions and a delayed retention test. | LLM-generated stories (GPT-4 performing best) enhance comprehension of legal concepts and interest in law among non-native English speakers compared to definitions alone. Stories also help participants relate legal concepts to their lives and show higher knowledge retention for non-native speakers. | Legal documents are challenging for non-experts due to unfamiliar terms and nuanced language, hindering access to justice and civic participation. Scalable legal storytelling education is limited by the high costs of legal experts. | Leveraging LLMs to generate legal stories and assessment questions in a scalable way, using an expert-in-the-loop pipeline to maintain quality and enhance legal literacy for non-experts. | Enhancing general legal literacy, learning intricate legal concepts, legal education for non-experts. | Non-experts, people without legal backgrounds, legal novices, with a particular focus on non-native English speakers. | General legal concepts and doctrines | International | Input data for generation (not model training) consists of 294 legal doctrines with definitions from Wikipedia, which is publicly available, domain-specific (legal), unstructured text. The study uses pre-trained LLMs (LLaMA 2, GPT-3.5, GPT-4). | Expert-in-the-loop pipeline combining LLM generation with human (Prolific workers, legal experts) evaluation and refinement. Randomized Controlled Trials (RCTs) for evaluating effectiveness. Iterative design for question refinement based on expert feedback. | Release of the 'LEGAL STORIES' dataset and associated code on GitHub. | True | True | The LEGAL STORIES dataset and code are available on GitHub. | Limited sample size in RCTs affecting statistical power for small effects. The cost and scalability of human expert involvement, though reduced, remain a factor. Need for further research into diverse prompting strategies and LLM-based explanation methods. | Ensuring high quality and factual accuracy of LLM-generated content. Cost and time for human/expert evaluation and refinement. Designing effective prompts for LLMs. Evaluating generated questions without gold standards. | LLM-generated content may contain misleading, biased, harmful, or wrong information if not supervised. Risk of over-simplifying or over-generalizing nuanced legal jargon. Potential for inherent biases in LLMs to be perpetuated. |
| vmJp9pKwcFwJ.pdf | Google_Scholar | Addressing the Failures of the U.S. Civil Legal System | This paper analyzes the failures of the U.S. civil legal system in providing access to justice, particularly for vulnerable populations, by examining the concepts of legal capability and legal consciousness. It advocates for interdisciplinary interventions, including improved self-help resources, community-based support, and thoughtful use of technology, to better address the complex barriers faced by individuals. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Low legal capability (lack of knowledge, skills, confidence, agency); low legal consciousness (failure to identify problems as 'legal', distrust of institutions); systemic barriers (complex/intimidating procedures, cost, arcane language, digital exclusion); psychological/emotional barriers (stress, scarcity mindset, fear, anxiety, shame, lack of motivation); insufficient community support; educational system gaps. | Enhance legal capability and consciousness through targeted education and support; leverage interdisciplinary insights (public health communication, behavioral economics/nudges, inclusive design, marketing, neuroscience); utilize non-legal community organizations as trusted intermediaries; redesign self-help materials (plain language, procedural focus, address psychological needs, visual aids, motivational elements); coordinate resources nationally; employ technology thoughtfully (user-centered design, potential of generative AI like ChatGPT); co-design solutions with users. | Access to civil justice, self-representation support, legal empowerment, understanding and navigating the legal system, community-based legal help. | Legally vulnerable populations including lower-income Americans, women, racial/ethnic minorities (specifically Black and Multi-racial non-Hispanic Americans), younger and middle-aged Americans, urban and rural residents, people with disabilities, individuals experiencing houselessness, formerly incarcerated individuals. | Civil Law (general), Housing Law, Debt Collection, Family Law, Public Benefits Law, Employment Law, Consumer Law. | U.S. Civil Legal System (primary focus), with references to research/initiatives in the United Kingdom, Canada, and Australia. | NaN | NaN | NaN | False | False | NaN | Need for national coordination of resources and branding; challenge of integrating interdisciplinary approaches into legal service delivery; bridging the digital divide; addressing deep-seated distrust in legal institutions; ensuring ethical and effective implementation of AI; securing funding and resources for proposed interventions. | NaN | Generative AI risks (knowledge gaps, ethical concerns, economic disruption, data security, privilege issues, generating trust); digital exclusion exacerbating inequality; ineffective interventions leading to further harm or disillusionment for vulnerable individuals; potential for poorly designed technology to be unhelpful or harmful. |
| BthWmIW7q08J.pdf | Google_Scholar | Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language Models | This paper introduces KBL, a new benchmark designed to evaluate the Korean legal language understanding capabilities of Large Language Models (LLMs). KBL comprises legal knowledge tasks, legal reasoning tasks, and Korean bar exam questions, developed with legal professionals and used to assess LLMs in both closed-book and RAG settings. | True | NaN | True | 2.0 | NaN | KBL benchmark for evaluating LLMs and RAG systems on Korean legal tasks. | Evaluation of various LLMs (GPT-4, Claude series, Qwen2, etc.) using the KBL benchmark (7 knowledge tasks, 4 reasoning tasks, Korean bar exam questions). Testing was performed in zero-shot, multiple-choice QA format, under both closed-book and Retrieval-Augmented Generation (RAG) settings. RAG used BM25 retrieval on Korean statute and precedent corpora. | GPT-4 generally performed best, achieving 72.0% average accuracy on knowledge tasks, 88.6% on two core reasoning tasks (CAUSAL, CONS), and 48.1% on the 2024 bar exam (closed-book). Using RAG with both precedent and statute corpora improved GPT-4's knowledge task accuracy to 75.3% and 2024 bar exam accuracy to 49.7%, though RAG effectiveness varied by task and corpus. | NaN | NaN | NaN | NaN | Civil Law, Criminal Law, Public Law (Constitutional Law, Administrative Law), Professional Responsibility, Food Sanitation Law, various specific statutes. | South Korea | The benchmark KBL was created using Korean precedents, statutes, bar exams, legal QA datasets from Korea Legal Aid Corporation, legal terminology reference documents, etc. The RAG evaluation used a public corpus of 150k Korean precedents (LBoxOpen) and a newly compiled corpus of 220k Korean statutes and municipal ordinances. | Benchmark development involved sourcing diverse Korean legal texts, structuring tasks as multiple-choice QA, categorizing tasks (knowledge, reasoning, bar exam), and close collaboration with 8 licensed lawyers for task design, verification, and quality assurance (including correction of external data errors). RAG evaluation used standard BM25 retrieval. | The KBL benchmark dataset, associated corpora for RAG, and evaluation code are stated to be released via GitHub under a CC BY-NC license. | True | True | Stated intention to release dataset, RAG corpora, and code via GitHub under a CC BY-NC license. | Significant room for improvement in LLM capabilities for Korean legal tasks, particularly in recalling specific legal knowledge (e.g., statute numbers) and applying knowledge/reasoning in bar exam scenarios. RAG effectiveness is inconsistent and depends on LLM, corpus, and task type. | Ensuring the quality and accuracy of legal benchmark data required extensive expert verification (correcting up to 21% errors in sourced data). Designing pragmatic tasks beyond standardized tests. Building relevant Korean legal corpora for RAG evaluation. | Implicit risk of LLM hallucination in legal context (addressed via specific QA tasks and citing external work). General risk of misuse of open-source LLMs (mentioned briefly). |
| Y4rTcW-hKRcJ.pdf | Google_Scholar | Better Call GPT, Comparing Large Language Models Against Lawyers | This paper compares Large Language Models (LLMs) against Junior Lawyers and Legal Process Outsourcers (LPOs) for legal contract review tasks based on accuracy, speed, and cost. It finds that top LLMs match or exceed human accuracy in identifying issues, are dramatically faster, and significantly cheaper, suggesting a potential disruption in legal services. | True | Market | True | 2.0 | Positive | Comparative evaluation of multiple Large Language Models (GPT-4 variants, GPT-3.5, Claude variants, Palm2) for legal contract review (issue determination and location) using specific prompts. | Compared LLM performance (GPT-4, GPT-3.5, Claude, Palm2) against Junior Lawyers and LPOs on 10 anonymized procurement contracts (US & NZ), using ground truth established by Senior Lawyers. Measured accuracy (Precision, Recall, F-score for issue determination and location), time, and cost. | Best LLM (GPT4-1106) matched LPO accuracy for issue determination (F-score 0.87), slightly beating Junior Lawyers (0.86). LPOs led in issue location (F-score 0.77), followed closely by GPT4-32k (0.74). LLMs were vastly faster (seconds/minutes vs hours) and cheaper (>99.9% cost reduction). | NaN | Use of LLMs for contract review to drastically reduce cost and time, potentially enhancing accessibility of legal services. | NaN | NaN | Contract Law | United States, New Zealand | The study uses pre-trained commercial LLMs (GPT, Claude, Palm2) with their original, unspecified training data. The evaluation dataset consisted of 10 anonymized, real-world procurement contracts from US and NZ jurisdictions. | Experimental design involving benchmark creation (contract dataset, ground truth annotations by senior lawyers), prompt engineering for selected LLMs, comparative evaluation against human reviewers (junior lawyers, LPOs) based on accuracy, speed, and cost metrics. | NaN | False | False | NaN | Need to evaluate LLMs on more contract types; need to explore LLM capabilities in contract negotiation; LLMs may struggle compared to experts in locating issues where contract language is absent. | Selecting LLMs with sufficient context windows to avoid inefficient document splitting; prompt engineering specific to each LLM; initial setup, testing, and validation time. | LLMs performing less accurately in locating contract issues (vs. determining their existence), potentially impacting automated markup; need for ongoing human supervision of LLM outputs; potential for industry resistance to adoption based on protecting existing business models. |
| informit.T2024051500023791292031947.pdf | Google_Scholar | Rethinking Jurisdictional Barriers to Practising Law Abroad: A Soft Technological Deterministic Approach | This paper examines restrictive jurisdictional barriers to cross-border legal practice, arguing that technology is a key driver of change. Using a soft technological deterministic approach, it posits that this change is shaped by an interplay of technological progress with non-technological factors like legal system similarities and trade affiliations. | True | Market | False | 3.0 | NaN | NaN | NaN | NaN | Restrictive regulations (e.g., nationality/local admission requirements), professional protectionism, and outdated geographical-based regulatory frameworks for lawyers. | Advocates for reassessing historical justifications for restrictions and understanding the interplay of technology with factors like legal system similarity and trade ties to foster more liberal cross-border practice regulations. | Cross-border practice of law; Foreign lawyer mobility; Regulation of the legal profession. | NaN | General legal practice regulation, with examples from contract law, data protection, intellectual property. | International (mentions OECD countries, EU, US, UK, Australia, New Zealand, Nigeria, etc.). | NaN | NaN | NaN | False | False | NaN | Resistance to liberalizing foreign lawyer mobility due to protectionism; outdated geocentric regulations misaligned with technological advancements and globalization; need for adaptive regulatory reforms. | NaN | Traditional justifications for restrictions include protecting the public from incompetent legal service providers and risks from unauthorized cross-border practice, though the paper questions these as primary motivations. |
| 6hCPZ8Fr_aMJ.pdf | Google_Scholar | Beyond Readability with RateMyPDF*: A Combined Rule-based and Machine Learning Approach to Improving Court Forms | This paper introduces RateMyPDF, a web application designed to help authors assess and enhance the usability of court forms for self-represented litigants. The tool provides a score and automated improvement suggestions by combining rule-based methods, traditional machine learning, and GPT-3, validated against expert reviews and a large dataset of US court forms. | True | Idealistic | False | 1.0 | Positive | RateMyPDF: A web application combining rule-based metrics (readability, field counts, page counts, etc.), traditional ML (field classification), and LLMs (GPT-3 for summarization and metadata extraction) to automatically score court form usability and suggest improvements. | Compared RateMyPDF scores with human expert ratings (6 experts) on a subset of 40 forms. Experts rated complexity on a 1-5 scale. Intraclass correlation (ICC) was used to measure agreement. | Statistically significant intraclass correlations were found among experts (ICC1=0.3139, p=0.02) and between the average human rating and RateMyPDF score (ICC3=0.5861, p=0.00). RateMyPDF scores correlated with average expert ratings. | Court forms are often difficult for self-represented litigants to comprehend, complete accurately, and provide complete responses due to complex language, poor design, and legal jargon. Forms impose time and emotional burdens, are often created without usability expertise or user input, and traditional usability testing is resource-intensive. | Provide an automated tool (RateMyPDF) that measures form usability based on multiple features (readability, field types, layout proxies, burden estimation) and offers specific improvement suggestions. Enable scalable analysis and benchmarking of large form libraries to prioritize simplification efforts. | Improving the usability and accessibility of court forms, reducing administrative burden. | Self-represented litigants. | General Civil Litigation (court forms cover various areas like eviction, restraining orders, divorce, fee waivers) | United States (dataset from 46 states and D.C.) | Benchmark dataset: ~24,000 PDF forms scraped from official court websites in 46 U.S. States and D.C. (unstructured text, some with form fields). Field classification ML model trained using features including adjacent text, field location, previous field, and topic (derived from the form dataset). Leverages pre-trained GPT-3 model. | Literature review (form design, readability), data collection (web scraping), feature engineering (readability scores, field classification, burden estimation), development of rule-based and ML models, integration of external libraries (OpenCV, spaCy, PassivePy, EyeCite) and LLMs (GPT-3), user-centered design (interviews, workshopping with legal aid providers and court staff), validation through expert evaluation. | Publicly accessible web application (RateMyPDF.com), companion website for exploring the form dataset (Form Explorer), open-source code repositories on GitHub (FormFyxer library and RateMyPDF frontend). | True | True | Available as a web tool at RateMyPDF.com and as open-source code on GitHub (SuffolkLITLab/RateMyPDF and SuffolkLITLab/FormFyxer). | Need for establishing normative target scores ('good' vs 'bad' forms beyond complexity), refining time-to-answer estimates with real-world user testing, developing more domain-specific difficult word lists for legal forms, improving detection of state-specific citations, measuring whitespace and field ordering more directly, extending the approach to interactive legal applications (guided interviews). | Handling variability in PDF quality and formats (including XFA), automatically recognizing and normalizing form fields, classifying field types accurately (slot-in, gathered, third-party, created), integrating multiple NLP and computer vision tools, balancing automated metrics with actionable design advice, evaluating usability beyond simple readability. | Large language models (like GPT-3) may hallucinate or produce factually incorrect responses (mitigated by anchoring tasks like summarization to source text). Readability formulas can be 'gamed' by authors without improving true usability (mitigated by providing specific, varied recommendations). |
| kkd5gfg1ZFcJ.pdf | Google_Scholar | A Pattern Language for Persona-based Interactions with LLMs | This paper proposes a pattern language to enhance large language model (LLM) interactions by extending the basic 'Persona' prompt engineering pattern. It introduces seven new interconnected patterns designed to make LLM personas more dynamic, context-aware, culturally sensitive, and collaboratively developed. | True | Market | True | 1.0 | NaN | A pattern language for persona-based prompt engineering, including seven specific patterns: Multi-Persona Interaction, Dynamic Persona Switching, Role-Playing Scenarios, Contextual Depth Enhancement, Multi-Language and Cultural Adaptation, Temporal Perspective, and Collaborative Persona Development. | NaN | NaN | NaN | NaN | NaN | NaN | General Legal Advice, Contract Law, Legal Education, Compliance | International | NaN | Conceptual design based on pattern language methodology, identifying limitations and extending existing patterns. | NaN | True | True | The paper describes the prompt patterns conceptually with examples. | NaN | Increased complexity in prompt design, ensuring consistency and coherence (especially with dynamic switching/multiple personas), time consumption (for collaborative development), complexity in managing feedback (for collaborative development). | Hallucinations (generating incorrect/fictional content), Inconsistent/incoherent/disjointed outputs, Cultural stereotyping or oversimplification, Overfitting personas to specific users/contexts, Perpetuating biases, Historical inaccuracies (with Temporal Perspective pattern). |
| wu2VSu_u2G0J.pdf | Google_Scholar | Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language Models | This paper introduces KBL, a benchmark for assessing Korean legal language understanding in LLMs. KBL consists of legal knowledge tasks, legal reasoning tasks, and Korean bar exam questions, and is used to evaluate LLMs in both closed-book and RAG settings, highlighting areas for improvement. | True | NaN | True | 1.0 | Neutral | KBL: KOREAN BENCHMARK FOR LEGAL LANGUAGE UNDERSTANDING. A benchmark consisting of 7 legal knowledge tasks, 4 legal reasoning tasks, and the Korean bar exam, evaluated in closed-book and Retrieval-Augmented Generation (RAG) settings. | Various LLMs (including GPT-4, Claude-3 series, Qwen2, KULLM3, EEVE) were evaluated on the KBL benchmark tasks using accuracy on multiple-choice questions. The RAG setting utilized a BM25 retriever with Korean statutes and precedents corpora. | GPT-4 achieved the highest performance among tested models, e.g., 72.0% average accuracy on knowledge tasks and 48.1% on the 2024 bar exam in a closed-book setting. With RAG (using precedents and statutes corpora), GPT-4's performance on knowledge tasks improved to 75.3% and reached 49.7% average on the 2024 bar exam. | NaN | NaN | NaN | NaN | Criminal Law, Civil Law, Public Law, Professional Responsibility. Specific tasks cover legal concepts, offense components, statute matching, legal hallucination, causal reasoning, statement consistency, and case relevance. | South Korea | For the RAG technique studied: 1) A corpus of 150k Korean precedents (unstructured text) from Hwang et al. (2022). 2) A new Korean statute corpus comprising 220k articles from active Korean statutes and municipal ordinances (unstructured text) collected from LAW OPEN DATA. | Benchmark development involved close collaboration with legal professionals for task design and quality assurance, including verification of answers by licensed lawyers. Data was compiled from Korean precedents, statutes, bar exams, and legal QA datasets. Fuzzy matching was used to ensure no overlap with existing benchmarks like KMMLU. | The KBL datasets, the corpus for RAG, and the evaluation code are stated to be released to the community under a CC BY-NC license via GitHub. | True | True | KBL datasets, RAG corpus, and evaluation code to be released on GitHub (https://github.com/lbox-kr/kbl) under a CC BY-NC license. | Identifies significant room for LLM improvement in Korean legal tasks, particularly for the Korean bar exam where even top models like GPT-4 perform well below U.S. bar exam levels. The efficacy of RAG is inconsistent and depends on multiple factors, indicating a need for better retrieval and integration methods. | Key challenges included ensuring benchmark data quality through expert collaboration due to high error rates (up to 21%) in freely available semi-expert data. Developing a Korean-specific legal ontology, rather than mere translation, was crucial. Defining 'relevance' for case retrieval tasks and the inherent difficulty in automatically evaluating generative legal NLU tasks were also significant challenges. | The paper notes general risks of LLM misuse and potential for unethical tuning of open-source models. Specific to legal applications, it implicitly highlights issues like LLM unreliability in recalling specific legal knowledge (evidenced by low STAT task scores) and the problem of legal hallucinations (addressed by the HALL dataset). |
| 1WKST3FL64cJ.pdf | Google_Scholar | Structured Legal Argumentation with LLMs: A Study in Landlord-Tenant Law | This paper proposes and evaluates a method using OpenAI's GPT-4o with context augmentation (Chicago's RLTO) and Chain-of-Thought instructions to generate structured legal arguments for landlord-tenant disputes. The study tests this approach on ten scenarios, finding reasonable accuracy and factuality but limitations in handling out-of-scope issues and relevance assessment. | True | Idealistic | True | 1.0 | Positive | Using GPT-4o with context augmentation (full text of Chicago's Residential Landlord and Tenant Ordinance - RLTO) and Chain-of-Thought (CoT) prompting to generate structured legal arguments (Exposition, Specific law, Why this Law Applies, Conclusion) for specific scenarios. | Evaluation of generated arguments for 10 hypothetical landlord-tenant scenarios (5 from legal aid, 4 AI-generated, 1 author-crafted) by a Landlord-Tenant lawyer based on metrics: Accuracy, Factuality, Comprehensiveness (0-1 scale), and Relevance (0-1 scale). | The method was accurate in 8/10 scenarios and 54/55 arguments were factual. Limitations identified include failing to recognize issues outside the scope of the provided RLTO and difficulties in filtering irrelevant details from emotionally charged scenarios or narrowing arguments to the core legal issue. | The implicit difficulty for laypersons in understanding their rights and drafting legal documents like demand letters in landlord-tenant disputes. | Providing LLM-generated, structured legal arguments based on specific scenarios and relevant law (RLTO) to assist laypersons in drafting documents and asserting rights, with outputs designed to be verifiable by legal professionals. | Generating legal arguments, assisting with drafting demand letters, understanding legal rights in landlord-tenant disputes. | Tenants, particularly those who might seek assistance from legal aid organizations. | Landlord-Tenant Law | Chicago | The technique uses context augmentation with the text of Chicago’s Residential Landlord and Tenant Ordinance (RLTO). The underlying LLM (GPT-4o) was pre-trained on general web data by OpenAI. | Prompt engineering (structured output format, Chain-of-Thought instructions), context augmentation, expert evaluation. | The scenarios, model parameters, and results are shared on GitHub, but no deployment of the tool/system itself is mentioned. | False | False | NaN | Limitations in classifying legal issues outside the provided context (RLTO), reliably assessing the relevance of generated arguments, robustness of the process, need for refined evaluation methods, difficulty filtering noise from emotionally charged descriptions. | LLM's inability to filter out less important concerns from user scenarios (especially when emotionally charged), difficulty in narrowing down arguments to the crux of legal issues, ensuring generated arguments stay within the scope of the provided legal text. | Inaccuracy (e.g., missing that an issue falls outside the scope of the provided law), lack of factuality (connecting premise and conclusion to the cited law), generating irrelevant arguments. |
| ftds8EOUbrIJ.pdf | Google_Scholar | The Continued Rise of Artificial Intelligence in Higher Education | This paper examines the rapid growth and integration of AI within higher education, specifically focusing on the University of North Carolina (UNC) System. It discusses current uses, future opportunities, significant risks (like bias, plagiarism, data privacy), and proposes a framework for developing institutional policies and risk mitigation strategies. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | Legal education, Legal profession automation, Ethical AI use | University students (including underrepresented groups) | General law, Legal education, Legal tech | USA (specifically North Carolina educational institutions) | NaN | NaN | NaN | False | False | NaN | Lack of institutional AI policies/strategy, Curriculum gaps in AI literacy/application, Need for ethical guidelines, Faculty training deficit, Potential for bias/discrimination, Job displacement concerns. | Policy development lag, Ethical concerns (plagiarism, bias, privacy), Faculty adoption and training, Ensuring validity/transparency of AI tools, Risk management complexity, Resource constraints, Potential degradation of critical thinking skills. | Data privacy breaches, Discrimination/Bias, Inaccurate/Unreliable outputs, Plagiarism/Academic integrity issues, Job displacement (legal sector), Loss of public trust, Security vulnerabilities (tampering), Disinformation propagation. |
| cQHRZiimZz0J.pdf | Google_Scholar | Large Language Models in Politics and Democracy: A Comprehensive Survey | This paper surveys the current and potential applications of large language models (LLMs) across various political domains, including policymaking, communication, analysis, national security, and law. It outlines both the opportunities for enhanced efficiency and inclusivity, and the significant challenges related to bias, transparency, reliability, and ethics. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Unreliability due to legal hallucinations, need for human oversight, potential biases favouring specific groups or jurisdictions. | Responsible development principles, creation of ethical guidelines and governance frameworks, ensuring human oversight, developing methods for bias mitigation, using domain-specific adaptation and curated data. | Legal information provision, legal research, legal drafting. | Under-resourced nations (mentioned generally in policy context), general public needing access to justice (implied). | General Legal Field | International | NaN | NaN | NaN | False | False | NaN | Technical: Robust bias mitigation, transparency, explainability, reliability (reducing hallucinations). Societal: Ensuring fairness, equity, representation; addressing impacts on polarization and democratic processes; establishing accountability frameworks. | Bias in models and data, reliability issues (hallucinations), lack of transparency and accountability, ethical concerns (e.g., manipulation, deception, lobbying), privacy risks, security vulnerabilities (adversarial attacks), need for effective human oversight, ensuring equitable access and outcomes. | Disinformation and manipulation, amplification of political polarization, biased or unfair policy outcomes, unreliable legal outputs ('hallucinations'), potential for unintended escalation in military/diplomatic contexts, erosion of democratic accountability, AI deception. |
| 26yOzn8f_vkJ.pdf | Google_Scholar | To Learn or Not to Learn: Replaced Token Detection for Learning the Meaning of Negation | The paper proposes continued pretraining of the ELECTRA model using its Replaced Token Detection objective on newly created negation-focused datasets (Expanded NLI and Expanded LAMA) to improve understanding of negation in Natural Language Inference (NLI). Results show significant gains on binary NLI tasks (RTE) using Expanded LAMA, but challenges remain for multi-class NLI (MNLI) and datasets with scarce negation examples (SNLI). | True | NaN | False | 1.0 | NaN | Continued pretraining of ELECTRA's discriminator using the Replaced Token Detection (RTD) objective on custom datasets (Expanded NLI, Expanded LAMA) designed to teach negation. | Models were evaluated on standard development sets of NLI benchmarks (RTE, MNLI, SNLI) and their corresponding negated subsets (Negated RTE, Negated MNLI, Negated SNLI from Hossain et al., 2020) using accuracy. | Continued pretraining with Expanded LAMA (+LAMA model) achieved a 19.9% accuracy increase on Negated RTE compared to the baseline ELECTRA-Small, reaching 70.3% accuracy. | NaN | NaN | NaN | NaN | NaN | International | Continued pretraining used two new datasets: 'Expanded NLI' (derived from RTE, MNLI, SNLI negation subsets from Hossain et al. 2020, plus Wikipedia/Books data) and 'Expanded LAMA' (derived from LAMA and Negated LAMA datasets, plus Wikipedia/Books data). Source datasets are publicly available; the derived datasets were created via specific processing rules for the RTD task. Unstructured text. | Dataset creation involved converting existing NLI and LAMA examples containing negation into a format suitable for ELECTRA's Replaced Token Detection (RTD) objective, including specific rules for generating 'original'/'replaced' token labels. Continued pretraining leveraged this modified dataset and the RTD task. | NaN | False | False | NaN | Difficulty handling negation cues in neutral-labeled NLI examples; overfitting on non-negated examples when negation data is scarce in finetuning dataset; limitation to specific overt negation cues (e.g., 'not', 'never'); need for pretraining tasks suitable for multi-class NLI with negation. | Adapting existing NLI/LAMA datasets for the RTD pretraining task; preventing overfitting during continued pretraining; handling the under-representation of negation in standard NLP benchmarks; difficulty learning negation's impact in multi-class (entailment/neutral/contradiction) settings. | NaN |
| pTUY-puzpdkJ.pdf | Google_Scholar | Technologically Competent Reprised: Ethical Practice in an AI Age and Considerations for Our Courts in a Burgeoning AI Era | This paper examines the ethical implications of generative AI for legal practice under existing professional conduct rules, particularly ABA Model Rule 1.1 regarding technological competence. It reviews recent court cases involving AI misuse, discusses court orders regulating AI, analyzes ABA and state bar guidance, and proposes revisions to ethical rules for greater clarity. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal Ethics and Professional Responsibility, Civil Procedure, General Litigation | United States | NaN | NaN | NaN | False | False | NaN | Lack of specific ethical guidelines and rules addressing generative AI; Insufficient lawyer training and competence regarding AI tools and risks; Inconsistent approaches by courts in regulating AI use. | NaN | AI Hallucinations leading to citation of nonexistent cases; Violation of client confidentiality through data input into AI tools; Lack of candor towards the tribunal; Frivolous claims/submissions; Inflated/unreasonable legal fees; Inadequate supervision of staff using AI; Bias in AI outputs; Misleading advertising by AI tools; Deceptive AI practices. |
| yRTzQcw3sdsJ.pdf | Google_Scholar | An Introduction to A Roadmap for Law School Modernity: Teaching Technology Competence | This paper introduces a law journal symposium focused on developing a 'Roadmap for Law School Modernity' by integrating technology competence into legal education. It highlights the professional duty for lawyers to be tech-competent and summarizes the symposium's articles, which cover curriculum framing, pedagogical considerations, competency testing, and the relevance of technology to access to justice. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | The access to justice gap; uneven technology access, especially for rural communities. | Improving legal education on technology competence to include how legal technology can address the justice gap and mitigate disparate impacts of technology access; incorporating technology competence across all law school curricula. | Access to justice gap; disparate impact of technology access in underserved areas like rural communities; legal education reform. | Underserved communities generally; rural communities specifically. | General legal practice | United States | NaN | NaN | NaN | False | False | NaN | Need for a unified approach to technology competence education in law schools; integrating rapidly evolving technologies like LLMs/Generative AI into legal education and understanding their impact. | Resistance from law school administration and faculty to curriculum changes; defining and assessing technology competence effectively; keeping legal education current with rapid technological advancements. | Professional and financial repercussions for lawyers lacking tech competence (e.g., cybersecurity breaches); ethical risks associated with the use of technology if lawyers are not properly educated on its benefits and risks. |
| iCe6v16i9SwJ.pdf | Google_Scholar | Friend or Foe – AI’s Invasion of the Legal Battlefield | This paper discusses the integration of AI into the legal profession, highlighting potential benefits like increased efficiency and access to justice through lower costs. It also examines significant risks, including ethical considerations, privacy concerns, AI errors ('hallucinations'), and the unauthorized practice of law. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | High cost of legal services; insufficient number of lawyers to meet population needs. | Leveraging AI for efficiency to enable lawyers to offer more affordable services (e.g., document drafting/review) and handle more clients, thereby increasing accessibility. | Cost of legal services, Efficiency of legal service delivery, Document automation, Legal research. | General public requiring affordable legal services. | General Legal Practice | United States (primarily, with brief mention of Italy/EU) | NaN | NaN | NaN | False | False | NaN | Need for clear governmental regulation and ethical guidelines for AI in law; ensuring lawyer competency in using AI; addressing AI limitations like bias and 'hallucinations'; defining boundaries related to the unauthorized practice of law. | NaN | AI 'hallucinations' (incorrect outputs); privacy violations due to handling client data on third-party platforms; unauthorized practice of law; potential for AI bias; cybersecurity threats (e.g., AI-generated malware); ethical concerns regarding lawyer competence, oversight, and accountability; potential legal liability for AI outputs. |
| lftOiX2IcekJ.pdf | Google_Scholar | Chain of Logic: Rule-Based Reasoning with Large Language Models | This paper introduces "Chain of Logic," a novel prompting method inspired by the IRAC legal framework, designed to improve rule-based reasoning in Large Language Models (LLMs). Evaluated on LegalBench tasks, Chain of Logic consistently outperforms existing prompting methods by decomposing rules into elements and then recomposing their logical resolution to arrive at a conclusion. | True | Idealistic | True | 1.0 | Positive | Chain of Logic prompting method | Evaluated across eight rule-based reasoning tasks involving three distinct compositional rules from the LegalBench benchmark. Compared against zero-shot, standard prompting, chain of thought, and self-ask methods using GPT-3.5, GPT-4, Llama-2-70b-chat, and Mistral-7B-OpenOrca models, using a one-shot example from a different rule application. | Chain of logic consistently outperforms other prompting methods across all tested models. On average, Chain of Logic achieved 79.3% accuracy across all rules and models (as per Table 1). | Language models are prone to hallucinations in legal settings and struggle with basic legal tasks and complex compositional rules. Annotated legal data is scarce, limiting fine-tuning capabilities. LLMs also show difficulty with in-context learning for legal reasoning. | The proposed 'Chain of Logic' prompting method guides LLMs to perform rule-based reasoning through explicit decomposition of rules into elements and recomposition of sub-answers to resolve the logical expression, thereby improving in-context learning and reducing the need for numerous rule-specific examples. | Improving rule-based legal reasoning in LLMs, enhancing the interpretability of AI-driven legal analysis, and potentially broadening access to justice by increasing the capacity of legal professionals. | NaN | Civil Procedure (Personal Jurisdiction, Diversity Jurisdiction), Contract Law (J.Crew Blocker covenant). | United States | The technique is a prompting method applied to pre-trained large language models (GPT-3.5, GPT-4, Llama-2, Mistral-7B-OpenOrca). The evaluation uses tasks from LegalBench, each providing a rule, fact pattern, and question. The method uses a single in-context example from a different rule application, not requiring model fine-tuning on task-specific data. | Inspired by the IRAC (Issue, Rule, Application, Conclusion) legal reasoning framework. The method involves: 1) Structured Input, 2) Rule Decomposition, 3) Logical Expression construction, 4) Question Answering per element, 5) Element Recomposition, and 6) Resolving the Expression. | NaN | True | False | The Chain of Logic prompting methodology and its steps are fully described in the paper, allowing users to implement it with compatible LLMs. The specific LLMs used have varying access models (commercial or open-source). | The rules in LegalBench are simplified compared to real-world legal rules. The current approach primarily addresses rule antecedents, not complex consequences. Future work areas include rule identification, dynamic sampling of reasoning paths, and incorporating retrieval augmented generation. | Models struggling with in-context learning in legal settings for compositional rules. Cost and scalability of requiring multiple reasoning examples per rule. Difficulties in correctly decomposing rules, identifying elements, and understanding logical relationships between them without explicit guidance. | Language models are prone to hallucinations in a legal setting. Potential for incorrect rule application or logical errors leading to inaccurate conclusions, even with advanced prompting. |
| bpVcEyHR4cQJ.pdf | Google_Scholar | HUMAN REALIGNMENT: AN EMPIRICAL STUDY OF LLMS AS LEGAL DECISION-AIDS IN MORAL DILEMMAS | This paper empirically investigates the alignment between human judgments and large language model (LLM) decisions in moral dilemmas, specifically trolley problems, finding significant misalignment with LLMs exhibiting utilitarian bias. It tests whether normative prompting can realign LLMs (GPT-3.5, GPT-4, GPT-o3-mini) with deontological or balancing principles, yielding mixed and often unsatisfactory results, raising concerns about their use as legal decision-aids under the Rule of Law. | True | NaN | True | 2.0 | Negative | Evaluating Large Language Models (specifically OpenAI's GPT-3.5, GPT-4, and GPT-o3-mini) as legal decision-aids in moral dilemmas (trolley problems), using normative prompting (deontological, utilitarian, balancing instructions) as a method to attempt human realignment. | LLMs were prompted with 41 moral dilemma vignettes multiple times (25-100 iterations per condition) via OpenAI API, varying prompts (no norm, deontological, utilitarian, balancing) and temperature settings (0.7, 1.0, 1.3 for GPT-3.5/4). LLM decision proportions (intervene/utilitarian vs. do nothing/abstain) were compared against human benchmark data from neal.fun and experimental studies (Mikhail 2002). LLM beliefs about human choices were also elicited and compared. | LLMs showed significant misalignment with human choices, exhibiting a stronger utilitarian bias. Normative prompting failed to reliably realign the models: GPT-3.5 often refused to decide when given deontological or balancing prompts; GPT-4 remained predominantly utilitarian and largely ignored deontological instructions; GPT-o3-mini responded strongly to deontological prompts but ignored instructions to balance concerns. | Misalignment of AI decisions with human values and established legal/normative principles (e.g., utilitarian bias overriding deontological concerns). Limited controllability or 'malleability' of LLMs through normative instructions, hindering efforts to ensure they act as faithful agents of the legislator (Rule of Law). Opacity of LLM reasoning processes, making it difficult to predict or understand their normative biases. | Investigated normative prompting as a realignment method but found it insufficient with current models. Suggests the need for rigorous, ongoing testing of LLMs against human and political/legal norms before deployment in morally laden legal contexts. Implies more intrusive methods like fine-tuning might be necessary, or 'heavy-handed' instructions, to achieve satisfactory alignment. | NaN | NaN | Constitutional Law (human dignity, balancing), Criminal Law (necessity by analogy), Tort Law (duty of care by analogy), Rule of Law principles. | Multiple (mentions US, UK, Germany) | Proprietary datasets used by OpenAI to train GPT-3.5, GPT-4, and GPT-o3-mini. Known to be large-scale, general-purpose, primarily unstructured text and code data derived from the internet, books, etc., not specifically legal domain data. | Experimental design: Comparing LLM outputs to human benchmarks across different conditions (normative prompts, LLM versions, temperature settings) using moral dilemma vignettes. Statistical analysis (t-tests, linear probability models, multinomial logistic regression) of quantitative choice data. Semantic clustering of qualitative justification data (for abstentions). | NaN | True | False | The evaluated LLMs (GPT-3.5, GPT-4, GPT-o3-mini) are accessible via the commercial OpenAI API. The specific prompts and vignettes used in the study are provided in the paper. | Technical: Current LLMs lack sufficient sensitivity and reliability in responding to nuanced normative instructions, especially balancing competing principles. Their opacity hinders trustworthiness and predictability. Societal: Need for governance mechanisms to ensure AI used in legal contexts aligns with democratic will and Rule of Law. Lack of established methods for reliably testing and ensuring normative alignment across different LLM versions and updates. | Inherent utilitarian bias in the studied LLMs. Difficulty in controlling LLM behavior via prompting (insensitivity, refusals to answer, unpredictable responses). Significant behavioral differences between LLM versions. Managing the stochastic nature of LLM outputs for systematic study. | LLMs acting as 'unauthorized normative rulers' imposing hidden or unintended biases (e.g., utilitarianism) contrary to legal principles or democratic will. Undermining the Rule of Law through misalignment. Users (e.g., judges) potentially making biased decisions based on flawed AI advice. Increased disconnect between legal decisions and societal values. Risk that technical improvements to LLMs may worsen normative alignment without specific testing. |
| PLFHrc0U1FoJ.pdf | Google_Scholar | “AI Takes the Gavel: Contract Laws' New Sidekick in Automated Decision -Making" | This paper explores the impact of Artificial Intelligence (AI) on contract law, focusing on opportunities like efficiency and risks such as errors, bias, and lack of transparency. It emphasizes the complexities of automated decision-making in law and the need for human oversight despite AI's growing capabilities. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Contract Law, General Legal Practice | India, Canada, US, EU, International | Discusses the need for labeled legal performance data (e.g., contract language variations) but notes challenges like context-specificity, jurisdiction variations, evolving law, privacy, and potential amplification of errors. No specific dataset described. | NaN | General market adoption by law firms and legal departments. | False | False | NaN | Technical gaps include AI's difficulty adapting to legal dynamics, ensuring confidentiality, achieving transparency without sacrificing accuracy, and handling legal complexity. Societal/Ethical gaps include establishing accountability, mitigating bias, preventing skill erosion, ensuring fairness, and addressing privacy concerns. | Data acquisition/labeling challenges (context, privacy, amplifying errors), ensuring accuracy/reliability, maintaining security/confidentiality, mitigating bias, achieving transparency/explainability, adapting to legal changes, avoiding deskilling/over-reliance, establishing accountability. | Errors in AI output (e.g., fake citations, flawed contracts), perpetuation of bias, data security/confidentiality breaches, over-reliance eroding human skills/judgment, lack of transparency hindering challenges to decisions, unclear accountability for mistakes, potential amplification of poor legal practices, workflow disruption from technical issues. |
| i3AWry70BicJ.pdf | Google_Scholar | STUDENT SCHOLARS: ACCESS -TO-JUSTICE RESEARCH IN THE LAW SCHOOL DIRECT REPRESENTATION CLINIC | The paper argues for integrating empirical access-to-justice research projects into traditional direct representation law school clinics. This model aims to enhance student learning about systemic legal issues and contribute to data-driven solutions for improving access to justice for low-income communities. | True | Idealistic | False | 1.0 | Positive | Integrating empirical access-to-justice research projects (qualitative and quantitative) into direct representation law school clinics. | Discussed using a case study of the author's related 3-year qualitative/quantitative research project on debt collection, and supported by literature on clinical education and A2J research. | NaN | Civil justice gap (unmet needs); A2J data gap (lack of data on court workings and community needs); limitations of lawyer-centric solutions; barriers for low-income litigants (e.g., lack of legal consciousness, systems avoidance, costs). | Conducting localized, empirical A2J research within law school clinics; using mixed methods (court data, qualitative interviews) to understand community needs and system failures; data-driven policy reform; training students as systems-change agents. | Consumer debt collection, housing/eviction, family law, high-volume state court litigation ('poor people's courts'). | Low-income litigants, self-represented litigants, marginalized communities (including racially/ethnically diverse and non-English speaking populations). | Civil Procedure, Consumer Law, Housing Law, Family Law, Poverty Law, Access to Justice. | USA (focus on state courts, with specific examples/case study in California, Texas, Utah, Arizona, New York, Massachusetts). | The proposed approach uses research data, not training data for a specific model. Data sources discussed include state court records (public but often unstructured/inconsistent), administrative data, and qualitative data from interviews with community members. | Integration of existing clinical models (direct representation, project-based, policy advocacy); application of social science research methodologies (quantitative analysis of court data, qualitative interviews); pedagogical theories (experiential learning, social justice lawyering). | Proposed for adoption by law school clinics; suggests resource sharing, network building, and partnerships with legal aid, other academic departments, and research centers. | False | False | NaN | Need for more granular data on SRLs and court processes; deeper understanding of legal consciousness and non-engagement; systematic integration of research into clinics; effective translation of research into policy/practice; evaluation of A2J interventions; improving inclusivity and scope of A2J research methodologies. | Securing grant funding; navigating IRB approval for human subjects research; fostering interdisciplinary partnerships; managing project continuity across student cohorts; training clinicians/students in empirical research methods; accessing and processing court data. | Ethical considerations in human subjects research (requiring IRB oversight for confidentiality and safety). |
| i1mVllezProJ.pdf | Google_Scholar | Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence | The paper critiques 23 existing LLM benchmarks using a novel evaluation framework based on people, process, and technology, identifying significant inadequacies related to functionality and integrity. It proposes a shift from static benchmarks towards dynamic behavioral profiling and regular audits for more accurate LLM evaluation. | True | NaN | True | 3.0 | NaN | Unified evaluation framework (based on People, Process, Technology) and proposal for behavioral profiling and regular audits for ongoing LLM assessment. | Structured Literature Review and Thematic Analysis applied to 23 LLM benchmarks using the proposed framework. | Identified widespread inadequacies in 23 benchmarks across Functionality and Integrity dimensions, including issues like response variability, inability to distinguish reasoning from optimization, linguistic bias, implementation inconsistency, lack of evaluator diversity, and overlooking cultural norms (detailed in Table II). | NaN | NaN | NaN | NaN | AI Evaluation / Computer Science (reviews benchmarks including some in law, finance, medicine, coding) | International | NaN | Structured Literature Review, Thematic Analysis, adaptation of the People, Process, Technology (PPT) framework. | NaN | False | False | NaN | NaN | Challenges identified with *current LLM benchmarking*: response variability, distinguishing reasoning vs. optimization, helpfulness/harmlessness tension, linguistic/logic diversity, installation/scalability, biases in LLM-generated evaluations, implementation inconsistency, slow iteration, prompt engineering difficulty, evaluator diversity, handling diverse cultural/social norms. *Challenges for the paper's own approach*: Subjectivity in evaluation (especially behavioral profiling), keeping evaluations current with rapid AI evolution, mitigating authors' own bias. | LLM 'gaming' benchmarks leading to misleading results, technical optimization mistaken for reasoning, data contamination/overfitting, perpetuation of biases, generation of harmful/unsafe content, security vulnerabilities (e.g., jailbreaking, delayed patching due to slow evaluation), misuse of the term 'benchmark', reliance on biased LLMs for evaluation. |
| aRJ0E_41Vj4J.pdf | Google_Scholar | Prompts for generative artificial intelligence in legal discourse | The paper examines the legal nature of prompts for generative AI in law, classifying them as legal actions and discussing copyright implications. It also explores their potential and risks in legal practice and education, advocating for standardization and interdisciplinary research. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | Restricted access to advanced AI models due to corporate control, which hinders broad technological development and legal diversity relevant for access to justice; general unreliability of AI (e.g., hallucinations, replication of non-compliant legal positions) if not properly managed. | Fostering interdisciplinary and international collaboration to balance diverse interests (including societal/access to justice needs) and ensure varied AI development; standardization of prompts and specialized legal education to improve reliability and effective use of AI for legal tasks, potentially extending to access to justice applications. | The general potential for AI to contribute to ensuring access to justice. | NaN | General legal practice, Copyright law, Contract law, Civil law theory, Judicial practice analysis. | International | N/A (The paper discusses training data for LLMs generally but does not propose or study a technique using a specific dataset.) | NaN | NaN | False | False | NaN | Lack of comprehensive legal understanding, regulation, and standardization of prompts for generative AI; insufficient development of specialized legal education for AI interaction; restricted access to advanced AI models controlled by corporations, limiting broader societal benefit including for access to justice. | NaN | Generation of incorrect or plausible but false information (hallucinations); replication of legally non-compliant common positions; unreliability of AI-generated legal documents and counsel without human validation; cognitive errors in prompt design or model training leading to flawed outputs; overlooking critical details in legal texts due to oversimplification by AI methods; narrow regulatory focus neglecting private AI use in legal services. |
| ldw0ALaiFLgJ.pdf | Google_Scholar | LAWYERING IN THE AGE OF ARTIFICIAL INTELLIGENCE | This paper presents a randomized controlled trial studying GPT-4's impact on law students performing legal tasks. AI assistance significantly increased speed and satisfaction, while quality improvements were slight, inconsistent, and most pronounced for lower-skilled participants. | True | Market | True | 2.0 | Positive | GPT-4 assistance for human legal analysis tasks. | Randomized controlled trial with 60 law students assigned to complete four legal tasks (complaint drafting, contract drafting, employee handbook section, client memo) with or without GPT-4. Outcomes were blind-graded for quality, and time taken was recorded; surveys assessed participant perceptions. | AI assistance slightly and inconsistently improved output quality (e.g., contract drafting +0.24, client memo -0.07 on a 4.0 scale) but consistently and largely reduced completion time (e.g., contract drafting -32.1%, complaint drafting -24.1%). Quality gains were larger for lower-skilled participants; time savings were consistent across skill levels. | High cost and inefficiency of legal services (implicitly identified as barriers AI could reduce). | Embracing generative AI to improve efficiency and potentially reduce costs, thereby lowering barriers to justice; proactive exploration and integration by lawyers, firms, and law schools. | Improved efficiency and potential cost reduction in legal service delivery through AI, thereby reducing barriers to justice. | NaN | Civil Litigation, Constitutional Law, Contract Law, Employment Law, Tort Law (Products Liability) | United States (Federal, Minnesota, Ohio) | NaN | Randomized controlled trial (RCT) with pre-registration of methods and hypotheses. | Participants accessed GPT-4 via a central 'ChatGPT “clone” website using the GPT-4 API' provided by the researchers. | True | False | GPT-4 is generally available via paid services like ChatGPT Plus from OpenAI. | Uncertainty about the higher-order impacts of AI on the legal services market (e.g., demand, billing) and how these will translate to tangible access to justice improvements; need for study on specialized legal AI tools and more complex tasks. | Recruiting and managing a specific participant pool (law students), designing realistic yet manageable legal tasks, providing controlled access to and training for GPT-4, and accounting for the rapid evolution of AI capabilities beyond those tested. | Hallucination of legal sources and facts by AI; over-reliance leading to decreased performance or malpractice; risks to client confidentiality with general-purpose AIs; hindering skill development in law students if AI use is not appropriately managed in education. |
| L8CImat85ScJ.pdf | Google_Scholar | REGULATION OF THE USE OF GENERATIVE ARTIFICIAL INTELLIGENCE TOOLS IN THE DELIVERY OF LEGAL SERVICES : VERIFICATION AND ACCOUNTABILITY | This paper discusses the challenges lawyers face in verifying the confidentiality and security of client data when using generative AI tools, as current guidance often requires diligence beyond their capabilities. It proposes that regulators, possessing technical expertise, should be responsible for assessing these AI tools and ensuring vendor accountability for data handling practices. | True | Market | True | 1.0 | NaN | Regulator-led assessment and verification of generative AI tools for legal services. | NaN | NaN | Lawyers' inability to verify AI tools for data confidentiality and security due to proprietary nature and lack of expertise; Unclear and unattainable due diligence standards in current legal guidance; Lack of comprehensive regulatory oversight for AI in legal services; Diverging incentives between lawyers seeking data protection and tech providers aiming to train models. | Shift the burden of AI tool verification from individual lawyers to legal regulators; Regulators, with technical experts, to establish and enforce standards for AI tools, including data security and handling; Mandate transparency and auditability from AI vendors for tools used in legal services; Implement consequences for non-compliant AI vendors. | Regulation of AI in legal practice; Lawyer's professional responsibility (confidentiality, competence); Accountability of AI technology providers; Cybersecurity and data privacy in legal tech. | NaN | General legal practice; Professional Responsibility/Legal Ethics; Patent Law. | United States (federal and state levels). | NaN | NaN | NaN | False | False | NaN | Absence of a comprehensive and effective regulatory framework in the U.S. for governing the use of generative AI in legal services, particularly concerning data security and vendor accountability; Lawyers' lack of access to AI vendors' proprietary information, hindering their ability to conduct proper due diligence; Unclear and often unattainable standards for lawyer diligence regarding AI systems; Insufficient mechanisms to ensure tech companies adhere to contractual and ethical obligations regarding data handling and safety. | Potential challenges in implementing the proposed regulator-led assessment include securing regulator preparedness and resources, developing requisite technical expertise within regulatory bodies, establishing clear and enforceable standards in a rapidly evolving technological landscape, and ensuring effective cross-jurisdictional coordination. | Breach of client confidentiality through AI data handling; Inadvertent disclosure of sensitive client or national security information; Violation of export control laws and secrecy orders; AI tools generating inaccurate or fabricated information (hallucinations); Copyright infringement by AI systems; Lack of transparency and accountability from AI providers regarding data use and model behavior; Potential for lawyers to violate ethical duties (competence, confidentiality) if relying on unverified AI. |
| mIXnP9q0bRsJ.pdf | Google_Scholar | OpenJustice.ai: A Global Open-source Legal Language Model | The paper critiques the use of generalized AI like ChatGPT for legal tasks due to risks like misinformation and lack of transparency. It introduces OpenJustice.ai, a proposed open-source, domain-specific legal language model designed to be reliable, transparent, and accessible, leveraging curated data and crowdsourced feedback. | True | Idealistic | True | 1.0 | Positive | OpenJustice.ai: An open-source, distributed legal language model using Retrieval Augmented Generation (RAG), instruction fine-tuning on legal data, and crowdsourced human feedback. | NaN | NaN | Risks associated with using general AI for legal tasks: legal misinformation/hallucinations, lack of transparency and precision, inability to offer diverse narratives, poor citation capabilities. Difficulty for non-lawyers in effective prompting. | Developing domain-specific, open-source, distributed legal AI (OpenJustice.ai) using: curated legal data, Retrieval Augmented Generation (RAG) for accuracy, multiplicity for diverse perspectives, assisted prompting for non-lawyers, crowdsourced feedback for improvement and transparency, and decentralized fine-tuning. | Access to justice, legal research, legal information provision, dispute resolution (negotiation), legal education, addressing legal misinformation. | Self-represented litigants, non-lawyers, legal students, legal clinics, Pro Bono Students Canada (PBSC), the broader legal community. | General Law (using legislation and case law), Employment Law, Consumer Protection, Personal Injury (mentioned for negotiation context). | International | Combination of: (i) Unstructured legal data (case law, journals, etc.) for self-supervised training. (ii) Structured data (annotated question-answer pairs since 2019) for instruction fine-tuning. (iii) Crowdsourced human feedback from the legal community. (iv) Proprietary data from industry partners for closed-system fine-tuning. | Retrieval Augmented Generation (RAG), Instruction Fine-tuning, Self-supervised Training (Masked Language Modeling), Crowdsourced Human Feedback, Decentralized Fine-tuning, Consortium-based development, Design Probes (for assisted prompting). | Rollout via a consortium of universities, legal clinics, and industry partners starting March 2023. A non-proprietary version intended to be openly accessible to the legal community for feedback, alongside custom models for partners. | True | True | Claims to be an open-source model launched in March 2023, intended to be openly accessible to the legal community via the OpenJustice.ai project/consortium. | Underlying reasons for LLM citation inaccuracies remain an unresolved computer science question. Need for better interfaces/tools (like assisted prompting) for non-expert users. Current LLMs lack true legal reasoning capability. | Ensuring factual accuracy and reliable citations; Training models for multifaceted legal reasoning; Making AI tools usable for non-lawyers; Managing crowdsourced feedback; Balancing open-source and proprietary data needs. | Legal misinformation or hallucinations, lack of transparency and precision, inability to offer diverse narratives (associated primarily with generalized AI but relevant context for legal AI). Poor citations. |
| H-SXQ38r3nMJ.pdf | Google_Scholar | Enhancing Semantic Validity in Large Language Model Tasks Through Automated Grammar Checking | This paper proposes integrating automated grammar checking tools into Large Language Model (LLM) workflows to improve the semantic validity of generated text. Experiments demonstrate significant enhancements in coherence, contextual accuracy, grammatical correctness, and readability across various text types. | True | NaN | True | 1.0 | NaN | Integration of automated grammar checking tools as a post-processing step for LLM-generated text. | LLM-generated text was assessed before and after applying an advanced grammar checking tool. Evaluation metrics included coherence (automated scoring), contextual accuracy (cross-referencing with contextual data), grammatical correctness (grammar checking tools), readability (formulas/scoring systems), lexical diversity (type-token ratio), and syntactic complexity (average dependency length). Analysis was done across different text types (news, academic, technical, conversational). | Significant improvements were observed: coherence (6.2 to 8.5), contextual accuracy (5.8 to 8.2), grammatical correctness (7.0 to 9.1), readability (65 to 85). Lexical diversity (TTR) increased from 0.45 to 0.55, and syntactic complexity (avg. dependency length) from 3.5 to 4.2. | NaN | NaN | NaN | NaN | NaN | NaN | Publicly available corpora from diverse domains (news articles, academic papers, technical documentation, conversational text) were pre-processed and fed into an LLM to generate text samples for the experiment. | An automated approach involving: LLM text generation, initial semantic validity assessment, integration of an advanced grammar checking tool for post-processing, re-evaluation of semantic validity, and comparative analysis of pre- and post-processing results. An algorithm for the integration process is provided. | NaN | False | False | NaN | NaN | Reliance on existing grammar checking tools not fully capturing LLM text subtleties; computational overhead of grammar checking affecting efficiency/scalability; evaluation focus on specific metrics may not cover all dimensions of semantic validity; limited generalizability due to dataset constraints. | Influencing public discourse and information dissemination; misuse for spreading misinformation or deepfakes; impact on human editors/writers; potential biases from the technologies. |
| n0Hs2VDVw4QJ.pdf | Google_Scholar | PanGu- π: Enhancing Language Model Architectures via Nonlinearity Compensation | This paper introduces PanGu-π, a novel Large Language Model (LLM) architecture designed to address the feature collapse problem by enhancing model nonlinearity through a series informed activation function and augmented shortcuts. The paper demonstrates its effectiveness and efficiency on general NLP tasks and through a domain-specific model, YunShan, applied to finance and law. | True | Market | True | 1.0 | NaN | PanGu-π architecture, incorporating Series Informed Activation Function (SIAF) in the Feed-Forward Network (FFN) and Augmented Shortcuts (AS) in the Multi-Head Self-Attention (MSA) module. | Evaluated PanGu-π (1B, 7B) on general NLP benchmarks (C-Eval, CMMLU, MMLU, AGI-Eval, BoolQ, AX-b, PIQA, CSL, EPRSTM, XSum, LCSTS) via OpenCompass. Evaluated domain-specific YunShan model (based on PanGu-π-7B) on financial (FinanceIQ, FinEval) and legal (LawBench) benchmarks. Included ablation studies and feature analysis (PCA, gradient visualization). | PanGu-π-7B achieved comparable performance to SOTA models with ~10% faster inference. PanGu-π-1B achieved SOTA performance for its size. YunShan surpassed similar-scaled models on financial (e.g., avg 61.34 on FinEval) and legal benchmarks (e.g. avg 31.75 on LawBench). | NaN | NaN | NaN | NaN | Chinese Civil Law (based on LawBench benchmark) | China (for domain-specific YunShan model evaluation); International (for base PanGu-π model training) | Base PanGu-π: 1.6 trillion tokens (1:1 English/Chinese) from diverse internet sources. YunShan Further Pre-training: Financial data (36.5B tokens - company announcements, news, articles, exams from FinCorpus, TuShare) and Legal data (111.7B tokens - regulations, cases, papers, exams from Pile of Law, LeXFiles, pkulaw.com, wenshu.court.gov.cn). YunShan Instruction Tuning: 995k domain instructions (JEC-QA, ChatLaw, Lawyer LLaMA, LawGPT, FinCorpus sources). Mix of public and crawled unstructured text data. | Theoretical analysis (feature collapse, nonlinearity), network architecture design (SIAF, AS with bottleneck), large-scale pre-training, ablation studies, supervised fine-tuning (SFT) for domain adaptation (YunShan). | Deployed as YunShan LLM for practical application in finance and law domains. | False | False | NaN | NaN | High computational cost of large models, complexity of LLM system engineering (data, architecture, training), balancing performance increase (nonlinearity) with efficiency (augmented shortcut cost addressed via bottlenecks), mitigating catastrophic forgetting during domain-specific further pre-training. | NaN |
| -FEDgvjRcnIJ.pdf | Google_Scholar | UNCOVERING THE FAIRNESS OF AI: EXPLORING FOCAL POINT , INEQUALITY AVERSION, AND ALTRUISM IN CHATGPT’S DICTATOR GAME DECISIONS | This paper investigates the social preferences of ChatGPT-3.5 and ChatGPT-4o using the Dictator Game with varying transfer efficiencies. It finds that GPT-3.5's tendency to give half its endowment is likely a focal point heuristic, while GPT-4o's decisions align more closely with altruistic motives, though inconsistently. | True | NaN | True | 2.0 | NaN | Using the Dictator Game experimental economics paradigm with varying transfer efficiency factors (f) to probe the social preferences (altruism, inequality aversion, focal point heuristic) of Large Language Models (ChatGPT-3.5-turbo and ChatGPT-4o). | Compared donation decisions of ChatGPT-3.5-turbo and ChatGPT-4o across 113 different transfer efficiency factors (f ranging from 0 to 1000). Each scenario (combination of LLM version and f value) was prompted 100 times via OpenAI's API (temperature=1). Donations were compared against theoretical predictions for payoff-equalizing, altruistic, and focal point strategies. | ChatGPT-3.5 consistently donated 50% regardless of transfer efficiency, suggesting a focal point heuristic. ChatGPT-4o's donations varied with efficiency, mostly aligning with altruistic motives (increasing donations with f), especially at higher efficiencies (donating 100% for f > 100), but showed some inconsistencies and trimodal distributions (50%, 100%, payoff-equalizing amount) for intermediate f values. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Experimental economics (Dictator Game with parameter variation), API-based interaction with LLMs, quantitative analysis of response distributions. | NaN | False | False | NaN | NaN | Interpreting AI decisions (distinguishing between heuristics like focal points and genuine preferences like fairness or altruism). Observed inconsistencies in GPT-4o's behavior across different transfer efficiency factors, making it difficult to conclude stable preferences. | Risk of misinterpreting AI behavior (e.g., concluding fairness from simple experiments when a heuristic is driving the behavior) if experimental parameters are not sufficiently varied. |
| kcm5NP6MOecJ.pdf | Google_Scholar | Language Model Fine-Tuning | This paper reviews language model fine-tuning, covering various methodologies like supervised and unsupervised techniques, and domain adaptation. It discusses applications in sentiment analysis, question answering, and conversational AI, along with challenges such as data quality, overfitting, and ethical bias. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal research, case law identification, legal document summarization, contract analysis | International | NaN | NaN | NaN | False | False | NaN | NaN | Data quality issues, risk of overfitting, ethical concerns (bias in data), substantial computational resources. | Ethical concerns surrounding bias in data. |
| _xt52fZFqmoJ.pdf | Google_Scholar | Quebec Automobile Insurance Question-Answering With Retrieval-Augmented Generation | This paper introduces two French corpora for Quebec automobile insurance and evaluates a GPT-4o based Retrieval-Augmented Generation (RAG) system for answering related questions. While RAG improves answer quality over a baseline, the study concludes that LLM-based QA is not yet reliable enough for critical applications due to a significant rate of false statements. | True | Idealistic | True | 1.0 | Neutral | Retrieval-Augmented Generation (RAG) using GPT-4o, a custom Quebec automobile insurance reference corpus for retrieval, and a custom question-answer corpus for evaluation. | Automatic metrics (BLEU, ROUGE, METEOR, BERTScore, MeaningBERT) and manual evaluation by an insurance expert using a predefined grading scale on 82 question-answer pairs assessing criteria for completeness and correctness. | The RAG approach using the complete custom reference corpus performed best, achieving a 51.74% score on manual expert evaluation. However, between 5% to 13% of LLM-generated answers included a false statement that could mislead a customer. | Lack of public's legal/insurance knowledge; complexity and jurisdiction-specific nature of insurance information; difficulty for individuals to find and correctly interpret relevant information online. | Developing AI-powered QA systems (like RAG) using curated, high-quality domain-specific corpora to provide more accurate and accessible information. Releasing these specialized corpora to foster further research. | Access to insurance information, understanding insurance products, consumer rights regarding automobile insurance. | General public / insurance customers in Quebec, particularly those seeking information online about automobile insurance. | Insurance Law (specifically Quebec automobile insurance). | Quebec, Canada. | The primary dataset used for the RAG system's retrieval component is the purpose-built 'Quebec Automobile Insurance Expertise Reference Corpus'. This French corpus consists of unstructured text from seven official and reliable online sources (legislation, legal insurance documents, regulator informative resources, domain-specific educative articles), manually extracted and cleaned. The LLM itself (GPT-4o) is pre-trained on general data not detailed by the paper. | Comparative evaluation of GPT-4o (zero-shot vs. RAG with incrementally added reference sources from the custom corpus); RAG architecture built using LangChain, OpenAI's text-embedding-ada-002 for embeddings, and GPT-4o for generation, including context compression. A manual evaluation protocol with a grading scale was developed and applied by a domain expert. | The research prototype uses proprietary OpenAI APIs for core LLM and embedding models. The developed corpora are released on GitHub. No public deployment of the QA system itself is mentioned. | False | True | The two custom corpora created for this research (Quebec Automobile Insurance Expertise References Corpus and Corpus of 82 Expert Answers to Laypeople Automobile Insurance Questions) are released on GitHub. | The reliability of LLM QA for critical legal/insurance applications remains insufficient (5-13% false statements). LLMs' tendency to hallucinate or not abstain when information is lacking, the impact of potential data leakage from pre-training, and the need for better alignment of automatic evaluation metrics with human judgment in specialized domains like law are remaining gaps. | Ensuring factual accuracy and avoiding misinformation in LLM outputs for specialized, high-stakes domains like insurance law. Potential for LLMs to be confused by incomplete or overly complex legal texts provided as context. LLM memorization versus true understanding and reasoning. Models defaulting to information from incorrect jurisdictions if not precisely prompted/contextualized. The labor-intensive and costly nature of high-quality manual evaluation for specialized QA. | Generation of false or misleading information by LLMs (study found 5-13% of answers contained false statements), potentially leading to customer misunderstanding and financial or legal harm. Premature deployment of inadequately vetted legal NLP tools. Inherent biases in training corpora and AI systems potentially leading to discriminatory outcomes. |
| Mr3hcqPrRuYJ.pdf | Google_Scholar | NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models | This paper details the NOWJ1 team's participation in the ALQAC 2023 competition, focusing on legal document retrieval and question answering. They propose hybrid systems combining classical statistical models (TF-IDF, BM25, QLD) and fine-tuned BERT models, utilizing techniques like learning-to-rank and specific pipelines for different question types. | True | Market | True | 1.0 | NaN | Hybrid approach combining traditional lexical models (TF-IDF, BM25, QLD) and fine-tuned BERT embeddings. For retrieval: features from lexical models and BERT-based classifiers (SVM, XGBoost, LightGBM) are ensembled using LightGBM (learning-to-rank). For QA: TF-IDF for matching, fine-tuned BERT for sentence classification (True/False, MCQs) and a two-stage BERT-based system for span extraction. | Evaluation within the ALQAC 2023 competition using its official training, public test, and private test datasets. Metrics: F2-score for retrieval, Accuracy for question answering. | For retrieval (Task 1), the system achieved the 1st rank on the public test (F2=0.94) and 2nd rank on the private test (F2=0.8358). For question answering (Task 2), it achieved 2nd rank on the public test (Accuracy=0.67) and lower on the private test (Accuracy=0.6545). | NaN | NaN | NaN | NaN | General Vietnamese Law | Vietnam | Competition datasets (ALQAC 2021, 2022, 2023 official samples; Zalo legal dataset) containing Vietnamese legal questions and articles (unstructured text), provided by organizers. | Pipeline approach involving rule-based pre-processing (word/article segmentation), feature extraction (TF-IDF, BM25, QLD, BERT embeddings), classification (SVM, tree-based models), and ensemble learning (LightGBM). | Submission to the ALQAC 2023 competition. | False | False | NaN | NaN | Handling long legal documents with model input limitations (addressed via segmentation). Adapting general pre-trained models (BERT) to the specific legal domain (addressed via fine-tuning). Managing computational time differences between models. Dealing with distribution shifts between public and private test datasets. Achieving high performance on legal question answering (noted lower results compared to retrieval). | NaN |
| SoCFwEeEKWUJ.pdf | Google_Scholar | A Cross-Lingual Statutory Article Retrieval Dataset for Taiwan Legal Studies | This paper introduces LawFactsQA-TW, a new cross-lingual (English-Chinese) statutory article retrieval dataset focused on Taiwanese law, aimed at improving legal information access for non-native speakers. It also proposes and evaluates several LLM-based retrieval methods as baselines, with LLM-augmented techniques showing improved performance metrics. | True | Idealistic | True | 1.0 | Positive | The LawFactsQA-TW dataset and LLM-augmented cross-lingual statutory article retrieval methods, including Answer Expansion, Statutory Article Expansion, and LLM-based Reranking. | Retrieval performance was evaluated using Recall and Average Precision (@10, @20, @50) on both human-labeled and synthetically A Cross-Lingual Statutory Article Retrieval Dataset for Taiwan Legal Studiesgenerated QA pairs within the LawFactsQA-TW dataset. Question-answering was evaluated using BLEU scores and an LLM-based 3-point scoring system. | On human-labeled data, LLM re-ranking with Breeze achieved the highest Recall@10 (0.472); Taide with Statutory Article Expansion achieved Recall@50 of 0.729. On synthetic data, BGE-m3 augmented with Breeze for Statutory Article Expansion achieved the highest Recall@50 (0.845). | Difficulties for non-native speakers in accessing and understanding legal information in a foreign language (cross-lingual retrieval challenge); scarcity of specialized, multilingual legal datasets for SAR. | Creation of LawFactsQA-TW, a cross-lingual (English-Chinese) dataset for Taiwanese statutory articles. Proposal and evaluation of LLM-based methods, particularly LLM-augmented retrieval, to enhance cross-lingual legal information access. | Cross-lingual statutory article retrieval; access to legal information (FAQs, statutes) for non-native speakers. | Foreign nationals in Taiwan; non-native Chinese speakers seeking legal information pertaining to Taiwan. | Taiwanese civil law, criminal law, and administrative regulations. | Taiwan | The LawFactsQA-TW dataset was constructed using: 1) A corpus of all Taiwanese civil, criminal, and administrative laws from the National Regulatory Database. 2) 92 human-labeled QA pairs derived from legal agency FAQs. 3) 173 synthetic QA pairs generated by gpt-4-turbo based on news articles and legal regulations. LLMs used for augmentation (GPT series, Breeze, Taide) are pre-trained models. | Dataset: Collection of official legal texts, manual annotation of FAQs, and an automated pipeline using gpt-4-turbo for synthetic QA generation. Retrieval Methods: Comparative analysis of sparse retrieval (BM25), dense retrieval (BGE-m3), and LLM-augmented retrieval (query expansion, hypothetical document generation, LLM-based reranking using various LLMs). | The LawFactsQA-TW dataset is introduced as a research resource. The paper presents LLM-based methods as baselines for this dataset. | True | False | The dataset is named LawFactsQA-TW and is presented as a key contribution of the paper, referenced via a footnote, implying it is a distinct resource associated with the research. | The synthetic portion of the dataset has not been evaluated by legal professionals, potentially affecting its credibility. The dataset primarily covers common public queries and may not address the specific retrieval needs of legal professionals. Further collaboration with legal experts is needed. | Mitigating translation errors in cross-lingual settings, enhancing retrieval accuracy for legal texts, and effectively evaluating the quality of LLM-generated legal content (answers and expanded queries/articles). | The paper notes a limitation that its synthetic dataset has not been evaluated by legal professionals, which could affect system credibility and expertise if deployed without such validation. This implies a risk of providing inaccurate or unreliable legal information. |
| ecxnpROAuQAJ.pdf | Google_Scholar | Integrating Generative AI into Legal Education: From Casebooks to Code, Opportunities and Challenges | This paper discusses the integration of Generative AI (GenAI) into legal education, highlighting the gap between traditional methods and modern practice needs. It explores opportunities like enhanced research and personalized learning, alongside challenges such as ethics, bias, academic integrity, and the need for curriculum reform. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General Legal Education / Legal Profession | International | NaN | NaN | NaN | False | False | NaN | NaN | General challenges discussed include: integrating AI without undermining critical thinking or enabling academic dishonesty; addressing AI inaccuracies ('hallucinations') and algorithmic opacity; mitigating bias amplification from training data; developing reliable methods for detecting AI-generated content in assessments; providing necessary resources (software, infrastructure, technical support); ensuring adequate faculty training; acknowledging and addressing the environmental and human costs of AI development. | Potential risks stated include: undermining students' critical thinking and skill development; increased academic dishonesty and plagiarism; generation of inaccurate legal information ('hallucinations'); perpetuation and amplification of societal biases leading to unfair or discriminatory outcomes; lack of transparency and accountability in AI decision-making; intellectual property violations; significant environmental costs (carbon emissions, e-waste) from AI model training and infrastructure; exploitation of human labor in AI development (e.g., data annotation). |
| vflh02DRLncJ.pdf | Google_Scholar | REGULATING ARTIFICIAL INTELLIGENCE AS A PERPETRATOR OF DEEPFAKE CRIMES IN INDONESIA | This paper examines the regulation of artificial intelligence (AI) as a perpetrator of deepfake crimes under Indonesian law, concluding that AI is not currently recognized as a legal subject. It discusses how existing laws (ITE Law, Criminal Code, Pornography Law) might apply to deepfake-related offenses like hoaxes, fraud, defamation, and pornography, while highlighting the need for specific AI legislation. | True | NaN | False | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Criminal Law, Cyber Law (Electronic Information and Transactions Law), Pornography Law | Indonesia, California (USA) | NaN | NaN | NaN | False | False | NaN | Lack of specific legal regulation for AI and deepfakes in Indonesia, particularly concerning AI's legal subjectivity and accountability. | NaN | Spreading hoaxes/disinformation, fraud (especially using deepfake audio), defamation, non-consensual pornography, manipulation of facts/circumstances, eroding public trust, social unrest, use as political propaganda, identity theft, privacy violations. |
| _rV9oWYXkzoJ.pdf | Google_Scholar | War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education | This paper reviews the development and compares the performance of major AI chatbots (ChatGPT 3.5/4, Bing Chat, Bard) on tasks relevant to higher education, finding current models perform poorly overall despite hype. It provides recommendations for faculty, students, and institutions on navigating AI's impact on assessment, teaching, learning, and academic integrity. | True | NaN | True | 2.0 | Neutral | Comparative evaluation of existing chatbots: ChatGPT (GPT-3.5), ChatGPT Plus (GPT-4), Bing Chat, and Bard. | Systematic comparison using 15 multi-disciplinary questions (Sociology, business, maths, history, economics, philosophy, literature, psychology, art history, Chinese non-fiction, literature search/annotation) relevant to higher education assignments/exams. Responses graded (A-F scale) based on accuracy, comprehensiveness, and clarity. | GPT-4 performed best (average C+), followed by ChatGPT-3.5 (average C). Bing Chat and Bard performed poorly (average F). Issues included lack of academic sources, hallucinations, factual errors, and inability to follow instructions. | Threats to academic integrity (plagiarism); difficulty detecting AI text; potential for misinformation/hallucinations; lack of critical evaluation by users; ethical concerns (data privacy, bias, exploitative data labeling); accessibility issues (bans, workarounds); potential deskilling; rapid pace of development. | Reform assessments (authentic, process-focused); teach responsible AI use, ethics, and limitations; require disclosure of AI use; update integrity policies; foster digital literacy; use AI to enhance teaching/learning; promote critical thinking; encourage stakeholder dialogue. | Higher Education: assessment, teaching, learning, academic integrity, research, employability. | Higher education stakeholders (students, faculty, institutions). | Multi-disciplinary including Law and Medicine (mentioned in literature review/testing examples). | International | Large-scale, primarily unstructured text and other data (web pages, books, articles, search data, image data, voice data, knowledge graphs); described as potentially 'internet scale'. Includes data from 'darkest recesses of the internet' labeled by outsourced workers for safety fine-tuning. Primarily proprietary datasets specific to each company (OpenAI, Google, Baidu). | NaN | Web interfaces (ChatGPT, Bard), Integration into existing products (Bing Chat in Bing Search/Edge), Paid subscription tiers (ChatGPT Plus), Initially restricted access via waitlists or geographically (Bing Chat, Bard), Planned enterprise focus/integration (Ernie). | True | False | ChatGPT (free version via web), ChatGPT Plus (paid subscription via web), Bing Chat (via Edge browser, likely free), Bard (via web, likely free). Ernie Bot access was restricted at time of writing. Availability may have geographical limitations. | Lack of reliable AI detection tools; need for updated assessment methods and academic integrity policies; need for improved AI digital literacy; need for more research on AI's educational effects; need for ethical guidelines/dialogue; current AI limitations (reasoning, bias, transparency); insufficient focus on equity in AI's educational use. | Inaccuracy and hallucinations in AI responses; poor sourcing (non-academic/fictitious references); bias in outputs; ethical issues (privacy, data sourcing); limitations in understanding context/instructions; ability to bypass safety features (jailbreaking); models lacking current information; difficulty accessing certain models for research (Ernie Bot). | Academic integrity threats (plagiarism); spread of misinformation/disinformation/fake news; harmful hallucinations; automation of nefarious activities (spam, malware, hacking); job displacement; deskilling; privacy violations; data breaches; algorithmic bias (racism, sexism); exploitation of data labelers; erosion of education as a public good; deepfakes; incitement of violence; risks to democracy; exposure of minors to inappropriate content. |
| 0NeSdgcY4UUJ.pdf | Google_Scholar | From Distributional to Overton Pluralism: Investigating Large Language Model Alignment | This paper investigates the effects of alignment on large language model (LLM) output distributions, particularly concerning response diversity. It finds that apparent diversity loss is often due to quality control and information aggregation into longer, more comprehensive responses, and demonstrates that aligned LLM behavior can be largely mimicked by base LLMs using advanced in-context learning prompting strategies. | True | NaN | True | 1.0 | NaN | In-context distillation prompting strategies, including static (URIAL with human/teacher outputs, random teacher outputs) and dynamic (kNN-selected teacher outputs, oracle kNN, URIAL with teacher summaries) approaches, to make base LLMs mimic aligned LLMs. | Evaluated on CONFLICTING QA and LIMA-OE datasets using Llama 2 and Mistral model families (base vs. aligned/instruct). Metrics included GPT-4 assessed quality (helpfulness, clarity, factuality, depth, engagement), lexical similarity (Jaccard-based Self-Sim and Max-Sim to teacher), semantic coverage (GPT-4 assessed), and stance analysis (GPT-4 assessed). | The dynamic 'URIAL Prompts and Summary' strategy for in-context distillation (referred as 'Llama 2 Base Summary Llama 2 Chat' in tables) allowed base Llama 2 to achieve a Max-Sim to Llama 2 Chat of 0.31 on CONFLICTING QA and 0.33 on LIMA-OE, closely approaching Llama 2 Chat's self-similarity (0.36 and 0.34_respectively). | NaN | NaN | NaN | NaN | NaN | International | The study utilizes pre-trained base LLMs (Llama 2, Mistral) and their instruction-aligned counterparts. For its in-context learning experiments, few-shot prompts were constructed using queries from the CONFLICTING QA and LIMA-OE datasets (or a separate corpus U) paired with responses that were either human-written or generated by the 'teacher' (aligned) LLM. The evaluation datasets (CONFLICTING QA, LIMA-OE) consist of open-ended questions. | Iterative design of few-shot prompting strategies. This included static prompts with fixed examples and dynamic prompts where examples were selected based on k-Nearest Neighbors (kNN) semantic similarity (using embeddings) to the input query or teacher's response. Some strategies incorporated summaries of teacher responses as additional hints. | Code and data are made available on GitHub. | True | True | The paper states 'Our code and data is available at https://github.com/thomlake/investigating-alignment'. The LLMs used (Llama 2, Mistral variants) are also generally publicly available. | The study is limited to two English-language QA datasets, LLMs up to 7B parameters, and uses imperfect evaluation metrics (lexical overlap, GPT-based assessment). The analysis does not cover information missing from base models themselves, potentially underrepresenting cross-cultural perspectives. | The imperfection of using lexical overlap and prompting GPT-4 to assess semantic similarity and other qualitative aspects. Difficulty in finding better intermediate semantic representations for evaluation. | The findings should not be taken as evidence that LLMs will appropriately handle diverse viewpoints in all high-stakes settings. The analysis does not address information missing from base models, a source of underrepresentation. The presented tools are for analytical purposes and not yet suitable for deployment. |
| meIFFFgdLAMJ.pdf | Google_Scholar | ChatGPT, Esq.: Recasting Unauthorized Practice of Law in the Era of Generative Al | This paper examines the conflict between traditional Unauthorized Practice of Law (UPL) rules and the capabilities of generative AI like GPT-4 in providing legal services. It proposes a novel UPL reform where bar associations would primarily regulate who can be designated a 'lawyer,' while allowing non-lawyers, including AI, to offer most legal services except for in-court representation, aiming to enhance access to justice. | True | Idealistic | True | 1.0 | Positive | A regulatory reform proposal: recasting Unauthorized Practice of Law (UPL) rules to focus on regulating the 'lawyer' designation, while permitting non-lawyers (including AI) to offer most legal services, excluding in-court representation. | NaN | NaN | Current Unauthorized Practice of Law (UPL) rules restricting non-lawyers (including AI) from providing legal services, leading to high costs, limited access to justice (especially for low-income individuals), and potential protectionism by the legal profession. | Recast UPL rules to primarily regulate who can claim the title 'lawyer' or 'attorney,' while allowing non-lawyers (including AI) to provide most legal services except for in-court representation. Consumer protection would rely on tort law (negligence, deceptive practices) and clear distinctions regarding lawyer status. | Reducing cost and increasing availability of legal information, advice, and document preparation for routine legal matters; reform of professional responsibility rules. | Low-income individuals and small businesses currently underserved by the legal system due to cost and access barriers. | General (Unauthorized Practice of Law regulation), Professional Responsibility, with examples from various fields like criminal law (trespassing), property law (eviction), and business law. | United States | NaN | Policy proposal developed through legal analysis, review of existing UPL jurisprudence and literature, and consideration of technological advancements in AI. | Adoption of revised Model Rules of Professional Conduct and corresponding changes in state-level UPL statutes and court rules, driven by bar associations and judiciaries. | False | False | NaN | Further development of tort law standards for AI/non-lawyer legal service providers; specifics of civil procedure adjustments; potential need for federalizing legal ethics for non-lawyer providers; ensuring equitable access to AI-driven legal services for all demographics; addressing potential for new forms of consumer exploitation if the new framework is not carefully managed. | Overcoming resistance from the established legal profession (judges, lawyers, bar associations); achieving consensus on the scope of UPL reform, particularly the definition of 'representation in legal proceedings'; ensuring the new framework adequately protects consumers while fostering innovation. | If UPL is not reformed: continued lack of access to justice, stifling of innovation, anticompetitive practices by the legal profession. With AI in law generally: errors (hallucinations), bias in AI systems if not properly developed and overseen, over-reliance by consumers. With the proposed reform: potential for consumer misunderstanding or exploitation if the distinction between lawyers and non-lawyer providers is not clear or if tort remedies prove insufficient; economic disruption to the traditional legal profession. |
| R0gkfcmKmPwJ.pdf | Google_Scholar | Will AI Replace Tax Practitioners? | This paper discusses the potential for AI to replace tax practitioners, arguing that AI will augment rather than replace human roles, particularly in tax law. It concludes that practitioners embracing AI will lead the profession, emphasizing a future of human-AI collaboration and the continued importance of human skills like ethics and complex reasoning. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | High cost and unequal access to AI tools, complexity of AI for non-proficient users, lack of transparency in AI decision-making (black box), AI bias perpetuating inequalities, profit-driven development neglecting marginalized communities, and deep-rooted systemic inequities hindering access to justice. | NaN | Accessibility of legal information and assistance in tax matters, particularly for low-income individuals and marginalized communities. | Low-income individuals, marginalized communities, individuals with disabilities, poor and minority communities. | Tax law | United States | NaN | NaN | NaN | False | False | NaN | Technical gaps include AI's limitations in handling ambiguity and novel situations, reliance on historical data, lack of nuanced legal reasoning, and opacity. Societal gaps include the digital divide, prohibitive costs, data access inequality for AI refinement, potential for AI bias, and insufficient infrastructure or political will for equitable AI deployment in justice. | NaN | Reinforcement of preexisting biases, privacy concerns, inaccurate or misleading AI outputs (hallucinations), potential job displacement for some tax professionals, reduced transparency in decision-making, exacerbation of inequalities in access to justice, and liability for AI errors. |
| LHYfQYjVfOUJ.pdf | Google_Scholar | A.I. In Law: Adversary or Ally? Addressing the Possible Implications of A.I. Technology in Law and the Necessity of Regulation | This paper examines the benefits and significant risks (like bias and inaccuracy) of integrating AI into the legal profession, focusing on impacts on marginalized communities. It argues for a comprehensive dual regulatory framework involving government and legal institutions to ensure ethical AI deployment and uphold justice. | True | Idealistic | True | 3.0 | Neutral | Discussion and evaluation of existing legal AI research tools (e.g., Lexis+ AI, Westlaw AI) and general LLMs (e.g., GPT-4), and proposal of a regulatory framework. | References empirical evaluation by Magesh et al. (2024) assessing hallucination rates in Lexis+ AI, Westlaw AI-Assisted Research, Ask Practical Law AI, and GPT-4. Also references Narayanan & Kapoor (2024) study on AI accuracy in predicting criminal justice outcomes (7%). References Gender Shades study on facial recognition bias. | Based on Magesh et al. (2024), hallucination rates in leading AI legal research tools remained between 17% and 33%, despite vendor claims about retrieval-augmented generation (RAG). | Algorithmic bias exacerbating systemic discrimination; unreliability and hallucinations leading to inaccurate legal information potentially harming vulnerable users; potential negative impact on employment equity for underrepresented groups in law; lack of AI literacy; exclusion of marginalized communities from AI governance. | Proposed dual regulatory framework (government oversight + internal legal institution governance), including mandatory sandbox evaluations, bias mitigation teams, transparency/accountability offices, mandatory education/certification for legal professionals, and inclusion of marginalized communities in policymaking (grassroots involvement/relational justice). Emphasizes human oversight. | Algorithmic bias and discrimination; Ethical AI use in law; Reliability and accuracy of legal AI; Regulation of AI; Impact on marginalized communities; Access to justice; Employment equity in the legal profession. | Marginalized communities, underrepresented groups, low-income individuals, early-career legal professionals and law students from historically underrepresented backgrounds, women, people of color. | General legal practice, Legal research, Contract review, Case prediction, Document drafting, Criminal justice. | US, EU | NaN | NaN | NaN | True | False | Discusses commercial tools like Lexis+ AI and Westlaw AI, available via subscription, and general models like GPT-4 with varied accessibility. | Need for reliable and unbiased legal AI tools; Effective regulatory frameworks balancing innovation and risk mitigation; Improved AI literacy among legal professionals; Mechanisms for community involvement in AI governance; Addressing AI's impact on diversity and equity in the legal workforce. | Ensuring AI accuracy and reliability (combating hallucinations); Mitigating algorithmic bias from training data and models; Achieving transparency and accountability in AI decision-making; Developing effective and adaptive regulations; Bridging the AI literacy gap among legal professionals; Managing data privacy and security. | Generation of fictitious legal citations/information (hallucinations); Amplification of systemic bias and discrimination; Privacy violations; Lack of transparency and accountability; Malpractice liability due to AI errors; Job displacement, particularly impacting marginalized groups entering the profession; Erosion of public trust; Misapplication in high-stakes legal decisions (e.g., criminal justice). |
| -ajvsbsAALoJ.pdf | Google_Scholar | Shariah Governance Standard on Generative AI for Islamic Financial Institutions | This paper proposes a comprehensive Shariah governance framework for integrating generative AI within Islamic Financial Institutions (IFIs), ensuring AI applications align with Islamic legal and ethical principles. The framework details a dual governance model and an operational standard to guide IFIs in mitigating risks and embedding Shariah compliance throughout the AI lifecycle. | True | Market | True | 1.0 | Positive | A Shariah governance framework and an 'Operational Shariah Governance Standard on Generative AI for Islamic Financial Institutions'. | NaN | NaN | Aligning AI with Shariah principles (avoiding ribā, gharar, maysir); AI opacity ('black box') hindering compliance verification; AI biases leading to discriminatory outcomes and undermining financial justice; AI-generated misinformation; lack of Shariah considerations in conventional AI governance. | A comprehensive Shariah governance framework integrating Islamic jurisprudence with AI governance principles, featuring dual governance (Shariah Supervisory Board and AI Governance Committee), ethical AI lifecycle management (data, model development, deployment, monitoring), and operational standards emphasizing transparency, fairness, accountability, explainable AI, and bias mitigation. | Ethical and Shariah-compliant financial services, financial inclusivity, fairness in financial decision-making, prevention of impermissible financial practices. | Customers of Islamic Financial Institutions, particularly low-income applicants or minority communities; underserved communities for zakāh distribution; women entrepreneurs for Islamic microfinance. | Islamic Finance Law, Shariah Law, Financial Regulation, AI Governance/Regulation | International | NaN | Integration of classical fiqh, maqāṣid al-Sharīʿah, and contemporary AI governance literature; conceptual framework development based on the AI lifecycle model. | The paper proposes the framework and standard for adoption by Islamic Financial Institutions and suggests it may be adopted or mandated by supervisory authorities (e.g., AAOIFI, IFSB). The standard itself is provided in an appendix for potential implementation. | True | True | The 'Operational Shariah Governance Standard on Generative AI for Islamic Financial Institutions' is provided in the Appendix of the paper, available for IFIs to adopt and implement. | Need for empirical validation of the proposed framework's efficacy in real-world settings; development of advanced technical tools for explainable and suitable AI in Islamic finance; ongoing updates to the governance framework to address evolving AI technology and challenges. | Synthesizing diverse and complex fields: Islamic jurisprudence (fiqh, maqāṣid al-Sharīʿah), contemporary AI governance, and specifics of generative AI in the financial sector to create a cohesive and practical framework. | Inadvertent promotion of impermissible financial practices (ribā, gharar, maysir); ethical lapses and biased AI decision-making leading to discrimination; AI-generated misinformation misleading stakeholders; 'black box' opacity undermining transparency and Shariah compliance; misuse of deepfake technology; data breaches. |
| 2dTgL-HM2fkJ.pdf | Google_Scholar | Topic classification of case law using a large language model and a new taxonomy for UK law: AI insights into summary judgment | This paper introduces a novel functional taxonomy for UK law and employs the Large Language Model Claude 3 Opus to classify UK summary judgment cases based on this taxonomy. The study evaluates the LLM's accuracy (achieving 87.13% F1) and analyzes the resulting topic distributions across legal domains, courts, and time. | True | Idealistic | True | 1.0 | Positive | Topic classification of UK case law using the Claude 3 Opus LLM, guided by a newly developed functional legal taxonomy and a specific prompt incorporating self-evaluation. | Manual classification by a legal expert on a statistically significant random sample (342 cases) from a dataset of 3078 summary judgment cases. Evaluation metrics included accuracy, precision, recall, F1 score (overall, macro, micro, weighted), and per-class analysis. | Claude 3 Opus achieved an overall accuracy of 87.13% (88.20% adjusted for minor naming errors) and a macro F1 score of 0.87 (weighted F1 0.89). Some topics showed lower performance, and a low rate of topic hallucination was observed (< 2%). | Summary judgments disproportionately affecting self-represented litigants; the challenge of balancing judicial efficiency with fairness and access to justice; lack of existing topic classifications for UK case law hindering analysis. | Developing a functional legal taxonomy and using LLMs for accurate topic classification to enable better analysis of case law trends (specifically summary judgment). This data-driven understanding can inform policy and judicial administration regarding fairness and efficiency. | Summary judgment procedure, fairness vs. efficiency in civil procedure, judicial administration, analysis of court trends. | Self-represented litigants. | Civil Procedure (specifically summary judgment), with topic classification covering multiple fields including Commercial law, Dispute Resolution, Personal/Consumer Matters, Public law, Criminal law (in civil contexts), and International law. | United Kingdom (UK) | The technique uses the pre-trained Claude 3 Opus LLM (proprietary data). Evaluation was performed on a curated dataset of 3,078 UK summary judgment cases (XML format, unstructured text) from the Cambridge Law Corpus. | Development of a new functional legal taxonomy using a grounded theory approach; Prompt engineering for the LLM, including closed-set prompting, detailed instructions, reasoning prompts, iterative refinement based on feedback, and adding self-evaluation instructions to mitigate hallucinations. | NaN | True | False | The prompt and taxonomy are published in the paper. The LLM (Claude 3 Opus) is commercially available. The dataset requires access permission from the Cambridge Law Corpus. | Lack of comparison with other models/methods; Relatively small dataset size; Subjectivity in manual evaluation; Potential for information leakage in LLM training data; Need for further research on hallucination mitigation; Need for more objective evaluation metrics; Limited generalizability beyond summary judgment/UK law without further testing. | Developing a suitable UK legal taxonomy; Effective prompt engineering for accuracy and hallucination reduction; Handling nuances/overlaps in legal topics; Evaluating performance accurately, especially for low-frequency topics; Distinguishing primary/secondary topics; Correcting LLM errors (hallucinations, naming discrepancies). | LLM hallucinations leading to incorrect topic assignments; Inaccurate classification impacting analysis reliability; Cascading errors from dataset identification and classification; Information leakage from LLM training data; The procedure itself (summary judgment) potentially sacrificing fairness for efficiency, especially for vulnerable litigants; Risk of non-specialist judges deciding complex cases via summary judgment. |
| dIjEiCOLmbgJ.pdf | Google_Scholar | Legal large language models (LLMs): legal dynamos or “fancifully \npackaged ChatGPT”? | This comment discusses the impact and perception of large language models specifically designed for legal tasks (Legal LLMs). It argues for a balanced view, positioning these tools as advanced assistants that require human oversight, rather than fully autonomous replacements for lawyers, while cautioning against both overhype and overly restrictive regulations. | True | Market | True | 3.0 | NaN | Legal LLMs (general category, mentioning specific examples like Harvey AI, Lexis+ AI, Westlaw AI, CoCounsel, etc.) | Cites a Stanford study [19] evaluating hallucination rates in Lexis+ AI, Westlaw AI-Assisted Research, and Ask Practical Law AI. Also mentions internal testing by Thomson Reuters. | Stanford study [19] found hallucination rates between 17% and 33%. Thomson Reuters claimed ~90% accuracy in internal testing dependent on customer usage patterns. | NaN | NaN | NaN | NaN | General legal practice, Contract law, Litigation, Regulatory compliance, Legal research, Tax law | USA, UK mentioned, but discussion seems broadly applicable. | Mentions specific tools trained on proprietary legal datasets (e.g., LexisNexis content) or combinations of legal and internet data. Kelvin LLM trained "from scratch" on legal data. | User feedback (e.g., from lawyers), fine-tuning by AI engineers and legal experts. | Commercial previews, partnerships with law firms, beta programs, direct product launches, software add-ins. | True | False | Specific commercial products (e.g., Lexis+ AI, PatternBuilder MAX, LawDroid Copilot) are stated as launched or available to customers. | NaN | Managing expectations (hype vs. reality), ensuring accuracy/reducing hallucinations, needing human verification, ethical integration, countering restrictive regulations. | Hallucinations/inaccurate output, uncritical reliance leading to errors, potential undermining of professional competence/judgment, ethical breaches if used without human supervision/verification. |
| SGGW0H1kypUJ.pdf | Google_Scholar | Beyond Words: A Controlled Experiment on the Role of Linguistic Empathy for Trust in Conversational AI | This paper develops and tests a theory of linguistic empathy in conversational AI using 9 specific rules. An online experiment with 277 participants solving a tenant law problem found that linguistic empathy increased perceived helpfulness, but decreased trustworthiness for angry users, while chatbots generally reduced cognitive effort compared to FAQs. | True | Idealistic | False | 1.0 | Positive | Rule-based chatbot designed with 9 specific rules for linguistic empathy (syntax, punctuation, rhetoric) built on a deterministic decision-tree logic. | A 2x3 factorial randomized online experiment with 277 Chicago residents. Participants used either an empathetic chatbot, a non-empathetic chatbot, or an FAQ page to solve a tenant security deposit problem. Anger was induced in half the participants. Outcomes (helpfulness, trustworthiness, cognitive effort) were measured using surveys (Likert scales) and analyzed using ANOVA and OLS regressions. | The linguistically empathetic chatbot was perceived as significantly more helpful. Trustworthiness increased with linguistic empathy for non-angry users but decreased for angry users. Using either chatbot significantly reduced cognitive effort compared to the FAQ page. | The primary challenge identified for AI tool effectiveness is building user trust, especially when users are experiencing negative emotions like anger, where linguistic empathy alone can be insufficient or counterproductive. | Designing conversational AI with specific linguistic empathy rules (based on syntax and rhetoric) to enhance helpfulness and trustworthiness. The findings suggest combining linguistic empathy with affective/emotional empathy capabilities, particularly for interactions involving negative user emotions. | Providing legal information and guidance in Tenant Law. | General public facing tenant law issues (specifically Chicago residents in the study). | Tenant Law / Landlord-Tenant Law | Chicago, Illinois, USA | N/A (The chatbot was rule-based using a decision tree, not trained on data in the ML sense). | Theoretical framework development (extending linguistic empathy theory), rule derivation (9 rules), rule-based system design (using decision trees and visual programming software Landbot), scenario design (collaboration with legal experts/non-profit), controlled behavioural experiment (2x3 factorial design). | Deployed within a controlled online experiment for recruited participants via the SONA research registry. | False | False | NaN | The need for conversational AI to possess affective/emotional empathy, in addition to linguistic empathy, to effectively build trust with users experiencing negative emotions. Further research is needed to examine the individual effects of the proposed linguistic empathy rules. | Disentangling the effects of linguistic empathy from cognitive ability and psychological empathy in experimental research. Designing AI that can effectively build trust, especially with users experiencing negative emotions like anger. | Linguistic empathy without corresponding emotional understanding can reduce trust in users experiencing negative emotions (e.g., anger). Training generative AI for empathy using non-expert labels can introduce biases. |
| rxTZXXLaMTcJ.pdf | Google_Scholar | Robots in the Middle: Evaluating LLMs in Dispute Resolution | This paper evaluates the performance of Large Language Models (LLMs), specifically GPT-4o, in acting as mediators for dispute resolution. Using a novel dataset of 50 dispute scenarios, the study found that LLMs can select appropriate intervention types and generate high-quality intervention messages, often outperforming human annotators in a blind evaluation. | True | Idealistic | True | 2.0 | Positive | Using GPT-4o to select mediation intervention types and generate intervention messages based on dispute scenarios, within the conceptual LLMediator framework. | A blind evaluation comparing GPT-4o with human annotators on a manually created dataset of 50 dispute scenarios. Evaluation included: 1) comparing choices of intervention types (5-point Likert scale), 2) comparing generated intervention messages (5-point Likert scale overall, and on understanding, neutrality, empathy, resolution quality), and 3) safety checks for LLM messages. | In 62% of cases, LLM-chosen intervention types were rated better than or equivalent to human-chosen types. In 84% of cases, LLM-generated intervention messages were rated better than or equal to human-written messages, with LLMs outperforming humans in 60% of these cases. | High cost of human intermediaries, scarcity of trained facilitators, limiting access to mediation, especially for low-value disputes or in certain areas. | Using LLMs in Online Dispute Resolution (ODR) to provide scalable, cost-effective mediation services, thereby increasing the availability of facilitated dispute resolution. | Online Dispute Resolution (ODR), mediation, access to justice. | Individuals facing cost or availability barriers to traditional mediation services. | General civil disputes (examples include parcel delivery, land property rights, noise complaints). | International | NaN | Experimental design involving: construction of 50 diverse dispute scenarios; human and LLM (GPT-4o) selection of intervention types and drafting of intervention messages for these scenarios; blind comparative evaluation of intervention types and messages by human evaluators using Likert scales and specific criteria; safety checks for LLM outputs. | The full data, code, and prompts for reproducing the experiment are made available on a GitHub repository. | True | False | The prompts, dispute data, and code for the experiment are available on GitHub, allowing replication using the commercial OpenAI GPT-4o API. | Lack of evaluation with expert mediators and in real-world ODR systems; limitations of structured intervention tasks not reflecting real mediator processes; evaluating complex, nuanced LLM outputs objectively; need for multi-modal data integration; determining when to intervene. | Scarcity of accessible real-world dispute data (due to sensitivity/privacy) necessitating manual dataset creation; difficulty in objectively evaluating LLM performance on complex, nuanced tasks like mediation where answers are not definitively right or wrong. | Potential for LLMs to hallucinate information or generate unsafe messages (though not observed in this study's specific experiment with GPT-4o). |
| A_Framework_for_LLM-Assisted_Smart_Policing_System.pdf | Google_Scholar | A Framework for LLM-Assisted Smart Policing System | This paper proposes and evaluates a framework using large language models (LLMs) like BART, GPT-3, and GPT-4 for crime prediction within smart policing systems. It applies zero-shot prompting, few-shot prompting, and fine-tuning techniques to crime datasets from San Francisco and Los Angeles, comparing LLM performance against traditional machine learning models. | True | Market | True | 2.0 | NaN | LLM-based framework (using BART, GPT-3, GPT-4) for crime prediction employing zero-shot prompting, few-shot prompting, and instruction fine-tuning. | Evaluated using weighted accuracy, precision, recall, and F1-score on crime datasets from San Francisco (SF) and Los Angeles (LA). Compared LLM approaches against each other and baseline ML models (Random Forest, XGBoost). Data split 80% training / 20% testing. | Fine-tuned GPT-3 achieved the best performance on the SF dataset (97% weighted accuracy and F1-score). On the LA dataset, few-shot GPT-4 performed best among the tested LLM approaches (68% weighted accuracy), but overall performance was lower. | NaN | NaN | NaN | NaN | Criminal law (crime prediction and policing) | United States (San Francisco, CA; Los Angeles, CA) | Publicly available historical crime incident report datasets from San Francisco (DataSF) and Los Angeles (LA Open Data), pre-processed and transformed into natural language descriptions. An instruction dataset was derived from this data for fine-tuning. | Application of existing LLMs (BART, GPT-3, GPT-4) using standard techniques (zero-shot prompting, few-shot prompting, instruction fine-tuning via API/HuggingFace). Comparison with baseline ML models (RF, XGBoost). Performance evaluation using standard metrics. | Conceptual framework and integration diagrams provided, suggesting cloud or local deployment for law enforcement, but no specific deployment of the prototype tool itself is described. | False | False | NaN | LLM performance variability across datasets; need for output calibration for reliable probability estimates in predictive policing; lack of a detailed framework for identifying and mitigating biases and ethical issues in deployment. | Significant variability in LLM performance between SF and LA datasets. Difficulty adapting LLMs to diverse dataset characteristics. Poor performance in classifying minority crime classes, particularly in the LA dataset. Computational costs of fine-tuning. Ensuring prompt quality. | Perpetuating biases from historical crime data leading to discrimination. Privacy violations from data collection/sharing. Lack of transparency and accountability in LLM decisions. Overreliance on AI systems in policing. |
| KthtaKV79LAJ.pdf | Google_Scholar | A Survey of Generative AI in Finance | This paper surveys real-world generative AI applications in the financial sector, analyzing tools from major institutions across different regions and segments. It examines their technologies, functionalities, impacts, and regional adoption patterns to provide insights for financial institutions. | True | NaN | True | 2.0 | NaN | DeepSeek R1: AI model for enhanced reasoning (math, coding, knowledge tasks) using reinforcement learning (GRPO). | Benchmarks: AIME 2024, MATH-500 (math); Codeforces (coding); MMLU, GPQA Diamond (knowledge); AlpacaEval 2.0 (QA). Compared to OpenAI o1-1217. | AIME 2024: 79.8% pass@1; Codeforces: 96.3 percentile; MMLU: 90.8%; MATH-500: 97.3%; AlpacaEval 2.0: 87.6% win rate. Matches/exceeds OpenAI o1-1217. | NaN | NaN | NaN | NaN | NaN | International | Built on DeepSeek-V3-Base. R1 version: multi-stage training pipeline including cold-start data, reasoning-oriented reinforcement learning, rejection sampling, and comprehensive fine-tuning. R1-Zero: pure reinforcement learning. | Reinforcement learning (GRPO - Group Relative Policy Optimization framework), multi-stage training pipeline, model distillation. | Described as having an open-source nature, making it a valuable resource for the research community. | True | True | DeepSeek R1 is described as having an open-source nature available to the research community. | NaN | For DeepSeek R1: Sensitivity to prompting, occasional language mixing issues. | Misinformation, harmful/discriminatory content, hallucinations, over-reliance on AI outputs, lack of transparency/explainability, data privacy and security breaches, regulatory compliance failures. |
| H5HwzgGHq2wJ.pdf | Google_Scholar | The Disrupting Influence of AI and the Potential Impact of ChatGPT on Maritime Law and Practice | The paper explores the disruptive potential of AI, particularly ChatGPT, within the field of maritime law and practice. It discusses potential applications like contract analysis and incident investigation, while also highlighting significant challenges such as accuracy, legal acceptance, data privacy, and ethical concerns. | True | Market | True | 3.0 | Neutral | ChatGPT | NaN | NaN | Legal acceptance by the community, data privacy and security concerns, integration challenges with existing systems, intellectual property disputes, liability uncertainties (especially with autonomous systems), AI inaccuracies and potential for misinformation, need for human expertise/oversight, ethical concerns (bias, transparency). | Keen human oversight and validation ('human in the loop'), transparency with users about AI interaction, development of robust guidelines and best practices, workforce adaptation and reskilling, creation of specific AI usage protocols (e.g., data security, content validation). | Automating legal tasks (contract review/analysis/generation, maritime incident investigation, environmental monitoring/compliance, legal research/information retrieval), potentially lowering cost of legal services and improving efficiency. | NaN | Maritime Law | International | General text data from the internet (pre-September 2021); potential for fine-tuning on maritime-specific data (laws, contracts, incident reports). | NaN | Discusses potential investigation and protocol development by companies like Maersk, but no specific deployment strategy detailed. | False | False | NaN | Need for established legal acceptance and frameworks for AI use, unresolved data privacy/security/IP/liability issues, requirement for improved AI accuracy/reliability and mitigation of biases, lack of integration standards, need for workforce adaptation strategies, limited knowledge base (e.g., past Sept 2021 cutoff for ChatGPT). | Ensuring accuracy and avoiding misinformation/hallucinations, understanding specific domain nuances (e.g., maritime nomenclature), preventing misuse, implementing quality control, integrating with existing systems, addressing legal acceptance, data privacy, IP, and liability concerns, managing implementation costs, mitigating ethical issues. | Spreading legal misinformation, misinterpreting legal/trade terminology, creating intellectual property disputes, complex liability issues from AI errors or autonomous operations, data privacy/security breaches, job displacement, perpetuating biases, lack of transparency. |
| 3yFwsD-ie9gJ.pdf | Google_Scholar | A Comprehensive Study of ChatGPT: Advancements, Limitations, and Ethical Considerations in Natural Language Processing and Cybersecurity | This review paper presents an in-depth study of ChatGPT, analyzing its architecture, training, capabilities, and limitations in NLP and cybersecurity. It compares ChatGPT with other language models and discusses ethical considerations, privacy risks, and diverse applications across various industries. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | Reviews training data for models like GPT-3 (175B parameters, 570GB text including Common Crawl, WebText2, Wikipedia, Books) and ChatGPT (fine-tuned GPT-3 using Reinforcement Learning from Human Feedback (RLHF) on dialogue datasets). Data is generally large-scale, web-based, unstructured text, proprietary in its final form used by OpenAI. | NaN | NaN | True | False | Available as a service/API from OpenAI (implied through discussion of the existing tool). | NaN | Bias from training data, lack of contextual understanding/common sense, high computational cost, large data dependency, lack of interpretability, potential for generating incorrect information ('hallucination'), limited knowledge cutoff (initially 2021), lack of direct internet search, inability to interpret images. | Misuse for generating fake news/disinformation/impersonation, generation/leakage of sensitive/personal data (privacy violation), use in phishing/scams, facilitating cybercrime (malware/script generation), replicating biases, potential copyright infringement, inappropriate substitution for human interaction. |
| DlbOhgReUPsJ.pdf | Google_Scholar | Developing Fictitious Country Maps through Generative AI Techniques | This thesis explores the application of diffusion models to generate high-resolution maps for the fictional country of Carana, a scenario used by international peacekeeping organizations for training and strategic planning. The study aims to address the limitations of existing map representations by developing a framework to produce synthetic, adaptable imagery for enhanced simulations. | True | Idealistic | False | 1.0 | Positive | Diffusion model based on U-Net architecture for generating synthetic satellite imagery tiles. | Validation was performed using histogram-based analysis of color channels and Fréchet Inception Distance (FID) scores, comparing generated tiles to real Sentinel-2 imagery. | The Fréchet Inception Distance (FID) score was 435.0111, indicating that further improvements are needed to enhance the quality of the generated tiles. | Lack of detailed, adaptable, and realistic maps for training international peacekeeping organizations, hindering effective simulation, strategic planning, and operational preparedness for complex crises. | Developing a framework using diffusion models to generate high-resolution, synthetic maps for the fictional country scenario (Carana) to improve the quality and realism of training materials for peacekeeping personnel. | Enhancing training effectiveness for peacekeeping operations, crisis management, humanitarian aid distribution, conflict resolution, and post-disaster response through improved geospatial visualization. | International peacekeeping organizations (e.g., UN), military leaders, policymakers, and humanitarian workers undergoing training. Indirectly, populations in regions affected by geopolitical conflicts, humanitarian crises, and natural disasters. | Public International Law, International Humanitarian Law (as relevant to the peacekeeping and crisis response training scenarios). | International (Carana is fictional; training data from Ethiopia, Kenya, Somalia; intended for international organizations). | Publicly available Sentinel-2 L2A RGB satellite imagery from regions in Ethiopia, Kenya, and Somalia, preprocessed into 64x64 pixel tiles after filtering for cloud cover and null data. | Iterative development following a data science project pipeline: data acquisition (Sentinel-2 imagery), preprocessing (tiling, cleaning), model training (U-Net based diffusion model, initially local, then on HPC), and validation (histogram analysis, FID). | The generated maps are intended for integration into training simulations, with a specific example of georeferencing an output map using ArcGIS Pro for use with a geodatabase. | False | False | NaN | Technical gaps include the lack of labeled geospatial features in training data for conditional generation, inconsistent color and unrealistic shapes (e.g., cloud-like patterns) in generated tiles, limitations of 10m resolution source imagery, and computational intensity of training/sampling. Need for improved tile coherence and boundary blending in assembled maps. | Ensuring spatial coherence and color consistency across assembled tiles, managing high computational requirements for model training (necessitating HPC), GPU configuration, robust preprocessing of satellite imagery (cloud-cover, black/white pixel filtering), and adapting validation metrics like FID to the specific tile characteristics. | General ethical concerns related to AI-generated maps, including data integrity, potential for misinformation, and biases inherited from training data (acknowledged from literature, not specific to this study's findings). |
| nI4pc9EGbUoJ.pdf | Google_Scholar | Exploring a GPT-based large language model for variable autonomy in a VR-based human-robot teaming simulation | This paper introduces and evaluates a simulation framework using Virtual Reality (VR) where users interact via natural language with multiple robot agents, each powered by a GPT-4 core. A user study explored interaction strategies, finding users often defaulted to simple commands despite the LLM's capabilities, highlighting challenges in shared understanding and perceived agent autonomy. | True | NaN | True | 1.0 | NaN | A VR-based simulation framework (using Unity) for human interaction with multiple simulated robot agents controlled by individual GPT-4 instances, employing OpenAI's function calling to map natural language commands to robot actions. | Exploratory within-subjects user study with 12 participants performing seven structured tasks of increasing complexity within the VR simulation, interacting with the agents via speech. Data collected included audio/video recordings, system logs, post-study questionnaires (SASSI), and semi-structured interviews. | Users often employed simple, command-like instructions and had preconceived expectations, seldom exploring the LLM's full conversational potential. Challenges included mismatches in expected autonomy, response latency, and occasional LLM meticulousness or errors, though some users successfully adopted more complex, conversational coordination strategies. | NaN | NaN | NaN | NaN | NaN | NaN | The system uses OpenAI's pre-trained GPT-4 model (gpt-4-0613) accessed via API. The model is initialized with specific prompts (role, restraints, few-shot examples) and structured function descriptions (JSON objects detailing available actions and parameters) relevant to the simulation environment. | Development of a custom software framework using Unity Engine for VR simulation, integration of OpenAI API (GPT-4 for control logic, Whisper for speech-to-text), Amazon Polly (text-to-speech), and implementation of OpenAI function calling for command interpretation. Design involved creating a multi-agent architecture with a central controller and individual agent GPT cores, task design for user studies, and thematic analysis of user interaction data. | The simulation framework is described as being available via a provided GitHub link. | True | True | The framework is available at a GitHub link provided in footnote 1. | NaN | Mapping unstructured natural language to structured robot actions (addressed via function calls but still imperfect); aligning user and agent conceptual/world models; LLM non-determinism, opacity, and planning limitations; response latency due to cloud API calls and sequential processing; achieving natural turn-taking and dialogue flow; designing effective inter-agent communication for collaborative tasks; providing adequate intervention/control mechanisms. | LLM hallucination/errors leading to incorrect actions or communication breakdown; potential for user frustration due to latency or misalignment of expected vs. actual agent autonomy/capabilities; ethical concerns regarding potential future empathetic channels (user deception, authenticity). |
| G4OomxoYXtoJ.pdf | Google_Scholar | On Evaluating Legal-Reasoning Capabilities of Generative AI | This paper critically examines recent studies on the legal-reasoning capabilities of generative AI, particularly large language models. It also discusses the potential roles of traditional symbolic AI approaches in legal reasoning and argumentation in the era of generative AI. | True | NaN | True | 3.0 | Neutral | Generative AI / Large Language Models for legal reasoning and argumentation, including various prompt engineering methods (e.g., zero-shot, few-shot, Chain-of-Thought). | The paper reviews studies that evaluated LLMs on tasks such as bar/law school exam performance, specific legal reasoning tasks (e.g., rule application, IRAC adherence, entailment), and legal document generation. Evaluation methods included qualitative expert assessment, quantitative metrics (accuracy, F1 score, precision, recall), and comparisons against human performance or other NLP models. | The paper reviews various results; one of the highest performances cited is from Servantez et al. (2024), where GPT-4 with a 'Chain of logic' prompting method achieved 92.3% accuracy on specific rule-based tasks from the LegalBench benchmark. | NaN | NaN | NaN | NaN | General legal reasoning, Tax Law, Cryptocurrency Securities Law, Criminal Law (based on reviewed studies). | Japan, United States (based on reviewed studies); discusses concepts broadly applicable internationally. | Reviewed studies use LLMs (e.g., GPT-3, GPT-4) pre-trained on broad general datasets. Some specific studies involved fine-tuning on legal datasets (e.g., COLIEE) or used retrieval-augmented generation with legal documents like statutes or case law. | The paper discusses approaches that utilize prompt engineering (zero-shot, few-shot, chain-of-thought, retrieval-augmented generation), selection/fine-tuning of LLMs, and structuring tasks according to legal frameworks like IRAC, as observed in the reviewed studies. | NaN | False | False | NaN | NaN | Challenges identified in applying/evaluating LLMs for legal reasoning include: LLM 'hallucinations'; difficulties in robustly evaluating reasoning beyond output; interpreting ambiguous natural language; ensuring models genuinely follow proclaimed reasoning methods (unfaithful explanations); avoiding training data memorization effects in evaluation; and moving beyond simplistic deductive tasks to full legal argumentation. | Potential risks stated include: LLMs producing factually incorrect information ('hallucinations'); biases (e.g., racial, gender) influencing LLM outputs and decision-making processes; unfaithful explanations misleading users about the actual reasoning; and over-reliance on LLMs for complex legal work for which they may be ill-suited or not robustly validated. |
| BO49BB8AYbkJ.pdf | Google_Scholar | From Text to Structure: Using Large Language Models to Support the Development of Legal Expert Systems | This paper investigates using large language models (LLMs), specifically GPT-4, to automatically extract structured representations (pathways) from legislative text. The goal is to support the efficient development of rule-based legal expert systems, like JusticeBot, for improving access to justice. | True | Idealistic | True | 1.0 | Positive | An LLM-based framework (JusticeCreator Automatic Pathway Generator - JCAPG) using prompted GPT-4 to extract structured pathways (criteria and conclusions) from legislative text, formatted for the JusticeBot/JusticeCreator system. | Evaluation by 4 experts on 40 articles from the Civil Code of Quebec. Compared GPT-4 generated pathways to manual ones using direct rating (textual/logical accuracy, usability) and a blind comparison test (E2). | In a blind comparison test, 60% of automatically generated pathways were rated equivalent or better than manual ones. In direct evaluation, 90% were rated as correct or needing only slight adjustment for use as a basis for a JusticeBot tool. | The manual analysis and encoding of legislation into formal representations is time-consuming and requires legal expertise, creating a bottleneck for developing legal decision support tools. | Using LLMs (GPT-4) to automatically generate draft pathways from legislative text, which can then be reviewed and refined by legal experts, thereby increasing efficiency in developing rule-based expert systems. | Development of legal decision support tools for laypersons based on legislation. | Laypeople seeking to understand how legislation applies to them. | Civil Law (based on the Civil Code of Quebec). | Quebec (Canada) | The technique uses OpenAI's GPT-4 model. The input data for the experiment consisted of 40 selected articles from the Civil Code of Quebec. | Iterative prompt engineering for GPT-4 based on the JusticeBot methodology. Development of the JCAPG tool integrating prompt execution and JSON conversion. | Output pathways can be imported into the JusticeCreator tool. Code and prompt shared on GitHub. | True | True | Code and prompt available on GitHub (link provided in footnote 3). | Generalizability to more complex/interconnected legislation, other jurisdictions/legal traditions (beyond Quebec Civil Code), need for integration of case law/doctrine to resolve ambiguities, need for robust study of efficiency gains, and application to related tasks (e.g., mapping case facts to pathways). | Ensuring logical correctness (avoiding errors like denying the antecedent), handling legal ambiguity inherent in texts, variability in valid pathway structuring, preventing model hallucination, and occasional technical errors in generating valid structures. | Inaccuracy of generated pathways (textual errors, missing elements, hallucinations, logical fallacies) potentially leading to incorrect legal information or flawed system logic if not diligently verified by human experts. |
| 5NF0TDdTxRgJ.pdf | Google_Scholar | Explaining Legal Concepts with Augmented Large Language Models (GPT-4) | This paper evaluates GPT-4's ability to explain legal terms from statutory provisions to legal professionals. It finds that augmenting GPT-4 with relevant sentences retrieved from case law significantly improves explanation quality and reduces factual errors compared to using GPT-4 directly. | True | Market | True | 2.0 | Positive | Retrieval-augmented generation using GPT-4 and a legal information retrieval component to explain statutory terms based on case law. | Manual comparison by two legal scholars evaluating pairs of explanations (short and long) generated by baseline vs. augmented GPT-4 for 42 statutory terms. Evaluation dimensions were Factuality, Clarity, Relevance, Information Richness, and On-pointedness. | The augmented GPT-4 approach was significantly preferred over the baseline across all evaluation dimensions, particularly Factuality. The augmentation appeared to eliminate the issue of hallucination (citing non-existent cases or misrepresenting content) present in the baseline model's outputs. | NaN | The paper suggests augmented LLMs can assist legal professionals; future work could adapt this for laypeople to enhance access to justice. | Statutory interpretation explanation. | Legal professionals (lawyers, judges, scholars). Mentions laypeople as a potential future audience. | Statutory interpretation | United States | The technique uses GPT-4 (trained on undisclosed large corpus) and augments it with data retrieved from the publicly available Statutory Interpretation Data Set, which contains manually classified sentences (high, certain, potential, no value) extracted from US case law (Caselaw Access Project) relevant to interpreting specific statutory phrases. | Comparative experimental evaluation using human annotators; Retrieval-Augmented Generation (RAG) approach. | NaN | False | False | NaN | The quality of the information retrieval component limits the augmented model's output quality. The approach needs evaluation for generating explanations suitable for laypeople to improve access to justice. Applicability to other legal tasks needs investigation. | Ensuring factuality and avoiding hallucinations in baseline LLMs. The quality of the retrieved information (e.g., relevance, source type like dissenting opinions, outdated cases) directly affects the augmented system's output. Proper formatting of legal citations. | Hallucination (generating non-factual information, citing non-existent cases, misrepresenting case holdings) in baseline LLMs. Propagation of errors or irrelevant information from the retrieval component in the augmented approach. Users potentially relying on incorrect information without verification. |
| OLZjlJlYtzIJ.pdf | Google_Scholar | Do Robot Lawyers Dream of Electric Clients? | This paper experimentally evaluates ChatGPT's ability to draft a legally sound last will based on a complex user prompt, analyzing its performance with and without jailbreaking compared to human drafting. It concludes that while ChatGPT shows potential as a lawyer's tool, it is currently unsafe for direct consumer use due to significant limitations in legal reasoning, handling ambiguity, and susceptibility to errors, especially when jailbroken. | True | Idealistic | True | 2.0 | Negative | ChatGPT (version 4) for drafting a last will and testament, including testing with a 'jailbreak' prompt (DAN). | A fictional client prompt for a Virginia will with embedded legal complexities was given to ChatGPT under different conditions: 1) standard interaction, 2) with a jailbreak primer, 3) with human co-piloting (the author), 4) using its output as a rough draft. Outputs were qualitatively analyzed and compared against a will drafted independently by the author. | ChatGPT's independently drafted wills contained significant legal flaws, errors, and ambiguities related to spousal disinheritance, asset distribution, libelous statements, and identification. The jailbroken version performed worse, exhibiting degraded reasoning. Human co-piloting was necessary to rectify major issues, highlighting the need for expert supervision. | The high cost of legal services motivating consumers to use potentially unreliable AI tools. Consumers' lack of legal expertise to evaluate AI outputs. AI's inability to correctly interpret complex/ambiguous instructions, understand legal nuances (like spousal elective share), and prioritize legal validity over problematic user requests. Widespread misconceptions about AI capabilities. | Human lawyers must supervise AI use, treating AI as a nonlawyer assistant under ethical rules (e.g., ABA Model Rule 5.3). Increased education for both the public and legal professionals about AI limitations is needed. Regulation for consumer protection is considered but noted as difficult due to technical challenges like jailbreaking. | Self-help legal document drafting (Wills), Consumer protection | General public / consumers seeking to avoid legal fees. | Wills and Estates, Legal Ethics | Virginia | Proprietary data used by OpenAI for ChatGPT, described generally as massive volumes of internet text (blogs, articles, Wikipedia, etc.) combined with reinforcement learning from human feedback. | NaN | NaN | True | False | ChatGPT 4 is described as a mass-market consumer product (paid), while ChatGPT 3.5 is mentioned as free. Access is via OpenAI's platform. | Lack of public understanding regarding AI limitations versus science fiction portrayals. Difficulty in ensuring AI prioritizes legal correctness over problematic user instructions. Need for reliable methods to evaluate AI output quality and failure rates. Effective consumer protection mechanisms for AI legal tools, especially considering jailbreaking. | Evaluating proprietary 'black box' AI models. AI tendency to prioritize user satisfaction over legal accuracy. Variability and unpredictability of AI outputs. Addressing AI misuse through techniques like jailbreaking. Overcoming user misconceptions. | Creation of invalid or legally flawed documents (e.g., wills) by consumers using AI without supervision. Financial loss or unintended consequences due to reliance on faulty AI legal advice/drafting. Potential for libel claims arising from AI-generated content. Ethical breaches or malpractice if lawyers inadequately supervise AI assistants. |
| AwmwqPYg6eIJ.pdf | Google_Scholar | EXPLORING THE FACTORS INFLUENCING ACTUAL USAGE OF GENERATIVE AI IN ACADEMIC RESEARCH | This master's thesis investigates factors affecting academic researchers' adoption and use of generative AI tools via a quantitative survey of 141 participants, primarily in Finland. Findings indicate that the perceived benefit of mutual adaptation and AI literacy significantly predict intention to use, while intention strongly predicts actual usage frequency. | True | NaN | True | 2.0 | NaN | Factors influencing academic researchers' adoption and usage of existing Generative AI tools (like ChatGPT), analyzed via a survey-based multi-stage model. | Quantitative survey distributed to 141 academic researchers (predominantly Finland). Statistical analysis included reliability tests (Cronbach's Alpha), factor analysis (PCA), correlation analysis, and regression analysis (including bootstrapping and moderation testing). | Benefit of mutual adaptation (β = 0.533, p < 0.001) and AI literacy (β = 0.330, p = 0.004) were the strongest positive predictors of intention to use Generative AI. Intention significantly predicted actual usage frequency (β = 0.582, p < 0.001). | NaN | NaN | NaN | NaN | General academic research (multi-disciplinary) | Predominantly Finland, with international participants | NaN | Development of a theoretical multi-stage model based on existing literature (e.g., Technosymbiosis, Source Credibility). Quantitative survey design using adapted, validated scales (Likert scale). Online data collection via Webropol. Statistical analysis using SPSS and Excel (Reliability, Factor, Correlation, Regression, Bootstrap, Moderation). | NaN | False | False | NaN | Limited understanding of human-AI interaction nuances in academic workflows; applicability of existing adoption models; lack of standardized ethical guidelines/acknowledgment protocols; need for discipline-specific and cross-cultural studies; limited understanding of barriers for older researchers; gap between AI's potential and perceived role (especially as collaborator); need to explore factors beyond intention for actual usage; impact on research quality/creativity. | Defining/measuring complex constructs (e.g., technosymbiosis, AI literacy); sampling challenges (low response rate, geographic/age skew); potential multicollinearity between predictors; capturing the full scope of 'actual usage' beyond just frequency. | Overdependence diminishing critical thinking; potential for bias/errors impacting research credibility; exacerbating inequalities; threats to academic integrity (plagiarism, data fabrication); challenges to authorship/IP; spread of misinformation. |
| T_UxWrCFaRQJ.pdf | Google_Scholar | LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods | This paper provides a comprehensive survey on the 'LLMs-as-judges' paradigm, where Large Language Models evaluate outputs based on natural language. It examines existing research across functionality, methodology, applications, meta-evaluation, and limitations, while also outlining future research directions. | True | NaN | True | 3.0 | NaN | LLMs-as-Judges paradigm (using LLMs as evaluators of natural language responses or other outputs based on defined criteria). | The paper surveys various meta-evaluation methodologies for 'LLMs-as-Judges'. This includes the use of diverse benchmarks categorized by application domain (e.g., code generation, machine translation, text summarization, dialogue generation, value alignment) and metrics (e.g., Accuracy, Pearson, Spearman, Kendall’s Tau, Cohen’s Kappa, ICC) to assess alignment with human preferences. | NaN | NaN | NaN | NaN | NaN | Legal (Section 5.4 mentions applications such as evaluation of law LLMs and relevance judgment in legal case retrieval). | International | NaN | NaN | NaN | False | False | NaN | NaN | The paper extensively discusses limitations of the LLMs-as-Judges paradigm (Section 7), including: Biases (position, verbosity, social like authority/bandwagon, content-related like sentiment/token/context, cognitive like overconfidence/self-enhancement); susceptibility to Adversarial Attacks; and Inherent Weaknesses (knowledge recency, hallucination, domain-specific knowledge gaps). | Potential for biased, inconsistent, or unfair judgments; manipulation through adversarial attacks leading to unreliable evaluations; propagation of errors due to hallucinations or outdated knowledge; negative impacts from domain-specific knowledge gaps leading to incorrect assessments in critical fields like medicine or law. |
| ysPWbU8zbbEJ.pdf | Google_Scholar | Construction of a Japanese Financial Benchmark for Large Language Models | This paper introduces a new benchmark designed to evaluate Large Language Models (LLMs) specifically within the Japanese financial domain. The benchmark comprises five distinct tasks, and the authors present evaluation results for various LLMs, highlighting GPT-4's superior performance and confirming the benchmark's effectiveness. | True | NaN | True | 1.0 | NaN | A new Japanese financial benchmark for LLMs comprising five tasks: chabsa (financial sentiment analysis), cma_basics (securities analysis knowledge), cpa_audit (CPA exam auditing questions), fp2 (financial planner exam questions), and security_sales_1 (securities broker representative test questions). | The benchmark's effectiveness was validated by applying it to evaluate various LLMs. Its ability to differentiate model performance across tasks of varying difficulty and consistency with known model capabilities (e.g., GPT-4's high scores) demonstrated its functionality. | The benchmark effectively differentiated LLM performance. The GPT-4 series demonstrated outstanding performance, with openai/gpt-4-32k achieving the highest average score of 66.27 across the five tasks. | NaN | NaN | NaN | NaN | Auditing, accounting law, financial planning regulations, consumer finance law, securities law, financial instruments regulation. | Japan | The benchmark datasets were constructed from publicly available sources: chabsa from a previous study's GitHub repository; cma_basics, fp2, and security_sales_1 from crawled and cleansed online sample exam questions and practice materials; cpa_audit data was from a previous study using Japanese CPA examination questions. | Dataset construction by sourcing from previous studies and web crawling/cleansing of public exam materials; data processing including format conversion (e.g., tables to markdown) and task adaptation (e.g., chabsa to binary classification); prompt engineering involving preparation and selection of best-performing prompts based on preliminary experiments. | The benchmark and model performance results are publicly released on GitHub. | True | True | Publicly available on GitHub: https://github.com/pfnet-research/japanese-lm-fin-harness. | NaN | Scarcity of existing domain-specific benchmarks for Japanese finance; data processing for diverse question types (e.g., removing figures, table conversion); ensuring stable performance evaluation with imbalanced datasets (e.g., chabsa neutral class); significant impact of prompt engineering on LLM performance; cost of API access for evaluating certain proprietary models. | NaN |
| 7PttF-rL6z8J.pdf | Google_Scholar | Through the AI -Looking Glass and What Consumers Find There* | This paper examines the risks and potential benefits of consumer-facing generative AI tools for access to justice, particularly for self-represented litigants in the US. It proposes an incentive-based regulatory framework to mitigate harms like misinformation and the unauthorized practice of law, while encouraging the development of trustworthy AI tools. | True | Idealistic | True | 1.0 | Positive | An incentive-based regulatory framework for consumer-facing legal AI tools, offering liability shields and presumptions against UPL findings for compliant providers. | NaN | NaN | High cost and complexity of the legal system; lack of legal representation (justice gap); difficulties for self-represented litigants in navigating the system; potential for misinformation from unregulated AI tools; protectionism within the legal profession (e.g., UPL enforcement). | Utilize generative AI to provide accessible legal information and assistance; implement the proposed incentive-based regulatory scheme requiring disclosures, clear disclaimers, data protection options, transparency, and expert review; offer liability shields/presumptions for compliant providers. | Access to legal information for self-represented litigants; document drafting assistance; understanding legal procedures; navigating civil litigation. | Self-represented litigants; consumers facing legal issues without lawyers; general public needing legal assistance. | General Civil Litigation, Family Law, Housing Law, Consumer Protection, Traffic Law (based on examples discussed) | United States (with comparisons to EU and China) | NaN | NaN | NaN | False | False | NaN | Lack of clear definition for 'practice of law' / 'legal advice' concerning AI; uncertainty about liability for AI-generated errors; absence of effective US regulation for consumer-facing legal AI; need for transparency in AI operations and data usage; ensuring AI accuracy and reliability; balancing innovation with consumer protection. | Defining 'legal advice' for AI regulation; ensuring AI provider transparency; designing effective enforcement for regulations; balancing access goals against UPL and misinformation risks; overcoming legal profession skepticism; keeping pace with AI development; avoiding stifling innovation through regulation. | AI providing inaccurate information (hallucinations); users over-relying on AI; deepening the justice gap and user distrust; AI engaging in Unauthorized Practice of Law (UPL); privacy violations/data misuse; user manipulation via hidden prompts; bias in AI outputs; provider liability. |
| JjKy892udNQJ.pdf | Google_Scholar | Developing aGenerative AIModel toEnhance Sentiment Analysis fortheSaudi Dialect | This PhD dissertation proposes a novel method using generative AI (AraGPT2) to create synthetic data for the low-resource Saudi Dialect (SD), addressing data scarcity. By combining collected Twitter data with generated data to fine-tune AraBERT, the study significantly improves sentiment analysis performance for SD compared to using only collected data. | True | Market | True | 1.0 | NaN | A hybrid approach using MARBERT (a BERT variant) for dialect annotation/filtering, AraGPT2 (a GPT-2 variant) for generating synthetic Saudi Dialect data, and fine-tuning AraBERT (another BERT variant) for sentiment analysis using a combination of collected tweets and generated data. | AraBERT fine-tuned for sentiment analysis was evaluated using Accuracy, Precision, Recall, and F1-score on combinations of datasets (collected Saudi Twitter Data - STD, generated data, AraCust dataset). Evaluation involved single runs and averaging over 10 iterations of data reshuffling and splitting (80% train, 20% test). MARBERT for annotation was evaluated using Accuracy, Precision, Recall, F1-score, and LIME (XAI). AraGPT2 for generation was evaluated using Perplexity and BLEU scores. | The best sentiment analysis performance was achieved by fine-tuning AraBERT on a combination of the AraCust dataset and the generated Saudi Dialect dataset, yielding an average accuracy of 96.47% and an average F1-score of 92.15% over 10 iterations. | NaN | NaN | NaN | NaN | NaN | Saudi Arabia | Initial data: ~50,000 Arabic tweets collected from X (Saudi Twitter Dataset - STD), geotagged to Saudi Arabia. Annotation step used ~27,870 preprocessed STD tweets, fine-tuning MARBERT. Generation step used ~19,251 dialectal tweets identified by MARBERT to fine-tune AraGPT2, producing 19,251 synthetic tweets. Sentiment analysis step used combinations of STD, generated data, and the public AraCust dataset (20,000 manually labeled Saudi telecom tweets). Data is unstructured text. | Multi-step process including: selecting pre-trained models (MARBERT, AraGPT2, AraBERT), web scraping (X API), data preprocessing (cleaning, normalization, tokenization with NLTK), model fine-tuning, synthetic data generation using generative AI, comparative model evaluation using standard metrics, and explainability analysis (LIME). | NaN | False | False | NaN | Significant lack of research and open datasets for Saudi Dialect (SD) NLP. Challenges include limited resources (datasets, tools, models), linguistic variation and ambiguity (lack of standardization, diglossia), and the nonconcatenative structure of Arabic. | Data scarcity and quality for the low-resource Saudi Dialect. Linguistic diversity, lack of standardization, and diglossia within the dialect. Technical difficulties in processing dialectal text. Time and effort required for data collection and annotation. Potential for model overfitting. Evaluating the quality and coherence of synthetically generated text. | NaN |
| ACmFBJB5spsJ.pdf | Google_Scholar | Enhancements for Developing a Comprehensive AI Fairness Assessment Standard | This paper proposes expanding the Telecommunication Engineering Centre (TEC) Standard for AI Fairness Assessment to cover images, unstructured text, and generative AI like LLMs. The goal is to create a more comprehensive framework for responsible AI deployment by addressing biases in diverse data modalities and advanced AI models. | True | Idealistic | True | 1.0 | Positive | The proposed enhanced TEC Standard for AI Fairness Assessment, incorporating specific methodologies for fairness in images (e.g., tabular reduction, XAI), unstructured text (e.g., WEAT, SEAT, GBETs), and LLMs (e.g., embedding-based, probability-based, generation-based metrics). | NaN | NaN | Biased AI systems leading to discriminatory outcomes that disproportionately affect vulnerable or marginalized groups, reinforcing prevailing societal inequities and undermining trust in AI applications. | Expanding and enhancing the existing TEC AI Fairness Standard to include specific assessment methodologies for images, unstructured text, and LLMs, thereby enabling more comprehensive identification and mitigation of biases in a wider range of AI systems. | Ensuring equitable and non-discriminatory outcomes from AI systems, especially for vulnerable and marginalized populations. This impacts fairness in diverse sectors such as telecommunications, finance, healthcare, public services, and touches upon areas like law enforcement actions and legal aid. | Vulnerable entities, marginalized or underrepresented groups, marginalized communities. | NaN | India (primary focus on the TEC Standard), with references to international frameworks (ITU, NIST). | NaN | NaN | NaN | False | False | NaN | The current TEC Standard's limitation to structured tabular data and supervised learning models, making it less applicable to AI systems using unstructured data (images, text) and advanced models like LLMs. | NaN | Biased or unjust AI outcomes disproportionately affecting vulnerable entities; inequalities in network access or resource allocation; perpetuation of harmful stereotypes or discrimination by image recognition systems; LLMs reinforcing societal biases and generating discriminatory or harmful content; potential for wrong medical diagnoses or autonomous vehicle accidents due to biased AI. |
| jhu4mHJ3DpUJ.pdf | Google_Scholar | LegalGuardian: A Privacy-Preserving Framework for Secure Integration of Large Language Models in Legal Practice | This paper introduces LegalGuardian, a framework using NER and local LLMs to mask PII in prompts for external LLMs, aiming to protect client confidentiality in legal practice. Evaluated on synthetic immigration law prompts, it achieved high PII detection accuracy (97% F1 with Qwen2.5-14B) and maintained semantic fidelity, demonstrating a method for safer LLM use by lawyers. | True | Idealistic | True | 1.0 | Positive | LegalGuardian: a framework using Named Entity Recognition (NER) techniques (specifically GLiNER) and local LLMs (specifically Qwen2.5-14B) to mask and unmask Personally Identifiable Information (PII) in prompts sent to external LLMs. | Evaluated using a synthetic dataset of 50 prompts in US immigration law scenarios. PII detection performance was assessed using precision, recall, and F1-score (overall and entity-level). Semantic consistency between original and masked/unmasked LLM outputs was measured using Cosine Similarity, Jaro-Winkler Distance, and Levenshtein Distance. | For PII detection, Qwen2.5-14B achieved an F1-score of 97% (Precision 99%, Recall 94%), while GLiNER achieved an F1-score of 93% (Precision 100%, Recall 88%). GLiNER showed slightly higher cosine similarity (0.9808) compared to Qwen2.5-14B (0.9731) for semantic consistency. | The primary obstacle is the risk of breaching client confidentiality when lawyers use LLM-based tools due to the inclusion of PII in prompts. This hinders LLM adoption, especially for practitioners with limited resources (e.g., legal aid, solo practitioners) who cannot afford custom secure solutions, thereby limiting AI's potential to democratize legal services. | The paper proposes LegalGuardian, a lightweight framework that allows lawyers to mask PII in prompts before sending them to external LLMs and subsequently unmask this PII in the LLM's response. This approach aims to preserve confidentiality while enabling the use of advanced AI tools by a broader range of legal professionals. | Protection of client confidentiality when using AI tools; Enabling access to advanced AI for a broader range of legal professionals, including those in legal aid or solo practice, thereby indirectly supporting access to justice goals. | Legal professionals, particularly legal aid workers and solo practitioners with limited resources. By extension, their clients, who may include individuals from underserved communities. | Immigration law (for the synthetic dataset and scenarios); the framework is intended for broader legal practice. | United States (references to ABA Model Rules, US state initiatives, and US immigration law scenarios). | The evaluation involved a synthetic dataset of 50 legal prompts in US immigration law, generated using the Faker library and the Qwen-2.5 14B model. PII detection relies on the pre-trained GLiNER model (GLiNER Multi PII-v1, fine-tuned for PII) and one-shot prompting of the pre-trained Qwen2.5-14B model. | The framework development included: 1. Synthetic legal prompt dataset generation. 2. A PII masking layer using NER (GLiNER) and a local LLM (Qwen2.5-14B via one-shot prompting). 3. A secure prompting layer for interacting with external LLMs. 4. An evaluation layer using accuracy and semantic similarity metrics. | NaN | False | False | NaN | Future work includes extending the framework to more legal areas, enhancing PII detection for complex data, integrating with cloud-based LLMs using privacy-preserving techniques (e.g., secure multi-party computation, federated learning), and conducting user studies with practicing lawyers. | Balancing PII masking accuracy (privacy) with the preservation of semantic integrity and utility of LLM outputs. Ensuring comprehensive PII detection across various PII types and contexts. Developing a lightweight solution to avoid high computational costs and complexity associated with some advanced privacy-preserving methods. | Unauthorized exposure of client PII to third-party LLM providers. Breaches of attorney-client privilege and data protection laws. LLM misinterpretation of prompts if masking techniques alter meaning or introduce ambiguity. Potential for sensitive information to surface in unrelated prompts if LLMs learn from input data (though LegalGuardian aims to prevent this by masking before external interaction). |
| crj8G8qyKYEJ.pdf | Google_Scholar | AI White Paper, consultation response | This paper is a consultation response by the British Irish Law, Education and Technology Association (BILETA) to the UK government's AI White Paper. BILETA critiques the proposed non-statutory, principles-based approach, advocating instead for a mandatory statutory framework for AI regulation to ensure adequate protection, fairness, and redress. | True | Idealistic | True | 2.0 | Negative | NaN | NaN | NaN | Inadequate, unclear, inaccessible redress mechanisms for AI-related harms; lack of mandatory regulation leading to potential abuse and weak enforcement; challenges in regulating foundation models (LLMs) including bias, hallucination, and societal impacts; risks to human rights (e.g., non-discrimination, fair elections). | Implement a mandatory statutory regulatory framework (akin to the EU AI Act); establish clear, strong redress mechanisms including class actions and judicial review; potentially establish a single coordinating regulatory body; enhance transparency requirements; implement auditing. | Fairness, accountability, contestability, redress, transparency, AI risk management, regulation of high-risk AI, foundation models (LLMs), human rights protection, statutory vs non-statutory regulation. | General public / users / marginalized groups | AI Regulation, Technology Law, Human Rights Law, Administrative Law | United Kingdom | NaN | NaN | NaN | False | False | NaN | Lack of a mandatory statutory framework in the UK proposal; inadequate redress mechanisms; insufficient clarity on handling foundation models and assigning legal responsibility; potential for overlapping/contradictory guidance from multiple regulators. | Challenges for regulators in applying principles consistently across diverse AI applications; determining legal responsibility across the AI lifecycle, especially with foundation models; potential for overlapping or contradictory guidance from different regulators under the proposed framework. | AI reinforcing biases against marginalized groups; LLMs 'hallucinating' (providing false information); adverse impacts on workforce and economy; inadequate redress for AI harms; insufficient protection of human rights (e.g., free elections, non-discrimination, health, fair pay, freedom of expression); risks associated with specific AI applications like social scoring, remote biometric identification, predictive policing, emotion recognition, indiscriminate scraping of biometric data. |
| feikXgtDjy8J.pdf | Google_Scholar | Continual Pre-Training is (not) What You Need in Domain Adaption | This paper investigates the efficacy of Domain-Adaptive Continual Pre-Training (DACP) for Legal Large Language Models (LLMs) in the Taiwanese legal system. It finds that while DACP enhances domain-specific knowledge, it does not uniformly improve performance across all legal tasks and can have trade-offs regarding generalization and prompt-based tasks. | True | Idealistic | True | 2.0 | Neutral | Domain-Adaptive Continual Pre-Training (DACP); Low-Rank Adaptation (LoRA); Direct Preference Optimization (DPO); Odds Ratio Preference Optimization (ORPO). Creation of LLAWA, BLLAWA, BLAWSTRAL models. | Custom benchmark for Taiwanese legal framework: multiple-choice questions from Bar/Judicial Exam and Jurist Journal (Tasks A, B; accuracy metric), argument-based decision-making in legal symposia (Task C; accuracy), and essay questions from Bar/Judicial Exam (Task D; GPT-4o evaluation against segmented golden answers based on 'Juristisches Gutachten' method). | DACP enhances domain-specific knowledge but does not uniformly improve performance across all legal tasks. For example, while BLAWSTRAL (LoRA-tuned Mistral-Nemo) achieved the highest accuracy on Task C (56.54%), models with DACP (LLAWA variants) did not consistently outperform base models or other fine-tuning methods on all tasks, and sometimes DACP led to performance degradation on prompting tasks. | Lack of resources and difficulty in accessing expert-level legal analysis for individuals and organizations. | Improving Legal LLMs through techniques like Domain-Adaptive Continual Pre-Training to provide more accessible expert-level legal analysis and democratize legal services. | Democratizing access to legal services; Making expert-level legal analysis more accessible. | Individuals and organizations that might otherwise lack the necessary resources. | Taiwanese law (general), including juvenile law, criminal law, laws, regulations, and court documents. Also references German law. | Taiwan (primary), Germany (secondary, for comparative pre-training data). | Pre-training: Publicly available Taiwanese legal data (laws, regulations, court documents from Judicial Yuan), a German law subset from MultiLegalPile, and self-curated data (ConceptNet, CBETA). Instruction tuning: Cleaned TAIWAN CHAT (general instructions) and a legal dataset from Taiwan's Bar/Judicial Exams and Taiwan High Court website (specific legal tasks). Data is largely unstructured text. | For LLAWA: Domain-Adaptive Pre-Training, full-parameter instruction tuning, preference alignment (DPO, ORPO). For BLLAWA & BLAWSTRAL: Low-Rank Adaptation (LoRA) for instruction tuning. | The paper states that models and a Hugging Face repository will be made publicly available upon acceptance or after anonymized review. | False | False | NaN | Need for hybrid approaches combining DACP with other methods; Refinement of evaluation benchmarks for legal reasoning; Addressing potential data contamination in LLM training; Finding optimal mixture ratios for general vs. domain-specific corpora; Limitations of current evaluation metrics (e.g., BLEU/ROUGE) and potential biases in LLM-as-evaluator setups. | DACP not uniformly beneficial, leading to trade-offs in generalization and prompt-based task performance; Fine-tuning can sometimes lead to suboptimal states (e.g., BLLAWA); Preference optimization techniques (DPO, ORPO) did not yield expected improvements under the study's conditions; Complexity in evaluating essay-type legal questions; Difficulty in modeling complex legal argumentation in settings like legal symposia. | Potential for LLM hallucinations; Ensuring ethical use of legal AI; Maintaining transparency in AI decision-making; Addressing concerns about AI bias; Risk of data contamination in training leading to inflated performance perception; Biases introduced by using LLMs as evaluators. |
| A3TgdbzreLMJ.pdf | Google_Scholar | Customizing Large Language Models for Legal Consultations | This paper introduces a multi-turn prompt engineering method to enhance large language model (LLM) performance for legal consultation, iteratively refining responses for improved accuracy and legal coherence. Evaluations using a curated legal dataset, with GPT-4 as a judge and human assessment, demonstrate the method's superiority over baselines in delivering precise and contextually relevant legal advice. | True | Idealistic | True | 1.0 | Positive | A multi-turn prompt engineering method for LLMs, designed to iteratively refine model responses in legal consultation tasks by dynamically adjusting prompts based on previous outputs. | The method was evaluated on a manually curated legal query dataset (covering contract, intellectual property, constitutional law) using GPT-4 as a judge to score outputs on legal coherence, legal precision, reasoning depth, and iterative improvement. Additionally, legal professionals conducted human evaluations based on relevance, completeness, clarity, and legality. | The proposed method (OM) achieved scores of 4.8 for Legal Coherence, 4.7 for Legal Precision, 4.6 for Reasoning Depth, and 4.5 for Iterative Improvement (on a 1-5 scale). Human evaluation rated OM at 4.7 for Relevance, 4.6 for Completeness, 4.8 for Clarity, and 4.7 for Legality, significantly outperforming baseline methods. | The high cost of traditional legal representation and limited availability of legal services, particularly in underserved or remote areas. Additionally, the inherent challenges of applying general AI to the complex legal domain (e.g., lack of precision, misinterpretation of legal nuances) without specialized approaches hinder reliable A2J applications. | The development and application of specialized AI techniques, such as the proposed multi-turn prompt engineering for LLMs, to generate more accurate, reliable, and contextually appropriate legal advice. This approach aims to democratize access to legal consultations, making them more affordable and broadly available, especially for underserved communities. | Access to legal advice and consultation, Democratization of legal services, Improving understandability and reliability of AI-generated legal information. | Individuals in underserved or remote areas, populations with limited access to traditional legal representation due to cost or geographical constraints. | General legal consultation, with evaluation dataset examples from contract law, intellectual property law, constitutional law. The method is suggested to be adaptable to other domains like family law and corporate law. | International | NaN | Iterative design; a multi-step pipeline involving an input layer (user query), processing layer (initial LLM response), refinement layer (iterative follow-up prompts guiding the LLM), and output layer (final, refined legal response). | NaN | False | False | NaN | Reliance on the quality of the initial user query; the current fixed sequence for iterative refinement could be improved with adaptive mechanisms. Further integration of domain-specific legal knowledge bases is needed. Broader ethical considerations, including privacy and bias in AI legal systems, require ongoing research. | Computational cost of multiple prompt iterations (though claimed to be manageable), susceptibility to errors from poorly framed initial user queries, and optimizing the iterative refinement process (e.g., determining when to stop iterations). | Potential for misinterpretation of legal terminology, errors in applying legal principles, and difficulties in adhering to jurisdictional rules by LLMs (which the method aims to mitigate). Broader AI in law risks include bias, data privacy concerns, and ethical implications of automated legal advice. |
| mDOOmREBPQoJ.pdf | Google_Scholar | Getting in the Door: Streamlining Intake in Civil Legal Services with Large Language Models | This paper investigates using Large Language Models (LLMs) to streamline the legal intake process for civil legal aid, focusing on eligibility determination. It describes and evaluates a digital intake platform piloted in Missouri that combines logical rules with LLMs, finding promising results with GPT-4-Turbo achieving an F1 score of .82. | True | Idealistic | True | 1.0 | Positive | A digital intake platform built on the Docassemble framework, which uses a combination of Python-encoded formal rules and zero-shot LLM prompting (with program-specific intake rules provided as text) to assess eligibility for legal aid and elicit further information from users. | Evaluated using two datasets: D1 (48 scenario-jurisdiction pairs generated via ChatGPT and manually coded) to test initial LLM response accuracy across 8 LLMs for predicting 'accept', 'deny', or 'question'; D2 (11 manually generated multi-turn conversational transcripts with GPT-4-turbo) for qualitative assessment of follow-up questions and overall interaction quality by an expert rater. | GPT-4-Turbo achieved the highest overall weighted F1-score of 0.82 on dataset D1, with high precision for the 'Deny' class. Qualitative analysis (D2) by an expert rater showed 73% correct overall results, and perfect scores (5/5) for understandability and satisfaction with the tool, though noting that additional follow-up questions could have been asked by the AI in 63% of cases. | Time-consuming nature of legal intake for legal aid, nuanced and frequently changing substantive eligibility criteria, high demand for services leading to long wait times for applicants. | A digital intake platform using LLMs combined with logical rules to provide 24/7 preliminary eligibility screening, inform applicants about their likelihood of qualifying before waiting, and potentially reduce staff burden by handling initial assessment. | Legal intake streamlining, eligibility determination for civil legal aid, reducing barriers to accessing legal help, client-facing legal technology. | Low-income individuals and applicants for free legal aid programs, specifically tenants facing housing issues in Missouri. | Civil legal aid, housing law, landlord-tenant law. | Missouri, USA (specifically, legal aid programs in Eastern Missouri, Mid-Missouri, and Western Missouri). | The technique uses pre-trained LLMs in a zero-shot setting. Program-specific substantive intake rules are provided as plain text within the prompt at inference time, along with the user's problem description. Evaluation datasets (D1 and D2) consist of scenarios generated using ChatGPT, manually reviewed, reworded, and coded, or entirely manually generated. | Iterative prompt engineering (specifically for GPT-4-turbo), development of a user-facing application using the Docassemble framework, pilot testing in collaboration with four legal aid programs in Missouri. | The intake application was piloted in Missouri, accessible on mobile phones, embedded in a legal help website (MOTenantHelp.org), and referred to in the on-hold message for callers to the phone intake system. | True | True | The full code and prompt are available on GitHub in two repositories. The piloted application is embedded in MOTenantHelp.org for Missouri tenants. | Integration with a seamless online intake experience, improved user analytics, simplifying rule updates (e.g., allowing staff to upload documents directly), potential for using semi-structured reasoning, further prompt and intake rule refinement, evaluation of human intake staff performance for comparison, exploration of potential LLM biases, and expansion to best-match eligibility recommendations across multiple providers. | Initial LLM tendency to give inappropriate advice (addressed by clarifying its task), LLMs generating example replies leading to hallucinations (addressed by omitting examples in prompt), content censorship by some LLMs (e.g., Google Gemini for a domestic violence scenario), and prompt optimization being model-specific. | Content censorship by LLMs may limit applicability to other legal topics (e.g., involving violence or abuse). Biased LLM training data could expose vulnerable legal aid applicants to risks (mitigated by human-in-the-loop design, focusing LLM on minimum qualification criteria, and prompting for explanations). |
| 3615859.pdf | Google_Scholar | Generative AI as a New Innovation Platform | This paper explores generative AI as a potentially transformative innovation platform, analyzing its ecosystem structure including foundational models, infrastructure, and applications. It discusses the significant opportunities alongside major concerns such as market concentration, content ownership, data privacy, information accuracy, and the need for effective regulation. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Intellectual Property Law (Copyright/Fair Use), Privacy Law, Tech Regulation | USA, Italy | Large, general datasets ('trillions of words and other data points', potentially including copyrighted content and open source software) used to train foundational LLMs. | NaN | NaN | False | False | NaN | Gaps in regulation and governance frameworks for AI, difficulties in controlling technological issues like hallucinations and detecting fake content, challenges in addressing societal impacts (misinformation, job displacement), and unresolved legal questions regarding data usage (e.g., fair use). | NaN | Diffusion of misinformation, detrimental societal impact, lack of ethical guardrails, concentration of market power, privacy violations, data leaks, algorithmic bias, copyright infringement, generation of inaccurate information (hallucinations), difficulty in detecting AI-generated fakes, high energy consumption, job displacement. |
| DoIEmP47jgoJ.pdf | Google_Scholar | The Legal AI: Justifying Justice | This paper reviews AI applications in the legal domain, focusing on tools to improve court efficiency like automated docketing in Florida and Brazil's VICTOR system, while also critically examining issues like algorithmic bias with the COMPAS example. It further touches upon commercial AI tools for litigation analytics such as Lex Machina. | True | Idealistic | False | 2.0 | Neutral | Automated docketing system (Florida) using classification, Learn by Example (LBX), Robotic Process Automation (RPA), and OCR for processing court filings. | The automated docketing system in Palm Beach County, Florida, was trained and evaluated on 'thousands of filings'. Its accuracy was compared to human performance. | The automated docketing system in Florida achieved 98 to 99 percent accuracy in classifying and docketing documents, which was reported as better than human counterparts. | Systemic court delays and backlogs; resource shortages (judges, lawyers); procedural inefficiencies (e.g., party absence, evidence issues); risk of algorithmic bias and embedding societal prejudices. | Utilizing AI to enhance efficiency in judicial processes (e.g., automated document processing, case management assistance); developing AI systems mindful of and adaptable to ethical considerations and bias mitigation. | Reducing court backlogs, improving judicial process efficiency (e.g., docketing, appeal categorization), and addressing algorithmic bias in legal AI. | General public affected by justice system delays; specific demographic groups (e.g., racial minorities) vulnerable to algorithmic bias. | Court administration, criminal justice (risk assessment), patent litigation, general civil and criminal procedure. | India, USA (Florida, Wisconsin), Brazil | For the automated docketing system in Florida: 'thousands of filings' from the Circuit Court & Comptroller, Palm Beach County (unstructured, domain-specific legal documents). | For the automated docketing system in Florida: Machine learning for classification, Learn by Example (LBX), Robotic Process Automation (RPA), and Optical Character Recognition (OCR). | The automated docketing system is deployed in the Circuit Court & Comptroller from Palm Beach County, Florida. Other systems mentioned (SUPACE, VICTOR) are deployed in their respective Supreme Courts. | False | False | NaN | Ensuring accountability for AI decisions; preventing errors, malfunctions, and misjudgments; effectively mitigating algorithmic bias; developing AI that can adapt to evolving societal norms and ethical considerations for complex legal decision-making. | Achieving high accuracy and efficiency in AI legal tools, handling diverse and complex data inputs (e.g., handwritten scripts requiring OCR), integrating AI into existing legal workflows, and overcoming limitations in mimicking nuanced human legal reasoning. | Algorithmic bias leading to discriminatory outcomes and unfairness (e.g., as seen in COMPAS); errors, malfunctions, or miscalculations in AI systems leading to misjudgment and negative societal impacts; lack of accountability and transparency in AI-driven legal decisions; perpetuation of existing societal biases through biased training data. |
| afRxufBh6fkJ.pdf | Google_Scholar | Artificial intelligence (AI) technology in OpenAI ChatGPT application: A review of ChatGPT in writing English essay | This paper reviews OpenAI's ChatGPT, focusing on its capability to generate English essays for educational purposes. It describes how to use ChatGPT and provides examples of generated essays, noting its ability to structure text appropriately but also highlighting concerns about accuracy and potential misuse like plagiarism. | True | NaN | True | 2.0 | Neutral | ChatGPT | Qualitative observation: The researcher prompted ChatGPT to write English essays on various topics (descriptive text, recount text, future plans) and analyzed the output for structure, grammar (tense, voice), and coherence. | ChatGPT generated essays considering writing structure (introduction, body, conclusion), appropriate voice (active/passive), and tense selection based on the topic. However, grammatical accuracy needs further verification. | NaN | NaN | NaN | NaN | NaN | International | Trained using Reinforcement Learning from Human Feedback (RLHF) on a large dataset (billions of words including text and books), based on OpenAI's GPT-3.5 model. | NaN | Web interface access via OpenAI website (chat.openai.com) after user registration. | True | True | Available for free public use via web interface (chat.openai.com) during its "research preview" phase, requiring user registration. | NaN | Tendency to generate plausible but incorrect/illogical answers; lack of deep contextual understanding, critical thinking, and ethical decision-making abilities; potential for perpetuating societal biases (race, gender, culture); difficulty in fixing inaccuracies. | Academic dishonesty (plagiarism by students); difficulty for educators in detecting AI-generated text; perpetuation of societal biases; dissemination of incorrect information presented as fact. |
| uTqP15w03YEJ.pdf | Google_Scholar | CHATGPT, I HAVE A LEGAL QUESTION? THE IMPACT OF GEN AI TOOLS ON LAW CLINICS AND ACCESS TO JUSTICE | This paper evaluates the accuracy of Generative AI tools like ChatGPT for providing legal advice, finding them prone to significant errors and jurisdictional confusion. It discusses the risks for non-lawyers and explores the potential for responsible use of Gen AI in law clinics to enhance access to justice and student skills, tempered by ethical considerations. | True | Idealistic | True | 2.0 | Negative | Evaluation of Generative AI tools: ChatGPT 3.5 (free version), ChatGPT 4 (paid subscription version), Bing Chat (balanced mode), and Google Bard. | Six generic legal queries (family, employment, consumer, housing, online contracts, child maintenance) reflecting common legal problems were posed to four Gen AI models. Responses were rated 0-5 by two qualified lawyers based on accuracy of legal advice (currency, comprehensiveness, correct application, need for prompts, clarity for non-lawyer) and clarity of practical next steps (practicality, ADR inclusion, links provided, completeness, clarity for non-lawyer). A follow-up question regarding English law was used if necessary. | ChatGPT4 (subscription model) performed best, scoring 73% for accuracy of legal advice and 70% for clarity of next steps. However, overall, only 13% of the initial queries across all tools were correctly answered based on UK law, with 42% of responses being too generic and 25% wrong in law. | High cost of legal advice and representation; limited availability of legal aid and geographical 'legal aid deserts'; public an DRAFTlack of awareness of reliable free legal helphuman-centeredsources; digital divide (cost of technology, internet access, digital literacy) disproportionately affecting low-income individuals; structural inequalities in the justice system not solely solvable by technology. | A public legal education campaign about Gen AI limitations for legal advice. Responsible integration of Gen AI in law clinics with appropriate training, policies, and student supervision. Development of bespoke, reliable legal AI solutions (though funding for free advice organisations is a challenge). Encouraging human-centered design for legal technologies. | Reliability and accuracy of Gen AI for legal queries; impact on litigants in person; role and risks of Gen AI in clinical legal education; addressing the access to justice gap. | Litigants in person (non-lawyers); individuals with unmet legal needs, particularly highlighting BAME communities, younger people, those on low income, or with low levels of education. | Family law, employment law, consumer law, housing law, online contract law, child maintenance law. | England and Wales (queries focused on English law). The paper notes issues with AI tools defaulting to US law. | The Gen AI tools studied (ChatGPT, Bard, Bing Chat) are described as being trained on 'vast amounts of internet text data.' | NaN | NaN | True | True | ChatGPT 3.5, Google Bard, and Bing Chat are freely available. ChatGPT 4 is available via paid subscription. | Need for improved accuracy, reliability, and jurisdictional awareness in Gen AI for legal advice. Ensuring equitable access to beneficial AI tools, avoiding a 'two-tiered system' based on ability to pay. Lack of public understanding of Gen AI's limitations and risks in legal contexts. Development of tailored, trustworthy AI solutions for free legal advice providers. Addressing ethical concerns regarding Gen AI training data, inherent biases, transparency, and data privacy. | Gen AI tools providing generic, incorrect, or outdated legal advice. Frequent jurisdictional confusion (e.g., defaulting to US law when UK law is needed). Outputs lacking crucial details (e.g., legal deadlines). Difficulty for non-lawyers to critically evaluate the veracity of Gen AI responses. The dynamic nature of law requiring continuous updates to AI models (implied). | Non-lawyers relying on inaccurate Gen AI legal advice, leading to detrimental consequences. Exacerbation of existing inequalities if more reliable AI tools are only available via paid subscriptions. Ethical issues including inherent bias in AI models, lack of transparency, and compromises to client confidentiality when sensitive data is input into Gen AI tools. Reputational and legal risks for law clinics if students misuse Gen AI. Potential for 'hallucinations' or fabrication of legal information by Gen AI. Over-reliance on AI potentially degrading research and writing skills. |
| PiELflBCXh8J.pdf | Google_Scholar | AI ASSISTANCE IN LEGAL ANALYSIS: AN EMPIRICAL STUDY | This paper investigates how AI (GPT-4) assistance affects human performance on law school exams. Results show significant improvement on multiple-choice questions but not essays, with lower-performing students benefiting most and top performers potentially seeing declines; optimally prompted AI alone can outperform both humans and AI-assisted humans. | True | Market | True | 2.0 | Neutral | Evaluating the performance of law students taking exams with GPT-4 assistance, compared to students without AI and GPT-4 alone using various prompting techniques (basic, chain-of-thought, few-shot, grounded). | Experiment involving University of Minnesota law students taking prior years' exams (Introduction to American Law, Insurance Law) with GPT-4 assistance after training. Performance compared against their own real exam scores (without AI) and prior year students' scores (without AI). Exams were blindly graded; results analyzed quantitatively (percentiles, grades, speed) and qualitatively. | With grounded prompting, GPT-4 alone outperformed both average human students and average AI-assisted students on both exams, achieving perfect scores on multiple-choice and high scores on essays (93rd percentile on Intro essay, 65th percentile on Insurance Law). | AI struggles with complex legal reasoning, issue-spotting, and incorporating nuanced legal details (cases, rule variations) without grounding. Integrating AI output is challenging for complex tasks. Top performers may be negatively impacted by AI assistance (potential over-reliance, stifled creativity). | Effective prompt engineering, particularly 'grounded' prompting (providing relevant source material like lecture notes), significantly improves AI performance, making AI alone potentially superior to humans or AI-assisted humans for some tasks. | Legal analysis, Legal reasoning, Legal education (law school exams), Performance evaluation, Future of legal profession | Law students (undergraduate and JD), Legal professionals (potential implications for elite vs. non-elite lawyers, paraprofessionals) | Legal Education, Introduction to American Law (Contracts, Torts, Criminal Law, Civil Procedure, Property, Constitutional Law), Insurance Law | United States | GPT-4 (pre-trained on broad data by OpenAI). The study utilized specific prompting techniques: 'grounded' prompting used domain-specific, unstructured text (instructor lecture notes); 'few-shot' prompting used sample questions and model answers from prior exams. | Experimental design, Human subjects research (law students), Between-subjects and within-subjects comparisons, Blind grading, Quantitative analysis (statistical testing, bootstrapping), Qualitative analysis (review of exam answer characteristics). | The experimental setup used a private website cloning ChatGPT via the GPT-4 API for participants; this was specific to the study. | False | False | NaN | Need for better understanding of why AI assistance harms top performers; difficulty integrating AI for complex tasks; generalizability beyond exam settings to real legal work; adequacy of short-term training; impact of unknown parallel forms reliability of exams; potential for automated prompt engineering. | Integrating AI insights with human reasoning for complex essay questions; potential for AI to crowd out independent thinking or high-order reasoning (e.g., spotting hidden issues); variation in student ability to effectively use AI; ensuring comparable effort levels between real exams and study exams; limitations of training. | Performance degradation for high-skilled users; potential job displacement for paraprofessionals due to AI superiority in certain tasks; over-reliance on AI leading to reduced effort or creativity; AI generating inaccurate or conclusory analysis (especially without grounding); organizational problems in AI-assisted writing. |
| 3Kw3imwyDSMJ.pdf | Google_Scholar | TOWARD NATIONAL REGULATION OF LEGAL TECHNOLOGY: A PATH FORWARD FOR ACCESS TO JUSTICE | The paper argues that state-by-state regulation of legal technology is inadequate for addressing the access-to-justice gap and potential harms like bias and inequality. It proposes a national, opt-in regulatory sandbox to test innovative legal services, generate data, and inform evidence-based regulatory reforms by states. | True | Idealistic | False | 1.0 | Positive | National, opt-in legal services regulatory sandbox. | NaN | NaN | Inadequate state-level regulation failing to keep pace with technology; regulatory uncertainty (UPL, ethics rules); risk of a technology-driven two-tiered system; lack of data on legal tech benefits and harms; resistance to innovation and reform (e.g., nonlawyer ownership); financial and logistical barriers to local reform efforts. | Establish a national, opt-in legal services regulatory sandbox to centralize expertise, generate empirical data through controlled testing of innovative services with temporary safe harbors, and provide data-driven recommendations to states for regulatory reform. | Access to civil legal services, regulatory innovation, legal technology regulation, unauthorized practice of law (UPL) reform, alternative business structures (ABS) / Rule 5.4 reform, data-driven regulation. | Low-income and moderate-income individuals facing the justice gap. | Civil law (broadly), including family law, business law, estate planning, consumer issues. | United States (discusses state initiatives but proposes a national mechanism) | NaN | Conceptual proposal based on analysis of existing regulatory issues, state-level sandbox examples (e.g., Utah), and academic literature. | Proposal for an opt-in system for US states. | False | False | NaN | Need for empirical data on legal tech impact (benefits, harms, A2J effects); lack of national coordination and expertise in regulating legal tech; need for clear definitions and safe harbors (e.g., UPL); need for flexible regulatory models (e.g., ABS) to foster innovation; overcoming resistance to change; ensuring technology doesn't exacerbate inequality. | Overcoming state-level resistance to national coordination, securing state participation (opt-in), designing effective sandbox processes (application, monitoring, data analysis, recommendation), funding the national oversight body. | Exacerbation of the justice gap / creation of a technology-driven two-tiered system; consumer harm from poorly designed/implemented tech (inaccurate advice, bias); automation of bias leading to discrimination; inadequate protection of client data; stifling innovation due to regulatory uncertainty; potential for "spontaneous deregulation". |
| _wFRigLwihMJ.pdf | Google_Scholar | Uniandes at the Regulations Challenge Task: A Scalable Framework for Legal Text Understanding in Regulatory and Financial Contexts. | This paper presents the development and fine-tuning of a domain-specific LLM (LLaMA-3.1-8B) for understanding regulatory and financial texts. The process involved creating a specialized corpus via web scraping, implementing data cleaning and preprocessing pipelines, and instruction fine-tuning using QLoRA for tasks defined in the Coling 2025 Regulations Challenge. | True | Market | True | 1.0 | NaN | Domain-specific further pretraining and instruction fine-tuning of LLaMA-3.1-8B using QLoRA. Methodology includes corpus creation (web scraping, TF-IDF filtering, GPT-4o-mini cleaning) and instruction dataset generation (GPT-4o prompting, external dataset integration). | Evaluated on nine tasks from the Coling 2025 Regulations Challenge (Abbreviation Recognition, Definition, NER, QA, Link Retrieval, Certificate Analysis, XBRL Analytics, CDM Processing, Financial Mathematics, License Compliance) using metrics like Accuracy, BERTScore, F1 Score, FActScore. Compared against baselines (GPT-4o, Llama 3.1 8B base, Mistral Large 2) on the challenge leaderboard. | The model achieved a final weighted score of 0.43929 (2nd place in the challenge). It showed strength in Question-Answering (0.7688 FActScore) but weaknesses in Named Entity Recognition (0.4302 F1) and XBRL Analytics (0.3444 FActScore). | NaN | NaN | NaN | NaN | Regulatory law, Financial law, Compliance | US, EU, International | A custom corpus of publicly available financial and regulatory documents scraped from sources suggested by the Coling 2025 Regulations Challenge (e.g., EUR-LEX, ESMA, FDIC, Fed Reserve, eCFR, SEC, CFA/CPA Exam info, XBRL Int'l, CDM Docs, OSI). Preprocessed using TF-IDF filtering and GPT-4o-mini cleaning. Instruction dataset generated using GPT-4o on the cleaned corpus and supplemented with public Hugging Face datasets (flare-cfa, XBRLBench). Unstructured text data. | Corpus creation (recursive scraping, source-specific scrapers), Data Filtering (TF-IDF relevance scoring), Data Cleaning (GPT-4o-mini with prompt engineering), Instruction Dataset Generation (task-specific prompts with GPT-4o, integration of external datasets), Model Selection (LLaMA-3.1-8B), Fine-tuning (further pretraining, two-stage QLoRA instruction fine-tuning with varying context windows). | Potential for local deployment discussed due to model size, but no specific deployment strategy described. | True | True | Code, prompts, and implementation details are available on GitHub. | Technical gaps identified: suboptimal performance in NER, XBRL Analytics, and Certificate tasks; challenges in handling long documents exceeding context windows; need for enhanced structured data processing; lack of comprehensive expert validation; potential for hallucinations without mitigation strategies like RAG. | Handling noisy web-scraped data; lack of standardized regulatory NLP benchmarks; computational resource management (addressed via QLoRA and 8B model); varying context window requirements across tasks; achieving high performance on complex structured tasks (NER, XBRL); potential for model hallucination. | Risk of model inaccuracy, particularly in complex tasks like NER and XBRL Analytics. Potential for generating factually incorrect information (hallucinations), especially without retrieval augmentation. |
| gScUXpSxSxgJ.pdf | Google_Scholar | PREDICTING CONSUMER CONTRACTS | This article empirically evaluates the ability of the GPT-3 language model to understand consumer contracts by testing its performance on a novel dataset of questions about online terms of service. While showing potential for consumer empowerment, the study finds GPT-3 exhibits brittleness regarding question wording and a possible anti-consumer bias, highlighting the need for safeguards before deploying such models in law. | True | Idealistic | True | 2.0 | Neutral | Evaluating GPT-3's ability to answer yes/no questions about consumer contracts (terms of service) when provided with relevant excerpts. | A novel dataset of 200 yes/no questions was created, relating to the terms of service of the 20 most-visited U.S. websites. GPT-3 (davinci engine, temperature=0) was prompted with a contract excerpt and a question, and its accuracy and calibration were measured against random chance, majority class, and a 'contract withheld' baseline. Regression analysis controlled for variables like question category and wording. | GPT-3 achieved 77% accuracy, outperforming baselines, suggesting it used contract information. However, it performed significantly worse on questions about pro-consumer provisions (60% accuracy) compared to pro-company provisions (84% accuracy), indicating potential anti-consumer bias. Performance was also highly sensitive to question wording (readability) but not contract length or readability. | Consumers lack time, expertise, and incentive to read/understand contracts. AI models may provide misleading advice, contain harmful biases (e.g., anti-consumer bias), lack reliability due to brittleness (sensitivity to input variations), and lack interpretability, making errors hard to diagnose and trust difficult to establish. | Language models could empower consumers by reading/explaining contracts. The paper proposes ongoing experimentation (e.g., varying prompts), development of prompt design guidance, establishing technical and institutional safeguards (transparency, accountability, auditing), and regulatory reform (e.g., regarding unauthorized practice of law) to ensure responsible deployment. | Understanding consumer rights and obligations in online terms of service. | General consumers interacting with online services. | Consumer Law, Contract Law | US (based on the dataset of terms of service from US websites) | GPT-3 was trained by OpenAI on vast unlabeled datasets (570GB+) including Common Crawl, Webtext2, online books, and Wikipedia. This data is proprietary and likely includes numerous online terms of service. | Creation of a novel test dataset (200 yes/no questions on 20 terms of service), specific prompt engineering for GPT-3 interaction via API (davinci engine, temp=0), quantitative evaluation based on accuracy and calibration metrics, comparison against defined baselines, and statistical analysis (OLS regression) to identify factors influencing performance. | The paper evaluates GPT-3 used via the OpenAI API; it does not deploy a tool itself but discusses the potential for future deployment of similar technologies for consumers. | True | False | The methodology relies on the GPT-3 API provided by OpenAI, which is commercially available (subject to OpenAI's terms and pricing). | Need for larger, more diverse, and robust legal benchmark datasets (including unanswerable questions). Deeper investigation into model biases (sources and mitigation). Improving model robustness and interpretability. Development and implementation of effective technical/institutional safeguards and governance frameworks. Addressing regulatory barriers like unauthorized practice of law rules. Need for real-world evaluation methodologies. | Methodological challenges in evaluation: avoiding test data contamination, ensuring question independence, managing model stochasticity, maintaining transparency. Limitations of the study: small dataset size, single author annotating questions, narrow scope (one model, one task), reliance on yes/no format due to difficulty evaluating open-ended legal answers. Identifying and controlling for all variables influencing performance (potential omitted variable bias). | Misleading legal advice from AI; amplification and entrenchment of societal biases (e.g., anti-consumer bias); model brittleness leading to unreliable outputs; lack of interpretability hindering error diagnosis and trust; misuse for malicious purposes (misinformation, phishing, spam); data protection/privacy violations (in training data or API use); high environmental costs of training; intellectual property ownership ambiguity; unequal performance/access across languages/groups; compounding bias via feedback loops where model outputs pollute future training data. |
| hfKbdgn8f08J.pdf | Google_Scholar | Linking Judgement Text to Court Hearing Videos: UK Supreme Court as a Case Study | This paper presents a system for automatically linking paragraphs in UK Supreme Court written judgements to relevant segments in the corresponding court hearing video transcripts. The system uses customized GPT text embeddings for information retrieval and aims to improve access to and understanding of lengthy court proceedings. | True | Idealistic | True | 1.0 | Positive | An Information Retrieval (IR) system using customized GPT-3 text embeddings (text-embedding-ada-002) and cosine similarity to link written judgement paragraphs (queries) to transcribed spoken hearing segments (corpus). Includes data augmentation via InstructGPT paraphrasing. | Initial IR models (BM25, GloVe, Entailment, Legal BERT, Asymmetric, GPT) were evaluated using MAP@k and Recall@k against human annotations on a subset to select candidates for full annotation. Supervised models (Logistic Regression, Cross-encoder, CT bi-encoder, customized GPT embeddings) were trained on annotated/augmented data and evaluated using Accuracy, Precision, Recall, F1 against gold-standard labels on a test set. | Best results achieved with customized GPT-3 embeddings combined with cosine similarity as a feature in a logistic regression model (Accuracy=0.85, Precision=0.85, Recall=0.84, F1=0.85 on the gold-standard test set). | Key A2J obstacles identified: 1) Time required to analyze lengthy hearing videos. 2) Scarcity and difficulty of using hearing transcripts. | Proposed solution: An automated tool/UI platform linking judgement text to relevant video moments via semantic search, aiding navigation and comprehension of UKSC proceedings. | Improving access to and understanding of Supreme Court proceedings and judgements; Navigating lengthy legal video recordings. | General public and legal professionals/researchers needing to understand UKSC proceedings. | General (UK Supreme Court cases) | United Kingdom | Dataset derived from 7 UKSC cases (judgements from UKSC website, transcripts from custom ASR of UK National Archive videos). Annotated by law postgraduates (3620 gold links). Augmented using InstructGPT paraphrasing and negative sampling (total 7248 links). Domain-specific, mixed written/spoken register, unstructured text. | Information Retrieval (IR) approach (semantic search), custom ASR model development, zero-shot IR evaluation, human annotation by legal experts, data augmentation using generative AI (InstructGPT), supervised model training, embedding customization (OpenAI method), User Interface (UI) development. | Presented via demos to stakeholders (UK National Archives, UKSC, legal AI companies) with interest expressed for integration into transcription software pipelines. No wide deployment mentioned. | False | False | NaN | Mentioned gaps: Need for larger datasets, exploring entity-based linking, improving model robustness against high-frequency irrelevant phrases. | Challenges: Data segmentation (judgements/transcripts), linking different linguistic modes (written/spoken), costly domain-expert annotation, distinguishing true semantic links from superficial term overlap. | NaN |
| JIrkB5Ps8MEJ.pdf | Google_Scholar | Legal Considerations in Machine -Assisted Decision -Making: Planning and Building as a Case Study | This paper examines the legal considerations for governments and businesses implementing machine-assisted decision-making, using planning permits and building approvals in Victoria, Australia, as a case study. It identifies challenges related to transparency, bias, privacy, liability, and administrative law, arguing that addressing these issues is crucial to minimize risks while harnessing AI's benefits. | True | Market | False | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Administrative Law, Planning Law, Building Law, Tort Law (Negligence/Liability), Privacy Law, Data Protection Law, Intellectual Property Law, Evidence Law | Victoria (Australia), Australia (Federal), EU, US, UK | NaN | NaN | NaN | False | False | NaN | Legislative frameworks not designed for AI (e.g., defining 'decision', authorising AI-made decisions, establishing liability standards, ensuring reviewability). Need for clarity on scope of AI use and criteria. | Technical black box (inscrutability of algorithms, especially deep learning), legal black box (proprietary claims/trade secrets hindering transparency), algorithmic bias stemming from training data, ensuring privacy and data protection (particularly with collaborative platforms like BIM), cybersecurity risks, defining accountability (who is the decision-maker?), adapting existing legal doctrines (e.g., definition of 'decision' for judicial review, standard of care for negligence), ensuring admissibility of AI-generated evidence. | Lack of transparency preventing judicial review and undermining legitimacy; Algorithmic bias leading to unfair or discriminatory outcomes; Privacy violations and misuse of personal/proprietary data; Cybersecurity breaches (data corruption/theft); Legal liability for damages caused by AI errors (e.g., faulty building approvals); Decisions potentially being non-reviewable under current administrative law; 'Hallucinations' or untruthful outputs from generative AI leading to incorrect decisions. |
| jcDJsdOzy-4J.pdf | Google_Scholar | Generative AI in the Law | This paper provides an overview of Generative AI, focusing on its current uses and misuses by lawyers, particularly exemplified by tools like ChatGPT. It extensively discusses court responses, sanctions in cases like Mata v. Avianca, and the significant ethical and professional responsibility implications for attorneys, emphasizing caution and the need for human oversight. | True | Market | True | 3.0 | NaN | Generative AI, specifically discussing platforms like ChatGPT, Google Bard, Bing Chat, and Harvey.AI. | The paper discusses outcomes from real-world misapplications (e.g., Mata v. Avianca, Ex Parte Lee, a Second Circuit case) and refers to informal testing by the author on ChatGPT regarding Texas procedural rules and substantive legal research questions. It also mentions DoNotPay's attempted in-court use of its 'robot lawyer'. | In Mata v. Avianca, ChatGPT fabricated case citations. The author's tests showed ChatGPT provided incorrect answers to legal questions and failed substantive legal research tasks. When later prompted by the author, ChatGPT acknowledged creating fictional case examples. | NaN | NaN | NaN | NaN | General legal practice, Civil Litigation, Criminal Law (Habeas Corpus), Legal Research, Legal Drafting. | United States (specifically mentioning federal courts like S.D.N.Y., N.D. Tex., E.D. Pa., N.D. Ill., U.S. Court of International Trade, Second Circuit, Fifth Circuit; and state-level mentions like Texas courts and the Florida Bar). | For ChatGPT, it is mentioned as being trained on "hundreds of billions of words scraped from the internet." | Generative AI is described as a "deep-learning model" that uses a "neural network algorithm." | Platforms like ChatGPT, Google Bard, and Microsoft Bing Chat are described as widely available or released. DoNotPay attempted to deploy its 'robot lawyer' in court. | True | False | ChatGPT is stated to be free, and Bing Chat is accessible on the Microsoft Edge internet browser. | NaN | Challenges in using Generative AI in law include: inaccuracy and 'hallucinations' (fabricating information like case citations); potential bias in outputs; ensuring client confidentiality and privilege when using third-party AI tools; the ethical obligation for lawyers to remain competent and diligent, requiring thorough verification of AI-generated content; and the risk of professional sanctions or disciplinary action from misuse. | Potential risks include: court sanctions for submitting AI-generated filings with fabricated information (Mata v. Avianca); professional discipline by bar associations; breach of client confidentiality and attorney-client privilege; harm to clients through incompetent representation; damage to the integrity of the judicial system and public trust; reputational harm to judges, parties, and attorneys involved with fabricated legal materials; and violating rules of professional conduct regarding competence, diligence, and candor to the tribunal. |
| NrBD0NLvApwJ.pdf | Google_Scholar | פרטיות ואוטונומיה בהליכי גישור דיגיטליים: מיפוי הסיכונים והצעות לפתרון | This paper introduces an information-flow model to analyze how digital mediation affects participants' privacy, data protection, and autonomy. It identifies key risks from digital platforms and proposes legal and regulatory solutions to address current framework shortcomings. | False | Idealistic | False | 1.0 | Neutral | An analytical model conceptualizing digital mediation as an information flow process along two axes: information disclosure (internal/external to mediation) and information processing (for internal/external mediation purposes). | NaN | NaN | The digitalization of mediation creates new types of information and processing methods, leading to risks for participants' confidentiality, privacy, data protection, and autonomy. Existing legal frameworks (mediation law, privacy law) are inadequate and fragmented in addressing these new risks, particularly concerning the role of digital platforms and AI. | Proposes: 1) Regulating the legal status and duties of dedicated mediation platforms regarding information handling, confidentiality, and party control. 2) Defining mediators' responsibilities when using digital platforms, including training and risk disclosure. 3) Adapting general data protection, privacy, and AI laws to address the specific challenges of digital mediation, emphasizing transparency and accountability. | Digital mediation, Online Dispute Resolution (ODR), Privacy, Data Protection, Participant Autonomy, Confidentiality of mediation. | NaN | Mediation law, Privacy law, Data protection law, Civil procedure (by implication). | Primarily Israel, with comparative references to US and EU law and regulations. | NaN | Conceptual modeling based on legal and theoretical analysis of mediation, privacy, and technology literature. | NaN | True | False | The analytical model presented in the paper can be understood and conceptually applied by reading and understanding the paper. | Legal and regulatory gaps in defining the status and obligations of digital mediation platforms; Insufficiency of current data protection frameworks (including consent mechanisms) for the mediation context; Need for enhanced mediator training on technological risks; Lack of transparency and accountability in AI tools used in mediation. | Developing a comprehensive analytical model to map complex information flows and multi-faceted risks in the evolving landscape of digital mediation, amidst a fragmented and lagging legal framework. | Compromise of party autonomy in decision-making (due to interface design, algorithmic bias, automation bias); Breach of confidentiality (within mediation and externally, e.g., AI tools sharing data, platform data misuse for external purposes like marketing or profiling); Collection of excessive data; Lack of transparency in platform data practices; 'Trust spillover' from mediator to platform; Creation of detailed user profiles by combining mediation and platform usage data. |
| 2rvZcTZWe3oJ.pdf | Google_Scholar | GEWICHTSFORMEL – \nWÖRTLICH GENOMMEN.\nEIN EMPIRISCHER TEST \nMIT DER HILFE EINES \nSPRACHMODELLS | This paper empirically tests Robert Alexy's "Gewichtsformel" (weight formula) for legal balancing using large language models (GPT-3.5 and GPT-4) as proxies in a constitutional law scenario. It investigates how varying prompts based on the formula influence the LLMs' decisions and consistency, finding that the formula can have a rationalizing effect but may also be perceived as too rigid. | False | NaN | True | 1.0 | NaN | Using LLMs (GPT-3.5 turbo and GPT-4) with varied prompts incorporating Robert Alexy's "Gewichtsformel" to simulate and analyze legal balancing decisions in a constitutional case. | LLMs (GPT-3.5 turbo, GPT-4) were presented with a hypothetical German constitutional law case concerning cannabis cultivation for medical reasons. Six main versions of prompts were administered, progressively incorporating elements of Alexy's "Gewichtsformel," including chain-of-thought reasoning and explicit aggregation instructions. Each prompt variant was queried 100 times with "temperature" set to 1 to obtain a distribution of binary (yes/no) decisions on whether a constitutional complaint should be granted. | When GPT-4 was used to aggregate individual interest assessments (previously generated by GPT-3.5) and then make a final decision based on explicit instructions for balancing according to the 'Gewichtsformel' (prompt version six), it resulted in granting the constitutional complaint in 27 out of 100 instances. This highlighted that explicit formulaic guidance significantly impacts outcomes and that GPT-4 showed more consistency in aggregation but also instances of deviating from strict formula application when it seemed too rigid. | NaN | NaN | NaN | NaN | Constitutional law | Germany | The study uses pre-trained GPT-3.5 turbo and GPT-4 models from OpenAI; the paper refers to the general, proprietary training data these models were trained on. | Empirical experimental design using LLMs as subjects. The methodology involved systematic variation of prompts (including simple queries, 'chain of thought' prompts, and prompts with explicit formulaic instructions for aggregation and balancing) to test hypotheses about the impact of Robert Alexy's "Gewichtsformel" on decision outputs. Different LLM versions (GPT-3.5 for assessment, GPT-4 for aggregation) were used for specific tasks based on their perceived strengths. | NaN | True | False | The methodology, including the specific prompts and parameters (e.g., model choice, temperature setting), is detailed in the paper, allowing replication by anyone with access to OpenAI's API for GPT-3.5 turbo and GPT-4. | NaN | Challenges included effective prompt engineering to elicit desired binary outputs, the known weakness of GPT-3.5 in mathematical or quasi-mathematical aggregation tasks, the imperfect replicability of results due to the proprietary nature of the LLMs, and the ongoing general uncertainty about how well LLM responses mirror human decision-making processes. | The paper notes normative problems with potential full delegation of judicial decision-making to AI, mentions that cognitive biases might persist in LLMs (citing other research), implicitly warns against using LLMs as direct substitutes in actual legal proceedings without due caution ("Sicher nicht, dass die Grundrechtsauslegung künftig einem amerikanischen Unternehmen... überlassen bleiben soll"), and observes that LLMs may show inconsistencies or find established legal formulas too rigid. |
| GqRsr_mXZ9UJ.pdf | Google_Scholar | Legal Literacy in Indonesia: Leveraging Semantic -Based AI and NLP for Enhanced Civil Law Access | This paper addresses low legal literacy in Indonesia by developing and evaluating CerdasHukum, an AI system using IndoSBERT and QDrant for semantic search of the Indonesian Civil Code. The system demonstrated good accuracy (76.66% expert-validated) and usability (SUS score 74.81) in retrieving relevant legal articles. | True | Idealistic | False | 1.0 | Positive | A semantic legal information retrieval system named CerdasHukum using IndoSBERT (an Indonesian BERT-based sentence embedding model) to generate 256-dimensional vectors from legal texts and QDrant (a vector database) for efficient cosine similarity-based search. | Accuracy was evaluated by legal experts using a binary grading scale on retrieved articles for sample queries. Usability was assessed with 30 participants using the System Usability Scale (SUS). Comparative case studies were conducted against IndoBERT and FastText using cosine similarity scores. | The system achieved a recommendation accuracy of 76.66% as validated by legal experts. It received a System Usability Scale (SUS) score of 74.81 (Grade B). In comparative case studies, IndoSBERT achieved higher cosine similarity scores (e.g., 0.9227 and 0.9089) than IndoBERT (0.7065, 0.6232) and FastText (0.6668, 0.6205) for specific legal queries. | Low legal literacy, complex legal language, limited resources hindering public understanding and access to justice, particularly for marginalized communities. Insufficiency of traditional keyword-based information retrieval systems. | Development of a semantic-based AI retrieval system (CerdasHukum) using IndoSBERT and QDrant to provide context-aware search results from legal texts, enhancing accessibility and understanding of civil law. | Access to legal information, Legal literacy | General public in Indonesia, particularly marginalized communities and individuals facing civil disputes (e.g., in South Kalimantan). | Civil Law (specifically the Indonesian Civil Code - KUHPerdata) | Indonesia | The system uses the text of the Indonesian Civil Code (KUHPerdata), comprising 2,074 articles, preprocessed and transformed into embeddings by the pre-trained IndoSBERT model. The source is domain-specific (legal code), unstructured text data. | Systematic approach including: Data collection (Indonesian Civil Code), Text preprocessing (lowercasing, normalization, WordPiece tokenization), Semantic vector embedding (IndoSBERT), Vector storage and retrieval (QDrant with cosine similarity), Evaluation (Expert validation for accuracy, System Usability Scale for usability). | NaN | False | False | NaN | Need to incorporate additional legal texts and expand to other legal domains (criminal, administrative law). Requirement for further refinement of IndoSBERT for domain-specific tasks. Potential for exploring multilingual embeddings or zero-shot learning. General lack of focus on AI for low-resource languages. | Handling the complexity and nuances of legal language. Overcoming limitations of traditional keyword search. Achieving high contextual relevance in information retrieval. Working with a low-resource language (Indonesian). | NaN |
| 0FONfFqRRU4J.pdf | Google_Scholar | Mini-CarbonGPT: A Domain-Specific Large Language Model \nfor Carbon Neutrality | This paper introduces Mini-CarbonGPT, an LLM tailored for the carbon neutrality domain, built by fine-tuning the GLM-4-9B model and integrating Retrieval-Augmented Generation (RAG). Evaluations show it outperforms the base model and several commercial LLMs on domain-specific objective questions and performs competitively on subjective tasks. | True | NaN | True | 1.0 | NaN | Mini-CarbonGPT: Integration of supervised fine-tuning (SFT) using LoRA on the GLM-4-9B base model and Retrieval-Augmented Generation (RAG) using a custom knowledge base. | Evaluated using a custom dataset of 700 objective (single-choice) and 249 subjective (open-ended) questions across five carbon neutrality subfields. Metrics included accuracy for objective questions, and F1 score, BERT score, METEOR score, GPT-o1 scoring (accuracy, completeness, clarity), and keyword coverage for subjective questions. Compared against base GLM-4-9B, fine-tuned GLM, RAG-only GLM, and commercial models (GPT-4o, Gemini-1.5 Flash, Kimi, ERNIE Bot-3.5). | Mini-CarbonGPT achieved the highest average accuracy (80.57%) on objective questions, outperforming the base GLM-4-9B model (70.00%) and commercial models like GPT-4o (79.43%). For subjective questions, it showed improved accuracy and completeness in GPT-o1 evaluations and better keyword coverage compared to the base model, though commercial models generally led in automated metrics (BERT, F1) and perceived clarity. | NaN | NaN | NaN | NaN | Environmental Science / Policy / Energy / Economics | International | Supervised Fine-Tuning (SFT) data: ~50k general instructions (cleaned GPT4-Alpaca) + 5,382 professional instructions (extracted from UltraChat). Retrieval-Augmented Generation (RAG) Corpus: 6,096 carbon neutrality documents (from CNKI, Web of Science) + 60,000 Wikipedia pages. Evaluation Datasets: 700 objective + 249 subjective questions (from Studocu, Baidu Wenku, national graduate entrance exam materials). Primarily unstructured text data from mixed public/academic/proprietary sources. | Base model selection (GLM-4-9B), Parameter-Efficient Fine-Tuning (PEFT) via Low-Rank Adaptation (LoRA), INT4 quantization, two-phase SFT (general then domain-specific), Retrieval-Augmented Generation (RAG) implementation using FAISS vector store and paraphrase-multilingual-MiniLM-L12-v2 embeddings, dynamic paragraph-priority chunking, weighted fusion retrieval strategy. | The paper details training on standard hardware (2x 2080Ti GPUs) enabled by quantization and PEFT, suggesting feasibility for resource-constrained environments. Discusses potential future deployment strategies like knowledge distillation but does not state current deployment status. | False | False | NaN | NaN | High computational cost of LLMs, scarcity of large-scale domain-specific datasets for carbon neutrality, data imbalances across sub-disciplines, handling interdisciplinary and heterogeneous data, potential knowledge conflicts between fine-tuned model and RAG results, balancing information coverage and conciseness in RAG-generated answers, ensuring semantic accuracy beyond surface keyword matching, potential accuracy loss from quantization techniques (e.g., INT4). | General LLM risks such as hallucinations and biased outputs. Potential for inconsistent or inaccurate outputs due to conflicts between the model's internal knowledge (from fine-tuning) and externally retrieved information (via RAG). Accuracy loss due to model compression techniques like quantization. |
| r0sO2A6Lo0UJ.pdf | Google_Scholar | Artificial Intelligence and Legal Transparency: A Comparative Analysis between Public and Private Law | This paper analyzes the legal dimensions and challenges of artificial intelligence, focusing on transparency issues arising from the differences between public and private law. It discusses the regulatory landscape, particularly the EU AI Act, and the need for both legal branches to adapt to frame AI effectively. | True | NaN | True | 3.0 | Neutral | NaN | NaN | NaN | Difficulty in challenging unfair or discriminatory AI decisions due to lack of transparency and established legal recourse mechanisms; potential for bias in AI systems used for justice. | Developing AI tools (e.g., Q&A bots) to automate legal tasks and assist with filings; implementing robust legal frameworks (like the EU AI Act) emphasizing transparency, accountability, and appeal rights; updating existing laws. | Automating lawyer tasks; Assistance with legal document filing. | General public needing legal assistance but lacking resources for lawyers. | Public Law, Private Law, Administrative Law, Human Rights Law, Data Protection Law, Civil Law (Contracts, Liability), Commercial Law, Competition Law, Intellectual Property Law, Consumer Protection Law, International Law | European Union, United States, International | NaN | NaN | NaN | False | False | NaN | Insufficiency of current legal frameworks (e.g., copyright for generative AI); lack of transparency and accountability in AI decision-making; difficulty defining legal liability for AI harm; challenges adapting contract, commercial, competition law; potential for bias and digital divides. | Ensuring AI respects fundamental rights; protecting data privacy; achieving algorithmic transparency and accountability; preventing bias and discrimination; defining legal liability; adapting existing legal frameworks; regulating effectively; addressing market concentration. | Violation of fundamental rights (e.g., privacy via facial recognition); unfair/discriminatory decisions due to AI bias; lack of legal recourse against AI decisions; data protection violations; misuse of personal data; market monopolies; unfair competition; copyright infringement. |
| 8Yv6l4FgOwUJ.pdf | Google_Scholar | Generative AI – Uses and Abuses in Litigation | This paper discusses the increasing use of Generative AI (GenAI) in litigation, outlining potential benefits for tasks like drafting and eDiscovery, alongside significant risks such as inaccuracies and ethical breaches. It emphasizes the need for responsible use, adherence to emerging court guidelines like NSW's Practice Note SC Gen 23, and ongoing development of GenAI literacy among legal professionals. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | Lack of effective participation in formal dispute resolution processes by unrepresented parties. | GenAI can potentially enable more effective participation by unrepresented parties in litigation. | Participation in formal dispute resolution. | Unrepresented parties/litigants. | Litigation | New South Wales (Australia), with references to other jurisdictions (e.g., US). | NaN | NaN | NaN | False | False | NaN | Need for GenAI literacy among legal professionals; technical limitations of GenAI (accuracy, bias, reasoning); need for clear governance frameworks and ethical guidelines. | Ensuring accuracy, avoiding hallucinations and bias, maintaining data security and privacy, verifying AI-generated content, integrating GenAI responsibly into legal workflows, keeping up with rapid technological development and evolving court rules. | Generation of fake/inaccurate citations or legal summaries, fallacious arguments, inadequate fact-checking, prolix/incorrect drafting, court 'flooding' with AI documents, litigation delays, increased workloads and costs, failure of proceedings, reputational damage, professional sanctions (e.g., costs orders), misuse in preparing evidentiary materials, data security and privacy breaches. |
| 3nlNDsmskIAJ.pdf | Google_Scholar | Incorporating Generative Artificial Intelligence into the Practice of Law: Utilizing Generative AI within the Framework of the California Rules of Professional Conduct | The paper explores the potential applications of generative AI in legal practice, such as document drafting and research, while highlighting the significant ethical challenges under the California Rules of Professional Conduct. It emphasizes the need for lawyers to understand AI limitations like hallucinations and adhere to duties of competence, confidentiality, communication, candor, and supervision. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | AI generating inaccurate information ('hallucinations'); ensuring lawyer competence with AI; protecting client confidentiality; maintaining candor to courts regarding AI use; proper supervision of AI; over-reliance hindering critical analysis; potential for bias. | Maintain human oversight and verify AI output; understand AI limitations; use AI platforms with strong data privacy; anonymize client data; communicate AI use to clients; disclose AI use to courts as required; supervise AI diligently; ongoing education; establish clear policies. | NaN | NaN | General legal practice | California | NaN | NaN | NaN | False | False | NaN | Reliability of AI (hallucinations), need for lawyer competence and clear ethical guidelines for AI use, ongoing research into mitigating AI flaws. | Ensuring ethical compliance (competence, confidentiality, candor, supervision) when using generative AI, dealing with AI hallucinations/inaccuracies, verifying AI output, avoiding over-reliance. | Producing inaccurate legal or factual statements (hallucinations); violating client confidentiality; lawyer incompetence; misleading courts; facing sanctions or discipline; potential for bias in AI output; charging unconscionable fees due to inefficient AI use or lack of cost pass-through. |
| VxfIMJROhukJ.pdf | Google_Scholar | How effectively can ChatGPT-4 draft data transfer agreements for health research? | This paper evaluates the effectiveness of ChatGPT-4 in drafting specialized Data Transfer Agreements (DTAs) for health research using a two-stage prompting methodology. While ChatGPT-4 can generate a comprehensive outline and detailed clauses, the resulting DTA requires significant refinement by legal experts due to issues with clarity, precision, and data protection compliance. | True | Market | True | 2.0 | Neutral | Using ChatGPT-4 with a two-stage iterative prompting methodology (contract-level outline generation until saturation, followed by clause-level drafting) to generate Data Transfer Agreements (DTAs) for health research. | ChatGPT-4 was prompted iteratively (10 sessions until saturation) to generate DTA outlines. Based on the aggregated outline, it was prompted clause-by-clause to draft the full DTA. The generated DTA was then qualitatively analyzed for comprehensiveness (comparison with best practices identified by Swales et al., 2024), content quality (clarity, precision, redundancy, ambiguity, overlap), and alignment with data protection compliance standards. | ChatGPT-4 produced a comprehensive outline after iteration and a 6847-word DTA covering standard clauses. However, the content suffered from redundancies, ambiguous terminology (e.g., 'derivative work'), overlapping provisions, and lacked sufficient detail to fully meet data protection best practices regarding legal justification for transfer, data handling lifecycle specifics, data subject rights implementation, technical/organisational security measures, and cross-border transfer rules. | NaN | NaN | NaN | NaN | Contract Law, Data Protection Law, Health Law | International | NaN | Iterative prompting (contract-level outline saturation and clause-level generation). Comparative analysis against best practices (Swales et al., 2024). Qualitative content analysis. | NaN | True | True | The technique involves using ChatGPT-4, which is publicly available (with free tiers), following the prompting methodology detailed in the paper. | NaN | Inconsistency in AI's initial outline generation (necessitating iteration to avoid omissions); limitations in generating extensive documents in one session; achieving consistent quality in generated legal text (redundancy, ambiguity, overlap); ensuring sufficient specificity for compliance with detailed data protection requirements. | Risk of significant omissions if relying on a single AI-generated draft without iteration. Potential for embedded bias in AI outputs (though none observed in this study). Risks associated with lack of human oversight and professional accountability when using AI tools for legal drafting. |
| gzrmVfqby74J.pdf | Google_Scholar | Generative Artificial Intelligence and Article 6 of the European Convention on Human Rights: The Right to a Human Judge? | This paper examines the implications of using generative AI in judicial processes under Article 6 of the European Convention on Human Rights (ECHR), focusing on the right to a fair trial. It argues that interpreting Article 6 through the lens of human dignity implicitly supports the right to a human judge to safeguard against dehumanisation. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Unaffordability of legal advice, significant court backlogs causing delays, potential for AI-driven advice to exacerbate system strain without improving resolution, risks of dehumanisation and undermining fair trial rights (voice, neutrality, respect, trustworthiness) through AI. | Advocating for a human dignity-based interpretation of Article 6 ECHR to establish the right to a human judge. Proposing the use of AI to complement, not replace, human judges (e.g., automating non-judicial tasks, research assistance, bias identification), emphasizing transparency, explainability, ethical review, and potentially using AI in ADR with consent. | Right to a fair trial (Article 6 ECHR), access to courts, judicial efficiency, judicial decision-making, human dignity in legal processes. | NaN | Human Rights Law, Civil Procedure, Civil Justice | European Convention on Human Rights (ECHR) signatory states, European Union (mentions AI Act) | NaN | NaN | NaN | False | False | NaN | Lack of explicit recognition of a 'right to a human judge' in ECHR Article 6 interpretation. Need for equitable access to AI tools, better understanding of AI's cognitive impact on judicial work, balancing transparency with proprietary IP, defining adequate human oversight. Technical limitations in AI 'understanding', 'reasoning', bias, empathy, and explainability. | NaN | Dehumanisation (loss of individuality, lack of genuine voice), discrimination (algorithmic bias), erosion of public trust (errors, opacity), undermining procedural fairness (neutrality, respect, trustworthiness), inaccurate outputs (hallucinations), compromised judicial independence/impartiality (external influence, hidden bias), inadequate reasoning ('black box' problem), erosion of judicial accountability. |
| pnYx_0Zyq1oJ.pdf | Google_Scholar | The Potential for Jurisdictional Challenges to AI or LLM Training Datasets | This paper critiques the use of Large Language Models (LLMs) for Access to Justice (A2J), arguing that their training datasets pose significant jurisdictional challenges related to bias, sovereignty, and the rule of law. It proposes a conceptual framework of "information sovereignty" to ensure AI tools are jurisdictionally appropriate and truly serve A2J goals. | True | Idealistic | True | 3.0 | Negative | NaN | NaN | NaN | Systemic bias in LLMs due to training datasets not reflecting specific communities/jurisdictions; challenges to legal sovereignty and the rule of law from extra-jurisdictional data; failure to ensure quality and legal compliance of datasets; AI exacerbating existing inequalities (digital divide, cost); lack of transparency and accountability in AI decision-making. | Proposes a conceptual framework of "information sovereignty" with four tenets: Population (limiting training data to jurisdictional individuals), Territory (defining jurisdiction by practitioners/systems), Recognition (auditable outputs reflecting community practitioners), and Regulation of borders (immutable outputs). Emphasizes the need for jurisdictionally bounded training data and encoded procedural logic. | Procedural justice; Rule of law; Legal information provision; Document drafting; Use of AI by self-represented litigants. | Underserved litigants; Self-represented litigants; General public unable to afford legal services. | General Law; Constitutional Law; Procedural Law | International | NaN | NaN | NaN | False | False | NaN | LLMs lack nuance for legal technicalities and edge cases; difficulty ensuring datasets represent community norms; lack of accountability mechanisms for AI. | NaN | Systemic bias leading to unfair outcomes; undermining the rule of law; lack of transparency and accountability; inaccurate information and fabricated citations (hallucinations); exacerbating inequalities; declining public trust in the justice system; lawyers over-relying on flawed AI outputs; AI acting as a liability shield; denial of justice due to incorrect AI guidance. |
| bY8xyMfAAK0J.pdf | Google_Scholar | NAVIGATING THE CHALLENGES OF GENERATIVE AI | This paper analyzes ABA Formal Opinion 512 and other ethical guidelines for lawyers using generative AI. It details key obligations like competence, confidentiality, and candor to ensure responsible AI integration in legal practice. | True | Market | True | 2.0 | Neutral | Ethical guidelines for legal professionals using generative AI (based on ABA Formal Opinion 512 and similar state bar guidances) | NaN | NaN | AI-generated biased information, inaccurate 'hallucinated' citations in legal filings, and unintentional disclosure of confidential client information, all of which risk undermining the justice system. | Adherence to clear ethical guidelines (e.g., ABA Opinion 512), including understanding AI capabilities and limitations, independently verifying AI outputs, protecting client confidentiality, establishing firm policies and training, and transparent billing practices. | Ethical and competent use of AI by legal professionals to maintain the integrity and fairness of the justice system. | NaN | Legal ethics, Professional responsibility, General legal practice | United States (referencing ABA, Colorado, California, Florida, New Jersey, New York, Texas) | NaN | NaN | NaN | True | True | The discussed ABA Formal Opinion 512 is available online via a provided URL. | The need for continuous adaptation of ethical rules to evolving AI, maintaining lawyers' up-to-date knowledge, and addressing AI's inherent limitations (e.g., bias, lack of human nuance). | Understanding GenAI capabilities and limitations (hallucination, reliability, bias); verifying AI outputs; protecting client confidentiality with AI tools; communicating AI use to clients; ensuring candor to tribunals; establishing firm AI policies and training; adjusting fee structures for AI efficiencies; staying current with evolving AI technology and guidance. | AI hallucinations (e.g., fake citations); unreliable, inaccurate, or biased AI outputs; breaches of client confidentiality through AI tools; misleading tribunals with AI-generated content; lawyers facing sanctions for AI misuse. |
| 1B6lhnMG9xwJ.pdf | Google_Scholar | Analysis of the Digital Transformation of Legal Services and the Role of Policy Brokers in KOREA through the Advocacy Coalition Framework | This paper analyzes the nine-year conflict between the Korean Bar Association and the legal tech platform LawTalk using the Advocacy Coalition Framework (ACF). It examines how different advocacy coalitions, their belief systems, and the intervention of policy brokers shaped the policy outcomes regarding digital legal services in South Korea. | True | Idealistic | False | 2.0 | Positive | NaN | NaN | NaN | Resistance from traditional legal institutions (Bar Association), regulatory ambiguity for new platforms, conflicting interpretations of law (Attorney-at-Law Act, Fair Trade Act), concerns over ethics vs. accessibility, information asymmetry in the traditional market. | Mediation by neutral policy brokers (e.g., Ministry of Justice, FTC), legal clarification and rulings supporting innovative platforms, adapting policy frameworks to technological change, promoting dialogue between stakeholders. | Access to legal information and consultation via online platforms, regulation of LegalTech. | General public / consumers | General legal services, Attorney regulation, Competition law | South Korea | NaN | NaN | Commercial platform available to the public in South Korea, facing legal/regulatory challenges. | True | False | Operational commercial platform (LawTalk) in South Korea. | Need for updated legal frameworks to address LegalTech innovation, risk of stifling domestic innovation due to prolonged conflicts, potential dominance by foreign platforms if domestic innovation is hindered. | Legal challenges from established professional bodies (KBA), navigating regulatory uncertainty ('grey area'), resistance from the traditional legal profession. | Lowering of lawyer ethical standards, commercialization undermining judicial justice and public trust, stifling innovation due to protectionism, dominance by foreign platforms if domestic innovation is hindered, significant social costs from prolonged disputes. |
| ChatGPTAndAcademicIntegrityAnalyzingItsInfluenceOn.pdf | Google_Scholar | Chat GPT And Academic Integrity: Analyzing Its Influence On College Students' Study Practices And Performance | This paper investigates the impact of ChatGPT on the study habits, academic performance, and perceptions of academic integrity among college students at Banaras Hindu University, India. Using questionnaires and interviews, the study identifies benefits like improved understanding and efficiency, alongside significant concerns about accuracy, over-reliance, and academic dishonesty. | True | NaN | True | 2.0 | Neutral | ChatGPT | Mixed-methods approach: Questionnaire survey (N=100 students from Banaras Hindu University using ChatGPT for >= 1 semester) analyzed with descriptive statistics and chi-square tests; semi-structured interviews (N=15 students) analyzed using thematic analysis. | 80% reported positive grade changes (though impact varied). 77% reported better understanding of complex subjects. Major benefits included concept simplification and research aid. Significant concerns included information accuracy, lack of depth/referencing, technical limitations, and threats to academic integrity. | NaN | NaN | NaN | NaN | NaN | India | NaN | NaN | NaN | True | True | ChatGPT is available online, with both free and paid ('Plus') versions mentioned. | NaN | Inaccuracy/misleading information, insufficient detail/depth, lack of proper referencing, difficulty answering complex queries effectively, platform technical limitations (login issues, response limits in free version), lack of graphical/visual aids. | Threats to academic integrity (plagiarism, authenticity concerns), potential decline in critical thinking and problem-solving skills due to over-reliance, ethical issues regarding authorship and originality, potential for increasing educational inequalities, concerns over surveillance and data exploitation associated with AI in education. |
| FEWa_jU34vsJ.pdf | Google_Scholar | SCALE :Scaling up the Complexity for Advanced Language Model Evaluation | This paper introduces SCALE, a large-scale, multilingual (5 languages) benchmark designed to evaluate Large Language Models (LLMs) on complex legal tasks using Swiss court data, focusing on long documents, domain-specificity, multilinguality, and multitasking. The authors establish baselines by evaluating various open and closed LLMs, including newly pretrained Swiss legal models, revealing significant challenges and low performance, particularly on tasks like Court View Generation and Information Retrieval. | True | Market | True | 1.0 | Neutral | SCALE benchmark suite, including 7 datasets for various legal tasks (IR, CE, CP, LAP, JP, CVG, LDS) and 3 pretrained Swiss legal language models (Legal-Swiss-RoBERTa Base/Large, Legal-Swiss-LF Base). | Evaluation of baseline models (MiniLM, DistilmBERT, mDeBERTa-v3, XLM-R, X-MOD, SwissBERT, mT5, BLOOM, GPT-3.5, Claude-2, LLaMA-2, PaLM-2) on the SCALE benchmark tasks. Metrics included Macro F1 (hierarchically aggregated for classification), BERTScore, BLEU, METEOR, ROUGE (generation), NDCG, and Capped Recall@k (IR). Zero-shot evaluation for large closed models, fine-tuning for smaller open models. | The best overall aggregated Macro F1 score on the text classification tasks was 48.6 (XLM-R Large). Performance on IR and CVG was particularly low, highlighting the benchmark's difficulty. Even large models like GPT-4 underperformed fine-tuned models on some tasks (e.g., CVG). | NaN | NaN | NaN | NaN | Civil, public, criminal, social law; legislation covers diverse areas including public health, education, civil rights, energy, environment, infrastructure, visa regulations. | Switzerland (Federal and Cantonal) | Publicly available, anonymized Swiss court rulings (638K) and legislation (36K) scraped from Entscheidsuche.ch and fedlex.admin.ch, covering German, French, Italian, Romansh, English. Unstructured text data. Pretraining also used EUR-LEX data. Downstream task datasets derived from the same Swiss sources. | Data scraping and processing pipeline (parsing, regex extraction, metadata utilization) for dataset creation. Standard language model pretraining techniques (warm-start, new tokenizer, MLM objective). Standard NLP evaluation metrics and aggregation methods for benchmarking. | Public release of datasets, pretrained models, and code via Hugging Face and GitHub under CC BY-SA license. | True | True | Datasets, pretrained models (Legal-Swiss-RoBERTa/LF base/large), and code are available on Hugging Face and GitHub under a CC BY-SA license. | Significant performance gaps exist for current LLMs in handling long legal documents, domain-specific reasoning, multilinguality (especially within one jurisdiction), and complex multi-tasking. Need for better models, potentially larger legally pretrained generative models, and methods incorporating retrieval/tool use. Current models struggle with tasks like Court View Generation and Information Retrieval. | Resource limitations (compute for pretraining/evaluation). Data curation challenges (algorithmic label generation, quality control for large scraped corpus). Handling long document contexts (truncation required). Evaluating diverse multilingual, multitask performance fairly. | Job market impact for legal professionals. Inaccuracy/misinformation due to model limitations in the high-stakes legal domain. Failure to capture cultural/contextual nuances. Misuse for generating misleading legal content at scale. |
| dhPMuPLW7IIJ.pdf | Google_Scholar | Al Cannibalism and the Law | This paper discusses Large Language Models (LLMs) used in law, focusing on the problem of "AI cannibalism" where models are trained on AI-generated content. It argues this could lead to model degradation, increased hallucinations and bias, impacting lawyers' use of these tools and potentially stifling legal development. | True | Market | True | 3.0 | Negative | NaN | NaN | NaN | AI hallucinations leading to incorrect legal filings; amplification of existing societal biases (gender, racial, political) through AI; training data limitations (knowledge cut-offs, bias towards existing norms); AI cannibalism causing model degradation and increased misinformation; potential for AI overuse to stifle legal creativity and development. | The paper highlights the need for AI developers to address AI cannibalism and suggests careful human oversight, fact-checking, and awareness of bias as necessary strategies for lawyers using current LLMs. It notes research indicating the importance of incorporating sufficient 'fresh' human-generated data in training. | Functioning and limitations of LLMs; AI hallucinations in legal practice; AI bias in legal contexts; AI cannibalism; Impact of AI on legal development and practice. | NaN | General Legal Practice | United States | The paper discusses training data for existing LLMs (e.g., Common Crawl, books, news, web content) and the problem of future models training on mixtures of human-generated and AI-generated (synthetic) text. | NaN | NaN | False | False | NaN | Methods to mitigate AI cannibalism; ensuring AI doesn't stifle legal development or exacerbate biases; reliably distinguishing human vs. AI content for training data curation; agreed-upon evaluation metrics for generative models. | Ensuring data quality for training LLMs; preventing bias propagation; avoiding AI hallucinations; high cost and difficulty of training/fine-tuning LLMs; evaluating generative models effectively; weeding out AI-generated content from future training datasets. | Increased misinformation and AI hallucinations undermining LLM utility and leading to legal errors/sanctions; amplification of existing societal and legal biases; stifling legal creativity and innovation, leading to stagnation; potential disclosure of confidential client information; overall degradation of LLM capabilities due to recursive training on synthetic data (AI cannibalism). |
| VniR1rNFOEwJ.pdf | Google_Scholar | MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applications | This paper introduces MindLLM, a series of lightweight (1.3B, 3B parameters) bilingual (English/Chinese) LLMs trained from scratch, detailing their development, training strategies, and evaluation. It highlights their competitive performance against larger models, explores efficient instruction tuning using entropy-based filtering, and demonstrates applications in law and finance. | True | Market | True | 1.0 | Neutral | MindLLM (1.3B and 3B parameter bilingual lightweight LLMs), pre-training strategies (bilingual from scratch vs. monolingual then transfer), entropy-based instruction tuning filtering, SFT/COT fine-tuning for specific domains. | Standard benchmarks (MMLU, AGIEval, C-Eval, CMMLU), specific capability tests (math, reasoning, bilingualism), zero-shot/few-shot evaluation pre/post-instruction tuning, domain-specific tests (ChatGPT ranking for law, accuracy for finance sentiment). | MindLLMs match/outperform larger models on some benchmarks (e.g., MMLU, AGIEval). MindLLM-3B excels in math/bilingual tasks for its size. Entropy-filtered instruction tuning improves specific capabilities significantly. Domain-specific fine-tuning yields competitive results (MindLLM-Law outperforms Baichuan2-7B; MindLLM-1.3B with COT achieves 47.79% accuracy in finance task). | NaN | NaN | Legal consultation, General legal NLP tasks | NaN | General Law | China, USA (implied by English data/benchmarks) | Pre-training: Public (Pile, Wudao, CBooks) and web-crawled English/Chinese text (unstructured). Instruction Tuning: Mix of public NLP/human-written/generated/translated datasets (Chinese MingLi, English Tulu, Bilingual MOSS), specific public datasets (Wanjuan, LogiCoT). Domain Fine-tuning: Public legal datasets (LaW-GPT, DISC-LawLLM) + public general instruction data; Proprietary web-crawled financial news (EastMoney). | Empirical evaluation on benchmarks, ablation studies (data mix, curriculum, tuning data), development of data filtering strategy (entropy-based), domain-specific fine-tuning (SFT, COT), LLM-based evaluation (ChatGPT ranking with Elo). | NaN | False | False | NaN | NaN | High cost/resource requirements for LLMs, data processing complexity (quality, deduplication, mix ratio), training instability and catastrophic forgetting (especially with transfer learning), limited capacity of lightweight models affecting complex tasks and instruction tuning effectiveness, balancing multilingualism vs. capacity, robust domain-specific evaluation. | Generation of harmful/sensitive content, privacy violations (PII leakage). |
| paper-icon.pdf | Google_Scholar | A TEXT INTELLIGENCE-BASED APPROACH FOR AUTOMATIC GENERATION OF FAULT TREES IN NUCLEAR POWER PLANTS | This paper introduces NuLLM-FTG, a large language model fine-tuned on a custom textual fault tree dataset to automate fault tree analysis (FTA) for Nuclear Power Plants (NPPs). NuLLM-FTG demonstrated performance comparable to experts and superior to GPT-4 in some aspects, aiming to assist non-experts in the complex FTA process. | True | NaN | True | 1.0 | NaN | Nuclear Large Language Model Fault Tree Generator (NuLLM-FTG): A fine-tuned Baichuan 2-13B-Chat model using a novel textual fault tree representation, Supervised Fine-Tuning (SFT), and prompt engineering techniques (Fault Tree Chain of Thought - FTCoT, Role-Playing - RP, few-shot learning). | Quantitative evaluation via cosine similarity and conversation pattern alignment against baseline, GPT-3.5, GPT-4 across different few-shot settings. Qualitative evaluation using Delphi method with domain experts (single-blind and double-blind comparisons with GPT-4 on professionalism, completeness, satisfaction). Ablation studies on FTCoT and RP. Language corpus impact assessment (English vs. Chinese). Case study integration with Risk Spectrum software. | NuLLM-FTG significantly outperformed baseline, GPT-3.5, and GPT-4 on cosine similarity (~0.94 vs. max ~0.84 for GPT-4) and conversation pattern alignment. Qualitative results showed performance comparable to experts and often preferred over GPT-4, particularly in single-blind setup. FTCoT prompting showed a stronger impact than RP. English corpus training/testing yielded better similarity scores. | NaN | NaN | NaN | NaN | NaN | International | A curated dataset of over 1700 examples (fault trees represented textually) collected by volunteers from academic papers (e.g., CNKI) covering multiple domains including nuclear, aerospace, transportation. Used for Supervised Fine-Tuning; likely proprietary in its curated form. | Supervised Fine-Tuning (SFT), Novel textual data structure design for fault trees, Prompt Engineering (Few-shot learning, Fault Tree Chain of Thought - FTCoT, Role-Playing - RP). | Demonstrated via integration with Risk Spectrum software in a case study for quantitative risk assessment. | False | False | NaN | NaN | Designing an effective textual representation for complex fault tree structures, collecting and curating specialized training data, evaluating the 'black box' nature of LLMs, optimizing few-shot prompting strategies (observing a performance threshold), assessing the impact of specific prompting techniques (FTCoT, RP). | NaN |
| TYEifIONNHsJ.pdf | Google_Scholar | NaN | This document is a brochure for the Group Legal Services Association (GLSA) Annual Conference scheduled for March 30 - April 2, 2025, in Washington D.C. The conference focuses on Access to Justice, featuring CLE sessions (including on AI, ethics, and rural access), networking opportunities for attorneys, legal plans, and tech companies, and guest speakers involved in access to justice initiatives. | True | Market | False | NaN | Neutral | NaN | NaN | NaN | Lack of essential services, including legal services, in rural areas due to perceived unviability (low population density, limited income) for traditional for-profit models. | Developing innovative, sustainable business models tailored to rural dynamics; leveraging technological innovations; promoting legal plans as delivery mechanisms. | Access to legal services in rural areas; Legal service delivery models (legal plans); Technology and innovation in legal services; AI applications and ethics in law. | Rural communities in America. | General Legal Practice, Legal Service Delivery, Access to Justice, Intellectual Property, Bankruptcy, ERISA, Legal Ethics. | United States | NaN | NaN | NaN | False | False | NaN | Need for viable and sustainable business models for legal service delivery in underserved rural areas; Ongoing need for technological innovation and adoption in legal services. | NaN | Ethical risks associated with the use of AI in the legal industry. |
| FbgEwaRT2gcJ.pdf | Google_Scholar | How Privacy-Savvy Are Large Language Models? A Case Study on Compliance and Privacy Technical Review | This paper evaluates the performance of various large language models (LLMs) on privacy-related tasks such as information extraction, key point detection, and question answering using specific datasets. It introduces a Privacy Technical Review (PTR) framework and finds that while LLMs show promise, significant gaps remain in their ability to fully meet privacy compliance requirements. | True | Market | True | 2.0 | Neutral | Evaluation of various LLMs (BERT, GPT-3.5, GPT-4, GPT-4o, Mistral_7b, gemini-1.5-flash, moonshot_8k_v1, Doubao, Doubao-pro, ComBERT, etc.) for Privacy Information Extraction (PIE), Key Point Detection (KPD), and Question Answering (QA) within a proposed Privacy Technical Review (PTR) framework. | Benchmarking LLMs on PIE, KPD, and QA tasks using custom datasets derived from privacy policies and agreement texts. Metrics included Precision, Recall, F1-score (Macro/Averaged), ROUGE-L, Exact Match (EM), and Re-85. | For PIE, gemini-1.5-flash had the best F1 (99.8%). For KPD, GPT-4 had the best F1 (94.8%). For QA, Doubao had the best F1 (95.4%). Overall, modern LLMs significantly outperformed older models but showed variance across tasks. | Significant gaps persist in LLMs' ability to fully comply with evolving legal standards and technical privacy requirements. | Implementing a Privacy Technical Review (PTR) framework within the software development lifecycle; enhancing LLM capabilities; better integration of LLMs with legal and regulatory requirements. | Privacy compliance review, Privacy Information Extraction (PIE), Key Point Detection (KPD) in legal/regulatory text, Question Answering (QA) on privacy policies. | NaN | Data Protection Law, Privacy Law, Compliance | International | Evaluation datasets were used: 1) Privacy Information Extraction Dataset (approx. 8,800 sentences from privacy policies, BIOE tagged). 2) Legal and Regulatory Key Point Detection Dataset (10 key legal concepts, binary labels). 3) Domain-Specific Question Answering Dataset (approx. 2,300 passages from agreements + queries). Data originates from https://github.com/alipay/ComBERT, suggesting publicly available, domain-specific (legal/privacy) unstructured text. | NaN | NaN | False | False | NaN | LLMs' capability gaps in fully adhering to evolving legal standards and technical privacy requirements. | NaN | Implicit risks of LLMs failing privacy compliance checks, potentially leading to non-compliance with regulations (e.g., GDPR, CCPA) and inadequate protection of user data. Mentions risks like data leakage, model inversion, and membership inference from related works. |
| w_7u2VP-ra8J.pdf | Google_Scholar | WenyanGPT: A Large Language Model for Classical Chinese Tasks | This paper presents WenyanGPT, a large language model derived from LLaMA3-8B-Chinese through continued pre-training and instruction fine-tuning specifically for Classical Chinese processing. The authors also introduce WenyanBENCH, a benchmark for evaluation, demonstrating WenyanGPT's superior performance over existing models on various Classical Chinese tasks. | True | NaN | True | 1.0 | NaN | WenyanGPT: A large language model created by continued pre-training and instruction fine-tuning of LLaMA3-8B-Chinese on Classical Chinese data. | Evaluation performed using a newly developed benchmark, WenyanBENCH, covering six tasks: Punctuation, Part-of-speech tagging, Named Entity Recognition (NER), Translation, Word Explanation, and Reverse Dictionary. Metrics used include Precision, Recall, F1-Score, BLEU, and BERT-Score. | WenyanGPT significantly outperformed baseline models (incl. GPT-4o, Deepseek-V3) across all tasks on WenyanBENCH. For example, it achieved F1 > 91% in NER, F1 > 75% in Punctuation, and BLEU1 = 0.47 in Translation. | NaN | NaN | NaN | NaN | NaN | NaN | Continued pre-training on a proprietary ~16GB corpus aggregated from publicly available Classical Chinese text sources (e.g., Daizhige, Wenyanguji, GitHub). Instruction fine-tuning used ~1.85 million data points derived from corpora and LLM-assisted generation (mix of structured/unstructured data). | Continued pre-training, Supervised Fine-Tuning (SFT), Development of a domain-specific instruction data construction framework (manual design, LLM expansion, testing, filtering). | The model, benchmark dataset, and instruction fine-tuning data are publicly released via Hugging Face and GitHub. | True | True | Model available on Hugging Face (Wenyanmuc/WenyanGPT); benchmark (WenyanBENCH) and data (WenyanGPT) available on GitHub (Wenyanmuc). | NaN | General challenges identified: poor performance of existing models on Classical Chinese; lack of standardized evaluation benchmarks. Model limitations: potential subjectivity in evaluating tasks like poetry generation (not included); reliance on large instruction datasets; room for improvement with long texts and complex syntax. | NaN |
| r57zQs5yHEMJ.pdf | Google_Scholar | Harmonizing Innovation and Ethics: The Complex Landscape of Artificial Intelligence in Legal Practice | This paper critically examines the transformative potential of Artificial Intelligence (AI) in legal practice, highlighting opportunities like enhanced efficiency and access to justice. It primarily focuses on the complex ethical challenges, such as algorithmic bias, liability, and data confidentiality, advocating for a collaborative approach to develop robust ethical frameworks for AI's responsible integration into the legal system. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | High cost of legal services for individuals and small businesses; Algorithmic bias in AI potentially perpetuating or amplifying societal injustices against vulnerable groups; Lack of transparency and explainability in AI decision-making processes; Risks to data privacy and confidentiality of sensitive legal information. | Development and deployment of AI-powered tools (e.g., chatbots, virtual assistants) to provide low-cost basic legal guidance and document drafting; Collaborative creation of comprehensive ethical frameworks involving all stakeholders (legal professionals, technologists, ethicists, policymakers, public); Implementation of robust regulatory frameworks addressing AI liability, data security, and algorithmic transparency; Designing AI systems to be fair, non-discriminatory, and explainable with human oversight; Integrating AI education into legal curricula. | Providing low-cost basic legal guidance and information; Assisting with simple legal tasks like drafting basic documents; Supporting self-represented litigants in understanding legal processes and preparing for court; Reducing the overall cost of accessing legal services; Alleviating the justice gap for disadvantaged and minority groups. | Individuals and small businesses unable to afford traditional legal services; Vulnerable members of society; Disadvantaged groups and minorities; Pro se (self-represented) litigants. | General legal practice, Family law, Housing law, Employment law | International | NaN | NaN | NaN | False | False | NaN | Need for evolving ethical frameworks to keep pace with AI development; Ensuring true fairness and non-discrimination in AI by mitigating bias in data and algorithms; Achieving transparency and explainability for complex AI systems; Maintaining meaningful human oversight and control in AI-assisted legal decision-making; Adapting legal education and professional roles for an AI-integrated future; Establishing clear lines of liability for AI errors. | NaN | Algorithmic bias perpetuating or amplifying societal discrimination, particularly towards vulnerable groups; Lack of transparency in AI decision-making hindering due process, accountability, and public trust; Breaches of client data confidentiality and privileged legal information; AI systems providing incorrect legal advice or flawed legal documents, leading to adverse legal outcomes; Difficulty in assigning liability for errors made by 'black box' AI systems; Potential for job displacement or negative transformation of roles within the legal profession. |
| 7i_a2xpSHxAJ.pdf | Google_Scholar | The future of court’s procurators with the advent of artificial intelligence technologies | The paper analyzes the impact of artificial intelligence technologies on the traditional functions of legal professionals in Spain, particularly focusing on the potential obsolescence of the court's procurator ('Procurador de los Tribunales') role due to automation. It discusses how AI applications challenge the necessity of procurators and, to some extent, lawyers, while also noting risks like job displacement, bias, and lack of transparency. | True | Market | True | 3.0 | Negative | NaN | NaN | NaN | Complexity and delays in the traditional justice administration system. | AI-driven automation, communication platforms (e.g., Lexnet), predictive analytics, generative AI tools for increased efficiency and streamlining of justice administration. | Efficiency of court procedures, Role of legal professionals in the justice system | NaN | Procedural Law, Civil Law, Criminal Law | Spain | Large databases of judicial decisions and legal texts (e.g., for predictive analytics and LLMs); specifics not detailed. | NaN | NaN | True | False | Systems like Lexnet (Spain), Prometea (Argentina), Ross (US law firm), and commercial services like Jurimetry and broadly available tools like ChatGPT are presented as operational. | Lack of AI transparency ('black box' issue), potential for algorithmic bias, significant risk of job displacement for legal professionals, potential for increased social inequality. | Ensuring algorithmic transparency and explainability, preventing bias in AI systems, aligning AI use with fundamental rights (due process, right of defense), adapting legal professions to automation. | Job displacement for legal professionals, erosion of fundamental rights (due process, right to defense) due to AI opacity and bias, potential for discriminatory outcomes, increased social inequality. |
| pCYBrSdeFH8J.pdf | Google_Scholar | Design Novel Effective Method for Large Language Model Compression BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation | This paper introduces BiLD (Bi-directional Logits Difference), a novel loss function for knowledge distillation designed to compress large language models (LLMs) by filtering noise in logits and leveraging their internal ranking. Experimental results on 13 NLP datasets show BiLD outperforms existing distillation methods and supervised fine-tuning. | True | NaN | True | 1.0 | NaN | Bi-directional Logits Difference (BiLD) loss for task-specific LLM knowledge distillation. | Evaluated on 13 NLP datasets (SuperGLUE, Arc-C, Arc-E, HellaSwag, PIQA, WinoGrande) using BLOOM and Qwen1.5 models. Compared against supervised fine-tuning, vanilla KL loss, top-k KL loss, DKD, NKD, NormKD, and RKL, using metrics like accuracy, EM, F1, and overlap@k. | BiLD loss, using only top-8 logits, achieved the highest average accuracy across four distillation settings, outperforming SFT, vanilla KL, and five other methods. For instance, in Qwen-4B to 0.5B distillation, BiLD surpassed vanilla KL by 3.52% in average accuracy. | NaN | NaN | NaN | NaN | NaN | International | 13 publicly available NLP benchmark datasets (SuperGLUE (BoolQ, CB, COPA, MultiRC, ReCoRD, RTE, WiC, WSC) and Arc-C, Arc-E, HellaSwag, PIQA, WinoGrande) consisting of unstructured text, used collectively for SFT and distillation. | Qualitative exploration of LLM logits characteristics (long-tail distribution, ranking information importance) followed by the design of BiLD loss. Evaluation through quantitative experiments comparing against baseline methods. | NaN | True | True | Code available in an open-source repository (mentioned on page 18). | NaN | Requires access to teacher logits and shared vocabularies. Computational complexity increases with more top-k logits considered. Clipping long-tail logits results in some knowledge loss. Significant computational overhead and memory for training/distillation. | NaN |
| Iyi-fuvhE5gJ.pdf | Google_Scholar | AI in the Courts: How Worried Should We Be? | This paper presents a multi-expert discussion on the applications and implications of AI in the legal system and courts, addressing both potential benefits like enhanced access to justice and serious risks such as bias and misinformation. The authors emphasize the need for rigorous verification, transparency, and human oversight to harness AI responsibly in the legal field. | True | Idealistic | True | 3.0 | Positive | Generative AI (e.g., ChatGPT), Technology-Assisted Review (TAR), predictive policing tools, algorithmic risk assessment tools (e.g., COMPAS), online adjudication systems. | References external evaluations (e.g., ChatGPT on bar exam, empirical evidence for TAR); discusses concerns about lack of transparency and testability (e.g., COMPAS in Loomis case). | ChatGPT-4 passed the Uniform Bar Exam at the 90th percentile. Technology-Assisted Review (TAR) has been shown to substantially reduce e-discovery time, cost, and burden. | High cost of justice; potential for AI misuse by litigants; systemic bias in AI systems leading to discriminatory outcomes; lack of verifiable reliability and fairness in AI tools. | AI assistance for self-represented litigants; online adjudication systems; ensuring AI systems are valid, reliable, equitable, and unbiased through rigorous testing, transparency, auditing, and human oversight, particularly judicial gatekeeping. | Legal aid for self-represented litigants, online dispute resolution for minor cases, cost reduction in legal services, algorithmic bias mitigation. | Self-represented litigants, individuals in small claims/housing/traffic disputes, general public seeking affordable legal help. | Civil procedure (e-discovery, pleadings), criminal law (sentencing, recidivism risk), general legal practice, administrative law (adjudication). | Primarily United States, with comparative examples from UK, Colombia, China; insights are broadly applicable. | NaN | NaN | NaN | True | True | Discussed tools like ChatGPT (free version available from OpenAI) and commercial Technology-Assisted Review (TAR) software are generally accessible. | Technical: Development of trustworthy and verifiable Generative AI; robust methods for ensuring AI fairness, reliability, and transparency_ Societal/Legal: Consensus on defining 'algorithmic fairness'; comprehensive legal and ethical regulations for AI in law; ensuring due process with AI; enhancing digital literacy among legal professionals; fostering public trust. | NaN | Use of untested, invalid, or unreliable AI systems; function creep; discriminatory outcomes from biased AI; proliferation of misinformation and deepfakes; increased fraud; threats to personal privacy; AI errors ('hallucinations') in legal documents or judicial decisions; due process violations; erosion of trust in evidence; decline in essential legal skills due to over-reliance on AI. |
| oLraehfrATYJ.pdf | Google_Scholar | Scenario-based Sociotechnical Envisioning (SSE) The Guide Book | This paper introduces Scenario-Based Sociotechnical Envisioning (SSE), a method for anticipating the societal impacts of new AI technologies by collecting and evaluating diverse written scenarios. It details the SSE data collection and analysis approach, aiming to equip researchers and policymakers to mitigate risks and steer towards desirable technological futures. | True | Idealistic | False | 1.0 | Positive | Scenario-Based Sociotechnical Envisioning (SSE) | SSE has been applied and refined through studies focusing on generative AI in the news environment, general-purpose chatbots, and (in preparation) access to legal justice. Data collection involved workshops and surveys where participants wrote scenarios, which were then analyzed using qualitative thematic analysis and axial coding. | The application of SSE produces collections of scenarios and sociotechnical risk/impact classification frameworks. For example, its use in studies on generative AI in news and chatbots led to the development of such human-centered frameworks for risk and impact. | Risk of AI systems providing incorrect or harmful legal advice, thereby undermining access to justice. | Employing the Scenario-Based Sociotechnical Envisioning (SSE) method to proactively identify, understand, and develop mitigation strategies for negative outcomes, such as incorrect AI-generated legal advice. | The impact of generative AI on access to justice, particularly concerning the reliability and quality of AI-provided legal information and advice. | NaN | General legal advice/justice | International | Human-generated fictional scenarios written by diverse participants (e.g., experts, citizens, stakeholders) based on their perspectives, experiences, and knowledge. This data is unstructured text. | The SSE method is built upon principles of scenario planning, sociotechnical studies, and anticipatory governance. Its design involves structured scenario-writing tasks for participants (via workshops or surveys) followed by qualitative data analysis techniques like thematic analysis and axial coding. | The SSE method is disseminated as a guidebook, with full materials (including questionnaires and data from previous studies) made available open access via an OSF repository and through academic publications. | True | True | The guidebook for the SSE method and associated materials (questionnaires, data from previous studies) are available open access via an OSF link (https://osf.io/8sdgh/). | The inherent difficulty in systematically anticipating the diverse, complex, and often unpredictable sociotechnical impacts of emerging AI technologies on society, including on access to justice, which SSE aims to address. | Ensuring high-quality (creative, specific, believable, plausible) scenarios from participants; effectively filtering out AI-generated scenarios when human-derived insights are paramount; scaling data collection to capture diverse perspectives and achieve conceptual saturation; guiding participants to create plausible scenarios, especially in unfamiliar contexts, without overly constraining their creativity (e.g., regarding character roles). | For emerging AI technologies generally: diverse and unpredictable societal impacts, potential for human rights infringements, and severe or transformative implications for users. Specifically related to AI in access to justice: generation of wrong or misleading legal advice. Other exemplified risks include AI-driven misinformation in news leading to false accusations and arrests, job displacement, erosion of trust in information, and a decline in original human thought due to over-reliance on AI. |
| lIQ28MAsj1IJ.pdf | Google_Scholar | Private Ordering and Generative AI: What Can We Learn From Model Terms and Conditions? | This paper reports on a pilot empirical study analyzing the Terms and Conditions (T&C) and privacy policies of 13 generative AI providers in early 2023, focusing on copyright and data protection. It finds providers assign output ownership but shift all risks to users, mimicking platform moderation practices while avoiding platform obligations, highlighting a governance gap that existing regulations like the EU's DSA fail to address. | True | NaN | True | 2.0 | NaN | Qualitative comparative analysis of Terms and Conditions (T&C) and privacy policies of generative AI providers. | Manual collection and legal analysis of T&C, privacy policies, and related documents from a sample of 13 generative AI providers (T2T, T2I, T2A/V; varied sizes and origins) during January-March 2023. Focused analysis on clauses related to copyright, data protection, and dispute resolution. | Providers typically assign copyright ownership of outputs to users but retain extensive licenses and disclaim all liability, shifting risks (copyright infringement, privacy breaches) entirely onto users. Most implement notice-and-takedown procedures akin to platforms but are argued not to fit platform definitions (e.g., under DSA), thus avoiding obligations. Data protection rights were poorly addressed in early 2023, with some improvement observed by late 2023, though implementation remains basic. | NaN | NaN | NaN | NaN | Contract Law, Copyright Law, Data Protection Law, Internet Law, Platform Regulation, Consumer Law, AI Regulation | EU, US, China, UK, International | NaN | Qualitative empirical legal research involving comparative analysis of legal documents (T&C, privacy policies). Sampling aimed for representativeness across modalities (text, image, audio/video), provider size, and geographic origin. | Publication as a working paper and forthcoming book chapter. | False | False | NaN | Regulatory gap where generative AI models avoid platform obligations (like those in the EU DSA) despite controlling content generation and moderation. Insufficient user protection against unfair terms and opaque moderation. Lack of standardized terms and poor enforcement mechanisms for data protection rights. Need for research into B2B contract fairness and the impact of market concentration. | Difficulty obtaining B2B T&C due to commercial secrecy. The dynamic nature of T&C requires automated tracking for longitudinal analysis. Managing the complexity of a multi-dimensional sample (modality, size, origin). | Copyright infringement by model outputs. Violation of data protection rights (e.g., unlawful processing of training data, inability to exercise erasure or rectification rights). Generation and dissemination of illegal or harmful content (deepfakes, hate speech, disinformation, bias). Lack of transparency and fairness in content moderation and user sanctions. Unfair allocation of risk and liability to users via T&C. Imbalance of power between large providers and users/SMEs. |
| pXk4HgLmMQMJ.pdf | Google_Scholar | Ushering In a New Era of User Rights | This paper introduces and argues for the necessity of establishing a distinct concept of "user rights" in the digital age, separate from traditional consumer rights, due to the unique power dynamics created by platform enterprises. It highlights the profound negative impacts of unchecked platform power on civil, political, economic, social, and cultural rights, especially for vulnerable groups, and proposes national and international governance reforms centered on user rights protection. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Inadequacy of existing consumer rights frameworks; unchecked digital power of platforms leading to infringements on civil, political, economic, social, and cultural rights; manipulation via algorithms and data (information cocoons, amplification of harmful content); increased risks for vulnerable groups (children, workers); lagging legal and governance structures unable to hold global platforms accountable; power asymmetry between concentrated platforms and dispersed users. | Establish a distinct legal concept and framework for "user rights"; create new national and international governance systems regulating platform power (e.g., specialized legislation like EU's DMA/DSA, defining platforms' international legal status and obligations); enforce platform responsibility (unity of power and responsibility); ensure platform transparency and fairness (rules, algorithms); develop user rights protection organizations and public interest litigation mechanisms. | Platform governance, fundamental rights protection in the digital sphere, regulation of digital power, protection of vulnerable groups online (children, gig workers), information integrity and democracy, corporate accountability. | General users of digital platforms, with specific attention to vulnerable groups like children, women, the elderly, and gig economy workers (e.g., delivery riders). | Human Rights Law, Internet Law / Digital Law, Platform Governance, Consumer Law, Competition Law, Labor Law, International Law | International | NaN | NaN | NaN | False | False | NaN | Lack of theoretical clarity and recognition of "user rights"; inadequacy of current national and international legal frameworks for platform regulation; insufficient research on platform impacts (especially on vulnerable groups); need for transparency in platform operations (algorithms, DTA); lack of effective enforcement mechanisms for user rights. | NaN | Erosion of civil and political rights (manipulation, polarization); exacerbation of economic inequality and worker exploitation; harm to vulnerable groups online (harmful content, addiction, abuse); undermining of democratic processes; abuse of digital power by platforms; potential for state power corruption or capture by platforms. |
| reBnNJjAlrgJ.pdf | Google_Scholar | Authors in the age of language -generation AI: to be or not to be, that is… the question? | This paper discusses the rise of large language models like ChatGPT and their impact on academic writing. It primarily focuses on the debate surrounding whether AI tools should be credited as co-authors on scientific publications. | True | NaN | True | 3.0 | NaN | Discussion of Large Language Models (specifically ChatGPT) regarding their use in academic writing and the ethics of AI co-authorship. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | Mentions training on massive amounts of diverse text data, but specifics are not provided in the paper. | NaN | User-friendly web interface (referring to ChatGPT). | True | False | ChatGPT is described as readily accessible to the general public via OpenAI's platform. | NaN | Ethical considerations and lack of consensus/guidelines regarding AI co-authorship in academic publishing. | Ethical risks related to authorship attribution and academic integrity. |
| H75nWp8KzkAJ.pdf | Google_Scholar | Artificial Intelligence and Quality of Composition Verdicts in Indonesia: Lessons from New Zealand | NaN | True | NaN | False | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Indonesia, New Zealand | NaN | NaN | NaN | False | False | NaN | NaN | NaN | NaN |
| A_oLE1bQogYJ.pdf | Google_Scholar | The potential Legal Chat Bots have in the context of Access to Justice . | This thesis explores the potential of legal chatbots to enhance access to justice within the European Union, focusing on improving legal aid availability and reducing the length of proceedings. It analyses the advantages, such as cost reduction and efficiency, alongside significant challenges including algorithmic bias, ethical concerns, regulatory gaps, and the digital divide. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | High cost of legal services (lawyer fees, court costs), lengthy court proceedings, lack of legal knowledge among laypeople, complexity of the legal system, procedural hurdles, psychological stress of litigation, digital illiteracy/divide limiting access to tech solutions. | Utilizing legal chatbots to provide affordable/free legal information and basic advice, automate tasks like document drafting, potentially shorten proceedings through efficiency gains, enhance public legal knowledge and self-help capabilities, and using online dispute resolution platforms. | Legal aid, length of proceedings, effective remedy, self-representation support, legal information provision, cost reduction. | Financially disadvantaged individuals unable to afford legal representation, individuals lacking legal knowledge, general public facing barriers to civil justice. | Civil Justice (broadly), including consumer law, family law (divorce), employment law, housing law (tenant agreements), tort law, administrative law (parking tickets), data privacy (GDPR), immigration law. | European Union (primary focus, including ECHR, EU Charter, CJEU/ECtHR references, proposed EU AI Act), with examples from the Netherlands, China, Estonia, UK, US, Canada, Australia. | NaN | NaN | NaN | False | False | NaN | Lack of comprehensive regulatory frameworks for AI in the legal field (need for adaptation, e.g., via proposed EU AI Act), ensuring digital inclusivity for chatbot use, mitigating algorithmic bias, resolving ethical dilemmas (confidentiality, competence, supervision, IP), difficulty coding complex legal reasoning/nuance/human emotion, balancing consistency and flexibility in AI responses, potential lack of user understanding of chatbot limitations. | NaN | Providing inaccurate or misleading legal advice, algorithmic bias leading to unfair or discriminatory outcomes, digital exclusion of vulnerable populations, confidentiality breaches/data security risks, unauthorized practice of law, undermining legal certainty, erosion of trust in the justice system, ethical violations (duty of competence, supervision), difficulty in enforcing chatbot decisions or advice, potential for AI errors leading to significant harm (e.g., incorrect fines). |
| qsyjdYBfGrUJ.pdf | Google_Scholar | Judge AI: Assessing Large Language Models in Judicial Decision -Making | This paper evaluates OpenAI’s GPT-4o by replicating a prior factorial experiment conducted on human judges, focusing on a simulated international war crimes appeal where defendant sympathy and legal precedent were varied. The study finds that GPT-4o's decisions are strongly influenced by precedent but not by sympathy, aligning it more with student subjects than with professional judges, who were swayed by sympathy. | True | NaN | True | 2.0 | Neutral | Using GPT-4o to replicate a 2x2 factorial experiment on judicial decision-making, varying defendant sympathy and precedent strength in a simulated international war crimes case appeal. | Replication of Spamann and Klöhn (2016, 2024) experiments. GPT-4o decided a simulated war crimes appeal under four conditions (Sympathetic/P-Affirm, Sympathetic/P-Reverse, Unsympathetic/P-Affirm, Unsympathetic/P-Reverse) across 25 random seeds per condition (n=100 total). Performance compared to original human judge and student subject data using frequency of affirming, Boschloo two-sided exact test, OLS, Logit, and Exact Logistic regression models. Prompt engineering techniques were also tested. | GPT-4o was strongly affected by precedent but not by sympathy (p<0.01 for precedent, not significant for sympathy). Its performance was similar to students and opposite to professional judges, who were influenced by sympathy. Prompt engineering had little success in making GPT-4o act like human judges. | NaN | NaN | NaN | NaN | International Criminal Law | International Criminal Tribunal for the Former Yugoslavia (ICTY) | The paper used GPT-4o (May model: gpt-4o-2024-05-13), a closed, pre-trained large language model by OpenAI. The specific training data for GPT-4o is proprietary and not detailed in the paper. The experiment input data consisted of modified materials from a real ICTY case (Prosecutor v. Momčilo Perišić), including instructions, statement of agreed facts, prosecution/defense briefs, ICTY statute, and GPT-4o generated summaries of precedent and trial judgments. | Experimental replication. Case materials from Spamann and Klöhn (2016, 2024) were adapted for LLM input (e.g., summarization of lengthy documents due to token limits). Prompt engineering, including system prompts and varied user instructions, was used. Temperature set to 0.7, and seed numbers were used to generate multiple trials (n=100). | NaN | False | False | NaN | NaN | Adapting experimental materials for LLM input (e.g., token limits requiring summarization of lengthy legal documents). Ensuring replicability with a closed model (GPT-4o) and its seed feature's beta status. Prompt engineering difficulties in steering LLM behavior to emulate human judges, particularly regarding non-formalist reasoning (e.g., considering sympathy). LLMs' tendency towards formalism and potential affirmance bias. The 'deep unintelligibility' of LLMs making it hard to understand their decision-making process. | LLMs may perpetuate a naive or 'official story' understanding of law, lacking the nuanced, realist decision-making of experienced human judges. Difficulty in trusting AI judges if they operate like human judges by deciding realistically but reasoning formally (lack of transparency). Replacing human judges with formalist AIs could lead to outcomes not aligned with social needs or policy judgments, especially where law requires discretion. LLMs might resist prompts for 'unethical' or non-standard judicial behavior derived from their training. |
| Szk2wzoXOGwJ.pdf | Google_Scholar | Ethics 3.0—Attorney Responsibility in the Age of Generative AI | This paper examines the heightened ethical responsibilities for lawyers in the digital era, focusing on the implications of generative AI and the metaverse. It underscores the importance of technological competence, client confidentiality, data security measures, and truthful online communication, referencing ABA Model Rules and real-world examples of misuse. | True | Market | True | 3.0 | NaN | Generative AI (e.g., ChatGPT), Extractive AI, Metaverse | NaN | NaN | NaN | NaN | NaN | NaN | Legal ethics, professional responsibility, data privacy, cybersecurity, civil procedure (related to legal research and filings) | United States | NaN | NaN | NaN | True | True | The paper discusses publicly launched generative AI services like OpenAI's ChatGPT, which offers free access tiers, as well as commercial legal AI tools from companies like LexisNexis and Thomson Reuters (Casetext). | NaN | Lawyers using generative AI face challenges including: ensuring factual accuracy and avoiding 'hallucinations'; maintaining client confidentiality with third-party services; understanding the fundamental limitations of generative AI (content generation vs. factual retrieval); addressing potential biases in AI outputs; and ensuring robust contractual safeguards with AI vendors regarding data security. | Potential risks include: submitting fabricated legal precedents to courts; breaching client confidentiality via data input into AI or insecure platforms; inadvertent disclosure of sensitive data (e.g., metadata); misleading online communications violating advertising rules; and violating duties of candor and professional competence, potentially leading to sanctions or malpractice claims. |
| b496aCfAJecJ.pdf | Google_Scholar | Generative AI and Entrepreneurial Entry* | This study investigates how access to generative AI (GenAI), particularly following ChatGPT's release, influences entrepreneurial entry. Using a difference-in-differences analysis of Current Population Survey data, it reveals that increased GenAI exposure significantly boosts incorporated entrepreneurship in the STEM sector, primarily through an 'augmentation channel' where GenAI automates peripheral business tasks. | True | Market | True | 2.0 | NaN | Analysis of GenAI access's (via ChatGPT release) impact on entrepreneurial entry. GenAI itself, exemplified by ChatGPT, relies on Large Language Models. The study methodology also uses LLMs (Llama 3, GPT-4o) to construct its 'GenAI Exposure' measure from O*NET task data. | Quasi-experimental difference-in-differences (DID) design using Current Population Survey (CPS) data (2021Q2-2024Q2) for STEM individuals. Compared changes in incorporated self-employment rates before/after ChatGPT release for high vs. low GenAI exposure groups, controlling for various factors. The GenAI exposure measure was validated using Semrush data on ChatGPT website traffic. | GenAI access generated a 0.3 percentage point increase in the likelihood of launching an incorporated business for each 1 standard deviation increase in an individual’s GenAI Exposure (a 15% increase from an average 2 percentage point likelihood). The effect is primarily driven by the augmentation channel (automation of peripheral tasks). | NaN | NaN | NaN | NaN | NaN | United States | The 'GenAI Exposure' measure was constructed using the O*NET database (task descriptions for STEM occupations), with task automation potential classified by a Large Language Model (Llama 3, with robustness checks using GPT-4o/GPT-4o-mini). The main empirical analysis uses Current Population Survey (CPS) data and Semrush website traffic data. | Quasi-experimental difference-in-differences (DID) research design. Construction of a novel 'GenAI Exposure' metric using Large Language Models (LLMs like Llama 3) to classify occupational task exposure to GenAI based on O*NET data, aggregated to industry-level for the STEM workforce. | NaN | True | True | The studied GenAI tool, ChatGPT, is publicly available with basic features being free, enabling democratized access. | NaN | Establishing a causal link between GenAI access and entrepreneurial entry due to potential selection and omitted variable biases. Creating a robust and valid GenAI exposure measure; potential noise in LLM-coded exposure. | Potential for labor displacement as GenAI automates core tasks, possibly leading to necessity-driven entrepreneurship (the 'automation channel'), even if findings support augmentation for the studied cohort. This implies broader labor market disruptions. |
| 9vDU08JcYqsJ.pdf | Google_Scholar | The Use of Artificial Intelligence and the Professional Duties of German Lawyers | This paper examines how the use of AI, particularly large language models, by German lawyers interacts with their professional duties under German law (BRAO and BORA). It analyzes potential conflicts with duties of independence, confidentiality, and faithfulness, highlighting legal uncertainties and advising caution. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal Ethics / Professional Responsibility / Regulation of the Legal Profession | Germany | NaN | NaN | NaN | False | False | NaN | NaN | Ensuring client confidentiality when using AI systems, especially those transferring data externally; verifying the reliability and accuracy of AI outputs (risk of hallucination); acquiring sufficient technical competence to assess and use AI tools; navigating legal uncertainty regarding professional duties; needing informed client consent in certain scenarios. | Violation of professional duties (confidentiality, independence, faithfulness); facing professional sanctions (warnings, fines, expulsion) or criminal liability (e.g. Sec 203 StGB); incurring contractual or tort liability towards clients due to AI errors; violating data protection laws; reputational damage. |
| R9Rm5fVzOc0J.pdf | Google_Scholar | IMPACT OF DİGİTAL TRANSFORMATİON ON ADMİNİSTRATİVE LAW İN THE FİELD OF LEGAL SERVİCES | This paper examines the impact of digital transformation, including AI and blockchain, on administrative law and legal services, with a focus on Uzbekistan and international examples. It argues that such technologies can simplify legal processes, improve public administration, reduce corruption, and enhance transparency in citizen-state interactions. | True | Idealistic | False | 3.0 | Positive | Discussion of digital transformation, encompassing e-government services, artificial intelligence, and blockchain technologies, as applied to administrative law and legal services. | NaN | NaN | Problems arising during the implementation of digital technologies; potential malfunctioning of AI systems; errors and inaccuracies in complaint mechanisms leading to legal problems; ensuring legal security and citizens' rights. | Aligning legal norms with technological development; clearly defining the responsibilities of artificial intelligence; strengthening international cooperation; harmonizing technological innovations and legal norms. | Digitization of public services; e-government; reducing corruption in public administration; transparency in public administration; efficiency of legal processes; improving relations between citizens and the state. | Citizens in general, particularly in developing countries like Uzbekistan, in the context of accessing public and legal services. | Administrative law; Public administration; Legal services | Uzbekistan; International | NaN | NaN | NaN | True | True | Uzbekistan's 'Unitary interactive public services portal' is mentioned as an existing, operational e-government service, implying availability to citizens. | Need for legal norms to align with technological development; lack of clarity in defining AI responsibilities; insufficient international cooperation; need to harmonize technological innovations and legal norms for full implementation of digital transformation. | General problems arising during the implementation of digital technologies; ensuring legal security and the rights of citizens during the introduction and use of digital technologies. | Malfunctioning of artificial intelligence systems; errors and inaccuracies in complaint mechanisms creating legal problems. |
| PoJ8D2VsNwoJ.pdf | Google_Scholar | Incorporating AI impacts in BLS employment projections: occupational case studies | This paper explains the U.S. Bureau of Labor Statistics' (BLS) methodology for incorporating potential AI impacts into its 10-year employment projections. It presents case studies for the 2023–33 cycle, analyzing selected occupations in computer, legal, business/financial, and engineering fields, projecting varied AI impacts from job growth to decline depending on the occupation. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General Legal Practice | United States | NaN | NaN | NaN | False | False | NaN | NaN | Difficulty in projecting AI's future employment impacts due to uncertainties (timing, scale, regulation, adoption pace/cost); Insufficient data infrastructure hindering AI adoption by businesses. | Job displacement/reduced demand in certain occupations (e.g., claims adjusters, credit analysts, paralegals); Errors and biases (e.g., hallucinations) in AI output requiring human oversight. |
| Research_on_the_Application_of_Mediation_Model_Based_on_Deep_Learning_in_Dispute_Resolution.pdf | Google_Scholar | Research on the Application of Mediation Model Based on Deep Learning in Dispute Resolution | This paper proposes an Attention-based Long Short-Term Memory (LSTM) model to automate the classification of dispute mediation outcomes (success or failure) from case texts. The model aims to improve the efficiency and accuracy of the dispute resolution process compared to traditional methods. | True | Market | False | 1.0 | Positive | Attention-based LSTM model for classifying dispute mediation outcomes. | Evaluated on a proprietary dataset of 5,000 mediation cases (split 70% train, 15% validation, 15% test) using accuracy, recall, precision, and F1 score. Compared against Logistic Regression, SVM, CNN, and conventional LSTM. | Achieved 92.5% accuracy, 90.0% recall, 88.0% precision, and 89.0% F1 score on the validation set. Outperformed baseline models (Logistic Regression, SVM, CNN, LSTM) on the test set across all metrics. | Limitations of traditional dispute resolution: reliance on human resources, time costs, inefficiency, subjective judgments, slow processing, inconsistency, increasing case complexity and volume. | Using an Attention-based LSTM model to automate the classification of dispute mediation outcomes, aiming to improve efficiency, accuracy, objectivity, and consistency, and reduce mediator workload. | Dispute Mediation Outcome Prediction | NaN | Civil Law, Commercial Law | NaN | A proprietary dataset of 5,000 mediation cases (text, initially also images, audio) covering civil and commercial disputes, obtained from multiple courts, mediation agencies, and law firms. Includes party statements, evidence, mediation records, and outcomes (labelled as success/failure). | Supervised learning, data preprocessing (cleaning, annotation, feature extraction), model training (Attention-LSTM with cross-entropy loss and Adam optimizer), hyperparameter tuning, quantitative evaluation. | NaN | False | False | NaN | Technical gaps: handling textual ambiguity, understanding complex legal issues, integrating external legal knowledge. Societal gaps: N/A | Handling formalized legal text, integrating domain knowledge, model interpretability ('black box' problem), processing long texts and complex semantic relationships. | Potential misclassification of cases, lack of interpretability ('black box' problem). |
| g8yPDVQinAIJ.pdf | Google_Scholar | The Impact of Artificial Intelligence Technologies on the Justice Administration and on the Judicial Office Personnel | This paper reflects on the potential impacts of artificial intelligence applications, including predictive and generative AI, on the administration of justice. It specifically examines the effects on judicial office staff's tasks and the role of judges, highlighting significant risks to fundamental rights and procedural guarantees. | True | NaN | False | 3.0 | Negative | NaN | NaN | NaN | Lack of transparency in algorithms ('black box' problem), algorithmic bias leading to discrimination, threats to judicial independence and democratic legitimacy, potential erosion of due process (right to defense, reasoned judgments), risk of significant job losses in judicial administration. | Use AI solely as a complementary tool, ensuring human judges retain ultimate decision-making authority ('last word') to safeguard judicial independence, fundamental rights, and due process guarantees. | Automation of judicial procedures, Predictive justice (risk assessment), Judicial decision-making support versus replacement, Protection of procedural rights (due process, right to defense) and judicial independence, Impact on judicial office personnel. | NaN | Criminal law, Civil law, Procedural law, Judicial Administration | Spain, USA, Argentina, Estonia, China | NaN | NaN | NaN | False | False | NaN | Ensuring algorithmic transparency, explainability, and accountability; Mitigating bias and ensuring non-discrimination; Defining the appropriate role of AI versus human judges to protect fundamental rights and judicial independence; Addressing socio-economic impacts like job displacement in the legal sector. | NaN | Lack of algorithmic transparency ('black box') hindering challenges and defense; Algorithmic bias leading to discrimination; Violation of due process, right to defense, and right to reasoned judgments; Threat to judicial independence and democratic legitimacy if AI replaces judges; Significant job losses for judicial office staff and legal professionals; Difficulty assigning accountability for algorithmic errors; Potential misuse of predictive AI leading to undue rights restrictions ('Minority Report' scenario). |
| _FX_fECYb00J.pdf | Google_Scholar | The future of Cyber crime: AI and Emerging Technologies are creating a Cybercrime tsunami | This paper reviews how AI and emerging technologies like generative AI, blockchain, and IoT are driving an unprecedented increase in sophisticated cybercrime, creating a 'tsunami' of threats. It argues that law enforcement and regulators are ill-prepared and must radically adapt by raising awareness and leveraging these same technologies for detection, prevention, and prosecution. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | Lack of awareness among law enforcement and regulators about the cybercrime ecosystem; outdated operational models ill-suited for dynamic, real-time threats; challenges posed by anonymous actors (humans, algorithms, avatars); difficulties with global jurisdictions and jurisdictional arbitrage; the speed and scale of technological innovation outpacing legal and regulatory responses. | Radically rethinking law enforcement and regulatory operational models; increasing awareness and knowledge transfer (e.g., 'Cyberwise'); leveraging AI and emerging technologies for automation, anomaly detection, and real-time intervention; adopting innovative approaches like tech sprints and sandboxes (similar to FCA); enhancing coordination (national and international) through secure infrastructures and standards; proactive horizon scanning; potentially establishing rapid-response legal provisions and specialist international agencies. | NaN | NaN | Criminal Law (Cybercrime), Information Technology Law, Regulation (especially financial), International Law | International | NaN | NaN | NaN | False | False | NaN | Technical gaps in real-time anomaly detection, agent (human/AI/avatar) authentication, deepfake detection/mitigation, securing decentralized infrastructures (Web3, IoT). Societal/Regulatory gaps include widespread lack of awareness, need for updated legal frameworks for AI/digital agents, ensuring AI alignment with human values, addressing ethical concerns (bias, fairness), establishing effective international coordination, and managing the risks of superintelligence. | Addressing the dynamic nature and rapid pace of emerging technologies; dealing with anonymous, global actors; shifting from retrospective analysis to real-time intervention; managing massive data volumes; ensuring ethical use of AI in law enforcement; overcoming lack of awareness and expertise; fostering cross-disciplinary collaboration (AI developers, cybersecurity, law enforcement). | AI-generated crimeware and enhanced social engineering; sophisticated deepfakes for fraud, impersonation, and misinformation; increased ransomware and denial-of-service attacks, especially on critical infrastructure; algorithmic manipulation of markets and public opinion; misuse of AI for surveillance and social control (e.g., predictive policing bias); data breaches; AI hallucinations leading to false accusations; potential for 'feral' or uncontrollable AI; digital addiction fueled by AI; jurisdictional arbitrage; erosion of privacy and trust. |
| deDSwE3z9PMJ.pdf | Google_Scholar | LLM4Causal: Democratized Causal Tools for Everyone via Large Language Model | This paper introduces LLM4Causal, a large language model fine-tuned to interpret user requests for causal analysis on tabular data, execute appropriate causal tools, and explain the numerical results in simple language. The authors also propose a data generation pipeline and two benchmark datasets (Causal-Retrieval-Bench and Causal-Interpret-Bench) used for fine-tuning and evaluation. | True | Idealistic | True | 1.0 | Positive | LLM4Causal: A fine-tuned LLM (Llama 2 base) designed for end-to-end causal analysis workflow, including natural language query interpretation (task classification, entity extraction), selecting/executing external causal analysis tools (from libraries like CausalML, CausalDM, causal-learn), and generating natural language interpretations of the results. Uses custom fine-tuning datasets (Causal-Retrieval-Bench, Causal-Interpret-Bench) created via LLM generation and human annotation. | Evaluated end-to-end on synthetic datasets generated for five causal tasks (CGL, ATE, HTE, MA, OPO) using Pass Rate, Relevance Rate, and Win Rate metrics. Ablation studies evaluated performance on causal entity extraction (Step 1, accuracy metric) and result interpretation (Step 3, human evaluation based on hallucination, incompleteness, non-fluency rubrics) against GPT-4-turbo. | LLM4Causal significantly outperformed GPT-4. The LLM4Causal-Mixed variant achieved an average end-to-end Win Rate of 80.6% (compared to very low rates for GPT-4), 98% overall accuracy in Step 1 entity extraction (vs. 77% for GPT-4), and comparable or better performance in Step 3 interpretation based on human evaluation rubrics. | The complexity of causal inference methods, the need for specialized knowledge to use existing tools, and the difficulty for non-experts to interpret quantitative results from these tools, hindering broader access. | An end-to-end system (LLM4Causal) that uses a fine-tuned LLM to automate causal analysis: understanding user queries in natural language, applying appropriate causal algorithms to user data, and explaining the findings accessibly. | Democratization of causal decision-making tools, specifically targeting tasks like Causal Graph Learning (CGL), Average Treatment Effect Estimation (ATE), Heterogeneous Treatment Effect Estimation (HTE), Mediation Effect Analysis (MA), and Off-Policy Optimization (OPO). | General audiences / everyone lacking specialized expertise in causal inference. | NaN | International | Two custom instruction-tuning datasets created for the paper: Causal-Retrieval-Bench (causal questions paired with structured JSON representations) and Causal-Interpret-Bench (context including query, task, method, numerical output paired with human-revised natural language interpretations). Data was generated using a combination of LLM (GPT-4) prompting and human/expert annotation; it is synthetic, domain-specific (causal inference), and includes structured elements. | Definition of causal tasks, design of a three-stage framework (interpret, execute Ttools, interpret results), development of a data generation pipeline (LLM prompting + human annotation), fine-tuning a pre-trained LLM (Llama 2) using Parameter-Efficient Fine-Tuning (LoRA), integration with existing causal libraries. | NaN | False | False | NaN | Need to extend support to more causal tasks/methods, potential for integrating LLM's internal knowledge with tool use, lack of interactive capabilities for user feedback and guidance. | Existing LLMs struggle with specialized causal tasks (hallucination, confusion with correlation, lack of end-to-end capability, outdated knowledge). Creating high-quality, diverse, and accurate fine-tuning data for these specialized tasks required a complex generation pipeline with human oversight. Efficiently fine-tuning large models (addressed via LoRA). | Potential for inaccurate causal inference leading to poor decisions. Risk of model hallucination or incomplete/misleading interpretations misguiding users. General risks associated with democratizing powerful analytical tools without ensuring user understanding or safeguards against misuse. |
| 309XxeqZV9EJ.pdf | Google_Scholar | Using Artificial Intelligence to Increase Access to Justice | This PhD thesis investigates how Artificial Intelligence (AI) can improve access to justice for laypeople facing legal issues. It proposes and details the 'JusticeBot' methodology, a hybrid rule-based and case-based reasoning approach implemented as an augmented intelligence tool to provide users with relevant legal information and similar case precedents. | True | Idealistic | False | 1.0 | Positive | JusticeBot methodology: A hybrid rule-based/case-based reasoning approach combined with an augmented intelligence tool, supported by the JusticeCreator interface for building tools. | Public deployment and use of JusticeBot TAL (landlord-tenant disputes) with over 17k uses; user survey (N not specified, 86% recommendation rate); analysis of usage analytics (time spent, pathways clicked). | JusticeBot TAL was used over 17k times, and 86% of survey respondents would recommend the system. | Cost, complexity, time consumption, and emotional difficulty of the legal system for laypeople; lack of legal knowledge and awareness of rights/solutions; difficulties for self-represented litigants. | Develop AI-powered augmented intelligence tools (like JusticeBot) that simplify legal information access for laypeople through guided questions, providing tailored information and relevant case law examples. | General everyday legal problems (high-volume, low-intensity), specifically landlord-tenant disputes in the case study. Potentially also consumer issues, debt, employment. | Laypeople / average citizens without legal training facing everyday legal problems. | Housing law (Landlord-Tenant), potentially Consumer law, Debt law, Employment law, Administrative law. | Québec, Canada | Structured legal knowledge (rules, criteria) encoded by experts using JusticeCreator; abstracted case data (reasoning paths, outcomes) derived from analyzing previous court decisions (e.g., 10k TAL decisions for the case study). Primarily processed legal texts (statutes, case law). | Literature review (AI&Law, Access to Justice, HCI), user-centered design (focus on laypeople), prototyping (FactorBot, JusticeBot), iterative development (incorporating feedback), case study evaluation, hybrid AI approach (rule-based + case-based reasoning). | Public website deployment (justicebot.ca) for the JusticeBot TAL tool; collaboration with relevant legal institutions (TAL, Legal Aid) for promotion/support. | True | False | The JusticeBot TAL tool for landlord-tenant disputes in Québec is available via a public website: https://justicebot.ca. | Need for expansion to more legal/administrative areas; improving user interaction (e.g., NLP); enhancing evidence handling; integration with ODR; reducing knowledge encoding effort; ensuring information accuracy and updates; addressing potential biases; managing user expectations; ensuring equitable technology access. | Encoding complex legal knowledge (including vague concepts and case law); designing intuitive interfaces for laypeople; matching user input to relevant rules/cases; evaluating tool effectiveness and user satisfaction; maintaining and updating the knowledge base. | Over-reliance on the tool by users; misinterpretation of provided information; potential for encoded biases in rules or case data; system failure on complex, novel, or edge cases; potential perception of providing legal advice rather than information. |
| 15NbsabryQwJ.pdf | Google_Scholar | Artificial Intelligence (AI) and the Practice of Law | This article provides an overview of Artificial Intelligence (AI) applications in the legal profession, discussing potential benefits such as increased efficiency and access to justice, alongside significant challenges like accuracy, bias, confidentiality, and ethical considerations. It calls for lawyers, courts, rules committees, and ethics bodies to understand AI technology, evaluate its risks, ensure human oversight, and consider necessary regulatory updates. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | High cost of legal services (implied). For AI in A2J: Risk of inaccurate or biased AI output harming pro se litigants or clients of pro bono services; need for human vetting and oversight; potential for AI misuse (e.g., unauthorized practice of law); confidentiality concerns with AI platforms. | Use of AI tools to automate tasks (legal research, document review, form completion), potentially reducing costs and increasing efficiency for pro bono providers and legal aid organizations. Exploration of Online Dispute Resolution (ODR) potentially enhanced by AI for small claims. Emphasizes lawyer supervision, vetting AI output for accuracy and bias, and maintaining confidentiality. | Cost reduction in legal services, automation of legal tasks (form completion, research, review), legal aid/pro bono service delivery, online dispute resolution (ODR) for small claims. | Pro se litigants, individuals unable to afford attorneys (general population needing legal aid/pro bono services). | Multiple fields including Litigation (eDiscovery, evidence, motions), Criminal Law (bail, sentencing, innocence projects, law enforcement), Intellectual Property (copyright, patents), Employment Law (hiring, discrimination), Contract Law (review, management), Healthcare Law (diagnosis, privacy), Immigration Law (form completion), ADR (mediation, arbitration, ODR), Corporate Law (due diligence). | Primarily US, with references to International (EU, Canada, Colombia, India). | NaN | NaN | NaN | True | False | The paper discusses various types of AI tools, some of which are commercially available (e.g., ChatGPT, Westlaw Precision, Lexis+, Clearbrief, eDiscovery platforms) or under development by firms (e.g., LAER.AI). Some have free versions (e.g., ChatGPT), while others are paid subscription services or proprietary. | Need for clear regulations and ethical guidelines for AI use in law; methods to mitigate bias and ensure fairness; improved accuracy, reliability, and explainability ('black box' problem) of AI; enhanced education/training for legal professionals and students; frameworks for liability regarding AI errors; reliable methods for authenticating AI-generated evidence (esp. deepfakes); ensuring AI use upholds due process. | Ensuring accuracy and avoiding 'hallucinations'; mitigating bias; maintaining client confidentiality and data security; need for human oversight and validation; lack of transparency/explainability; cost; ensuring ethical compliance (competence, supervision); integration into workflows; potential 'function creep'; authenticating AI evidence (deepfakes); need for specialized skills; navigating evolving regulations; cybersecurity. | Inaccurate legal filings leading to sanctions; violation of client confidentiality; perpetuation of societal biases (hiring, sentencing); use of deepfakes to mislead; AI-powered cybersecurity threats (scams, breaches); unauthorized practice of law; erosion of due process/transparency in AI adjudication; misinformation; potential job displacement; financial fraud via AI voice synthesis; violation of privacy laws (HIPAA, GDPR). |
| lxdPlzm8HF8J.pdf | Google_Scholar | Artificial Intelligence Cannibalism and the Law | This paper discusses the concept of "AI cannibalism," where future large language models (LLMs) trained on increasing amounts of AI-generated content may degrade in quality, coherence, and accuracy. It explores the specific risks this poses for the legal profession, including increased hallucinations, exacerbated bias, and potential stagnation of legal development. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General Legal Practice, Legal Research, Legal Writing, Appellate Advocacy | US | NaN | NaN | NaN | False | False | NaN | NaN | The challenge of maintaining LLM quality and avoiding model collapse when training data increasingly includes AI-generated (synthetic) content. Difficulty distinguishing human vs AI-generated content for curation. Need for massive datasets for LLM improvement potentially conflicting with the need to exclude synthetic data. Lack of agreed-upon evaluation metrics for generative models beyond technical error rates. | AI hallucinations leading to incorrect legal citations and professional sanctions for lawyers. Amplification and perpetuation of existing societal biases through biased training data and outputs. Stagnation in the development of law due to LLMs favouring existing legal precedent and potentially hindering novel legal arguments. Undermining lawyers' own creativity and critical thinking processes if over-relied upon. Potential disclosure of confidential client information when using external AI tools. |
| 5optiAllNawJ.pdf | Google_Scholar | The Impact of Artificial Intelligence on Access to Justice: Predictive Analytics and the Legal Services Market | This paper examines how developers of predictive analytics market their software regarding access to justice, analyzing claims about time savings, improved access, clarity, and certainty. It concludes that while these claims have merit, the economic realities of the legal services market limit the technology's actual positive impact on access to justice. | True | Idealistic | False | 2.0 | Negative | Predictive analytics software for legal outcome prediction (e.g., Blue J Legal, LexMachina). | Qualitative documentary analysis of company websites and social media (blogs, webinars, promotional materials) to assess marketing claims regarding access to justice. The paper mentions company claims about prediction accuracy (e.g., Blue J Tax 90%) but does not independently test the software. | Companies claim time savings, improved access to law, clarity, and certainty. However, the paper concludes these claimed benefits are unlikely to translate into significant access to justice improvements for individuals due to market factors (tools target firms/corporations, cost doesn't trickle down), required legal capability, and technology limitations (novelty handling, instrumentalism). | High cost of legal services; limited availability/scope of legal aid; legal system complexity; difficulty understanding rights/procedures; unmet legal needs causing negative life impacts; inequality between individual litigants and well-resourced entities; lack of legal capability. | The paper critiques predictive analytics as a solution in its current deployment model and mentions university projects (MyOpenCourt, JusticeBot) with limitations. It does not propose specific new high-level solutions but implicitly points towards market reforms and enhancing legal capability alongside technology. | Affordability of legal services; availability of legal services; legal capability; impact of technology on legal practice; fairness and equality in the justice system. | General public facing cost and complexity barriers in the legal system, particularly individuals contrasted with corporations/large firms. | Civil Law (broadly), with specific examples including Tax Law, Labour and Employment Law, Intellectual Property Law. | Canada, United States (primarily), with references to UK. | Large datasets of case law (implied to be court decisions). Source details (public vs proprietary) not specified, but limitations regarding unreported decisions and non-digitized evidence are noted. | Machine learning (statistical analysis of case law data), validation using test datasets. Specific software development methodologies are not described. | Commercial marketing and sales targeted at legal professionals (law firms, corporate counsel), accountants, and HR professionals through company websites, promotional materials, and direct outreach. Subscription-based model implied. | True | False | Commercial subscription-based software (Blue J Legal, LexMachina) marketed to legal professionals and corporations. Some limited, university-developed tools (MyOpenCourt, JusticeBot) are mentioned as publicly accessible. | Technical: Data limitations (unreported cases), inability to handle novelty/reason by analogy, algorithm opacity, potential bias. Societal: Lack of cost pass-through to clients, high technology cost, need for user legal capability, focus of commercial tools on market advantage, potential to stifle legal development. | Ensuring accuracy, avoiding bias from training data, algorithm opacity ('black box'), high cost of development and maintenance, limitations of data availability. | Lawyer over-reliance leading to competency issues; screening out novel/difficult cases; reinforcing historical biases; lack of transparency; hindering legal development through instrumentalism; potential increase in overhead costs; widening justice gap between resourced/unresourced parties; misinterpretation of probabilities. |
| uNE_TxZM_g0J.pdf | Google_Scholar | Enhancing Judicial Efficiency and Access to Justice Using AI | This study explores integrating AI into Indiana's legal system to enhance judicial productivity and access to justice. Using survey data from over 100 judges, the research applies NLP and Azure Language AI to identify concerns, informing the development of an AI awareness packet, an integration roadmap, and a comparative analysis of AI-generated content detection tools. | True | Idealistic | True | 1.0 | Positive | Application of NLP (Azure Language AI for sentiment analysis and key-phrase extraction) to judicial survey data, qualitative interviews with judges, and process mining. This informed the development of an AI awareness packet, an AI integration roadmap, and a comparative analysis of AI-generated content detection tools. | For the comparative analysis of AI content detection tools, publicly available benchmark datasets featuring AI-generated images, deepfakes, synthetic audio, and other artificial media were utilized to evaluate tool performance metrics, including precision and efficiency. The AI awareness packet was integrated into the IOCS learning management system. | An AI awareness packet was successfully integrated into the Indiana Office of Court Services (IOCS) learning management system. Pilot programs for AI-enhanced workflows were recommended to IOCS, and a proposal packet comparing multi-modal synthetic media detection tools was provided to IOCS leadership. | Judges' security concerns regarding AI tools, lack of knowledge about AI applications and their specific use-cases, and difficulty distinguishing between different types of AI tools. Broader issues include AI bias, transparency, accountability, and the unreliability of current AI-generated content detection methods against sophisticated attacks. | Development of an AI awareness packet to educate judges on AI concepts, tools, and ethical considerations. Creation of an AI integration roadmap suggesting AI applications for specific judicial workflows like document review, calendar management, and court transcriptions. Provision of a comparative analysis of available tools for detecting AI-generated or -altered media. | Judicial efficiency, AI literacy for judges, identification of AI-generated evidence, AI integration into court workflows, ethical AI adoption in the judiciary. | General public / litigants in Indiana (as indirect beneficiaries of improved access to justice and judicial efficiency). | Criminal law, tax law, mental health law, family law, misdemeanor cases, appellate procedure, general court administration. | Indiana (US) | Proprietary survey data from over 100 Indiana judges (quantitative and open-ended responses) and qualitative interview transcripts from 12 judges were used for NLP analysis. Publicly available benchmark datasets of AI-generated content were used for evaluating detection tools. | Survey design and administration, qualitative data collection (interviews, open-ended questions), NLP (sentiment analysis, key-phrase extraction using Azure Language AI), process mining, thematic analysis, comparative market analysis of existing tools, and literature review. | The AI awareness packet was integrated into the Indiana Office of Court Services (IOCS) Learning Management System. Recommendations for pilot programs and a proposal for AI detection tools were submitted to IOCS for consideration and potential implementation. | False | False | NaN | Limited empirical research on AI's impact on judicial bias and case outcomes. Current AI text detection methods are not robust against paraphrasing/spoofing attacks. Real-world deepfake detection requires more scalable and computationally lighter models. A general need for continuous research and adaptation of judicial AI policies. | Balancing efficiency gains from AI with accountability and the protection of sensitive court data. Addressing judicial skepticism and lack of familiarity with AI tools. Ensuring ethical AI integration within the legal framework and maintaining data security. Identifying and selecting appropriate AI tools for specific judicial needs. | Mis D_identification or failure to identify AI-generated/altered evidence, potentially undermining justice. Proliferation of deepfakes and synthetic media in legal proceedings. Inherent biases in AI models, lack of transparency, and accountability issues. Security vulnerabilities related to sharing sensitive court data with AI systems, and AI feedback loops impacting data integrity. |
| uVSVKWt3LiMJ.pdf | Google_Scholar | UNLOCKING LEGAL KNOWLEDGE WITH MULTI -LAYERED EMBEDDING -BASED RETRIEVAL | This paper proposes a multi-layered embedding-based retrieval method for legal and legislative texts, creating embeddings at various granularity levels to capture their hierarchical structure and semantic nuances. The method, demonstrated with the Brazilian Constitution, aims to enhance Retrieval Augmented Generation (RAG) systems for more accurate and contextually relevant legal information retrieval. | True | Market | True | 1.0 | Positive | Multi-layered embedding-based retrieval for legal texts, integrated with Retrieval Augmented Generation (RAG). | Comparative analysis against a traditional flat chunking approach using the Brazilian Constitution. Eight queries were used, and retrieval results (chunk relevance, similarity scores using text-embedding-3-large, token counts) were analyzed. Embeddings were visualized using PACMAP for dimensionality reduction and Plotly. The gpt-4-turbo-preview model was used for response generation. | The multi-layered approach yielded a higher proportion of essential chunks (37.86%) compared to the flat embedding method (16.39%), and a lower proportion of unnecessary chunks (58.25% vs 75.41%). The multi-layered approach also showed more semantically consistent chunks aligned with user queries. | The increasing volume and complexity of legal corpora; traditional keyword-based search methods failing to capture legal nuances, semantic content, and intricate relationships within hierarchical legal documents. | A multi-layered embedding-based retrieval method that captures the semantic content and inherent hierarchical structure of legal texts at varying levels of granularity. This allows RAG systems to provide more accurate and context-specific responses and enables queries in plain language. | Access to information on constitutional rights (e.g., foundations of the republic, social function of property, attributes of the vote, tax revenue distribution, rights of children and teenagers, jury rights, right to association, legal assistance for those with insufficient funds). | Individuals with insufficient funds (specifically regarding legal assistance information), General public (by making legal knowledge more understandable). | Constitutional Law, Legislative texts. | Brazil (primary focus on Brazilian Constitution and legislative structure). The paper suggests applicability to other civil and common law systems. | The text of the Brazilian Constitution was used as the corpus for creating and testing embeddings. The embedding model used was OpenAI's 'text-embedding-3-large', pre-trained on general text data. | Conceptual design of a multi-layered chunking strategy based on the inherent hierarchy of legal texts. Empirical comparison with a baseline flat chunking method using quantitative (similarity scores, token counts, relevance classification) and qualitative (response evaluation) metrics. | NaN | False | False | NaN | Representing inter-article relationships (e.g., cross-references), incorporating a temporal dimension for legal text evolution, and further investigation into optimal vector dimensions for embeddings. | Handling the complexity, hierarchical structure, and semantic nuances of legal texts; overcoming limitations of traditional search and flat chunking methods (e.g., overlooking intrinsic hierarchy); managing semantic overload in dense legal articles (like Article 5 of the Brazilian Constitution). | NaN |
| -e-mbKxBPzcJ.pdf | Google_Scholar | Exploring a GPT-based Large Language Model for Variable Autonomy in a VR-based Human-Robot Teaming Simulation | The paper introduces a VR simulation framework where users interact with multiple GPT-4 powered robot agents via natural language to explore human-robot teaming and variable autonomy. A user study suggests users have preconceived notions about robot communication and often underutilize the LLM's capabilities, although some benefit from more natural dialogue. | True | NaN | True | 1.0 | NaN | A VR-based simulation framework (Unity) enabling natural language interaction with multiple simulated robot agents, each controlled by a GPT-4 instance, using OpenAI's function calling to map language to actions. | User study with 12 participants performing 7 tasks in VR. Data collected via observation, recordings, SASSI questionnaire, and semi-structured interviews. Analysis involved qualitative thematic analysis and quantitative SASSI results. | Users often used simple, command-like language, influenced by preconceived ideas about robot interaction, and didn't fully leverage GPT's conversational abilities. Interaction styles varied (dialog engagement vs. command optimization; holistic coordination vs. task decomposition). Function calls were effective but sometimes led to overly meticulous agent behavior or mismatched autonomy expectations. System latency was a drawback. SASSI scores indicated positive ratings for accuracy, likeability, cognitive demand, and annoyance, but negative ratings for speed and habitability. | NaN | NaN | NaN | NaN | NaN | NaN | Uses OpenAI's pre-trained GPT-4 model (gpt-4-0613). The specific training data is proprietary, large-scale, general text and code data. The study used prompt engineering with few-shot examples, not custom fine-tuning. | Exploratory research; prototype development (VR simulation using Unity); integration of existing APIs (OpenAI GPT-4, Whisper, Amazon Polly); user study employing mixed methods (qualitative thematic analysis, quantitative SASSI questionnaire). | The framework was deployed in a controlled laboratory setting for the user study. A footnote mentions plans for future open-source release on GitHub upon acceptance. | False | False | NaN | NaN | Mapping unstructured language to structured actions; designing a scalable multi-agent LLM framework; latency from cloud APIs impacting interaction; GPT non-determinism and opacity; aligning user expectations with agent autonomy; need for robust inter-agent communication. | Risk of miscommunication leading to incorrect task execution. Risk of system unresponsiveness due to latency affecting usability. Risk of unpredictable agent behavior due to LLM non-determinism and opacity. Potential ethical concerns mentioned regarding future implementation of simulated emotions. |
| F6cddn0EKiIJ.pdf | Google_Scholar | The Rise of the Robotic Tax Analyst | This paper discusses the use of AI, specifically large language models like GPT-3 and predictive analytics tools like Blue J, in tax law. It reviews the accuracy of Blue J's tax case predictions from 2022 and speculates on the future integration of AI for tax research, analysis, and compliance by 2030. | True | Market | True | 2.0 | NaN | Blue J's machine learning models for predicting tax case outcomes; Use of GPT-3 for text generation. | Comparison of Blue J's 2022 predictions published in the 'Blue J Predicts' column against the actual court outcomes in the corresponding tax cases. | Blue J accurately predicted the outcome in 6 out of 8 cases where a prediction was made and the litigation outcome was known at the time of writing. The paper also states Blue J's models achieve over 90% accuracy generally. | NaN | NaN | NaN | NaN | Tax Law | USA | Blue J's models use 'massive amounts of data collected from tax cases and legal sources'. The nature (public/proprietary) is not explicitly stated but likely involves proprietary processing of public legal data (case law, statutes). | NaN | Blue J Tax is a commercial software product. Predictions are also shared via the 'Blue J Predicts' column in Tax Notes Federal. | True | False | Blue J Tax is commercially available via subscription from Blue J Legal Inc. | NaN | The complexity and perceived inconsistency of tax case law make it difficult for humans to analyze accurately; Wariness among some legal academics and practitioners regarding large language models. | NaN |
| 3599696.3612895.pdf | Google_Scholar | Analyzing the Use of Large Language Models for Content Moderation with ChatGPT Examples | This paper proposes an enhanced content moderation pipeline integrating Large Language Models (LLMs) to improve fairness, personalization, and user communication on online social networks. It demonstrates the approach with ChatGPT examples for sex-related texts, gender stereotypes, and ableist language, highlighting the potential for user-defined rules and decision explanations. | True | Idealistic | True | 1.0 | Positive | An enhanced content moderation pipeline that integrates an LLM (using ChatGPT as an example) to classify text based on user-customizable rules (provided via prompts) and to generate explanations for moderation decisions. | Qualitative demonstration using ChatGPT with specific prompts and predefined rules for three case studies: sex-related texts, texts containing gender stereotypes, and texts offensive to people with disabilities. The LLM's binary classification (violates rules: Yes/No) and its generated explanations were examined. | ChatGPT successfully adapted to different rule sets, classifying content and providing explanations. For instance, it correctly distinguished permissible medical sex-related content and identified non-inclusive language regarding disabilities. However, it sometimes failed to detect more subtle gender stereotypes without explicit phrasing or in isolated instances. | Current content moderation systems are often unfair to fragile users and minorities, lack personalization, fail to provide adequate explanations for decisions, and struggle with interpreting diverse languages and cultural contexts, thereby hindering safe and inclusive online environments. | Integrating LLMs into content moderation to enable personalization through user-specified rules (via prompts), provide explanations for moderation actions, enhance user-platform communication, and offer better support for human moderators. | Fairness and equity in online content moderation, protection of vulnerable groups from harmful content, transparency and explainability of automated moderation decisions, user empowerment in defining online content filtering. Indirectly relates to upholding principles of justice in digital spaces. | Fragile users (defined by age, digital literacy, education), minorities (e.g., LGBTQ+), marginalized people (e.g., based on race, religion, users from the Global South), and people with disabilities. | Online speech regulation, anti-discrimination principles as applied to online content, platform governance. | International | NaN | Conceptual framework proposal for an enhanced content moderation pipeline, demonstrated through illustrative case studies using prompt engineering with a pre-trained LLM (ChatGPT). | NaN | False | False | NaN | LLMs have inherent limitations such as 'hallucinations and knowledge recency.' Obtaining numeric confidence values from LLMs for their decisions is an open research problem. Designing user-friendly interfaces for rule customization and addressing privacy implications of such personalized systems are also needed. | Effectively designing prompts for LLMs to handle nuanced content moderation. LLMs' difficulty in interpreting subtle or highly contextual violations without explicit cues. The current inability of LLMs to provide numeric confidence scores for their decisions, limiting their comparability with traditional ML classifiers. | LLM limitations like 'hallucinations and knowledge recency' may lead to incorrect moderation decisions. The proposed system's reliance on binary (Yes/No) LLM outputs, due to the difficulty in obtaining confidence scores, might be insufficient for complex cases. |
| gkdm8RV9wjYJ.pdf | Google_Scholar | Mapping the Challenges of HCI: An Application and Evaluation of ChatGPT and GPT-4 for Mining Insights at Scale | This paper proposes and evaluates a two-step approach using ChatGPT and GPT-4 to mine research challenges from the HCI conference proceedings (CHI 2023). It demonstrates this method's cost-efficiency for analyzing large text corpora at scale and discusses broader implications of LLMs for academic research and insight mining. | True | NaN | True | 1.0 | NaN | A two-step insight mining approach: 1) ChatGPT (gpt-3.5-turbo-0301) extracts candidate research challenges from papers. 2) GPT-4 (gpt-4-0314) filters this list to the top five most significant challenges per paper. | Quantitative evaluation using NLP metrics (EM, ED, WER, BLEU, ROUGE, METEOR, BLANC, BERTScore) by comparing LLM outputs to best-matching text from the source or previous LLM step. Qualitative evaluation via human annotation of research challenges from a 5% random sample of papers, compared against GPT-4's output, with inter-rater agreement (Cohen's kappa) calculated. Semantic similarity analysis using embedding cosine distances. | Human evaluation showed high agreement (κ=0.97) that LLM-extracted statements are potential research challenges. The GPT-4 list matched human-identified challenges in approximately 65% of the sampled papers (κ=0.86 for alignment). GPT-4 selected 98.62% of challenges verbatim from ChatGPT's output, and no hallucinations were found in the sampled qualitative evaluation. The approach was deemed cost-efficient. | NaN | NaN | NaN | NaN | NaN | NaN | The underlying LLMs (OpenAI's ChatGPT and GPT-4) were pre-trained on opaque, internet-scale text corpora. The method described in the paper was applied to the CHI 2023 conference proceedings (879 papers), which served as input data for analysis by the pre-trained models. | Iterative prompt engineering using best practices for reliable prompting, with observation of outcomes on sample documents in Jupyter notebooks. Temperature parameter set to zero for determinism during prompt design. | The dataset of 4,392 extracted HCI research challenges and an interactive visualization were made publicly available on GitHub Pages and the Open Science Framework (OSF). | True | False | The described insight mining approach relies on OpenAI's commercial ChatGPT and GPT-4 APIs. The resulting dataset and visualization are open access. | NaN | Iterative and experimental nature of prompt design to achieve desired output quality and consistency; Managing API limitations such as context window length (requiring batching and error handling for InvalidRequestError) and rate limits; Cost considerations for using LLM APIs, which motivated the two-step approach for efficiency; The lack of a gold standard for the specific task, requiring approximation methods for quantitative evaluation metrics. | General LLM risks mentioned include potential for hallucinations (though not observed in their specific evaluation with context), sycophancy, reproduction of biases from training data, encoding of opinions and cultural values, and sensitivity to prompt phrasing. |
| kbfw6Fsq-qAJ.pdf | Google_Scholar | Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-Generation | This paper proposes EDC2-RAG, an efficient dynamic clustering-based document compression framework to improve Retrieval-Augmented Generation (RAG) for large language models (LLMs). The framework clusters retrieved documents based on semantic similarity to reduce noise and redundancy before compressing them using an LLM, leading to improved performance on knowledge QA and hallucination detection tasks. | True | NaN | True | 1.0 | NaN | EDC2-RAG: An Efficient Dynamic Clustering-based document Compression framework for Retrieval-Augmented Generation. It uses document embeddings for dynamic clustering, followed by LLM-based query-aware compression of clusters before feeding the context to the final generation LLM. | Evaluated on knowledge QA datasets (NQ, WebQ, TriviaQA) and hallucination detection datasets (FELM World Knowledge Subset, WikiBio GPT-3, HaluEval). Performance compared against baseline RAG methods (RALM, Raw Compression, CEG, Self Consistency) using F1 score, Balanced Accuracy, and Accuracy metrics under varying conditions (top-k retrieved documents, noise levels, redundancy rates). | The proposed EDC2-RAG method achieved consistent performance improvements over baseline methods across different datasets and experimental settings. For example, on WebQ (Top-100), it achieved an average F1-score improvement of +0.91 over RALM across varying noise rates. On HaluEval, it improved Accuracy by +1.05 over the CEG baseline (Top-10). | NaN | NaN | NaN | NaN | NaN | International | The technique utilizes pre-trained LLMs (GPT-3.5-Turbo, GPT-4o) and embedding models (SimCSE Bert, all-mpnet-base-v2). Evaluation was performed on publicly available datasets (NQ, WebQ, TriviaQA, FELM, WikiBio, HaluEval) using retrieval corpora derived from Wikipedia snapshots (2018, Oct 2023) and Freebase. | Algorithmic design involving document embedding, similarity calculation, rule-based dynamic clustering, and prompt-based LLM compression. | Code and datasets are publicly released on GitHub. | True | True | Code and datasets available on GitHub: https://github.com/Tsinghua-dhy/EDC-2-RAG | NaN | General RAG challenges addressed: Noise, redundancy, and repetition in retrieved documents; limited exploitation of inter-document relationships by standard RAG. Limitation of the specific method: Incurs API consumption costs for the compression step. | Implicitly addresses the risk of LLM hallucination (generating factually incorrect information) by improving the quality of retrieved context provided to the LLM. |
| cKasUkcMinkJ.pdf | Google_Scholar | Generative AI's Role in Reducing Transaction Costs \nin Finnish Legal Markets \nAn Analysis of Litigation Process Participants | This literature review examines the potential of generative AI (GenAI) to reduce transaction costs and improve efficiency in the Finnish litigation process. It analyzes empirical studies and theoretical frameworks, finding mixed results where GenAI shows promise but requires careful implementation to mitigate risks like reduced economies of scope. | True | Market | True | 3.0 | Neutral | Generative AI (GenAI) for tasks such as supporting legislative drafting and producing summaries of public consultation responses. | The paper reviews two Finnish pilot projects: 1) Futurice Oy: Finnish language models (e.g., FinGPT, Poro) were further trained with legislative texts and a chatbot demo was developed for legal drafters. 2) SiloGen AI Oy: An AI tool was used to create draft summaries of public consultation responses, with a demo solution evaluated by drafters. | The reviewed studies showed mixed results. The Futurice Oy pilot found Finnish language models 'are not yet at a sufficient level.' The SiloGen AI Oy pilot showed 'promising results' in generating preliminary summaries but also 'produced inaccurate and incomplete interpretations' requiring further refinement. | High transaction costs in legal markets, making legal services expensive and often inaccessible for ordinary consumers, citizens, and small businesses. Law is perceived as too expensive and low quality due to lack of innovation. | Integrating GenAI into the litigation process to automate routine tasks like legal research and document drafting. This could lead to cost savings, faster processes, better allocation of legal expertise, and potentially make legal aid more accessible. | Reducing transaction costs in the litigation process, improving efficiency of legal services, and enhancing accessibility of legal aid. | Ordinary consumers, citizens, and small businesses who currently find legal services unaffordable. | Litigation (civil, criminal, petitionary cases) and legislative drafting processes. | Finland (Finnish Legal Markets) | For the Futurice Oy pilot: Finnish legislative texts and related materials used to further train Finnish language models (e.g., FinGPT, Poro). For the SiloGen AI Oy pilot: Public consultation responses. | The paper reviews pilot projects that developed: 1) A service demo with a chatbot interface for legislative drafting support (Futurice Oy). 2) A demo AI tool for summarizing public consultation responses (SiloGen AI Oy). The paper itself is a literature review. | The discussed GenAI applications are at the pilot project/demo stage and not broadly deployed. | False | False | NaN | A significant research gap on legal market efficiency and litigation cost minimization globally. Need for further studies in Finland on welfare gains from GenAI in legal systems. Technical gaps include Finnish language models not being sufficiently advanced and AI summary tools requiring more training. | For the reviewed studies: Finnish language models not being sufficiently advanced (Futurice Oy). AI tools producing inaccurate/incomplete interpretations, generalizing feedback, overlooking comments, and requiring significant additional training (SiloGen AI Oy). For the thesis author: The difficulty of undertaking original empirical/theoretical work on a novel topic. | Privacy violations and cybersecurity concerns. Over-reliance on AI predictions leading to misuse. AI producing inaccurate or incomplete outputs. Reduction in economies of scope, potentially leading to humans losing holistic understanding and decreased productivity. Challenges in balancing regulation to foster innovation while protecting rights. |
| TAPs_final_CHI.pdf | Google_Scholar | Privacy Perceptions of Custom GPTs by Users and Creators | This paper explores the privacy perceptions of users and creators regarding OpenAI's custom GPTs through interviews (N=23). It reveals blurred user/creator roles, unclear mental models of data flow, significant privacy concerns about data handling and regulation, and proposes recommendations for improved transparency and platform oversight. | True | NaN | True | 2.0 | NaN | OpenAI Custom GPTs | Semi-structured interviews (N=23) with users and creators, analyzed via thematic analysis. | Participants exhibit blurred user/creator roles and uncertain data flow mental models. Key privacy concerns include data collection scope, processing misuse, unauthorized dissemination, and lack of regulation; creators also worry about knowledge exploitation. Users practice self-censorship and GPT evaluation, while creators employ knowledge protection techniques; expertise and responsibility shape perceptions. | NaN | NaN | NaN | NaN | Privacy Law (implicitly, through user concerns and GDPR mentions), General Technology Regulation | International | NaN | NaN | OpenAI GPT Store | True | False | Available via OpenAI subscription (ChatGPT Plus) through the GPT Store. | Need for clear platform regulations, GPT verification mechanisms, creator knowledge protection, effective privacy communication strategies, resolution of machine unlearning challenges. | Unclear data flows, third-party data sharing risks, potential for data misuse/profiling/leaks, lack of user control over data (e.g., deletion), inadequate privacy protections for creators' knowledge, proliferation of spam/malicious GPTs, lack of robust platform verification and regulation. | Data misuse (profiling, marketing, scams, political manipulation, deepfakes), data breaches/leaks, identity theft, financial loss, unauthorized exposure of personal/confidential information, exploitation of creator knowledge, malicious GPTs stealing user data, platform promotion of spam/scams. |
| yNavCh3Cl8gJ.pdf | Google_Scholar | AI Tools for Lawyers: A Practical Guide | This paper provides a practical guide for lawyers on how to ethically and efficiently use large language models (LLMs) like GPT-4 and Bing Chat for legal tasks. It offers generalizable strategies for prompting LLMs to analyze caselaw, identify legal issues, and draft legal documents such as memos, briefs, and contracts. | True | Market | True | 2.0 | Positive | Prompt engineering strategies for using LLMs (specifically GPT-4 and Bing Chat) in legal research, analysis, and drafting, including detailed prompting, iteration, chain-of-thought, and providing source material for verification. | Qualitative demonstration through examples of prompting GPT-4 and Bing Chat for various legal tasks (e.g., case summarization of Chipokas v. Hugg, legal analysis of hypothetical fact patterns) and assessing the LLM-generated output. No formal benchmarks were used. | The paper demonstrates that well-prompted LLMs like GPT-4 can produce highly readable and accurate case summaries (e.g., Chipokas v. Hugg synopsis described as more accurate than court-supplied or West headnotes), identify relevant legal issues, and generate good first drafts of legal arguments and contract clauses. | Historical lack of resources for lower-income individuals to pay for legal services (mentioned in footnote 55). General LLM limitations include potential for hallucinations, lack of access to nuanced facts without direct input, and (for some models like older GPT versions) lack of access to the latest legal sources if not connected to a search engine. | Expanded use of AI tools by lawyers could plausibly lower costs or increase efficiency, thereby helping to expand the availability of legal services. For LLM limitations, the paper suggests verifying outputs, providing specific source material to the LLM, and using tools like Bing Chat that access current information. | Expanding availability of legal services. | Lower-income individuals. | General legal practice, Torts (defamation), Statutory Interpretation, Contract Law (promissory estoppel, contract drafting). | United States (examples from US federal and state law, e.g., New York Times Co. v. Sullivan, U.S. v. Marshall, Chipokas v. Hugg (Iowa)). | The paper refers to the training data of GPT-4 as a large, historical corpus of text, and Bing Chat as having access to current information via search. These are general, large-scale, proprietary datasets from OpenAI and Microsoft. | The strategies were developed through the authors' experimentation and application of general LLM prompt engineering best practices (e.g., detailed input, iteration, chain-of-thought, few-shot prompting) to common legal tasks. | The paper guides lawyers on using existing, publicly accessible LLM platforms like ChatGPT Plus (for GPT-4) and Bing Chat. | True | True | The paper describes techniques for using LLMs like GPT-4 (accessible via paid ChatGPT Plus subscription) and Bing Chat (freely available). | The primary A2J gap mentioned is the cost of legal services for lower-income individuals. Technical gaps for LLMs include their propensity to hallucinate and their outputs requiring careful verification. A societal gap could be the digital literacy required to effectively use these tools for A2J, though not explicitly detailed. | Key challenges for users include the learning curve for effective prompt engineering, the need to constantly verify AI-generated content due to potential inaccuracies or hallucinations, managing context window limitations for lengthy legal documents, and ensuring client confidentiality when using third-party AI tools. | Risks include LLMs making mistakes or 'hallucinating' incorrect information/citations, providing inaccurate answers if their training data is limited or outdated, and confidentiality concerns due to potential data security bugs in third-party LLM services. Using AI-generated text without permission in academic settings is also noted as unethical. |
| HQ7IncDAqDQJ.pdf | Google_Scholar | Law Without Lawyers: Examining the Limitations of Consumer-Centric Legal Tech Services | The paper discusses the rise of business-to-consumer (B2C) legal tech driven by cost reduction and inclusion needs, analyzing examples like document automation, ODR, and chatbots. It argues that while promising for access to justice, these tools have significant limitations in handling complexity, ensuring quality, and replacing human lawyers, necessitating regulation and adaptation within the legal profession. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | High cost of traditional legal services creating a justice gap for low/middle-income earners; complexity of legal issues requiring human reasoning, interpretation, and empathy beyond current AI capabilities; the digital divide limiting access for vulnerable populations (cost of devices/internet, digital literacy). | Regulating legal tech (minimum standards, liability rules, ethical guidelines, transparency); increasing lawyer's technical competency through legal education reform; establishing legal tech innovation hubs and regulatory sandboxes; raising public awareness about legal tech's benefits and limitations. | Access to affordable legal services, bridging the justice gap, online dispute resolution (ODR), legal information access, basic legal document generation. | Low to middle-income earners, populations underserved by traditional law firms, digitally vulnerable populations (elderly, disabled, digitally illiterate), general public needing basic legal services, populations in Africa. | General Civil Law | International | NaN | NaN | NaN | True | False | Various commercial B2C legal tech platforms (e.g., LegalZoom, DoNotPay, JusDraft) and some government online services (e.g., MCOL) are presented as operational. | Lack of specific legal frameworks for legal tech and ODR (especially in Africa); regulatory gap in AI governance; need for AI explainability and independent quality assurance; difficulty encoding complex legal reasoning/ontology; digital divide limiting access; lack of lawyers' technical competency; limited academic research on legal tech in Africa. | Handling legal complexity and unstructured issues; translating legal ontology into algorithms; ensuring quality and accuracy (data bias, interpretational risk); lack of transparency (black-box problem); keeping pace with evolving law; addressing the digital divide; regulatory lag. | Unauthorized practice of law; ethical dilemmas (client protection, confidentiality); legal liability issues (algorithmic errors, lack of accountability); risk transfer to users via disclaimers; potential undermining of the rule of law; biased or inaccurate outcomes; data privacy breaches; user misinterpretation of guidance. |
| 3627673.3680020.pdf | Google_Scholar | LawLLM: Law Large Language Model for the US Legal System | The paper introduces LawLLM, a multi-task large language model fine-tuned on US legal data to perform Similar Case Retrieval, Precedent Case Recommendation, and Legal Judgment Prediction. LawLLM demonstrates superior performance over existing baselines, including GPT-4, particularly in Legal Judgment Prediction. | True | Market | True | 1.0 | NaN | LawLLM, a multi-task LLM fine-tuned from Gemma-7B with custom data preprocessing, instruction tuning, in-context learning (ICL), and advanced information retrieval methods for Similar Case Retrieval (SCR), Precedent Case Recommendation (PCR), and Legal Judgment Prediction (LJP). | Evaluated on a subset of the CaseLaw dataset for SCR, PCR (using top-k metrics: top-1, top-3, top-5 and not-found rate), and LJP (using accuracy and F1-score in zero-shot and few-shot ICL scenarios). Compared against baselines including LLaMa2-7b, Gemma-7b, Vicuna-13b, Guanaco-13b, GPT-3.5, and GPT-4. | For Legal Judgment Prediction (Few-shot), LawLLM achieved an accuracy of 0.794 and an F1 score of 0.758, outperforming all baselines including GPT-4 (0.732 accuracy, 0.712 F1). | NaN | NaN | NaN | NaN | General US Law (covering Similar Case Retrieval, Precedent Case Recommendation, Legal Judgment Prediction) | United States | A subset of 1,000,000 cases from the publicly available CaseLaw dataset (US court cases), preprocessed (summarized, verdicts extracted) using GPT-4. For SCR, training cases converted to vectors using OpenAI Embedding model. For PCR, precedent relationships from training data converted into a knowledge graph. | Instruction tuning of Gemma-7B using a combined dataset for three tasks (SCR, PCR, LJP). Custom data preprocessing for each task, including GPT-4 for summarization/verdict extraction, vector database creation for SCR, knowledge graph construction for PCR. Use of in-context learning (ICL) and 4-bit quantized Low-Rank Adaptation (LoRA). | Code and data made available via a GitHub repository. | True | True | Code and data are available on GitHub (https://github.com/Tizzzzy/Law_LLM). | Need for expansion to more legal tasks and further refinement of data processing techniques and in-context learning methodologies to improve the model’s understanding of legal nuances and precedents. | Handling voluminous and complex legal text, distinguishing nuanced legal concepts (similar vs. precedent cases), managing token size limitations of base LLMs, and developing effective multi-task learning for the legal domain. | Hallucination (model producing answers unrelated to the provided options, measured by 'not-found' rate). |
| nPNWRlE8MbcJ.pdf | Google_Scholar | The Effect of Race, Gender, and Priming on AI’s Conviction Predictions | This paper experimentally evaluates ChatGPT (GPT-3.5 and GPT-4) for race and gender biases in predicting criminal conviction probabilities using manipulated defendant descriptions and priming. It finds no significant race or gender bias in either model but observes significant priming effects and better performance (lower variance, lower conviction rates) in GPT-4. | True | Idealistic | True | 2.0 | Neutral | Evaluating ChatGPT (GPT-3.5 and GPT-4) for conviction probability prediction in a criminal case scenario using manipulated prompts (varying defendant race/gender, applying priming). | Experimental design using 90 queries (45 per model) based on a modified criminal case vignette (Rachlinski et al. 2009). Defendant attributes varied across a 3x5 matrix (Gender x Race Implicit/Explicit), with three priming conditions (positive, negative, neutral). Statistical analysis (t-tests, ANOVA, regression) of predicted conviction probability ranges (0-100%). | Neither GPT-3.5 nor GPT-4 showed statistically significant race or gender bias. Priming significantly affected predictions (especially GPT-3.5), generally lowering conviction rates compared to no priming. GPT-4 predicted significantly lower conviction rates and showed less variance than GPT-3.5. | Human cognitive biases (race, gender stereotypes) influencing judicial decisions. The 'black box' nature of proprietary LLMs hinders understanding and evaluation. | Exploring LLMs as potential decision-support tools to mitigate human biases in judicial decision-making, possibly due to algorithmic de-biasing or lack of visual cues. Need for transparency and robust evaluation. | Fairness in judicial decision-making, racial bias, gender bias, conviction prediction. | General racial (Black vs. White defendants) and gender (Male vs. Female defendants) categories, implicitly addressing disparities faced by Black individuals in the criminal justice system. | Criminal Law | United States (implied) | Proprietary datasets used to train GPT-3.5 and GPT-4 (details not publicly known or specified in the paper). | Experimental design (Factorial experiment), Quantitative analysis (Statistical testing: t-tests, ANOVA, linear regression). | NaN | False | False | NaN | Need for LLM transparency (training data, policies), better understanding of priming effects, development of legal LLM evaluation metrics (especially without ground truth), qualitative analysis of reasoning, larger scale testing to address randomness. | Lack of 'ground truth' for legal predictions, opacity of proprietary models, high sensitivity of LLMs to prompt variations (priming), randomness in LLM outputs, methodological limitations (sample size). | Potential for AI bias perpetuation (despite negative findings here), risks associated with 'black box' models (difficulty in auditing), susceptibility to manipulation via priming/prompting, potential for poor performance or hallucinations in legal tasks. |
| pMuzPPoMHigJ.pdf | Google_Scholar | Guarding the News Media’s Intellectual Property in the Age of Generative AI | This paper investigates the intellectual property challenges generative AI poses to the news media, emphasizing the unauthorized use of copyrighted journalistic content for training AI models. It argues that this practice threatens the financial viability of journalism and its democratic role, proposing legislative reforms, stronger regulation, and financial support to protect news creators. | True | Idealistic | True | 3.0 | Negative | NaN | NaN | NaN | Unauthorized and uncompensated use of copyrighted news content for training AI, leading to financial unsustainability of news outlets; spread of misinformation and distortion of news by AI, undermining journalism's democratic role; inadequate existing legal frameworks to protect journalistic IP from AI. | Legislative action (e.g., Journalism Competition and Preservation Act, new AI-focused laws); enhanced regulation and enforcement by agencies like the FTC (e.g., mandatory disclosures, fines); public funding or tax breaks for journalism (e.g., Local Journalism Sustainability Act, levy on digital advertising revenue from AI). | Protection of intellectual property for news media, ensuring financial viability of journalism, combating AI-generated misinformation, upholding the democratic role of the press, public access to reliable information. | The general public, whose access to reliable information and a functioning democracy is dependent on a viable press. | Copyright Law, Intellectual Property Law, Media Law, First Amendment Law | United States | The paper discusses AI models being trained on large, publicly available datasets scraped from the internet, which include copyrighted news articles, in-depth investigations, opinion pieces, and other journalistic content without permission or compensation. | NaN | NaN | False | False | NaN | Lack of solid legal standards for resolving disputes over AI's use of copyrighted material; uncertainty about the applicability and adequacy of current copyright law (especially fair use) to generative AI; disparities in bargaining power between news outlets and AI companies; need for comprehensive legislative and regulatory frameworks specifically addressing AI and news content. | NaN | Copyright infringement and financial deprivation for news outlets due to uncompensated use of their content for AI training; spread of AI-generated misinformation, disinformation, and fabricated news, potentially attributed to real news outlets; diminished work opportunities for journalists; reduced media diversity and public access to trustworthy information; undermining of the press's democratic and societal functions. |
| dkUIaaWEdX8J.pdf | Google_Scholar | AI-ASSISTED GERMAN EMPLOYMENT C ONTRACT \nREVIEW: A BENCHMARK DATASET | This paper presents a benchmark dataset of 1094 German employment contract clauses annotated for legality and fairness by legal experts. The authors provide baseline performance results using various NLP models, including fine-tuned and prompt-engineered GPT variants, for automatically identifying problematic clauses. | True | Idealistic | True | 1.0 | Positive | Creation and benchmarking of a dataset for German employment contract clause legality/fairness classification using transformer models (BERT, GPT-3.5, GPT-4) via fine-tuning and prompt engineering. | Evaluation on a 10% held-out test set from the created dataset (893 samples after deduplication). Metrics used were Precision, Recall, and F1-score for binary classification (okay vs. problematic). Various input formats incorporating clause text, section titles, and instructions were tested. | The best performance (highest weighted average F1-score 88.9%, positive class F1-score 61.5%) was achieved by fine-tuning the OpenAI gpt-3.5-turbo-1106 model with instructions and clause text only as input. | Cost and time of traditional legal review; insufficient legal knowledge among employers and employees; scarcity of expert-annotated legal datasets. | Developing AI-assisted tools for contract review to reduce costs, time, and improve accessibility. Providing an open benchmark dataset to facilitate research and development of such tools. | Legality and fairness review of employment contract clauses. | Employees (limited legal knowledge/financial resources) and employers (risk reduction). | Employment Law, Contract Law | Germany | A dataset of 1094 German employment contract clauses, sourced from a law firm's anonymized client data, annotated by two lawyers for legality (valid, unfair, void), category (14 types), and explanation. Released publicly (CC BY-NC 4.0). | Dataset creation involved sourcing, anonymization, clause segmentation, multi-round expert annotation with inter-annotator agreement calculation, categorization. Baseline evaluation involved standard NLP fine-tuning and prompt engineering techniques. | NaN | False | True | The annotated dataset is available on GitHub under a CC BY-NC 4.0 license. | Current dataset size potentially limits fine-tuning performance (plan to expand). Baselines lack extensive hyperparameter tuning/prompt exploration. Need for advanced classification pipelines (e.g., RAG) and evaluation of a prototype system (technical, economic, social). Need to bridge the gap between research and practical application. | Scarcity and cost of creating expert-annotated legal datasets, especially non-English. Handling sensitive data/privacy. Potential model bias (e.g., GPT models favouring employee protection). Data imbalance. Potentially insufficient dataset size for optimal fine-tuning. | Employees unknowingly accepting unfair/void contract terms. Employers facing lawsuits due to void clauses. AI models potentially misclassifying clauses (risk of overlooking problematic ones deemed higher). Privacy risks if data anonymization fails. |
| viewcontent.pdf | Google_Scholar | Continuing Legal Education in Germany – Digitalization | This paper discusses the increasing importance of digitalization in continuing legal education (CLE) for German legal professionals due to growing legal complexity and market changes. It outlines necessary digital skills, relevant CLE content, and innovative teaching methods, citing examples from Bucerius Law School. | True | Market | False | 3.0 | NaN | The paper broadly discusses digitalization in CLE but specifically mentions the 'dskrpt' platform for text-based legal education and the 'Bucerius Legal Tech Essentials' free online course. | dskrpt: Developed in-house over two years; aims to collect user data for future improvement. Bucerius Legal Tech Essentials: Evaluated via participant numbers (12,500+), geographic reach (120+ countries), Net Promoter Score (85.58), and median overall satisfaction (10/10). | Bucerius Legal Tech Essentials: 12,500+ participants from 120+ countries (2020-2022), NPS 85.58, median satisfaction 10/10, led to enrollments in paid programs. | NaN | NaN | NaN | NaN | Legal Education, Legal Profession, Technology Law, Civil Procedure, Criminal Procedure, Corporate Law | Germany, with references to USA and Canada. | The dskrpt platform aims to collect user interaction data for future ML applications, but no specific training dataset is described for its current state or other mentioned techniques. | dskrpt: Developed in-house. Bucerius Legal Tech Essentials: Deployed as a Massive Open Online Course (MOOC). | dskrpt: In-house use, plans for a separate company. Bucerius Legal Tech Essentials: Free MOOC offered online from 2020-2022. | False | False | NaN | Need for legal professionals to acquire digital skills; need for legal education and CLE providers to adapt curricula and methods to digitalization trends; historically slow pace of judicial digitalization in Germany (though improving). | Teaching foundational technical concepts effectively in a CLE setting (vs. university); keeping CLE content (like tech landscaping) current; potential overlap between advanced CLE and consulting services; developing new educational platforms like dskrpt. | Professional liability for lawyers failing to keep up with legal/technical developments; clients becoming competitors by developing/offering legal tech solutions; legal professionals being unprepared for market shifts due to digitalization. |
| 3xpB1xoOKekJ.pdf | Google_Scholar | Mapping the Potentials and Limitations of Using Generative AI Technologies to Address Socio-Economic Challenges in LMICs | This paper explores the potential of Generative AI (GenAI) to address socio-economic challenges in Low- and Middle-Income Countries (LMICs), drawing on experiences from 50 projects across various sectors like health, agriculture, and education. While highlighting significant opportunities, it also details substantial risks (bias, privacy, safety) and barriers (infrastructure, data, cost, language) that must be overcome for equitable and just AI deployment. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Lack of affordable compute and reliable infrastructure; Poor data quality, availability, and representativeness (incl. bias from Western datasets); Limited capabilities for low-resourced languages; Insufficient gender-sensitive capacity; Data privacy risks due to inadequate regulations; Safety and cultural sensitivity concerns; Potential to perpetuate bias and stigma; Ethical trade-offs in resource-poor settings. | Enable local innovation through funding and platforms; Build a solid evidence base via M&E and longitudinal studies; Foster public awareness, engagement, and critical digital literacy; Establish rights-based AI governance and regulation; Mobilize resources to build local ecosystems and strengthen capacity (infrastructure, expertise, data ownership). | Global health (healthcare access, health communication, SRH/MCH, evidence generation, disease surveillance), Agriculture (climate adaptation, crop disease detection, farmer advisory), Education (personalized learning, local content generation, literacy assessment), Financial inclusion (financial literacy/services for underserved populations), Gender equality (support for GBV survivors, access to information for women), Access to information in low-resourced languages. | Populations in Low- and Middle-Income Countries (LMICs), including frontline workers (health, agriculture), patients, smallholder farmers, students, rural populations, informal/small-business owners, women, survivors of Gender-Based Violence (GBV), marginalized communities (e.g., LGBTQAI+), low-literacy populations, users of low-resourced languages. | Data Privacy and Protection, Access to Justice (specifically for GBV), Human Rights, AI Governance and Regulation. | LMICs (various, including specific examples from Africa, Asia, and South America) | Varied across projects; included proprietary data collected from users (text, speech), domain-specific data (health records, agricultural info, financial queries, educational materials), sometimes requiring digitization or creation of new datasets (e.g., parallel corpora for low-resourced languages). Base models trained on large, often Western-biased datasets. | Co-creation with communities, human feedback loops, expert reviews, user-led testing, prompt engineering, Retrieval-Augmented Generation (RAG), fine-tuning, development of gold standard responses for evaluation, expert-in-the-loop models, gender-sensitivity training. | Deployed within specific projects/communities/institutions (e.g., hospitals, community programs) for testing or limited service provision. Some projects reported as 'live' providing services, others in user testing/validation. | False | False | NaN | Need for diverse, locally reflective data repositories; Lack of comprehensive M&E and longitudinal studies on AI impacts in LMICs; Insufficient local capacity for critical AI research; Underdeveloped AI governance and regulatory frameworks in many LMICs; Need for resource mobilization to build sustainable local AI ecosystems and address infrastructure/data ownership issues. | High cost and limited access to compute/infrastructure; Poor data quality, availability, and digitization needs; Supporting low-resourced languages; Mitigating bias, ensuring accuracy and cultural sensitivity; Protecting data privacy with inadequate regulations; Navigating ethical trade-offs; Budget constraints and unpredictability; Unstable connectivity; User training and adoption. | Data privacy breaches and misuse of personal/sensitive information; Harm from biased, inaccurate, or culturally insensitive AI outputs (e.g., incorrect health advice, reinforcing stereotypes); Perpetuation of discrimination and exclusion; Stigmatization; Overreliance on technology leading to neglect of human resources; Lack of accountability due to weak governance. |
| mMl9vQtSY1YJ.pdf | Google_Scholar | Generative AI Considered Harmful | This paper critiques generative AI like ChatGPT, outlining potential and actual harms arising from both its usage (e.g., plagiarism, misinformation) and its design/development (e.g., bias, copyright issues, human/environmental costs). It advocates for researchers to study the interactive contexts of AI use and development rather than viewing these systems as autonomous black boxes. | True | NaN | True | 3.0 | Negative | NaN | NaN | NaN | Risk of generating hallucinations and misinformation, particularly problematic for applications like legal advice. | NaN | Legal advice generation (as an example of risk) | NaN | General Legal Advice (examples include Contract Law, Traffic Law) | International | Discusses training data used by models like ChatGPT: massive datasets including Wikipedia, Common Crawl archive, books, websites, scientific articles. Characterized as vast, internet-based, unstructured text, potentially biased, and often lacking proper attribution. | NaN | NaN | False | False | NaN | Lack of reliable, verifiable, and attributable advice generation; need for understanding user interaction and adoption; lack of attribution/explainability; ethical issues in data sourcing and labor; environmental impact assessment needs; need for effective regulation and harm mitigation. | Managing bias, ensuring accuracy (avoiding hallucinations), addressing copyright/attribution, ethical labor practices, environmental sustainability, countering misuse (e.g., plagiarism). | False authorship/plagiarism; hallucinations/misinformation (e.g., incorrect legal advice); job displacement; replication of societal biases; copyright/IP infringement; exploitation of hidden human labor; environmental costs. |
| N0eYrm4EzjUJ.pdf | Google_Scholar | The Path of Tax Law: Toward Legal Singularity | This paper discusses the concept of the "legal singularity," a future where AI makes law fully comprehensive and predictable, primarily drawing insights from the book "The Legal Singularity.". It explores AI's potential to revolutionize tax law, improve access to justice by increasing legal literacy and addressing service unaffordability, and outlines ethical considerations for AI development in law. | True | Idealistic | True | 3.0 | Positive | AI-powered computational legal tools, including predictor-style machine learning models for outcome prediction (e.g., worker classification, innocent spouse relief) and generative AI (large language models) for tax research (e.g., Ask Blue J). | For predictor models: Evaluated using datasets of past court decisions (e.g., hundreds of cases for worker classification; all available cases for innocent spouse relief). For generative AI (Ask Blue J): Described as providing answers backed by relevant source documents for user verification. | For predictor models: Demonstrably able to extract key factors and predict future outcomes with confidence, providing detailed explanations. For generative AI (Ask Blue J): Delivers quality answers to challenging tax questions in seconds. | Law's inherent incompleteness and ambiguity; unaffordability of legal representation; complexity of the law; knowledge gap between legal professionals and clients; potential for AI to act as an expensive gatekeeper or entrench inequalities; algorithmic bias and decontextualization of data. | Achieving "complete law" through AI; developing dynamic rules and microdirectives for clearer, specific laws; promoting universal legal literacy via AI; democratizing access to legal information; improving algorithmic design to consider social context and extralegal factors to mitigate bias; maintaining human oversight in AI-assisted legal processes. | Legal predictability and clarity; accessibility of legal information and services; affordability of legal representation; universal legal literacy; fairness and equity in tax law application and administration; efficiency of legal and government services. | The general public, taxpayers, less well-resourced individuals, and specifically mentions Black taxpayers in the context of addressing algorithmic bias in IRS audits. | Tax law, General Law | United States (primarily, with references to IRS and US case law), Estonia (as an example of digital governance). | For predictor models: Datasets of past court decisions (e.g., "hundreds of past court decisions" for worker classification, "all available innocent spouse cases"). For generative AI (Ask Blue J): "Blue J’s vast tax database" (proprietary, domain-specific, includes source documents). General discussion of AI using "vast legal data sets." | Machine learning, big data analytics, predictor-style models, natural language processing, large language models. For addressing bias: improving algorithmic design by considering a wider range of social context and extralegal considerations. | IRS use of AI for tax-related Q&A and potential tax return processing; Estonia's digital government platform; Commercial AI tools for legal professionals (e.g., Blue J's platforms). | True | False | Ask Blue J is described as a "newly released" product from Blue J Legal. The book "The Legal Singularity" is commercially available. | Achieving full legal singularity; ensuring ethical and equitable AI development and deployment (addressing bias, fairness, accountability); continued need for legal advocacy and diverse perspectives; need for more research and multi-stakeholder collaboration; preventing AI from creating new access barriers; robustly solving data decontextualization. | Capturing the nuances and multidimensionality of legal reasoning with AI; addressing data and algorithmic biases (reflection, amplification, techno-epistemic problems); managing the decontextualization of legal data when building AI tools; drafting AI-generated rules that are both clearer and more specific. | AI being reductionist in legal reasoning; algorithmic decision-making tools perpetuating and amplifying existing societal inequalities (e.g., racial disparities in audits); embedding biases in institutions under a guise of technological objectivity; AI tools becoming expensive gatekeepers to legal information, exacerbating access to justice issues; generative AI entrenching inequalities if critical information remains behind paywalls. |
| l9PBmsLmLYwJ.pdf | Google_Scholar | Measuring Political Preferences in AI Systems – An Integrative Approach | This paper assesses political bias in various Large Language Models (LLMs) using an integrative approach combining four methods: linguistic comparison with US political speech, policy recommendation analysis, sentiment analysis towards public figures, and political orientation tests. The study finds a consistent left-leaning bias in most contemporary conversational AI systems, discusses potential sources and consequences, and recommends mitigation strategies like prioritizing accuracy, transparency, and independent monitoring. | True | NaN | True | 2.0 | NaN | Integrative approach combining: 1) Linguistic comparison of LLM text with US Congress members' language (using Jensen-Shannon Divergence on partisan bigrams). 2) Classification of political viewpoints in LLM-generated policy recommendations (using gpt-4o-mini for annotation). 3) Sentiment analysis of LLM text towards politically aligned public figures (using gpt-4o-mini for annotation). 4) Administration of standardized political orientation tests. | Applied the four methods to 20 conversational LLMs, 6 base LLMs, and 2 ideologically aligned LLMs. Data was generated by prompting models for policy recommendations (27 topics, 30 prompts each) and commentary on public figures (290 figures, 15 prompts each). Results from the four methods were standardized (Z-score) and averaged for a final bias ranking. Linguistic method validated by comparing its results on news media outlets with AllSides bias ratings (r=0.80). | Most conversational LLMs exhibit a statistically significant left-leaning bias across methods, though intensity varies. Google's Gemma 1.1 2b IT was ranked least biased (but still left-leaning); Google's Gemini 1.5 Flash was ranked most biased among conversational models tested. Base models showed milder left-leaning bias. Ideologically aligned models performed as expected. | NaN | NaN | NaN | NaN | NaN | United States (based on analysis focus: US Congress language, US public figures, US policy recommendations) | The study analyzes existing LLMs, discussing their likely original training data (diverse internet sources, potentially including biased sources like Wikipedia, news media, academic papers; often proprietary/undisclosed). For its own analysis, the study generated data (LLM policy recommendations, LLM commentary on public figures) and used external data (US Congressional Record 2010-2022, AllSides media bias ratings, Wikipedia political alignments, Politico journalist list). | Computational linguistics (bigram frequency analysis, Jensen-Shannon Divergence), automated text classification and sentiment analysis (using gpt-4o-mini as annotator), standardized testing (political orientation tests), statistical analysis (Z-score normalization, averaging). | NaN | False | False | NaN | NaN | Limitations of political orientation tests (calibration bias, constrained format); potential calibration bias in any single assessment method; difficulty measuring bias accurately in incoherent base model outputs; understanding the asymmetry observed in partisan term usage; lack of transparency regarding composition of LLM training data. | Reduced viewpoint diversity; increased societal polarization; public mistrust in AI; AI reinforcing pre-existing beliefs (echo chambers); biased autonomous AI agents impacting environments; potential for AI manipulation or control; AI providing deceptive or false information in critical roles (healthcare, finance, legal services). |
| 573DLIdsWUYJ.pdf | Google_Scholar | Artificial Intelligence (AI) Vs Academic Integrity (AI) in Law and \nSociety | This paper explores the conflict between Artificial Intelligence (AI), particularly generative models, and academic integrity within the educational and legal sectors of Bangladesh. It highlights concerns such as plagiarism and ethical breaches due to AI, and advocates for comprehensive policy frameworks, ethical guidelines, and legal reforms to manage AI use responsibly while upholding integrity in law and society. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Education Law, Copyright Law, Technology Law, Legal Ethics/Professional Responsibility, ICT Law, Cyber Security Law | Bangladesh, European Union, USA (California) | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Harming academic integrity through plagiarism and cheating; copyright infringement from AI-generated content; data breaches and misuse of sensitive student information; perpetuation of biases through AI algorithms; job displacement due to AI automation; blurring of lines between authentic skill and machine-generated output; lack of transparency/explainability in AI conclusions. |
| bEqaaktWOKwJ.pdf | Google_Scholar | Integrating Generative AI into Legal Education: \nFrom Casebooks to Code, Opportunities and \nChallenges | This paper examines the integration of Generative AI (GenAI) into legal education, discussing its potential to enhance practical skills and efficiency while highlighting significant ethical challenges like bias, hallucinations, and academic integrity. It proposes policy recommendations for law schools to adapt curricula and responsibly leverage AI, preparing students for a technologically evolving legal profession. | True | Market | True | 3.0 | Neutral | Generative AI (GenAI) | NaN | NaN | Ethical concerns (academic integrity, bias, hallucinations, lack of transparency), potential negative impact on critical thinking and skill development, environmental and human costs of AI development. | Mandatory AI ethics courses, AI literacy training, hands-on learning, faculty development, revised assessment methods, clear institutional policies, infrastructure investment, focus on human skills (judgment, accountability), critical evaluation of AI outputs, RAG techniques. | Legal Education Reform | Law students | General / Legal Education | International (with specific examples from US, India, Colombia, UK, Canada, Australia, Europe) | General discussion of training data issues (bias, RAG using domain-specific data); Example of student-generated data at Yale for specific project. | Curriculum design, policy development, pedagogical strategies (case studies, hands-on learning, interdisciplinary collaboration, assessment redesign). | Integration into curriculum, offering specific AI courses, providing access to commercial AI tools (e.g., Lexis+ AI), university AI labs/projects. | False | False | NaN | Effective pedagogical integration strategies, reliable AI detection, mitigating AI hallucinations reliably, addressing ethical/environmental/social costs, ensuring equitable access, understanding long-term impacts. | Integrating AI effectively into curricula while maintaining academic integrity, training faculty, redesigning assessments, addressing ethical concerns (bias, plagiarism, hallucinations), managing resource needs, keeping pace with rapid technological change. | Undermining critical thinking, academic dishonesty, AI factual errors ('hallucinations', fake citations), algorithmic bias/discrimination, lack of transparency/accountability, significant environmental and human costs in AI development/use. |
| Healthcare__A_Growing_Role_for_Large_Language_Models_and_Generative_AI3.pdf | Google_Scholar | The Expanding Function of Generative AI and Large Language Models in Healthcare | This preprint surveys the application of generative AI (GAI) and large language models (LLMs) in healthcare, covering techniques like GANs, VAEs, biomedical transformers, and multimodal models. It discusses their use in medical text analysis, image analysis, diagnosis support, and drug discovery, while also highlighting existing tools, benchmarks, challenges, and ethical considerations. | True | NaN | True | 3.0 | Positive | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Healthcare / Medical Law | International | Various publicly available and proprietary datasets including electronic health records (EHRs), biomedical literature (PubMed, scientific articles), clinical notes, medical images (MIMIC-CXR), biobanks (UK Biobank), and specific annotated biomedical NLP corpora (e.g., BC5-chem, NCBI-disease). Data types include unstructured text, structured data, images, genomics, and sensor data, largely domain-specific to healthcare and biomedicine. | NaN | Describes various deployment strategies including commercial tools, EHR integration, and public model repositories (e.g., Hugging Face). | True | True | Mentions publicly accessible tools (ChatGPT, Google Bard, DALL-E 2, Midjourney, Amazon Transcribe) and open-source/publicly released models (e.g., PathologyBERT on HuggingFace, PMC-LLaMA, ClinicalBERT, BioBERT). | NaN | Need for large diverse domain-specific data, data privacy/security, model interpretability, mitigating bias, regulatory hurdles, workflow integration, model robustness (hallucinations, instruction sensitivity), need for human oversight, computational resources. | Data privacy violations (PHI), biased/unfair diagnosis or treatment, generation/spread of misinformation, patient harm from inaccurate AI, legal liability, misuse for creating deceptive content, over-reliance, plagiarism/academic integrity issues. |
| 7j3b1GMYe48J.pdf | Google_Scholar | Integrating ChatGPT , Bard , and leading -edge generative artificial intelligence in \nbuilding and construction industry : applications, framework, challenges, and future \nscope | This paper reviews the diverse applications of generative AI models like ChatGPT and Bard across various stages of the building and construction lifecycle, including project management, design optimization, risk management, and safety monitoring. It proposes a high-level conceptual framework for integration and discusses implementation challenges, ethical considerations, and future potential. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Construction Law (implicitly, through discussions of compliance, risk, contracts), but primarily focuses on Construction Management and Engineering. | NaN | NaN | Conceptual Framework Development, Literature Review, Bibliometric Analysis. | NaN | False | False | NaN | NaN | Technical Integration (with existing construction software), Domain-specific Understanding (need for construction context/jargon), Safety and Compliance (ensuring AI adheres to regulations), Real-time Collaboration support, Data Privacy and Security, Training and Adoption by industry professionals. | Misinterpretations leading to errors in communication/decision-making, failure to adhere to safety guidelines leading to hazards, data privacy breaches, unauthorized data access, legal implications, compromise of project integrity. |
| 3T7NSdWW0p4J.pdf | Google_Scholar | Natural Language Processing in the Legal Domain | This paper surveys the field of Natural Language Processing applied to law (Legal NLP) over the past decade (2013-2022), analyzing trends in publication volume, tasks, methods, languages, and reproducibility based on a corpus of over 600 papers. It highlights increasing sophistication, methodological alignment with general NLP, and improved data/code sharing, while noting remaining challenges and future directions like legal text generation. | True | NaN | True | 3.0 | Positive | NaN | NaN | NaN | Complexity of legal language, culture of law (lawyers, judges, educators), regulation of the legal profession. | Application of NLP/AI technologies (especially modern LLMs) to process legal text, potentially enabling digital justice and justice at scale. Proposed creation of a 'living survey' for ongoing knowledge management in the field. | Improving delivery of justice, meeting legal needs, reducing case backlogs, improving access (mentioned broadly as context/motivation). | NaN | Broad coverage across various legal fields (constitutional, environmental, IP, labor, corporate, immigration, criminal, tax, family, maritime, contract law, patent law, human rights law, etc.). | International (mentions papers covering English, Chinese, Japanese, French, German; specific examples from ECHR, US, Germany, Singapore, Brazil). | The survey itself analyzes a corpus of >600 NLP & Law papers (peer-reviewed journals, conference proceedings, pre-prints) collected via targeted searches and snowball sampling. The papers *within* the surveyed corpus use various legal text datasets. | Corpus construction, qualitative review and categorization (tasks, reproducibility), keyword frequency analysis over time, citation analysis (including normalization), proposal for a 'human-in-the-loop' interactive living survey. | Proposal for an interactive 'living survey' website (availability stated as 'Coming Soon'). The survey data itself is planned for release on GitHub. | True | True | Survey data (corpus metadata, analysis results) claimed to be available on GitHub upon publication. | Understanding the comparative effects of domain-specific vs. large general models, data availability/sharing (though improving), effective application/integration of NLP into legal products/workflows, exploration of legal text generation, need for further research on training data effects, model architectures, and modeling techniques. | For the survey: Building a comprehensive corpus, qualitatively categorizing diverse papers, tracking evolving methods, assessing reproducibility across papers. For the field surveyed: Processing complex legal language, data availability and reproducibility, effectively adapting general NLP advancements to the specific legal domain. | Mentioned critique of LLMs ('dangers of stochastic parrots') in the general NLP context, but no specific risks for Legal NLP applications were detailed in this survey paper. |
| LX4k-4hBp0EJ.pdf | Google_Scholar | From Knowledge Management to Intelligence Engineering - A practical approach to building AI inside the law-firm using open-source Large Language Models | This paper explores options for law firms to build AI, advocating for a 'Creator Customiser Posture' that uses open-source LLMs fine-tuned with internal data to address privacy concerns. It introduces 'intelligence engineering' as an extension of knowledge management and demonstrates this approach with a proof-of-concept on contract data. | True | Market | True | 1.0 | Positive | The 'Creator Customiser Posture' for in-house AI development in law firms, involving layered fine-tuning (unsupervised and instruction-response) of open-source LLMs on internal and open-source legal data, and 'intelligence engineering' for knowledge management. | Proof-of-concept using Cerebras-GPT (590M variant) fine-tuned on combined text (1.8M tokens) from CUAD and MAUD datasets for unsupervised fine-tuning, and 8,000+ instruction-response pairs from CUAD for instruction fine-tuning. Evaluation was via perplexity scores on an 80:20 train/test split and qualitative analysis of outputs. | Unsupervised fine-tuning reduced perplexity on the test set from 8.33 to 4.68. Subsequent instruction fine-tuning reduced perplexity on its test set from 14.09 to 3.43. This demonstrated feasibility with reasonable data volumes, cost, and time. | NaN | NaN | NaN | NaN | Contract law, Corporate law (mergers and acquisitions) | US (based on datasets and examples like 'State of New York' law) | Base model (Cerebras-GPT) pre-trained on 'The Pile'. For fine-tuning: 1) Unstructured text (1.8M tokens) from public domain contract datasets (CUAD, MAUD) for unsupervised fine-tuning. 2) 8,000+ structured instruction-response pairs mined from the CUAD dataset for instruction fine-tuning. | The paper proposes the 'Creator Customiser Posture', which involves selecting an open-source foundational LLM, bringing it in-house, and performing layered fine-tuning: first, unsupervised fine-tuning on a domain-specific corpus, followed by instruction-response fine-tuning with structured task-specific data. It also introduces 'intelligence engineering' to enhance knowledge management. | The fine-tuned model is intended to be served internally within a law firm, potentially using on-premise or cloud compute resources, to be accessed by internal applications. | False | False | NaN | NaN | Requires internal infrastructure management and specialized AI skillsets for the 'Creator Customiser Posture'. Further quantitative evaluation on downstream tasks and investigation into the effect of model parameter size on performance are needed. | The paper highlights that other AI adoption postures (e.g., using vendor-managed services, sharing data with external vendors) raise risks related to data privacy, security, confidentiality, and IP ownership of models. The proposed 'Creator Customiser Posture' aims to mitigate these risks. |
| eL1CqE7Zs-EJ.pdf | Google_Scholar | ChatGPT and the Future of Legal Services | This article discusses the potential of ChatGPT to transform legal services in India by improving efficiency in research, drafting, and case prediction for advocates. It also highlights ChatGPT's potential to enhance public access to legal information and advice, particularly in underserved rural areas. | True | Market | True | 3.0 | Positive | ChatGPT | NaN | NaN | Limited access to legal services, particularly in rural areas. Difficulty for the public in finding and interpreting legal information through traditional means. | Utilizing ChatGPT for legal research, document drafting, case outcome prediction, and providing legal advice/information to the public. Adapting technology like machine learning for tasks such as translating court judgments. | Access to legal information, Access to legal advice | Rural populations, General public | General Law | India | NaN | NaN | NaN | True | False | ChatGPT is generally available as a service provided by OpenAI. | Need for continuous development and updates of the technology to ensure accuracy and keep pace with legal changes. Need to ensure ethical and responsible use. | Need for legal professionals (advocates) to adapt to the new technology to remain relevant. Ensuring ethical and responsible use of the technology. | NaN |
| FCFL8LhLaeMJ.pdf | Google_Scholar | COMMENTS IN RESPONSE TO THE GOVERNMENT OF CANADA ’S \nCONSULTATION QUESTIONNAIRE ON COPYRIGHT IN THE AGE OF GENERATIVE \nARTIFICIAL INTELLIGENCE | This paper responds to a Canadian government consultation, arguing against expanding copyright law to restrict Text and Data Mining (TDM) for AI training or to grant authorship to AI-generated content. It advocates for legal clarity favoring TDM (e.g., via fair dealing or exceptions) and maintaining the requirement of human authorship for copyright protection. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Lack of legal clarity on Text and Data Mining (TDM) under copyright law chills AI research and development; potential copyright restrictions might impede access to comprehensive training data, leading to biased or lower-quality AI; impossibility and inefficiency of clearing rights for vast training datasets; risk of copyright being expanded based on industry lobbying ('copyright trap') rather than public interest. | Amend the Copyright Act to clarify that TDM/informational analysis is permissible (e.g., new exception, broadening fair dealing); reject copyright protection for AI-generated works lacking human authorship; maintain human authorship requirement; apply existing infringement doctrines carefully; avoid specific remuneration rights for TDM training data use; focus copyright policy on public interest balance, not solely industry incentives. | Legal information summarization; Empirical legal research | NaN | Copyright Law | Canada | NaN | NaN | NaN | False | False | NaN | Lack of legal clarity regarding Text and Data Mining (TDM) permissibility under Canadian copyright law; potential difficulties in applying liability frameworks when AI outputs infringe copyright without clear human control; discrepancy between balanced public interest goals of copyright and industry-focused framing of policy debates. | Applying existing copyright concepts (authorship thresholds for AI-assisted work, substantial similarity and causality for infringement, authorization liability) to AI contexts; designing clear TDM exceptions that balance innovation and rights; avoiding biased AI outcomes potentially caused by restricted training data; practical impossibility of tracking/remunerating individual works in massive datasets. | Copyright restrictions chilling AI research and development; decreased AI quality/fairness due to biased/incomplete data; stifling human creativity by granting copyright to vast amounts of AI-generated content; undue expansion of copyright driven by lobbying; ineffective/burdensome TDM licensing; unethical uses of generative AI (e.g., misinformation, academic dishonesty); reduced competition and transparency in the AI field. |
| OTFgKz00ph8J.pdf | Google_Scholar | Automatic Linking of Judgements to UK Supreme Court Hearings | This paper describes J-HAL, an AI system using customized GPT embeddings to automatically link segments in UK Supreme Court written judgements to relevant timespans in court hearing videos. The goal is to create a user interface that bookmarks relevant video segments, improving access for legal professionals, academics, and the public. | True | Idealistic | True | 1.0 | Positive | Information Retrieval system (J-HAL) using customized OpenAI GPT embeddings (text-embedding-ada-002) to calculate semantic similarity between judgement paragraphs and hearing transcript segments. | Compared multiple IR models (BM25, GloVe, Entailment, Legal BERT, Asymmetric Search, GPT) on a human-annotated dataset of 3620 judgement-transcript segment pairs derived from 7 UK Supreme Court cases. Evaluated using Mean Average Precision (MAP) and Recall @ 5, 10, 15. Optimized GPT embeddings were evaluated by comparing cosine similarity distributions. | Customized GPT embeddings performed best. The overlap between cosine similarities for relevant and irrelevant links improved from 70.5% +/- 2.7% (original GPT) to 73.0% +/- 2.6% (customized GPT). Original GPT achieved MAP@5 of 0.691 and Recall@15 of 0.914 on the full dataset. | Court hearing recordings are extremely long, making manual review inefficient. Existing transcription methods make navigating recorded arguments difficult. | An automated tool (J-HAL) that uses AI to semantically link written judgments to specific timespans (bookmarks) in the corresponding hearing videos, facilitating navigation and understanding. | Access to court proceedings; Understanding judicial decision-making; Navigating legal audiovisual recordings. | Legal professionals, academics, and the general public. | UK Supreme Court cases (covering various fields, particularly public and constitutional law). | United Kingdom | Judgements (7 cases, 1.4M tokens) scraped from UK Supreme Court website; Video transcripts (53 hours) from UK National Archive transcribed via custom ASR; Pretrained embeddings (GloVe, MiniLM, Legal BERT, MS MARCO, OpenAI GPT); Human-annotated dataset of 3620 judgement-transcript pairs for evaluation and GPT customization. | Information Retrieval; Comparative evaluation of IR models; Zero-shot IR followed by human annotation; Embedding customization via classification model training and cosine similarity threshold optimization; User Interface development. | Deployed as a User-Interface (UI) platform. Mentioned application for a UK patent based on the UI. | False | False | NaN | Need for larger annotated datasets; Potential for more granular linking based on legal entities (articles, provisions, case names); Applicability to other domains needs exploration. | Difficulty of creating large-scale human annotations; Linking text across different language registers (written vs. spoken); Data preprocessing (segmentation, filtering); Balancing IR performance and computational speed. | NaN |
| xI22v_VkAogJ.pdf | Google_Scholar | Hallucinations and Truth:A Comprehensive Accuracy Evaluation of RAG,LoRA and DoRA | This paper empirically evaluates and compares Retrieval-Augmented Generation (RAG), Low-Rank Adaptation (LoRA), and Weight-Decomposed Low-Rank Adaptation (DoRA) using extensive FAQ-based datasets. The study finds that DoRA significantly outperforms RAG and LoRA in accuracy, relevance, and latency, making it a promising approach for accuracy-critical, domain-specific generative AI applications. | True | Market | True | 2.0 | Positive | Comparative evaluation of RAG, LoRA, and DoRA focusing on their performance in NLP tasks, with DoRA highlighted for its superior accuracy and efficiency. | Model fine-tuning and generation performance assessed on 20,000 FAQ-based queries/technical service tickets, with a knowledge base of 400,000 technical troubleshooting FAQs. Metrics included accuracy, relevance, latency, BLEU, ROUGE, MRR, NDCG. Generative output quality also evaluated on the SQuAD dataset. | DoRA achieved the highest accuracy (90.1%), a relevance score of 0.88, the lowest latency (110 ms per query), BLEU-4 score of 52.6, ROUGE-L score of 65.8, and a hallucination rate of 6.8%. | NaN | NaN | NaN | NaN | General legal services, legal research, legal document analysis. | International | A large-scale dataset of 400,000 technical troubleshooting FAQs (used as knowledge base and for fine-tuning LoRA/DoRA). Evaluation on a separate test set of 20,000 questions/technical service tickets. Publicly available SQuAD dataset also used for evaluating generative output quality. | NaN | NaN | False | False | NaN | Need for enhanced alignment with user expectations (e.g., via RLHF), multimodal capabilities, more efficient architectures, and addressing ethical concerns like bias and explainability. | Hallucinations, retrieval misalignment, cost-performance trade-offs, parameter selection, dataset quality, fine-tuning optimization, scalability, potential overfitting, and adaptation stability. | Generating factually incorrect outputs (hallucinations), retrieval errors leading to inaccuracies, misinformation, potential bias in AI models. |
| KjEhfsM_c_sJ.pdf | Google_Scholar | Enhancing Judicial Efficiency: The Role of AI and Blockchain in Modernizing Legal Systems | This paper explores how AI and blockchain technologies can improve judicial efficiency by addressing challenges like delays and backlogs. It reviews current applications, proposes an integrative framework, discusses associated risks and ethical considerations, and notably uses an AI pipeline involving generative AI for its own creation. | True | Idealistic | True | 3.0 | Positive | AI (for case management, legal research, predictive analysis), Blockchain (for record management, security, transparency), Smart Contracts (for automating agreements) | NaN | NaN | Judicial inefficiencies including procedural delays, case overload/backlogs, excessive costs, lack of transparency, and bureaucratic bottlenecks. | Integration of AI (for automation, predictive analysis), Blockchain (for secure, transparent records), and Smart Contracts (for automated agreements) within a strategic framework to streamline procedures and enhance efficiency. | Judicial efficiency, Case management, Reducing delays and backlogs, Transparency in judicial processes, Secure record-keeping, Access to justice | General public, potentially marginalized groups | General judicial processes | International | The paper reviews techniques using data like legal documents, case histories, and judicial records. The AI pipeline used for writing relied on the general large datasets of LLMs like ChatGPT. | An AI Pipeline utilizing tools like ChatGPT 4o, Perplexity, Consensus, Elicit, Zotero plugins, and Grammarly for topic selection, literature review, structuring, writing, and refinement. | NaN | False | False | NaN | Ethical concerns (privacy, bias, AI opacity), technical challenges (interoperability, skill development), resource constraints, need for human oversight, limitations of smart contracts complexity, underutilized opportunities (ADR, access to justice platforms, training tools). | Challenges implementing AI/Blockchain: Ethical issues, technical barriers (interoperability), resource needs, legal compliance, security. Challenges using AI pipeline for writing: Difficulty generating original reasoning, AI defaulting to reproduction, robustness of insights, managing context windows, tool instability (e.g., ChatGPT Canvas). | Ethical risks (privacy violations, algorithmic bias, lack of transparency/accountability), security risks (data manipulation/breaches if not secured), over-reliance on AI, potential for deskilling, technical failures, poor interoperability. |
| K9RrIhC9DNcJ.pdf | Google_Scholar | AI Luddites: Consumers Penalize Creative Work Output Generated by Arti cial Intelligence | This paper investigates consumer reactions to creative work (e.g., posters, scripts, logos) produced by generative AI versus humans through five experiments. It finds that consumers significantly penalize AI-generated creative output, particularly those holding 'Luddite' beliefs concerned about machines displacing humans, attributing this to a perceived lack of essential human process in creation. | True | Market | False | 3.0 | NaN | NaN | NaN | Participants significantly penalized creative works (posters, scripts, logos) when informed they were AI-generated compared to human-generated or baseline conditions. This negative reaction (penalization) was significantly stronger among individuals with higher 'Luddism' scores, linked to valuing the human creative process. | Negative consumer perception and penalization of AI-generated creative work, rooted in beliefs that AI lacks the appropriate 'human touch' or process for creativity, and concerns about job displacement (Luddism). | The paper highlights the difficulty in overcoming negative perceptions, noting that experimental interventions (educating about AI collaboration, co-creation exercises, transparency statements, premium 'human-made' labels) were unsuccessful. It calls for future research and proactive engagement from businesses and policymakers on AI integration strategies. | NaN | NaN | Briefly touches on disclosure (consumer protection implication) and labor issues (job security). | International (focus primarily on consumer reactions analogous to Western markets, e.g., US references) | NaN | Experimental design (between-subjects, pre-registered), surveys (MTurk, Prolific), statistical analysis (t-tests, regression, mediation analysis). | NaN | False | False | NaN | Need for effective interventions/communication strategies to mitigate negative consumer reactions to AI creativity; uncertainty about the future role of human creative professionals. | Consumer resistance and penalization of AI-generated creative work, particularly from those holding Luddite views. | Negative impact on brand image, perceived corporate unethical behavior, job displacement for creative professionals, societal disruption. |
| ZRf7TqNsvaYJ.pdf | Google_Scholar | Large Language Models (LLMs) for Legal Advice: A Scoping Review | This paper provides a scoping review of the use and potential use of Large Language Models (LLMs) for generating legal advice, focusing on the US and UK jurisdictions. It synthesizes literature on the benefits, such as reduced costs and improved access, and significant risks, including misinformation (hallucinations), bias, copyright issues, and the need for regulation. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | High cost of traditional legal services; Risk of LLMs providing false/misleading information (hallucinations); Potential for LLMs to encourage vexatious litigation, delaying justice for others; Lack of attorney-client privilege and ethical guarantees with LLMs; Privacy risks associated with LLM data use; LLM biases reinforcing societal inequalities. | Improving LLM accuracy (e.g., linking to verified sources); Developing "justice bots" to help laypeople navigate legal issues; Using LLMs to translate legal jargon into plain language; Implementing technical safeguards (watermarking, fine-tuning, censoring); Establishing clear regulations and industry standards (e.g., transparency obligations, risk-based approaches); Educating users (lawyers and public) about LLM limitations. | Reducing legal costs, Legal information access and understanding (plain language), Issue identification for laypersons, Assistance for self-represented litigants. | People with lower socio-economic status, General consumers facing corporations or bureaucracy. | Broad / Multiple fields including Civil litigation, Tax law, Contract law, Criminal law (sentencing/probation context), Consumer law. | US, UK | General LLMs: Terabytes of broad internet data, potentially including copyrighted materials. Legal-specific LLMs: Fine-tuned on legal text databases (cases, legislation) from sources like Westlaw, LexisNexis, Casetext, or proprietary curated legal/financial datasets (e.g., KL3M). | Sandbox testing, User evaluation, Red-teaming (for Harvey); Benchmarking using curated legal task datasets (LawBench, LegalBench); Reinforcement Learning with Human Feedback (RLHF) for alignment; Ontology creation from legal concepts (older ML example). | Internal deployment within law firms (e.g., Harvey); Public web/app access for consumers (e.g., DoNotPay, ChatGPT); Planned commercial release for industry professionals (e.g., KL3M); Research platforms/benchmarks. | True | False | Public access via web interfaces/APIs (e.g., ChatGPT, Gemini) some with free tiers; Consumer service model (e.g., DoNotPay). | Need for systematic empirical evaluation of LLM legal advice quality and user perception; Understanding and mitigating cross-jurisdictional/cultural biases; Continuous evaluation due to rapid model evolution; Need for qualitative research on lawyer adoption/experience; Legal clarification on AI copyright (input and output); Gaps and inconsistencies in regulatory approaches (US/UK). | Ensuring accuracy / mitigating hallucinations; Mitigating dataset bias; Navigating copyright complexities (training data and generated output); Implementing effective and robust safeguards (alignment, preventing misuse); Managing data poisoning and model collapse risks; Addressing the 'black box' transparency problem; Managing user expectations and avoiding misleading claims. | Generating false or misleading legal information (hallucinations); Wasting court resources and causing delays; Undermining trust in the legal system; Encouraging vexatious litigation; Lack of attorney-client privilege and confidentiality; Disclosure of private user data; Embedding and amplifying societal biases; Copyright infringement (input data and generated output); Data poisoning and pollution leading to model degradation; Circumvention of safety guardrails (jailbreaking). |
| Enhancing_the_Precision_and_Interpretability_of_Retrieval-Augmented_Generation_RAG_in_Legal_Technology_A_Survey.pdf | Google_Scholar | Enhancing the Precision and Interpretability of Retrieval-Augmented Generation (RAG) in Legal Technology: A Survey | This paper surveys the application of Retrieval-Augmented Generation (RAG) techniques within the legal technology domain, reviewing methods, applications, datasets, and evaluation metrics. It highlights challenges such as hallucination and computational cost, discusses ethical considerations, and proposes future research directions for improving RAG systems in legal contexts. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | Technical challenges hindering RAG application in law: Computational cost and complexity, potential for hallucination or no response, difficulty handling complex legal queries, heavy dependence on retrieval accuracy, and limitations of current evaluation metrics. | Improving RAG performance through technical strategies: Advanced pre/post-retrieval optimization (e.g., query rewriting, reranking, KG integration, adaptive chunking), hybrid retrieval approaches, adaptive retrieval mechanisms, fine-tuning models on legal corpora, and advanced sampling strategies. | NaN | NaN | Privacy law, legislative texts, public law, criminal law, statutory law, immigration law, contract law, case law, patent law, tax law, border inspection law. | Multiple specific jurisdictions (US, China, Australia, EU, France, Italy, Montenegro, Pakistan) and general applicability. | Discusses various datasets used in surveyed papers, including public and private sources like legal judgments, case law repositories (e.g., Caselaw Access Project), legislative texts, court records, contracts, privacy policies, EU laws (EUR-Lex), patent documents, and domain-specific Q&A pairs. Data includes structured and unstructured text. | Discusses various RAG pipeline design choices: embedding models (BERT-based, OpenAI, multilingual), retrieval methods (sparse, dense, hybrid), retrieval processes (one-time, iterative, adaptive), chunking strategies (sentence, semantic, pattern-based), augmentation (prompt engineering), generation models (GPT, Llama), fine-tuning (QLoRA, full), knowledge graph integration, reranking algorithms, query rewriting, and indexing techniques (HNSW). | NaN | False | False | NaN | Need to expand RAG applications to more legal domains and jurisdictions (especially non-English); lack of robust, open-source benchmark datasets; need for better multilingual RAG techniques; requirement for standardized RAG evaluation metrics (including interpretability and ethics); limited exploration of integrating RAG with other AI methods like reinforcement learning; insufficient attention to ethical considerations (privacy, bias, safety, trust) in existing systems. | Computational cost and complexity (API usage, in-house LLM maintenance); achieving robustness against hallucination and failure to respond (addressing RAG failure points); handling complex, ambiguous, or multi-hop legal queries; high dependence on the accuracy and relevance of the retrieval step; limitations and lack of standardization in evaluation metrics for factual correctness and semantic quality. | Bias propagation from data or models, privacy violations through handling sensitive legal data, generating hallucinated or factually incorrect legal information/advice, safety concerns arising from unreliable outputs, lack of transparency and accountability in RAG system decisions. |
| ck8Ac0neujYJ.pdf | Google_Scholar | AI and access to justice : How AI legal advisors can reduce economic and shame-based barriers to justice | This paper argues that publicly funded Artificial Intelligence Legal Advisors (AI LAs), particularly large language models specialized for law, can lower barriers to accessing the legal system. It focuses on how these tools can mitigate economic costs and shame-based cultural obstacles during the initial information-gathering stage of pursuing justice. | True | Idealistic | True | 3.0 | Positive | Artificial Intelligence Legal Advisors (AI LAs), described as specialized AI systems (potentially LLMs) providing legal information and preliminary assessment. | NaN | NaN | Economic barriers (financial costs, time/opportunity costs, transportation costs, lack of resources, lack of awareness of rights or affordable legal options) and shame-based cultural barriers (stigma associated with seeking legal help, particularly for victims of intimate partner violence, individuals disputing cultural norms like inheritance practices, or victims of fraud; fear of judgment or social reprisal). | Developing and deploying publicly funded AI Legal Advisors (AI LAs) that offer reliable, specific, and intelligible legal information, preliminary case assessment, and interactive explanations. This aims to reduce costs and provide a private, non-judgmental means of information gathering. | Access to legal information, preliminary case assessment, understanding legal rights and recourse, reducing barriers during the information-gathering stage. Specific examples include intimate partner violence (IPV) protection orders, inheritance rights disputes, and pursuing claims related to fraud. | People with low socio-economic status (SES), marginalized populations facing cultural barriers (e.g., women expected to relinquish inheritance rights), victims of intimate partner violence (IPV), victims of fraud. | General Civil Law, Housing Law (example: JusticeBot), Family Law (IPV context), Inheritance Law, Consumer Law (Fraud context). | Anglo-American common law systems (stated scope). Examples also draw from the US, UK, Canada, and Quebec (JusticeBot). | Implied to be case law, noted as potentially containing historical biases. | NaN | Advocates for public funding by governments and international organizations. | False | False | NaN | Ensuring AI LAs reach reliability and accuracy standards comparable to human lawyers. Developing methods to mitigate biases present in legal training data (case law). | Achieving sufficient reliability and accuracy for AI LAs. Mitigating inherent biases in training data. Potential for increased caseloads on the existing legal system. Establishing frameworks for legal responsibility and liability for AI errors. | AI LAs inheriting and perpetuating biases from historical case law. Potential for increased litigation burdening the legal system. AI LA malfunction or error leading to incorrect advice (e.g., dissuading a valid claim or encouraging a futile one), causing harm to users. Difficulty in assigning legal responsibility for harms caused by AI advice errors. |
| 2501.00957v3.pdf | Google_Scholar | Generative AI and LLMs in Industry: A text-mining Analysis and Critical Evaluation of Guidelines and Policy Statements Across Fourteen Industrial Sectors | This paper analyzes 160 guidelines and policy statements concerning Generative AI (GAI) and Large Language Models (LLMs) across fourteen industrial sectors using text-mining techniques. It identifies key governance themes, sector-specific variations, and gaps, proposing recommendations for adaptive, ethical, and human-centric AI integration in industry. | True | Market | True | 3.0 | NaN | Text-mining analysis (tokenization, stemming, lemmatization, TF-IDF, KMeans clustering, Sankey diagrams) and qualitative thematic analysis applied to AI policy documents. | Analysis of 160 GAI/LLM guidelines and policy statements collected from companies across 14 industrial sectors globally. Evaluation involved qualitative semantic analysis, frequency analysis, TF-IDF heatmap analysis, and Sankey diagram keyword co-occurrence analysis. | Identified common themes (e.g., privacy, data, risk, ethics, integrity) and sector-specific concerns across industries. Revealed gaps in guidelines regarding disclosure, human-centricity, democratization, alternative methods, misinformation, and skepticism. Highlighted varying levels of AI adoption maturity and governance approaches across sectors. | NaN | NaN | NaN | NaN | General AI Governance, Policy Analysis, Legal Tech / Legal Services / Intellectual Property Law (as one of the 14 sectors analyzed) | International | A dataset ('IGGA') of 160 industrial guidelines and policy statements for GAI/LLMs collected by the authors from company websites, official documents, and media interviews, covering 14 sectors across multiple continents. Claimed to be available on Harvard Dataverse. Consists of unstructured text. | Systematic document collection, Qualitative thematic analysis, Text-mining (tokenization, stopword removal, stemming, lemmatization, TF-IDF, KMeans clustering), Visualization (Sankey diagrams, frequency plots). | NaN | False | False | NaN | NaN | Data collection: Identifying companies with official guidelines, substituting with policy statements or interviews when guidelines were absent, ensuring diversity across sectors/geographies despite exclusions. | Data security breaches, misinformation generation, algorithmic bias, intellectual property infringement, cybersecurity vulnerabilities, job displacement, erosion of human oversight, privacy violations, lack of fairness and accountability, safety risks (e.g., healthcare, construction), ethical risks, marketing hype obscuring limitations and risks, lack of transparency. |
| BGBNDfe58egJ.pdf | Google_Scholar | Multidisciplinary collaboration : key players in successful implementation of ChatGPT \nand similar generative artificial intelligence in manufacturing, finance, retail, \ntransportation, and construction industry | This paper argues that successful implementation of ChatGPT and similar generative AI across industries like manufacturing, finance, retail, transportation, and construction requires collaboration among multidisciplinary teams. These teams, comprising experts like AI specialists, domain experts, ethicists, legal professionals, and UX designers, are essential for navigating technical, operational, ethical, and regulatory challenges. | True | Market | True | 3.0 | NaN | Multidisciplinary collaboration strategy for implementing generative AI (e.g., ChatGPT) in industry. | NaN | NaN | NaN | NaN | NaN | NaN | Data Privacy Law, AI Ethics Regulation, Regulatory Compliance (Industry-specific), Intellectual Property Law | International | Industry-specific data relevant to the target sector (e.g., manufacturing processes, financial terminology/regulations, retail customer data/transactions, transportation logistics, construction plans/codes/historical data). The paper discusses the need for such data but does not specify a dataset used. | User Experience (UX) design, Human-Computer Interaction (HCI) principles. | Integration into existing IT infrastructure, development of user interfaces/APIs, training programs for workforce, change management strategies, cybersecurity protocols implementation. | False | False | NaN | NaN | Communication gaps between diverse team members, addressing ethical implications (data privacy, algorithmic bias, job impact), keeping up with rapid AI evolution, managing data security concerns, overcoming resistance to technological change, need for continuous training and skill development. | Data security vulnerabilities (data breaches, cyber threats), bias in AI-generated decisions, ethical violations (privacy concerns), legal and regulatory non-compliance, negative impact on employment, potential for errors leading to operational or physical risks (e.g., structural issues in construction). |
| mdjWtUfQe6AJ.pdf | Google_Scholar | The Legal Ethics of Generative AI | The paper argues that lawyers can ethically use generative AI by following existing Model Rules, particularly regarding confidentiality, client consultation, competence, and oversight. It criticizes recent court orders banning or requiring disclosure of AI use as unnecessary and overbroad, and posits that competence may eventually require lawyers to use generative AI. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | High cost and lack of availability of traditional legal services leading to unmet legal needs (implied by the mention of the 'access-to-justice crisis' and AI's potential to help). | Suggests generative AI could become an important tool for addressing unmet legal needs and the access-to-justice crisis. | General access to justice crisis, unmet legal needs. | NaN | Legal Ethics, Civil Procedure | United States | NaN | NaN | NaN | False | False | NaN | The general 'access-to-justice crisis' and 'unmet legal needs'. Technologically, the tools are still evolving, their reliability needs improvement (e.g., hallucinations), and use cases are still emerging. A need for mandatory training on generative AI for law students and lawyers is also suggested. | NaN | Violation of client confidentiality (Rule 1.6), inaccuracy and hallucinations in AI output leading to flawed legal work or filings (violating Rule 1.1, Rule 3.1, FRCP 11), inherent bias in AI models, unauthorized practice of law, issues related to duties to prospective clients (Rule 1.18), incorrect billing or fee arrangements (Rule 1.5). |
| ti1sOnOBim4J.pdf | Google_Scholar | LEGILM: A F INE-TUNED LEGAL LANGUAGE MODEL FOR DATA COMPLIANCE | This paper introduces LegiLM, a legal language model derived from SaulLM-7B and fine-tuned on GDPR-specific data to automatically assess data protection compliance in contracts. Evaluated on a custom benchmark, LegiLM outperformed baseline models in accuracy and justification quality for GDPR compliance tasks. | True | Market | True | 1.0 | NaN | LegiLM: A legal language model based on fine-tuning SaulLM-7B for GDPR compliance detection in data-sharing contracts using instruction tuning and contrastive learning. | Custom benchmark created from GDPR texts, case law, data-sharing contracts, and privacy policies. The benchmark included 200 multiple-choice questions, 150 open-ended questions, and 50 real-world case studies. Metrics used were Accuracy, F1-Score, and Compliance Justification Quality, compared against models including Saul-7B, GPT-4, and various Chinese legal LLMs. | LegiLM-Advanced achieved the highest scores: 68.05% Accuracy, 68.21% F1-Score, and 'High' Justification Quality, outperforming Saul-7B (62.10% Accuracy, 63.15% F1-Score) and other baselines. | NaN | Develop domain-specific fine-tuned language models like LegiLM to automate and streamline compliance assessments for data protection regulations (e.g., GDPR), reducing the burden on legal professionals. | NaN | NaN | Data Protection Law, Privacy Law, Contract Law | EU (GDPR focus), USA (mentions CCPA), English-speaking jurisdictions (base model focus) | Fine-tuning dataset includes GDPR text, CCPA text, EDPB guidelines, EUR-Lex interpretations, EU case law, GDPR Fines Database, GDPR Enforcement Tracker dataset, custom-annotated data-sharing contracts, and various privacy policies. Derived from public sources (e.g., official websites, EUR-Lex) and custom creation/annotation. Base model (SaulLM-7B) trained on a large English legal corpus. | Supervised fine-tuning of a pre-trained LLM (SaulLM-7B), instruction tuning, contrastive learning for generating negative examples and improving answer diversity. | Resources made publicly available via GitHub. | True | True | Publicly available on GitHub: https://github.com/DAOLegalAI/LegiLM | Current model is specific to GDPR; future work needed to expand coverage to data protection regulations in other countries and regions. | Ensuring nuanced understanding of complex legal requirements (GDPR), maintaining answer diversity and avoiding bias during fine-tuning. | NaN |
| yU764-jHuYIJ.pdf | Google_Scholar | Do Large Language Model Benchmarks Test Reliability? | The paper argues that current LLM benchmarks fail to adequately measure model reliability due to pervasive label errors, proposing meticulously curated "platinum benchmarks" instead. Evaluating frontier LLMs on these cleaned benchmarks reveals significant remaining failures even on simple tasks and uncovers specific, consistent error patterns. | True | NaN | True | 1.0 | Neutral | Platinum benchmarks: A methodology for creating reliable evaluation datasets by systematically identifying and correcting/removing label errors and ambiguities in existing benchmarks. | Subsets of 15 existing benchmarks (e.g., GSM8K, SVAMP, MMLU Math, SQuAD2.0, VQA v2.0) were manually revised using LLM agreement flagging and inspection. Various frontier LLMs (e.g., GPT-4o, Claude 3.5, Llama 3, Gemini, o1 series) were then evaluated on these cleaned 'platinum' subsets, reporting error counts. | Frontier LLMs still make errors on simple tasks in the cleaned benchmarks (most models failed on most benchmarks). Many original benchmark errors were due to label noise (e.g., ~75% on SVAMP). More generally capable models were more reliable, but reliability varied by task. Consistent failure patterns (e.g., 'first event bias', 'rounding up primes') were identified. | NaN | NaN | NaN | NaN | NaN | International | NaN | Analysis of existing benchmarks, use of multiple LLMs to identify disagreements/errors, manual review and annotation, quantitative evaluation of LLM performance on revised benchmarks. | Release of the created platinum benchmark datasets and associated code on GitHub. | True | True | The platinum benchmark datasets are available via code release on GitHub: https://github.com/MadryLab/platinum-benchmarks | The primary gap identified is the lack of focus on reliability (vs. capability) in LLM evaluation, leading to benchmarks being retired before ensuring models are truly error-free. The paper also notes limitations in its own benchmark coverage, size, and the potential for remaining errors. | The difficulty and resource-intensive nature of creating error-free benchmarks through manual verification. Benchmark noise obscuring true model reliability. Ensuring LLM reliability even on simple tasks remains a challenge for current models. | Deploying unreliable LLMs in high-stakes domains (like legal services, healthcare, finance) due to inadequate reliability evaluation, potentially causing significant harm, financial loss, or legal liability (citing Moffatt v. Air Canada). |
| XurNiV9wTRQJ.pdf | Google_Scholar | Chat Kanoon: A Novel Approach to Legal Assistance in India | This paper introduces ChatKanoon, a multilingual AI chatbot leveraging GPT-4 and Llama2 70B through instructional techniques to provide legal assistance within the Indian legal system. It aims to democratize access to legal information, reduce costs, and enhance the efficiency of legal processes in India. | True | Idealistic | True | 1.0 | Positive | ChatKanoon: A multilingual AI chatbot using GPT-4 and Llama2 70B APIs via instructional techniques (not traditional fine-tuning), guided by Indian legal documents and case laws. | Descriptive evaluation through example user interaction scenarios and UI demonstrations with sample prompts in multiple Indian languages (e.g., Marathi, English) and corresponding system responses for legal queries. | The paper claims ChatKanoon successfully provides detailed and accurate legal information and advice in response to queries on topics like cyberbullying and distinctions between civil/criminal law, in multiple languages, based on example scenarios. | Limited access to legal information and assistance, high costs of legal services, complexity of legal procedures and laws, linguistic diversity challenges, scarcity of specialized legal guidance, and urban-rural disparities in legal service accessibility. | Developing and deploying AI-powered, multilingual chatbots like ChatKanoon, tailored to specific legal contexts (e.g., Indian law), to provide accessible, affordable legal information, simplify understanding of legal concepts, and enhance the efficiency of legal processes. | Access to legal information and advice, legal literacy and education, cost reduction for legal services, efficiency in legal processes, multilingual legal support. | General public in India, particularly economically weaker sections, low-income earners, those in rural areas, and individuals facing language barriers to accessing legal information. | General Indian Law, with examples from cyberlaw, civil law, and criminal law. | India | Utilizes pre-trained foundation models (GPT-4, Llama2 70B APIs). Instructional techniques are applied, informed by a 'diverse array of legal documents and case laws' from the Indian legal system, as opposed to fine-tuning the models. | Application of instructional techniques to pre-trained LLM APIs (GPT-4, Llama2 70B). System architecture built with Next.js (React) for the front-end, Node.js for server-side logic, employing a component-based design. | Hosted and deployed on the Vercel platform. | False | False | NaN | Technical gaps include high computational needs for LLMs, ensuring predictable and user-controlled outputs, refining instructional guidance precision, achieving comprehensive regional language support, and enhancing document processing capabilities. Societal and ethical gaps involve addressing user data privacy/security and the ongoing need for human oversight and verification of AI-generated legal advice. | High computational requirements for the large language models (GPT-4, Llama2 70B), ensuring model outputs are predictable and user-controllable, addressing user data privacy and security concerns for sensitive legal queries, effectively guiding LLMs through instructions (instructional techniques), and providing comprehensive support for India's diverse regional languages. | Potential for the AI to generate unexpected or inaccurate legal advice, risks to user data privacy and security if not robustly protected, and the possibility of users over-relying on AI-generated information without seeking verification from qualified legal professionals. |
| 3583780.3614953.pdf | Google_Scholar | Leveraging Event Schema to Ask Clarifying Questions for Conversational Legal Case Retrieval | This paper proposes LeClari, a method using a legal event schema (LEVEN) to improve the generation of clarifying questions by Large Language Models (LLMs) for conversational legal case retrieval. LeClari employs an event selection module optimized with ranking-oriented rewards to guide LLMs, significantly enhancing downstream retrieval performance compared to baseline methods. | True | Market | True | 1.0 | NaN | LeClari: A conversational search model using a legal event schema (LEVEN) for prompt construction and an Event Selection Module (with transformer-based interaction layers) optimized via Reward Augmented Maximum Likelihood (RAML) with ranking-oriented rewards to guide LLMs in generating clarifying questions for legal case retrieval. | Evaluated on two Chinese criminal case retrieval datasets (LeCaRD, CAIL2022-LCR) using simulated conversations with LLMs (ChatGPT, GPT-4) as user simulators. Performance measured by MAP, P@5, NDCG@10 using BERT-Crime and LawFormer as rankers, compared against baselines including direct LLM prompting ('w/o Event') and various event selection strategies (Random, MaxE, GBS, LinRel, GP+UCB/EI). | LeClari significantly outperformed all baselines on both datasets. For instance, on CAIL2022-LCR using GPT-4 + BERT-Crime, LeClari achieved NDCG@10 of 0.7104, compared to 0.6105 for the 'w/o Event' baseline. | NaN | NaN | NaN | NaN | Criminal Law | China | The Event Selection Module was trained using simulated conversational data derived from the LeCaRD and CAIL2022-LCR datasets, incorporating the LEVEN legal event schema (publicly available) as external knowledge. Training involved ranking-oriented rewards based on performance improvement using pre-trained legal language models (BERT-Crime, LawFormer). | Prompt engineering for LLMs, incorporation of external structured knowledge (legal event schema), transformer-based neural networks for interaction modeling, Reward Augmented Maximum Likelihood (RAML) optimization. | NaN | False | False | NaN | Dynamically determining when to stop asking clarifying questions is mentioned as future work. | LLMs directly prompted for clarifying questions in legal case retrieval often produce low-utility or redundant questions. Aligning the question generation process with downstream retrieval performance improvement. | NaN |
| 2hMq09zdNrgJ.pdf | Google_Scholar | Tech -Business Analytics – a Review -based New Model to Improve the Performances of Various Industry Sectors | This paper proposes a new conceptual model called Tech-Business Analytics (TBA), integrating traditional Business Analytics (BA) and Big Data with broader Information, Communication, and Computation Technologies (ICCT). The goal of TBA is to enhance decision-making and improve performance across various industry sectors. | True | Market | False | 1.0 | NaN | Tech-Business Analytics (TBA) model - a conceptual integration of Business Analytics/Big Data with broader ICCT underlying technologies (AI, Cloud, IoT, Blockchain, etc.). | The paper is review-based and proposes a conceptual model; no empirical testing or specific evaluation methodology is described. | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | Literature review, analysis of current status, prediction of desired status, research gap identification, qualitative ABCD analysis framework, conceptual model development. | NaN | False | False | NaN | NaN | General challenges related to implementing analytics solutions include ensuring data quality, managing model complexity, achieving timely results, gaining user trust and confidence, and integrating analytics into organizational capabilities. | Potential misuse of analytics knowledge for discrimination; complexity requiring specialised skills; risks related to data security and ethics (data breaches); potential for AI-integrated analytics leading to opacity, poor judgement, and operational inefficiency (citing Rana et al.). |
| o30m2SrIoEMJ.pdf | Google_Scholar | LEGAL LITERACY AND GENERATIVE ARTIFICIAL INTELLIGENCE: COMPARING THE EDUCATION LAW KNOWLEDGE OF PRACTICING EDUCATORS AND LARGE LANGUAGE MODELS LIKE CHATGPT | This paper compares the education law knowledge of practicing K-12 educators with several large language models (LLMs) like ChatGPT, using a pre-existing true/false survey. It finds that LLMs generally outperform educators but are not infallible, highlighting their potential to supplement, but not replace, educator legal literacy. | True | Idealistic | True | 2.0 | Positive | Evaluation of existing LLMs (ChatGPT GPT-3.5, GPT-4 with/without plugins, Google Bard, Microsoft Bing AI Chat Mode) for education law knowledge. | Zero-shot prompting of LLMs using the 34 true/false questions from the Principals’ Education Law Survey (Militello, Schimmel, & Eberwein, 2009). Performance was compared against established correct answers and historical scores of teachers and principals. | Four out of five LLMs (ChatGPT versions, Bing AI) achieved >70% proficiency (76.47% correct), outperforming average teacher (40.04%) and administrator (58.71%) scores. LLM performance varied by legal topic, scoring highest on constitutional law (80%) and lowest on liability (57.78%). | Educators' lack of legal knowledge and literacy, fear/anxiety towards legal issues, and reliance on potentially inaccurate sources. Limitations of LLMs including inaccuracies ('hallucinations'), inconsistent performance, inherent biases, and unresolved copyright/ownership issues. | Leveraging LLMs as tools to supplement educators' legal knowledge. Developing educators' technological proficiency and legal literacy skills to critically evaluate and verify LLM outputs ('trust but verify'). Rethinking educator preparation programs to incorporate responsible AI use. | Educator legal literacy; K-12 education law topics including student rights (discipline, free speech, general rights), teacher rights (free speech, general rights), liability, religion in schools, special education, school authority, student records, copyright. | K-12 Educators (teachers and school administrators). | Education Law | United States | The paper mentions LLMs are trained on "huge swaths of information from the internet and other sources" but does not provide specific details on the datasets used for the evaluated models (ChatGPT, Bard, Bing). Training data is implied to be vast, unstructured text, and largely proprietary. | NaN | NaN | True | False | The LLMs studied (ChatGPT, Google Bard, Microsoft Bing AI) are generally publicly accessible via web interfaces. | Need for research on LLM reliability and statistical significance of findings. Assessing LLM legal literacy (application) beyond knowledge recall. Updating assessment tools for contemporary legal issues. Understanding LLM training data limitations. Addressing ethical/legal issues (privacy, liability, equity). | Ensuring LLM accuracy and avoiding 'hallucinations'. Achieving consistent results from LLMs. Prompt engineering for specific answer formats. Evaluating models using potentially outdated survey instruments. Limitations due to lack of transparency regarding LLM training data. | LLM 'hallucinations' leading to false legal information and potential negative consequences (e.g., defamation, incorrect legal actions). Copyright infringement issues related to training data. Bias amplification. Over-reliance without critical verification. Student data privacy issues. Potential misuse for academic dishonesty. |
| The_Truth_s_About_AI_and_Legal_Education_A_Discourse_Analysis_of_the_Conflicting_Narratives_Regarding_the_Implications_of_Generative_AI_for_the_Teaching_of_Law.pdf | Google_Scholar | The Truth(s) About AI and Legal Education: A Discourse Analysis of the Conflicting Narratives Regarding the Implications of Generative AI for the Teaching of Law | This paper analyzes the conflicting narratives surrounding the impact of generative AI on legal education. It employs discourse analysis to explore different perspectives on how AI will affect the teaching of law. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal Education | NaN | NaN | NaN | NaN | False | False | NaN | NaN | NaN | NaN |
| 8JE3jvLmRJIJ.pdf | Google_Scholar | NATURALIZING LEGAL INTERPRETATION AFTER GENERATIVE AI | This essay explores how generative AI, particularly LLMs, can be integrated into legal interpretation by aligning with constitutive theories of language and complexity science. It argues for a conceptual framework that harmonizes AI's computational power with the contextual, moral, and emergent dimensions of human legal reasoning. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | The primary obstacle identified is the inadequacy of current AI approaches, often based on simplistic 'designative' views of language, to grasp the complex, contextual, moral, and emergent nature of legal interpretation, leading to biased or superficial outcomes that undermine justice. | Adopting a conceptual framework for legal AI based on constitutive theories of language and complexity science, where AI augments human judgment rather than replacing it, thereby aligning AI with the dynamic and morally-rich nature of law to foster fairer outcomes. | Ensuring AI contributes to justice in legal interpretation and reasoning, potentially enhancing accessibility and efficiency in legal practice. | NaN | General jurisprudence and legal interpretation, with examples from contract, family, tort, constitutional, and criminal law. | Primarily US (due to case law examples), but discusses principles with broader, potentially international, applicability. | NaN | NaN | NaN | False | False | NaN | The primary gap is the inadequacy of current legal AI to truly engage with the moral, contextual, and emergent dimensions of legal reasoning, stemming from a limited philosophical understanding of language and law. This leads to challenges in developing AI that is fair, just, and genuinely supportive of complex legal interpretation, thereby hindering its potential for improving access to justice. | NaN | Key risks include the perpetuation of systemic biases due to reliance on historical data (e.g., racial bias in predictive algorithms like COMPAS), the creation of a misleading 'facade of objectivity' by AI in value-laden legal decisions, and the lack of transparency and accountability in 'black-box' AI systems. |
| 4kOtMViO_DAJ.pdf | Google_Scholar | Data-Driven Justice: Effective Data Governance to achieve SDG 16 | This paper examines the role of effective data governance in achieving Sustainable Development Goal 16 (Peace, Justice, and Strong Institutions) in India, highlighting the challenges posed by the overburdened justice system and fragmented legal frameworks. It explores the potential of data analytics and AI to improve access to justice, transparency, and court efficiency while noting the need for robust governance to mitigate risks. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Lack of a holistic national data governance framework; fragmented legal framework for data; overburdened judicial system with large case backlogs and high numbers of unsentenced prisoners; low judge-to-population ratio; challenges in digital accessibility potentially leading to inequality. | Implement effective data governance frameworks (like the proposed NDGFP); adopt people-centric approaches prioritizing citizens' needs and experiences; utilize AI and data analytics to understand systemic problems, crime statistics, and legal aid needs; enhance court efficiency through technology (e-Courts, NJDG for scheduling, case classification); leverage online forums and AI (e.g., chatbots) for accessible legal support; develop local policies based on local data mapping; foster collaboration between government branches (executive/judiciary); establish clear leadership for justice data. | Access to justice; Sustainable Development Goal 16 (Peace, Justice, Strong Institutions); Data governance; Rule of law; Court efficiency; Case pendency reduction; Legal aid; Public access to judicial information. | Poor and marginalized communities disproportionately affected by the slow legal system; general citizens seeking access to justice; potentially people with disabilities facing digital accessibility barriers. | Justice System Administration, Data Privacy Law, Information Technology Law, Constitutional Law (Right to Privacy) | India | NaN | NaN | NaN | False | False | NaN | Lack of a holistic and binding national data governance framework; fragmented existing legal protection; slow integration and updating of technology within the judicial system (e.g., NJDG); lack of clear guidelines for data collection, processing, and sharing; insufficient technical capacity among justice system actors; need for a cultural shift towards data-driven justice; inadequate focus on people-centric approaches and understanding user needs/experiences; limited use of data for understanding local justice issues. | Legislative fragmentation and evolving data governance landscape; inconsistencies between policy Gaps between policy formulation and practical implementation; slow adoption and integration of rapidly advancing technologies like AI; ensuring data privacy and security amidst increased data sharing; preventing potential discrimination arising from data use; addressing cybersecurity threats; building technical capacity among court staff, judges, lawyers, and litigants; fostering a cultural shift within the justice system to embrace data-driven methods; complexity in assigning leadership responsibility for justice data governance. | Privacy violations through data sharing; potential for increased discrimination based on data analysis; unauthorized surveillance; cybersecurity threats to sensitive judicial data; inaccuracy and unreliability of generative AI tools (like ChatGPT) used for legal support, posing risks particularly for legally unrepresented individuals. |
| Hzv8CB3O47YJ.pdf | Google_Scholar | LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development | This paper introduces LeXFiles, a diverse English multinational legal corpus, and LegalLAMA, a legal knowledge probing benchmark, aimed at advancing the development and detailed analysis of legal pre-trained language models (PLMs). The authors also release and evaluate two new legal PLMs, LexLMs, finding that diverse pre-training corpora and model size are crucial for effective upstream, probing, and downstream performance. | True | Idealistic | True | 1.0 | Positive | LeXFiles (multinational English legal corpus), LegalLAMA (legal knowledge probing benchmark), and LexLM (RoBERTa-based legal PLMs). | LexLM models were evaluated on: 1. Upstream Masked Language Modeling (MLM) performance (Accuracy/P@1) on LeXFiles sub-corpora. 2. Probing performance (Mean Reciprocal Rank - MRR, P@1) on the LegalLAMA benchmark. 3. Downstream performance (micro-F1, macro-F1) on selected LexGLUE classification tasks after single-epoch fine-tuning. | The LexLM-L (large) model generally performed best. On LegalLAMA, LexLM-L achieved an average MRR of 77.4%. On selected LexGLUE downstream tasks, LexLM-L achieved an average micro-F1 of 73.3% and macro-F1 of 51.0%. | Lack of diverse, multinational legal corpora; insufficient benchmarks for probing specific legal knowledge in PLMs; limited understanding of how pre-training settings and model characteristics affect legal language understanding. | Release of LeXFiles, a diverse multinational English legal corpus; release of LegalLAMA, a benchmark for probing legal knowledge in PLMs; development and release of LexLMs, new PLMs trained on diverse legal data to improve legal language understanding. | Democratizing legal information, improving legal services and tools for legal professionals and laypersons. | Laypersons, legal professionals, and the NLP research community working on legal AI. | Legislation, Case Law, Contracts, Human Rights Law (ECHR), Criminal Law. | EU, CoE, Canada, US, UK, India. | LexLM models were trained on LeXFiles, a new corpus of approx. 19 billion tokens from 6 million publicly available, English, unstructured legal documents (legislation, case law, contracts) sourced from EUR-Lex, UK.LEGISLATION.GOV.UK, BAILII, Court Listener, SEC-EDGAR, Canadian official legislation portal, HUDOC, and re-distributions from Henderson* et al. (2022) and Malik et al. (2021). | For LexLM: Warm-starting from RoBERTa checkpoints, training a new BPE tokenizer on LeXFiles, continued pre-training using Masked Language Modeling on LeXFiles with sub-corpora sampling smoothing. For LegalLAMA: Creation of mask-filling probing tasks based on LAMA, extended for multi-token targets, using test subsets of LeXFiles. | LeXFiles corpus, LegalLAMA benchmark, and LexLM models are released on Hugging Face Hub. The codebase is available on GitHub. | True | True | LeXFiles corpus, LegalLAMA benchmark, and LexLM models are available on Hugging Face Hub. Associated codebase is on GitHub. | Need for more diverse corpora (more languages, legal systems); expansion of probing benchmarks (more tasks, topics, jurisdictions); exploration of larger/different model architectures (e.g., GPT-like) and advanced training P\nparadigms (instruction-tuning, RLHF); development of more robust evaluation methods for probing and fine-tuning; further research into trustworthiness, including model interpretability and fairness in legal AI. | Compiling diverse and representative legal corpora; avoiding overspecialization of models to specific jurisdictions or text types; designing effective methods to probe specific legal knowledge acquired by PLMs; balancing capacity across sub-corpora of varying sizes during pre-training; understanding the interplay between model size, pre-training data, and performance on diverse legal tasks. | Models may perpetuate biases from training data if not carefully curated (e.g., outdated or discriminatory legal standards). Lack of interpretability and fairness in models can lead to irresponsible deployment. Over-reliance on models without understanding their limitations. |
| 3477495.3531668.pdf | Google_Scholar | LawNet-Viz: A Web-based System to Visually Explore Networks of Law Article References | This paper presents LawNet-Viz, a web-based prototype tool for visualizing networks of legal article references extracted from statute law, demonstrated using the Italian Civil Code. The system aims to aid legal research for professionals and enhance understanding for laymen by displaying article connections, network statistics, and semantic similarities calculated using NLP techniques including BERT. | True | Idealistic | True | 1.0 | Positive | LawNet-Viz: A web-based system that extracts references from statute law, builds a network graph, calculates semantic similarity between articles using NLP (incl. BERT), and provides interactive visualization of the network with associated statistics (e.g., centrality) and search capabilities. | Demonstration of the system's functionalities using the Italian Civil Code (ICC) as a case study. A BERT-based model (LamBERTa) fine-tuned on the ICC was used for semantic analysis. No formal user study or quantitative benchmark evaluation reported. | NaN | Complexity of navigating legal corpora ("intricate regulatory systems"); knowledge gap for laypersons unfamiliar with the legal domain; time and cost involved in traditional legal research. | Providing an interactive visual exploration tool (LawNet-Viz) to map article references and semantic relationships, reducing the knowledge gap for laypersons and increasing efficiency for legal professionals through enhanced search and understanding capabilities. | Legal research support; Understanding statutory law structure; Navigating complex legal texts. | Legal professionals (lawyers, jurists) and citizens/laymen. | Statute law (specifically demonstrated with Civil Law / Private Law) | Italy (Italian Civil Code), designed to be adaptable. | Network structure derived from Italian Civil Code (ICC) text. Language models (including LamBERTa, a fine-tuned BERT model) trained/fine-tuned on the text of the ICC using unsupervised labeling for data augmentation. The ICC is public statutory law; resulting models/embeddings may be proprietary. | Modular architecture (network, text, integration modules), use of NLP libraries (Gensim, HuggingFace), web technologies (Bootstrap, DataTables, vis.js), JSON data format compatible with Gephi, focus on interactive user experience. | System prototype using web technologies (Bootstrap, DataTables, vis.js, Python backend). Planned for product development. A screen recording demo is provided via a shared drive link. | False | False | NaN | Social and ethical considerations related to automating legal research are acknowledged but not explored. The system is a prototype requiring further development. | Developing tailored methods for extracting article references according to specific legal syntax; processing and normalizing legal text; managing computational load (addressed via server-side processing); designing effective interactive visualizations for complex network and textual data. | Not explicitly stated, beyond acknowledging that social/ethical considerations are outside the paper's scope. |
| itbYunRMpiQJ.pdf | Google_Scholar | LLM vs. Lawyers: Identifying a Subset of Summary Judgments in a Large UK Case Law Dataset | This paper compares two computational methods, a traditional keyword-based NLP approach and an application of the Claude 2 LLM, for identifying summary judgment cases within the Cambridge Law Corpus of UK court decisions. The study finds that the LLM significantly outperforms the keyword method, achieving a weighted F1 score of 0.94, demonstrating AI's potential to enhance legal research and accessibility of legal information. | True | Idealistic | True | 2.0 | Positive | Application of Claude 2 Large Language Model with engineered prompts for classifying legal cases (summary judgments) and a traditional NLP keyword/RegEx-based search. | Manual review of statistically representative samples by a legal expert for both keyword-based and LLM-based classification. Performance evaluated using confusion matrices and F1 scores. | The Claude 2 LLM method achieved a weighted F1 score of 0.94, significantly outperforming the keyword-based method (weighted F1 score of 0.78). | Difficulty for self-represented litigants to navigate summary judgments; lack of automatic categorization of legal issues in UK case law; incomplete publication of court judgments; complexity of legal language hindering automated analysis and general access to legal information. | Employing advanced NLP and LLMs (like Claude 2) to efficiently identify and classify specific types of legal cases, thereby improving the accessibility of legal information and aiding legal research, which can help democratize access to legal resources. | Identifying specific case types (summary judgments) for legal research; improving accessibility of legal information; understanding procedural justice, particularly concerning summary judgments affecting self-represented litigants. | Self-represented litigants. | Civil procedure. | United Kingdom (primarily England and Wales regarding Civil Procedure Rules). | The Claude 2 LLM, one of the techniques studied, was pre-trained by Anthropic on large, general natural language datasets (details proprietary to Anthropic). The keyword-based method is rule-based and does not use a training dataset. The Cambridge Law Corpus was used as the input data for classification. | Keyword-based method: Expert-driven keyword generation, RegEx development, iterative refinement of search logic based on legal domain knowledge (CPR, case law). LLM-based method: Prompt engineering for Claude 2, utilizing insights from keyword analysis and LLM provider guidelines, including structured prompts with examples. | The identified dataset metrics are shared to support further research. Code is made available on GitHub. | True | True | The code implementing the methods is available on GitHub. The Claude 2 method relies on accessing the Claude 2 Chat console (used for final results and generally accessible). | Need for further refinement of LLM methodologies (e.g., prompt engineering) to improve accuracy in legal case classification; incompleteness of available legal datasets (e.g., CLC not containing all judgments); ongoing challenges with LLM reliability (e.g., errors, over-inclusivity, hallucinations); lack of standardized benchmarks for legal information retrieval tasks. | Keyword method: Capturing nuances and variability in legal language, distinguishing true cases from mere mentions or similar legal tests used in other procedures. LLM method: Effective prompt engineering, LLM output inconsistencies (API vs. Chat console), handling LLM context window limits for very long documents, general complexity of legal language for NLP. | Misclassification of legal cases by AI methods, potentially leading to incorrect legal research outcomes or flawed understanding of legal trends; inherent limitations of LLMs such as errors, over-inclusivity (incorrectly identifying non-summary judgment cases as summary judgments), and potential for hallucination when applied to complex legal tasks. |
| vkejhE-Ze-oJ.pdf | Google_Scholar | Human Resource Analytics in the Era of Artificial Intelligence: Leveraging Knowledge towards Organizational Success in Pakistan | This paper investigates how workplace coordination (implicit and explicit) influences organizational performance in Pakistani software houses, mediated by knowledge sharing. It finds that employee use of generative AI moderates this relationship, significantly boosting performance when knowledge sharing is low. | True | Market | True | 2.0 | NaN | Utilization of generative AI tools (e.g., ChatGPT) by employees as a moderating variable. | Cross-sectional survey data from employees in software houses in Islamabad, Pakistan. Analysis via Partial Least Squares Structural Equation Modeling (PLS-SEM) using SMART-PLS, including mediation and moderation analysis (Baron & Kenny's interaction term approach). | Generative AI infusion significantly moderated (p<0.05) the relationship between knowledge sharing and organizational performance. AI use substantially enhanced performance in low knowledge-sharing environments but had minimal impact in high knowledge-sharing environments. | NaN | NaN | NaN | NaN | NaN | Pakistan | NaN | Quantitative survey research, Partial Least Squares Structural Equation Modeling (PLS-SEM). | NaN | False | False | NaN | NaN | NaN | NaN |
| AIoPYeNkVewJ.pdf | Google_Scholar | VIOLATION OF HUMAN RIGHTS OF CHILDREN: A CASE OF JUDICIAL PRACTICES IN PROTECTION OF MINORS FROM SEXUAL OFFENCES | This paper examines judicial practices and the Protection of Children from Sexual Offences (POCSO) Act in India for protecting minors from sexual offences, highlighting the high incidence and underreporting of such crimes. It finds a significant lack of public awareness regarding protective legal provisions and notes that despite legal frameworks and judicial efforts, children are often denied justice. | True | Idealistic | False | 2.0 | NaN | Judicial practices and the Protection of Children from Sexual Offences (POCSO) Act, 2012 in India for protecting minors from sexual offences. | Doctrinal analysis of secondary sources (books, journals, legal reports), analysis of National Crime Record Bureau (NCRB) reports, and assessment of major judgments from the Supreme Court of India and High Courts related to child sexual abuse and the POCSO Act. | The POCSO Act, 2012 has made a substantial contribution to addressing child sexual abuse in India by outlawing harmful sexual behaviours. However, its effectiveness is hampered by a significant lack of public awareness of legal provisions and persistent issues in delivering justice, with many children remaining unprotected despite judicial efforts. | Most child sexual abuse cases go undetected; many child victims are victimized by known persons (family, relatives); lack of public awareness about protective laws; cases not being effectively addressed despite legal safeguards; poverty leading to child labour and marriage; societal and cultural norms accepting child marriage; government inaction on judicial suggestions for child protection. | The paper suggests a need for stricter enforcement mechanisms. While not detailing new solutions, it implicitly calls for increased public awareness of legal provisions, more effective implementation of the POCSO Act, and governmental responsiveness to judicial recommendations for child protection. | Child sexual abuse, protection of minors from sexual offences, access to justice for child victims, human rights of children, implementation of the POCSO Act. | Children in India, particularly minors who are victims or at risk of sexual offences. | Criminal Law, Child Protection Law, Human Rights Law, Constitutional Law. | India | NaN | NaN | The POCSO Act is a national law enacted by the Parliament of India and implemented through the Indian legal and judicial system, including the establishment of special courts. | True | True | The Protection of Children from Sexual Offences (POCSO) Act, 2012, is a public law in India and is accessible through official government legal resources. | Lack of public awareness of protective laws; high number of undetected/unreported abuse cases; poor implementation of existing legal frameworks like the POCSO Act despite its contributions; governmental inaction on judicial recommendations; persistence of child labor and child marriage due to poverty and societal norms. | NaN | Continued sexual abuse and exploitation of children; violation of children's human rights; severe emotional and psychological trauma to victims; denial of justice and impunity for perpetrators; perpetuation of child marriage and child labour due to lack of protection and awareness. |
| uq8bglKm_NoJ.pdf | Google_Scholar | WHERE’S THE LIABILITY IN HARMFUL AI SPEECH? | This paper examines potential legal liability for harmful speech generated by AI foundation models under US law, focusing on defamation, speech integral to criminal conduct, and wrongful death. It argues that liability and Section 230 immunity analyses are complex, depend heavily on technical design choices, and that current legal frameworks create potentially misaligned incentives for AI safety. | True | NaN | True | 3.0 | NaN | Generative AI / Foundation Models and associated design/mitigation strategies (extractive, retrieval-augmented, RLHF, inference-time processing, uncertainty calibration). | NaN | NaN | NaN | NaN | NaN | NaN | Torts (Defamation, Wrongful Death, Aiding and Abetting, Negligent Misrepresentation), Communications Law (Section 230), Criminal Law (Speech Integral to Criminal Conduct), First Amendment Law | United States | The paper discusses general practices for training foundation models, referencing data sources like web crawls (e.g., CommonCrawl, C4), CourtListener cases, books (e.g., from BitTorrent trackers like Bibliotik in The Pile dataset), and methods like instruction fine-tuning using human-generated datasets (potentially company-proprietary) and reinforcement learning from human feedback (RLHF). | NaN | NaN | False | False | NaN | Legal uncertainties regarding Section 230 applicability to generative AI; difficulties in applying traditional mens rea (state of mind) requirements to AI systems for liability purposes; misalignment between current legal incentives and the encouragement of optimal AI safety interventions (e.g., human feedback potentially increasing liability risk while improving safety); imperfect technical solutions for preventing harmful AI outputs (hallucinations, bias, dangerous instructions, manipulation); challenges in reliable fact-checking, uncertainty calibration, and preventing malicious misuse (e.g., jailbreaking). | Analyzing complex interactions between evolving AI technology (foundation models, various mitigation techniques) and established legal doctrines (Section 230, torts, First Amendment); assessing the incentive effects of different legal rules and interpretations on AI system design and safety investments. | AI generating defamatory falsehoods; AI providing instructions or encouragement for criminal acts (e.g., violence, terrorism, financial crimes); AI causing physical harm or wrongful death (e.g., encouraging self-harm, providing dangerous advice); AI generating biased or hate speech; AI facilitating disinformation, manipulation, or scams; AI generating malware. |
| 1286021.pdf | Google_Scholar | Generative AI in Practice: Pipeline Design, Implementation, and Ethical Considerations | This paper presents the design and implementation processes for generative AI pipelines, focusing on applications such as chat systems, Retrieval-Augmented Generation (RAG), and fine-tuning pre-trained models. It outlines the key components, implementation steps, and ethical considerations associated with deploying these AI technologies. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Mentions general concepts: Large datasets of human dialogue for chatbots, extensive databases/repositories (public like Wikipedia or private) for RAG, domain-specific data (e.g., question-answer pairs) for fine-tuning. Does not specify a concrete dataset used within the paper's scope. | Describes pipeline design in terms of components (e.g., UI, Input Processing, Model Inference, Post-processing, Memory Management, API/Deployment) and implementation steps for chatbots, RAG, and fine-tuning. | Mentions deployment via APIs using web frameworks (FastAPI, Flask), integration into applications, hosting on cloud platforms (AWS, Hugging Face). | False | False | NaN | NaN | Computational constraints, model interpretability, bias mitigation, data privacy, model selection, data preprocessing, integration of components, ensuring scalability and reliability. | Bias and fairness issues (skewed/discriminatory outputs), privacy concerns (use of personal data), lack of transparency (users unaware they interact with AI, how data is used). |
| CYTtXANcRR4J.pdf | Google_Scholar | Combat Security Barriers with state- of-the-art Tools and Techniques | This paper reviews common network and cybersecurity threats, including internal risks like BYOD and external attacks like phishing and DoS. It proposes solutions such as cryptography, firewalls, and enhancing security awareness to mitigate these threats. | True | Market | False | 3.0 | NaN | General cybersecurity tools and practices (Cryptography, Firewalls, Network Monitoring, Security Awareness Training) | NaN | NaN | NaN | NaN | NaN | NaN | Cybersecurity / Network Security | International | NaN | NaN | NaN | False | False | NaN | NaN | Network complexity, lack of technical resources, Bring Your Own Device (BYOD) risks, insider threats, attackers constantly developing new tactics. | Insider threats causing security breaches, virus infection and data theft via BYOD, phishing attacks leading to credential compromise or malware installation, Denial-of-Service (DoS/DDoS) attacks causing service unavailability and financial loss, wormhole attacks disrupting communication. |
| jC3rwCcyzLcJ.pdf | Google_Scholar | Artificial Intelligence (AI) in Legal System | This paper reviews the current state and impact of AI on the legal profession, highlighting its potential to enhance legal work but also noting risks like inaccuracies and the need for human oversight. It explores benefits such as increased efficiency and access to justice, alongside challenges including ethical concerns, bias, and the potential for errors if AI is used without proper safeguards. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Scarcity of attorneys in 'legal deserts'; high cost of legal services for the general public; risk of biased or erroneous AI decisions impacting fairness and perpetuating discrimination; ensuring the right to be heard ('Audi alteram partem') when AI is involved in decision-making. | AI-powered tools to provide legal information and assistance in underserved areas (e.g., 'legal deserts'); using AI to efficiently handle straightforward legal matters (e.g., petty cases, amicable divorces, Khula) potentially reducing costs; development and implementation of robust ethical frameworks, guidelines, and human supervision for AI in law to ensure fairness and mitigate bias; public awareness and discourse on AI's role in the legal system. | Addressing 'legal deserts' and scarcity of legal professionals; providing accessible legal information and assistance for common/minor legal issues; improving efficiency and potentially lowering costs for resolving straightforward legal cases (e.g., uncontested divorces, small claims); ensuring fairness, non-discrimination, and upholding legal rights in AI-assisted legal processes. | Individuals in 'legal deserts' (areas with limited access to legal professionals); general public needing assistance with common or minor legal issues (e.g., parking tickets, simple family law matters); individuals who could benefit from more efficient and less costly resolution of straightforward cases. | General legal practice, Contract Law, Real Estate Law, Commercial Law, Criminal Law (bail proceedings), Family Law (divorce, Khula), Human Rights, Intellectual Property Law, Small Claims. | Pakistan, UK, USA, India, International (due to discussion of global justice and broad applicability). | Vast text and code datasets for Generative AI like ChatGPT; general pre-existing legal data for other AI systems discussed. The paper notes these data can contain biases. | NaN | NaN | True | False | Some discussed tools like DoNotPay are presented as services available to individuals (likely paid). ChatGPT is generally accessible (with free/paid tiers). Other commercial tools (Kira Systems, LEVERTON, etc.) are mentioned as existing products from companies. | Limited research on AI's role in deciding legal cases where law is ambiguous or nonexistent; need for AI systems capable of reasoning from first principles or handling social dilemmas effectively; insufficient literature on AI's broader human rights impacts beyond privacy and expression; challenges in integrating societal values, moral principles, and ethical considerations into AI reasoning, especially in novel legal situations; developing effective oversight and auditability for AI systems. | High cost of developing and implementing sophisticated AI systems; ensuring accuracy and reliability of AI-generated information and avoiding errors; overcoming AI's difficulty in handling legal ambiguity or non-existent law due to reliance on pre-existing data; incorporating human expert knowledge and ethical considerations into AI decision-making; potential for inherited bias from training data leading to discriminatory outcomes; the 'black box' nature of some AI systems making them opaque. | AI errors leading to incorrect legal outcomes and miscarriages of justice (e.g., UK divorce software error, bogus citations from ChatGPT); job displacement for legal professionals; perpetuation of societal biases and discrimination through biased AI systems; violations of privacy due to large-scale data collection by AI; spread of misinformation through AI-generated content; lack of accountability for AI decisions; challenges in assigning authorship and intellectual property for AI-generated content. |
| n9YM6j_xIvUJ.pdf | Google_Scholar | Technology Competence as a Compass For Helping to Close the Justice Gap | This paper argues that the ethical duty of technology competence for lawyers can serve as a crucial guide in leveraging legal technology to address the access to justice crisis in the U.S. It explores the potential of this duty to influence legal service providers, regulators, and educators in promoting technology for social good, despite current obstacles and the rapid evolution of AI. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Cost of legal services; consumer sophistication and language barriers; cuts to legal aid; insufficiency of traditional legal aid/pro bono; poorly designed or irresponsibly used technology (e.g., one-size-fits-all tools, overhype, bias); ethical uncertainty regarding new technologies; lawyers' resistance to technology; difficulty for A2J organizations to gain tech competence due to resource/time constraints; rules stifling cross-industry collaboration (unauthorized practice of law, non-lawyer ownership). | Leveraging legal technology for cost, availability, and quality of legal services; using the duty of technology competence as a guide for all stakeholders; adopting a 'thick view' of technology competence; organizational leadership embracing 'people factors,' algorithmic literacy, and interdisciplinary collaboration (especially for bias and cultural competency); regulators clarifying ethics rules, reforming restrictive rules, and offering tech competence CLEs; legal education integrating tech competence, interdisciplinary approaches, and A2J focus; increasing transparency in legal tech use. | Closing the justice gap; access to legal services for low- and moderate-income individuals; ethical obligations of lawyers (technology competence, pro bono, reasonable fees); role of legal technology in legal service delivery (efficiency, cost reduction, self-help tools, connecting consumers to providers); democratizing access to legal information. | Low-income Americans, moderate-income individuals, people facing economic or social barriers to legal counsel, those with limited English proficiency, recent immigrants. | General Civil Law (examples include income maintenance, education, housing, family law, immigration, arbitration, traffic infractions). | United States | NaN | NaN | NaN | False | False | NaN | Significant unmet legal needs; lack of tech knowledge in A2J organizations; ethical ambiguity hindering innovation; insufficient resources for A2J tech adoption; lack of transparency in legal tech use; need for more interdisciplinary collaboration and algorithmic literacy. | Rapid pace and complexity of technological evolution leading to overwhelm; conservative nature of the legal profession and resistance to change; ethical uncertainties and lack of clear guidance; risk of bias in AI requiring vigilance and interdisciplinary solutions; ensuring technology is culturally competent and user-centric; integrating unbundled services into business models. | Poorly designed/irresponsibly used tech exacerbating the justice gap; 'one-size-fits-all' tools failing diverse needs; overhyped expectations leading to negative impacts; failure to account for consumer differences; overreliance on tech for tasks requiring human judgment or interaction; technology creating/automating/magnifying bias; uncertainty about unauthorized practice of law, data protection, and business structures; passive tech adoption for marketing leading to ineffective use. |
| TM1elPIE7dsJ.pdf | Google_Scholar | ChatGPT and GPT-4: utilities in the legal sector, functioning, limitations and risks of foundational models | This paper examines the architecture, operation, applications, and significant limitations (such as hallucinations and biases) of large language models like ChatGPT and GPT-4 within the legal sector. It also analyzes the associated legal risks, particularly concerning data protection and intellectual property, and discusses emerging EU regulatory frameworks for AI. | False | Market | True | 3.0 | Neutral | ChatGPT and GPT-4 (OpenAI's foundational large language models) | NaN | NaN | NaN | NaN | NaN | NaN | General legal practice, Contract law, Intellectual Property law, Data Protection law, Litigation, Criminal law, Administrative law, AI law | Multiple (EU, USA, Spain, and others cited) | Publicly available internet data (e.g., Common Crawl, WebText2, Wikipedia), licensed third-party data, and user/reviewer-generated data, largely collected via web scraping. Primarily unstructured text data. | Pre-trained transformer architecture, trained using semi-supervised learning (unsupervised pre-training, supervised fine-tuning) and Reinforcement Learning from Human Feedback (RLHF). | Publicly available chatbot (ChatGPT), API access (GPT-4), and integration into specialized legal tech tools (e.g., Harvey, CelIA) used by law firms. | True | False | ChatGPT is available as a publicly accessible chatbot (with free and paid tiers). GPT-4 is accessible via paid subscriptions (e.g., ChatGPT Plus) and API. | NaN | Technical limitations (hallucinations, biases, explainability, handling long texts), data acquisition and quality for legal domain training, ensuring responsible and ethical use, navigating legal compliance (data privacy, IP). | Hallucinations and biases leading to incorrect information; data protection violations (improper use of personal data in training/prompts, international transfers, difficulty exercising data subject rights); intellectual property infringement (use of copyrighted training data, infringing outputs, authorship issues); over-reliance by users; professional liability for legal professionals. |
| 1FuR9e3J6qUJ.pdf | Google_Scholar | PDF-WuKong : A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling | This paper introduces PDF-WuKong, a multimodal large language model designed for question answering on long PDF documents containing interleaved text and images, such as academic papers. It utilizes an end-to-end sparse sampler to efficiently select relevant text and image evidence based on user queries, improving accuracy and reducing computational cost compared to existing methods. | True | NaN | True | 1.0 | NaN | PDF-WuKong: A multimodal large language model (MLLM) incorporating an end-to-end sparse sampler that selects relevant text paragraphs and images based on query similarity using contrastive learning. | Evaluated on a newly created bilingual (English/Chinese) dataset 'PaperPDF' (1.1M training pairs, 10k test pairs from academic papers) using ANLS, F1, Rouge, and GPT-Acc metrics. Also tested on public benchmarks: DocVQA, ChartQA, InfoVQA, MPDocVQA, DUDE, and MM-NIAH. Compared against open-source MLLMs (with/without RAG) and commercial products. Ablation studies performed on sampler, dataset size, document length, and sampling strategy. | PDF-WuKong surpassed baseline open-source MLLMs and proprietary commercial products on the PaperPDF benchmark (e.g., by an average of 8.6% F1 over proprietary products). It achieved competitive performance on other document VQA benchmarks, particularly on multi-page (DUDE) and long-context tasks (MM-NIAH @ 64K). Performance remained stable with increasing document length. | Limitations of existing methods for long multimodal PDFs: text-only approaches lose visual information; vision-only approaches suffer from scalability issues (high token counts, computational cost) with many pages/high resolution; difficulty processing interleaved text and images efficiently; attention dilution in LLMs with long inputs. | Proposed PDF-WuKong model with an end-to-end sparse sampler integrated with the MLLM's vision encoder. The sampler uses text and image embeddings (trained via contrastive learning) to retrieve top-k relevant evidence (text blocks, images) based on query similarity, reducing LLM input tokens. Created PaperPDF dataset with question-answer-evidence triplets for training and evaluation. | NaN | NaN | NaN | International | Primary dataset: PaperPDF, a newly created dataset of 1.1M bilingual (English/Chinese) QA pairs with evidence grounding, automatically generated using Gemini Pro/GPT-4V from ~70k parsed academic PDF documents (text blocks and images). Also trained on public datasets: DocVQA, ChartQA, InfoVQA, MPDocVQA, DUDE. | Document parsing (Grobid, MinerU), sparse sampling (contrastive learning, similarity matching), multimodal large language model fine-tuning (IXC2-VL-4KHD backbone, BGE-M3 text encoder), end-to-end joint training of sampler and LLM, automatic QA data generation using LLMs/VLMs. | Code and dataset planned for release on GitHub. | True | True | Code and dataset will be released at https://github.com/yh-hust/PDF-Wukong. | Current dataset limited to academic papers; model not specifically designed for global queries requiring synthesis of the entire document rather than sampled evidence. | Efficiently processing long multimodal documents, integrating text and visual information, reducing computational cost for LLMs, mitigating attention shift in long sequences, generating high-quality training data with evidence. | NaN |
| LLMSurvey-MBS.pdf | Google_Scholar | Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future Prospects | This paper provides a broad survey of Large Language Models (LLMs), covering their history, architectures, training methods, diverse applications across various domains including law, and associated challenges. It discusses technical aspects, ethical considerations, limitations like hallucination and bias, and future research directions for LLMs. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General Legal Practice, Civil Law | US, Colombia, Japan, International | Large corpora of diverse text data (e.g., CommonCrawl, Wikipedia, books, articles, code) used for pre-training; domain-specific data sometimes used for fine-tuning. | Transformer architecture, large-scale pre-training, supervised fine-tuning, Reinforcement Learning from Human Feedback (RLHF), prompt engineering, in-context learning. | General discussion of deployment in applications (chatbots, search engines, virtual assistants) via APIs, including monitoring. | True | True | Discusses numerous publicly available models like ChatGPT (free/paid tiers), Bard (free), Llama 2 (open source download). Provides GitHub repository for the survey itself. | NaN | Computational requirements (cost, energy, water), Training data issues (size, quality, bias, privacy), Tokenization problems, Fine-tuning complexity, Inference latency, Limited context length, Knowledge updating difficulties, Lack of explainability, Reasoning errors, Susceptibility to adversarial attacks (prompt injection, jailbreaking, data poisoning), Behavioral drift over time, Factual errors (hallucination), Spelling/Counting errors. | Bias amplification, information hallucination, lack of explainability, reasoning errors, adversarial attacks (prompt injection, jailbreaking, data poisoning), security vulnerabilities, privacy leaks, environmental costs (energy/water consumption), copyright infringement, training data contamination, generation of harmful/offensive content, erosion of trust, potential job displacement, undermining critical thinking skills. |
| Ugs9Xq5VlCIJ.pdf | Google_Scholar | Parameter-Efficient Legal Domain Adaptation | This paper proposes 'prefix domain adaptation', a parameter-efficient method using unsupervised data from legal forums (Reddit, Stack Exchange) to pre-train prompts for language models. This approach improves few-shot performance on legal classification tasks compared to standard fine-tuning and existing methods like LEGAL-BERT, while only tuning ~0.1% of parameters. | True | Idealistic | False | 1.0 | Positive | Prefix Domain Adaptation: Pre-training a prefix prompt using Masked Language Modeling (MLM) on unsupervised, domain-specific legal text (from public forums), then using this pre-trained prefix for parameter-efficient tuning (P-Tuning v2) on downstream few-shot legal tasks. | Evaluated on few-shot classification tasks using three datasets: Legal Advice Reddit (new), Law Stack Exchange (new), and ECHR. Performance (Macro F1) and calibration (ECE) were compared against baselines including full fine-tuning (FT), LEGAL-BERT + FT, full domain adaptation + FT, P-Tuning v2, and prefix adaptation (general corpus pre-training). | Prefix Domain Adaptation matched or outperformed full fine-tuning and LEGAL-BERT in most few-shot settings across datasets (in terms of Macro F1), while tuning only ~0.1% of parameters. It also achieved competitive or better calibration (ECE). | High cost of legal advice; large model sizes requiring parameter-efficient methods; poor performance of existing parameter-efficient methods in low-data settings common in law due to high labeling costs. | Leveraging abundant unsupervised legal text from public forums (Reddit, Stack Exchange) for domain-specific pre-training of prompts using Masked Language Modeling (MLM). This 'prefix domain adaptation' improves the few-shot performance of parameter-efficient tuning methods, reducing computational and data labeling costs. | Legal area classification based on layperson questions; improving access to legal information/services for laypersons. | Laypersons seeking legal advice, particularly users of online legal forums like Reddit and Stack Exchange. | General / Multiple (based on classification tasks covering various areas like criminal, copyright etc., and human rights law via ECHR) | International (Uses data from Reddit/LSE with unspecified/broad user base and ECHR covering European states) | Prefix pre-training uses unsupervised, unstructured text data from public legal forums (Legal Advice Reddit, Law Stack Exchange). Downstream tasks use labeled, unstructured text data (forum posts/titles mapped to legal area tags; ECHR case facts mapped to violation status) in few-shot settings. The forum data is publicly available via Pushshift/StackExchange dumps, and ECHR data is also public. | Builds on RoBERTa architecture, Masked Language Modeling (MLM), Prefix Tuning (P-Tuning v2), and domain adaptation principles. Combines domain-specific MLM pre-training with prefix tuning. | NaN | False | False | The two new datasets (Legal Advice Reddit, Law Stack Exchange) are claimed to be available via Hugging Face. | Focus is on classification; need for extension to more complex legal tasks (Q&A, reasoning). Potential robustness issues related to data distribution shifts. Need for more extensive hyperparameter tuning for larger models. | Adapting parameter-efficient methods to perform well in few-shot legal settings. Processing noisy, informal (Reddit) and formal (Stack Exchange) text data. Computational resource limitations for hyperparameter search. | Misuse or over-reliance on model predictions due to poor calibration, especially given the high-stakes nature of law. Models trained on formal legal text may perform poorly on informal layperson language. |
| i4jm_4PwR-IJ.pdf | Google_Scholar | THE LEGAL TECH BRO BLUES : GENERATIVE AI, LEGAL INDETERMINACY , AND THE FUTURE OF LEGAL RESEARCH AND WRITING | This paper critiques techno-optimism surrounding generative AI in law, arguing its proponents ignore legal indeterminacy and the importance of human experience, potentially stifling legal creativity and reinforcing biases. It proposes a four-step model for lawyers to responsibly integrate LLMs into research and writing, emphasizing traditional research, critical prompting, verification, and human oversight. | True | NaN | True | 1.0 | Neutral | A conceptual 4-step normative model for integrating LLMs into legal research and writing: 1. Research: Edification (using traditional sources first), 2. Prompting and Generation (LLM drafting), 3. Research: Verification (independent validation), 4. Writing: Polishing and Preparation (human review and editing). | NaN | NaN | The complexity and indeterminacy of law which simplistic AI ignores; the risk of AI automating the status quo and reinforcing existing biases; potential for AI to justify cuts to legal aid or devalue human legal representation. | Adopt a critical stance towards legal tech ('legal bibliographer' vs 'legal tech bro'); implement a responsible model for using AI in practice (the proposed 4-step model); reform legal education with standalone courses on critical legal information literacy and technology evaluation. | NaN | NaN | General law (focus on legal research, writing, reasoning, education) | US | The paper discusses LLMs trained on large, potentially biased text corpora, including private repositories of legal materials used by legal tech companies, but does not specify a dataset for its proposed model. | Conceptual analysis based on legal theory, critique of AI capabilities, and established legal research practices. | NaN | False | False | NaN | Disconnect between techno-optimist visions ('legal singularity') and legal indeterminacy; need for improved legal education integrating critical technology assessment; lack of understanding of how AI may entrench bias or devalue human expertise, especially experience-based reasoning relevant to justice; technical limitations like LLM non-determinism and bias. | Managing LLM 'hallucinations' and ensuring factual accuracy/verification; overcoming inherent biases in training data; accounting for legal indeterminacy; preventing automation from stifling legal creativity and critical thinking. | AI automating the legal status quo and reinforcing bias; hindering law reform; 'determinization of law' stifling creativity; malpractice due to LLM errors/misuse; LLMs subtly influencing user judgment; AI justifying cuts to legal aid or devaluing human lawyers; undermining stare decisis; mistaking automated outputs for true legal process; dulling sensitivity to legal complexity and values. |
| vFkrzAPX5eMJ.pdf | Google_Scholar | Generative AI and the Rule of Law⋆ | This exploratory paper discusses the emergence of Large Language Models (LLMs) and Multimodal Foundation Models (MFMs), examining their potential to model the rule of law and serve regulatory purposes. It analyzes responses from models like ChatGPT and Claude, highlighting both their capabilities in generating plausible legal discourse and the ongoing challenges related to accuracy, ethics, and semantic understanding. | True | Idealistic | True | 2.0 | Neutral | Prompting of LLMs (ChatGPT, GPT-4, Claude) for modeling the rule of law; discussion of Semantic Injection and Constitutional AI. | Qualitative experiment involving prompting ChatGPT3, GPT-4 (via Lex.page), and Claude with the question "How can we model the rule of law?" and analyzing the generated responses. | LLM responses were generally plausible and detailed, outlining various components of the rule of law, but exhibited cultural legal biases and operated at a symbolic level requiring user interpretation for meaning. The quality and comprehensiveness of responses varied and improved with newer models/versions. | Unreliability of LLMs (hallucinations, lack of true understanding of meaning vs. symbols), inherent biases in models, unresolved legal and ethical issues (e.g., copyright, privacy, defamation), and challenges in aligning AI with regulatory compliance and democratic values. | Improving LLM accuracy and reliability through semantic injection and knowledge graphs; utilizing advanced prompt engineering (e.g., Moral Chains of Thought) and Constitutional AI principles to align models with ethical and legal norms; adopting a 'Law informs AI' approach for better legal reasoning; and conducting further empirical testing and benchmarking. | Modeling the rule of law; Regulatory applications of AI; Computational ethics; Legal reasoning in AI. | NaN | Constitutional law, Jurisprudence, Regulatory law, Tax law (in an example), Intellectual Property law, Privacy law. | International; references to US and EU. | For LLMs in general: Large, diverse corpora of unlabeled text scraped from the internet for pre-training; specific fine-tuning datasets (e.g., for Constitutional AI, instruction-following datasets based on principles and examples). | For LLMs: Unsupervised pre-training, fine-tuning, Reinforcement Learning (RLHF/RLAIF). For Constitutional AI: Principle-based design, self-critique, preference modeling. For semantic injection: Knowledge Graph integration techniques. | Web-based interfaces and APIs for models like ChatGPT and Claude; some models with geographically restricted access initially. | True | False | ChatGPT is publicly accessible via a web interface (with free and paid tiers). Claude's access was stated as limited to USA and UK at the time of writing (via application). Lex.page is a commercial writing assistant. | Technical: Scalability of knowledge injection methods, achieving genuine legal/ethical reasoning beyond pattern matching, mitigating hallucinations and biases reliably. Societal/Legal: Establishing clear legal frameworks for LLM use (copyright, liability, privacy), ensuring alignment with democratic values and societal norms, adapting regulation to fast-evolving AI, and the conceptual challenge of adequately modeling the rule of law itself. | Design/Development: Sourcing quality training data, effective and scalable fine-tuning/alignment (e.g., Constitutional AI), robust knowledge integration (Semantic Injection), bias mitigation, lack of transparency in model development. Use: Effective prompt engineering, critical interpretation of outputs, avoiding over-reliance due to potential inaccuracies. Deployment: Ensuring safety and preventing misuse, managing computational costs, navigating unclear regulatory environments. | Generation of false information ('hallucinations'); perpetuation of biases leading to disproportionate negative impacts on minority groups; legal infringements (copyright, privacy, defamation); generation of hate speech; challenges to existing regulatory frameworks due to undefined purpose and scale of use; potential for misuse (e.g., flawed legal document generation). |
| AKc59QWPmfEJ.pdf | Google_Scholar | Generative AI in Education From the Perspective of Students, Educators, and Administrators | This dissertation explores the integration of generative AI in education through five studies, covering legal text summarization, stakeholder perspectives (students, educators, administrators) on AI tools and policies, and AI adoption models. The research highlights AI's transformative potential for teaching, learning, and information access, while also underscoring challenges related to ethics, equity, and practical implementation in educational settings. | True | Idealistic | True | 3.0 | Positive | PEGASUS CourtOp, a domain-adapted transformer-based model (fine-tuned from PEGASUS LARGE) for abstractive summarization of legal court opinions (detailed in Chapter 2). | The PEGASUS CourtOp model was evaluated using ROUGE scores (ROUGE-1, ROUGE-2, ROUGE-L) by comparing its generated summaries of court opinions against human-written reference summaries from Justia. A test set of 25% of 4814 court opinions was used. | PEGASUS CourtOp achieved a ROUGE-1 F1 score of 0.53 and a ROUGE-1 Recall of 0.66, outperforming baseline models PEGASUS LARGE and Legal Pegasus. | Cost and complexity of accessing and understanding legal information, particularly lengthy court opinions, which forms a barrier to legal help for people with lower incomes. | Automated legal text summarization using NLP models like PEGASUS CourtOp to reduce the time, effort, and cost associated with parsing and understanding legal documents, thereby potentially lowering barriers to legal services. | Automated summarization of court opinions to enhance accessibility to legal precedents and reduce the cost of legal services. | People of lower-income brackets. | Case Law / Court Proceedings (specifically, summarization of court opinions). | United States (supreme courts of Utah, Idaho, Arizona, New Mexico, Nevada, and Colorado). | A dataset of 4814 US state supreme court opinions paired with human-generated summaries from Justia. For fine-tuning PEGASUS CourtOp, 3661 such pairs were used. This is domain-specific (legal court opinions) unstructured text data, provided under a data-sharing agreement. | Domain adaptation of a pre-trained language model (PEGASUS LARGE) by fine-tuning it on a specific corpus of legal opinions and their summaries. Standard NLP data processing techniques and evaluation using ROUGE metrics were employed. | NaN | False | False | NaN | Technical gaps in AI model capabilities for legal text summarization, including the need for more powerful and potentially open-source language models, domain-specific Named Entity Recognition, and improved generation of highly abstractive, human-like summaries not strictly tied to source text phrasing. | General difficulty of abstractive summarization due to natural language complexity, the need for substantial domain-specific training data (paired opinions and summaries), potential data imbalances, and inherent challenges in objectively evaluating the quality of generated summaries. | Incorrect or fabricated results (hallucinations), misuse for academic dishonesty (cheating, plagiarism), data privacy and security vulnerabilities, algorithmic bias, lack of transparency and accountability in AI systems, negative impacts on critical thinking and creativity, and challenges in ensuring equitable access to AI tools and their benefits. |
| 3627673.3679154.pdf | Google_Scholar | LeDQA: A Chinese Legal Case Document-based Question Answering Dataset | This paper introduces LeDQA, a new Chinese legal dataset for question answering based on civil case documents, featuring a question schema designed by legal professionals and annotations generated using GPT-4. The authors evaluate several LLMs and retrieval methods on this dataset, finding that relevant sentence retrieval improves QA performance but challenges like irrelevant retrieval and incorrect reasoning remain. | True | Idealistic | True | 1.0 | Positive | LeDQA dataset, a Chinese legal case document-based question answering dataset, along with a methodology for its creation and baseline evaluations of retrieval and QA models. | Relevant sentence retrieval was evaluated using R@3, R@5, MRR with models like BM25, TF-IDF, and pre-trained dense retrievers. Question answering was evaluated using Accuracy and Macro-F1 with various LLMs (e.g., Baichuan2, Qwen-7B-Chat, GPT3.5-turbo) using the full document, chain-of-thought prompting, retrieved sentences, or oracle sentences. | For question answering, the Qwen-7B-Chat model, when using the top-5 retrieved sentences from TF-IDF (Retrieve setting), achieved an accuracy of 0.7623 and an F1 score of 0.5605 on the binary classification task ("yes" vs. "no and unknown"). Using oracle (human-annotated) relevant sentences generally yielded the best performance across models, highlighting the importance of accurate sentence retrieval. | The general public's limited knowledge of their rights and fundamental legal processes, and the inherent complexity of legal texts. The high cost of human annotation for creating legal AI resources. | Developing legal question answering systems based on case documents to bridge the gap between people and the law. Creating specialized datasets like LeDQA to facilitate research and development in legal AI. Using LLMs like GPT-4 for cost-effective annotation of legal data, with human validation. | Legal question answering, legal information access and understanding, legal document analysis, element extraction from legal cases. | General public with limited legal knowledge, individuals involved in legal disputes (specifically private lending cases initially). | Chinese civil law, specifically private lending cases. | China | For LeDQA dataset creation: 100 private lending case documents selected from authoritative cases published by the Supreme People’s Court of China. Relevance and answer annotations for these documents were generated using GPT-4 and subsequently validated by human legal experts (PhD students in Chinese civil law). | For LeDQA dataset creation: Question schema construction by a legal expert team through review of laws, element listing, group discussions, and categorization. Case document selection based on authoritativeness and coverage of question schema categories. Annotation of relevant sentences and answers using GPT-4, followed by human validation with inter-annotator agreement checks. | The LeDQA dataset is made publicly available on GitHub. | True | True | The LeDQA dataset is available on GitHub via the link https://github.com/BulouLiu/LeDQA. | Insufficiency of current retrieval models to accurately extract all relevant sentences from long legal documents. LLMs struggle with correct multi-sentence reasoning even when provided with relevant sentences. Difficulty for models in correctly identifying 'unknown' answers. | High cost of human annotation for legal datasets. Ensuring questions are designed from a legal knowledge perspective and cover complex legal elements. Dealing with the length and noisy information in legal case documents compared to typical MRC datasets. Achieving accurate retrieval of relevant sentences and enabling models to perform correct, multi-step reasoning based on these sentences. | Models may retrieve irrelevant sentences or fail to perform correct reasoning even with relevant sentences, leading to potentially incorrect legal interpretations or answers. |
| kLpjOdGODhMJ.pdf | Google_Scholar | Robots vs. Predators: Can Generative Artificial Intelligence Help to Address the Justice Gap in Consumer Debt Litigation? | This paper explores the potential for Generative Artificial Intelligence (GenAI) to alleviate the access-to-justice crisis, particularly in the context of US consumer debt litigation where low-income individuals are often unrepresented. It proposes a 'digital continuum of care' utilizing GenAI and related technologies while also discussing the significant technological, practical, and ethical challenges involved. | True | Idealistic | True | 3.0 | Positive | Proposal for a 'Digital Continuum of Care' for consumer debt cases, leveraging GenAI, chatbots, document assembly/generation tools, and automated discovery. | NaN | NaN | High cost of legal services, individuals not recognizing their problems as legal issues, lack of knowledge on how/where to find legal help, asymmetry of representation (creditors represented, debtors not), sewer service, digital divide. | Deploying technology (specifically GenAI) to provide legal information (chatbots, know-your-rights), automate repetitive tasks (document assembly, discovery, drafting basic pleadings/motions) to make legal assistance more efficient and affordable, creating targeted interventions like a 'digital continuum of care' for high-need areas like consumer debt. | Consumer debt litigation defense, providing legal information and guidance, automating legal tasks (pleadings, discovery, motion practice), self-representation support. | Low- and moderate-income Americans, specifically those facing consumer debt lawsuits. Also mentions disproportionate impact on women, minority populations, and urban communities. | Consumer Law (specifically debt collection), Civil Procedure. | United States | The paper proposes using existing pro se resources curated by non-profits for chatbots and suggests the potential use of scanned court filings (via OCR) or curated/restricted LLMs for document generation, but does not detail a specific implementation or dataset. | NaN | NaN | False | False | NaN | GenAI accuracy/hallucinations, need for human oversight ('lawyer in the loop'), required human capital/resources (especially for under-staffed non-profits), funding for technological innovation in legal aid, the digital divide (access to internet/technology), language and accessibility barriers for users. | Technological feasibility (especially for more complex tasks like analyzing/opposing summary judgment motions), securing human resources for implementation and oversight within budget-constrained legal aid organizations, addressing the digital divide and accessibility issues, navigating ethical concerns (standard of care, confidentiality, UPL). | GenAI producing inaccurate results ('hallucinations'), lawyers/litigants relying on fictitious sources, increased burden on courts due to AI-generated filings (especially pro se), sharing confidential client information with AI tools, potential violations of Unauthorized Practice of Law (UPL) rules, possibility of widening the justice gap if technology disproportionately benefits well-resourced parties. |
| 21M7EwsP0fIJ.pdf | Google_Scholar | What will ChatGPT revolutionize in the financial industry ? | This paper explores the potential transformative impact of ChatGPT on the financial industry by examining its use cases, comparing it to existing financial chatbots, and analyzing its responses to specific queries. It also identifies key challenges such as data quality, regulatory compliance, bias, and cybersecurity, and discusses the future outlook for generative AI in finance. | True | Market | True | 2.0 | NaN | ChatGPT (specifically version 3.5) | Qualitative analysis of ChatGPT 3.5's responses to nine predefined questions regarding its applications in the financial industry. The outputs were interpreted using academic literature and articles on ChatGPT in finance. | ChatGPT's responses indicated potential for customer engagement, personalization, data analysis, stock price prediction, and compliance assistance in finance, claiming advantages over existing chatbots in conversational ability, learning, and customization. However, it acknowledged limitations and the need for legal consultation regarding regulatory approval for its use in the financial industry. | NaN | NaN | NaN | NaN | Finance (primary); Law (mentioned as an area where ChatGPT's capabilities have been tested and discussed by other cited studies) | International | NaN | The paper's methodology involved a conversational approach using prompts to elicit responses from ChatGPT 3.5 on its applications in finance, followed by critical analysis of these outputs in conjunction with existing academic literature. | NaN | True | True | ChatGPT is stated to be an 'open AI chatbot... available to everyone,' accessible via OpenAI, which offers free and paid tiers. | NaN | Data quality and quantity limitations, ensuring regulatory compliance (e.g., GDPR, FINRA), lack of interpretability and transparency in model decision-making ('black box' nature), potential amplification of existing data biases, risk management associated with synthetic data accuracy, cybersecurity vulnerabilities, and establishing clear lines of responsibility for decisions made by AI models. | Data inaccuracies in AI outputs, cybersecurity vulnerabilities (e.g., new attack vectors for financial institutions), breaches of customer data privacy and security, amplification of existing biases leading to discriminatory outcomes in financial services, inaccurate predictions or decisions due to model limitations or poor data, and regulatory non-compliance issues. |
| MAPiegzikAIinFamilyLaw.pdf | Google_Scholar | The Adoption of Artificial Intelligence in Family Law – Brand New or Well-known Idea? | This paper reviews the historical development and current state of Artificial Intelligence (AI) adoption in family law, contrasting early systems ('Wave 1') with modern machine learning approaches ('Wave 2'). It assesses AI's application across administrative efficiencies, client support, and decision-making, concluding that progress is accelerating despite ongoing challenges. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Complexity of human emotions in family law; lawyer skepticism ('dehumanization'); jurisdictional inconsistencies; usability challenges for non-specialists; AI's difficulty with nuance/context; ethical concerns (bias, privacy); accuracy limitations; large amounts of unstructured data. | Keeping 'humans in the loop'; developing AI for specific tasks (administration, ODR, information provision, decision support); leveraging AI (ODR, virtual assistants) to improve access to justice; advancing AI capabilities (machine learning, LLMs). | Access to legal information/aid, Online Dispute Resolution (ODR), child welfare (risk assessment, contact scheduling), divorce/separation processing (document drafting, property division). | Self-represented litigants in family law, children. | Family Law, Dispute Resolution (ODR, ADR). | International | Varies depending on the tool; includes social care data, demographic/historical/legal data for predictive models; legislation and case law for legal advice tools; large corpora of legal documents for drafting/review tools. | NaN | Adoption by law firms, courts (ODR), government agencies; commercial software releases; integration into existing legal tech platforms. | True | False | Numerous commercial AI tools for document review/drafting, case management, translation, legal advice (e.g., Casetext, Claude, CoCounsel Drafting, numerous ODR platforms) are mentioned as available on the market. | Knowledge gaps on AI's impact in family law; need for improved accuracy (advice, translation, prediction); need for tools better suited for non-specialists; addressing ethical concerns and bias; regulation/oversight needs; better handling of unstructured data. | Handling emotional complexity; ensuring accuracy/reliability; overcoming lawyer skepticism; cross-jurisdictional integration; addressing ethical issues (bias, privacy, accountability); obtaining quality training data; usability for laypeople. | Dehumanization of justice; algorithmic bias; privacy violations; inaccurate legal information/advice; misleading translations; over-reliance on flawed predictive models; AI becoming an unaccountable source of 'law'. |
| fYmtydY0ZpUJ.pdf | Google_Scholar | FAIRNESS AND FAIR USE IN GENERATIVE AI | This paper advocates for applying the 'non-expressive use' doctrine to assess fair use for generative AI, arguing that AI training on copyrighted works is permissible if it doesn't reproduce original expression in outputs. It contends that fair use analysis should stem from copyright principles, not broad policy considerations, while acknowledging specific fairness issues like lawful data access or systematic substitution. | True | Market | True | 1.0 | NaN | The legal theory/analytical framework of 'non-expressive use' for assessing whether the use of copyrighted materials in training and operating Generative AI models (such as LLMs and text-to-image models) constitutes fair use. | NaN | NaN | NaN | NaN | NaN | Disabled artists and people lacking specific artistic/musical competencies (in the context of broader access to creative tools, not legal services or justice). | Copyright Law, Intellectual Property Law | United States (primarily), with comparative mentions of UK, Japan, EU, Canada and others. | Massive quantities of text and images scraped from the internet, including copyrighted works, publicly available data (e.g., Project Gutenberg, Wikipedia, Github, arXiv, Common Crawl datasets like C4), licensed data, and potential use of 'shadow libraries' (e.g., for Books2, Books3 in ThePile). Specific datasets mentioned include Books2, LAION 5B, ThePile. | NaN | NaN | False | False | NaN | NaN | Applying existing copyright law (specifically the fair use doctrine) to the novel context of generative AI training and output; navigating legal uncertainty for AI developers regarding copyright liability for training data and model outputs; balancing copyright protection with technological innovation and public benefit. | Generation and propagation of misinformation, hate speech, cyberattacks, phishing emails; disclosure of private information; perpetuation and exacerbation of biases from training data; cultural homogenization; unhealthy dependence on technology; job displacement in creative industries; potential for AI to become deceptive, power-seeking, and pose existential risks. Also, 'memorization' by AI models leading to reproduction of copyrighted training data, and AI models being used as tools for copyright infringement by users. |
| U5ILlHRdNAkJ.pdf | Google_Scholar | Amusing Inventions Not to Be Thrown Away: ChatGPT and the Future of Tax | This article discusses the potential applications and implications of Generative AI, specifically ChatGPT T, for tax practice and research. It highlights potential benefits like increased efficiency while emphasizing the significant ethical risks and current limitations practitioners must consider. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Tax Law | US | NaN | NaN | NaN | True | True | ChatGPT has free and paid versions publicly available. BlueJ Ask is mentioned as a commercial product. | NaN | Inaccuracy (hallucinations, fake citations), outdated knowledge (pre-2021 data), lack of source citations, difficulty in verification, inability to handle nuanced judgment or complex analysis, need for human oversight. | Violation of professional competence duties (ABA Rule 1.1, Circular 230), breach of client confidentiality (ABA Rule 1.6), failure of supervisory duties (ABA Rule 5.1), reliance on incorrect or fabricated information leading to incorrect legal advice/filings, professional sanctions or disciplinary actions against lawyers/practitioners. |
| UiT7QntcF2wJ.pdf | Google_Scholar | Understanding National, Regional, and Global Priorities for the Social Justice and Economic Inclusion of Persons with Disabilities: Analyzing CRPD State Reports Using Text Mining, NLP, and LLMs | This paper analyzes 170 State Reports submitted under the UN Convention on the Rights of Persons with Disabilities (CRPD) using traditional text mining/NLP techniques and Large Language Models (LLMs). The study aims to identify global, regional, and national implementation priorities, assess the focus on social justice and economic inclusion, and evaluate the hybrid analytical approach. | True | Idealistic | True | 2.0 | Positive | Hybrid approach using traditional text mining/NLP (N-grams, TF*IDF, LDA topic modeling, NER with spaCy, custom dictionary/lexicon analysis) and LLMs (Gemini 1.5 Flash, GPT-4o) to analyze CRPD State Reports. | Analysis applied to a corpus of 170 CRPD State Reports scraped from the OHCHR website (subset of 20 used for LLM analysis due to token limits). Evaluation involved frequency analysis, LDA topic coherence assessment (0.461 score achieved), NER entity extraction, lexicon-based quantification of CRPD article/paragraph representation and disability model prevalence, and comparison of traditional NLP results with LLM outputs generated via multi-shot prompt engineering. | Identified key themes (e.g., awareness-raising, family rights, regional variations), found a general shift towards a social justice model (64% representation), quantified representation of specific CRPD articles (Art. 8 most represented, Art. 10 least), extracted relevant named entities, and demonstrated that LLMs could produce coherent analyses comparable to traditional methods on the tested subset. | Challenges in effectively monitoring the global implementation of the CRPD due to the volume and complexity of State Reports. Data collection and monitoring challenges are mentioned generally in the literature review. | Proposes a hybrid computational text analysis methodology (NLP and LLMs) to systematically analyze State Reports, enabling researchers, civil society, and monitoring bodies to identify implementation priorities, gaps, and regional variations, thereby facilitating accountability and strategic planning. | Monitoring implementation of the UN Convention on the Rights of Persons with Disabilities (CRPD). | Persons with disabilities. | International Human Rights Law, Disability Law. | Global (analyzing reports from 170 State Parties to the CRPD, with regional breakdowns). | The analysis corpus consists of 170 CRPD State Reports (publicly available PDFs from OHCHR website, unstructured text). NER uses spaCy's pre-trained 'en_core_web_sm' model. LLMs (Gemini, GPT-4o) utilize their own pre-training. Custom lexicons were created based on CRPD text and disability studies literature. | Corpus collection (web scraping), data preprocessing, lexicon development (manual, literature-based, validation via KWIC and robustness checks), text mining (N-grams, TF*IDF), NLP techniques (NER via spaCy, LDA Topic Modeling via Gensim), LLM analysis (Prompt Engineering with Google AI Studio/Gemini and OpenAI/ChatGPT). | Findings presented in an academic paper. The methodology is proposed as a framework to enable broader analysis by scholars, practitioners, and civil society, facilitated by more user-friendly GenAI tools. | False | False | NaN | Methodological limitations: reliance on self-reported state data, potential dictionary limitations, NER model not fine-tuned, LLM analysis constrained by token limits (subset used). Need to incorporate shadow reports for a balanced view. Substantive gaps: Less emphasis found on addressing stigma and barriers in reports. | Developing robust custom lexicons, achieving high coherence in topic modeling (LDA score was moderate), managing LLM token limits for large corpus analysis, standard PDF text extraction and cleaning. | The paper primarily focuses on benefits but limitations imply a risk of drawing inaccurate conclusions if relying solely on the analysis of self-reported data without considering its inherent biases or the methodology's limitations. |
| X_xiJkMBc48J.pdf | Google_Scholar | Generative AI and Tax Professionals: Current PR Guidance | This presentation outlines professional responsibility guidance for tax professionals using generative AI. It discusses ethical considerations regarding competence, confidentiality, supervision, client communication, and fees based on recent bar association directives. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Tax Law, Professional Responsibility | United States | NaN | NaN | NaN | False | False | NaN | NaN | Challenges for users include: AI hallucinations, lack of transparency ('black box'), user error (untrained users, poor prompting), limitations of training data, ensuring competence, maintaining confidentiality, supervising AI and staff use, accurate billing, and client communication. | Inaccurate outputs (hallucinations); violation of client confidentiality (through input, data breaches, third-party access, discovery); violation of attorney-client privilege; over-reliance hindering critical judgment; inaccurate billing (charging for saved time, training time, or general fees); failure to supervise subordinates' AI use; reputational damage. |
| ZqGazwM9LhwJ.pdf | Google_Scholar | Higher Education After Artificial Intelligence: An Invitation to a New Kind of Conversation About the Future | This paper argues that higher education must move beyond reactive measures to address the profound challenges posed by AI, particularly LLMs. It proposes a 'responsive' approach, inviting a new kind of collective conversation to fundamentally rethink the meaning and future of education. | True | NaN | True | 3.0 | NaN | A conceptual approach called 'responsiveness' involving experiential self-awareness, taking notice of embedded meanings, and retrieving historical possibilities to guide future action in higher education. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | The proposed 'responsiveness' approach is developed through philosophical argumentation, drawing on concepts from philosophy of technology, hermeneutics, and historical analysis of educational transformations. | Proposed through publication as an essay and an open invitation for the higher education community to join a new kind of conversation facilitated by the American Council on Education (ACE). | True | False | Readers are invited to join a conversation by contacting a representative at the American Council on Education (ACE). | NaN | Challenges in fostering adoption of the proposed 'responsiveness' approach include: overcoming the prevailing 'reactive' mindset, the dominance of instrumental rationality, and entrenched disciplinary silos within academia. | Risks from AI to higher education: academic integrity (cheating, plagiarism); hindrance to student skill development; spread of misinformation/disinformation; perpetuation of AI biases; disruption of the knowledge workforce and labor markets; existential threat to the role and authority of universities. |
| FET1CXtySgkJ.pdf | Google_Scholar | AHOW ARTIFICIAL INTELLIGENCE CAN HELP TO RESHAPE LEGAL PROFESSION THROUGHOUT THE WORLD | This paper explores how AI, including machine learning and NLP, can reshape the global legal profession by automating tasks, enhancing efficiency, and improving accuracy in areas like research and document review. It also discusses benefits such as cost-effectiveness and improved access to justice, alongside challenges like job displacement and the need for lawyers to adapt. | True | Market | True | 3.0 | Positive | AI tools for document review (e.g., Kira Systems), legal research (e.g., ROSS Intelligence), contract drafting/analysis (e.g., ContractZen), and virtual legal assistance (e.g., LegalShield, AI chatbots). | NaN | NaN | High cost of legal services and limited availability of legal assistance in underserved areas. | Automating legal processes with AI to reduce costs; using AI-powered tools like chatbots and virtual assistants to provide basic legal information and guidance. | Providing basic legal information and guidance, making legal services more affordable. | Underserved communities | General legal practice | International | NaN | NaN | NaN | False | False | NaN | Ensuring responsible, ethical, transparent, and accountable deployment of AI in legal services; upskilling legal professionals for AI integration. | Ethical concerns (bias, transparency, accountability), data privacy and security, accuracy and reliability of AI, potential job displacement, and the need for legal professionals to acquire AI competency. | Job displacement, breach of attorney-client privilege, AI bias, lack of accuracy/authority in AI outputs, ethical issues in AI decision-making, data privacy and security vulnerabilities. |
| -_ghJP3E10kJ.pdf | Google_Scholar | From Briefs t o Bytes: How Gener ative AI is T ransforming Legal Writing and Pr actice | This paper explores how Generative AI (GAI), particularly tools like ChatGPT, is revolutionizing legal practice with a focus on legal writing. It examines GAI's capabilities, practical applications for lawyers, ethical considerations, limitations, and provides a framework for effective use through prompt engineering. | True | Market | True | 3.0 | NaN | The paper focuses on the use of Generative AI (GAI), exemplified by ChatGPT and GPT models, for legal tasks, particularly legal writing. It details Prompt Engineering as the key technique for effectively interacting with and guiding these AI models. | The paper does not present a formal evaluation or systematic testing procedure. It uses illustrative examples generated by the author using GAI tools (e.g., editing text, summarizing, generating captions), cites external studies on GAI productivity, and relies on the author's expertise and experience. | NaN | Primary obstacles discussed relate to GAI use by legal professionals, not access to justice directly: GAI inaccuracy and 'hallucinations', inherent bias in training data leading to discriminatory outputs, risks to client confidentiality and data privacy, the knowledge gap among legal professionals regarding GAI, potential for overreliance diminishing critical skills, and ensuring ethical compliance. | Solutions focus on responsible GAI use by legal professionals: educating practitioners about GAI, employing careful prompt engineering techniques, verifying AI outputs, taking steps to mitigate bias, protecting client confidentiality, continuous learning and skill development, and adhering to ethical obligations. It mentions potential for legal aid to use GAI for app development. | NaN | NaN | General legal practice, Legal Writing, Legal Research, Contract Law, Litigation (briefs, motions), E-discovery, Legal Education, Law Firm Management, Marketing. | United States | The paper states GAI models like GPT are pre-trained on vast amounts of text data (hundreds of billions of pieces) gathered from the web, noting this includes diverse sources and copyrighted material, and may contain biases. Specific datasets are not detailed. | The paper primarily discusses prompt engineering as a method for *using* existing GAI tools, synthesizing best practices from research (e.g., Chain-of-Thought prompting) and practical experience. It does not detail the design methodologies for creating the underlying GAI models beyond mentioning transformer architecture and pre-training. | Discusses publicly available GAI chatbots (like ChatGPT), integration into commercial legal tech platforms (Thomson Reuters, LexisNexis), and the potential for custom tool creation and service productization by law firms. | True | False | Publicly available chatbots like ChatGPT (some versions free, advanced versions paid, e.g., GPT-4) and integrations into commercial legal tech software (requiring subscriptions). Prompt engineering techniques can be applied to available tools. | Knowledge gap among legal professionals on how GAI works and how to use it effectively and safely. Technical gaps in GAI include ensuring accuracy (reducing hallucinations), mitigating bias, improving reasoning, and maintaining data privacy. Societal gaps include adapting legal ethics and education to GAI. | Challenges for users include understanding complex GAI technology, mastering effective prompt engineering, critically evaluating AI outputs for accuracy and bias, ensuring confidentiality and ethical compliance, integrating GAI into existing workflows, and keeping pace with rapid technological advancements. | Inaccuracy and fabrication ('hallucinations', e.g., fake case citations), perpetuation of biases from training data leading to discrimination, breaches of client confidentiality and data privacy, failure to meet ethical duties (competence, diligence), overreliance diminishing lawyers' critical thinking and writing skills, potential copyright infringement issues related to training data and generated outputs. |
| dPkZcjcHFQsJ.pdf | Google_Scholar | Generative AI and Finding the Law | This paper outlines six principles for evaluating generative AI large language models in legal research, focusing on the shift in cognitive authority and the instability of AI outputs. It applies ecological holistic media theory, explains generative AI concepts, analyzes AI performance on legal tasks with examples, and concludes that law librarianship must evolve towards legal information science. | True | Market | True | 2.0 | Neutral | Evaluation of commercial legal research AI tools (Casetext CoCounsel, Lexis+ AI) and general LLMs (ChatGPT-4) employing Retrieval-Augmented Generation (RAG) and large language models for legal research tasks. | Qualitative evaluation through specific legal research problems/prompts across various legal fields (e.g., special needs trusts, slip and fall, ADA, boxing regulation, securities law, case summarization, fair use). Analysis focused on accuracy, consistency, handling complexity, abstraction capabilities, and hallucination. | AI demonstrated strengths like abstraction and analogical reasoning but showed significant weaknesses: inconsistent answers over time, difficulty with complex multi-issue/jurisdictional prompts, sensitivity to syntax, potential bias towards case law, prompt rewriting, and severe hallucinations (including reversing case holdings). No single tool was consistently superior; performance varied significantly. | AI unreliability (hallucination, instability), ethical issues for lawyers (competence, candor, supervision), need for human oversight and traditional research skills, potential AI bias, difficulty verifying AI outputs, and the existing digital divide in access to legal information tools. | Emphasizing traditional legal research skills for verification, adapting the profession towards legal information science, employing careful prompt engineering and iterative questioning, using Retrieval-Augmented Generation (RAG) grounded in authoritative legal sources, and adhering to ethical rules and court mandates regarding AI use. | NaN | NaN | General Law / Multiple Fields (including Estate Planning, Torts, Disability Law, Sports Law, Securities Law, Tax Law, Criminal Law, Copyright Law, Civil Procedure, Professional Ethics) | United States (Federal and various States including Missouri, Pennsylvania, Wisconsin) | Combination of general web data (for base LLM like GPT-4) and proprietary, domain-specific legal text databases (cases, statutes, regulations, secondary sources like Matthew Bender treatises) used for Retrieval-Augmented Generation (RAG) by commercial tools (CoCounsel, Lexis+ AI). | NaN | Commercial web-based subscription services (CoCounsel, Lexis+ AI) and publicly available web interfaces (ChatGPT). | True | False | Commercial subscription services (CoCounsel, Lexis+ AI, ChatGPT-4) and free web access (ChatGPT-3.5). | Need for improved AI reliability and stability; mitigating AI bias; developing robust ethical frameworks and user competence; ensuring effective human oversight; better AI handling of complex queries and non-caselaw sources; persistence of the digital divide; conceptual need for law librarianship to evolve into Legal Information Science. | Ensuring AI accuracy/avoiding hallucination; maintaining output consistency; handling complex legal queries; appropriate training/augmentation with diverse legal sources; effective prompt engineering; managing user trust vs. skepticism; ethical integration into legal workflows. | AI hallucination leading to misinformation and citing fake cases (Mata v. Avianca example); instability undermining legal certainty; violation of lawyers' ethical duties (competence, candor, FRCP Rule 11); AI bias; automation complacency; erosion of traditional skills; AI making false statements of law. |
| MMjoMWJmYBkJ.pdf | Google_Scholar | Legal Practitioners' Views on the Effectiveness of Virtual Courts | This study explores legal practitioners' perspectives on the effectiveness of virtual courts through semi-structured interviews, identifying key themes around technological adoption, procedural changes, and access to justice. It finds that while virtual courts can improve efficiency and accessibility, their success depends on addressing technological, procedural, and equity challenges, requiring ongoing adaptation and training. | True | Idealistic | False | 2.0 | Neutral | Virtual courts | Qualitative research design using semi-structured interviews with 30 legal practitioners (lawyers, judges, paralegals, court clerks). Data were analyzed using thematic analysis. | The analysis revealed four main themes: Technological Adoption, Procedural Changes, Impact on Justice Access, and Future Directions. Key findings include the importance of user-friendly technology, the potential of virtual courts to improve access to justice, and the need for continuous adaptation and training to address technological and procedural challenges. | Digital divide (lack of digital literacy and technology access); economic barriers (technology costs, funding disparities); physical barriers (disability access, age-related challenges); psychological barriers (privacy concerns, lack of trust in technology, perceived intimidation). | Enhancing user satisfaction, ensuring equitable access to justice, and adapting to evolving technological landscapes. Prioritizing user-friendly technology, robust security measures, comprehensive training programs, and providing support and resources to address the digital divide. | Effectiveness of virtual courts in relation to technological adoption, procedural changes, and impact on geographical, economic, physical, and psychological barriers to justice. | Individuals in remote areas, persons with mobility issues or other disabilities, those facing economic disparities, and individuals with low digital literacy. | General | International | NaN | Qualitative research design, semi-structured interviews, purposive sampling, thematic analysis (using NVivo software). | NaN | False | False | NaN | Need for research including broader stakeholders (litigants, witnesses, technical experts); quantitative and longitudinal studies; adapting laws and regulations to keep pace with technology; fully addressing the digital divide and ensuring equitable participation. | Ensuring ease of use and accessibility of platforms; implementing robust security measures; integrating with existing case management systems; managing technical glitches; adapting communication dynamics for virtual settings; overcoming resistance to change and learning curves; addressing the digital divide and technology costs. | Compromised effectiveness of legal procedures (e.g., cross-examinations) in virtual settings; exacerbation of accessibility issues due to digital divide or technology costs; negative psychological impacts (anxiety, distrust); security and confidentiality breaches if measures are inadequate; potential diminution of therapeutic justice aspects in legal proceedings. |
| WB8suT_r-MIJ.pdf | Google_Scholar | The Rapid Rise of Generative AI Assessing risks to safety and security | This report examines the national security implications of generative AI, based on literature reviews and expert interviews. It assesses political, digital, and physical security risks (e.g., disinformation, cyberattacks, CSAM, weapon instruction) and potential opportunities for intelligence agencies, proposing policy recommendations for governance and safe deployment. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | National Security Law, Cybersecurity Law, Criminal Law, Intelligence Law, International Law, Technology Regulation | Primarily UK, with discussion of US, EU, China, and Global governance. | Discusses models trained on large, pre-existing internet/text corpora (e.g., for GPT, LLaMA). Case study 1 (LLM_OSINT) involves agents using web searches. Case study 2 (Gen-MAS-Sim) uses OpenAI models (davinci-003, GPT-3.5 Turbo, GPT-4). | NaN | NaN | False | False | NaN | Need for better evaluation frameworks (socio-technical approach); reliable methods for identifying/watermarking AI content; techniques for 'machine unlearning' for compliance; robust evidence on terrorist uses; improved voice cloning detection; common technical standards and measurement theory for AI; enhanced government expertise, compute infrastructure, and data access; addressing hardware supply chain issues (semiconductors). | Unreliability, inaccuracy, and hallucinations of LLMs; user over-trust and over-reliance; data security risks (sensitive data input); prompt injection and jailbreaking vulnerabilities; data poisoning; difficulty purging information from LLMs; lack of transparency and explainability; distinguishing real vs. fake content; ensuring human validation and oversight; managing autonomous agents securely; aligning global standards; government skills and infrastructure gaps. | Political risks: Disinformation, electoral interference, surveillance, geopolitical fragmentation, erosion of trust. Digital risks: Enhanced cyberattacks (lowering skill barrier), targeted fraud/phishing (quality/scale), voice cloning scams, AI-generated CSAM creation/proliferation, terrorist radicalisation/propaganda. Physical risks: Lowering barriers to weapon (especially biochemical) instruction/development. Broader risks: Improper adoption in critical sectors (CNI, public services), unintended consequences from DIY experimentation, degradation of code integrity, potential for AI race-to-the-bottom dynamics impacting safety, accidental misuse leading to crises, undermining strategic stability. |
| H5rHkJZPK4QJ.pdf | Google_Scholar | Evaluating Errors and Improving Performance of Chatgpt: a Research Paper | This paper analyzes common errors (grammatical, semantic, contextual, factual) made by ChatGPT, identifying underlying causes like insufficient training data and model biases. It proposes and discusses various mitigation strategies (e.g., fine-tuning, reinforcement learning, bias mitigation) to improve ChatGPT's performance, reliability, and user experience. | True | Market | True | 2.0 | NaN | Error analysis of ChatGPT and proposed mitigation strategies for its improvement. | The paper proposes error identification through: Human Evaluation (rating responses on fluency, relevance, grammaticality, overall quality), Automatic Evaluation Metrics (Perplexity, BLEU, ROUGE, METEOR), Error Annotation (manual annotation of error types), Comparison with Gold Standard, and User Feedback and Surveys. It states that experimental evaluations were conducted to assess proposed mitigation strategies. | The paper states that 'The experimental results demonstrated the effectiveness of the employed error mitigation strategies in reducing errors and enhancing ChatGPT's performance.' No specific quantitative results are provided. | NaN | NaN | NaN | NaN | General (mentioned as a potential application area, not a focus) | International | ChatGPT is trained on 'a vast corpus of text data from the internet.' The error analysis dataset described comprises 'a collection of user interactions with chatgpt,' potentially constructed from human-generated conversations, simulated interactions, crowdsourced dialogues, or scraped public dialogue data (anonymized and unstructured). | Proposed error mitigation strategies include: Fine-tuning, Reinforcement Learning with User Feedback, Context-Awareness Enhancements (e.g., memory mechanisms, attention mechanisms, dialogue state tracking), Error-Specific Training Data Augmentation, Bias Mitigation techniques, Ethical and Safety Constraints (e.g., rule-based filtering, human-in-the-loop), Active Learning, Multi-Model Ensembles, and User Interface/Interaction Design. | NaN | False | False | NaN | Remaining LLM challenges include: handling long-range dependencies, understanding complex reasoning, generating context-aware responses, insufficient training data for specific scenarios, contextual ambiguity, and lack of common sense or comprehensive world knowledge. | Causes of errors (challenges) in ChatGPT include: Insufficient Training Data, Contextual Ambiguity, Lack of Common Sense or World Knowledge, Biases in Training Data, Overconfidence or Insufficient Uncertainty Estimation, Lack of continuous Feedback and Reinforcement Learning, Data Skewness or Bias, Sensitivity to Input Phrasing, and Algorithmic Limitations. | Potential risks include: generating biased, offensive, or harmful content; providing factually incorrect or misleading information; erosion of trust in AI systems; and providing inappropriate or insensitive suggestions or advice. |
| RiRt0XNLpwEJ.pdf | Google_Scholar | Assessing Information Literacy in the Age of Generative AI: A Call to the National Conference of Bar Examiners | This paper argues for the National Conference of Bar Examiners (NCBE) to incorporate information literacy assessment, especially concerning generative AI, into the Multistate Professional Responsibility Exam (MPRE). This is presented as crucial for ensuring newly licensed lawyers meet their duty of technology competence and to protect the public from the risks of incompetent AI use in legal practice. | True | Idealistic | True | 1.0 | Positive | Incorporating information literacy assessment for generative AI into the Multistate Professional Responsibility Exam (MPRE). | NaN | NaN | The primary obstacle identified is the risk of newly licensed lawyers' incompetent use of generative AI, stemming from a lack of assessed information literacy. This incompetence can lead to flawed legal research, ethical breaches, and ultimately harm to clients, thereby undermining access to competent legal services. | The paper proposes that the National Conference of Bar Examiners (NCBE) address this by incorporating specific assessments of information literacy related to generative AI into the Multistate Professional Responsibility Exam (MPRE). This would ensure a minimum standard of technological competence for newly licensed lawyers. | Lawyer competence in using AI, professional responsibility, public protection, legal research ethics in the age of AI. | The general public seeking legal services. | Professional Responsibility, Legal Ethics, Legal Research | United States | NaN | Conceptual analysis, review of legal and educational literature, argumentation based on existing institutional frameworks (e.g., NCBE history, AALL standards), and analysis of current technological impacts on the legal profession. | Proposed deployment through the National Conference of Bar Examiners (NCBE) by integrating new assessment components into the Multistate Professional Responsibility Exam (MPRE). | False | False | NaN | The current lack of formal assessment of AI-related information literacy in lawyer licensing exams (specifically the MPRE), which fails to ensure newly licensed lawyers are competent in using emerging AI technologies responsibly and ethically. | The primary challenge for the NCBE would be the rapid pace of AI development, requiring continuous updates to assessment content and methodologies, and ensuring the validity, fairness, and psychometric soundness of new question types related to AI and information literacy. | Risks identified include lawyers producing inaccurate legal work due to AI 'hallucinations,' breaching client confidentiality through improper AI use, and a general decline in critical legal skills if AI is used without adequate oversight. These issues can lead to disciplinary actions for lawyers and significant harm to clients, thereby eroding public trust in the legal profession. |
| VWi01BsHzJwJ.pdf | Google_Scholar | USING KAZAKH NER DATASETS FOR MULTICLASS CLASSIFICATION IN THE LEGAL DOMAIN: A COMPARATIVE STUDY OF BERT, GPT, AND LSTM MODELS | This study comparatively analyzes the performance of BERT, GPT, and LSTM models for multiclass text classification within the Kazakh legal domain, utilizing a specialized Named Entity Recognition (NER) dataset. The research highlights the models' effectiveness and challenges in processing a low-resource language, emphasizing the need for specialized datasets and algorithms for applications like legal document automation and decision support. | True | Market | True | 2.0 | Neutral | Comparative study of BERT, GPT, and LSTM models for multiclass text classification in the legal domain. | Models were evaluated on a specialized Kazakh NER dataset (KazNERD) adapted for legal multiclass text classification. Evaluation metrics included accuracy, recall, precision, and Area Under the Curve (AUC). | BERT demonstrated the best performance on validation data, achieving: Loss 0.0481, Accuracy 0.9202, Precision 0.9712, Recall 0.9585, and AUC 0.9781. | Scarcity of linguistic resources (annotated data, research) and NLP tools for low-resource languages like Kazakh, hindering the development of advanced AI tools for the legal domain, which could impact potential access to justice applications. | Development of specialized datasets (like the adapted KazNERD) and NLP models (BERT, GPT, LSTM) tailored for the Kazakh language and its legal domain to improve legal information processing, potentially making legal services more efficient and systems more accessible. | Automation of legal document management, analysis of court decisions, development of intelligent decision-support systems for the legal sector, growth of digital jurisprudence. | NaN | General legal domain, including legal document management, court decision analysis, and legal services automation. | Kazakhstan | The KazNERD dataset, an annotated corpus for Named Entity Recognition in the Kazakh language, adapted for legal topics. The data was preprocessed and transformed from NER annotations to suit multiclass text classification. | Comparative analysis of existing NLP models (LSTM, BERT, GPT). Data preparation involved tokenization specific to each model and creation of binary/multiclass labels from NER data for the classification task. | NaN | False | False | NaN | The Kazakh language is under-researched in computational linguistics; critical importance of creating more specialized datasets for training and testing models; need for development of advanced methods like cross-sentence entity recognition for deeper text understanding. | Adapting NLP models to the agglutinative structure and complex legal terminology of the Kazakh language; scarcity of annotated training data for specific legal tasks; ensuring model generalization from training data to unseen data. | NaN |
| GocnXfuRjPsJ.pdf | Google_Scholar | ARTIFICIAL REASON AND ARTIFICIAL INTELLIGENCE: THE LEGAL REASONING CAPABILITIES OF GPT-4 | This paper explores the concept of "artificial" legal reasoning, comparing it to the capabilities of artificial intelligence, specifically GPT-4. Through testing, it concludes that GPT-4 can generate outputs in legal tasks like fact-finding, interpretation, qualification, and decision-making that mimic human legal reasoning. | True | NaN | True | 2.0 | NaN | GPT-4 (via ChatGPT) | Qualitative testing using zero-shot prompting (with requests for step-by-step reasoning) on hypothetical legal scenarios, primarily traffic law examples set in a Serbian context. The study involved presenting these scenarios to ChatGPT (using GPT-4) and analyzing its responses, comparing outputs from May 2023 and March 2024 versions. | ChatGPT (GPT-4) can generate outcomes in fact-finding, interpretation, qualification, and decision-making that appear as if it reasons legally. It identified factual and interpretative problems, and when prompted to decide with underdetermined information, it relied on general legal principles (e.g., 'beyond a reasonable doubt', 'reasonable person standard'). | NaN | NaN | NaN | NaN | Traffic law (used in the paper's own illustrative test cases); General legal reasoning (as the broad subject of investigation). Mentions various fields covered by UBE/LSAT for context. | Serbia (setting for the paper's own illustrative test cases); USA (context for referenced LSAT/UBE benchmarks). The philosophical discussion aims for broader applicability. | GPT-4's training data: proprietary, large-scale, general text and multimodal data. The paper refers to it as 'large amounts of text' and 'large quantity of text the model was trained on'. | NaN | Commercial availability through OpenAI's ChatGPT service (the paper mentions using a paid version). | True | False | ChatGPT (based on GPT-4) is accessible as a commercial service; the paper specifically mentions use of a paid version. | NaN | Hallucinations (providing factually incorrect information), the 'black box' nature of LLMs (lack of transparency in reasoning processes), performance variability and potential degradation of models over time, and managing user/researcher 'expectation of perfection' bias when evaluating LLMs. | Generation of and reliance on 'hallucinated' or factually incorrect legal information (e.g., lawyers citing fake cases from ChatGPT). Emergent 'risky' AI capabilities (e.g., agency, long-term planning) if not understood or controlled. |
| JxMDLxGZzF4J.pdf | Google_Scholar | Competitive Advantage in B2B Marketing and Sales Through Generative AI | This paper explores how B2B firms can use generative AI in marketing and sales to gain a competitive advantage, applying the Situated AI Framework through three case studies. It finds that generative AI enhances efficiency, customer engagement, and strategic decision-making, with grounding, bounding, and recasting activities playing key roles. | True | Market | True | 2.0 | NaN | Application of Generative AI (ChatGPT-4, DALL-E, custom applications using GPT-4) in B2B marketing and sales, analyzed through the Situated AI Framework (grounding, bounding, recasting activities). | Qualitative analysis based on three case studies involving semi-structured interviews with company representatives and review of public documents/articles. | Generative AI enhanced operational efficiency (e.g., proposal generation time reduced by 75% in C3), improved customer engagement (personalized communications), enabled data-driven decisions, and built new capabilities (e.g., visual design in C1). Grounding, bounding, and recasting activities were identified as relevant for leveraging AI strategically. | NaN | NaN | NaN | NaN | B2B Marketing, B2B Sales, Business Strategy, Logistics. Peripheral: Corporate Law, IP Law, Data Privacy Law. | International | Mix of proprietary internal data (historical communications, RFPs, proposals) for custom models/fine-tuning/RAG, and user prompts/criteria combined with potentially public data for publicly available models (e.g., ChatGPT, DALL-E). | Case study research; Qualitative interviews; Thematic analysis; Application of Situated AI framework. For tools developed: Proof of Concept (POC), iterative development, agile methodology, user feedback, blind testing. | Use of public tools (ChatGPT, DALL-E) integrated into workflows; Development and internal deployment (or planned deployment) of custom AI applications (chatbot, proposal generator) often built on platforms like Azure OpenAI. | False | False | NaN | NaN | Technological limitations (hallucinations, image generation quality); Context alignment (difficulty achieving desired outcomes, e.g., C1 India campaign); Maintaining competitive advantage with widespread AI adoption; Change management; Ensuring data quality for training; Protecting AI capabilities; Effective adaptation/recasting based on feedback. | Data leakage/security breaches; Knowledge expropriation; AI errors/hallucinations; Ineffective implementation/poor ROI; Failure to adapt AI; Legal/compliance risks (data privacy, AI regulations); Reputational damage from inaccurate outputs. |
| BqZr04cxhiwJ.pdf | Google_Scholar | GPT Takes the Bar Exam | This paper evaluates the performance of OpenAI's GPT-3.5 (text-davinci-003) on the multiple-choice section (MBE) of the US Bar Exam using zero-shot prompting. GPT-3.5 significantly outperformed random guessing, achieving 50.3% accuracy overall and passing scores in Evidence and Torts, suggesting future LLMs may pass the full MBE. | True | Market | True | 2.0 | Positive | Evaluating OpenAI's GPT-3.5 (text-davinci-003) via zero-shot prompting on the Multistate Bar Examination (MBE). | Assessed performance on a complete official NCBE MBE practice exam (purchased December 2022) using zero-shot prompting with the text-davinci-003 API. Involved prompt engineering (testing 7 types, finding rank-ordering top 3 choices best) and hyperparameter tuning (temperature, top_p, best_of, max_tokens). Results were compared against baseline guessing, average human scores, and passing thresholds. | Best configuration (rank-ordering top 3 choices prompt) achieved 50.3% overall accuracy on the MBE practice exam. GPT-3.5 achieved passing rates in Evidence (63%) and Torts (62%). Its top two and top three choices were correct 71% and 88% of the time, respectively. Fine-tuning attempts did not improve performance over zero-shot. | NaN | NaN | NaN | NaN | Civil Procedure, Constitutional Law, Contracts, Criminal Law and Procedure, Evidence, Real Property, Torts | USA | The evaluated model (GPT-3.5 / text-davinci-003) was pre-trained by OpenAI on proprietary data described as "a blend of text and code from before Q4 2021". An unsuccessful fine-tuning attempt used 200 unseen, simulated MBE questions with explanations from an NCBE answer guide. | The evaluation method involved zero-shot prompting, extensive prompt engineering (comparing different prompt structures), and hyperparameter optimization using the OpenAI API. An attempt at fine-tuning via the API was also conducted. | NaN | True | False | The core technology (GPT-3.5 model family) is accessible via OpenAI's commercial API. | Performance gap between GPT-3.5 (50.3%) and the MBE passing threshold (~60%), particularly in subjects like Criminal Law. Nascent scientific understanding of LLM behavior and limitations due to the proprietary nature of GPT. Need for evaluation on other Bar exam components (essays, performance tests). | High sensitivity of LLM performance to prompt engineering. Difficulty in interpreting model reasoning due to lack of access to internal states. Failure of fine-tuning attempts with limited data. | NaN |
| nvJ-YKrRcQAJ.pdf | Google_Scholar | Artificial Intelligence in Civil Justice Systems : An Empirical and Interdisciplinary Analysis and Proposal for Moving Forward | This paper analyzes the systemic and individual harms posed by generative AI to civil justice systems (litigation and arbitration), drawing on empirical social science research. It proposes restructuring the legal profession and education based on England's split bar model to balance AI's benefits with the need to preserve human expertise and system legitimacy. | True | Idealistic | True | 1.0 | Negative | A proposed restructuring of the legal profession into a 'split bar' (post-AI solicitors using AI for routine tasks, post-AI barristers avoiding AI for complex/novel work), inspired by the English system. | The proposal is based on theoretical analysis, empirical social science research on AI's effects, legal scholarship, and comparative analysis of the English legal system; no empirical testing of the proposal itself is described. | NaN | Systemic threats to the legitimacy and integrity of civil justice (e.g., algocracy, path dependency, erosion of diffuse support); individual cognitive harms (e.g., automation bias, cognitive atrophy, skill degradation, metacognitive laziness, cognitive loafing, AI addiction); difficulty ensuring expertise development for junior lawyers; ethical challenges; risk of inequitable two-tiered justice. | Adopt a 'split bar' model distinguishing lawyers who use AI extensively (post-AI solicitors) from those who do not for complex tasks (post-AI barristers). Implement corresponding differentiated legal education pathways focused on either AI proficiency or traditional independent legal analysis, ensuring a baseline legal understanding before specialization. | Legitimacy of justice systems, Quality of legal services, Professional ethics and competence, Legal education reform | NaN | Civil justice systems (litigation and arbitration) | US, UK, EU, International | NaN | Interdisciplinary analysis, review of empirical social science research, legal analysis, comparative legal systems analysis (England and Wales). | NaN | False | False | NaN | Need for development of practical implementation details for the proposed split bar system; overcoming status quo bias for adoption; addressing potential negative impacts of pre-collegiate AI education on foundational skills required for the 'post-AI barrister' path; underexplored choice-of-law issues related to AI. | Ensuring responsible AI use by legal professionals; overcoming cognitive biases (automation bias, cognitive loafing, anchoring bias); preventing skill degradation and ensuring expertise development; maintaining system legitimacy and public trust amidst technological change; adapting legal education effectively; addressing ethical concerns (hallucinations, self-dealing); managing potential AI addiction. | Erosion of civil justice system legitimacy (algocracy, undermining judicial independence, loss of public trust); degradation of critical thinking, legal skills, and creativity (cognitive atrophy, path dependency); increased errors due to automation bias and hallucinations; reinforcement of societal biases through algorithms; creation of inequitable two-tiered justice systems; ethical violations; AI addiction among professionals. |
| 3613904.3642700.pdf | Google_Scholar | How Knowledge Workers Think Generative AI Will (Not) Transform Their Industries | This paper presents findings from participatory research workshops with knowledge workers across seven industries in the US regarding their expectations of generative AI's impact. Participants largely view generative AI as a tool for menial tasks requiring human review, not anticipating major industry disruption, but fearing it may amplify existing negative social forces like deskilling and dehumanization. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Law | USA | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Deskilling (especially elimination of entry-level jobs, reduction in value of skills, shift to precarious work), dehumanization (loss of human connection, joy in work, critical thinking), disconnection (from reality, human interaction, exacerbating loneliness), disinformation (proliferation of false/low-quality content, deepfakes, propaganda), job loss, inaccuracy/hallucinations in AI output, reflection of societal biases, privacy breaches (memorization), undermining professional expertise, potential for misuse under capitalism. |
| l1iBcAN9nccJ.pdf | Google_Scholar | Legal Validity w ith Artificial Intelligence Technology on Gpt Chat as Legal Aid | This paper analyzes the legal validity of using AI, specifically ChatGPT, for legal aid in Indonesia, highlighting the absence of a clear regulatory framework for liability and user protection. It argues for the urgent need for specific regulations to ensure AI's safe and ethical application in the legal field without compromising legal certainty for users. | True | Idealistic | True | 3.0 | Neutral | ChatGPT for legal aid | NaN | NaN | Uncertainty of legal liability for AI errors; AI (ChatGPT) lacking legal capacity/qualifications under Indonesian law; risk of inaccurate/outdated AI advice; AI's inability to make ethical judgments; data privacy/confidentiality concerns; lack of a clear regulatory framework for AI in legal aid. | Adopting specific regulations for AI in law (defining limits, accountability); public education on AI limitations; collaboration between tech developers and legal institutions; ensuring compliance with data protection laws; setting quality/accuracy standards for legal AI; clarifying provider liability and consumer protection. | Legal validity of AI-provided legal aid; legal liability for AI errors; data privacy and consumer rights in AI legal services; regulation of AI in the legal field; accessibility of legal information. | General public, especially those unable to afford professional legal advocates and individuals unfamiliar with the law. | Advocate Law, Consumer Protection Law, Personal Data Protection Law, provision of legal aid. | Indonesia | NaN | NaN | NaN | False | False | NaN | Lack of a clear legal framework for AI liability in legal aid; absence of specific regulations for AI use ensuring legal/ethical standards; unclear application of data protection laws to legal AI; no established mechanism for holding AI or developers liable for erroneous advice. | Ensuring legal validity of AI-generated advice; establishing accountability for AI errors; protecting user data; AI's lack of contextual/ethical understanding; potential for AI inaccuracies; public over-reliance or misunderstanding of AI capabilities. | Inaccurate or irrelevant AI-generated legal advice leading to adverse outcomes for users; misuse or leakage of personal/sensitive legal data; users relying on AI without understanding its lack of legal authority or accountability compared to human advocates. |
| 10._Efficient_prompt_engineering_Techniques_and_Trends_for_maximizing_LLM_output.pdf | Google_Scholar | Efficient Prompt Engineering: Techniques and T rends for Maximizing LLM Output | This paper reviews prompt engineering techniques (e.g., structured prompting, iterative refinement, chain-of-thought) aimed at optimizing Large Language Model (LLM) performance. It also discusses emerging trends like automated prompt generation and multi-modal prompting, along with challenges such as response bias, ambiguity, security, and ethical concerns. | True | Market | True | 3.0 | NaN | General prompt engineering techniques for LLMs (e.g., structured prompting, role-based prompting, iterative refinement, chain-of-thought, few-shot learning, automated generation, multi-modal prompting). | NaN | NaN | NaN | NaN | NaN | NaN | General Legal Field | International | NaN | NaN | NaN | False | False | NaN | Need for bias reduction, improved interpretability (explainable AI), automatic/secure prompt optimization, handling long contexts, robust evaluation standards. | Prompt ambiguity/vagueness, response bias, vulnerability to adversarial attacks (e.g., prompt injection), computational cost/latency, ethical concerns (misinformation, fairness, transparency). | Generation of irrelevant, incorrect, or biased responses; perpetuation of societal stereotypes; manipulation via adversarial prompts to produce false, dangerous, or unethical content; distribution of misinformation and deepfakes; lack of accountability due to opacity. |
| Cozd6dhwLwwJ.pdf | Google_Scholar | PR Council Guidelines on Generative AI | These guidelines provide ethical and legal advice for public relations professionals using generative AI, emphasizing responsible use, human oversight, and client confidentiality. The document outlines risks like bias, copyright infringement, and misinformation, and recommends transparency and accuracy. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Public Relations Law, Copyright Law, Contract Law, Intellectual Property Law, Confidentiality | US | NaN | NaN | NaN | True | True | Guidelines publicly released by the PR Council on April 26, 2023. | NaN | Ensuring accuracy of AI output, managing and mitigating algorithmic bias, protecting client confidentiality when using AI tools, maintaining intellectual property integrity, ensuring proper disclosure of AI use, need for adequate staff training, keeping guidelines current with rapid AI evolution. | Spreading deepfakes, misinformation, or disinformation; Inaccuracy and fabrication of information by AI; Inadvertent plagiarism, copyright infringement, or trademark infringement; Violation of confidentiality agreements; Bias in AI-generated text and images; Contract violations related to 'work for hire' clauses; Increased legal risk due to indemnification clauses; Misuse of voice/music generation tools. |
| f4RWySu8iFcJ.pdf | Google_Scholar | Ten Thousand AI Systems Typing on Keyboards: Generative AI in Patent Applications and Preemptive Prior Art | This paper examines the potential negative impacts of generative AI on the US patent system. It specifically analyzes how AI could be misused to create massive databases of preemptive prior art and flood the Patent and Trademark Office (PTO) with low-quality patent applications, proposing policy solutions to mitigate these risks. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Patent Law, Intellectual Property Law | United States | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Use of generative AI to publish massive online databases of preemptive prior art intended to foreclose patentability. Use of generative AI to automate the writing and filing of enormous numbers of low-quality provisional and utility patent applications, potentially overwhelming the PTO. Weakening of the conception requirement if AI-generated text is accepted without a substantive nexus to human inventorship. Misuse of the provisional application system and fee structures to file excessive disclosures cheaply. Potential for findings of egregious misconduct/inequitable conduct if inventors falsely claim inventorship over AI-generated application content. |
| DOSrEgjcnAoJ.pdf | Google_Scholar | REVOLUTIONIZING JUSTICE: UNLEASHING THE POWER OF ARTIFICIAL INTELLIGENCE | This paper provides an overview of artificial intelligence (AI) and machine learning (ML) applications in the legal field, discussing their history, benefits (efficiency, research, contract analysis, predictive analytics), and potential uses by lawyers and courts. It also explores ethical considerations, potential disadvantages (confidentiality, misuse, inaccuracy, job loss), and legal liability issues associated with AI in law. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | Lack of affordability and accessibility of traditional legal services; Potential for systemic bias in AI tools used in the justice system (e.g., risk assessment). | Using AI tools like virtual assistants and chatbots for basic legal guidance and resource direction; Automating legal processes for pro bono and legal aid services to increase efficiency and reach. | Providing basic legal guidance; Answering common legal questions; Client intake/direction; Automation for pro bono/legal aid; Bail decisions; Sentencing (recidivism risk assessment). | General public needing affordable legal services; Clients of pro bono services, public interest organizations, and legal aid clinics. | General Litigation, Contract Law, Criminal Law, Legal Research, Practice Management | US | NaN | NaN | NaN | False | False | NaN | Systemic bias in AI datasets and algorithms leading to unfair outcomes; Lack of transparency ('black box') in AI decision-making; Ensuring AI accuracy and preventing 'hallucinations'; Need for human oversight, accountability, and clear ethical/legal frameworks; Ensuring AI tools for access to justice are affordable, accessible, accurate, and do not constitute unauthorized practice of law. | NaN | Disclosure of confidential information; Misuse of AI (e.g., plagiarism); Inaccurate or outdated AI outputs ('hallucinations'); Creation and use of deepfakes; Potential job displacement in the legal sector; Systemic bias leading to discrimination (e.g., in hiring, sentencing); Lack of transparency and accountability; Ethical violations (competence, confidentiality, unauthorized practice of law); Legal liability uncertainty. |
| XKWZNKbyZE0J.pdf | Google_Scholar | Lawful Grounds to Share Justice Data for Lawtech Innovation in the UK | This paper analyzes the legal framework under UK data protection law (UK GDPR) for sharing publicly held 'justice data' (like court judgments and pleadings) with commercial lawtech entities. It argues that 'public interest' or 'legitimate interests' could serve as lawful bases, facilitating innovation aimed at improving access to justice. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Lack of a clear legal basis under data protection law (UK GDPR) for secondary processing (reuse) of justice data containing personal information by commercial entities for innovation purposes; difficulty balancing public interest, commercial goals, and individual data protection rights. | Proposes using GDPR Article 6(1)(e) 'public interest' or Article 6(1)(f) 'legitimate interests', potentially in a hybrid model, as lawful bases for sharing justice data. Emphasizes the need for careful assessment of purpose limitation, necessity, controllership, and the balancing test. | Access to legal information, Cost-effective legal advice, Predicting case outcomes. | Litigants in person. | Data Protection Law, Access to Justice | UK | Conceptual discussion of using 'justice data' (e.g., judgments, potentially pleadings and submissions) held by UK public bodies (e.g., HMCTS, Ministry of Justice), which contains personal data and is largely unstructured text. | NaN | NaN | False | False | NaN | Regulatory uncertainty regarding the interpretation and application of 'public interest' and 'legitimate interests' grounds for data sharing involving commercial entities; practical challenges in establishing robust data sharing agreements and governance; potential impact of future data protection law reforms. | Interpreting complex GDPR provisions (e.g., necessity test, balancing test, purpose limitation, research exemptions) in the context of public-private data sharing; defining the scope of 'public authority tasks'; determining data controllership roles. | Unlawful processing of personal data leading to sanctions; infringement of individuals' data protection rights and privacy; potential for inaccurate or biased outputs from legal analytics tools; misuse of sensitive justice data; undermining public trust in the justice system due to opaque data use or data breaches. |
| HmTfEfhHkRMJ.pdf | Google_Scholar | AI Diversity and the Future of “Fair” Legal AI | This article examines the potential for AI to reshape legal practice, highlighting the critical issue of embedded bias, particularly in automated legal decision-making. It proposes that using a diversity of AI systems ("AI diversity" or a "multisystem approach"), benchmarked against public standards, can help mitigate bias and lead to fairer legal outcomes. | True | Idealistic | True | 1.0 | Neutral | AI Diversity / Multisystem Approach: Employing multiple, distinct AI models (developed by diverse teams, trained on different data) in parallel for legal tasks, comparing their outputs to enhance reliability and mitigate bias. | The paper proposes the technique conceptually and discusses the general importance of testing and benchmarking AI, including using public benchmarks, but does not report any specific testing or evaluation conducted on the proposed "AI diversity" approach itself. | NaN | Algorithmic bias stemming from training data that reflects historical societal and legal system inequalities; Lack of transparency in AI systems ("black box" problem) hindering trust, accountability, and regulation. | Adopt an "AI diversity" or "multisystem approach" using multiple AI models benchmarked against public standards; Ensure diversity in development teams and training data; Promote transparency and public participation in AI implementation and oversight; Use consensus among models for credibility and discrepancies to trigger human review. | Fairness in legal AI, Bias mitigation in automated legal/governmental decision-making (administrative decisions, judicial rulings, sentencing), Algorithmic accountability and transparency. | Minority and underrepresented groups disproportionately affected by bias in the legal system (e.g., racial minorities mentioned in context of COMPAS and juror questioning). | General / Multiple (Criminal Law, Administrative Law, Constitutional Law, Litigation, Legal Research, Document Drafting, Appellate Review) | International | The paper discusses general types of data used for legal AI (historical text, cases, statutes, dockets) and emphasizes the source of bias often lies in this data reflecting societal inequalities or specific legal system biases (e.g., COMPAS data, voir dire data). It advocates for diverse, cleaned, and vetted data but does not describe a specific dataset. | Conceptual proposal; The paper does not detail specific design methodologies used to develop the proposed 'AI diversity' technique itself. | The paper proposes parallel deployment of multiple benchmarked AI systems for government functions and potentially in the appeals process, but does not describe any actual deployment. | False | False | NaN | Lack of emphasis on incorporating AI system diversity (multisystem approach) in current proposals for AI adoption, regulation, and transparency, especially in government legal processes; Need for effective public benchmarks for legal AI; Need for truly democratized processes for AI implementation and oversight in the public sector. | Ensuring fairness and eliminating bias in AI systems; Dealing with the opacity ('black box') of complex models; Developing appropriate and timely regulations; Establishing trust and accountability; Sourcing diverse data and development teams; Creating meaningful benchmarks; Managing and interpreting outputs from multiple AI systems. | Replicating and amplifying societal biases leading to unfair or discriminatory legal outcomes (e.g., in sentencing, administrative decisions); Degrading trust in the legal system due to biased or opaque AI; Lack of accountability for AI-driven decisions; Hindering access to justice or exacerbating inequities if AI implementation is flawed. |
| oS44t3l-DBgJ.pdf | Google_Scholar | A Question and Answering Service of Typhoon Disasters Based on the T5 Large Language Model | This paper proposes a question-answering (Q&A) system for typhoon disaster information using the T5 large language model, integrating domain fine-tuning and retrieval-augmented generation (RAG). The system aims to improve access to accurate and timely disaster information for the public. | True | NaN | True | 1.0 | NaN | A Q&A system (Typhoon-T5) based on the T5 model, enhanced through continuous pre-training on typhoon-related text, fine-tuning on typhoon Q&A pairs, and retrieval-augmented generation (RAG) using external knowledge. | Evaluation using text similarity metrics (cosine similarity with 'all-MiniLM-L6-v2', Jaccard similarity coefficient), text generation quality metrics (ROUGE-1, ROUGE-2, ROUGE-L), intelligent evaluation (using ChatGPT), and manual evaluation by human workers. Compared different configurations: T5-large vs T5-base, with/without fine-tuning (Typhoon-T5 vs T5), and with/without RAG context. | The proposed method (Typhoon-T5-large_with_context) integrating fine-tuning and RAG outperformed other configurations across all evaluation metrics (Cosine similarity, Jaccard index, ROUGE, ChatGPT, Manual). For instance, it achieved the highest ROUGE scores (e.g., 40.82% ROUGE-1) and demonstrated the highest frequency of outputs in high-similarity score ranges. | NaN | NaN | NaN | NaN | NaN | China | Textual information about typhoon disasters collected from open-source databases like Baidu Encyclopedia and Wikipedia, plus news website reports (specifically focusing on Typhoon 'In-Fa'). This unstructured text data was processed to create a corpus for continuous pre-training using masked language modeling and a dataset of 2204 Q&A pairs for fine-tuning. | Data collection from public web sources, data cleaning and classification, masked language modeling for pre-training data construction, Q&A pair generation (using open-source self-instruct method), continuous pre-training of T5 model, supervised fine-tuning on Q&A pairs, and implementation of RAG using ColbertV2 for vector DB creation and retrieval. | NaN | False | False | NaN | NaN | Increased complexity and hardware/computational resource requirements due to integrating fine-tuning and RAG, especially with larger knowledge bases. Potential for poor quality of RAG-retrieved text negatively impacting results ('hallucinations'). Limitations of the T5 model itself, such as inability to automatically adjust response length appropriately. Current data representation focuses on spatiotemporal text descriptions, lacking integration with other key factors like meteorological, political, or socioeconomic data. | LLM hallucination leading to incorrect or misleading information, which is particularly problematic in the context of disaster response. Poor quality of retrieved text in the RAG process could mislead the LLM, resulting in inaccurate answers. |
| O3Q_xyM4nOAJ.pdf | Google_Scholar | Exploring the Impact of Attention Mechanisms in Big Data Analysis and Large Language Models | This paper reviews the transformative effect of attention mechanisms on big data analysis and large language models (LLMs), highlighting their improvements over traditional sequence models. It discusses applications in generative AI, business intelligence, and prompt engineering, noting challenges like computational cost and interpretability. | True | Market | True | 3.0 | NaN | Attention Mechanisms / Transformer Models (e.g., BERT, GPT) | Comparison of Transformer/Attention-based models (BERT, GPT) against traditional models (LSTM) using performance metrics (Accuracy, F1-Score, Inference Time, Training Time) on tasks like time-series forecasting, text classification, anomaly detection, and text summarization. Datasets included text corpora (Common Crawl, Wikipedia) and structured data (financial transactions, sensor logs). | Attention-based models substantially outperformed traditional LSTM models across tasks. For example, BERT achieved 94% accuracy in text classification vs. 81% for LSTM, and a Transformer model achieved 94% accuracy in financial anomaly detection vs. 87% for LSTM. | NaN | NaN | NaN | NaN | NaN | International | Textual corpora from open repositories (e.g., Common Crawl and Wikipedia) and structured big data sources such as financial transactions and sensor logs. | Comparative analysis of different model architectures (LSTM vs. Attention/Transformer), hyperparameter tuning (Bayesian optimization), standard dataset splitting (train/validation/test). | NaN | False | False | NaN | NaN | High computational costs and model interpretability are identified as key challenges associated with attention mechanisms and large models. | NaN |
| nMYAEiY8Io4J.pdf | Google_Scholar | If You Give an LLM a Legal Practice Guide | This paper examines how providing Large Language Models (LLMs) with information from legal practice guides, using Retrieval Augmented Generation (RAG) and structured propositional prompting, impacts their ability to answer legal questions and predict case outcomes. Findings indicate that while practice guides generally enhance performance, effectiveness varies significantly across models, legal domains, and prompting techniques, with structured approaches sometimes substantially improving or degrading results. | True | Market | True | 1.0 | NaN | Retrieval Augmented Generation (RAG) using legal practice guides, and a structured propositional prompting methodology breaking down legal rules from these guides into discrete queries. | Evaluated various LLMs (GPT-3.5, GPT-4, Claude Haiku, Sonnet, Opus) on legal question answering and outcome prediction using real cases (California res ipsa loquitur, Minnesota eminent domain) and expert-written hypothetical cases (same domains plus New Jersey pretrial detention). Performance was measured by accuracy across different prompting strategies: baseline (case name only), facts only, RAG with practice guide excerpt (+Guide), and propositional logic-based multi-query (Prop.). | The propositional prompting method (Prop.) achieved 100% accuracy on Minnesota-specific eminent domain hypotheticals for GPT-3.5, Claude Haiku, and Claude Sonnet (Prop. 2/3 variants), significantly outperforming other methods for these specific model-task combinations. However, overall results were highly variable, with no single method consistently superior across all models and legal areas. | NaN | NaN | NaN | NaN | Tort law (res ipsa loquitur), Real estate law / Constitutional law (eminent domain), Criminal procedure (pretrial detention). | California, Minnesota, New Jersey (USA). | The technique uses existing legal practice guides (California torts, Minnesota real estate, New Jersey criminal procedure) as the source for RAG and for structuring propositional prompts. Evaluation data consists of manually extracted facts and holdings from real cases referenced in these guides, and hypothetical examples written by a legal expert. | Comparative evaluation of different prompting strategies: baseline (case name only), facts-only, RAG with full practice guide excerpts, and a propositional logic-based multi-query approach derived from practice guides (with two levels of breakdown, Prop. 2 and Prop. 3). | NaN | False | False | NaN | NaN | High variability in LLM performance across different models, legal subject areas, and prompting methods. Difficulty in separating facts from legal reasoning in real case opinions. Real cases used for evaluation often concern gray areas of law, making 'ground truth' complex. Appellate court decisions can be nuanced (e.g., remands) rather than clear yes/no outcomes. | LLMs may be misled by their creative capacity, especially in nuanced legal doctrines like res ipsa loquitur. Providing practice guides or using more complex prompting can sometimes worsen performance (inverse scaling). Risk of LLMs generating erroneous legal analyses or predictions. |
| vsJBIMMWpjwJ.pdf | Google_Scholar | ChatGPT for Legal and Tax Professionals: ‘World-Altering Power’ Requires Kid Gloves | The paper examines the ethical challenges legal and tax professionals face when using ChatGPT, specifically concerning confidentiality and accuracy under the MRPC and Circular 230. It concludes that due to significant risks like data privacy issues and AI hallucinations, professionals should use ChatGPT with extreme caution and primarily for low-stakes tasks. | True | Market | True | 2.0 | Negative | ChatGPT (specifically referring to GPT-4 capabilities and limitations at the time of writing) | NaN | NaN | NaN | NaN | NaN | NaN | Legal Ethics, Tax Law | United States | Large datasets of unlabeled text, user interactions/content (as disclosed by OpenAI privacy policy). Mentions training data cutoff of Sept 2021 for GPT-4. Also notes legal challenges regarding the use of potentially proprietary material in training AI models generally. | NaN | Available via OpenAI website; paid subscription for latest version (GPT-4 at time of writing). | True | False | Accessible via OpenAI website link provided in the paper. A paid subscription version (GPT-4) is mentioned. | NaN | Ensuring compliance with ethical rules (competence, confidentiality, diligence) given ChatGPT's inaccuracy and data usage policies; verifying AI output; obtaining informed client consent; staying updated on AI risks/benefits; navigating prohibitions on AI reliance for written tax advice. | Violation of client confidentiality; providing inaccurate advice (malpractice/ethics violations); reputational harm; disciplinary action; legal liability; malware from spoofed sites. |
| qOSNB97orXcJ.pdf | Google_Scholar | AI-Powered Platforms for Access to Justice: The Case of Hear Me Out | This paper introduces Hear Me Out, an AI-powered platform using GPT-4o and RAG to help disadvantaged Australians navigate complex complaint pathways, thereby enhancing access to justice. It details the platform's user-centered design, technical architecture, ethical considerations, and initial impact, outlining plans for future expansion. | True | Idealistic | True | 1.0 | Positive | Hear Me Out: An AI chatbot platform using Azure OpenAI (GPT-4o) with tool-based Retrieval Augmented Generation (RAG), OpenAI Ada embeddings, Pinecone vector database, and Cosmos DB backend to guide users through legal complaint processes. | Usability testing with potential users during prototype development; ongoing user feedback collection (surveys, forms) for iterative improvement. | Positive user feedback led to refinements (response sensitivity, language adjustments); qualitative descriptions of enhanced user self-advocacy and potential efficiency gains for legal aid providers; systemic impact illustrated via analogous case studies. | Complexity and fragmentation of the legal complaint system, lack of centralized guidance, resource constraints in legal aid, lack of legal representation, difficulty understanding legal language, traditional barriers (cost, time, location). | An AI-powered platform (Hear Me Out) to simplify complaint navigation, provide automated guidance and triage, offer plain-language explanations, overcome traditional access barriers, and facilitate data collection for systemic advocacy. | Navigating complaint systems, lodging complaints, self-advocacy support, systemic advocacy. | Disadvantaged communities in Australia experiencing discrimination and disadvantage, including First Nations, CALD communities, and people with disabilities. | Administrative Law (complaint procedures), Discrimination Law, Human Rights Law, potentially others depending on the specific complaint. | New South Wales (Australia), with planned expansion across Australia and potentially internationally. | Information on NSW complaint bodies and pathways (stored in Cosmos DB); synthetic scenarios based on real data linked via metadata to complaint bodies (stored in Pinecone vector DB using OpenAI Ada embeddings); base model is GPT-4o. | User-centered design (prototype testing with target users), collaborative development (non-profit, universities, tech company), technical investigation, iterative development based on design principles derived from user testing. | Web application accessible via www.hearmeout.org.au. | True | False | Available as a web application at www.hearmeout.org.au (currently focused on NSW). | Need for broader geographic/jurisdictional coverage, enhanced AI capabilities (complaint drafting, translation, accessibility), deeper integration with public systems, development of comprehensive AI governance policies for justice, need for public awareness and trust. | Adapting AI to diverse legal jurisdictions, ensuring data privacy/security, managing ethical AI considerations (bias, transparency, accountability), balancing content filtering, maintaining accuracy and relevance of legal information, securing collaborations and resources for development/expansion. | Data breaches, unauthorized data access, AI model drift impacting response quality, inaccurate AI guidance, suppression of valid complaints via content filtering, potential AI bias. |
| ib5lJKhwk9AJ.pdf | Google_Scholar | Topic Classification of Case Law Using a Large Language Model and a New Taxonomy for UK \nLaw: AI Insights into Summary Judgment | This paper develops a new functional legal taxonomy for UK law and employs the Large Language Model Claude 3 Opus to classify summary judgment cases by topic, achieving an accuracy of 87.13%. The research reveals trends in the application of summary judgments across legal domains, highlighting AI's potential for legal analytics and providing a foundation for policy discussions in judicial administration. | True | Idealistic | True | 1.0 | Positive | Topic classification of UK summary judgment case law using the LLM Claude 3 Opus, guided by a novel, researcher-developed functional legal taxonomy and a specific multi-step prompting strategy including closed-set prompting and self-evaluation instructions. | Manual evaluation by a legal expert on a statistically significant random sample of 342 cases from a dataset of 3,078 UK summary judgment cases. Performance was measured using accuracy, precision, recall, and F1 score (weighted, macro, micro). Initial prompt refinement was done on a separate sample of 50 cases. | Claude 3 Opus correctly classified the topic with an overall accuracy of 87.13% and a macro F1 score of 0.87. Adjusting for minor naming discrepancies where the three-letter abbreviation was correct improved accuracy to 88.20%. | Summary judgments are often used against self-represented litigants lacking legal knowledge, raising concerns about balancing efficiency, fairness, and access to justice. The lack of existing keyword or topic classifications for UK case law hinders research and analysis of these issues. | The paper proposes a methodology (new taxonomy and LLM-based topic classification) to systematically analyze case law, enabling the identification of patterns and trends in summary judgment use. This improved understanding can inform future legal analyses, policymaking, and research in judicial administration to better address fairness and access to justice concerns. | Access to justice (particularly concerning the impact of summary judgments on fairness and potentially self-represented litigants), efficiency of judicial procedures, transparency of case law trends. | Self-represented litigants (mentioned as a group potentially disadvantaged by summary judgment procedures). | The developed taxonomy aims to cover UK law broadly. The study specifically applies to summary judgment cases, which cut across various fields including Commercial law (dominant), Dispute resolution law, Personal and consumer matters, Criminal law (civil proceedings related to criminal cases), Public law, and International law. | United Kingdom (UK) | The study used a curated dataset of 3,078 UK summary judgment cases (in XML format, including full text) from the Cambridge Law Corpus for the LLM to classify. An initial sample of 50 cases from this dataset was used for prompt refinement and iterative development of the classification approach; Claude 3 Opus itself was pre-trained by Anthropic. | Development of a novel functional legal taxonomy for UK law, starting from The Law Society’s list of practice areas and refined using a grounded theory approach based on the case dataset. Prompt engineering for Claude 3 Opus involved task framing, closed-set prompting with the developed taxonomy, detailed instructions, example inclusion, encouraging reasoning and confidence scores, and an iterative refinement process based on LLM feedback and initial expert checks on a small case sample. A self-evaluation step was incorporated into the prompt to reduce hallucinations. | NaN | False | False | NaN | Lack of comparison with other LLMs or traditional topic modelling techniques. The dataset size (3078 cases) is relatively small for generalising across all UK law. Subjectivity in manual evaluation of LLM classifications. The inherent challenge of information leakage in commercial LLMs. Literature on effectively mitigating LLM-generated hallucinated topics is still limited. Difficulty in identifying very specific subtopics useful for practitioners. | Traditional topic modelling methods struggle with the complexity and nuance of legal language. Absence of a standardised, universally accepted legal taxonomy for UK law necessitated creating a new one. Effective prompt engineering for LLMs to ensure accurate and constrained outputs. Occurrence of topic hallucinations (LLM generating topics not in the provided list or misnaming them). Ensuring reliability of LLM outputs, requiring manual expert verification and iterative refinement. Cascading errors from inaccuracies in the initial dataset. Small support values for some topic categories in the evaluation sample, limiting confidence in per-class metrics. | LLM hallucination, where the model generates incorrect or non-existent topics. Potential for information leakage if case details were part of the LLM's training data, though considered unlikely for this specific task structure. Risk of over-reliance on automated classification systems without expert oversight. The paper also discusses systemic risks related to summary judgments (e.g., potential for misuse impacting fairness, especially during periods like the COVID-19 pandemic, or application by non-specialist judges in complex areas like IP) that their analytical tool helps to study. |
| informit.T2024121000001400747097470.pdf | Google_Scholar | ALLA CONFERENCE 2024: TAKE THE LEAP | This paper is a personal reflection by a law librarian on her attendance at the ALLA Conference 2024, summarizing key presentations. Topics include the AI tool 'amica' for assisting couples in separation (access to justice), space law, and the role of librarians in navigating generative AI and LLMs. | True | Idealistic | True | 3.0 | Positive | amica: an AI tool by the Legal Services Commission of South Australia, designed by family lawyers, to help couples navigate separation and asset division. | For amica: Ongoing quality assurance on every case by a team of people; if a case is unusual or out of range of the AI's training scenarios, it is flagged for the team to contact the couple with resource suggestions. | For amica: Described as a valuable tool for those who cannot afford lawyers during separation, with a free version ('amica one') available to provide an estimate of asset division. | The high cost of hiring lawyers for couples going through separation, preventing access to legal assistance. | The 'amica' platform, an AI tool designed to guide couples through separation and asset division, offering a free version ('amica one') for initial estimates. | Access to legal assistance for relationship separation, financial settlements, and property division. | Couples, particularly those with limited financial means, undergoing relationship separation and needing guidance on legal processes. | Family Law | Australia (specifically, amica is a government platform, with the Legal Services Commission of South Australia involved in its development). | For amica: The AI tool was trained on over one thousand scenarios and is a closed model system. These scenarios were presumably related to family law separations. | For amica: Designed by family lawyers; incorporates quality assurance for every case by a team of people, with out-of-scope cases escalated for human intervention. | For amica: Deployed as a government web platform (amica.gov.au), with a free version called 'amica one' also available. | True | False | The 'amica' platform (amica.gov.au) is described as an existing, usable government service, with a free version 'amica one' accessible online. | AI systems, while beneficial for access to justice (e.g., amica), still require human oversight, especially for unusual cases, and lack human qualities like empathy and discretion critical in legal matters. The need for human input to review AI outputs. | NaN | Generative AI risks include lack of empathy and discretion, potential for inaccuracies ('hallucinations'), and the need for critical human review. Broader concerns about AI involve human rights implications. For amica, the risk of unusual cases falling outside its AI capabilities is managed by human review. |
| BB-GeoGPT-IPM1.pdf | Google_Scholar | BB-GeoGPT: A Framework for Learning a Large Language Model for Geographic Information Science | This paper introduces BB-GeoGPT, a Large Language Model specialized for Geographic Information Science (GIS), developed by fine-tuning LLaMA-2-7B on curated GIS-specific datasets. It also presents the framework for creating such domain-specific LLMs and benchmark datasets, demonstrating BB-GeoGPT's improved performance over general LLMs on GIS tasks. | True | NaN | True | 1.0 | NaN | BB-GeoGPT, a GIS-specific Large Language Model created by adapting LLaMA-2-7B through continued pretraining (on BB-GeoPT) and supervised fine-tuning (on BB-GeoSFT) using LoRA, along with a framework for curating domain-specific datasets and evaluation. | Evaluated using the custom BB-GeoEval dataset (600 objective, 150 subjective GIS questions across 5 domains: Spatial Analysis, Geodatabase, Cartography, Remote Sensing, Surveying). Also tested on toponym extraction (Harvey2017, Ju2016 datasets) and temporal reasoning (TEMPREASON-L1 dataset). Compared against LLaMA-2-7B, Alpaca-7B, Vicuna-7B, K2 (geoscience LLM), and GPT-3.5-turbo. Subjective evaluation involved GPT-4 as a referee and 12 human GIS professionals. | On BB-GeoEval objective tasks, BB-GeoGPT achieved an average accuracy of 0.608, outperforming similar-sized open-source LLMs by 10.55%-47.57%. On subjective tasks, it showed improvements of 7.87%-27.73% and outperformed K2, though it lagged behind GPT-3.5-turbo, particularly in completeness. | NaN | NaN | NaN | NaN | NaN | NaN | Custom-curated datasets: BB-GeoPT (26,907 GIS-related papers and Wikipedia documents for pretraining). BB-GeoSFT (35,876 instructions for fine-tuning, including general instructions from GPT4-Alpaca, self-instructed GIS Q&A from BB-GeoPT, rule-based text summarization/generation from GIS papers, and data from open-source professional datasets like BroadTwitterCorpus, LNEx, SemEval-2015 Task 8, and UltraChat). | Domain adaptation of LLaMA-2-7B. Continued pretraining on GIS-specific text corpus (BB-GeoPT) and supervised instruction fine-tuning on a mixed general and GIS-specific instruction dataset (BB-GeoSFT). Utilized Parameter-Efficient Fine-Tuning (PEFT) method LoRA. Self-instruct method employed for generating GIS-specific instruction data using LLaMA-2-7B-chat. | NaN | False | False | NaN | NaN | High demand for computing resources for training LLMs; scarcity of large-volume, high-quality professional training data for specialized domains like GIS; general LLMs' lack of deep understanding of specific disciplinary knowledge; deployment challenges for large models (compute/memory-intensive requirements); model limitations such as hallucination, toxicity, stereotypes, and limited non-English support. | Hallucination, toxicity, and stereotypes inherited from foundational LLMs. Potential for factual inaccuracies despite domain-specific training, which is crucial in geographic information. |
| k1-G1sD5mA0J.pdf | Google_Scholar | AI and Tools for Expanding Access to Justice | This paper explores how artificial intelligence, encompassing traditional expert systems and modern large language models, can significantly improve access to justice by automating legal tasks and enhancing the accessibility of legal support. Through case studies like MADE, the Resurrection Project, and Rentervention, it demonstrates practical applications of AI in assisting unrepresented individuals and legal aid organizations. | True | Idealistic | True | 2.0 | Positive | Expert systems for document automation, and their enhancement with Large Language Models, including conversational AI chatbots. | User-centered design, iterative feedback from users (tenants, clinic staff, volunteers), usage analytics (e.g., Google Analytics, internal metrics), and qualitative impact assessment (e.g., time saved, error reduction). | The Resurrection Project's tool, built with LLM-assisted development, reduced legal form processing time for migrant families from 2 hours to 45 minutes, assisting 4,440 individuals (1,097 family groups) between February and May 2024, and saving over 1,370 hours. | High cost and limited availability of lawyers, difficulty for people to understand legal processes or recognize their legal problems, restrictive regulations on providing legal help (unauthorized practice of law), and the sheer scale of unmet legal needs. | Deploying AI-powered tools like expert systems, document automation, and conversational chatbots to guide self-represented litigants; using LLMs to enhance these tools' capabilities and reduce development costs; promoting regulatory reforms (e.g., sandboxes); and developing accessible, interactive legal applications. | Eviction defense, immigration assistance (work authorization, Temporary Protected Status), tenant rights, and broader civil legal aid for self-represented litigants. | Low-income individuals, tenants, migrants (particularly recent arrivals), self-represented litigants, and other vulnerable populations facing civil legal issues. | Housing Law, Immigration Law, Civil Law (general, including family law and administrative benefits). | Primarily United States (Massachusetts, Illinois), with mentions of broader U.S. applicability (e.g., CourtFormsOnline.org in a dozen states) and global access to justice issues. | For rule-based expert systems: encoded legal knowledge, procedures, and template documents. For LLM-assisted development (e.g., GitHub CoPilot for Resurrection Project tool) or LLM-powered features (e.g., OpenAI models in Rentervention, Weaver tool): large, general pre-trained models based on public code, web text, and other diverse data sources. | User-centered design, iterative development, co-development with users and stakeholders, rapid prototyping, feedback loops using direct observation and analytics, and leveraging existing development frameworks (e.g., Docassemble, Assembly Line). | Web applications accessible on various devices (including smartphones), deployment in legal aid clinics and for remote assistance, integration with existing legal aid workflows and intake processes, online chatbots, virtual help desks, and dissemination of standardized development frameworks to other organizations. | True | False | Specific tools like MADE are available online for their target audience (e.g., Massachusetts tenants). Rentervention is an operational service for Illinois renters. CourtFormsOnline.org provides access to forms for users in several US states. | The vast scale of unmet legal needs persists. Low adoption of existing automation due to cost/rigidity, scarcity of deployed public interest LLM applications, challenges in safely and effectively integrating LLMs (ensuring accuracy, reliability, handling sensitive topics with current LLM moderation), need for investment in open-source LLMs for legal aid, and restrictive regulations on legal service provision. | Ensuring user comprehension and managing complexity for self-represented litigants, development costs and timelines for robust tools, inflexibility of traditional rule-based systems, potential for LLM errors ('hallucinations') and bias, the necessity for human oversight with AI, LLM moderation policies interfering with legally relevant content, and ensuring the overall accuracy and safety of AI-driven legal assistance. | LLM 'hallucinations' leading to incorrect information, algorithmic bias in AI systems, inappropriate or unethical application of AI in legal contexts, lack of fairness and transparency potentially hindering rather than helping access to justice, and LLM moderation filters preventing the processing of essential (but sensitive) legal topics like human rights violations or domestic abuse. |
| 62PlXWw-qiYJ.pdf | Google_Scholar | Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence | This paper evaluates the performance of several Large Language Models (LLMs), particularly OpenAI's GPT series, on U.S. tax law questions using synthetically generated multiple-choice problems. It finds that newer models like GPT-4 show emerging legal reasoning capabilities, significantly improving with few-shot prompting and access to relevant legal texts, though still falling short of expert human performance. | True | Idealistic | True | 2.0 | Positive | Retrieval-Augmented Generation (RAG) using various OpenAI LLMs (davinci, text-davinci-002, gpt-3.5-turbo, gpt-4) combined with different prompting strategies (zero-shot, few-shot, chain-of-thought) and retrieval methods (no retrieval, lecture notes, similarity search with GTR-large embeddings on CFR/US Code, gold standard retrieval). | Evaluation on two synthetic multiple-choice exams (one based on CFR, one on U.S. Code), each with multiple 100-question sections covering specific tax law question types. Questions were randomly generated using Python code to avoid training data contamination. Answers were graded for accuracy using GPT-4 comparing the model's choice to the ground truth across 28,700 evaluated answers. | GPT-4 combined with few-shot prompting, chain-of-thought (CoT) prompting, and retrieval using the 'gold standard' correct legal text ('mega_run') achieved the highest accuracy, approaching or exceeding 80% on average for both CFR and U.S. Code exams. Performance increased consistently with newer OpenAI model releases. Few-shot prompting and providing relevant legal text significantly improved GPT-4's accuracy. | Complexity of legal reasoning; need for accurate legal source retrieval; current LLM performance limitations compared to human experts; need for safeguards regarding data privacy, bias, and accountability; cost of legal counsel for potential users. | Using enhanced LLMs (with retrieval augmentation, few-shot prompting, CoT) to potentially provide legal information/advice, increase lawyer productivity, and lower costs. Further research into advanced prompting, better retrieval, and fine-tuning models for law is proposed. | Answering fact-specific legal questions; providing basic legal information/advice; augmenting lawyer tasks. | People who currently cannot afford legal counsel; consumers not engaging a traditional lawyer; general public needing tax law information. | Tax Law | United States | The evaluated LLMs (OpenAI GPT series) were pre-trained on general web corpora. Retrieval augmentation used vector databases built from the U.S. Code of Federal Regulations (Treasury Regulations) and Title 26 of the U.S. Code, embedded using the GTR-large model (trained on general domain data). Evaluation data was synthetically generated via Python code. | Experimental design varying LLM model, retrieval method, and prompting technique. Synthetic data generation for evaluation. Retrieval-augmented generation (RAG). Automated evaluation using a separate LLM (GPT-4). | NaN | False | False | NaN | LLM performance gap compared to expert lawyers; sub-optimal performance of similarity search retrieval compared to gold standard; need for improved prompting techniques (e.g., self-reflection); need to explore legal-specific model fine-tuning; need for better safeguards (privacy, bias, accountability). | Ensuring evaluation validity (avoiding data contamination); developing effective legal text retrieval; optimizing prompting strategies; accurate automated grading of LLM outputs; managing varying model capabilities and context window limitations. | Inaccurate legal information/advice; model bias; lack of accountability; LLM hallucinations; vulnerability to misleading prompts; potential disruption of the legal profession; challenges for regulations like unauthorized practice of law. |
| DaI7QWpvj28J.pdf | Google_Scholar | The Role of Artificial Intelligence (AI) in the Academic Paper Writing and Its Prospective Application as a Co-Author: A Letter to the Editor | This letter discusses the use of AI, specifically ChatGPT, in academic writing, highlighting potential benefits like refining text. It cautions against significant risks such as factual inaccuracies, bias, ethical concerns, and the inappropriateness of AI co-authorship under current academic norms. | True | NaN | True | 3.0 | Neutral | Use of ChatGPT for academic writing assistance | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | False | NaN | Need for more nuanced AI programs that can provide accurate information with proper references and address ethical concerns in academic writing. | Ensuring accuracy, avoiding bias, addressing ethical concerns (including plagiarism), and defining the appropriate role of AI (e.g., co-authorship). | Factual inaccuracies, biased text generation, potential undermining of researcher credibility, ethical concerns, plagiarism, inappropriate co-authorship attribution. |
| nexxQ6MBzmYJ.pdf | Google_Scholar | TRANSFORMING LEGAL PRACTICE: THE RISE OF AI FOR EFFICIENCY AND ACCESS TO JUSTICE” | This paper discusses the historical evolution and contemporary applications of AI in the legal domain, highlighting its role in improving efficiency and access to justice internationally and specifically in India. It also outlines key challenges such as data privacy, ethics, accuracy, training, and cost, while maintaining a positive outlook on AI's future in law. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Key obstacles include data privacy concerns, ethical considerations surrounding AI use in law, ensuring the accuracy and reliability of AI systems, the need for adequate training for legal professionals, and the high costs associated with implementing AI technologies. | The paper suggests that adopting AI innovations such as tools for legal research, document analysis, live transcription of proceedings, and Online Dispute Resolution can enhance efficiency and access to justice. It emphasizes addressing identified challenges (privacy, ethics, cost etc.) to fully leverage AI. | Improving efficiency in legal practice, enhancing access to justice, AI in judicial processes (e.g., transcription, decision support), legal research automation, document analysis and management, Online Dispute Resolution (ODR). | General public | General Law / Multiple Fields (including litigation, criminal justice, arbitration, and contract law). | Multiple (India, USA, China, UK, Colombia mentioned as examples, with a focus on India in one section). | NaN | NaN | NaN | False | False | NaN | The paper highlights the gap between current AI capabilities and full human-level intelligence, meaning AI cannot yet replace human judgment in complex legal tasks. Broader gaps include the need to fully address data privacy, ethical issues, AI system accuracy, training requirements for legal professionals, and AI implementation costs to realize its full potential in the legal field. | NaN | Risks related to data privacy, ethical misapplication of AI, and consequences of AI inaccuracies in legal contexts. |
| 231FJFX2Ms8J.pdf | Google_Scholar | LEEC for Judicial Fairness: A Legal Element Extraction Dataset with Extensive Extra-Legal Labels | This paper introduces LEEC, a large-scale dataset of 15,919 Chinese criminal judicial documents annotated with 155 legal and extensive extra-legal labels designed by legal experts. Experiments show current DEE models and LLMs struggle with legal element extraction, while empirical analysis using LEEC reveals potential sentencing biases based on defendant demographics, highlighting the dataset's value for promoting judicial fairness research. | True | Idealistic | False | 1.0 | Positive | NaN | NaN | NaN | Influence of extra-legal factors (e.g., demographics) on sentencing leading to potential unfairness; lack of comprehensive datasets covering these factors; difficulty for researchers in extracting labels from large volumes of text; potential disadvantages for socio-economically disadvantaged defendants due to legal aid system structure. | Creation of a comprehensive, large-scale dataset (LEEC) incorporating both legal and extra-legal factors, annotated based on legal expertise and empirical research, to facilitate research on judicial fairness and develop better AI tools. | Judicial fairness; Sentencing disparities; Criminal sentencing; Extra-legal factors in judicial decisions; Element extraction from legal documents. | Individuals involved in the criminal justice system potentially subject to bias based on demographic factors (gender, ethnicity, age, employment status, education level); socio-economically disadvantaged defendants. | Criminal Law | China | The LEEC dataset itself, constructed from 15,919 publicly available Chinese criminal judicial documents (sourced from LEVEN and LeCaRD datasets). Documents were annotated by law students with 155 labels covering legal and extra-legal factors based on a system designed by legal experts. | Literature review (Chinese and international empirical legal studies), expert interviews (legal researchers, legal aid officers, lawyer), legal expert knowledge consolidation, development of an extensive label system (knowledge graph), manual annotation by law students following detailed guidelines. | Dataset sample released on GitHub; full dataset available upon email request for non-commercial research purposes. | True | False | Sample available on GitHub; full dataset available upon email request for non-commercial use. | Limitations of existing datasets (incomprehensive labels, lack of domain focus); limitations of current DEE models and LLMs (low accuracy, context length issues, poor handling of multiple entities, erroneous legal reasoning); potential selection bias in publicly available court decisions; need for deeper investigation into specific sentencing disparities; uncertainty about whether AI model biases reflect real-world judicial biases. | Designing a truly comprehensive and nuanced label system for complex legal documents; ensuring high-quality, consistent annotation across a large dataset; limitations of current NLP models (context length, reasoning ability, output formatting) when applied to lengthy and complex legal texts. | Potential misuse of the dataset for discriminatory purposes or violations of the rule of law; risk of AI models trained on the data perpetuating or amplifying existing societal biases if not carefully developed and evaluated. |
| 11KlEG9Q9e8J.pdf | Google_Scholar | LEGAL RELECTRA : Mixed-domain Language Modeling for Long-range Legal Text Comprehension | This paper introduces LEGAL RELECTRA, a language model using REFORMER within the ELECTRA framework, pre-trained on mixed legal-medical texts for improved long-range comprehension in legal documents. Tested on Named Entity Recognition, it outperformed existing models in processing personal injury case texts. | True | Market | True | 1.0 | NaN | LEGAL RELECTRA: an adaptation of the ELECTRA framework using REFORMER for its generator and discriminator, trained on mixed-domain legal and medical corpora, and utilizing a custom domain-specific tokenizer. | Named Entity Recognition (NER) on a legal domain dataset (labels: case type, plaintiff, defendant) and a mixed legal-medical domain dataset (labels: case type, plaintiff, defendant, medical problem). Performance was measured by F1 scores and compared against BERT, CLINICAL-BERT, LEGAL-BERT, REFORMER, and LEGAL RELECTRA with BERT tokenizer. | LEGAL RELECTRA (with custom tokenizer) achieved an overall F1 score of 85.93% on the legal domain NER task and 78.57% on the mixed-domain NER task, outperforming other tested models. | NaN | NaN | NaN | NaN | Personal injury civil suits, Civil law | United States | A 12GB corpus consisting of: 6GB legal text (excerpts from US case law), 3GB medical text (doctor’s notes and letters from MIMIC and MIMIC-CXR databases), and 3GB mixed-domain personal injury text (Supreme Court opinions, academic literature, COURT LISTENER, BYU LAW, anonymized case descriptions from attorneys). | Adaptation of the ELECTRA pre-training framework by replacing BERT-based generator and discriminator with REFORMER models. Pre-training on a mixed-domain corpus (legal and medical). Development and use of a custom domain-specific tokenizer trained via Byte-Pair Encoding. | NaN | False | False | NaN | NaN | Processing long legal documents (exceeding typical 512-token limits of models like BERT), handling specialized terminology from multiple domains (e.g., legal and medical terms in personal injury texts), and the difficulty of collecting a sufficiently large corpus of specific pre-training data (e.g., personal injury texts). | NaN |
| sEHknHKUxvUJ.pdf | Google_Scholar | ChatGPT: A New Era in Legal \nResearch and its Sustainable Impact \non Judicial Decision Making | This paper examines the use of ChatGPT in the legal field, particularly for legal research and potential judicial decision-making assistance in India. It highlights ChatGPT's limitations, such as inaccuracy and bias, arguing for caution, human oversight, and the need for contestability frameworks. | True | Idealistic | True | 2.0 | Negative | ChatGPT | Analysis of two court cases (India, Colombia) where judges used ChatGPT; interactive prompting of ChatGPT by the authors with legal questions (focused on bail, capabilities, limitations, data) and analysis of its responses. | ChatGPT responses were found to be potentially inconsistent, inaccurate (e.g., citing fake cases), biased, lacking legal nuance, limited by a knowledge cut-off (Sept 2021), not fully comprehensive in accessing case law, and acknowledging its own limitations and lack of liability. | Inaccuracy and unreliability of AI; potential for bias amplification; lack of transparency and explainability; digital divide limiting access; inability to replicate human judgment, equitable justice, and discretion; resistance to change in the legal profession; inadequate regulatory frameworks. | Maintaining human intervention and oversight; using AI as an assistive tool, not a replacement; implementing a 'right to contestability' for AI decisions; developing robust legal/regulatory frameworks for AI governance (transparency, accountability, fairness); verifying AI outputs. | Bail jurisprudence, judicial decision-making, legal research, access to justice, legal information services. | Individuals and Small/Medium Enterprises ("people law"), general public seeking legal information, citizens interacting with the justice system. | General Legal Practice, Criminal Law (Bail), Constitutional Law (Due Process), Civil Procedure. | India, Colombia, USA, EU, UK | Described by ChatGPT as a large preprocessed text database including news articles, legal documents, case law (including Indian statutes and court decisions up to Sept 2021 available in the public domain), and academic literature. Mix of publicly available and potentially proprietary data curated by OpenAI. | NaN | Publicly accessible web application by OpenAI. | True | True | Available online as a "Free Research Preview" (ChatGPT May 3 Version mentioned). | Technical gaps include the need for up-to-date, accurate, unbiased, and contextually nuanced information, along with transparency. Societal/Regulatory gaps include the lack of comprehensive AI governance laws (especially in India regarding contestability, liability), the digital divide, and the need for legal professional training. | Unreliability, inaccuracy, potential for bias, lack of genuine legal understanding, limitations of training data scope and recency when using ChatGPT for legal tasks. | Inaccurate legal research/advice; perpetuation of bias; erosion of trust in justice; violation of due process/fundamental rights; automation bias; lack of accountability for AI errors. |
| RG_Manuscript_Avatarjudgesandvirtuousadjudication.pdf | Google_Scholar | GenAI avatar judges and virtuous adjudication | This paper examines the potential use of GenAI avatars as judges through the lens of virtue ethics and jurisprudence. It argues that fully autonomous AI judges cannot achieve 'virtuous adjudication' due to lacking genuine virtuous agency, but suggests that advice-giving AI avatars could potentially support human judges' virtuous practice, while also identifying significant risks to moral responsibility and potential deskilling. | True | Idealistic | True | 3.0 | Neutral | Conceptual discussion of 'GenAI avatar judges', distinguishing 'automated decision-making' (Mode A) and 'supportive advice-giving' (Mode B) types, personalized using adjudication records. | NaN | NaN | AI lacks genuine virtuous agency (internal states, right reasons, phronesis) needed for virtuous adjudication; Difficulty in training AI for virtue (data curation, ensuring virtuous output); Potential undermining of human judges' moral responsibility (control, freedom, knowledge, deliberation); Risk of human cognitive/moral deskilling. | Use advice-giving GenAI avatars (Mode B) as 'virtue cultivators' to support, not supplant, human judges; Enhance judges' perceptual capacity and contextual knowledge using AI trained on curated exemplary adjudication records; Potential use in VR training simulations for judges. | Judicial decision-making (adjudication); Judicial ethics; Virtue jurisprudence; Moral responsibility; Access to justice via digital courts. | General public / Litigants using digital courts | General | International | Hypothesized use of personal/exemplary adjudication records (from one or multiple judges), legislation, and jurisprudence; likely proprietary/court-held, domain-specific, potentially structured and unstructured. | NaN | Hypothesized deployment in digital/online/Metaverse courts, potentially via VR for training. | False | False | NaN | Philosophical/ethical gap: AI's inability to replicate genuine virtue and moral responsibility for virtuous adjudication. Societal gap: Ensuring AI supports rather than undermines human judicial qualities. Technical gaps: Difficulty curating appropriate training data for virtue; Ensuring AI output aligns with virtuous deliberation (explainability, bias mitigation). | Defining and implementing 'virtuous adjudication' in AI; Aligning AI statistical methods with human phronesis; Curating training data (identifying/labelling virtue/vice); Ensuring meaningful human control; Avoiding psychological coercion, automation bias, or under-trust; Addressing explainability issues; Mitigating human deskilling. | Undermining human judges' moral responsibility; Psychological coercion by AI; Automation bias; Under-trusting AI; Infringement on deliberation due to black box issues; Human cognitive and moral deskilling; Potential for AI to act viciously if truly autonomous; Difficulty ensuring virtuous AI output. |
| 0b5FFMvGIoYJ.pdf | Google_Scholar | The Implications of ChatGPT For Legal Services and Society | This paper explores the potential impact of large language models, specifically ChatGPT, on legal services and society by demonstrating its capabilities through generated text examples. It discusses use cases like legal research and document drafting, alongside significant challenges, ethical considerations (like accuracy and bias), and the rapid evolution of AI in law. | True | Idealistic | True | 2.0 | Positive | Large Language Models: ChatGPT (GPT-3 based) and Bing Chat (reportedly GPT-4 based) | Demonstration through prompting ChatGPT and Bing Chat on various legal tasks (research, document generation, information provision, analysis). Includes qualitative assessment of outputs and reports Bing Chat's performance on 15 legal ethics multiple-choice questions (12/15 correct) and a civil procedure problem. | ChatGPT outputs were imperfect, incomplete, and sometimes problematic, lacking nuance and detail. Bing Chat (GPT-4 based) showed better performance, answering 12/15 legal ethics MCQs correctly and providing plausible legal analysis comparable to a B/B+ law student. | High cost and complexity of the US legal system, lack of right to counsel in most civil cases, legal profession regulations (monopoly, fee-sharing rules), limited government funding for legal aid, and the cost of legal education contribute to a significant justice gap. | Leveraging technology, particularly AI tools like ChatGPT, to create self-help resources and enhance lawyer efficiency to serve more clients. | General civil legal needs (e.g., family law, debt, housing), disability rights (IEP), government benefits (Social Security). | Low-income individuals and middle-income Americans. | Civil Procedure, Torts, Contract Law, Constitutional Law, Estate Planning, Education Law, Social Security Law, Legal Ethics. | USA (primarily Massachusetts and Federal) | General, large-scale text data used to train OpenAI's GPT-3 and GPT-4 models (implied to be broad web text and other sources). | NaN | NaN | True | False | ChatGPT and Bing Chat are available online via OpenAI and Microsoft, possibly with free and paid tiers. | Accuracy and reliability of AI outputs, handling legal nuance, need for user prompt engineering skills, digital divide/cost of access to advanced AI, need for integration into legal education, potential for misuse, broader societal risks including existential concerns. | Ensuring accuracy and reliability, handling legal complexity/nuance, potential job displacement for lawyers, misuse for generating false information or manipulation, ethical concerns (UPL, competence, confidentiality), attribution problems, over-reliance, potential for bias, societal disruption, managing AI's rapid development responsibly. | Inaccurate legal information/advice, job displacement, generating false/misleading documents or information, manipulation of user beliefs/emotions, unauthorized practice of law, breaches of competence/confidentiality, algorithmic bias, exacerbating digital divide, difficulty in attributing authorship, existential risks. |
| v19LnyREUAsJ.pdf | Google_Scholar | Let’s Have a Chat! A Conversation with ChatGPT: Technology, Applications, and Limitations | This paper reviews ChatGPT, exploring its underlying Transformer and reinforcement learning technology, historical context, and diverse applications in fields like healthcare, education, and research. It summarizes evaluations of ChatGPT's capabilities, including exam performance, alongside its limitations and significant ethical and privacy concerns. | True | NaN | True | 3.0 | Neutral | ChatGPT | Review of multiple studies evaluating ChatGPT: Performance on professional/academic exams (medical, law, CS, etc.), qualitative assessments, plagiarism detection comparisons, text summarization (Rouge scores), reasoning tasks, translation benchmarks, clinical decision support tasks. | Average accuracy of 59.53% across various exams reviewed, though performance varied significantly by domain and task (e.g., high on USMLE, low on math-heavy tasks). Showed potential in text summarization, detecting its own generated text, and deductive reasoning, but limitations in inductive reasoning, accuracy, and citation reliability. | NaN | NaN | NaN | NaN | Law (general), Constitutional Law, Torts, Taxation | USA, China | Large corpus (>300 billion words) of text data from varied sources (books, articles, websites) up to September 2021. Includes public internet data, potentially containing personal information. Unsupervised pre-training followed by Reinforcement Learning from Human Feedback (RLHF). | Deep learning (Transformers, LLMs), unsupervised learning, prompt engineering, Reinforcement Learning from Human Feedback (RLHF). | Released publicly by OpenAI via a web interface in November 2022. | True | False | Publicly released by OpenAI, accessible via a web interface (URL provided in footnote). | Factual inaccuracies ('hallucinations'), reasoning errors (especially inductive), knowledge cutoff (Sept 2021), potential biases, limited context window, lack of multimodal input capabilities, need for better evaluation metrics, improving performance consistency across diverse domains. | Ensuring factual accuracy, avoiding harmful/biased outputs, detecting AI-generated text (for plagiarism/cheating), aligning AI with human values (via RLHF), limitations in reasoning and specific tasks (math, low-resource languages), computational cost of training/running LLMs, potential for misuse. | Generation of misinformation ('infodemic'), potential for race/gender bias, privacy risks due to training data potentially containing personal information or memorizing user inputs, copyright infringement and plagiarism risks, misuse for cheating in education or generating misleading content. |
| Aota7JmCmSEJ.pdf | Google_Scholar | Chapter 22: AI and the future of private dispute resolution mechanisms | This chapter reviews how artificial intelligence, including natural language processing, predictive analytics, machine learning, and generative AI, is transforming private dispute resolution mechanisms such as arbitration, mediation, and negotiation. It discusses current AI tools and their applications in enhancing case preparation, predicting outcomes, and automating dispute resolution, while also considering future prospects, implementations around the world, and ethical implications. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | High costs, lengthy processes, perceived biases and inconsistencies in traditional private dispute resolution; waning confidence in courts due to expenses, delays, and impartiality concerns; complexity of the legal system. | Leveraging AI tools (NLP, predictive analytics, machine learning, generative AI) to enhance efficiency, fairness, and accessibility in dispute resolution through enhanced case preparation, predictive analytics, and automated dispute resolution platforms (ODR). | Private dispute resolution (arbitration, mediation, negotiation), Online Dispute Resolution (ODR), improving efficiency and reducing costs of legal processes, enhancing fairness and consistency in dispute outcomes, increasing accessibility to justice mechanisms. | General public involved in disputes (e.g., consumer, small claims, family law matters such as divorce and asset division), laypeople needing legal information (e.g., landlord-tenant issues), legal practitioners, and dispute resolution providers. | Private dispute resolution, including arbitration, mediation, negotiation. Specific applications cover family law (divorce, asset division), consumer law, small claims, commercial disputes, landlord-tenant law, and insurance claims. | International | Various, including large language models trained on general and legal text (documents, statutes, case law, opinions); historical case data for predictive analytics; specific datasets curated for particular tools (e.g., lawyer-reviewed randomized scenarios for Amica). | Includes rule-based systems, case-based reasoning, machine learning (including deep learning and LLMs with pre-training and fine-tuning), game-theoretical algorithms, and expert systems methodologies. Some tools also incorporate user-centered design and iterative development based on expert input. | Online platforms, web applications, integration into existing legal/judicial systems (e.g., Jupitice for courts), APIs, educational initiatives for stakeholders. | True | False | Many tools discussed are presented as launched and accessible, either as commercial products (e.g., Relativity, Lex Machina, various ODR platforms) or as public/research initiatives with websites (e.g., Amica, JusticeBot, CREA platform). | Technical gaps include AI accuracy (e.g., LLM hallucinations) and data privacy. Societal gaps include ethical concerns, ensuring meaningful human control and oversight, addressing the digital divide, preventing bias and discrimination, and the need for education and training for legal professionals on AI capabilities and limitations. | NaN | Generation of incorrect or biased information (hallucinations) by AI, especially LLMs; ethical and privacy concerns regarding sensitive client data; potential for misuse of generative AI (e.g., deepfakes, misinformation); erosion of human responsibility and oversight in decision-making; risk of unjust outcomes if AI errors are not mitigated by human control. |
| nN70shbEoP0J.pdf | Google_Scholar | Ketergantungan Mahasiswa Universitas Jember Terhadap Artificial Intelligence (AI) | This study investigates the dependency of Jember University students in Indonesia on AI tools, specifically Chat GPT, for their academic tasks. Using a qualitative ethnographic approach with 5 student interviews, the research finds students use AI for inspiration opportunistically rather than continuously, acknowledging its limitations such as answer accuracy and the risk of reduced critical thinking. | False | NaN | True | 2.0 | NaN | Chat GPT | Qualitative ethnographic study: observation, interviews, and documentation with 5 students at Jember University. | Students at Jember University use Chat GPT for inspiration and to help with assignments but are not continuously dependent. They recognize that its answers are not always accurate and that overuse can lead to reduced critical thinking and laziness. | NaN | NaN | NaN | NaN | NaN | Indonesia | NaN | NaN | NaN | True | True | Chat GPT, the tool discussed, is accessible online with a free usage tier provided by OpenAI. | NaN | For users (students): Inaccuracy of AI-generated answers which require verification and supplementation; potential to foster laziness and reduce critical thinking if overused. | Decline in students' critical thinking and problem-solving abilities, increased laziness and lack of independence, potential for plagiarism, and over-dependence on AI. |
| 1-s2.0-S1877050924011177-main.pdf | Google_Scholar | Artificial Intelligence as an Innovative Element of Support in Policing | This paper explores the potential application of large language models (LLMs), specifically GPT, to reduce the administrative burden within the Czech Republic police force. It outlines various conceptual use cases, including document creation, data analysis, investigation support, and public communication, while emphasizing the need for further research and addressing ethical concerns. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | Increasing administrative burden limits police officers' ability to focus on core security tasks. | Integrating LLMs (like GPT) into police work for tasks such as: supporting document creation (using speech-to-text and fine-tuning), acting as personal assistants for information retrieval, supporting investigations (data analysis, chronology generation, pattern detection), enhancing analytical processes (using plugins for data analysis/visualization), facilitating international police cooperation (translation, document analysis, request generation), supporting forensic activities (data processing, calculations), and creating public communication tools (chatbots for inquiries and reporting). | NaN | NaN | Policing, Criminal Law, Criminal Procedure, International Law | Czech Republic | NaN | NaN | NaN | False | False | NaN | Absence of empirical data and practical research to validate the proposed LLM applications in policing. Need for developing secure, locally operated LLMs to protect sensitive police data. | Ethical, legal, and security considerations (transparency, accountability, privacy). Ensuring human oversight due to potential AI errors. Obtaining high-quality, unbiased training data. Need for secure infrastructure (potentially separate from the internet). Requires interdisciplinary collaboration. | Potential negative impact on privacy and human rights. Misuse of AI by criminals or terrorists. Risk of bias propagation from training data. Over-reliance on potentially inaccurate AI outputs. Data security vulnerabilities. |
| x2Rhas8fUBAJ.pdf | Google_Scholar | ChatGPT: Literacy or intelligence about UN sustainable development goals? | This paper evaluates ChatGPT's literacy and intelligence regarding the UN Sustainable Development Goals (SDGs) using two assessment tools: the SDG Fitness Test and the SULITEST. While ChatGPT demonstrates high SDG literacy, its intelligence, particularly concerning core competencies like critical and systems thinking, is found to be at an intermediate level, and the assessment tools themselves show limitations in coverage. | True | Idealistic | True | 2.0 | Neutral | Evaluation of ChatGPT (GPT-3.5 based model) using standardized sustainability literacy tests. | ChatGPT's performance was assessed using the UN SDG Fitness Test and the SULITEST (Sustainability Literacy Test). Questions from both tests were input into ChatGPT, and the responses were scored according to the tests' frameworks. ChatGPT was also used to map test questions to SDG competencies and SDG types. | ChatGPT scored highly on literacy tests (<90% on SDG Fitness Test, 80.9% on SULITEST). However, its performance on core SDG competencies (evaluated via SDG Fitness Test) was mostly intermediate, particularly in areas like Collaboration, Systems Thinking, Anticipatory skills, Integrated problem-solving, Critical thinking, and Self-awareness. Both assessment tests were found to have inadequate coverage of SDG competencies and SDG types. | Current limitations of LLMs like ChatGPT, including intermediate-level capabilities in crucial SDG competencies (e.g., critical thinking, systems thinking, self-awareness); inadequacy and unbalanced coverage of existing SDG assessment tools (SULITEST, SDG Fitness Test); potential for LLMs to generate misinformation ('hallucinations'). | Improve future LLM versions to enhance specific SDG competencies (collaboration, critical thinking, systems thinking, etc.); refine SDG assessment tools (SULITEST, SDG Fitness Test) for better coverage of competencies and types; use LLMs cautiously for SDG-related tasks, primarily for information gathering and suggesting actions, not decision-making. | UN Sustainable Development Goals (SDGs) literacy and intelligence; Assessment of AI capabilities related to sustainability; Core cross-cutting SDG competencies (e.g., Systems Thinking, Critical Thinking, Collaboration). | NaN | Sustainable Development / UN SDGs | International | The paper evaluates a pre-trained model (ChatGPT). Its underlying training data (e.g., for GPT-3) is described as vast (e.g., 45TB text dataset), web-sourced, largely proprietary, unstructured text and code data. | Experimental evaluation using existing standardized tests (SULITEST, SDG Fitness Test). Utilized ChatGPT itself to map test questions to SDG competencies and types as a methodology step. | The evaluated technique (ChatGPT) is deployed by OpenAI via web interface and API. The study itself did not involve deployment. | True | False | ChatGPT is available via web interface and API from OpenAI, often with free and paid access tiers. | ChatGPT's intermediate performance in key SDG competencies; Inadequate and unbalanced coverage of SDG competencies and types by existing assessment tools (SULITEST, SDG Fitness Test); Need for validated mappings between test questions and competencies/SDGs; Need for AI development specifically targeting SDG competencies; Societal gap in safely integrating LLMs for SDG advancement. | Lack of validated mappings for test questions to SDG competencies and types, requiring the use of ChatGPT itself for mapping; Potential inconsistency in LLM responses; Evaluating the 'intelligence' beyond simple 'literacy'. | Over-reliance on LLMs for SDG decision-making; Generation of misinformation or 'hallucinations'; Ethical issues (bias, misuse); Potential negative impact on human critical thinking skills; Test security and validity if LLMs can easily pass assessments. |
| FrIATqlyPS4J.pdf | Google_Scholar | FIGHTING THE HYPOTHETICAL: WHY LAW FIRMS SHOULD RETHINK THE BILLABLE HOUR IN THE GENERATIVE AI ERA | This paper analyzes how generative AI (GenAI) challenges the traditional billable hour model in law firms, forcing a shift towards value-based billing. Based on interviews with firm leaders, it predicts GenAI will automate routine tasks, disrupt existing staffing models, and require firms to innovate their pricing and service delivery to remain profitable. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | High cost of legal services inherent in the billable hour model; affordability barrier for low-income individuals (e.g., needing upfront fees for Chapter 7 bankruptcy). | AI-powered tools for self-help (e.g., Upsolve for bankruptcy); potentially redeploying lawyers made efficient by AI to serve lower-cost markets. | Bankruptcy (Chapter 7), Affordability of legal services | Low-income individuals needing bankruptcy assistance; middle-income households; small/midsize organizations. | General Legal Practice (Law Firms), Corporate Law, Mergers and Acquisitions, Litigation (Document Review, Discovery), Bankruptcy Law, Contract Law | United States | NaN | NaN | NaN | False | False | NaN | Technical: AI accuracy (hallucinations), potential for bias, AI's inability to replicate human judgment, empathy, creativity, and contextual reasoning. Societal: Training gap for junior lawyers losing learning opportunities from routine tasks, ethical challenges (confidentiality, UPL, reasonable fees, bias), resistance to change within the legal profession, need for new business/pricing models, potential digital divide. | Law firm resistance to changing the profitable billable hour model; cost of AI investment and implementation; ensuring data security and client confidentiality; training lawyers to use AI effectively and ethically; managing the disruption to traditional staffing (pyramid/leverage) models; developing and implementing new value-based pricing structures; overcoming lawyer skepticism and change aversion. | Ethical violations (incompetence, lack of diligence, confidentiality breaches via AI input, filing AI-generated 'hallucinations', unauthorized practice of law, unreasonable fees due to unchanged billing despite efficiency gains); generation of inaccurate or incomplete AI output leading to bad advice; deskskilling of junior lawyers due to automation of foundational tasks; data security breaches; perpetuating biases present in AI training data; increased pressure/burnout if efficiency gains aren't managed well; potential negative impact on firm revenue/profitability if transition is poorly managed; possibility that AI benefits primarily accrue to well-resourced firms/clients, widening the justice gap. |
| iCJapnvrHUoJ.pdf | Google_Scholar | Artificial Intelligence in the Workforce National and Regional Implications | This report analyzes the impact of artificial intelligence, particularly generative AI, on the workforce and economy at the national (US) level and within the Rio Grande Valley (RGV) region. It identifies industries and occupations most likely to be affected, highlights potential economic shifts, and suggests strategies for workforce development and adaptation for community stakeholders. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | USA (National and Rio Grande Valley, Texas) | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Potential job displacement/automation in specific sectors (e.g., food service, office support, production) and for low-skilled workers; regional disparities in automation impact (affecting areas like the RGV more); over-dependence on AI; legal and ethical issues; security concerns regarding AI models (open vs closed source). |
| 3584931.3606955.pdf | Google_Scholar | Shaping the Emerging Norms of Using Large Language Models in Social Computing Research | This paper proposes a Special Interest Group (SIG) to discuss the impacts, opportunities, and challenges (validity, privacy, ethics) of using Large Language Models (LLMs) in social computing research. The goal is to facilitate community discussion and collectively shape emerging norms for LLM use across various research stages. | True | NaN | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | NaN | NaN | False | False | NaN | NaN | Ensuring validity of findings (non-determinism, prompt sensitivity, bias), protecting privacy (captured training data, user data during analysis), ethical concerns (consent, potential misuse, equity), effectively evaluating LLM performance in research tasks, lack of interpretability, preventing over-reliance, managing resource requirements (cost, expertise). | Potential misuse of LLMs for manipulation or deception, propagation of biases and stereotypes, privacy violations (PII exposure, interdependent privacy), ethical breaches regarding informed consent and testing on users (especially in sensitive domains), equity concerns due to unequal access to resources, potential for economic, reputational, or psychological harms to users of LLM-enabled systems. |
| 37KsD-fAzisJ.pdf | Google_Scholar | How generative AI Is shaping the future of marketing | This paper distinguishes Generative AI (Gen AI) from analytical AI and proposes a four-quadrant framework based on input type (general vs. custom) and human augmentation level (low vs. high) to guide marketers in selecting and implementing Gen AI tools. It discusses the benefits, risks, and strategic trade-offs of different Gen AI approaches in marketing contexts. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | Literature review, industry reports review, expert interviews. | NaN | False | False | NaN | Need for research on: boundary conditions for choosing custom vs. general inputs, lower-cost custom input methods, user trade-offs regarding privacy/IPR protection, managing bias in outputs, ethical use boundaries, risk measurement/mitigation, reducing opacity, maximizing returns, enabling production deployment, appropriate classification frameworks for Gen AI, impact of regulation, and long-term societal/social impacts. | Deciding between general vs. custom inputs; determining the optimal level of human augmentation; ensuring high-quality data for custom inputs; managing costs of implementation; moving from experimentation to production deployment; addressing privacy and transparency concerns. | Inaccuracy/hallucinations, intellectual property rights (IPR) infringement, creation of misinformation and deepfakes, privacy breaches (data leakage), perpetuating/amplifying algorithmic bias, opacity of algorithms, potential legal liability for infringing or inappropriate outputs. |
| hjat-xhoNUwJ.pdf | Google_Scholar | AI Legal Innovations: The Benefits and Drawbacks of Chat-GPT and Generative AI in the Legal Industry | This paper reviews the impact of generative AI, particularly large language models like ChatGPT, on the legal industry. It details potential benefits like increased efficiency and expanded access to justice, alongside significant drawbacks including inaccuracies, bias, copyright issues, and privacy concerns. | True | Market | True | 3.0 | Neutral | Generative AI / Large Language Models (LLMs) for legal applications | The paper reports on external findings, such as a Stanford study showing high hallucination rates (69-88%) in LLMs for legal queries and mentions ChatGPT-4 passing the Uniform Bar Exam. | NaN | High cost of legal services leading to unmet legal needs, particularly for poor and middle-class individuals (cites 80% unmet need). | Using AI/LLMs to decrease the cost of legal services, potentially allowing lawyers to serve the previously unmet needs of the poor and middle class and expand the overall market for legal services. | Access to justice cost barriers. | Poor and middle-class people. | General / Multiple Fields (including corporate tax, regulatory, litigation, e-discovery, contract analysis, immigration) | US, EU, UK, International | Publicly available web data (books, websites, social media), potentially including copyrighted material; specific datasets for proprietary tools not detailed. | Mentions Anthropic's 'Constitutional AI' approach; general references to machine learning, NLP, neural networks. | Commercial software offerings (SaaS, licensed), integration into existing platforms (e.g., MS Office, Westlaw), mobile apps, potential for in-house development by firms. | True | False | Mentions publicly accessible tools like ChatGPT and lists numerous commercial AI legal tech products available through companies like vLex, Thomson Reuters (Casetext), LegalMation, DoNotPay, etc. | High rates of legal hallucinations/inaccuracies in LLMs. Lack of clear regulatory frameworks. Persistence of bias in AI outputs. Unresolved copyright issues in training data. Insufficient safeguards for data privacy and attorney-client privilege. Need for better AI integration into legal workflows that accounts for limitations (e.g., specificity for contracts). Ensuring AI benefits improve access to justice effectively. | Ensuring accuracy and avoiding hallucinations ('fabrications'). Addressing algorithmic bias. Managing data privacy, security, and attorney-client confidentiality. Navigating copyright complexities. Ethical integration into legal practice (requiring human oversight). Overcoming professional resistance and adapting business models (e.g., billable hours). Developing appropriate regulations. Combating misuse (e.g., deepfakes, AI washing). | Generating incorrect legal information (hallucinations/fabrications) leading to flawed legal work and potential sanctions. Amplifying societal biases (racism, sexism). Copyright infringement liability. Breaches of data privacy and attorney-client privilege. Facilitating misinformation and election interference (AI-generated deepfakes). Financial misrepresentation ('AI washing'). Job disruption within the legal profession. |
| _OXxj01xIJYJ.pdf | Google_Scholar | An Empirical Study of Production Incidents in Generative AI Cloud Services | This paper analyzes production incidents from a major GenAI cloud service provider (Microsoft) over four years, detailing their characteristics, root causes, and mitigation strategies. It reveals unique reliability challenges for GenAI services, including content quality issues and difficulties in incident management, and identifies areas for future improvement. | True | Market | True | 2.0 | NaN | Incident management practices (detection, triage, diagnosis, mitigation) within production Generative AI cloud services. | Analysis of anonymized production incident data from Microsoft's Incident Management system (IcM) over four years. The study involved quantitative analysis of hundreds of thousands of GenAI incidents and qualitative_in-depth analysis of high-severity incidents, using manual open coding by multiple annotators with inter-rater reliability checks (Cohen's kappa). | GenAI incidents show high rates of human detection (38.3%) and monitor false alarms (11.0%), take longer to mitigate (avg. 1.12 normalized time units vs 0.65 for other services), and commonly manifest as performance degradation (49.8%). Key root causes include infrastructure issues (27.2%), configuration issues (24.5%), and code bugs (21.5%), with fixes often being ad-hoc (22.4%) or rollbacks (15.2%) rather than immediate code changes (7.6%). | NaN | NaN | NaN | NaN | NaN | International | NaN | Empirical study using quantitative and qualitative analysis of production incident data from Microsoft's Incident Management system. This involved collection of GenAI and non-GenAI incidents over four years, selection of high-severity incidents for in-depth analysis, and manual open coding by multiple annotators with inter-rater reliability checks (Cohen's kappa) to categorize incident symptoms, root causes, and mitigation strategies. | NaN | False | False | NaN | NaN | Unique challenges for GenAI cloud services include large scale, high hardware demands, ensuring content quality (e.g., invalid inference) and privacy, immature automated monitoring systems leading to high human detection rates and false positives, and increased complexity and time for incident diagnosis and mitigation due to diverse root causes. | Operational failures leading to user dissatisfaction and monetary loss; degraded service quality including invalid, harmful, or low-quality AI-generated content (e.g., hallucinations); privacy violations; and security vulnerabilities from model fine-tuning or prompt exploitation (e.g., 'hidden text' attacks). |
| 3696319.pdf | Google_Scholar | “This Verdict was Created with the Help of Generative AI...?” On the Use of Large Language Models by Judges | This paper discusses the emerging use of Large Language Models (LLMs) like ChatGPT by judges in various jurisdictions, citing specific real-world cases. It explores the complex interdisciplinary questions (legal, ethical, technical) arising from this practice and calls for increased research collaboration to address the implications for the judiciary. | True | NaN | True | 3.0 | Neutral | General purpose LLMs (e.g., ChatGPT) used by judges for tasks like legal research, analysis, summarization, and calculations. | NaN | NaN | Lack of transparency/explainability (black-box problem); Potential for bias and discrimination; Risk of inaccuracies (hallucinations); Threats to judicial independence and impartiality; Accountability challenges; Data privacy and confidentiality issues; Ensuring fairness of trial and due process. | Emphasis on the need for interdisciplinary research (involving legal studies, ethics, information systems, etc.); Suggestion of frameworks (e.g., 'orders of change') to analyze impact; Development of official guidelines (referencing UK example); Call for collaboration with courts. | Impact of LLM use by judges on judicial process integrity (fairness, transparency, accountability, independence). | NaN | General (examples from Health Law, Family Law, Election Law, Criminal Law) | Colombia, Peru, Mexico, India, Brazil, UK, USA, China, Singapore, Germany | Not specified, but implicitly refers to the large-scale, general (and often proprietary) datasets used to train commercial LLMs like ChatGPT. Concerns about data bias, quality, and privacy are raised. | NaN | Ad hoc use of publicly available LLMs (e.g., ChatGPT) by individual judges; Issuance of official guidelines (e.g., UK). | True | True | Publicly available LLMs (e.g., ChatGPT) accessible via web interfaces, often with free tiers. | Lack of sufficient interdisciplinary research on the legal, ethical, technical, and societal implications of LLM use by judges; Need for better understanding of impacts on judicial independence, fairness, and accountability; Challenges in managing bias and ensuring transparency (XAI); Need for appropriate guidelines and potentially domain-specific models; Addressing public trust concerns. | Need for interdisciplinary research collaboration; Lack of understanding of LLM impacts on judiciary; General LLM challenges (accuracy, bias, transparency, privacy, cost, hallucinations); Developing specific guidelines; Integration into existing court IT systems. | Inaccurate judgments due to LLM errors/hallucinations; Erosion of judicial independence and impartiality; Violation of fairness/due process rights; Discrimination due to biased outputs; Breach of confidentiality/privacy; Undermining public trust in the judiciary; Lack of accountability for AI-influenced decisions. |
| RAoTkOxbBj0J.pdf | Google_Scholar | CHATGPT, PROFESSOR OF LAW | This paper experiments with using ChatGPT for seven common tasks faced by law professors related to teaching and service. The results suggest ChatGPT can generate usable first drafts quickly, especially for routine service tasks, potentially reducing faculty workload. | True | Market | True | 2.0 | NaN | ChatGPT | Qualitative evaluation of ChatGPT's output for seven hypothetical law professor tasks (exam question, handout, recommendation letter, bio, symposium remarks, committee plan, syllabus) based on usability as first drafts. | ChatGPT produced usable first drafts for six out of seven tasks in 23 minutes. It performed best on routine service tasks (recommendation letter, bio, remarks, committee plan) but required personalization, while performance on teaching tasks was mixed (good for syllabus brainstorming, weaker/inaccurate for exam question details and handout content). | NaN | NaN | NaN | NaN | Torts, Employment Law, Legal Education, Academic Administration | USA (implied) | NaN | NaN | NaN | True | False | The paper uses ChatGPT, accessible via OpenAI's website. | NaN | Need for prompt engineering/tweaking; potential for factual inaccuracies in output requiring significant expert revision; output may lack depth/detail for complex analytical tasks. | Inaccuracy of generated legal content; potential unstated ethical concerns regarding AI use in academic work (though scholarship was explicitly excluded). |
| QWltlnjUlekJ.pdf | Google_Scholar | NEW RULES FOR A NEW ERA: REGULATING ARTIFICIAL INTELLIGENCE IN THE LEGAL FIELD | This paper argues that the legal industry should be cautious about fully integrating AI due to its current flaws and limitations, which could lead to negative consequences for legal professionals and the legal system. It proposes that jurisdictions amend professional conduct rules to restrict the use of generative AI for specific litigation purposes until the technology matures. | True | Market | True | 1.0 | Negative | Generative AI / Large Language Models (e.g., ChatGPT) | Author's informal test of ChatGPT's legal research capabilities (checking for case law on vehicular battery in Ohio) and references to other anecdotal tests and OpenAI's stated limitations. | ChatGPT provided incorrect and fabricated legal information (hallucinations), such as citing non-existent court cases to support its legal explanation. | AI's current unreliability (hallucinations, inaccuracies); AI's potential to stagnate legal development due to reliance on historical data and lack of true understanding/morality; Risk of entrenching and amplifying biases present in training data; AI's inability to replicate crucial human elements of legal practice (e.g., emotional intelligence, complex strategic thinking). | Proactive self-regulation by the legal profession to restrict AI use in specific legal tasks (e.g., drafting persuasive legal communications, client communications, judicial rulings) until it matures, enforced by AI-detection software. | Ensuring the integrity, reliability, and fairness of the legal system and legal representation in the face of AI adoption; Regulation of AI in legal practice; Ethical use of AI by legal professionals. | NaN | General legal practice, litigation, professional conduct/ethics, criminal law, contract law. | United States (implicitly, for proposed rule changes), but arguments are broadly applicable. | Massive, diverse, largely unsupervised textual data from the internet (e.g., Common Crawl, Reddit, news, Wikipedia, historic books) for pre-training, supplemented with supervised labeled prompt-answer pairs and human-ranked outputs for fine-tuning (for models like ChatGPT). | Machine learning, including unsupervised and supervised learning, deep learning (artificial neural networks, transformer models using attention/self-attention mechanisms), reinforcement learning with human feedback (RLHF) for large language models like ChatGPT. | Publicly accessible web interfaces (e.g., ChatGPT), APIs for integration into other software and services, commercial product integrations (e.g., search engines, workplace tools). | True | True | ChatGPT, a primary example discussed, has a publicly accessible free version for use. The paper also mentions GPT-4 as a paid service and a free AI detection tool from OpenAI. | Technical gaps include AI's unreliability (hallucinations, inaccuracies), data staleness, black-box nature, and lack of true understanding or human-like intelligence. Societal/Ethical gaps include the absence of a moral compass in AI, unresolved issues of bias encoding and amplification, and the need for robust regulatory frameworks. | Challenges for AI developers include mitigating misalignment (inaccuracy, bias, harmful outputs), keeping models updated with current and unbiased information, overcoming the black-box problem for transparency, and instilling genuine understanding and ethical reasoning in AI systems. | Generation of incorrect legal information (hallucinations) leading to professional misconduct or malpractice; Stagnation of legal development and misalignment with current social values; Entrenchment and amplification of societal biases through AI; Compromised quality of legal representation due to AI's lack of human skills (e.g., emotional intelligence, strategic thinking, negotiation); Erosion of public trust in the legal system. |
| lm9K0vSCKEcJ.pdf | Google_Scholar | A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law | This survey extensively reviews the methodologies, applications, challenges, ethics, and future advancements of Large Language Models (LLMs) in the critical domains of finance, healthcare, and law. It highlights LLMs' transformative potential in these high-stakes sectors, such as enhancing diagnostics in healthcare, financial analytics, and legal interpretation, while also critically examining ethical concerns and advocating for responsible AI development. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Lack of access to legal services due to cost or knowledge barriers; ethical issues in LLMs (bias, fairness, robustness, hallucination); difficulty in acquiring high-quality, domain-specific (legal) training data; risk of LLMs worsening existing societal inequalities and creating technology access gaps. | Using LLMs to democratize legal information, education, and advice; improving quality and availability of training data for legal AI; promoting interdisciplinary collaboration; establishing robust ethical frameworks, security measures, open-source tools, and educational programs to ensure equitable access and responsible deployment. | Democratizing access to legal information, education, and advice; facilitating online dispute resolution; ensuring fairness, equity, and non-discrimination in legal AI; providing legal guidance for marginalized and under-resourced communities. | Individuals with limited financial or knowledge resources for legal help; marginalized communities; self-represented litigants; underrepresented groups; smaller organizations and non-profits. | General legal tasks (question answering, judgment prediction, text classification, summarization, information retrieval), Tax law, Transportation law, Privacy law, Criminal law, Contract law, EU law, Copyright law, Online dispute resolution. | US, China, Japan, European Union, Switzerland, Vietnam, Greece. | NaN | NaN | NaN | False | False | NaN | Significant ethical challenges (explainability, bias, fairness, robustness, privacy, accountability, potential for inequality exacerbation); insufficient reliability and advanced reasoning in legal-specific LLMs; difficulties in curating comprehensive, high-quality legal datasets; unresolved knowledge gap between NLP developers and legal domain experts. | NaN | Severe consequences from LLM errors in high-stakes FHL decisions (e.g., financial losses, incorrect medical diagnoses, wrongful legal outcomes); breaches of sensitive confidential data; propagation of biases leading to discriminatory outcomes and eroded trust; generation of 'hallucinated' or misleading information, especially harmful in legal and medical advice; exacerbation of societal inequalities and job displacement due to automation. |
| JQl5IoVQjuAJ.pdf | Google_Scholar | Hallucinating Law: Legal Mistakes with Large Language Models are Pervasive | This paper reports on a study evaluating the performance of three large language models (GPT-3.5, Llama 2, PaLM 2) on various U.S. legal tasks. The study found alarmingly high hallucination rates (69%-88%), particularly for complex tasks, lower court cases, and when presented with incorrect premises, suggesting current LLMs are unreliable for legal applications and may worsen access-to-justice issues. | True | Idealistic | True | 2.0 | Negative | Evaluation of existing Large Language Models (GPT 3.5, Llama 2, PaLM 2) on legal tasks. | Tested over 200,000 queries against GPT 3.5, Llama 2, and PaLM 2. Queries covered tasks like identifying opinion authors, determining precedential relationships, and identifying case holdings, stratified by court hierarchy, case prominence/age, and circuit. | Hallucination rates ranged from 69% to 88%. Performance deteriorated with task complexity and for lower court/less prominent cases. Models exhibited overconfidence and susceptibility to contra-factual bias. GPT-3.5 generally performed best but showed biases. | Current LLMs perform poorly on localized legal knowledge (lower courts) and complex reasoning tasks. They exhibit overconfidence, fail to correct user misconceptions (contra-factual bias), and are least reliable for the users (e.g., pro se litigants, those needing complex advice) who could most benefit from democratized legal information. | The paper advocates for caution, responsible integration requiring human supervision, transparency in model trade-offs, and a human-centered AI approach rather than specific technical fixes. | Access to legal information, Legal research accuracy, Reliability of AI in law, Case law analysis (precedent, holdings), Judicial system structure. | Litigants in lower courts, individuals in less prominent jurisdictions, users lacking legal expertise, general public seeking legal advice. | General US Case Law, Litigation | United States | NaN | Systematic evaluation using a large dataset (~200,000) of structured legal queries targeted at existing LLMs, stratified along dimensions like court level, case prominence, and task type. | NaN | True | False | The paper evaluates existing LLMs (GPT-3.5, PaLM 2, Llama 2) which are generally available, though access modalities vary (e.g., API, open release for Llama 2). | Significant gaps exist in LLM reliability for legal tasks, including handling complexity, local nuance (lower courts), calibration (confidence vs accuracy), and robustness against incorrect premises (contra-factual bias). Current models risk deepening legal inequalities rather than alleviating them. Need for transparency and normative judgment in model development. | NaN | Providing inaccurate legal information; deepening existing legal inequalities; fostering legal monoculture; representational harms (e.g., misattributing judicial opinions); users being misled by overconfident or factually incorrect responses (contra-factual bias). |
| laae003.pdf | Google_Scholar | Large Legal Fictions: Profiling Legal \nHallucinations in Large Language Models | This paper presents the first systematic empirical evidence of legal hallucinations in large language models (LLMs) like ChatGPT 4, PaLM 2, and Llama 2, finding they hallucinate at least 58% of the time when queried about US federal case law. It also documents their susceptibility to users' incorrect legal assumptions and poor self-awareness of errors, cautioning against unsupervised integration into legal tasks and highlighting risks for access to justice. | True | Idealistic | True | 2.0 | Negative | Public-facing LLMs: OpenAI’s ChatGPT 4, OpenAI’s ChatGPT 3.5, Google’s PaLM 2, and Meta’s Llama 2. | Evaluation using 14 legal knowledge query tasks (categorized by complexity) on a random sample of US federal case law (SCOTUS, USCOA, USDC). Employed reference-based querying (comparison to ground-truth metadata from legal databases) and reference-free querying (detecting self-contradiction across multiple LLM responses generated at a non-greedy temperature, with contradictions assessed by GPT-4). | LLMs hallucinate between 58% (ChatGPT 4) and 88% (Llama 2) of the time on direct, verifiable questions about federal court cases. GPT-4 performed best in terms of raw hallucination rates but was less calibrated than PaLM 2 and GPT 3.5. Models also demonstrated susceptibility to contrafactual bias and imperfect self-awareness of their propensity to hallucinate. | High rates of factual hallucination in LLM responses, poor model calibration (overconfidence in errors), susceptibility to contrafactual bias (uncritically accepting users' incorrect legal premises), and uneven legal knowledge (better for prominent/newer cases and major jurisdictions, worse for localized or older law). These issues risk exacerbating existing inequalities in legal services and creating a 'legal monoculture'. | The paper discusses potential mitigation techniques from other research (e.g., retrieval-augmented generation, advanced prompting, specialized fine-tuning, factuality-focused decoding, external database checks) but notes their current limitations. It advocates for human-centered AI approaches and emphasizes the need for developers to be transparent about the types of hallucinations their LLMs might produce and the choices made to minimize them. | Accuracy of LLMs in retrieving and stating US case law facts; factual hallucinations; implications of LLM errors for legal research, legal advice, and access to justice for pro se litigants. | Pro se and under-resourced litigants. | US federal case law. | United States (federal judiciary: US Supreme Court, US Courts of Appeals, US District Courts). | The paper states the LLMs were trained on vast text corpora including public domain American case law. Specific training datasets for the evaluated commercial/open-source LLMs (OpenAI, Google, Meta) are generally proprietary to the developers and not detailed further by the paper. | Construction of a test dataset of legal queries based on American case law, stratified by court level, jurisdiction, and time. Application of reference-based evaluation (comparing LLM output to known metadata) and reference-free evaluation (measuring self-contradiction in LLM outputs to infer hallucinations). Statistical analysis of hallucination rates and their correlation with case/court characteristics. | The evaluated LLMs (ChatGPT, PaLM 2, Llama 2) are deployed by their respective developers (OpenAI, Google, Meta) via APIs and public interfaces. | True | True | The discussed LLMs (ChatGPT 4, ChatGPT 3.5, PaLM 2, Llama 2) are generally accessible via APIs or public interfaces, with Llama 2 being open-source (e.g., Llama-2-13b-chat-hf). The paper's evaluation dataset is also available on HuggingFace and replication materials on Harvard Dataverse. | Technical: persistent high rates of factual hallucination in LLMs despite ongoing research into mitigation, poor model calibration (especially LLMs being overconfident in errors), difficulty handling localized or less prominent legal information, and an inability to reliably correct users' legal misconceptions. Societal: the risk of LLMs exacerbating the access to justice gap for vulnerable populations, the potential for creating a 'legal monoculture' due to biased knowledge, and the need for normative frameworks and transparency regarding which types of hallucinations are minimized by developers. | For LLMs in legal tasks: Ensuring factual accuracy and reliability in open-domain legal question answering. For the evaluation: Designing comprehensive and scalable methods (reference-based and reference-free) to detect and quantify legal hallucinations. General limitations of hallucination mitigation techniques like RAG (dependency on retrieval quality, query ambiguity, computational cost, handling conflicting information in databases) and evaluation metrics. | Generation of factually incorrect legal information leading to harmful or inaccurate legal advice. Worsening disparities in access to legal services due to LLMs' uneven knowledge distribution (e.g., better on prominent law, worse for specific needs of pro se litigants). Creation of a 'legal monoculture' by promoting a homogenized and potentially biased understanding of the law. Misleading users due to LLMs' overconfidence in false statements and their tendency to uncritically accept and respond to queries based on incorrect legal premises (contrafactual bias). |
| eWEN5-3w78IJ.pdf | Google_Scholar | Generative AI and the Future of Legal Scholarship | This paper proposes "Generative Synthesis" as a new paradigm for legal scholarship, advocating for the integration of generative AI as a co-creator of knowledge alongside human researchers. It explores the potential transformation of scholarly practices while detailing significant challenges like AI bias, deskilling, authorship ambiguity, and the need for ethical guidelines and institutional adaptation. | True | NaN | True | 1.0 | Neutral | Generative Synthesis: Integrating generative AI (LLMs) as a co-creator in the legal scholarship process. | Demonstration through AI generation of the paper's main body (Parts I-IV) using ChatGPT (OpenAI o1) based on specific prompts provided by the human author. | AI-generated text demonstrated creativity and sophistication comparable to a competent legal scholar, though with acknowledged gaps and flaws. | NaN | NaN | NaN | NaN | Legal Scholarship (meta-level) | International | Not specified, but uses ChatGPT (OpenAI o1, Dec 2024) which is known to be trained on large, diverse datasets. The paper mentions the risk of bias embedded in AI training data. | Prompt engineering with ChatGPT (OpenAI o1). Initial high-level prompt followed by section-specific prompts requesting law-review suitable text. Minimal iteration reported. | NaN | True | False | The conceptual approach ('Generative Synthesis') can be used with available LLMs; the specific prompts/outputs transcript from the paper's generation process is shared via a link. | Need for robust verification protocols for AI output; methods to mitigate algorithmic bias; development of new norms for authorship, disclosure, and citation; addressing deskilling risks; ensuring AI use aligns with normative legal values (justice, fairness); adapting peer review and legal education; establishing institutional oversight; considering global/cross-jurisdictional applicability and sensitivity. | Over-reliance on AI, deskilling, epistemic complacency, algorithmic bias perpetuating inequality, accountability/authorship issues, IP concerns, need for evolving standards (publishing, ethics), adapting peer review, updating legal education, ensuring AI aligns with normative values, potential for a narrow Western/Anglo-American focus. | Inaccurate/flawed AI outputs, deskilling of legal analysis, perpetuation/amplification of systemic bias, automated discrimination, IP infringement, undermining field credibility, creating digital divides. |
| Q_89nrnh9yYJ.pdf | Google_Scholar | Creative and Strategic Capabilities of Generative AI: Evidence from Large-Scale Experiments | This study experimentally compares generative AI (ChatGPT-4, Bard) with US adults on creative and strategic tasks. Results indicate ChatGPT-4 often surpasses human creativity, and AI augmentation improves human creativity but not beyond AI alone; in strategic games, AI adapts but humans can outperform it. | True | NaN | True | 2.0 | NaN | ChatGPT-4 and Bard (Google's AI chatbot) | Large-scale experiments with over 4,000 US adult participants. Creative tasks involved generating text based on prompts, rated by other humans on creativity, novelty, surprise, and usefulness. Strategic tasks involved playing 24 rounds of Rock-Paper-Scissors against a pre-determined opponent strategy (either balanced/equilibrium or unbalanced/biased). | For creativity, ChatGPT-4's ideas were rated highest, surpassing unassisted humans, humans augmented with AI, and Bard. In strategic games (Rock-Paper-Scissors against a biased opponent), humans earned significantly more points than ChatGPT-4, despite both showing adaptation to the opponent's strategy. | NaN | NaN | NaN | NaN | NaN | USA | NaN | NaN | NaN | True | False | ChatGPT-4 and Bard are accessible via their standard chat interfaces. Access to ChatGPT-4 as used in the study (version 4) typically requires a subscription. | NaN | Effective prompting of AI for optimal creative output (HumanPlusAI underperformed AI alone). Understanding AI's adaptive limits in strategic contexts and how humans interact with AI. | Algorithm aversion where humans rate suspected AI output lower. Potential for AI competition to disproportionately affect certain demographics (e.g., women's creativity). Public skepticism or resistance towards AI. |
| xqPlbTspskYJ.pdf | Google_Scholar | Legal Market Decartelization | This paper critically examines the trend towards legal market decartelization in the United States, arguing that deregulation, while intended to improve access to justice, presents significant risks such as increased information asymmetry, corporate dominance, and negative litigation externalities. The authors advocate for policymakers to consider solutions beyond mere decartelization, including targeted aid, process simplification, and the cautious adoption of new technologies like AI, to address the maldistribution of legal services. | True | Idealistic | False | 3.0 | Positive | Legal market decartelization (as a policy approach, including reforms to rules on nonlawyer ownership of law firms/Alternative Business Structures and the unauthorized practice of law) | NaN | NaN | Maldistribution of legal services; high rates of unmet legal needs; consumers' lack of awareness of their legal problems or available solutions; asymmetric information between consumers and legal service providers; high information costs; distrust of providers; complexity of legal processes. | Targeted interventions to reduce asymmetric information; subsidized legal services (e.g., "civil Gideon") in critical areas like housing; simplification of legal and court processes; leveraging new technologies like generative AI (with appropriate safeguards) to lower information costs; government and professional investment in civics training and dissemination of legal information; partnerships with trusted community organizations; specific process-based reforms like court appearance reminders, remote hearings, and automatic criminal record expungement ('clean slate' laws). | Addressing unmet civil legal needs; improving access for low- arid middle-income consumers; housing law (eviction); criminal record expungement; consumer debt; simplification of legal processes; self-represented litigants; regulation of legal services. | Low-income Americans; ordinary consumers of legal services; individuals of limited means; rural communities facing 'legal deserts'; tenants in eviction proceedings; individuals with criminal records. | Legal Profession Regulation; Access to Justice; Civil Law; Housing Law; Criminal Law (specifically expungement); Consumer Law; Litigation. | United States (with comparative references to the UK, Europe, Canada, and Australia, and specific examples from US states like Arizona, Utah, Washington, Minnesota, New York, Pennsylvania). | NaN | NaN | NaN | True | True | Discusses existing generative AI technologies like GPT-4, which are publicly accessible, with some versions available for free. | Vast scale of unmet legal needs unresolved by current approaches; lack of comprehensive studies on the impact of deregulation (e.g., in the U.K.); persistent information costs and consumer unawareness; the digital divide limiting technology-based solutions in 'legal deserts'; potential for AI misuse (e.g., hallucinations, lack of accuracy) requiring regulatory oversight; need for public/philanthropic funding for non-market solutions like Civil Gideon. | NaN | Legal market decartelization exacerbating asymmetric information; private equity and well-capitalized entities gaining market dominance, potentially leading to consolidation and higher prices without service improvement; increased moral hazard and negative externalities in litigation (e.g., frivolous suits, reduced attorney gatekeeping); diminished role of lawyers in constructive law development, potentially worsening regulatory capture; 'one-size-fits-all' deregulation failing due to spatial localization of legal markets; digital-first solutions marginalizing vulnerable populations or those in digital deserts; deregulation leading to reduced competition if capital is primarily used for consolidation rather than innovation; misuse of AI if deployed without proper regulation and disclosure of limitations. |
| RTRKZVYlBPYJ.pdf | Google_Scholar | DETERMINANTS OF SOCIALLY RESPONSIBLE \nAI GOVERNANCE | This paper proposes justice, equity, and the rule of law as core determinants for socially responsible AI governance, ensuring AI actively promotes fairness and inclusivity. It analyzes AI's impact on access to justice, discusses risks like bias, and offers a proactive governance framework by comparing approaches in the US, EU, China, and Singapore. | True | Idealistic | False | 1.0 | Positive | Proactive governance framework (incorporating transparency, equity audits, tailored regulatory approaches); 'Equity by Design' framework; justice, equity, and the rule of law as yardsticks for socially responsible AI. | NaN | NaN | Algorithmic bias (stemming from data, code, and existing legal frameworks), lack of transparency and accountability in AI systems (e.g., due to trade secrets, complexity), exacerbation of existing socio-economic inequalities and the digital divide, and the potential for AI to undermine the rule of law and democratic processes (e.g., through disinformation or unscrutinized norm-setting). | Proactive governance frameworks (featuring transparency, equity audits, tailored regulation), adherence to 'Equity by Design' principles, fostering diversity in development teams and training data, promoting explainable AI, ensuring human oversight, implementing AI literacy programs, encouraging international collaboration and standards, and establishing normative oversight bodies for AI. | Access to legal information and representation, fairness and non-discrimination in legal processes, efficiency of legal services for underserved populations, overcoming language and literacy barriers in legal contexts. | Marginalized communities (including low-income individuals, ethnic minorities, Indigenous groups, unskilled immigrants, senior citizens), self-represented litigants, individuals with limited English proficiency, and tenants facing eviction. | Civil law (especially housing, debt collection), criminal justice, due process, intellectual property, litigation generally, constitutional law. | United States, European Union, China, Singapore, International | NaN | Comparative legal analysis, conceptual framework development, literature review. | Policy adoption by governments and regulatory bodies, international collaboration and standard-setting, implementation of proposed principles by AI developers and deployers. | False | False | NaN | Ensuring equitable access to AI benefits and mitigating the digital divide, developing effective and balanced regulations that foster innovation while safeguarding rights, managing AI's autonomous norm-setting capabilities, achieving truly unbiased and fair AI systems (addressing technical and data limitations), and establishing international harmonization for AI governance. | Synthesizing diverse international governance models, balancing innovation with ethical safeguards (justice, equity, rule of law), addressing the rapid evolution and multifaceted risks of AI, and conceptualizing abstract principles for practical AI governance. | Exacerbation of existing inequalities, algorithmic bias and discrimination, lack of transparency and accountability, AI errors such as hallucinations, misuse by malicious actors (e.g., disinformation, fraud, predatory practices), undermining of due process and the rule of law, erosion of privacy and free expression, and threats to national security. |
| yV9JSc0E-YYJ.pdf | Google_Scholar | Regulatory Framework for Artificial Intelligence in the Legal System of Pakistan | The paper discusses the growing use of AI in Pakistan's legal system, highlighting applications like e-discovery and judicial assistance (e.g., ChatGPT) and emphasizing the urgent need for a comprehensive regulatory framework. It outlines potential benefits for efficiency alongside significant risks such as bias, errors, job displacement, privacy violations, and the current lack of a coherent national AI strategy. | True | Market | True | 3.0 | Neutral | General AI applications in law (incl. TAR, ChatGPT/GPT-4) | NaN | NaN | Lack of coherent national AI strategy and regulation; potential for AI bias and errors; risks to privacy; need for human oversight/judgment; job displacement concerns. | Develop a comprehensive legal and regulatory framework for AI; create a clear national AI strategy; provide education and training for legal professionals; launch public awareness campaigns; ensure human judgment remains central in legal decision-making. | Judicial efficiency, Regulation of AI in legal services, E-discovery, AI-assisted judicial decision-making | NaN | Civil Law, Criminal Law, Technology Law, Procedural Law, Privacy Law | Pakistan | NaN | NaN | NaN | False | False | NaN | Lack of a comprehensive and strategic national AI policy; inadequate regulation for ethical/societal implications (bias, privacy, liability, data scraping); insufficient cyber/internet governance policy for AI; need for greater public awareness and digital literacy; need for mechanisms for redress for AI-related harms. | Integrating AI while maintaining human oversight; ensuring fairness and avoiding bias; addressing job displacement; protecting privacy; developing effective regulations; overcoming lack of strategic policy implementation. | AI bias leading to inequitable outcomes; errors in AI judgments causing injustice; job displacement; privacy violations; misuse for crime (e.g., voice cloning); erosion of human rights (dignity, privacy); lack of transparency. |
| Y4hGJX6FeicJ.pdf | Google_Scholar | NEW FRONTIERS IN ATTORNEY REGULATION : INTRODUCTION TO VOLUME II OF II | This paper introduces Volume II of a symposium on attorney regulation, summarizing articles on topics including the NextGen Bar Exam, lawyer competence, Generative AI in law and legal education, and professional responsibility. It highlights how Generative AI is discussed in the context of legal practice and education, its potential to provide DIY legal solutions for low-income individuals, and emerging regulatory considerations. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Inability of low-income individuals to afford or secure free legal services. | Encouraging the use of Generative AI tools for DIY legal solutions and permitting nonlawyers to assist consumers in using these tools effectively. | DIY legal solutions for low-income individuals using Generative AI; Access to legal services for the underserved. | Low-income individuals; persons unable to afford or secure free legal services. | Attorney regulation; Delivery of legal services; Legal education; Legal ethics; Professional responsibility. | United States | NaN | NaN | NaN | False | False | NaN | Need for more specific guidance and proactive regulatory frameworks for the legal profession's use of Generative AI. | NaN | Compromise of confidential client information when inputted into Generative AI tools; lawyers encountering problems using AI tools without proper training or guidance. |
| vgbfX5Ex6JoJ.pdf | Google_Scholar | VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs | This paper introduces VANE-Bench, a new benchmark dataset designed to evaluate the ability of Large Multi-modal Video Models (Video-LMMs) to detect anomalies in videos. The benchmark includes real-world surveillance clips and synthetically generated videos with subtle inconsistencies, finding that current models struggle significantly with this task. | True | Market | True | 1.0 | NaN | VANE-Bench: A benchmark dataset and evaluation methodology for video anomaly detection using Video-LMMs via a Multiple-Choice Video Question Answering (MC-Video QA) task. | Evaluation of nine Video-LMMs (7 open-source, 2 closed-source like GPT-4o and Gemini-Pro) on the VANE-Bench dataset (325 videos, 559 QA pairs from real-world and AI-generated sources). Human evaluation also performed on SORA videos. | Most Video-LMMs, particularly open-source ones, performed poorly (<26% accuracy). Closed-source models (GPT-4o: 72.82%, Gemini-Pro: 69.64% average accuracy) performed better overall but still struggled with subtle AI-generated anomalies. Human performance on subtle anomalies was also sub-optimal. | NaN | NaN | NaN | NaN | Criminal Law, Evidence Law | International | The benchmark dataset (VANE-Bench) comprises videos from existing publicly available real-world anomaly datasets (CUHK Avenue, UCF-Crime, UCSD Pedestrian) and synthetically generated videos from state-of-the-art text-to-video models (SORA, Open-Sora, Runway Gen2, ModelScopeT2V, VideoLCM). Associated question-answer pairs were generated using GPT-4o based on annotations. | Benchmark design involved collecting real-world and AI-generated videos, semi-automatic annotation of anomalies (Frame Annotation Module - FAM), caption generation using GPT-4o (Caption Generation Module - CGM), and multiple-choice question-answer pair generation using GPT-4o (Question Answer Generation Module - QAGM). | The VANE-Bench dataset and associated code are made publicly available via GitHub. | True | True | Code and data for the VANE-Bench benchmark are publicly available on GitHub. | Current Video-LMMs lack robustness in detecting subtle video anomalies, especially in AI-generated content. Existing benchmarks do not sufficiently focus on this specific challenge. | Detecting subtle anomalies in high-fidelity videos (challenging for AI and humans), inconsistent predictions from Video-LMMs depending on query phrasing, variability in performance across different anomaly types, limitations in accessing/evaluating closed-source models, limited availability of samples from cutting-edge models like SORA. | The difficulty in detecting anomalies in high-fidelity AI-generated videos poses risks related to misinformation, deepfakes, and distinguishing real from synthetic content, particularly during critical events like elections. |
| Paper_113-Accurate_AI_Assistance_in_Contract_Law1.pdf | Google_Scholar | Accurate AI Assistance in Contract Law Using Retrieval-Augmented Generation to Advance Legal Technology | This paper proposes an AI chatbot using Retrieval-Augmented Generation (RAG) to provide accurate legal assistance in contract law, demonstrated with Moroccan legislation. The system aims to enhance understanding for non-experts by grounding responses in verified legal documents, thereby mitigating Large Language Model (LLM) hallucinations. | True | Idealistic | True | 1.0 | Positive | A chatbot system integrating Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs like GPT-4 Turbo, Llama 3) and a vector database (FAISS) containing embedded legal documents (Moroccan Code of Obligations and Contracts). | Comparative evaluation of GPT-4 Turbo and Llama 3 within the RAG system using the RAGAS framework, measuring 'Faithfulness' and 'Answer Relevance' metrics, alongside response time. | GPT-4 Turbo achieved higher Faithfulness (1.0 vs 0.84) and Answer Relevance (0.971 vs 0.79) compared to Llama 3, although Llama 3 was faster (0.86s vs 3.12s). GPT-4 Turbo was selected for its higher accuracy. | Complexity of legal documentation, prevalence of misinformation, need for specialized legal skills to understand/draft contracts, limitations of LLMs (outdated knowledge, hallucinations). | An AI chatbot using RAG to provide accurate, contextually relevant responses based on integrated official legal documents, simplifying legal information access for non-experts and reducing reliance on potentially inaccurate LLM knowledge. | Contract law understanding and assistance. | General public / citizens / non-expert users. | Contract Law (specifically mentioning Moroccan Code of Obligations and Contracts, and potential application to Property Law). | Morocco (with stated adaptability to other jurisdictions). | The knowledge base used for RAG consists of the Moroccan "Code of Obligations and Contracts (COC)", extracted from official PDF documentation using OCR. This is unstructured, domain-specific legal text. The underlying LLMs (GPT-4 Turbo, Llama 3) were pre-trained on general datasets by their respective organizations. | System architecture development involving data collection (OCR), preprocessing (text splitting), embedding (LLM-Embedder), vector storage (FAISS), retrieval (ANN similarity search), response generation (RAG with LLMs), and comparative evaluation (RAGAS framework). | NaN | False | False | NaN | Need for automated legal updates, integration of multimodal capabilities, improved explainability (providing explicit legal references), enhanced adaptability to different legal frameworks, the system cannot replace human expertise in complex cases. | Balancing LLM speed vs. accuracy/relevance, ensuring factual consistency and avoiding hallucinations, managing and updating the legal knowledge base, processing PDF legal documents effectively (OCR, chunking). | Providing incorrect legal information due to LLM hallucination or outdated data, users over-relying on the system for complex legal matters requiring professional advice. |
| JDIxmlFdiQYJ.pdf | Google_Scholar | THE USE OF ARTIFICIAL INTELLIGENCE IN ACADEMIC PUBLISHING: PRELIMINARY REMARKS AND PERSPECTIVES | The paper discusses the potential applications of Artificial Intelligence (AI) across various stages of the academic publishing workflow, including manuscript analysis, reviewer selection, and communication. It highlights how AI can enhance efficiency but also notes limitations, risks like bias and copyright issues, and ethical considerations. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | NaN | NaN | False | False | NaN | NaN | Integrating AI software with existing journal management systems; need for custom programming (e.g., using APIs) to connect different tools; some tasks are already adequately handled by existing non-AI systems. | AI systems struggling with nuance and context; perpetuation or amplification of biases from training data; difficulty assigning accountability for AI errors or biased content; potential job displacement for human editors; concerns regarding authorship of AI-generated text; copyright infringement/piracy risk. |
| DrMmT3gajroJ.pdf | Google_Scholar | NYAYA ANUMANA and INL EGAL LLAMA : The Largest Indian Legal Judgment Prediction Dataset and Specialized Language Model for Enhanced Decision Analysis | This paper introduces NyayaAnumana, the largest dataset for Indian Legal Judgment Prediction (LJP), containing over 700,000 cases from various courts. It also presents INLegalLlama, a LLaMa-based language model specifically adapted for the Indian legal domain via continued pretraining and supervised fine-tuning, designed for both predicting judgments and providing explanations. | True | Idealistic | True | 1.0 | Positive | NyayaAnumana dataset creation and INLegalLlama model development (LLaMa-2 7B adapted via Continued Pretraining and Supervised Fine-tuning with LoRA for Legal Judgment Prediction and Explanation). | Evaluated LMs (InLegalBERT, InCaseLaw, XLNet) and LLMs (including INLegalLlama) on NyayaAnumana splits for binary/ternary classification across court types and temporal data. Also tested on external datasets (ILDC, PredEx, ILDC_expert). Metrics included Precision, Recall, F1, Accuracy, Rouge, BLEU, METEOR, BERTScore, BLANC, and expert evaluation using a Likert scale. | Achieved approximately 90% F1-score/accuracy in binary prediction tasks on the NyayaAnumana dataset using domain-specific models. INLegalLlama (CPT+SFT) outperformed baseline LLaMa-2 and other LLMs on PredEx and ILDC_expert datasets for prediction and explanation tasks, achieving 76.05% accuracy on PredEx. | Significant backlog of lakhs of pending cases burdens the Indian legal system. | Develop AI-driven systems for legal judgment prediction and explanation (like NyayaAnumana and INLegalLlama) to enhance efficiency, accessibility, and transparency in the legal process. | Legal Judgment Prediction (LJP), Explainable AI (XAI) in law. | General population interacting with the Indian judicial system (implicitly, by addressing case backlog). | General Litigation (covering multiple fields adjudicated by Supreme Court, High Courts, Tribunals, District Courts). | India | NyayaAnumana: A new publicly sourced (IndianKanoon) corpus of 702,945 preprocessed, English-language, unstructured Indian court case documents from various court levels. Subsets used for model training (CPT: SCI + 100k HCs subset; SFT: PredEx dataset with expert annotations). | Data compilation and preprocessing (web scraping, regex cleaning, keyword filtering, label extraction). Model development involved Continued Pretraining (CPT) of LLaMa-2 7B on a subset of NyayaAnumana, followed by Supervised Fine-tuning (SFT) using the PredEx dataset and Parameter-Efficient Fine-Tuning (PEFT) with LoRA. | Dataset and code made available via a GitHub link. | True | True | Dataset (NyayaAnumana) and code for prediction and explanation models available on GitHub. | Lack of datasets in regional Indian languages. Need for larger, more advanced models and refined fine-tuning techniques incorporating diverse legal documents (statutes, contracts). LLM applicability for complex legal reasoning requires further investigation. | Resource constraints (GPU memory, compute time, cost) leading to model quantization (4-bit) and limiting the use of larger models. High cost and time for obtaining expert annotations. Difficulty for generative models in processing long legal documents and performing complex reasoning. Inconsistencies and preprocessing errors in existing benchmark datasets (e.g., ILDC). Reducing model hallucination. | Generative models may produce hallucinated or factually incorrect content. Over-reliance on AI without human oversight in legal decision-making (mentioned as a need for caution). |
| p4PiylqM104J.pdf | Google_Scholar | Exploring Possibilities of AI-Powered Legal Assistance in Bangladesh through Large Language Modeling | This paper details the development of UKIL-DB-EN, a corpus of Bangladeshi legal documents, and the fine-tuning of GPT-2 (creating GPT2-UKIL-EN) on this corpus to provide legal assistance in English. The model shows promising results in evaluations including expert opinions but requires further refinement for accuracy and safety. | True | Idealistic | True | 1.0 | Positive | Fine-tuning the GPT-2 medium model on a custom-built corpus of Bangladeshi legal documents (UKIL-DB-EN) using instruction-tuning prompts to create the GPT2-UKIL-EN model. | Quantitative semantic similarity analysis (Cosine similarity, Jaccard index) comparing model output to original texts, and qualitative evaluation through three case studies (varying difficulty) assessed by five legal experts using a rating scale and providing feedback. | GPT2-UKIL-EN achieved the highest scores on semantic similarity metrics (Cosine: 0.515, Jaccard: 0.133), outperforming baseline GPT-2, Mistral-7b, and Gemma-2b. Expert evaluation (average score 4.81/7) indicated good reasoning and approach but issues with accuracy and clarity, especially in complex cases. | Significant delays, procedural complexity, high legal costs, large case backlogs (over 3.7 million), police harassment, inadequacies in legal provisions, lack of legal knowledge, and financial constraints preventing access to representation, particularly for lower-income/marginalized communities. | Developing a specialized LLM (GPT2-UKIL-EN) to automate legal assistance, simplify legal language, provide affordable support, streamline administrative processes, democratize access to legal information, and empower individuals to understand their rights and navigate the system. | Access to legal information, understanding legal rights and procedures, reducing legal costs, mitigating judicial delays, improving case management efficiency. | General population of Bangladesh, particularly lower-income or marginalized communities facing financial or educational barriers to accessing the legal system. | General Legal Assistance (derived from scraping various acts like civil, criminal, administrative and case studies on property, criminal law). | Bangladesh | UKIL-DB-EN: A publicly available corpus of English-language Bangladeshi legal documents (595 Acts, ~18,023 sections) collected via web scraping from an open-access government portal (bdlaws.minlaw.gov.bd) and preprocessed. | Data collection (web scraping), data curation (cleaning, noise reduction, standardization, verification), model selection (GPT-2 medium), model fine-tuning (instruction tuning, LoRA, quantization), prompt engineering, quantitative evaluation (semantic similarity), qualitative evaluation (case studies, expert review). | The dataset (UKIL-DB-EN) and the fine-tuned model (GPT2-UKIL-EN) are publicly released on Hugging Face. | True | True | Dataset and model available on Hugging Face under Apache-2.0 license. | Need for improved model accuracy, credibility, and safety; limitations in contextual comprehension for complex cases; handling of multilingual requirements (Bangla and English); need for larger models; language simplification for lay users; information gaps requiring more comprehensive data. | Limited computational resources restricted experimentation with larger/multilingual models and RAG; ensuring accuracy and reliability in the sensitive legal domain; handling legal complexities and context-specific nuances. | Potential inaccuracies and reliability issues in the model's responses could lead to incorrect legal understanding or advice, posing ethical concerns due to the critical nature of legal applications. |
| h2e8YjKeFzcJ.pdf | Google_Scholar | OBJECTION! USE OF AI! \nEvaluating the Role of Generative Arti/f_icial Intelligence in Litigation: \nRisks and Regulations | This paper reviews the significant risks posed by generative AI, particularly ChatGPT, in litigation, including misinformation, privilege breaches, data collection, and ethical concerns regarding judicial integrity. Based on a literature review, it argues that current generative AI should be prohibited in litigation pending further societal consideration and regulation. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Litigation | United States, Colombia | NaN | NaN | NaN | False | False | NaN | Need for societal discussion and consensus on whether and how generative AI should be used in litigation; Need for evaluated regulations. | NaN | Misinformation and generation of fake legal citations/cases; Breaches of legal professional privilege and confidentiality due to data handling practices (review, retention, training use, third-party sharing); Undermining judicial integrity if judges rely on potentially inaccurate AI outputs; Ethical concerns about the appropriateness of AI influencing high-stakes legal decisions; Potential for biased AI outputs. |
| Assessing_the_Benefits_of_ChatGPT_for_Business_An_Empirical_Study_on_Organizational_Performance.pdf | Google_Scholar | Assessing the Benefits of ChatGPT for Business: An Empirical Study on Organizational Performance | This paper empirically investigates the impact of ChatGPT's system, information, and service quality on user satisfaction and benefits, and subsequently on organizational performance in businesses, using the DeLone and McLean's Information Systems Success model. Findings from a survey of 361 Korean business users indicate that these quality attributes positively affect satisfaction and benefits, which in turn enhance organizational performance. | True | Market | True | 2.0 | NaN | ChatGPT (a conversational generative AI by OpenAI) | Survey of 361 business users in Korea; data analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test hypotheses derived from the D&M IS Success model. Measurement model assessed for reliability, convergent validity, and discriminant validity. | System quality, information quality, and service quality of ChatGPT positively impact user satisfaction and benefits; service quality had the most significant impact on satisfaction (β=0.451, p<0.001). Both satisfaction (β=0.262, p<0.001) and benefits (β=0.269, p<0.001) significantly enhanced organizational performance. The model explained 46.1% of variance in organizational performance. | NaN | NaN | NaN | NaN | NaN | Republic of Korea | The study analyzes ChatGPT, which is trained by OpenAI on large-scale, diverse text and code datasets using Reinforcement Learning from Human Feedback (RLHF); this data is proprietary to OpenAI. | Quantitative survey-based study applying the DeLone and McLean’s Information Systems Success (D&M IS) model; instrument development based on verified measurements; data analysis through structural equation modeling (PLS-SEM). | ChatGPT, the tool studied, was publicly released by OpenAI in November 2022 and is accessible via a web interface and API. | True | True | ChatGPT is available via a web interface and API provided by OpenAI, with both free and paid access tiers. | NaN | Identifying ChatGPT's impact on organizational performance due to its novelty; generalizability of findings as ChatGPT is in its nascent stage and the user base may not be representative; potential for same-method bias in self-report survey data. | Disinformation; ethical issues, including those related to academic writing and test integrity. |
| vbPLjHykthcJ.pdf | Google_Scholar | Experimenting with Legal AI Solutions: The Case of Question-Answering for Access to Justice | This paper introduces a human-centric pipeline for legal question-answering aimed at laypeople, featuring a new dataset (LegalQA) with expert-written answers and citations. It demonstrates that retrieval-augmented generation (RAG) using a small, domain-specific set of expert-approved documents can match or outperform RAG using internet-wide retrieval for factual accuracy. | True | Idealistic | True | 1.0 | Positive | A human-centric legal NLP pipeline involving: 1) A new dataset (LegalQA) of real layperson legal questions and expert-written answers/citations. 2) Domain-specific Retrieval-Augmented Generation (RAG) using only expert-approved documents. 3) An automatic, expert-vetted evaluation protocol focused on factuality. | Evaluated using the created LegalQA dataset (323 questions). Factual accuracy was assessed using an automatic evaluation protocol (GPT-4 comparing model output to expert answers), measuring the percentage of factual disagreement. Compared domain-specific RAG (using 850 expert-sourced documents) against non-RAG models (GPT-3.5, GPT-4, Mixtral-8x7B) and internet-wide RAG (GPT-3.5 with Google search, Cohere Command R+). | Domain-specific RAG using GPT-3.5 ('GPT-3.5 Legal', 8.7% disagreement) performed better than non-RAG GPT-3.5 (11.8%) and internet-wide RAG ('GPT-3.5 Internet', 8.3%; Command R+, 14.4%). However, the non-RAG GPT-4 model performed best overall (4.4% disagreement). | Lack of high-quality, expert-vetted structured legal data (question-answer pairs) suitable for laypersons; factual incorrectness (hallucination) and outdated information in LLMs; prohibitive costs of high-performing models (like GPT-4) limiting accessibility. | Creating and releasing high-quality, expert-verified datasets (like LegalQA). Employing domain-specific retrieval-augmented generation (RAG) using a curated set of trusted legal sources to improve factual grounding and reduce costs compared to retrieving from the entire internet. Developing human-centric evaluation protocols focused on factuality. | Providing factual answers to specific legal questions asked by laypeople. | Laypeople seeking legal information. | Employment and labour law, Family and juvenile law, Real estate law, Corporate law, Personal injury law, Civil rights law. | Canada (specifically Ontario mentioned in an example, and expert annotators were knowledgeable in Canadian law). | The study uses a retrieval dataset comprising 850 legal documents (citations) provided by legal experts corresponding to answers for real layperson questions sourced from Reddit (r/legaladvice). The evaluation dataset (LegalQA) is a subset (323 Q&A pairs) released publicly. This data is structured (question, expert answer, citation) and domain-specific (Canadian law). | Human-centric design involving legal experts (law professors and students) for data sourcing (writing answers, providing citations) and evaluation design. Technical methodology involves Retrieval-Augmented Generation (RAG) based on embedding similarity (dot product) between questions and a curated document set. | The LegalQA evaluation dataset was released publicly on Hugging Face. | False | False | The evaluation dataset (LegalQA) is claimed to be publicly released, but not the full RAG system or the 850-document retrieval corpus. | Performance gap between open-source and closed-source models; need for continual updating of legal knowledge in AI systems; lack of expert involvement in sourcing unstructured data for pre-training legal models; accountability issues with black-box models. | Sourcing high-quality, expert-approved legal data suitable for laypersons; developing reliable automatic evaluation methods for factual correctness in open-ended legal answers; difficulty answering highly specific/nuanced questions, particularly in certain legal areas (e.g., civil rights, real estate); managing computational/storage costs of retrieval. | LLMs providing factually incorrect or misleading legal advice (hallucination); lack of accountability and transparency in closed-source models used for legal purposes. |
| 8fuknjhzvVkJ.pdf | Google_Scholar | “Balancing Innovation and Copyrights: The Legal Framework for AI Training in the European Union” | This master's thesis examines the European Union's copyright framework concerning the use of copyrighted materials for training Generative AI systems. It analyzes the legal challenges, compares EU, US, and Japanese approaches, and explores potential remedies like licensing, unlearning, and exceptions for non-expressive use. | True | Market | False | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Copyright Law, Intellectual Property Law, AI Law/Regulation | European Union (EU), United States (US), Japan | Discusses the use of large datasets (text, images) scraped from the internet, including public domain, licensed, and copyrighted works (e.g., Getty Images, books, news articles) for training Generative AI, without using a specific dataset for its own study. | NaN | NaN | False | False | NaN | Lack of conclusive EU case law on AI training copyright infringement and remedies. Technical difficulty and feasibility of AI 'unlearning'. Impracticality of obtaining licenses for vast amounts of scraped data. Tension between fostering innovation (especially for SMEs) and protecting creators' rights. Lack of standardized machine-readable opt-out mechanisms. | Legal challenges in obtaining authorization/licenses for copyrighted training data. Balancing innovation needs with creator rights under existing legal frameworks. Legal uncertainty (especially with doctrines like fair use). Technical difficulty of implementing remedies like 'unlearning' or selective data removal without impacting model performance. Ensuring effective copyright compliance mechanisms (e.g., scalable opt-out, filtering). | Risk of copyright infringement lawsuits leading to damages, injunctions, or model destruction. Stifling innovation due to overly restrictive regulations or high compliance/licensing costs. Market harm to creators if AI outputs devalue their original work. Potential degradation of AI model quality if access to diverse data is heavily restricted ('garbage in, garbage out', 'Habsburg AI'). Lack of transparency regarding training data potentially leading to legal or ethical issues. |
| idl5o7BDS4YJ.pdf | Google_Scholar | A KNOWLEDGE GRAPH MODELING APPROACH FOR AUGMENTING LANGUAGE MODEL-BASED CONTRACT RISK IDENTIFICATION | Large language models (LLMs) show promise for automating contract review but struggle with domain-specific knowledge and factual accuracy. This paper proposes augmenting LLMs with a nested Knowledge Graph (KG) modeling approach to enhance automated contract risk identification in the construction industry. | True | Market | True | 1.0 | NaN | A nested Knowledge Graph (KG)-augmented Large Language Model (LLM) framework for automated contract risk identification. | Case study comparing the KG-augmented LLM (gpt-3.5-turbo) against a baseline LLM using sample clauses from international construction projects. Evaluation was conducted manually by domain experts assessing risk label identification and analysis accuracy against a gold standard review. | The KG-augmented LLM correctly identified 'No risk' for the sample clause and provided analysis aligned with expert review, whereas the baseline LLM incorrectly identified risk due to hallucination and lack of domain knowledge. | NaN | NaN | NaN | NaN | Construction Law, Contract Law | International | The Knowledge Graph was constructed semi-automatically using an ontology and LLM-based prompting on unstructured contract text (potentially standard forms and project-specific clauses), requiring manual intervention. The underlying LLM (gpt-3.5-turbo) is pre-trained on general data. Testing data comprised clauses from international construction projects. | Ontology development (using Protégé), nested Knowledge Graph modeling (using RDF-star), semi-automated knowledge extraction (using LLMs and ontology prompting), Retrieval-Augmented Generation (RAG). | NaN | False | False | NaN | NaN | Difficulty in fully automating the complex, multi-layer knowledge graph construction (requiring human intervention); trade-off between KG expressivity and scalability; lack of suitable evaluation benchmarks for LLM-based contract risk analysis that align with expert judgment. | The paper highlights risks associated with using unaugmented LLMs for contract review, namely hallucinations (factual errors) and inability to leverage domain-specific knowledge accurately. |
| qj8laKSbW8gJ.pdf | Google_Scholar | Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education | This paper proposes a conceptual framework for integrating fine-tuned Large Language Models (LLMs) into interactive visualization systems, focusing on alignment between domain problems, visualizations, and interactions. The framework is applied to develop Tailor-Mind, a system using a fine-tuned LLM and visualizations to support self-regulated learning (SRL) for AI beginners. | True | NaN | True | 1.0 | Positive | Tailor-Mind: An interactive visualization system using a fine-tuned LLM (Baichuan2-7B-chat based) to support self-regulated learning (SRL) in AI education, implementing a proposed framework for LLM-visualization integration. | The fine-tuned LLM (SFT-2.0) was compared against other models (Base, EduChat, GPT-3.5, SFT-1.0) on a custom AI education test set (280 examples), evaluated by human experts and GPT-4 on Accuracy, Completeness, Clarity. The Tailor-Mind system was evaluated via a comparative user study (N=24) against GPT-4 for learning the Transformer model, measuring objective learning outcomes and subjective feedback. | The fine-tuned model (SFT-2.0) outperformed comparison models in human expert evaluations (Avg 4.30) and GPT-4 evaluation (Avg 4.20). The user study showed Tailor-Mind users achieved significantly better learning outcomes on objective tests and reported higher satisfaction and engagement compared to using GPT-4. | Challenges specific to Self-Regulated Learning (SRL) identified: Limited student knowledge of SRL, lack of motivation/guidance, difficulty understanding complex/esoteric knowledge, and lack of immediate/personalized feedback. | Tailor-Mind system addresses SRL challenges by: Providing SRL guidance, optimizing learning depth via structured explanations/visualizations (mind maps, learning paths), offering personalized recommendations/assessments, and creating an engaging interactive environment. | NaN | NaN | Education | International | Proprietary dataset (74,932 entries) for AI education, mixing domain texts (textbooks, notes etc.), open-source data (Alphaca_gpt4_data, ChatGPT-Corpus), and generated dialogues, structured for instruction fine-tuning. | User-centered design involving preliminary study (expert interviews, student surveys), requirement analysis, iterative design based on a proposed workflow (Task Identification, Design Mapping, User Alignment), incorporating SRL models (Zimmerman) and educational frameworks (Bloom's Taxonomy), and prototype testing with user feedback. | NaN | False | False | NaN | Limitations in handling multimodal data (images, audio, video); lack of integration with web resources; potential for LLM hallucinations; challenges in automated personalized fine-tuning; need for better error reporting/trustworthiness mechanisms. | Aligning LLMs with domain problems, visualization systems, and user interactions; creating high-quality domain-specific fine-tuning data; ensuring model outputs match visualization requirements; evaluating domain-specific model performance without standard benchmarks. | LLM 'hallucinations' (producing plausible but nonsensical responses). |
| tC9KkckGsnoJ.pdf | Google_Scholar | Law and Economics of Language Model Development: Empirical Examination of Corporate Strategies and Vaporware Claims | This paper analyzes corporate strategies for Large Language Model (LLM) development in Japan using a law and economics perspective, specifically investigating if announcements constitute 'vaporware' with antitrust implications. Using a stock event study, it finds no significant abnormal returns following LLM development announcements, suggesting the market reacted calmly and did not perceive these as vaporware. | True | Market | True | 2.0 | NaN | Stock price event study analysis | Calculated Cumulative Abnormal Returns (CARs) for four Japanese companies (CyberAgent, Fujitsu, Hitachi, NTT) following their LLM development announcements using daily stock price data from two months prior to the announcement, benchmarked against the Nikkei Stock Average. Robustness checks involved varying the event window and using control group companies (GREE, NEC, Toshiba, KDDI). | No statistically significant positive CARs were found for the companies following their LLM development announcements; market reaction was calm, suggesting the announcements were not perceived as vaporware. | NaN | NaN | NaN | NaN | Antitrust Law, Competition Policy, Securities Law | Japan, United States (references to law and cases) | Daily closing stock price data for Nikkei Stock Average, CyberAgent, Fujitsu, Hitachi, NTT, GREE, NEC, Toshiba, and KDDI. | Econometric analysis (Stock Price Event Study) | NaN | False | False | NaN | NaN | Standard limitations of event studies (e.g., benchmark choice, confounding events, potential information leaks prior to announcement). Difficulty in directly observing and evaluating vaporware characteristics. | Vaporware announcements leading to anti-competitive effects (antitrust violations under Sherman Act Section 2) or securities fraud (misleading investors). |
| Mo87jYlV5roJ.pdf | Google_Scholar | CLOSING ACCESS TO JUSTICE GAPS GLOBALLY | This chapter argues that closing the global justice gap requires a shift to a people-centered, data-driven, innovative, and collaborative approach, moving beyond traditional justice institutions. It highlights the critical role higher education institutions can play by leveraging their expertise in data science, fostering multidisciplinary innovation, and initiating cross-sectoral collaborations. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Lack of public understanding that problems are legal or solvable by the justice system; cost barriers; insufficient information, advice, or representation; inadequate justice data collection, analysis, and use; data silos; institutional resistance to innovation and collaboration; regulatory barriers limiting non-lawyer assistance; professional protectionism. | Adopt a people-centered justice approach; build robust justice data ecosystems leveraging legal needs surveys and administrative data analysis; foster innovation in justice services (e.g., prevention, systemic reforms, ADR, paralegals, technology, holistic defense); promote cross-sectoral collaboration; strategically engage higher education institutions to provide expertise in data science, multidisciplinary research, and innovation incubation. | General civil justice problems (housing, family, debt, consumer, public services, land tenure, probate), legal identity, criminal justice (sentencing, prosecution, defense, holistic approaches), domestic violence, eviction. | Poor and marginalized populations, people lacking legal identity, informal workers, those without land/housing proof, self-represented litigants, indigenous communities, women and girls, tenants facing eviction, people involved in the criminal justice system, victims of domestic violence. | Civil Law, Criminal Law, Legal Identity, Administrative Law, Human Rights Law | International | NaN | NaN | NaN | False | False | NaN | Inadequate justice data ecosystems; institutional resistance to innovation and collaboration; regulatory hurdles; lack of funding for innovation; insufficient data science and interdisciplinary skills in legal education and practice; disconnect between local/national A2J efforts and global agendas (e.g., SDGs, human rights). | NaN | NaN |
| W4582WozKXQJ.pdf | Google_Scholar | Strengthening Legal Mechanisms for Consumer Protection in the Digital Marketplace | This paper discusses the challenges to consumer rights in the digital marketplace and proposes strategies to strengthen legal mechanisms for consumer protection. These strategies include enhancing transparency, strengthening data protection laws, facilitating access to justice, promoting consumer education, and fostering international cooperation. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Difficulties for consumers in obtaining redress in cases of dispute or fraud; Lack of transparency and accountability in digital transactions; Insufficient protection of consumer data. | Developing legal frameworks for accessible and effective redress; Enhancing transparency and accountability in the digital marketplace; Strengthening data protection laws; Promoting consumer education; Fostering international cooperation on consumer protection standards. | Consumer protection in the digital marketplace; Access to redress mechanisms for consumers in online disputes or fraud. | Consumers in the digital marketplace. | Consumer law; Data protection law; E-commerce law. | International | NaN | NaN | NaN | False | False | NaN | Existing legal frameworks are insufficient to protect consumers in the evolving digital marketplace, particularly regarding transparency, data protection, and access to redress mechanisms. | NaN | Fraud, misinformation, and privacy breaches for consumers in the digital marketplace. |
| 9Eriq0jTDrAJ.pdf | Google_Scholar | AI, plurality and democracy \nReflecƟons on the impact of Large Language Models like ChatGPT on the rule of law and \ndemocracy | This paper critically analyzes the impact of large language models, like ChatGPT, on the legal system, the rule of law, and democracy, highlighting risks such as the reduction of plurality, monopolization of legal language and values, and potential manipulation. It evaluates the EU AI Act's capacity to mitigate these systemic threats, expressing doubt about its sufficiency. | True | NaN | True | 3.0 | Negative | NaN | NaN | NaN | Potential creation of a two-tier justice system where disadvantaged groups rely on potentially lower-quality AI legal services, while the privileged access human lawyers. | Raising awareness of AI's impact; deeper democratic discussion on AI's role in law; potential for stricter regulations including transparency, licensing, restricting AI use in the legal sector (e.g., limiting AI responses to legal queries); treating the legal system as critical infrastructure requiring protection. Critically evaluates the EU AI Act as a potential, but possibly insufficient, regulatory solution. | Quality of legal services, potential for unequal access/two-tier system, impact on rule of law, impact on democracy | Broad masses (as opposed to privileged groups) | General Law / Multiple Fields (discusses impact on legal practice broadly, including examples related to litigation, contracts, administrative law, judicial processes, constitutional law) | EU (primarily discusses the EU AI Act), with broader international context. | General description: Large amounts of diverse text data, using supervised learning and learning from human feedback. Notes the EU AI Act requirement for providers of general-purpose AI models to make publicly available a summary of the content used for training. | NaN | NaN | True | False | The paper discusses generally available LLMs like ChatGPT, some of which have public access tiers provided by commercial entities. | Societal: Insufficiency of current regulations (specifically the EU AI Act) to address systemic risks; lack of awareness and democratic control over AI's role in the legal system; erosion of pluralism. Technical/Regulatory: Difficulty in effectively monitoring, controlling, and mitigating harmful systemic effects, manipulation, and value imposition by LLMs within legal contexts. | NaN | Reduction of linguistic and cognitive plurality (monoculture); corporate monopolization and control over legal processes; dependency on AI; undermining the rule of law and separation of powers; creation of a two-tier justice system; deskilling of legal professionals; potential overload of courts with AI-generated content; manipulation of legal discourse and outcomes; facilitation of authoritarian control; ecological costs; disinformation; threats to democratic values and human rights. |
| MiabMAWShnEJ.pdf | Google_Scholar | The Impact of ChatGPT Technological Innovation on Civil Law Practices: Challenges, Opportunities, and Implications of Article 1338 of the Civil Code | This paper reviews the impact of ChatGPT on civil law practices, discussing challenges such as technological skill gaps, data security, and legal validity, alongside opportunities like enhanced efficiency and accessibility. It emphasizes the need for collaboration between legal and technology experts to develop ethical guidelines, particularly considering Indonesia's Civil Code. | False | Idealistic | True | 2.0 | Neutral | ChatGPT | The paper is a literature review based on 23 articles from Google Scholar (2019-2024) using a qualitative approach and descriptive analysis. It does not involve direct empirical testing of ChatGPT by the authors. | NaN | Lack of technological skills among legal practitioners, data security and privacy concerns, questions regarding the legal validity and credibility of AI-generated content, potential for errors and biases in AI outputs, and the risk of exacerbating inequality in access to legal services. | Collaboration between legal and technology experts to develop guidelines and standards for ethical AI use, fostering technological literacy among legal practitioners, implementing robust data protection measures, establishing transparency and accountability mechanisms for AI systems, developing strategies to ensure equitable access to technology-assisted legal services, and continuous monitoring and adaptation of legal practices. | Accessibility of legal services, equitable access to justice, validity of AI-generated legal documents, ethical use of AI in law. | Individuals who may lack sufficient access to or skills in using technology. | Civil Law, Contract Law (specifically referring to Article 1338 of the Civil Code). | Indonesia | The paper states that ChatGPT is trained on massive text data from various sources, allowing it to learn and mimic human language patterns. It does not specify the exact datasets but notes the importance of representative data to avoid bias. | The paper mentions that ChatGPT is built on the Transformer architecture and utilizes machine learning techniques. | NaN | True | False | ChatGPT is generally accessible as a web-based service provided by OpenAI, with both free and paid tiers. | The need for clear guidelines, standards, and updated legal regulations for the ethical and effective use of AI in law; insufficient technological literacy among legal practitioners; and the necessity for continuous adaptation of legal practices to keep pace with technological advancements. | Ensuring legal validity and credibility of AI-generated texts, addressing intellectual property rights, managing data privacy and security, ensuring transparency and accountability of AI systems, overcoming potential biases in AI, and fostering adequate technological skills among legal professionals. | Use of AI-generated text that may not be legally valid or admissible as evidence, infringement of intellectual property rights, data breaches of sensitive client information, perpetuation of biases through AI outputs leading to unfair outcomes, and increased disparities in access to justice due to unequal tech access or skills. |
| 1.9781611977653.ch111.pdf | Google_Scholar | Making a Computational Attorney | This paper outlines a vision for a "computational attorney," an AI agent capable of assisting human lawyers with complex legal tasks using Large Legal Language Models (L3Ms). It discusses the current state of L3Ms in law, highlights their potential to democratize legal services, and identifies key future research challenges for their development. | True | Idealistic | True | 3.0 | Positive | The 'computational attorney' concept as a future AI system based on advanced Large Legal Language Models (L3Ms). | NaN | NaN | Prohibitively expensive legal fees leading to inadequate or no legal assistance for a large percentage of low-income individuals with civil legal problems. | Development of advanced AI like 'computational attorneys' using L3Ms, which could democratize legal services. | Affordability of legal services, access to legal aid for civil matters. | Low-income Americans with civil legal problems. | General law, covering tasks like drafting legal briefs, analyzing legal judgments, opinions, and contracts. | US (primary focus, especially for access to justice aspects and legal system examples); Japan (referenced for specific AI model evaluations). | Large-scale legal text data, including publicly available corpora (e.g., Pile-of-Law) and potentially proprietary datasets. The paper discusses pre-training L3Ms on general and legal-specific corpora. | NaN | NaN | False | False | NaN | Technical gaps in current L3M capabilities (making them updatable, stable, provable, communicable, and predictable) hinder the creation of a 'computational attorney' capable of democratizing legal services. The societal gap is the current widespread lack of affordable legal assistance. | Developing L3Ms that are: updatable with new legal precedents and laws efficiently; stable against generating false information ('hallucinations') and robust to out-of-distribution data; provable in their reasoning by linking to legal sources; communicable for effective human-lawyer interaction and learning; and predictable in anticipating legal outcomes and strategic implications. | AI models providing outdated or incorrect legal analysis; models 'hallucinating' or inventing non-existent legal facts/precedents; lack of verifiability or provability for AI-generated legal opinions; potential liabilities associated with AI outputs if not properly managed. |
| 4oxIKlQvBHAJ.pdf | Google_Scholar | MAINDZ at SemEval-2024 Task 5: CLUEDO - Choosing Legal oUtcome by Explaining Decision through Oversight | This paper presents CLUEDO, an ensemble LLM system for legal reasoning, where fine-tuned collaborator models generate answers and explanations for a zero-shot 'detective' model. Evaluated on U.S. civil procedure cases, the system demonstrated strong performance in determining answer correctness and improved prediction stability compared to individual models. | True | Market | True | 1.0 | Positive | CLUEDO: An ensemble system using multiple fine-tuned collaborator LLMs (Llama 2 13B, Mistral v0.1 7B, Zephyr beta 7B) with multiple-choice prompting for label prediction and explanation generation, overseen by a zero-shot 'detective' LLM (GPT-4) for final answer selection. | The system was evaluated on the SemEval-2024 Task 5 dataset (U.S. civil procedure cases). Performance was measured using F1 macro score and accuracy on development and test sets. Stability was assessed by running experiments five times and measuring standard deviation of the metrics. | CLUEDO achieved an F1 macro score of 0.77 and accuracy of 0.82 on the test set. It also demonstrated higher prediction stability, with a standard deviation for F1 macro of ±0.017 on the test set, compared to the zero-shot detective model alone (±0.022). | Limited scope and diversity of existing legal benchmarks; inherent risks of LLMs generating incorrect, misleading, or offensive content; challenges in accurately assessing LLM legal reasoning capabilities. | The CLUEDO framework, an ensemble of LLMs with collaborator models and a 'detective' overseer, utilizing multiple-choice prompting and explanation generation to improve legal reasoning accuracy and the stability of predictions. | Legal reasoning; Correctness evaluation of legal arguments in response to specific questions based on case facts. | NaN | U.S. Civil Procedure | United States | SemEval-2024 Task 5 dataset: derived from a U.S. civil procedure textbook for law students. Each instance includes a case introduction, a specific question, a potential solution argument, an annotated label (correct/incorrect), and a detailed analysis. Collaborator models were fine-tuned on this data. | Multiple-choice prompting (MCP); Supervised Fine-Tuning (SFT) using 8-bit quantization and Parameter-Efficient Fine-Tuning (PEFT) for collaborator models; Ensemble learning (multiple collaborators combined with a zero-shot 'detective' model). | NaN | True | True | Code available on GitHub: https://github.com/irenebenedetto/PoliToHFI-SemEval2024-Task5 | The limited nature of existing legal benchmarks to capture diverse legal tasks; the ongoing general need to enhance legal reasoning capabilities and reliability in LLMs. | Variability in performance across different LLMs; achieving reproducibility and stability in predictions from large models like GPT-4 (even with deterministic settings); effectively fine-tuning smaller open-source LLMs for specialized legal tasks. | LLMs generating offensive, misleading, or factually incorrect content, which could disproportionately affect marginalized or under-resourced populations; instability and unreliability of LLM predictions in critical legal contexts. |
| VIrPJN95W2sJ.pdf | Google_Scholar | Human Law, Human Lawyers and the Emerging AI Faith | This paper critiques the growing 'AI faith' in the legal sector, questioning its transformative promises regarding efficiency and democratization. It argues for caution, emphasizing the unique human dimensions of law and legal practice that current AI cannot replicate and might undermine. | True | Market | False | 3.0 | Neutral | NaN | NaN | NaN | The 'black box' problem hindering transparency and trust; AI's inability to replicate human empathy, ethical reasoning, and contextual understanding; risk of errors and lack of accountability; potential to increase complexity and create knowledge divides rather than simplify access. | Adopt a critical perspective towards AI in the legal sector; engage in careful reflection by individual operators, firms, and regulators on AI's impacts rather than blindly accepting the hype ('AI faith'). | Democratization of legal services, Cost reduction, Efficiency gains | NaN | General / Cross-domain | International | NaN | NaN | NaN | False | False | NaN | Societal: Potential undermining of public trust and legitimacy of law; widening knowledge divide. Technical: AI's inability to fully replicate human legal reasoning, ethics, and empathy; lack of transparency and explainability ('black box'); potential for errors ('hallucinations'). | Reconciling AI capabilities with human law's foundations (authority, function, reactivity); ensuring AI aligns with legal ethics; addressing opacity/explainability; managing AI-induced complexity; dealing with potential human lawyer substitution; establishing accountability. | Generation of incorrect information (e.g., fake citations); erosion of public trust due to opacity, errors, or lack of human values; undermining legal authority and legitimacy; creation of inaccessible 'artificial law'; loss of human skills (reasoning, ethics) in legal practice; potential for unchallenged abuses of power. |
| ilFk-RDHRnYJ.pdf | Google_Scholar | Beyond Human Discretion: Reconciling AI Systems With Traditional Legal Frameworks | This paper argues that the increasing use of artificial intelligence in the legal field presents significant challenges, including algorithmic bias, opacity, and accountability issues, which traditional legal frameworks rooted in human discretion are ill-equipped to address. It calls for comprehensive regulatory reforms, and fundamental changes in legal education and ethics to ensure AI integration upholds justice and fairness. | True | Idealistic | False | 3.0 | Negative | NaN | NaN | NaN | Algorithmic bias perpetuating societal inequities and leading to unfair outcomes, particularly for minority groups; Lack of transparency (algorithmic opacity) in AI decision-making, hindering accountability and trust; AI systems lacking human-like nuanced judgment, empathy, and moral reasoning essential for equity. | Implementing a comprehensive legal and regulatory framework tailored to AI's characteristics; Reforming legal education and ethics standards to address AI; Embedding legal values like fairness and transparency into AI design ('legal protection by design'); Ensuring human oversight and accountability mechanisms. | Algorithmic bias in legal and justice systems (e.g., criminal sentencing, predictive policing, tenant screening); Ensuring fairness, equity, and non-discrimination in AI-driven legal processes; Accountability and transparency of AI in legal decision-making; Impact of AI on vulnerable and minority communities. | Racial minorities (specifically Black and Hispanic individuals mentioned in examples), economically disadvantaged individuals (e.g., those using housing vouchers), and individuals interacting with the criminal justice system. | General legal practice, Criminal Justice, Housing Law, Civil Litigation, Constitutional Law, Legal Ethics, Administrative Law. | United States (primary focus), United Kingdom, European Union. | The paper generally refers to AI systems being trained on 'historical data,' 'past data,' 'large datasets,' or 'historical records and data points,' which can encode and perpetuate existing societal biases. | NaN | NaN | True | False | The paper discusses several existing AI tools; some, like ChatGPT (which has free tiers), are publicly accessible, while others (e.g., Lex Machina) are commercial products or institutionally deployed (e.g., COMPAS). | Technical gaps include algorithmic opacity and the difficulty of embedding nuanced human judgment and moral reasoning into AI. Societal gaps include the lack of comprehensive regulatory frameworks, insufficient ethical guidelines, the need for extensive reform in legal education to prepare professionals for AI, and the unpreparedness of the legal community to manage AI risks, all contributing to potential erosion of public trust and perpetuation of discrimination. | NaN | Generation of fictitious legal citations by GAI (e.g., ChatGPT); Algorithmic bias leading to discriminatory outcomes in criminal justice (COMPAS, predictive policing), corrections (PACT), and housing (SafeRent); Violations of privacy due to AI's data needs; Lack of accountability for AI-driven decisions; Erosion of public trust in the legal system; Misleading consumers or users of AI tools; Undermining of legal precedent and established judicial reasoning; Professional negligence by legal professionals using AI without adequate verification; Unjust denial of rights or services (e.g., healthcare coverage, housing). |
| oO6c-Wwoy2sJ.pdf | Google_Scholar | The CLC-UKET Dataset: Benchmarking Case Outcome Prediction for the UK Employment Tribunal | This paper introduces the CLC-UKET dataset, a collection of ~19,000 UK Employment Tribunal cases annotated using an LLM to facilitate research on access to justice. The dataset is used to benchmark various models, including LLMs, on the task of predicting case outcomes based on facts and claims, comparing results against human expert performance. | True | Idealistic | True | 2.0 | Positive | Benchmarking dataset (CLC-UKET) creation using LLM-aided annotation (GPT-4) and evaluation of case outcome prediction models (BERT, T5, GPT-3.5, GPT-4) on this dataset. | Evaluation on a test split of the curated CLC-UKET pred dataset (1,371 cases) using manually annotated gold-standard outcome labels. Metrics: Accuracy, Precision, Recall, F-score. Comparison with human expert predictions. | The fine-tuned T5 model achieved the best performance among the models tested (F-score: 0.564), significantly outperforming random guessing but still falling short of human expert performance (F-score: 0.672). | Uncertainty regarding the likely outcome of court procedures hinders access to justice and amicable dispute resolution. | Creating large-scale, annotated legal datasets (like CLC-UKET) and developing/benchmarking AI models for case outcome prediction to provide insights into likely results. | Case outcome prediction, Facilitating dispute resolution, Access to legal information | Claimants in the UK Employment Tribunal system. | Employment law | United Kingdom (UK Employment Tribunal) | The CLC-UKET dataset, derived from the publicly available Cambridge Law Corpus (CLC) containing UKET judgments (2011-2023). Facts, claims, and initial outcome labels were extracted from unstructured judgment text using GPT-4 (LLM-aided annotation). Gold-standard outcome labels for the test set were manually annotated by a legal expert. | Dataset curation (filtering public legal documents), LLM-aided annotation (GPT-4 with prompt engineering), manual validation (for test set outcome labels), standard ML benchmarking (train/val/test split, baseline models), Human evaluation (expert prediction task with guidelines). | The CLC-UKET dataset is planned to be made available via the Cambridge Law Corpus (CLC) website, with access restricted to qualified researchers adhering to ethical and legal standards. | False | False | NaN | Reliance on extracted facts/claims from judgments rather than original filings (potential bias), limitations of LLM-based annotation quality, need for more detailed factual information, dataset representativeness uncertainty, handling legal evolution over time, performance gap between AI models and human experts. | Cost and time of manual legal annotation, potential inaccuracies in LLM-based annotation, complexity of legal cases (e.g., preliminary issues, procedural decisions), potentially insufficient information in extracted facts/claims for accurate prediction, ensuring ethical use of legal data. | Information bias in facts/claims extracted from judgments, potential inaccuracies from LLM annotation, models learning spurious correlations (e.g., sentiment), misinterpretation or over-reliance on prediction results in legal practice, data privacy concerns (mitigated by CLC protocols). |
| 4Nbz7njEtzoJ.pdf | Google_Scholar | Fighting the Knowledge Representation Bottleneck with Large Language Models | This paper investigates using Large Language Models (GPT-4o) to tackle the knowledge representation bottleneck in developing legal expert systems. It proposes and evaluates a human-in-the-loop, prompt-based methodology for formalizing legal articles and case law into Prolog rules, using the Facilex system as a case study. | True | Idealistic | True | 1.0 | Positive | Using GPT-4o with a 'Chain of Prompts' methodology (few-shot learning) and human-in-the-loop validation to: 1) formalize legal articles into Prolog rules by refining LLM-generated code based on existing system facts and structure; 2) extract key legal principles from case law and formalize them into new Prolog rules, integrating them with existing legal provisions in an expert system (Facilex). | Two-tiered evaluation: 1) Formal validation (automated check for syntactic correctness and executability of Prolog rules within the Facilex system). 2) Expert validation (by knowledge engineers) assessing Accuracy (completeness of legal elements), Relevance (adherence to legal reasoning and text), Human Alignment (support for model-engineer dialogue), and Fluency (consistency and readability of Prolog code). | For article generation, LLM-generated Prolog rules passed formal validation. Expert validation showed high accuracy (23 out of 27 expert-formalized conditions captured), with minor issues like redundant conditions or structural variations. For case generation, rules also passed formal validation, and expert validation confirmed strong accuracy in identifying and representing key legal elements from case law, though significant prompt engineering was needed for relevance. | The primary obstacle addressed is the Knowledge Representation Bottleneck (KRB) in legal expert systems, which makes the acquisition, formalization, and constant updating of expert knowledge time-consuming, error-prone, and limits the systems' flexibility, scalability, and longevity. | The paper proposes leveraging Large Language Models (GPT-4o) within a human-in-the-loop 'Chain of Prompts' framework. This approach semi-automates the generation and revision of Prolog rules from legal articles and case law, aiming to make expert systems more scalable, adaptable, and easier to update. | Enhancing the development, maintainability, and scalability of rule-based legal expert systems, particularly for complex legal domains such as EU mutual recognition instruments in criminal matters (e.g., European Arrest Warrant procedures), by using LLMs to formalize legal knowledge. | Individuals involved in EU cross-border criminal proceedings (indirectly, through improved tools and systems for the legal professionals representing or adjudicating their cases). | EU procedural law, mutual recognition instruments in criminal matters, European Arrest Warrant. | European Union (EU) | The approach uses a pre-trained LLM (GPT-4o). For its few-shot prompting methodology, it utilizes: 1) existing Prolog rules and facts from the Facilex expert system, 2) natural language text of legal articles (e.g., EU Framework Decision on European Arrest Warrant), and 3) raw text of EU case law (e.g., CJEU judgments). | Human-in-the-loop approach, 'Chain of Prompts' methodology for LLM interaction, few-shot learning, iterative refinement of LLM outputs by knowledge engineers, and a two-tiered evaluation process (formal and expert-driven validation). | NaN | False | True | The Jupyter Notebook containing prompts, inputs, and outputs of the experiments is available on GitHub at https://github.com/LegalMachineLab/JURIX24-fighting_krb. | The need for continuous human supervision to ensure legal correctness, consistency, and alignment with expert system's domain. The challenge of achieving full automation in knowledge formalization due to LLM limitations and the nuanced nature of legal interpretation. Making the resulting expert systems truly user-friendly for diverse end-users. | Ensuring consistency and avoiding redundancy in LLM-generated Prolog rules. Aligning the LLM's rule generation style with specific expert preferences (e.g., structure of sub-rules, use of negation). Significant prompt engineering effort required to achieve desired relevance and scope in outputs. Managing the LLM's tendency to introduce legally accurate but contextually irrelevant information. Potential for structural errors in generated code when processing large or complex inputs. The necessity of an iterative human-in-the-loop process for refinement and validation. | Generation of syntactically correct but legally inaccurate, incomplete, or subtly flawed Prolog rules if expert oversight is insufficient. Introduction of inconsistencies, redundancies, or out-of-scope information into the expert system's knowledge base. Unpredictability in LLM outputs regarding naming conventions or rule structures, potentially affecting code maintainability and expert alignment. |
| _3PICPHoZiIJ.pdf | Google_Scholar | LLMediator: GPT-4 Assisted Online Dispute Resolution | This paper introduces LLMediator, an experimental platform using GPT-4 to enhance Online Dispute Resolution (ODR) for low-intensity legal disputes. It discusses and qualitatively evaluates features like reformulating user messages to be less emotional and drafting mediator responses to facilitate amicable settlements. | True | Idealistic | True | 1.0 | Positive | LLMediator platform using GPT-4 API calls with specific prompts for: F1 (reformulating inflammatory messages), F2 (drafting message suggestions for human mediators), F3 (experimental autonomous AI intervention). | Initial qualitative evaluations through illustrative examples and discussion of potential outputs generated by GPT-4 in different scenarios. | Qualitative examples demonstrate GPT-4's promising ability to perform the intended tasks (reformulation, drafting interventions) appropriately, relevantly, and adaptively based on context and instructions. | Difficulty understanding rights, costs (monetary, temporal, psychological) of traditional courts, challenges in reaching resolution for laypeople in low-intensity disputes. | Enhancing ODR platforms with AI (specifically LLMs like GPT-4) to reformulate inflammatory messages, assist human mediators, and potentially provide automated mediation support for low-value cases. | Online Dispute Resolution (ODR), Negotiation, Mediation | Laypeople facing low-intensity disputes (debt, consumer, employment). | Consumer law, Debt collection, Employment law, Landlord-tenant law, Torts (minor) | International | Pre-trained GPT-4 model accessed via OpenAI API; no specific training data mentioned by the authors. | Prototyping, Prompt Engineering, Qualitative evaluation via examples. | Experimental prototype, proof of concept. | False | False | NaN | Need for empirical evaluation of efficacy and bias, refinement of prompt engineering, development of improved triggers for AI intervention, exploring further LLM applications (e.g., summarization). | Potential for LLM hallucination and inaccuracy, risk of AI taking sides, user frustration/self-expression concerns (for F1), anchoring bias/over-reliance (for F2), high risks with autonomous intervention (F3). | LLM hallucination and inaccuracy, biased outputs leading to unfair outcomes or loss of trust, mediators developing anchoring bias or over-reliance, user frustration with automated message changes. |
| LERC_Book_of_abstracts_website.pdf | Google_Scholar | DIGITAL SAVIOUR OR JUST ANOTHER PROBLEM TO DEAL WITH: A DISCOURSE ANALYSIS OF THE CONFLICTING NARRATIVES REGARDING THE IMPLICATIONS OF GENERATIVE AI FOR THE TEACHING OF LAW | This paper analyzes the diverse and conflicting narratives surrounding Generative AI's impact on legal education using discourse analysis. It aims to identify dominant discourses and predict their evolution, helping legal educators form strategic responses. | True | NaN | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal Education | Australia | NaN | Discourse Analysis | NaN | False | False | NaN | NaN | NaN | NaN |
| 3w_RoYmbStkJ.pdf | Google_Scholar | Human Centered AI for Indian Legal Text Analytics | This position paper proposes a human-centered, compound AI system using Large Language Models (LLMs) for legal text analytics in India to improve access to justice. It introduces a new Indian legal dataset and outlines 'InLegalLLaMA', an LLM to be trained on Indian legal texts, to address current AI limitations like low trustworthiness and lack of specialized resources. | True | Idealistic | True | 1.0 | Positive | Human-centered compound AI system integrating LLMs (specifically a proposed 'InLegalLLaMA') with human input for Indian legal text analytics, supported by a novel domain-specific dataset. | LLaMA-2-70B-Chat for case similarity (few-shot prompting on 2,626 document excerpt pairs, ROC-AUC); LLaMA-2-34B-Instruct for relation/tail prediction on a legal KG subset (Hits@k). | For case similarity, LLaMA-2-70B-Chat achieved a ROC-AUC score of 0.566. For relation/tail prediction, LLaMA-2-34B-Instruct achieved Hits@1: 0.520, Hits@5: 0.556, Hits@10: 0.617. | Overwhelmed legal system with case backlogs and time-consuming processes; low trustworthiness of current AI; lack of AI focus on common citizens; scarcity of specialized legal datasets; citizens' unfamiliarity with legalese; poorly written petitions leading to inefficiencies and dismissals; complexity of legal documents for laypersons. | Development of Human-Centered AI (HCAI) as a compound system eliciting human input; creation of specialized Indian legal datasets; using LLMs to help citizens understand legal documents, conduct research, and draft better petitions; abstractive summarization for layperson comprehension; LLM-based conversational QA for identifying missing information in petitions; pre-training and fine-tuning LLMs (e.g., InLegalLLaMA) on Indian legal texts and infusing them with domain knowledge. | Speeding up justice delivery; improving legal understanding for common citizens and self-represented litigants; aiding legal research; assistance with petition drafting; reducing system burden from poorly prepared documents; democratizing legal knowledge. | Common citizens, self-represented litigants, individuals not well-versed in legal language, and the general public in India seeking access to justice. | General Indian Law / Indian Case Law | India | A new dataset composed of: 1) A Legal Knowledge Graph derived from 2,286 Indian legal documents (court cases, judgements, laws from public repositories, IndianKanoon, Casemine), processed using Stanza, SystemT, and manually curated dictionaries. 2) A Question-Answering dataset from 45 Delhi High Court judgments, with QA pairs generated by gpt-3.5-turbo using few-shot prompting. 3) A Text2SQL dataset extended from the QA dataset. The proposed InLegalLLaMA will use general Indian legal domain corpora. | Human-Centered AI (HCAI) principles; compound AI systems approach; dataset creation via web scraping, automatic/manual annotation, LLM-based generation (gpt-3.5-turbo, few-shot prompting); proposed LLM development includes pre-training, instruction-tuning, concept-enhanced pre-training, PEFT, knowledge infusion, and Retrieval Augmented Generation (RAG). | NaN | False | False | NaN | Low trustworthiness of current generative AI; scarcity of specialized legal datasets for training LLMs; existing LLMs not adequately tailored to specific legal domains like the Indian legal system; poor performance of European-trained legal models in the Indian context due to document structural differences; need to mitigate hallucinations in LLMs for domain tasks with societal impacts; lack of focus on common citizens in current AI applications; general unavailability of resources for AI in domains directly touching human lives. | Scalability of supervised methods due to extensive annotation needs; ensuring factual accuracy and avoiding misrepresentation in AI-generated legal text (e.g., summaries); adapting general LLMs to the nuances of the Indian legal domain; developing trustworthy LLMs for high-stakes legal applications; creating comprehensive, high-quality specialized legal datasets; mitigating LLM hallucinations in critical legal tasks. | Low trustworthiness of generative AI; misleading readers with AI-generated content (e.g., abstractive summaries generating information absent in original documents, or altering meaning through subtle word changes); inaccuracies in generated text (e.g., altered proper nouns, locations, numbers); societal consequences from LLM hallucinations in domain tasks; potential for poorly written petitions (if AI is faulty) adding costs and risking dismissal. |
| 4gUeSLlHb8MJ.pdf | Google_Scholar | Large Vision-Language Model Security: A Survey | This paper surveys security issues in Large Vision-Language Models (LVLMs), covering malicious attacks like jailbreaking and backdoors, alongside defenses. It also discusses application risks such as hallucinations and privacy leaks, reviewing mitigation methods and highlighting areas for future research. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Jailbreak attacks (inducing harmful content generation), Backdoor attacks (implanting hidden triggers for malicious behavior), Controllable misinformation generation (producing targeted, deceptive content), Hallucinations (generating factually incorrect or prompt-irrelevant responses, risky in areas like medical/legal aid), Privacy leakage (extraction of Personal Identifiable Information (PII) from training data, Membership Inference Attacks). |
| iL5Ltm0_mAcJ.pdf | Google_Scholar | The unreasonable effectiveness of large language models in zero-shot semantic annotation of legal texts | This paper evaluates the zero-shot semantic annotation performance of GPT-4 and GPT-3.5-turbo(-16k) on diverse legal texts (adjudicatory opinions, contracts, statutes), comparing them to earlier GPT models and supervised baselines. It finds GPT-4 performs well, especially on contract clauses, and analyses the trade-offs between performance, cost, and batch processing for practical applications. | True | Idealistic | True | 2.0 | Positive | Zero-shot semantic annotation (classification) of short legal text snippets using large language models (GPT-4, GPT-3.5-turbo(-16k), text-davinci-003) instructed via prompts containing category names and definitions. | Evaluation on three manually annotated datasets: BVA (rhetorical roles in veterans' appeal decisions), CUAD (clause types in commercial contracts), PHASYS (purpose of public health statutes/regulations). Performance measured by Precision, Recall, F1-score (micro-average overall). Compared against Random Forest and fine-tuned RoBERTa baselines. Tested both single-instance and batch prediction. | GPT-4 achieved F1 scores of 0.82 (BVA), 0.90 (CUAD), and 0.54 (PHASYS), outperforming GPT-3.5 models and matching Random Forest on BVA/CUAD, but below fine-tuned RoBERTa. Cost-effective GPT-3.5-turbo matched the more expensive text-davinci-003. Batch processing significantly lowered costs with only a minor performance decrease compared to single-instance prediction, but large batches degraded performance. | High cost of current AI workflows requiring manual annotation or expensive enterprise solutions. Potential cost of using LLM APIs, especially for high-volume or non-batched tasks. Performance limitations compared to fine-tuned models, particularly for nuanced or ambiguous categories. Difficulty handling domain-specific nuances with simple definitions. Constant evolution of proprietary models. | Leveraging zero-shot capabilities of LLMs with simple prompts (type lists and definitions) to perform semantic annotation without task-specific training data. Employing batch prediction within prompts to significantly reduce API costs, making sophisticated annotation workflows more accessible and economically feasible for experimentation and deployment. | Semantic annotation, Rhetorical role classification, Contract clause classification, Statutory provision classification, Contract review, Case law analysis, Empirical legal studies. | Legal professionals, legal researchers, potentially smaller law firms or organizations unable to afford traditional high-cost AI legal tech solutions. | Veterans Law, Contract Law, Public Health Law, Administrative Law | United States (based on BVA, PHASYS datasets; CUAD likely US-centric) | The evaluated LLMs (GPT-4, GPT-3.5) used their large, general, proprietary pre-training data. No task-specific fine-tuning data was used for the evaluated zero-shot approach. Baseline models were trained on the specific BVA, CUAD, and PHASYS datasets (manually annotated legal texts). | Prompt engineering: Designing specific text prompts instructing the LLMs to classify text snippets based on provided type definitions. Experimental comparison across models, datasets, and batching strategies. | The approach relies on accessing LLMs via the OpenAI API. Prompts and model settings are shared via a GitHub repository for replication. | True | False | Prompts and settings are available on GitHub; execution requires access to the commercial OpenAI API. | Performance gap between zero-shot and supervised/fine-tuned models, especially for complex/nuanced tasks. Handling imbalanced datasets and ambiguous definitions in zero-shot settings. Need for methods applicable to longer texts and more complex reasoning. Understanding and optimising effects of batching (e.g., ordering). Addressing cost barriers for wider adoption. Research challenges due to proprietary, evolving models. | Designing effective prompts for diverse legal annotation tasks. Balancing performance vs. cost (especially regarding batch size). Handling model context length limitations. Achieving high accuracy for nuanced legal distinctions. Dealing with dataset imbalance. Reproducibility issues with closed models. | Inaccuracy of annotations, potentially leading to incorrect analysis or decisions if used without verification. Cost can still be a barrier depending on scale and approach (batched vs. single). Dependence on proprietary, changing models. |
| VzUbPp4kve8J.pdf | Google_Scholar | Automated User Story Generation with Test Case Specification Using Large Language Model | This paper introduces "GeneUS", a tool using GPT-4.0 and a novel "Refine and Thought" (RaT) prompting technique to automatically generate user stories, deliverables, and test case specifications from software requirements documents. The tool aims to improve software engineering productivity by automating parts of the Requirements Engineering phase. | True | Market | True | 1.0 | NaN | GeneUS tool using GPT-4.0 with a Refine and Thought (RaT) prompting technique for automated user story and test case generation. | Tested with 7 Requirements Engineering documents (6 from a textbook, 1 from industry). Output quality evaluated via a RUST (Readability, Understandability, Specifiability, Technical-aspects) survey questionnaire distributed to 50 software developers. | The RaT prompting technique improved results and reduced LLM hallucinations compared to basic prompting. The RUST survey yielded a median score of 4 out of 5 ('Good'), indicating general acceptance by developers, although Specifiability and Technical Aspects showed more room for improvement. | NaN | NaN | NaN | NaN | NaN | NaN | The approach uses a pre-trained LLM (GPT-4.0). The system was tested using 7 textual Requirements Engineering documents (some sourced from a Software Engineering textbook, one from industry). | Prompt engineering (development of the Refine and Thought - RaT technique), tool development (GeneUS), qualitative evaluation via expert survey (RUST questionnaire). | An online REST API was made available for researchers to test the application. Future plans include making the tool publicly accessible. | False | False | Online REST API mentioned as available for researchers. | NaN | LLM Hallucinations (generating incomplete, incorrect, or inconsistent information), especially with long and complex input documents (like Requirement Analysis Documents). Processing requirement documents containing non-text elements (images, diagrams) which become meaningless tokens. | Risk of generating factually incorrect or incomplete user stories due to LLM hallucinations. |
| Ey5B4UxN4Q8J.pdf | Google_Scholar | Bridging the Gap: Mapping Layperson Narratives to Legal Issues with Language Models | This paper proposes a system using language models to automatically map layperson factual descriptions of their problems to relevant legal issues, aiming to improve access to justice. Integrated into the JusticeBot tool, the system was evaluated on real-world user data and demonstrated high accuracy in suggesting appropriate legal pathways to users. | True | Idealistic | True | 1.0 | Positive | A system using a multilingual universal sentence encoder to create vector embeddings of layperson factual descriptions and pre-defined example situations. It employs an approximate nearest neighbor search (Annoy library) to match the user's description to the most similar example situations, thereby suggesting relevant legal issues and pathways, integrated within the JusticeBot. | The system was evaluated using real-world, anonymized user-submitted factual descriptions from the JusticeBot. Performance was measured by Precision@1 (P@1) and Precision@3 (P@3) against annotated ground truth pathways. Two main experimental setups were used: 1) training on seed examples and testing on user submissions, and 2) training on seed examples plus user submissions (excluding the test instance, in a leave-one-out manner) and testing on user submissions. A cold-start scenario comparing the language model approach to an SVM baseline was also conducted. | When trained with both seed examples and user-submitted data, the system achieved 93.5% P@3 (relevant legal issue suggested within the top 3 options) and 74.5% P@1 (relevant legal issue suggested as the top option) on user-submitted descriptions. | The 'gap' between layperson language (focusing on facts) and legal language (requiring identification of legal issues), causing laypeople to struggle in identifying their rights or relevant legal remedies. This hinders their ability to use self-help tools effectively. | An 'augmented intelligence' system that analyzes layperson's factual descriptions to suggest potentially relevant legal issues and pathways. The system provides factual explanations for its suggestions, allowing users to verify the system's understanding before exploring a suggested legal pathway within tools like JusticeBot. | Legal issue identification from layperson narratives, improving usability of legal self-help tools, bridging the language gap in legal information. | Laypeople (individuals without legal training) facing legal disputes, particularly those who might self-represent or use online legal information tools. | Landlord-tenant disputes (primary focus of JusticeBot and evaluation), with potential applicability to other areas like consumer rights, debt, and employment law. | Quebec, Canada (based on the JusticeBot project and data source). | A combination of: 1) 'Seed example descriptions' (58 examples) created by the research team, formulating potential layperson descriptions for various legal issues. 2) 'User-submitted example descriptions' (3,250 annotated examples) from real JusticeBot users, representing genuine layperson narratives. Data is unstructured text. | User-centered design (addressing observed user difficulties), augmented intelligence approach, use of pre-trained multilingual sentence encoders, approximate nearest neighbor search, iterative improvement based on user data (seed examples and real user feedback). | The proposed feature is integrated into the JusticeBot (https://justicebot.ca), an online legal decision support tool. Users can type a description of their situation, and the system suggests relevant pathways. | True | False | The feature is described as part of the JusticeBot tool, which is accessible online at https://justicebot.ca. | Need to expand the dataset to cover more legal issues and domains. Further empirical evaluation with end-users is needed to assess real-world utility. Exploration of alternative embedding models (including newer LLMs like GPT-4) and classification approaches for potential performance improvements. | Handling the variability and ambiguity of layperson language compared to structured legal text. Overcoming the 'cold-start problem' when introducing new legal topics or tools. Ensuring suggestions are not misleading if a user's specific issue is not covered. | The system might provide irrelevant suggestions if a user's situation is not covered by the pre-defined pathways. Misinterpretation of the system's suggestions as legal advice rather than legal information, potentially leading to concerns about the unauthorized practice of law. |
| DynamicUniversallyAdaptiveLanguageModelANewApproachtoNaturalLanguageProcessinginMachineLearning.pdf | Google_Scholar | Dynamic Universally Adaptive Language Model – A New Approach to Natural Language Processing in Machine Learning | This paper introduces the Dynamic Universally Adaptive Language Model (DUALM), a novel NLP approach designed for adaptability, efficiency, and reduced resource consumption compared to traditional LLMs. DUALM features a modular, dynamically adjusting architecture enabling real-time learning and context-aware interactions across diverse tasks and languages. | True | NaN | True | 1.0 | NaN | Dynamic Universally Adaptive Language Model (DUALM), featuring modular design (ModuleRegistry), dynamic layer adjustment (DynamicLayerAdjustment), adaptive attention, hierarchical processing, and real-time learning. | The paper mentions that benchmarking is planned or ongoing, but does not present any specific testing procedures or results. | NaN | NaN | NaN | NaN | NaN | General legal domain | International | The abstract mentions enabling proficiency in multiple Romance languages with a model trained primarily in English, but details of the specific training corpus are not provided. | Conceptual design, modular architecture, dynamic adaptation principles based on task complexity and feedback. | Open-source release via GitHub repository and encouragement of community contributions. | True | True | Code available on GitHub repository: https://github.com/NeeravSood/DUALM | Potential computational efficiency issues, difficulties in training dynamic systems, limitations in handling certain language tasks or languages, scalability, generalization across diverse languages and domains, ensuring fairness, transparency, accountability, and mitigating biases. | NaN | Lack of fairness, transparency, accountability; perpetuation of biases; non-compliance with data protection and privacy laws, particularly in sensitive domains. |
| lnLVibnxH7AJ.pdf | Google_Scholar | Access to Justice and the Legal Profession: Three Questions | This article argues that the Canadian legal profession faces a critical access to justice crisis and has moral, regulatory, and economic imperatives to act now. It advocates for a people-centered approach, listening to public needs and exploring diverse solutions like increased funding, regulatory reform, technology, and community services to bridge the justice gap. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | High costs of legal help; significant court delays (civil, family, criminal); limited legal aid; alienation of communities (e.g., Indigenous, racialized) from the justice system; lack of public/government awareness and funding; inefficient court procedures; underlying societal issues (poverty, racism, homelessness). | Adopt a people-centered justice approach; increase public funding for justice; explore broad legal care models (expanded legal aid, legal insurance); regulatory reform and experimentation; expand service providers (community services, technology/AI); court reform (efficiency, case management, settlement counsel). | Access to justice; everyday legal problems; civil justice; family justice; criminal justice delays; legal aid; court delays; legal profession's role; public perception of justice; people-centered justice; funding for justice. | General public experiencing everyday legal problems, low-income households, middle-class individuals, Indigenous communities, racialized communities, people facing homelessness, poverty, systemic discrimination, or other societal barriers. | Civil Law, Family Law, Criminal Law (re delays), Administrative Law (re delays), General Access to Justice | Canada (primarily Ontario, with national context), USA (comparative statistics), Global (context) | NaN | NaN | NaN | False | False | NaN | The "justice gap" between public needs/barriers and solutions; insufficient funding; lack of public awareness; need for regulatory innovation; need for more efficient court processes; disconnect between providing legal services and achieving substantive justice outcomes; insufficient focus on public perspectives ("people-centered justice"). | NaN | Loss of the legal profession's self-regulation privilege if inaction continues; erosion of public trust in justice and democracy; continued negative societal costs (financial, health, social) from unresolved legal problems. |
| av1Arye_3y4J.pdf | Google_Scholar | Generative AI systems in legal practice offering quality legal services while upholding legal ethics | This paper examines the impact of generative AI systems, like ChatGPT, on Luxembourg lawyers' ethical duties of competence and confidentiality, drawing on doctrinal analysis, a survey, and interviews. It finds lawyers use AI for efficiency but face challenges with accuracy and data privacy, suggesting a need for client-centric approaches, informed consent, training, and potentially updated regulations. | True | Market | True | 2.0 | Neutral | Use of Generative AI systems (primarily LLMs like ChatGPT) in legal practice; mentions fine-tuning and Retrieval Augmented Generation (RAG). | Empirical research: Anonymous online survey distributed to members of the Bar Association of Luxembourg (28 responses analyzed); four semi-structured interviews with representatives of two law firms and two legal tech companies active in Luxembourg, France, and Belgium. | Survey: 54% use ChatGPT, mainly for drafting (emails, some legal docs), research, translation; 64% find it improves efficiency, but concerns exist over hallucinations/verification need; only 25% receive training; opinions split on duty to use AI/inform clients; 79% believe AI use compromises confidentiality; 93% avoid inserting client data. Interviews: Firms/companies develop fine-tuned GPT-based systems for similar tasks, emphasizing efficiency but acknowledging hallucinations/context limitations; stress lawyer verification; divided on informing clients; generally avoid processing client data but desire access for improvement; highlight security measures (encryption, EU servers, vendor reviews). | Risk of AI 'hallucinations' leading to inaccurate outputs; Threat to client confidentiality due to potential data disclosure to third-party AI providers; Lack of transparency in how AI systems process data; Need for constant verification of AI outputs, counteracting efficiency gains; Potential deskilling or over-reliance on AI. | Adopt client-centric approach prioritizing quality service and client interests; Obtain specific, informed, freely given client consent before processing confidential data via AI; Conduct thorough due diligence on AI vendors; Implement robust security and compliance measures (contracts, encryption, access controls, audits); Provide mandatory lawyer training on AI use, risks, limitations, and prompt engineering; Bar Associations should consider issuing clear guidelines or rules. | Lawyer competence, Client confidentiality, Professional ethics, Quality of legal services. | NaN | General legal practice | Luxembourg (primary focus), EU (secondary, via GDPR and AI Act references) | Discusses public LLMs (trained on broad internet data) and fine-tuned systems. Fine-tuned systems use controlled legal data (legislation, case law), possibly public/subscribed legal content. Some systems leverage internal law firm databases (non-confidential or anonymized data preferred). Explicit avoidance of using client confidential data for training is emphasized. | For the systems discussed (not the paper itself): Fine-tuning pre-trained models, Retrieval Augmented Generation (RAG), Evaluation using legal expert scenarios, User feedback loops, Implementation of security by design (encryption, data silos, access controls, auditing), Due diligence processes for vendors. | Public LLMs (e.g., ChatGPT) accessed via web; Fine-tuned systems deployed internally within law firms or offered as commercial products by legal tech companies. | False | False | NaN | Lack of clear professional conduct rules/guidelines specific to generative AI use; Tension between improving AI performance (requiring data) and maintaining client confidentiality; Insufficient training for lawyers on AI tools and associated risks; Need for improved AI explainability and context-awareness in legal tasks. | Ensuring accuracy and reliability of AI outputs (mitigating hallucinations); Protecting client confidentiality when using third-party AI tools; Integrating AI ethically and effectively into legal workflows; Addressing user (lawyer) reservations and ensuring proper usage; Need for context-specific AI performance in complex legal tasks; Balancing innovation with ethical obligations. | Disclosure of confidential client information to AI providers or other third parties; Generation of inaccurate or fabricated information (hallucinations); Breaches of data protection regulations (e.g., GDPR); Unauthorized access to sensitive data; Deskilling of legal professionals; Erosion of client trust due to opaque AI use; Cybersecurity risks (e.g., prompt injection, data poisoning). |
| e3vCn9f3qxcJ.pdf | Google_Scholar | GPT, Ontology, and CAABAC: Attribute-based personalized access control model anchored by compliance, context, and attribute | This paper proposes GPT-Onto-CAABAC, a novel framework integrating Generative Pre-trained Transformers (GPT), ontologies, and Context-Aware Attribute-Based Access Control (CAABAC) for enhancing access control to Electronic Health Records (EHRs). The system aims to provide dynamic, personalized, and compliant EHR access by interpreting legal/policy documents and adapting to contextual changes in healthcare settings. | True | Market | True | 1.0 | NaN | GPT-Onto-CAABAC (GPT-powered Ontology-Driven Decision of Context-Aware Attribute-Based Access Control). It integrates GPT (specifically ChatGPT-4) for natural language understanding and policy interpretation, ontologies (dynamically constructed from legal texts) for structured knowledge, and CAABAC for managing access permissions based on attributes and real-time context in EHR systems. | Empirical evaluation using over 120 use-case scenarios across 12 categories, cross-referenced with Australian legislation (Privacy Act 1988, My Health Records Act 2012). Scenarios included anonymized real-world EHR data and constructed artificial situations. Evaluation metrics: 'context comprehension' and 'recommendation effectiveness' (scored 0-1 using a rubric), compliance, adaptability, and conflict resolution efficiency. Fault injection testing was also performed. | The GPT-Onto-CAABAC framework showed high capability in handling extrinsic (environmental context, access subject) and intrinsic factors (ontology, GPT) for access control. In scenario testing across 12 healthcare categories, it achieved average scores for 'context comprehension' and 'recommendation effectiveness' generally above 0.8 (on a 0-1 scale), demonstrating robust interpretation of legal requirements and adaptive decision-making. | NaN | NaN | NaN | NaN | Health Law, Data Privacy Law, Healthcare Compliance (specifically citing Australian Privacy Act 1988, My Health Records Act 2012, Health Records Act 2001 (Victoria), and mentioning GDPR, HIPAA). | Australia (specifically Victoria for some legislation, and federal acts for testing), with conceptual applicability to international standards like GDPR and HIPAA. | The core GPT model (ChatGPT-4) is pre-trained by OpenAI. For this framework's development and testing: 1) Australian legislation (Privacy Act 1988, My Health Records Act 2012, Health Records Act 2001 (Victoria)) loaded as PDFs via 'AskYourPDF' plugin for dynamic, implicit ontology construction. 2) A dataset of over 120 use-case scenarios (in 12 categories) combining anonymized real-world EHR data and constructed artificial scenarios for evaluation. | Proof-of-concept development using a constructive research approach. Involved literature review of existing access control models, development of the GPT-Onto-CAABAC algorithm and architecture, and iterative refinement. Evaluation through scenario-based testing with predefined metrics and fault injection. | The framework is at a proof-of-concept stage. The paper discusses future steps for real-world implementation, including pilot testing, optimization, and regulatory approvals, but it is not currently deployed for general use. | False | False | NaN | NaN | Key challenges include ensuring stability and validity of GPT-generated outputs (mitigating hallucinations), managing GPT model performance (response times for real-time decisions), fostering societal trust in opaque AI systems, achieving scalability for large healthcare environments, high resource requirements for deployment and maintenance, ensuring data privacy during integration, and maintaining continuous adaptation to evolving legal and technological landscapes. | Stated risks include data breaches from system vulnerabilities, non-compliant or harmful decisions due to GPT hallucinations or misinterpretations, erosion of societal trust due to AI opacity or errors, and challenges in maintaining interpretability and accountability of AI-driven access control decisions. The paper also notes the complexity and potential for misinterpretation if human oversight is not robust. |
| CXzDSayL0SEJ.pdf | Google_Scholar | 7th Annual Innovation and Technology Law Conference: Generative AI: Infringement or \nInnovation? | This paper introduces the 7th Annual Innovation and Technology Law Conference held by Seattle University School of Law, focusing on Generative AI. It summarizes the conference's history, goals, and the specific legal and societal panels presented, including copyright, publicity rights, tort liability, digital resurrection, and the impact on professions. | True | Market | False | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Copyright law, Rights to publicity, Tort liability | US | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Copyright infringement, violation of rights to publicity, tort liability from bad generative AI advice, displacement of professions (e.g., voice artists, journalists), potential aggravation or perpetuation of existing societal inequities. |
| zbCs_xPzI6IJ.pdf | Google_Scholar | NaN | This paper outlines AI and Law research at the University of Liverpool, focusing on developing explainable AI models for legal reasoning and argumentation. It describes successful collaborations translating this research into practical tools used by UK law firms to improve efficiency. | True | Market | True | 2.0 | Neutral | Hybrid AI approaches combining symbolic AI (for modeling legal argumentation and ensuring explainability) and machine learning to analyze legal cases and assist decision-making. | Pilot projects and daily use within collaborating law firms (Weightmans, Fletchers Solicitors). Evaluation appears qualitative, based on successful application, transformed working practices, efficiency gains, cost reduction, and improved turnaround times. | The AI tool developed with Fletchers Solicitors is used daily, reportedly transforming workflows, increasing efficiency, reducing costs, and speeding up client service for medical negligence claims. Successful pilots conducted with Weightmans for argument identification and reasoning in case settlements. | NaN | NaN | NaN | NaN | Medical Negligence, General Case Assessment and Settlement | UK | Unspecified, likely domain-specific legal data (case law, legislation, potentially proprietary firm data from collaborations) combined with expert knowledge, used for both symbolic modeling and potentially machine learning. | Symbolic AI (rule-based systems, argumentation frameworks like ADF), Machine Learning, Hybrid AI, Collaboration with legal experts and law firms, Pilot testing. | Deployed as decision-support tools ('digital legal assistant') within collaborating law firms following pilot projects. Supported by the broader LawtechUK ecosystem. | False | False | NaN | Current generative AI lacks capability for complex legal tasks; need for better explainability ('black box' issue); need for hybrid approaches; future gaps include AI alignment with human values, privacy preservation, robust evaluation methods, regulatory alignment, and workforce upskilling. | Modeling complex legal reasoning/argumentation; explainability of AI decisions; scalability of symbolic AI; integrating symbolic and ML methods effectively (hybrid AI); translating research prototypes into robust, deployed tools for legal practice. | Lack of explainability in AI tools ('black box' problem). Implicit future risks if unaddressed: misalignment with human values, privacy violations, poor outcomes due to inadequate evaluation, non-compliance with regulations regarding data use. |
| Zz495LiJ5oAJ.pdf | Google_Scholar | Exploring the Nexus of Large Language Models and Legal Systems: A Short Survey | This paper surveys the applications of Large Language Models (LLMs) in the legal domain, covering tasks like text comprehension, case retrieval, analysis, and legal education. It also discusses key challenges such as bias, hallucination, and ethical concerns, along with available datasets and fine-tuned models for various legal systems. | True | Idealistic | True | 3.0 | Positive | Survey covering various techniques including fine-tuning LLMs (e.g., LawGPT, Lawyer LLaMA, LexiLaw, ChatLaw, DISC-LawLLM, LexGPT, LLaMandement), Prompt Engineering (LPE, CoT), Retrieval-Augmented Generation, and neuro-symbolic methods. | NaN | NaN | Lack of true understanding (stochastic parrots), spurious correlations, biases (racial, gender, religious, LGBTQ+), hallucination, privacy encroachments, interpretability issues, challenges in distinguishing authentic AI-generated evidence, potential negative impacts on fairness and fundamental values. | Fine-tuning on diverse/representative data, adversarial prompts, retrieval augmentation, integration of external knowledge bases, development of methods to mitigate bias and ensure transparency, aligning models with human values ('Law Informs Code'), evolving legal frameworks, interdisciplinary collaboration. | Legal text processing and understanding, legal case retrieval and analysis, legal education and examinations, legal practice assistance, dispute resolution, legal advice provision, enhancing accessibility to legal knowledge. | NaN | General/Multiple (including criminal law, constitutional law, contract law, tort law, tax law, privacy law, parliamentary procedure) | Multiple (including China, Taiwan, Palestine, France, US, UK, EU, CoE, Canada, India) | Surveys works using various legal datasets (e.g., CAIL2018, LeCaRD, Pile of Law, LeXFiles, CaseHOLD, Cambridge Law Corpus, MultiLegalPile) from multiple jurisdictions and languages, including court cases, legislation, contracts, Q&A. | NaN | NaN | True | True | The survey provides GitHub repository links for several specific fine-tuned LLMs it reviews (e.g., LawGPT, Lawyer LLaMA, LexiLaw, ChatLaw, DISC-LawLLM). The survey paper itself is available on arXiv. | Need for further mitigation of biases, enhanced interpretability, development of specialized data resources (especially multilingual), establishment of ethical guidelines, improved robustness and reliability for legal tasks, better handling of complex legal reasoning and causality, improved performance on benchmarks (e.g., LexGLUE), advanced multimodal capabilities. | Need for domain-specific data/training, preventing hallucination and ensuring factual accuracy (necessitating retrieval augmentation/human oversight), adapting general models to specialized legal tasks efficiently, addressing inherent biases in models and data, evaluating performance accurately in complex legal scenarios, ensuring transparency and interpretability. | Privacy violations, perpetuation of societal biases (racial, gender, religious, etc.), generation of inaccurate or misleading information (hallucination), lack of genuine understanding leading to errors, potential for misuse in generating false evidence or overwhelming legal systems, undermining judicial integrity and fairness, threats to fundamental human values (autonomy, equality). |
| 9I7B1zrXEvgJ.pdf | Google_Scholar | KRAG Framework for Enhancing LLMs in the Legal Domain | This paper introduces Knowledge Representation Augmented Generation (KRAG), a novel framework using structured knowledge graphs to improve Large Language Models' (LLMs) performance in the legal domain. Its implementation, Soft PROLEG, enhances legal reasoning, argumentation, and explainability, demonstrating improved accuracy and stability on Japanese Bar Exam questions. | True | Market | True | 1.0 | NaN | Knowledge Representation Augmented Generation (KRAG) framework, with Soft PROLEG as an implementation model. It uses inference graphs derived from structured legal knowledge (conditions, subconditions, exceptions) to guide LLM reasoning and generate explanations. | Evaluated on the English version of the Japanese Bar Exam (Heisei 29 (2017) to Reiwa 03 (2021)). SoftPROLEG (using GPT-3.5 and GPT-4 backbones) was compared against vanilla GPT-3.5 and GPT-4 on accuracy and stability (consistency of answers across two trials). | GPT-4-SP (SoftPROLEG with GPT-4 backbone) achieved the highest stability (91.9%) and demonstrated improved accuracy on the Japanese Bar Exam compared to baseline GPT-4 (e.g., 0.8765 for GPT-4-SP vs 0.8018 for GPT-4 in the Reiwa 02 exam year). | NaN | NaN | NaN | NaN | Civil Law (exemplified through JUF theory and scenarios); evaluation conducted on the Japanese Bar Exam which covers multiple legal fields. | Japan | The KRAG system uses a 'Knowledge Set' (not for LLM fine-tuning). This set, comprising 1,287 samples for the PoC, contains (Query, Related Legal Articles, Graph Structure) triplets. These were semi-automatically constructed: LLMs generated initial data points from Japanese Bar Exam-like scenarios, which were then reviewed, verified, and structured into graphs by human legal experts. | The KRAG framework and Soft PROLEG were designed based on derivational analogy and Presupposed Ultimate Fact Theory (JUF theory), employing graph-based knowledge representation. The Knowledge Set construction involved a semi-automated process: LLM-based data generation followed by human expert review and verification. | A Proof of Concept (PoC) system (Soft PROLEG 1.0) was developed. No specific deployment or diffusion strategies for wider external use are mentioned. | False | False | NaN | NaN | Computational complexity of handling large knowledge graphs and real-time inference with LLMs. Scalability limitations due to the semi-automated, human-expert-reliant process for Knowledge Set construction. Need for refinement in methods for evaluating the quality and relevancy of graph-based explanations. | The paper notes the 'high stakes of incorrect information' in legal applications generally as a motivation for improving LLMs. It does not explicitly list new risks introduced by KRAG/Soft PROLEG but aims to mitigate existing LLM risks like inconsistency and inadequate knowledge representation. |
| SP6gNobGvBUJ.pdf | Google_Scholar | HACKING GENERATIVE AI | This paper analyzes whether prompt injection attacks, which manipulate generative AI like ChatGPT into producing harmful or illegal content, violate existing US computer crime law, specifically the Computer Fraud and Abuse Act (CFAA). The author argues that such attacks constitute accessing a computer in excess of authorization under the CFAA and offers recommendations for applying the law. | True | NaN | True | 3.0 | NaN | Prompt Injection Attacks | Reviews examples and research demonstrating prompt injection attacks on platforms like ChatGPT and Clyde, citing specific instances like generating instructions for bombs, meth, napalm, and malicious code. | Researchers successfully used prompt injection to bypass AI safety restrictions, generating harmful content (bomb instructions, meth recipes, malicious code, hate speech) and extracting sensitive information. | NaN | NaN | NaN | NaN | Computer Crime Law, Cybersecurity Law, Criminal Law | United States | NaN | NaN | NaN | False | False | NaN | NaN | Legal challenges in applying existing computer crime law (CFAA) to prompt injection, including defining authorization (code-based vs. contract-based, intended function, norms), distinguishing information obtained vs. generated, valuing generated content based on nature rather than monetary value, First Amendment concerns regarding speech, protecting legitimate security research, and the AI black box problem making internal workings opaque. | Generation of harmful, offensive, dangerous, or illegal content (e.g., instructions for bombs, meth, napalm; hate speech; phishing emails; malicious code, including polymorphic malware). Disclosure of sensitive or personal information. Lowering the barrier of entry for malicious hacking. Increased sophistication and adaptability of hacking attacks. Potential for indirect prompt injection to poison training data or exfiltrate user data. |
| T4UCpfvU-usJ.pdf | Google_Scholar | laws clearly: large language models and plain language transformation | This paper investigates the capability of OpenAI's GPT-4 large language model to automatically transform complex Hungarian legal texts into plain language to improve access to legal information. The study manually evaluates the model's performance on specific linguistic simplification tasks, assessing both comprehensibility improvements and the preservation of legal meaning. | True | Idealistic | True | 2.0 | Neutral | Using GPT-4 with specifically crafted prompts to perform plain language transformations on legal text excerpts. | Manual analysis of GPT-4 outputs based on four specific linguistic features (avoiding long/interjected clauses, replacing light verb constructions, splitting long sentences, clarifying ambiguous conjunctions like 'illetve'). Evaluation focused on prompt adherence and preservation of normative legal content. | GPT-4 showed mixed performance. While promising for simplifying sentence structures (clause shortening, sentence splitting), it struggled to accurately replace light verb constructions (potentially due to internal translation issues altering meaning) and incorrectly interpreted the conjunction 'illetve', changing the legal meaning from 'or' to 'and'. Normative legal content was altered in almost all tested cases. | The complexity, specialized terminology, and convoluted sentence structures inherent in legal language (legalese) prevent citizens from understanding legal texts and representing themselves effectively. | Leveraging Large Language Models (specifically GPT-4) to automatically simplify complex legal texts into more understandable plain language versions for laypeople. | Access to legal information, Comprehensibility of legal texts, Plain language transformation | Laypeople / citizens without legal expertise. | Land Transaction Law (specifically Act CXXII of 2013 on Transactions in Agricultural and Forestry land) | Hungary | The study utilizes the pre-trained GPT-4 model from OpenAI; details of its training data are proprietary but known to be vast text corpora. | Experimental approach using prompt engineering to guide GPT-4, followed by manual qualitative analysis of the generated text. | NaN | False | False | NaN | Current LLMs like GPT-4 are unsuitable for fully automatic plain language paraphrasing of legal texts due to the high risk of altering normative content. The task still requires significant human legal expertise and oversight. | Ensuring the preservation of normative legal content during simplification; potential misinterpretation of prompts or linguistic nuances by the LLM (e.g., function verbs, conjunctions); issues arising from the model's internal processing/translation for non-English languages. | The potential alteration or violation of the normative legal content during automatic simplification, leading to misinterpretations of the law by citizens relying on the simplified text. |
| oCY_5uUnGtIJ.pdf | Google_Scholar | Artificial intelligence in the analysis and screening of criminal processes: implications for speed and access to justice | The paper examines how AI can accelerate the analysis and screening of criminal cases within the Brazilian judicial system, potentially enhancing access to justice. It discusses existing AI initiatives in Brazil and internationally, while also considering the ethical challenges and risks, such as algorithmic bias and lack of transparency. | False | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | High volume of cases, lack of human and technological resources, slowness and delay (morosidade processual) in the judicial system, difficulty identifying priority cases, compromising the right to a reasonable duration of proceedings and access to justice. | Using AI for automated analysis, classification, and triaging of cases to increase speed and efficiency, improve resource allocation, standardize decisions, and potentially enhance transparency and predictability. | Celerity/speed of judicial processes, access to justice, efficiency of the judicial system, case triaging and analysis. | General population facing delays in the judicial system, particularly economically vulnerable individuals. | Criminal Law, Criminal Procedure, Constitutional Law | Brazil | NaN | NaN | NaN | False | False | NaN | Need for robust regulatory frameworks and ethical guidelines (transparency, explainability, bias mitigation), continuous human supervision, addressing algorithmic opacity ("black box"), ensuring AI respects constitutional principles and human rights, avoiding reinforcement of existing biases (racial, social, gender), preventing desumanization of the justice process. | Lack of technological infrastructure, need for human resource training, ensuring ethical design and use, managing large volumes of data, integrating AI with existing systems, balancing efficiency with fundamental rights protection. | Algorithmic bias (including racial bias, termed 'racismo algorítmico'), lack of transparency/explainability ('caixa-preta'), reinforcement of structural inequalities, potential violations of fundamental rights (due process, privacy), unjust decisions due to lack of human oversight, over-dependence on technology, compromising judicial autonomy. |
| VhD8GBNm_7QJ.pdf | Google_Scholar | Text Mining Legal Documents for Clause Extraction | This paper investigates the feasibility of using pre-trained language models (BERT, RoBERTa, DeBERTa) for legal clause extraction with limited training data. The study finds that acceptable performance (within 10% of results using 3.3x more data) can be achieved with just 120 contracts, suggesting potential for smaller law firms. | True | Market | True | 2.0 | NaN | Fine-tuning pre-trained Transformer-based language models (RoBERTa, DeBERTa, BERT) for clause extraction formulated as a question-answering task, varying the amount of training data. | Evaluation on the Contract Understanding Atticus Dataset (CUAD) using F1-Score (token-level), Precision, Recall, and AUPR (text-level). Tested varying numbers of training contracts (50-400) and epochs. | RoBERTa fine-tuned with 120 training contracts achieved an F1-Score (token comparison, best prediction) within 10% of the score achieved with 400 training contracts (54.8% vs 57.9% for the 'Total' of 8 clause types). RoBERTa generally performed best, especially with smaller datasets. | High resource requirements (large annotated datasets, labelling effort) traditionally needed for training NLP models, making them inaccessible to smaller law firms. | Demonstrating that fine-tuning pre-trained language models like RoBERTa requires significantly less labelled data (e.g., 120 contracts) for reasonable clause extraction performance. Suggests using model predictions to aid further data labelling. | NaN | NaN | Contract Law | US | Contract Understanding Atticus Dataset (CUAD), a publicly available dataset of ~510 English-language commercial contracts labelled for 41 clause types, structured as a Question-Answering dataset (unstructured text). | Empirical evaluation and comparative study, involving fine-tuning existing pre-trained models and varying experimental parameters (training set size, epochs) to measure performance. | NaN | False | False | NaN | Need for improved performance, potentially through exploring smaller models or enhancing pre-training with diverse legal corpora. Challenges remain in handling multi-answer clauses and reconciling text vs. token level evaluation. | Achieving robust performance with limited training data. Optimizing hyperparameters (epochs). Selecting appropriate evaluation metrics and methods, especially for multi-answer clauses. Performance variation across clause types and models. | NaN |
| fyLCMIyr3Q4J.pdf | Google_Scholar | Generative AI, Cybersecurity And Cybercrime For Lawyers: Myths, Risks And Benefits | This paper discusses the historical context, risks (security, privacy, legal), and benefits of Generative AI for legal professionals, focusing on its implications for cybersecurity, cybercrime, and enhancing access to justice. It aims to debunk myths about AI replacing lawyers while highlighting its potential to improve efficiency, fairness, and reduce backlogs within the legal system if implemented responsibly. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Suboptimal access to justice, especially for socially vulnerable groups, racial or ethnic minorities; Sluggish, expensive, and operationally inefficient legal and judicial systems leading to wrongful convictions and miscarriages of justice; Overloaded public defenders; Judicial system backlogs due to mounting cases and overcriminalization, impacting the quality and fairness of due process. | Properly implemented GenAI systems to streamline litigation and reduce judicial bottlenecks; AI tools for lawyers to summarize cases and assemble relevant information from diverse legal documents; AI assistance for lawyers, court clerks, and judges in prioritizing and summarizing case content; AI use by prosecutors to predict conviction chances (with safeguards against bias) for better resource allocation. | Improving efficiency of legal and judicial systems; Reducing wrongful convictions and miscarriages of justice; Aiding overloaded public defenders; Streamlining litigation and case management for lawyers, judges, and prosecutors; Addressing judicial backlogs. | Socially vulnerable groups, racial or ethnic minorities, indigent defendants in criminal cases. | Criminal law, General legal practice, Cybersecurity law, Data protection law. | US, UK, EU, Switzerland | NaN | NaN | NaN | False | False | NaN | Resolving AI hallucinations; Ensuring human oversight in AI-generated legal content; Developing effective guardrails for AI use in law firms; Addressing and mitigating AI bias, especially in criminal justice predictions to uphold principles like presumption of innocence; Technical limitations in managing personal data within AI models (e.g., deletion requests). | Ensuring data security and confidentiality when using third-party AI tools or training proprietary models; Compliance with evolving data protection and AI regulations (e.g., EU AI Act, DSRs under privacy laws); Preventing copyright infringement when using data for AI training; Overcoming the technical difficulty of removing specific data from trained AI models; Dealing with AI-generated misinformation (hallucinations) and ensuring outputs are reviewed by legal professionals; Protecting against cyber-attacks targeting AI systems, such as data poisoning. | Disclosure of confidential client information through AI systems; Legal liability and sanctions for lawyers relying on inaccurate AI-generated content (e.g., fake case law); Copyright infringement issues related to AI training data and outputs; Increased vulnerability to sophisticated cyber-attacks like deep fakes and data poisoning targeting AI; Perpetuation or amplification of biases through AI systems, particularly in criminal justice, leading to unfair outcomes or infringement of rights; Misuse of AI for malicious activities such as creating convincing phishing content or impersonation. |
| 2jqrUByqR-8J.pdf | Google_Scholar | LEGAL ANALYTICS WITH LARGE LANGUAGE MODELS AND STRUCTURED KNOWLEDGE BASES | This paper explores how integrating legal analytics with large language models (LLMs) and structured knowledge bases (SKBs) can enhance the efficiency and effectiveness of legal services. It discusses the roles, capabilities, benefits, and challenges of these technologies, advocating for a more data-driven approach to law. | True | Market | True | 3.0 | Positive | Integration of Large Language Models (LLMs) and Structured Knowledge Bases (SKBs) for legal analytics | NaN | NaN | NaN | NaN | NaN | NaN | General Legal Practice / Multiple Fields | International | LLMs trained on large, diverse corpora (web text, potentially legal texts); Structured Knowledge Bases containing proprietary legal data (caselaw, statutes, regulations) | NaN | NaN | False | False | NaN | NaN | Data quality and scarcity, computational complexity, potential for false positives, model interpretability ('black box' issue), need for expertise and resources, vulnerability to adversarial attacks | Data privacy violations, security risks, algorithmic bias leading to discrimination, lack of fairness and accountability in automated decision-making |
| YimleaMoY5QJ.pdf | Google_Scholar | Towards Human-Centered Standards for Legal Help AI | This paper presents findings from interviews and design sessions with community members on their use of large language model-based AI tools (like Google Bard) for legal problems, specifically an eviction scenario. It highlights user preferences, trust factors, and concerns, advocating for participatory, human-centered approaches to design and policymaking for legal AI to enhance access to justice. | True | Idealistic | True | 2.0 | Positive | Users interacting with Google Bard (a large language model) for a fictional legal problem (eviction notice) as part of a research study. | Qualitative research study with 15 US adults involving: 1) background questions, 2) a scenario exercise using Google Bard for an eviction notice, 3) feedback/brainstorming. Data collected via online interviews with structured and open-ended questions. | Participants generally found Bard helpful (average rating 3.6/6), and trust in the AI tool increased after use (from an average of 2.7/6 to 4.2/6). Key desires included hyperlinks/citations for information, features like "People Also Ask," and simple responses with options for more detail; reactions to prominent warnings were mixed to negative. | General public's lack of awareness that life problems may have a legal dimension; inability to resolve problems via the formal justice system due to lack of capacity or limited help. For AI: risk of providing incorrect legal information, AI tools becoming a second-class service, and inequitable access due to digital divide or literacy barriers. | Adopting human-centered design and participatory policy-making involving community members in AI development. Designing AI tools that are user-friendly, provide clear and actionable information, and incorporate safeguards. Specific suggestions include better referral systems, guardrails against case law hallucinations, jurisdiction-specific information, and prominent links to reliable human help. | Access to civil justice, specifically for issues like evictions. Use of AI for legal issue spotting, triage, guidance on options, finding free assistance, and understanding legal-procedural steps. | General community members in America who have faced civil legal problems and might use AI for legal help. The study sample was a convenience sample with some demographic limitations. | Civil justice, with a specific focus on landlord-tenant law (eviction). Also mentions debt collection, family law (divorce, custody), and employment law. | United States (participants from California, New York, Maryland, New Jersey; scenario included elements like 'Alameda Eviction laws'). | NaN | Qualitative research methods derived from design research, participatory policymaking, and human-computer interaction. Scenario-based research protocol involving structured interviews, observation of AI tool use (Google Bard), and co-design discussions. | NaN | True | False | The study used Google Bard, which is a publicly accessible web service provided by Google. | Need for more extensive and ongoing research with representative samples. Development of a comprehensive risk typology for legal AI. Creation of interface and technical solutions to mitigate specific harms like 'ersatz legal help' (correct-seeming but flawed information). Understanding how to design effective disclosures and warnings that users engage with meaningfully. | For the study: limitations of a convenience sample (underrepresentation of certain demographics). For legal AI in general: ensuring accuracy and reliability of AI-generated legal information (avoiding hallucinations, providing context, jurisdictional accuracy); user over-reliance on AI; designing interfaces that meet diverse user needs and literacy levels; balancing simplicity with the complexity of legal matters and necessary warnings. | AI providing incorrect legal information (hallucinations, e.g., non-existent case law). Users misapplying information due to lack of context or jurisdictional errors. AI tools becoming a 'second-class' service. Inequitable access due to digital divide or varying tech literacy. Data privacy concerns (over-harvesting data). Users over-relying on AI without verification. 'Ersatz legal help' leading to poor outcomes (e.g., bad referrals, cherry-picking details). |
| I6Ful7p1yP0J.pdf | Google_Scholar | ARTIFICIAL INTELLIGENCE, ETHICS AND SPEED PROCESSING IN THE LAW SYSTEM | This paper reviews how generative AI can enhance the Brazilian Judiciary's efficiency by automating tasks and aiding sentence generation, exemplified by tools like VitorIA and Victor. It highlights the importance of embedding ethical considerations in AI to ensure fair, accessible, and non-discriminatory justice. | True | Idealistic | True | 2.0 | Positive | Generative AI applications in the Brazilian Judiciary, specifically VitorIA (appeal profiling/binding) and Victor (appeal admissibility analysis). | Qualitative review of secondary data and documentary evidence concerning the functionalities and operational impact of existing systems (VitorIA, Victor) in the Brazilian Judiciary. | Generative AI significantly expands judicial operational capacity by automating tasks and aiding sentence generation, leading to improved decision-making, effective legal strategies, and enhanced overall judicial efficiency. | Risk of algorithmic bias leading to unfair/discriminatory outcomes; slowness and case overload in traditional judicial systems; high operational costs; complexity of ensuring ethical AI judgments, especially in heterogeneous societies. | Use generative AI to automate tasks for speed and cost reduction; embed ethical standards in AI design for fairness; free human judges for complex ethical considerations; promote extrajudicial resolution for simpler cases identified by AI. | Improving judicial efficiency (speed, cost); enhancing access to justice; ensuring fairness and reducing discrimination; supporting judicial decision-making and sentence generation; ethical application of AI in law. | Society at large; specific challenges noted for heterogeneous societies (e.g., Brazil's indigenous populations) regarding ethical AI. | General judicial processes and litigation. | Brazil | Not explicitly detailed, but implied to be case files, appeals, and jurisprudential databases from the Brazilian Federal Supreme Court for tools like VitorIA and Victor. | N/A (Paper discusses existing tools, does not detail their specific design methodologies beyond Victor being developed by STF's IT staff). | Deployed within the Brazilian Federal Supreme Court (STF) for internal use (e.g., VitorIA for appeal analysis, Victor for admissibility checks). | False | False | NaN | Teaching AI nuanced social values and ethical behaviors for sentencing; developing AI for ethical complexities in heterogeneous societies; current AI's inability to handle all circumstantial/mitigating factors like humans; need for AGI for more complex judicial tasks. | Ensuring ethical considerations, neutrality, and avoiding bias in AI for judicial tasks; defining and embedding ethical standard value criteria; adapting AI for culturally heterogeneous societies; balancing efficiency with human oversight. | Algorithmic bias leading to discriminatory or unfair sentences; doctrinal bias in AI-processed information; unjust punishment due to lack of nuanced human judgment; creation of legal uncertainty. |
| Generative_Artificial_intelligence_Applications_in.pdf | Google_Scholar | Generative Artificial intelligence Applications in Banking and Finance sector | This paper reviews the applications of Generative AI in the banking and finance sector, focusing on improving customer support services and operational efficiency. It discusses benefits like enhanced personalization, fraud detection, risk assessment, and compliance automation, while also outlining challenges and ethical considerations. | True | Market | True | 3.0 | NaN | Generative AI (incl. models like GPT-3, LLaMA), Graph Neural Networks (GNNs), Retrieval-Augmented Generation (RAG), fine-tuning methods | NaN | NaN | NaN | NaN | NaN | NaN | Banking Law, Financial Regulation, Compliance (KYC/AML), Data Privacy, Contract Law | USA, South Korea, International (mentions EU regulations like GDPR, MiFID II) | Discusses use of public PLM training data (web text, books), private conversational data (customer service chats), transaction data, customer profiles, regulatory documents, network traffic data; mentions fine-tuning on real chat discussions and specialized instruction datasets in cited studies; discusses RAG using custom knowledge bases. | NaN | NaN | False | False | NaN | NaN | Data privacy and security; model output accuracy (hallucination); skills/expertise deficit; scaling and integration difficulties; regulatory compliance; cost of fine-tuning; handling disjointed data; potential for bias; need for explainability (XAI). | Biased/discriminatory outputs; exposure/misuse of sensitive data; model inaccuracy/hallucination; non-compliance with regulations (AML, GDPR, KYC); data breaches; cybersecurity threats; reputational damage; legal vulnerabilities. |
| 5ofcHggE0bkJ.pdf | Google_Scholar | Generative AI and LLMs in Industry: A text - mining Analysis and Critical Evaluation of Guidelines and Policy Statements Across Fourteen Industrial Sectors | This paper analyzes 160 corporate guidelines and policy statements on Generative AI (GAI) and Large Language Models (LLMs) across fourteen diverse industrial sectors using text mining techniques. It identifies common themes, sector-specific priorities, and gaps in current AI governance practices, offering recommendations for more adaptive, ethical, and human-centric approaches. | True | Market | False | 2.0 | NaN | Text mining (tokenization, stemming, lemmatization, TF-IDF, KMeans clustering, Sankey diagrams) and qualitative semantic analysis applied to a corpus of industrial AI guidelines and policy statements. | The analysis techniques were applied to a dataset of 160 company documents (guidelines, policy statements, interviews) collected across 14 industries and multiple continents. Evaluation involved identifying term frequencies, co-occurrence patterns, and thematic clustering. | Identified key themes (e.g., privacy, ethics, innovation, data security) and sector-specific priorities. Revealed gaps including underrepresentation of concepts like disclosure, human-centricity, democratization, and skepticism. Highlighted varying approaches across industries (e.g., caution in finance vs. rapid exploration elsewhere) and the prevalence of marketing hype. | NaN | NaN | NaN | NaN | Legal Tech/Services, Intellectual Property Law, Data Privacy Law, AI Regulation, Corporate Governance | International | The study analyzed 'IGGA: A Dataset of Industrial Guidelines and Policy Statements for Generative AIs', comprising 160 documents collected by the authors from company websites, official guidelines, and media interviews across 14 industries globally. The dataset itself is claimed to be publicly available on Harvard Dataverse. | Systematic document collection, qualitative semantic analysis, established text mining techniques (using NLTK and scikit-learn libraries). | The analysis was conducted by the researchers and results published in the paper. The dataset used for the analysis (IGGA) is made available on Harvard Dataverse. | False | False | NaN | Underrepresentation of concepts like disclosure, human-centricity, democratization, alternative methods, and skepticism in AI guidelines. Lack of focus on embedding values in pre-design/in-design phases. Disconnect between claims of 'responsible AI' and actual implementation. Insufficient guidelines tailored to employee/management roles. | NaN | Data security breaches, misinformation, algorithmic bias, accountability gaps, intellectual property infringement, cybersecurity vulnerabilities, job displacement, erosion of human oversight, privacy violations, unrealistic expectations due to marketing hype, erosion of public trust, lack of transparency, potential for unfairness. |
| T6NjEju5IEEJ.pdf | Google_Scholar | LegalTech in the Light of the Upcoming Artificial Intelligence Act | This paper introduces Artificial Legal Intelligence (ALI) and reviews various LegalTech tools aimed at automating legal tasks, enhancing consumer access to legal services. It further analyzes the implications of the upcoming European Artificial Intelligence Act (AIA) on these technologies and discusses the future of legal services. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | High cost of legal services, limited access to legal aid, unaffordability of pursuing small claims, and consumers' lack of legal skills and expertise. | Utilizing LegalTech tools for automated, cost-effective legal information and services; liberalizing legal markets; reforming legal education to include technology skills. | Affordable legal information and services, self-representation tools (do-it-yourself), assistance with small claims, Online Dispute Resolution (ODR). | Low-income individuals and general consumers lacking legal expertise. | General legal services, consumer law, contract law, intellectual property law, AI regulation. | Primarily European Union (due to focus on the AIA), with references to the USA and UK. | NaN | NaN | NaN | True | False | Various commercial and some potentially free/low-cost LegalTech tools and platforms (e.g., legal intermediation platforms, DIY document tools, small claims services) are mentioned as existing and accessible online. | Technical gaps include unreliable fuzzy logic systems and the need for further research in computational legal argumentation. Regulatory gaps include the AIA's coverage of hybrid AI systems and the classification of certain LegalTech tools as high-risk. Societal gaps include the slow implementation of high-risk AI systems, the need for legal market liberalization, and updated legal education. | NaN | Inadequate representation of ambiguous legal rules by logic-based AI, difficult-to-understand AI outputs for laypersons, ethical conflicts with online legal platforms, interpretation difficulties and potential for social engineering with Legal Design, AI-driven manipulation or harm through subliminal techniques or exploitation of vulnerabilities, social scoring leading to detrimental treatment, biases in AI leading to discrimination and unfair decisions, lack of transparency, and general impact on fundamental rights. |
| UMyqIKz4N7YJ.pdf | Google_Scholar | Knowledge Pyramid Construction for Multi-Level Retrieval-Augmented Generation | This paper proposes PolyRAG, a multi-layer knowledge pyramid approach (Ontologies, Knowledge Graphs, raw text) for Retrieval-Augmented Generation, aiming to balance precision and recall in domain-specific question answering. Evaluated on academic and financial benchmarks, PolyRAG demonstrated significant improvements over existing RAG techniques and base LLMs. | True | Market | True | 1.0 | NaN | PolyRAG: A multi-layer knowledge pyramid (Ontologies, Knowledge Graphs, chunk-based raw text) with cross-layer augmentation (knowledge completion) and cross-layer filtering (knowledge condensation), using a waterfall model for retrieval in Retrieval-Augmented Generation (RAG). | Evaluated on two domain-specific benchmarks: AcadChall (academic, self-created from XXX University data) and R-FLUE-FiQA (financial, extended public FiQA dataset). Compared against 19 SOTA methods using metrics including Precision, Recall, F1 score, BLEU, BERT similarity, and HitRate. | PolyRAG combined with GPT-4 achieved an F1 score of 0.8109 on the AcadChall benchmark, representing a 395% F1 gain from GPT-4's baseline performance of 0.1636 on the same benchmark. | NaN | NaN | NaN | NaN | NaN | International | Raw text from a self-constructed academic benchmark (AcadChall: based on XXX University data including staff, courses, departments) and an extended public financial Q&A dataset (R-FLUE-FiQA from FiQA). This text is processed using LLMs and Open Information Extraction to construct the knowledge pyramid's layers: Ontologies, Knowledge Graphs, and raw text chunks. | Iterative knowledge pyramid construction involving: 1) Initial layer creation (Ontology, Knowledge Graph, Raw Text) from domain-specific corpora. 2) Knowledge Completion through cross-layer interaction, identifying and integrating missing concepts from lower layers (KGs) into higher layers (Ontologies) using semantic distribution divergence (KL-divergence) and k-medoids clustering. 3) Knowledge Condensation via top-down refinement, using Ontology anchors to filter and summarize KG triplets with LLM assistance. | The paper states that implementations are available in a GitHub repository, and the two benchmarks (AcadChall, R-FLUE-FiQA) will also be made available to the community. | True | True | Implementations available in a Github repository. Benchmarks also stated to be made available. | NaN | Significant human effort required for initial Ontology schema definition; Noisy output from direct Open Information Extraction for Knowledge Graph construction; Effectively integrating heterogeneous knowledge bases (Ontologies, Knowledge Graphs, raw text). | General LLMs are prone to hallucinations in domain-specific tasks; Supervised Fine-Tuning (SFT) of LLMs can lead to catastrophic forgetting of general knowledge and model hallucination. (PolyRAG is proposed to mitigate these issues). |
| BuN0HcT9T0sJ.pdf | Google_Scholar | Regenerating Justice: ChatGPT and the Legal Minefield of Generative AI | This paper critically examines Generative AI (GenAI), particularly systems like ChatGPT, and its profound implications for the legal field. Adopting an automation bias lens, it argues that unthinking reliance on GenAI risks undermining law's truth-seeking functions and core epistemic foundations through the propagation of inaccurate and sourceless information. | True | Idealistic | True | 2.0 | Negative | Generative AI / Large Language Models (specifically GPT models like ChatGPT) | Theoretical analysis using an automation bias lens, literature review, and examination of real-world incidents and GenAI capabilities (e.g., hallucinations, performance claims). | GenAI fundamentally threatens legal truth-seeking and epistemic integrity due to inherent issues like hallucinations and sourceless information, compounded by human automation bias. | Misinformation and hallucinations from AI leading to incorrect legal guidance; lack of accountability for AI-provided advice; erosion of trust if AI is unreliable/biased; entrenchment of biases from training data; ethical issues (unlicensed practice of law, loss of solicitor-client privilege); over-reliance due to automation bias. | Enhanced critical thinking and awareness of AI limitations (automation bias, inherent nature of hallucinations); robust human oversight (while acknowledging its limits); caution in deploying AI, especially solutions that obviate human participation in legal reasoning and storytelling. | Automated legal advice for consumers/self-represented litigants; consumer protection in automated legal services; reliability and trustworthiness of AI tools for those unable to afford traditional legal services. | Individuals unable to afford legal services; self-represented litigants (especially with low-value claims); consumers seeking rights protection. | Legal Practice, Legal Ethics, Consumer Law, Contract Law, Civil Procedure, Copyright Law. | International / Multiple (primarily US and Canada examples, but broadly applicable concerns) | Vast quantities of text scraped from publicly accessible internet sites (e.g., websites, social media, digital books like BooksCorpus, Wikipedia), largely unlabelled and collected via webcrawling bots. | Machine learning (supervised, unsupervised, reinforcement learning), transformer architecture, pre-training on large unlabelled text datasets, fine-tuning for specific tasks like dialogue. | Publicly accessible web interfaces (often with free tiers), APIs for developers, beta releases for public testing, integration into existing software products. | True | True | Publicly accessible web interfaces (e.g., ChatGPT free tier) and APIs. Some models (e.g., Meta's Llama) are stated to be open-source and downloadable. | Technical: Inherent unreliability (hallucinations, factual inaccuracies, lack of true reasoning). Societal/Legal: Absence of robust legal/ethical frameworks for AI in law, accountability vacuum, risk of exacerbating inequalities, erosion of solicitor-client privilege, public over-trust and misunderstanding of AI capabilities. | Technical: Managing massive datasets, reducing hallucinations (though seen as inherent), addressing bias in training data, ensuring factual accuracy, resolving tokenization issues. Ethical/Societal: Preventing misuse (e.g., disinformation), managing copyrighted material in training, ensuring safety and avoiding harmful or biased outputs. | Undermining law’s truth-seeking functions with sourceless/incorrect information; automation bias leading to over-reliance on flawed AI; erosion of legal meaning and narrative; spread of misinformation; ethical violations by legal professionals; harm to individuals relying on faulty AI advice; entrenchment of societal biases; threats to privacy and solicitor-client privilege. |
| EA5UKSipTqkJ.pdf | Google_Scholar | Are Robot Lawyers the Future of Increasing Access to Justice? | The paper discusses the potential of AI-powered legal tools ("robot lawyers") to improve access to justice by providing affordable information and self-help options. It also highlights risks like exacerbating inequalities and excluding vulnerable populations if not developed responsibly. | True | Idealistic | True | 3.0 | Neutral | AI-powered legal information and self-help tools (e.g., AdviceNow, Farewill, Valla, Amicable) | N/A (No specific evaluation performed by the author; cites tool provider claims) | N/A (No independent results reported; cites provider claims) | Digital exclusion (affecting elderly, non-English speakers, digitally illiterate), varying digital/legal capabilities, lack of access to devices/digital literacy. | Responsible development, diverse training data, auditing/testing AI, Assisted Digital services, leveraging AI to free up human advisors for vulnerable clients. | Access to legal information, self-representation tools, cost reduction in legal services, specific issues like benefits challenges, wills, employment claims, divorce. | General public needing legal assistance, with specific concern for vulnerable groups (elderly, non-English speakers, digitally excluded, marginalized populations). | Family law, Wills & Estates, Welfare Benefits, Employment Law, Civil Procedure. | UK, USA (mentioned briefly) | N/A (Mentions the need for diverse data but doesn't describe data used by specific tools). | NaN | Online websites/platforms, integration into government digital justice services. | True | False | Online services (some free guidance/tools, some paid). | Ensuring equitable access, preventing digital exclusion, mitigating AI bias, ensuring tools accommodate varying needs and capabilities. | Designing effective/accurate tools, addressing digital literacy/access issues, ensuring fairness/avoiding bias, integrating with existing legal systems. | Amplifying existing inequalities (racial, gender, socioeconomic, geographic bias), digital exclusion, inaccurate AI outputs. |
| 1237243.pdf | Google_Scholar | Leveraging the Use of ChatGPT: Exploring Its Real-World Applications Including Their Related Ethical and Regulatory Considerations | This paper explores twenty real-world applications of ChatGPT across diverse sectors, detailing its operational functionalities and benefits. It also systematically discusses the ethical and regulatory considerations, alongside potential risks, for each application, emphasizing the need for human oversight and verification. | True | Market | True | 3.0 | NaN | ChatGPT | NaN | NaN | NaN | NaN | NaN | NaN | Legal document review, contract drafting, legal research | International | Massive, diverse data from the internet, primarily unstructured text, used to train ChatGPT on human language, context, style, and common patterns. | Based on Generative AI, Large Language Models (specifically Transformer architecture), trained via pre-training and potentially Reinforcement Learning from Human Feedback (RLHF). | Web-based service, API access, embeddable in other tools, custom chatbot creation via GPT Builder. | True | False | ChatGPT is accessible via a web interface and API, with both free and paid tiers. Custom GPTs can be built on its platform. | NaN | Ensuring accuracy and reliability of generated content, necessity of human verification and quality control, adherence to ethical and regulatory considerations, issues of content ownership and accountability, debuggability, and maintenance of applications built using it. | Inaccuracy of information, ethical breaches, data privacy violations, security vulnerabilities, copyright and trademark infringement, plagiarism, legal liabilities, financial and reputational damage, potential for user manipulation or exploitation, perpetuation of bias from training data, lack of emotional understanding in sensitive human interactions, and potential harm if advice is followed without expert human oversight, especially in health and legal domains. |
| oZsXj4Vs990J.pdf | Google_Scholar | Prof Felix Steffek November 2024 PRESENTATIONS | This document is a list of academic presentations and convened conferences by Prof Felix Steffek, covering topics primarily in AI in law, corporate insolvency, dispute resolution, and access to justice. Several presentation titles highlight AI for legal tasks like court outcome prediction and the development of legal datasets such as the Cambridge Law Corpus. | True | Idealistic | True | NaN | Positive | NaN | NaN | NaN | NaN | NaN | Application of AI to dispute resolution for access to justice, Online Dispute Resolution (ODR), consumer dispute resolution (ombuds proceedings, conciliation), people-centered justice services. | Consumers, Small and Medium-sized Enterprises (SMEs) | Corporate Insolvency Law, Employment Law, Dispute Resolution, Civil Procedure Law, Consumer Law, Company Law, Private Law, Commercial Law | UK, Singapore, Latvia, Germany, EU, Japan, US, Hong Kong, International | NaN | NaN | NaN | False | False | NaN | NaN | NaN | NaN |
| _7IvdJ0dADYJ.pdf | Google_Scholar | A Review on Alex AI Legal Assistant | This paper reviews Alex AI Legal Assistant, a specialized AI system for legal tasks, comparing it favorably to general-purpose AI models like ChatGPT for accuracy and legal reasoning. It discusses Alex AI's architecture, benefits such as real-time legal updates and structured case law retrieval via 'Gorq', its limitations including computational demands, and future research directions for AI in law. | True | Market | True | 2.0 | Positive | Alex AI Legal Assistant (powered by Gorq) | Feature-based comparative analysis (qualitative) against ChatGPT, DeepSeek, and Gemini across attributes like legal accuracy, IPC interpretation, and API specialization. | Alex AI Legal Assistant is claimed to achieve 'Very High' legal accuracy and 'Expert-Level' IPC interpretation, outperforming ChatGPT, DeepSeek, and Gemini, due to its real-time legal database integration and jurisdiction-specific analysis via Gorq. | Limited accessibility and efficiency in exploring case law for legal education. | Employing AI tools like Alex AI to facilitate efficient case law exploration for students and researchers, making legal education more accessible. | Improving access to legal education and research materials. | Law students and researchers. | General law, with specific mention of compliance verification, case law interpretation, legal document analysis, Indian Penal Code (IPC) interpretation, and contract analysis. | India (specifically for IPC interpretation), with aspirations for global applicability across multiple legal regimes. | Proprietary legal datasets, real-time legal databases, specialized legal databases with legal texts, case laws, regulations, and continuously updated legal precedents. The NLP engine is fine-tuned on legal datasets. | System architecture includes a three-tier structure: Legal Document Retrieval Module (leveraging Gorq), NLP Engine (fine-tuned on legal datasets), and Legal AI Assistant. | NaN | False | False | NaN | Need for multilingual and jurisdiction-specific AI for global legal accessibility; lack of transparency and interpretability (explainability) in AI legal tools; incomplete integration with live court systems for real-time assistance; maturity of AI for reliable legal prediction and risk assessment. | High computational demands requiring cloud-based AI acceleration; ensuring adaptability across multiple jurisdictions and languages; ethical considerations regarding AI bias and the need for human expert validation to ensure fairness and accountability. | Risk of bias in AI-generated legal interpretations; potential for AI to produce unfair or unaccountable outcomes without human review and validation; risk of "hallucinated" or inaccurate content from general-purpose AI models if not specifically designed for legal domain accuracy. |
| assyXFv39zkJ.pdf | Google_Scholar | The Cost of Justice at the Dawn of AI | This paper examines the historical and potential future impact of legal service costs, particularly in light of AI, on the legal system, including access to justice and trial rates. It analyzes whether law suffers from 'cost disease' and urges the legal system to proactively adapt its doctrines and procedures to either continued cost stagnation or an AI-driven productivity revolution. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | High cost of legal services, perceived stagnation in legal sector productivity (cost disease), leading to diminished access to justice, the 'vanishing trial' phenomenon, and difficulties for individuals to afford legal representation. | Proactive adaptation of legal doctrines and procedures to explicitly incorporate and respond to changes in legal costs (e.g., in summary judgment, class actions, contracts of adhesion, arbitration, rules vs. standards). AI itself is presented as a potential solution to lower costs and thereby improve access to justice and potentially revive trials. | Cost of legal services, access to legal representation (especially for those with limited means), efficiency of the civil and criminal justice systems, trial rates, plea bargaining, summary judgment, class actions, rule of law, impact of technology on the legal profession. | The general public, particularly individuals with limited financial means and underrepresented groups who face barriers to accessing legal services due to high costs. | General civil litigation, criminal justice, contract law, administrative law, constitutional law (due process), intellectual property (as an example). | United States (federal and state systems), with brief comparative mentions of the United Kingdom and Ontario (Canada) regarding trial rates. | NaN | NaN | NaN | False | False | NaN | Persistent difficulty in accurately measuring legal productivity and service quality; technical limitations of current AI (e.g., reasoning depth, context limits, hallucination); potential exhaustion of high-quality AI training data; societal and professional inertia in adapting legal systems and practices to technological change and varying cost structures; uncertainty regarding the elasticity of demand for legal services and AI's impact on lawyer employment/wages. | NaN | AI-driven efficiencies in criminal justice leading to harsher, unintended sentencing outcomes; continued cost stagnation exacerbating access to justice problems; lower legal costs due to AI causing undesirable overenforcement or frivolous litigation in some areas; potential for increased wage inequality among lawyers; ethical challenges and errors from AI use (e.g., hallucinations, lack of human judgment). |
| Log48v1Ok7AJ.pdf | Google_Scholar | AI and LLMs in Legal Technology: Revolutionizing Research and Document Analysis | This paper provides an overview of how Artificial Intelligence (AI) and Large Language Models (LLMs) are transforming the legal field, particularly in research and document analysis. It highlights benefits like increased efficiency, accuracy, and predictive capabilities, while briefly noting associated challenges. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General legal practice | International | NaN | NaN | NaN | False | False | NaN | NaN | Potential bias in AI algorithms, need for transparency in AI operations, dependence on data quality and completeness for accuracy, ensuring responsible use that complements human judgment. | Potential bias leading to unfair outcomes, inaccurate predictions due to poor data quality, over-reliance replacing human judgment. |
| Paper23272RetrainingUSWorkforceintheAgeofAgenticGenAIRoleofPromptEngineeringandUp-SkillingInitiatives.pdf | Google_Scholar | Retraining US Workforce in the Age of Agentic Gen AI: Role of Prompt Engineering and Up- Skilling Initiatives | This review synthesizes research on the importance of prompt engineering skills for the US workforce in the age of generative AI. It discusses applications across various sectors, highlights available training initiatives, and identifies challenges and future directions for workforce development. | True | Market | True | 3.0 | NaN | Prompt Engineering | NaN | NaN | NaN | NaN | NaN | NaN | Legal, Finance, Education, Healthcare, Human Resources, Project Management | US | NaN | NaN | Discussion of various online courses, workshops, and educational programs (free and paid) offered by different providers (e.g., Alison, deeplearning.ai, Rutgers, Siemens, Deloitte, Google, Microsoft). | False | False | NaN | Lack of standardized training frameworks, limited accessibility to affordable training, inadequate focus on domain-specific applications, insufficient evaluation of training outcomes, weak integration between academia and industry. | Developing effective curricula, keeping training up-to-date with rapid technological advancements, addressing ethical concerns (bias, fairness), ensuring accessibility and equity of training, measuring training impact. | Bias and fairness issues in LLMs, lack of interpretability, security risks (malicious content generation, bypassing security), job displacement, potential for misinformation and manipulation. |
| 6RYeLVaZ8VgJ.pdf | Google_Scholar | THE DUTY OF EFFICIENCY & GENERATIVE AI PEDAGOGY | This article argues that lawyers have an ethical duty of efficiency which necessitates embracing generative AI, and that law schools must proactively teach students to use these tools responsibly and effectively. It examines lawyers' ethical obligations concerning AI, critiques restrictive regulations, and proposes a pedagogical approach for AI integration in legal education. | True | Market | True | 3.0 | Positive | Generative AI (GenAI) / Large Language Models (LLMs), specifically mentioning ChatGPT, Lexis+ AI Assistant, and Westlaw Practical Law AI as examples. | Illustrative comparison of outputs from ChatGPT 4.0, Lexis+ AI Assistant, and Westlaw’s Practical Law AI in response to a sample legal question about California slip and fall tort claims. | GenAI tools provide a starting point for legal questions but their outputs vary, may lack citations or cite non-precedential sources, and require careful lawyer verification for accuracy and legal soundness. They cannot yet perform genuine legal analysis. | The legal profession's reluctance to embrace AI, concerns about accuracy (hallucinations) and confidentiality, lack of technological competency among lawyers, and slow integration of AI training in law schools. These hinder the adoption of AI which could otherwise lower costs and broaden access to legal services. | Proactive and comprehensive AI education in law schools, lawyers embracing their 'duty of efficiency' by responsibly adopting AI tools, and reliance on existing professional conduct rules rather than overly restrictive, AI-specific regulations to manage AI use, thereby fostering an environment where AI can enhance efficiency and potentially lower costs for clients. | Increasing lawyer efficiency through AI to potentially reduce the cost of legal services and thereby improve broader public access to legal advice. | NaN | General legal practice, Professional Ethics, Civil Procedure (including discovery and Rule 11 sanctions), Torts (specifically premises liability/slip and fall examples). | United States (Federal and various States including California, Florida, Missouri, New York, North Carolina, Colorado); Canada (Ontario). | LLMs are generally trained on 'a vast corpus of texts.' For some tools like ChatGPT, user-inputted information may also be used for training, raising confidentiality concerns. | NaN | NaN | True | True | ChatGPT is mentioned as a free and popular product. Commercial tools like Lexis+ AI and Westlaw AI are also discussed as being used by the authors for examples. | Technical gaps include GenAI's inability to perform true legal analysis, the risk of 'hallucinations,' and potential biases. Societal/professional gaps include the slow adoption and insufficient understanding of GenAI within the legal field and legal education, preventing the full realization of AI's benefits for efficiency and potential A2J improvements. | For lawyers using GenAI: ensuring the accuracy and reliability of AI-generated content (avoiding 'hallucinations'), maintaining client confidentiality, overcoming personal lack of technological competency, and correctly prompting AI for useful outputs. | Misreliance on AI leading to inaccurate legal filings (e.g., 'hallucinated' cases), breach of client confidentiality through inputting sensitive data into non-secure AI, perpetuation of biases embedded in AI training data, lawyers abdicating professional judgment, and potential for fraudulent billing. |
| informit.T2025011900000390025191863.pdf | Google_Scholar | Introduction: Law as Data, Data as Law | This paper introduces a symposium on "Law as Data, Data as Law," summarizing diverse contributions that analyze data-driven approaches and AI in law. It emphasizes the need for critical reflection, methodological rigor, and interdisciplinary engagement to navigate impacts on legal practice, education, and access to justice. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Unreliability and inaccuracy of current AI tools for complex legal tasks, potential for algorithmic bias and mismatches with legal reasoning principles in sensitive areas like asylum claims, and the risk of creating opaque systems that hinder rather than help justice. | Enhancing AI reliability through further research, ensuring human oversight and expert legal involvement in AI system design and deployment, fostering interdisciplinary dialogue and critical interrogation of AI tools, and adopting human-centered design methodologies that incorporate stakeholder input. | Automated decision-making in refugee status determination and its fairness; reliability of LLMs for legal tasks crucial for accessing legal information or support; ethical integration of AI in legal education to prepare future professionals for promoting access to justice. | Asylum seekers/Refugees | Administrative Law (specifically refugee/asylum law), Legal Education, General Legal Practice (research, reasoning) | International | NaN | NaN | NaN | False | False | NaN | Methodological gaps in legal research for evaluating data-driven law, lack of robust benchmarks for AI legal tools, need for deeper understanding of AI's societal impacts (bias, fairness, environmental costs), and insufficient interdisciplinary collaboration and expertise within the legal academy. | Synthesizing diverse and technical contributions from various disciplines, evaluating research outside traditional legal expertise, and fostering a coherent, critical dialogue on the complex and rapidly evolving field of law and AI. | Fossilization of law into opaque and difficult-to-challenge infrastructures, perpetuation of harmful bias and feedback loops through AI systems, negative social and environmental consequences of AI, uncritical adoption of AI tools by students and practitioners, and adverse transformations to legal processes if new technologies are not carefully vetted and implemented. |
| hdPHnUO15h0J.pdf | Google_Scholar | Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review | This paper provides a comprehensive review of prompt engineering for Large Language Models (LLMs) and Vision-Language Models (VLMs). It details foundational and advanced prompting techniques, methods for evaluation, diverse applications, significant security concerns like adversarial attacks and model stealing, and outlines future research directions such as understanding model structures and AI agents. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | NaN | NaN | False | False | NaN | NaN | Crafting effective prompts requires significant manual effort and expertise; ensuring accuracy and avoiding hallucinations in LLM outputs, especially for complex reasoning; addressing security vulnerabilities (e.g., adversarial attacks, data poisoning, prompt injection); overcoming lack of reproducibility and transparency due to limited understanding of model internal structures. | Adversarial attacks causing unintended or harmful outputs; data poisoning compromising model integrity and leading to erroneous outputs; backdoor attacks embedding hidden vulnerabilities activated by specific prompts for malicious behavior; prompt injection manipulating model outputs for misinformation or harmful content; prompt leaking exposing sensitive or proprietary information; prompt hacking leading to unintended model actions, misinformation, or data breaches; model stealing for intellectual property theft, loss of competitive advantage, and unauthorized replication of models. |
| dwaKJ69S2wEJ.pdf | Google_Scholar | Emerging Artificial Intelligence Risk Management Considerations for Law Firms | The paper discusses the emerging risk management considerations for law firms using AI tools, focusing on competence (Rule 1.1), confidentiality (Rule 1.6), and billing (Rule 1.5) under a framework of ABA Model Rules. It emphasizes the need for firms to evaluate AI tools, set clear policies, and train personnel, highlighting both known and unknown risks associated with rapidly evolving AI technology. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | Lack of ready access to lawyers for individuals navigating the legal system pro se. | AI tools may potentially help individuals navigate the legal system pro se by reshaping the delivery of legal services. | Access to legal services for self-represented litigants. | Self-represented litigants / Individuals who do not currently have ready access to lawyers. | Legal ethics, Professional responsibility, Civil litigation, Risk management for law firms. | United States | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Incompetent use of AI leading to errors (e.g., fabricated citations) and professional misconduct; breach of client confidentiality through insecure AI tools; improper billing practices related to AI cost or time saved; failure in supervision of AI use by lawyers and staff; legal sanctions, civil liability, and reputational damage; copyright infringement issues; and the general uncertainty of 'unknown unknowns' as AI substitutes or replaces lawyer professional judgment. |
| SulcX-It8GoJ.pdf | Google_Scholar | Robustness of Structured Data Extraction from In-plane Rotated Documents using Multi-Modal Large Language Models (LLM) | This paper investigates the impact of in-plane document rotation (skew) on the structured data extraction accuracy of three multi-modal LLMs: Anthropic Claude V3 Sonnet, GPT-4-Turbo, and Llava:v1.6. The study finds skew significantly degrades performance, identifies safe rotation angles for each model, notes varying hallucination tendencies under skew, and suggests solutions like de-skewing or building skew-robust models. | True | Market | True | 2.0 | NaN | Evaluation of multi-modal LLMs (Anthropic Claude V3 Sonnet, GPT-4-Turbo, Llava:v1.6) for structured data extraction from skewed documents using LMDX-derivative JSON schema prompting. | Synthetically generated sample documents containing first and last names were rotated in 5-degree increments (0-355 degrees). LLMs extracted key-value pairs, and accuracy was measured by the average Levenshtein distance between extracted values and ground truth across different skew angles. | GPT-4-Turbo demonstrated the widest Safe In-plane Rotation Angles (SIPRA) ([0°, 35°] and [330°, 360°]), suggesting highest robustness to skew among the tested models, but also exhibited the most hallucinations outside its SIPRA. | NaN | NaN | NaN | NaN | General document processing (potential application in legal services) | International | Synthetically generated sample documents with manually annotated ground truth key-value pairs (first name, last name). Details of synthetic data generation not provided. | NaN | NaN | True | False | The evaluated models (GPT-4-Turbo, Claude V3 Sonnet, Llava:v1.6) are available, some commercially via API, one open-source. | Need for comprehensive testing on diverse real-world document quality (older, scanned, stained). Need for multi-modal architectures inherently robust to skew or models pre-trained with skew augmentation. | Document skew degrades data extraction accuracy. Identifying critical skew angles for reliable performance. Models hallucinate outside safe rotation angles. Applying de-skewing techniques adds computational overhead and complexity. Evaluating performance on noisy, real-world documents. | Model hallucinations (generating incorrect or fabricated information) when processing skewed documents, especially noted for GPT-4-Turbo outside its Safe In-plane Rotation Angles (SIPRA). Propagation of erroneous data downstream. |
| oKOMNS2NmFsJ.pdf | Google_Scholar | BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation | This paper proposes BiLD, a novel loss function for knowledge distillation in large language models (LLMs) that focuses on top-k logits differences to filter noise and capture ranking information. Experimental results on multiple NLP benchmarks show BiLD outperforms standard distillation methods. | True | NaN | True | 1.0 | NaN | Bi-directional Logits Difference (BiLD) loss: A knowledge distillation method that selects top-k logits from teacher and student, calculates pairwise differences within these logits, and minimizes the KL divergence between the distributions of these differences. | Evaluated on 13 public NLP datasets (SuperGLUE subset, Arc-C, Arc-E, HellaSwag, PIQA, WinoGrande) using BLOOM and Qwen1.5 models. Compared against Supervised Fine-Tuning (SFT), vanilla KL loss, top-k KL loss, RKL, DKD, NKD, NormKD based on standard task metrics (Accuracy, F1/EM) and a proposed 'overlap@k' metric. | BiLD loss achieved the highest average accuracy across all datasets and teacher/student model pairs, outperforming all baselines. It also demonstrated superior performance on the overlap@8 metric, indicating better imitation of the teacher's key logit patterns. | NaN | NaN | NaN | NaN | NaN | NaN | Publicly available NLP benchmark datasets (SuperGLUE subset: BoolQ, CB, COPA, MultiRC, ReCoRD, RTE, WiC, WSC; Others: Arc-C, Arc-E, HellaSwag, PIQA, WinoGrande) used for task-specific distillation. | Theoretical analysis of LLM logit characteristics, formulation of a novel loss function (BiLD), comparative empirical evaluation on benchmark tasks. | Code made available on GitHub. | True | True | Code is available at https://github.com/fpcsong/BiLD. | NaN | Computational complexity associated with calculating pairwise differences (O(k^2)), especially for larger k. Requirement for shared vocabularies between teacher and student models. Inability to use teacher models with restricted access (e.g., output text only). | Potential loss of knowledge contained in the clipped long-tail logit distribution (mentioned as a limitation). |
| OpPoPkNx0W4J.pdf | Google_Scholar | Intelligent Legal Assistant: An Interactive Clarification System for Legal Question Answering | This paper introduces 'Intelligent Legal Assistant', an interactive legal question-answering system using large language models (LLMs). The system addresses incomplete user queries by asking for location, detecting missing information, generating clarifying questions with options, and then providing a detailed legal analysis. | True | Idealistic | True | 1.0 | Positive | An interactive legal Q&A system ('Intelligent Legal Assistant') using LLMs (Llama-3.1-8B, GPT-4o) for information deficiency detection, Reinforcement Learning (DDPG) with GNNs for predicting missing information elements (nodes in a fact-rule graph), and LLMs/retrieval models for generating clarifying questions/options and final responses. | Blind human evaluation with 100 users comparing the proposed system against GPT-4o, AI Lawyer, and Callidus AI. Users rated systems on accuracy, satisfaction (1-5 scale), and usage preference. | The proposed system scored 4.8/5 for accuracy, 4.8/5 for satisfaction, and was preferred by 90% of users, significantly outperforming GPT-4o, AI Lawyer, and Callidus AI. | The general public often lacks professional legal knowledge, leading to incomplete questions that omit critical information, hindering traditional Q&A systems. The complexity and specialized nature of legal terminology and procedures act as barriers. | An interactive LLM-based system that: 1) asks for user location for jurisdiction-specific laws, 2) detects information deficiency in user questions, 3) generates clarifying questions and options to gather missing details, and 4) provides comprehensive legal analysis based on the completed information. | Legal question answering, access to legal information/advice. | The general public, non-specialists who lack financial resources or opportunity to consult lawyers directly. | General (not specified) | NaN | Uses case law data processed by GPT-4o to generate questions for fine-tuning Llama-3.1-8B (deficiency detection). Constructs a fact-rule node graph from case law documents parsed via LLM into IRAC structure, used for RL training (missing node prediction). Utilizes a legal document database for retrieval. Sources (public/proprietary) not specified. | Information deficiency detection via prompt-based fine-tuning of Llama-3.1-8B. Missing node prediction via Deep Deterministic Policy Gradient (DDPG) reinforcement learning using Graph Neural Networks (GNNs) on a fact-rule graph. Clarifying question/option generation via LLMs using predicted missing nodes. Response generation via retrieval models (text-embedding-3-large, cosine similarity) and LLMs. | A demonstration system is described. A GitHub link is provided for 'more materials'. | False | False | A GitHub repository containing 'more materials' is mentioned: https://github.com/RujingYao/Intelligent-Legal-Assistant | NaN | Implicit challenges include generating varied quality training data (questions), constructing the detailed fact-rule graph, training the reinforcement learning agent effectively for the legal domain, ensuring relevant legal document retrieval, and integrating multiple complex AI components. | NaN |
| 3NLzN6i5MaIJ.pdf | Google_Scholar | Artificial Intelligence & Criminal Justice: Cases and Commentary | This open-access casebook provides a comprehensive exploration of artificial intelligence's integration into the criminal justice system, featuring curated cases, commentary, and policy documents. It examines AI applications in areas like policing, lawyering, access to justice, and AI governance, while critically discussing associated benefits, risks, and ethical considerations. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | AI potentially worsening access to justice; biased AI disadvantaging vulnerable groups like self-represented litigants and legal aid recipients; AI-generated misinformation; complexity of AI creating new barriers. | Promoting AI literacy for all stakeholders; ethical guidelines and professional standards for AI in legal services; human oversight and accountability in AI systems; leveraging AI to empower self-represented litigants; open-access educational resources. | Legal aid, self-represented litigants, judicial interim release, mental health disorders and AI. | Self-represented litigants, individuals needing legal aid, persons with mental health disorders involved in the justice system, Indigenous communities. | Criminal Justice | Canada, United States, European Union | NaN | NaN | NaN | True | True | The casebook is available for free and open access via Allard Research Commons and the Canadian Legal Information Institute (CanLII) under a CC BY-NC-ND 4.0 license. | Lack of reliable, unbiased AI tools for access to justice; insufficient frameworks for ethical AI deployment in A2J; digital divide and literacy issues hindering equitable access; need for ongoing research on AI's A2J impact and development of safe tools. | NaN | Generation of false/misleading information (hallucinations) by AI; perpetuation of societal biases leading to discriminatory outcomes; threats to privacy and data security; lack of transparency and accountability in AI decision-making; deepfakes and AI-generated misinformation undermining legal processes; AI exacerbating access to justice issues if not implemented equitably; misuse of AI for surveillance; potential for AI to be used for malicious purposes (adversarial AI). |
| l4r5s2gwfukJ.pdf | Google_Scholar | Generative AI and Access to Justice in Canada: The Case of Self-Represented Litigants [SRLs] | This article examines the potential benefits and significant limitations of using Large Language Models (LLMs) like ChatGPT for self-represented litigants (SRLs) in the Canadian legal system. It argues that while LLMs can assist SRLs, their effectiveness is limited by factors like accuracy, cost, and the user's literacy, potentially causing more harm than good for those without legal knowledge. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | High cost of legal representation leading to self-representation; SRLs finding law/litigation difficult; Lack of clear/practical legal information; Need for assistance with forms, drafting, court preparation; Ensuring a 'level playing field'; High cost of bespoke legal AI tools; Reliability/accuracy limitations of LLMs (hallucinations, jurisdictional errors); Lack of legal expertise for SRLs to verify AI output; Over-reliance on inaccurate AI; Basic language/digital literacy gaps; Lack of access to technology/internet. | Using customized LLMs for SRLs; Developing AI tailored to SRL demographics (e.g., form completion); LLM interfaces directing users to verified resources; Calibrating AI reliance; Requiring disclosure of AI use in filings; Enhancing SRL AI literacy; Combining LLM use with existing free legal resources. Creating public AI models mentioned but feasibility questioned. | Access to legal information; Legal document drafting (pleadings, correspondence); Case preparation; Understanding legal rights and procedures; Facilitating settlement. | Self-Represented Litigants (SRLs) in Canada (acknowledged as a diverse group). | General litigation, Family Law, Civil Procedure | Canada | NaN | NaN | NaN | False | False | NaN | Affordability gap (cost of bespoke AI); Reliability/Accuracy gap (especially with generic LLMs); Literacy gap (digital, legal, AI); Lack of evaluation of everyday SRL use of LLMs; Funding gap between A2J tech and commercial legal tech; Uncertainty about market-driven development addressing SRL needs. | Accuracy/Hallucinations in LLMs; Bias from training data; Limited contextual understanding; Jurisdictional confusion; Cost/Affordability of bespoke tools; Need for user literacy (AI and legal). | LLMs potentially harming SRLs without legal knowledge; Over-reliance on inaccurate/hallucinated information; Distortion of public understanding of law; SRLs submitting AI-generated false citations to courts; Widening justice gap due to AI cost disparities; Potential increase in frivolous litigation. |
| o1rPy5FGPjIJ.pdf | Google_Scholar | Mitigating Translationese with GPT-4: Strategies and Performance | This paper investigates using GPT-4 with linguistically informed prompts to reduce translationese in human-translated German and English texts derived from the Europarl corpus. The study demonstrates that prompts incorporating specific linguistic instructions lead to revised translations more similar to original target language texts, particularly for English. | True | NaN | True | 1.0 | NaN | Prompting GPT-4 with linguistically informed instructions (self-guided vs. feature-guided, with varying detail) to rewrite human translations and reduce translationese. | Evaluation involved SVM-based translationese classification (F1 score reduction on rewritten text vs. human translation), COMET scores for content preservation, statistical analysis of linguistic feature shifts, and expert human assessment of accuracy and fluency. | The feature-guided detailed mode for English translations was most successful, reducing the F1 score for translationese classification by 7.63 points on the top-15 features (and 4.07 on all 58 features) compared to human translations. | NaN | NaN | NaN | NaN | EU Parliamentary Proceedings | European Union (German-English language pair) | The technique relies on the pre-trained GPT-4 model. Experiments used a publicly released, segment-aligned, bidirectional German-English dataset built from the Europarl corpus (parliamentary speeches). | Prompt engineering varying in guidance: self-guided modes (relying on LLM's internal knowledge with minimal or detailed task description) and feature-guided modes (providing specific, segment-tailored linguistic instructions based on feature deviations from target language norms). | The segment-aligned bidirectional German-English dataset from Europarl and multiparallel datasets including LLM-generated outputs are released on GitHub and Zenodo. Prompt examples are provided in the paper's appendix. | True | False | The experimental dataset and prompt examples are publicly available (GitHub/Zenodo, paper appendix). The core LLM, GPT-4, is accessible via OpenAI's API (paid). | NaN | Extensive cleaning of GPT-4 output was required due to meta-comments and inconsistent formatting. Limiting the number of instructions per segment was important, as too many instructions were less effective. The model showed different willingness to edit text across languages in self-guided modes. | Excessive application of linguistic instructions can lead to 'overtransformed renditions' and decreased translation accuracy. Content preservation requires further attention. Rewritten texts sometimes exhibited new, unintended linguistic deviations, including over-normalisation effects. |
| _UgzRabzPPEJ.pdf | Google_Scholar | Generative AI, Fake Law and Professional Guidance | The paper discusses the risks associated with lawyers using Generative AI (GenAI), particularly the emergence of 'fake law' (hallucinated case citations) in court filings, drawing on examples primarily from the US and Canada. It reviews existing professional guidelines and ethical obligations, emphasizing the need for diligence, verification, and further guidance for the Australian legal profession to ensure responsible AI adoption and maintain public trust in the justice system. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | The primary hurdle identified is the unverified use of Generative AI by legal professionals leading to the submission of inaccurate or fabricated legal citations ('fake law'), which undermines court processes, professional integrity, and public confidence in the justice system. | The paper advocates for increased education of lawyers and the judiciary, adherence to existing professional ethical obligations (diligence, honesty, duty to the court), and the development and adoption of clear, consistent professional guidelines for the responsible and ethical use of GenAI in legal practice. | NaN | NaN | Professional Conduct/Ethics, Litigation/Court Procedure | Australia (NSW, Victoria, ACT, WA), United States (Federal, New York, Colorado, Massachusetts, Florida), Canada (British Columbia), United Kingdom, New Zealand | NaN | NaN | NaN | False | False | NaN | The need for more comprehensive, consistent, and updated professional guidelines and education regarding GenAI use across the Australian legal profession is highlighted. | NaN | Generation of fake cases and citations ('fake law'); misleading courts; undermining the administration of justice; erosion of public confidence; professional sanctions against lawyers (fines, suspension, cost orders); harm to clients' cases; wasting court and opposing party resources; reputational damage (judges, courts, legal profession); potential breaches of confidentiality/privacy with public GenAI tools; outputs may be inaccurate, incomplete, misleading, outdated, or biased. |
| W5ZX8VFbaeIJ.pdf | Google_Scholar | Evaluating AI for Law: Bridging the Gap with Open-Source Solutions | This study evaluates general-purpose AI like ChatGPT for legal question-answering, highlighting significant risks and performance issues such as lack of citations and verbosity. It proposes OpenJustice.ai, a domain-specific, open-source legal AI platform, advocating for collaborative development and improved benchmarks to enhance accuracy, transparency, and access to justice. | True | Idealistic | True | 1.0 | Positive | Evaluation of LLMs (GPT-4, Mixtral-8x7B) on legal Q&A tasks using the curated LegalQA benchmark; proposal of OpenJustice.ai, an open-source legal AI platform and development framework. | GPT-4 and Mixtral-8x7B were evaluated on legal question-answering using two datasets: LegalQA (curated from Reddit, >2000 questions, answers by law students) and Law Stack Exchange (200 popular questions, top-voted answers). Evaluation involved automatic comparison (using GPT-4 via OpenAI Evals) of model-generated answers to expert answers based on factuality categories (subset, superset, same, disagree, incomparable), supplemented by qualitative review by law students. | On the LegalQA task, GPT-4 had under 5% factually incorrect responses. Mixtral-8x7B performed significantly worse. Qualitative feedback indicated GPT-4's answers lacked citations and were often verbose compared to concise human expert answers. | Reliability issues of current AI (hallucinations, bias, lack of legal nuance, poor citation practices); limited accessibility and transparency of specialized legal AI tools (closed, proprietary systems benefiting mainly large firms); lack of diversity in AI-generated content and potential for creating AI echo chambers; inadequate regulatory frameworks and evaluation benchmarks for legal AI. | Develop domain-specific, open-source legal AI systems (e.g., OpenJustice.ai); revise benchmarks and protocols for evaluating legal AI in real-world settings, focusing on bias, fact-checking, legal reasoning, and narrative diversity; foster collaborative, crowdsourced development with expert feedback loops; emphasize high-quality data curation and advanced AI methodologies (DPO, world models, etc.). | Legal question-answering for laypeople; improving accuracy, transparency, and narrative diversity in legal AI; addressing legal misinformation; assisting self-represented litigants; reducing legal fees and research costs. | Self-represented litigants, laypeople with legal questions, broader legal communities (beyond large firms), legal aid centers, law students, legal professionals. | General law (covering various topics as found in public legal advice forums and general legal Q&A sites). | Canada (primary for LegalQA annotation context), US (source of some LegalQA questions, OpenJustice.ai data), France (OpenJustice.ai data), EU (OpenJustice.ai data, EU AI Act). | For the proposed OpenJustice.ai: A mix of curated open-source legal data (annotated question-answer pairs, case law from US, Canada, France, EU), crowdsourced human feedback, and proprietary partner data. For the LegalQA benchmark created: Publicly available questions from r/legaladvice with expert answers written by law students. | For OpenJustice.ai: Open-source development, crowdsourcing human feedback from legal experts, iterative improvement, data curation, LLM fine-tuning, training Small Language Models (SLMs), and leveraging advanced AI techniques like Direct Preference Optimization (DPO), world models, Flash Attention 2, rejection sampling, reward modeling, supervised fine-tuning, and alignment research. | OpenJustice.ai launched in March 2023 by Conflict Analytics Lab, operating as a natural-language processing interface (www.OpenJustice.ai). The open version is intended for sophisticated users (legal background) to provide quality feedback. It aims to partner with law schools and aid centers. | True | True | The OpenJustice.ai platform (www.OpenJustice.ai) is described as launched and operational. It has a core open-source component. Access to the 'open version' of the platform for feedback contribution is restricted to sophisticated users with a legal background. | Existing legal benchmarks lack real-world complexity; insufficient empirical data on AI performance in diverse legal tasks; need for improved automatic evaluation methods for the legal domain; understanding the utility of unstructured legal databases for pretraining/domain-adaptation is unexplored; current AI struggles with nuanced legal reasoning, citation, and conciseness. | Ensuring factual accuracy and avoiding hallucinations in legal AI; addressing and mitigating bias; achieving diversity in narrative representation; handling the dynamic nature of law with static training data; reliable source citation; modeling complex, non-algorithmic legal reasoning; high cost of developing purpose-built models; curating high-quality, representative, and unbiased legal datasets. | Overreliance on unreliable general-purpose AI for legal tasks by both laypeople and professionals, leading to incorrect advice or actions; generation of 'hallucinated' or fictitious legal information (e.g., fake citations, case law); propagation of biases present in training data; misleading users with AI tools that appear specialized but are general-purpose; widening the access to justice gap if specialized tools remain closed and expensive; creation of AI echo chambers stifling diverse legal thought and democratic discourse; potential for ossification of law due to static models. |
| Iv6wOJNR-lkJ.pdf | Google_Scholar | GoalAct: A Globally Adaptive Dynamic Legal Multi-agent Collaboration System | This paper proposes GoalAct, a multi-agent legal collaboration system using the GLM-4 language model, designed to provide legal services by accessing legal databases. GoalAct aims to improve accuracy and adaptability through integrated planning, reflection, and memory mechanisms at both global and local levels. | True | Idealistic | True | 1.0 | Positive | GoalAct, a globally adaptive dynamic legal multi-agent collaboration system composed of five agent types (Processor, Memorizer, Actor, Judge, Reflector) built on GLM-4, accessing legal databases through APIs. | The paper mentions that "experimental results also demonstrate its superior performance for legal services" but provides no specific details on the testing procedure within the provided text. | The paper claims "superior performance for legal services" but does not provide specific metrics or quantitative results in the provided text. | Limited availability and high cost of legal professionals, especially in regions with restricted access; complexity of user inquiries requiring AI systems to effectively filter information, generate logical plans, and self-correct. | Developing advanced AI-driven multi-agent systems like GoalAct, leveraging LLMs (GLM-4) with integrated planning, reflection, and memory to provide more efficient and adaptable legal services. | Access to legal information and consultation services. | Individuals in regions with limited access to legal professionals or those facing high costs for legal services. | General legal services / Legal consultation | International | The system uses the pre-trained GLM-4 language model. It accesses unspecified external legal databases through API calls for information retrieval during operation, not explicitly for further training of the core model. | Multi-agent system design with specialized agents (Processor, Memorizer, Actor, Judge, Reflector); integration of planning, reflection (self-correction), and memory (short-term and long-term) mechanisms; emphasis on balancing local task accuracy with global objective consistency. | NaN | False | False | NaN | Ensuring robust filtering of user inputs, coherent logical planning, effective self-correction, and reliable memory formation in legal AI systems. Balancing local task accuracy with global objectives in multi-agent systems for complex legal problem-solving. | Effectively filtering irrelevant or redundant information from user inputs; generating logical and coherent planning paths while avoiding local search loops; developing robust self-correction mechanisms; forming memory and accumulating experience to reduce repeated errors; balancing local task accuracy with global objective consistency in a multi-agent system. | Risk of the system getting trapped in local search loops, leading to no responses; potential for degraded system performance if individual agents' tasks do not align with the overall global objective. |
| SSRN.pdf | Google_Scholar | Uncovering the Influence of ChatGPT’s Prompts on Scientific Writings using Machine Learning-Based Text Mining Approaches | This paper investigates how variations in prompts given to ChatGPT affect the quality and content of generated scientific text, specifically introduction sections of traffic safety articles. It compares outputs from basic versus enhanced prompts against human-written texts using text similarity and network analysis, finding minimal quality differences based on prompt detail. | True | NaN | True | 2.0 | NaN | Prompt engineering for ChatGPT (comparing basic vs. enhanced prompts with persona/citation info) evaluated using text similarity (Cosine/LSA) and Text Network Analysis (TNA). | Generated ChatGPT introductions for 327 traffic safety paper titles using two prompt types (initial vs. improved). Measured similarity (Cosine/LSA via text2vec) between generated/human texts and between the two generated texts. Used Text Network Analysis (TNA) to compare content themes and collocations. | Improved prompt offered negligible improvement (avg similarity 0.56 vs 0.54) over initial prompt when compared to human text. High similarity (avg 0.82) between outputs of the two prompt types. ChatGPT generated more generic phrases than human text. | NaN | NaN | NaN | NaN | NaN | International | The study uses ChatGPT; evaluation data are introductions from 327 published traffic safety papers (Web of Science). | Experimental comparison of prompt variations; computational text analysis (Cosine similarity, LSA, Text Network Analysis). | NaN | False | False | NaN | Need for exploring specific personas, other scientific paper sections, different domains, and human-AI collaboration in writing. | Crafting effective prompts to generate high-quality, human-like scientific text. | NaN |
| fXjnV8ksgc0J.pdf | Google_Scholar | Professionals Beware: The Opportunities and Risks of Generative AI in L egal P ractice | This paper reviews the opportunities and significant risks associated with using generative AI tools like ChatGPT in legal practice. It highlights issues such as hallucinations, copyright infringement, bias, privacy concerns, and breaches of professional responsibility, urging practitioners to use these tools cautiously and responsibly. | True | Market | True | 3.0 | NaN | Generative AI tools (e.g., ChatGPT, CoPilot, Dall-E) | Author tested ChatGPT 3.5 and ChatGPT-4 with prompts requesting Australian patent law cases related to inventorship. DALL-E was tested with prompts about lawyers. | ChatGPT 3.5 generated mostly fabricated or inaccurate case references regarding Australian patent law. ChatGPT-4 showed improvement but still generated non-relevant cases and one hallucination (among other correct, but not directly relevant, cases). DALL-E image generation showed potential gender bias. | NaN | NaN | NaN | NaN | Legal practice, Intellectual Property (Copyright, Patents), Litigation, Professional Responsibility, Privacy Law | USA, Australia, EU, UK | The paper discusses general issues with training data for generative AI, noting it often involves vast datasets (text, images) scraped from the internet, potentially including millions of copyrighted works (e.g., news articles, images, code) obtained without author/owner consent. | NaN | NaN | True | True | Publicly available tools like ChatGPT and CoPilot are discussed, which have both free and paid/enterprise versions. | NaN | NaN | Hallucinations (generating inaccurate/fabricated information); Copyright infringement (use of copyrighted material in training data, generation of infringing outputs, memorization/regurgitation); Moral rights violations; Lack of copyright protection for AI-generated outputs; Algorithmic bias (racial, gender) reflected in outputs; Privacy violations (disclosure of personal data via prompts); Breach of confidentiality (disclosure of client information, trade secrets); Breach of professional duties (competence, diligence, honesty, integrity, independence). |
| kIhJxHbh8BgJ.pdf | Google_Scholar | ChatGPT, Esq.: Recasting Unauthorized Practice of Law in the Era of Generative AI | This paper analyzes the conflict between generative AI's legal capabilities, exemplified by GPT-4, and restrictive Unauthorized Practice of Law (UPL) rules that impede access to justice. It proposes a novel legal reform: recasting UPL rules to primarily regulate who can claim the title of 'lawyer,' while allowing AI and other non-lawyers to provide most legal services, except for in-court representation. | True | Idealistic | True | 1.0 | Positive | A legal reform proposal to recast Unauthorized Practice of Law (UPL) rules as primarily regulation of entity-type claims (i.e., who can call themselves a 'lawyer' or 'attorney'), while allowing non-lawyers, including AI-powered entities, to provide most legal services, save for representation in legal proceedings. | NaN | NaN | Restrictive Unauthorized Practice of Law (UPL) rules that limit competition, create high costs, and hinder innovation in legal service delivery, thereby exacerbating the access-to-justice gap. Ill-defined scope of 'practice of law' leading to inconsistent enforcement and protectionist tendencies within the legal profession. | Recast UPL rules to focus on regulating the 'lawyer' designation rather than the provision of most legal services; allow AI and other non-lawyers to provide legal services (excluding in-court representation); utilize tort law (negligence, deceptive practices) and market competition to ensure service quality and consumer protection. | Reform of Unauthorized Practice of Law (UPL) rules; leveraging AI/LLMs for legal service delivery; improving affordability and availability of legal services for the general public; ethical regulation of AI in law; consumer protection in the evolving legal market. | Low-income individuals, small businesses, and the general public who currently lack adequate access to affordable legal services. | Professional Regulation (Unauthorized Practice of Law, Legal Ethics), Torts, Constitutional Law (First Amendment, occupational freedom), Antitrust Law, Civil Procedure. Examples touch on various areas like criminal, housing, and securities law. | United States | NaN | NaN | NaN | False | False | NaN | Need for clear standards of care and accessible redress mechanisms (e.g., tort law) for services provided by non-lawyers/AI; addressing potential AI bias and ensuring ethical AI deployment; updating civil procedure and establishing consistent (perhaps federalized) ethical rules for all legal service providers; mitigating the digital divide to ensure equitable access to AI-driven legal tools; potential for increased deceptive practices despite reforms. | NaN | Consumers receiving incompetent or unethical advice from non-lawyer providers or AI (e.g., AI 'hallucinations'), although the paper argues tort law can mitigate this. Unintended consequences from major legal reforms affecting fundamental rights and legal system stability. Current UPL rules enabling selective enforcement and perpetuating access-to-justice issues. |
| SafrZAuaSrMJ.pdf | Google_Scholar | GENERATIVE ARTIFICIAL INTELLIGENCE AND REVOLUTION OF MARKET FOR LEGAL SERVICES | This paper discusses the transformative potential and challenges of generative AI for the legal services market, focusing on efficiency gains, business model shifts, and competitive dynamics. It highlights risks related to quality control, liability, data privacy, ethical standards, and vertical dependency on large technology providers. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | The paper identifies obstacles within the legal market structure rather than specific A2J hurdles: high implementation costs creating disparities between large and small firms, potential for market concentration disadvantaging smaller players, risks of technology/data lock-in and dependency on upstream AI/cloud providers, maintaining quality and accuracy of AI outputs (hallucinations), ensuring data privacy and confidentiality, ethical challenges (bias, transparency), and managing liability for AI errors. | The paper suggests strategies for law firms to mitigate market risks: pursuing multi-homing strategies to avoid vendor lock-in, implementing robust contractual data protection mechanisms, potential use of decentralized training techniques, developing strong internal quality control processes and ethical guidelines, adapting business models (e.g., alternative fee arrangements), and potentially forming collaborations or using shared resources (especially for smaller firms). | NaN | NaN | General legal services, Contract law, Litigation, Compliance | International (with specific examples from US, EU, France) | NaN | NaN | NaN | False | False | NaN | Ensuring smaller firms can access/afford AI to prevent market concentration; managing vertical dependencies on large tech/LLM providers; establishing clear liability frameworks for AI-related errors; developing robust quality control and methods to prevent AI hallucinations/bias; balancing innovation with data privacy regulations and ethical standards; addressing potential skill gaps created by automation; uncertainty about long-term productivity gains versus investment costs. | N/A (paper discusses challenges for firms adopting AI, not for the authors in creating a tool) | Poor quality AI outputs (errors, hallucinations) impacting reputation and liability; data privacy/confidentiality breaches; ethical issues (bias, lack of transparency); dependency and lock-in with technology providers leading to unfair terms or anticompetitive practices (self-preferencing, envelopment); increased market concentration hurting smaller firms; erosion of core legal skills; stifled innovation due to compliance costs or liability fears; manipulation risks with open-source models. |
| 4IGMF7HagUQJ.pdf | Google_Scholar | Lawyers Should Not Trust AI: A call for an Open-source Legal Language Model | The paper argues that general AI like ChatGPT is unsuitable for legal tasks due to significant risks such as misinformation and lack of transparency. It advocates for the development of domain-specific, open-source legal AI, like the proposed OpenJustice.ai, built through multi-layered fine-tuning and legal community feedback to improve legal research and access to justice. | True | Idealistic | True | 1.0 | Positive | Open-source and distributed legal AI (specifically OpenJustice.ai) developed through multi-layered fine-tuning: Raw Data Fine-tuning, Instruction Fine-tuning, Open-Source Feedback Fine-tuning from legal professionals, and Decentralised Fine-tuning combining open and closed datasets. | The paper outlines the design and development process for OpenJustice.ai, involving supervised annotation and feedback from law students and legal professionals on real-world questions and generated legal scenarios. It does not present specific benchmark testing or quantitative evaluation results for OpenJustice.ai within this paper. | NaN | Limitations of general AI (legal misinformation/hallucinations, lack of transparency and precision, bias, inability to offer diverse narratives or perform contextual legal reasoning, unexplainability) hindering their safe use for legal tasks and access to justice. The risk of AI leading to ossification of law and undermining legal diversity. The current absence of reliable, open-source domain-specific legal AI. | Development of OpenJustice.ai: an open-source, domain-specific legal LLM. This involves multi-layered fine-tuning (on raw legal data, instruction-response pairs) and reinforcement learning with human feedback from the legal community (law schools, legal professionals), with initial feedback restricted to experts to ensure data integrity. A decentralized approach allows incorporating proprietary data while keeping it localized. | Improving legal research, enhancing legal reasoning tools, addressing shortcomings of general AI in legal problem-solving and dispute resolution, and ultimately providing access to justice for self-represented litigants through reliable legal information. | The legal community (law schools, legal professionals, legal clinics, industry partners) for development, feedback, and initial use. Potentially self-represented litigants in the future, once the system is mature and reliable. | General Law, with applications in legal research, legal reasoning, and potentially specific areas like contract drafting. The focus is on foundational capabilities extendable to various legal domains. | International (as a general call and framework), with specific examples from Canada and the United States, implying the need for jurisdiction-specific adaptation for deployed systems. | A combination of: 1) Unstructured legal data (case law, journals, other legal resources). 2) Structured data: question-response pairs from online forums (e.g., Reddit, Law Stack Exchange) annotated by law students and legal professionals. 3) Synthetic data: legal scenarios and contracts generated by other LLMs (e.g., Llama2) for further annotation. 4) Proprietary data from industry partners (used in a closed, decentralized fine-tuning manner). | Multi-layered fine-tuning of foundational language models (raw data fine-tuning, instruction fine-tuning, reinforcement learning from human feedback). A staged development process involving data collection from public and legal sources, annotation by law students under professional supervision, and iterative model refinement. Proposed use of decentralized learning to combine open and proprietary data sources. | Initially, a secured interface enabling law students and legal professionals to interact with and provide feedback to the model (OpenJustice.ai). The aim is a non-proprietary version openly accessible to the legal community, but not to the general public for feedback in early stages to maintain data quality. Decentralized learning architecture where industry partners can fine-tune on proprietary data locally. | False | False | NaN | The need for empirical performance evaluation of domain-specific legal LLMs using clear, industry-specific metrics (for hallucinations, reasoning, citation accuracy, narrative diversity). Research into effective human-AI collaboration, particularly the 'end-user prompt engineering' abilities of non-lawyers for legal AI. Persistent limitations in LLMs' legal citation retrieval capabilities. | Ensuring data integrity and quality during the open-source feedback process. Developing robust methods for legal citation retrieval within LLMs. Addressing the challenge of effective prompt engineering for users, especially non-experts, to extract useful information from legal AI. Scaling the annotation and expert feedback process. | Use of general AI for legal tasks: legal misinformation (hallucinations, fake citations), biased outputs, lack of transparency and explainability, creation of 'AI echo-chambers' narrowing perspectives, ossification of law. Premature public release of even domain-specific legal AI: providing incorrect legal information to self-represented litigants. Inaccurate feedback from non-experts if feedback mechanisms are opened too broadly too soon. |
| 7VLS4kLM-rYJ.pdf | Google_Scholar | ChatGPT and service: opportunities, challenges, and research directions | This paper explores the potential applications, opportunities, and challenges of using ChatGPT within the service sector. Leveraging expert opinions, it outlines implications for service marketing, customer experience, digital services, cost-effectiveness, and ethics, proposing future Mresearch directions. | True | Market | True | 2.0 | NaN | ChatGPT | NaN | NaN | NaN | NaN | NaN | NaN | General Service Industry, Marketing, Customer Experience, Digital Services | International | NaN | Expert-oriented perspective approach, literature review. | NaN | True | True | ChatGPT is a publicly accessible tool offered by OpenAI, with free and paid tiers. | NaN | Accuracy (including hallucination), Bias in training data and output, Privacy and data security concerns, Ethical issues (fairness, corporate digital responsibility), Intellectual Property infringement, Potential for Misuse (malicious use, manipulation), Lack of Transparency and Explainability, Accountability for outputs, Potential job displacement, Ensuring equitable access (digital divide), Need for regulation and legal frameworks. | Perpetuation of biases, Inaccuracy leading to harm or mistrust, Privacy violations (data misuse, surveillance, data leaks), Security threats (spamming, phishing, fraud, impersonation, misinformation), Intellectual property infringement, Dehumanization, social isolation, loss of autonomy/dignity in interactions, Manipulation of users, Addiction, Identity theft. |
| Thirdofglobaljournalofmultidisciplinarysciencesarts-Copy.pdf | Google_Scholar | Transformative Applications of ChatGPT: A Comprehensive Review of Its Impact across Industries | This paper provides a comprehensive review of ChatGPT's applications and impacts across various industries, including healthcare, education, business, legal services, creative sectors, and social media. It highlights the tool's potential for enhancing efficiency, personalization, and automation while also discussing associated challenges like ethics, bias, technical limitations, and the need for human-AI collaboration. | True | NaN | True | 3.0 | NaN | ChatGPT | NaN | NaN | Potential for bias replication from training data leading to unfair outcomes; issues of legal responsibility for AI errors; need for ethical guidelines and oversight. | Efforts to identify and reduce bias in training data; continuous monitoring of outputs; ensuring transparency; establishing clear protocols and regulations; balancing AI assistance with human judgment and expertise. | Legal research, document drafting, regulatory compliance, risk management. | NaN | General Legal Services, Compliance | International | The paper states ChatGPT is trained on large datasets containing diverse human-generated content, but does not specify the exact data sources. It notes this data can contain inherent biases. | NaN | NaN | True | False | The paper discusses ChatGPT, which is a widely available tool developed by OpenAI, accessible via web interface and API. | Need for improved accuracy, reliability, and robustness, especially for novel or specialized topics; enhancing contextual understanding; integrating real-time data; developing multimodal capabilities; achieving hyper-personalization. | Addressing bias and ethical concerns; overcoming technical limitations (e.g., dependence on training data, factual inaccuracies); balancing human-AI collaboration effectively (ensuring AI augments rather than replaces human expertise). | Replication and perpetuation of societal biases; generation of factually inaccurate or incoherent information; potential for misdiagnosis or inappropriate recommendations in healthcare; data privacy concerns (especially with sensitive information like patient data); over-reliance on AI leading to erosion of human skills or judgment; unclear legal liability for AI errors; potential job displacement. |
| z68SfimV9U0J.pdf | Google_Scholar | LLMs & Legal Aid: Understanding Legal Needs Exhibited Through User Queries | The paper presents a preliminary analysis of 3,847 user queries submitted to a GPT-4 powered legal aid tool by Frank Bold in the Czech Republic. Using GPT-4o for zero-shot classification, it categorizes queries to understand user needs and interaction patterns when seeking legal help from LLMs. | True | Idealistic | True | 1.0 | Neutral | A method for understanding user legal needs exhibited in queries to LLM-based legal aid tools, involving: 1) iterative development of a query categorization scheme (facts provided, information vs. advice, user control over answer) and 2) zero-shot classification of queries using GPT-4o based on this scheme. | The outcome of the zero-shot classification performed by GPT-4o was not formally evaluated for accuracy by the authors. | Classification of 3,847 queries: 29.95% provided facts, 64.93% sought information (vs. 35.07% advice), and 71.43% posed open-ended questions, granting control to the model. Only 3.35% of queries treated the LLM as a human expert, and 3.04% as a sophisticated search engine. | High cost of traditional legal services; users oversharing personal/sensitive information with LLMs; unfeasible user expectations of LLM capabilities; users granting excessive control to LLMs, increasing vulnerability; the blurry line between users seeking legal information versus actionable legal advice. | Increase AI literacy among the public. Develop and implement technical and policy safeguards by LLM providers and legal aid organizations. Further research into augmenting LLMs with curated, reliable legal information (e.g., RAG). | Understanding user needs in legal aid; distinguishing between legal information seeking and legal advice seeking via LLMs; patterns of user interaction with LLM-based legal tools; user expectations of LLMs in legal contexts. | General public in the Czech Republic seeking legal aid, laypeople, low-income and marginalized individuals. | The underlying experiment covered environmental law, whistleblowing and corruption-related issues, civic rights, municipal laws, and civic engagement issues. The query analysis is broadly applicable to general legal queries. | Czech Republic | The user query classification was performed by GPT-4o using zero-shot learning with prompts defining categories. The data classified was a corpus of 3,847 anonymized user queries in Czech collected from the Frank Bold experiment. The original Frank Bold RAG system (which users interacted with) used internal Frank Bold documents (guidelines, blog posts, articles) and selected Czech legal acts (proprietary, domain-specific, unstructured text). | For the query categorization approach: Iterative development of descriptive codes based on existing literature (Cheong et al.) and pilot analysis of 200 random queries. For the Frank Bold experiment (context): Experimental design with a web platform for query submission to GPT-4 with RAG, user registration, and single question-answer interaction. | The Frank Bold experiment tool was accessible via a public website (www.ai.frankbold.org, now defunct) from May 3, 2023, to July 25, 2023. It was publicized through Frank Bold’s internal mailing lists and several prominent online media outlets. | False | False | NaN | Need for more rigorous experiments with controlled variables and demographic user data. Deeper understanding of user behaviors and query types that fall between the extremes of treating LLMs as search engines versus human experts. Lack of widespread AI literacy among lay-users. Insufficient safeguards in existing LLM-based legal aid tools. | For the query analysis presented: Limitations of using unvalidated zero-shot classification. For the original Frank Bold experiment: Uncontrolled variables during the experiment (e.g., different GPT-4 model versions, RAG adjustments over time); lack of detailed demographic data about users. | Oversharing of personal and sensitive information by users to LLMs. Users holding unfeasible expectations regarding LLMs' capabilities to provide personalized and actionable legal advice. Users ceding significant control over the response to LLMs, increasing their vulnerability to hallucinations and irrelevant information. Users developing a false sense of competence based on LLM-generated answers without proper verification. |
| Wgt3m-_XVr8J.pdf | Google_Scholar | Artificial Intelligence & the Future of Law Libraries: Mid-Atlantic Roundtable Report | This paper reports on a roundtable discussion among legal experts and information professionals about the impact of AI, particularly generative AI, on law libraries. It highlights opportunities for AI to improve library services, accessibility, and access to justice, while also discussing challenges such as rapid adoption pressures, the need for staff training, and budget constraints. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Existing access to justice gaps, such as difficulties for self-represented litigants in navigating legal procedures and understanding legal information. | Development of AI-driven information retrieval and document automation systems for self-represented litigants; leveraging AI to adapt information for diverse needs and improve court processes. | Assisting self-represented litigants (e.g., in child custody, landlord-tenant, criminal appeals); enhancing legal information accessibility and court processes. | Self-represented litigants; individuals with diverse needs and limited resources. | Family law, Landlord-tenant law, Criminal law, General legal information services. | United States | NaN | NaN | NaN | False | False | NaN | Need for development of user-friendly AI tools tailored for access to justice; lack of open, machine-readable legal data for AI development; ensuring ethical AI deployment that promotes fairness and equity. | Pressure for rapid AI adoption without strategic evaluation; staff skills gaps and need for training; high costs and complex procurement; privacy, security, and ethical concerns; advocating for the library's value and role; budget and resource limitations. | Data privacy and security vulnerabilities with generative AI; potential for biased AI-driven collections or information; unethical AI use if vendor accountability is lacking; marginalization of librarians not adapting to AI. |
| jxLBw6Jkp30J.pdf | Google_Scholar | LLM-Datasets: An Open Framework for Pretraining Datasets of Large Language Models | This paper introduces LLM-Datasets, an open-source Python framework designed to standardize and simplify the collection, processing, and compilation of large-scale, multilingual pretraining datasets for Large Language Models. The framework emphasizes reproducibility, modularity, and HPC-readiness, demonstrated through the creation of a 2.3 trillion token dataset covering 32 European languages. | True | NaN | True | 1.0 | NaN | LLM-Datasets: An open framework integrating tools for downloading, text extraction, filtering, deduplication, sampling, and tokenization of diverse data sources to create reproducible LLM pretraining datasets. | Showcased by compiling a 2.3 trillion token, 32-language European dataset using the framework, detailing the data sources (e.g., Colossal OSCAR, Wikipedia, Pile of Law, Starcoder) and processing steps involved (e.g., filtering web data based on quality warnings, perplexity, blocklists). | Successfully compiled a 2.3 trillion token, 32-language European dataset, demonstrating the framework's capability to handle large-scale, multilingual data processing and composition. | NaN | NaN | NaN | NaN | General Law (via included datasets) | International | The framework itself does not use training data, but compiles it. The showcase dataset uses numerous sources, including public web crawls (Common Crawl via OSCAR), Wikipedia, code repositories (Starcoder), legal datasets (Pile of Law, EURLex, LegalMC4, Open Legal Data DE, Slovak court decisions), scientific papers (peS2o), mathematical datasets (Proof Pile, AMPS), project Gutenberg, patents (BigPatent), parliamentary proceedings (ParlaMint), etc. Data is multilingual, largely unstructured text, from public and curated sources. | Modular design, HPC-readiness (network file system considerations, chunking), extensibility (custom datasets/registries), reproducibility (config files, seeds), model agnosticism, support for private data. | Released as an open-source Python package on GitHub (Apache-2.0 license) and installable via PyPI. | True | True | Available as an open-source Python package on GitHub (https://github.com/malteos/llm-datasets) and installable via PyPI. | Identifies the lack of open frameworks and reproducibility for creating LLM pretraining datasets as a major gap in current LLM research infrastructure. | Managing complexity of large-scale data processing; Handling diverse data formats and sources; Ensuring reproducibility; Designing for HPC environments; Integrating multilingual data effectively; Filtering noisy or harmful web data. | Implicitly acknowledges risks of harmful content in web-crawled data by detailing filtering steps (quality warnings, perplexity-based filtering, URL blocklists for categories like adult, dangerous material, malware etc.). |
| 9ocGP8hgKUoJ.pdf | Google_Scholar | Rapid Response Information Report Generative AI: Language models and multimodal foundation models | This Australian government-commissioned report analyzes the opportunities and risks of generative AI (LLMs and MFMs) across various sectors over the next decade. It also reviews international strategies to address the impacts of these technologies, aiming to inform national policy. | True | Idealistic | True | 3.0 | Neutral | LLMs and MFMs (e.g., ChatGPT, GPT-3, GPT-4, LLaMa, Ernie Bot) | NaN | NaN | Bias in AI reproducing social inequalities (e.g., in law enforcement, social services); risks to human rights; lack of digital inclusion for communities like regional/older Australians, hindering access to AI-driven services; opacity and lack of accountability in AI systems. | Development of legal/regulatory frameworks (e.g., risk-based approaches, human rights due diligence); promoting transparency and accountability; multi-stakeholder collaboration; public investment in national AI capabilities and accessible infrastructure; measures to improve digital inclusion. | Protecting human rights in AI deployment; mitigating bias and discrimination in AI impacting legal and social outcomes; ensuring equitable access to AI technologies and legal information; accountability for AI harms. | Regional Australians, older Australians (digital inclusion); minority groups, over-policed populations (bias in AI); women (bias in data generally). | Law enforcement, contract law, privacy law, copyright law, anti-discrimination law, consumer law | Australia, with references to international jurisdictions (US, EU, China, Canada, Singapore, Thailand). | Vast, diverse datasets (text, images, code) often scraped from the internet, including public-domain content (e.g., Wikipedia, books) and potentially personal or copyrighted material; specific datasets for models like GPT-3 are mentioned generally, but specifics for newer models like GPT-4 are often not disclosed by commercial entities. | Model pre-training, fine-tuning (supervised learning, reinforcement learning with human feedback), input/output filtering, red-teaming, fuzzing, staged release strategies, post-release monitoring and auditing. | Controlled release via APIs and web interfaces (e.g., OpenAI's ChatGPT); open-sourcing of some models (e.g., Meta's LLaMa, Stanford's Alpaca) often aimed at researchers; integration into existing software products. | False | False | NaN | Lack of transparency in commercial LLM/MFM development (datasets, pre/post-processing); insufficient national capacity for AI development and oversight in some countries (e.g., Australia); persistent challenges in ensuring fairness, accuracy, and robustness of models; digital divide limiting equitable benefit; need for effective governance, standardized reporting, and redress mechanisms. | High resource requirements (monetary, computational, human); managing accuracy, bias, and safety of models; preventing misuse for harmful purposes (e.g., misinformation); ensuring data privacy, security, and sovereignty; addressing the environmental impact of large-scale computation; establishing robust ethical guidelines and governance. | Generation of 'hallucinations' (erroneous/misleading information); perpetuation/amplification of biases leading to discrimination and social inequalities; misuse for misinformation, deepfakes, and malicious activities; privacy violations (data scraping, re-identification, unauthorized use of personal/copyrighted data); security vulnerabilities; lack of transparency and accountability ('black box' effect); negative impacts on democratic processes, labor markets, and the environment; erosion of trust; market concentration. |
| Safe_and_Responsible_AI_in_Australia_-_Submission_-_Dr_Francina_Cantatore.489fe100215e6.pdf | Google_Scholar | Submission to government on the safe and responsible use of AI in Australia | This submission responds to the Australian government's discussion paper on AI, advocating for a risk-based regulatory approach centered on consumer protection, ethical principles, and human oversight. It highlights risks like algorithmic bias and data misuse, suggesting national standards, mandatory policies in sectors like law, and considering the impact on employment and intellectual property. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Risks to consumers (data misuse, lack of awareness, misleading conduct, algorithmic bias, unfair choices, market collusion), lack of regulatory consistency across sectors, insufficient protection of privacy rights, potential negative impact on human employment. | Implement a nationally consistent, risk-based regulatory framework for AI underpinned by ethics and human rights; mandate AI policies in specific sectors (e.g., legal profession); enhance consumer protection laws (like ACL) and privacy regulations; require transparency from developers; ensure human oversight ('human in the loop'); conduct impact assessments (including employability); foster public education. | Consumer protection, Data privacy, Regulation of AI, Ethical AI, AI in the legal profession. | Australian consumers | Consumer Law, Competition Law, Privacy Law, Intellectual Property Law, Legal Profession Regulation | Australia | NaN | NaN | NaN | False | False | NaN | Lack of empirical data on AI's longitudinal impact, need for updated legal frameworks (e.g., ALRC report) post-generative AI, jurisdictional differences potentially hindering adoption of international approaches, inconsistent application of ethical principles across sectors. | NaN | Algorithmic bias, breaches of consumer law (misleading/deceptive conduct), online market collusion, misuse of personal data, erosion of privacy rights, unfair consumer choices, potential for devastating effects in high-risk sectors (e.g., health), detrimental impact on livelihoods (employment), lack of public trust without human oversight. |
| OO-3hRkHauEJ.pdf | Google_Scholar | Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset | This paper introduces Pile of Law, a large-scale, open-source dataset of English legal and administrative text, intended for pretraining models and studying data filtering. It argues for grounding AI data filtering techniques in established legal norms for privacy and toxicity, demonstrating how such contextual rules can be learned from the dataset. | True | Idealistic | True | 1.0 | Positive | Pile of Law dataset and the approach of learning context-aware data filtering rules (for privacy and toxicity) directly from legal text. | Case studies involving training models (distill-BERT) to predict pseudonymity in Board of Immigration Appeals cases (~80% F1), comparing Masked Language Model (MLM) scores for pseudonym use in civil litigation, analyzing outputs of existing privacy (HIPAA tool) and toxicity filters (Perspective, Detoxify, etc.) on dataset subsets (BVA, DOL, Supreme Court opinions), and using causal lexicon induction. | Pseudonymity prediction model achieved ~80% F1 and aligned with legal rules; models pretrained on legal data better encoded pseudonymity norms. Existing toxicity filters showed low agreement, context/time sensitivity, and poor handling of nuance on legal text, highlighting limitations. | Lack of responsible, legally-grounded, and context-aware data filtering practices for AI pretraining data, hindering development of trustworthy AI, including for legal applications potential applications. | Grounding AI data filtering practices in established legal norms; providing the large-scale, open-source Pile of Law dataset as a resource; proposing methods to learn contextual filtering rules directly from the dataset. | General (as a potential application area for models trained on the dataset) | NaN | Court opinions, contracts, administrative law, legislation, constitutional law, immigration law, criminal law, civil litigation. | Primarily U.S. federal, with comparative examples from Germany, China, Canada. | Pile of Law dataset: ~256GB of publicly available, open-source, English-language, unstructured legal and administrative text (court opinions, contracts, administrative rules, legislation, etc.). | Dataset curation by compiling public sources. Case studies using standard ML methods (classification, MLM scoring, causal inference) to analyze and learn patterns from the dataset. | Dataset released publicly on Hugging Face. | True | True | The Pile of Law dataset is available for download on Hugging Face. | Need for better text-based causal attribution methods for identifying drivers of filtering decisions; need for robust, value-aligned toxicity filters that handle legal context, domain shift, and long documents; further exploration of legal system differences (e.g., civil vs common law); challenge of reliably performing context-aware filtering at scale; limitations of model context windows for assessing toxicity. | Compiling a large-scale legal dataset from diverse public sources; handling the inherent context-dependency of legal text for filtering purposes; limitations of existing NLP tools (e.g., privacy, toxicity filters) when applied to the specialized legal domain; addressing potential sensitivity of information within publicly available legal data. | Biased/harmful model outputs due to pretraining data; filtering negatively impacting representation or utility; privacy violations via model memorization of sensitive information; release of sensitive information contained within the Pile of Law dataset despite its public sourcing; incorrect application of toxicity filters leading to censorship of important legal discussions (e.g., civil rights cases) or failure to flag genuinely harmful content. |
| d4pkaJu5lpAJ.pdf | Google_Scholar | Dallma: Semi-Structured Legal Reasoning and Drafting with Large Language Models | This paper introduces Dallma, a framework combining predefined templates, logical rules, user input, and Large Language Models (LLMs) for semi-structured legal tasks like drafting and reasoning. The framework aims to improve the safety and utility of LLMs in law, with potential applications in enhancing access to justice. | True | Idealistic | True | 1.0 | Positive | The Dallma framework combines expert-defined templates (containing content, logic, variable specifications) with user interaction and calls to LLMs (e.g., GPT-4o) to perform semi-structured legal reasoning and document drafting tasks. | Two illustrative examples are presented using GPT-4o: one for spotting legal issues based on user input and another for reasoning about a Quebec Civil Code article concerning tenant eviction. Formal evaluation across various tasks is planned for future work. | The provided examples demonstrate plausible outputs where the system identifies relevant legal areas and applies legal criteria according to the template structure. No quantitative performance metrics are reported. | Difficulty for laypeople in understanding legal issues and completing complex legal forms; inherent limitations of LLMs such as hallucinations and difficulties with logical reasoning. | The Dallma framework proposes using semi-structured templates created by legal experts to guide LLMs and users. This approach constrains LLM outputs, integrates deterministic logic, allows user verification, and breaks down complex tasks into smaller steps to improve accuracy and safety. | Legal issue spotting, legal form completion, applying for social aid/benefits, automating legal reasoning and drafting. | Laypersons / Self-represented litigants. | General (issue spotting), Landlord-tenant law (specific example). | Quebec (for one specific example); potentially general applicability. | N/A (Uses pre-trained LLMs like GPT-4o; templates contain expert-defined content and logic, not ML training data). | NaN | Templates created by experts can be shared with target users, who can run the tool on their own computer. | False | False | NaN | Need to establish best practices for creating Dallma templates; requires formal evaluation of accuracy and performance; potential for extensions like Retrieval-Augmented Generation (RAG) and automatic template generation. | Designing effective and comprehensive templates; ensuring LLM reliability and adherence to constraints within the framework; developing user-friendly interfaces for both template creators and end-users; mitigating general LLM limitations (hallucinations, reasoning errors). | Potential for inaccurate LLM output despite the framework's constraints, although the design aims specifically to mitigate this risk common to LLM applications in law. |
| 5TdSakvWXuwJ.pdf | Google_Scholar | Expanding Access to Justice Through \nRegulatory Reform and Innovation: Arizona \nLessons from the Past, Present and Future | This paper details Arizona's historical and ongoing regulatory reforms and innovations aimed at expanding access to justice, covering changes in lawyer regulation, the introduction of non-lawyer legal service providers, and various court and technology initiatives. It presents Arizona's experience, including successes and challenges, as potential lessons for other jurisdictions working to narrow the justice gap. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | The large unmet need for civil legal services, particularly for low-income individuals; limitations of the traditional lawyer-centric model; cost of legal services; geographic disparities (legal deserts); the digital divide (lack of computer/internet access); resistance to regulatory reform. | Regulatory liberalization for lawyers (e.g., limited scope representation, admission by motion) and non-lawyers (e.g., LDPs, LLLPs, Legal Advocates, FHIP employees); creation of new non-lawyer roles; Alternative Business Structures (ABS); court process improvements (navigators, self-help, kiosks, remote hearings, digital evidence portals, ODR); leveraging technology (data analysis, potential of GAI); inter-agency/inter-jurisdictional collaboration; targeted programs (Lawyer Apprentice, tax credits, community justice workers). | Access to civil legal services, Domestic violence, Housing stability/Eviction, Family law, Consumer law/Debt collection, Public benefits, Administrative law, Fair housing, Legal needs of older adults/veterans/crime victims/homeless individuals. | Low-income Arizonans, Self-represented litigants, Domestic violence survivors, Individuals facing housing instability/eviction, Residents of rural areas/legal deserts, Older adults, Veterans, Crime victims, Immigrants, Children in foster care system. | Civil Law (Family, Housing, Consumer, Administrative, Fair Housing), Protective Orders, Wills/Estates, Criminal Law (minor offenses, post-conviction, LLLP area), Immigration Law, Torts (ABS area). | Arizona | NaN | Task forces, Committee reports and recommendations, Pilot programs authorized by court administrative orders, Public comment periods on rule changes, Development of training curricula and certification processes for non-lawyer providers (e.g., by i4J, AOC). | Arizona Supreme Court Administrative Orders, Amendments to court rules and Code of Judicial Administration, State Bar initiatives, Arizona Bar Foundation programs, Specific court programs (navigators, kiosks, remote hearings), State agency initiatives, Partnerships between academic institutions (i4J, ASU), legal aid organizations, and community groups. | True | False | Various described reforms and programs (e.g., LDPs, LLLPs, ABS, DVLA/HSLA pilots, remote hearings, court navigators, AZPOINT, online resources) are operational within Arizona's legal system or specific organizations/courts. | The significant overall access to justice gap remains; need for more service providers, especially in rural areas (legal deserts); the digital divide limits technology-based solutions; insufficient funding for legal aid; specific unmet needs in areas like eviction, domestic violence, debt, public benefits, mental health; ensuring fairness and mitigating bias in emerging AI tools. | Opposition to regulatory reform; establishing training, certification, and oversight infrastructure for new provider types; ensuring quality and consumer protection for non-lawyer services; addressing the digital divide; scaling pilot programs effectively; securing adequate funding. | Potential harm to the public from inadequately regulated or poorly delivered non-lawyer services; perceived threats to the traditional legal profession or justice system from reforms like non-lawyer ownership; potential for bias and lack of fairness in AI applications. |
| jVZShwYu2OUJ.pdf | Google_Scholar | Computational Law and AI Alignment in the Era of Large Language Models | This article examines the intersection of computational law, AI alignment, and risk mitigation concerning large language models (LLMs), discussing key concerns for various legal stakeholders. It explores AI alignment strategies, regulatory approaches, and concludes that a balanced approach between innovation and safety is essential to harness AI's transformative potential. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Concerns about reliability, correctness, and ethical implications (fairness, accountability, transparency) of AI. For individuals, ensuring quality legal support from AI tools and addressing the digital divide are key hurdles. | AI alignment strategies (explainability, transparency, fine-tuning, guardrails), comprehensive regulatory frameworks, development of benchmarks, and embedding legal principles into AI systems. | Affordable and accessible legal help, quality of AI legal support for consumers, digital divide in AI access. | General public/consumers, especially those needing affordable or accessible legal services. | General / Multiple | EU and US | The paper discusses techniques using various data. Examples include Anthropic's Constitutional AI (human principles, public input) and Pile of Law (open-source legal texts for filtering/research). | Techniques discussed include Constitutional AI (developed using supervised learning and reinforcement learning, including AI feedback and public input for drafting principles) and the Pile of Law (involves data gathering of legal texts, distillation of legal norms, and data-driven learning of filtering rules). | NaN | True | True | Mentions several available tools/datasets: e.g., Pile of Law (open-source dataset), Guardrails.ai (open-source), NeMo Guardrails (GitHub link provided), HELM (living benchmark), LegalBench (open for contribution). | Defining/tracing AI harm, achieving LLM explainability/transparency, robust guardrails, integrating symbolic AI with LLMs, and broadening benchmark contributions. | NaN | Reliability issues, bias, lack of accountability, malpractice, job displacement, privacy infringement, societal division, unauthorized practice of law, and generation of illegal/harmful content. |
| ys2Ue4MeS4MJ.pdf | Google_Scholar | Can GPT-4 Support Analysis of Textual Data in Tasks Requiring Highly Specialized Domain Expertise? | This paper evaluates GPT-4's ability to semantically analyze sentences from court opinions for interpreting legal concepts, a task requiring specialized legal expertise. It finds GPT-4 performs comparably to well-trained law students and explores prompt engineering, batch processing efficiency, and model sensitivity. | True | Idealistic | True | 2.0 | Positive | GPT-4 with zero-shot prompting using detailed annotation guidelines, including batch processing and chain-of-thought variations. | Comparison against gold-standard labels (consensus annotations by legal scholars) on a subset of the Statutory Interpretation Data Set, using Precision, Recall, F1-score, Accuracy, and Krippendorff's alpha. Compared performance against human (law student) annotators. | Best configuration (updated guidelines, single sentence processing) achieved F1=0.57, Accuracy=0.57, and Krippendorff's alpha=0.48 (or alpha=0.53 for labels only setting). Performance was comparable to well-trained law students. Batch processing workable but slightly less accurate; CoT ineffective. | High cost and requirement for specialized domain expertise for annotating legal texts, acting as a bottleneck for research and development. | Employing large language models (GPT-4) with detailed prompts derived from annotation guidelines for automated semantic analysis, potentially reducing cost and reliance on human experts. Iterative refinement of prompts/guidelines based on model output analysis. | Interpretation of legal concepts in statutory law; Legal understanding; Legal argumentation support. | NaN | Statutory Interpretation (Task); Data from multiple fields including Intellectual Property, Criminal Law (Cybercrime). | United States | The technique uses pre-trained GPT-4 (trained on broad web data). Evaluation uses the 'Statutory Interpretation Data Set' (publicly available on GitHub) containing sentences from US court opinions labeled by legal experts. | Prompt engineering (translating human guidelines, varying prompt structure for batching and CoT), comparative evaluation against human performance and gold standard, error analysis for iterative prompt refinement. | NaN | True | False | Requires access to OpenAI's commercial GPT-4 API. The annotation guidelines used for prompting are available on GitHub. | Need for broader testing across more tasks and larger datasets; Improving model robustness to prompt formatting; Addressing reproducibility challenges with proprietary models; Exploring few-shot/fine-tuning approaches. | Achieving high accuracy on complex legal tasks; Cost of API usage; Ineffectiveness of standard prompt techniques like CoT for this task; Model brittleness/sensitivity to prompt formatting. | Brittleness leading to unreliable predictions based on minor prompt changes; Potential for inaccurate analysis in complex legal tasks; General concerns about misuse of powerful LLMs (mentioned indirectly via OpenAI report). |
| dofdWxvXYDgJ.pdf | Google_Scholar | Can AI make a case? AI vs. Lawyer in the Dutch Legal Context | This paper investigates the quality of AI-generated (GPT-4) legal argumentation compared to human-written arguments in the Dutch legal context using an experiment with 25 legal professionals. Results showed a strong preference (80%) for the AI-generated document, highlighting AI's potential for tasks like legal drafting and information retrieval. | True | Idealistic | True | 2.0 | Positive | GPT-4 was used to generate a legal letter. The input for GPT-4 was prepared using prompt engineering, which included manual co-reference resolution on 9 case documents, a 'Prompt Reducer' technique (using a Python script) to compress these documents into a summary fitting token limits, and a specific prompt instructing GPT-4 to rewrite an original lawyer's letter based on this summary and the original letter. | An online survey was conducted with 25 Dutch legal professionals (judges, lawyers, other legal professionals). Participants were given a case summary and two anonymized legal letters (one human-written, one AI-generated by GPT-4) arguing the same side. They rated both texts on persuasiveness, clarity/coherence, strength of arguments, and use of evidence (1-10 scale), and then chose the more effective text, providing justification. | 80% of participants chose the GPT-4 generated legal document (Text B) as more effective. GPT-4's text received higher average scores than the human-written text (Text A) across all four evaluated dimensions (persuasiveness, clarity & coherence, strength of arguments, use of evidence) and across nearly all demographic subgroups (age, profession, gender). | Prohibitively expensive cost of traditional legal advice, slowness and poor quality of free legal aid services, language barriers excluding non-Dutch speakers from accessing free legal aid. | AI generating legal arguments and advice efficiently and in multiple languages to increase accessibility, timeliness, and equity. AI-driven tools for faster and more cost-efficient case preparation. AI-driven mediation processes. | Access to legal advice, cost of legal services, language barriers in legal services, efficiency in legal processes, quality of legal representation, legal drafting. | Economically disadvantaged individuals, expatriate population in the Netherlands, younger populations. | Employment Law | Netherlands (Dutch legal context) | Input for the GPT-4 generation task consisted of a processed summary derived from 9 proprietary, unstructured legal documents (e.g., letters, emails, reports) from a real-world Dutch employment dispute case, and the original lawyer's letter. The processing involved manual co-reference resolution and a 'Prompt Reducer' technique for text compression. | Experimental design comparing human-written vs. AI-generated text. Pre-processing of input case documents for GPT-4 involved manual co-reference resolution and a Python-scripted 'Prompt Reducer' technique. A specific instructional prompt was designed for GPT-4 to generate the alternative legal letter. | NaN | False | True | The Python script for the 'Prompt Reducer' technique and OpenAI API interaction is provided in Appendix 2 of the paper. | The study has limitations and requires replication in varied settings. Future research should explore client's unique circumstances as input, AI's impact on legal education, and client perspectives on AI-generated legal texts. Current AI, as used, may miss nuanced client context unless explicitly provided. | Initial AI summarization tests had factual inaccuracies due to differing author perspectives and pronoun ambiguity. The primary challenge was the token limitation of GPT-4, necessitating text compression techniques (Prompt Reducer) for the case documents. | AI 'hallucinations' (generating incorrect output). AI lacking nuanced understanding of case-specific information or client's broader, unstated circumstances unless explicitly provided. Ambiguity in legal responsibility and accountability for AI-assisted services. Perpetuation of human biases by AI models. Potential job displacement in the legal field. Risk of AI creating an imbalance in legal disputes if one party has superior AI tools. Potential for the judicial system to be overwhelmed if AI-generated filings increase significantly before the system can adapt. |
| 98-JoACmT0MJ.pdf | Google_Scholar | GENERATIVE ARTIFICIAL INTELLIGENCE PROMPT-KIT FOR ENHANCED LEGAL LEARNING AND ANALYSIS | This paper introduces a prompt-kit utilizing generative AI (specifically ChatGPT) to enhance legal education by providing structured guidance for tasks like case analysis, legal research, and mooting. The proposed kit aims to address limitations in traditional legal learning, such as lack of personalized feedback, and improve analytical skills for law students and professionals. | True | Market | True | 1.0 | Positive | A generative AI prompt-kit (compilation of >150 prompts) designed for use with ChatGPT to guide users through various legal learning tasks (case analysis, legal research, mooting, problem-based questions). | NaN | NaN | Limitations in traditional legal education: lack of personalized feedback and real-time guidance in legal analysis. | Develop a virtual legal research and analysis assistant (the prompt-kit) powered by generative AI to offer real-time feedback, answer queries, and guide students in applying legal principles via structured prompts. | Legal education, Legal analysis skills development, Legal research assistance. | Law students, Legal educators, Legal professionals. | General Legal Education | NaN | N/A (The technique is a set of prompts for an existing LLM, not a newly trained model). | Prompts developed based on identified processes for each aspect of legal education (e.g., case identification, summarization, analysis for case studies; pre-research inquiries for legal research). | NaN | False | False | NaN | Lack of research on leveraging and maximizing the potential of ChatGPT for legal education (prior research focused more on ethical concerns); Need for accuracy filters in ChatGPT; Concerns about potential deterioration in critical thinking. | NaN | Potential for student plagiarism, lack of accuracy in AI responses, deterioration in critical thinking skills, ethical and legal concerns regarding AI use in education. |
| AywYUXCuvmoJ.pdf | Google_Scholar | The Duty of Efficiency & Generative AI Pedagogy | This paper argues that lawyers have a professional duty of efficiency, which supports the adoption of Generative AI tools to enhance productivity and reduce costs for clients. It advocates for law schools to proactively teach students the ethical and effective use of AI, addressing its capabilities, limitations like hallucinations and bias, and the importance of human oversight. | True | Market | True | 3.0 | Positive | Generative AI (GenAI) / Large Language Models (LLMs), exemplified by tools like ChatGPT, Lexis+ AI, and Westlaw Practical Law AI. | Comparative analysis of responses from ChatGPT 4.0, Lexis+ AI Assistant, and Westlaw’s Practical Law AI to a legal question ('What are the elements of a slip and fall tort claim in California?'), critiquing their accuracy, citation quality, and practical utility. | The evaluated AI tools (ChatGPT, Lexis+ AI, Westlaw Practical Law AI) provide only a starting point for legal queries, requiring substantial human oversight. They exhibit issues such as lack of citations (ChatGPT), use of non-precedential sources (Lexis+ AI), or conceptual errors (Westlaw AI), and cannot be relied upon for direct legal work without verification. | The paper primarily discusses obstacles to effective AI adoption by legal professionals, which indirectly affects access to justice. These include: lawyers' resistance to technology and lack of technological competency; misuse of AI leading to inaccurate work; and courts' misguided responses to AI use. An implied obstacle to access to justice is the high cost of legal services. | The paper proposes education for lawyers and law students on AI's ethical and effective use, emphasizing existing professional duties (care, competence) for verifying AI output. Increased lawyer efficiency through proper AI use is presented as a way to potentially lower legal service costs, thereby improving access to justice. | Lowering cost of legal services through increased lawyer efficiency. | General public (by potentially making legal services more affordable and accessible if AI enhances efficiency). | General legal practice, litigation, legal research, professional ethics, legal education, tort law (example given). | United States (federal and various states including California, Missouri, Colorado, New York, North Carolina). A Canadian case is also mentioned (Ontario). | Details not provided in the paper for the specific tools evaluated (ChatGPT, Lexis+ AI, Westlaw AI), beyond general statements that LLMs are trained on large text corpora. Lexis+ AI and Westlaw AI likely use their proprietary legal databases. | NaN | NaN | True | False | ChatGPT has a publicly accessible version (including a free tier); Lexis+ AI and Westlaw Practical Law AI are commercial products available to subscribers. | The paper highlights gaps in lawyers' and law schools' current understanding and pedagogical approaches to AI. Regarding access to justice, it implies a general gap in the affordability of legal services, which AI's efficiency gains might help address if properly implemented. | Challenges discussed include: ensuring factual accuracy and avoiding 'hallucinations' in AI-generated content; maintaining client confidentiality when using AI tools; addressing and mitigating biases inherent in AI models; overcoming lawyers' technological illiteracy and resistance to change; and developing effective pedagogical methods for teaching AI in law schools. | Filing inaccurate or fabricated legal documents due to AI errors ('hallucinations'); Breaches of client confidentiality through inputting sensitive information into AI tools; Propagation of societal biases embedded in AI models; Misunderstanding of AI capabilities by legal professionals leading to misuse; Damage to the reputation of the legal profession and judicial system through AI abuse; Fraudulent billing practices if time saved by AI is not passed on to clients. |
| HZ-4I9MfOCgJ.pdf | Google_Scholar | COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis | This paper introduces DEBUG EVAL, a comprehensive benchmark for evaluating LLM code debugging abilities across multiple stages (localization, identification, repair, recognition). It also proposes the COAST framework, a multi-agent system for synthesizing high-quality training data, which significantly improves the debugging performance of smaller LLMs. | True | NaN | True | 1.0 | NaN | COAST (COmmunicative Agent-based data SynThesis) framework for generating SFT data, and the DEBUG EVAL benchmark for evaluating code debugging. | The COAST framework and resulting NeuDebugger models were evaluated on the newly proposed DEBUG EVAL benchmark. DEBUG EVAL includes four tasks (BUG Localization, BUG Identification, Code Repair, Code Recognition) across Python, C++, and Java, using Accuracy and Pass@1 as metrics. | COAST-generated data enabled 7B-scale LLMs (NeuDebugger models) to achieve debugging performance comparable to GPT-3.5. NeuDebugger-DS-6.7B improved by 27.7% and NeuDebugger-Llama3-8B by 4.1% over their respective base models on the DEBUG EVAL benchmark. | NaN | NaN | NaN | NaN | NaN | NaN | For fine-tuning NeuDebugger models: Synthesized Supervised Fine-Tuning (SFT) data generated by the COAST framework. This data covers bug localization, identification, repair, and recognition, and is created through interactions between Code Quizzer, Code Learner, and Code Teacher agents, initially seeded with examples from the DEBUG EVAL benchmark. | For COAST: Multi-agent communicative framework (Code Quizzer, Code Learner, Code Teacher); data synthesis based on critic-guided selection (problems incorrectly solved by Code Learner are curated); Chain-of-Thought (CoT) explanations by Code Teacher. For DEBUG EVAL: Emulation of human debugging process; data collection from existing benchmarks and human trials, with manual review. | All data for DEBUG EVAL and codes for the COAST framework are made available on GitHub. | True | True | All data and codes are available at https://github.com/NEUIR/COAST . | NaN | The effectiveness of COAST is constrained by the performance of a_foundation models used for its Code Quizzer and Code Teacher agents. The quality of synthesized data heavily relies on the capabilities of these foundation models. Chain-of-Thought reasoning can negatively affect performance in the Code Repair task. | NaN |
| --chwZiMxA0J.pdf | Google_Scholar | Generative Contracts | This paper explores how consumers can use generative AI like GPT-4 to draft their own basic contracts, presenting this as an opportunity to improve access to justice for underserved populations. It demonstrates GPT-4's capabilities through generated contract examples and a case study, while also discussing the implications, limitations (like potential inaccuracies), and risks (technological, privacy, regulatory). | True | Idealistic | True | 2.0 | Positive | Using OpenAI's GPT-4 large language model via ChatGPT to generate various types of consumer contracts based on simple user prompts. | Qualitative evaluation based on generating drafts of over a dozen different contracts (employment, lease, bill of sale, etc.) using GPT-4 with simple prompts, plus a proof-of-concept case study of hypothetical consumers using GPT-4 to draft and modify a car sale contract. | GPT-4 generated contracts that were generally functional, enforceable, short, and simple, though susceptible to errors and inconsistencies. The case study highlighted ease of use, speed, low cost, flexibility, and modifiability. Quality was deemed lower than lawyer-drafted contracts but likely superior to undocumented 'handshake' deals. | High cost of legal services, shortage of lawyers (particularly in rural 'legal deserts'), and the difficulty consumers face in reading and understanding legal documents. | Leveraging generative AI (like GPT-4) to provide consumers with low-cost, easily accessible, and user-friendly tools ('generative contracts') to draft their own basic contracts. | Drafting basic consumer contracts (e.g., contracts for sales, services, leases, employment, NDAs). | Consumers underserved by the legal system, particularly low-income Americans and rural populations facing lawyer shortages. | Contract Law, Consumer Law, Transactional Law | Primarily California, USA (used for examples and specific legal references), but the concept is presented with broader applicability. | The study used OpenAI's GPT-4, which is generally known to be trained on massive, diverse datasets scraped from the internet. The paper does not specify further details or mention fine-tuning on legal data for this study. | NaN | NaN | True | False | The method relies on OpenAI's ChatGPT interface with the GPT-4 model, accessible via a paid subscription (ChatGPT Plus). | Current limitations in drafting long, complex business contracts using generative AI. Need for further research into fine-tuning LLMs for specific legal applications and prompt engineering in law. Implicitly, the need for consumer adoption and mitigation of technological/societal risks. | NaN | Technological risks (inscrutability, accuracy/hallucination, bias, adversarial attacks), privacy/data protection risks (violation of privacy laws like GDPR, breach of client confidentiality), intellectual property infringement risks (use of copyrighted training data), and regulatory risks (unauthorized practice of law, impact of emerging AI regulations). |
| B09Gn6auhTQJ.pdf | Google_Scholar | Comprehensibility and Automation: Plain Language in the Era of Digitalization | The paper presents a pilot machine-learning experiment using SVM and fastText models to automatically classify the comprehensibility of official Hungarian texts addressed to lay readers. The goal is to identify problematic sentences to assist experts in rephrasing them, thereby improving access to justice and the transparency of governmental organizations. | True | Idealistic | False | 1.0 | Positive | Machine learning (Support Vector Machine and fastText models) for binary classification of official text sentences into 'original' (less comprehensible) and 'rephrased' (more comprehensible). | A hand-crafted corpus of original and rephrased sentences from the National Tax and Customs Administration of Hungary documents from 2021. SVM models were evaluated using 10-fold cross-validation (metrics: precision, recall, F1-score). FastText models were evaluated on an 80/20 train/test split (metric: F1-score), with multiple runs per epoch averaged. | SVM models achieved a stable precision of approximately 0.72 in identifying original (less comprehensible) sentences. | Linguistic complexity, specialized language, and over-complicated sentence structures in official/legal documents that hinder comprehension by laypersons. | Developing machine learning models to automatically identify sentences in official texts that are likely difficult to comprehend, thereby assisting human experts in the process of rephrasing these texts into plain language. | Improving comprehensibility of official administrative and legal texts for laypersons; enhancing access to justice; promoting transparency of governmental organizations; supporting the rule of law. | Laypersons, including individuals interacting with governmental (e.g., tax administration) and legal systems, who lack specialized domain knowledge. | Administrative law, Tax law (specifically informational texts from tax administration). | Hungary | A proprietary, domain-specific corpus of 10,883 Hungarian sentences (original and expert-rephrased pairs) from 2021 informational documents of the National Tax and Customs Administration of Hungary. Data is unstructured text. | Supervised machine learning (binary classification). Data preparation included sentence segmentation (using a heuristic approach), creation of 'original' and 'rephrased' sub-corpora, noise reduction (e.g., removing sentences <10 tokens), text preprocessing (lowercasing, removing numbers/punctuation, lemmatization using HuSpaCy). Hyperparameter tuning was conducted for both SVM (with TF-IDF vectorization) and fastText (with corpus-trained embeddings) models. | NaN | False | False | NaN | The developed models are highly domain-specific due to limited quantity and diversity of training data. Further research is needed for comprehensive error analysis and exploration of more advanced NLP models to improve performance and generalizability. | Challenges included: 1) Reliable sentence segmentation of Hungarian official texts, particularly handling legal references and listings. 2) Overfitting when using pre-trained fastText word vectors on the specific corpus. 3) Limited customization options within the fastText library for fine-tuning neural network architecture. | NaN |
| Wx_p4tUveXoJ.pdf | Google_Scholar | A Question-Answering Approach to Evaluating Legal Summaries | This paper proposes a novel method using GPT-4 to evaluate the quality of legal summaries by generating question-answer pairs based on argumentative structure (Issue, Reason, Conclusion). The approach involves using GPT-4 to answer these questions based on a generated summary and then grading the answers, showing reasonable correlation with human evaluations. | True | Idealistic | True | 1.0 | Positive | QA-based evaluation framework for legal summaries using GPT-4. It involves: 1) Generating QA pairs from a reference summary based on argumentative structure (Issue, Reason, Conclusion). 2) Answering these questions based on the generated summary. 3) Grading the generated answers against the reference answers. | Compared GPT-4 evaluation grades (0-10 scale, binarized at thresholds 5 and 6) with human binary evaluations ('YES'/'NO') for answers derived from summaries created by BART, LED, and GPT-4. Evaluation used 10 Canadian case summaries (48 QA pairs) and Pearson/Spearman correlation metrics. | Correlations varied by summary generation model and argumentative component (Issue, Reason, Conclusion). The LED model showed the highest overall correlation (IRC Pearson 0.87/0.88, Spearman 0.84/0.85 at thresholds 5/6). Negative correlations were observed between GPT-4 grades and human evaluation for 'Reason' type questions on BART and GPT-4 generated summaries. | Difficulty in automatically evaluating the quality and argumentative structure of legal summaries, hindering the reliable assessment of tools meant to make legal text more accessible. | A QA-based evaluation framework using LLMs (GPT-4) to assess summary quality by focusing on argumentative structure (Issue, Reason, Conclusion), potentially improving summary generation and accessibility. | Evaluation of legal text summarization quality | NaN | General Case Law | Canada | Summarization models (BART, LED) fine-tuned on a dataset of 1,049 annotated Canadian legal case summaries (Issue, Reason, Conclusion annotations) paired with full texts from the Canadian Legal Information Institute. GPT-4 used zero-shot. The evaluation method itself (GPT-4 based QA) did not require specific training data beyond GPT-4's pre-training. | Experimental design involving prompt engineering for GPT-4 (QA generation, answer prediction, grading) and comparison with human evaluations using correlation analysis (Pearson, Spearman). | Code made available on GitHub. | True | False | Code available on GitHub (https://github.com/JoyceXu02/QA_evaluation), requires GPT-4 API access. | Sensitivity to prompt engineering, need for larger-scale evaluation, quality control for LLM generation (hallucinations, consistency, especially with longer input), potential exploration/calibration using open-source models. | Cost of GPT-4 API and human evaluation limited test size; aligning automated scores with human perception; controlling for hallucination or out-of-context answers generated by the LLM; achieving consistent correlation across different argumentative components (Issue, Reason, Conclusion). | LLM hallucination in generated answers. Potential divergence between automated evaluation scores and human judgments of quality (e.g., negative correlations found for 'Reason' component). |
| EM0LbiC9NWUJ.pdf | Google_Scholar | Exploring the Comprehension of ChatGPT in Traditional Chinese Medicine Knowledge | This paper introduces TCM-QA, a novel question-answering dataset for Traditional Chinese Medicine, and uses it to evaluate ChatGPT's (GPT-3.5) comprehension abilities. The study finds ChatGPT performs moderately, with better results on true/false questions and using Chinese prompts, but struggles with complex reasoning and can generate misinformation. | True | NaN | True | 2.0 | NaN | ChatGPT (gpt-3.5-turbo model) for question-answering in Traditional Chinese Medicine (TCM) using zero-shot and few-shot prompting, evaluated on a new dataset (TCM-QA). | Evaluation on a custom-built dataset called TCM-QA, comprising 801 questions (574 single-choice, 131 multiple-choice, 97 true/false) categorized into knowledge-based, diagnostic-based, and treatment-based reasoning. Performance was measured by precision and responsiveness, with human evaluation for explanation quality (readability, reliability, integrity). | ChatGPT performed best in true or false questions, achieving the highest precision of 0.688 (few-shot, Chinese prompt), while scoring the lowest precision (0.241, few-shot, English prompt for multiple-choice, though Chinese zero-shot reached 0.240). Chinese prompts generally outperformed English prompts. Human evaluation of explanations for correct answers showed high readability (avg. ~2.9) and good reliability/integrity (avg. ~2.6), but lower for incorrect answers, where ChatGPT generated 'illusions'. | NaN | NaN | NaN | NaN | NaN | China | The paper evaluates ChatGPT (GPT-3.5), whose general pre-training data is primarily English web text. The evaluation dataset, TCM-QA, was newly constructed by the authors from BaiduWenKu, refined, and verified by TCM experts, containing Chinese language questions specific to Traditional Chinese Medicine. | Creation of a new domain-specific QA dataset (TCM-QA). Application of prompt engineering (zero-shot and few-shot settings in English and Chinese). Automated evaluation using precision and responsiveness metrics. Human evaluation of AI-generated explanations based on readability, reliability, and integrity. | NaN | False | True | The TCM-QA dataset, a core component of the study for evaluating ChatGPT, is released on GitHub (https://github.com/yizhen-buaa/TCM-QA-datasets). The overall approach involves using this dataset with ChatGPT and can be replicated. | NaN | ChatGPT's shallow understanding of TCM knowledge due to limited TCM content in training data; misinterpretation by focusing on keywords over sentence context; generation of 'illusions' (erroneous TCM knowledge); linguistic bias affecting comprehension of non-English (Chinese) questions, though Chinese prompts helped. | Generation of 'illusions' by ChatGPT, where it invents erroneous TCM knowledge to substantiate its rationale, posing a risk of misinformation if used without expert oversight. |
| A_Hybrid_Transformer-Based_Framework_for_Multi-Document_Summarization_of_Turkish_Legal_Documents.pdf | Google_Scholar | A Hybrid Transformer-Based Framework for Multi-Document Summarization of Turkish Legal Documents | This paper presents a novel hybrid framework combining extractive (TF-IDF, TextRank) and abstractive (LED, Long-T5, BART-large, GPT-3.5 Turbo) techniques for multi-document summarization of Turkish legal texts. It also introduces a new dataset of 2,000 Turkish civil cases, with GPT-3.5 Turbo demonstrating the best summarization performance. | True | Market | True | 1.0 | Positive | A hybrid framework for multi-document summarization combining extractive methods (TF-IDF, TextRank) with abstractive transformer-based models (LED, Long-T5, BART-large, GPT-3.5 Turbo). | Extractive methods (TF-IDF, TextRank) were evaluated using cosine similarity, content precision, recall, and F1-score against keyword-based summaries. Abstractive transformer models were evaluated using ROUGE scores (ROUGE-1, ROUGE-2, ROUGE-L, ROUGE-Sum) against human-written reference summaries on a custom dataset of 2,000 Turkish civil cases. | GPT-3.5 Turbo achieved the highest ROUGE scores (ROUGE-1: 55%, ROUGE-2: 35%, ROUGE-L: 42%, ROUGE-Sum: 44%) for abstractive summarization. | The volume and complexity of legal documents, and unique linguistic challenges (e.g., agglutinative structure, domain-specific terminology) in languages like Turkish, hindering efficient processing of legal information which can be a barrier to access to justice. | Development of automated multi-document summarization tools using a hybrid AI framework (extractive + abstractive methods) to make large volumes of legal text more manageable and understandable, thereby enhancing the efficiency of legal professionals which indirectly supports access to justice. | Improving the efficiency of legal information processing and understanding (specifically case law) for legal professionals, which can contribute to a more accessible justice system. | NaN | Civil law (specifically consumer rights cases) | Turkey | A new dataset of 2,000 Turkish civil cases (court rulings related to consumer rights), curated and validated from publicly accessible legal platforms. The data is unstructured legal text. | Dataset creation through web scraping and preprocessing; application and fine-tuning of existing extractive (TF-IDF, TextRank) and abstractive (transformer models) NLP techniques; empirical evaluation and comparison of different models. | NaN | False | False | NaN | Need for further development and refinement of AI-powered legal document summarization tools for under-resourced languages like Turkish, particularly in addressing technical limitations such as handling long texts, ensuring factual accuracy (mitigating hallucination), preserving legal nuances, and expanding domain coverage, to improve their utility for legal professionals and potentially broaden access to justice. | Collecting and cleaning legal documents (removing metadata, retaining essential content); handling complex grammar and domain-specific legal terminology of the Turkish language; managing long input sequences due to token limits of transformer models; computational challenges (GPU memory, training efficiency); ensuring content completeness within token limits; mitigating hallucination in abstractive summaries. | Hallucination in abstractive summaries (generating information not present in the original text); failure of models to preserve critical legal details or nuances during summarization; loss of context during the abstraction phase, impacting legal accuracy. |
| Enhancing_the_Precision_and_Interpretability_of_Retrieval-Augmented_Generation_RAG_in_Legal_Technology_A_Survey (1).pdf | Google_Scholar | Enhancing the Precision and Interpretability of Retrieval-Augmented Generation (RAG) in Legal Technology: A Survey | This paper surveys the application of Retrieval-Augmented Generation (RAG) techniques within legal technology, focusing on enhancing the precision and interpretability of Large Language Models (LLMs). It reviews RAG methods, evaluation metrics, datasets, ethical considerations, challenges, and future research directions specific to the legal domain. | True | NaN | True | 3.0 | Positive | NaN | NaN | NaN | Hallucination/accuracy issues in generated legal information, difficulty handling complex legal queries, high computational cost potentially limiting access, ethical concerns (bias, privacy, safety). | Technical improvements to RAG (retrieval/generation optimization, better models, fine-tuning), development of comprehensive legal datasets and standardized evaluation methods, addressing ethical concerns through design and policy, exploring advanced techniques like knowledge graph integration and reinforcement learning. | Legal information access, Legal question answering, Simplifying legal processes. | NaN | Multiple fields including immigration law, tax law, EU law, case law (various domains), contract law, patent law, privacy law. | International (mentions US, China, Australia, France, EU, Pakistan, Montenegro) | The surveyed papers utilize a variety of datasets, including publicly available legal corpora (case law, statutes, EU law), domain-specific Q&A pairs, court records, contracts, privacy policies, and potentially proprietary legal documents. Data is largely unstructured text but sometimes includes structured elements like Q&A or knowledge graphs. | RAG pipeline design (standard, iterative, adaptive), Knowledge Graph (KG) integration, embedding model selection/fine-tuning, various retrieval techniques (sparse, dense, hybrid), prompt engineering, LLM fine-tuning (QLoRA, full FT), contrastive learning for retrieval, human/expert curation for datasets. | NaN | False | False | NaN | Need for datasets covering more legal domains and languages; lack of benchmarks evaluating interpretability/explainability; difficulty handling cross-jurisdictional differences; challenges in scaling RAG for complex multi-domain scenarios; inadequate addressing of ethical concerns; limitations in handling complex reasoning; high computational costs. | Computational cost and complexity; Achieving robustness (avoiding 'no response' or 'hallucination'); Handling complex queries (ambiguity, multi-hop reasoning); Dependence on retrieval accuracy (noise, relevance); Evaluation metric limitations (lack of standardization, inadequacy for assessing factual/semantic quality). | Generating hallucinated or factually incorrect legal information; Bias in models or data leading to unfair outcomes; Privacy violations through mishandling of sensitive legal data; Lack of system safety and robustness; Erosion of trust due to poor performance or opacity; Misleading users with incorrect/incomplete answers. |
| 6kXvecJ69wAJ.pdf | Google_Scholar | Generative AI as Tax Attorneys: Exploring Legal Understanding Through Experiments | This paper investigates the legal understanding and reasoning capabilities of OpenAI's GPT-4 and GPT o1-preview models in the context of Polish tax law. It finds that while models show significant improvement and can support tax professionals, they are not yet reliable for independent advice due to inaccuracies and a high rate of hallucination in citing legal precedents. | True | Market | True | 2.0 | Positive | OpenAI's GPT-4 and GPT o1-preview models, evaluated using a proposed Quality of Legal Reasoning Indicator (QLR). | Four experiments: 1) GPT models answered 100 Polish tax advisor exam questions. 2) GPT o1-preview answered 40 practical tax law questions from LEX service, compared to expert answers. 3) GPT o1-preview predicted National Revenue Administration Information Centre (NRAIC) positions for 45 private tax rulings. 4) Legal reasoning of GPT o1-preview for 45 private tax ruling scenarios was evaluated by 5 experts using the Quality of Legal Reasoning Indicator (QLR). | The GPT o1-preview model achieved 81% accuracy on a test of 100 questions from the Polish tax advisor exam, passing the 80% threshold. In another experiment, it demonstrated a 73.33% accuracy in predicting NRAIC positions. However, it showed a 50% hallucination rate in citing court decisions and PTRs. | Current LLM limitations (accuracy, high rates of hallucination, comprehension of complex and evolving legal data, multilingual/multicultural nature of law) hinder their direct application for widespread, reliable access to justice without professional oversight. | Using LLMs to assist legal professionals to increase efficiency and reduce service costs, thereby indirectly making legal aid more affordable. Technically, developing domain-specific models, using Retrieval Augmented Generation (RAG) architecture, and employing external models to validate reasoning quality. | Improving affordability and accessibility of professional legal advice (tax law) through AI-driven efficiencies. | General public, particularly those who currently find professional tax advisory services unaffordable. | Tax law (including PIT, CIT, VAT, Excise tax, Property tax, Inheritance and gift tax, Tax on means of transport) | Poland (with reference to European Union law for VAT) | The paper uses OpenAI's pre-trained GPT-4 and GPT o1-preview models, which are trained on very large, general, and proprietary datasets. The study notes that performance is influenced by the volume of domain-specific data encountered, e.g., EU-harmonized VAT data resulted in better performance. | The Quality of Legal Reasoning Indicator (QLR) was developed based on the clarification concept (Dascal and Wróblewski, 1988) and the derivation concept (Zieliński, 2017) of legal interpretation, involving expert assessment on five key aspects of legal reasoning. | NaN | True | False | OpenAI's GPT-4 is commercially available. The paper states GPT o1-preview premiered on 12.09.2024 and was used in experiments in October 2024, implying availability to researchers, likely through OpenAI. | LLMs are not yet accurate or reliable enough for independent legal advice or direct use by laypersons in access to justice contexts. Significant issues like hallucination in citing legal precedents (50% in one experiment) need to be addressed, possibly through RAG architectures or domain-specific models. | The main challenges faced in applying LLMs to law include high hallucination rates (especially in citing case law), ensuring understanding of complex legal language and reasoning, keeping up with the ever-changing nature of law, and the need for large, high-quality domain-specific training data. | Risk of providing incorrect legal advice due to model inaccuracies and hallucinations. Specifically, a strong hallucinatory effect (50% of cases) was observed in the analysis of court decisions and PTRs, where models cited non-existent rulings. |
| 7hNr4uL27jwJ.pdf | Google_Scholar | Dynamic LLM Routing and Selection based on User Preferences: Balancing Performance, Cost, and Ethics | This paper introduces OptiRoute, an advanced model routing engine designed to dynamically select the optimal LLM for tasks by balancing user-defined functional (cost, speed, accuracy) and non-functional (ethical) criteria. OptiRoute utilizes lightweight task analysis, complexity estimation, and kNN search to match tasks with models from a diverse repository, aiming to optimize LLM deployment across various applications. | True | Market | True | 1.0 | NaN | OptiRoute: A model routing engine using a Task Analyzer (fine-tuned LLM like FLAN-T5 for task/complexity/domain prediction), a Model Registry Evaluation Store (MRES - in-memory vector DB with model metrics), and a kNN-based Routing Engine with filtering to select LLMs based on user-specified functional (accuracy, speed, cost) and non-functional (helpfulness, honesty, harmlessness) requirements. | The Task Analyzer component is described as being fine-tuned using supervised and synthetic data derived from production query logs (human annotated and semi-supervised). The overall OptiRoute system incorporates a user feedback loop (thumbs up/down) for continuous refinement of the routing policy, but the paper does not present specific benchmark evaluations or comparative experimental results for the complete system. | NaN | NaN | NaN | NaN | NaN | General legal services / legal document processing | International | For the Task Analyzer: Proprietary query logs from a production MLaaS cloud provider, labeled via human annotation and semi-supervised learning, supplemented with synthetically generated data (self-align, self-instruct). Unstructured text queries. | System architecture design involving: user preference capture (explicit and implicit), a Task Analyzer (fine-tuned autoregressive encoder-decoder LLM), a Model Registry and Evaluation Store (MRES - in-memory vector database with normalized model metrics), a kNN-based Routing Engine with filtering and scoring using cosine similarity, and a user feedback loop for continuous policy refinement. Batch and interactive modes of operation are supported. | The paper mentions its potential deployment on Freshworks' Freddy ML enterprise platform and discusses its applicability to cloud-based MLaaS platforms (e.g., AWS, Google Cloud, Azure). | False | False | NaN | NaN | Challenges addressed by OptiRoute's design include: balancing cost, latency, accuracy, and ethical considerations in LLM selection; navigating the vast number of available models; efficient task analysis and complexity estimation; integrating diverse user preferences (functional and non-functional); normalizing diverse model performance metrics; and ensuring ethical AI behavior. | Propagation of biased or harmful content, erosion of user trust, potential regulatory repercussions, detrimental effects of latency in real-time applications, and hallucination risks in LLMs. |
| RtbC1q5BGA0J.pdf | Google_Scholar | How well do SOTA legal reasoning models support abductive reasoning? | This paper introduces L'ART, a new logic-augmented dataset, and a redefined task (𝛼𝑁𝐿𝐼*) to evaluate abductive reasoning in AI models, particularly in the legal domain. Experimental results show that current state-of-the-art legal models and large language models generally perform poorly, highlighting limitations in their abductive reasoning capabilities. | True | Idealistic | True | 1.0 | Neutral | L'ART dataset, a logic-augmented dataset for abductive reasoning, and the 𝛼𝑁𝐿𝐼* task, a redefined binary classification task for evaluating abduction. | The L'ART dataset and 𝛼𝑁𝐿𝐼* task were used to evaluate several SOTA transformer models (BERT Base/Large, BERT-PLI, Legal BERT, BERTLaw, NFSP ParaLaw Nets, GPT-3) on their abductive reasoning capabilities. Models were trained for binary classification (valid/invalid triple) and performance was measured by accuracy on a held-out test set. | BERT Base achieved the highest test accuracy (0.6162), outperforming specialized legal models (e.g., Legal BERT 0.5619, BERTLaw 0.5371) and even BERT Large (0.5000). GPT-3 (zero-shot) had the lowest accuracy (0.4959). | Current AI models, including SOTA legal-specific LLMs, exhibit poor performance on abductive reasoning tasks, which are crucial for legal argumentation and interpretation. This deficiency limits their reliability for complex legal applications that could support access to justice. | Developing more robust datasets (like L'ART) and evaluation tasks (like 𝛼𝑁𝐿𝐼*) for abductive reasoning. Future research should explore alternative pretraining approaches, novel model architectures, and better integration of legal domain knowledge to improve AI's abductive reasoning capabilities. | Improving foundational AI capabilities for legal reasoning (specifically abductive reasoning) to support the development of more reliable AI tools for legal services and access to justice. | Underserved communities (mentioned generally). | General legal reasoning (examples include statute law retrieval, contract risk analysis, case law retrieval). | International | The L'ART dataset (498,697 samples) is built upon the ART dataset (crowdsourced commonsense narrative contexts). It includes: 1) high-plausibility positive samples from ART, 2) newly generated positive samples using a logic-based theorem generator on logically consistent inference chains, 3) augmented positive samples by interchanging the first observation (𝒪1) and hypothesis (ℋ), and 4) negative samples derived by logically negating the second observation (𝒪2) based on a truth table for the expression 𝒪1∧ℋ =⇒ 𝒪2. | Task redefinition (binary classification of (O1, H, O2) validity instead of choosing between two hypotheses), logic-based data generation and augmentation (observation-hypothesis interchangeability), and systematic negative sample creation using logical negation and truth tables, based on an initial crowdsourced dataset (ART). | NaN | False | False | NaN | Current SOTA models, including legal-specific ones, lack robust abductive reasoning. Technical gaps include: need for alternative pretraining approaches tailored to abductive reasoning, development of novel model architectures for legal reasoning, better understanding of model capacity vs. performance on such tasks, and effective integration of legal domain knowledge into pretraining. | Ensuring logical consistency and quality in initial crowdsourced data (ART). Defining abductive reasoning tasks precisely for evaluation. Generating meaningful and logically sound negative samples for abductive reasoning. Overcoming the bias in legal models trained primarily on legal reasoning rather than abductive reasoning. | Over-reliance on current SOTA LLMs for legal tasks that require significant abductive reasoning, given their demonstrated poor performance, potentially leading to flawed legal analyses or applications. Misdirection of development efforts if the limitations in abductive reasoning are not addressed. |
| E7b4JLhQct8J.pdf | Google_Scholar | The Courtrooms Strikes Back: Generative AI’ s Force in Courts | This paper explores the increasing use of generative AI by judges in judicial decision-making, highlighting its potential to enhance court legitimacy and efficiency, which can support access to justice. However, it also details significant risks, such as bias, unreliability, and ethical concerns from AI systems like ChatGPT, which could undermine court legitimacy if not properly managed. | True | Idealistic | True | 3.0 | Neutral | Generative AI systems (e.g., ChatGPT) for judicial assistance tasks like legal drafting, case law summarization, and acting as a 'virtual sparring partner'. | NaN | NaN | Bias and unreliability of AI systems due to opaque 'black box' nature and unrepresentative training data, risk of AI 'hallucinations', erosion of public trust and court legitimacy, and ethical concerns regarding AI's normative impact and the influence of private developers on judicial independence. | Fostering AI literacy within the judiciary through training, and enhancing transparency and accountability in judicial decision-making, potentially by strengthening the judicial duty to state reasons when AI is used. | Improving efficiency of judicial processes (leading to faster case resolution) and enhancing the quality of judicial decisions, which are pre-requisites for effective access to justice; maintaining court legitimacy. | NaN | General (judicial decision-making across various fields) | Colombia, India, UK, Council of Europe (CEPEJ). Discussion is broadly applicable. | NaN | NaN | NaN | True | False | The paper discusses the use of existing generative AI systems like ChatGPT, which are commercially available with free or paid access tiers. | Technical gaps include lack of transparency, bias, and unreliability in current generative AI. Societal gaps include insufficient AI literacy in the judiciary, need for enhanced transparency and accountability mechanisms (e.g., duty to state reasons), and concerns over democratic oversight and private sector influence on AI used in courts. | NaN | Unreliable or biased outputs due to opaque models and unrepresentative training data, AI 'hallucinations' (generating false information), compromised judicial independence and impartiality from private developers' influence, privacy and data protection violations when handling sensitive data, judges' over-reliance due to automation bias, and overall erosion of court legitimacy and public trust. |
| zrpW9rvsnukJ.pdf | Google_Scholar | Digitalisation of the Slovenian Justice System and Its Discontents | This paper investigates the digitalisation of the Slovenian justice system, revealing a significant gap between the high expectations for digital tools and the practical challenges of their implementation, such as technical issues and user resistance. It advocates for an integrated, strategic approach with stakeholder involvement to improve judicial efficiency, accessibility, and transparency effectively. | True | Idealistic | False | 2.0 | Neutral | Digitalisation tools in the Slovenian justice system (e.g., electronic case files, automated transcription software 'Tipko', videoconferencing, use of social media by courts) | The paper's authors evaluated the impact and reception of these digitalisation efforts through qualitative analysis of in-depth interviews and focus groups with 85 diverse court users in Slovenia. | Digital tools showed potential benefits (e.g., efficiency, accessibility for some), but their implementation faced significant practical difficulties including outdated hardware, digital literacy gaps, increased workload, slow deployment, privacy concerns, and mixed reception among users, often leading to new problems. | Outdated infrastructure and insufficient budgets, varying digital literacy and resistance to change among users, digital divide affecting equitable access, lack of user consultation in technology deployment, slow adoption of effective tools, and potential for technology to be misused (e.g., as an excuse for inaccessible facilities). | Adopting an integrated and strategic approach to digitalisation, ensuring comprehensive stakeholder involvement and user training, investing in adequate infrastructure, developing clear communication strategies (including social media use by courts), and tailoring technology to specific needs while safeguarding against misuse. | Enhancing accessibility of court services and information (especially for vulnerable groups), improving transparency of judicial processes, increasing system efficiency, ensuring fairness in technologically-mediated proceedings, and fostering public understanding and trust in the judiciary. | People with special needs and disabilities (e.g., mobility impaired, deaf/hard of hearing), elderly individuals, foreigners, and the general public in terms of understanding and accessing the justice system. | General court system, including criminal justice, civil litigation, and administrative court processes. | Slovenia | NaN | NaN | For specific tools like 'Tipko' (automated transcription): pilot program at one court with slow/stalled broader rollout. For electronic case files: gradual transition, varying across legal areas. | False | False | NaN | Discrepancy between the promise of technology and implementation realities; significant digital divide; inadequate digital infrastructure in courts; lack of a cohesive digital strategy and stakeholder buy-in; slow deployment of useful technologies; insufficient consideration of ethical implications of AI. | Technical issues (e.g., outdated hardware, software incompatibility, unreliable connections for videoconferencing), user resistance and varying digital literacy, increased workload from new processes (e.g., longer transcripts from recordings), slow deployment due to administrative or budgetary hurdles, and lack of user consultation during implementation leading to mismatches with needs. | De-skilling of legal professionals, loss of nuanced information in automated processes (e.g., "lost in translation" with transcription), algorithmic bias and lack of fairness, privacy violations, cybersecurity threats, exacerbation of digital divide, and potential for technology to be misused or lead to unintended negative consequences (e.g., LLM hallucinations, videoconferencing as a substitute for physical accessibility). |
| cZ5qYLunKBoJ.pdf | Google_Scholar | Is disclosure and certification of the use of generative AI really necessary? | This paper critiques the proliferation of individual judicial standing orders requiring disclosure and certification of generative AI (GenAI) use in legal filings, arguing they are often redundant, inconsistent, and may stifle innovation beneficial for access to justice. It proposes instead the adoption of consistent, court-wide rules developed through public consultation, or public notices, and emphasizes the applicability of existing legal and ethical rules. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Inconsistent and burdensome individual judicial regulations regarding GenAI; the inherent unreliability of general GenAI (e.g., hallucinations, erroneous outputs) especially for pro se litigants; and potential discouragement of technology that could enhance access to justice. | Implement consistent, court-wide rules for GenAI use, developed after public notice and comment, instead of individual standing orders. Provide public guidance, especially for pro se litigants, on responsible GenAI use and verification obligations. Leverage existing rules of civil procedure and professional conduct, and encourage education by bar associations. | Judicial regulation of AI in legal practice; impact of AI governance on access to justice; enabling unrepresented parties (pro se litigants) to utilize legal tech; reducing legal costs and increasing efficiency through AI. | Pro se litigants (unrepresented parties). | Civil litigation | United States, Canada | NaN | NaN | NaN | False | False | NaN | The need for a nuanced, consistent, and less burdensome regulatory approach to GenAI in the legal system that encourages beneficial uses for access to justice. The current unreliability of general-purpose GenAI for complex legal tasks and the limited access for pro se litigants to more specialized and verified legal AI tools. Lack of comprehensive institutional guidance from bodies like bar associations. | NaN | Generation of inaccurate legal information (hallucinations, fake citations) by GenAI; infringement on attorney work product due to overly broad disclosure orders; chilling innovation and use of technology beneficial for access to justice; inconsistent judicial orders creating confusion and increasing costs; disclosure of confidential client information when using public GenAI tools; difficulty in accurately detecting AI-generated content; lawyers violating ethical duties (competence, candor, confidentiality) through improper GenAI use. |
| CMs1hDgYQYEJ.pdf | Google_Scholar | AI vs. Human Translators: Navigating the Complex World of Religious Texts and Cultural Sensitivity. | This paper evaluates the performance of AI translation tools, specifically ChatGPT and Google Translate, against human translators in rendering English religious texts into Arabic. The findings indicate that while AI tools offer fairly accurate translations, human translators consistently provide superior quality, particularly in conveying depth, cultural relevance, and nuanced understanding inherent in complex religious content. | True | NaN | True | 2.0 | NaN | ChatGPT and Google Translate (Neural Machine Translation) | Qualitative and comparative data analysis of translations of seven English to Arabic religious texts. Evaluation criteria included word choice, word count, readability, overall translation quality (fluency, accuracy, meaning loss), and punctuation, with Nvivo software used for coding and analysis. | Human translation consistently outperformed machine translations (ChatGPT and Google Translate), maintaining depth, cultural relevance, and nuanced understanding. Machine translations, while sometimes accurate, were often more concise, potentially missing significant elements, and exhibited issues like repetition or grammatical errors in complex sentences. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | True | False | ChatGPT and Google Translate are publicly accessible online translation services. | NaN | Machine translation tools (ChatGPT and Google Translate) faced difficulties in preserving linguistic nuances, context, and complex sentence structures accurately, especially in culturally and religiously loaded texts. Repetition was observed in machine translations, and achieving correct grammatical structure in Arabic was a challenge. | Mistranslation or meaning loss of essential elements, blurring of intended meaning due to inaccurate word choice, loss of cultural and religious nuances, grammatical errors in the translated text, and repetition affecting readability. |
| B-J7tTJ2YCMJ.pdf | Google_Scholar | Breaking the Loop: Detecting and Mitigating Denial-of-Service Vulnerabilities in Large Language Models | This paper addresses the issue of recurrent generation (looping) in Large Language Models (LLMs), which causes increased latency and potential Denial-of-Service (DoS) vulnerabilities. It proposes 'RecurrentGenerator', a black-box evolutionary algorithm to find inputs triggering loops, and 'RecurrentDetector', a lightweight classifier trained on LLM activation patterns to detect loops in real-time. | True | Market | True | 1.0 | NaN | RecurrentGenerator: A black-box evolutionary algorithm using a self-similarity fitness function to generate inputs that trigger recurrent generation in LLMs. RecurrentDetector: A lightweight Multi-Layer Perceptron (MLP) classifier trained on features derived from LLM activation state similarities to detect recurrent generation in real-time. | RecurrentGenerator was evaluated by comparing the number of attempts needed to find recurrent samples against a random sampling baseline across eight LLMs (Llama versions, Gemma-2, GPT-4o, GPT-4o mini). RecurrentDetector was evaluated on six open-source LLMs using metrics like accuracy, F1 score, false positive rate, recall, and inference time, based on a dataset combining ShareGPT samples, benign generated prompts, and harmful prompts identified by RecurrentGenerator. | RecurrentDetector achieved an average accuracy of 95.24%, an F1 score of 0.87, and a false positive rate of 2.59% across six open-source LLMs, with a fast average inference time of 0.36 ms. | NaN | NaN | NaN | NaN | NaN | International | RecurrentDetector was trained on activation pattern features extracted from LLM responses. The training dataset included prompts sampled from the public ShareGPT dataset, benign prompts generated during experiments, and harmful prompts known to cause recurrent generation (identified using RecurrentGenerator) across six open-source LLMs. The core data consists of internal LLM activation states and derived similarity metrics. | RecurrentGenerator: Evolutionary algorithm (black-box generative testing). RecurrentDetector: Supervised machine learning (MLP classifier) based on white-box analysis of LLM internal activation states. | Code and results are released on the authors' project website. | True | True | Code and results released on the authors' website [8]. | NaN | Efficiently generating test inputs that trigger recurrent generation, especially for black-box models. Reliably distinguishing benign long outputs from harmful recurrent generation loops in real-time without significant overhead. Understanding the internal model behavior leading to recurrent generation. | Increased latency in LLM responses. Degraded user experience due to repetitive content and long wait times. Potential for Denial-of-Service (DoS) attacks. Increased operational costs for developers and LLM providers (token usage, compute resources, energy consumption). |
| KYZmCs74QmQJ.pdf | Google_Scholar | Investigating the Effectiveness of ChatGPT in Mathematical Reasoning and Problem Solving: Evidence from the Vietnamese National High School Graduation Examination | This paper evaluates ChatGPT's performance on mathematics questions from the Vietnamese National High School Graduation Examination (VNHSGE) from 2019-2023. It finds ChatGPT performs well on knowledge-based questions but struggles significantly with increasing difficulty levels, especially application-level problems and those requiring graphical interpretation. | True | NaN | True | 2.0 | Neutral | ChatGPT | Evaluation on a dataset of 250 multiple-choice questions from the Vietnamese National High School Graduation Examination (VNHSGE) mathematics tests (2019-2023). Questions were categorized by difficulty (Knowledge, Comprehension, Application, High Application) and topic. ChatGPT was prompted with questions formatted to request a specific output structure (Choice + Explanation). | ChatGPT achieved an average score of 5.88/10 (58.8%) across 2019-2023 exams. Accuracy significantly decreased with question difficulty: 83% (Knowledge), 62% (Comprehension), 27% (Application), 10% (High Application). Performance varied by topic, with notable weaknesses in Derivatives/Applications (especially graphical questions), Spatial Geometry, and Oxyz Spatial Calculus. | NaN | NaN | NaN | NaN | NaN | Vietnam | The paper evaluates ChatGPT, which was pre-trained by OpenAI on a large text corpus (details not provided by this paper). The evaluation dataset consists of 250 multiple-choice questions from the publicly available Vietnamese National High School Graduation Examination (VNHSGE) mathematics tests (2019-2023). | Evaluation methodology: Data collection from official VNHSGE papers, formatting questions into LaTeX then JSON, structured prompting of ChatGPT, comparison of generated answers with correct solutions. | NaN | True | False | The paper uses the ChatGPT model accessible via the OpenAI website (chat.openai.com) and the OpenAI API as of the time of the study. | NaN | Key challenges identified for ChatGPT include: difficulty with complex, higher-level reasoning problems (Application and High Application levels); inability to interpret graphical data (tables, charts, diagrams) within questions; inconsistent performance across different mathematical topics. | NaN |
| QWScjUiMBQwJ.pdf | Google_Scholar | Evolving Norms Governing AI Engagement in Legal Practice and the Prospective Alignment of Law School Curriculum | This paper investigates pioneering US professional standards for regulating generative AI in legal practice, emphasizing the need for lawyers to understand AI's benefits and risks. It argues for aligning law school curricula with AI advancements and regulatory norms to cultivate AI-empowered legal professionals and upholds the importance of access to AI. | True | Idealistic | True | 3.0 | Positive | Generative AI (e.g., ChatGPT, LLMs) | NaN | NaN | The potential for an "AI divide" denying equitable access to AI's benefits in the legal field; risks of AI bias, lack of transparency, and generation of erroneous information undermining client rights and fair justice; breaches of client confidentiality. | Comprehensive AI education for legal professionals starting in law school; development and enforcement of robust professional and judicial standards for AI use focusing on accountability and human oversight; ensuring equitable access to the benefits of AI for all. | Equitable access to AI benefits in legal services; protection of client rights (confidentiality, competence, due diligence) in the context of AI use; mitigating AI bias in legal applications and the justice system. | General public / All clients of legal services; implicitly, communities vulnerable to AI bias (e.g., racial or economic bias in criminal justice). | General legal practice, Professional responsibility, Legal education, Criminal justice (briefly mentioned in context of risk assessment tools). | United States (primarily); relevance for other common law jurisdictions discussed. | NaN | NaN | NaN | True | True | The paper discusses generally available generative AI tools like ChatGPT, which has publicly accessible free and paid versions. | The risk of an "AI divide" if benefits are not equitably distributed; inadequacy of current continuing legal education for comprehensive AI training, necessitating law school curriculum reform; persistent issues of AI bias, lack of transparency, and explainability in legal AI tools. | NaN | AI bias leading to unfair outcomes (e.g., racial, economic); lack of explainability and transparency in AI decision-making; generation of false information (hallucinations) by AI; breaches of client confidentiality; over-reliance on AI compromising lawyers' professional judgment; inadvertent creation of attorney-client relationships via AI; potential for an "AI divide" in society. |
| Ub_Ju_UT7V4J.pdf | Google_Scholar | Artificial Intelligence and Employment: A Look into the Crystal Ball | This paper investigates the impact of AI exposure on employment dynamics in European regions between 2011 and 2018, using occupation-based AI exposure indicators mapped to European data. Results suggest a positive correlation between AI exposure and regional employment growth on average, though this may be negatively moderated by robot density in some regions. | True | NaN | False | 2.0 | NaN | Econometric analysis using AI Occupational Exposure (AIOE) indicators (Felten et al.) mapped to European regional employment data (ISCO/NUTS2) via crosswalk. | Econometric panel data analysis (OLS, Fixed Effects, Instrumental Variable) on 202 EU NUTS-2 regions (2011-2018), controlling for structural, labour market, R&D, and demand factors. Daily internet users used as IV for AI exposure. | Higher regional AI exposure (AIRE) significantly correlates with positive employment growth (IV estimate: +2.1 p.p. employment growth for 1 std. dev. increase in AIRE). Interaction effects with robot density suggesting negative moderation were found in FE models but were not robust in IV specifications. | NaN | NaN | NaN | NaN | NaN | European Union (23 countries, 202 NUTS-2 regions) | The analysis uses: 1) AIOE indicators (derived from Felten et al.'s work linking AI applications to O*NET occupational abilities via surveys/expert input). 2) European Labour Force Survey (EU LFS) for regional employment and occupational structure (ISCO codes). 3) International Federation of Robotics (IFR) data for robot density. 4) Eurostat data for controls (internet use, demographics, firm size, manufacturing share, R&D, GVA). Mix of publicly available statistical data and derived indicators based on survey/expert knowledge. | Crosswalking US-based AIOE scores to EU ISCO codes; Aggregating occupational scores to regional level (AIRE) using employment weights; Applying econometric panel data models (OLS, FE, IV). | NaN | False | False | NaN | NaN | Measuring actual AI adoption vs. potential exposure; Early diffusion stage limiting observable impacts; Accounting for interaction with automation (robots); Addressing spatial heterogeneity; Data limitations (EU data granularity vs. US); Potential endogeneity; Difficulty predicting impact of disruptive technology based on past data. | Potential for technological unemployment; Negative employment effects from AI-robot interaction in specific regions; Potential negative impacts on job quality (e.g., monotony, lack of meaning), although not measured in the study. |
| 8xT5fS0mGskJ.pdf | Google_Scholar | Better Bill GPT: Comparing Large Language Models against Legal Invoice Reviewers | This paper empirically compares Large Language Models (LLMs) against human legal invoice reviewers (early-career lawyers, experienced lawyers, Legal Operations Professionals) on accuracy, speed, and cost-effectiveness for legal invoice review. Findings reveal that LLMs significantly outperform humans across all metrics, indicating the arrival of LLM-powered legal spend management. | True | Market | True | 2.0 | NaN | Using pre-trained Large Language Models (e.g., Gemini 2.0 Flash Thinking, GPT-4o) with prompt engineering for legal invoice review. | LLMs and human reviewer groups were benchmarked against a ground truth (set by expert legal professionals) on a dataset of 50 legal invoices (anonymized client and synthetic). Performance was measured by F-scores for invoice approval and line-item classification, speed (seconds per invoice), and cost (USD per invoice). | The best performing LLM (Gemini 2.0 Flash Thinking) achieved 92% F-score for invoice approval decisions and an 81% F-score for line-item classification. This significantly surpassed the best human group (experienced lawyers at 72% and 43% respectively), with LLMs also being 50-80x faster and over 99% cheaper. | NaN | NaN | NaN | NaN | General (Legal Spend Management, Billing Practices) | US (implied by industry norms referenced for reviewer classification and salary guides) | The LLMs studied are general-purpose models pre-trained on broad datasets. For this study, they processed a specific evaluation dataset of 50 legal invoices (anonymized client and synthetic; unstructured text) with billing guidelines provided as contextual information within prompts. | Experimental design comparing LLM performance against human reviewers based on a ground truth. LLM approach involved model selection and iterative prompt engineering including role definition, task instructions, and contextual information (billing rules, policies). | LLMs were accessed via API endpoints (OpenAI, Claude, Gemini) or hosted internally on AWS (DeepSeek R1) for the study. No wider public deployment from this specific study is mentioned. | True | False | Several of the evaluated LLMs (e.g., GPT-4o, Claude 3.5 Sonnet, Gemini 2.0 Flash Thinking) are accessible via their respective APIs, some commercially, and one (Gemini) was reported as free during a preview period. | NaN | Iterative prompt engineering for different LLMs; ensuring optimal integration into workflows balancing AI strengths with human oversight; overcoming industry adoption challenges (regulatory, client expectations, stakeholder resistance); handling supporting documentation and nuanced professional judgment. | Potential for over-reliance on AI leading to compromised judgment if human oversight is not appropriately integrated; ensuring data privacy (e.g., PII) when processing invoices with LLMs. |
| rzMD0sznpJsJ.pdf | Google_Scholar | Access to Justice: The Role of Legal Aid in Society | This paper qualitatively explores the role of legal aid in enhancing access to justice through interviews with stakeholders, identifying key themes like access barriers, service quality, challenges, societal impact, and future directions. It concludes that legal aid is essential for equitable justice and social/economic development, highlighting the need for improvements. | True | Idealistic | False | 3.0 | Positive | NaN | Semi-structured interviews with 22 participants (providers, recipients, stakeholders) followed by thematic analysis. | Key findings highlight barriers (awareness, eligibility, financial, systemic, cultural, technological), quality factors (expertise, client relationship), positive societal impacts (social justice, economic stability, community well-being), and need for improvements (policy, innovation, partnerships). | Lack of awareness, restrictive eligibility, complex application processes, limited service availability, financial constraints/underfunding, systemic policy/institutional barriers, cultural/language barriers, technological divide. | Increase awareness, simplify processes, expand service availability (incl. tech), secure more funding, reform policies, address cultural/tech barriers, foster partnerships, enhance professional development. | Legal aid provision, barriers to access, quality of legal services, societal impact of legal aid. | Vulnerable and marginalized populations, low-income individuals, immigrants, people facing demographic discrimination. | General (Civil Law mentioned, potentially others) | NaN | NaN | Qualitative research: semi-structured interviews, purposive sampling, thematic analysis. | NaN | False | False | NaN | Need for increased funding, policy reform, service delivery innovation (incl. technology), better public awareness, enhanced professional development. Methodological limitations (generalizability) and need for quantitative, comparative, and longitudinal research. | Financial constraints, systemic issues (policy, bias), cultural/language barriers, technological divide impacting legal aid provision and access. | NaN |
| l4PMeM8YyF0J.pdf | Google_Scholar | How can we manage the risks and liabilities associated with legal translation in the age of machine translation and generative AI? | This paper examines the legal and ethical challenges, particularly liability, copyright, and professional rules, associated with using NMT and generative AI for legal translation. It argues for a narrative shift to enhance the role of human translators and proposes due diligence standards and appropriate liability solutions to manage risks. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Increased demand for legal translation unmet by human translators; difficulty ensuring availability of legal information in people's own languages; risks of bias, mis/disinformation, confidentiality breaches, and inaccuracies (e.g., omissions) from AI translation; inadequate liability frameworks for AI-generated translation errors; disruption of translators' professional standing and liability. | Short-term: Implementing due diligence standards for certifying legal translations generated with NMT or generative AI. Long-term: A narrative change to enhance and support the role of the human expert (legal translator), coupled with developing appropriate liability solutions. | Access to legal information in native languages; fair trial (translation of court documents); governmental transparency. | Individuals who do not speak the language of the court; vulnerable individuals (e.g., in asylum adjudications); general public needing access to legal information in their own language. | General legal (transactional documents), Criminal law (court documents, fair trial), Civil procedure (court-related translation), Asylum law. | International (mentions Canada and Europe as examples but discusses issues broadly). | NaN | NaN | NaN | False | False | NaN | The current 'human-in-the-loop' narrative is misleading and doesn't adequately value human expertise; lack of appropriate liability solutions that support human translators in AI-assisted workflows; need for better risk management for inaccuracies, bias, and confidentiality in AI legal translation. | NaN | Bias in translation; exacerbation of mis- and disinformation; breaches of confidentiality (e.g., lawyer-client relationship); increased vulnerability for individuals in sensitive contexts (e.g., asylum adjudications); inaccuracies and omissions in translations; misallocation of legal liability for translation errors. |
| Z5qcyozSxVQJ.pdf | Google_Scholar | Artificial intelligence at the bench: Legal and ethical challenges of informing —or misinforming —judicial decision-making through generative AI | This paper examines the legal and ethical challenges of using Generative AI (GenAI) in judicial decision-making, highlighting risks like bias and misinformation. Through case studies and analysis of regulatory approaches, it proposes a comprehensive framework for the responsible and equitable deployment of GenAI in the judiciary to enhance access to justice and uphold the rule of law. | True | Idealistic | True | 2.0 | Neutral | Generative AI (specifically, the use of Large Language Models like ChatGPT by judicial officers in decision-making processes). | Analysis of case studies from Colombia, Mexico, Peru, and India where judges used ChatGPT. Review of proactive regulatory approaches to AI/GenAI in other jurisdictions (UK, New Zealand, EU, Canada, Singapore, Estonia). | The analysis of case studies reveals unregulated, ad-hoc use of GenAI (like ChatGPT) by judges, leading to significant risks including bias, generation of misinformation ('hallucinations'), lack of transparency, accountability gaps, data privacy issues, and potential erosion of public trust and judicial independence. | Bias amplification, lack of transparency and explainability ('black box' problem), generation of fabricated information ('hallucinations'), undermining judicial independence and discretion, accountability and legal liability gaps, data protection risks, and potential for GenAI to widen access to justice disparities due to resource constraints and ad-hoc implementation. | A dual-prong framework for responsible GenAI integration: 1) Foundational standards for GenAI systems (capacity assessment, stakeholder engagement, licensing/verification, trusted datasets, explainability, clear responsibility allocation, prompt engineering). 2) Application principles for GenAI deployment (updating ethical standards, continuous legal education, case-based risk assessment, disclosure to parties, verification systems, specific procedural rights, ongoing audits). | Access to justice, fairness in judicial decision-making, responsible use of AI in courts, ethical AI governance, rule of law. | General public interacting with the judicial system, with specific mentions of implications for marginalized communities and individuals with disabilities (e.g., a case involving a child with autism). | General judicial decision-making. Case studies cover health law/social security, civil procedure, family law (child support), electoral law, and criminal law (bail applications). | Case studies from Colombia, Mexico, Peru, India. Comparative regulatory approaches from UK, New Zealand, EU, Canada, Singapore, Estonia. The proposed framework is intended for general applicability. | The paper discusses challenges with GenAI (like ChatGPT used in case studies) trained on vast, often unverified, non-legal, and potentially biased internet-scale data. It advocates for the use of 'trusted datasets', potentially closed-network and jurisdiction-specific, for judicial GenAI. | NaN | NaN | False | False | NaN | Lack of consensus and comprehensive frameworks for GenAI in judiciaries; technical limitations of current GenAI (accuracy, bias, explainability, hallucinations); societal challenges (public trust, ensuring equitable access, resource disparities); unclear legal liability; need for standardized AI audits and refined prompt engineering for legal contexts; insufficient legal education on AI. | Key challenges identified in using GenAI in judiciaries include: ensuring transparency and interpretability of AI outputs, mitigating algorithmic bias, addressing poor data quality and AI 'hallucinations', establishing clear accountability and legal liability, protecting data privacy, preserving judicial independence, and ensuring GenAI equitably improves access to justice rather than exacerbating inequalities. | Bias propagation leading to discriminatory outcomes, opacity in decision-making undermining due process, factual inaccuracies ('hallucinations') in AI-generated content, compromised judicial independence and discretion, erosion of public trust, unclear legal liability for AI-induced errors, data privacy breaches, increased justice disparities, and technological solutionism. |
| 14.pdf | Google_Scholar | Natural Language Understanding in Big Data: AI-Driven Approaches for Automated Insights | This paper explores AI-driven Natural Language Understanding (NLU) techniques, focusing on deep learning and transformer models, for extracting insights from large-scale unstructured textual data. It discusses current advancements, applications across various industries including legal services, common challenges like bias and interpretability, and future research directions. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal analytics, legal text analysis, legal decision-making, contract analysis, regulatory monitoring, legal document summarization. | International | The paper refers to large-scale pre-trained datasets (e.g., for models like BERT, GPT, T5, often derived from general web text) and the use of domain-specific corpora (e.g., financial, biomedical, legal texts) for fine-tuning. It primarily discusses unstructured textual data and mentions bias in training data. | NaN | NaN | False | False | NaN | NaN | Bias in AI models and training data, lack of interpretability (black-box nature), high computational costs for training and deployment, scalability, handling domain-specific language, ensuring fairness and transparency, and data privacy. | Bias leading to unfair or discriminatory outcomes (e.g., in legal decision-making, hiring), reinforcement of stereotypes, misinformation, and data privacy violations. |
| XcEkh7h2e9kJ.pdf | Google_Scholar | GUIDE Q: Framework for Guided Questioning for progressive informational collection and classification | This paper introduces GUIDE Q, a novel framework that uses LLMs combined with classifier-derived explanations (keywords) to generate guided questions. This progressive information collection aims to improve text classification accuracy when initial user input is partial or incomplete, demonstrating benefits across various domains. | True | NaN | True | 1.0 | NaN | GUIDE Q framework: It employs a fine-tuned classifier (e.g., BERT) to identify the top-k most probable labels for a partial input text. Keywords representative of these labels are learned via occlusion. A Large Language Model (specifically Llama-3 8B) then uses the partial input, top-k labels, their keywords, and a structured prompting strategy (with few-shot examples) to generate a targeted question. The answer to this question augments the initial input for more accurate final classification. | The framework was evaluated on six text classification datasets (Symptom2Disease, Crypto News, Human Stress Prediction, 20 Newsgroups, DBpedia, SALAD-Bench) using BERT and DeBERTa as classifiers and Llama-3 8B for question generation. Performance was primarily measured by F1-Score improvement after appending answers to guided questions, compared against three baselines: 'Partial' (classification on partial input only), 'LLM' (LLM-generated questions based on partial input), and 'LLM-nk' (LLM with top-3 labels but no keywords). Question quality was assessed via win rate against baselines, and analyses included the effect of keyword n-gram type and multi-turn interactions. | GUIDE Q consistently outperformed baselines in F1-score improvement across both BERT and DeBERTa classifiers on the six datasets. For example, with the DeBERTa classifier, GUIDE Q achieved a 22.1% F1-score increase (from 64.7% to 86.8%) on the Symptom2Disease dataset, and a 20.7% increase (from 38.0% to 58.7%) on the SALAD-Bench dataset, compared to classifying only partial information. The framework also demonstrated higher quality question generation with win rates >50% (often much higher) against baselines. | NaN | NaN | NaN | NaN | NaN | NaN | The classifiers (BERT, DeBERTa) were fine-tuned on various publicly available text classification datasets (Symptom2Disease, Crypto News, Human Stress Prediction, 20 Newsgroups, DBpedia, SALAD-Bench). These datasets consist of unstructured text and corresponding labels, specific to domains such as healthcare, finance, general news, and safety. The LLM (Llama-3 8B) component utilized few-shot exemplars for prompting but was not fine-tuned on these datasets specifically for the GUIDE Q methodology. | The GUIDE Q framework development involved: 1) Fine-tuning pre-trained transformer models (BERT, DeBERTa) for text classification. 2) Employing the occlusion method, an explainability technique, to identify significant keywords for each class label. 3) Designing a structured prompting strategy for an LLM (Llama-3 8B), incorporating few-shot examples, to generate guided questions based on partial input, top predicted labels, and their keywords. 4) An iterative process for multi-turn question generation where guiding words are dynamically updated. | Code is made available on GitHub. | True | True | Code is available on GitHub: https://github.com/SDRMp/DRPG. | NaN | Performance dependency on the quality of the initial classifier model and the relevance of extracted keywords. Reliance on LLMs for question generation introduces potential biases and inconsistencies inherent to these models. Computational resources required for running large language models may pose scalability challenges. | Risk of LLM hallucination (generating plausible but incorrect information). Potential biases and inconsistencies inherent to LLMs used for question generation. Misclassification if the initial classifier or keyword extraction is suboptimal. |
| _KwbPCDd_GwJ.pdf | Google_Scholar | Automatic Information Extraction From Employment Tribunal Judgements Using Large Language Models | This paper evaluates the performance of GPT-4 in automatically extracting eight key pieces of information (facts, claims, outcomes, statutes, precedents, reasons, etc.) from UK Employment Tribunal judgments. The study finds GPT-4 achieves high accuracy, suggesting its potential for legal information processing and facilitating downstream tasks like outcome prediction. | True | Idealistic | True | 2.0 | Positive | Using GPT-4 with engineered prompts for automatic information extraction of specific fields (facts, claims, statutes, precedents, outcomes, remedies, reasons) from legal judgments. | Manual verification by a legal expert and senior legal expert on a stratified sample of 260 UKET judgments. Accuracy was scored (0 or 1) for each of the eight extracted fields. A second check assessed suitability for a downstream prediction task. | High accuracy across all extraction tasks (generally >0.9). Perfect accuracy (1.0) for references to legal statutes and precedents; near-perfect accuracy (0.996) for general outcomes, detailed outcomes, and reasons. Lowest accuracy for labelled outcomes (0.912) and facts (0.942 overall, 0.919 for prediction-suitable cases), still considered high. | Knowledge imbalance between employers and employees regarding access to legal knowledge and predictive tools derived from tribunal data. | Develop accurate and open information extraction and predictive systems using AI, accessible to the general public (both employers and employees), to democratize access to legal knowledge and reduce imbalances. | Information extraction from court judgments, analysis of employment law disputes, case outcome prediction, access to legal information. | Employees involved in or considering UK Employment Tribunal claims, potentially lacking resources compared to employers; the general public. | Employment Law | United Kingdom (UK Employment Tribunal - England, Wales, Scotland) | The study uses GPT-4, a large language model pre-trained by OpenAI on diverse text corpora. The input data for the extraction task consisted of 260 publicly available UK Employment Tribunal judgments from the Cambridge Law Corpus. | Iterative prompt engineering based on OpenAI guidelines (clear instructions, persona definition, delimiters, task specification, examples, systematic testing). Manual quality checks by legal experts to assess accuracy and suitability for prediction. | NaN | True | False | The technique uses the GPT-4 API (32k version), which is commercially available from OpenAI. The prompts are detailed in the paper. | Need for improved prompting to consistently distinguish procedural vs. substantive facts. Potential information bias when using facts/claims extracted from judges' post-outcome decisions for prediction. Predictive models based solely on tribunal judgments lack context from original claim forms and out-of-court settlements. | Designing prompts for accurate and consistent extraction across varied judgments (e.g., handling subsequent claims, rule types, outcome labelling nuances, multiple parties). Difficulty in making GPT-4 distinguish procedural/substantive facts contextually. Ensuring consistent labelling logic for ambiguous situations (e.g., withdrawals, preliminary rulings). | Potential inaccuracies or biases in LLM extractions. Information bias in prediction models trained on post-hoc judicial summaries. Incompleteness of models based solely on adjudicated cases (missing settlements, original filings). Exacerbation of inequality if powerful AI tools are only accessible to resourceful parties. Systems assisting judicial authorities may be classified as high-risk (EU AI Act context). |
| MSfmdl3ZpvMJ.pdf | Google_Scholar | Bridging Law and Data: Augmenting Reasoning via a Semi-Structured Dataset with IRAC methodology | This paper introduces LEGAL SEMI, a new benchmark dataset for legal reasoning based on the IRAC framework, focusing on Malaysian Contract Law. It includes 54 annotated scenarios and a structured knowledge graph (SKG), demonstrating through experiments with LLMs that integrating the SKG improves performance on IRAC tasks like issue identification and rule retrieval. | True | Idealistic | True | 1.0 | Positive | Creation and use of the LEGAL SEMI benchmark dataset, including a Structured Knowledge Graph (SKG), to augment Large Language Models (LLMs) for IRAC (Issue, Rule, Application, Conclusion) analysis of legal scenarios. | Experiments were conducted using four LLMs (GPT-3.5 Turbo, Llama 2, Mistral, Gemini) on the LEGAL SEMI dataset. Tasks included legal concept identification, issue identification, rule retrieval, application generation, and conclusion generation. Evaluation involved automatic metrics (e.g., F1 score for concepts, precision/recall/F1 for rules, GPT-3.5 Turbo as judge for generation tasks) and comparative human evaluation using legal rubrics and Spearman correlation. | Integrating the SKG improved issue generation quality by over 21.4% across LLMs. Using the SKG (legal concepts + textbook interpretations) for rule retrieval achieved the best F1 score of 16.3% at top-5 results, significantly outperforming baseline retrieval. Providing identified issues and rules improved application generation (+18.9% for GPT-3.5 Turbo). Providing the application section improved conclusion generation (+71.4% for GPT-3.5 Turbo). | Backlogs in courts, complexity of legal practice, scarcity of legal professionals, limitations of LLMs in accurate legal reasoning (wrong conclusions, incorrect rule citations, difficulty with legalese vs. everyday language). | Developing high-quality, structured legal datasets (like LEGAL SEMI with its SKG) to enhance LLM reasoning capabilities for legal tasks, specifically automating IRAC analysis to potentially assist legal professionals and improve efficiency. | Automating IRAC analysis, Legal reasoning, Legal document analysis, Legal Information Retrieval. | NaN | Contract Law (specifically Formation of Contract) | Malaysia | The LEGAL SEMI dataset: 54 legal scenarios covering Malaysian Contract Law, annotated by law students/junior lawyers using the IRAC framework. Structured Knowledge Graph (SKG): automatically constructed via rule-based extraction from a Malaysian business law textbook ('Law for Business'), the Malaysian Contracts Act 1950, and 76 Malaysian court cases. The LLMs used (GPT-3.5, Llama 2, Mistral, Gemini) are pre-trained models. | Dataset construction involved scenario selection (human-written and LLM-generated/human-refined), expert review, and detailed human annotation according to IRAC guidelines using a custom-built annotation tool. SKG construction involved rule-based information extraction from structured legal texts (textbook index/content, legislation). Human evaluation rubrics based on legal education standards were used. | The paper states the dataset (LEGAL SEMI) will be made publicly available upon acceptance. | False | False | LEGAL SEMI will be publicly available upon acceptance of this paper. | LLMs struggle with identifying lower-level legal concepts compared to high-level ones. Generating lay-language interpretations of legal rules using LLMs can suffer from hallucination. The dataset scope is limited to Malaysian Contract Law (formation). | High effort and expertise required for reliable legal annotation. Bridging the semantic gap between lay language in scenarios and legalese in rules. Ensuring factual accuracy and reasoning fidelity in LLM outputs for legal tasks. Evaluating complex generative tasks in the legal domain. Automating the construction of comprehensive and accurate legal knowledge graphs. | LLM limitations leading to incorrect legal conclusions, citation of wrong legal rules, and hallucination, which pose risks if used without expert oversight in real-world legal analysis. |
| 2fOPE9ql6n0J.pdf | Google_Scholar | Large Language Models: AI’s Legal Revolution | This paper reviews the history of chatbots leading to modern Large Language Models (LLMs) and examines current LLMs like ChatGPT, Bing Chat, Bard, CoCounsel, and Lexis+ AI. It argues strongly for the integration of LLMs into legal academia, private practice, and the judiciary, advocating for understanding and nuanced regulation over bans to enhance legal efficiency. | True | Market | True | 2.0 | Positive | Large Language Models (LLMs), specifically differentiating between general-purpose (e.g., ChatGPT, Bing Chat, Bard) and legal-specific (e.g., CoCounsel, Lexis+ AI). | NaN | NaN | Lack of understanding of LLM capabilities and types within the legal profession; concerns about hallucinations and data privacy (especially with non-legal LLMs); misguided attempts to ban the technology; inability to effectively detect LLM-generated content. | Educate legal professionals (students, practitioners, judges) about LLM types and responsible use; integrate LLMs into legal workflows (practice, judiciary) to improve efficiency, with human oversight; develop nuanced regulations recognizing differences between LLM types (legal vs. non-legal). | NaN | NaN | General Legal Practice | United States | Discusses various LLMs using different data: general LLMs (e.g., ChatGPT, Bing Chat, Bard) trained on vast internet text scrapes (some static, some live); legal-specific LLMs (e.g., CoCounsel, Lexis+ AI) trained on proprietary, curated, up-to-date legal databases (caselaw, statutes, etc.) in addition to base LLM capabilities. | NaN | NaN | True | False | General LLMs (ChatGPT free tier, Bing Chat, Bard) available online; Legal-specific LLMs (CoCounsel, Lexis+ AI) available as commercial products. | Need for better education/understanding of LLMs in the legal profession; lack of nuanced regulations distinguishing LLM types; technical unreliability (hallucinations) and privacy risks of non-legal LLMs for legal tasks; lack of reliable methods to detect AI-generated text. | For the legal profession: Understanding the technology, balancing efficiency gains with risks (hallucinations, privacy), developing appropriate regulations, adapting education and practice. For LLM creators: Reducing hallucinations, ensuring data privacy and security, training models on accurate and up-to-date (legal) data. | Hallucinations (generating incorrect information/citations); violation of data privacy and client confidentiality (especially with non-legal LLMs); inaccurate legal work due to over-reliance without human verification. |
| gre9EWR6YS0J.pdf | Google_Scholar | Mixed-domain Language Modeling for Processing Long Legal Documents | This paper introduces LEGAL RELECTRA, a specialized language model for personal injury text, trained on mixed legal and medical corpora. It demonstrates that this smaller model, utilizing an ELECTRA framework with REFORMER components to handle long documents, outperforms general and single-domain models on tasks like NER and case retrieval in the personal injury domain. | True | Market | False | 1.0 | NaN | LEGAL RELECTRA: a specialized language model using an ELECTRA framework with REFORMER for its generator and discriminator, trained on mixed-domain (legal and medical) corpora, and utilizing a custom domain-specific tokenizer. | Evaluated on Named Entity Recognition (NER) for legal and mixed-domain text (using custom annotated data and public datasets conll2003, MIMIC III) and Legal Case Retrieval (on a proprietary dataset of 500 cases). Performance was compared against BERT, LEGAL-BERT, CLINICAL-BERT, and REFORMER. | On legal domain NER, LEGAL RELECTRA achieved an overall F1 score of 85.93. For legal case retrieval, it achieved 90.00% accuracy in matching claim type and 92.00% in matching injury categories, outperforming baseline models. | NaN | NaN | NaN | NaN | Personal injury civil suits | United States (specifically mentions Kentucky and Louisiana for some datasets, general US legal data otherwise) | A 12GB corpus of unstructured text: 6GB legal text (e.g., CourtListener, proprietary case descriptions), 3GB medical text (MIMIC, MIMIC-CXR), and 3GB mixed legal-medical text (personal injury cases from Supreme Court opinions, academic literature, BYU LAW, attorney descriptions). Mix of public and proprietary data. | Novel model architecture (RELECTRA: ELECTRA with REFORMER components), mixed-domain pre-training, development of a custom domain-specific tokenizer using Byte-Pair Encoding. | NaN | False | False | NaN | NaN | 1. Processing long legal documents (beyond typical token limits of models like BERT). 2. Handling specialized terminology from multiple domains (e.g., legal and medical) within legal texts. 3. Limited access to large, high-quality, curated training datasets for specialized legal AI. | Potential biases from training data (e.g., court opinions) impacting model predictions. Privacy concerns with legal and medical data, although anonymization was employed in this study. |
| HealthcareGrowingroleofGAI.pdf | Google_Scholar | Healthcare: A Growing Role for Large Language Models and Generative AI | This paper surveys the application of large language models (LLMs) and generative artificial intelligence (GAI) in healthcare, discussing their use in medical text analysis, image processing, and multimodal tasks. It reviews specific models, benchmarks, tools, challenges (like data privacy, bias, interpretability), and ethical considerations within the healthcare domain. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Healthcare | International | Various publicly available and potentially proprietary biomedical datasets (EHRs, text, images, genomics) mentioned across referenced studies. | NaN | Discusses commercial tools integrated with EHRs and models released via public repositories. | True | True | Some models (e.g., PathologyBERT, PMC-LLaMA) stated available via public repositories (e.g., Hugging Face). Some commercial tools (e.g., Suki Assistant, Glass AI, Amazon Transcribe Medical) discussed as available services. | Need for interpretability, bias mitigation, data security/privacy, regulatory clarity, model robustness (hallucinations), human oversight, better instruction handling, and further study of legal implications. | Data privacy/security, interpretability, data bias, regulatory hurdles, potential for generating false information/hallucinations, workflow integration, handling sensitive data (ePHI). | Data privacy violations, biased/unfair outcomes, generation of false/harmful information, liability issues, deception via synthetic media, plagiarism, copyright infringement. |
| sdjBd5vNE04J.pdf | Google_Scholar | Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learning | This paper proposes D3LM (Diagnostic Legal Large Language Model), a novel framework that uses adaptive lawyer-like diagnostic questions, driven by a Positive-Unlabeled Reinforcement Learning (PURL) algorithm, to gather comprehensive case information from users for improved legal consultations. The research also introduces a new English-language dataset for Court Views Generation (CVG) based on US criminal case law to support LLM research in the legal domain. | True | Idealistic | True | 1.0 | Positive | Diagnostic Legal Large Language Model (D3LM) incorporating a graph-based Positive-Unlabeled Reinforcement Learning (PURL) algorithm for adaptive question generation and Court Views Generation (CVG). | Evaluated using ROUGE and BLEU scores on the authors' newly created US-CVG dataset (derived from US criminal case law). Evaluation also involved human judgment (fluency, accuracy, adoptability) by legal professionals and usability testing (reliability, satisfaction, preference) comparing D3LM to GPT-4.0. | D3LM achieved ROUGE-1 63.3%, ROUGE-2 53.1%, ROUGE-L 59.2%, BLEU-1 38.7%, BLEU-2 31.7%, and BLEU-N 26.9%. In human evaluation, D3LM scored 4.48 for accuracy and 4.19 for adoptability. 62.3% of users preferred D3LM over GPT-4.0 in usability tests. | Scarcity and high cost of legal resources, inequities in legal proceedings disadvantaging the underprivileged, and the difficulty for laypersons (users without legal backgrounds) to formulate effective queries and provide all critical factual details to LLMs. | Development of D3LM, an LLM-based system that actively engages users with diagnostic questions to elicit comprehensive case details, aiming to provide more accurate, tailored, and cost-effective legal guidance, especially for those lacking legal expertise. | Improving legal consultation for laypersons, interactive legal information gathering, court view generation, enhancing AI-driven legal assistants for better accuracy and user understanding. | Individuals with modest means, economically disadvantaged individuals, and users lacking a legal background seeking legal assistance. | Primarily US Criminal Law (based on the dataset, case study, and stated limitations on PURL algorithm effectiveness). | USA | A new English-language Court Views Generation dataset (US-CVG) created by the authors from US criminal legal documents (Caselaw Access Project). GPT-4.0 was used with the IRAC framework to summarize narratives into fact descriptions and court views, and to create fact-rule graphs for each case; dataset integrity ensured by review from legal professionals. | LLM fine-tuning (Llama2-13B), Positive-Unlabeled Reinforcement Learning (PURL) with a bandit approach (NeuralUCB), graph-based knowledge representation (fact-rule graphs processed with DiGCN), IRAC framework for legal text summarization, and development of a new user-LLM interaction paradigm (LLM-navigated diagnostics). | The US-CVG dataset used for training and evaluation is made available on GitHub. The paper does not state other deployment strategies for the D3LM model itself. | False | False | NaN | The PURL algorithm's effectiveness is confined to the criminal cases domain; evaluation restricted to English language cases; the model demands significant computational and human annotation resources; operational speed lags behind existing large models. | Creating domain-specific knowledge graphs (resource-intensive), handling narrative length and complexity of US legal cases within LLM token limits, ensuring LLM reading comprehension for question generation, integrating reinforcement learning for optimal fact selection, and conducting rigorous human expert evaluations. | NaN |
| 3614407.3643708.pdf | Google_Scholar | Rethinking Machine Learning Benchmarks in the Context of Professional Codes of Conduct | This paper argues that current machine learning benchmarks, often focused on accuracy mimicking professional exams, fail to capture essential skills mandated by professional codes of conduct. It proposes using these codes, particularly illustrated through a case study of legal machine translation, to guide the development of more comprehensive benchmarks that incorporate requirements like expressing uncertainty and adhering to specific professional rules. | True | Idealistic | True | 1.0 | Neutral | Proposal to use Professional Codes of Conduct to guide ML benchmark creation, incorporating specific tests based on rules (e.g., preserving filler words, units, double negatives) and integrating 'Know-What-You-Know' (KWYK) checks for uncertainty quantification and abstention. | Case study on legal machine translation with demonstrative experiments: 1) 'Unit tests' checking gpt-3.5-turbo's compliance with specific California court interpreter rules (unit/filler word/error/repetition/double negative preservation, idiom identification, clarification needed). 2) KWYK check experiments using NLLB/flan-t5-xl models on translation (Opus100/Flores/EAC-TM datasets) and bar exam tasks (MMLU), measuring abstain rate vs acceptability rate. | For rule compliance tests with gpt-3.5-turbo, adherence varied significantly by rule (e.g., 100% unit preservation, 10% word repetition preservation, 61% double negative preservation). For KWYK checks on translation, a verifier KWYK check (xlm-roberta-base) achieved a target of 75% acceptability with an 18.9% abstain rate on a legal-adjacent translation task; KWYK checks failed to reach target accuracy on the bar exam task. | Unreliability and lack of accountability of general-purpose AI tools (like MT) in high-stakes legal contexts; inaccuracies leading to severe negative consequences (asylum denial, rights violations); tendency for users to rely on tools without understanding limitations due to perceived general competence; shortage of qualified human professionals (e.g., translators) creating demand for potentially unsafe AI solutions. | Align ML benchmarks with professional codes of conduct; integrate specific tests based on professional rules into benchmarks; standardize evaluation of uncertainty quantification ('Know-What-You-Know' checks) allowing models to abstain; provide runtime transparency regarding model limitations and adherence to rules. | Machine translation quality and reliability in legal contexts; AI safety and evaluation; Professional ethics in AI applications. | Individuals with Limited English Proficiency (LEP) interacting with the legal system, such as asylum seekers and individuals in police encounters. | Immigration Law, Criminal Procedure, Evidence, Professional Responsibility (Interpreters, Lawyers) | United States (primarily California and federal context) | The paper primarily evaluates existing models. Demonstrative experiments used public datasets: Opus100 (parallel corpora), Flores 200 (Wikipedia), EAC-TM (EU documents), MMLU Bar Exam (professional exam questions), Tang 2022 & Fadaee et al. 2018 (idiom datasets). KWYK checks used models pre-trained on large general corpora. | Comparative analysis (benchmarks vs. professional rules), Case study (legal machine translation), Conceptual proposal (rule-based benchmarks, KWYK checks), Demonstrative empirical evaluation. | NaN | False | True | Code for demonstrative experiments stated to be available in Supplementary Material. | Technical: Need for improved KWYK check calibration and reliability, methods for robustly incorporating professional rules into models, handling conflicting rules. Societal: Need for broader adoption of professionally-grounded benchmarks, effective user communication of uncertainty, extension to other professional domains. | Current benchmarks focus narrowly on accuracy, neglecting professional standards; implementing robust KWYK checks is a research challenge; ensuring consistent rule adherence in stochastic models is difficult; quantitatively evaluating nuanced rule compliance; generalizing the proposed evaluation approach. | Mistranslations leading to asylum denial, misunderstanding consent to search (Fourth Amendment violations), inadmissible evidence, or misinterpretation of law; over-reliance on seemingly capable AI leading to errors in critical situations; AI potentially removing crucial context (e.g., filler words indicating uncertainty); potential unauthorized practice of law; AI hallucinations in legal work. |
| PEdAAU7Q1McJ.pdf | Google_Scholar | Exploring the feasibility of developing an education tool for pattern identification using a large language model: focusing on the case of a simulated patient with fatigue symptom and dual deficiency of the heart-spleen pattern | This paper explores using large language models (LLMs) to create simulated patients for Korean Medicine education, specifically focusing on pattern identification training. The authors developed a prototype using prompt engineering based on standardized patient data and implemented web interfaces for students and evaluators. | False | NaN | True | 1.0 | NaN | Using an LLM (ChatGPT) via prompt engineering to simulate a patient based on Korean Medicine clinical practice examination (CPX) modules for educational purposes, coupled with a web interface (Django/WebSockets). | Prototype development and demonstration via a web interface. No formal user testing or benchmark results described. | Successfully developed a prototype simulated patient using prompt engineering and implemented web interfaces for examinees and evaluators. | NaN | NaN | NaN | NaN | NaN | Republic of Korea | Domain-specific (Korean Medicine) standardized patient information (characteristics, symptoms, history, sample Q&A) extracted from clinical practice examination (CPX) modules provided by the National Institute for Korean Medicine Development. Used for prompt engineering, not model training. | Prompt engineering (system, user, assistant prompts) based on standardized patient data; Web development using Django framework and WebSockets. | A web-based prototype was developed and made accessible via a URL. | True | False | The tool is available for testing via a specific URL provided in the paper: https://aicpx.seungho.kr/registration | NaN | Ensuring simulated patient realism, mitigating LLM hallucination, creating a user-friendly and accessible educational interface. | LLM hallucination (generating incorrect or non-existent information) in the simulated patient scenario. |
| 7kzQWOd65PQJ.pdf | Google_Scholar | Generative AI on the Loose: Impact of Improved AI and Expert Oversight on Knowledge Sharing | This study examines the impact of Generative AI advancements (specifically GPT-4) and expert oversight on knowledge sharing platforms, using Stack Overflow as a natural experiment. It finds that combining improved AI with strict oversight reduces contribution volume but enhances quality, whereas reduced oversight increases volume but lowers quality, especially from novices. | True | NaN | True | 2.0 | NaN | Analysis of the impact of Generative AI (GPT-4) and expert oversight on knowledge sharing dynamics. | Natural experiment using Stack Overflow data (Jan 2022 - Dec 2023) around GPT-4 release and moderation policy changes (moderator strike). Econometric analysis (Difference-in-Differences, Triple-Differences) on contribution quantity, quality (votes, upvote share), reputation gains, and moderation activity. Heterogeneity analysis by user reputation and supplementary analysis using Stack Overflow Developer Surveys. | Combining GPT-4 with strict expert oversight reduced the quantity of knowledge sharing but significantly improved quality. Relaxed oversight increased quantity but decreased quality (especially for answers), particularly from novice users. GPT-4 improved middle-skilled users' answer quality and low-skilled users' question quality, but oversight remained crucial for novices. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | False | NaN | NaN | General empirical challenges identified include obtaining detailed organizational knowledge-sharing data, observing both significant AI improvement and adoption, and observing relevant managerial policy shifts within a suitable timeframe. Specific identification challenges included establishing a comparable control group, ensuring parallel trends, avoiding SUTVA violations, and validating the significance of the policy changes (treatments). | GenAI risks: Hallucinations, lack of contextual understanding, generating content that appears correct but contains errors, compromising content quality and user trust. User risks: Novices struggling to assess AI-generated content, reputation farming or misuse of GenAI under weak oversight leading to lower-quality contributions. Platform risks: Strict moderation might discourage genuine participation through false positives. Applying GenAI to tacit knowledge domains without expert oversight may pose greater risks. |
| jdzBM0w9BAQJ.pdf | Google_Scholar | Discussion on the Reform of Higher Legal Education in China \nBased on the Application and Limitation of Artificial \nIntelligence in Law Represented by ChatGPT | This paper examines the applications (e.g., text processing, legal aid, education support) and limitations (e.g., data scarcity, inaccuracy, lack of value judgment) of ChatGPT in the legal field. It primarily discusses the resulting challenges and the need for reform in Chinese higher legal education to adapt to the AI era. | True | Market | True | 3.0 | Positive | ChatGPT | NaN | NaN | For AI application in law: Lack of high-quality, open Chinese legal training data; potential for generating false statements; lack of value judgment and empathy. For legal education: Risk of job displacement for graduates; student over-reliance on AI leading to reduced critical thinking, autonomy, and academic dishonesty. | For access to justice: Use AI like ChatGPT for low-cost/free online legal aid and consulting. For legal education reform: Cultivate 'AI + Law' talents, focus on legal thinking/skills over rote learning, adopt student-centered/personalized teaching methods, and integrate AI ethics education. | Legal aid, Online legal consulting | Vulnerable groups, General public | General Law | China | The paper discusses ChatGPT, which uses a large general pre-trained corpus. It highlights the lack of high-quality, publicly accessible, specialized Chinese legal data (e.g., from Judgment Document Network) as a limitation for applying such models effectively in China. | NaN | NaN | True | True | ChatGPT is available via web interface and API, with free and paid tiers offered by OpenAI. | Lack of accessible high-quality Chinese legal training data; Need for updated legal education curricula incorporating 'AI + Law' and AI ethics; Ensuring AI aligns with human ethical values. | Applying LLMs effectively given data limitations; Preventing generation of false information; Addressing the lack of nuanced value judgment; Reforming legal education curricula and teaching methods; Mitigating student over-reliance and academic integrity issues. | Generation of false statements/fabrications; Misuse in litigation; Algorithmic bias reinforcement; Erosion of students' critical thinking/autonomy; Academic dishonesty/plagiarism; Job displacement for legal professionals; National security threats; Privacy disclosure. |
| yMRuEJga_zIJ.pdf | Google_Scholar | GPT-4 Technical Report | This paper introduces GPT-4, a large-scale multimodal model that processes image and text inputs to produce text outputs, demonstrating human-level performance on diverse benchmarks including a simulated bar exam. It details GPT-4's predictable scaling, its inherent limitations such as potential unreliability, and the safety measures undertaken, including adversarial testing and model-assisted safety pipelines. | True | NaN | True | 1.0 | NaN | GPT-4, a large-scale, multimodal, Transformer-based model, pre-trained to predict the next token and fine-tuned using Reinforcement Learning from Human Feedback (RLHF) and a model-assisted safety pipeline with rule-based reward models (RBRMs). | Evaluated on diverse benchmarks: professional/academic exams (Uniform Bar Exam, LSAT, SAT, GRE, APs), NLP benchmarks (MMLU, HellaSwag, ARC, WinoGrande, DROP, GSM-8K), coding benchmarks (HumanEval, Leetcode), multilingual MMLU, visual input tasks, and safety evaluations including internal factuality evaluations and adversarial testing with domain experts (e.g., for cybersecurity, biorisk). | GPT-4 achieved a score around the top 10% of test takers on a simulated Uniform Bar Exam (298/400). On MMLU, it scored 86.4% (5-shot). It significantly reduces hallucinations compared to GPT-3.5 and shows improved adherence to safety policies. | NaN | NaN | NaN | NaN | General US law (as tested by the Uniform Bar Exam) | United States (for the Uniform Bar Exam). Multilingual capabilities tested more broadly. | Pre-trained on a large dataset of publicly available data (such as internet data) and data licensed from third-party providers. Fine-tuned using Reinforcement Learning from Human Feedback (RLHF). Specific dataset construction details are not provided. | Development of a deep learning stack that scales predictably. Pre-training followed by RLHF fine-tuning. Model-assisted safety pipeline including rule-based reward models (RBRMs). Adversarial testing with domain experts. Iterative model improvement based on evaluations. | GPT-4 is made available via ChatGPT and the OpenAI API. Deployment includes monitoring for abuse and a pipeline for fast iterative model improvement. OpenAI Evals, a benchmarking framework, is open-sourced. | True | False | Available via OpenAI API and ChatGPT. The OpenAI Evals benchmarking framework is open-sourced on GitHub. | NaN | Developing predictably scaling deep learning infrastructure. Ensuring model reliability (hallucinations, limited context, no experiential learning). Addressing significant safety challenges: bias, disinformation, over-reliance, privacy, cybersecurity, proliferation. Managing adversarial attacks ("jailbreaks"). | Hallucinations (generating false facts), reasoning errors, limited context window, lack of learning from experience, perpetuating societal biases, generating harmful content (e.g., hate speech, illicit advice, planning attacks), disinformation and influence operations, privacy violations (e.g., identifying individuals with external data), cybersecurity risks (e.g., aiding social engineering, vulnerability explanation), proliferation of weapons information, overreliance by users, potential for risky emergent behaviors (e.g., power-seeking, though preliminary assessment found current model ineffective at autonomous replication), economic impacts (e.g., job displacement). |
| 6Yzvwm5r5_kJ.pdf | Google_Scholar | LawBench: Benchmarking Legal Knowledge of Large Language Models | This paper introduces LawBench, a comprehensive benchmark designed to evaluate the legal knowledge and capabilities of Large Language Models (LLMs) within the Chinese civil law system across three cognitive levels: memorization, understanding, and application. Based on evaluations of 51 LLMs, the study finds that while GPT-4 leads, all current models, including legally fine-tuned ones, have significant room for improvement in performing diverse and realistic legal tasks reliably. | True | Idealistic | True | 2.0 | Neutral | LawBench: A benchmark suite comprising 20 diverse legal tasks for evaluating LLMs under the Chinese civil law system. | Evaluation of 51 LLMs (multilingual, Chinese-oriented, legal-specific) on LawBench (20 tasks across memorization, understanding, application levels; 5 task types). Tests performed in zero-shot and one-shot settings using task-specific metrics (Accuracy, F1, rc-F1, soft-F1, nLog-distance, F0.5, Rouge-L) and answer extraction rules. | GPT-4 performed best overall (average score 52.35 zero-shot, 53.85 one-shot), significantly outperforming other models. Legal-specific fine-tuning improved over base models but did not surpass top general models. Most models struggled to utilize provided legal article content effectively. | Current LLMs lack sufficient legal knowledge, understanding, and reasoning abilities for reliable performance on diverse legal tasks. Models struggle with instruction following, abstention on legal queries, and effectively integrating retrieved knowledge like legal articles. | Develop stronger foundation models; use high-quality legal-specific fine-tuning data and methods (potentially improving SFT and reconsidering RLHF impact); improve models' ability to utilize retrieved context; foster collaboration to overcome data confidentiality challenges. | Legal information provision, document analysis, case assessment, legal consultation simulation. | Non-professionals needing legal assistance | Criminal Law, Civil Law (including Family Law), Procedural Law, General Legal Practice | China | NaN | Benchmark designed using a cognitive hierarchy (Bloom's taxonomy adapted for legal skills: Memorization, Understanding, Applying). Tasks selected and adapted from existing public legal NLP datasets (e.g., CAIL, LAIC, JEC-QA) and other sources, formatted for instruction-following LLMs. | Benchmark and evaluation code released via GitHub (OpenCompass platform). | True | True | Benchmark data, model predictions, and evaluation code released on GitHub. | Technical gaps: Current LLMs lack robustness and reliability for complex legal reasoning, understanding, and application. They struggle to effectively integrate retrieved legal knowledge and can be hampered by safety alignments (RLHF). Societal gaps: Data confidentiality hinders the development of high-quality legal LLMs. | Evaluation: Designing diverse tasks, reliable answer extraction, appropriate metrics (esp. for generation), preventing data contamination. LLM Development/Application: Effective scaling, domain-specific fine-tuning, balancing helpfulness and harmlessness (instruction following vs. abstention), enabling effective use of retrieved information. | Lack of reliability and accuracy in performing legal tasks; potential for models to refuse to answer relevant legal queries (abstention); risk of evaluation invalidity due to test set contamination. |
| ieWkuwRsfCYJ.pdf | Google_Scholar | EMPOWERING JUSTICE: BLOCKCHAIN AND LEGAL CHATBOTS AS CATALYSTS FOR ACCESS TO LEGAL AID | This paper explores how integrating blockchain technology and AI-powered legal chatbots can improve access to justice by addressing barriers like cost, complexity, and geographical distance. It reviews existing applications, discusses potential benefits, ethical challenges, regulatory needs, and proposes a roadmap for future development focusing on inclusivity and global cooperation. | True | Idealistic | True | 3.0 | Positive | Integration of blockchain technology and legal chatbots | NaN | NaN | Economic constraints (cost), lack of legal literacy/awareness, geographical barriers, systemic discrimination/bias, complexity/inefficiency of legal systems, digital divide. | Leveraging blockchain for secure document/evidence/identity management and smart contracts; using legal chatbots for accessible information, guidance, and document drafting automation; fostering interdisciplinary collaboration, ethical guidelines, regulatory frameworks, inclusive design, investment, transparency, education, and global cooperation. | Access to legal information, advice, representation, document verification/management, identity protection, evidence management, dispute resolution. | General public, economically disadvantaged individuals, people in rural/remote areas, refugees, stateless individuals, marginalized populations. | General/Multiple | International | NaN | Conceptual framework design, discussion of general AI/chatbot development. | Discussion of existing case study deployments (e.g., government initiatives, commercial platforms), proposed pilot programs. | True | False | Mentions several existing platforms (e.g., DoNotPay, Casetext, Kleros, LegalMation, Juro etc.) available as commercial services or platforms, some with free tiers or specific initial free uses. | Digital divide, need for ethical guidelines and robust regulatory frameworks, lack of global convergence/standards, technical limitations (scalability, interoperability), need for AI/tech literacy training, addressing AI bias. | Technical complexity of integration, scalability issues, interoperability challenges, designing for user accessibility (digital divide), legal and regulatory uncertainty/hurdles, data privacy and security concerns, ethical AI development (bias, fairness, accountability), cost of implementation, integration with traditional legal systems, ecological impact of certain blockchains. | Providing inaccurate or oversimplified legal information, errors in automated document drafting, perpetuating societal biases through AI, data privacy violations, potential for misuse (e.g., manipulation), lack of clear accountability for errors, non-compliance with regulations, exacerbating the digital divide. |
| jVRZDuKmAFkJ.pdf | Google_Scholar | SwiLTra-Bench: The Swiss Legal Translation Benchmark | This paper introduces SwiLTra-Bench, a large multilingual benchmark for Swiss legal translation, and SwiLTra-Judge, an LLM-based evaluation system. It evaluates various LLMs, showing frontier models achieve the best performance, and while fine-tuning improves open SLMs, they still trail top zero-shot frontier models like Claude-3.5-Sonnet. | True | Idealistic | True | 1.0 | Positive | SwiLTra-Bench: a multilingual benchmark of over 180K aligned Swiss legal translation pairs. SwiLTra-Judge: an LLM-based evaluation system for legal translation quality assessment. | Various LLMs (translation-specific, frontier, reasoning, open, and fine-tuned SLMs) evaluated on SwiLTra-Bench using metrics like GEMBA-MQM, XCOMET, METEOR, ChrF. Human expert evaluations conducted on top models; SwiLTra-Judge's correlation with human scores was assessed. | Frontier models like Claude-3.5-Sonnet and o1 demonstrated superior performance; fine-tuned open SLMs improved significantly but did not surpass zero-shot frontier models. SwiLTra-Judge, using GPT-4o-mini with a deduction prompt and diverse few-shot examples, achieved the highest correlation (Spearman 0.5 ± 0.07) with human expert judgments. | Lack of specialized, high-quality multilingual legal translation data; inherent complexity (terminology, structure) of legal texts for NMT; translation bottlenecks hindering access to justice and governmental efficiency. | Creation of a large, high-quality multilingual Swiss legal translation benchmark (SwiLTra-Bench) for training and evaluation. Development of an LLM-based evaluation tool (SwiLTra-Judge) aligned with human legal expertise. Systematic evaluation and fine-tuning of LLMs to advance legal NMT capabilities. | Legal machine translation, multilingual access to legal information, automated evaluation of translation quality, enhancing governmental efficiency and civic participation through NMT. | Swiss citizens (especially speakers of official languages including the low-resource Romansh), legal professionals, and governmental bodies in Switzerland. | Swiss law, including legislation (laws), court decisions (headnotes), and official communications (press releases). | Switzerland | SwiLTra-Bench comprises over 180K publicly available, officially translated Swiss legal document pairs (laws, headnotes, press releases) in German, French, Italian, and partially Romansh and English, aligned at various granularities (e.g., paragraph/text level). | For SwiLTra-Bench: collection of official multilingual legal texts, segmentation, and strategic splitting into train/validation/test sets. For SwiLTra-Judge: ablation studies on LLM judge models, prompt engineering (testing basic, detailed, codebook styles), and few-shot example selection, validated against human expert judgments. | The SwiLTra-Bench datasets and associated code (including for SwiLTra-Judge evaluation) are made available on Hugging Face. | True | True | The SwiLTra-Bench datasets and code are available at https://huggingface.co/collections/joelniklaus/swiltra-bench-67c569a2ada47e4549733deb. | Fine-tuned open models still underperform large closed models. Need for further research into techniques like model merging to improve open models. Human expert evaluation scope was limited by resources (e.g., for Romansh, sample sizes). | Limited resources for comprehensive human expert evaluation, particularly for low-resource languages and large sample sizes. Some LLMs (Claude Sonnet/Haiku, o1/o1-mini) proving unsuitable as evaluators in SwiLTra-Judge development due to instruction-following failures or low correlation with human judgment. Token limits of certain automated evaluation metrics when processing longer texts (e.g., press releases). | NaN |
| G99pB0RXN5AJ.pdf | Google_Scholar | Uniandes at the Regulations Challenge Task: A Scalable Framework for Legal Text Understanding in Regulatory and Financial Contexts. | This paper presents the development and evaluation of a domain-specific LLM (fine-tuned LLaMA-3.1-8B) for regulatory and financial text understanding. The methodology includes corpus creation via web scraping, filtering, GPT-4o-mini based cleaning, and instruction fine-tuning for the COLING 2025 Regulations Challenge tasks. | True | Market | True | 1.0 | Positive | Domain-specific fine-tuning of LLaMA-3.1-8B using QLoRA, based on a custom corpus created via web scraping, TF-IDF filtering, GPT-4o-mini cleaning, and instruction generation. | Evaluation on the nine tasks from the Coling 2025 Regulations Challenge (Abbreviation, Definition, NER, QA, Link Retrieval, Certificates, XBRL, CDM, Licensing) using metrics like Accuracy, BERTScore, F1 Score, FActScore. Comparison against baseline models (GPT-4o, Llama 3.1 8B, Mistral Large 2). | The fine-tuned model achieved a final weighted score of 0.43929 on the challenge leaderboard. It showed slight improvements over the base LLaMA-8B in complex QA (0.7688 FActScore) and acronym expansion (0.2748 Accuracy), but performance significantly lagged in NER (0.4302 F1 Score). | NaN | NaN | NaN | NaN | Regulatory Law, Financial Law/Compliance | International | A custom corpus of 2,286 documents created by scraping publicly available financial and regulatory text from sources specified by the Coling 2025 Regulations Challenge (e.g., EUR-LEX, SEC, FDIC). The scraped data was filtered using TF-IDF and cleaned/structured using GPT-4o-mini. This corpus was used for further pretraining and generating an instruction dataset, supplemented by existing Hugging Face datasets for CFA and XBRL tasks. | Web scraping, TF-IDF based relevance filtering, LLM-based data cleaning (GPT-4o-mini), prompt engineering, domain adaptation (further pretraining), instruction fine-tuning (QLoRA). | NaN | True | True | Code, prompts, and implementation details available on GitHub. | Need for improved performance (especially NER, XBRL, Certificates tasks), better handling of long context documents, robust expert validation, improved data quality assessment, and hallucination reduction. | Obtaining clean domain-specific data from noisy sources, computational/memory constraints (addressed by LLaMA-8B and QLoRA), lack of standardized benchmarks (addressed by using the challenge), handling varying context window needs across tasks, task complexity (NER, XBRL), resource constraints for expert validation. | Hallucination. |
| mIAZXGRNg7QJ.pdf | Google_Scholar | JurisCTC: Enhancing Legal Judgment Prediction via Cross-Domain Transfer and Contrastive Learning | This paper proposes JurisCTC, a model using unsupervised domain adaptation (UDA) and contrastive learning to improve legal judgment prediction (LJP) accuracy. It focuses on transferring knowledge between Chinese civil and criminal law domains to address data scarcity, particularly in criminal law. | True | Idealistic | False | 1.0 | Positive | JurisCTC: A model combining Unsupervised Domain Adaptation (UDA) via Domain-Adversarial Neural Networks (DANN) with a BERT feature extractor and Gradient Reversal Layer (GRL), enhanced by Maximum Mean Discrepancy (MMD) loss and Contrastive Learning for cross-domain Legal Judgment Prediction (LJP). | Evaluated on LJP tasks transferring between Chinese Civil Law (LJP-MSJudge dataset) and Criminal Law (CAIL-2018 dataset) using Accuracy, Macro-Precision, Macro-Recall, and Macro-F1 metrics. Compared against baseline NLP models (TextCNN, BERT, TOPJUDGE, MPBFN), large language models (GPT-4o, Gemini-1.5-Flash, DeepSeek-V3-Chat), and included an ablation study. | JurisCTC achieved peak accuracies of 76.59% (Civil to Criminal transfer) and 78.83% (Criminal to Civil transfer), outperforming baseline models and tested LLMs in the specific LJP tasks. | Scarcity of annotated legal data, especially the decreased availability of public criminal law judgments in China, hindering LJP model development. Also mentions the general difficulty of handling lengthy, complex legal texts. | Using Unsupervised Domain Adaptation (UDA) and transfer learning to leverage data from a source legal domain (e.g., civil law) to improve performance in a target, data-scarce domain (e.g., criminal law) via the proposed JurisCTC model. | Legal Judgment Prediction (LJP) - predicting case outcomes (Guilty/Not Guilty; Support/Not Support appeal). | NaN | Civil Law, Criminal Law | China | Uses existing public research datasets: LJP-MSJudge (Chinese Civil Law judgments) and CAIL-2018 (Chinese Criminal Law judgments). These contain unstructured text from court documents. | Integration of established ML techniques: BERT embeddings, Unsupervised Domain Adaptation (UDA) via Domain-Adversarial Neural Networks (DANN) with Gradient Reversal Layer (GRL), Maximum Mean Discrepancy (MMD) loss, and Contrastive Learning. | Code available on GitHub. | True | True | Code available on GitHub: https://github.com/Zhaolu-K/JurisCTC | Need for investigation into specific linguistic features driving performance; exploration of alternative domain adaptation strategies; potential limitation in precision compared to some LLMs noted. | Handling lengthy and complex legal texts; data scarcity, particularly for Chinese criminal law; achieving model generalization across legal domains; balancing precision and recall. | NaN |
| OGl1CpY-kTkJ.pdf | Google_Scholar | Retrieval-based Evaluation for LLMs: A Case Study in Korean Legal QA | This paper proposes Eval-RAG, a novel method for evaluating large language model (LLM) generated text, particularly for legal question answering (QA), by using retrieved relevant documents to assess factual accuracy. Experiments on Korean legal QA show that Eval-RAG improves the correlation between LLM-based evaluations and human expert judgments. | True | NaN | True | 1.0 | Positive | Eval-RAG: An evaluation framework combining a retriever (to find relevant legal documents for a given query) and an LLM-based evaluator (whose prompt is augmented with the retrieved document) to assess the quality of LLM-generated answers. | Evaluated on a Korean legal QA task (n=100 divorce-related questions) by comparing the scores from Eval-RAG (combined with FairEval/ChatEval using GPT-3.5/GPT-4) against human grading by lawyers, using Pearson, Spearman, and Kendall correlation coefficients. Qualitative analysis of specific examples was also conducted. | Combining Eval-RAG with GPT-4 based evaluators significantly increased the correlation with human lawyer evaluations compared to using the evaluators alone. FairEval-RAG (GPT-4) achieved the highest correlation (Pearson 0.5923, Spearman 0.5841, Kendall 0.4991), outperforming FairEval (GPT-4) alone. | Factual errors (hallucinations) in LLM outputs; difficulty of existing evaluation methods in detecting these errors in domain-specific contexts like law. | Using retrieval-augmented generation (RAG) principles for evaluation (Eval-RAG) to ground LLM-based evaluations in relevant documents, thereby improving factual accuracy assessment. | Legal Question Answering (QA) evaluation | NaN | Family Law (specifically Divorce Law) | South Korea | Data used for the retriever component: Publicly available Korean legal documents including 287 QA pairs (Korea Legal Aid Corporation), 84 legal provisions, and 240 legal cases (Korea Legislation Research Institute). Questions for provisions/cases were generated using GPT-4. This data is domain-specific, unstructured text converted into question-document pairs for retrieval. | Conceptual framework design; Experimental evaluation comparing against baselines using correlation metrics and qualitative analysis. | NaN | False | False | NaN | Need for improved evaluation metrics that better align with human expert judgment and can robustly detect factual inaccuracies in domain-specific LLM outputs. | LLM hallucination; inadequacy of existing evaluation metrics; potential limitations in retriever accuracy; prompt engineering for incorporating retrieved documents; LLM context window limits. | LLMs generating factually incorrect legal information (hallucination); standard evaluations failing to detect these errors, leading to potential reliance on inaccurate AI-generated legal output. |
| SYHocniYWCEJ.pdf | Google_Scholar | Literature Review: AI and the Law | This literature review examines AI's role, particularly LLMs, in the legal profession, covering applications in legal practice, judicial processes, and access to justice through tools like legal apps. It also discusses significant ethical concerns including professional competence, algorithmic bias, and data privacy. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | High cost and scarcity of legal services leading to unmet legal needs for low-income and many middle-income individuals; disadvantages for self-represented litigants; the digital divide, including lack of access to technology and digital literacy. | Employing AI-powered legal apps for information, advice, and document creation; leveraging AI to enhance lawyer efficiency and extend services; developing self-help resources powered by AI; implementing online dispute resolution platforms. | Affordability of legal services, access to legal information and advice, self-representation, online dispute resolution for small claims and specific civil matters. | Low-income individuals, individuals below the poverty line, middle-income individuals, self-represented litigants. | General civil law, contract law, dispute resolution (small claims, condominium, motor vehicle accidents), constitutional law, torts, legal ethics. | International (with examples from USA, Canada, China, Colombia, Italy, UK) | NaN | NaN | NaN | True | True | The paper discusses tools like ChatGPT (free tier available) and publicly accessible services like Canada's Civil Resolution Tribunal, as well as commercial legal tech tools. | The digital divide (socio-economic, geographic, and literacy barriers to technology access); insufficient research on privacy and security of legal apps; outdated or lacking ethical guidelines and governance for AI tools in law. | Ensuring accuracy and completeness of AI-generated legal information; preventing user misinterpretation or over-reliance on AI outputs; addressing the digital divide for equitable access to AI-powered legal resources; managing data privacy and security risks; overcoming potential biases in AI systems and judicial applications; adapting legal education to incorporate AI ethically and effectively. | Breaches of lawyers' duty of competence from unverified AI use; dissemination of misleading or incomplete legal information by AI; perpetuation of systemic biases (e.g., racial, gender) by AI algorithms; privacy violations and data misuse from legal apps and AI systems; cybersecurity vulnerabilities (e.g., jailbreaking, prompt injection); negative impacts on judicial integrity, such as automation bias or perceived devaluation of human judgment. |
| PO2gt4t0fl4J.pdf | Google_Scholar | Decoding Legalese Without Borders: \nMultilingual Evaluation of Language Models on Long Legal Texts | This doctoral dissertation summarizes a body of research focused on advancing multilingual legal Natural Language Processing (NLP). It details the curation of extensive legal datasets and benchmarks for evaluating Large Language Models (LLMs) on long legal texts, and presents multidimensional analyses of model performance, explainability, fairness, and re-identification risks within the legal domain. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Lack of comprehensive multilingual legal datasets; suboptimal performance of models on low-resource languages and long legal texts; unique challenges of domain-specific legal tasks; difficulties in ensuring transparency and ethics in algorithmic jurisprudence. | Curation and open release of extensive multilingual legal datasets (e.g., MultiLegalPile) and benchmarks (e.g., LEXTREME, LegalBench, SCALE); training and analysis of language models for legal text; proposing methods for anonymization, re-identification assessment, and explainability; advocating for dataset extension to unexplored legal tasks and underrepresented jurisdictions. | Multilingual legal NLP; evaluation of LLMs on legal texts; legal judgment prediction; anonymization and re-identification; legal reasoning; creation of open legal corpora and benchmarks for broader development and application, including for underrepresented languages and jurisdictions. | The broader legal NLP research community; users and developers in underrepresented jurisdictions and languages. | Multiple legal fields (e.g., public, penal, civil, social, insurance law, class actions, depending on the specific dataset/benchmark described within the summarized works). | International (covers multiple jurisdictions including Switzerland, US, India, EU, CoE, and aims for broader global coverage including underrepresented jurisdictions). | The dissertation describes the creation and use of large multilingual legal corpora such as MultiLegalPile (689GB of diverse legal texts from public and other sources covering 24 languages/17 jurisdictions) and various specialized datasets for tasks like judgment prediction, sentence boundary detection, and negation scope resolution. | NaN | Many of the described resources (datasets, models, code) are deployed via open platforms like Hugging Face, Zenodo, and GitHub, often under permissive licenses (e.g., CC-BY, CC BY-SA). | True | True | Numerous datasets, pretrained models, and codebases (detailed in Table 1 and individual publication summaries) are publicly available on platforms like Hugging Face, Zenodo, and GitHub, often under open licenses like CC-BY. | Need for improved model performance on legal benchmarks (e.g., via domain adaptation, instruction tuning, advanced prompting); further analysis of dataset overlaps and model interpretability/explainability; extension of datasets to more legal tasks, languages, and jurisdictions, particularly with expert annotations. | NaN | Potential for re-identification of individuals in anonymized legal texts; lack of transparency and ethical concerns in algorithmic jurisprudence; inherent biases in models influencing predictions (e.g., from lower court decisions or training data). |
| LDrTM_lyDkQJ.pdf | Google_Scholar | GENERATIVE AI AND THE DIGITAL COMMONS | The paper discusses how Generative Foundation Models (GFMs) rely on and potentially degrade the "digital commons" (shared information resources and infrastructure). It proposes governance-based solutions, such as consortia for monitoring and standards, GFM company contributions to the commons, and input-data-based governance, to mitigate risks and ensure collective benefit. | True | NaN | True | 1.0 | NaN | Governance-based solutions including: 1) Consortia for monitoring, auditing, and standards-setting; 2) Norms or rules for GFM companies to contribute high-quality data to the commons; 3) Governance structures based on input data to model training (e.g., human feedback for stake, fine-tuning data from experts, data trusts for private data). | NaN | NaN | NaN | NaN | NaN | NaN | Copyright law, Fair Use doctrine, Data rights, Data protection, Governance, Regulation. | International | NaN | Conceptual analysis of risks posed by GFMs to the digital commons, review of existing regulatory/governance approaches, and development of novel governance proposals based on commons theory. | Proposals include voluntary memberships/subscriptions by GFM companies for consortia, potential regulation/taxation for funding, encouraging data contributions through norms or rules, and piloting input-data-based governance structures (e.g., data trusts, collective model governance by specific groups). | False | False | NaN | NaN | For consortia: avoiding standards-capture by incumbents, ensuring broad representation. For data contributions: ensuring validity of collected data, encouraging company participation. For input-data governance: developing detailed and feasible models, addressing privacy and fiduciary responsibilities, data tracing and valuation. | GFM risks include: poisoning the information sphere with easy-to-create low-quality data; eroding self-determination and democracy; homogenizing content; misaligning incentives for humans to contribute to the open digital ecosystem; driving further economic concentration; contributing to precarious labor conditions and large-scale automation; accelerating unpredictable risks from highly capable AI systems. |
| QE9gSMM7MkYJ.pdf | Google_Scholar | Artificial Intelligence & Criminal Justice: A Primer | This primer provides a high-level overview of AI's current impact on the criminal justice system, covering applications like deepfakes, predictive policing, facial recognition, risk assessment algorithms, and AI in legal practice. It also addresses critical perspectives including Indigenous viewpoints, AI governance, access to justice concerns, and future AI issues. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Inaccessibility (cost, proprietary nature, opacity) of sophisticated AI tools for the general public; lack of transparency and disclosure about AI use; ensuring accountability and freedom from bias; upholding due process rights in automated decision-making; and potential for inaccurate AI-generated legal information to mislead. | Implementing robust regulatory frameworks (e.g., EU AI Act, Canada's proposed AIDA), adherence to ethical guidelines and bills of rights (e.g., White House AI Bill of Rights), ensuring transparency and explainability in AI systems, providing human oversight and recourse mechanisms, specific recommendations for legal aid plans, and professional guidelines for legal practitioners and judges. | Affordable legal support and information, tools for self-represented litigants, transparency and accountability in automated legal decision-making, addressing bias in AI, ensuring due process rights, and the role of legal aid in the age of AI. | Self-represented litigants, racialized communities, Indigenous peoples, and individuals requiring legal aid or affordable legal support. | Criminal Justice | Canada (primary), United States, European Union | NaN | NaN | NaN | True | True | The primer itself is available for free and open access via Allard Research Commons and Canadian Legal Information Institute. A more detailed free ebook is planned for January 2025. | How to effectively regulate AI to ensure it narrows the access to justice gap without creating new harms (e.g., reliance on inaccurate AI, exacerbating inequalities); addressing and mitigating bias in AI tools for legal applications; defining and implementing procedural fairness in automated legal processes; ensuring meaningful transparency and accountability for AI systems. | NaN | Perpetuation and automation of systemic bias and discrimination (e.g., in policing, corrections); privacy violations through data scraping and surveillance; generation and misuse of deepfakes for criminal activities; reliance on inaccurate or 'hallucinated' information from generative AI in legal contexts; lack of transparency and 'black box' nature of some AI systems hindering accountability and due process; potential for AI to facilitate new forms of crime and digital colonialism. |
| Andrew Phang (Gen. Ed.) Pioneer polymath and mentor_ The life a.pdf | Google_Scholar | Andrew Phang (gen ed), Pioneer, Polymath and Mentor: The Life and Legacy of Yong Pung How | This book review examines "Pioneer, Polymath and Mentor: The Life and Legacy of Yong Pung How," detailing Dr. Yong's contributions to modernizing Singapore's judiciary and financial sector. It also notes his influence on enhancing access to justice, with current efforts in Singapore including the development of Generative AI to assist self-represented litigants. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Case backlogs, procedural gridlock, and challenges for self-represented individuals in accessing and navigating the justice system due to limited resources or legal literacy. | Judicial modernization (e.g., electronic filing, disciplined case management initiated by Dr. Yong), ongoing simplification of procedural frameworks, and development of Generative AI tools to assist self-represented litigants. | Judicial reform, court efficiency, procedural simplification, access to justice for self-represented persons, legal aid through technology. | Self-represented litigants, individuals with limited financial resources or legal literacy, those most in need. | Administration of Justice, Civil Procedure, Small Claims | Singapore | NaN | NaN | NaN | False | False | NaN | Ensuring equitable access to justice for all, particularly for self-represented individuals lacking sufficient resources or legal knowledge to effectively present their case. | NaN | NaN |
| 6aFMjU4sNkIJ.pdf | Google_Scholar | Computational Legal Studies Comes of Age | This paper surveys the field of computational legal studies, outlining the 'law-as-code' and 'law-as-data' paradigms and their evolution with computational text analysis. It further explores the impact and potential of recent generative AI (LLM) developments, discussing hybrid approaches, ongoing opportunities, and challenges for empirical legal research. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Various, including constitutional, criminal, administrative, civil, patent, human rights, contract, tax, and anti-discrimination law. | International, referencing U.S., U.K., E.U., Korea, Malawi, Germany. | Discusses techniques using various legal text corpora (e.g., case law, statutes, court dockets); specific examples include annotated Malawi criminal cases and the German credit dataset. For LLMs, it refers to large, broad internet-scale text datasets (some sources undisclosed). | NaN | NaN | False | False | NaN | NaN | Language specificity (tools primarily designed for English may not handle non-English texts well), domain specificity (a tool for one legal field might not work in another), data issues (quantity, quality, scope, and potential biases in data), LLM-specific issues (hallucination, interpretability challenges due to model complexity and large parameter counts, opacity of training data provenance). | LLM hallucination (e.g., generating fictitious case law), biased outputs resulting from biased training data, potential unfairness in automated systems like smart contracts, and socioethical concerns regarding fairness in law-as-code applications. |
| T2yiP3KjaMUJ.pdf | Google_Scholar | WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation | This paper introduces WANLI, a novel dataset creation approach combining AI generation (GPT-3) with human evaluation to improve Natural Language Inference (NLI) datasets. Training models on the resulting WANLI dataset significantly improves robustness and out-of-domain generalization compared to training on the larger original dataset (MultiNLI). | True | NaN | True | 1.0 | NaN | Worker-AI collaborative pipeline (WANLI) for dataset creation: Uses dataset cartography to identify challenging examples from an existing dataset (MultiNLI), prompts GPT-3 to generate similar examples, automatically filters generations using 'estimated max variability', and uses human crowdworkers for revision and final labeling. | Finetuned RoBERTa-large and T5-base models on WANLI versus MultiNLI (and its augmentations). Evaluated accuracy on eight out-of-domain NLI challenge sets (NLI Diagnostics, HANS, QNLI, WNLI, NQ-NLI, ANLI, FEVER-NLI, BIG-Bench NLI) and the MultiNLI development set. | Training RoBERTa-large on WANLI (103K examples) outperformed training on MultiNLI (393K examples) on all eight OOD test sets considered (e.g., +11% on HANS, +9% on ANLI). Similar improvements were observed for T5-base. | NaN | NaN | NaN | NaN | NaN | International | The method uses the publicly available MultiNLI dataset as a source for seed examples. It then employs GPT-3 (a proprietary LLM) to generate new examples. The final WANLI dataset consists of these AI-generated examples, filtered automatically and then reviewed, revised, and labeled by human crowdworkers. | Multi-stage pipeline involving: 1) Data analysis (dataset cartography), 2) AI generation (few-shot prompting of GPT-3), 3) Algorithmic filtering (custom 'estimated max variability' metric), 4) Human-in-the-loop validation (crowdsourcing via Amazon Mechanical Turk for revision and labeling). | The WANLI dataset, code for the pipeline, and a demo are made available online via a dedicated website. | True | True | Demo, data, and code are available at https://wanli.allenai.org/ | NaN | Ensuring GPT-3 reliably replicates complex DLI reasoning patterns (especially contradiction); balancing human revision freedom with avoiding annotation artifacts; potential for generation-specific artifacts (e.g., lexical correlations, entity biases); cost of large-scale generation and human review. | Perpetuation of social harms and toxic language from LLMs; potential for subtle biases missed by human annotators; over-representation of Western entities in generated data; creation of new, model-specific artifacts. |
| s1yPEHov7IcJ.pdf | Google_Scholar | Generative AI in Business: Visual Illustrations of Applications and Insights | This paper explores generative AI's business applications, benefits, and challenges using a visual framework based on recent literature and industry reports. It covers use cases like content creation, knowledge management, process automation, and decision support, highlighting implementation, risks, and future trends. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal Services | International | NaN | NaN | NaN | False | False | NaN | NaN | Integration with existing systems, data quality, performance optimization, security risks, high cost, ethical concerns (especially in HR), compliance requirements, need for governance frameworks, lack of organizational readiness and planning, need for new skills (e.g., prompt engineering). | Security risks (data breaches, model vulnerabilities), high implementation/operational costs, ethical risks (bias, fairness, transparency, especially in HR), compliance risks (regulatory non-compliance), data privacy risks, model risks (accuracy, reliability), output risks (e.g., generating incorrect or harmful content), operational risks (system failures, integration issues). |
| pEztotuCUbAJ.pdf | Google_Scholar | Ethics guidance for generative AI use | This article summarizes ABA Formal Opinion 512 regarding the ethical obligations for lawyers using generative AI tools in their practice. It highlights key duties including competence, confidentiality, client communication, supervision, candor to the tribunal, and reasonable fees. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General legal practice | Minnesota (USA) | NaN | NaN | NaN | False | False | NaN | NaN | Ensuring ethical compliance (competence, confidentiality, communication, supervision, candor, reasonable fees) when incorporating generative AI into legal practice. | Unauthorized disclosure of confidential client information, generation of inaccurate information ('hallucinations'), advancing non-meritorious claims, lack of candor to tribunals, inadequate supervision of AI use, improper billing practices. |
| bGDGljyWU4MJ.pdf | Google_Scholar | Gracenote.ai: Legal Generative AI for Regulatory Compliance | This paper introduces Gracenote.ai, a platform utilizing LLMs (GPT-4) and prompt engineering for governance, risk, and compliance (GRC) tasks. It details three tools: a regulatory horizon scanner, an obligations generator, and an LLM-based expert system, emphasizing human-in-the-loop control to ensure accuracy. | True | Market | True | 1.0 | NaN | A platform (Gracenote.ai) proposing three tools: 1) Regulatory newsfeed generation via automated horizon scanning, scraping, and LLM-based summarization/categorization. 2) Obligations register generation from legislation using LLM summarization and NLP-based duplicate detection (TF-IDF, cosine similarity). 3) LLM-based expert system using LangChain, text embeddings on an obligations register, and GPT-4 for querying. | Newsfeed: Compared summaries to original content via human review in an authoring environment. Obligations generator: Compared GPT-4 outputs to GPT-3.5 and human-generated reference registers; used cosine similarity for duplicate detection with human validation. Expert system: Lawyer reviewed outputs for relevance and accuracy based on user prompts; initial user feedback collected. | GPT-4 produced more concise and sometimes more legally precise obligations than GPT-3.5 and human experts. Similarity detection for obligations was mostly accurate but required human validation. Lawyer reviews confirmed the expert system's outputs were relevant and accurate for given prompts. Newsfeed tool estimated to save significant time (approx. 1 FTE) for corporate legal teams. | NaN | NaN | NaN | NaN | Governance, Risk and Compliance (GRC), Financial Services Law, Insurance Law, Cybersecurity Law, Corporate Law, Administrative Law | Australia (primary focus), UK (planned), Singapore (planned), USA (planned) | The approach primarily uses prompt engineering on pre-trained GPT-4. Data inputs for the tools include: publicly available regulatory documents scraped from official sources (press releases, alerts, case reports, legislation, policy documents) and user queries. For the expert system, it uses text embeddings generated from a CSV file of an obligations register. | Prompt engineering (incl. specific prompts for summarization, categorization, obligation extraction), text embeddings (OpenAI's text-embedding-ada-002), web scraping (custom scripts, headless browser), LangChain framework (CSVLoader, Character Text Splitter, ChatGPT Plugin Retriever), NLP for similarity detection (TF-IDF, cosine similarity), chunking for long documents, human-in-the-loop validation. | Platform trialled with law firms and consultancies. Tools generate content presented in authoring and client interfaces. Generated content can be used in external workflows (e.g., email systems, newsletters) or pushed via the platform's client interface. | False | False | NaN | NaN | LLM context window limitations requiring text chunking; prompt engineering difficulties (e.g., preventing embellishments in GPT-3.5); balancing conciseness and legal accuracy in generated text; limitations of similarity detection algorithms (identifying functionally non-equivalent but textually similar obligations); handling varied website structures for scraping; need for human oversight to ensure accuracy. | LLM hallucinations (generating plausible but incorrect information); privacy breaches from sending personally identifying information to public LLM endpoints; confidentiality breaches from using sensitive commercial information in prompts. |
| mI1P374fIKEJ.pdf | Google_Scholar | Confirmation Bias in Generative AI Chatbots: Mechanisms, Risks, Mitigation Strategies, and Future Research Directions | This perspective article analyzes the phenomenon of confirmation bias within generative AI chatbots based on large language models. It explores the mechanisms driving this bias, discusses the associated risks and ethical implications, proposes mitigation strategies, and outlines future research needs. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | General discussion of LLM training data: massive, often unfiltered internet datasets (news, social media, forums, etc.), potentially skewed, often lacking transparency. | NaN | NaN | False | False | NaN | NaN | Inherent LLM design favouring coherence; skewed training data; trade-offs in alignment/fine-tuning; iterative reinforcement in conversation; user perception; difficulty implementing mitigations without negative side-effects (e.g. paternalism, cost). | Spread of misinformation; reinforcement of harmful beliefs/conspiracy theories; poor individual decision-making (health, finance, legal); social fragmentation/polarization; potential legal liability; undermining user autonomy/critical thinking. |
| gOypQXV-mAMJ.pdf | Google_Scholar | Chat GPT and the Future of Work “Banking Industry Use Cases” | This paper explores the impact of generative AI, specifically ChatGPT, on jobs and functions, with a focus on its use cases within the banking, HR, and L&D sectors. It also discusses the technology's limitations, ethical considerations, and provides recommendations for its adoption by organizations. | True | Market | True | 3.0 | NaN | Generative AI (specifically ChatGPT) | NaN | NaN | NaN | NaN | NaN | NaN | Banking Law (KYC/AML, regulatory compliance), Contract Law (document drafting), Labor Law (HR compliance and policies) | International | The paper discusses ChatGPT, generally trained on large-scale public internet text and code. It mentions the potential for organizations to customize models using their own data. | NaN | NaN | True | True | ChatGPT is available as a free research preview and through paid subscriptions/API access. | NaN | Key challenges discussed for adopting and using ChatGPT in organizations include: implementation and maintenance costs; data security and privacy; ethical considerations such as bias and misuse; the model's lack of emotional intelligence; risk of over-reliance and skill degradation; intellectual property concerns; the need for workforce reskilling and cultural change; keeping pace with rapid AI developments; and ensuring human oversight for output validation. | Stated risks include: job displacement; security vulnerabilities (e.g., data breaches, intensified phishing); ethical issues like bias and discriminatory outputs; reduced quality of human interaction; over-dependence on technology eroding critical thinking; intellectual property infringements; financial crime enablement if not properly implemented in KYC/AML; reputational damage from errors or non-compliance; and dissemination of inaccurate information. |
| A74EjRLf2AgJ.pdf | Google_Scholar | The Rise of Generative AI: Modelling Exposure, Substitution, and Inequality Effects on the US Labour Market | This paper models the exposure of 711 US occupations to advancing AI capabilities by considering skill difficulty and computer interaction, proposing the AISA index. It distinguishes between AI complementing side skills and substituting core skills, finding that while AI initially complements all workers, advancing AI threatens substitution primarily in lower-wage jobs, potentially increasing inequality despite higher exposure of white-collar side skills. | True | NaN | True | 1.0 | NaN | The paper proposes a modelling approach called the AI Share Automatability (AISA) Index, which uses O*NET data on skill importance and difficulty level, combined with a measure of computer interaction per occupation (derived using GPT-4 and O*NET task descriptions) and a hypothetical AI capability parameter (κAI), to simulate AI exposure. This exposure is further analyzed by differentiating between an occupation's core and side skills to model complementarity versus substitution. | The AISA model was evaluated through simulations on 711 US occupations using O*NET and US Bureau of Labor Statistics data. Results were compared to existing literature on AI exposure (e.g., Eloundou et al. (2023), Hatzius et al. (2023)), and robustness checks were performed by varying model components (e.g., computer interaction measure, O*NET data categories like abilities/work activities, core/side skill definitions). | At low AI capabilities (κAI=2.0), 7% of skills are uniformly exposed. At moderate (κAI=3.0) and high (κAI=4.0) capabilities, 17% and 36% of skills are exposed on average, respectively, with up to 45% exposure in the highest wage quartile at high AI capability. Low AI capabilities complement all workers by affecting side skills; as AI advances, core skills in lower-wage jobs become exposed, threatening substitution and increased inequality, while high-wage core skills remain less exposed, though their side skills are significantly affected. | NaN | NaN | NaN | NaN | NaN | United States | The AISA model uses publicly available data: O*NET (version 27.2) for occupational skills (importance, difficulty level, task descriptions) and the 2022 Occupational Employment and Wage Statistics (OEWS) Survey from the US Bureau of Labor Statistics (employment, wages). O*NET task descriptions were processed using GPT-4 (a pre-trained LLM) to estimate the proportion of time spent on computer, social, and physical interaction types for each occupation. | Bottom-up quantitative simulation model. Key steps include: 1) Classifying share of time in occupations spent on computer interaction (using GPT-4 on O*NET task descriptions). 2) Quantifying skill automation based on O*NET skill level data and a variable AI capability parameter (κAI). 3) Combining these into an AI Share Automatability (AISA) index. 4) Differentiating skills into 'core' and 'side' based on O*NET importance ratings to model complementarity vs. substitution. 5) Aggregating results by industry and wage quantiles. | NaN | False | False | NaN | NaN | Predicting AI's impact due to technology's nascent stage and rapid evolution. Quantifying AI capability (κAI) abstractly. Simplifications made in modelling, such as assuming only computer interaction is automatable (in baseline) and statistical independence between computer interaction time and specific skills. Differentiating 'exposure' from actual 'automation' and distinguishing complementarity from substitution required further modeling (core/side skills). | Substitution of human labor, particularly in lower-wage jobs as AI capabilities advance to affect their core skills. Increased income inequality due to differential impacts across the wage spectrum (complementarity for high-wage side skills, substitution for low-wage core skills). Job displacement and the need for workforce adaptation. Reshaping of job roles, even in high-wage professions, as side tasks are automated. |
| hCFGY1A7TzoJ.pdf | Google_Scholar | Ethical Foresight: Confronting Misinformation, Representation and Toxicity in Generative AI | This thesis investigates socio-technical harms such as misinformation, biased representation, and toxicity in Large Language Models (LLMs). It proposes a new comprehensive framework and taxonomy, developed through a systematic review of existing safety evaluations, to guide the ethical development and regulation of AI. | True | Idealistic | True | 1.0 | Positive | A comprehensive framework, taxonomy of harms (focusing on misinformation, representation, and toxicity), and evaluation guidelines for LLMs, developed through a systematic literature review. | NaN | NaN | Pervasiveness of misinformation, representational errors, and toxicity in LLMs; inherent biases in training data and algorithms; challenges in AI transparency and explainability ('black boxes'); difficulties in defining and operationalizing ethical principles like fairness; shortcomings in current AI regulations; underrepresentation of diverse perspectives in AI development; abstraction traps in applying technical solutions to social problems. | Development of a comprehensive framework and taxonomy for AI harms; context-aware, interdisciplinary evaluation strategies with human oversight; clear definitions and criteria for AI assessment; improved AI governance and regulation (e.g., EU AI Act, regulatory sandboxes); fostering Human-Centred AI (HCAI) and multi-stakeholder collaboration; addressing abstraction traps by integrating social context into technical solutions; providing actionable guidelines for developers and policymakers. | Ethical AI development, AI governance, Bias detection and mitigation, Fairness and equity in AI systems, Misinformation, Representational harms, Toxicity in LLMs, Sociotechnical AI safety. | Marginalized and underrepresented communities generally (e.g., based on race, gender, language, socio-economic status, LGBTQ+ identity) who are disproportionately affected by AI biases and harms. | AI Law and Regulation, AI Ethics, Data Protection, Non-discrimination principles in AI. | EU (focus on GDPR, DSA, AI Act), USA (examples cited). Principles and framework are intended for broad applicability (International). | Systematic review of over 170 academic papers on AI ethics, safety evaluations, misinformation, representation, and toxicity, primarily sourced from the DeepMind Sociotechnical Safety Evaluation repository and other academic databases. | Systematic literature review, qualitative analysis of academic papers, synthesis of findings to construct a new taxonomy of harms, and development of evaluation guidelines and recommendations. | NaN | True | True | The proposed framework, taxonomy, and evaluation guidelines are detailed within this thesis, making the intellectual contribution accessible to readers. | Lack of comprehensive, context-aware AI safety evaluation frameworks; insufficient human oversight in AI development and evaluation; limited understanding and mitigation of multimodal risks; inadequacy of current regulations for rapidly evolving AI (e.g., general-purpose AI, misinformation definition); difficulty in translating ethical principles into concrete technical practices; underrepresentation of diverse (especially non-Western and marginalized) perspectives and languages in AI development and evaluation; challenges in achieving true algorithmic fairness beyond statistical metrics. | Synthesizing a vast and evolving body of literature on AI ethics and safety; developing a comprehensive yet practical taxonomy of harms; ensuring proposed guidelines are actionable and adaptable across diverse AI systems and contexts; overcoming the limitations of existing evaluation methods (e.g., artificial setups, metric-related issues, generalizability). | Spread of AI-generated misinformation impacting public trust and democracy; perpetuation and amplification of societal biases and stereotypes leading to discrimination (representational harms); generation of toxic and harmful content (hate speech, harassment); infringements on privacy; challenges to human autonomy and decision-making; socio-economic disruption (e.g., job displacement); misuse of AI for malicious purposes (e.g., information warfare, astroturfing). |
| CQsEueGL1vcJ.pdf | Google_Scholar | ChatGPT + Generative AI Systems as Quasi-Expert Legal Advice Lawyers- Case Study considering Potential Appeal Against Conviction of Tom Hayes | This paper uses OpenAI's ChatGPT (Jan 2023 version) to generate quasi-expert legal advice regarding a potential appeal for Tom Hayes' Libor conviction, assessing the AI's current capabilities. It concludes that while AI shows significant potential, current versions lack deep legal knowledge but predicts rapid advancements will significantly disrupt the legal profession, potentially replacing many human lawyers. | True | Market | True | 2.0 | Neutral | OpenAI's ChatGPT (specifically the 9 January 2023 Version Free Research Preview). | Qualitative analysis of ChatGPT's generated text based on specific prompt questions regarding the Tom Hayes case, comparing arguments generated for and against the appeal, and assessing perceived strengths and weaknesses. | ChatGPT produced a good initial background summary and articulated generic arguments, but lacked detailed knowledge of specific case law (e.g., USA v Connolly & Black), UK statutes, legal tests (conspiracy, dishonesty), and recent data (post-2021 cutoff). This was attributed to limitations in its training data. | NaN | NaN | NaN | NaN | Criminal Law, Financial Regulation, Legal Practice/Profession | United Kingdom, United States | ChatGPT was trained on 'a massive amount of text data', predominantly public domain general data (like news articles) up to the end of 2021. The paper notes a lack of specific training on technical legal databases (caselaw, legislation). | Qualitative analysis of AI-generated text resulting from structured prompt engineering focused on a specific legal case study. | The paper discusses potential future deployment within law firms (e.g., Allen & Overy's 'Harvey') and potential direct use by consumers, predicting AI will take increasingly dominant roles. | True | False | Accessed via OpenAI as a 'Free Research Preview' version from January 2023. | Technical gaps in current AI: lack of deep, up-to-date knowledge of specific case law, statutes, legal tests, and reliance on potentially incomplete training data. | Limitations in ChatGPT's current capabilities due to its training data (time constraints, lack of specialized legal data) prevented it from providing granular, technical legal analysis. | Significant disruption to the legal profession (job losses, especially for junior lawyers; devaluation of traditional legal training), potential inaccuracies in AI legal advice due to data/knowledge limitations, lack of nuanced understanding or professional judgment compared to human lawyers, over-reliance on AI without expert human oversight. |
| HZyLbSmv6fAJ.pdf | Google_Scholar | LLMs Provide Unstable Answers to Legal Questions | This paper investigates the stability of leading Large Language Models (LLMs) like GPT-4o, Claude-3.5, and Gemini-1.5 when answering difficult legal questions. Using a novel dataset of 500 legal questions derived from split U.S. court decisions, the authors find significant instability (LLMs providing different answers to the same question) even with deterministic settings, raising concerns about their reliability for legal applications. | True | Market | True | 2.0 | NaN | Evaluation of specific LLMs (GPT-4o, Claude-3.5 Sonnet, Gemini-1.5 Pro, o1) regarding their output stability on legal questions. | A novel dataset of 500 legal questions derived from split U.S. Federal Courts of Appeal decisions was created. Each question was posed 20 times to each LLM (GPT-4o, Claude-3.5, Gemini-1.5) via API with temperature=0 and other parameters set for maximum determinism. Stability was measured as the frequency of the most common answer; accuracy was measured against the actual court outcome. A subset of 50 questions was tested on o1 (temp=1.0). | All tested LLMs exhibited instability (<100% stability) on a fraction of the 500 questions: Claude-3.5 (10.6%), GPT-4o (43.0%), Gemini-1.5 (50.4%). Instability patterns were largely model-specific. Accuracy relative to actual court outcomes: GPT-4o (53.9%) and Claude-3.5 (52.9%) performed statistically significantly better than chance; Gemini-1.5 (46.4%) performed worse than chance. | The inherent instability of current leading LLMs, where they produce different answers to the identical legal question even under deterministic settings, making their outputs unreliable for legal decision-making or analysis. | NaN | NaN | NaN | Various areas of U.S. federal law (including criminal law, civil procedure, employment law, social security, First Amendment, Native-American tribal jurisdiction, bankruptcy, civil rights, pensions, military, immigration law). | U.S. federal law | The paper evaluates proprietary LLMs (GPT-4o, Claude-3.5, Gemini-1.5, o1). Their training data is not disclosed by the vendors but is presumed to be very large-scale and general-purpose, likely including public domain legal text. | NaN | The evaluated LLMs are deployed and accessed via commercial APIs provided by OpenAI, Anthropic, and Google. | True | False | The evaluated LLMs (gpt-4o, claude-3.5, gemini-1.5, o1) are accessible via commercial APIs. The novel dataset of 500 legal questions and the authors' code are available on GitHub. | NaN | The proprietary, closed-source nature of the LLMs makes it impossible to determine the exact causes of instability. The ongoing development of these models may affect future reproducibility. Cost and time limited the number of models tested. | Using unstable LLMs for legal tasks carries the risk of inconsistent and potentially arbitrary outcomes, analogous to judicial misconduct (like flipping a coin). This undermines the reliability of legal AI products, lawyers' work product relying on these tools, and any potential use in formal legal processes or dispute resolution. |
| Healthcare__A_Growing_Role_for_Large_Language_Models_and_Generative_AI.pdf | Google_Scholar | Large Language Models and Generative AI’s Expanding Role in Healthcare | This paper surveys the application of large language models (LLMs) and generative AI (GAI) in the healthcare sector, covering medical text analysis, image analysis, and multimodal applications. It discusses various models, benchmarks, tools, challenges, and ethical considerations associated with using these AI technologies in healthcare. | True | Market | True | 3.0 | NaN | The paper surveys various techniques including Large Language Models (e.g., GPT-3, Biomedical Transformers like BioBERT, ClinicalBERT, GatorTron, PMC LLaMA), Generative AI (GANs, VAEs), and Multimodal Models (e.g., Med-PaLM M, ELIXR, Visual ChatGPT, LLaVA-Med). | NaN | NaN | NaN | NaN | NaN | NaN | Healthcare Law (implicitly, concerning data privacy and regulation) | International | NaN | NaN | NaN | False | False | NaN | NaN | Data privacy and security (including compliance with regulations like GDPR, handling PHI), interpretability ('black box' models), data bias leading to biased generation, need for regulatory permission, potential for AI chatbots to be unfit for clinical use due to bias/hallucinations, need for human oversight ('garbage-in, garbage-out'), model sensitivity to instructions. | Violation of data privacy and security, generation of biased, incorrect, or unfair diagnoses/treatments, spread of false information or damaging content, legal issues concerning liability and intellectual property/copyright for AI-generated content, potential for misuse of synthetic media (images, video, audio) for deception, manipulation, harassment, or defamation, risk of patient harm due to reliance on inaccurate AI-generated information. |
| o6B30NBG7GIJ.pdf | Google_Scholar | Some Emerging Hypotheses about Using Generative AI in Public Sector Operations | This paper reviews the potential applications and productivity benefits of Generative AI (like ChatGPT) for public sector operations, targeting leaders and managers. It also highlights significant risks, including alignment problems, hallucinations, and bias, proposing principles and a framework for responsible implementation. | True | NaN | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | USA | Discusses models (like ChatGPT) trained on vast corpora of internet text, noting this data contains societal biases. | NaN | NaN | False | False | NaN | Lack of transparency and explainability ('black box' nature) in Generative AI models; ensuring alignment with human values; gap between AI capabilities and reliable, safe, unbiased deployment in public sector contexts; need for workforce adaptation strategies. | Managing risks (alignment, hallucination, bias); ensuring fairness, reliability, safety, accountability, privacy, security, inclusiveness, transparency; developing effective policies, training, monitoring, and audit procedures; integrating AI into workflows; managing reputational risk and public trust. | Alignment problem (AI not aligning with human values/intentions), hallucination (generating false information), algorithmic bias (unfair outcomes, reinforcing societal biases), lack of replicability, potential for misuse in critical services (e.g., emergency response), reputational risk for government, reduced public trust, potential negative impacts on lower-skilled workers. |
| 3700789.pdf | Google_Scholar | Integrating Content Moderation Systems with Large Language Models | This paper proposes integrating Large Language Models (LLMs) into content moderation systems to enable personalized moderation and improve user-platform communication. It evaluates the content moderation capabilities of GPT 3.5 and LLaMa 2 against commercial products, discussing the approach's benefits and limitations for creating fairer online environments. | True | Idealistic | True | 1.0 | Positive | An approach integrating LLMs (specifically GPT 3.5 and LLaMa 2) into content moderation pipelines. This uses prompting with user-defined or community-specific rules to enable personalized content evaluation (zero-shot) and generate explanations for moderation decisions. | Quantitative analysis using F1 score to compare LLMs (GPT 3.5, LLaMa 2 7B) with commercial products (Perspective API, OpenAI Content Moderation) on two publicly available datasets: OpenAI's content moderation dataset and an English subset of the Reddit Multilingual Content Moderation dataset (r/judaism, r/feminism, r/naruto) with their respective community rules. | On the OpenAI dataset, GPT 3.5 (F1 score 0.7762) performed comparably to commercial solutions (e.g., OpenAI Content Moderation F1 0.7859). For the Reddit r/naruto dataset, whose rules often pertain to topics not inherently harmful, GPT 3.5 (F1 score 0.6410) outperformed traditional commercial solutions. | Current content moderation systems exhibit unfairness towards historically marginalized individuals, fragile users, and minorities; policies are often hardcoded, hindering personalized moderation. There is a lack of effective communication between users and platforms, and platform policies can be difficult to understand and ambiguously defined, failing to address diverse cultural nuances. | Integrating LLMs into content moderation systems to allow for personalized moderation based on customizable rules (user preferences or community norms). LLMs can also be used to generate explanations for moderation decisions, thereby improving transparency, user understanding, and communication between users and platforms. | Fairness in content moderation, protection of marginalized groups online, reducing online harm, transparency and accountability of platform decisions, user empowerment in digital spaces, enabling personalized online experiences. | Historically marginalized individuals, fragile users, minorities (including LGBTQ+ individuals, users from the Global South, Arab females, teenagers), and diverse online communities with specific norms. | Platform governance, digital law, freedom of speech, human rights (related to non-discrimination, participation, and access to information). | International | The LLMs studied (GPT 3.5, LLaMa 2) are pre-trained on large-scale, general web corpora. The paper's evaluation of these LLMs used: 1) A publicly available dataset from OpenAI (1680 text samples labeled for harmful content categories). 2) The Reddit Multilingual Content Moderation dataset (comments and subreddit-specific rules), available upon request from its original authors. | The proposed integration approach is based on a conceptual framework utilizing LLMs for rule-based content evaluation via prompting and dialogue for explanations. The evaluation of this approach employed a quantitative experimental methodology comparing LLM performance against benchmarks. | N/A (The paper proposes an approach and evaluates models; no specific deployment of the integrated system by the authors is described). | True | True | The approach can be replicated using LLaMa 2 (7B), an open foundation model available for download, or GPT 3.5, accessible via OpenAI's commercial API. The prompting technique is described in the paper. | Technical gaps include LLMs' limited mathematical capabilities for confidence scoring, binary decision outputs needing more nuance, potential safety degradation from fine-tuning, high costs, and performance issues with low-resource languages, hallucinations, and knowledge recency. Societal and ethical gaps involve privacy concerns, mitigating biases, ensuring accountability, the need for multi-stakeholder collaboration for governance, and assessing efficacy across diverse global contexts. | Obtaining consistent, machine-parsable (e.g., Yes/No) responses from LLMs for classification. Effectively interpreting and applying ambiguous or context-dependent community rules. The financial and environmental costs associated with using large language models. Ensuring LLMs accurately understand and apply nuanced or lengthy rule sets. | Perpetuation of existing societal biases and harm against marginalized communities due to biased training data in LLMs. Exposure of users to harmful, false, or inappropriate content generated by LLMs (hallucinations or safety-compromised models). Privacy violations stemming from the handling of user data by LLM providers, especially closed-source models. Degradation of LLM safety alignment through fine-tuning processes. Negative impacts on freedom of speech and economic opportunities for content creators due to flawed or unfair moderation. |
| KsIty3_cK1AJ.pdf | Google_Scholar | Book Review—Shaping the Bar: The Future of Attorney Licensing | This paper reviews Joan Howarth's book advocating for reforms to attorney licensing, arguing the current bar exam fails public protection and equity, and needs alignment with actual practice competence. The reviewer supports these points, adding concerns about AI's impact on competence definitions and emphasizing the need to educate lawyers on their 'public citizen' role to address access to justice. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Current bar exam inadequately measures competence, functions as an elitist filter hindering diversity (racial/ethnic disparities), fails public protection, high cost of legal education/licensing, institutional resistance to change, neglect of lawyer's 'public citizen' role contributing to access to justice crisis. | Reform bar exam (e.g., NextGen) to focus on skills/application, evidence-based competency assessment, supervised practice/residencies, competence-based education (diploma privilege, portfolios), address disparities, reduce costs, portable licenses, reform character & fitness reviews, educate on 'public citizen' duties, adapt legal practice/education for AI. | Attorney licensing reform, Bar examination reform, Lawyer competence assessment, Legal education reform, Equity and diversity in the legal profession, Access to Justice (linked to systemic reform and lawyer's public role). | Historically excluded groups (immigrants, Jewish people, people of color), African American and Latinx students, individuals with criminal/mental health histories, the poor unable to afford legal assistance. | General Legal Practice / Legal Education / Professional Regulation | United States | NaN | NaN | NaN | False | False | NaN | Failure of current competency definitions/assessments, especially with AI; inadequate education on lawyers' 'public citizen'/stewardship role; insufficient integration of interdisciplinary perspectives; lack of focus on 'access to justice' as core lawyer responsibility; need for better transparency/review of licensing mechanisms. | NaN | AI automating tasks defining lawyer competence, causing economic disruption; licensing systems perpetuating racial/ethnic disparities; failure of legal profession/education to adapt to change; neglecting ethical/civic dimensions of lawyering; misuse of character/fitness reviews; inadequate public protection from incompetent lawyers. |
| CAIdSeWjm04J.pdf | Google_Scholar | ChatGPT and Generative AI Systems as Corporate Ethics Advisors | This paper explores the potential of ChatGPT and generative AI systems to serve as corporate ethics advisors. It tests ChatGPT's ability to provide ethical advice in a hypothetical scenario involving a high-tech space company facing a critical launch decision and subsequent ethical dilemmas. | True | Market | True | 2.0 | Positive | Using ChatGPT as a corporate ethics advisor by posing specific ethical questions related to a hypothetical scenario. | ChatGPT was prompted with a detailed hypothetical test case concerning a commercial high-tech space company, loosely based on the Columbia Space Shuttle disaster, including questions about launch decisions, transparency after failure, and handling of whistleblowers. ChatGPT's responses (text in italics) were then analyzed. | ChatGPT provided c.130 paragraphs of detailed applied advice for the specific scenario and c.30 paragraphs of general ethical advice, demonstrating its potential to offer significant corporate ethics guidance. | Corporate ethical blindness is common, or existing ethics provisions (like human hotlines or guidelines) are insufficient. | ChatGPT and successor generative AI systems, especially those trained on corporate ethical questions, can provide readily accessible ethics advice to company employees and officers, leading to improved ethical awareness and accountability. | Corporate ethics, ethical decision-making in high-risk situations, corporate transparency, whistleblower protection, role of ethics committees. | Company employees, company officers, company ethics committees. | Corporate Law, Business Ethics. | International | The version of ChatGPT used was predominantly trained on general data up to the end of 2021. The paper suggests future systems could be specifically trained on corporate ethical questions. | NaN | NaN | True | True | The analyses used a 'test version of OpenAI’s ChatGPT, 13 February 2023 Version Free Research Preview'. | The ethics advice from AI may not be perfect or complete. There is a need for generative AI systems to be specifically trained to focus on corporate ethical questions. | NaN | NaN |
| pqvZKAEc7eIJ.pdf | Google_Scholar | Building GenAI Benchmarks: A Case Study in Legal Applications | This paper discusses the importance and challenges of creating domain-specific benchmarks for evaluating Generative AI, using the legal field as a case study. It outlines benchmark components, highlights difficulties like evaluating unstructured text, high costs, train-test leakage, and subjectivity, and emphasizes the potential for interdisciplinary collaboration in benchmark development. | True | NaN | True | 3.0 | Neutral | Benchmarking methodologies for Generative AI in specialized domains (specifically law) | The paper discusses general benchmarking practices and specific legal benchmarks (e.g., CUAD, CaseHOLD, LegalBench) and evaluation methods (accuracy, error analysis, human expert review, LLM-as-judge), but does not perform a new evaluation itself. It focuses on the challenges of evaluation (e.g., cost, subjectivity, text evaluation). | NaN | The high cost of legal expertise needed for annotation hinders the creation of robust benchmarks, particularly for access-to-justice applications; evaluating complex, subjective legal reasoning and unstructured text output from AI is difficult; the priorities of institutions with resources to build benchmarks may not align with access-to-justice needs. | Develop benchmarks through interdisciplinary collaboration; use cost-effective data strategies like deriving labels from existing public data (e.g., CaseHOLD) or crowdsourcing smaller, diverse task datasets from experts (e.g., LegalBench); explore automated evaluation techniques like LLM-as-judge; focus evaluation on explanations rather than just predictions for subjective tasks. | Benchmarking AI for legal applications | NaN | Law (general), Contract Law (example) | International | The paper discusses benchmark dataset construction, not model training data. Benchmark examples mentioned use: expert annotations on contracts (CUAD), summaries automatically extracted from judicial opinions (CaseHOLD), and crowdsourced tasks from legal experts (LegalBench). The data is domain-specific (legal) and primarily unstructured or semi-structured. | Benchmark design principles (representativeness, size, multi-annotator labeling), data collection strategies (expert annotation, leveraging existing annotations, crowdsourcing), evaluation methods (comparison of model outputs to desired outputs using metrics, manual inspection, human evaluation, LLM-as-judge). | Discusses distribution of benchmarks via public online platforms (e.g., Huggingface, Github) and the associated risks (train-test leakage). | False | False | NaN | Difficulty and cost of evaluating unstructured text and complex legal reasoning; high cost limiting benchmark scale and scope (potentially biasing towards commercial interests over A2J); subjectivity in legal tasks complicating evaluation; train-test leakage rendering public benchmarks less reliable over time; need for better automated evaluation metrics/methods. | Evaluating unstructured text generation for semantic correctness and legal soundness; high cost of involving legal subject-matter experts for annotation and evaluation; preventing train-test leakage for publicly distributed benchmarks; designing benchmarks for subjective legal tasks; creating tasks that genuinely measure complex legal reasoning. | GenAI models hallucinating false information; replicating social biases; lawyers facing sanctions for using unreliable AI; potential for significant financial loss or deprivation of liberty due to AI errors; high costs limiting benchmark development to well-resourced entities, potentially neglecting access-to-justice needs; inflated performance metrics due to train-test leakage. |
| Sg6cPoNeAzoJ.pdf | Google_Scholar | A PROPOSAL FOR THE JOINT DEVELOPMENT OF GENERATIVE AI FOR THE DISPUTE RESOLUTION PROFESSION | This paper proposes the collaborative development of a specialized generative AI system, based on large language models, for the dispute resolution field. The goal is to create a fine-tuned, reliable tool to assist practitioners and parties, enhance access to justice, and mitigate risks associated with general-purpose AI. | True | Idealistic | True | 1.0 | Positive | Collaborative development of a fine-tuned generative AI system (based on LLMs like ChatGPT) specific to the dispute resolution field, involving shared data curation, guardrail setting, and privacy parameter definition. | NaN | NaN | Complexity and inaccessibility of dispute resolution information and processes, particularly for non-English speakers or individuals with impairments. | Develop a collaboratively built, fine-tuned generative AI tool for dispute resolution to provide accessible information (text/voice, multiple languages, 24x7) and assistance to parties and neutrals. | Providing information about dispute resolution processes (mediation, arbitration), facilitating negotiation, drafting agreements, addressing ethical questions for neutrals. | Parties involved in disputes, particularly non-English speakers and individuals with hearing or visual impairments. | Dispute Resolution (Mediation, Arbitration) | International | Proposed: A collaboratively curated dataset using dispute resolution-specific supervised learning inputs, potentially including existing literature/materials from industry authors, to fine-tune a base LLM. | Collaborative development involving a centralized advisory board, expert curation of training data, supervised fine-tuning of LLMs, establishment of guardrails and privacy parameters, and ongoing feedback. | Proposed: Access to the collaboratively developed system/dataset potentially via a fee, allowing individuals/providers to build applications. Neutrals could embed access on their websites. | False | False | NaN | Lack of a reliable, collaboratively developed AI tool specifically tailored for dispute resolution. Need for ongoing refinement and addressing concerns (privacy, accuracy) as the technology is used. | General LLM issues (accuracy, bias, IP, hallucinations); specific challenges for the proposal include organizing collaboration, securing data/cooperation, funding, defining/implementing guardrails and privacy standards. | Generation of biased/inappropriate content, factual inaccuracy/fabrications ('hallucinations'), intellectual property infringement related to training data, potentially misleading 'emotional' or 'sentient-like' responses. |
| PHjZDne92UkJ.pdf | Google_Scholar | What are Models Thinking about? Understanding Large Language Model Hallucinations through Model Internal State Analysis | This paper introduces HaluProbe, a framework for analyzing the internal states (attention, activation, logits) of Large Language Models (LLMs) during different inference stages to understand and detect hallucinations without external data sources. It systematically evaluates various internal features and token selection strategies, finding attention-based features offer robust detection but notes challenges like limited transferability across datasets. | True | NaN | True | 1.0 | NaN | HaluProbe: A framework using LLM internal state features (e.g., attention lookback ratio, attention allocation sharpness, last layer representation, activation map/entropy, token probabilities/ranks) extracted during understanding, query, and generation stages for hallucination detection. | Evaluated on HaluEval, CNN/Daily Mail (CNNDM), and Natural Questions (NQ) datasets using Llama-2-7B and Vicuna-7B models. Metrics included accuracy, recall for hallucinated outputs, and recall for factual outputs. Ablation studies on features and token selection strategies (All tokens, First/Last token, Per token, Sliced window) were performed, along with transferability tests across datasets. | The sliced window token selection strategy (specifically Window(4, 2)) achieved the highest accuracy (e.g., 0.89 on HaluEval with Vicuna-7B). Attention-based features like Lookback Ratio showed relatively robust performance across datasets compared to logit or activation features. However, transferability across different datasets (e.g., from HaluEval to CNNDM/NQ) was found to be limited for most features. | NaN | NaN | NaN | NaN | NaN | International | The study uses publicly available datasets (HaluEval, CNN/Daily Mail, Natural Questions) to generate factual and hallucinated responses from LLMs (Llama-2-7B, Vicuna-7B). Features are extracted from the internal states of these models during generation on these datasets; these features are implicitly used to train/evaluate a classifier for hallucination detection. | Systematic analysis of LLM inference process stages, extraction of internal states (attention scores, layer representations, logits), definition and computation of specific features from these states, experimental evaluation on benchmark datasets, ablation studies on features and token selection methods, transferability analysis across datasets. | The paper commits to making the source code, datasets, and implementation details publicly available on a repository like GitHub upon acceptance. | False | False | NaN | NaN | Limited transferability of internal state features across different datasets and task scenarios. High computational and storage overhead associated with extracting and processing some internal state features. | The primary risk addressed is LLM hallucination (generating factually incorrect or contextually inconsistent content). The paper does not explicitly state risks associated with the proposed detection method itself. |
| HsqxFTOulbAJ.pdf | Google_Scholar | InternLM-Law: An Open Source Chinese Legal Large Language Model | This paper introduces InternLM-Law, an open-source Large Language Model specialized for the Chinese legal domain, detailing its novel two-stage fine-tuning process and the construction of a comprehensive legal dataset. InternLM-Law demonstrates state-of-the-art performance on the LawBench benchmark, outperforming existing models including GPT-4 on many Chinese legal tasks, and is released to foster further research. | True | Idealistic | True | 1.0 | Positive | InternLM-Law, a specialized LLM for Chinese legal queries, developed using a two-stage supervised fine-tuning (SFT) process on a new Chinese legal dataset. | Evaluated on LawBench (20 legal subtasks covering memorization, understanding, and application), subjective evaluation (comparison with GPT-4 on legal consultation, case analysis, legal reasoning judged by GPT-4), and long-text evaluation (analyzing Chinese law judgments over 20k characters). | InternLM-Law-7B achieves the highest average performance on LawBench (67.71% zero-shot, 67.67% one-shot), outperforming GPT-4 on 13 out of 20 subtasks. In subjective evaluation, it achieved a 46.67% win-rate against GPT-4, and 87.5% on legal consultation. On long context evaluation, it achieved an 84.73% F1 score. | NaN | NaN | Legal consultation, consumer rights protection, criminal case analysis, financial remedy calculation, legal document understanding and information retrieval. | NaN | Chinese civil, criminal, and constitutional law, and other regulations. | China | A dataset of over 1 million queries in the Chinese legal domain, sourced from public legal datasets (e.g., CAIL, LawBench), online legal consultation platforms (6 million anonymized Q&A records), and the Chinese National Legal Database (100K entries of laws & regulations). It also includes 1 million general SFT instruction instances from InternLM2-Chat training. | Two-stage supervised fine-tuning (SFT) pipeline. Stage 1: fine-tuning on a mixture of legal and general-purpose tasks. Stage 2: refining the model on high-quality legal tasks. Data processing includes rule-based filtering, semantic filtering using LLMs (Qwen-1.5-72B), instruction generation using GPT-4, and data synthesis using GPT-4 with human feedback. | The model, dataset, and code are made publicly available on GitHub. | True | True | Dataset, code, and models will be released on GitHub (https://github.com/InternLM/InternLM-Law). | Model hallucinations and limitations in complex legal reasoning due to model size, which could hinder reliable application. | Collecting and cleaning a comprehensive SFT dataset; ensuring data quality and diversity; enabling the model to transfer general skills to legal tasks; designing an effective SFT strategy to learn crucial datasets and adjust response style; handling long legal texts. | Model hallucinations and generation of inaccurate responses. |
| gchkptrNhsIJ.pdf | Google_Scholar | Is ChatGPT Leading Generative AI? What is Beyond Expectations? | The paper provides an overview of Generative AI, focusing on ChatGPT and its competitors, discussing their technical fundamentals and societal impact by reviewing existing literature. It explores user expectations and the current landscape, highlighting both the potential and limitations of these technologies across various fields including law. | True | Market | True | 3.0 | Neutral | ChatGPT and other Generative AI Large Language Models (e.g., Bard, Claude, GPT-4) | Literature review of studies testing various LLMs, and illustrative queries by the authors to ChatGPT to demonstrate capabilities and limitations (e.g., reference generation, factual accuracy). | Authors' illustrative queries showed ChatGPT can generate coherent text but may fabricate references and provide incorrect factual information. It apologized for errors but repeated some. | Unreliability (hallucinations, factual inaccuracies), potential for bias, ethical and regulatory challenges, fabrication of information, and the risk of misuse. | Emphasizes human oversight, critical evaluation of AI outputs, development of safety protocols and explainable AI, legislation for high-risk AI use, and industry initiatives for responsible AI development. | Legal information provision, legal document drafting, legal reasoning assessment (e.g., Bar exam performance), transformation of legal services, and ethical/regulatory considerations in legal AI. | NaN | General law, Torts, Evidence, Civil law, Litigation, AI liability Ddrectives. | International | For ChatGPT and similar models: Proprietary, large and diverse datasets of text and code. Some mentioned models (e.g., OPT, GPT-J) use public datasets like The Pile and BookCorpus. | Deep learning using Transformer architectures, pre-training on large corpora, and fine-tuning techniques (e.g., supervised learning, Reinforcement Learning from Human Feedback - RLHF for some models like Claude). | Primarily via publicly accessible web-based user interfaces (e.g., ChatGPT, Bard). Some models are available as open-source software. | True | True | ChatGPT and other models like Bard are publicly accessible via web interfaces, often with free tiers. Some specific models discussed, like EleutherAI's GPT-J/NeoX and OpenAI's Whisper, are available as open-source software. | Need for improved reliability and factual accuracy, robust ethical and legal frameworks, mitigation of bias, better explainability, security against misuse, and reliable methods for detecting AI-generated content. | Ensuring accuracy and reliability (avoiding hallucinations), managing bias from training data, high computational costs for training, addressing ethical considerations and data privacy, and preventing misuse. | Generation of misinformation and fabricated content, privacy violations, academic and professional dishonesty, entrenchment of biases, job displacement for knowledge workers, misuse for malicious purposes (e.g., cybersecurity threats), and broader unforeseen societal disruptions. |
| Y167Kf-vw-gJ.pdf | Google_Scholar | Large language models and their possible uses in law | This paper explores the workings of Large Language Models (LLMs) like ChatGPT and their potential applications in the legal field, particularly focusing on enhancing access to legal information and services. It discusses uses like text retrieval, generation, and analysis, and details an experiment building a law firm chatbot, while also acknowledging limitations and suggesting paths towards democratizing access to justice. | True | Idealistic | True | 3.0 | Positive | A chatbot demo for a small law firm using the OpenAI GPT-3.5 API, customized with prompts and examples. | An informal experiment conducted by one author to build and explore the capabilities and limitations of a demo chatbot. | The demo chatbot could provide basic firm information entertainingly but was unsuitable for actual legal advice due to limitations (hallucinations, token limits, policy restrictions, lack of reality check/emotional intelligence). Customization required prompts and examples. | LLM limitations: unreliability/hallucinations, inability to perform reality checks or understand deeper context/client needs, lack of emotional intelligence, token limits restricting input/customization, potential for misuse (e.g., unauthorized practice of law), non-transparency of models. | Using LLMs for specific tasks (retrieval, generation, analysis) within professional workflows, staged approaches (e.g., retrieval + ranking), connecting LLMs to curated knowledge bases, prompt engineering, fine-tuning, responsible API use, and domain-specific evaluation by legal experts. | Providing legal information to the public, increasing efficiency of obtaining legal assistance. | The broader public / laypeople. | General / Multiple (including contract law, inheritance law). | International (with Hungarian context/examples). | The underlying LLM (GPT-3.5) was pre-trained on a vast, general internet corpus. The specific chatbot demo was customized using hand-crafted prompts and question/answer examples specific to the law firm and ethical rules, provided via the API. | Prompt engineering, few-shot learning (via examples provided in API calls). | A web interface for the demo chatbot was created and made publicly accessible (as stated in footnotes). | True | False | Source code for demo front-end and examples available on GitHub; requires paid access to OpenAI API. | Need for domain-specific accuracy benchmarks and evaluation by legal experts; understanding LLM capabilities with higher-level legal concepts; determining reliability limits, especially for direct client use; need for more large-scale experimentation across jurisdictions. | Adhering to deontological rules, preventing factual 'hallucinations', managing strict token limits (restricting customization and context length), ensuring accurate multilingual performance. | Providing incorrect legal information/advice (hallucinations); unauthorized practice of law; misrepresenting firm details; potential data confidentiality issues with API usage (mitigated by OpenAI policy); potential for misuse (e.g., generating misinformation). |
| 7ME5PVaLogYJ.pdf | Google_Scholar | Justifiable Artificial Intelligence: Engineering Large Language Models for Legal Applications | This paper introduces the concept of "Justifiable Artificial Intelligence" (JAI) as an alternative to Explainable AI (XAI) for Large Language Models (LLMs) in the legal domain. JAI proposes to enhance the trustworthiness of LLM outputs by providing users with supporting and potentially contradicting evidence from reliable sources, rather than focusing on explaining the LLM's internal workings. | True | Market | True | 1.0 | NaN | Justifiable Artificial Intelligence (JAI), conceptualized through two pipelines: 1) LLM prompted to extract evidence from documents, 2) Fact-checking an LLM's claim using external trustworthy sources/knowledge bases, a retriever, and an entailment classifier to display supporting/contradicting evidence. | NaN | NaN | Lack of trust in LLM outputs by legal experts due to issues with explainability, accuracy (hallucinations, outdated information), coherence, transparency, interpretability, and ethical concerns (bias, privacy). | Proposes Justifiable Artificial Intelligence (JAI), where LLM outputs are accompanied by retrievable evidence from trustworthy sources, allowing users to validate claims. This involves providing both supporting and (if applicable) contradicting evidence to enable informed decision-making by the user. | Enhancing trustworthiness and reliability of AI in legal applications, fact-checking AI-generated legal information, human-in-the-loop validation of AI outputs. | NaN | General legal domain. | International | The proposed JAI approach relies on retrieval from external data sources like 'trustworthy websites, database of fact-checked documents,' or a 'Document Collection.' The LLMs discussed in the background are trained on large, general text corpora. | Conceptual framework development; proposal of system architectures involving LLMs, information retrieval modules, and entailment classifiers. | NaN | False | False | NaN | The need to validate the acceptance of JAI-enhanced LLMs by legal experts. The underlying limitations of LLMs (accuracy, bias) are not fully resolved by JAI, only made more transparent for validation. | Ensuring the reliability and accuracy of the evidence retrieval and entailment classification components within the JAI framework; defining and maintaining a collection of 'trustworthy sources'; managing potential error propagation from these components to the final justification presented to the user. | General LLM risks: misinformation (hallucinations), copyright violations, bias in training data leading to unfair outputs, privacy issues, high energy consumption. Specific to JAI: flawed justification mechanisms (e.g., incorrect evidence retrieval or entailment classification) could lead to misplaced trust in an erroneous AI output. |
| lNX-6qdZr2wJ.pdf | Google_Scholar | How LLMs Can Help Address the Access to Justice Gap through the Courts | This paper explores how Large Language Models (LLMs) can improve access to justice for low-income individuals in the U.S. court system, focusing on externally-facing applications. It demonstrates five use cases using Arizona courts, including translation and GPT-powered chatbots for eviction and expungement, while also discussing potential risks and providing illustrative tools. | True | Idealistic | True | 1.0 | Positive | Demonstration of five LLM use cases: 1) multi-language translation of court website text, 2) finding pro bono legal help, 3) building no-code AI chatbots for criminal expungement guidance, 4) building no-code AI chatbots for landlord/tenant disputes and eviction guidance, and 5) internal court brainstorming/strategic planning. Two GPT-powered chatbots for Arizona expungement and eviction were built using OpenAI's GPT builder. | Translation: Prompts given to ChatGPT 4.0, ChatGPT 3.5, Bard, Claude 1 & 2; reviewed by native speakers. Pro bono help finding: Tested with ChatGPT 4, Bard, Perplexity Pro; links/numbers checked. Chatbots (Expungement & Eviction): Built with OpenAI's GPT builder using Arizona court documents, tested with sample user queries for eligibility, form-filling guidance, and procedural explanations. Internal brainstorming: Prompts given to Claude 2 and ChatGPT 4. | For Spanish translation of legal text, ChatGPT 4 received a native speaker rating of 9/10. | Lack of adequate legal assistance for low-income individuals, difficulties for self-represented litigants in navigating the legal system (e.g., understanding rights, procedures, finding help, completing forms), and language barriers. | Utilizing LLMs for language translation of legal information, curating legal provider information, guiding users through self-help forms and procedures (e.g., for eviction and expungement via AI chatbots), and assisting courts with internal planning and improving IT infrastructure. | Language access in courts, legal aid referrals, criminal record expungement, housing law (eviction, landlord-tenant disputes), support for self-represented litigants, and court administration. | Low-income Americans, self-represented litigants, individuals with limited English proficiency, and individuals with criminal records. | Civil law, criminal law (specifically record clearing/expungement), housing law (landlord-tenant disputes, eviction), immigration law (for referral finding). | United States (with Arizona courts as a specific case study) | Publicly available, unstructured textual information and forms (over 150 pages total for both bots) from the Arizona state courts' websites (specifically azcourts.gov, azcourthelp.org) regarding expungement, landlord-tenant disputes, and eviction. | For chatbots: Utilization of OpenAI's GPT builder (a no-code approach), involving uploading relevant documents from Arizona court websites to create a knowledge base, and iterative testing with sample conversations. For other use cases: Prompt engineering with various LLMs (ChatGPT, Claude, Bard). | Two GPT-powered chatbots (AZExpungement and AZ-evictionbot) were made accessible via URLs. Prompts and instructions for implementing the five use cases are provided in an appendix. | True | False | Two GPT-powered chatbots (Arizona Expungement Bot and Arizona Eviction Bot) accessible via provided URLs, requiring a ChatGPT 4 subscription. Prompts for all five use cases are in the appendix. | Current limitations of LLMs in accuracy and reliability (hallucinations), need for more sophisticated and reliable legal AI tools tailored for courts, and institutional/cultural challenges within courts for technology adoption. | Ensuring accuracy and reliability of LLM-generated information (hallucinations, e.g., incorrect legal deadlines), managing the risk of providing unauthorized legal advice, technical difficulties in translation (terms of art, less common languages), and the need for significant IT infrastructure upgrades and staff training for court adoption. | Generation of inaccurate or false information (hallucinations) by LLMs, perpetuation of bias from training data or model inferences, exacerbation of existing inequalities in the legal system (e.g., creating a two-tiered system of justice), potential for misuse (e.g., flooding courts with frivolous filings), and diversion of resources from other access to justice initiatives like right to civil counsel. |
| Hb7vkjE6mpUJ.pdf | Google_Scholar | Artificial Intelligence and the Sustainable Development Goals: An Exploratory Study in the Context of the Society Domain | This paper evaluates AI's potential contributions and risks concerning the United Nations' Sustainable Development Goals (SDGs) within the society domain by analyzing responses from the GPT-3 language model. The study highlights AI's capabilities for these goals while emphasizing the critical need for ethical guidelines and robust regulations. | True | Idealistic | True | 2.0 | Neutral | The paper evaluates the capabilities and response patterns of GPT-3 (specifically the text-davinci-003 model) when queried about AI's role in achieving Sustainable Development Goals (SDGs). | GPT-3 (text-davinci-003 model) was prompted with queries regarding 9 societal SDGs and their 58 outcome targets, asking it to shorten target titles, maintain numbering, and provide 3-5 sentences on AI's benefits and risks. The generated outputs were then descriptively analyzed for content, structure, word counts, and patterns/errors. | GPT-3 (text-davinci-003) generated relevant responses discussing potential benefits and risks of AI for societal SDGs. However, the study identified inconsistencies in output format, varying sentence structures, and increased punctuation mistakes in longer texts, indicating it is not fully reliable or error-free. | High-level obstacles/risks for AI in access to justice (derived from SDG 16 discussion) include: potential for targeting specific populations (e.g., minority groups), misinterpretation of data leading to false accusations, biased algorithms causing unfair discrimination, increased surveillance infringing on privacy, misuse by corrupt actors, racial biases in technologies like facial recognition for legal identity, and violation of fundamental freedoms through profiling. | The paper advocates for proper regulations and oversight for responsible, transparent, safe, and ethical AI use. It calls for a global debate leading to science-driven shared principles and legislation, careful monitoring of AI, building safeguards against discrimination into algorithms, and strict oversight for technologies like facial recognition. | Access to Justice for All (as part of SDG 16), promoting the rule of law, reducing illicit financial flows, reducing corruption and bribery, providing legal identity for all. | Minority groups, disadvantaged groups, and vulnerable populations are mentioned as potentially at risk or in need. | Public law, human rights, criminal justice (related to reducing violence, corruption), administrative law (effective institutions, rule of law). | International | The GPT-3 model text-davinci-003, used in the study, was trained on general internet text data up to June 2021. This is large-scale, mostly unstructured text data. | The evaluation of GPT-3 involved AI model selection (comparing GPT-3 models), prompt engineering (designing specific queries), and descriptive analysis (analyzing GPT-3's output for patterns, word counts, etc.). | NaN | True | False | The study used OpenAI's GPT-3 text-davinci-003 model, accessible via its platform (e.g., API, playground), which was available as a 'publicly available beta for research.' | Technical gaps include the unreliability and error-proneness of GPT-3 for complex, evidence-based tasks, its potential for bias, and lack of robust interpretability. Societal and regulatory gaps include the need for frameworks for ethical AI deployment in justice, mechanisms to ensure AI enhances freedoms, and robust legislation for AI governance and accountability. | Challenges faced by the authors in using GPT-3 included obtaining consistent output format, ensuring accuracy (avoiding 'hallucinations'), the AI's tendency to mimic human writing errors, and potential capacity issues with the free beta tier. | For SDG 16 (Access to Justice): AI could enable targeting of specific populations; misinterpret data leading to false accusations; use biased algorithms for discrimination; increase surveillance infringing privacy; be misused by corrupt actors; exhibit racial bias in facial recognition for legal identity; and violate fundamental freedoms through profiling and targeted ads. |
| CZsvZ6XE5O8J.pdf | Google_Scholar | Enhancing Generative AI Usage for Employees: Key Drivers and Barriers | This study investigates the factors influencing employee adoption and usage frequency of Generative AI (Gen-AI) tools in the workplace using the Technology, Organization, and Environment (TOE) framework. Based on a survey of 316 US employees, results show perceived competence, peer influence, and regulatory support positively impact usage, while perceived severity has a negative impact. | True | Market | True | 2.0 | NaN | Application of the Technology-Organization-Environment (TOE) framework, integrated with concepts from UTAUT (performance expectancy, effort expectancy) and social cognitive dimensions (perceived competence, warmth), to model employee adoption of Generative AI (Gen-AI). | Quantitative survey administered to 316 American employees via the Prolific platform. Data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed model relationships. | Perceived Gen-AI competence positively impacted usage intensity, mediated by both performance expectancy and effort expectancy. Perceived warmth positively impacted usage intensity via performance expectancy only. Peer influences and regulatory concerns positively impacted usage intensity directly, while perceived severity had a negative direct impact. Technological resource proficiency had a positive impact, but absorptive capacity did not significantly influence usage intensity. | NaN | NaN | NaN | NaN | NaN | United States | Quantitative survey data from 316 American employees regarding their perceptions and professional use of Gen-AI, collected via the Prolific online research marketplace. | The study developed a theoretical framework (model) based on existing theories (TOE, UTAUT, social cognitive dimensions) identified through a literature review. Hypotheses derived from this framework were tested using survey data and statistical modeling (PLS-SEM). | NaN | False | False | NaN | NaN | Study limitations mentioned include: relatively small sample size, cross-sectional data limiting causal inference, reliance on self-reported data, lack of input from various employee levels, focus solely on the US market, overlooking dynamics of change over time and individual adoption contexts. | Perceived severity risks (amplifying discrimination/bias, perpetuating stereotypes, wrong objectives, poor performance). Potential job security concerns. Doubts about AI dependability. Risk of over-regulation hampering innovation. |
| Ra4RfztIjSYJ.pdf | Google_Scholar | Governing Data and AI to Protect Inner Freedoms Includes a Role for IP | This policy brief argues for comprehensive governance of data and AI, highlighting the crucial role of intellectual property, to protect fundamental human rights such as freedom of thought from the impacts of technologies like generative AI. It identifies current regulatory inadequacies and proposes solutions including enhanced international cooperation, technology pre-deployment testing, and increased corporate accountability. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Lack of regulatory clarity and data governance hindering intellectual property protection for smaller entities, creating socio-economic and access-to-justice issues; pervasive data monetization without assured human rights protection; inadequate, siloed, and non-globalized AI/data regulations; manipulative AI practices (e.g., dark patterns, disinformation) threatening freedom of thought. | Integrating intellectual property rights into AI governance frameworks to enhance transparency and algorithmic monitoring; establishing national personal data protection laws; fostering international regulatory cooperation (e.g., a Digital Stability Board); implementing technology testing (e.g., regulatory sandboxes) before deployment; promoting corporate responsibility through duty-of-care frameworks. | Fairness in intellectual property protection, particularly for smaller entities; protection of fundamental human rights (especially freedom of thought) through AI and data governance. | Smaller companies (regarding intellectual property rights and access to justice); the general public (regarding the protection of freedom of thought and other fundamental rights). | Intellectual Property Law, Data Governance, AI Regulation, Human Rights Law, Competition Law | International (with specific examples and discussions related to Canada, United States, European Union, United Kingdom, Australia, Japan, and bodies like G7, OECD). | NaN | NaN | NaN | False | False | NaN | Lack of coherent global guardrails, standards, and regulations for generative AI and data governance; insufficient mechanisms for multi-stakeholder international cooperation on AI regulation; unresolved international differences in IP treatment for AI-generated works and data used in AI training; inadequate protection for freedom of thought against technological encroachments. | NaN | Monetization of nearly all human activity as data without upholding human rights (including freedom of thought, privacy, freedom of speech); covert tracking, surreptitious surveillance, and pervasive monitoring; opaque consent agreements; IP rights used by digital giants to impede competitors; uncertainty in IP law regarding AI-generated inventions and copyright for AI inputs/outputs; use of trade secrets to hinder transparency; web scraping of copyrighted data; AI-driven subtle influence on individuals and generation of social tensions (e.g., disinformation); weaponization of personal data through 'dark patterns'; AI 'hallucinations' and inaccuracies misrepresenting individuals. |
| YfVyFeUpCzoJ.pdf | Google_Scholar | AI for Data Science: A Benchmark for Differentially Private Text Dataset Generators | This paper introduces a benchmark design for evaluating differentially private text dataset generators, particularly for high-stakes, domain-specific applications. Preliminary results using healthcare datasets demonstrate that current methods significantly underperform in utility and fidelity, underscoring the need for such robust, domain-specific benchmarks. | True | Market | True | 1.0 | NaN | A benchmark design for differentially private text dataset generators. | The benchmark design's validity was demonstrated by applying it to evaluate two existing methods (AUG-PE, DP-Generator) on three healthcare text datasets (HOC, N2C2 2008, PSYTAR). Evaluation measured utility (F1 scores on downstream classification) and fidelity (MAUVE, text length distributions, entity mentions, lexical diversity) under various privacy budgets (ϵ). | Existing methods (DP-Gen and AUG-PE) showed significant degradation in utility (e.g., achieving only 34-58% of real data performance at ϵ≤4) and fidelity (MAUVE scores near zero, indicating substantial distribution differences) on domain-specific healthcare data. | NaN | NaN | NaN | NaN | NaN | International | Three semi-publicly available healthcare text datasets employed for validating the benchmark: HOC (scientific abstracts, public), N2C2 2008 (clinical discharge summaries from MIMIC-III, gated access), and PSYTAR (adverse drug effect reports from social media posts, gated access). These are domain-specific, unstructured text. | The benchmark design incorporates: 1) Use of datasets with gated access for realistic evaluation on sensitive data. 2) Integration of fully open foundation models with transparent training corpora to verify non-exposure of private data during pre-training. 3) Development of diagnostic datasets for membership inference attacks. 4) Evaluation of both utility (downstream task performance) and fidelity (statistical similarity to real data using metrics like MAUVE, text length distributions, entity mentions, and lexical diversity). | NaN | False | False | NaN | NaN | Key challenges motivating the benchmark design include: 1) Lack of realism and representativeness in existing benchmarks, which often use general domain data instead of specialized, sensitive data. 2) Problematic privacy budget assumptions in current approaches, such as assuming public label distributions or not accounting for privacy costs of hyperparameter tuning on private data. 3) Insufficient empirical privacy verification, with many works omitting rigorous checks for privacy leakage or using simplified checks. | Privacy leakage from synthetic data, especially for records with rare attribute combinations. Implementation errors in privacy-preserving mechanisms invalidating formal differential privacy guarantees. Generation of low-utility or low-fidelity synthetic data that is unrepresentative of real domain-specific data, leading to poor performance in downstream tasks. |
| 17mwWRXSIXUJ.pdf | Google_Scholar | Eliciting the Priors of Large Language Models using Iterated In-Context Learning | This paper introduces 'iterated in-context learning,' a prompt-based Markov chain Monte Carlo method to elicit implicit prior distributions from Large Language Models (LLMs) like GPT-4. Experiments demonstrate that these elicited priors align with human priors in known cognitive tasks and can uncover LLM priors for speculative events. | True | NaN | True | 1.0 | NaN | Iterated in-context learning, a prompt-based workflow using a Markov chain Monte Carlo (MCMC) method, to elicit prior distributions from LLMs. | Validated by comparing priors elicited from GPT-4 with known human priors in three settings: causal learning (gene/protein cover story, noisy-OR/noisy-AND-NOT likelihoods, compared against uniform and sparse/strong priors using RMSD and Pearson's r), proportion estimation (coin flips, visual comparison with human data), and predicting everyday quantities (e.g., lifespan, movie grosses, visual comparison). The method was also applied to elicit priors for speculative events (superhuman AI, zero carbon emissions, Mars colony). | For generative causal induction, the empirical prior elicited from GPT-4 using iterated in-context learning best explained GPT-4's judgments, achieving a Pearson's r of 0.86 and an RMSD of 0.19, outperforming uniform and sparse/strong priors. | NaN | NaN | NaN | NaN | NaN | International | NaN | The method is based on iterated learning, a Markov chain Monte Carlo (MCMC) method from cognitive psychology, adapted for in-context learning with LLMs (GPT-4) using prompt engineering. It relies on the theoretical connection between iterated learning with Bayesian agents and sampling from the prior distribution. | NaN | True | False | The paper describes the 'iterated in-context learning' methodology and provides prompt examples (Appendix A), allowing replication of the technique by implementing the iterative prompting procedure with an LLM like GPT-4 (which requires separate API access). | NaN | The key assumption that LLMs function as approximate Bayesian agents requires further investigation. It is also unclear how LLMs learn to encode human-like priors from pretraining on text. LLMs typically avoid direct speculation on sensitive future events, making direct elicitation of certain priors challenging. | The paper highlights the general risk that errors or biased decisions from LLMs can have profound implications in critical sectors (including legal services) if their decision-making processes are not well understood. The proposed method aims to improve this understanding. |
| F5YEH5n2YDoJ.pdf | Google_Scholar | Prompting Minds: Evaluating how Students Perceive Generative AI’s Critical Thinking Dispositions | This study introduces and validates the Perceived Critical Thinking Disposition of Generative Artificial Intelligence (PCTD-GAI) scale, designed to measure students' perceptions of GAI's critical thinking dispositions (reasoning, access to justice, search for evidence, search for truth, open-mindedness, and systematicity). Results from surveying 931 university students in Portugal and Poland demonstrate the scale's effectiveness in capturing these perceptions, which were found to be moderately positive regarding ChatGPT's capabilities. | True | NaN | True | 1.0 | NaN | Perceived Critical Thinking Disposition of Generative Artificial Intelligence (PCTD-GAI) scale, adapted from the Marmara Critical Thinking Dispositions Scale (MCTDS). | A quantitative cross-sectional survey study was conducted with 931 university students from Portugal and Poland. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were used to assess the PCTD-GAI scale's validity and reliability. | The PCTD-GAI scale effectively captures students’ perceptions of ChatGPT’s critical thinking dispositions across six dimensions. Overall, students in both Portugal and Poland showed moderately positive perceptions, with 'systematicity' rated highest and 'search for truth' most neutral. | NaN | NaN | NaN | NaN | NaN | Portugal, Poland | NaN | Adaptation of the Marmara Critical Thinking Dispositions Scale (MCTDS) involving item rewording and cognitive shifts. Translation (forward and backward) into Portuguese and Polish with validation by bilingual experts. Pilot testing with students (n=20) in Portugal and Poland. Statistical validation using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). | NaN | True | True | The PCTD-GAI scale items are fully listed in Table 1 within the paper, which is an open access article under CC Attribution 4.0. | NaN | Adapting MCTDS items from assessing individual's own critical thinking to perceptions of GAI’s abilities; ensuring linguistic and cultural equivalency during translation to Portuguese and Polish; statistical validation requiring item removal to achieve robust factor models for different samples (Portuguese and Polish). | Potential for AI to diminish students' independent reasoning and critical thinking skills. Overreliance on AI, leading to passive acceptance of AI-generated content and superficial learning habits. Students who perceive AI as highly competent may exert less independent cognitive effort, potentially weakening problem-solving abilities. General risks from cited literature include procrastination, memory loss, and dampened academic performance. |
| UEryaxmiBzIJ.pdf | Google_Scholar | Generative AI for the Legal Profession: Facing the Implications of the Use of ChatGPT through an Intradisciplinary Approach | This paper analyzes the use of generative AI, particularly ChatGPT, in the legal profession, focusing on EU regulatory implications such as the AI Act, DSA, and GDPR. It proposes a multi-faceted approach involving legal, technical, and societal measures to ensure safe and effective integration, while also considering the evolving role of legal professionals. | True | Market | True | 3.0 | Neutral | ChatGPT and general Generative AI systems. | Illustrative example of submitting a legal query (“What is the punishment for robbery according to the Greek Criminal Code?”) to ChatGPT and qualitatively assessing the response for accuracy and completeness against the Greek Criminal Code. | ChatGPT's response directly addressed the question by mentioning imprisonment duration but omitted specific circumstances for life imprisonment detailed in Article 380 of the Greek Criminal Code. The paper concluded the system showed overall accuracy for initial information, pending legal professional verification. | Misinformation and inaccuracy of AI-generated legal information, potentially misleading individuals seeking legal assistance. Privacy risks stemming from processing sensitive personal data and potential compromise of lawyer-client confidentiality. Lack of transparency and explainability in AI systems, hindering trust and verification. Potential for discriminatory outputs affecting fairness. | A holistic approach for safe AI use, comprising: legal measures (technologically neutral regulation, rules against misinformation, strict data protection enforcement, transparency in privacy policies, regulatory sandboxes); technical measures (diligent data collection/processing, human oversight, explainability, risk-based approach for high-risk AI); and societal measures (training for legal professionals, awareness of AI limitations). These would generally improve reliability for any A2J applications. | Legal information and advice, particularly for small claims cases or routine matters (e.g., parking tickets, as exemplified by DoNotPay). | General public with minor legal issues or seeking initial legal information, as implied by discussions of small claims and tools like DoNotPay. | General legal services (legal advice, legal research, document drafting), Criminal law (specifically Greek Criminal Code as an example), Contract law. | Primarily EU (referencing AI Act, Digital Services Act, GDPR), with specific examples or mentions relating to Greece (Criminal Code), Italy (Data Protection Authority), and USA (ROSS jurisprudence retrieval). | ChatGPT was trained using Reinforcement Learning from Human Feedback (RLHF) on general dialogue datasets and InstructGPT data, with knowledge cutoff around 2021. The paper does not specify domain-specific legal data for its core training but notes it can process legal prompts. | NaN | NaN | True | True | ChatGPT is discussed as a publicly available online chatbot offered by OpenAI, with both free and paid access tiers. | Direct applicability of current EU regulations like the Digital Services Act to generative AI systems like ChatGPT. Insufficient preparedness and training among legal professionals for effectively and safely using generative AI. Lack of specific, homogeneously enforced rules against AI-generated misinformation at a regional (EU) level. Limited external scrutiny of AI development due to proprietary protection of training datasets and algorithms. | Ensuring the accuracy, truthfulness, and timeliness of legal information generated by AI. Protecting personal data and maintaining client confidentiality when using AI systems. Navigating the complex and evolving regulatory landscape for AI. Addressing the lack of transparency in AI models' training and operational logic. Mitigating risks of bias and discrimination in AI outputs. | Dissemination of harmful content and misinformation. Compromise of clients' personal data and lawyer-client confidentiality due to data reuse for AI training or insecure systems. Lack of user control over personal data. Inaccuracy of AI-generated information due to outdated training data. Potential for algorithmic discrimination based on processed personal features. Cybersecurity threats such as inversion attacks to recover training data. Users being misled by inaccurate or non-current AI-generated legal advice. |
| 7WtIL9mLIEQJ.pdf | Google_Scholar | Integrating Generative AI into Legal Education: \nFrom Casebooks to Code, Opportunities and \nChallenges | This paper discusses the integration of Generative AI (GenAI) into legal education, exploring its benefits like efficiency and personalized learning alongside challenges such as ethics, bias, and academic integrity. It advocates for adapting curricula and policies to equip law students with AI literacy and critical evaluation skills for the evolving legal profession. | True | NaN | True | 3.0 | Positive | NaN | NaN | NaN | Gap between theoretical knowledge and practical legal skills; Ethical concerns (plagiarism, bias, hallucinations, lack of transparency); Difficulty detecting AI use; Skepticism among academics; High human and environmental costs of AI development; Need for faculty training and resources; AI limitations in judgment, innovation, and accountability. | Integrate AI literacy and ethics into curricula; Develop hands-on AI learning experiences (simulations, projects); Foster interdisciplinary collaboration; Use AI for personalized learning; Train faculty in AI; Redesign assessments for AI use; Establish clear, adaptable AI usage policies; Invest in AI infrastructure; Focus on enduring human skills (judgment, innovation, accountability); Improve AI techniques (e.g., RAG) to reduce errors. | NaN | NaN | General/Multiple Fields | International | NaN | NaN | NaN | False | False | NaN | Need for reliable AI detection methods; Limited research on AI in legal education; Need for research on AI's long-term impact on legal practice; Development of sophisticated AI educational tools; Ensuring equitable access to AI tech; Further work on reducing AI hallucinations (e.g., RAG improvements); Need for effective AI-inclusive assessment methods; Lack of widespread faculty AI proficiency. | Balancing AI integration with academic integrity; Overcoming academic skepticism; Ensuring ethical use and mitigating bias; Dealing with AI inaccuracies; Developing effective AI teaching strategies; Providing faculty training and resources; Redesigning assessments; Keeping policies current with technological changes; Addressing environmental and human costs. | Undermining critical thinking; Academic dishonesty; AI inaccuracies ('hallucinations'); Algorithmic bias leading to unfair outcomes; Lack of transparency/accountability; IP violations; Environmental damage; Exploitation of labor in AI development; Over-reliance hindering skill development; Use of fake citations in practice. |
| nPlqBz7DawEJ.pdf | Google_Scholar | Enhancing Privacy and Security in Large -Language Models: A Zero-Knowledge Proof Approach | This paper proposes a novel approach using Zero-Knowledge Proofs (ZKPs) to enhance the security, reliability, and privacy of Large Language Models (LLMs). It introduces a 'zk-LLM' framework and a prototype, zk-GPT, to demonstrate its effectiveness in user authentication, data validation, and malicious prompt detection. | True | Market | True | 1.0 | NaN | zk-LLMs (Zero-Knowledge Proof based Large Language Models) using zk-SNARKs (specifically Groth16 and Powers of Tau), with a prototype called zk-GPT built using Circom and SnarkJS. | The zk-GPT prototype was evaluated in three experimental stages: 1) User Authority Analysis (100 iterations testing ZKPs for user authentication and differentiating access levels), 2) Supplemental Data Relevance (80 experiments with 40 research papers to validate LLM use of relevant supplemental data), and 3) Malicious Prompt Detection (60 iterations with a dataset of 200 prompt injection keywords to detect and prevent malicious prompts). | The ZKP-based user authentication (User Authority Analysis experiment) demonstrated the highest success, correctly identifying and rejecting all 40 unauthorized login attempts and successfully processing all 60 authorized user logins with appropriate privilege separation over 100 iterations. | NaN | NaN | NaN | NaN | NaN | NaN | The zk-GPT prototype utilizes the pre-trained Llama-2 7b-GPTQ model. For its specific experiments, it used a custom dataset of 40 research papers (unstructured text, for supplemental data relevance testing) and a custom dataset of 200 prompt injection keywords (for malicious prompt detection testing). | A ZKP framework for creating zk-LLMs was proposed, covering user authentication, prompt analysis, source data verification, and source data relevance filtering. The zk-GPT prototype was developed using Circom for zk-SNARK circuits, SnarkJS for circuit binding and witness generation, and the Groth16 proof system with Powers of Tau ceremonies, built upon the localGPT platform. | A prototype application named zk-GPT was developed and tested. It builds upon the localGPT platform to perform on-device LLM computations. | False | False | NaN | NaN | Computational overhead impacting LLM responsiveness; challenges with data availability and context for large datasets; effective malicious prompt injection detection. Specific to the zk-circuit implementation: limited circuit flexibility (requiring modifications for input deviations), SHA256 hashing inefficiency for larger circuits, and reliance on trusted setups (e.g., Groth16). | General LLM risks: unreliability, susceptibility to manipulation, data poisoning, spread of misinformation, creation of deep-fakes, exposure of sensitive/mission-critical data, database corruption. Risks related to ZKP implementation in LLMs: computational overhead potentially impacting user experience. |
| ju14jCLQ_TMJ.pdf | Google_Scholar | Bekenbey AI: Innovative Solutions at the Intersection of Deep Learning and Law | This paper introduces Bekenbey AI, a system integrating generative artificial intelligence (including GANs, VAEs) and deep learning models like BERT for legal applications such as document analysis, generation, and predictive analytics. The model, tested on real-world legal data, demonstrates high performance on various metrics, aiming to enhance the efficiency, accuracy, and accessibility of legal services for professionals, organizations, and the public. | True | Idealistic | True | 1.0 | Positive | Bekenbey AI model: a hybrid system integrating Natural Language Processing (NLP) techniques, deep learning architectures (RNN, LSTM, BERT, CNN), and Generative AI technologies (GANs, VAEs) for legal text analysis, document generation, and predictive analytics. | The model was evaluated using metrics such as accuracy, precision, recall, F1-score, ROUGE (R-1, R-2, R-L), and BLEU scores on datasets of legal documents. Computation time and memory usage were also assessed across different dataset sizes. The datasets were compiled from legal databases, government/corporate websites, academic resources, and digital libraries, anonymized by Torun Law and Consulting. | With 50 samples, the Bekenbey AI model achieved an accuracy of 88.73%, precision of 89.00%, recall of 88.00%, and F1-score of 88.50%. For text generation tasks with 50 samples, it achieved ROUGE-1: 97.50%, ROUGE-2: 93.80%, ROUGE-L: 96.50%, and BLEU: 93.00%. | High cost, time-consuming nature of traditional legal procedures; limited accessibility of legal services; complexity of legal texts and data management challenges in the legal sector. | The Bekenbey AI model is proposed to streamline complex legal processes, enhance legal document management and analysis, provide predictive analytics, and support decision-making. This is intended to reduce time and costs, and improve the accuracy and accessibility of legal services. | Legal document generation, predictive legal analytics, legal text analysis, case outcome prediction, document management, enhancing accessibility of legal services. | The public/citizens, legal professionals, and organizations. | General legal domain (adaptable across various legal sectors and frameworks, not specified further). | International (not specified, model described as adaptable). | Proprietary datasets anonymized by Torun Law and Consulting, compiled from multiple sources including legal databases, government and corporate websites, academic resources, and digital libraries. The datasets include a mix of structured data (e.g., legal codes, statutes) and unstructured data (e.g., case law texts, legal opinions). | The Bekenbey AI model uses a multi-layered architecture involving: data preprocessing (cleaning, tokenization, stemming, vectorization); embedding layers (Word2Vec, BERT); deep learning layers (CNN, RNN/LSTM, Transformer with attention mechanisms); classification layer (densely connected layers, softmax). The system integrates NLP techniques, generative AI (GANs, VAEs), and uses SQL/NoSQL databases (PostgreSQL, MongoDB) with a Python-based backend (Django, Flask) and FastAPI for APIs. | The backend infrastructure uses Python with Django and Flask, and APIs are developed using FastAPI for integration. The paper mentions model deployment as part of its system architecture but does not detail broader public deployment or diffusion strategies. | False | False | NaN | The paper suggests future work to: analyze different generative models in legal contexts, conduct comparative analyses with other models, test the model on diverse datasets and application domains, and explore advanced techniques to enhance accuracy and overall performance. | Addressing data management challenges within legal processes; ensuring compliance with stringent security standards and privacy regulations (e.g., GDPR); meeting high demands for security and operational efficiency in legal applications; parsing and comprehending complex legal texts. | The paper does not explicitly state concrete risks of the Bekenbey AI model itself, though it mentions the implementation of encryption and anonymization techniques for GDPR compliance, implicitly acknowledging data privacy as a concern to be managed. |
| pjmf6r2ahe8J.pdf | Google_Scholar | PROFESSOR GPT: HAVING A LARGE LANGUAGE MODEL WRITE A COMMENTARY ON FREEDOM OF ASSEMBLY | This paper demonstrates that a large language model (GPT-4o) can generate a comprehensive legal commentary on freedom of assembly under the European Convention on Human Rights, comparable in quality to human-written equivalents. The authors also develop and apply a validation methodology, using retrieval-augmented generation, to assess the commentary's utility in predicting court rulings. | True | Market | True | 1.0 | Positive | Using GPT-4o with a multi-step prompting and iterative refinement process to generate a structured legal commentary on Art. 11 ECHR from ECHR jurisprudence. This includes case classification, batch summarization of extracted paragraphs per doctrinal element, topic identification, and cleaning for relevance and redundancy, with the output presented on a website. A validation method using RAG for case outcome prediction is also introduced. | The GPT-written commentary was validated through: 1) Comparative citation analysis against human-written commentaries for comprehensiveness. 2) Using GPT-4o with Retrieval Augmented Generation (RAG) to predict outcomes of 55 test cases (18 actual ECtHR cases, 27 German Constitutional Court cases, 10 fictitious cases), with access to either the GPT-commentary or the ECHR's official Guide. 3) Comparison of these predictions against base GPT predictions (without RAG) and actual/quasi-actual court decisions. | The GPT-written commentary cited significantly more cases (12,254 citations from 572 unique cases) than human competitors (e.g., ECHR Guide: 267 citations from 118 cases). For predicting ECtHR case outcomes (10 repetitions, temp 0), GPT-4o with RAG access to the ECHR Guide achieved 89% accuracy, while access to the GPT-commentary achieved 82% accuracy. | The difficulty, time, and cost for legal practitioners and academics to comprehensively read, synthesize, and stay updated with a vast and growing body of jurisprudence. | Automating the generation of detailed, structured, and up-to-date legal commentaries using large language models. This provides practitioners and academics with easier and more reliable access to the current state of legal doctrine and jurisprudence. | Improving access to synthesized legal information and understanding of legal doctrine for legal professionals, which could indirectly enhance access to justice. | NaN | Human Rights Law (specifically Freedom of Assembly) | European Court of Human Rights (ECHR). Validation also used cases from the German Constitutional Court. | The GPT-4o model was pre-trained by OpenAI. For the commentary generation, the input consisted of 1198 ECHR case documents (filtered to 691 relevant ones for freedom of assembly) discussing Art. 11 ECHR, publicly available from the ECHR's HUDOC database. These were unstructured texts extracted from PDFs. | An iterative development process involving: data acquisition and pre-processing of ECHR cases; multi-dimensional case classification using GPT-4o with detailed system prompts; iterative, batch-wise summarization of case law snippets per doctrinal element using GPT-4o; topic extraction and coherence refinement across batches; automated cleaning of summaries; and website presentation. | The generated commentary is deployed as a publicly accessible, hierarchically structured website (http://professor-gpt.coll.mpg.de/html/overview.html). | True | True | The generated commentary ('Professor GPT') on Art. 11 ECHR is available online for free at http://professor-gpt.coll.mpg.de/html/overview.html. The paper states its code is 'available for scrutiny.' | The technology is not yet at a 'push-button' stage for generating commentaries on any legal provision, and significant human expert intervention is still needed for quality control and usability. The process for ensuring coherence and managing large inputs needs further refinement. | 1. Managing LLM context window limitations and the 'lost in the middle' effect for large legal text corpora, necessitating batch processing. 2. Ensuring stylistic and substantive coherence across summaries generated from different batches of data. 3. LLM tendency for redundancy and inclusion of off-topic material, requiring multiple cleaning steps. 4. Technical difficulties in programmatically downloading case law from dynamic websites. 5. Initial concerns about LLM hallucinations, requiring skillful prompting and process design. | 1. LLM hallucinations (generating non-factual information), though mitigated by their process. 2. Systematic errors in LLM outputs, although diminishing with newer models. 3. Potential for undetected bias to be introduced or amplified by the automated process (mentioned in broader context of AI in law). 4. Insufficient accuracy of automated systems (mentioned in broader context of AI in law). |
| zYPa4rOtSP0J.pdf | Google_Scholar | Judicial training to prepare criminal justice professionals for #digitalisation and #artificialintelligence | This editorial describes a multi-annual training project by the Academy of European Law (ERA) aimed at equipping EU criminal justice professionals (judges, prosecutors, lawyers) with skills to handle digitalisation and AI challenges. The project involves seminars across EU cities, podcasts, and focuses on topics like e-evidence, videoconferencing, and AI in criminal justice. | True | Market | False | 3.0 | Positive | NaN | NaN | NaN | NaN | A multi-annual training program (2024-2027) for EU judges, prosecutors, and lawyers covering digitalisation challenges, including e-evidence, videoconferencing, and AI, using seminars and podcasts. | NaN | NaN | EU Criminal Law | EU / Member States | NaN | Training program design combining face-to-face seminars (presentations, case studies, discussions, demos, simulations), video podcasts, and audio podcasts. | Face-to-face seminars in various EU cities hosted by partners, podcasts available online via ERA website, dissemination through consortium partners, EJTN, and ECBA. | False | False | NaN | Lack of skills and knowledge among EU criminal justice professionals regarding digitalisation (e-evidence, videoconferencing, computer forensics) and AI (impacts, machine evidence, risks). | Keeping legal professionals updated with rapid technological advancements (digitalisation, AI) in the context of EU cross-border criminal justice. Addressing the practical and legal complexities of e-evidence, videoconferencing, computer forensics, and AI applications. | Risks associated with videoconferencing (affecting suspects' rights), AI malfunction, lack of AI legal liability, misuse of AI for crime, ethical issues with biometric surveillance/facial recognition, procedural difficulties with machine evidence. |
| 0epoEcgwBXoJ.pdf | Google_Scholar | PSA-VLM: Enhancing Vision-Language Model Safety through Progressive Concept-Bottleneck-Driven Alignment | This paper introduces PSA-VLM, a novel method to improve the safety of Vision-Language Models (VLMs) against harmful visual content by using a progressive, concept-bottleneck-driven alignment strategy. The approach involves two-stage training and integrates specific safety modules to enhance interpretability, controllability, and robustness against risks like pornography and political sensitivity, while minimizing impact on general performance. | True | NaN | True | 1.0 | NaN | PSA-VLM: Progressive Safety Alignment for VLMs using a Concept Bottleneck Model (CBM) framework with specific safety modules (Safety Projector, Safety Tokens, Safety Head). | Evaluated on VLM safety benchmark (RTVLM) and additional risk datasets (harmful politics, pornography, cyberbullying) using GPT-4 scoring and human subjective assessment. General performance evaluated on MMBench, SEEDBench, and MME. | Achieved state-of-the-art safety scores on the RTVLM benchmark (e.g., 8.46 average score for PSA-VLM-13B+LoRA) and significantly improved safety on other risk datasets (e.g., Politics, Porn, Cyberbullying) compared to baseline models, while maintaining competitive general multimodal benchmark performance. | NaN | NaN | NaN | NaN | NaN | International | A mix of unsafe data (approx. 11,000 image-text pairs compiled from sources like RTVLM, porn datasets, cyberbullying datasets, Stable Bias, etc., manually categorized into 6 risk types and 3 levels) and clean data (LLaV A, COCO datasets used for SFT). Data sources are mixed (open-sourced, accessible by application, close-sourced). | Concept Bottleneck Model (CBM) framework, two-stage training strategy (safety module training with frozen LLM, then LLM fine-tuning), LoRA for parameter-efficient fine-tuning, cross-attention mechanism in Safety Head. | NaN | False | False | Code planned to be open-sourced after anonymous review. | NaN | Ensuring VLM safety alignment against diverse risks without degrading general performance; balancing clean/unclean data for training; potential for needing human intervention or customization for specific safety requirements. | VLMs bypassing safety alignments through visual inputs; generation of harmful/inappropriate content (pornography, violence, discrimination, politically sensitive content, cyberbullying, misleading information, privacy violations); potential for sophisticated adversarial attacks; false positives in safety filtering (exaggerated safety behavior). |
| UsGFKAW4Sj4J.pdf | Google_Scholar | AI, UPL, & A2J — GENERATIVE AI’S DISRUPTIONS IN THE DELIVERY OF LEGAL SERVICES TO LOW-INCOME INDIVIDUALS | This paper examines how generative AI (GenAI) is transforming legal services for low-income individuals, highlighting its potential for access to justice (A2J) alongside concerns about accuracy and unauthorized practice of law (UPL). It argues against restrictive regulations, advocating instead for integrating GenAI into guided legal assistance programs and relaxing UPL rules to foster innovation and expand access. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Cost of legal services; inaccessibility of attorneys; individuals not recognizing problems as legal; restrictive Unauthorized Practice of Law (UPL) doctrines hindering innovation; digital divide (access, tech literacy, reading literacy); limitations and risks of AI tools (accuracy, bias, hallucinations). | Relax UPL restrictions to permit nonlawyer and technology assistance; integrate GenAI with expert-guided systems (like document automation); foster collaboration between lawyers and AI developers; improve AI reliability (e.g., using RAG); focus on self-help resources beyond lawyer-centric models. | Legal information provision; document automation; self-help legal resources; addressing common civil legal needs of low-income populations. | Low-income individuals; disadvantaged persons; self-represented litigants. | Civil Law; Estate Planning; Housing Law; Bankruptcy Law; Professional Responsibility (Unauthorized Practice of Law). | United States (with specific examples and caselaw from Missouri, Colorado, North Carolina, Ohio, New York, Maryland, etc.) | The paper discusses GenAI tools (like ChatGPT) trained on broad internet data and mentions Retrieval-Augmented Generation (RAG) using curated, authoritative sources, but does not specify datasets for any single tool studied. | Mentions principles for guided interview systems like A2J Author (legal expertise, user-centered design, community engagement) and techniques for proposed integrated systems (RAG, enhanced prompting), but does not detail a methodology used by the author to develop a specific tool. | Discusses generally available online tools (search engines, document automation sites, chatbots) and court-deployed systems (guided interviews), but no specific deployment strategy for a novel tool proposed in the paper. | False | False | NaN | Technical: AI reliability (hallucinations, accuracy), need for better integration of GenAI with expert systems, addressing AI bias. Societal: Digital divide, consumer trust issues (under/over-reliance), need for UPL reform, funding/resources for A2J tech development, ensuring tech serves low-income communities, UPL enforcement ambiguity with open-source AI. | NaN | Inaccurate/unreliable AI outputs (e.g., fake case law); violation of UPL rules; exacerbating inequality due to digital divide or poor AI design; creation of a two-tiered justice system; consumers over-trusting AI leading to poor decisions; potential for AI bias. |
| W7292Ow-LfoJ.pdf | Google_Scholar | Empowering Air Travelers: A Chatbot for Canadian Air Passenger Rights | This paper presents a chatbot designed to help Canadian air travelers understand their rights by retrieving relevant information from legal documents. The system decomposes complex user queries and presents relevant passages directly to the user, aiming to avoid hallucinations common in generative models. | True | Idealistic | True | 1.0 | Positive | A chatbot using LLM-based query decontextualization and decomposition (GPT-4 with in-context learning), followed by dense retrieval (OpenAI embeddings, cosine similarity) from a domain-specific knowledge base. Relevant passages are presented directly to the user, bypassing generative summarization. | Comparative usability study (N=15) against Google Search using USE questionnaire on 4 air travel scenarios per participant. Hallucination analysis comparing the chatbot's retrieval-only output to a standard RAG approach on 40 examples. Evaluation of retrieval performance (P@5, R@5, F1@5, MAP@5) on 40 examples. | User study: Chatbot rated significantly more useful and satisfying than Google Search, with comparable ease of use/learning. Hallucination analysis: Chatbot achieved 0% hallucinations versus 27.5% for the standard RAG approach. Retrieval achieved MAP@5 of 0.88. | Passengers' lack of knowledge about their rights, difficulty navigating complex regulations, deficient regulations and enforcement in Canada, high volume of inquiries overwhelming volunteer support systems. | An automated chatbot to provide quick, accurate information about passenger rights by understanding complex user narratives and retrieving relevant passages from reliable sources, thereby empowering users and reducing volunteer workload. | Access to information about air passenger rights. | Canadian air travelers facing issues such as flight delays, cancellations, and baggage problems. | Consumer protection law, Air passenger rights, Transportation law | Canada | A knowledge base constructed from 88 public web pages containing regulatory details, practical guides, and legal glossaries from the Air Passenger Rights (Canada) website and the Canadian Air Passenger Protection website. The system uses pre-trained LLMs (GPT-4, OpenAI embeddings) fine-tuned via in-context learning with provided prompts. | Retrieval-Augmented Generation (RAG) architecture modified to present retrieved passages directly instead of generating summaries. Use of LLMs (GPT-4, OpenAI Embeddings) via API. Development of a web application prototype (Python/FastAPI backend, Next.js frontend). User study for evaluation. | Implemented as a web application prototype. The code is made available on GitHub. | False | True | Code is available on GitHub (link provided in footnote 1). | Knowledge base requires continuous updates to remain current. The chatbot lacks interactive dialogue capabilities to clarify ambiguous queries. Users may need help understanding and applying the presented legal information; simplified summaries are needed. | Handling complex, multi-part user queries; ensuring high accuracy and avoiding hallucinations in a high-stakes domain; selecting and structuring the knowledge base; designing an intuitive user interface. | Providing incorrect information (hallucinations) leading to financial loss or missed opportunities for passengers. Undermining user trust. Users potentially misinterpreting the retrieved legal passages. Privacy concerns regarding user inputs (partially addressed by using paid API). |
| YMpBXSigfgQJ.pdf | Google_Scholar | What Should ChatGPT Mean for Bioethics? | This paper discusses the implications of Large Language Models like ChatGPT for bioethics, comparing many issues to existing medical AI concerns. It also highlights new ethical dilemmas such as medical deepfakes, the need for AI interaction disclosure, and challenges posed by foundational models including equitable access and potential biases. | True | Idealistic | True | 3.0 | Positive | ChatGPT (a chatbot interface for OpenAI's GPT Large Language Models). | The paper cites other studies where ChatGPT was tested by its performance on: law school exams, the bar exam, United States Medical Licensing Exam (USMLE) steps, and a Stanford Medical School final exam in clinical reasoning. | According to cited studies, ChatGPT passed law school exams (GPT-3 just barely, GPT-4 scored above 90th percentile), passed the bar exam (earlier versions with fine-tuning, GPT-4 aced it scoring above 90th percentile), and performed at or near the passing threshold for all three USMLE exams without specialized training. GPT-3 also achieved a passing grade on a Stanford Medical School clinical reasoning exam. | For access to justice, the paper implies obstacles for "low-income people" and "pro se prisoners" in accessing legal help. Broader AI-specific obstacles pertinent to A2J include model unreliability (hallucinations), bias, and ensuring equitable access to such technologies. | The paper suggests chatbots, like ChatGPT, could enhance access to justice by providing direct legal services, such as helping low-income individuals get a head start in "lawyer for a day programs" or assisting pro se prisoners in bringing litigation. | Direct legal services, assistance for pro se litigants, initial legal drafting and support. | Low-income people, pro se prisoners. | General legal services (e.g., drafting complaints, contracts, wills), litigation by pro se individuals. | US (based on examples like bar exams and legal services), with broader implications. | GPT-3 was trained on 175 billion parameters from a large amount of internet text; GPT-4 on approximately 1 trillion parameters. Both models were further refined through reinforcement learning from human feedback (supervised reinforcement learning). | ChatGPT is a general LLM based on the GPT architecture, designed as an autoregressive model to predict subsequent text based on prior context. It is refined using Reinforcement Learning from Human Feedback (RLHF). | ChatGPT is deployed as a chatbot, accessible via a prompt-based interface. | True | True | ChatGPT is publicly available through OpenAI, offering both free and paid access tiers. | Unreliability and "hallucinations" in LLM outputs; data representativeness and algorithmic bias; privacy vulnerabilities; ethical need for users to know they are interacting with an AI; potential for generating misleading deepfakes (legal or medical); market concentration risks affecting access and ethical standards; significant environmental impact of large models; linguistic limitations (dominance of English); risk of model homogenization stifling diversity and ethical considerations. | The paper discusses general challenges inherent to LLMs like ChatGPT: their immense size requiring substantial computational resources; their general-purpose nature often necessitating fine-tuning; their autoregressive functioning; their output unreliability including factual inaccuracies ("hallucinations"); and variability in responses to identical prompts (lack of consistent test-retest reliability). | Data ownership and consent issues for training data; perpetuation of biases from training data; privacy infringements via data breaches, user input leaks, or re-identification; deception if users are unaware they are interacting with AI; generation and dissemination of false information or deepfakes (e.g., in legal or medical contexts); market oligopolies by a few large AI developers affecting equitable access and ethical priorities; substantial environmental footprint; potential for misuse (e.g., patients relying on flawed AI medical advice, or flawed AI legal advice) leading to harm and liability issues. |
| WdgRnAnuIh0J.pdf | Google_Scholar | cLegal-QA: a Chinese legal question answering with natural language generation methods | The paper introduces cLegal-QA, a new large-scale dataset for Chinese civil law question answering, comprising user questions, lawyer responses, and expert-annotated gold answers. It benchmarks several natural language generation models, finding fully-supervised models outperform zero-shot ones, highlighting the dataset's utility and areas for model improvement. | True | Idealistic | False | 1.0 | Positive | cLegal-QA dataset creation and benchmarking of NLG models (UniLM, T5, BART, ChatGLM-6B, ChatYuan, ChatGPT) for Chinese legal question answering. | Evaluation using ROUGE (ROUGE-1, ROUGE-2, ROUGE-L) and BLEU scores on a held-out test set (20% of cLegal-QA). Expert evaluation (3 judges) on 100 samples assessing adequacy, factuality, and fluency (Fleiss' kappa = 0.719). Transfer learning tested on 1000 private lending dispute cases. | Fully-supervised models (UniLM, T5, BART) significantly outperformed zero-shot models. BART achieved the best scores on the 'Question-Gold Answer' split (ROUGE-1: 34.73, ROUGE-2: 17.65, ROUGE-L: 31.71, BLEU: 15.01) and also performed best in transfer learning. | Shortage of lawyers to meet public demand for legal consultation services in China. Lack of large-scale, high-quality annotated datasets for Chinese generative legal QA due to the need for specialized legal knowledge and the costly/time-consuming nature of annotation. | Creation of large-scale, high-quality annotated datasets (like cLegal-QA) using professional annotators and reviewers. Application of automatic QA technology (specifically NLG models) to provide efficient, accurate, and low-cost legal consulting services. | Legal consultation services, answering legal questions from the public. | General public in China seeking legal assistance/consultation. | Chinese Civil Law (specifically focusing on Labor disputes, Marriage and family disputes, Housing disputes, and tested on Private lending disputes). | China | cLegal-QA dataset: ~14,000 instances collected from Chinese legal advice websites (e.g., www.12348.gov.cn, www.66law.cn). Contains questions, dispute types, scenarios, multiple lawyer answers, and gold standard answers annotated by law students and reviewed by judges. Unstructured text data. | Dataset creation involved web scraping, keyword matching for categorization, manual verification, professional annotation by law students, review by judges using specific criteria (adequacy, factuality, fluency), and inter-annotator agreement checks. Model evaluation used standard NLG metrics, expert review, and transfer learning experiments. | NaN | True | False | Dataset will be made publicly available after peer review; requires email contact for a license. | Performance of current QA models needs improvement. Models struggle with complex legal queries requiring multi-faceted reasoning. Need for models to adapt to evolving legal statutes and norms. Potential for inherent biases in datasets derived from source texts and human annotation. | Ensuring data quality (accuracy, consistency) during annotation requiring legal expertise. Cost and time of annotation. Adapting general NLG models to the specific nuances and terminology of the Chinese civil law domain. Evaluating generative models effectively beyond automated metrics. | Generative models may produce inaccurate, incomplete, or unreasonable responses. Lack of precise control over generated answers affecting reliability and trustworthiness. Potential for inherent biases in datasets influencing model outputs and fairness. |
| 2_gchPzjPIEJ.pdf | Google_Scholar | DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services | This paper introduces DISC-LawLLM, an intelligent legal system for Chinese legal services, developed by fine-tuning large language models using legal syllogism prompting and retrieval augmentation. The authors also present DISC-Law-Eval, a comprehensive benchmark to evaluate such systems, demonstrating DISC-LawLLM's effectiveness across various legal scenarios. | True | Idealistic | True | 1.0 | Positive | DISC-LawLLM: A fine-tuned LLM (Baichuan-13B-Base) using custom supervised fine-tuning datasets (DISC-Law-SFT) constructed with legal syllogism prompting strategies, and augmented with a retrieval module for external legal knowledge. | Evaluated using the custom DISC-Law-Eval benchmark. Objective evaluation involved multiple-choice questions from Chinese legal exams (NJE, PAE, CPA, UNGEE, PFE, LBK) across three difficulty levels, measuring accuracy. Subjective evaluation used 300 Q&A cases assessed by GPT-3.5 as a referee on accuracy, completeness, and clarity. | DISC-LawLLM outperformed other LLMs, including GPT-3.5-turbo, on objective evaluation (e.g., 42.09% average accuracy on hard questions, improving over GPT-3.5-turbo by an average of 7%) and subjective evaluation (average score of 3.39 across accuracy, completeness, and clarity). | High demand for specialized legal reasoning capabilities and the need for reliable access to accurate, up-to-date external legal knowledge to avoid hallucinations and outdated information. | Fine-tuning LLMs with supervised datasets (DISC-Law-SFT) specifically constructed using legal syllogism prompting to enhance reasoning, and augmenting the LLM with a retrieval module to access external, current legal knowledge. | Legal consultation for dispute resolution, statute interpretation, legal document summarization, legal question answering, and assistance with legal examinations. | General public / everyday individuals seeking legal advice, legal professionals, and law students. | Chinese Judicial domain, covering areas such as Civil Law, Criminal Law, Administrative Procedure Law, Copyright Law, Patent Law, and Bidding Law. | China | DISC-Law-SFT dataset, constructed from: 1) Publicly available NLP legal task datasets (e.g., LEVEN, CAIL, JEC-QA, CJRC) for the Chinese justice domain. 2) Legal raw text (e.g., laws, judicial verdicts, consultation platform data). 3) Open-source instruction datasets. Data was processed using rule-based methods and LLM-assisted (GPT-3.5-turbo) refinement into supervised fine-tuning samples (pairs and triplets). | Supervised fine-tuning (SFT) of a base LLM (Baichuan-13B-Base). Dataset construction involved collecting data from diverse legal sources and processing it with rule-based methods and LLM-assisted refinement (behavior shaping with legal syllogism, knowledge expansion, law-specific chain of thought - LCoT). Retrieval augmentation with an external knowledge base was also implemented. | The paper states that detailed resources, including constructed datasets and model weights, are made available on GitHub. | True | True | Datasets and model weights are released on GitHub (https://github.com/FudanDISC/DISC-LawLLM). | The paper implies a continued need for comprehensive benchmarks for legal AI systems, as evidenced by their development of DISC-Law-Eval due to the lack of established alternatives. Other specific future gaps beyond improving model capabilities are not detailed. | Ensuring intricate legal reasoning capabilities in LLMs, reliably integrating up-to-date and precise external legal knowledge to mitigate issues like outdated information and hallucinations, and constructing high-quality, diverse supervised fine-tuning datasets that effectively instill legal reasoning patterns like syllogism. | The potential for LLMs to produce inaccurate responses due to hallucinations or reliance on outdated knowledge, which could lead to incorrect legal information or advice. |
| TheImpactofGenerativeAIonBusinessConsultingsuprit.pdf | Google_Scholar | The Impact of Generative AI on Business Consulting Engagements: A New Paradigm for Client Interaction and Value Creation | This paper explores the transformative impact of Generative AI on the business consulting industry, examining its capabilities, applications, and the challenges of integration. It emphasizes the need to balance AI's efficiency with human-centric aspects, Gdiscussing how AI is reshaping client interactions and value creation in consulting. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Developed markets | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Key risks include lack of AI transparency and explainability, algorithmic bias leading to unfair outcomes, data privacy and security vulnerabilities, AI 'hallucinations' producing incorrect information, potential misuse of the technology, significant environmental impact from training models, and job displacement. |
| 1_Qa-6uaUUcJ.pdf | Google_Scholar | GENERATIVE AI IN AMERICAN AND CANADIAN COURTS : A “TRAINING” APPROACH TO REGULATION | This paper reviews judicial directives on generative AI use by lawyers in Canadian and US courts, noting concerns about inconsistency and overbreadth. It advocates for a "training" approach to regulation, focusing on user competence and ethical responsibility rather than prohibition, to constructively integrate AI while upholding professional standards. | True | Market | True | 1.0 | Positive | A proposed "training" approach/framework for judicial guidance on generative AI use in legal proceedings, emphasizing user competence, human oversight, and responsibility. | NaN | NaN | Risks associated with lawyer's use of generative AI, including inaccuracy ('hallucinations'), potential for bias from training data, breaches of client confidentiality, lack of clarity in regulations, and varying technological literacy among legal professionals. | A 'training' approach to judicial guidance focusing on user competence (including understanding limitations), mandatory human oversight and verification of AI outputs, and lawyer responsibility for AI-generated content, rather than outright bans or overly broad disclosure rules. Development of nuanced guidelines by courts and law societies. | Regulation of AI use in legal practice and court proceedings; Lawyer competence and ethics in using AI. | NaN | General Litigation, Legal Practice, Professional Ethics, Court Procedure | Canada, United States (Primary focus); mentions examples/guidelines from Colombia, Pakistan, India, South Africa, UK, Peru, Mexico, New Zealand, Australia (NSW), EU. | The paper discusses generative AI tools (like ChatGPT, Harvey AI, Lexis+ AI) that use various data, including publicly available information, licensed third-party data, user inputs, and proprietary/curated legal content. | Conceptual/analytical legal research, including review of existing judicial directives, analysis of professional conduct rules, synthesis of academic/expert commentary, and proposing a normative regulatory framework. | Proposed for adoption by judicial bodies as practice directives or guidelines. | False | False | NaN | Need for enhanced lawyer competence and AI literacy; development of clear, nuanced regulations adaptable to evolving technology; lack of consensus on appropriate AI use; need for effective human oversight mechanisms; ongoing ethical considerations. | Rapid evolution of AI technology; varying levels of technological literacy within the legal profession; defining the scope of regulations (e.g., what constitutes 'AI use'); balancing innovation with ethical obligations (accuracy, confidentiality, bias); ensuring meaningful human oversight; avoiding overly restrictive or vague directives. | Inaccuracy and 'hallucinations' in AI output (e.g., citing fake cases); propagation of biases present in training data; breach of client confidentiality and data privacy; deskilling junior lawyers; undermining the attorney-client privilege; misleading users (including self-represented litigants) with inaccurate information; potential misuse leading to reputational damage for lawyers and undermining the administration of justice. |
| chatgptisplayingaroleinArtificialIntelligent1.pdf | Google_Scholar | How Chatgpt is Playing a Role in Artificial Intelligent bases Applications | This paper provides a general overview of ChatGPT's capabilities and its applications across diverse domains, including conversational interfaces, content generation, and legal assistance. It highlights ChatGPT's role in improving efficiency and user experiences while briefly noting associated ethical and legal considerations. | True | NaN | True | 3.0 | Neutral | ChatGPT | NaN | NaN | NaN | NaN | Legal research, document generation, legal insights | NaN | General Legal | International | Based on OpenAI's GPT-3.5 training data (large-scale general text data, implicitly proprietary). | NaN | NaN | True | False | ChatGPT is generally accessible via platforms like OpenAI's website, often with free tiers. | NaN | Ethical and legal challenges, bias, privacy issues, potential inaccuracies. | Bias in language generation, privacy issues, inaccuracies, security risks related to AI-generated content. |
| flw_DqScUF4J.pdf | Google_Scholar | Roles and challenges of ChatGPT and similar generative a rtificial intelligence for \nachiev ing the Sustainable Development Goals (SDGs) | This paper discusses the potential roles of generative AI like ChatGPT in achieving the UN's Sustainable Development Goals (SDGs), including for access to justice (SDG 16). It also outlines significant challenges, such as ethical concerns, data issues, security risks, and the need for robust governance to harness AI's benefits responsibly. | True | Idealistic | True | 3.0 | Positive | ChatGPT and similar generative artificial intelligence | NaN | NaN | Key obstacles include ethical concerns (data privacy, AI bias, accountability, misinformation), data quality and accessibility issues, language and cultural diversity barriers, security risks from misuse (e.g., deepfakes), environmental impact of AI, scalability challenges, the digital divide, and the need for robust regulatory frameworks. Specific to access to justice (SDG 16), challenges are ensuring legal accuracy, addressing AI bias in legal contexts, and overcoming unequal access to justice technologies. | Proposed solutions involve ethical, responsible, and inclusive AI development and deployment, global collaboration, developing robust policy and regulatory frameworks, fostering human-AI collaboration, enhancing digital literacy, and bridging the digital divide. For access to justice, AI can be used for disseminating legal information, promoting awareness of rights, and offering conflict resolution advice. | Legal information provision, access to justice promotion, legal rights awareness, conflict resolution, crime prediction, human rights awareness, strengthening legal institutions. | Citizens, marginalized communities, vulnerable populations. | Public legal information, human rights law, criminal justice (related to crime prediction), dispute resolution, general access to justice. | International | NaN | NaN | NaN | True | True | The paper discusses ChatGPT and similar generative AI, which are existing technologies. ChatGPT offers publicly accessible versions, including free and paid tiers. | Technical gaps include developing AI that ensures legal accuracy and contextual understanding across diverse legal systems and languages, and creating robust, unbiased legal AI models. Societal gaps include establishing ethical guidelines and regulations for AI in law, bridging the digital divide for equitable access to legal AI, fostering public trust, and integrating AI effectively into existing legal institutions and workflows. | Inherent challenges of generative AI like ChatGPT include managing ethical dimensions (data privacy, algorithmic bias, accountability), ensuring high-quality and accessible training data, adapting to language and cultural diversity, enabling effective human-AI collaboration, mitigating environmental impact, preventing security threats and misuse (e.g., generating harmful content, misinformation), achieving scalability and equitable resource allocation for widespread deployment, and navigating an evolving regulatory landscape. | Potential risks include perpetuation of societal biases, data privacy violations, spread of misinformation and deepfakes, security threats from malicious use, negative environmental impact from high energy consumption, and exacerbation of the digital divide and social disparities if not implemented equitably. |
| wOJSYY9rFZIJ.pdf | Google_Scholar | NATURAL LANGUAGE PROCESSING IN LEGAL TECH | This chapter provides a non-technical overview of Natural Language Processing (NLP) techniques relevant to legal technology, discussing their potential applications like document review and case outcome prediction. It critically examines the capabilities and inherent limitations of current NLP, particularly its difficulty with complex legal reasoning, and outlines key challenges like data availability and the need for legal-specific benchmarks. | True | Market | True | 3.0 | Neutral | NaN | Conceptual testing of GPT-3's legal reasoning capabilities using a hypothetical liquidated damages clause scenario. The authors input the scenario and the relevant legal rule into GPT-3 and evaluated its response. | GPT-3 correctly stated the general legal rule regarding liquidated damages but failed to apply it correctly to invalidate an 'exorbitant' clause in a specific fact pattern, even when prompted with the rule and the term 'exorbitant'. | The primary obstacles identified are the significant technical limitations of current NLP in performing legal reasoning and extracting legal ontologies, scarcity and representativeness issues with training data (especially pre-litigation or for 'easy' cases), potential for biased outputs, and difficulty processing complex legal document structures. These hinder the development of reliable legal tech, including for potential A2J applications. | The paper suggests that advancing NLP for legal reasoning requires specific, domain-focused efforts beyond general NLP improvements, potentially relying on human experts to define legal ontologies. It also highlights the need for creating and utilizing legal-specific benchmark datasets (mentioning the Atticus Project as an example). | NaN | NaN | Litigation (discovery, case outcome prediction), Contract Law, Tax Law | International | The paper discusses various types of data: large general text corpora (e.g., web data used for training models like BERT and GPT-3); hand-labeled legal documents for specific tasks like document review (potentially proprietary); existing case law and related documents (often unstructured, facing access issues like paywalls or non-collection); specific legal datasets like CUAD (annotated contracts, publicly available) and CaseHOLD (algorithmically extracted holdings, publicly available). Challenges related to data availability, representativeness, and cost are highlighted. | NaN | NaN | False | False | NaN | The main technical gap identified is the inability of current NLP models to perform genuine legal reasoning, extract legal ontologies, or accurately process complex legal document structures and references. Additional gaps include the lack of sufficient, representative, and accessible training data for many legal tasks, and the absence of robust, large-scale benchmark datasets specifically for the legal domain. | Key challenges include: automating legal reasoning and extracting legal concepts (ontologies) from text; effective document segmentation and handling complex structures/references in legal documents; obtaining sufficient, representative, and unbiased training data; overcoming computational complexity for long legal texts; developing and adopting legal-specific benchmark datasets to evaluate model performance accurately in the legal domain. | The primary risks stated are inaccurate or biased predictions resulting from limitations in legal reasoning capabilities (e.g., misapplying legal rules) and unrepresentative or biased training data. The reliance on distributional patterns rather than true understanding can lead to failures in novel situations or tasks requiring nuanced legal interpretation. |
| vNG_5kHTgG0J.pdf | Google_Scholar | Evaluating the Use of Artificial Intelligence for \nan Effective Justice System in Sri Lanka | This paper evaluates the potential of Artificial Intelligence (AI), including chatbots and ChatGPT, to enhance Sri Lanka's legal system, particularly in improving access to justice. It discusses AI's applications, advantages, and challenges, recommending steps like robust data governance, ethical standard-setting, and capacity building for successful integration. | True | Idealistic | True | 3.0 | Positive | AI (general), Chatbots (e.g., NALA), ChatGPT, Robotics | NaN | NaN | Lack of complete legal data; slow adoption by legal professionals; potential for AI bias; infrastructure limitations (technology, language, digital literacy); high cost and accessibility issues for AI; large case backlogs and inefficient court processes; general resource constraints in the justice system; resistance to change from legal experts. | Implement robust data governance and security measures; establish clear ethical and legal standards for AI use; conduct thorough cost-benefit analyses of AI implementation; foster collaboration with international organizations; perform socio-economic impact assessments; invest in capacity building and training for legal professionals; create systems for collecting and distributing legal data for AI training; ensure AI systems are designed to be transparent and auditable. | Improving timely dispensation of justice; enhancing efficiency and reducing costs of legal services; supporting legal research, contract analysis, and case law analysis; enabling legal translation services; providing legal information and decision support for legal professionals; reducing court case backlogs. | Underserved communities, neglected populations, individuals unable to afford conventional legal services, and non-native speakers in Sri Lanka. | General legal system | Sri Lanka | NaN | NaN | NaN | True | False | Discusses ChatGPT, a generally accessible AI model. Mentions 'NALA' chatbot developed by the Legal Aid Commission of Sri Lanka; its public accessibility is not detailed by the paper. | Absence of sufficient, comprehensive legal data for AI development; resistance to technological change among legal professionals; risk of AI systems reinforcing existing societal biases if not carefully designed; need for greater transparency and auditability in AI decision-making processes; loopholes and inadequacies in existing Sri Lankan law concerning AI liability and regulation; insufficient focused research and practical adoption of AI within Sri Lanka's legal sector. | Technological infrastructure limitations (e.g., reliable internet, data security); language barriers in a multilingual context; low digital literacy among potential users and some professionals; high cost and difficult accessibility of advanced AI technologies; scarcity of comprehensive and high-quality legal data for training AI; slow adoption rate of new technologies within the legal sector; ensuring fairness and mitigating algorithmic bias; lack of transparency in the operational mechanisms of some AI tools; addressing data privacy and security concerns related to sensitive legal information. | AI bias reinforcing or perpetuating systemic discrimination (e.g., the COMPAS example); lack of transparency in AI decision-making processes leading to 'black box' problems; over-reliance on AI potentially diminishing human judicial discretion and the human element in sensitive cases; AI systems generating inaccurate, fabricated, or misleading legal information (e.g., ChatGPT citing fake cases); breaches of data privacy and security of sensitive legal information; socio-economic disruption such as job displacement within the legal profession; inadequacy of existing legal and ethical frameworks to address harm or errors caused by AI; potential for AI to magnify social injustice if not implemented equitably. |
| L4nL0nO_ZeIJ.pdf | Google_Scholar | AI Catalyst: Cracking the code \nfor MSME productivity | This paper reports on 'The AI Catalyst' project, a participatory action research initiative investigating AI adoption by UK Micro-, Small-, and Medium-sized Enterprises (MSMEs) to enhance productivity. It identifies key motivators, barriers (such as resource access, digital readiness, and sociotechnical integration challenges), and outcomes, demonstrating increased AI adoption and investment among participating firms. | True | Market | True | 1.0 | Positive | The AI Catalyst programme: a participatory action research initiative involving a 'scan tool' (to map resources, capabilities, and processes) and tailored 'Knowledge Exchange' sessions based on established business/management frameworks (e.g., SOSTAC, Dynamic Capabilities) to facilitate AI adoption in MSMEs. | Participatory Action Research conducted with fifteen UK MSMEs over five months (March-July 2024). Data was collected using a 'scan tool' (Microsoft Excel workbook) and through 100 hours of fortnightly online 'Knowledge Exchange' sessions. Programme effectiveness and AI adoption outcomes were assessed via changes in a weighted technology diffusion score and qualitative participant interviews. | Twelve of the fifteen participating firms chose to adopt Generative AI solutions. Collectively, firms made an estimated investment of over £100,000 in AI, supporting more than 360 users. The cohort's average weighted score for technology diffusion increased by 0.25 (from 2.0 to 2.25). | For MSMEs: Limited access to finance and STEM/digital talent; operational burdens overshadowing strategic orientation; sub-optimal effectiveness of existing resources/capabilities; burden of researching technology; insufficient in-house tech capabilities, digitalisation, and data analytics; deployment of non-complementary technologies; inconsistent digital broadband infrastructure. | For MSMEs: The 'AI Catalyst' programme approach, including tailored knowledge exchange and a sociotechnical perspective on AI integration. Guidance on AI strategy, ethics policy development, and implementation of specific AI solutions. Recommended government support through access to expertise, training, targeted assistance, financial support/subsidies, and development of regulatory frameworks. | AI adoption for productivity enhancement in Micro-, Small-, and Medium-sized Enterprises (MSMEs); Digital transformation challenges and enablers for MSMEs; Sociotechnical factors in AI integration within business processes. | UK Micro-, Small-, and Medium-sized Enterprises (MSMEs) across various sectors including manufacturing, legal services, food and beverage, real estate, sales, services, and event services. | The study's cohort of MSMEs included one firm from 'Legal Services'. The paper's overall focus is on general MSME productivity across sectors, not specifically on legal fields. | United Kingdom (UK) | NaN | Participatory Action Research (PAR). Theoretical frameworks underpinning the research design included Dynamic Capabilities, Knowledge-Based View, Resource-Based View, and Stakeholder Theory. The SOSTAC model guided 'Knowledge Exchange' sessions. A 'scan tool' (Microsoft Excel workbook) was developed for data collection. A weighted scoring model was developed and used to assess digital technology diffusion. | The AI Catalyst programme was deployed via collaboration with 'Be The Business' for firm recruitment. 'Knowledge Exchange' sessions were conducted online using Microsoft Teams with 15 MSMEs over 5 months. Tailored content (slides, research papers, web resources) was provided to participating firms. | False | False | NaN | For MSMEs: Need for continued research into MSME productivity drivers and AI adoption factors. Requirements for enhanced, tailored support mechanisms (expertise, training, sector-specific guidance) for digital and AI adoption. Necessity for clear AI regulatory/ethical frameworks. Addressing financial barriers and improving digital infrastructure (e.g., broadband) for MSMEs. Better understanding and measurement of intangible asset investments at the firm level. | Recruitment of MSME firms for the action research (15 firms recruited against a target of 20). Ensuring programme content was sufficiently tailored to diverse business needs. Determining optimal structure for knowledge exchange sessions (e.g., length, frequency). Addressing MSMEs' existing burdens of technology research and limited in-house digital capabilities. Facilitating a sociotechnical approach to AI integration. | Risk of business obsolescence for MSMEs if AI is not adopted. Inherent risks and complexities of integrating new technologies into existing workflows. Cybersecurity risks. Potential for AI-induced job displacement. (Literature review also cites: liability for AI-induced damage, data exchange standards, lack of citizen trust, reputational risks for AI use). |
| TGKE4_F0dVoJ.pdf | Google_Scholar | Continuous Training and Fine-tuning for Domain-Specific Language Models in Medical Question Answering | This paper proposes a two-stage method (continual training and instruction fine-tuning) to adapt Llama 2 base models for the Chinese medical domain using domain-specific datasets. The resulting model achieves performance comparable to GPT-3.5-turbo on a Chinese medical exam benchmark (CMExam). | True | NaN | True | 1.0 | NaN | Two-stage adaptation of Llama 2 models: 1) Continual pre-training on Chinese medical text (1B tokens from Huatuo-26M). 2) Instruction fine-tuning on Chinese medical exam questions with reasoning (CMExam dataset). | Evaluation on the CMExam test set (Chinese medical QA) using accuracy and F1-score (greedy decoding). Catastrophic forgetting assessed using MMLU (English) and CMMLU (Chinese) benchmarks (5-shot prompting). | The best performing model (Chinese-Llama-2-13B after continual training and fine-tuning with reasoning) achieved 46.0% accuracy and 45.7% F1 score on CMExam, comparable to GPT-3.5-turbo (46.4% accuracy, 46.1% F1). | NaN | NaN | NaN | NaN | NaN | China | Continual training: ~1B tokens (unstructured text, question-answer pairs) from the Huatuo-26M dataset (Chinese medical encyclopedias/articles). Fine-tuning: 54K multiple-choice questions with explanations from the CMExam dataset (Chinese medical licensing exam). Base models Llama 2 and Chinese-Llama 2 pre-trained on general corpora. | Machine learning development pipeline: Base model selection, dataset curation (Huatuo-26M, CMExam), two-stage training process (continual training, fine-tuning), benchmark evaluation (CMExam, MMLU, CMMLU). | NaN | False | False | NaN | NaN | Catastrophic forgetting of general knowledge during continual domain-specific training. Difficulty in achieving performance gains through cross-lingual fine-tuning (mixing English and Chinese medical data). Computational constraints. | Amplifying dataset biases during continual training. Dissemination of inaccurate or misleading medical information without rigorous validation by experts. |
| _Q1r5ohGDz8J.pdf | Google_Scholar | Intention and Context Elicitation with Large Language Models in the Legal Aid Intake Process | This paper proposes a proof-of-concept framework using Large Language Models (LLMs) to improve the legal aid intake process. The method uses conversational prompting to actively elicit clients' underlying intentions and relevant contextual details, aiming to generate more useful responses compared to direct one-shot LLM answers. | True | Idealistic | True | 1.0 | Positive | An LLM-based conversational system designed to elicit client intentions and contextual information through guided dialogue prompts during the legal intake process. | Qualitative comparison of the proposed method's output against a baseline one-shot LLM response using example scenarios (e.g., tenancy law). No formal benchmarks or extensive experimental evaluation. | The combined intention and context elicitation approach generated qualitatively more useful and tailored responses compared to generic one-shot LLM outputs, which were often too broad or non-specific. | Clients often lack legal expertise, leading them to ask suboptimal questions that don't reveal their true intentions or necessary context. Limited capacity of legal aid organizations. | Employing LLMs in a conversational manner to actively probe for and elicit underlying client intentions and specific contextual details before formulating a response, thereby improving the quality of information gathered and provided during intake. | Legal intake and triage for legal aid services. | Clients of legal aid organizations and court centers, particularly those with limited legal knowledge. | General legal aid intake, with examples drawn from Family Law, Immigration Law, Tenancy Law. | United States context mentioned, but the technique appears generally applicable internationally. | N/A (The proposed technique uses pre-trained LLMs like GPT-4 without specific fine-tuning on new datasets for the proof-of-concept. Proposes future generation of datasets for training). | Proof-of-concept development based on prompt engineering and structuring conversational interactions with LLMs. | NaN | False | False | NaN | Lack of quantitative experimental evaluation and ablation studies with machine and human evaluators. Need for attorney review of LLM outputs in production settings. Need for verified conversational datasets for future training. | LLM tendency to provide overconfident 'best guess' answers without probing. Difficulty in reliably prompting LLMs to assess information completeness due to overconfidence and lack of metrics. | LLM inaccuracy (hallucination, incorrect information, inapplicable laws/organizations). Client over-reliance on potentially flawed AI output. Potential for unauthorized practice of law. Privacy concerns regarding client data. |
| BXfGjxwV8VQJ.pdf | Google_Scholar | Exploring ChatGPT: An Extensive Examination of Its Background, Applications, Key Challenges, Bias, Ethics, Limitations, and Future Prospects | This paper provides a broad overview of OpenAI's ChatGPT, detailing its development history, underlying technologies (GPT series), and key features. It surveys applications across diverse domains including healthcare, finance, law, and education, while also discussing significant challenges, ethical concerns, biases, limitations, and future research directions. | True | NaN | True | 3.0 | NaN | ChatGPT | NaN | NaN | NaN | NaN | NaN | NaN | General Law | International | General description of GPT/ChatGPT training: Pre-training on large, diverse text corpora (books, papers, webpages); Fine-tuning using Reinforcement Learning from Human Feedback (RLHF). | NaN | NaN | True | False | Available via OpenAI's platform (e.g., chat.openai.com, mentioned implicitly). | NaN | Reliability/accuracy, bias (various forms including dataset bias), overreliance by users, quality control, generalization to new data, real-time responsiveness, model explainability, adapting to domain-specific knowledge. | Inaccuracy/misinformation, bias perpetuation (gender, racial, cultural, linguistic, commercial, etc.), privacy/security vulnerabilities, lack of accountability/responsibility, generation of harmful/inappropriate/verbose content, undermining critical thinking, potential for misuse (e.g., propaganda, sensationalism). |
| Lips_et_al._2023_Potential_value_of_Generative_AI_legal_services.pdf | Google_Scholar | Sizing the potential value of Generative AI for legal services | This paper estimates the potential efficiency gains from using Generative AI in legal services within Switzerland. It calculates significant potential reductions in full-time equivalents (around 25%) and annual costs (around 20%) for both law firms and internal legal/tax departments. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General Legal Services / Tax Advisory | Switzerland | NaN | NaN | NaN | False | False | NaN | NaN | Achieving estimated savings requires ideal circumstances (specially trained LLMs, full data access, interoperability); difficulty in reallocating saved labor to other tasks; need to adapt professional training structures (e.g., traineeships). | NaN |
| Cr-3p3D0J4gJ.pdf | Google_Scholar | Copyright Protection in Generative AI: A Technical Perspective | This paper provides a comprehensive technical overview of copyright protection methods for generative AI, covering both data copyright (for source data owners) and model copyright (for DGM providers). It discusses various techniques like unlearning, watermarking, and de-duplication across image, text, code, and audio domains, highlighting current limitations and future research directions. | True | Market | False | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Copyright law, Intellectual Property law | International | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Copyright infringement of source data (e.g., memorization, unauthorized replication/modification, style mimicry); model theft and unauthorized commercial use of DGMs; uncertainty in copyright ownership of AI-generated content; potential for misuse of DGMs for generating misinformation. |
| x4foJ0wSk_kJ.pdf | Google_Scholar | The Role of Generative AI in developing new Supply Chain Strategies - Future Trends and Innovations | This review explores how Generative AI is transforming supply chain management by enabling innovative strategies for demand forecasting, inventory optimization, and risk assessment. It discusses future trends like autonomous systems and AI-driven collaboration, while also highlighting challenges such as data privacy and implementation costs. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Data privacy breaches, unauthorized data access, and misuse; AI models vulnerable to cyberattacks (e.g., data manipulation, reverse-engineering); financial losses from ineffective AI or biased data; perpetuation or amplification of algorithmic biases in decision-making. |
| JOrIKdo7d-kJ.pdf | Google_Scholar | Private ordering, generative AI and the ‘platformisation paradigm’: What can we learn from comparative analysis of models terms and conditions? | This paper analyzes the terms and conditions (T&C) and privacy policies of various generative AI providers from early 2023, focusing on copyright and data protection. It finds providers adopt a "platformisation paradigm," positioning themselves as neutral intermediaries by assigning output ownership and all liability to users, despite not fitting the legal definition of platforms, thus creating power imbalances and regulatory gaps. | True | Idealistic | True | 2.0 | NaN | Comparative analysis of Terms & Conditions (T&Cs) and Privacy Policies of Generative AI providers (Private Ordering). | Manual collection and qualitative comparative analysis of T&Cs, privacy policies, and related documents from a sample of 13 generative AI services (categorized by function: T2T, T2I, T2A/V) selected based on mode, size, jurisdiction, and open/proprietary nature. Initial data collected Jan-Mar 2023, with a follow-up review of privacy policies in Dec 2023. | Providers consistently assign copyright ownership of outputs to users but grant extensive back-licenses to themselves and assign all liability for infringement or other harms to users. Initial privacy policies were often inadequate but improved over 2023. Providers implement platform-like content moderation (e.g., NTD) and position themselves as neutral intermediaries ('platformisation paradigm'), despite not fitting legal definitions. | NaN | NaN | NaN | NaN | Contract Law, Terms and Conditions, Privacy Law, Data Protection Law (GDPR, CCPA), Copyright Law, Platform Regulation, Internet Law, Consumer Law, Comparative Law. | International | NaN | Qualitative comparative legal analysis based on manual collection of terms and conditions and privacy policies. | NaN | False | True | The paper is published as an Open Access article under a Creative Commons Attribution licence. | Regulatory gaps (e.g., EU DSA not clearly covering foundation models). Lack of transparency and due process in content moderation by providers. Need for fairer risk allocation between providers and users. Need for better operationalization and enforcement of data protection rights (rectification, erasure) for foundation models. Lack of insight into B2B T&Cs and market competition effects. | Rapidly changing T&Cs requiring manual tracking. Difficulty obtaining B2B T&Cs due to commercial secrecy. Difficulty identifying the underlying models used by downstream applications. Complexity of the multi-dimensional comparative analysis within the given timeframe. | Risks from GenAI models: Bias, fake news, illegal/harmful content, hallucinations, copyright infringement, privacy violations. Risks from private ordering: Abuse of provider power via unfair/non-negotiable T&Cs, inadequate enforcement of user rights (privacy, due process), unfair shifting of liability to users, arbitrary content moderation and sanctions. Risk of regulatory gaps allowing platform-like entities to evade platform-specific obligations (e.g., under DSA). |
| pDsTcmAOZK4J.pdf | Google_Scholar | THE USE OF ARTIFICIAL INTELLIGENCE IN CORPORATE DECISION -MAKING AT BOARD LEVEL: A PRELIMINARY LEGAL ANALYSIS | This paper discusses the 'Assisted, Augmented, Autonomous' classification for AI, applying it to 'artificial governance intelligence' in corporate boards, and conducts a preliminary legal analysis of its implications. It highlights legal uncertainties in current corporate law regarding AI's role, decision rights, oversight, and liability across different autonomy levels, suggesting needs for regulatory adaptation. | True | Market | False | 2.0 | NaN | The 'Assisted, Augmented, Autonomous' (AAA) classification framework for AI (acknowledged as originally from A. Rao), applied and detailed by the author as 'artificial governance intelligence' with three levels (assisted, augmented, autonomous) to categorize AI's role in corporate board-level decision-making based on system autonomy and allocation of decision rights. | The classification's implications are evaluated through a legal analysis of corporate law principles (delegation, fiduciary duties, liability, director qualifications) as they apply to each proposed level of AI autonomy (assisted, augmented, autonomous) in corporate governance. | The legal analysis, structured by the classification, reveals significant legal unpreparedness and uncertainty in current corporate law for AI governance beyond simple assistance. Key issues identified include ambiguity in delegation rules, inapplicability of fiduciary duties to AI, lack of clarity on human oversight requirements, and complex liability attribution for AI failures. | NaN | NaN | NaN | NaN | Corporate law, company law, technology law (including AI regulation), liability law. | International (with specific examples from EU, US (Delaware, LLCs), Hong Kong, UK, Italy, Belgium, Germany, Spain, Switzerland). | N/A (The paper discusses AI generally; no specific AI model or its training data is proposed or analyzed in detail). | The paper's approach to applying and detailing the existing AAA classification for 'artificial governance intelligence' involves conceptual analysis, literature review on AI and corporate law, and mapping AI autonomy levels to corporate decision-making functions and their legal implications. | N/A (The classification is an analytical framework, not a deployable tool). | False | False | NaN | NaN | Challenges in applying the AAA classification to analyze corporate governance include: defining clear legal boundaries for 'core management functions' that cannot be delegated to AI; establishing appropriate human oversight mechanisms for each autonomy level without stifling AI benefits; resolving liability attribution for algorithmic failures; and adapting human-centric corporate law concepts (like fiduciary duties, director eligibility) to AI systems, especially at higher autonomy levels. | Legal uncertainty discouraging AI adoption in corporations; biases in AI systems (from data, code, or learning process); lack of transparency ('black box' issue); algorithmic failure leading to corporate harm; difficulties in attributing liability for AI-driven decisions; inadequacy of traditional corporate fiduciary duties (loyalty, care) for AI; AI not being recognized as a legal director; potential for AI to be used to mask human self-interest or biases; erosion of human judgment/over-reliance on AI; challenges to board collegiality and oversight. |
| HkNIuWlMdoIJ.pdf | Google_Scholar | The Future of Advocacy: The Trial Lawyer’s Guide to Large Language Model Generative AI | This paper introduces Large Language Model (LLM) generative AI to trial lawyers, discussing its mechanisms, potential impacts on legal practice, and significant ethical considerations. It emphasizes the need for lawyers to maintain competence, verify AI outputs, ensure confidentiality, and exercise independent judgment when using these tools. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General legal practice, Trial advocacy, Criminal defense | United States | NaN | NaN | NaN | False | False | NaN | Lack of clear legal and ethical guidelines for LLM use; Need for lawyer technological competence; Limitations in AI's ability for nuanced legal/ethical judgment and empathy. | Ensuring accuracy and avoiding hallucinations; mitigating inherent biases in AI systems; maintaining client confidentiality when using AI tools; fulfilling the duty of technological competence; avoiding overreliance and automation bias; maintaining independent professional judgment. | Generating inaccurate or fabricated information (hallucinations); perpetuating systemic biases; breaching client confidentiality; violating ethical duties (competence, candor, independent judgment); deskilling due to overreliance; potential job displacement for certain legal roles; unjustified billing practices. |
| bByRJ9_vmPAJ.pdf | Google_Scholar | RE-REGULATING UPL IN AN AGE OF AI | This paper argues that US state Unauthorized Practice of Law (UPL) statutes should be re-evaluated to allow AI tools, particularly LLMs, to help address the access to justice gap. It proposes focusing consumer protection on transparency, liability, and private rights of action rather than broad prohibitions based on vague definitions of legal practice. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Vague, restrictive, and inconsistently enforced Unauthorized Practice of Law (UPL) statutes prevent potentially helpful AI tools from assisting consumers. A vast 'justice gap' exists where most people with civil legal problems cannot afford or access legal help. | Re-evaluate and reform state UPL statutes to permit AI legal assistance tools. Shift consumer protection focus from broad UPL prohibitions to measures like transparency requirements (clear disclaimers that AI is not a lawyer, no attorney-client privilege), mandatory liability insurance for AI providers, and enabling private rights of action (e.g., for negligence, fraud, consumer protection violations) against incompetent or deceptive AI providers. | Access to justice gap in civil legal matters, Self-represented litigants, Legal form completion (e.g., debt collection defense) | Low-income households, Individuals/families/small businesses unable to afford lawyers, Self-represented litigants | General Civil Law (focusing on areas with high unmet need like debt collection, employment, housing, benefits, insurance), Unauthorized Practice of Law Regulation | United States (State-level UPL regulations, mentioning New York specifically) | NaN | NaN | NaN | False | False | NaN | Lack of clear, updated regulatory frameworks for AI legal tools that balance innovation and consumer protection. Need for effective consumer redress mechanisms beyond traditional UPL enforcement. Persistent societal gap in access to affordable legal assistance. | Ambiguity and restrictiveness of current UPL laws, which can chill innovation in legal AI for consumers. Technical limitations of current AI (e.g., hallucinations, ensuring confidentiality). | AI providing incompetent, fraudulent, or negligent legal advice. AI 'hallucinations' leading to reliance on false information or non-existent case law. Breach of user confidentiality if prompts/data are used for model training or exposed. Consumers being deceived into believing AI software is a licensed lawyer or provides attorney-client privilege. |
| cpMmdLuTaPkJ.pdf | Google_Scholar | Who Wants a Robo-Lawyer Now?: On AI Chatbots in China’s Public Legal Services Sector | This essay discusses the potential for large language model (LLM) chatbots to be widely adopted within China's public legal services (PLS) sector to address the access to justice gap. It examines the political economy driving this adoption, potential benefits like reinforcing legality, and associated risks such as errors and confidentiality concerns. | True | Idealistic | True | 3.0 | Positive | AI chatbots (specifically mentioning Ernie LLM-powered ones) deployed within a government-run public legal services (PLS) system. | The paper mentions that chatbots deployed in Yunnan performed 620,000 consultations in the initial months, but provides no formal testing methodology or results for this specific deployment. It cites general LLM benchmark studies like LegalBench and LawBench. | NaN | Scarcity of legal professionals, particularly in rural/disadvantaged regions; geographic disparities in access to legal services; high demand for basic legal information unmet by existing resources. | Leveraging AI chatbots within the government-funded Public Legal Services (PLS) system to provide automated, widely accessible basic legal information and advice, particularly for routine, statute-based questions. | Access to basic legal information and advice; Routine legal inquiries; Dispute resolution guidance (mediation, negotiation, litigation options). | General populace in China, particularly rural residents and those in disadvantaged regions (illustrated by the Yunnan case). | General civil law (e.g., labor law, family law, contract/property issues like landlord-tenant disputes mentioned implicitly via routine questions). | China (with specific examples from Yunnan province). | The paper mentions the use of Baidu's Ernie LLM for the Yunnan deployment. It discusses general LLM development approaches like fine-tuning on specific legal Q&A tasks, training specialized legal models with large legal datasets, and Retrieval Augmentation Generation (RAG) connecting models to external knowledge databases. | The paper discusses general approaches like fine-tuning general LLMs, developing specialized legal LLMs, and using Retrieval Augmentation Generation (RAG). It mentions the specific chatbot in Yunnan is based on Baidu's Ernie LLM. | Deployment via government-run public legal services stations in rural villages (Yunnan example), accessible through devices stationed at local government offices. Government procurement of third-party (Legal Tech) services. | False | False | NaN | Need for systemic methodologies for assessing LLM legal task performance; Optimal solutions for LLM hallucination are still developing; Technical limitations in ensuring confidentiality of user input; Need for regulatory frameworks and public oversight mechanisms for PLS chatbots; Potential for entrenching inequality if chatbot services remain inferior to human lawyers. | Overcoming regulatory barriers (e.g., unauthorized practice of law) for Legal Tech firms; Finding viable, large-scale use cases attractive to the Legal Tech industry; Ensuring user-friendly interfaces compared to previous technology generations; Managing risks associated with LLM limitations (hallucination, errors); Balancing confidentiality needs with the ability to use interaction data for model improvement. | Loss of confidentiality for user information; Hallucination and errors in legal information provided; Potential for scams and malicious manipulation (e.g., fake platforms); Misuse by government officials (e.g., manipulating information, biasing advice); Exacerbating inequality by creating a two-tiered system or diverting resources from human legal aid. |
| 6G3ocYCwgrcJ.pdf | Google_Scholar | A Debate-Driven Experiment on LLM Hallucinations and Accuracy | This paper investigates LLM hallucinations using a novel debate-like framework where multiple GPT-4o-Mini models interact, with one model intentionally providing false information. The findings suggest these inter-model interactions, particularly with a 'saboteur', can improve overall accuracy on the TruthfulQA benchmark. | True | NaN | True | 1.0 | NaN | Debate-like interaction framework involving multiple GPT-4o-Mini instances (Saboteurs, Fact-Based Models, Moderator) to improve response accuracy against misinformation. | Evaluation on the TruthfulQA benchmark, calculating overall and per-category percentage accuracy based on a multiple-choice question format derived from correct/incorrect answer sets. | The 5/1 configuration (5 total personas, 1 saboteur) of the debate framework achieved the highest overall accuracy of 78.93% on TruthfulQA, improving from a 61.94% baseline. | NaN | NaN | NaN | NaN | General; the TruthfulQA benchmark used for evaluation includes a 'Law' category. | International | N/A (the proposed debate framework uses pre-trained LLMs and does not involve training a new model). | Experimental setup with GPT-4o-Mini models assigned roles (Saboteur, Fact-Based, Moderator) engaging in two-round debates on TruthfulQA prompts, varying N (3,4,5) personas with 1 Saboteur. | NaN | True | False | The experimental methodology can be replicated using the GPT-4o-Mini API (not free) and the publicly available TruthfulQA dataset, as described in the paper. | NaN | Limitations cited include generalizability (fixed debater/saboteur configurations), comprehensiveness of the TruthfulQA dataset for diverse misinformation, and understanding long-term effects of misinformation exposure in model interactions. | LLM hallucinations (generating plausible but incorrect information), impacting accuracy in various sectors. Vulnerability to misinformation, especially in subjective and culturally nuanced areas. |
| 9VZt6DyvzXsJ.pdf | Google_Scholar | THE USE OF AL IN TODAY'S TECHNOLOGY DEVICES | This paper offers a broad overview of Artificial Intelligence (AI), detailing its core concepts like machine learning and deep learning. It highlights diverse AI applications in sectors such as e-commerce, healthcare, robotics, education, and business, while also discussing the economic influence of major tech companies and associated societal concerns like job displacement and cybercrime. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | NaN | NaN | False | False | NaN | NaN | General challenges include ensuring equitable AI benefits, managing socio-economic impacts (e.g., job displacement), simulating human expertise for complex tasks, addressing AI underutilization, potential for algorithmic bias, integration difficulties with existing systems, and combating evolving cyber threats. | Potential risks include the development of new cybercrimes, general safety concerns regarding advanced AI, widespread job displacement, perpetuation of algorithmic bias, violations of privacy and data protection, and increased data security breaches. |
| HIO1C6XNEh8J.pdf | Google_Scholar | DO USO DE IA GENERATIVA NOS TRIBUNAIS A UMA JUSTIÇA DEGENERATIVA: QUANDO A TECNOLOGIA “ALUCINA”... | This paper discusses the use of generative AI in courts, focusing on European ethical and legal frameworks like the AI Act and CEPEJ guidelines. It reviews Portuguese AI projects in the justice sector and highlights significant risks, especially AI "hallucinations" leading to erroneous judicial outcomes. | False | Idealistic | True | 3.0 | Negative | Generative AI / Large Language Models for court support and potentially decision-making assistance. | NaN | NaN | Risk of inaccurate, biased, or "hallucinated" outputs from generative AI undermining fairness and trust; Potential for a "digital divide" excluding the digitally illiterate ("infoexcluídos"); Amplification of procedural inequality ("desigualdade de armas") based on access to AI tools. | Adherence to ethical guidelines (e.g., CEPEJ) and legal frameworks (e.g., EU AI Act); Emphasis on transparency, non-discrimination, quality, security, user control, and robust human oversight (especially for judicial decisions); Use of certified/controlled AI systems; Critical evaluation of AI outputs; Transparency about AI use. | Judicial efficiency; Fairness of judicial process (processo equitativo); Transparency; Non-discrimination; Judicial independence; Reliability of AI-generated legal information; Digital divide. | Digitally excluded individuals ("infoexcluídos"), economically disadvantaged litigants. | General Judicial Process, Administrative Law, Civil Law, Criminal Law, Public Procurement Law. | Portugal, European Union | Not specified for most systems discussed; mentions use of court decisions (Projeto IRIS) and public procurement data (Court of Accounts project); recommends using certified and official data; Guia Prático da Justiça trained on specific legal topics (Marriage, Divorce, Company Creation). | Mentions use of machine learning, deep learning, NLP, OCR; emphasizes adherence to ethical principles (CEPEJ) and legal frameworks (EU AI Act). | Mix of deployed systems (Guia Prático da Justiça), systems under development (IRIS, Assistente Virtual, Court of Accounts), and informal use of general tools (e.g., ChatGPT). | True | False | The "Guia Prático da Justiça" chatbot is available online via a government website. | Need for improved regulation and understanding of generative AI risks (especially hallucination); Lack of robust certification mechanisms; Need for better data quality, transparency, explainability, and bias mitigation in judicial AI; Ensuring equal access and bridging the digital divide; Training for legal professionals. | Ensuring accuracy, reliability, and non-bias of AI (especially generative AI prone to hallucination); Maintaining transparency and explainability; Protecting judicial independence; Safeguarding sensitive data; Developing effective human oversight protocols; Training legal professionals. | Production of factually incorrect information ("hallucinations", bias); Disclosure of sensitive/confidential data; Lack of verifiable sources; Intellectual property/copyright violations; Inconsistent/unreliable outputs; Amplification of cognitive biases; Threats to fundamental rights (fair trial, non-discrimination); Undermining judicial independence and the rule of law; Algorithmic injustice; Digital divide amplification. |
| informit.T2025011900000101519001919.pdf | Google_Scholar | Using Generative AI to Identify Arguments in Judges’ Reasons: Accuracy and Benefits for Students | This paper evaluates the ability of Large Language Models (LLMs) like ChatGPT and Claude to identify and reconstruct legal arguments from Australian High Court judgments, finding significant performance variations. It concludes that while some LLMs show promise for legal education and potential A2J benefits through efficiency, critical human oversight remains essential due to varying accuracy and the risk of skill degradation. | True | Idealistic | True | 2.0 | Neutral | Evaluation of Large Language Models (ChatGPT versions 4 and 4o; Claude versions 3.0 Opus and 3.5 Sonnet) for identifying and reconstructing legal arguments from judicial reasons in a modus ponens structure using single-shot prompting. | Two human assessors (a lawyer/legal academic and a philosopher) blind-marked LLM-generated argument reconstructions for five recent High Court of Australia decisions. Outputs were compared against pre-determined sample answers and assessed using a detailed rubric (20 marks total) covering identification of disposition, premises/conclusions, argument location (paragraph numbers), and modus ponens structure. | Claude 3.5 Sonnet performed best, achieving average grades up to 18/20 (90%), with an overall system average of 16.2/20 for this version. In contrast, ChatGPT versions averaged around 8/20, with the lowest individual ChatGPT output scoring 4/20. | General barriers to access to justice include: financial cost, time, complexity of justice systems, lack of legal capability, and language skills. Specific to GAI, obstacles include inaccuracy, unreliability (e.g., hallucinations), and the current inability of GAI to perform all aspects of legal reasoning accurately. | Accurate GAI could facilitate low- or no-cost legal advice and increase the speed and efficiency of legal processes, thereby potentially reducing costs associated with legal services and increasing individuals' access to justice. | Reducing costs of legal advice/services, increasing efficiency in legal analysis, improving legal education outcomes, potential for enhanced access to justice through technology. | Individuals who do not seek legal advice due to high costs or overworked judicial systems; law students and junior lawyers. | The study used cases covering native title, criminal law, statutory interpretation, and immigration law. | Australia (High Court of Australia cases). | The LLMs (ChatGPT, Claude) were trained on 'enormous volumes of data – for example, text corpora scraped from vast swathes of the internet.' Input for the specific task was the PDF text of five High Court of Australia judgments. | N/A (The paper evaluates existing LLMs, it does not detail their internal design methodologies beyond general statements about deep learning. The paper's methodology is for evaluation, see 'testing'). | The LLMs (ChatGPT and Claude) are commercially deployed and accessible via web interfaces, which were used in the study. The study's specific prompting approach is described but not deployed as a standalone tool. | True | False | ChatGPT and Claude models are commercially available through their respective platforms (OpenAI and Anthropic). The specific advanced versions tested (e.g., GPT-4, Claude 3.5 Sonnet) are typically part of paid subscription tiers. | Significant variance in accuracy across different LLMs and versions for legal argument identification. LLMs' tendency for 'hallucinations' and unreliability for certain legal tasks. The 'skill-gap' in users' ability to critically evaluate LLM outputs. Need for further research before LLMs can revolutionize the legal industry. | For the study: The 'high cost of labour involved in the analysis of legal documents, which necessitates small numbers of annotators/assessors' was a limitation. For users (e.g. students): The varied accuracy of LLMs, the superficial plausibility of even poor answers, and the need to develop critical engagement skills to assess LLM outputs. | LLM 'hallucinations' (fabricating information, e.g., non-existent cases). Inaccurate legal advice leading to safety issues. Unsupervised creation of legal arguments by GAI. Over-reliance on LLMs leading to degradation of essential human legal skills, particularly argument analysis, if used uncritically by students. |
| qHJ-ypmosE4J.pdf | Google_Scholar | Hallucination is the last thing you need | The paper discusses the significant risk of LLM hallucinations corrupting legal research and common law, proposing theoretical solutions like multi-length tokenization and ensemble models. It evaluates current GPT models, finding frequent non-verbatim but semantically similar outputs when quoting case law, highlighting the subtlety of the hallucination problem. | True | Market | True | 3.0 | Neutral | Evaluation of GPT-4 and text-davinci-003's ability to accurately complete text sequences with verbatim quotes from provided UK case law judgments. | Empirical testing using OpenAI's Playground (Complete, Chat, Insert modes) with specific prompts asking models to finish sequences with the correct legal quote from UK case law. 20 trials analysed for verbatim match, close match, semantic similarity, or unrelated output. | Out of 20 trials: 1 verbatim match, 2 close matches, 11 non-verbatim matches with similar semantic intent (often summaries), 6 non-verbatim matches with unrelated intent. Models frequently hallucinate subtly or produce semantically similar but inaccurate quotes. | Common law contamination: Subtle, non-obvious hallucination errors from LLMs used in legal research risk altering the understanding and application of law if incorporated into legal documents and judgments. | Proposes theoretical architectural changes (multi-length tokenization, ensemble models separating problem/commentary/fact). Suggests using verification tools, relying on curated data from established providers, and promoting open justice data initiatives. | Integrity of legal research; Accuracy of case law citation; AI safety in legal practice. | NaN | Common Law (general principles, citations across various fields like contract, tort, family law), Statutory Interpretation | United Kingdom (specifically England & Wales based on case law cited) | The study evaluated existing models (GPT-4, text-davinci-003) trained on general internet data. The proposed theoretical models would require segmented legal judgments (unstructured text data). | The evaluated technique used empirical testing. The proposed techniques are theoretical concepts without specified design methodologies. | The evaluated techniques are accessed via OpenAI's platform. The proposed theoretical techniques are not deployed. An enhancement to the authors' open-source YCNBot (blocking copy/paste of detected case law) is mentioned as deployed on GitHub. | False | False | The paper states that the authors' related tool, YCNBot, including a feature discussed for mitigating copy-paste risk, is available open-source on GitHub. | Current LLMs lack reliable mechanisms to handle factual legal text (e.g., quotes) without hallucination. There's a gap in access to curated justice data for open-source research compared to large vendors. Need for robust verification methods to prevent legal knowledge contamination. | Defining and measuring legal hallucination; Designing LLM architectures that distinguish between reasoning and factual recall; Segmenting legal texts accurately for potential ensemble models; Linking ensemble components effectively; Keeping models updated; Subtle nature of errors making detection hard. | Contamination of common law with subtle errors; Lawyers facing sanctions for using hallucinated precedents; Undermining the credibility of the legal profession and AI tools in law. |
| _J-yDG-ZRmwJ.pdf | Google_Scholar | ChatGPT Practices: Finance and Banking Domain | This paper reviews the applications, challenges, limitations, and future potential of ChatGPT in the finance and banking sectors. It also recommends the development of legislation and regulations to mitigate risks associated with ChatGPT use. | True | NaN | True | 2.0 | NaN | ChatGPT | NaN | NaN | NaN | NaN | NaN | NaN | Legislation and Regulation (general for AI/ChatGPT), Criminal Law (e.g., phishing, fraud), Intellectual Property Law (infringement), Data Privacy Law, Legal Liability (for AI outputs) | International | Large-scale corpora of text data (e.g., books, web pages) used for unsupervised pre-training of the underlying GPT models (GPT-3.5, GPT-4). | NaN | NaN | True | False | ChatGPT is accessible via OpenAI's platform, with free and paid subscription tiers for usage. | NaN | Ensuring data privacy and security, addressing ethical dilemmas, model bias and accuracy, need for real-time information, limitations in handling precise logic (e.g. math), and susceptibility to misuse or instruction attacks. | Data leakage, misuse for creating phishing websites or disseminating misinformation, infringement of intellectual property, and compromised personal/property safety due to false or biased information. |
| jM9T-EjTRBcJ.pdf | Google_Scholar | The Use of LLMs in the Legal Field: Optimizing Contract Management with Generative Artificial Intelligence | This Master's thesis explores using Large Language Models (LLMs), specifically Retrieval-Augmented Generation (RAG), to develop a proof-of-concept web application for lawyers. The application aims to optimize contract management by aiding in contract analysis and clause generation, thereby increasing efficiency for legal professionals. | True | Market | True | 1.0 | NaN | A web application using Retrieval-Augmented Generation (RAG) with LLMs (GPT-3.5-Turbo via Azure OpenAI). It incorporates semantic search via embeddings (Text-Embedding-Ada-002) stored in Redis, and prompt engineering for contract analysis (key point extraction) and clause generation. | The RAG system's retriever component (for clause generation) was evaluated using Langsmith. Metrics included Context Relevance, Contextual Recall, latency, and user feedback from lawyers on the correctness of generated clauses, testing different similarity score thresholds. | For clause generation retrieval, a similarity score threshold of 0.75 in Redis yielded the best balance, achieving Context Relevance of 0.90, Context Recall of 0.92, latency of 3.83s, and positive user feedback. | NaN | NaN | NaN | NaN | Contract Law | Italy | The RAG system retrieves information from a proprietary dataset of legal clauses provided by the collaborating law firm (Orbyta Legal), originally stored in an Excel file. These clauses are vectorized using Azure OpenAI's Text-Embedding-Ada-002. The underlying LLM (GPT-3.5-Turbo) is pre-trained by OpenAI/Microsoft Azure. | Proof of Concept (POC) development, RAG pipeline implementation, Front-end development (Streamlit), Back-end development (Python, LangChain, Redis), Prompt Engineering, Text Chunking, Embedding Generation, User Feedback Collection (via Langsmith). | Containerized using Docker and Docker Compose, deployed via Azure Container Registry (ACR). Access limited to the company's internal network. | False | False | NaN | Technical limitations mentioned: Inability to process scanned (non-text) documents (requires OCR integration). Lack of an integrated user feedback mechanism within the application to directly guide model improvement. | Handling LLM context length limitations for large documents. Effective document chunking. Selecting appropriate embedding models (considering performance, cost, language support). Designing effective prompts (Prompt Engineering). Evaluating RAG system components (especially retrieval). Ensuring data privacy. | Data privacy issues (mitigated by using Azure OpenAI). Potential for inaccuracies in AI outputs requiring human supervision. Loss of context due to document segmentation techniques. |
| Research-Paper-TokenOps.pdf | Google_Scholar | TokenOps: A Compiler-Style Architecture for Token Optimization in LLM API Workflows | This paper introduces TokenOps, a compiler-style middleware architecture designed to optimize Large Language Model (LLM) API workflows. By employing pre-processing and post-processing layers to compress and restructure inputs and outputs, TokenOps aims to reduce token usage, thereby lowering costs, latency, and the carbon footprint of LLM deployments. | True | Market | True | 1.0 | Positive | TokenOps architecture V2: A middleware stack wrapping LLM API calls, featuring a Preprocessing Layer (input optimization using rule-based cleaning and LLM summarization like DistilBERT/TinyLlama) and a Postprocessing Layer (output minimization using template-matching, summarization, and hierarchical reduction). An optional Semantic ZIP Layer for caching/macros is also proposed. | Simulations using 5,000 anonymized enterprise prompts on GPT-4, comparing raw vs. optimized calls. A/B testing on client production data. Semantic fidelity assessed via blind human review using a 3-point Likert scale. Cost and latency calculated based on OpenAI pricing/stats and simulation results. Tools included Python (NLTK, spaCy), DistilBERT, TinyLlama, GPT-4, LangChain. | Simulations showed 40-46% token reduction across scenarios (customer support, document search, content generation) with 97% of outputs rated semantically 'Accurate' or 'Acceptable' by human reviewers. Average latency was reduced by 29-36%, and cost savings were estimated at $9K/month per 10M API calls. | The primary obstacles identified relate to enterprise LLM deployment: high token costs, increased latency with larger prompts/outputs, and the environmental impact (carbon footprint) associated with high token volume. | The proposed solution is the TokenOps architecture itself, which acts as middleware to systematically reduce token usage in LLM workflows. Policy recommendations include standardizing token accounting, incentivizing token efficiency, recognizing optimization middleware as critical infrastructure, and incorporating token usage into AI sustainability reporting. | Lowering cost barriers for technology deployment | Under-resourced regions / Budget-sensitive environments | NaN | International | The evaluation used a proprietary dataset of 5,000 anonymized, unstructured enterprise prompts from production logs across varied industries. The TokenOps technique itself uses smaller pre-trained LLMs (DistilBERT, TinyLlama) but doesn't describe their training data. | Compiler-style architecture design, rule-based systems, use of smaller pre-trained LLMs (DistilBERT, TinyLlama) for specific tasks (summarization), template matching, simulation, A/B testing, cost/energy profiling. | Integration into LLM pipelines using tools like LangChain, FastAPI wrappers, and deployment on cloud platforms (GCP, Azure). Implemented for clients of the author's consulting firm. | False | False | NaN | Future technical development needs include token-aware reinforcement learning, a full 'LLM Compiler Stack™', and a marketplace for domain-specific TokenOps modules. Societally, while cost reduction may improve access, deeper gaps in equitable AI deployment remain unaddressed. | Maintaining semantic fidelity while aggressively reducing tokens (especially for creative tasks), integrating middleware seamlessly into diverse enterprise workflows, managing the complexity of the optimization layers. | Risk of semantic degradation or loss of nuance/style due to over-optimization (observed at a 3% rate in testing, primarily in creative content generation). |
| 32IjpX6kRLwJ.pdf | Google_Scholar | A Survey on Symbolic Knowledge Distillation of Large Language Models | This paper surveys the field of symbolic knowledge distillation (SKD) for Large Language Models (LLMs), reviewing methods for converting implicit LLM knowledge into explicit, symbolic forms to enhance interpretability and efficiency. It categorizes techniques, discusses applications, highlights challenges, and identifies opportunities for future research. | True | NaN | True | 3.0 | NaN | Symbolic Knowledge Distillation (SKD) | The paper reviews studies that evaluated SKD techniques using various benchmarks specific to the application domain (e.g., commonsense reasoning benchmarks, machine translation metrics like BLEU, mathematical reasoning benchmarks like miniF2F, summarization quality metrics, visual reasoning tasks, instruction following evaluation). | Reviewed studies generally show that SKD can create smaller, more efficient student models that sometimes outperform larger teacher models on specific tasks like commonsense reasoning, translation, summarization, and mathematical reasoning, or achieve better controllability or specialized capabilities. | NaN | NaN | NaN | NaN | NaN | International | The surveyed techniques use large pre-trained LLMs (teachers) trained on massive, general corpora (web text, books, code etc.). Symbolic knowledge distillation often involves generating intermediate datasets (e.g., knowledge graphs, instruction pairs, rationales, sentence-summary pairs) from the teacher model, sometimes with filtering or human feedback, to train smaller student models. | The paper reviews various methodologies including prompt engineering, NLP techniques for knowledge extraction (NER, POS, parsing), rule/graph generation, iterative distillation, reinforcement learning (RLHF, offline RL), self-instruction, expert iteration, and filtering using critic models or human evaluation. | NaN | False | False | NaN | Need for specific evaluation benchmarks for SKD/neurosymbolic AI; ensuring quality/diversity/representativeness of distilled knowledge; balancing automation and human oversight in data generation; achieving high performance in compact models across broad domains (not just narrow tasks); adaptability and continuous learning for distilled models. | Ensuring quality, diversity, and representativeness of distilled data; balancing automation and human oversight in dataset generation; developing compact models that retain high performance across diverse applications without loss of nuance; effective instruction tuning for varied use cases; ensuring adaptability and continuous learning in distilled models. | Propagation of biases and inaccuracies from teacher LLMs to distilled datasets and student models; potential issues with factual accuracy and safety in distilled models. |
| 8t--pP6kmsIJ.pdf | Google_Scholar | Generative Artificial Intelligence and the Practice of Law: Impact, Opportunities, and Risks | This article discusses the transformative impact of generative AI on the legal profession, including improving efficiency in tasks like drafting motions and enhancing legal education. It also explores the significant potential of generative AI to broaden access to legal services for underserved communities, while acknowledging associated challenges and risks like AI hallucinations and the need for regulatory adaptation. | True | Idealistic | True | 3.0 | Positive | Generative AI / Large Language Models (LLMs) (e.g., ChatGPT, GPT-4) | The paper cites studies by Choi, Monahan, and Schwarcz where GPT-4's impact on human legal analysis was assessed through a randomized controlled trial, and its performance was tested on law school exams. | Cited studies found that GPT-4 slightly and inconsistently improved the quality of legal analysis but induced large and consistent increases in speed. On law school exams, GPT-4 alone outperformed both students alone and students with AI assistance on simple multiple-choice questions; worst-performing students saw the largest gains from AI assistance, while best-performing students saw declines. | Need for AI systems of sufficient quality and reliability; concerns about privacy and privilege of personally identifiable information collected by AI systems; potential for unauthorized practice of law litigation against AI tool providers; and the time/effort required for software development, testing, navigating legal challenges, and updating regulatory frameworks. | Continued technological development to improve AI quality; careful system design ensuring user-friendliness, privacy, and verification features; navigating potential litigation; adapting regulatory frameworks over time to permit AI deployment while ensuring consumer protection; and learning from regulatory reforms in states like Utah and Arizona. | Support for pro se litigants, addressing tenant harassment, and mitigating common civil legal problems for low-income individuals (e.g., consumer issues, healthcare, housing, income maintenance). | Low-income households, low-income tenants, tenants in rent-stabilized dwellings, and pro se litigants. | Civil litigation, Housing Law, Consumer Law, Healthcare Law, and general legal practice. | United States (citing federal rules, and state/local examples from Texas, Los Angeles, New York, Utah, Arizona). | LLMs are described as being trained on 'massive datasets' of text and other content. The paper does not specify the exact composition or sources of these datasets for the general LLMs discussed, beyond noting they are trained on the data they have access to. | NaN | Existing tools like ChatGPT were released publicly by companies (e.g., OpenAI), leading to rapid adoption. Commercial products (e.g., CoCounsel by Casetext, Lexis+AI by LexisNexis) are being adopted by law firms and made available to law students. Future access to justice tools might be deployed by public/private legal aid organizations or directly to pro se litigants. | True | False | ChatGPT is publicly accessible (with free and paid tiers). CoCounsel and Lexis+AI are commercially available products, with Lexis+AI also being made available to many law students. | Ensuring high quality, reliability, and accuracy (reducing 'hallucinations') of generative AI in legal contexts; developing robust data privacy and attorney-client privilege protection mechanisms for AI systems; establishing clear regulatory frameworks for AI-driven legal services, particularly concerning unauthorized practice of law and consumer protection; time needed for development, testing, and societal/professional adaptation. | For users/adopters: managing AI 'hallucinations' and ensuring factual/legal accuracy of AI outputs; cost and market volatility of AI tools; training legal professionals to use AI effectively and ethically; protecting client data confidentiality; adapting existing workflows and business models; and effectively integrating AI into legal education. | AI 'hallucinations' leading to incorrect legal information or filings; breaches of client confidentiality and data privacy; unauthorized practice of law claims against AI service providers; premature deployment of low-quality AI tools for access to justice, potentially harming users or discrediting the approach; over-reliance on AI without sufficient human oversight and critical judgment; challenges in maintaining academic integrity and ensuring effective student learning with AI in legal education. |
| CeA1rreEv_sJ.pdf | Google_Scholar | InLegalLLaMA: Indian Legal Knowledge Enhanced Large Language Model | This paper introduces InLegalLLaMA, a Large Language Model adapted for the Indian legal domain through continual pre-training on Indian legal texts and knowledge infusion from a legal knowledge graph. The paper also proposes a Retrieval Augmented Generation (RAG) based framework utilizing this model for petition drafting to improve access to legal processes. | True | Idealistic | True | 1.0 | Positive | InLegalLLaMA: a LLaMA-2 model continually pretrained on Indian legal documents and instruction-tuned using legal knowledge graph triples and domain-specific tasks. A RAG-based framework for petition drafting is also proposed. | InLegalLLaMA was evaluated on: 1) In-context masked triple prediction using data from Vasisht et al. (2023), with metrics Hits@1, BLEU, ROUGE-L. 2) Legal sentence rhetorical role classification using data from Bhattacharya et al. (2023), with metrics Precision, Recall, and F1-Score. | For in-context triple prediction, InLegalLLaMA achieved Hits@1: 0.984, BLEU: 98.224, ROUGE-L: 99.191. For legal sentence rhetorical role prediction, InLegalLLaMA achieved F1-Score: 0.585. InLegalLLaMA outperformed LLaMA-2-7B on these Indian legal domain tasks. | Complexity of legal procedures for individuals; poorly written petitions leading to information omission, incorrect filings, and dismissals, thereby increasing costs and hindering justice; significant backlog and volume of petitions in courts. | Development of domain-specific LLMs (InLegalLLaMA) enhanced with legal knowledge from Indian legal documents and knowledge graphs. Proposal of a RAG-based framework for petition drafting involving template selection, AI-assisted content generation, refinement, and evaluation, with human oversight. | Petition drafting assistance; access to legal information and processes; understanding legal notices. | Citizens unfamiliar with legal processes; individuals seeking redressal of grievances; lawyers (for improving petition quality). | General Indian law; court petitions. | India | For continual pretraining: A new dataset of 10,000 Indian legal documents (Supreme Court judgments and legal statutes) and 5% replay data from RedPajama. For instruction-tuning: Triples from an Indian legal knowledge graph (derived from public Indian court/legal sources), datasets from Vasisht et al. (2023) for triple prediction, Bhattacharya et al. (2023) for rhetorical role classification, and LIMA instructions (Zhou et al., 2023). | Knowledge graph construction (building on prior work); continual pretraining of LLaMA-2 on domain-specific corpus using LoRA; instruction tuning with domain-specific tasks and general instructions; design of a RAG architecture for petition drafting. | The InLegalLLaMA model (base and instruction-tuned versions) is publicly available on HuggingFace. The petition drafting framework is a proposal. | True | True | The base and instruction-tuned versions of InLegalLLaMA are publicly available on HuggingFace. | Need for more extensive instruction tuning for complex legal text analytics tasks; the current triple prediction task may not be sufficiently challenging, requiring a larger knowledge graph; further work needed to make LLMs useful for tasks requiring human expertise; need for code fine-tuned versions of InLegalLLaMA for RAG tools (e.g., Text-to-SQL). | Resource constraints for large model training (addressed by LoRA); catastrophic forgetting during continual pretraining (addressed by replay data and learning rate strategies); designing LLMs to identify *missing* salient information in legal documents; ensuring legal soundness, completeness, and admissibility of AI-generated petitions (requiring human expert monitoring). | Generation of poorly written or legally unsound petitions if AI assistance is inadequately supervised, potentially leading to negative legal outcomes for users. Regulatory challenges with LLMs trained on data with unestablished provenance (though this work aims to address this by using domain-specific Indian legal data). |
| PxXf3m9gRGsJ.pdf | Google_Scholar | Traditional and Computational Canons | This paper empirically investigates whether judges align with linguistic consensus (from lawyers and laypeople) when using traditional interpretive tools like canons and dictionaries, finding they generally do. It also evaluates large language models (LLMs) like GPT-4o and o1, concluding they match, but do not exceed, judges' alignment with human consensus on plain meaning tasks. | True | Market | True | 2.0 | NaN | Evaluation of judicial use of traditional interpretive tools (canons, dictionaries) against linguistic consensus derived from human experiments, and evaluation of Large Language Models (GPT-4o, o1) prompted to perform similar plain meaning interpretation tasks. | Study 1: Behavioral experiments with lawyers (n=2,373) and laypeople (n=4,533) interpreting 180 real-world plain-meaning cases. Study 2: Prompting experiments with LLMs (GPT variants, o1 models) on the same 180 case materials, comparing outputs to human consensus from Study 1. | Judges' interpretations aligned with human linguistic consensus (both lay and lawyer) in a supermajority of cases. The best-performing LLMs (o1-mini or GPT-4 depending on metric) aligned with consensus at a similar rate to human judges, matching but not exceeding their performance. | NaN | NaN | NaN | NaN | Multiple fields including statutory interpretation, contract law, constitutional law, administrative law, wills, trusts, deeds. | United States (Federal and State) | Large, general, proprietary datasets used to train base LLMs (e.g., GPT-4o, o1), likely including significant amounts of publicly available text but specifics not disclosed. | Study 1: Experimental design, survey methodology, statistical analysis (mixed-effects logistic regression). Study 2: LLM prompting, comparison of LLM output to human benchmark data, statistical analysis. | NaN | True | False | The LLM part of the approach uses existing commercial models (GPT-4o, o1) accessible via OpenAI's API, typically requiring an account and potentially payment. | NaN | Potential for LLM data contamination affecting evaluation; ensuring LLMs accurately proxy human interpretation; potential misuse of LLMs by judges (e.g., cherry-picking models/prompts); accurately applying canons like ejusdem generis and rule of last antecedent which showed inconsistencies with consensus. | Potential misuse of interpretive tools (canons, dictionaries, LLMs) by judges as a smokescreen for policy preferences; LLMs potentially overestimating clarity/consensus in ambiguous cases; judges potentially misusing LLMs (e.g., cherry-picking models or settings). |
| Y81SvrL3LE0J.pdf | Google_Scholar | A Brief Report on LawGPT 1.0: A Virtual Legal Assistant Based on GPT-3 | This paper introduces LawGPT 1.0, a virtual legal assistant created by fine-tuning the GPT-3 language model on a large corpus of legal text. It briefly discusses the system's architecture, potential to improve legal service accessibility, and requirements like explainability for real-world application. | True | Idealistic | True | 1.0 | Positive | LawGPT 1.0: GPT-3 fine-tuned on a large corpus of legal text. | Evaluated on a set of legal benchmark tasks including answering legal questions, generating legal documents, and providing legal advice. No specific benchmark names or detailed procedures provided. | The system is reported capable of providing high-quality legal assistance, with accuracy rates competitive with other virtual legal assistant systems. No specific metrics are provided. | The need for cost-effective, efficient, and accessible (e.g., 24/7) legal services. For AI deployment: ensuring explainability and establishing responsibility for AI-generated recommendations. | Develop and deploy virtual legal assistants like LawGPT 1.0 to provide conversational legal assistance (answering questions, generating documents, advice) to improve efficiency and accessibility. | Answering legal questions, generating legal documents, providing legal advice (general legal assistance). | Individuals and businesses, particularly those needing legal assistance outside normal business hours. | General legal domain. | NaN | A large corpus of legal text. Proprietary status implied by NDA, details not disclosed. | Fine-tuning of a pre-trained large language model (GPT-3) using standard deep learning techniques (stochastic gradient descent, backpropagation). | NaN | False | False | NaN | Need for explainability in AI recommendations; need to establish responsibility frameworks for AI use in legal decisions; current version lacks RLHF; requires expansion to support multiple languages and legal systems. | Incorporating explainability; establishing responsibility for AI decisions; addressing legal and ethical considerations (data privacy, IP, confidential information); adapting the model for different languages and legal systems; current limitation of not supporting RLHF. | Serious consequences from incorrect legal decisions made with AI assistance; lack of explainability hindering trust and accountability; potential issues with data privacy, intellectual property rights, and handling sensitive/confidential information. |
| dDbdmiHfrYoJ.pdf | Google_Scholar | Persuading across Diverse Domains: A Dataset and Persuasion Large Language Model | This paper introduces DailyPersuasion, a large-scale, multi-domain dataset for persuasive dialogues generated using GPT-4. It also proposes PersuGPT, a large language model fine-tuned on this dataset using intent-to-strategy reasoning and simulation-based preference optimization, outperforming baselines including GPT-4. | True | NaN | True | 1.0 | NaN | PersuGPT: An LLM (LLaMA-2 based) trained for persuasive dialogue using intent-to-strategy reasoning and multi-turn simulation-based preference optimization (using a learned user model, GPT-4 for reward estimation, and DPO). Also introduces the DailyPersuasion dataset. | Evaluated on unseen scenarios from the DailyPersuasion dataset and the PersuasionForGood dataset. Metrics included Win-Rate (comparing model vs. GPT-4+ISR output using ChatGPT as judge) and ROUGE-L against GPT-4+ISR generations, plus human ratings (1-5 scale). Simulation-based preference optimization evaluated paths using GPT-4. | On DailyPersuasion, PersuGPT achieved a Win-Rate of 60.4% against GPT-4+ISR generations and a human rating of 4.35, outperforming GPT-4+ISR (Human Rating 4.17) and other baselines. It also outperformed baselines on the PersuasionForGood dataset. | NaN | NaN | NaN | NaN | NaN | International | Introduces DailyPersuasion: a synthetic dataset of 78,000 persuasive dialogue sessions across 35 daily life domains, generated using GPT-4. It includes scenarios, background, goals, strategy sets, dialogue turns (user utterances, persuader responses), and annotated intent-to-strategy reasoning paths. Also uses the public PersuasionForGood dataset. | Dataset Creation (DailyPersuasion): GPT-4 based generation using keyword induction for scenarios, guideline-based prompts for strategies, and third-person narrative prompts for dialogues. Model Training (PersuGPT): Supervised fine-tuning of LLaMA-2 Chat (13B) on DailyPersuasion including intent-to-strategy reasoning paths. Simulation-based preference optimization using a fine-tuned LLaMA user model, k-turn path simulation, GPT-4 for pairwise path comparison/reward estimation, and Direct Preference Optimization (DPO). | Code and data are made available via a GitHub link (https://persugpt.github.io). | True | True | Code and data available at https://persugpt.github.io. | Limitations mentioned include potential inconsistencies between synthetic data (DailyPersuasion) and real-world conversations, and the trained user model not fully capturing human personality variations. | Difficulty in collecting large-scale, diverse, high-quality human persuasion data across domains; enhancing LLM multi-turn following and planning for persuasion; anticipating user feedback and optimizing for long-term persuasive success; avoiding unnatural, role-inconsistent responses from LLMs during data generation. | Persuasive dialogue systems are a 'double-edged sword' with potential for misuse in harmful scenarios. The system should not replace human interaction and requires human supervision and regulation. |
| YT8QBByRvOoJ.pdf | Google_Scholar | Transdisciplinary research as a way forward in AI & Law | This position paper argues for a transdisciplinary approach in AI & Law, emphasizing hybrid AI systems (combining knowledge-based and data-driven methods) and practical evaluation with stakeholders. Two case studies, a police complaint intake system and a court decision support tool, illustrate these principles. | True | Idealistic | False | 3.0 | Positive | Advocacy for hybrid AI systems combining knowledge-based (e.g., rules, argumentation) and data-driven (e.g., NLP, machine learning) approaches. Illustrated by: 1. A police complaint intake system using rule-based argumentation and NLP (regex, experimented with ML). 2. A court decision support system using NLP for information extraction, document vectorization for case similarity, and legal text classification for outcome prediction. | Police system: Internal police evaluations (accuracy 80-90% vs human, efficiency), controlled experiment on citizen trust with explanations (N>1700), ethnographic case study of police case workers. Court system: Extensive testing and feedback sessions with three paralegals at a Dutch court. | Police system: Explanations significantly increased citizen trust and compliance with system recommendations; case workers valued structured data but ignored system recommendations without explanations. Court system: Information extraction and similar-case matching were valued by paralegals; outcome prediction without explanation was deemed useless. | General 'algorithmic drama' (complex, opaque, uncontrollable AI). Specific to access to justice: lack of citizen/professional trust in AI without clear explanations; resistance from legal professionals if AI impinges on discretion or is opaque; inherent difficulty for AI to handle legal nuance, ambiguity, and open-textured concepts. | 1. Combine knowledge-based and data-driven AI for transparency, reasoning, and handling legal complexities. 2. Evaluate AI in real-world legal practice through collaboration with stakeholders (courts, police, citizens) using diverse methods (computational, experimental, ethnographic, participatory). 3. Foster transdisciplinary research involving AI builders, legal experts, social scientists, ethicists, and others. | Citizen complaint intake for online trade fraud, decision support for processing traffic violation appeals. | Citizens reporting online trade fraud, citizens appealing traffic fines, legal professionals (police case workers, court paralegals). | Criminal law (online trade fraud), Administrative law (traffic violations). | Netherlands (for the specific projects discussed); International (for the general discussion of the AI & Law field). | Police system: Dutch Criminal Code and police policy rules (for knowledge-based argumentation model); citizen complaint forms containing free text (for NLP, final implementation used regular expressions after experimenting with ML). Court system: PDF files of appeal cases and a database of previous court cases (for information extraction, case similarity, and legal text classification). | Action research, participatory design, collaborative development with end-users (police, court personnel), ethnographic studies, controlled experiments. | Police complaint intake system: Implemented and in operational use by the Dutch police since 2018. Court decision support system: Developed and tested as a prototype in collaboration with a Dutch court for a case study; not stated as being in routine operational use. | False | False | NaN | Need for more truly integrated neuro-symbolic AI systems capable of legal reasoning (not just modular combinations); scarcity of AI & Law research applications being practically used and evaluated in real-world legal settings; insufficient transdisciplinary integration and consideration of broader socio-technical impacts; ongoing challenge of ensuring AI systems are transparent, contestable, and legally sound. | Computational efficiency for complex reasoning tasks (e.g., argument-based inquiry); gaining user trust and acceptance, particularly for AI recommendations lacking explanations; integrating AI into established professional workflows without undermining expert discretion; modeling complex legal language, open-textured concepts, and nuanced legal reasoning. | Over-reliance on opaque or poorly understood AI systems ('algorithmic drama'); AI systems being complex, uncontrollable, and leading to unjust outcomes; AI recommendations being ignored if not explainable, thus failing to deliver benefits; potential for AI to introduce or perpetuate biases if not carefully designed and evaluated; systems not being transparent, contestable, or aligned with legal principles. |
| EX0pygEbHrgJ.pdf | Google_Scholar | ACCESS TO JUSTICE FOR SELF -REPRESENTED LITIGANTS THROUGH THE NEW HAMPSHIRE CIRCUIT COURT NAVIGATOR PROGRAM: A PATH FORWARD | This report evaluates the effectiveness of the New Hampshire Circuit Court Navigator Program, which provides legal information and assistance to self-represented litigants (SRLs). It finds the program highly effective for both SRLs and the court system but highlights limitations due to staffing constraints and proposes recommendations for expansion and improvement. | True | Idealistic | False | 2.0 | Positive | New Hampshire Court Navigator Program | Evaluation based on observational analyses within courthouses, 19 stakeholder interviews (Navigators, court staff, judges, administrators, national experts), and an original survey administered to 34 SRLs served by the program. | The Navigator Program is highly effective, significantly increasing SRL confidence and satisfaction (rated 9 or 10 out of 10 by all survey respondents). It successfully serves target demographics (low-income, female, disabled, senior citizens), eases court staff/judge workload, and contributes positively to procedural justice perceptions, primarily in estate and guardianship cases. | Structural challenges to justice (court/legal aid staffing shortages, rural/demographic barriers, infrastructure limits). Program-specific barriers include limited data collection on SRLs, Navigator overburden due to only two staff members, lack of widespread advertising and schedule continuity, court technology issues (website usability, TurboCourt errors), need for staff mental health training, potential loss of institutional knowledge, and staff pay discrepancies causing tension. | Proposed short-term solutions include systematic data collection (tablets, platform), hiring two more Navigators (including a Volunteer Program Manager), and increasing program accessibility (advertising, scheduling system). Medium-term solutions cover technology upgrades (website redesign, AI exploration, TurboCourt audit, language accessibility), human infrastructure (mental health training, preserving knowledge, career paths), and program enhancements (volunteer program, expanded hours, coordination with Community Navigators). The long-term vision is ensuring no litigant appears in court alone. | Providing legal information and procedural assistance to self-represented litigants (SRLs) within the court system. | Low-income residents, women, disabled persons, senior citizens, rural populations, and self-represented litigants (SRLs) generally within the New Hampshire Circuit Court system. | New Hampshire Circuit Court matters, primarily civil cases including: estates, guardianship, small claims, landlord-tenant, name changes, low-level criminal offenses (misdemeanors/violations), stalking, adoptions, trusts, equity matters, involuntary commitments, and civil claims under $25,000. | New Hampshire (specifically the NH Circuit Court system) | NaN | The paper evaluates an existing program. The evaluation methodology included observational analyses, semi-structured stakeholder interviews, and an original SRL experience survey. | The program utilizes two state-funded court employees (Navigators) based in specific courthouses (Nashua and a Concord office for the Travelling Navigator). SRLs access services via phone/email appointments or walk-ins at these locations during business hours. | True | False | Available to SRLs in New Hampshire via appointment or walk-in at specific Circuit Court locations (primarily Nashua and where the Travelling Navigator is scheduled), subject to limited Navigator availability. | Need for comprehensive statewide data on SRL location and needs; insufficient number of Navigators to cover all courthouses, case types, and operating hours; inadequate court technology (frustrating website, error-prone TurboCourt); language accessibility barriers on forms and potentially services; challenges reaching rural SRLs; need for structured volunteer program; need for better institutional knowledge preservation and staff support (mental health, burnout prevention). | Operating with only two Navigators leading to overburden and inability to fully develop components like the volunteer program; difficulty advertising widely due to capacity constraints; dependence on current Navigators' extensive institutional knowledge; navigating court technology flaws (e.g., TurboCourt errors); ensuring consistent service quality and availability across different locations and times. | Potential for Navigator burnout due to high workload and emotional toll of cases. Risk of losing significant institutional knowledge if current Navigators leave. Risk of providing incorrect legal information, potentially harming SRL cases and procedural justice. Inconsistent service availability leading to perceptions of inequity. Data privacy concerns related to proposed data collection expansion. Court technology failures (e.g., TurboCourt) hindering effective assistance. |
| 5U2GMFXt3YIJ.pdf | Google_Scholar | Man or Machine? An exploratory study of the performance of Chat GPT 3.5 in the CFC Sufficiency Exam | This paper evaluates ChatGPT 3.5's performance on Brazilian accounting proficiency exams, finding it achieved passing scores on all tests. While demonstrating AI's potential in accounting, the study highlights the need for human oversight due to risks of inaccuracies and plagiarism. | False | NaN | True | 2.0 | NaN | ChatGPT 3.5 (with some comparison to GPT-3) | ChatGPT 3.5 (and GPT-3 for comparison) was prompted with questions from the Brazilian Federal Council of Accounting's (CFC) 1st Sufficiency Exam of 2022 and 1st Technical Qualification Exam of 2023. Multiple-choice answers were directly evaluated; essays were assessed for quality criteria including originality using Plagiarism Detector.net. | ChatGPT 3.5 scored above the 50% passing threshold on all exams: 74% on the Sufficiency Exam. For the Technical Qualification exams, scores were: QTG 64%, CVM 56%, BCB 52%, SUSEP 56%, and PREVIC 80%. Essays were generally coherent but showed some inaccuracies and variable originality scores. | NaN | NaN | NaN | NaN | NaN | Brazil | ChatGPT 3.5 was pre-trained by OpenAI on diverse public domain text data from the internet, with a knowledge cut-off around September 2021. The study used exam questions from 2022 and 2023 as input prompts. | NaN | NaN | True | False | Accessible via the OpenAI website (https://chat.openai.com/). The paper implies use of a free access tier for ChatGPT 3.5 at the time of research (March-June 2023). | NaN | Ensuring accuracy and avoiding plausible-sounding but incorrect answers from the model. Overcoming the model's knowledge cutoff (post-September 2021 events). Formatting complex inputs (e.g., tables from exams) for the model. Issues with originality and potential plagiarism in generated essay responses. Some difficulties in the AI understanding nuanced aspects of questions, leading to minor errors in essay framing. | Generation of plausible-sounding but incorrect or nonsensical answers. Potential for user misinterpretation or over-reliance leading to errors. Ethical concerns about AI-generated content, including plagiarism, ownership of AI-generated content, and the role of knowledge workers. Regulatory concerns regarding AI-generated data and its use. |
| x_J2nC_89RsJ.pdf | Google_Scholar | The Impact of Artificial Intelligence on the Evolution of Digital Education: A Comparative Study of OpenAI Text Generation Tools including ChatGPT, Bing Chat, Bard, and Ernie | This paper reviews the use of AI text generation tools (ChatGPT, Bing Chat, Bard, Ernie) in digital education, comparing their capabilities and applications. It discusses potential benefits for teaching and learning alongside significant challenges, particularly regarding academic integrity and ethical considerations. | True | NaN | True | 2.0 | Neutral | Comparative analysis of Large Language Models / AI Text Generation Tools: ChatGPT (GPT-3.5 & GPT-4), Bing Chat, Google Bard, Baidu Ernie. | Comparative evaluation using a set of questions posed to ChatGPT 3.5, ChatGPT 4, Bing Chat, and Bard. Analysis combined with review of existing literature. Access to Ernie was not possible. | ChatGPT-4 demonstrated enhanced performance in the authors' comparison, providing more comprehensive, detailed, precise responses and better understanding of context and complex topics compared to Bing Chat. Bing Chat provided briefer responses and integrated web search. Bard was noted for conversational ability but also hallucinations. Ernie could not be tested. | Within the educational context: potential for academic dishonesty (plagiarism, cheating); generation of inaccurate or biased information; difficulty detecting AI output; need for ethical guidelines and pedagogical adaptation; lack of digital literacy; ensuring equitable access; data privacy concerns. | Within the educational context: Develop clear policies and ethical guidelines; adapt assessment methods; provide digital literacy training for educators and students; integrate AI tools constructively; improve AI detection; foster stakeholder collaboration; research effective integration and bias mitigation. | Digital education, AI in education, large language models, ChatGPT, academic integrity, assessment methods, personalized learning, teacher support, ethical AI use, digital literacy. | Higher education community (students, educators, institutions). | NaN | International | Large corpora of text and code (e.g., 45TB for GPT-3, billions of parameters mentioned for GPT-3 and Ernie). Includes diverse sources like web text, books, articles, conversations. Data sources are generally large-scale, mixed public/proprietary, unstructured/semi-structured. Specific models leverage web data (Bing Chat) or human feedback (RLHF for ChatGPT). | The paper employs a comparative literature review approach combined with direct testing of accessible chatbot tools (ChatGPT, Bing Chat, Bard) using a set of questions to evaluate and compare their performance and features in an educational context. | NaN | True | False | ChatGPT (free tier via OpenAI website), Bing Chat (via Microsoft products), Google Bard (via Google website). | Lack of clear institutional policies for AI use in education; insufficient digital literacy training; need for research on effective pedagogical integration and assessment adaptation; inadequate AI detection methods; need to address AI bias and inaccuracies; limited inclusion of student perspectives; inability to test all compared tools (Ernie) due to access restrictions. | For the reviewed LLMs: Generating incorrect/biased information (hallucinations); potential for misuse (academic dishonesty); context understanding limitations; real-time information access limitations (for some models); ethical concerns (bias, privacy, safety). For the authors: Inability to access Baidu's Ernie for direct comparison. | Undermining academic integrity; spread of misinformation/bias; deskilling students (e.g., critical thinking); exacerbating inequalities due to access issues; data privacy violations; potential job displacement in education and related fields; over-reliance on AI; ethical hazards inherent in large language models. |
| xJJvD-ECVPIJ.pdf | Google_Scholar | Access to Civil Justice in the Age of AI: Mindsets & Pathways to New Practices | This paper explores how Artificial Intelligence, particularly generative AI, can enhance access to civil justice by enabling new, scalable legal service models focused on legal information products. It argues that lawyers must adopt new mindsets and innovate beyond traditional practice to effectively leverage AI and address the justice gap in the PeopleLaw sector. | True | Idealistic | True | 3.0 | Positive | Using generative AI (e.g., ChatGPT, GPT-4) for creating, simplifying, organizing, and diversifying legal information products for consumers. | NaN | NaN | High cost (affordability) of traditional legal services; Lack of scalability in the one-to-one lawyer service model; Limited funding and reach of legal aid; Neglect of the 'missing middle' income group; Difficulty navigating the complex legal system without help; Prevalence of poor quality online legal information ('sea of junk'). | Lawyers adopting new mindsets (learning, adaptation, innovation); Leveraging generative AI for practice efficiency and cost reduction; Developing scalable legal information products (handouts, guides, videos) using AI; Implementing new business models (freemium, tiered pricing, subscription) centered around information products; Courts providing self-help resources and simplified procedures. | Bridging the justice gap in civil law; Providing affordable legal help (information and services); Serving self-represented litigants; Innovating legal service delivery models in the PeopleLaw sector. | Low-income individuals (including the ALICE population) and the 'missing middle' (middle-class individuals often priced out of legal services). | Civil Justice (general), Family Law (divorce, custody, support mentioned as examples), Small Business Law (implied by PeopleLaw sector). | United States | NaN | NaN | Discusses potential business models for lawyers (freemium, tiered pricing, subscription models) to deploy AI-assisted legal information services. | False | False | NaN | Need for lawyers to overcome resistance to change and adopt new mindsets/business models; Ensuring quality and reliability of AI-generated legal information; Addressing regulatory frameworks around AI in legal services; Scaling legal advice, not just information; Need for continued focus on simplifying legal processes. | Understanding AI capabilities and limitations; Integrating AI ethically and competently into legal practice (duty of competence); Overcoming professional inertia and traditional practice models; Rethinking the value proposition beyond bespoke legal advice. | AI 'hallucinations' leading to inaccurate legal information or citation of non-existent cases; Lawyers violating ethical duties (competence) through improper AI use; Potential for AI to exacerbate low-quality online information if not curated; Broader societal risks associated with advanced AI (job transformation, unforeseen consequences). |
| 7dlcyVpmL_gJ.pdf | Google_Scholar | ІNFORMATION AND LEGAL SUPPORT FOR BALANCING THE INTERESTS OF JUSTICE AND HUMAN RIGHTS PROTECTION | The paper discusses balancing effective justice administration and human rights protection in Ukraine's criminal justice system. It proposes leveraging modern IT, including AI models like GPT-4 and associative rule mining, to analyze case data and support judicial decision-making in sentencing, while acknowledging potential risks. | True | Idealistic | True | 1.0 | Neutral | Using multimodal language models (like GPT-4) for text generation/analysis combined with associative rule models (mining) to extract relationships from unstructured case data for sentencing support (risk assessment, societal danger, analysis of similar cases). | NaN | NaN | Balancing effective justice administration with human rights protection; Manual analysis of large volumes of unstructured text data in criminal proceedings is labor-intensive, inefficient, and subject to human bias; Potential risks of IT implementation like privacy violations, digital divide, and misuse for surveillance or rights violations. | Leveraging modern IT (electronic document management, integrated databases, videoconferencing, etc.); Proposing the use of AI (GPT-4, associative rules) to automate analysis of unstructured text data for sentencing decisions; Emphasizing the need for legal frameworks, regulatory control, cybersecurity, training, and resources for IT implementation. | Sentencing support, Risk assessment (recidivism, danger to society), Analysis of similar cases, Balancing efficiency and human rights in criminal justice, Justice system transparency. | NaN | Criminal Law, Criminal Procedure | Ukraine | The paper mentions analyzing "large collections of unstructured text documents" from criminal proceedings. The specific source, availability, and nature (beyond unstructured text) are not detailed. | NaN | NaN | False | False | NaN | Need for effective tools to analyze large amounts of unstructured legal text efficiently and objectively; Need for improved risk assessment tools for sentencing; Need for strategies to implement IT in justice effectively while mitigating risks (privacy, digital divide, misuse). | Ensuring privacy and data protection; Bridging the digital divide; Preventing misuse of IT for surveillance or rights violations; Establishing proper regulatory control; Ensuring cybersecurity; Providing adequate staff training and resources; Overcoming inefficiency and bias in manual analysis of unstructured case data. | Privacy violations; Breach of personal data protection; Creation of a digital divide; Potential for systematic human rights violations; Potential for mass surveillance through IT systems. |
| HjuAmkWUb1QJ.pdf | Google_Scholar | Equitable Access to Justice: Logical LLMs Show Promise | This paper explores integrating Large Language Models (LLMs) with logic programming to enhance their reasoning capabilities for legal applications, aiming to improve access to justice. It demonstrates that OpenAI's o1-preview model significantly outperforms GPT-4o in translating a health insurance contract into logical Prolog code, suggesting potential for creating 'computable contracts'. | True | Idealistic | True | 1.0 | Positive | Using LLMs (GPT-4o and OpenAI o1-preview) to automatically generate logical representations (Prolog code) of a health insurance policy to create "computable contracts", enabling automated reasoning about policy coverage. | GPT-4o and OpenAI o1-preview were prompted to translate a simplified health insurance policy into Prolog code. Then, both models were prompted to translate nine natural language yes/no questions about the policy into Prolog queries on their respective encodings. The number of correct answers from executing these queries was recorded over ten trials. | OpenAI o1-preview averaged 7.5 out of 9 correct answers across ten trials when its generated Prolog code for the insurance policy was queried. GPT-4o averaged 2.4 correct answers. | High cost of legal services, complexity of the judicial system, widespread distrust of attorneys, large number of self-represented litigants, and consumer difficulty in understanding legal documents like insurance policies. | Developing reliable and transparent technological solutions using AI (LLMs combined with logic programming) to create 'computable contracts' that simplify understanding and automate interpretation of legal documents like insurance policies, thereby scaling the encoding of legal text into logic programs. | Understanding legal documents (insurance contracts), automated legal reasoning for contract interpretation, improving consumer access to information about their legal rights and obligations under contracts. | General public/consumers, especially those facing challenges in understanding complex legal documents like insurance policies, and self-represented litigants. | Contract law, Insurance law (specifically health insurance). | USA (reference to American judicial system and California statistics; the example insurance policy specifies New York law). | The LLMs (GPT-4o, OpenAI o1-preview) are pre-trained models; their specific training data is not detailed in the paper. The input for the experiment was a simplified version of the Chubb Hospital Cash Benefit insurance policy text, provided in the paper's appendix. | Experimental comparison of LLM outputs (Prolog code) generated from a legal text (insurance policy). Evaluation involved qualitative analysis of the code's logical structure and interpretability, and quantitative empirical testing based on the accuracy of answers to nine natural language questions translated into Prolog queries, run over ten trials. | NaN | False | False | NaN | Technical gaps include the accuracy and quality of LLM-generated logic (risk of misinterpretation, omission, inconsistency, overgeneralization), LLM struggles with legal nuances and temporal relationships, and potential biases in training data. Societal gaps include the need for consistent, transparent, reliable, and trustworthy AI solutions for legal applications. | Ensuring the accuracy and quality of logical representations generated by LLMs from legal texts. LLMs may misinterpret terms, omit details, create logical inconsistencies, or overgeneralize. They also struggle with legal nuances, ambiguities, and conditional/temporal relationships. Potential biases in LLM training data can affect the validity of the generated logic. | LLMs may produce hallucinations and inconsistent answers. They can misinterpret legal terms, omit critical details, generate logical inconsistencies, or overgeneralize legal principles. Biases in their training data could compromise the validity of the generated legal logic, potentially leading to incorrect legal interpretations. |
| uySo4wqTXNAJ.pdf | Google_Scholar | Design at the Center for Future Strategy Making: Generative AI as an Affordance Catalyst | This paper investigates how Generative AI (Gen-AI) acts as a technological affordance to make design central to strategic decision-making for top management. Based on qualitative interviews and archival data, it proposes a framework where Gen-AI enhances the entire design process, fostering human-AI collaboration and improving strategic performance. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Strategic Management / General Business (covering Finance, Manufacturing, Software, Education, IT, Transport, Legal Services) | International | NaN | NaN | NaN | False | False | NaN | NaN | Need for training/upskilling; ethical concerns (bias, privacy, misinformation, responsibility); integrating Gen-AI with human skills and existing processes; potential tension between analytical and design-based strategy approaches; rapid evolution of technology; need for governance frameworks. | AI bias, data privacy breaches, data security issues, misinformation generation, negative societal implications. |
| -1qPa25dIGUJ.pdf | Google_Scholar | Lex Ex Machina: Forging a New Ethical Framework for AI and Technology in the Law | The paper argues that current legal ethics rules, particularly regarding competence, are insufficient to address the challenges and opportunities presented by Generative AI (GAI). It proposes a new, detailed, yet flexible ethical framework ('Rule X' and commentary) to guide legal professionals in the responsible use of GAI and other advanced technologies, emphasizing continuous learning and balancing innovation with ethical integrity. | True | Market | True | 1.0 | Positive | Proposed new ethical framework ('Rule X: Technology Use in Legal Practice' and associated commentary) for regulating technology use, particularly GAI, in legal practice. | NaN | NaN | The primary identified obstacle is the inadequacy and vagueness of current legal ethics rules (e.g., ABA Model Rule 1.1 Comment 8) to provide sufficient guidance for lawyers using advanced technologies like GAI. Other related obstacles include the technology gap among legal professionals, potential for misuse (inaccuracy, bias, confidentiality breaches), and resistance to adopting new technologies. | Proposes the adoption of a new, detailed, flexible, and aspirational ethical framework (Rule X and commentary). This framework aims to guide lawyers on competence, confidentiality, client communication, fees, supervision, and other ethical duties concerning technology use, fostering tech literacy and responsible innovation. | NaN | NaN | Legal Ethics, Professional Responsibility, General Legal Practice | United States | NaN | Legal analysis, Review of existing regulations, Argumentation | NaN | False | False | NaN | Lack of specific, detailed, yet flexible ethical guidance for lawyers using advanced technologies like GAI within existing professional conduct rules. Existence of a 'technology gap' (disparity in tech proficiency) among lawyers. Potential for AI bias to exacerbate societal discrimination if unaddressed. | Ensuring data privacy and security when using AI tools, Verifying the accuracy of AI-generated content (avoiding 'hallucinations'), Addressing potential algorithmic bias in AI tools, Maintaining lawyer competence through continuous learning about evolving technology, Supervising subordinate lawyers and nonlawyers using AI, Communicating the use of AI and associated fees appropriately to clients. | Breach of client confidentiality; Use of inaccurate AI-generated information ('hallucinations') leading to incompetent representation or court sanctions; Perpetuation of societal biases through biased AI tools; Unauthorized practice of law if AI replaces lawyer judgment; Charging unreasonable fees related to AI use; Creation of unintended attorney-client relationships via AI chatbots; Failure to supervise subordinate use of AI; Misleading courts or opposing counsel. |
| IDsoJMvT-Q8J.pdf | Google_Scholar | TOWARDS HIGH -QUALITY, PRIVACY -FOCUSED BLOG GENERATION: AN OPEN -SOURCE APPROACH USING LLAMA -2 | The paper presents BlogGen, a system using a fine-tuned Llama-2 model for generating customized, high-quality blog content. It highlights the benefits of using an open-source model like Llama-2 over proprietary alternatives like GPT-3.5, focusing on customization, privacy, and control. | True | Market | True | 1.0 | NaN | BlogGen: A system fine-tuning the open-source Llama-2 LLM for blog generation, featuring a Streamlit-based UI for user input (topic, audience, length). | Evaluation focused on content relevance, coherence (logical flow, structure), and user satisfaction assessed via feedback surveys across various test cases and user demographics. | BlogGen consistently generated content aligned with user specifications (topic, audience type, tone) and produced well-structured outputs. User feedback indicated satisfaction with accuracy and engagement. | NaN | NaN | NaN | NaN | NaN | NaN | Publicly available data from StackExchange and Kaggle focusing on blog-related prompts and responses. Unstructured text data, preprocessed and augmented. | Fine-tuning of a pre-trained LLM (Llama-2), data preprocessing (tokenization, normalization, noise removal), data augmentation (paraphrasing, synonym replacement), user interface development (Streamlit). | Implemented as a web application with a Streamlit user interface. | False | False | NaN | NaN | General LLM challenges mentioned include hallucination (factual inaccuracy), ensuring coherence and relevance, and ethical considerations (privacy, bias, misinformation, authorship). Specific challenges in development were not detailed beyond the need for effective fine-tuning and data preparation. | Hallucination (generating factually incorrect information), propagation of bias or misinformation from training data, data privacy concerns (mitigated by using self-hostable Llama-2), challenges to authorship and credibility of generated content. |
| hUHRaiZY4LYJ.pdf | Google_Scholar | Judicial Administration 4.0: strengths, \nchallenges and opportunities of a proactive \napproach to AI regulation in Spain | This paper discusses the benefits and risks of predictive and generative AI in the Spanish judicial system, particularly in criminal procedures, crime prevention, and judicial efficiency, advocating for a proactive regulatory approach. It examines whether binding regulations or soft law (codes of conduct) are sufficient to mitigate risks to fundamental rights while promoting compliance. | True | NaN | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Criminal procedural law, Criminal justice | Spain | NaN | NaN | NaN | False | False | NaN | Lack of consolidated ethical standards governing the use of predictive or generative AI in the legal field in Spain (though a policy document was recently approved). Insufficiencies in the current legislative framework (e.g., Royal Decree-Law 6/2023 lacks specificity on automated, proactive, or assisted judicial actions). | Ensuring AI neutrality and avoiding bias; malicious manipulation; opacity diminishing transparency and fairness; erosion of reasoning in judicial decisions; assigning responsibility for AI errors; replication and intensification of judicial biases; difficulties in challenging AI outputs due to 'black box' nature; potential infringement of judicial principles like contradiction of parties, impartiality, and party-driven justice when judges use AI-generated data not admitted as evidence. | Violation of citizens' fundamental rights (e.g., privacy, honor, due process, effective judicial protection); discriminatory outcomes from biased AI; undermining fairness and validity of judicial outcomes if parties cannot challenge AI training data or algorithms; erosion of judicial independence and impartiality; potential for AI to be used for indiscriminate surveillance and identification without consent. |
| _y_E2nhQTJwJ.pdf | Google_Scholar | Reading Law with ChatGPT (With Special Emphasis on Contextual Canons) Version of Apr. 3, 2024 | This paper evaluates the performance of ChatGPT in interpreting legal prompts related to 'Contextual Canons' from Scalia & Garner's "Reading Law." The findings indicate that ChatGPT is exceptionally successful in applying these canons to specific legal scenarios, offering sound and detailed legal reasoning. | True | Market | True | 2.0 | NaN | ChatGPT (free version, specifically mentioned as GPT-3.5) | Qualitative evaluation of ChatGPT's responses to prompts based on 14 contextual canons of legal interpretation, with scenarios adapted from Scalia & Garner's 'Reading Law'. The author engaged in personal interaction with the free version of ChatGPT. | ChatGPT was found to be 'exceptionally successful' in taking contextual canons into account, providing sound, detailed, and often court-aligned legal reasoning for the presented scenarios across all 14 tested canons. | NaN | NaN | NaN | NaN | Statutory Interpretation, applying to various fields including criminal law, property law (leases), employment law, family law, torts, and administrative law. | United States (examples from New York, Arizona, Missouri, Montana, Minnesota, Texas, South Dakota, Pennsylvania, District of Columbia, Federal law) | NaN | NaN | NaN | True | False | The paper states the author used the free version of ChatGPT, available at https://chat.openai.com. | NaN | The author noted surprise at how well simple prompting worked, implying complex prompt engineering was not a significant challenge. No other major challenges in using or evaluating ChatGPT were detailed. | NaN |
| moqz6a30twoJ.pdf | Google_Scholar | Development of AI Prototype for Generating Construction Safety Guidelines Through Fine-Tuning of Large-Scale Language Model | This paper develops an AI chatbot prototype to generate construction safety guidelines by fine-tuning a Korean large language model (KoAlpaca-Polyglot-12.8B) on a custom QA dataset derived from official safety documents. The fine-tuned model demonstrated improved performance in providing specific and accurate construction safety information compared to general LLMs like GPT-4. | False | Idealistic | True | 1.0 | Positive | Fine-tuning the KoAlpaca-Polyglot-12.8B large language model using Parameter-Efficient Fine-Tuning (PEFT) with Low-Rank Adaptation (LoRA) on a custom construction safety Question-Answering (QA) dataset to create a guideline-generating chatbot. | Qualitative comparison of response accuracy and expertise against GPT-4, Palm2, and baseline KoAlpaca. Quantitative evaluation using BLEU score and BERT Similarity score on 100 construction safety questions against reference answers. | The fine-tuned model achieved the highest BERT Similarity score (0.8416) and highest BLEU scores (Unigram: 0.3497, Bigram: 0.2604), outperforming GPT-4 by 7.21% in BERT Similarity and 7.5% in BLEU (unigram). Qualitatively, it provided more specific and accurate answers on construction safety topics. | Difficulties accessing, understanding, and applying construction safety guidelines provided in static formats (PDFs); Lack of safety capacity and resources (e.g., managers) in small to medium-sized construction sites; High accident rates due to non-compliance on these sites; Limitations of general-purpose LLMs in providing accurate, domain-specific safety knowledge. | Develop a specialized AI chatbot fine-tuned on construction safety guidelines using PEFT-LoRA; Provide easy access to specific and accurate safety information tailored to job conditions via the chatbot; Overcome limitations of general LLMs for domain-specific tasks. | Construction safety regulations and guidelines application; Workplace accident prevention in construction. | Small to medium-sized construction sites/companies and their managers/workers. | Construction Law, Occupational Safety and Health Law/Regulations | South Korea | A custom dataset of 5,114 Korean question-answer pairs generated via prompt engineering using GPT-3.5-turbo based on 86 publicly available construction safety guideline PDF documents from the Korean Occupational Safety and Health Agency (KOSHA) covering 2010-2023. Manually reviewed by researchers. | Prompt engineering (Chain of Thought, Self-Consistency); Parameter-Efficient Fine-Tuning (PEFT); Low-Rank Adaptation (LoRA); Qualitative evaluation; Quantitative evaluation (BLEU, BERT Similarity). | NaN | False | False | NaN | Dataset may not reflect the absolute latest regulations; Potential for errors in the AI-generated dataset despite review; Evaluation metrics (BLEU, BERT Similarity) measure textual similarity, not practical safety/validity; Model's utility may be limited to QA based on guidelines; Need for real-world validation and potentially multimodal models. | Creating an accurate, domain-specific QA dataset; Efficiently fine-tuning an LLM with resource constraints; Ensuring accuracy and specificity of the fine-tuned model for safety information; Evaluating model performance in a specialized domain. | Generating incorrect or unsafe safety advice due to dataset limitations (outdated info, errors) or model flaws; Over-reliance on AI without proper validation leading to potential harm; Data security issues when handling sensitive information like accident reports. |
| cepniqfn3kwJ.pdf | Google_Scholar | Legal Tech Abolition: Using legal technology to free them all | The paper discusses the disproportionate impact of the US criminal justice system and the underutilization of the 821 Criminal History Amendment for sentence reduction. It describes the creation of a legal tech tool using Docassemble to help incarcerated individuals determine eligibility for this amendment and connect with Federal Public Defenders. | True | Idealistic | False | 1.0 | Positive | A website built using the open-source platform Docassemble to guide users through eligibility questions for the 821 Sentencing Amendment and facilitate requests for assistance from Federal Public Defender's Offices. | Informal feedback from assistant federal public defenders during development. The website was never launched, so no deployment testing occurred. | NaN | The access-to-justice gap for incarcerated individuals, including lack of awareness of relief mechanisms (like the 821 Amendment), lack of legal guidance, and communication barriers with counsel. Underutilization of the 821 Amendment. | Developing and deploying a user-friendly legal tech tool (Docassemble website) to inform incarcerated individuals about 821 Amendment eligibility and connect them with legal assistance (Federal Public Defenders). | Sentence reduction (821 Amendment), Post-conviction relief, Access to legal information, Access to legal assistance for prisoners. | Incarcerated individuals in the US federal system potentially eligible for sentence reduction under the 821 Amendment, noting disproportionate impact on Black and Latino individuals. | Criminal Law, Federal Sentencing Law, Post-Conviction Procedure. | United States (Federal) | NaN | Use of open-source platform (Docassemble), iterative design based on stakeholder feedback (federal public defenders), development of guided user interview, multi-language support (English/Spanish). | Technical deployment plan using Amazon Lightsail and Docker developed, but the website was never launched. | False | False | NaN | Underutilization of legal relief by eligible prisoners due to lack of awareness/assistance. Need for better communication channels between legal aid and prisoners (especially those outside federal facilities). Lack of legal tech focused specifically on prisoners' needs. | Securing ongoing funding and resources (led to the project not being launched). Implicit challenges: Complexity of federal sentencing law, reaching incarcerated individuals, tool maintenance. | The paper primarily discusses risks of other AI applications in criminal law (biased algorithms in predictive policing, bail decisions, facial recognition; increased surveillance in prisons), rather than specific risks of the proposed Docassemble tool. Implicit risks include inaccurate legal information or eligibility assessment. |
| 3-4xM4xi2w4J.pdf | Google_Scholar | Interoperable Legal AI for Access to Justice | The paper argues that siloed progress in AI for consumers, legal providers, and courts is insufficient to close the access-to-justice gap without coordination and interoperability. It advocates for courts to lead the development of interoperable legal AI systems, using examples like Brazil, to enhance fairness and scalability. | True | Idealistic | False | 1.0 | Positive | Interoperable legal AI driven by courts, encompassing technical, organizational, legal/policy, semantic, and socially informed interoperability. | NaN | NaN | Growing access-to-justice gap; procedural barriers; underresourced courts and public defenders; lack of coordination across consumer/provider/court fronts; regulatory uncertainty (UPL, ownership); jurisdictional variations; data silos; funding disparities; potential for AI bias and inequality magnification. | Achieve technological and procedural legal interoperability driven by courts; adopt common standards and open-source software; establish collaborative governance structures; implement data integration/standardization; pursue legal regulatory reform (e.g., national sandboxes); align with AI governance principles. | Access to justice (civil and criminal), court procedures (filing, case management, ODR), self-help legal tools, legal service delivery efficiency, legal data standardization, regulatory reform. | Low-income Americans, self-represented litigants, individuals facing economic or social barriers to legal help. | Civil Law, Criminal Law, Administrative Law, Court Administration | United States (with comparative examples from Brazil and the European Union) | NaN | NaN | NaN | False | False | NaN | Lack of nationwide scale and impact in AI for justice; funding gap between commercial and A2J tech; lack of coordination, standards, and interoperability; insufficient regulatory reform; absence of comprehensive, standardized, and accessible court data; need for bias mitigation in AI design; persistence of the justice gap and risk of a two-tiered system. | Regulatory fragmentation across jurisdictions; lack of funding for courts and A2J technology; institutional inertia (courts as followers); complexity of the US federalist system; achieving stakeholder buy-in; need for interdisciplinary expertise; difficulties in data standardization. | Automating bias; magnifying inequality; entrenching a two-tiered justice system; inaccurate or harmful AI outputs (e.g., racist statements, flawed risk assessments); undermining fairness and the legitimacy of the legal system. |
| VK3sHji0IeoJ.pdf | Google_Scholar | ARTIFICIAL INTELLIGENCE AND LAW — AN OVERVIEW OF RECENT TECHNOLOGICAL CHANGES : KEYNOTE ADDRESS AT THE 2024 IRA C. ROTHGERBER J R. & SILICON FLATIRONS CONFERENCE ON ARTIFICIAL INTELLIGENCE AND CONSTITUTIONAL LAW | This paper, a keynote address, offers an overview of artificial intelligence, detailing its historical evolution with a focus on recent advancements in large language models like GPT-4. It explores their applications and limitations within the legal field, particularly constitutional law, while stressing the importance of AI literacy for legal professionals and expressing cautious optimism about AI's potential to enhance the legal system. | True | Idealistic | True | 2.0 | Positive | Primary focus on Large Language Models (LLMs) such as OpenAI's GPT series (GPT-3, GPT-3.5, GPT-4, GPT-4o), Anthropic's Claude, and mentions of Google's Gemini Ultra and Meta's Llama 3. Also discusses specialized legal AI systems like Lexis+ AI and Westlaw CoCounsel, which utilize underlying LLM technology. | The author evaluates LLMs through illustrative examples and personal experimentation. This includes posing commonsense questions (e.g., "How many legs does an apple have?"), legal queries (e.g., a Third Amendment scenario), requesting legal document drafting (e.g., merger agreement, motion for summary judgment), and analyzing legal texts (e.g., insurance contracts, torts fact patterns). Comparisons between models (e.g., GPT-4 vs. Claude) are also used. | GPT-4 is presented as significantly more capable than its predecessors, able to produce comprehensive legal document drafts, perform sensible legal analysis, and answer complex questions. However, it's noted that even advanced LLMs can 'hallucinate,' provide outdated information, make reasoning errors, and generate conflicting answers depending on the model or prompt. | Lack of AI literacy among legal professionals; inherent limitations of AI (e.g., hallucinations, potential for bias, lack of transparency, sensitivity to prompts); risk of over-reliance and misinterpretation of AI outputs; privacy and confidentiality concerns with certain AI usage models; the potential for AI to make non-transparent value judgments in legal interpretation. | Enhancing AI literacy for legal professionals through education and hands-on experience; promoting careful, supervised use of AI tools with thorough verification of outputs; utilizing specialized legal AI systems for better reliability, security, and access to curated legal data; focusing on high-quality training data and improved AI architectures for future models; fostering a thoughtful societal adoption of AI to make the legal system more transparent, equitable, and accessible. | Improving transparency, fairness, and general accessibility of the legal system; legal analysis; legal document generation; legal research; contract analysis; constitutional law interpretation. | General public / society at large, with the aim of a fairer and more accessible legal system for all. | Constitutional law, contract law, torts law, general legal practice, legal research, and document drafting. | United States (e.g., references to U.S. Constitution, Colorado Governor), though many principles discussed have international relevance. | Large-scale, primarily unstructured text data from diverse sources such as unpublished books, public webpages, Wikipedia, and other internet content. The paper notes a trend towards using higher-quality, curated data like textbooks and research papers for training newer models. | Machine learning, particularly deep learning utilizing neural networks and the transformer architecture. The development of models like ChatGPT also involves engineering improvements based on training with large datasets and techniques to enhance instruction following and problem-solving capabilities. | General-purpose LLMs like ChatGPT are accessible via web-based interfaces and apps. Specialized legal AI systems (e.g., Lexis+ AI, Westlaw CoCounsel) are offered as commercial products to legal professionals. Some models (e.g., Llama 3) are available as open-weights. | True | False | General-purpose LLMs like ChatGPT (available via OpenAI with free and paid subscription tiers) and Claude (available from Anthropic) are accessible online. Specialized legal AI systems such as Lexis+ AI and Westlaw CoCounsel are commercially available through subscriptions. | Technical gaps include the need for improved reliability, reduction of hallucinations, enhanced transparency and interpretability, better bias mitigation, and reduced sensitivity to prompt variations in LLMs. Societal gaps involve fostering widespread AI literacy, ensuring equitable access to and fair application of AI in the legal domain, and addressing the challenge of AI making implicit value judgments without clear human oversight. | Key challenges include ensuring the reliability and accuracy of LLM outputs (avoiding hallucinations and outdated information); managing and mitigating biases present in training data; overcoming the lack of transparency ('black box' nature) in how LLMs arrive at conclusions; addressing the sensitivity of LLMs to prompt phrasing; and managing the significant computational and hardware requirements for training and deploying advanced models. | Inaccurate AI-generated outputs (hallucinations) leading to errors in legal work and potential professional sanctions; reliance on outdated information; propagation of biases embedded in training data; breaches of privacy and client confidentiality, especially with non-enterprise AI versions; over-reliance on AI by legal professionals without critical evaluation; lack of transparency in AI’s decision-making, particularly concerning for legal and constitutional interpretation where AI might make implicit, unscrutinized value judgments. |
| oiF84vWI26YJ.pdf | Google_Scholar | CERTIFYING LEGAL AI ASSISTANTS FOR UNREPRESENTED LITIGANTS: A GLOBAL SURVEY OF ACCESS TO CIVIL JUSTICE, UNAUTHORIZED PRACTICE OF LAW, AND AI | This paper surveys global approaches to AI, unauthorized practice of law (UPL), and access to civil justice for unrepresented litigants. It proposes a capability-based framework using public benchmarks to certify legal AI assistants, allowing their exemption from UPL rules to improve access to justice. | True | Idealistic | True | 1.0 | Positive | A capability-based framework for certifying legal AI assistants based on testing accuracy against public benchmark datasets for specific legal tasks. | The paper proposes a framework that requires testing AI capabilities against public benchmark datasets (e.g., LegalBench, LawBench, JEC-QA, SARA) using metrics like f-measure, Bleu, MCC, or Task Success Rate to meet predefined accuracy thresholds. It does not test a specific tool itself. | NaN | The large number of unrepresented litigants lack access to affordable legal help; restrictive Unauthorized Practice of Law (UPL) rules prevent potentially helpful AI tools; risk of harm from inaccurate advice provided by unregulated AI; absence of a standardized certification framework for legal AI. | Amend UPL rules to explicitly exempt certified legal AI assistants; establish a capability-based certification framework evaluating AI accuracy on specific tasks using public benchmarks; create a third-party body to manage certification; foster collaboration and investment in developing necessary benchmark datasets. | Access to legal information, guidance, advice; navigating legal procedures; drafting legal documents; case outcome prediction; dispute resolution for unrepresented litigants in civil justice. | Unrepresented litigants (self-represented litigants, litigants in person, pro se litigants), particularly those with limited financial resources. | Civil Justice (broadly, including examples from housing, consumer law, family law (protection orders), small claims). | Global survey covering Argentina, Australia, Brazil, Canada (incl. provinces), China, European Union, Germany, India, New Zealand, Nigeria, Singapore, United Kingdom, United States (incl. 50-state/6-territory survey). Proposed framework intended for global adoption with local implementation. | NaN | NaN | NaN | False | False | NaN | Need for more public benchmark datasets specifically designed for unrepresented litigant tasks; need for international coordination among legal regulators on AI; inconsistent definitions of 'practice of law' and UPL across jurisdictions; technical issues like data contamination potentially affecting benchmark evaluations. | Defining the practice of law and UPL consistently; getting regulatory bodies to adopt UPL exemptions for AI; ensuring sufficient and representative benchmark datasets are created and maintained; addressing technical issues like data contamination in evaluating LLMs. | Unrepresented litigants receiving inaccurate legal guidance from AI, leading to negative outcomes; potential for data contamination to lead to overly optimistic evaluations of AI accuracy; bias in AI systems (mentioned generally in cited sources/context). |
| r84CfYBTdlEJ.pdf | Google_Scholar | Position: Stop Acting Like Language Model Agents Are Normal Agents | This paper argues that Language Model Agents (LMAs), built on LLMs, possess inherent pathologies (statelessness, stochasticity, semantic sensitivity, linguistic intermediation) that undermine key agentic properties like identity, continuity, persistence, and consistency. Treating LMAs like normal agents leads to problems with utility and trustworthiness, necessitating specific evaluation of their ontological properties. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | NaN | NaN | False | False | NaN | NaN | Fundamental challenges stem from the underlying LLM pathologies: statelessness (lack of inherent memory), stochasticity (unpredictable outputs), semantic sensitivity (small input changes cause large output shifts), and linguistic intermediation (interactions filtered through text tokens). These pathologies make it difficult to establish stable LMA identity conditions: identifiability, continuity, persistence, and consistency. | Unreliability and unpredictability, particularly in high-stakes applications; undermined trustworthiness; compromised utility due to inconsistent performance; potential for misuse via prompt manipulation or tool misuse; difficulties in AI alignment and governance due to unstable identity; known LLM risks like hallucination and jailbreaking propagating to agentic systems. |
| dGh5QjYACUIJ.pdf | Google_Scholar | Generative AI -Driven Storytelling: A New Era for Marketing | This paper explores the use of generative AI for storytelling in marketing, enhancing personalization and customer engagement. It discusses applications by companies like Netflix and Google, highlights benefits, ethical challenges, data dependencies, and future research directions. | True | Market | True | 3.0 | Positive | Generative AI-driven storytelling using Recurrent Neural Networks (RNNs) and Transformers | NaN | NaN | NaN | NaN | NaN | NaN | Marketing, Business Ethics | International | Large datasets of existing narratives (e.g., articles, books, movies); customer data (preferences, demographics, engagement patterns). Importance of diverse, high-quality, unbiased datasets stressed. | NaN | Adoption by major tech and consumer companies (e.g., Google, Netflix, Amazon, Adobe, OpenAI, IBM) for marketing, recommendations, content creation, and bias mitigation tools. | True | True | Discusses commercial services (e.g., Netflix, Stitch Fix, Adobe Firefly) and publicly available tools/platforms (e.g., ChatGPT, Google Translate, IBM AI Fairness 360 toolkit). | Need for robust fairness metrics, effective bias identification/mitigation techniques (considering cultural context), research into human-AI collaboration for storytelling quality and ethics. | Ethical concerns (bias, manipulation, misinformation, copyright, deepfakes), dependence on high-quality and diverse training data, requirement for skilled professionals. | Generation of manipulative or misleading narratives, dissemination of false information, perpetuation of biases leading to discrimination, copyright infringement, creation/use of deepfakes, malicious use for targeted attacks. |
| eZOdC8SrDBkJ.pdf | Google_Scholar | Legal-Emotional BATNA: AI Chatbot Addressing Divorce Legalities and Emotional Complexities, and Research of Social Implementation in Japan | This paper introduces "Legal-Emotional BATNA," an AI chatbot designed to assist individuals in Japan undergoing divorce negotiations by integrating legal calculations (support, asset division) with emotional support, using GPT-4 for emotion analysis. User surveys indicate general satisfaction but highlight needs for improved privacy measures and handling of complex emotional and legal issues. | True | Idealistic | True | 1.0 | Positive | "Legal-Emotional BATNA" AI chatbot integrating legal calculations (child/spousal support, property/pension division, solatium based on Japanese legal standards/precedents) and emotional aspect analysis (using GPT-4) to guide divorce negotiations. | Online user survey with 100 participants recruited via CloudWorks (Japanese crowdsourcing platform). Evaluation used demographics questions and a 5-point Likert scale assessing ease of use, trustworthiness, speed, clarity, accuracy, helpfulness, communication smoothness, privacy protection, and overall satisfaction, plus open-ended feedback. | Generally positive: >80% found it user-friendly (avg 1.65), trustworthy (avg 1.79), and were satisfied overall (avg 1.82). 77% expressed willingness to use it again. Areas needing improvement included privacy protection (avg 2.62) and desire for more personalized advice. | The complexity of divorce negotiations involving both legal calculations and emotional factors, lack of tools integrating both aspects, and the potential cost of traditional professional support. | An AI chatbot ("Legal-Emotional BATNA") that provides calculations based on legal standards and incorporates emotional considerations (using GPT-4 analysis) to offer early-stage guidance and bridge users to professional services. | Divorce negotiation support, Online Dispute Resolution (ODR) for divorce. | Individuals undergoing divorce negotiations in Japan. | Family Law (specifically divorce, child support, spousal support, property division, pension splitting, solatium/compensation for emotional distress). | Japan | Legal calculations use Japanese court standards ('Calculation Tables for Child Support / Expenses arising from Marriage') and legal precedents. Emotional analysis relies on GPT-4, implying its general training data. No specific proprietary dataset is mentioned. | A framework separating Legal-BATNA Calculation (using legal data, precedents, tables) and Emotional-BATNA Estimation (using GPT-4 emotion analysis). The system interacts with users, processes legal/emotional data, and provides integrated recommendations. | The chatbot is accessible via a chatgpt.com link (likely requiring ChatGPT access). Evaluation involved deployment on a crowdsourcing platform (CloudWorks). The paper discusses potential future "societal implementation in Japan". | True | False | Available as a custom GPT accessible via a specific chatgpt.com URL provided in a footnote. | Need for improvement in handling complex legal scenarios (e.g., international divorces, mixed-income households), enhancing privacy protection clarity, increasing financial accuracy depth, providing more personalized advice vs. general calculations, and managing user expectations regarding preliminary vs. definitive legal advice. | Balancing legal accuracy with nuanced emotional support, ensuring user trust regarding privacy and data handling, addressing user desire for personalized advice while maintaining scalability, managing complexity in legal calculations for non-standard cases. | Potential for users to misunderstand preliminary guidance as definitive legal advice. Privacy concerns related to handling sensitive personal and financial data during divorce negotiations. |
| Y8UaRGVOxgoJ.pdf | Google_Scholar | ChatGPT accuracy analysis for legal field and anticipation of potential problems | This paper evaluates the accuracy of different ChatGPT versions (3.5, 4.0, 4.0 Omni) on Korean legal questions, comparing performance against human police trainees. It finds improved accuracy with newer versions, particularly Omni, but highlights significant issues with inconsistency and the confident presentation of incorrect information. | True | NaN | True | 2.0 | Neutral | Evaluation of ChatGPT versions 3.5, 4.0, and 4.0 Omni. | 1) Accuracy assessment on 100 Korean legal questions (objective facts, simple questions, case questions). 2) Comparison of ChatGPT scores with 158 human police trainees on a 20-question multiple-choice test. | ChatGPT 4.0 Omni achieved the highest average accuracy (84.96%) on the 100 questions. On the multiple-choice test, Omni scored up to 80/100, comparable to post-training human average (84.53/100), but showed inconsistency (match rate 80% between two runs). | NaN | NaN | NaN | NaN | Korean Law (including criminal law, general legal principles relevant to policing) | Korea | NaN | NaN | NaN | True | False | ChatGPT 4.0 and 4.0 Omni are accessible via OpenAI's platform (potential subscription required). | NaN | Inconsistent answers from ChatGPT to the same questions asked at different times. | AI confidently providing incorrect answers (Uncertainty/hallucination); Difficulty in verifying the truthfulness of AI-generated answers (Possibility of error judgment); Inconsistency in AI responses; Potential devaluation of human knowledge and expertise; Exacerbation of data bias, privacy, and copyright issues. |
| EABTT6lmmYYJ.pdf | Google_Scholar | Sociological Phenomenology: Understanding Neighborhood Development and Local Culture | This paper proposes an approach integrating sociological phenomenology with AI technologies (big data analysis, LLMs, generative AI) to understand neighborhood development and local culture. The research suggests this combined methodology can provide deeper insights for urban planning and policy-making, fostering more culturally sensitive and inclusive community growth. | True | Idealistic | True | 1.0 | Positive | An integrated research approach combining sociological phenomenology with AI techniques, including big data analysis (with attention mechanisms), large language models (LLMs), generative AI for simulations, and prompt engineering. | A mixed-methods approach: qualitative ethnographic methods (in-depth interviews, participant observation, document analysis of local histories and community archives) to understand lived experiences, complemented by big data analysis of datasets (e.g., demographic, socioeconomic, real estate, social media data) using attention mechanisms and LLMs. Generative AI was used to simulate neighborhood development scenarios informed by resident and planner input. | AI-driven tools and big data analysis identified critical factors (e.g., gentrification patterns, employment changes, social network shifts) influencing neighborhood transformation and predicted areas of significant cultural change. LLMs extracted evolving narratives of neighborhood identity from textual data (social media, news). Generative AI simulations visualized potential cultural shifts under different conditions, offering insights for culturally sensitive urban planning. | Loss of cultural identity, community cohesion, and sense of belonging for residents due to neighborhood changes like gentrification if development is not sensitive to lived experiences and local culture. | Employing sociological phenomenology integrated with AI (big data, LLMs, generative AI) to deeply understand residents' lived experiences, cultural narratives, and social dynamics. This understanding can inform urban planning and policy-making for more culturally sensitive, inclusive, and equitable neighborhood development that preserves community heritage and identity. | Equitable urban development, preservation of local culture and community identity, mitigating negative impacts of gentrification, inclusive policy-making for neighborhoods, understanding social dynamics of urban change. | Residents of urban and rural neighborhoods undergoing development or transformation, particularly those whose cultural identities, social cohesion, and sense of place might be threatened by such changes (e.g., long-term residents in gentrifying areas). | NaN | International | The approach uses mixed data: Qualitative data from ethnographic methods (interviews, participant observation, local historical/cultural artifacts). Quantitative/textual data for AI analysis includes demographic information, socioeconomic indicators, real estate data, social media interactions, local news, and community forum discussions. | Mixed-methods research design combining qualitative sociological phenomenology (ethnographic methods like participant observation, interviews, document analysis) with advanced data analytics techniques (big data analysis with attention mechanisms, large language models, generative AI, prompt engineering). | NaN | False | False | NaN | The need to further refine methodologies to more effectively incorporate complex cultural nuances into AI-driven decision-making processes for neighborhood development. Ensuring AI tools are guided by ethical considerations to genuinely reflect and serve community values and lived experiences. | Ensuring AI models accurately understand and incorporate complex cultural nuances for relevant, insightful, and ethically sound data generation and simulation, highlighted by the stated need for prompt engineering and culturally sensitive approaches. | NaN |
| TUZpZNjejiYJ.pdf | Google_Scholar | CONCENTRATING INTELLIGENCE: SCALING AND MARKET STRUCTURE IN ARTIFICIAL INTELLIGENCE | This paper analyzes the market structure and competition dynamics for foundation models, particularly LLMs, highlighting significant economies of scale and scope that drive towards concentration. It discusses key inputs (compute, data, talent), explores competition risks like market tipping and vertical integration, and evaluates potential policy remedies primarily from an antitrust perspective. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Competition Law, Antitrust, Regulation | US, UK, EU, International | NaN | NaN | NaN | False | False | NaN | Potential for an 'intelligence divide' between regions; balancing competition promotion with AI safety; ensuring equitable distribution of AI benefits; lack of clarity in liability frameworks for AI; uncertainty regarding future AI capabilities and economic impact. | NaN | Market tipping leading to monopolies; anti-competitive effects of vertical integration (foreclosure, reduced innovation); regulation potentially stifling competition (especially for smaller players); AI safety risks (malfunctioning, malicious use, human disempowerment/extinction); lopsided liability burdens impacting human judgment; excessive concentration of economic, social, and political power. |
| bIi8Ve6d_s8J.pdf | Google_Scholar | Prompt Packer: Deceiving LLMs through Compositional Instruction with Hidden Attacks | This paper introduces Compositional Instruction Attacks (CIA), a method to bypass Large Language Model (LLM) safety measures by embedding harmful prompts within seemingly harmless instructions like dialogue generation or story writing. The authors propose and evaluate automated techniques (T-CIA and W-CIA) demonstrating high success rates in tricking models like GPT-4 and ChatGPT into generating harmful content. | True | NaN | True | 1.0 | NaN | Compositional Instruction Attacks (CIA), including automated methods Talking-CIA (T-CIA) based on adversarial personas and Writing-CIA (W-CIA) based on disguising prompts as novel writing tasks. | Evaluated on GPT-4, ChatGPT (gpt-3.5-turbo), and ChatGLM2-6B using safety assessment datasets (Safety-Prompts, Harmless Prompts) and harmful prompt datasets (Forbidden Question Set, AdvBench). Attack success measured by Non-Rejection Rate (NRR) and Attack Success Rate (ASR), primarily evaluated using ChatGPT judgments, validated partially with human evaluation. | High ASR achieved. T-CIA: >95% ASR on safety datasets, >83% (GPT-4) / >91% (ChatGPT/ChatGLM2) on harmful prompt datasets. W-CIA: >91% ASR on Harmful Behaviors dataset. NRR approached 100% for both methods. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Prompt engineering using specific instruction templates (APE, RUAP, DWPC, SDWP), in-context learning (one-shot example for W-CIA), leveraging psychological principles (similarity-attraction for T-CIA), iterative attack attempts. | NaN | False | False | NaN | LLMs lack the ability to identify underlying harmful intentions in multi-intent compositional instructions. LLM vulnerability to repetitive attacks due to random factors in the decoding stage. | Manually designing compositional attacks is labor-intensive and costly, motivating automated methods. Bypassing advanced LLM safety mechanisms (e.g., RLHF) is the core challenge. | Generation of harmful content (insults, bias, PII leakage, misinformation, criminal instructions), abuse of LLMs for malicious purposes (hate campaigns, internet fraud), negative social impacts. |
| K32P7Y_q8boJ.pdf | Google_Scholar | GENERATIVE AI IN BUSINESS: VISUAL ILLUSTRATIONS OF APPLICATIONS AND INSIGHTS \nFROM Q1 2025 | This paper reviews and visually illustrates the applications, benefits, challenges, and future directions of generative AI in various business domains using literature and reports primarily from 2025. It presents a visual framework to analyze implementation, ROI, functional impact, and risk management for Gen AI adoption in businesses. | True | Market | True | 3.0 | NaN | Visual framework for analyzing generative AI applications in business. | N/A (Framework presented based on literature review, not empirically tested within the paper) | N/A (Reports results from cited literature, doesn't present evaluation results for a specific technique proposed *in this paper*) | NaN | NaN | NaN | NaN | Business (General), Marketing, HR, Operations, Data Analytics, Risk Management, Decision Support, Knowledge Management, Legal Services (mentioned) | International | NaN | Literature review, Synthesis, Visual framework development. | NaN | False | False | NaN | Need for robust governance frameworks, longitudinal studies on adoption impact, addressing gaps between technical potential and organizational/operational maturity, challenges in dynamic adaptation and context-aware processing for AI systems. | Security risks, implementation cost, ethical concerns, integration with existing systems, data quality, lack of organizational readiness/planning, need for workforce skills development (AI literacy, prompt engineering). | Security risks, compliance risks, ethical risks (e.g., bias in HR), data privacy risks, risks related to model accuracy and reliability, operational risks. |
| UQg4PX163KgJ.pdf | Google_Scholar | DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning | This paper introduces DISC-FinLLM, a Chinese financial large language model developed using a Multiple Experts Fine-tuning Framework (MEFF) and a novel financial instruction-tuning dataset, DISC-FIN-SFT. The model is designed to enhance general LLMs with capabilities in financial consulting, NLP tasks, computation, and retrieval-augmented generation, showing improved performance over baseline models in various financial scenarios. | True | NaN | True | 1.0 | NaN | DISC-FinLLM, a Chinese financial LLM built using a Multiple Experts Fine-tuning Framework (MEFF). This involves training four individual Low-rank adaptation (LoRA) modules on a base LLM (Baichuan-13B) using specialized parts of the DISC-FIN-SFT dataset for financial consulting, financial NLP tasks, financial computing (with tool use), and retrieval-enhanced generation. | Evaluated on multiple benchmarks: 1) FinCUGE for financial NLP tasks (sentiment analysis, relation extraction, summarization, text classification, event extraction); 2) FinEval for human-generated multiple-choice questions (finance, economy, accounting, certificate); 3) A manually created dataset of over 100 financial calculation problems; 4) A dataset of financial questions requiring up-to-date information for retrieval-based tasks, with answers evaluated by GPT-3.5 on accuracy, usefulness, linguistic quality, and reflectiveness. | On financial computing tasks, DISC-FinLLM (Computing LoRA expert) achieved an accuracy of 0.35 for both formula construction and result calculation, significantly outperforming the base Baichuan-13B-Chat (0.12) and GPT-3.5-turbo (0.26). | NaN | NaN | NaN | NaN | NaN | Chinese | A custom financial instruction-tuning dataset named DISC-FIN-SFT (approx. 250k examples). It includes: Financial Consulting Instructions (from FiQA translated to Chinese, QA for financial terms, multi-turn QA from Chinese financial forums, all using ChatGPT); Financial Task Instructions (from public Chinese financial NLP datasets and self-constructed reading comprehension from financial news/reports); Financial Computing Instructions (handwritten/report-derived financial calculation questions, general math questions, augmented by ChatGPT); Retrieval-enhanced Instructions (financial news/reports with ChatGPT-generated questions/answers and retrieved references). | Multiple Experts Fine-tuning Framework (MEFF), Low-rank adaptation (LoRA) for parameter-efficient fine-tuning. Task-specific instruction dataset (DISC-FIN-SFT) creation using various prompting strategies with ChatGPT (e.g., self-chat, self-instruction, Chain-of-Thought, Chain-of-Retrieval). | During deployment, different LoRA modules can be loaded onto the base model to switch between functionalities without retraining. Resources, including the model, are made available via a GitHub repository. | True | True | Further resources can be found at https://github.com/FudanDISC/DISC-FinLLM. | NaN | Developing a model with specialized, task-oriented financial functionalities (consulting, NLP, computation, retrieval) that operate effectively without interference. Creating a large-scale, diverse, and high-quality instruction-tuning dataset (DISC-FIN-SFT) specific to the Chinese financial domain and its varied tasks. | NaN |
| 4y_1wDzhPbUJ.pdf | Google_Scholar | A Comprehensive Framework for Reliable Legal AI: Combining Specialized Expert Systems and Adaptive Refinement | This paper proposes a framework combining a mixture of expert systems, knowledge graph-enhanced retrieval-augmented generation (RAG), and reinforcement learning from human feedback (RLHF) to improve AI accuracy and reliability in legal tasks, addressing issues like hallucinations. This approach utilizes specialized modules and human feedback, aiming to offer more precise, accessible, and affordable legal services. | True | Idealistic | True | 1.0 | Positive | A framework integrating Mixture of Experts (MoE), Knowledge Graph (KG) enhanced Retrieval-Augmented Generation (RAG), and Reinforcement Learning from Human Feedback (RLHF). | Comparative evaluation of the framework (using LLMs like GPT-4, LLaMA-3 enhanced with RAG/KG/RLHF) against baseline and fine-tuned models on nine legal tasks using multiple public datasets (LegalQA, CaseHold, LEDGAR, LEXTREME, COLIEE, SARA, LexGlue, Billsum, CUAD, Super-SCOTUS, EUR-LEX, ECHR). Metrics included Accuracy, Rouge-L, F1 Score, BLEU, and abstention rates. | The integrated framework, particularly using advanced models like GPT-4 and LLaMA-3 enhanced with RAG and RLHF, consistently outperformed baseline and solely fine-tuned models across various legal tasks. For example, GPT-4 with RLHF achieved approximately 10% higher accuracy than with KG integration on complex tasks, demonstrating enhanced reliability and reduced abstention. | High cost and time consumption of traditional legal support limiting access; unreliability and potential for inaccuracies (hallucinations) in existing AI models hindering their effective use in legal contexts. | Utilize the proposed AI framework featuring MoE, KG-enhanced RAG, and RLHF to provide more reliable, accurate, scalable, and affordable legal assistance, thereby improving access to justice. | Making general legal assistance tasks (e.g., document review, research, contract drafting, Q&A, case analysis, judgment prediction) more accessible and affordable through reliable AI. | General population needing affordable legal services. | General / Multiple (covers tasks related to contracts, legislation, case law, etc.) | Mixed (Uses datasets coveringUS, European, and potentially other/general legal contexts). | The framework leverages multiple publicly available legal datasets for evaluation and potentially fine-tuning (LegalQA, CaseHold, LEDGAR, LEXTREME, COLIEE, SARA, LexGlue, Billsum, CUAD, Super-SCOTUS, EUR-LEX, ECHR), containing diverse structured and unstructured legal text. It relies heavily on retrieval from external knowledge sources (documents, KGs) rather than a single training dataset. | Modular integration of Retrieval-Augmented Generation (RAG), Knowledge Graphs (KG), Mixture of Experts (MoE), Reinforcement Learning from Human Feedback (RLHF), Supervised Fine-Tuning (SFT) with Low-Rank Adaptation (LoRA), and an 'Experts Collaboration Workflow' modeling human legal teams. | NaN | False | False | NaN | Need for expansion to more legal domains, integration of real-time legal updates, enhanced explainability, and ongoing refinement through collaboration with legal professionals. Persistent difficulty with tasks requiring precise verbatim reproduction. | Integrating multiple complex AI components (RAG, KG, MoE, RLHF); mitigating AI hallucinations and ensuring reliability in the legal domain; selecting and utilizing appropriate datasets; modeling complex legal reasoning; scaling human feedback processes (RLHF). | Generation of inaccurate or misleading information ('hallucinations') by AI, leading to serious legal consequences and undermining trust. Potential for toxic outputs if not properly managed (addressed via RLHF). |
| pbtKJPybqNYJ.pdf | Google_Scholar | Generative AI and the Purpose of Legal Scholarship | This article critiques the potential use of generative AI in producing legal scholarship, arguing it undermines the intrinsic value and purpose of academic writing. It contrasts an instrumental view focused on quantity and prestige with the benefits derived from the traditional, effortful process of research and writing. | True | NaN | True | 3.0 | Negative | NaN | NaN | NaN | The paper argues generative AI threatens the intrinsic benefits of legal scholarship, such as knowledge development, collaboration, critical thinking, writing improvement, and personal satisfaction ('fun'), by prioritizing speed, efficiency, and quantity over the value derived from the difficult process itself. Overlooking AI flaws like inaccuracies and lack of critical engagement are also implicit obstacles. | The paper implicitly advocates for preserving the traditional, time-intensive process of legal scholarship to retain its intrinsic benefits. It suggests prioritizing the joy and inherent value of writing, fostering critical engagement rather than blind adoption of AI, and potentially rethinking academic evaluation norms that overly emphasize quantity and instrumental goals. | Impact of AI on the purpose, process, and intrinsic value of legal scholarship; Critique of instrumentalism in academia; Role of personal satisfaction and 'fun' in scholarship. (Access to justice is mentioned tangentially as an example). | Legal academics / scholars | Legal Academia / Legal Scholarship | United States | NaN | NaN | NaN | False | False | NaN | The need to re-evaluate academic norms favoring quantitative output over intrinsic scholarly values and качеств; Addressing systemic issues in academia (like disproportionate burdens) directly rather than masking them with AI; Lack of critical engagement with AI's limitations and potential negative impacts. | NaN | Erosion of scholarly skills (research, critical thinking, writing); Production of shallow, repetitive, inaccurate, or unoriginal scholarship; Loss of collaborative aspects and interpersonal dynamics in academia; Stifling of creativity and personal voice; Over-reliance on AI leading to decreased human engagement and understanding; AI becoming a distraction from addressing fundamental problems in academia or access to justice. |
| Pnl5_5sEE-gJ.pdf | Google_Scholar | LawPal : A Retrieval Augmented Generation Based System for Enhanced Legal Accessibility in India | This paper proposes LawPal, a Retrieval-Augmented Generation (RAG) based chatbot using DeepSeek-R1:5B and FAISS, to improve legal accessibility and literacy in India by providing accurate legal information. The system demonstrated over 90% legal accuracy in evaluations, aiming to democratize legal knowledge and combat misinformation. | True | Idealistic | True | 1.0 | Positive | LawPal, a Retrieval-Augmented Generation (RAG)-based legal chatbot using DeepSeek-R1:5B for language understanding and FAISS for document retrieval. | Retrieval accuracy (Precision@K, Mean Reciprocal Rank, Normalized Discounted Cumulative Gain), response quality (BLEU, ROUGE, Legal Consistency Score, human expert review), computational efficiency (query processing time analysis), robustness against adversarial inputs, and user feedback from lawyers, law students, and legal aid seekers. Comparative testing against rule-based chatbots and keyword-based search engines. | LawPal achieved over 90% legal accuracy. FAISS-based retrieval takes 10-50 milliseconds, and DeepSeek-R1:5B response generation ranges from 800 to 1500 milliseconds. User feedback indicated 85% satisfaction for accuracy and reliability. | Lack of awareness, misinformation, limited accessibility to judicial resources, difficulty for individuals in navigating complex legal frameworks, frequent misuse of laws, and inadequate legal protection. | Development of a Retrieval-Augmented Generation (RAG)-based legal chatbot (LawPal) to provide accurate, efficiently retrieved legal information, enhance legal literacy, and prevent the spread of misinformation. The platform also includes features like real-time legal news, blogs, and access to law-related books. | Legal information retrieval, enhancing legal literacy, combating legal misinformation, navigating complex legal frameworks, improving access to judicial resources. | The general public in India, particularly individuals struggling with legal complexities, and those with limited access to legal resources. | Indian Law, including the Indian Constitution, statutory laws, and case law. Specific examples like Criminal Law and Civil Law are mentioned for data categorization. | India | Publicly available legal texts from authoritative sources such as government websites, Supreme Court archives, legal research papers, legal books, official documentation, and the Indian Constitution. The dataset includes structured and unstructured texts, preprocessed with OCR and segmentation. | Retrieval-Augmented Generation (RAG) architecture, data collection from diverse legal sources, data preprocessing (cleaning, OCR correction, text normalization, chunking with LangChain’s RecursiveCharacterTextSplitter), vector embedding generation (DeepSeek-R1:5B), FAISS for efficient vector indexing and similarity search, prompt engineering, hierarchical indexing of legal topics, and a Streamlit-based user interface. | A Streamlit-based user interface was developed for user interaction. Broader deployment or diffusion strategies are not detailed. | False | False | NaN | Need for multilingual support for regional Indian languages, improved handling of multi-jurisdictional queries, enhanced capability for processing long-context legal arguments, further fine-tuning for specialized legal domains (e.g., corporate and international law), and continuous bias mitigation. | Handling multi-jurisdictional legal queries, effectively processing long-context legal arguments, ensuring consistent up-to-date legal information, occasional errors in ambiguous legal queries, and the need for fine-tuning in specialized or niche legal fields. | Potential for legal misinformation due to occasional errors in ambiguous queries, and general ethical concerns in legal AI regarding bias (as noted in the literature review). |
| 18FrJ_f-ToAJ.pdf | Google_Scholar | Large language models in cryptocurrency securities cases: can a GPT model meaningfully assist lawyers? | This paper investigates the capabilities of GPT-3.5 for legal reasoning and ChatGPT for legal drafting in the context of cryptocurrency securities cases. It finds that GPT-3.5's legal reasoning is currently weak, while ChatGPT shows promise for legal drafting, potentially assisting lawyers but highlighting the need for significant improvements in LLM reasoning abilities for legal tasks. | True | Market | True | 2.0 | Neutral | Evaluation of OpenAI's GPT-3.5 models (text-davinci-003 for legal reasoning, and ChatGPT based on gpt-3.5-turbo-0301 for legal drafting) using zero-shot prompting with specific prompt engineering techniques (e.g., IRAC for reasoning, section-by-section generation for drafting). | For legal reasoning (GPT-3.5): Fact patterns from 20 real-life cryptocurrency securities cases were fed to the model to identify potential legal violations. Performance was evaluated against actual charges using precision, recall, and F1-score. For legal drafting (ChatGPT): Complaints for 9 real-world securities class action cases were drafted by ChatGPT and compared to lawyer-drafted (abridged) versions. 88 mock jurors assessed these complaints, and their decisions, confidence levels, and the linguistic concreteness of the complaints were analyzed. | For legal reasoning, GPT-3.5 performed poorly, with an average F1-score of 0.324. Precision (0.658) was higher than recall (0.252), indicating it missed more correct violations than it identified spurious ones. For legal drafting, ChatGPT performed well; juror decisions were not statistically significantly associated with the document's author (ChatGPT vs. lawyer), and ChatGPT-drafted complaints were found to be significantly more concrete than lawyer-drafted ones. | Resource constraints for enforcement attorneys in complex legal areas like cryptocurrency securities law, potentially hindering effective legal enforcement and justice for victims. | Proposing LLMs (specifically ChatGPT for drafting, and acknowledging the need for future improved models for reasoning) as potential tools to assist lawyers, thereby potentially alleviating resource burdens and improving efficiency in the legal process. | Application of AI in litigation, particularly in securities law and cryptocurrency cases, to support legal reasoning and legal drafting for legal professionals. | Victims of cryptocurrency securities violations, who may rely on enforcement actions or class action lawsuits for justice. | Securities law, Cryptocurrency law, Litigation (U.S. civil procedure, class actions). | United States (federal law, referencing U.S. District Court cases). | The study utilizes OpenAI's pre-trained models (GPT-3.5: text-davinci-003, gpt-3.5-turbo-0301; ChatGPT). This data is proprietary, large-scale, and primarily unstructured text and code. ChatGPT is further fine-tuned with Reinforcement Learning from Human Feedback (RLHF). The study notes specific model training data cut-off dates (e.g., June 2021 for text-davinci-003). | Experimental design involving: 1) For legal reasoning: prompt engineering (zero-shot, specifying IRAC method), systematic case selection, and quantitative evaluation against ground truth. 2) For legal drafting: iterative prompt engineering for section-by-section complaint generation, mock jury survey design with specific jury instructions, human editing of lawyer-drafted complaints for comparability, and statistical analysis of juror responses alongside linguistic analysis (concreteness) of complaints. | NaN | True | False | The studied models (GPT-3.5, ChatGPT) are accessible via OpenAI's API (e.g., gpt-3.5-turbo-0301, text-davinci-003) and the ChatGPT user interface (May 24, 2023 GPT-3.5 version mentioned for drafting). API access is typically paid. | Current LLMs like GPT-3.5 exhibit weak legal reasoning capabilities (high false negatives). There's a need for significant improvement in LLM accuracy and reliability for complex legal reasoning. Further research is required on LLM performance with longer texts, more advanced models, and in diverse legal jurisdictions and contexts. | For legal reasoning: Ensuring models (text-davinci-003) had no prior knowledge of test cases by selecting cases filed after its training data cut-off. Significant prompt engineering was required. For legal drafting: Encountering API errors which necessitated using the ChatGPT user interface and modifying prompts (e.g., adding 'for educational purposes only'). Substantial manual effort was needed to edit lawyer-drafted comparator complaints to ensure fairness. Managing mock juror recruitment and quality control. | LLM 'hallucinations' or fabrication of facts/statutes (e.g., adding 'John Doe' defendants, inconsistent case details). Poor legal reasoning by LLMs can lead to missing salient legal violations or identifying incorrect ones. Over-reliance on current LLMs for tasks they are not proficient in, like complex legal reasoning, poses a risk to legal practice quality. |
| QH89sWPQoGIJ.pdf | Google_Scholar | Lawma: The Power of Specialization for Legal Annotation | This paper introduces CaselawQA, a benchmark of 260 legal annotation tasks, and the Lawma family of fine-tuned language models. It demonstrates that these smaller, specialized Lawma models significantly outperform large commercial LLMs for legal annotation in empirical research, advocating for task-specific fine-tuning. | True | Market | True | 1.0 | NaN | Lawma: a family of fine-tuned open-source language models (SmolLM2, Llama 3.2, Llama 3.1, Llama 3.3) specialized for legal annotation tasks through fine-tuning on the CaselawQA benchmark. | Evaluated on CaselawQA, a new benchmark of 260 legal classification tasks derived from U.S. Supreme Court and Court of Appeals databases, using accuracy as the primary metric. Compared against commercial models (e.g., GPT-4.5, Claude 3.7 Sonnet) and other open-weights models. | Lawma 70B (largest fine-tuned model) achieved 88% accuracy on CaselawQA, outperforming the best-performing commercial model (Claude 3.7 Sonnet at 78%) by 10 percentage points. Lawma 135M (smallest fine-tuned model) achieved 83% accuracy, also surpassing commercial models. | NaN | NaN | NaN | NaN | General (court case annotation covering various aspects such as issue area, case source, disposition, ideological direction based on U.S. Supreme Court and Courts of Appeals data) | United States (federal courts: Supreme Court, Courts of Appeals) | A dataset of 24,916 court cases (majority opinions from Caselaw Access Project) with labels from the U.S. Supreme Court Database (SCDB) and U.S. Courts of Appeals Database (USCAD), forming 260 classification tasks (~553,000 task examples for fine-tuning after processing). Data is publicly derived and consists of unstructured text (court opinions) paired with structured annotations. | Supervised fine-tuning of pre-trained open-weights language models (SmolLM2, Llama 3.2, Llama 3.1, Llama 3.3) on a collection of 260 legal annotation tasks simultaneously. Evaluation uses a multiple-choice prompt template with chain-of-thought prompting. | Code, datasets, and fine-tuned models are made available on GitHub. | True | True | Code, datasets, and fine-tuned models are available at https://github.com/socialfoundations/lawma. | NaN | High variability in model performance across different legal annotation tasks. Fine-tuned models still do not reach human intercoder agreement rates on many tasks. The need for task-specific fine-tuning, as broadly specialized legal models may not suffice. Managing compute resources for fine-tuning. Dealing with long document lengths, high number of classes in some tasks, and imbalanced datasets. | Caution regarding the use of LLMs for consequential legal tasks without further substantive investigation. Findings may not generalize to other legal domains within the U.S. or legal systems in other countries. |
| P6imolD5eSQJ.pdf | Google_Scholar | A negation detection assessment of GPTs: analysis with thexNot360 dataset | This paper evaluates the negation detection capabilities of GPT-2, GPT-3, GPT-3.5, and GPT-4 using a custom dataset, xNot360, and a zero-shot prediction approach. Findings reveal that while GPT-4 performs best, overall LLM proficiency in negation is modest, highlighting limitations in logical understanding crucial for high-stakes domains like law. | True | NaN | True | 2.0 | NaN | Zero-shot negation detection assessment of GPT models (GPT-2, GPT-3, GPT-3.5, GPT-4) using the custom xNot360 dataset. | GPT models were prompted to predict whether a second sentence negates a first sentence from the xNot360 dataset in a zero-shot setting. Performance was measured using accuracy, precision, recall, F1-score, and confusion matrices. | GPT-4 performed best, achieving an accuracy of 0.7833, F1-score of 0.7706, precision of 0.8187, and recall of 0.7278 on the xNot360 dataset. | NaN | NaN | NaN | NaN | General Law (mentioned as a high-stakes application domain where logical reliability is critical) | International (negation is a general language feature); USA (Uniform Bar Examination mentioned as a benchmark for GPT-4) | The custom eXploring Negation Over Text with 360 samples (xNot360) dataset: 360 English sentence pairs (5-20 words each), with 180 positive (negating) and 180 negative (non-negating) examples, constructed using sentence templates and classical logic principles. Publicly available on Hugging Face. | For xNot360 dataset: Manual design of sentence templates with negated components, guided by classical logic. For evaluation strategy: Zero-shot prediction prompting of GPT models. | The xNot360 dataset is available on Hugging Face. GPT-2 was accessed via HuggingFace's zero-shot-classification pipeline; GPT-3, GPT-3.5, and GPT-4 were accessed via the OpenAI API. | True | True | The xNot360 dataset is available on Hugging Face (https://huggingface.co/datasets/nguyenthanhasia/xNot360). GPT-2 is publicly available. GPT-3, GPT-3.5, and GPT-4 are accessible via OpenAI API. The evaluation methodology is described. | NaN | Difficulty in creating logically sound negation examples for datasets, even for humans. LLMs struggle with correct negation handling, especially in complex sentence structures (e.g., conditionals). Reinforcement Learning from Human Feedback (RLHF) might inadvertently degrade logical performance if not carefully aligned with logical consistency. LLMs exhibit performance drops on out-of-distribution logical reasoning datasets and face difficulties in resolving semantic ambiguity. | Erroneous generations, hallucinations, and misinterpretations by LLMs due to poor negation handling in high-stakes domains like law, healthcare, and science, potentially leading to incorrect decisions or flawed communication. |
| h_pgclBU5VYJ.pdf | Google_Scholar | Sentimental Analysis of Legal Aid Services: A Machine Learning Approach | This paper uses sentiment analysis with various machine learning models (Naive Bayes, SGD, Random Forest, SVC, Logistic Regression, XGBoost) to evaluate client perceptions of Legal Aid South Africa based on feedback from Twitter and an internal system. The study finds a predominantly neutral or positive sentiment but identifies areas for improvement, with SVC and XGBoost showing the best classification performance. | True | Idealistic | False | 2.0 | Positive | Sentiment analysis comparing multiple machine learning classifiers (Naive Bayes, SGD, Random Forest, SVC, Logistic Regression, XGBoost) on client feedback text, using TextBlob for initial labelling and TF-IDF for vectorization. Best performers were SVC and XGBoost. | Compared six ML models on an 80/20 train/test split of client feedback data using accuracy, precision, recall, and F1-score metrics. Hyperparameter tuning using Grid Search was mentioned for Random Forest and Logistic Regression. | XGBoost and SVC demonstrated superior performance, achieving testing accuracies of 90.10% and 90.00%, respectively. Both achieved F1 scores generally above 85%. | Public perception that state-funded legal aid is of substandard quality; lack of client trust; clients feeling uninformed about case progress; potential issues related to using newly graduated attorneys; limited access to digital platforms (like Twitter) for feedback among indigent clients. | Utilizing sentiment analysis of client feedback (primarily internal) to understand perceptions, identify specific service issues (e.g., delays, communication), and inform improvements like business process reengineering or automation. Emphasizes the need to keep clients informed. | Quality and perception of legal aid services. | Vulnerable individuals lacking financial resources, indigent clients in South Africa. | Criminal law, Civil law | South Africa | Primary: 5,246 unstructured text entries from Legal Aid SA's proprietary internal client feedback system (2019-2024). Secondary: 100 text entries from Twitter queries (2019-2024). | Data collection, text pre-processing (cleaning, stemming/lemmatization, tokenization, stopword removal), sentiment labelling (TextBlob), feature extraction (TF-IDF), comparative machine learning model training and evaluation (Naive Bayes, SGD, RF, SVC, LR, XGBoost), hyperparameter tuning (Grid Search). | NaN | False | False | NaN | Need for further model optimization (hyperparameter tuning, ensembles, feature engineering), cross-validation, evaluation on broader datasets, and exploration of advanced NLP models (Deep Learning: RNN, Transformers, GPT, LSTM). Limited representativeness of social media data for the target demographic. | Limited quantity and representativeness of social media (Twitter) data for Legal Aid clients; potential for model overfitting; standard challenges of selecting and tuning ML models. | Potential bias in feedback data (especially the small Twitter sample); risk of model overfitting leading to poor generalization; misinterpretation of sentiment potentially leading to ineffective service changes. |
| aKgI4nl8ulwJ.pdf | Google_Scholar | Komodo: A Linguistic Expedition into Indonesia’s Regional Languages | This paper introduces Komodo-7B, a 7-billion-parameter Large Language Model family optimized for Indonesian, English, and 11 Indonesian regional languages. Komodo-7B-Instruct surpasses existing models on various benchmarks, aiming to improve linguistic inclusivity and access to education in Indonesia. | True | Idealistic | True | 1.0 | Positive | Komodo-7B (Komodo-7B-Base and Komodo-7B-Instruct), a family of 7-billion-parameter Large Language Models based on Llama-2, with an expanded vocabulary for Indonesian and regional languages, trained with a bilingual next-token prediction strategy. | Evaluated on discriminative tasks (IndoMMLU, ID-EN Entailment, X-Copa-ID, Intent-Classification, Colloquial-Detection, NusaXSenti, ID-Hatespeech) and generative tasks (NusaX-MT, TydiQA-ID, IndoSum) using metrics like Accuracy, F1, CHRF++, Rouge-L-F1. Also tokenizer fertility, embedding position analysis, English capability regression (Perplexity, common benchmarks), and qualitative analysis. Compared against GPT-3.5, GPT-4, Llama-2, Mixtral, Gemma, Sealion, Aya, Bactrian-X, Qwen. | Komodo-7B-Instruct achieved state-of-the-art performance in several tasks, outperforming models like GPT-3.5 and Aya-101. For example, it scored 90.5% accuracy on ID-EN entailment, 79.3% accuracy on NusaX-Senti, 90.3% accuracy on TydiQA-ID, and a 43.0 Rouge-L-F1 score on IndoSum. Overall average score on the benchmark suite was 71.1%. | The primary obstacles identified are the significant gap in linguistic resources and high-performing LLMs for low-resource Indonesian regional languages. This digital language barrier hinders access to information and education for communities speaking these languages, contributing to educational disparities. | The paper proposes Komodo-7B, a specialized LLM family, to address these obstacles. This involves creating comprehensive, high-quality datasets (including legal/jurisprudential corpora and textbooks), expanding tokenizer vocabularies for regional languages, and using advanced training techniques to enhance performance in these languages, thereby promoting informational and educational equity. | Linguistic inclusivity in digital resources, Access to education in regional languages, Access to information in regional languages, Bridging language barriers with AI. Potential for access to legal information given training data and stated domains. | Speakers of 11 Indonesian regional languages: Acehnese, Balinese, Banjarese, Buginese, Dayak Ngaju, Javanese, Lampungnese, Madurese, Minangkabau, Sundanese, and Toba Batak, particularly those in regions with lower educational quality compared to Java island. | Legal Services, Jurisprudence (based on training data and mentioned application domains). | Indonesia | A combination of diverse open-source datasets, manually collected data for Indonesian regional languages, Indonesian textbooks (grades 1-12, covering subjects like local cultures, engineering, legal and jurisprudential corpus), colloquial data (subtitles, news, conversations), and English datasets with alternate parallel data for code-mixing. Preprocessing involved repetition removal, quality filtering, and deduplication. About 8.79 billion tokens were processed for pretraining. SFT data included open-source tasks, manually labeled data, and ChatGPT responses. | Built on Llama-2-7B. Methodologies include vocabulary expansion for target languages, new embedding initialization by averaging existing ones, incremental pretraining and Supervised Fine-Tuning (SFT) using LORA, and a bilingual next-token prediction strategy. Data preprocessing techniques were also applied. | NaN | False | False | NaN | The paper suggests that achieving optimal performance across all regional languages may require larger models (e.g., a future 13B parameter version). The current 7B model's English mathematical reasoning capability is also noted as an area with relative underperformance due to training data composition. The general challenge of subpar performance of models for low-resource languages persists, with Komodo-7B being a step towards addressing this. | Effectively expanding tokenizer vocabularies for languages like Indonesian that share Latin script with English. Balancing vocabulary size with computational resources. Initializing new embeddings properly. Mitigating catastrophic forgetting during incremental pretraining. Managing hardware and cost requirements for large model training. Objectively evaluating generative outputs, sometimes requiring human or advanced AI (GPT-4) assistance. | NaN |
| RBzOwCxynRAJ.pdf | Google_Scholar | STATE BAR OF CALIFORNIA | This paper reports on the activities of the State Bar of California between late 2023 and early 2024, covering new reports (including on legal profession diversity and AI regulation), rulemaking proposals, legislative updates, and recent litigation. Key themes include attorney discipline, regulation of generative AI use by lawyers, and efforts related to access to justice such as pro bono programs and alternative bar exam pathways. | True | Idealistic | False | 3.0 | Neutral | N/A_No specific technique or tool related to AI for A2J discussed. | NaN | NaN | The text implicitly identifies lawyer misconduct, fee disputes, ethical challenges posed by new technologies like AI, potential gaps in pro bono service provision, and diversity disparities as hindering public protection and potentially access to justice. | Proposed solutions include enhancing the attorney discipline system, implementing diversion programs (mediation, Attorney-Client Bridge Program), expanding pro bono programs, regulating AI use by lawyers (including mandatory training), proposing alternative licensure pathways (Pilot Portfolio Bar Exam), and promoting diversity. | Attorney discipline, Attorney regulation, Legal ethics, Generative AI in legal practice, Pro bono services, Diversity and inclusion in the legal profession, Bar examination alternatives, Access to justice. | The general public interacting with the legal system in California, clients of attorneys, potentially underserved communities benefiting from pro bono services. | Legal profession regulation, Professional Responsibility, Ethics, Administrative Law. | California | NaN | NaN | NaN | False | False | NaN | Need for clear ethical guidelines and regulations for lawyer use of generative AI. Potential insufficiency or lack of insight into pro bono service provision. Ongoing need for improvements in attorney discipline, diversity, and oversight within the Bar itself. | N/A_The text discusses regulatory and administrative challenges for the State Bar, not challenges in developing a specific AI technique/tool. | Risks associated with lawyers' use of generative AI (data confidentiality breaches, inaccurate outputs, ethical violations). Risk of attorney misconduct harming clients. Risks related to conflicts of interest and lack of transparency within regulatory bodies. |
| Ll274-1nqZsJ.pdf | Google_Scholar | Navigating the Digital Dispute Resolution \nLandscape: Challenges and Opportunities | This paper explores the concept of digital dispute resolution (DDR), charting its progress and highlighting enabling technologies like blockchain and AI (including ChatGPT). It discusses the significant challenges DDR faces, such as jurisdiction, fairness, enforcement, and security, while proposing solutions like enhanced digital literacy and uniform legal frameworks. | True | NaN | True | 3.0 | Neutral | NaN | NaN | NaN | Jurisdictional complexities in cross-border digital environments, concerns regarding due process and fairness safeguards, challenges in enforcing digital dispute resolution outcomes, security and privacy vulnerabilities of digital platforms, potential for errors and biases in AI-driven tools. | Enhancing digital literacy across populations, implementing robust security and privacy measures (e.g., encryption, secure channels), adopting uniform international laws and consistent dispute resolution mechanisms to address jurisdictional and enforcement issues, ensuring the appropriateness, accuracy, and fairness of digital platforms, particularly AI. | General dispute resolution efficiency and cost-effectiveness, Consumer protection (mentioned via Kenyan blueprint). | NaN | Alternative Dispute Resolution (ADR), Commercial Law, Technology Law, International Law (conflict of laws) | Kenya, UK, International | NaN | NaN | NaN | False | False | NaN | Need for widespread digital literacy, lack of robust security/privacy standards and practices for DDR platforms, absence of clear international legal frameworks for jurisdiction and enforcement in digital disputes, challenges in ensuring fairness, transparency, and accuracy, especially with AI tools. | Jurisdictional ambiguity in transnational digital disputes, ensuring due process and fairness comparable to traditional methods, establishing effective cross-border enforcement mechanisms, protecting data security and user privacy on digital platforms, mitigating risks of errors and bias in AI applications for dispute resolution, requirement for enhanced digital literacy among users and practitioners. | Privacy breaches due to cyberattacks or platform vulnerabilities, security failures leading to data theft or manipulation, AI errors or biases resulting in unfair outcomes or incorrect legal analysis/advice, lack of adequate due process safeguards, difficulty in enforcing digital dispute resolution decisions across borders, unresolved jurisdictional conflicts. |
| _C_qVU_R_oMJ.pdf | Google_Scholar | LLM4Causal: Democratized Causal Tools for Everyone via Large Language Model | This paper introduces LLM4Causal, an LLM fine-tuned to make causal decision-making tools accessible to general audiences by interpreting queries, executing causal functions, and explaining results. It also presents two new benchmark datasets, Causal-Retrieval-Bench and Causal-Interpret-Bench, used to train and evaluate LLM4Causal, which showed superior performance over baselines. | True | Idealistic | True | 1.0 | NaN | LLM4Causal, a Llama-2 (7B) model fine-tuned for causal decision-making, incorporating a three-stage process: user request interpretation, causal tool assignment and execution, and output interpretation. This is supported by two newly proposed datasets: Causal-Retrieval-Bench and Causal-Interpret-Bench. | End-to-end evaluation on five causal tasks (CGL, ATE, HTE, MA, OPO) using 150 synthetic datasets. Ablation studies were conducted for causal entity extraction (accuracy metric) and output interpretation (human evaluation for hallucination, incompleteness, fluency). Comparisons were made against GPT-4/ChatGPT. | LLM4Causal-Mixed achieved an average win rate of 80.6% in end-to-end tasks, significantly outperforming ChatGPT. In causal entity extraction, LLM4Causal-Mixed achieved 0.98 overall accuracy compared to GPT4-turbo's 0.77. In interpretation, LLM4Causal models showed comparable or superior performance to GPT-4-turbo, notably in reducing hallucination. | NaN | NaN | NaN | NaN | NaN | International | Two new benchmark datasets: 1) Causal-Retrieval-Bench (1,500 pairs of causal questions and JSON outputs) for causal problem identification and input parameter extraction. 2) Causal-Interpret-Bench (400 interpretations revised by annotators) for in-context causal interpretation. Both generated via a pipeline combining LLM (GPT-4) generation and human annotation. | A three-stage framework: 1) User request interpretation (seq2seq), 2) Causal tool assignment and execution (integrating public causal packages), 3) Output interpretation. Fine-tuning of Llama-2 (7B) model using Low-Rank Adaptation (LoRA). Data generation for benchmarks involved a three-course procedure: value generation, JSON generation, and question generation with human oversight. | NaN | False | False | NaN | NaN | Adapting general LLMs for specialized causal tasks (e.g., hallucination, irrelevance, lack of end-to-end delivery). Generating high-quality, diverse, and accurate fine-tuning data for causal reasoning (e.g., controlling topics, ensuring JSON compliance, distinguishing causality from association). | NaN |
| UmP53qugiQoJ.pdf | Google_Scholar | OPENING THE VIRTUAL WINDOW: HOW ON-LINE PROCESSES COULD INCREASE ACCESS TO JUSTICE IN THE CRIMINAL LEGAL SYSTEM | This paper argues that online processes and technology, drawing from Online Dispute Resolution (ODR), can improve access to justice (A2J) in the US criminal legal system, particularly for misdemeanor cases. It proposes evaluating technologies using a traffic light system (green, yellow, red) based on A2J impact, suggesting specific applications like auto-notifications, information tools, remote appearances, and bias mitigation techniques while highlighting significant challenges and risks. | True | Idealistic | False | 3.0 | Positive | Discusses applying Online Dispute Resolution (ODR) concepts and various technologies (auto-notifications, option support tools, AI for lawyers, online communication/hearings, anonymization, bias detection AI) to improve A2J in criminal misdemeanor processing. | References existing studies and pilots (e.g., Michigan ODR for traffic tickets via Matterhorn, San Francisco/Yolo County blind charging pilots, studies on auto-reminders) but does not present new empirical testing. | Referenced study on Michigan ODR for traffic tickets found text-based online processes reduced outcome disparities based on age, gender, and race compared to face-to-face hearings (e.g., Black defendants received slightly lower fines online vs. higher fines F2F). | High costs and burdens of the traditional court process (time, transport, childcare); pressure to plead guilty; lack of access to information, legal counsel, and confidential communication channels (esp. for jailed defendants); collateral consequences; conviction of innocents; systemic bias; underfunding; resistance to change; digital divide. | Implement defendant-centric technology: optional remote appearances, auto-notifications, accessible information/option support tools, AI assistance for defense lawyers, online case management, secure communication platforms, text-based processes to reduce bias, anonymization/blind charging, AI for bias detection, technology-based alternatives to bail (e.g., GPS apps). | Access to information, access to legal representation, fairness and efficiency of court processes, reducing bias (racial, socioeconomic), alternatives to pretrial detention/bail, improving attorney-client communication. | Individuals charged with misdemeanors, particularly low-income individuals (often eligible for indigent defense). Also notes challenges for those with limited English proficiency, mental illness, cognitive disabilities, or substance abuse problems. | Criminal Law, Criminal Procedure, Dispute Resolution (ODR) | United States | NaN | NaN | NaN | False | False | NaN | Need for secure/confidential attorney-client communication platforms; resource constraints (funding, personnel); digital divide; resistance to change; lack of research on effectiveness/unintended consequences; need for ethical guidelines/oversight for AI; ensuring technology serves justice, not just efficiency; unaddressed structural power imbalances. | Resource constraints (cost, training); ensuring data privacy and confidentiality (security, surveillance, attorney-client privilege); overcoming resistance to change; bridging the digital divide; potential lack of empathy in remote interactions; risk of prioritizing efficiency over justice; potential for technology to exacerbate power imbalances; ensuring AI accuracy/avoiding bias; navigating Unauthorized Practice of Law (UPL) concerns. | Erosion of attorney-client confidentiality; exacerbation of bias (algorithms, video bail hearings); widening the digital divide; decreased empathy; over-reliance on inaccurate/biased AI; privacy infringements; using tech as a substitute for adequate defense funding; unintended consequences (e.g., anonymization); exacerbating power imbalances; focus on efficiency detrimental to justice (e.g., net-widening). |
| m5lEJ--ziNcJ.pdf | Google_Scholar | The Disruption of Generative AI in Real Asset Markets | This paper empirically examines the impact of Generative AI (GenAI), specifically post-ChatGPT, on the commercial real estate (CRE) market using private lease data and public stock market analysis. Findings suggest higher tenant GenAI exposure leads to lower rents, reduced space demand, and lower CRE firm valuations, indicating GenAI primarily acts as a labor substitute in this context. | True | Market | True | 2.0 | NaN | Measurement of industry/firm GenAI exposure using LLM (GPT-3.5) evaluation of O*NET tasks, combined with Difference-in-Differences (DID) regression on lease data and Event Study / Portfolio Analysis on CRE firm stock data. | Analysis of ~270,000 US commercial leases (CompStak, 2019-2024) using DID regression around ChatGPT release. Analysis of US REIT stock returns (S&P Global) using event study and portfolio sorts based on calculated GenAI exposure. Robustness checks include placebo tests, alternative samples, and controlling for confounders (e.g., Work-From-Home). | Higher GenAI exposure (1 std dev) is associated with a 3.5% reduction in net effective rent post-ChatGPT, increased tenant downsizing, lower lease renewal rates, and increased landlord concessions. In public markets, higher exposure REITs experienced lower stock returns (short-long portfolio yielded -11.83%), lower FFO forecasts, decreased occupancy, lower cash flows (FFO, NOI), and lower valuations (Tobin's Q). | NaN | NaN | NaN | NaN | Commercial Real Estate, Corporate Law, Securities Law (viewed through finance/economics lens) | United States | GenAI exposure measure derived using OpenAI GPT-3.5 Turbo API calls based on prompts assessing tasks from the public O*NET V28.0 database, aggregated using public BLS Occupational Employment Survey (OES) data. Analysis performed on proprietary CompStak lease data and proprietary S&P Global financial/property/tenant data. | Task-based occupational analysis aggregated to industry/firm level, Econometric analysis (Difference-in-Differences, Event Study, Portfolio Analysis), Data integration (linking tenants, properties, REITs), LLM-based task scoring. | NaN | False | False | NaN | NaN | Acquiring and integrating large-scale proprietary datasets (leases, property ownership, tenant details); constructing a meaningful measure of GenAI exposure across diverse industries; econometric identification to isolate GenAI impact from confounding factors (e.g., WFH trends, pre-existing trends). | Economic risks to Commercial Real Estate sector including reduced demand for space, lower rents, lower occupancy rates, and declining asset valuations, driven by GenAI-induced labor substitution. |
| -CZiMBSVZrgJ.pdf | Google_Scholar | Large Language Model Agent as Insurance Law Assistant | This thesis proposes and evaluates an LLM agent using Retrieval-Augmented Generation (RAG) to make Finnish traffic insurance law more accessible to ordinary individuals. Evaluation by a legal expert showed the agent could provide satisfactory answers and mitigate hallucinations, but highlighted the need for improved document retrieval. | True | Idealistic | True | 1.0 | Positive | An intelligent agent employing Retrieval-Augmented Generation (RAG) with the GPT-4 Turbo Large Language Model, facilitated by the Embedchain library, to answer user questions based on custom-selected legal documents. | The agent was evaluated through human feedback from a legal expert who assessed the quality of responses to 10 predefined scenarios based on real-life situations. Quantitative metrics (context relevance, answer relevance) from Embedchain's evaluation method were also used. | The expert rated 9 out of 10 responses as satisfactory (score 10/10), demonstrating reduced hallucination compared to the base LLM. However, quantitative evaluation showed low context relevance scores (2-44%), indicating suboptimal document retrieval, while answer relevance scores were high (84-90%). | The complexity of traffic insurance law for laypeople, and the general lack of knowledge, networks, and financial resources needed to access legal support. | Develop an accessible web-based LLM agent that utilizes RAG to retrieve information from specific legal documents (Finnish traffic insurance law, precedents) and answer user questions, thereby guiding them through the complexities of the legal domain. | Understanding rights to compensation under traffic insurance law after an accident, accessing relevant legal information. | Ordinary individuals involved in traffic accidents in Finland who find it difficult to navigate insurance law. | Traffic Insurance Law | Finland | The RAG system uses publicly available Finnish legal documents (e.g., from Finlex, Liipo) and potentially undisclosed proprietary sources related to traffic insurance law. This unstructured text data (HTML, PDF) is chunked, embedded using OpenAI's 'text-embedding-ada-002', and stored in a ChromaDB vector database. The underlying LLM is OpenAI's GPT-4 Turbo. | Design Science Research Methodology (DSRM), including problem explication, requirements definition, iterative design/development (brainstorming, assessment, sketching, building, reflection), demonstration, and evaluation. Peer review via the Walk-Through method was also employed. | The agent is accessed via a web application (built with Next.js, Nginx, Django API) requiring user registration/login. The system is containerized using Docker and hosted on Google Cloud. | False | False | NaN | Suboptimal document retrieval performance in the RAG system (low context relevance). Need for more extensive evaluation with more test cases and potentially more experts. Lack of sufficient data and expert resources for developing a planned Multi-Agent System (MAS). Need for self-hosted LLMs to improve privacy and reduce costs. | Selecting/developing LLMs with robust Finnish language understanding; optimizing RAG retrieval performance; effective prompt engineering; acquiring comprehensive real-world case data (esp. for MAS); limited expert availability for evaluation and multi-agent design; evaluating and fine-tuning open-source models for the task; managing costs associated with third-party APIs. | LLM hallucination leading to incorrect legal information. Failure to retrieve or include critical legal details in responses. Data privacy concerns associated with using third-party LLM APIs. Potential for sensitive information leakage between users (e.g., via improperly implemented caching). |
| ukGcRPmEsjkJ.pdf | Google_Scholar | MODERN INNOVATIVE MACHINE LINGUISTICS | The paper provides an overview of modern innovative machine linguistics, highlighting the role of Data Mining, machine learning (especially deep learning and transformers like BERT and GPT), and Big Data. It discusses various applications such as machine translation, speech recognition, and sentiment analysis, while also touching upon ethical considerations and evolutionary computing. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal services (general) | International | NaN | NaN | NaN | False | False | NaN | NaN | Need for high-quality Big Data; ethical issues (data privacy, model bias, transparency); complexity of search spaces in linguistic tasks. | Data privacy violations, model bias leading to unfair outcomes, lack of transparency in AI decision-making. |
| aH2ZKJ7gUmYJ.pdf | Google_Scholar | Free LLMs Hallucinate and Rarely Signal Their Limitations in Solving Legal Problems | This study evaluates the ability of two free large language models (GPT-4o mini and Bielik-11B-v2) to answer simple Polish legal questions. The results show the models perform poorly on moderately complex issues, often hallucinate, fail to correct erroneous user assumptions, and rarely indicate their own limitations. | True | Idealistic | True | 2.0 | Negative | Evaluation of GPT-4o mini and Bielik-11B-v2 | Models were prompted with 12 questions across 3 Polish legal fields (Constitutional, Criminal, Inheritance). Prompts included sensible/nonsensical assumptions and varied phrasing. 120 responses (5 per prompt/model) were evaluated by human experts for correctness and signalling of limitations. | Models answered correctly only on the simplest constitutional law issue. They struggled significantly with criminal and inheritance law, especially when prompts contained nonsensical assumptions (Bielik: 0-20% correct, GPT: 20-100% correct depending on phrasing). Limitations were rarely signalled (17% overall). | LLMs hallucinate and provide incorrect legal analysis, especially for non-trivial questions; they fail to correct user misconceptions posed in prompts; they rarely signal their own limitations (e.g., lack of access to real-time/accurate legal databases); opacity of commercial models. | The paper suggests lawyers should use LLMs very carefully, be aware of their limitations, and calls for more research to scrutinize these limitations and raise awareness. | Legal analysis accuracy, hallucination in legal contexts, LLM limitations signalling, legal information retrieval. | NaN | Constitutional Law, Criminal Law, Inheritance Law (Civil Law) | Poland | NaN | NaN | NaN | True | True | The paper explicitly studies 'free LLMs'. GPT-4o mini is available via OpenAI. Bielik-11B-v2 is available on Hugging Face (as per reference [9]). | Unreliability of current free LLMs for legal analysis beyond simple cases; lack of precision and tendency to hallucinate; failure to signal limitations; need for more research on limitations of free models; issue of LLM transparency. | Evaluating legal correctness of LLM outputs; designing realistic prompts; high variability and sensitivity of LLM responses to prompt phrasing. | Users receiving incorrect legal information due to hallucinations; users being misled when LLMs confirm false premises; over-reliance on LLMs due to lack of signalled limitations; risks associated with the opacity (black-box nature) of LLMs in legal applications. |
| cauuCB_XXSkJ.pdf | Google_Scholar | Let's Chat About ChatGPT: A Practical Guide to Risks in Attorney Use of Generative AI | This paper analyzes recent instances where attorneys misused generative AI tools like ChatGPT, resulting in fabricated case citations, court sanctions, and potential malpractice claims. It reviews the responses from courts, bar associations, and ethics bodies, concluding that existing ethical rules suffice but emphasizing the need for attorney education, caution, and human oversight. | True | Market | True | 2.0 | NaN | General-purpose generative AI (e.g., ChatGPT, Google Bard) | Analysis of court cases involving attorney misuse of generative AI; mentions a Stanford study testing LLMs on legal questions. | Attorneys misusing generative AI faced sanctions, discipline, and reputational harm due to fabricated cases and inaccuracies. A cited Stanford study found general LLMs hallucinate >75% on core legal questions. | NaN | NaN | NaN | NaN | Legal Ethics, Civil Procedure, Criminal Procedure, Torts (Personal Injury), Housing Law | United States (Federal and State courts including NY, TX, CO, CA, PA, IL, OK, NJ, MT, OH, MO, HI, MI) | NaN | NaN | NaN | True | True | Publicly available generative AI tools like ChatGPT and Google Bard have free accessible tiers. | NaN | Challenges discussed are those faced by attorneys *using* generative AI: verifying accuracy, avoiding hallucinations ('fake cases'), maintaining client confidentiality, need for human oversight and supervision, lack of technological competence. | Generation of inaccurate information ('hallucinations', fake cases), breach of client confidentiality, violation of ethical duties (competence, diligence, candor, supervision), professional sanctions, disciplinary action, legal malpractice claims, potential bias in AI outputs, undermining trust in the legal system. |
| GIrhz2GUiNgJ.pdf | Google_Scholar | Evaluating the Performance of ChatGPT in the Automation of Maintenance Recommendations for Prognostics and Health Management | This paper proposes and applies a methodology using a rubric based on Accuracy, Concordance, and Insight (ACI) to evaluate ChatGPT's performance in generating maintenance recommendations for Prognostics and Health Management (PHM). The evaluation reveals ChatGPT has some understanding of industrial concepts but suffers from inaccuracies, verbosity, lack of specificity, and potential safety risks, indicating limitations for practical PHM application without significant safeguards and domain adaptation. | True | Market | True | 2.0 | Neutral | Evaluation of ChatGPT for generating PHM maintenance recommendations using a custom rubric (adapted ACI) and multi-stage testing. | Three-stage evaluation: 1) 76-item Maintenance & Reliability multiple-choice exam (adapted from Gulati & Smith, 2021). 2) 63-item PHM Industrial Domain knowledge exam (adapted from internal GE Vernova test). 3) Troubleshooting assessment using historical case prompts. Responses scored using an adapted Accuracy, Concordance, Insight (ACI) rubric by domain experts. | ChatGPT (AI1) scored 72% on M&R exam and 67% on PHM exam (below 80% passing threshold). It grasped central concepts well but struggled with accuracy, consistency, specificity (tended towards verbosity/generality), deduction, and sometimes physical soundness. Troubleshooting recommendations were too verbose and lacked practical specificity. | NaN | NaN | NaN | NaN | NaN | International | The evaluated model (ChatGPT based on GPT-3.5) was pre-trained on a large, general corpus of text data from online sources (books, websites, articles) up to 2021, fine-tuned using RLHF. This is not domain-specific PHM data. | Evaluation framework design based on adapted ACI scoring rubric and multi-stage knowledge/task testing using expert-derived questions and prompts. | NaN | True | False | ChatGPT was accessible via OpenAI's public interface. | NaN | Evaluating rapidly evolving LLMs, laborious grading requiring domain expertise, ensuring grading consistency, designing effective prompts, assessing subtle qualities (physical soundness, safety, deduction) within verbose/inconsistent outputs. | Generating inaccurate/outdated/unsafe maintenance guidance, data security issues, lack of interpretability, misinformation, hallucinations, potential bias from training data, lack of contextual understanding of specific industrial processes/safety protocols/regulations. |
| EUlupy7HOaEJ.pdf | Google_Scholar | The Generative AI Revolution: Opportunities, Shocks, and Risks | This policy report analyzes the rapid rise of generative AI, focusing on the UK context. It outlines economic and geopolitical opportunities, potential labor market and macroeconomic shocks, and AI safety risks (especially alignment), proposing UK government strategies in response. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General legal practice, Intellectual Property | United Kingdom | NaN | NaN | NaN | False | False | NaN | Strategic gaps for UK: insufficient compute power, lack of sovereign LLM capability, suboptimal AI talent attraction/retention, regulatory/IP framework (TDM) limitations, need for AI safety evaluation standards, lack of dedicated AI regulator. Technical gaps: AI alignment problem. Societal gaps: managing labor market transitions, ensuring AI aligns with human values. | NaN | Misinformation, lack of transparency, privacy issues, AI alignment problem (unintended/harmful consequences, potential existential risk), manipulation/disruption of critical infrastructure (reliance on foreign AI), job displacement/unemployment, increased frivolous lawsuits, environmental impact of compute. |
| rM1eMjqvg9cJ.pdf | Google_Scholar | Efficiency, Ethics , and Algorithms : The Implications of AI on the Legal Profession and the \nABA Model Rules | This paper analyzes the potential impacts of AI, particularly tools like ChatGPT, on legal drafting, research, and decision-making within the legal profession. It further examines the ethical considerations under the ABA Model Rules for attorneys using such AI and explores AI's potential to enhance access to justice. | True | Market | True | 2.0 | Positive | ChatGPT (and other generative AI tools like CoCounsel and Harvey AI used as examples) | N/A (The paper discusses capabilities and issues based on existing reports and general knowledge of the tools, but does not present its own empirical testing or evaluation against benchmarks.) | NaN | High cost of legal services; rules barring unauthorized practice of law and sharing of legal revenue with non-lawyers; risk of lower-quality AI services for underserved populations. | Utilizing AI for drafting and research to lower costs; reforming rules (e.g., Rule 5.4) to allow innovative partnerships and service delivery models (like legal sandboxes); establishing state bar task forces for AI regulation and service standards. | Affordability of legal services; provision of services for common legal issues (family law, debt, landlord/tenant); innovative legal service delivery models including non-lawyer providers and AI. | Low-income individuals and the general population facing affordability barriers to legal services. | General legal practice (drafting, research, decision-making); Ethics and Professional Responsibility; Criminal Justice; Family Law; Debt Collection; Landlord/Tenant Law; Alternative Dispute Resolution. | USA (primary, with references to ABA Model Rules, state-level initiatives, and US court cases); some international examples (EU). | ChatGPT: Vast amounts of general internet text. CoCounsel: Customized GPT-4 for the legal industry (details not specified). Harvey AI: Legal documents (details not specified). Public Safety Assessment: 750,000 cases from US jurisdictions. | N/A (The paper does not detail the specific design methodologies for the AI tools it discusses, beyond general descriptions like 'large language model' or 'machine learning' for ChatGPT, or factor-based scoring for PSA.) | ChatGPT: Public release (free and subscription tiers). CoCounsel: Market launch after beta testing with law firms. Harvey AI: Beta phase with partnerships (e.g., Allen & Overy for internal integration). | True | True | ChatGPT is available for public use, with a free version (based on GPT-3.5) and a paid subscription version (GPT-4). | Insufficient use of AI and non-lawyers to address the access to justice gap; lack of robust testing and standards for AI to ensure accuracy and lack of bias in A2J contexts; absence of clear ethical guidelines and regulations from state bars for AI use; need for transparency in AI tools used in the justice system. | For users (attorneys): Ensuring client confidentiality, verifying accuracy and avoiding bias in outputs, maintaining professional competence and supervision, navigating lack of clear ethical guidelines. For developers: Mitigating inherent biases from training data, ensuring system security. | Legal malpractice from negligent AI use; threats to due process from biased/opaque AI in criminal justice; perpetuation of societal biases (e.g., racial); unauthorized practice of law; disclosure of confidential client information; security vulnerabilities (breaches, jailbreaking); misleading tribunals with inaccurate or fabricated AI outputs; discriminatory conduct through use of biased AI. |
| blackham-2025-interrogating-new-methods-in-socio-legal-studies-content-analysis-case-law-and-artificial-intelligence.pdf | Google_Scholar | Interrogating new methods in socio-legal studies: Content analysis, case law and arti ficial intelligence | This article critically examines the use of artificial intelligence (AI) and large language models (LLMs) for empirical legal research, specifically focusing on content analysis of case law. It highlights significant risks such as inaccuracy, bias, hallucinations, and lack of reproducibility, concluding that researchers should be extremely cautious and implement stringent evaluation procedures when considering these tools. | True | Idealistic | True | 3.0 | Negative | Using AI and LLMs for content analysis of case law | NaN | NaN | Current unreliability and limitations of AI/LLMs (e.g., hallucinations, bias, inaccuracy) prevent their effective and trustworthy use in legal research tasks that could support access to justice, such as analysing case law to identify systemic enforcement gaps or practical legal issues. | Researchers should exercise significant caution when using AI/LLMs, implement stringent procedures for evaluating AI outputs (e.g., blind, independent testing), and develop a clear understanding of the tools' limitations before applying them in legal research, especially research with potential A2J implications. | Analysis of case law to understand legal operations in practice (e.g., who brings claims, claim resolution, identifying systemic patterns); identifying barriers to justice and gaps in legal enforcement. | Implicitly, communities that are underserved by the legal system whose issues might be illuminated by thorough case law analysis (example given in text: older women and young people in age discrimination cases). | Equality law, employment law (used as primary examples for content analysis). | Australia, UK (examples and studies discussed are from these jurisdictions). | General LLMs (e.g., GPT-4, Llama2-70B) are trained on large-scale, often uncurated internet-based data. Specific studies discussed use datasets like UK Employment Tribunal decisions for analysis. | NaN | NaN | False | False | NaN | Significant gap between the potential of AI/LLMs to assist in socio-legal research (including for A2J purposes) and their current capabilities, particularly regarding accuracy, reliability, reproducibility, and freedom from bias and hallucinations. | Automation bias in human evaluation of AI outputs, AI 'hallucinations' (generating false or nonsensical information), inherent biases in LLMs derived from training data or design, and practical difficulties in ensuring reproducibility of results from third-party LLM services. | Lack of reproducibility, automation bias, inherent LLM bias, hallucinations leading to inaccurate or unfaithful outputs, general inaccuracy, production of poor or misleading research, potential for creating more work due to the need for extensive fact-checking and correction of AI outputs. |
| Mc-1PNuCNIsJ.pdf | Google_Scholar | ChatGPT as an Artificial Lawyer? | This paper qualitatively evaluates ChatGPT's ability to provide legal information to laypeople using simulated landlord-tenant cases, comparing its performance against the expert system-based JusticeBot. While ChatGPT excels at user interaction and language comprehension, it suffers from significant inaccuracies and hallucinations, making it currently unsuitable for direct use, unlike the more reliable but less flexible JusticeBot. | True | Idealistic | True | 2.0 | Neutral | Evaluating ChatGPT's capability for providing legal information to laypeople compared to JusticeBot (an expert system). | Qualitative evaluation using three simulated landlord-tenant cases (generated by ChatGPT) set in Quebec. Researchers interacted with ChatGPT and JusticeBot as layperson parties involved in these cases, assessing performance against criteria including language comprehension, accuracy, completeness, trustworthiness, harmlessness, and user-friendliness. | ChatGPT demonstrated good language comprehension and user-friendliness but lacked accuracy, completeness, and trustworthiness, often 'hallucinating' incorrect legal provisions and case law. JusticeBot provided accurate and trustworthy information within its defined scope but was less flexible and interactive. | Cost of legal services leading to 'legal advice deserts'; difficulty for laypeople in understanding their rights and legal procedures; information asymmetry and power imbalances in disputes (e.g., housing). | Using AI tools like ChatGPT and JusticeBot to provide legal information. The paper suggests combining the conversational strengths of LLMs (like ChatGPT) with the accuracy of verified knowledge bases or expert systems (like JusticeBot). | Provision of legal information; self-help tools for laypeople; everyday legal disputes. | Laypeople, self-represented litigants, individuals facing everyday legal problems without access to professional legal help. | Landlord-Tenant Law (Housing Law) | Quebec, Canada | ChatGPT: Trained on 'enormous corpora of text data' (general, not specified as legal). JusticeBot: Based on content created by legal experts using an expert system methodology. Evaluation Data: Simulated landlord-tenant cases generated by ChatGPT. | Qualitative evaluation based on predefined criteria (Language comprehension, Accuracy, Completeness, Trustworthiness, Harmless, User-friendly) using simulated case interactions. | JusticeBot is deployed online (justicebot.ca) and has been accessed by users. ChatGPT is available via OpenAI's web interface and API. | True | False | ChatGPT is available via OpenAI's interface/API. JusticeBot is available online at https://justicebot.ca. | Accuracy, reliability, and trustworthiness of LLMs for legal information; ensuring information is up-to-date and properly sourced; difficulty in verifying AI-generated legal content for laypeople; potential for harmful reliance on incorrect information. | ChatGPT's tendency to 'hallucinate' legal facts (false provisions, non-existent cases); ensuring accuracy and reliability for lay users who cannot easily verify information; the limited scope and inflexibility of expert systems like JusticeBot compared to LLMs; defining the boundary between providing legal information and unauthorized legal advice. | Laypeople making harmful decisions based on inaccurate or hallucinated information from AI; provision of misleading or incomplete information; privacy risks associated with user interaction data; potential for bias in AI responses. |
| XZX5nvn_88QJ.pdf | Google_Scholar | Summary of Young -OGEMID Symposium No. 13: “The Role of Artificial Intelligence in Shaping ADR Practices” (July 2023 ) | This paper summarizes a Young-OGEMID virtual symposium discussing the integration of Artificial Intelligence (AI) into Alternative Dispute Resolution (ADR), particularly arbitration. Experts explore AI's opportunities (efficiency, data analysis) and challenges (bias, ethics, transparency), its role in decision-making, and its potential future impact, including on access to justice. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Bias in AI algorithms; unequal access to AI tools leading to a 'digital divide' and tiered justice; lack of transparency ('black box' problem); potential increase in costs/inefficiencies due to verification needs; ethical issues of offloading underserved cases to potentially inferior AI; language barriers for less common dialects. | Promote responsible AI use through education; develop unbiased and transparent AI; ensure broad access to AI tools; maintain human oversight and intervention (Human Experience + AI); focus AI on augmenting human capabilities rather than replacement; leverage AI to empower self-represented litigants. | Empowering self-represented litigants; reducing legal costs; increasing efficiency; overcoming language barriers; addressing resource inequality (digital divide); ensuring procedural fairness and due process. | Self-represented litigants; parties with fewer resources (e.g., SMEs, individuals); parties from developing countries. | Alternative Dispute Resolution (ADR), International Arbitration (Commercial, Investment), Consumer Arbitration, Employment Arbitration. | US, Canada, India, Colombia, Estonia, China, International | NaN | NaN | NaN | True | True | Publicly available tools like ChatGPT (free tier) and free legal databases (worldlii.org, CLOUT) are discussed. Commercial tools (e.g., Lexis+ AI, Jus Mundi) and specific pilot projects (e.g., SUPACE) are also mentioned. | Technical: Improving AI accuracy, reducing bias, handling legal complexity/nuance, ensuring transparency, dealing with limited/confidential data, supporting more languages. Societal/Ethical: Addressing the digital divide, establishing clear ethical guidelines/regulations, building public trust, defining human oversight roles, preventing misuse (deepfakes), ensuring procedural justice. Need for more research on AI vs. human decision-making fairness/effectiveness. | Data limitations (availability, confidentiality, bias); ensuring fairness/mitigating bias; maintaining confidentiality/privacy; addressing the 'black box'/explainability issue; integrating AI with human judgment; overcoming user distrust; cost of sophisticated tools; rapid technological change; potential for misuse. | Unfair/biased outcomes; confidentiality/privacy violations; erosion of public trust in ADR; creation of 'tiered justice'; inaccurate legal research/submissions; anchoring bias in human decision-making; use of deepfakes; challenges to award enforcement due to improper AI use; deskilling professionals. |
| L8yvUKWzscwJ.pdf | Google_Scholar | RULE 11 IS NO MATCH FOR GENERATIVE AI | This paper analyzes whether Federal Rule of Civil Procedure 11 can effectively sanction attorneys who negligently submit court filings containing fictitious cases or false statements of law generated by AI. It concludes Rule 11 is ill-suited for this task and evaluates the emerging judicial response of issuing standing orders, discussing their benefits and drawbacks. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Civil Procedure, Legal Ethics, Professional Responsibility | United States Federal Courts | NaN | NaN | NaN | False | False | NaN | NaN | The paper argues that Federal Rule of Civil Procedure 11 is poorly suited to sanction negligent attorney use of generative AI that results in filings with fictitious cases or false law, due to the safe harbor provision and the higher standards (subjective bad faith or 'akin to contempt') required for sua sponte sanctions. It also identifies challenges with emerging judicial standing orders, such as drafting errors (imprecise terminology), discouraging technology adoption, potential appearance of judicial bias, and creating a problematic patchwork of rules. | The primary risk discussed is attorneys negligently relying on generative AI, leading to the submission of filings containing fictitious cases and false statements of law ('hallucinations'), resulting in ethical breaches and potential sanctions. Secondary risks mentioned include the potential disclosure of confidential information when using AI and the chilling effect of poorly drafted standing orders on the adoption of potentially beneficial technology. |
| -miNsCrBVBwJ.pdf | Google_Scholar | AI-POWERED LAWYERING: AI REASONING MODELS, RETRIEVAL AUGMENTED GENERATION, AND THE FUTURE OF LEGAL PRACTICE | This paper presents a randomized controlled trial assessing the impact of AI reasoning models (OpenAI's o1-preview) and Retrieval Augmented Generation (VLex's Vincent AI) on legal tasks completed by law students. Both AI tools significantly improved the quality and speed of legal work compared to no AI, with o1-preview showing stronger quality gains and Vincent AI demonstrating effectiveness against hallucinations. | True | Market | True | 2.0 | Positive | AI reasoning models (OpenAI o1-preview) and Retrieval-Augmented Generation (RAG) integrated into a legal AI tool (VLex's Vincent AI). | Randomized controlled trial (RCT) with 127 upper-level law students assigned to complete six realistic legal tasks (drafting client email, legal memo, complaint analysis, NDA, motion to consolidate, persuasive letter) using either Vincent AI, o1-preview, or no AI. Work products were blindly graded by experienced lawyers using standardized rubrics assessing quality (accuracy, analysis, organization, clarity, professionalism) and time spent. | Both Vincent AI and o1-preview significantly improved quality in 4/6 tasks and speed in 5/6 tasks compared to no AI. o1-preview yielded larger quality improvements, particularly in analytical depth (significant gains in 3/6 tasks), but resulted in more hallucinations (11 vs 3 for Vincent, 4 for no AI). Vincent AI improved clarity, organization, and professionalism, had the fewest hallucinations, but did not significantly improve analysis scores and had mixed effects on accuracy. | NaN | NaN | NaN | NaN | Litigation (defamation, insurance law, class action procedure, covenants not to compete, civil procedure), Transactional Law (contract drafting - Non-Disclosure Agreement). | USA (Tasks involved Tenth Circuit, Massachusetts, New Hampshire, Minnesota, Indiana, Nevada law). | Vincent AI uses RAG, integrating foundational LLMs (e.g., GPT-4, GPT-4o) with VLex's legal database (case law, statutes, regulations, etc.). OpenAI's o1-preview is a general-purpose reasoning model; its specific training data is not detailed. | The paper evaluates existing tools. Vincent AI uses Retrieval-Augmented Generation (RAG) and automated prompting. o1-preview uses enhanced compute at inference for step-by-step processing and internal chain-of-reasoning, refined through large-scale reinforcement learning. | The study provided participants with free access to o1-preview (via OpenAI Plus accounts) and Vincent AI (via institutional subscription or complimentary access from VLex) through a Canvas interface for the duration of the experiment. | True | False | Vincent AI is a commercial product available via subscription from VLex. o1-preview was available via OpenAI API access (typically paid). | Lack of standardized benchmarks for complex lawyering tasks; need for more empirical evaluation (like RCTs) of AI tools in law. Potential reduction in benefit for higher-skilled individuals. Need to study integration of RAG and reasoning models. Understanding AI's impact on legal education and skill development. | Evaluating AI's impact on nuanced aspects of legal work like analysis and accuracy. Ensuring AI tools minimize hallucinations while maximizing relevance. Variability in AI performance across different types of legal tasks (e.g., litigation vs. transactional). Tools may benefit lower-skilled users more, potentially reducing quality for high-skilled users. | Hallucinations (generating fake cases or inaccurate information), particularly with reasoning models like o1-preview tested here. Potential for AI tools to decrease accuracy in some contexts. Over-reliance on AI potentially undermining human judgment or skill development. Risk of reduced performance quality for high-skilled individuals when using AI. |
| GV6mowVAwRsJ.pdf | Google_Scholar | Legal AI: Enhancing Justice through Technology, Practical Considerations | This paper explores the applications, benefits, and limitations of AI, particularly LLMs, in the Indian legal field, aiming to improve efficiency and access to justice for legal professionals, government bodies, and citizens. It discusses various AI models and emphasizes the need for careful implementation, oversight, and customization while considering risks like hallucinations and the digital divide. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | AI hallucinations/errors, data privacy/security concerns, need for customization, digital divide, resistance to technology, cost, vendor dependence, need for professional training. | Human oversight, robust data protection, AI customization, developing inclusive use cases, training professionals, tailored implementation architecture, policy changes, prompt engineering, tuning models for local contexts. | Access to legal information/resources, efficient case resolution, cost reduction in legal services, automation of legal/administrative tasks (e.g., filing applications, drafting), overcoming language barriers. | General public/citizens, litigants, legal professionals, government departments (collectorate, police), particularly targeting issues relevant to developing regions (digital divide). | General Law | India | NaN | NaN | NaN | False | False | NaN | Need for improved AI accuracy and robustness against hallucinations, better customization for specific legal/local contexts, addressing the digital divide, ensuring data privacy, effective integration into workflows, training for legal professionals. | Prompt engineering, effective communication (especially local languages), contextual tuning, ensuring accuracy/avoiding hallucinations, data privacy, cost, vendor dependence, need for end-to-end architecture design. | AI hallucinations leading to incorrect information, data privacy and security breaches, exacerbation of the digital divide, potential for erratic, divisive, or harmful outcomes due to lack of contextual understanding. |
| S4wC53qpRXQJ.pdf | Google_Scholar | How to Retain Being a Human Lawyer While Using Generative AI | This paper examines the transformative impact of generative AI on the legal profession, outlining potential issues like overreliance and embedded biases. It advocates for legal professionals and educational institutions to adapt by emphasizing and cultivating uniquely human skills such as emotional intelligence, storytelling, ethical judgment, and practical experience. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | Generative AI providing false or biased information to self-represented individuals, leading to harm; overreliance on AI; AI 'hallucinations' and deepfakes; inherent biases in AI models. | Regulation and potential licensing of AI products offering legal assistance to the public; revision of rules on unauthorized practice of law to address AI; emphasis on human oversight, professional judgment, and ethical responsibilities for lawyers using AI; adaptation of legal education to cultivate human-centric skills. | Reliability and regulation of AI tools providing legal information/assistance to self-represented individuals; ethical use of AI in legal practice impacting service delivery. | Self-represented individuals | General legal practice | United States (with a focus on California) | NaN | NaN | NaN | False | False | NaN | Inadequate specific regulations and ethical guidelines for AI systems providing legal assistance directly to the public, especially self-represented individuals; risk of harm from unreliable or biased AI-generated legal information; need for legal education and the profession to fully adapt to AI's impact while preserving human-centric lawyering skills. | NaN | Overreliance on AI; AI 'hallucinations' (false outputs); deepfakes; displacement of legal professionals (knowledge workers); algorithmic bias (e.g., racist, gender) from training data and developer demographics; harm to self-represented individuals from inaccurate AI-generated legal information; invasion of privacy interests; potential for unauthorized practice of law by AI if not properly regulated. |
| EnhancingConversationalAgentswithGenerativeAI.pdf | Google_Scholar | Enhancing Conversational Agents with Generative AI: A Framework for Creating More Adaptive and Context-aware chatbots | This paper explores how generative AI, particularly models like GPT based on Transformer architecture, can enhance conversational agents (chatbots) by making them more adaptive, context-aware, and capable of human-like interactions. It proposes a general framework for developing such chatbots and discusses applications, challenges (technical and ethical), and future trends like multimodal AI. | True | NaN | True | 3.0 | NaN | Proposes a general framework for building adaptive and context-aware chatbots using Generative AI (e.g., GPT, Transformer models). | Illustrative case studies of existing chatbots (e.g., ChatGPT, Sephora, Babylon Health, Netflix, Bank of America's Erica) are mentioned, but no specific evaluation of the proposed framework itself is described. | NaN | NaN | NaN | NaN | NaN | NaN | International | Discusses the need for diverse, high-quality data (e.g., customer service transcripts, FAQs, product descriptions) and mentions large pre-trained models (like GPT) trained on general web text, advocating for domain-specific fine-tuning. | Proposes a conceptual framework involving: defining objectives/use cases, data collection/preprocessing, model selection (e.g., GPT), contextual integration (state tracking, personalization), and continuous learning (reinforcement learning, human-in-the-loop). | Discusses potential deployment environments (website, mobile app) and application areas (customer service, healthcare, e-commerce) but no specific strategy for the proposed framework. | False | False | NaN | NaN | Technical limitations (memory constraints, computational power requirements, training time) and ethical challenges (bias inherited from data, privacy concerns, potential for misinformation). | Bias leading to unfair or inappropriate responses, privacy risks due to collection of personal data, generation and spread of misinformation (noted as particularly problematic in domains like healthcare or legal services). |
| AE9Y5NtXFKEJ.pdf | Google_Scholar | Large Language Scholarship: Generative AI in the Legal Academy | This paper argues that generative AI will inevitably transform legal scholarship production, analyzing the systemic impacts on academics, law schools, and the legal system. It predicts AI adoption trends, examines implications for stakeholders, and offers guidance for responsible integration, including practical advice on using AI tools. | True | Market | True | 3.0 | Positive | Generative AI / Large Language Models (broad discussion) | NaN | NaN | NaN | NaN | NaN | NaN | General Legal Scholarship | United States | NaN | NaN | NaN | True | False | Discusses commercially available AI tools (e.g., ChatGPT, Claude, Gemini, Perplexity) with both free and paid tiers; recommends paid plans. Also mentions legal-specific tools requiring subscriptions (e.g., Lexis+ AI, Westlaw CoCounsel). | NaN | Managing information overload; preventing cognitive deskilling among scholars; adapting evaluation metrics (hiring, tenure) to account for AI-assisted productivity; developing effective institutional norms, policies, and training for AI use; addressing ethical concerns (bias, data appropriation); avoiding negative impacts on teaching quality; navigating potential reinforcement of academic hierarchies. | Increased misinformation (e.g., "scholarly deepfakes" with manufactured citations or analyses); cognitive deskilling of scholars; alienation from the scholarly craft; reinforcement of biases embedded in AI training data; erosion of traditional academic credentials as signals of expertise; creation of unsustainable publication pressures; potential negative impacts on teaching quality; ethical risks related to appropriation of author works in training data. |
| q-DTIJ8ci6YJ.pdf | Google_Scholar | Bridging the Gap t o Every American: How a National Regulat ory Sandbo x Can Pr ompt Radical Collabor ation t o Adopt Legal Artificial Intelligence T ools | The paper highlights the significant access to justice gap in the United States, particularly for low-income individuals facing civil legal issues. It advocates for the creation of a national regulatory sandbox, overseen by the U.S. Supreme Court, to foster the development and responsible adoption of AI-powered legal tools to provide affordable legal services. | True | Idealistic | True | 1.0 | Positive | Proposal for a National Regulatory Sandbox overseen by a National Office of Legal Services Innovation under the U.S. Supreme Court to regulate and foster AI-driven alternative legal service providers. | The proposed national regulatory sandbox is a policy recommendation and has not been tested. The paper references the operational data (consumer complaints, number of people served) from the existing Utah regulatory sandbox as evidence of the potential success of such an approach. | N/A (The proposed national sandbox has not been implemented or tested). Results cited for the analogous Utah sandbox include assisting over 2,500 people with a low rate of consumer harm complaints (approx. 1 per 6,851 services). | High cost of legal services, insufficient legal aid for low-income populations leading to unresolved civil matters, negative life consequences (financial, health, housing) stemming from lack of legal help, complexity of the legal system, underfunding and overwork in traditional legal aid and public defender systems, unequal access based on income. | Establishment of a National Regulatory Sandbox to allow controlled experimentation and deployment of AI-powered alternative legal service providers. Encouraging innovation in legal tech, particularly AI tools (like chatbots, document simplifiers, legal marketplaces), to offer low-cost, accessible legal information and services. | Access to justice (civil), Alternative legal service providers, Legal technology regulation, Housing law, Immigration law, Domestic violence, Healthcare law, Discrimination law, Employment law, Consumer contracts (leases, mortgages, credit cards), Dispute resolution. | Low-income Americans, Economically vulnerable populations, Underserved communities facing civil legal issues. | Civil Law (broadly, including family, housing, consumer, contract, immigration, employment law) | United States | NaN | Policy design based on existing models (Utah regulatory sandbox, EU initiatives) and policy guidelines (CGAP's Practical Guide for Policy Makers), focusing on eligibility criteria, governance structure, experimentation timelines, evaluation metrics, and exit options. | N/A (The paper proposes the creation and implementation of the sandbox, but it is not currently deployed). | False | False | NaN | Lack of affordable and accessible civil legal services, inadequacy of traditional service models, regulatory barriers to innovation in legal services, potential for AI to exacerbate inequality if not implemented equitably, digital divide limiting access to tech-based solutions. | Distrust of AI among legal professionals and judiciary, data privacy concerns, potential job displacement in the legal sector, ensuring AI tools are effective and do not cause consumer harm, overcoming the digital divide, gaining stakeholder buy-in for regulatory innovation. | Consumer harm (inaccurate advice/results, unnecessary services), Data privacy violations, Widening the access-to-justice gap if AI tools are costly or inaccessible, Creation of a two-tiered legal system (high-quality human lawyers vs. potentially inferior AI for the poor), Potential job displacement for legal professionals. |
| rpfKPBonepgJ.pdf | Google_Scholar | AI CHATBOT CHAT GPT AND THE THEMES IT CREATES ON TURKEY'S INTERNET AGENDA | This paper analyzes Turkish internet news articles about ChatGPT published between November 30, 2022, and January 31, 2023, to identify key discussion themes, perceived opportunities, and concerns. The analysis reveals major themes including education, business life, IT sector, coding, daily life, investment advice, and creative content generation, highlighting anxieties around plagiarism, job displacement, misinformation, and bias, alongside potential benefits like efficiency and content generation. | False | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General (mentions law, copyright) | Turkey | NaN | Document analysis (doküman incelemesi) and inductive content analysis (tümevarımsal içerik analizi) of news articles. | NaN | True | True | ChatGPT was available via the OpenAI website, including a free access tier at the time the analyzed articles were published. | Need for more research on societal impacts of large language models, user awareness and digital literacy concerning AI outputs, and critical algorithm studies perspectives (bias, inequality etc.), particularly within the Turkish research context. | NaN | Educational integrity issues (plagiarism, cheating); misinformation, bias, and discrimination in AI outputs; job displacement across various sectors (e.g., journalism, law, coding); cybersecurity threats (malware generation); potential for misuse in generating harmful content (e.g., deepfake scripts); over-reliance on potentially inaccurate AI advice; ethical concerns regarding AI development and lack of transparency ('black box'). |
| tuhct2ee6vgJ.pdf | Google_Scholar | Unveiling Retail Insights with Generative AI | This internship report explores using Generative AI (GPT-4) combined with change detection algorithms (PELT) to analyze retail transactional data from Sonae MC's loyalty program. The goal is to automatically detect significant changes in business metrics, interpret them using GPT-4, and generate communication drafts for category directors, improving responsiveness. | True | Market | True | 1.0 | NaN | A combination of change detection algorithms (specifically PELT with RBF cost function) to identify shifts in time-series retail data, followed by GPT-4 for interpreting these changes and generating automated communication drafts. | Phase 1: GPT-4 tested on ability to extract key performance indicators (RH values) from dashboard images, comparing general vs. refined prompts (accuracy measured). Phase 2: PELT change detection algorithm tested on 4 years of historical Sonae MC transactional data (weekly aggregated), compared visually against CUSUM, parameter tuning (penalty factor). Phase 3: GPT-4 interpretation of PELT-detected recent change-points evaluated qualitatively by internal Sonae MC teams (Advanced Analytics, Analytical, Business User/Category Director). | Phase 1 showed GPT-4 unreliable for extracting precise numerical values (max 40% accuracy with refined prompt) but capable of generating useful business insights/suggestions. Phase 2 validated PELT as suitable for detecting significant shifts, with a penalty factor of 14 for historical analysis and 1 for recent change monitoring. Phase 3 successfully demonstrated GPT-4 interpreting changes detected by PELT and generating insightful, actionable communication drafts positively received by internal stakeholders. | NaN | NaN | NaN | NaN | NaN | Portugal | Proprietary Sonae MC transactional data from the Continente Loyalty Card program (4 years of historical data, aggregated weekly), covering metrics like sales, customers, quantity, price, etc. for specific product categories. GPT-4 was prompted using this data (or derived information) and potentially 'base knowledge' like internal documentation/glossaries. | CRISP-DM (Cross-Industry Standard Process for Data Mining) | Internal proof-of-concept simulation within Sonae MC. No external deployment mentioned. | False | False | NaN | NaN | GPT-4 unreliability in extracting exact numerical values from dashboards; need for effective prompt engineering; difficulty in accurately removing non-cyclical seasonality from time-series data before change detection; selecting appropriate penalty factors for change detection sensitivity. | GPT-4 hallucinations or inaccuracies leading to incorrect business insights or communications; change detection model misinterpreting residual seasonality or noise as significant shifts, causing false alerts; reliance on external LLMs (GPT-4 via Azure OpenAI API). |
| 6Lu9Wgf2rMcJ.pdf | Google_Scholar | Public Consultation Response on “Copyright and AI” [Docket No. 2023-06] | This paper is a response to the U.S. Copyright Office regarding AI and copyright, arguing AI is a human-controlled tool that requires adapting existing laws, not new ones. It discusses fair use, authorship, transparency, and the need for a balanced approach to protect creators and foster innovation for societal benefit. | True | Idealistic | True | 3.0 | Positive | Generative AI / Large Language Models (e.g. ChatGPT, DALLE-3) | NaN | NaN | Misconceptions about AI's nature (e.g., anthropomorphism), hindering clear legal discourse; Lack of transparency in AI training data, impeding creators' ability to enforce rights; Difficulty in applying traditional legal concepts (e.g., authorship, fair use) to AI. | Promote an accurate understanding of AI as a human-controlled tool; Adapt existing legal frameworks, relying on courts for interpretation, rather than rushing new AI-specific laws; Increase transparency in AI systems (e.g., training data disclosure) and explore robust accountability mechanisms like 'networked responsibility'. | Ensuring fairness for creators (e.g., regarding use of their works in AI training, compensation); Upholding human authorship in AI-assisted creations; Enhancing transparency of AI systems for copyright enforcement. | Creators (e.g., artists, writers) and individuals whose data is used for AI training. | Copyright Law, Intellectual Property Law, Data Privacy | United States | The paper discusses that these models are trained on vast amounts of data, including copyrighted works, web-scraped material, proprietary enterprise data, and user-generated content. It does not specify a single dataset for a technique it studies. | NaN | NaN | True | False | The paper mentions existing generative AI tools like ChatGPT and DALLE-3, some of which are publicly accessible for use (e.g., ChatGPT via OpenAI's platform). | Need for clear application of fair use to AI training and outputs; Development of effective, feasible opt-in/opt-out mechanisms and compensation models for creators; Establishing clear lines of authorship and responsibility for AI-assisted works. | For enterprises adopting Generative AI: regulatory compliance (e.g., data use, privacy, security), ownership of proprietary data used for training, integration with existing workflows. For consumer data applications: gaining access to data, overcoming privacy/security concerns. | Hasty regulatory interventions based on misconceptions leading to stifled innovation or ineffective rules; Infringement of copyright through AI outputs if fair use and licensing are not clarified; Unfair exploitation of creators' works for AI training without consent or compensation; Generation of harmful or undesirable content if AI tools are not properly controlled or are misused. |
| 3631935.pdf | Google_Scholar | Why Are Lawyers Afraid of AI? | The article discusses the rapid adoption and impact of generative AI, particularly ChatGPT, in the legal profession, highlighting both its potential to revolutionize legal work and the significant concerns lawyers have regarding its accuracy, ethics, and impact on jobs. It also touches upon early instances of misuse and the legal community's efforts to establish guidelines and responsible deployment strategies. | True | Market | True | 3.0 | Neutral | Generative AI (e.g., ChatGPT, CoCounsel, Harvey.AI, Google Bard) | Reports on Andrew Perlman's experiment using ChatGPT to generate a paper and testing Bing Chat. Perlman prompted ChatGPT for his paper and assessed Bing Chat's answers to legal questions. | Perlman found Bing Chat operated at the level of a B to B+ law student, but its knowledge of certain legal doctrines like personal jurisdiction was problematic and incomplete. | High cost of legal services making them unaffordable for a large portion of the population (lower-income and middle-class individuals), with an estimated 80% of lower-income individuals unable to afford a lawyer and 40-60% of middle-class legal needs unmet. | Development of AI-powered curated platforms offering basic professional-level legal expertise at lower costs, similar to tax preparation software, to improve affordability and access to legal services. | Affordability and accessibility of legal services, unmet legal needs | Lower-income individuals and middle-class Americans | General legal practice (covering document generation, legal research, case briefing) | U.S., U.K., Canada | General internet data and other sources for foundational models like GPT-4 (used by ChatGPT and CoCounsel); specific training data for proprietary tools like CoCounsel or Harvey.AI is not detailed but likely involves legal texts for fine-tuning. | NaN | Gradual rollout in law firms (e.g., Dykema Gossett adopting CoCounsel), use of LLM governance tools (e.g., Lega) to monitor compliance and manage risks. | True | True | Publicly available tools like ChatGPT (with a free tier) and Google Bard are mentioned. Commercial tools like CoCounsel are available to subscribing firms. | Persistent unaffordability of legal services for many, leading to unmet legal needs. Current AI's limitations in accuracy (e.g., 'problematic and incomplete' knowledge of certain doctrines) and the need for skilled prompting also present gaps in its effectiveness for broad A2J application. | User misunderstanding of AI capabilities (e.g., treating LLMs as search engines), lack of established guidelines for using new AI tools, ensuring data confidentiality with client information, the rapid pace of AI development outpacing governance, and integrating AI responsibly into legal workflows without compromising professional responsibilities or accuracy. | Generation of inaccurate or fictitious information ('hallucinations') by AI, such as citing non-existent cases. General ethical concerns about AI use in legal work. Potential for misuse due to lack of user understanding of the technology's limitations. |
| AiNuo2C-gn4J.pdf | Google_Scholar | Interpretable Long-Form Legal Question Answering with\nRetrieval-Augmented Large Language Models | The paper proposes a retrieval-augmented LLM methodology for generating interpretable, long-form answers to French statutory law questions to improve access to legal information. It introduces the LLeQA dataset for this task and finds that while models generate fluent answers, they often suffer from factual inaccuracies. | True | Idealistic | True | 1.0 | Positive | A retrieve-then-read pipeline using a fine-tuned bi-encoder retriever (CamemBERT-based) and instruction-tuned Large Language Models (LLMs like Vicuna, WizardLM, TULU, Guanaco) adapted via in-context learning or QLoRA finetuning. Includes extractive rationale generation (paragraph IDs). | Retriever evaluated using Recall@k (k=5, 10) and MRR@10 on LLeQA dev set. Generator evaluated using METEOR for answer quality and F1 score for rationale extraction on LLeQA test set, supplemented by qualitative analysis. | Fine-tuned CamemBERT retriever achieved R@5=48.6, R@10=60.6. Fine-tuned WizardLM-1.0 (7B) generator achieved the best METEOR score (20.4). Qualitative analysis revealed significant hallucination issues despite syntactic correctness. Rationale extraction F1 was very low (<3.5%). | Lack of legal understanding/literacy, prohibitive cost of legal assistance, difficulty navigating legal complexity, prevalence of unhelpful/commercial online legal advice. | Develop automated, interpretable long-form legal question answering systems using retrieval-augmented LLMs to provide affordable, accessible legal information. | Access to legal information, automated legal aid, statutory law question answering (covering housing, healthcare, family, work, immigration, money, privacy, justice). | Vulnerable individuals, laypersons, Belgian citizens, marginalized parties, people unable to afford legal assistance. | Statutory law (multiple domains) | Belgium | LLeQA dataset: 1,868 expert-annotated French legal questions with detailed answers and references to relevant Belgian statutory articles (27,942 article corpus). Paragraph-level rationales partly expert-annotated, partly synthetically generated (gpt-3.5-turbo). Sourced via partnership with Belgian non-profit Droits Quotidiens. | Retrieve-then-read pipeline; Bi-encoder retriever fine-tuned contrastively; LLM reader adapted via in-context learning and parameter-efficient fine-tuning (QLoRA); Dynamic NTK-aware scaling for context extension; Extractive rationale generation via prompting. | Public release of code, data, and models on GitHub. | True | True | Public release of code, dataset (LLeQA), and model checkpoints on GitHub. | Inadequacy of automatic metrics for evaluating long-form QA factuality; LLM propensity for hallucination; Need for improved retrieval performance; Scalability of reliable rationale generation for multi-document contexts. | Handling long legal document context within LLM limits; Effective domain adaptation for retrieval; Ensuring factual accuracy and mitigating hallucinations in generation; Accurate evaluation of long-form answers; Generating faithful and interpretable rationales; Computational resource constraints. | Laypersons relying on potentially inaccurate or hallucinated AI-generated legal advice, leading to detrimental real-world consequences; Potential for misuse despite research purpose limitations. |
| LMgkDC4nxD0J.pdf | Google_Scholar | ChatGPT as a Copilot for Investigating Digital Evidence | This paper explores using ChatGPT (specifically GPT-4) to assist digital forensic investigators with tasks like generating structured queries from natural language, summarizing and analyzing chat communications, and analyzing search results. It finds that ChatGPT shows significant promise as an investigative assistant once provided with relevant domain knowledge, like query languages and data models. | True | Market | True | 2.0 | NaN | Using ChatGPT/GPT-4, prompted with domain-specific knowledge (Hansken trace model, Hansken Query Language documentation), to perform digital forensics tasks: natural language to structured query conversion, chat summarization/analysis/visualization, and cross-evidence analysis. | Three experiments using ChatGPT/GPT-4: 1) Generating Hansken Query Language (HQL) queries from natural language after being prompted with HQL documentation and examples. 2) Summarizing, analyzing roles, and visualizing chat messages from a fictitious case dataset (Crystal Clear). 3) Analyzing and correlating browser history, chat summaries, and GPS data from the same fictitious case. | ChatGPT successfully generated valid HQL queries after being prompted with the model and language specifics. It produced accurate summaries, role descriptions, and network visualizations (TikZ) from chat data. It also demonstrated capability in analyzing and cross-referencing diverse digital evidence types (browser history, chats, GPS data) within a case scenario. | NaN | NaN | NaN | NaN | Digital Forensics, eDiscovery, Criminal Law | Netherlands, UK, US mentioned, but techniques potentially International. | The underlying model (GPT-4) was pre-trained by OpenAI on broad data. The experiments involved prompting the model with provided Hansken documentation (manual, trace model, cheat sheet) and data from a fictitious case (Crystal Clear training case: chat logs, browser history, GPS coordinates). | Empirical evaluation via prompt engineering experiments on specific digital forensics tasks. | NaN | True | False | Access via OpenAI's ChatGPT web application (GPT-4 access typically requires subscription). | Need for user experience evaluation in real investigations with larger datasets; potential limitations of current models (hallucinations, context size); need for fine-tuning models specifically for investigative tasks (SleuthGPT concept). | Prompt size limits requiring data splitting, maintaining consistency across prompts, necessity for format reminders, requirement for human correction of AI mistakes, need for detailed domain-specific context prompting, potential limitations of RLHF alignment ("harmlessness") for analyzing criminal content. | Hallucinations (factual inaccuracies), cost and privacy concerns associated with cloud-based models handling sensitive legal/investigative data, RLHF alignment potentially hindering effective analysis of criminal communications. |
| 1hDJ716g7tIJ.pdf | Google_Scholar | TOWARDS THE EXPLOITATION OF LLM-BASED CHATBOT FOR PROVIDING LEGAL SUPPORT TO PALESTINIAN COOPERATIVES | This paper presents the development and evaluation of an LLM-based chatbot designed to provide legal support to Palestinian cooperatives by answering questions related to cooperative law. The chatbot, utilizing ChatGPT and LlamaIndex with curated legal documents and Q&A datasets, achieved an overall accuracy of 82% on expert-generated queries. | True | Idealistic | True | 1.0 | Positive | LLM-based chatbot using ChatGPT API and LlamaIndex for vectorization and indexing of Palestinian cooperative law documents and Q&A datasets. | Evaluation using 50 queries generated by a legal expert. Chatbot's answers were compared to the expert's answers, and metrics including overall accuracy, average satisfaction score (rated by legal counsel), precision, recall, and F1-score were calculated. | The chatbot achieved an overall accuracy of 82% (41 out of 50 questions answered correctly or relevantly). The F1 score was 79%, and the average satisfaction score was 78.3%. For distinguishing right/related answers, precision was 1.0, recall 0.79, and F1-score 0.88. | The urgent need for readily available legal answers for cooperative members due to new laws, the labor-intensive effort required for manual responses, and the large number of cooperative members needing timely assistance. | Developing an LLM-based chatbot available 24/7 to provide legal information and answer inquiries about Palestinian cooperative law. | Access to legal information and support regarding Palestinian cooperative law. | Palestinian cooperatives, cooperative societies, cooperative unions, and their members. | Cooperative law | Palestine | A dataset comprising: 1) Formal Legal Documents (Law No. 20 of 2017 on Cooperatives, Cooperatives Bylaws, Housing Cooperatives Bylaws) - text data. 2) Question and Answers Dataset consisting of a human-generated set (40 Q&A by a legal advisor) and a ChatGPT-generated set (350 Q&A based on Law No. 20 of 2017, prompted to format like a legal advisor). These documents were indexed by LlamaIndex for the chatbot. | The system uses LlamaIndex to index legal documents and Q&A datasets, creating vectors for document chunks (600 tokens, 50 token overlap) to overcome ChatGPT's token limits. A LlamaIndex query engine, leveraging ChatGPT, is used to answer legal queries. Prompt engineering was used to generate a portion of the Q&A dataset. | NaN | True | True | The paper states a GitHub repository is available for more information and details, with a placeholder link: "Github". | Instances of incorrect chatbot answers, need for continuous development to improve accuracy and reliability, the necessity of transparency about chatbot limitations, insufficient Q&A data for long or complex legal articles, and the need for post-processing of chatbot answers. | The primary challenge was handling the large volume of textual data, which exceeded ChatGPT GPT-4’s token processing limit, necessitating the use of LlamaIndex for document chunking and vectorization. Other challenges included ensuring sufficient Q&A data for comprehensive coverage of all legal articles, especially longer ones, and providing context for specific bylaws. | The chatbot providing incorrect answers, which could lead users to unintentionally violate legal regulations. |
| tags21nqSE4J.pdf | Google_Scholar | AI, Justice, and the Ecosystem Approach – Notes from the OpenNyAI Mission | OpenNyAI is an Indian initiative leveraging AI to enhance access to justice by developing open-source public goods like AI models and APIs, supported by a collaborative ecosystem of legal and tech communities. The paper highlights projects like Jugalbandi, a conversational AI for legal information in local languages, and emphasizes transparent, inclusive practices to make justice more accessible. | True | Idealistic | True | 1.0 | Positive | OpenNyAI's initiatives including: NLP models for legal text analysis (Rhetorical Roles Model, Legal Named Entity Recognition Model, Judgment Summarizer); Jugalbandi Stack (LLM-based conversational AI for multilingual information access); Jugalbandi Studio (open-source chatbot development platform). | User adoption (7000+ unique users for models) and an open-source testing environment provided by Jugalbandi Studio. No formal benchmarks or detailed evaluation procedures are mentioned. | The models (Rhetorical Roles, NER, Summarizer) are reported as generating value across law firms, government bodies, and other institutions. Jugalbandi enables multilingual access to information on government schemes and legal aid. Jugalbandi Studio allows organizations to rapidly iterate on chatbot development without extensive technical expertise or large capital investments. | Initial landscape: significant divide between legal and tech professionals, lack of open-source reference solutions, and poor data quality. Broader A2J issues: information asymmetry, language barriers. Tech deployment challenges: understanding AI capabilities, resource constraints for SMEs/NGOs. | Building a collaborative ecosystem (OpenNyAI mission); developing open-source AI public goods (models, Jugalbandi Stack, Jugalbandi Studio); creating data annotation pipelines; using AI for language access and information retrieval; providing tools to lower deployment barriers; ensuring data privacy. | Language access in legal information, access to government schemes/entitlements, legal aid, access to laws and court procedures, improving efficiency of legal processes, enhancing capacity of legal professionals and civil society. | General public in India, particularly those facing information asymmetry and language barriers, including farmers, women, victims of domestic abuse, litigants, students, lawyers, judges, SMEs, and NGOs. | Administrative law (government schemes), family law (domestic abuse), criminal law (investigation guidelines), civil procedure (court processes), dispute resolution (ODR), general legal information access. | India | For NLP models: Meticulously annotated datasets of Indian court judgments, created via a data annotation pipeline involving law students. For Jugalbandi: Verified knowledge bases curated by subject matter experts. These are domain-specific (legal) and structured (annotated) data. | Ecosystem approach, interdisciplinary collaboration (legal, tech, academia, civil society), community building (Maker Residency, learning circles), open-source development, development of a data annotation pipeline. | Models used by over 7000 users. Jugalbandi Stack is a free and open tech stack. Jugalbandi Studio is an open-source platform running on an organization's own cloud server. General strategy is creating AI public goods and community empowerment. | True | True | Jugalbandi Stack is described as a 'free and open tech stack'. Jugalbandi Studio is an 'open-source platform'. GitHub links for OpenNyAI and Jugalbandi are provided in the references, indicating open accessibility of resources. | A 'sheer knowledge gap that exists in accessing these technologies' among potential users and deployers of AI solutions. | Understanding AI technology's capabilities by non-technical users/organizations, lack of resources (financial, technical) for SMEs/NGOs to deploy AI at scale, ensuring high-quality data for training models, bridging communication gaps between legal and tech communities. | Data privacy of users interacting with AI systems, particularly concerning sensitive personal information. Mitigation includes PII filtering and local/private cloud deployment. |
| 4tKvGBLOdNEJ.pdf | Google_Scholar | How We Learned to Stop Worry ing and Love AI: Analyzing the Rapid Evol ution of Generative Pre - Trained Transformer (GPT) and its Impacts on Law, Business, and Society | This paper surveys the rapid evolution of Artificial Intelligence (AI), focusing on Generative Pre-trained Transformer (GPT) models like ChatGPT and their broad impacts across law, business, society, and national security. It examines AI's history, current capabilities, potential applications, significant risks (including bias, disinformation, job displacement, and security threats), and emerging governance efforts in the US and EU. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | Lack of access to legal help, potential for algorithmic discrimination and bias, inaccuracy/unreliability of AI tools (hallucinations, fake citations). | Leveraging technology for ADR (e.g., online platforms), promoting responsible AI development through governance frameworks and supporting R&D infrastructure. | Alternative Dispute Resolution (ADR), potential courtroom applications, general legal problem resolution. | General population facing legal problems; minorities and people of color susceptible to algorithmic bias. | AI Regulation, Corporate Governance, Data Protection/Privacy, Cybersecurity Law, Practice of Law (general), ADR, Antitrust, Employment Law, Intellectual Property, National Security Law. | US, EU, Italy, China, International | Refers to training data used by others, e.g., GPT-3 trained on large unlabeled text datasets (Wikipedia, websites), largely English; AlphaZero trained via self-play; RLHF uses human feedback. | NaN | NaN | False | False | NaN | Need for robust governance frameworks (explainability, accountability, fairness), closing the digital divide in AI resource access, ensuring AI reliability (addressing hallucinations), mitigating bias and discrimination, bridging the gap between technology development and legal/policy adaptation. | NaN | Disinformation/Deepfakes, cybersecurity threats, job displacement, algorithmic bias/discrimination, algorithmic collusion, privacy violations, national security risks (arms race, autonomous weapons), erosion of trust, manipulation, safety risks (unsafe/ineffective systems), intellectual property infringement, exacerbation of inequality. |
| Yyq2ZqBnxDMJ.pdf | Google_Scholar | CHATMAP : LARGE LANGUAGE MODEL INTERACTION WITH CARTOGRAPHIC DATA | This paper presents a proof-of-concept for fine-tuning a small large language model (LLM) using an artificially generated dataset to enable natural language queries about OpenStreetMap (OSM) cartographic data. The approach aims to provide a linguistic interface for users to inquire about various attributes of specific urban locations. | True | NaN | True | 1.0 | NaN | Fine-tuning a 1B parameter LLM (Falcon 1B RW) using Low Rank Adaptation (LORA) and 8-bit quantization with an artificially curated dataset (prompt-answer pairs generated by ChatGPT 3.5-turbo from OpenStreetMap data descriptions) to enable natural language interaction with cartographic data. | The fine-tuned model was queried with preprompts (verbal descriptions of OpenStreetMap data) for geolocations not in the fine-tuning dataset, using various question types. Qualitative examples of model responses were provided, and validation loss was monitored during training. | The fine-tuned model demonstrated 'early signs of emerging abilities' by providing plausible responses to queries about urban areas based on OSM data, including for locations not in the fine-tuning set. Examples showed the model classifying regions, assessing suitability for living/tourism, and suggesting business viability. | NaN | NaN | NaN | NaN | NaN | Istanbul, Turkey | An artificial dataset of 4111 prompt-answer pairs generated by OpenAI ChatGPT 3.5-turbo. These pairs were based on 81 'preprompts'—verbal descriptions of OpenStreetMap (OSM) data (amenities, buildings, land use, roads, etc.) for circular areas (300m radius) in selected districts of Istanbul. The data is domain-specific (geospatial/urban) and unstructured (natural language text). | Proof-of-concept development involving: 1) Extraction and verbalization of OSM data into 'preprompts'. 2) Artificial dataset curation using a teacher LLM (ChatGPT 3.5-turbo) to generate prompt-response pairs. 3) Fine-tuning a pre-trained LLM (Falcon 1B RW) using Low Rank Adaptation (LORA) and 8-bit quantization. | NaN | False | False | NaN | NaN | Simplifying user interaction with complex cartographic datasets and overcoming the need for specialized tools or domain expertise, particularly under constraints of minimal computational budget and limited human-labeled data by using a teacher model for dataset creation and efficient fine-tuning. | Potential for the LLM to generate answers not sufficiently supported by the provided cartographic data, highlighted by the need to carefully instruct the teacher model during dataset creation to avoid responses based on insufficient information. |
| BrenoNiero_theFutureofLawFirmsRG.pdf | Google_Scholar | AI-Law Firms of the future. The integration of artificial intelligence and other cutting-edge technologies for value creation | This paper explores the integration of AI (including generative AI), SuperApps, Metaverse, and AI cybersecurity into law firms to enhance operational efficiency and client services, citing a case study of Allen & Overy using the Harvey platform. It proposes a conceptual prototype of a 'SuperApp' for secure lawyer-client interaction and streamlined workflows, while acknowledging challenges like data privacy and potential job displacement. | True | Market | True | 1.0 | NaN | Conceptual prototype of an integrated system for law firms featuring a SuperApp, Adaptive AI, Metaverse integration for meetings, and AI-based Cybersecurity. | NaN | NaN | NaN | NaN | NaN | NaN | General legal practice | International | The paper mentions the Harvey platform used by Allen & Overy is based on GPT-3 architecture and trained on 'a vast amount of legal documents and contracts'. The proposed prototype implies use of private client data and legal knowledge bases, but specifics are not provided. | Conceptual design based on analysis of technology trends (Gartner: SuperApps, Adaptive AI, Metaverse, AI Cybersecurity) and layered architecture concepts (McKinsey: Client Engagement, AI-powered systems, Core Technologies and Data). | NaN | False | False | NaN | Need for AI systems to be transparent, explainable, and accountable; requirement for ethical and legal frameworks governing AI use in law. | Balancing AI performance with data security (especially regarding sensitive client data); potential widening of the social gap between clients and lawyers; need for continuous learning and adaptation for AI systems; integrating diverse technologies effectively. | Job displacement for human lawyers and legal staff; potential for AI algorithms to reinforce existing biases and perpetuate discrimination; misuse or mishandling of sensitive client data; cyber-attacks. |
| KFrN-E4j064J.pdf | Google_Scholar | Generative AI in the Attorney-Client Relationship: An Exercise in Critical Revision and Client Management | The paper proposes educational exercises for law students simulating scenarios where clients present flawed AI-generated legal documents like motions to suppress. These exercises aim to develop students' skills in critically revising AI output and managing client expectations and resistance, particularly regarding cost or misguided confidence in AI. | True | Market | True | 1.0 | Neutral | Pedagogical exercises for law students involving the review and revision of AI (ChatGPT-3) generated legal motions (motions to suppress) within hypothetical client scenarios. | NaN | NaN | Clients misusing generative AI (like ChatGPT) due to cost concerns or overconfidence, leading them to present flawed, AI-generated legal documents to their attorneys and resist professional revision or advice. | Training law students through specific exercises to critically evaluate AI-generated legal text, identify errors (like misstated case law), revise appropriately, and develop diplomatic client communication strategies to explain AI limitations and the value of legal expertise. | Access to justice (briefly mentioned as a potential area impacted by AI for laypeople generating documents) | NaN | Criminal Law, Criminal Procedure, Legal Education, Legal Ethics | United States | NaN | Creation of hypothetical client scenarios and legal fact patterns, use of ChatGPT-3 to generate sample legal documents for analysis. | Presented as adaptable examples within the academic paper for use by legal educators. | False | False | NaN | Lack of training in legal education for handling client misuse of generative AI; limited discussion in legal commentary on client-side AI use compared to attorney-side use. | Designing effective pedagogical methods to teach critical evaluation of AI output and related client management skills. Inherent limitations of AI like generating convincing but flawed or incorrect legal arguments (e.g., misstating case holdings like Whren v. United States) and omitting relevant authorities (e.g., Kyllo v. United States). | Clients relying on inaccurate AI-generated legal documents; attorneys potentially filing flawed documents leading to sanctions or poor outcomes; damage to attorney-client relationships; undermining credibility with courts; AI enabling unauthorized practice of law. |
| mfP-piOctqgJ.pdf | Google_Scholar | BianCang: A Traditional Chinese Medicine Large Language Model | This paper introduces BianCang, a Large Language Model specialized for Traditional Chinese Medicine (TCM), developed using a two-stage training process involving domain-specific knowledge injection and targeted alignment. BianCang was evaluated on multiple TCM-specific tasks and datasets, demonstrating superior performance in syndrome differentiation and diagnosis compared to existing models. | True | NaN | True | 1.0 | NaN | BianCang, a Traditional Chinese Medicine (TCM) Large Language Model based on Qwen2/2.5, developed through a two-stage training process: continuous pre-training for knowledge injection and supervised fine-tuning (SFT) for alignment with TCM tasks. | Evaluated on 11 test sets across 4 tasks: TCM syndrome differentiation (TCMSD, TCMSD-BC), TCM disease diagnosis (TCMDD, TCMDD-BC), medical exams (MLEC-QA, CMB), and subjective medical record analysis (BC-Analytical). Performance was compared against 29 other models using metrics like accuracy and human-rated win/tie/loss rates. | BianCang-Qwen2.5-7B-Instruct achieved an accuracy of 82.10% on the TCMSD test set (Chain-of-Thought) for TCM syndrome differentiation. BianCang models generally outperformed baselines across TCM syndrome differentiation, disease diagnosis, and medical exam tasks. | NaN | NaN | NaN | NaN | NaN | China | A combination of publicly available and proprietary data. Pre-training: TCM/medical books, encyclopedias, literature, the Pharmacopoeia of the People’s Republic of China, real patient case records, TCM syndrome differentiation/diagnosis records, ChatMed-TCM, CMB-Train, and general domain corpora (COIG-CQIA, APE-210K, Evol-Instruction-66K). SFT: Custom instruction sets from the Pharmacopoeia and real hospital records (ChP-TCM, TCMSD-DD-SFT, TCM-WM-DiffDiag-SFT, TCM-Plan-SFT), existing medical dialogue (DISC-Med-SFT), exam (MLEC-SFT), and general domain datasets. | A two-stage training process: 1) Continuous pre-training on a foundational LLM (Qwen2/2.5) to inject extensive TCM and medical knowledge, including real medical records. 2) Supervised fine-tuning (SFT) using a curated set of TCM-specific instructions to activate and align the model's internal knowledge. | Code, datasets, and models are made available on GitHub. | True | True | Code, datasets, and models are available at https://github.com/QLU-NLP/BianCang. | NaN | General challenges in TCM LLM development addressed by BianCang: 1) Significant differences between TCM and modern medical theory. 2) Scarcity of specialized, high-quality TCM corpora. 3) Existing LLMs' limited capabilities in real-world TCM syndrome differentiation and diagnostic analysis. | BianCang cannot guarantee accuracy in all its responses and is an auxiliary research tool, not a substitute for professional TCM consultation. Users are advised to exercise caution with generated information and seek expert advice due to potential serious consequences of inaccurate medical data. |
| atliUSlYSC0J.pdf | Google_Scholar | WHY LAWYERS MUST RESPONSIBLY EMBRACE GENERATIVE AI | This paper argues that legal professionals must responsibly adopt Generative AI (GenAI) to enhance efficiency, maintain competence, and remain competitive, addressing common counterarguments and ethical concerns. It proposes a framework and best practices for navigating GenAI integration while managing risks like inaccuracy, bias, and confidentiality breaches. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | The high cost of legal representation and advice, rendering the judicial process inaccessible to a substantial portion of the population. | Widespread adoption of GenAI could revolutionize legal service delivery, enabling more providers to offer affordable services, thus potentially narrowing the access-to-justice gap. | Affordability and accessibility of legal services. | Low-income individuals or populations unable to afford legal representation. | General legal practice, with examples from mergers and acquisitions, due diligence, contract management, litigation, e-discovery, legal research, employment law, intellectual property, data privacy. | United States (references ABA Model Rules, US case law, federal agencies like EEOC/FTC/DOJ, White House initiatives, NYC Local Law 144, Colorado AI Act, California State Bar opinions, Utah sandbox program). | NaN | NaN | NaN | False | False | NaN | The access-to-justice gap due to high costs remains a significant challenge. Ensuring GenAI development and deployment is equitable, accurate, unbiased, and affordable enough to meaningfully address this gap are remaining technical and societal gaps. | Managing risks (confidentiality, ethics, bias, accuracy/hallucinations, IP infringement), ensuring legal compliance, need for extensive training and education, overcoming skepticism, requirement for human verification of outputs, developing robust governance policies and AI risk management frameworks, adapting to rapid technological change. | Generating biased or inaccurate information (hallucinations); violating client confidentiality or unauthorized disclosure of sensitive business information/IP; violating ethical rules (competence, diligence, supervision, communication, combating bias); intellectual property infringement; employment law violations (e.g., discrimination via biased AI); security vulnerabilities and data breaches; reputational damage; potential liability for privacy/data protection violations; competitive disadvantage and negative talent impact if adoption is resisted; risks from unmonitored 'shadow IT' use of AI; increased social engineering attack vectors. |
| 21TvEm4C_3QJ.pdf | Google_Scholar | Argumentative Segmentation Enhancement for Legal Summarization | This paper proposes a method to improve legal case summarization by first identifying argumentative segments within legal decisions using a novel classification task. These segments are then summarized by GPT-3.5, reportedly yielding higher quality summaries and overcoming token limits compared to baseline GPT models and non-GPT approaches. | True | Idealistic | True | 1.0 | Positive | An approach combining argumentative zoning principles (using IRC triples for legal arguments) with a C99 text segmentation algorithm to identify argumentative segments in legal decisions. These segments are then classified using LegalBERT and subsequently summarized using prompted GPT-3.5. | Argumentative segment classification was evaluated using F1 score on a test set (LegalBERT vs. BERT). Summarization quality was evaluated using ROUGE-1, ROUGE-2, ROUGE-L, BLEU, METEOR, and BERTScore, comparing the proposed argumentative segmentation enhanced GPT-3.5 method against baseline GPT-3.5, GPT-4, and fine-tuned non-GPT models (LED, T5, BART) on a test set of CanLII decisions. | For argumentative segment classification, LegalBERT achieved an 80.14% F1 score. For summarization, the argumentative segmentation enhanced GPT-3.5 (temp 0, max_tokens 128) achieved Rouge-1: 49.42, Rouge-2: 23.98, Rouge-L: 46.07, BLEU: 17.54, METEOR: 0.32, and BERTScore: 87.30, outperforming baselines on several metrics. | The difficulty in consuming and understanding long, complex legal documents, and the technical challenge of input token limitations in large language models when processing such documents. | An AI-driven method for legal summarization that focuses on argumentative segments to make legal texts more digestible and to overcome token limitations of LLMs for processing long documents. | Improving understanding of legal documents (case decisions) through automated summarization to make legal information more accessible. | NaN | General case law (variety of legal claims). | Canada | A corpus of 1,049 Canadian legal case decisions from CanLII. These decisions were sentence-split and annotated by researchers with Issue, Reason, Conclusion (IRC) triples. Text segments (derived using C99 algorithm) were then labeled as 'argumentative' or 'non-argumentative' based on the presence of IRC sentences. This dataset was used for training the argumentative segment classifier and fine-tuning baseline summarization models. | Linear text segmentation (C99), sentence embedding (Sentence-BERT), supervised classification (LegalBERT), prompt-based learning with LLMs (GPT-3.5, GPT-4), and principles of Argumentative Zoning and IRC triple annotation. | NaN | False | False | NaN | Coherency issues in generated summaries; need for systematic human evaluation; reproducibility challenges with proprietary LLMs; need for reliable performance of proprietary models and alternative prompt engineering techniques. | Input token limitations of LLMs for long legal documents; ensuring summaries capture important argument-related information; cost of using advanced LLMs; potential coherency issues in generated summaries; reproducibility of results with proprietary models. | Coherency issues in generated summaries; reproducibility challenges with proprietary LLMs and potential changes to these models by their providers. |
| AMAOheVRUmwJ.pdf | Google_Scholar | SoK: Prompt Hacking of Large Language Models | This paper provides a systematic overview of three types of prompt hacking attacks on Large Language Models (jailbreaking, leaking, and injection), differentiating their nuances and goals. It also proposes a novel framework for categorizing LLM responses to such attacks and experimentally evaluates the robustness of several common LLMs. | True | NaN | True | 1.0 | NaN | A 5-category framework for classifying LLM responses to prompt hacking attacks (Reject - Irrelevant Output, Reject - Safety Mechanism Triggered, Prompt too Long, Partial Response, Full Response). | Seven existing LLMs (Gemini, Copilot, Perplexity, You.com, ChatSonic, ChatGPT-3.5, ChatGPT-4) were tested against crafted jailbreak (DAN, Pretending), injection (direct, indirect), and leaking prompts. Responses were categorized using the paper's proposed 5-class framework based on seven illegal questions adapted for each attack type. | Gemini demonstrated robust defenses: it triggered safety mechanisms in 100% of jailbreak attempts and 71% of injection attempts (though 29% of injections still yielded partial or full harmful content). For leaking attacks, Gemini produced irrelevant output or only a partial response in all instances. | NaN | NaN | NaN | NaN | NaN | International | NaN | Conceptual categorization for the response classification framework; empirical testing with crafted prompts for LLM evaluation. | NaN | True | True | The evaluation framework, illegal questions, and example prompts/templates are described in the paper (available on arXiv), allowing for replication of the testing approach. | NaN | NaN | Generation of harmful, biased, deceptive, or illegal content; exposure of sensitive information (e.g., personal data, proprietary algorithms, intellectual property, custom instructions); generation of malware; undermining LLM system security, reliability, and integrity; perpetuation of biases. |
| uQbnPMyYpagJ.pdf | Google_Scholar | PREPARING STUDENTS F OR THE ARTIFICIAL \nINTELLIGENCE ERA: THE CRUCIAL ROLE OF CRITICAL \nTHINKING SKILLS | This paper argues that AI's integration into legal practice necessitates strong critical thinking skills for lawyers to evaluate AI outputs and handle complex analytical tasks AI cannot perform. It highlights a current deficit in these skills among incoming law students and urges law schools to adapt curricula and assessment methods to address this gap effectively. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | Potential widening of the digital divide due to high resource/expertise requirements for AI; Bias in AI outputs; Accuracy issues (hallucinations); Ethical concerns (confidentiality, IP). | NaN | NaN | NaN | General Legal Practice, Legal Education | USA | General discussion mentions training on extensive (often public) data, including legal texts (case law, statutes, contracts, commentary), with copyright concerns noted. | NaN | NaN | False | False | NaN | Critical thinking skills deficit among law students/graduates needed to effectively use and evaluate AI in legal practice; Potential widening of the digital divide. | NaN | AI hallucinations/inaccuracy; Misuse (e.g., deepfakes); Bias amplification; Intellectual property infringement (training data & output); Client confidentiality breaches; Security vulnerabilities; Job displacement for routine tasks; Widening digital divide; Environmental impact; Over-reliance hindering critical thinking skill development. |
| fqMuO36AeyQJ.pdf | Google_Scholar | Preparing Future Lawyers to Draft Contracts and Communicate with Clients in the Era of Generative AI | This paper argues for the necessity of integrating generative AI into transactional law curricula, addressing both the capabilities and significant risks (confidentiality, hallucinations, bias) of these tools. It further proposes practical pedagogical strategies and assignment adaptations for law professors to prepare students effectively for an AI-augmented legal profession. | True | Market | True | 3.0 | Positive | Discussion and pedagogical integration of existing GenAI tools (e.g., ChatGPT, Spellbook, Harvey, CoCounsel, Lexis Plus AI) in legal education, exemplified by exercises like AI-assisted client email drafting and contract review with critical analysis. | Pedagogical approaches were explored through classroom exercises, such as having students use ChatGPT or a university GPT to draft a client email based on a hypothetical scenario, followed by in-class critique of the AI's output and the students' prompts. The author also describes adaptations to assignments like mock contract redlining. | For the AI-assisted email drafting exercise, results indicated that prompt quality significantly influenced output quality, students with existing legal knowledge formulated better prompts leading to superior AI outputs, and the AI-generated drafts served as effective starting points for class discussions on legal accuracy, communication strategies, and AI limitations. | Inefficiency and high cost of traditional legal work, limiting its accessibility and scope. | GenAI tools can significantly increase lawyer efficiency, potentially freeing up resources for more pro bono or lower-cost legal services and enabling innovative applications like AI-assisted case screening for innocence projects, thereby helping to reduce barriers to justice. | Increasing lawyer efficiency to potentially expand legal aid or pro bono capacity; AI-assisted case review and analysis for meritorious claims (e.g., innocence projects). | General underserved populations (by potentially reducing overall barriers to justice); wrongfully convicted individuals (implied by the Innocence Project example). | Transactional law (contract drafting, review), client communication, legal research, litigation support (brief drafting, deposition preparation), due diligence, regulatory compliance, employment law (intern classification). | Primarily United States (examples from US law schools, US legal cases, California Innocence Project). Mentions global law firms using AI tools, suggesting broader applicability. | For ChatGPT: Described as trained on 'everything on the Internet' and user inputs. For Harvey: GPT model base, further trained with 'general legal data, including case law and reference materials,' and a firm's 'own work product and templates.' For Lexis Plus AI: 'treatises in LexisNexis.' | NaN | For tools discussed: ChatGPT is publicly available. Harvey, CoCounsel, Lexis Plus AI, and Spellbook are commercial products for legal professionals, deployed via firm licensing, cloud platforms, or Word plug-ins. Spellbook mentioned potential future availability to law schools. | True | True | ChatGPT (free version) and DALL·E are described as freely and publicly available. Spellbook offered the author a trial and was exploring making its tool available to law schools, potentially for free. | Equitable access to robust, reliable, and ethically sound AI tools for A2J practitioners and underserved communities, especially since free AI models have significant limitations (confidentiality, bias, hallucinations) and more advanced tools are often costly. Ensuring AI development and deployment considers A2J needs. | For the author's pedagogical approach: Adapting curriculum to effectively integrate AI while ensuring students develop foundational legal skills and critical thinking. Addressing student over-reliance on AI, teaching ethical AI use (confidentiality, accuracy, bias), and managing equitable access to AI tools among students. | Confidentiality breaches from inputting sensitive client data into open AI models. AI hallucinations leading to reliance on false information or non-existent case law. Perpetuation and amplification of biases present in training data. Deskilling of lawyers or hindering the development of foundational legal skills in students due to over-reliance. Potential job displacement or significant changes in the nature of legal work. Unrealistic client expectations regarding speed and cost. Broader societal risks including intellectual property infringement, liability issues for AI-generated errors, discrimination, and challenges in developing appropriate AI regulation. |
| SBSqIelFtX8J.pdf | Google_Scholar | AI-POWERED ANALYSIS OF COURT DECISIONS: THE UKRAINIAN EXPERIENCE | This paper proposes using the GPT-4 language model to automatically extract relevant predefined facts from unstructured Ukrainian court decisions, specifically criminal verdicts. The aim is to replace labor-intensive manual analysis, thereby increasing efficiency, improving consistency in judicial practice, and potentially aiding in judicial competence assessment. | True | Market | True | 1.0 | Positive | Using GPT-4 with a specific prompt structure (listing criteria to be extracted) to generate relevant facts from unstructured court decisions via natural language generation. | Illustrative example showing input prompt and GPT-4 generated output for a single court decision. | Provided a single qualitative example demonstrating successful extraction of predefined facts from a court decision text using the proposed GPT-4 prompting method. | Large volumes of unstructured text data in court registries (USRCD), complexity and time-consuming nature of manual analysis, need for consistency in judicial practice, limitations in current search functionalities. | Automated fact extraction and analysis of court decisions using AI (specifically GPT-4) to enhance efficiency, ensure consistency, analyze trends, and potentially assess judicial competence. | Analysis of judicial decisions, consistency of judicial practice, sentencing criteria in criminal cases, judicial efficiency, judicial competence assessment. | NaN | Criminal Law | Ukraine | The technique applies a pre-trained LLM (GPT-4). Data used for application consists of unstructured court decisions from the Unified State Register of Court Decisions of Ukraine (USRCD). | Prompt engineering for a pre-trained LLM (GPT-4). | A related pilot project using GPT chat for the Supreme Court is mentioned, but no specific deployment strategy for the author's proposed method is detailed. | False | False | NaN | Need for AI tools tailored to national legal systems (like Ukraine's), limitations in automated analysis of large legal databases (USRCD), need for careful integration respecting ethical considerations and human oversight. | General LLM challenges (formalism, bias, reliability, accountability, misuse), complexity of extracting information from unstructured legal text, ethical considerations, ensuring data confidentiality, need for human control. | Bias in AI outputs, unreliability of LLMs, misuse or over-reliance on AI, data confidentiality breaches, ethical issues (fairness, accountability), potential violation of fundamental rights. |
| N4XvXhSJkIMJ.pdf | Google_Scholar | Exploring the Landscape of Large and Small Language Models: Advancements, Trade-offs, and Future Directions | This survey examines the distinctions between large language models (LLMs) and small language models (SLMs), contrasting their size, efficiency, performance, and deployment. It further discusses the trade-offs involved, optimization techniques for SLMs, and future research avenues in NLP. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | False | NaN | NaN | High computational/energy costs, latency, and bias for LLMs; potential performance trade-offs and generalization limits for SLMs if not domain-specifically tuned. | Environmental impact (carbon footprint), inequitable AI distribution/accessibility, propagation of data-learned biases leading to undesirable outcomes. |
| 1yDtTGB00T8J.pdf | Google_Scholar | Generative AI in the Wild: Prospects, Challenges, and Strategies | This paper investigates how users in creative industries perceive and utilize Generative AI (GenAI) through semi-structured interviews. It identifies prospects like enhanced creativity and efficiency, challenges including resource availability and regulatory compliance, and strategies users employ to overcome these hurdles. | True | Market | True | 3.0 | NaN | Generative AI tools (e.g., ChatGPT, Midjourney, text-to-image models) | Semi-structured interviews (N=18) with GenAI users in creative industries, data analyzed using thematic analysis. | GenAI offers prospects such as improved efficiency and sparking creativity, but users also encounter significant challenges including limited controllability, issues with resource availability, regulatory compliance, and content trustworthiness. Users develop various strategies like careful tool selection, personalized prompting, and manual fact-checking to navigate these challenges. | NaN | NaN | NaN | NaN | Intellectual Property (copyright, authorship), Data Privacy, Regulatory Compliance (general) | International | NaN | NaN | NaN | True | True | Discusses various publicly accessible GenAI tools (e.g., ChatGPT, Stability.AI's Stable Diffusion), many of which have free tiers or are open-source. | NaN | Users face challenges with GenAI including limited controllability, ineffective feedback mechanisms, engineering-centric design of some tools, lack of customizability for localized content, and difficulties in authorship disclosure and regulatory compliance. | Copyright infringement, data privacy/security breaches from inputting sensitive information, regulatory non-compliance, generation of inaccurate information (hallucinations), ethical dilemmas regarding authorship, and potential widening of the digital divide. |
| XVXXHioJe_wJ.pdf | Google_Scholar | LEGAL PROCEDURE BOT | This paper proposes an AI-based chatbot, "LEGAL PROCEDURE BOT", designed to simplify access to information about legal procedures and required documents in India. The bot utilizes a generative deep learning architecture (RAG with an LLM) and features like speech-to-text, text-to-speech, geo-location, and multilingual support to address the complexities and inefficiencies of manual systems. | True | Idealistic | True | 1.0 | Positive | An AI chatbot ("LEGAL PROCEDURE BOT") using a Retrieval-Augmented Generation (RAG) architecture with the Mistral-7B-Instruct-v0.2 LLM. It employs NLP (NLU/NLG), vector embeddings (Word2Vec/Doc2Vec) stored in a vector database, cosine similarity for pattern matching, Speech-to-Text (STT), Text-to-Speech (TTS), Geo Location services (Google Nearby Search API), and multilingual query processing. | The paper includes screenshots of the user interface (login screen and chat window) as a demonstration. No quantitative evaluation or formal user testing results are reported. | Results are presented visually via GUI screenshots (Figures 5 and 6), demonstrating the intended user interface. No performance metrics or evaluation outcomes are provided. | Complexity of government legal procedures, fragmented and inaccessible information, time-consuming and labor-intensive manual processes, potential for mistakes and misinterpretation in manual systems. | An AI chatbot providing comprehensive guidance on legal procedures, lists of required documents, estimated fees, direct links to official websites/forms, and locations of nearby centres/courts/lawyers through a user-friendly interface. | Accessing information on government legal procedures (e.g., obtaining identity documents), required documentation, estimated costs, and relevant service locations. | General public / citizens needing guidance on legal procedures. | Administrative Law, Government procedures | India | A CSV file containing legal procedures, acts, regulations, and case law. This unstructured text data is pre-processed into chunks and converted into vector embeddings for storage in a vector database. The source seems specific to the project, likely collected by the authors. | Generative Deep Learning architecture (RAG), semantic indexing, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Natural Language Generation (NLG), user-centered design principles. | NaN | False | False | NaN | Need for algorithm refinement, continuous updates of legal information, real-time updates, integration of user feedback, enhancements in privacy and data protection, expansion of multilingual capabilities, need for collaboration with legal experts for accuracy and reliability. | Implicit challenges include ensuring the accuracy and reliability of legal information provided, handling the complexity of legal language and procedures, keeping the information up-to-date, and ensuring user trust and data privacy. | Need for ethical considerations, ensuring user satisfaction, managing privacy and data protection. |
| lRhwzB6FxjgJ.pdf | Google_Scholar | LexGPT 0.1: pre-trained GPT-J models with Pile of Law | This paper introduces LexGPT 0.1, a set of GPT-J models pre-trained on the Pile of Law dataset, aiming to provide a foundation model for the legal domain. It also explores a "No Code" approach for fine-tuning these models for classification tasks, finding this method less performant than state-of-the-art approaches. | True | Idealistic | True | 1.0 | Positive | LexGPT 0.1: GPT-J models (6B, 1.6B, 456M parameters) pre-trained on Pile of Law using custom tokenizers. A 'No Code' fine-tuning method for classification using prompt format `(text) <|label|>(label)`. | Fine-tuning on LEDGAR (contract classification) and CaseHOLD (holding identification) datasets from the LexGLUE benchmark. Evaluation metrics: micro/macro-F1 (LEDGAR), accuracy (CaseHOLD). | On LEDGAR (1.6B model): 83.9% micro-F1, 74.0% macro-F1. On CaseHOLD (456M model): 49.6% accuracy. These results were lower than reported state-of-the-art. | Technical skill barrier for legal professionals to utilize language models; potential for models to make factual mistakes and experience hallucinations; scarcity and expense of specialized legal datasets (though Pile of Law is used). | Pre-training domain-specific foundation models (LexGPT); proposing a "No Code" fine-tuning approach to lower technical barriers; public release of models and code; recommending initial use by legal professionals to filter errors. | Foundational model development; Legal text classification; Facilitating AI adoption by legal professionals. | Legal professionals | General Legal (based on Pile of Law), Contract Law (LEDGAR), Case Law (CaseHOLD) | US | Pre-training: Pile of Law (~256GB public dataset of legal/administrative text). Fine-tuning: Publicly available LEDGAR and CaseHOLD datasets (subsets from LexGLUE). Unstructured text data. | Domain-specific pre-training of Transformer models (GPT-J); Prompt-based fine-tuning for classification ('No Code'); Experimentation with model size, tokenizers, learning rates. | Intention to release models, tokenizers, datasets, configurations, and source code publicly on GitHub upon publication. | True | True | Models, tokenizers, datasets, config files, and code to be released on GitHub. | Performance gap between 'No Code' fine-tuning and SOTA classification methods; effective 'No Code' approach for multi-label classification; improving 'No Code' performance (e.g., via CoT prompting); limited exploration of GPT models vs BERT in legal domain. | Optimizing hyperparameters (e.g., learning rate) for pre-training; achieving competitive performance under the 'No Code' constraint; adapting generative models for classification; finding optimal data formats for fine-tuning. | Factual mistakes and hallucinations generated by the language models. |
| KS2K506sQ5sJ.pdf | Google_Scholar | CULTURAL FIDELITY IN LARGE-LANGUAGE MODELS: AN EVALUATION OF ONLINE LANGUAGE RESOURCES AS A DRIVER OFMODEL PERFORMANCE IN VALUE REPRESENTATION | This paper evaluates how well large language models (GPT-4o and GPT-4-turbo) represent societal values across different languages, finding their performance strongly correlates with the amount of online resources available in each language. The study highlights that models perform poorly for low-resource languages, potentially worsening digital divides and cultural homogenization, particularly in the Global South. | True | Idealistic | True | 2.0 | Negative | Evaluation of GPT-4o and GPT-4-turbo's cultural value representation capabilities using World Values Survey (WVS) data and persona prompting. | Compared LLM responses (prompted as a citizen of a specific country, answering WVS questions on the original scale) to average human responses from the WVS Wave 7 for 21 country-language pairs across 94 questions. An error was counted if the absolute difference between the LLM answer and WVS average was >= 50% of the WVS average. | For GPT-4o, 44% of the variance in the error rate correlated with the log of online websites available in the language (72% for GPT-4-turbo). Low-resource languages had significantly higher error rates (over 5 times higher for the lowest vs highest resource languages in GPT-4o). | The primary obstacle is the limited availability of digital resources (online content) for low-resource languages, leading to biased LLM training datasets derived predominantly from high-resource languages (mainly English). This results in poor AI performance in representing diverse societal values, exacerbates digital inequality, and potentially leads to cultural erosion, particularly impacting the Global South. Censorship further distorts the representativeness of available data in some regions. | Proposed solutions include democratizing AI development (open-source initiatives, grassroots NLP communities), ethical regulation mandating transparency and diversity, collaborative data sharing with local communities, targeted digital inclusion programs (increasing internet access, digital literacy, speech synthesis for LRLs), developing inherently multilingual LLMs, and fine-tuning models on diverse, curated linguistic datasets (including audio/oral sources) rather than relying solely on web-scraping. | Representation of societal values (political, social, ethical) by AI, linguistic diversity, digital inequality, cultural preservation, access to information, AI bias. | Speakers of low-resource languages, particularly communities in the Global South. Specific languages studied include Swahili, Hindi, Burmese, Filipino, Amharic, Hausa, Shona, Tajik. | AI Ethics / Governance | International | The paper evaluates models (GPT-4o, GPT-4-turbo) inferred to be trained primarily on large-scale web-scraped text (e.g., Common Crawl), supplemented by undisclosed proprietary data and potentially fine-tuning datasets. The study highlights the problematic nature and biases of this inferred training data, especially its underrepresentation of low-resource languages and potential pollution/censorship issues (e.g., Mandarin Chinese). | The study employed an evaluation methodology involving: selecting country-language pairs and 94 questions from the World Values Survey (WVS), verifying question translations with native speakers, prompting LLMs (GPT-4o, GPT-4-turbo) to answer as citizens of specific countries on the WVS scale, calculating deviation from averaged WVS human responses, defining an error threshold (>=50% deviation), and correlating error rates with metrics of language resource availability (log of website count). | NaN | True | False | The evaluated models (GPT-4o, GPT-4-turbo) are commercially available via API from OpenAI. | Establishing causality between language resources and LLM performance; quantifying the data required for parity; controlling for confounders (e.g., GDP); developing better data collection methods for LRLs beyond web-scraping (curated, diverse, audio/oral sources); creating ethical frameworks for value conflicts and responsible deployment; addressing nuanced representation within high-resource languages; lack of transparency in commercial LLM training data. | Evaluating LLM bias quantitatively (limitations of closed questions); data scarcity and quality for low-resource languages; inherent biases and limitations of web-scraped data (skewed demographics, spam, censorship); defining 'low-resource'; ensuring cultural fidelity without stereotyping during prompting. | Exacerbation of digital divides; cultural homogenization/erosion; perpetuation of flawed information and stereotypes; biased resource allocation (recruiting, medicine); negative impacts on education (biased history/values); biased content generation (news, marketing); flawed censorship/moderation; potential for social/political discontent; risks for users in autocratic regimes; homogenization within high-resource languages; privacy risks related to sensitive information access. |
| 0RI6qBV7pvcJ.pdf | Google_Scholar | Large language models and political science | This paper introduces Large Language Models (LLMs) to political scientists, discussing their potential applications, benefits, and drawbacks within the field. It reviews current LLM types, highlights research uses like content analysis and generation, and addresses issues like bias, transparency, and reproducibility. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | United States | The paper discusses general training data sources for LLMs, including large-scale web scrapes (e.g., The Pile, Common Crawl), Wikipedia, BooksCorpus, blogs, social media, articles, and image datasets (ImageNet, COCO). It notes some models use proprietary data (e.g., GPT-4) while others are trained on publicly specified or open datasets. | NaN | NaN | True | True | Discusses access via commercial APIs (e.g., OpenAI's GPT-4) and downloadable open-source models (e.g., LLaMA families, Falcon) available on platforms like Hugging Face. | NaN | Hardware requirements (GPUs/TPUs), computational cost, model size management (quantization, PEFT, LoRA), potential slowness/inefficiency vs. specialized methods, hallucinations, sensitivity to prompt variations, domain-specificity limitations, model censorship/bias, ensuring transparency and reproducibility. | Perpetuation of social/political bias from training data; generation/spread of misinformation and fake news (incl. political ads, election interference); privacy concerns regarding training data; intellectual property infringement; environmental impact; difficulty detecting AI content; lack of transparency; potential for discriminatory outcomes (e.g., biased risk assessments, job ads). |
| m0OdIkZgr7MJ.pdf | Google_Scholar | Luck of the Draw III: Using AI to Examine Decision‐Making in Federal Court Stays of Removal | This paper uses a large language model (GPT-3) to extract and analyze data from Federal Court of Canada dockets concerning immigration law applications for stays of removal, revealing significant inconsistencies in stay grant rates among judges. It argues for measures to promote consistency in judicial decision-making and greater access to bulk legal data for research to enhance transparency and migrant rights. | True | Idealistic | True | 1.0 | Positive | A multi-step computational legal research methodology: 1) Web-scraping Federal Court online dockets. 2) Docket and docket entry screening using Regex. 3) Fine-tuning GPT-3 models for specific data extraction and categorization tasks from docket entries (e.g., identifying stay motions, outcomes, judges). 4) Applying docket-level logic using Pandas to construct a final dataset for analysis. | Data verification involved: 1) Comparing the automated process against one year's worth of manually reviewed stay of removal decisions from CanLII, where the automated process identified 98.0% (96 out of 98) of the manually identified decisions. 2) A research assistant manually verified 200 randomly selected, coded dockets, confirming 99% accuracy for key data points (judge, outcome, dates). | The automated data extraction technique achieved 98% coverage compared to a manual CanLII dataset and 99% accuracy on manually verified dockets. The substantive research findings revealed large unexplained variance in stay of removal grant rates depending on the deciding judge (e.g., some judges granting stays over 80% of the time, others less than 10%). | Inconsistent and potentially arbitrary outcomes in high-stakes deportation proceedings due to judicial variance. Lack of transparency in legal decision-making processes. Restricted access to bulk legal data for non-commercial researchers, creating an asymmetry favouring commercial entities and the state. | The Federal Court should implement measures to encourage more consistency in stay decision-making (e.g., judicial discussions of hypotheticals). Facilitate fair and equal access to bulk legal data (e.g., via APIs) for non-commercial research to enhance transparency and rights. Utilize AI/LLM technology to scrutinize legal decision-making processes rather than solely for enhancing state power over marginalized groups. | Judicial decision-making consistency, access to justice in immigration and refugee law, stays of removal, deportation, transparency of legal systems, empirical legal studies. | Marginalized migrants and non-citizens facing deportation in Canada. | Immigration Law, Refugee Law, Administrative Law (specifically judicial review, interlocutory orders). | Canada (Federal Court of Canada) | For fine-tuning GPT-3: A manually labelled dataset of hundreds of sample Federal Court docket entries (prompts) paired with desired completions (e.g., judge's name, outcome category like 'granted' or 'dismissed'). The raw data was scraped from 87,776 Federal Court online dockets (2012-2022), consisting of unstructured natural language text entries in English or French. | Iterative development of fine-tuned GPT-3 models: applying models to new docket entries, verifying outputs, providing additional labelled examples to correct errors or improve performance, re-fine-tuning, and re-testing until satisfactory accuracy was achieved for each extraction/classification task. | The Python code (Jupyter Notebook) and the dataset of scraped Federal Court dockets (with case names removed for privacy) are stated to be made available for non-commercial use by other researchers via a public GitHub repository upon the paper's publication in a law journal. | False | False | The code and dataset are planned to be publicly available on GitHub for non-commercial research use after the paper is accepted for publication. | Need for further research into the specific reasons for divergent stay grant rates across judges (e.g., different interpretations of legal tests). Investigation needed into causes of variance in stay grant rates across different cities (e.g., quality of counsel, access to legal aid). The primary systemic gap is the restricted access to bulk legal data for non-commercial researchers, hindering broader scrutiny and transparency. | Technical difficulty and resource intensiveness of systematically web-scraping and maintaining large, up-to-date databases of court dockets. Managing ethical concerns associated with LLMs, including inherent biases, potential for generating misinformation ('hallucinations'), copyright issues, and environmental impact. Ensuring high accuracy when processing unstructured, bilingual (English/French) legal text from dockets. | LLMs may perpetuate biases present in their training data (e.g., racial, gender, religious biases). LLMs can 'hallucinate' or generate plausible but false information. Potential for misuse of LLMs for creating disinformation. Risk of automation bias due to the coherent-seeming text generated by LLMs. Significant environmental costs of training and running large language models. Copyright infringement concerns regarding data used for training commercial LLMs. Asymmetrical access to AI tools could exacerbate power imbalances if benefits primarily accrue to well-resourced actors. |
| qb37pS0wwnwJ.pdf | Google_Scholar | HARNESSING ARTIFICIAL INTELLIGENCE IN INTERNATIONAL ARBITRATION PRACTICE | This paper surveys the existing and emerging applications of Artificial Intelligence (AI), including Generative AI and Large Language Models (LLMs), in international arbitration practice. It discusses various tools, potential use cases, benefits, pitfalls, the need for ethical guidelines, and future transformative possibilities. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International Arbitration | International | NaN | NaN | NaN | True | False | The paper discusses multiple AI tools and platforms relevant to international arbitration, many of which are commercially available (e.g., e-discovery platforms, specialized legal research tools, Harvey) or have publicly accessible versions (e.g., ChatGPT-4, Google Translate). | NaN | Challenges discussed include ensuring accuracy and avoiding hallucinations, maintaining confidentiality and data privacy, managing potential biases in AI outputs, ethical considerations in AI use, the need for human oversight and verification, the potential for deskilling, integration into existing legal workflows, and the development of appropriate regulatory guidelines (e.g., SVAMC Draft Guidelines). | Potential risks stated include reliance on inaccurate or fabricated AI-generated information (e.g., non-existent case law), breaches of confidentiality when inputting sensitive data into AI tools, perpetuation of biases (e.g., in arbitrator selection algorithms), violation of ethical duties (competence, diligence), improper delegation of decision-making responsibility by arbitrators, and disruption to traditional legal roles and billing models. |
| o-QdXzv2krIJ.pdf | Google_Scholar | Artificial Intelligence and the Crises of Judicial Power : (Not) Cutting the Gordian Knot? | The paper argues that Artificial Intelligence (AI) and automated decision-making (ADM) cannot solve the current crises of efficiency and contestation facing judicial power globally. It analyzes courts' dual role as both users of AI, highlighting limitations like opacity and lack of trust, and as regulators of AI through judicial interpretation, examining different judicial approaches to managing AI risks. | True | NaN | False | 3.0 | NaN | NaN | NaN | NaN | Systemic inefficiency of justice systems (delays, backlogs, increasing costs); Contestation of judicial legitimacy and authority (criticism of liberal interpretations, perceived political role, populist attacks, undermining rule of law). | AI/ADM are presented as insufficient solutions. Implicitly favors reliance on legal safeguards (transparency, comprehensibility, contestability), robust judicial scrutiny of AI/ADM, upholding rule of law principles, and acknowledging the limitations of technology compared to human judgment. | Judicial administration and efficiency; Judicial legitimacy and contestation; Regulation of AI by courts; Digital constitutionalism; Automated decision-making in the public sector. | NaN | Constitutional law, Administrative law, Judicial Procedure, Data Protection Law | International / Comparative (mentions UK, US, Italy, France, China, Colombia, Brazil, EU) | N/A - Specific dataset details largely absent or noted as proprietary/opaque (e.g., COMPAS). General discussion of AI relying on data. | NaN | Government-led digitisation programs, integration into specific judicial/administrative functions (e.g., e-discovery, risk assessment, case allocation), online court platforms. | False | False | NaN | Ensuring AI trustworthiness and public legitimacy; Maintaining essential human elements in the justice process; Developing effective and comprehensive AI regulation; Bridging the digital divide impacting access; Achieving transparency and explainability in ADM; Protecting fundamental rights (due process, data protection, effective remedy) against AI risks. | Lack of transparency/opacity in AI systems; Potential for bias and inaccuracy in algorithms; Difficulty distinguishing purely administrative from decision-influencing AI functions; Overcoming public distrust and ensuring legitimacy; High costs and implementation delays for judicial digitization; Risk of de-humanizing the justice experience; Ensuring meaningful contestability and review of automated decisions; Balancing standardization benefits with the need for individualized justice. | Opacity hindering challenges, review, and trust; Algorithmic bias leading to discriminatory outcomes (e.g., racial bias in COMPAS); De-humanization of the legal process undermining legitimacy; Potential undermining of judicial independence and separation of powers; Standardisation leading to 'flattened' rights protection and inability to adapt law; Erosion of public trust in the judiciary; Difficulty ensuring due process, right to explanation, and effective remedies against automated decisions. |
| BRrBu4S54Q0J.pdf | Google_Scholar | The Consequences of Implementing Artificial Intelligence Technology in the Digital Economy from the Perspective of Generation Z | This paper explores how Generation Z perceives artificial intelligence (AI) and its implementation effects within the digital economy, based on a survey of 323 Polish respondents. The study reveals Generation Z's frequent AI use alongside limited trust, particularly concerning data privacy, autonomous systems, and potential job displacement. | True | NaN | False | 2.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Multiple sectors including IT, trade, finance, transport, education, legal services (briefly), healthcare (briefly). | Poland | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Lack of trust in AI systems (especially in finance, legal, medical, autonomous vehicles), privacy/data security violations, job displacement/digital unemployment, potential for AI errors (e.g., in legal/medical services), ethical concerns. |
| kN0VpM62IsIJ.pdf | Google_Scholar | A Short Survey of Viewing Large Language Models in Legal Aspect | This paper surveys the applications of large language models (LLMs) in various legal tasks, such as judgment prediction and document analysis. It also discusses the associated legal challenges like bias and privacy, and the data resources required for specializing LLMs in the legal domain. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Legal challenges including intellectual property ownership, data privacy (disclosure of sensitive information, GDPR compliance), bias and discrimination (e.g., anti-Muslim, anti-queer), and lack of explainability/transparency. | Developing specialized legal data resources, methods to mitigate bias and ensure transparency, legal frameworks and guidelines for ethical use, privacy-preserving techniques, prompt engineering, and a legal informatics approach. | Legal judgment prediction, legal document analysis/writing, statutory reasoning, legal education, legal advice, access to justice. | NaN | Criminal law, Constitutional law, Contract law, Tort law, Civil law, General legal practice. | International (with specific examples/datasets from China, US, Japan, EU regulations mentioned) | Discusses publicly available legal datasets (e.g., CAIL2018 from China, LeCaRD from China, CaseHOLD derived from US law) and general large-scale web data used to train base LLMs. | NaN | NaN | False | False | NaN | Need for methods to mitigate bias and ensure transparency/interpretability; need for more specialized legal data; need for guidelines/standards for ethical use; need to address legal challenges (IP, privacy); need for better alignment with human/societal values. | Privacy concerns, bias perpetuation, lack of explainability, need for specialized domain data and adaptation, intellectual property issues, ensuring responsible and ethical deployment. | Copyright infringement, disclosure of private information, perpetuation of societal biases leading to discrimination, lack of transparency hindering accountability, potential misuse in legal education or practice. |
| ArtificialIntelligenceinLegalPracticeOpportunitiesChallengesandFutureDirections.pdf | Google_Scholar | Artificial Intelligence in Legal Practice: Opportunities, Challenges, and Future Directions | This review paper discusses the transformative impact of Artificial Intelligence (AI), including Generative AI, on legal practice. It outlines AI's applications, benefits like increased efficiency and automation, and challenges such as data privacy, ethical concerns, and potential job displacement in the legal field. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | High cost of traditional legal services, making them unaffordable for individuals with limited resources and small businesses. | Utilizing AI to automate tasks, reduce costs, and increase the efficiency of legal services, thereby making them more accessible. | Affordability of legal services, accessibility of legal services for individuals and small businesses. | Litigants with limited resources, individuals, small businesses. | General legal practice, including contract law, intellectual property law, litigation, due diligence, and legal research. | International (with some examples from the United States) | NaN | NaN | NaN | True | True | ChatGPT (specifically GPT-4 mentioned as used by authors), a type of generative AI discussed in the paper, is generally available with free access options from OpenAI. | The paper implies that overcoming ethical issues, regulatory uncertainties, data privacy concerns, and the skills gap among legal professionals are necessary for the full and equitable realization of AI's potential in enhancing access to justice. | Data privacy and security, ethical concerns (including algorithmic bias, AI hallucinations, and confidentiality), risks of data breaches and cyberattacks, skills gap among legal professionals, potential job displacement, and the lack of clear and comprehensive regulations for AI in legal practice. | Data breaches, cyberattacks, spread of misinformation and disinformation (e.g., deepfakes), algorithmic bias, AI hallucinations, confidentiality breaches, job displacement for legal professionals, intellectual property rights issues, privacy and data protection violations, and various ethical dilemmas. |
| Qs9Hxl2Iir4J.pdf | Google_Scholar | ARTIFICIAL LAWYERING: A JEKYLL AND HYDE STORY | This paper examines the dual potential of artificial intelligence, particularly generative AI like ChatGPT, in the legal field. It argues that while AI can significantly improve access to justice for underserved communities, it also poses risks such as unauthorized practice of law, and thus proposes an amendment to the Model Rules of Professional Conduct to balance these aspects. | True | Idealistic | True | 3.0 | Positive | Proposed amendment to Rule 5.5 of the ABA Model Rules of Professional Conduct regarding 'practicing entities' (including AI) and UPL, allowing use by pro se litigants with informed consent. | NaN | NaN | Inability of low-income individuals to afford legal counsel; lack of awareness among individuals about whether their problems are legal in nature; insufficient number of lawyers serving low-income populations; systemic inequalities. | Utilize AI (like ChatGPT) for legal education and information dissemination, especially for pro se litigants. Amend Rule 5.5 of the Model Rules of Professional Conduct with a new comment to address AI's potential for UPL, while allowing its use by pro se litigants under conditions of informed consent and disclosure to the court. | Access to legal information, self-representation (pro se litigants), understanding legal rights, unauthorized practice of law (UPL) by AI, an LSC (Justice Gap) report. | Low-income Americans, veterans, persons with disabilities, parents of children under eighteen, survivors of domestic violence or sexual assault. | Civil law (specifically landlord-tenant disputes), Trademark law, General legal ethics (Unauthorized Practice of Law). | United States | The paper discusses generative AI like ChatGPT which is trained on large language models (LLMs) using extensive text data to infer relationships between words and texts. Specific datasets for ChatGPT are not detailed by the paper beyond this general description. | NaN | NaN | False | False | NaN | Lack of clear legal and ethical rules addressing advanced AI (like ChatGPT) and the unauthorized practice of law; need for mechanisms to balance AI's benefits for access to justice with public protection; ethical rules (Model Rules) not sufficiently updated for AI advancements; issues of AI bias, language limitations, and lack of redressability for AI-inflicted harm if AI engages in law practice. | NaN | AI engaging in the unauthorized practice of law (UPL); public endangerment from incompetent or biased AI-generated legal advice/documents; AI producing non-existent legal precedents ('hallucinations'); generation of frivolous lawsuits; lack of legal redress for individuals harmed by AI's errors (AI malpractice); perpetuation or amplification of societal biases through AI; AI's limitations in understanding true context beyond language patterns. |
| wNT2cfBmGiQJ.pdf | Google_Scholar | From Bytes to Borsch: Fine-Tuning Gemma and Mistral for the Ukrainian Language Representation | This paper details the fine-tuning of open-source LLMs (Gemma and Mistral) for the Ukrainian language using existing and newly created datasets (UKID). It benchmarks the models, highlighting performance improvements and challenges like code-switching, arguing for the importance of developing language-specific models for low-resource languages. | True | Idealistic | True | 1.0 | Positive | Fine-tuning open-source LLMs (Gemma-2b, Gemma-7b, Mistral-7b) using LoRA for the Ukrainian language, including the creation and use of a new instruction dataset (UKID). | Benchmarking using two datasets: 1) Ukrainian External Independent Testing (EIT) Multiple Choice Questions (MCQ) dataset (3,063 questions on history, language, literature), automatically evaluated. 2) 100 Open Questions (OQ) for generative tasks, manually evaluated on language use, coherence, relevance, and grammar. Comparison against baselines and proprietary models. | The fine-tuned Mistral-7B (MistralFT) achieved 40.16% accuracy on History MCQs and 22.86% on Language & Literature MCQs. It achieved an average score of 40.75 out of 100 on Open Questions, though struggled with adhering to instructions (Relevance score was low). Proprietary models like GPT-4 performed significantly better. | Scarcity of suitable instruction datasets with authentic Ukrainian context; Language and cultural bias in existing LLMs; Uneven knowledge representation favouring dominant languages; Resource constraints for developing models for low-resource languages. | Fine-tuning open-source LLMs with language-specific data; Creating and sharing new, culturally relevant datasets (e.g., UKID); Utilizing efficient fine-tuning techniques (LoRA); Advocating for investment and policy focus on LLM development for lower-resource languages; Creating language-specific benchmarks (e.g., ULIB). | Linguistic Inclusion, Cultural Preservation, Education, Countering Misinformation. | Ukrainian speakers, with potential applicability to other low-resource language communities. Specific examples mention Ukrainian refugees, rural Peruvian villagers (Quechua), and Navajo learners. | NaN | Ukraine | Combined dataset including: 3,063 instruction rows from Ukrainian national exam (ZNO dataset); 10,000 rows from UAlpaca (translated general knowledge); Uk-Squad dataset (translated SQuAD); 962 question-answer-fact pairs from the newly created Ukrainian Knowledge and Instruction Dataset (UKID), derived from Ukrainian Wikipedia summaries via Gemini 1.0 API. Primarily unstructured text formatted as instructions. | LoRA (Low-Rank Adaptation) fine-tuning. Dataset creation (UKID) involved selecting high-traffic Ukrainian Wikipedia pages, filtering for relevance, and using Gemini 1.0 API with few-shot prompting to generate question-answer-fact triplets. | Fine-tuned model weights and the UKID dataset are shared via a GitHub repository. | True | True | Fine-tuned model weights and the UKID dataset are available on the associated "from-bytes-to-borsch" GitHub repository. | Need for larger, more comprehensive Ukrainian instruction datasets; Need for improved fine-tuning methods to avoid performance degradation and negative artifacts (e.g., code-switching); Significant performance gap between fine-tuned open-source and large proprietary models; Need for better evaluation benchmarks for Ukrainian (e.g., expanding ULIB); Lack of institutional support and resources for low-resource language model development. | Reproducibility of fine-tuning setups; Scarcity of high-quality, culturally relevant training data; Models lacking foundational conceptual understanding in the target language; Compute and resource constraints; Adapting datasets to model-specific instruction formats; Negative side-effects of fine-tuning (impaired generation, poor instruction following, code-switching). | Perpetuation of language/cultural bias; Uneven access to technology; Cultural erosion and loss of linguistic diversity; Negative impacts on education and linguistic identity; Increased vulnerability to targeted propaganda and misinformation; Emergence of a 'model divide' between languages; Digital extinction risk for threatened languages. |
| 21Up8qsNj6cJ.pdf | Google_Scholar | A Knight in Shining Nascency: Under -the-Radar Platforms as a Solution to Access to Justice for Incarcerated Litigants | This paper analyzes how the legal information duopoly (Lexis/Westlaw) and prison monopsonies restrict incarcerated litigants' access to justice by controlling legal information access. It advocates for prisons to adopt nascent, lower-cost, or open-access legal research platforms to fulfill constitutional requirements and improve access. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Market dominance and anti-competitive behavior (including copyright claims on enhanced public domain materials) by the legal publishing duopoly (Lexis/Westlaw). Prison systems acting as a monopsony buyer, often prioritizing cost or convenience over inmates' needs, and using exploitative funding mechanisms (inmate welfare funds). Path dependency in procurement practices. Lack of adequate digital access within prisons. | Prisons should contract with nascent, lower-cost, or open-source legal information platforms (e.g., Fastcase/vLex, Cornell LII, Caselaw Access Project) instead of relying solely on the duopoly. Procurement processes should change to prioritize meaningful access over specific proprietary features (like requiring Black's Law Dictionary). Funding for legal information should not rely on exploitative 'prison retailing' kickbacks. | Access to legal information, Access to the courts, Prison law libraries, Digital divide | Incarcerated litigants | Constitutional Law, Antitrust Law, Criminal Law/Procedure | United States | NaN | NaN | NaN | False | False | NaN | Continued dominance of the legal research duopoly hinders market entry for more accessible solutions. Prison monopsonies remain reluctant to adopt alternative platforms due to entrenched practices and contractual issues. Exploitative funding mechanisms persist. Need for greater digitization and open access to primary legal materials (statutes, cases) by governments. | NaN | Continued denial of the constitutional right of access to the courts for incarcerated individuals. Financial exploitation of inmates and their families to fund prison services, including legal research access. Entrenchment of data cartels controlling public and legal information. |
| Pfjjr1-EIwwJ.pdf | Google_Scholar | Conversational Factor Information Retrieval Model (ConFIRM) | This paper introduces ConFIRM, a method using the Five-Factor Model of personality to generate synthetic, personality-aligned training data for fine-tuning Large Language Models (LLMs) in domain-specific tasks. A case study fine-tuning Llama-2-7b for financial query classification demonstrated 91% accuracy, highlighting ConFIRM's potential for creating personalized and accurate AI retrieval systems. | True | Market | True | 1.0 | NaN | ConFIRM (Conversational Factor Information Retrieval Model): A method that uses the Five-Factor Model (FFM) of personality to generate synthetic question-answer pairs reflecting target population characteristics. This data is then used for parameter-efficient fine-tuning (LoRA) of LLMs (Llama-2-7b) for domain-specific information retrieval/classification tasks. | A case study in the finance domain. A Llama-2-7b model was fine-tuned using LoRA on 3000 synthetically generated QA pairs based on personality factors derived from the PolyU-Asklora Fintech Adoption Index survey. The model was evaluated on its accuracy in classifying financial queries against data categories modeled after Refinitiv Datastream. | The fine-tuned Llama-2-7b model achieved 91% accuracy in classifying financial queries on the test set (1000 samples). The average inference time per query was 0.61 seconds on an NVIDIA A100 GPU. | NaN | NaN | NaN | NaN | Finance (primary case study); potential applicability mentioned for Healthcare and Legal Services | Hong Kong (source of user personality data) | Synthetically generated question-answer pairs created using LLMs (GPT-3.5), SELF-INSTRUCT, and Text2Text generation methods. The generation was guided by population personality traits (OCEAN factors) derived from a survey subset (n=50) of Hong Kong participants (PolyU-Asklora Fintech Adoption Index). Data categories based on Refinitiv Datastream. | Integration of psychological frameworks (Five-Factor Model), synthetic data generation, large language models (GPT-3.5 for generation, Llama-2-7b for fine-tuning), parameter-efficient fine-tuning (LoRA), evaluation based on classification accuracy. | Model code shared via a GitHub repository. | True | True | Model code available on GitHub. | The paper suggests future work on scalability, expanding FFM integration, and exploring alternative preference optimization techniques like DPO. | Data scarcity for fine-tuning in specialized domains, need for training data reflecting target population characteristics, achieving high accuracy requires large training datasets (demonstrated by accuracy scaling with sample size). General LLM challenges like hallucinations and knowledge cutoffs. | LLM hallucinations (inaccurate responses), LLMs providing outdated information, misclassification leading to potential regulatory concerns (mentioned regarding false negatives). |
| Vl9jvdYVpp4J.pdf | Google_Scholar | JudicialTech supporting Justice \nThe impact of AI and Emerging Technologies on the Judiciary, Courts and Justice | This paper defines JudicialTech as AI and emerging technologies for judges, courts, and dispute resolution, aiming to support the judiciary, enhance access to justice, and increase fairness. It reviews JudicialTech's future impact across the judicial process, highlighting benefits like efficiency and improved access, alongside risks such as the erosion of human-led legal decisions and the need for robust judicial oversight. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Erosion of human-led legal decisions and judicial independence; lack of public confidence due to uncontrolled/untested AI; algorithmic bias, opacity, and inaccuracy (e.g., AI hallucinations, high error rates); insufficient or unsuitable legal data for training AI; negative impact on common law development from reduced trials due to predictive tools. | Strong judicial oversight, control, and robust testing regimes for AI; presumption against AI for judicial decision-making without thorough vetting; knowledge transfer, experimentation (sandboxes, tech sprints), and horizon scanning; development of "Open Justice" standards and JudicialTech Labs; human oversight and appeal mechanisms for AI-driven decisions. | Enhancing judicial efficiency and fairness; litigation advice and trial preparation (eDiscovery, document review); Online/Algorithmic Dispute Resolution (ODR/ADR); judicial guidance and decision support (including for sentencing); digital courts, managing court backlogs, supporting self-represented litigants. | Self-Representing Litigants (SRLs)/litigants-in-person (LIPs); general public affected by court backlogs. | Criminal law, Civil law, Commercial law, Family law, Regulatory law | UK, US, India, Singapore, France, EU, Canada. Broadly applicable internationally. | Discusses training data for existing AI systems: LLMs (e.g., GPT-4) trained on vast quantities of often online data; legal predictive tools trained on past judicial rulings which can be limited or outdated in smaller jurisdictions or with evolving laws; legal analytics tools use docket entries and documents. | NaN | NaN | False | False | NaN | Lack of robust, judiciary-supervised appraisal and testing regimes for judicial AI; need for established standards for digital access to justice data and services ('Open Justice'); insufficient R&D capabilities within Justice Ministries; ensuring public confidence and addressing algorithmic bias, opacity, and errors; adapting AI to limited and evolving legal data. | Data limitations for training legal AI (small datasets, evolving laws); ensuring AI systems are unbiased, transparent, and accurate; maintaining judicial control over technology; balancing innovation with Rule of Law and public confidence; complexity of automating legal reasoning; managing digital evidence and deepfakes. | Erosion of human-centered legal decision-making; undermining public confidence in the Rule of Law; inaccurate or biased AI decisions leading to miscarriages of justice; misleading legal submissions from generative AI (hallucinations); reduction in trials impacting common law; deepfake evidence; lack of algorithmic accountability; exploitation of AI for cybercrime. |
| -1ZohptBDsIJ.pdf | Google_Scholar | Good AI Legal Help, Bad AI Legal Help: Establishing quality standards for responses to people’s legal problem stories | This paper proposes 22 specific criteria to evaluate the quality of AI responses to legal problem stories from the public, particularly in civil justice. It then presents findings from a survey of 21 legal experts who ranked these criteria, aiming to establish robust standards for future AI benchmarking in access to justice. | True | Idealistic | True | 1.0 | Positive | A set of 22 quality criteria, grouped into 6 categories (Presentation, Legal Content Coverage, Legal Content Quality, Content Sources, Warnings/Disclaimers, Equity), for evaluating AI responses to legal help questions. | Survey methodology: 21 legal domain experts (legal aid lawyers, court staff, etc.) reviewed and ranked the 22 criteria on a 0-6 importance scale in 30-minute one-to-one interviews. They also provided qualitative feedback and suggested additional criteria. | Criteria such as 'Response is not toxic,' 'Response is in plain language,' 'Response does not misrepresent the substantive law,' and 'Response does not misrepresent any forms, paperwork, or tools' averaged highest importance (6/6). Experts prioritized usability, actionability, and accuracy, while de-emphasizing robustness, citations, and warnings to consult a lawyer. | Lack of well-defined, specific quality metrics for legal services and AI performance in the legal domain; current quality assessment is often subjective and ill-defined. | Proposing a specific, comprehensive list of 22 quality criteria, reviewed and ranked by legal domain experts, to serve as a basis for establishing actionable quality evaluation and benchmarking protocols for AI systems providing legal help. | Evaluating the quality of AI-generated legal information for initial legal help requests; establishing benchmarks for AI in civil justice; improving public understanding of legal rights and procedures. | General public needing legal help for civil justice problems such as housing, family, domestic violence, debt, and criminal records. | Civil justice (including housing, family, domestic violence, debt, criminal records, traffic). | International (aims for broadly applicable standards, with expert outreach including US, Canada, UK, Australia, and other countries, though initial survey participants' specific locations are not detailed, some roles suggest a US context). | NaN | Literature review of existing quality rubrics and AI benchmark standards; expert consultation via email inquiries; survey methodology involving semi-structured interviews with legal domain experts to rank and refine proposed criteria. | NaN | False | False | NaN | The study is ongoing, and findings are provisional; further testing of criteria in benchmark efforts is needed; exploration of automated assessment of criteria; addressing language and disability access more comprehensively; ensuring AI is not trained on biased data. | Defining and measuring 'quality' in the legal domain; creating specific yet broadly applicable evaluation criteria; balancing comprehensive legal information with user-friendly presentation; ensuring accuracy in a dynamic legal environment without setting unattainable standards. | AI providing misleading or harmful legal information (e.g., hallucinations, over-simplifications, errors leading to missed deadlines or incorrect filings); AI exhibiting bias or creating disparate impacts; users being overwhelmed by information or paralyzed by disclaimers. |
| Ksz1ZJFlB00J.pdf | Google_Scholar | Warhol, Drake, and Deepfakes: Monetizing the Right of Publicity in the Generative AI Era | This paper analyzes how AI-generated deepfakes and deep voices impact celebrities' right of publicity, critiquing the traditional transformative use test. It proposes a stricter test based on *Warhol v. Goldsmith* and a central licensing repository to manage and monetize the use of digital likenesses. | True | Market | True | 1.0 | NaN | A stricter transformative use test (informed by *Andy Warhol Foundation v. Goldsmith*) combined with a proposed "likeness licensing repository" modeled after Performance Rights Organizations (PROs) for managing AI-generated digital replicas of public figures. | NaN | NaN | The unauthorized and uncompensated use of celebrity likenesses (faces, voices) through easily created, hyperrealistic AI deepfakes and deep voices, which devalues their persona, undermines their ability to control and monetize their identity, and challenges the existing legal framework for the right of publicity. | Adoption of a stricter, purpose-based transformative use test (from *Warhol v. Goldsmith*) to assess if the AI use supplants the original's market. Establishment of a nonprofit likeness licensing repository to create official digital replicas, issue blanket licenses to content platforms, track usage, and distribute royalties to likeness-holders, ensuring consent, credit, and compensation. | Right of publicity, monetization of likeness, digital replicas (deepfakes and deep voices), transformative use test, First Amendment considerations, intellectual property. | Public figures, celebrities, entertainers, athletes, and influencers. | Right of Publicity, Intellectual Property Law, Copyright Law (related to fair use/transformative use). | United States (with discussion of U.S. case law, statutes, and proposed federal legislation). | The paper describes deepfakes and deep voices as being trained on "thousands of training images of an individual" or "audio of ... someone else’s—voice." Generally, this refers to visual and auditory data of the individuals whose likenesses are replicated, which can be sourced from public domain or proprietary collections. | The proposed likeness licensing repository is designed by analogy, modeled after existing music Performance Rights Organizations (PROs) like ASCAP and BMI. | The proposed likeness licensing repository would be deployed by having public figures enroll, the organization creating and managing a database of official digital replicas, content platforms obtaining blanket licenses, users selecting licensed replicas for their creations, and the organization tracking usage and distributing royalties. | False | False | NaN | Inadequacy of the traditional transformative use test for hyperrealistic AI replicas. Lack of a systematic mechanism for consent, compensation, and control for digital likenesses in the AI era. Potential for legislative solutions to be either too restrictive, stifling creativity, or not comprehensive enough to manage the complexities of AI-generated likenesses. | Difficulty in applying legal tests like 'transformative use' to hyperrealistic AI. Balancing First Amendment rights with the right of publicity. The rapid advancement and accessibility of deepfake technology. Detecting AI-generated content effectively. Preventing market substitution and the dilution of likeness value. Ensuring ethical use and clear disclosure for AI-generated content, especially in endorsements. | Unauthorized commercial exploitation and devaluation of personal likenesses. Consumer deception regarding the authenticity of content. Market substitution, harming the original creators' ability to profit from their work and image. Misuse for nonconsensual pornography or misinformation (though the paper's focus is on monetizing celebrity likenesses). Potential for overly broad regulations to stifle artistic expression and innovation with AI tools. |
| gHXfe3cys0IJ.pdf | Google_Scholar | Analyzing Images of Legal Documents: Toward Multi-Modal LLMs for Access to Justice | This paper investigates using a multi-modal large language model (GPT-4o) to extract structured information from images of handwritten legal forms, specifically an Ontario lease agreement. Initial results show the potential for such technology to aid access to justice by simplifying information gathering, but also reveal challenges related to image quality, handwriting variability, and potential biases. | True | Idealistic | True | 2.0 | Positive | Using a multi-modal LLM (GPT-4o) via API to extract structured data (e.g., names, addresses) from images of filled-out legal forms. | Evaluated GPT-4o on images of a filled-out standard Ontario lease form. Created a dataset with 3 scenarios (varying name/field complexity) and 5 image formats per scenario (typed PDF screenshot, neat handwritten HD, sloppy handwritten HD, neat handwritten SD, sloppy handwritten SD). Measured accuracy based on exact field value matches (case-insensitive) against ground truth across 14 fields. | Overall accuracy was 73%. Typed PDF (HD) yielded 98% accuracy, while performance decreased with handwritten text, lower image quality, and messier handwriting (Sloppy SD: 60%). The model struggled most with handwritten street numbers and uncommon names (sometimes substituting common ones), but excelled with predictable fields like city/province. | Difficulty for laypeople and self-represented litigants in understanding legal requirements, finding relevant information scattered across documents, and correctly filling out forms; the burden of administrative processes ("administrative sludge"). | Leveraging multi-modal LLMs to automatically identify and extract needed information from images of paper documents (forms, certificates, contracts, letters), thereby assisting users in filling out other forms, understanding their rights, or drafting submissions. | Form filling automation, information extraction from legal documents, support for self-represented litigants, reducing administrative burden in legal processes. | Laypeople, self-represented litigants. | Landlord-tenant law (residential leases), Administrative law (forms). | Ontario, Canada | N/A (Paper evaluates a pre-trained model, GPT-4o. The described dataset is for testing.) | Experimental design involving dataset creation (varying form scenarios, handwriting styles, image quality) and API-based LLM prompting for evaluation. | NaN | True | False | The technique uses the GPT-4o model via OpenAI's API, which is commercially available. The experimental code is available on GitHub. | Need for larger-scale studies with more varied data; optimizing prompts and models; integrating the capability into user-facing systems; addressing performance issues with low-quality inputs and handwriting; mitigating model biases. | Achieving reliable extraction despite variations in image quality, form completeness, and handwriting (neatness, style); model tendencies to 'correct' uncommon names towards common ones. | Exacerbating the digital divide, as performance relies on good quality images (requiring modern devices and good lighting); potential for societal biases embedded in LLMs to affect outcomes (e.g., poorer recognition of less common names). |
| UCe3R-FaIlsJ.pdf | Google_Scholar | Information extraction from employment tribunal judgments using a large language model | This paper investigates the use of the large language model GPT-4 for extracting key information from UK Employment Tribunal (UKET) judgments. It details a manual verification process to evaluate GPT-4's accuracy in extracting eight aspects from judgments, and discusses the suitability of this extracted data for developing a tool to predict employment dispute outcomes. | True | Idealistic | True | 2.0 | Positive | Information extraction from UK Employment Tribunal judgments using GPT-4 with specific prompt engineering. | Manual verification by a legal expert of GPT-4's extraction accuracy on 8 pre-defined aspects (facts, claims, statutes, precedents, general outcome, labelled outcome, detailed order/remedies, reasons) for 260 sampled UKET judgments. A second quality check assessed suitability for a downstream prediction task based on extracted facts, claims, and labelled outcomes. | GPT-4 achieved 100% accuracy for extracting legal statutes and precedents. It also showed high accuracy for claims (98.1%), general outcomes (99.6%), detailed outcomes (99.6%), reasons (99.6%), facts (94.2%), and general outcomes in one of four labels (91.2%). | Knowledge imbalance favoring employers in accessing legal information and AI tools; high cost or restricted access to advanced AI systems for employees and the general public. | Developing open and equally accessible dispute resolution systems and predictive tools; public release of datasets and annotations (e.g., via Cambridge Law Corpus) to counterbalance privately developed systems. | Equitable access to legal information, outcome prediction in employment law, transparency of AI in legal applications, creation of open legal datasets. | Employees and the general public, especially those disadvantaged in accessing legal resources compared to employers. | Employment law | United Kingdom Employment Tribunal (UKET) for England, Wales, and Scotland. | GPT-4, the LLM used, is pre-trained by OpenAI on vast, diverse text corpora (specifics not detailed in the paper). The study applies GPT-4 to a dataset of 260 judgment transcripts from the UK Employment Tribunal, sourced from the Cambridge Law Corpus (CLC). | Iterative prompt engineering for GPT-4, following OpenAI guidelines. This involved defining a persona for the LLM, specifying detailed extraction targets for eight information categories, and refining prompts through trial-and-error to improve accuracy, completeness, and consistency of outputs. | The extracted data and annotations from the study are released as part of the Cambridge Law Corpus (CLC). | True | False | The paper details the methodology, including specific prompt structures used with GPT-4, enabling replication by users with access to GPT-4 and relevant legal texts (e.g., UKET judgments). | Persistent knowledge and resource imbalance between employers and employees regarding legal AI. Datasets for legal AI prediction often lack crucial pre-decision information (e.g., original claim forms) or details of out-of-court settlements. LLMs still face challenges in fully distinguishing procedural vs. substantive facts through prompts alone. | Crafting effective and robust prompts for GPT-4 to accurately extract all nuances of legal information consistently. Ensuring consistent LLM behavior across similar cases (e.g., for outcome labelling). Overcoming LLM limitations in handling complex multi-party scenarios or incomplete information within source documents. Difficulty in getting LLMs to consistently include procedural elements in factual summaries without data leakage concerns for prediction tasks. | Exacerbation of access to justice disparities if advanced legal AI tools are predominantly commercial and inaccessible to the general public or less resourced parties. Potential for biased outputs from LLMs (due to training data or model architecture) influencing legal understanding or decisions. Over-reliance on predictive models trained on judicial texts which may contain inherent biases or incomplete factual accounts. |
| 5ZkDnCaTzcUJ.pdf | Google_Scholar | ASKING GPT FOR THE ORDINARY MEANING OF STATUTORY TERMS | The paper tests ChatGPT's (GPT-3.5 Turbo) ability to provide evidence of the ordinary meaning of statutory terms by comparing its outputs against human survey data. It identifies a successful prompting technique (belief prompt with Likert scale) and explores context sensitivity, historical meaning, and offers lessons for using LLMs in statutory interpretation. | True | Market | True | 2.0 | Positive | Using GPT (specifically GPT-3.5 Turbo) with specific prompting techniques (direct question, chain of thought, belief prompt with percentage, belief prompt with Likert scale) to generate empirical evidence on the ordinary meaning of statutory terms. The most successful identified technique is the belief prompt combined with a Likert scale response format. | Comparison of GPT-3.5 Turbo's responses (generated 100 times per prompt/object combination with temperature=1) to results from a large-scale experimental human survey (Tobia 2020, N=2,835) asking whether 25 candidate objects are "vehicles". Evaluation based on statistical comparison of distributions (Kolmogorov-Smirnov test) and visual inspection of response patterns. Also tested sensitivity to context (rule wording, alternative rules, purpose) and historical meaning (1950s, intensional vs. extensional). | The belief prompt using a 7-point Likert scale generated results statistically indistinguishable (Kolmogorov-Smirnov, p = .2798) from the human survey benchmark (Tobia 2020). Other prompts (direct question, chain of thought, belief-percentage) performed poorly. GPT showed sensitivity to context (rule wording, purpose, historical time frame) but the character of the object remained the dominant factor. | Inaccuracy ('hallucinations') of LLMs, lack of transparency ('black box' nature, proprietary algorithms/data), potential for misuse/over-reliance on LLMs in legal interpretation, high cost of alternative empirical methods (surveys, corpus linguistics expertise). Methodological difficulty in finding reliable prompting techniques. | Careful benchmarking of LLM responses against human data, using specific validated prompting techniques (e.g., belief prompt + Likert scale), generating distributions of replies (not single answers), testing sensitivity to context, using LLMs to triangulate meaning with other methods, developing best practices for LLM use in interpretation. Democratizing access to empirical evidence via low-cost LLMs. | Statutory interpretation (determining ordinary meaning of terms). Access to legal information. | NaN | Statutory Interpretation | International | Standard GPT-3.5 Turbo training data: Common Crawl (410B words), WebText2 (19B words), Books1 (12B words), Books2 (55B words), Wikipedia (3B words). Not specifically trained on legal text. | Experimental design comparing LLM outputs to a human benchmark (Tobia 2020). Iterative testing of different prompting strategies. Statistical analysis (Kolmogorov-Smirnov test). Exploration of contextual variations. | NaN | True | False | Relies on OpenAI's commercial API for GPT-3.5 Turbo. | Need for more benchmarking against human data for various legal terms and tasks. Development of robust prompting methodologies. Lack of transparency of LLMs. Understanding LLM handling of technical vs. ordinary meaning. Further validation of historical meaning capabilities. | Finding effective prompting techniques yielding reliable results comparable to human judgment. Overcoming technical/coding challenges using the API. Analyzing large volumes of generated data. Interpreting variance in GPT responses. Avoiding over-reliance given limitations. | Misleading results from poor prompting ('junk science'). Over-reliance on LLM output due to perceived precision ('false allure of quantitative objectivity'). Lack of transparency ('black box') hindering accountability. General LLM risk of 'hallucinations'. Potential for over-inclusivity in classification observed in some tests. |
| 1237244.pdf | Google_Scholar | Applications of AI Chatbots Based on Generative AI, Large Language Models and Large Multimodal Models | This paper explores numerous applications of AI chatbots, built on Large Language Models (LLMs) and Large Multimodal Models (LMMs), categorizing them into personal and organizational uses. It details potential benefits like efficiency and personalization across various sectors, while also emphasizing crucial ethical considerations, regulatory compliance, and the need for human oversight for each application. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | Accuracy limitations, legal soundness of outputs, potential for synthetic media misuse, risks of IP theft, identity theft, digital privacy and security breaches, need for professional legal review, lack of accountability. | Human verification and supervision by legal professionals, adherence to ethical considerations (accuracy, privacy, security) and regulatory compliance. | NaN | NaN | General Legal Tasks | International | NaN | NaN | NaN | True | True | The paper discusses applications of generally available chatbots like ChatGPT and Gemini, which have public access (including free tiers). | Accuracy, reliability, bias mitigation, need for robust human oversight and verification especially for critical tasks, development of clear ethical guidelines and regulatory frameworks specific to legal applications, ensuring accountability. | NaN | Inaccuracy and hallucination leading to incorrect information or advice, data privacy and security breaches, propagation of bias, intellectual property (IP) theft, identity theft, misuse for generating harmful or misleading synthetic media, potential manipulation or exploitation of users (e.g., customers, employees), legal and reputational risks for organizations, over-reliance leading to deskilling, lack of accountability. |
| AT2VUVl9UdYJ.pdf | Google_Scholar | Attributing AI Authorship: Towards a System of Icons for Legal and Ethical Disclosure | This paper proposes the Artificial Intelligence Attribution (AIA) system, featuring icons similar to Creative Commons licenses, to standardize the disclosure of AI's role (research, writing, editing) in text generation across legal, academic, and corporate contexts. The authors argue, supported by original empirical research, that this system can mitigate legal risks, improve public perception, and foster ethical AI use by enhancing transparency and accountability. | True | Market | True | 1.0 | Positive | AIA (Artificial Intelligence Attribution) system using visual badges (Research, Writing, Editing, AI-Free) | Experimental survey (N=423) based on Mata v. Avianca case facts, comparing public perception and legal risk assessment of an attorney's negligent AI use with vs. without AIA badge disclosure. | Attorneys disclosing AI use with AIA badges faced significantly lower perceived likelihood of malpractice suits, lower recommended punishments (guilt verdicts, sanctions, suspension, fines), and were less likely to be seen as unfit for future practice compared to non-disclosing attorneys. | Lack of transparency regarding AI use in professional text generation (legal, academic, corporate); absence of norms and mechanisms for disclosure leading to legal risks, potential deception, erosion of trust, and ethical concerns. | Proposes the AIA (Artificial Intelligence Attribution) system, a set of visual badges inspired by Creative Commons, to provide a standardized, efficient way to disclose the nature and extent of AI involvement (Research, Writing, Editing, AI-Free) in text generation. | Professional ethics in law, Disclosure obligations, Transparency in AI use, Risk management for legal professionals, Accountability in legal document production. | NaN | Legal Ethics/Professional Responsibility, Contract Law, Consumer Protection Law, Intellectual Property Law, Civil Procedure, Tort Law (Legal Malpractice). | Primarily US (based on cases, institutions, laws cited), but proposed system has potential international applicability. | NaN | Conceptual design based on analogy (Creative Commons, iconography), legal analysis, ethical reasoning, empirically validated through a survey experiment. | Proposed for use in legal practice, academia, and corporate communications; no specific deployment strategy detailed. | True | True | The concept and visual designs for the AIA badges are presented within the paper, published in an open-access journal, implying they are available for adoption. | Need for evolving norms around AI disclosure; potential need for more granular disclosure than current badges offer; limitations of current AI detection tools and need for better verification/enforcement mechanisms; need for widespread adoption. | Designing an intuitive, comprehensive, yet simple system; promoting adoption and establishing norms; addressing potential negative perceptions of disclosed AI use; ensuring flexibility for future AI evolution; empirically validating the system's impact. | Risks of non-disclosure: Legal liability (malpractice, contract breach, consumer fraud, IP infringement), ethical violations (plagiarism, deception), professional sanctions, reputational damage, erosion of trust. Potential risks/criticisms of disclosure via AIA: Negative bias against AI-assisted work, revealing potentially sensitive process information, incomplete information conveyed by badges alone. |
| iboDfGK_-oEJ.pdf | Google_Scholar | It Cannot Be Right If It Was Written by AI: On Lawyers’ Preferences of Documents Perceived as Authored by an LLM vs a Human | This paper investigates whether lawyers' and law students' perception of legal documents (acknowledgement of debt) varies based on the belief that they were AI-generated versus human-crafted. The study found a significant bias against documents labeled as AI-generated, which were rated lower in correctness and language quality, despite being identical to those labeled human-crafted. | True | Idealistic | True | 2.0 | Neutral | Experimental survey designed to measure perception bias. Participants evaluated identical human-written legal documents (acknowledgement of debt), where the only difference was a label indicating whether the document was supposedly 'AI-GENERATED' or 'HUMAN-CRAFTED'. | 75 Czech lawyers and law students were randomly assigned to two groups. Each group evaluated two human-written 'acknowledgement of debt' documents (one Brief, one Verbose). Document labels ('AI-generated' vs 'human-crafted') were swapped between the groups. Participants rated documents on correctness and language quality (1-5 scale) via an online survey and provided qualitative explanations. Statistical analysis (Fisher exact test) and thematic analysis were performed. | Documents labeled 'human-crafted' were rated significantly higher than identical documents labeled 'AI-generated' on both correctness (mean 4.69 vs 4.21) and language quality (mean 4.55 vs 3.97). Thematic analysis revealed more negative comments regarding aspects like stylistics, structure, and formal correctness for documents perceived as AI-generated. Despite this bias, 93% of participants believe full automation of such documents is feasible. | Negative perception and bias (algorithmic aversion) against AI-generated legal documents among legal professionals, even when the documents are objectively correct. This bias could disproportionately harm lower-income individuals who might rely on AI-powered legal aid or self-help tools, potentially undermining the goal of increasing access to justice. | The paper highlights the need for awareness of this perception bias among legal practitioners, policymakers, and legislators. It suggests responsible implementation and adoption strategies for legal document generation technology and calls for discussions on updating legal processes. | Perception of AI-generated legal documents, Automated document drafting (specifically, acknowledgement of debt), Potential impact on access to justice for self-represented litigants or users of AI-powered legal aid. | Lower-income groups (potentially relying on AI tools for legal aid or self-help). | Civil Law (specifically Contract Law / Obligations Law related to debt acknowledgement). | Czechia | NaN | Experimental design involving manipulation of document labels (AI-generated vs. human-crafted) presented to two groups of participants (lawyers and law students). Data collection via online survey with Likert scale ratings and open-ended questions. Analysis using quantitative statistical tests (Fisher exact test) and qualitative thematic analysis. | NaN | True | True | The documents and the survey used in the experiments are released in an accompanying online repository on GitHub. | Need for research involving other populations (e.g., judges, officials, general public), different types/complexity of legal documents, varying participant AI exposure/experience, and cross-jurisdictional/-linguistic validation. Societal gap: Addressing the identified perception bias to ensure AI fairly benefits access to justice. | NaN | Over-reliance on or unfounded scepticism towards AI-generated documents influencing legal outcomes. Algorithmic aversion acting as a bias against users of AI tools, particularly affecting lower-income groups and potentially increasing social inequalities. Negative perceptions undermining the potential benefits of AI for access to justice. Potential conflict between transparency (disclosing AI use) and fairness due to perception bias. |
| jUunUV6T7O0J.pdf | Google_Scholar | Access to A.I. Justice: Avoiding an Inequitable Two -Tiered System of Legal Services | The paper discusses the potential of AI to improve access to justice but warns of the risk of creating inequitable two-tiered systems where the poor receive inferior services or only the wealthy benefit. It proposes a framework for calibrating AI use based on consumer, issue, and process considerations and advocates for regulatory reforms like sandboxes to overcome barriers and foster equitable AI development. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | High cost of legal services; lack of legal knowledge/experience/resources among consumers; language barriers; geographic isolation (rural areas); inadequate legal aid funding and staffing; digital divide (lack of access/skills); algorithmic divide; difficulty recognizing legal needs; complexity of legal issues for underserved populations (often overlapping with other issues); lack of data on marginalized communities (e.g., heirs' property); conservatism/resistance to change in the legal profession. | Regulatory reforms (clearer UPL definitions, relaxing non-lawyer ownership rules); regulatory sandboxes/laboratories for testing innovations; promoting competition in legal tech; increasing transparency (public accuracy rates, certifications, code comments); careful "calibration" of AI based on consumer, issue, and process considerations; culturally competent and user-centric AI design; involving diverse teams in design; using AI to identify/combat bias (if calibrated well); promoting algorithmic literacy; fostering collaboration between legal and tech sectors. | Access to legal information, self-help services, legal aid, document automation, client intake, legal analytics, pro bono services, legal service delivery models, regulation of legal services, unauthorized practice of law (UPL), non-lawyer ownership of law firms. | Low-income individuals, middle-income individuals, rural populations, those with limited English proficiency, recent immigrants, non-profits, small businesses, entrepreneurs, self-represented litigants, marginalized communities with undigitized records (e.g., heirs' property owners, affecting Black, Hispanic, Indigenous populations). | Civil Law, Family Law, Mediation, Transactional Law, Litigation, Property Law, Ethics/Professional Responsibility. | US | NaN | NaN | NaN | False | False | NaN | Need for regulatory reform (UPL, ownership); insufficient data/research on legal AI impact; lack of transparency in AI systems ("black box"); need for better methods/frameworks for AI calibration; addressing digital/algorithmic divides; ensuring culturally competent AI; fostering collaboration between legal and tech fields; lack of funding/resources for A2J tech; overcoming legal profession's conservatism. | High cost of AI development/deployment; need for significant resources (data, compute power, talent); overcoming legal profession's conservatism/resistance; ensuring algorithmic literacy; managing ethical risks (competence, UPL, bias); time/resilience needed for trial-and-error; difficulty establishing cross-industry collaboration due to regulations/market structures; designing for diverse users/contexts; ensuring data quality and mitigating bias; lack of transparency. | Creation/exacerbation of inequitable two-tiered legal service systems; AI causing harm to vulnerable consumers (errors, predatory services); amplification of societal biases (racial, gender, economic) through AI; exclusion of certain communities; erosion of trust in the legal system; decline in human-centered legal aid; stifling innovation due to regulatory uncertainty or market consolidation; ethical violations by lawyers; malpractice liability. |
| zWAOn2V0xsMJ.pdf | Google_Scholar | ArabLegalEval: A Multitask Benchmark for Assessing Arabic Legal Knowledge in Large Language Models | This paper introduces ArabLegalEval, a new benchmark dataset sourced from Saudi legal documents and translated English legal tasks, designed to assess the legal knowledge and reasoning of Large Language Models in Arabic. The authors detail the benchmark's creation methodology, evaluate several state-of-the-art LLMs, and release the dataset and code to foster research in Arabic legal AI. | True | Market | True | 1.0 | Positive | ArabLegalEval: A multitask benchmark dataset for Arabic legal LLMs, including methodologies for its creation from Saudi legal documents, publicly available FAQs, and translated LegalBench tasks. This also covers specific methods for MCQ generation (e.g., in-context learning from ArabicMMLU examples) and QA pair curation. | Various LLMs (GPT-4, GPT-4o, Jais, Command R, Command R Plus, Llama3) were benchmarked on ArabLegalEval tasks: MCQs (evaluated by accuracy), open-ended QA (evaluated using GPT-4 as a judge for answer similarity), and translated LegalBench subtasks (Consumer Contracts QA, Contracts QA, Privacy Policy QA, Privacy Policy Entailment, evaluated by F1 scores). Prompt optimization techniques (e.g., few-shot, CoT using DSPy) were explored. Human expert performance was also baselined on a sample. | On the generated MCQs, GPT-4o achieved the highest accuracy of 79.10% using few-shot prompting. For translated LegalBench tasks, top F1 scores varied: 90% by GPT-4 (one-shot) and Llama3-70B (zero-shot basic) on Consumer Contract QA; 99% by Command R Plus (few-shot) on Contract QA; 66% by Command R Plus (one-shot) on Privacy Policy Entailment. | Under-explored evaluation of LLM legal knowledge in Arabic, hindering development of reliable AI legal tools. Scarcity and difficulty in obtaining comprehensive, structured Arabic legal data suitable for training and benchmarking AI. | Creation and release of ArabLegalEval, a public benchmark with methodologies, open-source code, and dataset to stimulate and guide the development of more capable Arabic legal LLMs. Development of workflows for generating questions with automatic validation. | NaN | NaN | General legal domain, specifically Saudi Arabian law (regulations, statutes, circulars from Ministry of Justice and Board of Experts) and translated universal legal tasks (consumer contracts, general contracts, privacy policies). | Saudi Arabia (for native Arabic tasks); International (for translated LegalBench tasks considered universal). | The benchmark dataset (ArabLegalEval) was constructed from: publicly available Saudi legal documents (from Ministry of Justice and Board of Experts) scraped from official websites; publicly available human-written FAQs (NajizQA); and translated subsets of the publicly available LegalBench dataset. The ArabicMMLU legal subset was used for style guidance in MCQ generation. | For MCQ generation: Iterative prompt engineering (QA to MCQ, CoT, retrieval-based in-context learning from ArabicMMLU examples), automatic filtering using GPT-4, and manual review by legal experts. For QA data: Filtering of existing FAQs, semantic similarity matching. For LegalBench translation: Comparative evaluation of machine translation models (Opus MT chosen) with ROUGE scores and expert review. | The ArabLegalEval dataset and code are released on GitHub. | True | True | The ArabLegalEval dataset and code are available on GitHub: https://github.com/Thiqah/ArabLegalEval | Limited geographic representation in the current benchmark (primarily Saudi Arabian law). The dataset lacks granular categorization for more nuanced model training and evaluation. The broader under-exploration of LLM legal capabilities in Arabic. | Obtaining comprehensive Arabic legal data. Formulating high-quality MCQs (questions and plausible distractors). Mitigating evaluation bias where models might favor their own generated questions. Difficulty in evaluating open-ended QA due to semantic variability. Degradation of reasoning capability in Arabic for some smaller LLMs. | Potential for evaluation bias if models are tested on questions generated by themselves (a mitigation strategy was employed). Implicit risk of deploying LLMs with unverified or poor legal reasoning capabilities, which the benchmark aims to help identify and assess. |
| xIDsCy5DQfsJ.pdf | Google_Scholar | Legal Ethics, Artificial Intelligence, and Mindfulness, Oh My! | This paper explores the intersection of artificial intelligence, particularly generative AI like ChatGPT, with legal ethics rules governing lawyers' conduct. It highlights key ethical obligations (competence, confidentiality, supervision, billing) and suggests mindfulness as a tool for lawyers to navigate the benefits and risks of AI responsibly. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General Legal Practice / Legal Ethics | USA (primarily ABA Model Rules, New York court case) | NaN | NaN | Internal deployment within a law firm (Dentons' fleetAI example) with staff training and usage guidelines. | False | False | NaN | NaN | Ensuring ethical compliance (competence, confidentiality, supervision, candor), verifying AI accuracy, managing client communication and billing, adapting professional standards to new technology. | Inaccuracy/fabrication (hallucinations), breach of confidentiality, bias, violation of ethics rules (competence, candor), professional sanctions, reputational damage to lawyers/courts/profession, undermining client representation. |
| Yeadon_2023_Phys._Educ._58_035027.pdf | Google_Scholar | The death of the short-form physics essay in the coming AI revolution | This paper experimentally demonstrates that current AI language models like davinci-003 and ChatGPT can produce high-quality short-form physics essays achieving top university grades. This capability, combined with low plagiarism scores and challenges in AI text detection, poses a significant threat to the validity of such essays as an assessment method in physics education. | True | NaN | True | 2.0 | NaN | Use of AI language models (OpenAI's davinci-003 and ChatGPT) to generate short-form physics essays for university-level assessment. | Ten AI-generated submissions (each with five 300-word essays) were created using davinci-003. These were independently marked by five separate markers using an existing university Physics module's assessment proforma. Plagiarism detection software (Grammarly and TurnitIn) and AI text detection software (OpenAI's classifier, GPTZero) were also used on the essays. | AI-generated submissions achieved an average mark of 71±2%, comparable to the human student module average of 71±5% and meriting a First-Class grade. Plagiarism scores were low (Grammarly: 2±1%; TurnitIn: 7±2%). Current AI detection tools showed limited reliability in identifying AI-generated text (OpenAI's tool: 8/10 'Very unlikely AI'; GPTZero: 9/10 'May include parts written by AI'). | NaN | NaN | NaN | NaN | NaN | United Kingdom | The paper refers to GPT-3 being 'Trained on a large dataset of human-generated text,' which is a proprietary, large-scale, general text dataset from OpenAI. | The LLMs (davinci-003, ChatGPT based on GPT-3) are autoregressive language models trained on large-scale text datasets using statistical techniques to predict and generate coherent text based on prompts. | davinci-003 was accessible via OpenAI's 'playground' web application; ChatGPT is available as a chatbot. Both are online services provided by OpenAI. | True | False | davinci-003 accessible via OpenAI 'playground' web application; ChatGPT available as a chatbot, both stated as 'freely available to anyone with an internet connection' (though API/playground access may involve costs after initial free tiers). | NaN | Crafting effective prompts that elicit desired, high-quality, and varied essay responses from the LLMs often requires trial-and-error, including rephrasing questions and specifying output length or style. Ensuring generation of multiple unique, high-quality responses on nuanced topics might require some subject familiarity for prompt engineering. | Significant threat to the fidelity of short-form essays as an assessment method in education. Students could submit AI-generated work as their own, potentially passing undetected by plagiarism software and achieving unmerited high grades. AI models may also produce subtly incorrect or superficial content, especially in complex technical questions. |
| FuGSzl4d1IIJ.pdf | Google_Scholar | Citation-Enhanced Generation for LLM-based Chatbots | This paper proposes Citation-Enhanced Generation (CEG), a novel post-hoc framework to mitigate hallucinations in LLM-based chatbots by verifying generated content against retrieved documents using NLI. CEG can regenerate responses if unsupported statements are found, working as a training-free plugin for various LLMs. | True | NaN | True | 1.0 | NaN | Post-hoc Citation-Enhanced Generation (CEG) framework. It comprises: 1) A retrieval augmentation module (e.g., SimCSE BERT) to search for relevant documents (from a corpus like Wikipedia) for each segment of an LLM's response. 2) A citation generation module using Natural Language Inference (NLI) (e.g., prompted LLMs like GPT) to determine if retrieved documents support the response segments. 3) A response regeneration module that prompts the LLM to create a new response if segments are found to be nonfactual, incorporating the original query and relevant retrieved documents. | CEG was evaluated on hallucination detection benchmarks (WikiBio GPT-3, FELM WorldKnowledge subset) and a hallucination regeneration benchmark (HaluEval QA subset). Metrics included AUC-PR, Balanced Accuracy, and Accuracy. Custom datasets (WikiRetr-GPT3, WikiRetr-GPT4, based on Wikipedia) were also used to analyze the retrieval and NLI module performance using recall@k and precision@k. | CEG outperformed state-of-the-art methods in both hallucination detection and response regeneration on three benchmarks. For instance, on the HaluEval QA subset, CEG with GPT-3.5-Turbo-Instruct achieved 69.45% accuracy. On the FELM dataset with GPT-4, CEG achieved a balanced accuracy of 69.9%. | NaN | NaN | NaN | NaN | NaN | NaN | The CEG framework itself is training-free. For retrieval, it uses a processed snapshot of Wikipedia (October 20, 2023), segmented into ~100-word candidate documents. The NLI module employs pre-trained LLMs (e.g., GPT-3.5, GPT-4) with specific prompts, without requiring fine-tuning for CEG. | The CEG framework is designed as a post-hoc, plug-and-play system. Key design elements include segmenting LLM responses, using dense retrieval for document fetching, employing LLMs as NLI models via prompting for fact verification, and an iterative regeneration process with a maximum attempt limit. | The paper states the method is a 'training-free plug-and-play plugin'. The code and datasets are made available on GitHub to facilitate use and further research. | True | True | Code and datasets are available on GitHub: https://github.com/Tsinghua-dhy/CEG | NaN | Previous methods often require additional model training and data annotation. CEG aims to overcome this by being training-free. Challenges in CEG's development include selection of optimal retrieval models, balancing the number of retrieved documents (k) for effectiveness versus computational cost, and the performance of LLMs as NLI engines. API costs for LLM usage (NLI and regeneration) are also a practical consideration. | The paper identifies hallucination in LLM responses as the primary risk it addresses. Limitations that could be potential risks include: dependency on the quality of the retriever and corpus (current experiments use Wikipedia), reliance on the LLM's inherent world knowledge for NLI which could be flawed, and API costs associated with regeneration and NLI calls. |
| Sneddon_et_al_2024_A_servant_of_two_masters_How_Academic_Fears_about_Artificial_Intelligence_map_to_Employer_Engagement.pdf | Google_Scholar | SERVANT OF TWO MASTERS: HOW ACADEMIC FEARS ABOUT ARTIFICIAL INTELLIGENCE MAP TO EMPLOYER ENGAGEMENT | This paper discusses the impact of Generative AI on the legal and healthcare professions and their respective university education programs, highlighting key themes like risks (hallucinations, bias, skills gaps) and opportunities (efficiency, new job roles). It proposes the BATTEL model as a framework for guiding the ethical and effective integration of AI in education to prepare graduates for an AI-transformed professional landscape. | True | Market | True | 3.0 | Neutral | BATTEL (Best Available Techniques in Technology Enhanced Learning) model, applied to GenAI integration in education. | The paper mentions future research plans involving interviews and Q methodology to triangulate themes, but no testing of the BATTEL model's application is reported within this paper itself. | NaN | Replication of societal biases by AI systems leading to injustices; potential displacement of junior legal roles impacting service cost and availability; skills gaps among legal professionals and educators to effectively and ethically use AI. | Emphasis on human oversight and input to mitigate AI bias; adapting legal education to equip professionals with skills to manage AI, evaluate AI outputs, understand AI ethics, and fill new AI-related legal roles; utilizing frameworks like the BATTEL model to guide appropriate AI integration. | AI bias and fairness in legal outcomes; impact of AI on legal service delivery models and workforce (e.g., roles of paralegals and junior lawyers); ethical application of AI in the legal domain; ensuring legal education prepares students for AI in legal practice. | NaN | Legal education, General legal practice (e.g., contract negotiation). | International (with examples/mentions from UK, EU, US). | For GenAI (e.g., ChatGPT) discussed: The paper mentions the New York Times lawsuit against OpenAI and Microsoft for copyright infringement related to data scraping, implying large, scraped datasets including copyrighted material. For the BATTEL model itself, training data is not applicable as it's a conceptual framework. | The BATTEL model was developed by adapting the existing BAT (Best Available Technique) framework used in industrial emissions control. | The BATTEL model is proposed for adoption within higher education institutions to guide the appropriate and ethical use of AI, particularly Generative AI, in learning and teaching for professions like law and healthcare. | True | True | The BATTEL model is a conceptual framework described in a cited 2021 open-access journal article by one of the authors, making its principles available for application. | The need for ongoing development of sustainable curricula that keep pace with AI evolution; addressing the skills gap in both academic staff and students regarding AI; establishing clear policies and legislation for AI in education and professional practice; balancing technophobia and technophilia. | For GenAI: risk of 'hallucinations' (incorrect information), ethical use, ensuring data privacy and security, potential for bias replication, copyright issues related to training data. For the BATTEL model: achieving consensus among technological, subject, and pedagogic experts; evolving the 'best' standard as technology advances rapidly. | AI generating 'hallucinations' (false information) leading to incorrect decisions; replication of societal biases by AI, leading to injustices; privacy and security breaches of sensitive data; ambiguities in legal liability and accountability for AI actions; job displacement in legal roles (e.g., paralegals, junior lawyers) if not balanced by new role creation; students misusing AI, leading to academic misconduct and lack of genuine learning; graduates being unprepared for AI-driven industries. |
| BuildingTrustwithGenerativeAIChatbots-ExploringExplainabilityPrivacyandUserAcceptance.pdf | Google_Scholar | Building Trust with Generative AI Chatbots: Exploring Explainability, Privacy, and User Acceptance | This paper discusses the importance of building user trust in generative AI chatbots by examining the key factors of explainability, privacy, and user acceptance. It explores challenges and techniques related to transparency, data protection (including regulations like GDPR/CCPA), and factors influencing user adoption across various industries. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General AI, Customer Service, Healthcare, E-commerce, Finance, Law (Data Privacy Regulation, Legal Tech example) | EU, USA | NaN | NaN | NaN | False | False | NaN | NaN | General challenges discussed include: achieving reliability, transparency, user control; balancing complexity/performance with transparency/explainability; preventing user overload; managing data privacy (breaches, profiling, transparency); adhering to regulations; ensuring user acceptance (usefulness, ease of use, social factors, trust); designing good UX (conversational flow, personalization); managing perceived risks (inaccuracy, loss of control); user education; limitations in handling nuance, context, empathy; ensuring security; ethical issues (bias, fairness). | Data breaches, identity theft, surveillance, user profiling without consent, lack of transparency in data use, inaccurate/wrong information, harmful/biased responses (esp. in healthcare/law/finance), loss of user control, ethical risks (bias, fairness, stereotyping), privacy violations, reliance on potentially flawed AI advice. |
| h6yAuPmgJMMJ.pdf | Google_Scholar | LegalBench.PT: A Benchmark for Portuguese Law | This paper introduces LegalBench.PT, the first benchmark dataset specifically designed to evaluate Large Language Models (LLMs) on their knowledge and application of Portuguese law. The benchmark was created by collecting questions from real law exams and synthetically converting them into multiple-choice, true/false, and matching formats using GPT-4o, followed by filtering and validation. | True | NaN | True | 1.0 | Neutral | A methodology for creating a legal benchmark (LegalBench.PT) by synthetically generating multiple-choice, true/false, and matching questions from existing long-form law exam questions and answers using an LLM (GPT-4o), followed by rule-based and semantic filtering, and option shuffling. | Evaluated several LLMs (GPT-4o, GPT-4o-mini, Claude-3-Opus, Claude-3.5-Sonnet, Llama-3.1-8B/70B/405B, Mixtral-8x7B) on the benchmark in a zero-shot setting using balanced accuracy and F1-score. Also conducted human evaluation with 22 Portuguese lawyers on a 1,000-question subset and compared performance. Investigated potential generation bias by recreating a subset with Claude-3.5-Sonnet. | GPT-4o performed best overall (85.4% score), closely followed by Claude-3.5-Sonnet (85.1%) and Llama-3.1-405B (83.8%). Human lawyers generally performed closer to the lower-performing models (Llama-3.1-8B, Mixtral-8x7B). The bias investigation found no significant performance difference favouring the generating model. | NaN | NaN | NaN | NaN | Public Law (Environmental, Administrative, Constitutional, Energy, Public Finance, Financial, Tax, Criminal, Administrative Procedure, Civil Procedure, Criminal Procedure, Labor Procedure, Urban Planning), Private Law (Contract, Family, Obligations, Property, Succession, Commercial, Banking, Maritime, Corporate, Securities, Transportation, Aviation, Insolvency, Private International, Labor), Public-Private Law (Competition), Public International Law, EU and Community Law | Portugal | The benchmark generation technique used GPT-4o. The source data for generating the benchmark questions consisted of 341 PDF law exams with solutions from the Faculty of Law at the University of Lisbon (academic years 2021-2024), manually segmented and processed. | Corpus collection (law exams), synthetic data generation (using GPT-4o prompts), rule-based filtering (removing questions referencing specific articles), duplicate removal (using ROUGE-L and semantic similarity), statistical analysis (answer distribution), option shuffling, expert review (sample validation by a lawyer), human evaluation (lawyer performance assessment). | The benchmark dataset (LegalBench.PT) is made publicly available on Hugging Face. | True | True | Dataset publicly available at: https://huggingface.co/datasets/BeatrizCanaverde/LegalBench.PT | Underrepresentation of some legal areas; need for tasks beyond legal knowledge/reasoning (e.g., contract analysis, summarization); presence of easy, ambiguous, or incorrect questions needing filtering; need for more thorough human expert evaluation for validation. | Ineffectiveness of automatically evaluating long-form answers; LLM output token limits; filtering undesirable generated questions (references to specific articles/laws, duplicates); bias in generated answer option distribution; noise in synthetically generated data (incorrect answers, improper legal terminology, ambiguity). | Potential biases in the dataset derived from the synthetic generation process or inherent in the legal system. Misuse of the benchmark as a substitute for professional legal advice. |
| g4WP7TIImlgJ.pdf | Google_Scholar | From Flowchart to Questionnaire: Increasing Access to Justice via Visualization | This paper introduces F2Q (Flowchart to Questionnaire), an open-source toolbox designed to enable legal experts without programming expertise to create web-based interactive questionnaires from flowcharts. These questionnaires aim to guide clients, particularly from underserved populations, in understanding their legal issues and identifying potential solutions, thereby improving access to justice. | True | Idealistic | False | 1.0 | Positive | F2Q (Flowchart to Questionnaire): An open-source toolbox with a back-end designer for legal experts to create flowcharts and a client-facing front-end that automatically generates interactive web-based questionnaires from these flowcharts. | Demonstration through four use cases (debt collection, protection, eviction, small claims) developed with input from a legal expert. Feedback was gathered from a collaborating legal expert. Formal usability assessment is stated as future work. | NaN | Lack of awareness of legal rights, procedures, and available resources. Difficulty for individuals to identify their legal problems and understand potential solutions, leading them to abandon seeking justice. | Providing an open-source toolbox (F2Q) that allows legal experts to easily create and deploy interactive questionnaires. These questionnaires act as virtual assistants to guide users in understanding their legal situation and finding resources. | Legal problem categorization, identification of legal remedies/solutions, guidance on legal procedures (debt collection, protection orders, eviction, small claims), self-help legal resources. | Minorities and underserved populations without legal representation, clients of legal self-help centers or legal clinics. | Civil law, specifically debt collection, protection orders, eviction, and small claims. | United States (with specific examples from Utah) | NaN | User-centered design incorporating feedback from a legal expert. Use of visual flowcharts for representing legal decision paths. Design requirements included ease of use for non-programmers, client privacy protection, and an emergency exit feature. | The F2Q toolbox is released as open-source software on GitHub. Core ideas are reported to be under development by contractors for deployment at a local help center. | True | True | Open-source toolbox available on GitHub (https://github.com/tdavislab/F2Q). | Need for in-depth consideration of privacy and scalability for practical deployment. Formal usability assessment to evaluate effectiveness and efficiency is pending. The current tool only addresses a small part of the potential for visualization in access to justice. | NaN | Potential privacy concerns if client data were stored (addressed by current design not storing data). Risk of incorrect guidance if flowcharts are not accurately designed or updated by legal experts. |
| zho6ctN2dzQJ.pdf | Google_Scholar | Working Smarter: A Quantitative Investigation into Higher Education Faculty's Perceptions, Adoption, and Use of Generative Artificial Intelligence (AI) in Alignment with the Learning Sciences and Universal Design for Learning. | This dissertation investigates higher education faculty's perceptions, adoption, and use of generative AI, exploring alignment with Universal Design for Learning (UDL) principles using a quantitative survey approach. The study examines predictors of AI adoption and use, finding high adoption rates influenced by perceived relative advantage and professional development, with variations based on faculty demographics and roles. | True | NaN | True | 2.0 | NaN | Generative AI (e.g., ChatGPT, Gemini) use by higher education faculty | Quantitative survey research design involving 214 higher education faculty. Data analyzed using descriptive statistics, independent samples t-tests, nested logistic regression, chi-square tests of independence, and nested multiple linear regression. | 86% of faculty adopted generative AI. Relative advantage and professional development participation were significant predictors of adoption. Adoption rates were significantly higher among men, tenured/tenure-track faculty, and those with more knowledge of learning sciences or generative AI. Professional development on generative AI and several perceived attributes significantly predicted UDL-aligned use. | NaN | NaN | NaN | NaN | NaN | International (sampled faculty from US, Canada, Japan, Kazakhstan, Russia, UK, though predominantly US) | NaN | Quantitative survey research design; adaptation of existing validated scales (Moore & Benbasat’s Perceived Attributes of Innovations Scales, Grassini's AI Attitude Scale, Brougham & Haar’s STARA Awareness Scale). | NaN | True | False | Publicly available generative AI tools (e.g., ChatGPT, Gemini). | Need for further research addressing sample representativeness, the evolving nature of generative AI and UDL guidelines, correlations between key factors (perceptions, PD, use), and refining measurement instruments for perceived attributes. | Methodological limitations including non-probability sampling (convenience, snowball), potential for selection bias, cross-sectional design limiting generalizability and ability to track changes over time, managing data quality (detecting duplicate/bot submissions). | Academic integrity concerns (plagiarism, cheating on exams), AI bias (from training data, in detection tools disadvantaging non-native English speakers), AI unreliability (hallucinations, misinformation), potential threats to faculty job security, ethical concerns regarding data privacy and intellectual property, inappropriate use for sensitive tasks (e.g., mental health support without proper safeguards). |
| P1n84Z_tYPUJ.pdf | Google_Scholar | A quantitative study on the negative and positive impacts of using artificial intelligence (AI) in the information technology field | This paper investigates the perceived positive and negative impacts of Generative AI tools within the Information Technology (IT) sector using a quantitative survey of IT professionals and educators. Results indicate a significant positive relationship between understanding AI tools and perceiving positive impacts, alongside strong agreement on the need for ethical guidelines. | True | Market | True | 2.0 | NaN | NaN | Quantitative survey of 52 IT professionals/educators via LinkedIn using a 10-item Likert scale questionnaire. Data analyzed using SmartPLS4. | Significant positive relationship found between understanding GenAI tools and perceiving positive impacts (H1 supported). 84% of participants agreed/strongly agreed on the need for ethical guidelines. Hypotheses linking informed decision-making to minimizing risks (H2) or maximizing positive impacts (H3) were not supported. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | False | NaN | NaN | Study limitations: Small sample size (N=52), potential participant bias based on prior experience. General AI challenges discussed: Job displacement, bias, privacy issues, security risks, lack of transparency/explainability, AI hallucinations ('inaccurate statements'), manipulation for harmful content generation, existential/'singularity' risk. | Job displacement due to automation, ethical concerns about bias and privacy, security dangers from hacking, unfair or dangerous decisions due to lack of explainability, generation of inaccurate statements ('AI hallucination'), potential for autonomous weapons or surveillance violating rights, existential risk ('singularity'). |
| WUp5XdawNLoJ.pdf | Google_Scholar | A(I)ccess to Justice: How AI and Ethics Opinions Approving Limited Scope Representation Support Legal Market Consolidation | This article argues that while general AI tools like ChatGPT pose risks due to misuse, legal-specific AI combined with ethically approved practices like limited scope representation and ghostwriting can enhance access to justice by lowering costs. This convergence, however, may also lead to the corporatization and consolidation of the legal market for low- and middle-income clients. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | High cost of traditional legal representation bars access for low- and middle-income individuals. Unauthorized Practice of Law (UPL) rules prevent direct use of advanced AI by pro se litigants. Potential unsuitability of limited scope representation for complex cases or clients with limitations. | Utilizing legal-specific Generative AI (like Westlaw Precision, Lexis+ AI) to improve efficiency and lower costs. Employing Limited Scope Representation (LSR) and ghostwriting, supervised by attorneys (potentially contract attorneys), to provide affordable, discrete legal tasks. Developing a 'TurboLaw' model combining AI tools and virtual attorney oversight. | Affordability of legal services, Limited Scope Representation, Ghostwriting, Unauthorized Practice of Law, Legal technology adoption, Market structure of legal services. | Low- and middle-income individuals and families, pro se litigants (by enabling more affordable attorney assistance). | General legal practice | United States (references ABA Model Rules, federal courts, D.C., Texas, Maryland, New York, New Hampshire) | NaN | NaN | NaN | False | False | NaN | Need for reliable, legal-specific AI tools accessible at low cost. Clear frameworks to address Unauthorized Practice of Law issues with AI-assisted pro se litigants. Ensuring attorney competence and adequate supervision when using AI within LSR models. Addressing cybersecurity and confidentiality risks inherent in virtual practice and AI use. Potential negative socioeconomic impacts of market consolidation on solo and small firms. | NaN | Use of general GenAI (ChatGPT, Google Bard) leading to fabricated legal citations and court sanctions. AI providing legal advice constituting Unauthorized Practice of Law (UPL). Inadvertent disclosure of confidential client information through technology / virtual practice / outsourcing. Limited scope representation agreements being unreasonable or insufficient for a client's needs. Market consolidation driven by AI potentially harming smaller legal practices. |
| YOXCYWgzfXYJ.pdf | Google_Scholar | CaseGen: A Benchmark for Multi-Stage Legal Case Documents Generation | The paper introduces CaseGen, a benchmark for evaluating Large Language Models (LLMs) on multi-stage legal case document generation in the Chinese legal domain. It uses real case samples annotated by experts and an LLM-as-a-judge evaluation framework, finding current LLMs still struggle with these complex tasks. | True | Market | True | 1.0 | Neutral | CaseGen benchmark for multi-stage legal case document generation (Defense Statements, Trial Facts, Legal Reasoning, Judgment Results) using an LLM-as-a-judge evaluation framework. | Evaluation of several general-domain (GLM-4, Claude-3.5, GPT-3.5, GPT-4o-mini, Qwen2.5, LLaMA-3.3) and legal-specific (ChatLaw, LexiLaw) LLMs using the proposed CaseGen benchmark and LLM-as-a-judge (GPT-4o) framework. Human evaluation (3 experts, 50 cases, 3 LLMs) was used to validate the LLM-as-a-judge consistency via correlation coefficients (Kappa, Spearman, Kendall, Pearson). | Current LLMs perform unsatisfactorily (most LLM-judge scores below 6/10). Qwen2.5-72B-Instruct achieved competitive scores among LLMs. LLM-as-a-judge evaluation showed high consistency with human annotations (Spearman 0.750), superior to ROUGE-L and BERTScore. | Rising caseloads putting pressure on manual drafting; lack of reliable AI tools for complex legal document generation due to tendency for hallucinations and need for high accuracy/professionalism; absence of suitable benchmarks. | Develop and utilize specialized benchmarks (like CaseGen) and robust evaluation methods (like LLM-as-a-judge) to assess and improve LLM capabilities for legal document generation. | Court/Judicial efficiency through automated document drafting. | NaN | Civil Litigation | China | NaN | Data collection (public legal documents), filtering, K-Means clustering for sampling, text parsing, expert annotation (evidence content), annotator training & QC, LLM-based checks, expert cross-checks, multi-stage task design, LLM-as-a-judge pipeline design (pointwise scoring, task-oriented criteria, CoT, reference-based). | Public release on GitHub. | True | True | Dataset and code publicly available on GitHub (https://github.com/CSHaitao/CaseGen) under CC BY-NC-SA 4.0 license for non-commercial academic use. | Unsatisfactory LLM performance on complex legal document generation; limitations of legal-specific LLMs (base model constraints, reasoning degradation); LLM-as-a-judge needs refinement for legal nuances and robustness; current benchmark limited to Chinese jurisdiction. | Handling long legal texts; ensuring factuality, legal accuracy, and logical coherence in generation; designing robust automated evaluation for nuanced legal criteria; high cost and effort of expert annotation for benchmark creation. | LLM hallucinations generating misleading legal content; potential undermining of judicial fairness if AI outputs are flawed; over-reliance on AI; vulnerability of LLM judges to adversarial attacks; potential data privacy violations if PII is not properly handled (mitigated by anonymization). |
| Z673F12s3tUJ.pdf | Google_Scholar | Advancing Legal Tech and Education - Developments in the United States and South Korea - | This paper compares the integration of artificial intelligence (AI) into legal practice and education in the United States and South Korea. It examines trends in AI tools, their adoption by law firms and law schools, regulatory challenges (particularly in Korea), and implications for preparing future lawyers. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Limited access to legal services for the general populace and small businesses, particularly in South Korea due to cost, lawyer concentration, and historical undersupply; Regulatory resistance from professional bodies (e.g., Korean Bar Association); Concerns about AI accuracy, privacy, ethics, and copyright. | Development and adoption of AI-driven legal tech tools for efficiency and service delivery; Creation of legal tech platforms to connect clients with lawyers and provide accessible legal information/consultation (e.g., LawTalk, AI DR & Aju); Integration of AI training, ethics, and practical application into law school curricula. | Access to legal services for underserved populations, Affordability of legal services, Lawyer-client matching platforms, Legal consultation accessibility, Legal education reform. | General populace, individuals, small-business clients (especially in Korea), Consumers with everyday legal issues (US). | General legal practice, Legal research, Document review, Contract analysis, E-discovery, Litigation analytics, Legal education. | United States, South Korea | NaN | NaN | NaN | False | False | NaN | Need for improved AI accuracy and reliability (reducing hallucinations); Addressing AI biases; Resolving data privacy and copyright concerns; Developing robust ethical and regulatory frameworks; Adapting legal education curricula and training faculty effectively; Overcoming resource limitations in educational institutions. | Regulatory pushback and conflicts with traditional legal practice norms (e.g., LawTalk controversy in Korea); Resistance from established legal professionals; Keeping legal education curricula current with rapid technological advancements; Training law faculty in AI and legal tech; Market consolidation. | AI inaccuracy leading to flawed legal analysis or advice ('hallucinations'); Data privacy breaches and misuse of sensitive legal information; Copyright infringement issues related to training data and AI outputs; Algorithmic bias perpetuating inequalities; Undermining professional ethics and standards; Commodification of legal services. |
| yytdIHOdBqkJ.pdf | Google_Scholar | Using deep learning to predict outcomes of legal appeals better than human experts: A study with data from Brazilian federal courts | This paper evaluates deep learning models (ULMFiT, BERT-LSTM, BigBird) for predicting the outcome of appeals in Brazilian Federal Small Claims Courts, using only the first-instance decision text. The best model outperformed human legal experts in prediction accuracy, suggesting AI's potential to enhance judicial efficiency and predictability. | True | Idealistic | True | 2.0 | Positive | Comparison of three deep learning architectures (ULMFiT, BERT-LSTM, BigBird) for binary classification of appeal outcomes (affirm vs. reverse) based on first-instance court decision text. The best performing was the bidirectional ULMFiT. | Models trained and evaluated on the BrACJ-4 dataset (729,830 Brazilian Federal Small Claims Courts appeals, 2007-2020). Performance measured primarily by Matthews Correlation Coefficient (MCC) using a time-based split (train < Mar 2018, validation/test > Mar 2018). Compared against a baseline of 55 human legal experts (judges and clerks) who evaluated a subset of 810 cases. | The bidirectional ULMFiT model achieved the highest MCC (0.3881 on the test set, 0.3647 on the human-evaluated subset), significantly outperforming the human expert baseline (MCC 0.1954). | High volume of litigation and appeals in the Brazilian judiciary, leading to backlogs and slow processes. Significant economic impact of appeal costs, especially on poorer litigants in Small Claims Courts. High affirmance rate makes prediction challenging but necessary. Difficulty in achieving judicial consistency and predictability. | Using AI (deep learning NLP models) to predict appeal outcomes can provide valuable information to litigants and lawyers considering appeals, potentially reducing unnecessary litigation. AI tools could also assist courts in managing caseloads, increasing efficiency, and promoting jurisprudential stability. | Judicial efficiency, case outcome prediction, reducing court backlogs, litigation costs, access to justice for low-income individuals, consistency in judicial decisions. | Litigants in Brazilian Federal Small Claims Courts (LSCIIs), particularly those in the 4th Region (TRF-4), often described as the 'poorest people' seeking social security or assistance benefits. | Social Security Law, Public Law (Administrative Law), Civil Procedure (specifically appeals in Small Claims Courts). | Brazil (Federal Justice, 4th Regional Federal Court - TRF-4, and associated Federal Small Claims Courts - LSCIIs). | A publicly derived dataset (BrACJ-4) containing text from 729,830 first-instance decisions and corresponding appeal outcomes from Brazilian Federal Small Claims Courts (4th Region) between 2007-2020. Pre-training also utilized general Portuguese corpora (Wikipedia, BrWaC). The data is unstructured text. | Data collection from public court repositories, data cleaning and labeling, time-based data splitting for training/validation/testing, transfer learning (pre-training on general/domain corpora, fine-tuning on task), hyperparameter tuning using Bayesian Optimization, evaluation using Matthews Correlation Coefficient (MCC), baseline comparison against human experts. | The dataset (BrACJ-4), code, and pre-trained models are made publicly available on Kaggle and GitHub to facilitate further research. | True | True | Dataset and pre-trained models available on Kaggle (https://www.kaggle.com/eliaskjacob/bracj4). Code available on GitHub (https://github.com/eliaskjacob/paper-bracj4). | Models currently only use first-instance decision text, ignoring potentially valuable information from appeal briefs and counter-arguments. The study is limited to one specific court region and type; generalizability needs testing. Lack of model explainability. | Handling very long legal documents, managing large datasets, avoiding data leakage through time-sensitive splitting, establishing reliable ground truth labels from court data, creating a robust human baseline for comparison, mitigating potential biases in data, computational resource requirements for training large models. | NaN |
| KRoJJ5fKn0IJ.pdf | Google_Scholar | Assessing ChatGPT as a Power Analysis Tool: An Empirical Investigation | This empirical study evaluates ChatGPT's (GPT-3.5-turbo and GPT-4) proficiency in conducting power analysis for sample size calculations, finding it capable, especially when GPT-4 generates R code. The paper suggests ChatGPT can serve as an accessible supporting tool for researchers, reducing barriers to performing power analysis. | True | Idealistic | True | 2.0 | Positive | ChatGPT (GPT-3.5-turbo and GPT-4) for power analysis, specifically sample size calculation for t-tests, ANOVA, and chi-square tests. Evaluation included direct querying and code generation (R and Python) strategies. | Two experiments: Exp1 assessed accuracy of sample size calculation using GPT-3.5-turbo and GPT-4 with three methods (direct, R code, Python code) for three test types (two-sample t-test, one-way ANOVA, χ² goodness-of-fit test), with 100 trials per condition, comparing results to G*Power. Exp2 assessed GPT-3.5-turbo's ability to identify missing input parameters for power analysis across these tests, with 100 trials per condition. | In Experiment 1, the GPT-4 model generating R code achieved 100% accuracy in calculating the required sample size for two-sample t-tests, one-way ANOVA, and χ² goodness-of-fit tests. | For researchers conducting power analysis: need for specialized statistical expertise, cost and accessibility of specialized software, cognitive load of learning new statistical programs, and limitations of existing software for complex designs or all desired statistical tests. | Proposes leveraging Large Language Models like ChatGPT as an interactive and accessible alternative or supplement for power analysis. ChatGPT can provide guidance, explain statistical parameters, and generate R or Python code for sample size calculations, thereby lowering cognitive and resource barriers for researchers. | Access to statistical analysis tools; democratizing research methods; sample size calculation; power analysis support for researchers. | Researchers with limited access to specialized statistical software, funding for such software, technical training, or expert statistical consultation; graduate students; early-career researchers. | NaN | International | The study utilized pre-trained OpenAI models (GPT-3.5-turbo and GPT-4). These models were trained by OpenAI on extensive, diverse datasets of text and code, including statistical information and programming code (e.g., R, Python) relevant to power analysis. | Factorial experimental design (2 models × 3 methods × 3 test types), prompt engineering techniques (e.g., 'Take a deep breath', 'Use the existing code as it is', setting temperature to 0), quantitative accuracy assessment by comparing LLM-generated sample sizes to those from established statistical software (G*Power), and McNemar tests for statistical comparisons of performance. | NaN | True | False | The approach involves using OpenAI's GPT-3.5-turbo (which has some free access tiers) and GPT-4 (a paid model) via their API. The prompting strategies are described and can thus be replicated by users with access to these models. | Need for research on LLM performance for more complex statistical tests and simulation-based power analysis. Limited evaluation of other LLMs beyond GPT-3.5/GPT-4 and the impact of ongoing model updates on result consistency. Validation could be strengthened (e.g., multiple raters). Ensuring overall reliability, safety, and precision of LLMs in statistical applications remains an area for further investigation. | Authors faced challenges related to the probabilistic nature of LLMs (mitigated by temperature settings and prompt engineering), ChatGPT's known limitations in direct mathematical calculations (addressed by using code generation), and achieving consistent high accuracy. For users, challenges include ensuring research accountability when using AI-generated results and potential data security concerns if sensitive contextual information is inputted. | Risk of incorrect sample size calculations leading to underpowered or inefficient research if AI outputs are not critically verified. Over-reliance on AI may lead to errors. Accountability issues for researchers using AI-generated results. Potential for data privacy breaches if users input sensitive study information, although power analysis parameters are typically not PII. |
| Cnozql-PhfgJ.pdf | Google_Scholar | ARTIFICIAL INTELLIGENCE AND THE FUTURE OF LAW AND JUSTICE IN NIGERIA | This paper reviews the potential applications and impact of Artificial Intelligence (AI) on the legal system and administration of justice in Nigeria. It discusses existing AI tools, potential benefits like increased efficiency and accessibility, significant challenges such as bias and job displacement, and the current regulatory landscape, offering recommendations for responsible integration. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Potential job replacement for legal professionals (paralegals, legal assistants); Likelihood of bias in AI systems due to training data, leading to unfair or discriminatory outcomes. | Enhance AI system transparency and accountability; Implement human review mechanisms with diverse, independent reviewers (potentially overseen by an ethics board); Provide comprehensive AI training for future lawyers; Invest in capacity building and integrate tech literacy into education/policy; Develop a comprehensive AI-specific legal and ethical framework. | Efficiency, cost reduction, accessibility, transparency in legal processes and justice delivery (e.g., legal research, document review, case prediction, dispute resolution, sentencing). | General population / litigants in Nigeria | General / Multiple legal fields | Nigeria | NaN | NaN | NaN | False | False | NaN | Lack of comprehensive AI-specific legislation and regulatory framework in Nigeria (beyond draft policies and data protection); Need for widespread AI literacy and training within the legal profession; Establishing robust mechanisms (like independent ethics boards) to ensure AI objectivity and combat bias; Addressing ethical implications of AI-driven job displacement. | The potential for AI bias and discriminatory outcomes; The risk of AI automating and replacing traditional legal tasks and jobs. | AI systems reproducing biases present in training data, leading to unfairness and discrimination; Automation of legal jobs (e.g., paralegals, legal assistants); Potential misuse or misinterpretation of AI in tasks requiring nuanced human judgment or emotional understanding. |
| jpcoqVYi6QcJ.pdf | Google_Scholar | Generative AI or the Doom of Translation as we Know it? | This paper explores the transformative impact of generative AI on the field of translation studies, outlining both significant challenges and promising opportunities. It discusses concerns like job displacement, quality issues, and ethics, alongside potential benefits such as efficiency, augmented human translation, domain specialization, and innovation in the field. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal (mentioned as an example domain) | International | NaN | NaN | NaN | False | False | NaN | NaN | Ensuring quality and accuracy (context-dependent nuances, idiomatic expressions, stylistic fidelity, cultural subtleties); ethical concerns (data privacy, bias in training data, potential propagation of misinformation/offensive content); potential for translator job displacement. | Job displacement for human translators, lack of quality and accuracy in translation outputs (especially regarding nuances, idioms, context, style, culture), perpetuation of biases encoded in training data, data privacy violations, inadvertent propagation of misinformation or offensive content. |
| NJWO2V-bSKAJ.pdf | Google_Scholar | Better Transcription of UK Supreme Court Hearings | This paper describes a method to improve automated transcription of UK Supreme Court hearings by fine-tuning an off-the-shelf Automatic Speech Recognition (ASR) system. The approach utilizes a custom language model trained on legal texts and gold-standard transcripts, along with a specialized vocabulary of legal terms, aiming to reduce word error rates and enhance the accuracy of domain-specific language. | True | Idealistic | False | 1.0 | Positive | Domain adaptation of an ASR system (AWS Transcribe) by fine-tuning with a custom language model (CLM) trained on in-domain legal texts and gold-standard transcriptions, and infusing a custom vocabulary of common legal phrases and entities extracted using NLP techniques (PMI-based collocation detection and Blackstone/spaCy NER). | The proposed models were evaluated on 12 hours of UK Supreme Court Hearings (2 cases). Performance was measured by Word Error Rate (WER) and the ratio of correctly transcribed legal entities, compared against AWS Transcribe base and OpenAI Whisper ASR systems. | The best performing model (CLM2+Vocab) achieved an average Word Error Rate (WER) of 11.6. This was an improvement over the baseline AWS Transcribe (12.3 WER) and OpenAI Whisper (12.4 WER). CLM2+Vocab also demonstrated superior accuracy in transcribing specific legal entities, such as 'Judge' (0.84 correct ratio vs. 0.66 for AWS and 0.77 for Whisper) and 'Provisions' (0.97 correct ratio vs. 0.88 for AWS and 0.95 for Whisper). | High cost and slowness of manual speech transcription. Generic ASR systems exhibit high Word Error Rates (WER) on specialized legal audio due to long hearings, multiple speakers, complex speech patterns, unique pronunciations, and legal jargon, leading to critical errors and information loss. Limited availability of large in-domain datasets for training specialized ASR systems. | Developing an automated transcription tool by fine-tuning a generic ASR system with an in-domain custom language model (trained on legal documents and gold-standard transcripts) and infusing a custom vocabulary of common legal phrases and entities to improve transcription accuracy and reduce critical errors specific to legal terminology. | Access to court proceedings/records through improved transcription of hearings. | General public and legal professionals requiring affordable and accurate access to court transcripts. | Court proceedings / Litigation (specifically UK Supreme Court hearings). | United Kingdom (UK Supreme Court). | For the Custom Language Model (CLM): 1) Publicly available written judgements from 43 UK Supreme Court cases (3.26M tokens, unstructured text, scraped from official website). 2) Approximately 81 hours of proprietary gold-standard transcripts from 10 UK Supreme Court hearings (unstructured text, created by post-editing ASR output). For vocabulary list: Approximately 139 hours of gold-standard transcripts and the aforementioned written judgements. | Data collection (web scraping, manual post-editing of ASR output to create gold-standard transcripts), fine-tuning of a pre-trained ASR system (AWS Transcribe) with a custom language model, NLP techniques for custom vocabulary creation (PMI-based phrase detection, named entity recognition using Blackstone and spaCy), and comparative evaluation (WER, entity recognition accuracy) against baseline systems. | Not explicitly stated for general public use; described as part of a combined research and industrial project involving Kingfisher Labs Ltd and Just Access. | False | False | NaN | Need for improved handling of diverse accents in British court audio beyond Supreme Court homogeneity. Potential for using NLP topic modeling to connect transcribed legal entities to case decisions. | Domain mismatch between generic ASR training data and specialized legal audio. Accurately transcribing domain-specific terminology, names, and numbers. Achieving low WER while managing transcription time and computational costs. Acquiring sufficient in-domain training data. | Inaccurate transcription, especially of critical legal terms, names, and numbers, can lead to serious information loss and cause confusion, potentially impacting legal outcomes or understanding. |
| Addressing_Technical_Challenges_in_Large_Language_Model-Driven_Educational_Software_System.pdf | Google_Scholar | Addressing Technical Challenges in Large Language Model-Driven Educational Software System | This paper discusses key technical challenges (integration, explainability, testability, scalability) in developing LLM-driven educational software. It proposes and evaluates a set of tactics, including a chain-of-reasoning-and-action pattern and an event-driven microservice architecture, implemented in a system called AITeach. | True | NaN | True | 1.0 | NaN | The AITeach system, which employs a chain-of-reasoning-and-action design pattern for integration, metadata and an algorithmic process for explainability, regression testing for response consistency, and an event-driven microservice architecture for scalability. | Response consistency (RQ1) was tested on 100 tasks using LLMs (Gemini-1.5-Pro, Gemini-1.0-Pro, GPT-3.5-Turbo, GPT-4-Turbo), comparing generated thoughts via cosine similarity (using OpenAI's text-embedding-3-small) and next-action accuracy against baselines. Scalability (RQ2) was assessed via load tests (20-100 requests/sec) on AITeach components deployed as microservices on Google Cloud Run, monitoring response times and instance scaling. | For response consistency, Gemini-1.5-Pro-002 performed best, achieving 97% accuracy for next action determination and the highest average cosine similarity (d-value of 0.9483) for thought generation. The event-driven microservice architecture demonstrated effective autoscaling, maintaining performance under increasing system load. | NaN | NaN | NaN | NaN | NaN | International | Learning reference materials (PDF, DOC, PPT) uploaded by instructors, converted into embeddings for Retrieval-Augmented Generation (RAG). The system utilizes pre-trained LLMs (Gemini, GPT models). | Chain-of-reasoning-and-action pattern, event-driven microservice architecture, metadata and algorithm design for explainability, regression testing methodology. | Core components (Thinker, Planner, Worker) of the AITeach system were deployed as independent microservices on Google Cloud Run for evaluation, using containerization technology. | True | True | The complete source code for AITeach is stated to be available on GitHub: https://github.com/cnacha-mfu/aiteach | NaN | The main challenges addressed are integration of complex LLM components (especially for RAG), the black-box nature of LLMs hindering explainability and trust, ensuring consistent and reliable outputs (testability) given the probabilistic nature of LLMs and prompt sensitivity, and achieving scalability for systems with interconnected components and sequential reasoning processes. | Risk of LLM hallucinations (generating inaccurate or fabricated information) in educational applications. |
| SQQr7rMNtGMJ.pdf | Google_Scholar | PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluation | This paper introduces PsycoLLM, a large language model specialized for psychological understanding and evaluation, fine-tuned on a newly constructed high-quality psychological dataset. The authors also propose a comprehensive psychological benchmark based on Chinese counseling examinations to evaluate PsycoLLM, demonstrating its superior performance over other LLMs in this domain. | True | NaN | True | 1.0 | NaN | PsycoLLM: a psychological LLM developed by fine-tuning Qwen1.5-14B-Chat on a novel, high-quality psychological dataset. The dataset includes single-turn QA, KimiChat-generated multi-turn dialogues, and knowledge-based QA primarily from Chinese sources. A new psychological benchmark was also developed. | PsycoLLM and other LLMs were evaluated on a newly proposed psychological benchmark based on Chinese psychological counseling examinations (MCQs and QA covering professional ethics, theoretical proficiency, case analysis). PsycoLLM was also tested on general benchmarks (MMLU, CMMLU, GSM8K, CEVAL). | PsycoLLM demonstrated superior performance on the psychological benchmark, achieving an overall average standard accuracy of 61.71% on MCQs (64.70% when using elastic accuracy for MMCQs). It also showed strong performance in case-based QA (e.g., R-1 24.45, BS 65.29) and comparable results to its base model on general benchmarks. | NaN | NaN | NaN | NaN | NaN | China | A proprietary, domain-specific (psychology) dataset constructed by the authors. It includes: 1) 155k+ single-turn QAs from Chinese online platforms (e.g., Yixinli, Zhihu). 2) 11.5k+ multi-turn dialogues generated by KimiChat from selected QAs using a 3-step pipeline. 3) 10k knowledge-based QAs from psychology books (extracted via Qwen-72B and exercises). Mainly unstructured text. | Dataset construction involved web scraping, LLM-based data generation (KimiChat, Qwen-72B with RAG and teacher-student model), a three-step dialogue generation pipeline (generation, evidence judgment, refinement), and manual proofreading. Model development involved supervised fine-tuning (SFT) of Qwen1.5-14B-Chat. | A GitHub link (https://github.com/MACLAB-HFUT/PsycoLLM) is provided for PsycoLLM. | True | True | Publicly available on GitHub (https://github.com/MACLAB-HFUT/PsycoLLM). | NaN | Primary challenges include creating high-quality, domain-specific training data that avoids simply distilling from larger models, developing comprehensive domain-specific benchmarks, mitigating biases from LLM-generated data, and balancing specialized knowledge with general reasoning capabilities to prevent overfitting. | Potential risks include the introduction or amplification of biases from LLM-based data generation processes, and overfitting to the specialized psychological domain, which can degrade the LLM's general reasoning capabilities. |
| sC5dwrTUpGUJ.pdf | Google_Scholar | Black-Box Analysis: GPTs Across Time in Legal Textual Entailment Task | This paper analyzes the performance of GPT-3.5 and GPT-4 on the COLIEE Task 4 Japanese legal textual entailment dataset from 2006 to 2021. Results show GPT-4 generally outperforms GPT-3.5, especially in recent years, but performance fluctuates, indicating sensitivity to data distribution and potential temporal knowledge gaps. | True | NaN | True | 2.0 | NaN | GPT-3.5 and GPT-4 APIs for zero-shot legal textual entailment. | Evaluation using the COLIEE Task 4 dataset (Japanese statute law entailment, H18-R03 / 2006-2021) via API calls. Accuracy was the primary metric. | GPT-4 generally outperformed GPT-3.5 (average accuracy 0.7670 Eng / 0.7843 Jap vs 0.7109 Eng / 0.6412 Jap), especially in recent years, but performance varied significantly across years. | NaN | NaN | NaN | NaN | Statute Law | Japan | The study uses proprietary models (GPT-3.5, GPT-4) with undisclosed, general pre-training data. The evaluation dataset is COLIEE Task 4 (Japanese statute law entailment questions, context, and labels from 2006-2021). | Black-box testing via API calls with specific prompts for zero-shot evaluation. | NaN | True | False | Commercial API access from OpenAI. | Need for better generalizability, adaptability, explainability/interpretability in legal reasoning models. Challenges in understanding black-box models due to undisclosed architecture/data. | Understanding performance variations of black-box models across different years, potentially due to training data distribution and temporal knowledge limitations. Evaluating performance across different languages (English vs Japanese translations of the dataset). | Limitations in reliability due to performance fluctuations and lack of transparency/explainability. |
| t8zFmiNLlnkJ.pdf | Google_Scholar | AI-Based Contract & Legal Document Generator using Machine Learning | This paper proposes an AI system using an LSTM-CNN model to automate the generation of legal documents such as rental agreements. The system aims to improve efficiency and accessibility within the Indian legal system, and was trained on 4,825 legal articles, showing promising initial results via a web interface but also identified overfitting issues. | True | Idealistic | False | 1.0 | Positive | An AI-based legal document generator using a phrases-based LSTM-CNN model. The system incorporates NLP techniques (tokenization, stop-word removal, stemming, POS tagging) for text preprocessing and is built using TensorFlow/PyTorch and spaCy/NLTK. | The LSTM-CNN model was trained for ten epochs on 4,825 legal articles. Evaluation metrics included training/validation accuracy and loss. A prototype web interface was developed to demonstrate the generation of a rent agreement. | The model achieved high training accuracy (approx. 0.95) but showed signs of overfitting, with validation accuracy plateauing around 0.8 after the 7th epoch. | Slow legal system in India with a high number of pending cases; short hearing times leading to pressure on legal professionals and potential for human error; high cost of legal document preparation; lack of standard automation techniques in the Indian legal system; legal system being 'far off for most of the residents'. | Automating legal document generation using AI/ML to reduce time, workload, and human error; improving consistency and accuracy of legal documents; making legal services more accessible and affordable, especially for those who cannot afford expensive lawyers; increasing efficiency in case resolution. | Automated legal document generation (e.g., contracts, rental agreements), improving legal system efficiency, enhancing access to legal services for low-income individuals, reducing legal costs. | Primarily individuals who cannot afford expensive legal representation in India. Secondarily, legal professionals (lawyers, judges) by improving their efficiency. | Contract law (rental agreements, partnership agreements, loan agreements are mentioned as examples) and potentially wills. | India | A dataset of 4,825 legal articles. The source and specific nature (e.g., public, proprietary, structured/unstructured beyond 'articles') are not detailed. | A standard machine learning workflow: data collection, preprocessing (NLP techniques), feature extraction, model selection (LSTM-CNN), training, validation, testing, and UI development for demonstration. | A prototype web application with a login and form-based input for generating a rent agreement was developed for demonstration purposes. No information on broader deployment. | False | False | NaN | Technical: Model overfitting, requiring further fine-tuning. Societal: The paper implies ongoing issues with access to justice and legal system efficiency in India that the tool aims to partially address. | The primary technical challenge identified was model overfitting. General challenges implied include acquiring and preprocessing suitable legal text data for training. | NaN |
| Vp2PeZVWrfAJ.pdf | Google_Scholar | Investigating Code Generation Performance of ChatGPT with Crowdsourcing Social Data | This paper presents a framework using crowdsourced social media data (Twitter, Reddit) to analyze ChatGPT's code generation performance. It finds Python and JavaScript are most popular, usage includes debugging and interview prep, and the dominant user sentiment is fear. | True | NaN | True | 2.0 | NaN | ChatGPT for code generation | Analysis of 316K tweets and 3.2K Reddit posts (Dec 2022-Jan 2023) using topic modeling (LDA), sentiment analysis (Text2Emotion), and code quality evaluation (Flake8) on Python code snippets extracted from shared images. | Python and JavaScript were the most popular languages. Tasks included debugging, interview prep, and assignments. Fear was the dominant sentiment across languages. Flake8 analysis showed most Python code errors were style-related (pycodestyle E/W codes), primarily E501 (line too long). | NaN | NaN | NaN | NaN | NaN | International | Publicly available social media posts (316K Tweets, 3.2K Reddit submissions) and associated images from Dec 1, 2022, to Jan 31, 2023, filtered by keywords related to ChatGPT and programming. | Data collection from social media (Twitter API, Pushshift Reddit API), Keyword expansion (LDA, expert review), NLP (LDA topic modeling, Text2Emotion sentiment analysis), Image processing (easyOCR), Code quality analysis (Flake8). | NaN | False | False | NaN | NaN | Diversity of programming languages and tasks making comprehensive evaluation difficult; cost/time of traditional user studies; potential biases from single data sources; difficulty in accurately reconstructing code (especially indentation) from images. | User sentiment of fear concerning code quality and potential negative impact on human jobs (job displacement). Potential for biased code generation (mentioned via citation). |
| 1279738.pdf | Google_Scholar | DOL-LLM - Optimizing Large Language Model Inference with Domain-Specific Adaptations and Efficiency Techniques via Quantization, Pruning, and Distillation | This paper proposes DOL-LLM, a methodology for developing lightweight, domain-specific large language models (LLMs) optimized for resource-constrained edge devices. It combines quantization, pruning, and knowledge distillation with domain-specific training to enhance LLM accessibility and performance on devices like mobile phones and embedded systems. | True | Market | True | 1.0 | NaN | DOL-LLM: A methodology combining domain-specific adaptations with optimization techniques (quantization, pruning, and knowledge distillation) for LLMs. | Benchmarking covered inference speed, memory usage, model accuracy, and energy efficiency. Comparative evaluations against SmolVLM variants were conducted on NVIDIA A100 GPUs and ARM mobile processors. | Achieved a 4x reduction in memory bandwidth with less than 2% accuracy degradation using mixed-precision quantization; structured pruning removed 30-40% of non-critical parameters while maintaining over 98% of original functionality. DOL-LLM showed 2-4x speedups on NVIDIA A100 GPUs, 30-50% reduction in energy consumption on ARM mobile processors, and 5.7 GB GPU RAM utilization. | NaN | NaN | NaN | NaN | NaN | International | Acquired or generated large corpus of text for pre-training; domain-specific datasets (textual and visual) for fine-tuning, curated to filter out low-quality and sensitive information. Specific sources or composition details are not extensively provided. | A pipeline approach: 1) Base model selection, 2) Domain-specific data acquisition and preparation (textual and visual), 3) Pre-training of the base model, 4) Optimization pipeline applying quantization, pruning, and distillation (sequentially or iteratively), 5) Fine-tuning and customization, 6) Comprehensive benchmarking. | Targeted for edge deployment on resource-constrained devices such as mobile devices and embedded systems. Implementation is facilitated by frameworks like PyTorch, TensorFlow Lite, and specialized toolkits such as NVIDIA NeMo. | False | False | NaN | NaN | High computational requirements of LLMs for resource-limited devices; need for domain-specificity in LLMs; balancing model compression with performance and accuracy; hardware limitations impacting the effectiveness of certain optimization techniques (e.g., unstructured pruning); maintaining robustness across different calibration datasets and context lengths. | Potential minor degradation in model accuracy due to quantization; risk of significant accuracy loss from excessive pruning; performance degradation in longer context scenarios for quantized models; difficulty in realizing theoretical benefits of unstructured pruning on standard hardware. |
| cesta-2024-large-language-models-and-community-legal-centres-could-chatbots-help-reduce-australia-s-justice-gap.pdf | Google_Scholar | Large language models and community legal centres: Could chatbots help reduce Australia ’s justice gap? | This paper explores whether LLM-based chatbots can alleviate the unmet demand for services from Australian community legal centres (CLCs). It argues that while client-facing legal information chatbots hold potential to reduce Australia's justice gap, their realization is hindered by significant challenges including LLM accuracy, legal uncertainties, and implementation costs. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Unmet demand for legal assistance due to under-resourced community legal centres (CLCs), limited government funding not based on needs assessment, and individuals not seeking necessary legal help. | Proposing the careful development and deployment of client-facing legal information chatbots by CLCs to provide accessible legal information, while highlighting the need to overcome significant technical, legal, and practical challenges. | Provision of legal information and services by community legal centres (CLCs) to address the justice gap and unmet legal need for disadvantaged individuals. | Individuals in Australia who cannot afford commercial legal services and rely on Community Legal Centres, including those facing digital exclusion or residing in regional areas with poor internet access. | General legal issues typically handled by community legal centres in Australia. | Australia | NaN | NaN | NaN | True | True | The paper refers to existing LLM tools; some are commercial (e.g., Ailira, CoCounsel), some have free access tiers (e.g., ChatGPT), and one specific AI model by Justice Connect is mentioned as available with a free license for Non-For-Profit organizations. | Technical gaps include LLM accuracy (hallucinations), explainability, cost-effective development for legal domains, and ensuring user prompts are effective. Societal/systemic gaps include legal/regulatory uncertainty regarding AI in law, the digital divide, ethical considerations for AI use by CLCs, and insufficient funding for CLCs to adopt such technology. | NaN | Risk of LLM hallucinations misleading clients or lawyers; privacy and data protection breaches with sensitive information; systems being misconstrued as unauthorized legal advice or practice; inadvertent waiver of legal professional privilege; exacerbating digital exclusion and inequality; and potential professional negligence or ethical breaches from over-reliance on AI by CLCs. |
| 1PICXeaunP8J.pdf | Google_Scholar | THE GPTJUDGE: JUSTICE IN A GENERATIVE AI WORLD | This paper analyzes Generative AI's impact on the legal system, focusing on challenges to evidence authenticity, intellectual property, and litigation. It provides practical recommendations for courts and lawyers to manage GenAI-related evidentiary issues and discusses broader implications for justice and legal practice. | True | Idealistic | True | 3.0 | Neutral | A proposed step-by-step procedural approach for courts and attorneys to handle evidentiary challenges posed by Generative AI, utilizing existing Federal Rules of Evidence, involving scheduling orders, disclosure, discovery, evidentiary hearings, and judicial rulings on admissibility. | NaN | NaN | Lack of legal representation for many citizens, particularly from marginalized communities; the risk of AI-generated vexatious lawsuits overwhelming courts; individuals' reliance on potentially faulty AI-generated legal advice; unpreparedness of courts for high-volume AI-generated filings. | The paper acknowledges GenAI's potential to assist unrepresented litigants by helping draft pleadings. However, it primarily focuses on managing the risks of AI misuse (e.g., vexatious lawsuits and faulty evidence) through judicial gatekeeping and procedural recommendations, rather than proposing specific high-level A2J solutions. | Assisting unrepresented litigants (pro se) in drafting legal documents; potential for generating vexatious or low-quality lawsuits; role of AI in providing legal information/advice to individuals. | Litigants who lack legal representation, often individuals from racialized or otherwise marginalized communities; ordinary people needing legal advice or facing debt collection. | Evidence law, Intellectual Property (Copyright, Trademark), Civil Procedure, Criminal Procedure, Torts (defamation, liability for bad advice), Academic/University Law, Judicial Ethics. | United States (primarily federal, but also state implications and examples). | NaN | The proposed procedural approach is based on an analysis of existing legal rules (Federal Rules of Evidence) and their flexible application to new technological challenges, informed by legal scholarship and judicial experience. | NaN | False | False | NaN | Courts are largely unprepared to differentiate beneficial uses of AI for A2J from misuse (e.g., vexatious litigation); current AI-detection capabilities are unreliable; lack of quality control and accountability for AI-generated legal advice for laypersons; underdeveloped ethical guidelines for AI in A2J. | The rapid pace of GenAI development versus the time-consuming process of revising formal rules of evidence, necessitating an approach that works within the existing legal framework while being adaptable to new technologies. Ensuring judicial gatekeeping is effective without unduly stifling GenAI's potential benefits. | Proliferation of deepfakes and difficulty in authenticating evidence; increased litigation costs due to need for experts; generation of misinformation and 'hallucinations' by AI; potential for AI to overwhelm courts with vexatious or low-quality lawsuits; undermining of intellectual property rights; misuse for scams and providing harmful advice; ethical breaches if judges or lawyers inappropriately use or rely on GenAI. |
| FLt-qPRZA6YJ.pdf | Google_Scholar | Answering legal questions from laymen in German civil law system | This paper introduces GerLayQA, a new German dataset of 21k laymen's legal questions paired with lawyers' answers and grounded in law book paragraphs, to benchmark AI for legal question answering. Experiments with retrieval and generation models show moderate performance, highlighting challenges in understanding German legalese and the need for legally-trained models and expert evaluation. | True | Idealistic | True | 1.0 | Neutral | A two-step QA pipeline involving document retrieval (embedding-based similarity) and answer generation (using GPT-3.5-turbo). Creation of a new dataset GerLayQA. | Document retrieval: Precision, Recall, F1, MRR, MAP compared against random and oracle baselines on GerLayQA. Answer generation: ROUGE and BERTScore compared against lower and oracle baselines on GerLayQA. | For document retrieval, OpenAI's text-embedding-ada-002 performed best (F1=0.055, MRR=0.146, MAP=0.108). For answer generation, GPT-3.5-turbo with legal paragraphs achieved ROUGE-1=0.2910 and BERTScore=0.6550. | Laypeople avoid law books due to incomprehensibility; cost of lawyers creates a barrier favoring those with more financial resources; online resources often unhelpful. | Leveraging NLP tools for legal question answering, specifically by creating datasets and models that can understand laymen's questions and provide understandable answers grounded in legal text. | Legal question answering, understanding legal texts, obtaining legal advice. | Laypersons in Germany without legal expertise. | German Civil Code (BGB), with mentions of German Criminal Code (StGB) and German Code of Civil Procedure (ZPO) for future work. Specific top categories mentioned are Tenancy law, condominium law; Labor law; Family law; Contract law; Inheritance law. | Germany | GerLayQA dataset: 21,538 QA pairs scraped from a German legal online forum (frage-einen-anwalt.de), filtered for quality (lawyer references to paragraphs, user ratings). This is publicly available. | Dataset creation involved web scraping, Regex-based extraction, and quality filtering. The QA pipeline involved standard NLP techniques: embedding generation for retrieval and prompting large language models for generation. | All datasets, source codes and models are publicly available at https://github.com/trusthlt/eacl24-german-legal-questions. | True | True | The GerLayQA dataset, source codes, and models are publicly available on GitHub. | Need for bespoke models trained on German legal text (both laymen and expert); inclusion of legal expertise in evaluation; expanding to more law books beyond BGB; manual filtering of dataset by legal experts; engaging secondary lawyers to verify gold standard answers. | Models' difficulty in understanding German legal texts (legalese); creating accurate semantic embeddings for both legalese and everyday language for effective retrieval; models struggling to grasp legal nuances and correlations; limited legal knowledge of the researchers for evaluation. | Misguided legal counsel from NLP models leading to severe consequences; users not being aware they are interacting with an NLP model versus a certified lawyer; reliance on non-binding legal advice. |
| M75_cXLMCPYJ.pdf | Google_Scholar | ChatGPT and GPT-4: utilities in the legal sector, functioning, limitations and risks of foundational models | This paper analyzes the architecture, functionality, and potential applications of large language models like ChatGPT and GPT-4 within the legal sector. It emphasizes the significant limitations, including hallucinations and biases, and discusses key legal risks, particularly concerning data protection and intellectual property rights, alongside emerging EU regulatory frameworks. | False | Market | True | 3.0 | Neutral | ChatGPT, GPT-4 | NaN | NaN | NaN | NaN | NaN | NaN | Legal Tech, AI Law, Data Protection, Intellectual Property Law, Civil Liability | EU, US, Spain | Sources include publicly available internet data (e.g., Common Crawl, WebText2, Wikipedia), data licensed from third parties, and information from users and human reviewers. Data collected partly through web scraping. | NaN | NaN | True | False | ChatGPT is available via OpenAI's website/apps (free and paid tiers). GPT-4 access is available via paid ChatGPT Plus or the OpenAI API. | NaN | Technical limitations in handling unique legal language and long documents, prevalence of hallucinations (factual inaccuracies, fabricated information) and biases (demographic, linguistic, temporal etc.), lack of reliability and explainability, difficulty in completely mitigating biases, risk of user over-reliance (cognitive mirage). | Data protection risks (GDPR compliance, use of personal data in training/prompts, international data transfers, enforcing data subject rights), Intellectual property risks (copyright/database right infringement from training data/web scraping, infringement via model outputs, authorship uncertainty), Legal and professional liability (sanctions for using inaccurate AI outputs, contractual/extracontractual liability, violation of professional duties), Confidentiality risks (inputting sensitive client data). |
| C6i925q78D8J.pdf | Google_Scholar | The Judicial Duty to State Reasons in the Age \nof Automation? The Impact of Generative AI \nSystems on the Legitimacy of Judicial \nDecision-Making | This paper explores how the use of generative AI systems by judges for tasks like legal drafting impacts the judicial duty to state reasons, focusing specifically on the normative goal of legitimacy. It argues that while such systems might offer efficiency gains, they pose significant risks to judicial legitimacy through issues like bias, opacity, and potential undermining of judicial independence and reasoning. | True | NaN | True | 3.0 | Neutral | Generative AI systems (e.g., ChatGPT) used to assist judges in legal drafting (summarising case law, answering legal questions, calculating damages, drafting judgments). | NaN | NaN | The paper implicitly mentions cost and delays in justice as potential A2J obstacles that AI efficiency *might* alleviate, but its main focus is on the risks AI poses to the judicial system itself. | The paper suggests safeguarding measures for using AI in courts, such as promoting AI literacy for judges, potential development of systems by the judiciary itself (though deemed likely infeasible), using value-sensitive design methodologies, and potentially strengthening the judicial duty to state reasons to be more substantive. These are solutions to problems AI *creates* for the judicial system, not AI solutions *for* A2J. | Judicial duty to state reasons, legitimacy of judicial decision-making. | NaN | General (applicable across civil and criminal law, focuses on judicial process). | European level (focus on ECHR, CEPEJ), with illustrative examples from Colombia, India, US, UK, Netherlands, China, Ukraine, Belgium. | The paper discusses general issues with LLM training data: large text corpora (potentially including legal data), but often opaque regarding sources, representativeness (e.g., limited published judgments in NL/BE), up-to-dateness (ChatGPT noted as trained up to early 2022), potential biases (political, racial - citing COMPAS), copyright limitations (hindering use of legal literature), predominance of English sources. | Value-sensitive design is mentioned as a potential future approach, not one used for the existing systems discussed. | Discusses anecdotal use by judges in various countries and emerging guidelines (UK, CEPEJ, Ukraine) suggesting wider adoption. | True | True | Publicly available commercial systems like ChatGPT (with free tiers) are the primary examples discussed. | Lack of comprehensive theory on the judicial duty to state reasons in the age of automation; lack of understanding of AI's precise impact on this duty and its normative goals; need for AI literacy among judges; need for research on effective remedies/safeguards; challenges in implementing a strengthened (substantive) duty to state reasons; need for transparency regarding training data and system functioning; unresolved accountability issues; need for development of robust, locally run LLMs for legal applications. | Bias in training data and outputs; opacity ('black box' problem); potential for hallucinations/inaccurate outputs; influence of private tech companies (embedded values, IP); risk of automation bias in judges; potential undermining of judicial independence; inherent limitations of LLMs (lack of true understanding/reasoning); privacy and data protection risks; ethical concerns (dehumanization, undermining judges' role, inappropriate legitimization of AI). | Biased judicial outcomes; lack of transparency undermining fair trial rights; unclear accountability for AI-influenced decisions; compromised judicial independence; generation of incorrect legal information (hallucinations); privacy violations through handling of sensitive case data; erosion of public trust and perceived legitimacy of courts; undermining the judicial duty to state reasons. |
| NYeeny7dKncJ.pdf | Google_Scholar | PhiloGPT: A Philology-Oriented Large Language Model for Ancient Chinese Manuscripts with Dunhuang as Case Study | This paper introduces PhiloGPT, the first Large Language Model (LLM) specifically designed for analyzing ancient Chinese manuscripts, trained on a newly curated corpus, PhiloCorpus-ZH. It also proposes the PhiloCoP reasoning framework and PhiloBenchmark, demonstrating improved performance on philological tasks like restoration and attribution, particularly using Dunhuang manuscripts. | True | NaN | True | 1.0 | NaN | PhiloGPT, an LLM based on Qwen-1.5-7b, trained on the domain-specific PhiloCorpus-ZH, utilizing the PhiloCoP (Chain-of-Philology) multi-step reasoning framework. | Evaluated using the authors' proposed PhiloBenchmark, which includes 9 tasks (Restoration, Conjugation, Attribution, Judgment, Topic Modeling, NER, Common QA, Analysis, Reasoning). Metrics included Character Error Rate (CER), Dynasty Shift, F1 score, Accuracy, and GPT-4o judged win rate. Compared against Qwen-7b-chat, Baichuan2-7b, LLaMA2-Chinese-7b. | PhiloGPT with the PhiloCoP framework (PhiloGPT+CoP) achieved the best results across tasks, significantly outperforming baseline LLMs which struggled or failed on specialized philology tasks. Example best results: CER 0.579 (Restoration), F1 0.590 (Conjugation), Accuracy 86.7% (Judgment). | NaN | NaN | NaN | NaN | NaN | China | PhiloCorpus-ZH: A curated collection of ancient Chinese texts (spanning a millennium, 30 diverse topics, including original folk documents) sourced from publicly available data (museum collections, research papers, academic publications). Instruction data generated via manual construction, Self-Instruct, and Self-QA methods, filtered by GPT-4o and checked by experts. General training data from open-source corpora (Wikipedia, SkyPile, Wanjuan 1.0) was also used. | Corpus curation (PhiloCorpus-ZH) involving expert collaboration, development of a reasoning framework (PhiloCoP), benchmark creation (PhiloBenchmark), language model pre-training and supervised fine-tuning (LoRA) on a base model (Qwen-1.5-7b). | Deployed in specific research collaborations with Dunhuang specialists for tasks like analyzing copying relationships and suggesting text restorations. | False | False | NaN | NaN | Scarcity of suitable large-scale ancient Chinese training data; significant linguistic differences between ancient and modern Chinese (phonetic loans, polysemy, syntactic inversions, semantic shifts); fragmentation of previous research efforts; ensuring factual accuracy. | Potential for factual inaccuracies in model outputs, requiring secondary verification by philology experts. |
| XIFkKbcErtcJ.pdf | Google_Scholar | Blockchain for Ethical and Transparent Generative AI Utilization by Banking and Finance Lawyers | This paper proposes a framework integrating blockchain technology with Explainable AI (XAI) and Generative AI to ensure ethical and transparent use by banking and finance lawyers. The framework uses blockchain to create an immutable audit trail of AI-generated content derived from anonymized XAI outputs, aiming to address data confidentiality and accountability concerns. | True | Market | True | 1.0 | NaN | A framework combining an Explainable AI algorithm (Evidential Reasoning - ER) for legal decision support, Generative AI (GPT models, Google Bard) for drafting assistance based on anonymized ER outputs, and Blockchain (Ethereum/Hyperledger Fabric) for immutable auditing of AI usage. | A case study on bank data breach tort liability claims involving 2712 cases. The ER model's accuracy was evaluated using AUC scores. Generative AI models' (GPT-3.5, GPT-4, Bard) output usability was assessed by 25 legal professionals via text similarity analysis (Turnitin). Blockchain networks (Ethereum, Hyperledger Fabric) were benchmarked for throughput (TPS) and latency. | GPT-4 generated the most usable text for legal drafting (60.5% utilization by lawyers). Hyperledger Fabric showed higher throughput and lower latency compared to Ethereum for blockchain auditing. The ER algorithm demonstrated good performance in predicting components of bank liability (AUC > 0.85). | NaN | NaN | NaN | NaN | Banking Law, Finance Law, Tort Law (Data Breach Liability) | UK (based on case study details mentioning ICO, FCA, NCSC) | For the XAI (ER) component: A proprietary dataset of 2712 bank data breach cases from a law firm. Pre-trained LLMs (GPT, Bard) were used via API. | Integration of existing technologies (XAI-ER, LLMs, Blockchain), Case study methodology, Usability testing with legal professionals, Performance benchmarking (blockchain). | Pilot study within a law firm using an API gateway in a controlled environment; Utilizes Infura for blockchain node management. | False | False | NaN | Need for prompt engineering to optimize LLM outputs; Further investigation needed on whether lawyers overlook errors in AI-generated text despite the framework. | Integrating multiple complex technologies; Ensuring compliance with data protection laws (e.g., GDPR's 'Right to be Forgotten') alongside blockchain immutability; Managing blockchain storage limitations (addressed via off-chain/on-chain storage); Reliance on fixed prompts in the study. | Ethical concerns with Generative AI (replicating legal reasoning, accountability for errors, confidentiality); Potential for data leakage to LLMs; Risk of AI hallucinations or inaccuracies in generated text; Tampering with AI inputs/outputs if not properly monitored. |
| IRq0hYe6WSYJ.pdf | Google_Scholar | AI and the law | This paper argues that generative AI, by reducing the costs of contracting and litigation, will have uneven effects on the evolution of common law. It predicts AI will accelerate the evolution of tort law towards efficiency but have ambiguous effects on property and contract law. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Tort law, Property law, Contract law | International | NaN | NaN | NaN | False | False | NaN | NaN | NaN | The paper briefly discusses frivolous litigation but concludes it will not affect the long-run evolution of law within the model presented. |
| n1VAE2-uj0UJ.pdf | Google_Scholar | AI Law and Legal Training Interim Report | This interim report describes a UKRI-funded project developing open educational resources (OERs) to enhance understanding and responsible use of Generative AI in legal contexts. It outlines stakeholder engagement through workshops and details the planned course content targeting the public, free advice sector, students, and legal professionals. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Lack of knowledge/skills regarding GenAI use in legal contexts across different groups (public, advice sector, legal professionals); risk of societal harm from irresponsible AI use (e.g., misinformation, bias); potential exacerbation of inequalities; digital exclusion and resource limitations, particularly in the free advice sector; regulatory uncertainty. | Develop and provide free, open-access, engaging educational resources (OERs) co-produced with stakeholders to build knowledge, confidence, and skills for the ethical and responsible use of GenAI in legal contexts. Offer tailored learning pathways for different groups. | Education and guidance on the ethical, responsible, and effective use of Generative AI (GenAI/LLMs) for accessing legal information and support; Mitigating risks associated with AI in legal contexts (misinformation, bias, digital exclusion). | Public, free advice and voluntary sector organisations (advisors, volunteers, managers), small and medium-sized law firms, law students, legal academics. | General Legal Field | United Kingdom (specifically England and Wales context) | NaN | Co-production through stakeholder engagement: three online learning design workshops were held with distinct groups (free advice sector, legal academics/students, legal practitioners) to gather insights and inform course development. | The courses are planned to launch in Summer 2025 as Open Educational Resources on The Open University’s OpenLearn platform. | False | False | NaN | Significant gap in knowledge, awareness, and confidence regarding GenAI use in legal contexts among the public, advice sector, students, and practitioners. Lack of trustworthy, accessible educational resources, especially OERs. Regulatory and guidance gaps concerning AI use in legal services. | Ensuring accuracy and reliability of GenAI; addressing ethical, privacy, and data security concerns; preventing skill degradation; overcoming digital exclusion and resource disparities; balancing AI augmentation with human oversight; managing stakeholder (e.g., funder, client) perceptions; developing effective training and governance strategies. | Inaccuracy and 'hallucinations' leading to misinformation; ethical issues (bias, interference with legal processes); privacy violations and data security breaches; degradation of legal skills; digital exclusion and exacerbation of inequalities; lack of regulatory clarity and liability issues; potential reduction in funding/support if AI is misconceived as a replacement for humans; referencing/plagiarism/fraud risks. |
| 3708530.pdf | Google_Scholar | LLM App Store Analysis: A Vision and Roadmap | This paper analyzes the emerging ecosystem of Large Language Model (LLM) app stores, highlighting their rapid growth, key stakeholders, and operational mechanisms. It proposes a research roadmap focusing on data collection, security/privacy analysis, and market dynamics, concluding with challenges and recommendations for stakeholders. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General (mentions privacy law, intellectual property, regulation) | International | NaN | NaN | NaN | False | False | NaN | Lack of in-depth studies on LLM app stores, their market dynamics, security implications, ethical considerations, and societal impact. Need for tailored analysis techniques (e.g., for security, recommendations) specific to LLM apps. | Data privacy and security (compliance, third-party risks, inadvertent data collection); Intellectual property protection (app cloning, generative content issues); Ensuring app quality and reliability (vetting, unpredictable AI outputs); Addressing algorithmic biases and fairness; Balancing innovation and responsibility; User education and awareness; Regulatory and policy challenges (adapting frameworks). | App cloning, app vulnerabilities (e.g., prompt injection, insufficient input validation, jailbreaking), malicious apps (tainted knowledge, harmful outputs, low description-to-behavior fidelity), third-party service integration risks, user tracking and profiling without consent, security risks from app protection techniques (e.g., obfuscation hiding threats), advertisement fraud, market policy violations, fake apps, ranking fraud, malicious App Store Optimization (ASO), spam reviews, developer data privacy leakage (in instructions/knowledge files), user input privacy data leakage, potential misuse (spread of misinformation), erosion of privacy, algorithmic bias, generation of harmful content (violating ethics/policy). |
| O-v-HRHTS3wJ.pdf | Google_Scholar | Reducing Hallucinations in Large Language Models Through Contextual Position Encoding | This paper proposes Contextual Position Encoding (CPE), a novel technique to enhance positional information in LLMs, aiming to reduce hallucinations and improve factual accuracy. Integration of CPE into the Mistral Large model demonstrated significant improvements in accuracy metrics and a reduction in hallucination rates compared to baseline models. | True | NaN | True | 1.0 | NaN | Contextual Position Encoding (CPE) integrated into the Mistral Large model. | Compared CPE-enhanced Mistral Large against baseline Mistral Large, GPT-3, and BERT using metrics like precision, recall, F1-score, BLEU score, perplexity, and a custom hallucination rate metric on diverse text corpora (news articles, academic papers, web content). | The CPE-enhanced Mistral Large achieved higher accuracy (F1-score 0.90 vs 0.84 baseline), reduced hallucination rates (0.15 vs 0.35 baseline), and outperformed GPT-3 and BERT on BLEU score (0.89) and perplexity (11.0). | NaN | NaN | NaN | NaN | NaN | International | Diverse text corpora (news articles, academic papers, general web content), preprocessed for quality and consistency. Specific sources and public/proprietary nature not explicitly stated. | Proposal of a novel architectural modification (CPE), integration into an existing LLM (Mistral Large), empirical training, and quantitative evaluation against baselines using standard NLP metrics. | NaN | False | False | NaN | NaN | Mitigating hallucinations in LLMs; Computational cost and scalability of the proposed CPE technique. | Risks associated with LLM hallucinations in critical domains (healthcare, legal, finance); Potential ethical risks of advanced LLMs (bias, fairness, transparency). |
| LHiwGkmrSvcJ.pdf | Google_Scholar | JUDICIAL ECONOMY IN THE AGE OF AI | This paper argues that AI, particularly LLMs, will dramatically lower access to justice barriers, creating a potential litigation boom that threatens judicial economy. It advocates for proactively integrating AI into the judicial process itself to enhance capacity and manage increased caseloads without curtailing substantive rights. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | High costs of legal services (lawyer and court fees); complexity of legal processes; sociolegal barriers including lack of legal consciousness (naming-blaming-claiming); potential for increased litigation volume overwhelming the judicial system. | Proactive integration of AI tools into the judicial process itself (e.g., for case management, summarization, document Q&A, drafting assistance, generative interpretation) to scale up the system's capacity, rather than reactive adjustments like raising fees or tightening procedural/substantive standards ('legal thermostats'). | Access to legal services; judicial economy; litigation volume; legal consciousness (naming-blaming-claiming); impact of technology on courts. | Low-income individuals/Americans, ordinary people facing unresolved civil legal problems. | General Civil Litigation, Administrative Law, Civil Procedure | United States | NaN | NaN | NaN | False | False | NaN | Ensuring the judicial system can scale to handle increased access without compromising substantive justice; need for robust, reliable, ethical AI tools tailored for judicial use; bridging the gap between AI's potential for access and the system's current capacity and readiness. | AI unreliability (hallucinations, inaccuracy); ensuring confidentiality; costs and complexity of integrating AI into judicial systems; need for careful testing and validation; ethical concerns (human oversight, judicial authenticity); potential for AI misuse (e.g., facilitating frivolous claims); judicial skepticism and resistance. | Overwhelming judicial caseloads; erosion of substantive rights through reactive 'legal thermostat' adjustments; AI errors impacting case outcomes; potential bias in AI applications; confidentiality breaches; misuse of AI for vexatious litigation; loss of judicial authenticity and reasoned deliberation. |
| IRJMETS70300034830-VenkateshSriram.pdf | Google_Scholar | RECENT ADVANCES IN NATURAL LANGUAGE PROCESSING SYSTEMS: A TECHNICAL OVERVIEW | This paper provides a technical overview of recent advancements in Natural Language Processing (NLP), focusing on transformer architectures (e.g., BERT, GPT-3), few-shot learning, multimodal integration, and associated challenges like bias and computational cost. It also surveys efficiency optimization techniques and industrial applications in sectors like healthcare, finance, and maintenance. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal Services (briefly mentioned) | International | Describes datasets used by cited works (e.g., WMT, BooksCorpus, Wikipedia, large web crawls, image-text pairs) | NaN | Discusses general deployment scenarios (cloud, edge) and impact of optimizations | False | False | NaN | NaN | Bias detection and mitigation, high computational resource requirements, model scaling limitations (e.g., attention complexity), need for better evaluation metrics. | Biased outputs leading to unfair or discriminatory outcomes against specific demographic groups. |
| BabyFaceandChatGPT.pdf | Google_Scholar | Don’t Trust ChatGPT: A Case Study of a Defective Research Tool | This paper details a case study where the author queried ChatGPT about the historical reasons for a film's setting. ChatGPT provided factually incorrect information, demonstrating its unreliability as a research tool. | True | NaN | True | 2.0 | NaN | ChatGPT | The author asked ChatGPT specific questions about the film "Baby Face" and screenwriter Darryl Zanuck's connection to Erie, PA, and cross-referenced the answers with internet searches. | ChatGPT provided plausible but factually incorrect information regarding Darryl Zanuck's connection to Erie, PA, including claiming he attended a specific high school there, and later retracted the claims when confronted. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | True | False | ChatGPT is accessible via its website (chat.openai.com). | NaN | The primary challenge identified is the factual inaccuracy and unreliability of ChatGPT for research purposes. | Reliance on inaccurate information provided by ChatGPT for research. |
| MCZA3RN2BbcJ.pdf | Google_Scholar | Economic and Financial Analysis of Artificial Intelligence's Impact on Law and Legal Profession | The paper discusses the disruptive potential of Large Language Models (LLMs) like ChatGPT on the legal profession, focusing on productivity gains and market changes. It argues that regulatory hurdles, particularly restrictions on outside investment in law firms, may hinder the development and adoption of proprietary AI solutions necessary for competitiveness and managing privacy concerns. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General legal practice / Legal industry | International | NaN | NaN | NaN | False | False | NaN | NaN | High cost of training proprietary LLMs; restrictions on outside investment in law firms due to bar association rules limiting non-lawyer ownership; misalignment of investment incentives between current partners and long-term benefits; privacy concerns with using third-party LLMs. | Hallucinations (factual errors) leading to professional embarrassment and costs for lawyers; competitive disadvantage for firms in jurisdictions with strict investment rules. |
| pf5cO5kK6_QJ.pdf | Google_Scholar | Chatlaw: A Multi-Agent Collaborative Legal Assistant with Knowledge Graph Enhanced Mixture-of-Experts Large Language Model | This paper introduces Chatlaw, an AI legal assistant using a Mixture-of-Experts (MoE) LLM and a multi-agent system mimicking law firm workflows to enhance reliability and reduce hallucination. Chatlaw integrates knowledge graphs and RAG, outperforming GPT-4 on Chinese legal benchmarks and expert evaluations. | True | Idealistic | True | 1.0 | Positive | Chatlaw system: Mixture-of-Experts (MoE) LLM combined with a multi-agent framework (Legal Assistant, Legal Researcher, Senior Lawyer, Legal Editor) using Standardized Operating Procedures (SOP), Knowledge Graphs, and Retrieval-Augmented Generation (RAG). | Evaluation on LawBench (Chinese legal benchmark), China's Unified Qualification Exam for Legal Professionals (2018-2022), and real-world legal consultations assessed by legal experts based on Completeness, Correctness, Guidance, and Authority. | Outperformed GPT-4 on LawBench by 7.73% avg accuracy and on the Unified Qualification Exam by 11 points avg score. Achieved highest scores and win rates in real-case expert evaluations. | Limited availability of legal professionals, high cost of legal services, gap between legal aid need and provision capacity, leading to restricted access and impacting justice/equity. | Proposes Chatlaw, an automated legal assistant using an MoE LLM and multi-agent collaboration, integrating knowledge graphs and RAG to provide reliable, accurate, and accessible legal consulting services. | General legal consultation, Divorce/family law (used as example). | General population in China lacking resources to navigate the legal system. | Multiple fields (based on benchmarks and dataset description), including Divorce Law (example). | China | Proprietary 'Chatlaw legal dataset' (approx. 4 million samples) constructed from multi-source data, processed via deduplication, denoising, and human finetuning (students, experts). Covers 10 major/44 minor legal categories, includes knowledge graphs and agent task datasets. Formatted using LLaMA chat template. | Data Collection Pipeline, Mixture-of-Experts (MoE) Architecture Design, Multi-Agent System Design (Roles, SOPs), Knowledge Graph Integration, Retrieval-Augmented Generation (RAG), Human-in-the-loop Refinement. | An online trial phase was conducted, gathering user feedback. Plans mentioned to popularize the framework. | True | True | Dataset, codes and deploy details are released in the GitHub repository: github.com/PKU-YuanGroup/ChatLaw. | Need for model compression (for on-device deployment), addressing user privacy concerns vs. record-keeping needs, managing computational resources at scale, ensuring robustness against adversarial inputs. | Creating high-quality legal dataset, effective MoE model training, multi-agent coordination, hallucination mitigation (via RAG/SOPs), ensuring robustness, computational resource intensity, addressing privacy issues identified in trials. | LLM hallucination (providing incorrect/fabricated legal information), privacy risks related to sensitive user data in consultations. |
| 2CGGojIZFmYJ.pdf | Google_Scholar | REPLACING THIS OLD HOUSE : CERTIFYING AND REGULATING NEW LEGAL SERVICES PROVIDERS | This paper critiques current state regulation of new non-lawyer legal services providers, arguing it's too rigid and costly by mimicking lawyer licensing. It proposes state courts collaborate to create flexible, independent provider certification and proactive regulation, thereby improving access to justice. | True | Idealistic | False | 1.0 | NaN | A proposed systemic approach for state courts to certify and regulate new non-lawyer legal services providers, characterized by collaborative design, flexible and focused training, pathways to independent practice, and proactive, data-driven regulation. | NaN | NaN | High cost of lawyers; restrictive Unauthorized Practice of Law (UPL) rules; traditional lawyer licensing serving as an inappropriate and overly burdensome model for new provider categories; current new provider programs often having overly rigid/expensive certification, limited service scope, and mandatory lawyer supervision, hindering their impact on access to justice. | States should collaboratively design and implement new, distinct certification and regulation systems for non-lawyer legal services providers. These systems should feature affordable, flexible, and competency-focused training for discrete legal work; pathways to independent, fee-generating practice; and proactive, data-driven, and ongoing regulation, with an aim for eventual uniformity. | Access to affordable legal help for essential civil legal problems (e.g., family law, housing, consumer debt, domestic violence, estate/probate) concerning basic human needs. | Low- and middle-income people/Americans; ordinary people; legally vulnerable communities. | Family law, housing law (eviction), consumer debt, estate and probate law, administrative law, low-level civil litigation, low-level criminal litigation (misdemeanors not subject to incarceration), domestic violence. | United States (state courts generally, with specific examples from Arizona, California, Colorado, Delaware, Minnesota, New Hampshire, Oregon, South Carolina, Texas, Utah, Washington). Also discusses federal administrative agencies. | NaN | The proposed approach is developed through critical legal analysis of existing regulatory systems, comparative study of different models (lawyer, federal agency, state pilots), and application of educational design principles (e.g., 'backward design'). It advocates for future iterative refinement based on data collection and stakeholder consultation. | Proposed deployment through state supreme court leadership, fostering robust experimentation, data collection and sharing among states, and collaboration (e.g., via the Conference of Chief Judges) to develop model guidelines and eventual uniformity. Involves diverse stakeholder participation in designing and refining programs. | False | False | NaN | Lack of uniformity in state approaches to new legal service providers; insufficient data on the effectiveness of different models (need for more experimentation and data sharing); political resistance to reform from the unified bar; inadequate inclusion of diverse stakeholder perspectives (especially clients and prospective providers) in designing reforms; need for national recognition and integration of new providers into the legal profession. | General challenges identified for implementing such regulatory reform include: overcoming resistance from the unified bar, ensuring programs are economically viable for providers, balancing public protection with increased access, achieving state-level consensus and collaboration, and securing adequate resources and political will for experimentation and implementation. | The paper acknowledges the general concern of protecting clients from incompetent or unscrupulous providers, which often underpins restrictive regulations. For Gen AI (briefly mentioned): potential to run afoul of UPL, create a two-tiered justice system, and exacerbate inequalities. |
| 1265947.pdf | Google_Scholar | Reducing hallucination of Generative AI via Agentic AI and Edge Computing | This paper proposes a novel framework combining Retrieval-Augmented Generation (RAG) with agentic workflows, specifically generator and critic agents, within 6G networks and edge computing to reduce hallucinations in Generative AI. The framework aims to improve AI response quality and factual accuracy through iterative self-criticism and real-time integration of external knowledge sources. | True | Market | True | 1.0 | NaN | A framework integrating agentic workflows (generator and critic agents) with Retrieval-Augmented Generation (RAG) deployed in 6G-enabled edge computing environments, utilizing iterative self-criticism, external tool integration, and game theory principles for agent collaboration. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | The proposed framework uses a multi-agent system (generator and critic agents), iterative self-criticism, Retrieval-Augmented Generation (RAG), tool integration (APIs, search engines, IoT data), game theory principles for agent interaction, and planning (task decomposition). It is designed for deployment in 6G-enabled edge environments. | The framework is proposed for deployment in 6G-enabled mobile edge computing environments to facilitate scalable, real-time knowledge integration, data fusion, dynamic knowledge base updates, and customizable AI service delivery. | False | False | NaN | NaN | Predictability issues with planning and multi-agent collaboration components of agentic AI; insufficiency of RAG alone to fully eliminate hallucinations; the need for developing robust agent memory systems. | Generation of inaccurate or fabricated information by LLMs (hallucinations), including potentially harmful outputs such as fabricated legal precedents or falsified medical advice. |
| F_ecQdtwRv4J.pdf | Google_Scholar | Bias Transmission in Large Language Models: Evidence from Gender-Occupation Bias in GPT-4 | This paper investigates gender-occupation bias in GPT-4, examining both underlying associations (e.g., surgeon=male) and potential outcome bias in generated job cover letters. It introduces the LLM Bias Transmission Assessment (LLM BTA) method, finding that while GPT-4 exhibits association biases similar to humans, these biases do not necessarily translate into generating or evaluating cover letters unfairly based on gender. | True | NaN | True | 1.0 | NaN | LLM Bias Transmission Assessment (LLM BTA): A two-stage method involving 1) prompting an LLM (GPT-4) to generate output (cover letters) with potentially biasing input (gendered names) and 2) prompting the same LLM to evaluate the quality of the generated output. | The LLM BTA method was applied to GPT-4. Testing involved: 1) Probing association bias using LLM IAT methods with job lists and gendered/racial names/labels (Exp 1-2, Supplemental). 2) Benchmarking GPT-4's evaluation ability on human-written strong/weak letters (Exp 3). 3) Testing GPT-4's evaluation bias on identical human letters with different gendered names (Exp 4). 4) Applying LLM BTA: Generating cover letters for 30 jobs with male/female names and having GPT-4 provide relative (hiring choice) and absolute (13-dimension rating scale) evaluations (Exp 5). Gender prediction of generated letters using Gemini. | GPT-4 showed strong gender-occupation association bias. However, it accurately assessed human-written letter quality and did not show bias when evaluating identical letters differing only by gendered name. In the LLM BTA test, GPT-4 generated cover letters for male and female applicants that it rated as equally strong overall (absolute ratings varied <1% across gender; relative hiring choice showed no significant bias for 19/30 jobs). A 'male voice' bias was detected in generated letters. | NaN | NaN | NaN | NaN | Employment / Anti-discrimination (implicit focus) | United States (Implicit) | N/A (Evaluates existing model GPT-4; uses job lists, names derived from US SSA data, and pre-written/generated cover letters for evaluation prompts) | Experimental design comparing LLM outputs and evaluations under controlled variations of input prompts (gendered names). The LLM BTA method was developed adapting concepts from human implicit association tests but focusing on outcome bias through generation and self-evaluation stages. Aimed for increased ecological validity over pure association tests. | NaN | True | False | The LLM Bias Transmission Assessment (LLM BTA) methodology is described in the paper, including prompts and stimuli lists, allowing replication if one has access to the evaluated LLM (GPT-4). | Need for more systematic studies on bias propagation (association vs. outcome); exploration of mechanisms behind bias; application to other bias types (race, age) and domains (admissions); understanding 'male voice' bias; investigating potential LLM 'correction' mechanisms; disentangling confounding job characteristics. | N/A (Not explicitly stated, but implies challenges in designing ecologically valid bias probes for LLMs and interpreting the relationship between association and outcome bias) | Generative AI used for job application materials may inherit and propagate societal biases (e.g., gender-occupation stereotypes), potentially disadvantaging groups in hiring, even if outcome bias isn't always direct. Risk of models exhibiting biased 'voice' (e.g., male voice). Risk of racial/ethnic bias (explored in appendix). |
| ghPzKgistSIJ.pdf | Google_Scholar | YOU JUST CAN’T BEAT TH E MACHINE: A LAWYER’S DUTY TO ADAPT IN THE AGE OF ARTIFICIAL INTELLIGENCE | This paper argues that lawyers have an ethical duty to embrace generative AI to enhance competence, ensure reasonable fees, and improve client communication. It discusses the evolution of legal technology, addresses AI-related risks like inaccuracy and confidentiality breaches, and emphasizes the necessity of responsible adoption and supervision of AI tools by legal professionals. | True | Market | True | 3.0 | Positive | Generative AI tools in legal practice (e.g., ChatGPT, Westlaw Precision, Lexis+ AI, Harvey) | NaN | NaN | High cost of legal services due to inefficiency; lack of technological adoption and competence among lawyers. | Lawyers should adopt AI to enhance efficiency and technological competence, potentially lowering client costs, fulfilling ethical duties. | Affordability of legal services; Lawyer's ethical duty of technological competence. | NaN | General legal practice; Legal ethics | United States | Proprietary legal databases (cases, statutes, regulations, editorial content) for legal tech platforms; vast amounts of internet text and proprietary data for general LLMs. | NaN | Commercial subscription services for specialized legal AI (e.g., Westlaw, LexisNexis, Harvey); public access for general LLMs (e.g., ChatGPT, with premium tiers for advanced models). | True | False | Specialized legal AI tools (Westlaw Precision, Lexis+ AI, Harvey) and advanced general LLMs (e.g., GPT-4) are available via commercial subscription; some general LLMs (e.g., basic ChatGPT) have free access tiers. | Technological incompetence and resistance to AI adoption among lawyers, hindering efficiency gains that could improve affordability and access to legal services. | For lawyers using AI: ensuring accuracy and reliability of AI outputs, maintaining client confidentiality, exercising adequate supervision, understanding AI capabilities and limitations, keeping pace with evolving technology and judicial expectations. | Ethical violations (competence, confidentiality, fees, candor, supervision), Rule 11 sanctions from inaccurate AI-generated filings, AI 'hallucinations' (fabricated information), breaches of client data security, biased outputs from AI, over-reliance leading to deskilling. |
| VXGVZ8xwopIJ.pdf | Google_Scholar | Application of Generative AI to the business context: analysis and assessment | This master's thesis explores the potential, applications, and challenges of Generative Artificial Intelligence (GenAI) within the business context, focusing on opportunities for enhanced productivity and efficiency. It includes a theoretical overview and an empirical evaluation comparing the performance of ChatGPT, Google Translate, and DeepL in translating business-related documents between Russian and English. | True | Market | True | 2.0 | NaN | Language translation using Large Language Models (LLMs) and related technologies, specifically comparing ChatGPT, Google Translate, and DeepL. | Comparative analysis of Russian-to-English translations of business profile text, investor relations text, and academic management engineering text using ChatGPT, Google Translate, and DeepL. Evaluation criteria included quantitative measures (word count, sentence length) and qualitative aspects (accuracy, tone, fluency, handling of specialized terminology, idiomatic expressions). | Translation performance varied across tools and text types. DeepL often produced the most polished and formally appropriate translations for business contexts. ChatGPT Translator made specific errors in terminology and phrasing in academic text translation, while Google Translate was generally accurate but sometimes less fluent or nuanced compared to DeepL. | NaN | NaN | NaN | NaN | NaN | International | The paper generally describes GenAI models (like ChatGPT) being trained on vast and varied datasets, including billions of texts (books, articles, webpages), using techniques like semi-supervised learning, RLHF, and large amounts of unlabeled data followed by smaller amounts of labeled data. Specific proprietary datasets for the tested versions of ChatGPT, Google Translate, or DeepL are not detailed. | The paper discusses underlying technologies for GenAI like deep learning, artificial neural networks (input, hidden, output layers), backpropagation, Generative Adversarial Networks (GANs), Transformer architecture, and Attention Networks (AN). It also mentions pre-training, fine-tuning (SFT), and Reinforcement Learning from Human Feedback (RLHF) as utilized by models like ChatGPT. | Discusses adoption patterns for enterprises ('Pay and Use', 'Integrate Your Apps', 'Enrich with Your Data', 'Train on Your Data') and mentions widespread adoption (e.g., ChatGPT adoption by Fortune 500 companies and specific examples like Block, Canva, PwC). Also mentions use cases like Zendesk Answer Bot, Zoom transcription, e-commerce chatbots (Shopify, WooCommerce), LegalZoom document assistance, and Babylon Health virtual assistant. | True | False | ChatGPT, Google Translate, and DeepL are publicly accessible tools, often via web interfaces, with free and paid tiers available. ChatGPT Enterprise is mentioned as a specific offering for businesses. | NaN | General challenges discussed include: technical (scalability, security, integration, explainability, limited predictability, data quality, IT infrastructure), operational (cost management, maintenance, data governance, lack of benchmarks), ethical/regulatory (bias, fairness, hallucinations, transparency, compliance, copyright, privacy, toxicity, accountability, lack of regulation), strategic (ROI, skill gaps, organizational readiness, rapid change). Specific translation challenges noted include variable performance across languages/text types, handling complex grammar/idioms, limited vocabulary range, ensuring accuracy/fluency/appropriate tone, and specific errors made by tools. | Bias in training data leading to discriminatory outputs, hallucinations (generating incorrect information), copyright infringement (training on copyrighted data), data privacy issues (PII leakage), security risks, creation of fake news/deepfakes/misinformation, lack of transparency ('black box' problem), potential job displacement, employee demotivation, over-reliance diminishing human skills, cost management difficulties, vendor lock-in. |
| Cccdwh7GboYJ.pdf | Google_Scholar | ETHICAL PITFALLS WHEN LAWYERS ARE USING ARTIFICIAL INTELLIGENCE | This paper discusses the ethical challenges lawyers face when using artificial intelligence, particularly generative AI like ChatGPT. It highlights concerns regarding client confidentiality, professional competence, the accuracy of AI-generated information, and the duty of supervision, referencing US legal ethics rules and recent incidents. | True | Market | True | 3.0 | Negative | Generative AI (e.g., ChatGPT, Bard) | NaN | NaN | Deepening unequal access to legal information due to AI. Lack of transparency in AI models regarding training data and operations, hindering assessment of fairness and reliability for A2J purposes. | NaN | Unequal access to legal information | NaN | Legal Ethics / Professional Responsibility, General Legal Practice, Intellectual Property | United States (specifically Colorado, New York, Texas, California, Florida Rules of Professional Conduct/Bar considerations, Michigan legislation on AI in political ads), United Kingdom | Vast amounts of generally undisclosed online materials, books, and articles used to train existing LLMs like ChatGPT. | NaN | NaN | True | True | Publicly available generative AI tools like ChatGPT (some with free access tiers) and commercial AI solutions from legal tech vendors (e.g., Thomson Reuters). | Lack of transparency in AI models (training data, internal workings, safety testing). Potential for AI to exacerbate existing inequalities in access to legal information. Insufficient regulation and understanding of AI development and deployment. | NaN | Breaches of client confidentiality through data input into AI; lawyers' lack of competence in using AI leading to errors or over-reliance; generation of inaccurate information or 'hallucinations' by AI; undermining of attorney-client privilege; use of AI-generated fakes (deepfakes, voice clones) in legal contexts; intellectual property infringement related to AI training data and outputs; lack of transparency about AI model operations; security vulnerabilities; attorneys failing to supervise AI use adequately; ethical issues in billing for AI use and advertising AI capabilities. |
| K7hkE1GDNtkJ.pdf | Google_Scholar | Analysis of barriers and proposals for inclusive access to justice for vulnerable groups | This paper analyzes the structural, social, and cultural barriers that hinder access to justice for vulnerable groups, using census participation in Ecuador as a case study. It proposes solutions focusing on cultural adaptation, privacy protection, inclusive policies, enhanced training for officials, and technological tools like online self-censuses. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Structural, social, and cultural barriers; Lack of culturally and linguistically adapted materials; Lack of accessible formats for people with disabilities; Privacy concerns regarding sensitive data collection; Inequalities in access to basic services (health, housing) and employment; Lack of trust in official processes; Insufficient training for officials; Uneven implementation of inclusive strategies. | Implement differentiated protocols; Strengthen training for justice operators (human rights, cultural sensitivity); Foster collaboration with community organizations; Use technology like online self-censuses for privacy; Develop adapted/accessible materials; Enhance confidentiality guarantees; Implement inclusive public policies for basic services/employment; Ensure uniform implementation of strategies. | Access to justice; Procedural barriers; Vulnerable groups; Participation in state processes; Inequality in basic services; Census participation. | Indigenous communities, people with disabilities, women, ethnic minorities, older adults, children and adolescents, migrants, LGBTIQ+ communities. | Human Rights Law, Access to Justice | Ecuador | NaN | NaN | NaN | False | False | NaN | Persistent accessibility barriers (linguistic, cultural, physical); Lack of trust and privacy concerns hindering participation; Uneven implementation of inclusive policies and training; Need for better tools for sensitive data collection; Deep-rooted structural inequalities in access to basic services and employment. | NaN | Privacy violations in sensitive data collection; Perpetuation of discrimination and exclusion; Erosion of trust in institutions; Exploitation and abuse of vulnerable groups. |
| vKMGIQ_M6YUJ.pdf | Google_Scholar | Towards Robust Legal Reasoning: Harnessing Logical LLMs in Law | The paper proposes a neuro-symbolic approach combining large language models (LLMs) with logic programming (Prolog) to improve the accuracy and reliability of legal reasoning, specifically for insurance contract analysis. Experiments show that an expert-guided framework for LLMs generating Prolog code outperforms vanilla LLMs and unguided LLM-Prolog generation. | True | Market | True | 1.0 | Positive | Expert-guided neuro-symbolic approach using LLMs to generate Prolog code from legal text (insurance policies) for logical reasoning. | Comparative evaluation of 'Vanilla LLM', 'Unguided LLM-generated Prolog', and 'Guided LLM-generated Prolog' approaches using multiple LLMs (including OpenAI o1, GPT-4o, DeepSeek-R1) on claim coverage questions derived from a simplified Chubb policy and specific coverages (ART, CI) from the Stanford Cardinal Care Aetna policy (using CodeX Insurance Analyst test cases). Accuracy and consistency across multiple trials were measured. | The guided approach significantly improved accuracy and consistency over vanilla and unguided methods, especially with advanced reasoning models like OpenAI o1 (achieving up to 100% accuracy on simpler tasks and 87-95% on complex ones). Unguided Prolog generation often resulted in poor quality, ambiguous, or syntactically incorrect code, sometimes performing worse than vanilla LLMs. | Lack of accuracy, consistency, transparency, interpretability, and susceptibility to hallucination in current LLMs, undermining trust for high-stakes legal applications. | A neuro-symbolic approach combining LLMs' natural language capabilities with the reliability of logic programming, particularly using expert guidance (structured frameworks, documentation) to help LLMs generate accurate and consistent logic code (computable contracts). | Insurance contract analysis / Coverage determination | NaN | Insurance Law, Contract Law | US | NaN | Comparative analysis framework evaluating three distinct methods (Vanilla LLM, Unguided LLM-Prolog, Guided LLM-Prolog). The guided approach involved prompt engineering providing LLMs with policy text, documentation on valid claim facts, and documentation on supporting pre-defined Prolog predicates (helper rules). | NaN | False | False | NaN | Limited scope (only health insurance); need for broader architectural exploration (fine-tuning, RAG, RL); requirement for larger and more diverse datasets; need to test more LLMs and logic interpreters; requires more sophisticated prompt/encoding strategies; need for formal evaluation of explainability/auditability. | Generating accurate, consistent, and logically sound Prolog encodings from complex legal text using LLMs; handling ambiguity, syntax errors, and logical fallacies in LLM outputs; integrating generated code with existing logic frameworks; variability in reasoning capabilities across different LLMs. | Inaccuracy, inconsistency, hallucination (e.g., citing fictitious cases), lack of transparency/explainability in LLM outputs leading to incorrect legal conclusions, misinterpretation of contracts, erosion of trust, and potential non-compliance with regulations (e.g., GDPR, EU AI Act). |
| AXew9YzxxOgJ.pdf | Google_Scholar | The First Hardware Circuit Emulating Italian Road Homicides Legal Logic, DAJE! | This paper proposes DAJE, a hardware circuit designed to emulate the legal logic for Italian road homicide cases using Boolean functions derived from law. The approach, demonstrated via FPGA emulation, aims to enhance security and efficiency in legal decision support, achieving 86% accuracy against real case verdicts. | True | Idealistic | False | 1.0 | Positive | DAJE (Digital Assurance Judicial Enforcer): A Boolean function-based hardware circuit designed to emulate Italian road homicide legal logic. | Comparison of DAJE's output against the verdicts of 100 real Italian road homicide cases (2016-2024) sourced from public legal portals (DeJure Giuffre Francis Lefebvre, Legisway). Features for testing were extracted from case texts using an LLM. | Achieved 86% accuracy (73% True Positives, 13% True Negatives, 8% False Positives, 6% False Negatives). Hardware implementation details (FPGA emulation): 6 LUTs, 24 FFs, max frequency 642 MHz, average power 89 mW. | Judicial system backlogs leading to lengthy case resolutions; security vulnerabilities and privacy concerns associated with software systems handling sensitive legal data. | Implementing legal logic directly in hardware (DAJE) to provide enhanced security (physical isolation, tamper resistance), reliability, and efficiency (automating preliminary analyses, expediting case management). | Legal decision support, automation of legal reasoning for preliminary case analysis. | NaN | Criminal Law (road homicide) | Italy | NaN | Boolean logic synthesis based on legal rules (Italian Road Code, principles of foreseeability/avoidability); Hardware description and implementation (FPGA emulation). | FPGA emulation for testing and demonstration. | False | False | NaN | The approach needs expansion to other legal domains beyond road homicide; further refinement and validation of the hardware design are needed. | Ensuring security and privacy of sensitive legal data in digital systems; reliably integrating technology into legal workflows; overcoming potential software vulnerabilities. | Risk of misclassification (False Positives/False Negatives) in determining whether a crime occurred according to the legal logic encoded. |
| 2023SingCompLRev130.pdf | HeinOnline | AI Regulation for the AI Revolution | This paper examines the adequacy of current legal frameworks for Artificial Intelligence, focusing on human rights infringements, AI bias, and intellectual property issues related to AI-generated content. It reviews existing and proposed AI regulations in key jurisdictions and proposes a multi-faceted regulatory approach aimed at balancing innovation with the protection of fundamental rights and achieving substantive equity. | True | Idealistic | False | 1.0 | Positive | A multi-faceted regulatory framework for AI, emphasizing rights protection, clarified liability (e.g., using a modified Hand Formula), transparency, risk management (e.g. layered approach based on use-case), and a hybrid structure of baseline blanket regulation coupled with sector-specific guidelines. | NaN | NaN | Systemic bias embedded in AI perpetuating societal inequities and discrimination; inadequacy of existing legal frameworks to address AI-specific harms and attribute liability; challenges in balancing innovation with the protection of fundamental rights (Collingridge dilemma); opacity of AI systems (black-box problem) hindering accountability and redress. | Implement comprehensive AI governance frameworks focused on substantive equity, transparency, and accountability; establish clear legal rules for liability (e.g., modified Hand Formula) and intellectual property in the context of AI; adopt a flexible, risk-based regulatory approach combining general principles (baseline blanket regulation) with sector-specific rules and guidelines. | Non-discrimination and fairness in AI decision-making (addressing AI bias); protection of human rights (dignity, privacy, liberty, fair trial, freedom of expression, non-discrimination); accountability and redress for harms caused by AI systems; fair intellectual property rights concerning AI-generated content. | Groups susceptible to AI bias and discrimination, including racial minorities (e.g., African-American patients), women, and socio-economically disadvantaged individuals (e.g., low-income students). | AI Regulation, Human Rights Law, Tort Law, Intellectual Property Law (Copyright), Anti-Discrimination Law, Data Protection Law, Contract Law, Administrative Law. | International (primarily EU, UK, Singapore, US, with references to China and OECD initiatives). | NaN | Legal analysis, comparative regulatory review of multiple jurisdictions, jurisprudential reasoning, and ethical analysis balancing competing societal values. | NaN | False | False | NaN | Lack of effective and harmonized global AI legal and regulatory mechanisms; persistent societal biases amplified by AI, despite awareness; difficulty in proving AI bias and obtaining redress for affected individuals; regulations struggling to keep pace with rapid AI development (the "barn door problem"); unclear liability attribution for AI harms despite proposals. | Designing a regulatory framework that is comprehensive yet flexible enough to adapt to rapid technological advancements; effectively balancing the promotion of innovation with the imperative to protect fundamental rights and ensure safety; defining complex technical and ethical concepts (like AI bias, high-risk AI) in legally operational terms; achieving international consensus and avoiding regulatory fragmentation. | AI bias leading to discrimination in critical sectors (healthcare, employment, finance, justice); infringement of human rights (privacy, fairness, dignity); infringement of intellectual property rights by AI-generated content; lack of transparency and accountability in AI decision-making (black-box algorithms); uncompensated harm to individuals due to unclear liability rules; stifling innovation through poorly designed or overly restrictive regulation; market consolidation in the AI industry, entrenching advantages of large tech companies. |
| 10TexAMJPropL389.pdf | HeinOnline | Beyond the Binary: AI, Ethics, and Liability in the Legal Landscape | This paper examines the ethical challenges and liability risks for attorneys using AI in legal practice, particularly tools like generative AI. It advocates for proactive strategies such as comprehensive AI training, rigorous oversight, and a balanced approach integrating human expertise to ensure competent representation and uphold professional ethics. | True | Market | True | 2.0 | Neutral | Use of AI tools (e.g., generative AI like ChatGPT, e-discovery tools, automated drafting tools) in legal practice. | Analysis of ethical rules, real-world incidents (e.g., Samsung data leak, LoDuca & Schwartz case involving fabricated citations), and hypothetical scenarios to evaluate risks and implications. | AI tools present substantial ethical challenges (confidentiality, competence, counseling) and malpractice risks for attorneys, necessitating proactive measures like training, oversight, and maintaining human judgment. | NaN | NaN | NaN | NaN | General legal practice, including litigation, legal research, e-discovery, contract law, and intellectual property. | United States | Discusses general AI training on user inputs (e.g., ChatGPT) and case data; notes risks with confidential client data being used for training or AI models having biased/incomplete training datasets (e.g., AI trained only on litigated cases). | NaN | NaN | True | True | Discusses various AI tools, including prominent examples like ChatGPT which offers free access tiers, alongside other commercial tools. | NaN | Challenges discussed relate to the use of AI by legal professionals: ensuring confidentiality with data-hungry AIs, maintaining professional competence when AI can err or fabricate, upholding the attorney's counseling role requiring empathy and ethical judgment beyond AI capabilities, and navigating the lack of established legal/ethical frameworks for AI use. | Key risks include client confidentiality breaches, waiver of attorney-client privilege, professional incompetence from overreliance on AI (e.g., AI fabricating information), legal malpractice claims, and ethical violations due to AI's lack of human judgment or empathy. |
| 23AustlJAsianL63.pdf | HeinOnline | Using Aceh's Qanun to Expand Protection for Domestic Violence Victims | This paper examines the underutilization of Indonesian domestic violence law and proposes a specific legal reform for Aceh, Indonesia. It suggests amending Aceh's Islamic criminal law (Qanun Jinayat) to empower the Mahkamah Syariah to adjudicate domestic violence cases, thereby aiming to improve access to justice for victims by bridging the gap between state criminal and religious law systems. | True | Idealistic | False | 1.0 | NaN | A legal reform proposal: amending Aceh's Qanun Jinayat (Islamic criminal law) to incorporate domestic violence protections similar to those in Indonesia's national Domestic Violence Law (Law No. 23 of 2004), and expand the Mahkamah Syariah's jurisdiction to hear these cases. | NaN | NaN | Underreporting of domestic violence to police; victims' preference for Islamic courts (Mahkamah Syariah) for divorce, which currently lack jurisdiction over criminal domestic violence; separation between state criminal and religious law systems leading to victims' cases not being fully addressed; police-related barriers (e.g., bias, lack of understanding); prevailing interpretations of Islamic law that may not support criminalization of domestic violence within Qanun. | Amend Aceh's Qanun Jinayat to criminalize domestic violence, granting Mahkamah Syariah authority to adjudicate these criminal cases, potentially alongside divorce proceedings. Advocate for interpretations of Islamic sharia that are consistent with the elimination of domestic violence. This reform aims to improve victim protection and perpetrator accountability. | Access to justice for domestic violence victims; Legal pluralism; Law reform; Women's rights under Islamic law. | Domestic violence victims, particularly women, in Aceh, Indonesia. | Family law; Criminal law; Islamic law (Sharia); Human rights. | Aceh, Indonesia | NaN | Legal analysis of existing national and local laws (including Qanun and Islamic law); review of court practices and statistics; consideration of socio-legal research on domestic violence reporting and victim experiences; and policy formulation for law reform. | The proposed deployment involves legislative enactment of amendments to the Qanun Jinayat by Acehnese lawmakers. Subsequent implementation would require changes to Mahkamah Syariah's procedural laws and specialized training for judges on domestic violence. | False | False | NaN | The need for concurrent changes to the Mahkamah Syariah's procedural laws; the necessity of specialized training and certification for judges handling domestic violence cases; overcoming patriarchal interpretations of Islamic sources that might hinder the reform's acceptance and effectiveness. | Convincing lawmakers of the theological appropriateness and necessity of including domestic violence criminalization within the Qanun Jinayat; overcoming prevalent patriarchal interpretations of Islamic texts that may impede reform; ensuring effective police handling of cases if they are to be prosecuted under the amended Qanun; the need for comprehensive judicial training and adjustments to court procedures. | NaN |
| 92FordhamLRev.pdf | HeinOnline | FAIRNESS AND FAIR USE IN GENERATIVE Al | This paper proposes the "non-expressive use" theory as a framework for analyzing fair use in generative AI, arguing that training AI on copyrighted data is often fair if it doesn't communicate the original expression to new audiences. It advocates for a copyright-centric approach to fair use, rather than one based on broader, speculative public policy concerns about AI. | True | Market | True | 1.0 | NaN | The "non-expressive use" theory as an analytical framework for assessing fair use of copyrighted works in training generative AI models. | The framework is supported by legal analysis, derivation of principles from copyright law's structure and purpose, and examination of existing U.S. case law on fair use (e.g., reverse engineering, search engine indexing, plagiarism detection cases). | The paper concludes that training generative AI models by copying works is often a 'non-expressive use' and thus highly transformative, favoring fair use, provided the AI output does not substitute for the original expression. It also suggests considering factors like lawful access to data and mitigation of infringing outputs under the fourth fair use factor. | NaN | NaN | NaN | NaN | Copyright Law | United States | The paper discusses that generative AI models are trained on massive quantities of data, including internet-scraped text and images (e.g., Common Crawl, LAION 5B), books (e.g., Project Gutenberg, Books2, Books3 in ThePile), and open licensed content, which may include copyrighted materials obtained without explicit permission or from 'shadow libraries'. | Legal scholarship, including doctrinal analysis of copyright law, common law reasoning, interpretation of statutes (U.S. Copyright Act) and case law, and derivation of legal principles. | Dissemination through academic publication (law review essay), public lectures, and testimony to governmental bodies (e.g., U.S. Senate Judiciary Committee). | True | True | The analytical framework ('non-expressive use' theory) is detailed in the published academic paper and related public discussions (lecture, testimony). | NaN | Challenges for this legal framework include its consistent judicial adoption and application, ensuring courts accurately understand AI's technical aspects and distinguish between non-expressive uses and those causing cognizable copyright harm, and balancing copyright principles with evolving AI capabilities. | The paper acknowledges general risks of AI (e.g., disinformation, job displacement, bias). Risks specific to the fair use context include: AI outputs memorizing and reproducing training data, AI being used as a tool for infringement, AI developers bypassing markets for lawful access to data, or AI causing pervasive indirect expressive substitution that undermines incentives for original content creation. |
| 86UPittLRev1.pdf | HeinOnline | CULTURALLY PROFICIENT LAWYERING: A FRAMEWORK AND RUBRIC SUPPORTING LEARNING OUTCOMES AND OBJECTIVES | This paper proposes the Culturally Proficient Lawyering (CPL) Framework and an accompanying CPL Rubric to help Toge educators meet the ABA's Standard 303(c) for teaching Toge, cross-cultural competency, and racism. These tools aim to develop law students' awareness, knowledge, and skills to foster a more inclusive and equitable Toge profession capable of serving diverse communities effectively. | True | Idealistic | False | 1.0 | Neutral | Culturally Proficient Lawyering (CPL) Framework and CPL Rubric. | The paper illustrates the CPL Framework and Rubric's utility through sample exercises and descriptive use-cases, such as the 'Client Interview Vignette,' rather than formal empirical evaluation. | NaN | Lack of cultural proficiency among lawyers; cultural and language barriers hindering access to Toge help; systemic Toge, discrimination, and racism embedded in the Toge system and its institutions; historically exclusionary nature of the Toge profession and education. | Implement comprehensive cultural proficiency education in law schools using the proposed CPL Framework and Rubric. This includes developing students' awareness of personal and systemic biases, knowledge of historical and social contexts of inequality, and practical skills for cross-cultural interaction and inclusive representation. | Improving lawyer-client communication and representation for diverse populations; addressing systemic bias and discrimination within the legal system through lawyer education; enhancing equal access to justice. | The paper addresses the need to serve diverse clients and communities broadly, including people of color (specifically mentioning African Americans, Indigenous peoples, Latine Americans, Asian Americans, MENA Americans), LGBTQ+ individuals, people with disabilities, and those from various socioeconomic backgrounds, aiming to improve representation for marginalized groups generally. | General legal practice / All fields | United States | NaN | Literature review, adaptation from other fields (e.g., medical education, education), synthesis of existing theories (e.g., critical race theory), and pedagogical principles. | Publication in a law review. The CPL Rubric is provided in Appendix A of the paper. | True | False | The Culturally Proficient Lawyering Framework is detailed in the paper, and the CPL Rubric is provided in Appendix A of the paper. | The ongoing need for development in cross-cultural competence assessment tools and methodologies; the challenge of ensuring legal education truly incorporates and values the voices and experiences of marginalized communities; the continuous, evolving nature of cultural proficiency requiring life-long learning; potential for over-reliance on frameworks stifling creativity if not critically applied; fostering genuine institutional change beyond superficial compliance. | Integrating cultural proficiency into a traditionally static and resistant law school culture; overcoming resistance to change and unawareness of the need to adapt; addressing the inherent subjectivity and complexity in assessing cultural proficiency; ensuring faculty are equipped to teach these topics effectively and create safe learning environments. | For AI: Perpetuation of societal biases by AI systems, potentially exacerbating legal disparities and power imbalances, especially for low-income individuals. For the proposed framework: Over-reliance on the CPL framework could stifle creativity and flexibility; the framework's effectiveness depends on the critical engagement of its users; poorly handled classroom discussions on sensitive topics like race can lead to negative outcomes for students. |
| 20UStThomasLJ53.pdf | HeinOnline | GEORGIA STATE LEGAL TECHNOLOGY COMPETENCY MODEL: A FRAMEWORK FOR EXAMINING AND EVALUATING WHAT IT MEANS TO BE A TECHNOLOGICALLY COMPETENT LAWYER | This paper introduces the Georgia State Legal Technology Competency Model, a framework designed to assess and guide the development of technological competence among lawyers and law students. The model features a foundational 'BASE' level of essential skills, complemented by a conical structure with four topical quadrants (Practice Technology, Data, Automation & Efficiency, Emerging Technology) and three graduated knowledge levels (Know, Integrate, Create), offering flexibility for different roles and educational goals. | True | Market | False | 1.0 | NaN | Georgia State Legal Technology Competency Model, which includes a 'BASE' (Basic Applications, Software, & Expectations) level and a conical framework with four topical quadrants (Practice Technology, Data, Automation & Efficiency, Emerging Technology) and three knowledge levels (Know, Integrate, Create). | The model's application is illustrated through several hypothetical scenarios: designing a stand-alone legal tech course, planning a law school legal tech certificate program (drawing from experiences at Georgia State College of Law), onboarding a new associate in a large law firm, and guiding a partner in a mid-sized firm to modernize technology usage. | The paper presents the conceptual model and demonstrates its potential utility for self-assessment, curriculum design, and training program development in law schools and law firms through illustrative examples. No quantitative results are provided as it's a proposed framework. | NaN | NaN | NaN | NaN | Legal education, Legal practice (general), Legal technology, Professional Responsibility/Ethics | United States | NaN | The model was developed by synthesizing and building upon existing concepts such as 'T-shaped lawyers,' the 'Delta Model,' and 'Bloom's Taxonomy of Educational Objectives' (as modified by Anderson and Krathwohl). It also incorporates the authors' experiences in curriculum planning. | The model is proposed for use in self-assessment, assessing students or employees, and evaluating and designing teaching curricula or training programs in law schools or firms. It is noted as being used in curriculum planning for the Georgia State College of Law Legal Analytics & Innovation Initiative. | True | False | The conceptual model/framework is fully described in the paper, allowing readers to understand and apply its principles after accessing the publication. | NaN | Addressing the lack of clarity regarding what constitutes legal technology competency; overcoming limitations of existing list-based competency models by creating a more flexible and pedagogically sound framework that accommodates graduated skill levels and diverse roles. | Risks associated with lawyers' lack of technological competency, including ethical violations (e.g., incompetent representation as per ABA Model Rule 1.1, breach of client confidentiality under Rule 1.6 due to poor cybersecurity or metadata mishandling), and failing to meet market expectations for efficiency and modern legal practice. |
| 55CumbLRev53.pdf | HeinOnline | LEX EX MACHINA: FORGING A NEW ETHICAL FRAMEWORK FOR AI AND TECHNOLOGY IN THE LAW | The paper argues that the rise of generative AI (GAI) necessitates a new, more detailed ethical framework for technology use in law, going beyond current general competence rules. It proposes flexible, aspirational standards to encourage continuous learning, ethical innovation, and address GAI's unique challenges and opportunities in legal practice. | True | Idealistic | True | 1.0 | Positive | A proposed new ethical framework and specific model rules (Rule X and commentary) for the use of technology, particularly Generative AI, in legal practice. | NaN | NaN | The primary obstacles identified are the 'technology gap' among legal professionals (lack of consistent tech proficiency and adoption) and the inadequacy of current ethical rules to guide the safe and effective use of advanced AI. These hinder the potential of technology to make legal services more efficient, transparent, and accessible. | The paper proposes the adoption of a new, detailed, and flexible ethical framework, including specific model rules and aspirational standards. This framework aims to enhance technological literacy, guide ethical innovation, and ensure lawyers leverage technology (like GAI) to make legal services more efficient, transparent, and accessible, thereby improving access to justice. | Enhanced efficiency of legal services, affordability of legal assistance, transparency in legal processes, general accessibility of legal services, legal productization for wider reach. | NaN | General Legal Practice, Legal Ethics | United States (primarily focusing on ABA Model Rules and state-level adoption, with references to federal actions) | NaN | NaN | NaN | False | False | NaN | Current ethical rules are too general for advanced AI; significant 'technology gap' (inconsistent proficiency and adoption) among lawyers; lack of clear, actionable guidance for ethical AI implementation; early GAI regulations are often imbalanced or insufficient. Societally, a gap exists in translating AI's potential into broadly accessible and efficient legal services. | NaN | GAI producing inaccurate information ('hallucinations'); breaches of client data privacy and confidentiality when using external AI tools; perpetuation of biases from training data leading to unfair outcomes; unauthorized practice of law through over-reliance or unsupervised AI use; unreasonable client fees related to AI use or saved inefficiencies; lawyer deskilling due to over-reliance on AI; AI chatbots creating unintended lawyer-client relationships or misrepresenting capabilities. |
| 25GermanLJ.pdf | HeinOnline | Image-Based Sexual Abuse and EU Law: A Critical Analysis | This paper critically analyzes the EU's new Directive on Violence Against Women, the Digital Services Act, and the AI Act concerning their effectiveness in addressing image-based sexual abuse (IBSA). It finds these legal instruments to be an initial step but identifies significant shortcomings in comprehensively protecting victims and holding perpetrators and platforms accountable. | True | Idealistic | False | 2.0 | Neutral | EU legal framework (Directive on Violence Against Women and Domestic Violence, Digital Services Act, AI Act) for regulating Image-Based Sexual Abuse | NaN | The analysis concludes that the EU's legal framework (Directive on Violence Against Women, DSA, AI Act) represents an initial effort but does not provide a comprehensive solution to IBSA, falling short in capturing its full scope, addressing diverse victim experiences, and ensuring effective redress. | Narrow legal definitions of offenses not covering all forms of abuse; high evidentiary burdens for victims (e.g., proving 'serious harm'); fragmented and inconsistent legal responses; underreporting by victims due to fear, shame, and lack of trust; inadequate training and sensitivity among law enforcement and legal professionals; difficulties in removing abusive content and ensuring platform accountability. | Adoption of comprehensive legal definitions for IBSA and related offenses reflecting victims' experiences; removal of undue evidentiary burdens focusing on lack of consent; enhanced specialized training for legal and law enforcement professionals; improved victim support services and safer reporting mechanisms; stronger, binding regulations for online platforms regarding content moderation, removal, and accountability; robust and harmonized national implementation of EU laws. | Image-based sexual abuse (IBSA); Deepfake pornography; Cyberflashing; Victims' rights; Criminalization of online gender-based violence; Regulation of online platforms; AI regulation. | Victims of image-based sexual abuse, predominantly women and girls, including those from LGBTQIA* communities, ethnic and religious minorities, younger women, and individuals in public positions. | EU Law; Criminal Law; Fundamental Rights Law; Digital Law; Cyberlaw; Victims' Rights Law. | European Union (EU) and its Member States. | NaN | NaN | NaN | False | False | NaN | The EU Directive's narrow scope of criminalized IBSA conduct and images, its high 'serious harm' evidentiary threshold, and limited content removal mechanisms. Insufficient accountability for online platforms under DSA and AI Act regarding IBSA, with the AI Act's deepfake labeling being inadequate for harm reduction. Lack of comprehensive coverage for all forms of IBSA (e.g., non-consensual creation/taking of images). Potential for inconsistent national implementation of EU laws. | NaN | Continued emotional, psychological, professional, and relational harm to IBSA victims; self-censorship and withdrawal of victims from online spaces; inadequate legal redress for victims due to narrow laws and high proof burdens; perpetuation of victim-blaming; insufficient platform accountability leading to continued proliferation of IBSA; AI-generated deepfakes causing harm despite labeling. |
| 99NYULRev451.pdf | HeinOnline | GENERATIVE INTERPRETATION | This paper introduces "generative interpretation," a novel approach using large language models (LLMs) to estimate contractual meaning, quantify ambiguity, fill gaps, and assess extrinsic evidence. It argues that this method can offer a cheaper, more accessible, and predictable way to interpret contracts, potentially bridging the gap between textualist and contextualist approaches and improving access to justice. | True | Idealistic | True | 1.0 | Positive | Generative interpretation using large language models (LLMs) for contractual interpretation, including querying models (GPT-4, Claude 2, Llama-2) with contract text and specific questions, analyzing embedding distances, and examining probabilistic outputs for meaning, ambiguity, and gap-filling. | The approach was evaluated through grounded case studies using actual contracts from well-known contract law opinions (e.g., In re Katrina, C & J Fertilizer, Famiglio v. Famiglio, Trident Center, Ellington v. EMI, Haines v. City of New York, Stewart v. Newbury). This involved feeding contract text and specific queries to LLMs and analyzing their responses, sometimes with multiple prompts and temperature settings. | LLMs demonstrated capabilities in ascertaining ordinary meaning in context (e.g., 'flood' in Katrina), quantifying ambiguity (e.g., prepayment clause in Trident), filling gaps (e.g., duration in Haines), and calculating the probative value of extrinsic evidence (e.g., phone call in Stewart). Model outputs were often plausible and offered nuanced perspectives, sometimes supporting and sometimes challenging judicial outcomes. | The high cost and inaccessibility of current contract interpretation methods for ordinary parties and resource-constrained firms, leading to an access-to-justice problem. The uncertainty and potential biases in traditional methods like dictionary reliance and judicial intuition. | Proposes generative interpretation as a cheaper, more accessible, transparent, and predictable methodology for contract interpretation. This can democratize access to sophisticated textual analysis, reduce litigation costs, and make outcomes more certain, thus improving access to justice for the "99%". | Contract interpretation, access to legal understanding for non-wealthy individuals and resource-constrained parties, reducing costs and uncertainties in contract litigation. | Non-wealthy individuals, ordinary parties, resource-constrained firms, and potentially judges in resource-deprived courts. | Contract Law, Insurance Law. | United States (primarily, with case law examples from various US state and federal courts like New York, California, Iowa, Fifth Circuit, Florida, Alabama). | The LLMs used (GPT-4, Claude 2, Llama-2) are trained on vast, general corpora of text ('torrents of existing texts'). The paper itself does not detail the specific datasets beyond what is generally known about these models' pre-training, but notes they are trained on 'trillions of words'. | The authors developed their "generative interpretation" approach by: 1) Obtaining and analyzing original contract texts from litigated cases. 2) Designing prompts and queries to elicit interpretations from LLMs. 3) Using techniques like embedding distance analysis. 4) Iterative querying with varied prompts and temperature settings to assess robustness. 5) Comparing LLM outputs to judicial reasoning and academic commentary. | The paper provides a GitHub link (https://github.com/yonathanarbel/generativeinterpretation/tree/main) for the code to replicate their results, suggesting the methods can be implemented using accessible LLMs. | True | True | The code for replicating results is available on GitHub. The LLMs discussed (e.g., Llama-2 is open source, GPT-4 and Claude 2 are accessible via APIs or chat interfaces) are generally available. | Technical gaps include model hallucinations, susceptibility to manipulation (adversarial attacks, prompt injection), majoritarian bias in outputs, sensitivity to linguistic drift over time, and the 'black box' nature of LLM reasoning (interpretability). Societal gaps include the need for a new 'language' or sociological framework for courts to justify and explain LLM-aided interpretations to ensure legitimacy. | Hallucinatory outputs, sensitivity of models to prompts ('leading prompts'), model biases towards majoritarian interpretations, adversarial attacks or prompt injections, models' insensitivity to the specific time of contract formation (linguistic drift), and the lack of full interpretability of model reasoning. | Generation of false or misleading information (hallucinations) by LLMs (e.g., citing fake cases). Manipulation of LLM outputs through carefully crafted prompts or adversarial attacks. Reinforcement of majoritarian biases, potentially silencing linguistic conventions of underrepresented communities. Difficulty in auditing or understanding the precise reasoning behind an LLM's interpretation ('black box' problem). Linguistic drift, where models trained on contemporary text misinterpret older contracts. E_DECREASE_IN_JUDICIAL_LEGITIMACY_IF_NOT_PROPERLY_INTEGRATED_AND_EXPLAINED. |
| 15CaseWResJLTechInternet1.pdf | HeinOnline | NEW RULES FOR A NEW ERA: REGULATING ARTIFICIAL INTELLIGENCE IN THE LEGAL FIELD | The paper argues for regulating generative AI in the legal field due to its current flaws, particularly its potential to stagnate legal evolution, undermine effective human representation, and embed biases. It proposes amending professional conduct rules to restrict AI use for persuasive legal communication and judicial decision-making until the technology matures. | True | Idealistic | True | 1.0 | Negative | Generative AI (e.g., ChatGPT, GPT-4) | Author's informal test: Asked ChatGPT for Ohio law on car accidents constituting battery and supporting caselaw, then verified the citations. | ChatGPT provided two case citations that did not exist or were irrelevant to vehicular battery, demonstrating 'hallucinations'. | AI inaccuracy (hallucinations), risk of legal stagnation due to reliance on outdated data, ingrained AI biases leading to unfair outcomes, and AI's lack of human emotional intelligence and moral judgment necessary for effective representation and legal development. | Amend professional rules of conduct to prohibit lawyers from using AI for persuasive legal/client communication and judges from using AI for drafting rulings/opinions, with enforcement via AI-detecting software. Continuously amend rules as AI evolves. | Quality and effectiveness of legal representation, fairness and non-discrimination in legal outcomes, reliability of legal information, and the just evolution of law. | NaN | General legal practice, litigation, judicial decision-making, professional ethics. | Primarily US (with references to ABA Model Rules, state law like Ohio, and US federal issues), though arguments and proposed regulatory principles could be applicable internationally. | For Generative AI (like GPT-3/ChatGPT, which the paper discusses): Publicly available and proprietary large-scale textual data, including internet crawls (e.g., Common Crawl with websites like news outlets and social media like Reddit), historic books, and Wikipedia. Mixed unstructured text. | For Generative AI (like ChatGPT, which the paper discusses): Large language model development (transformer architecture), pre-trained on vast text corpora, followed by fine-tuning using supervised learning and Reinforcement Learning from Human Feedback (RLHF) including a reward model. | For Generative AI (like ChatGPT, which the paper discusses): Public release by OpenAI (e.g., ChatGPT initially free, GPT-4 paid), rapid user adoption, and integration into various commercial applications (e.g., search engines, productivity tools). | True | True | ChatGPT (discussed in the paper) is available for use through OpenAI's website, with a free version and paid access for more advanced models like GPT-4. | Technical gaps: AI inaccuracy, outdated knowledge, lack of true understanding/morality, and difficulty in creating unbiased AI. Societal/Ethical gaps: AI's lack of human emotional intelligence, risk of over-reliance by professionals, amplification of societal biases, and absence of clear regulatory frameworks. | For developers of Generative AI (like OpenAI with ChatGPT): Addressing 'alignment' issues such as inaccuracy, bias, and generation of harmful content despite extensive fine-tuning efforts; overcoming inherent limitations like sensitivity to input phrasing and verbosity. | Stagnation of legal development, ineffective legal representation due to AI's lack of human skills, entrenchment of systemic biases, generation of incorrect legal information ('hallucinations'), erosion of public trust in the legal system, and deskilling of legal professionals due to over-reliance on AI. |
| 28AALLSpectrum10.pdf | HeinOnline | Making the Justice Leap: Using Generative AI to Bridge the Literacy, Equity, Access, and Privilege Gaps for Self-Represented Litigants | This paper discusses the potential of generative AI (GenAI) to assist self-represented litigants (SRLs) in navigating the civil legal system, addressing literacy, equity, access, and privilege gaps. It proposes a conceptual GenAI tool named "Gideon" and calls for law librarians to advocate for SRLs and the ethical use of such technologies. | True | Idealistic | True | 1.0 | Positive | A conceptual GenAI tool named "Gideon" designed to assist self-represented litigants by leveraging a sophisticated language model trained on legal resources. Also, an advocacy strategy for law librarians to promote AI tools and SRL support. | NaN | NaN | Intimidating court procedures, confusing legal forms, unfamiliar legal jargon, complex judicial rules leading to case dismissals; insufficient legal aid resources and unaffordability of lawyers; SRLs' personal limitations in language fluency, digital proficiency, and social exclusion; restrictive Unauthorized Practice of Law (UPL) rules. | Develop a powerful GenAI tool (e.g., "Gideon") for pro se litigants, trained on extensive legal resources. Establish ethical AI guidelines through impact assessments and continuous outcome evaluations. Law librarians to advocate for SRLs and the use of AI by publishing articles in bar journals and promoting alternative legal service models. | Access to justice for self-represented litigants; legal information navigation; legal document drafting; understanding court procedures; overcoming literacy and digital divides; role of law librarians in promoting legal tech; addressing Unauthorized Practice of Law (UPL) concerns. | Self-represented litigants (SRLs), particularly those with modest or limited means, middle-income individuals who cannot afford an attorney, and those facing literacy, digital proficiency, or social exclusion challenges. | Civil law (explicitly mentions eviction, foreclosure, repossession, domestic violence, and child welfare cases). | United States (implied by discussion of U.S. legal aid, ULC, state bar magazines, and specific U.S. locations like Washington D.C. and Harris County, TX). | For the conceptual tool "Gideon": An extensive range of legal resources, including legal aid websites, primary law (statutes, case law), legal practice guides for various jurisdictions, form books, and other secondary legal sources. | N/A (Gideon is described as "purely conceptual in design"; no specific design methodologies for its creation are detailed). | N/A (Gideon is conceptual. The advocacy part suggests publishing articles in bar magazines as a diffusion strategy for ideas). | False | False | NaN | Limited empirical research on GenAI's impact, especially for SRLs; murky and divergent Unauthorized Practice of Law (UPL) definitions across jurisdictions hindering innovation; need for robust ethical guidelines for GenAI development and use by SRLs; societal gaps related to literacy, digital proficiency, access, and privilege affecting SRLs. | Managing fears and navigating regulations concerning Unauthorized Practice of Law (UPL) violations; developing and implementing clear ethical guidelines for GenAI use by SRLs (covering fairness, transparency, non-discrimination); ensuring the proposed GenAI tool is accessible and effective for users with limited language fluency or digital proficiency. | GenAI tools producing fabricated or incorrect legal information (e.g., fake case citations); potential for AI to engage in the Unauthorized Practice of Law (UPL); negative consequences for SRLs if AI tools are not properly designed, ethically guided, or effectively used; model UPL legislation being used to curtail rather than expand access to justice programs. |
| 28LegalWritingJLegalWriti (2).pdf | HeinOnline | WHAT SOCIAL SCIENCE CAN TEACH US REGARDING BRIEFING | This paper reviews social science research on effective legal briefing, primarily at the U.S. Supreme Court, identifying controllable factors such as coordination, writing style, and information types that influence judicial decisions. It also presents new quantitative analyses demonstrating that these controllable factors can significantly impact case outcomes and opinion content, while cautioning against overusing certain strategies. | True | Market | False | 3.0 | NaN | The paper's original contribution involves new quantitative analyses using multiple regression models (derived from the authors' prior work) on a large dataset of Supreme Court briefs. These analyses estimate the maximum cumulative impact of controllable briefing factors on case outcomes and opinion language similarity, and identify potential negative effects of overusing certain factors. | The new quantitative analyses are based on statistical models applied to a dataset of over 26,000 merit briefs from the U.S. Supreme Court (1984-2015). The impact of factors is assessed by changes in predicted probabilities (e.g., of winning) or similarity scores (cosine similarity between briefs and opinions), considering statistical significance (p-values). | Maximizing all controllable factors in briefing can increase the probability of winning by 0.47 (best-case scenario for uncontrollables) to 0.88 (worst-case for uncontrollables) and increase similarity to majority opinion language by up to 0.99 (cosine similarity). However, excessive use of certain factors, like >90 Supreme Court citations or readability outside a college graduate level (approx. grade 16-18), can diminish positive effects. | NaN | NaN | NaN | NaN | Appellate practice, Constitutional Law, Voting Rights Law (by example) | United States (primarily Supreme Court, with some discussion of other federal and state appellate courts) | For the new quantitative analyses: A dataset of over 26,000 litigant and amicus briefs from the 1984 to 2015 terms of the U.S. Supreme Court, along with related court opinions. Data and replication materials are stated to be publicly available via the authors' website. | Statistical modeling (multiple regression), computational text analysis (implicitly, as these feed into the variables used in the regression models, drawing from prior work which used tf-idf, cosine similarity, LIWC, readability indices). | Academic publication (journal article); findings are intended to inform legal practitioners and scholars. Data and replication materials for underlying research are stated to be available online. | True | True | Data and replication materials for the authors' analyses (from their prior book, used for the new analyses in this paper) are stated to be available at https://www.rachaelkhinkle.com/research.html. | NaN | The paper notes that estimates calculated at extreme data values (minimums/maximums for controllable factors) can be fairly imprecise due to infrequent observation of such values (footnote 203). | The paper identifies risks of 'going too far' with certain briefing strategies: excessive citations to Supreme Court precedent (over ~90), language clarity that is too simple (below college graduate reading level) or too complex, excessive technical language, and too much future-oriented language can diminish a brief's impact. It also briefly notes generative AI's risk of 'hallucinating' citations. |
| 16IntlInHouseCounselJ.pdf | HeinOnline | Legal Profession in an Age of Generative Artificial Intelligence* | This paper provides an overview of generative AI (GAI) and its implications for the legal profession, focusing on challenges such as confidentiality, intellectual property risks, the potential for "hallucinations" in tools like ChatGPT, and impacts on junior lawyer training. It presents suggested guardrails for the use of GAI in legal services, emphasizing human oversight, verification of AI-generated content, and the use of enterprise-grade, legally-customized AI systems. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | High cost of generative AI tools and required technical competency potentially worsening access to justice; risk of inaccurate AI-generated information harming self-represented litigants; GAI deepening existing inequalities in legal access; dehumanization of law and erosion of public trust if AI is improperly implemented; difficulties for vulnerable individuals in accessing and using technology for legal processes. | Development of specialized AI tools to provide basic legal information, procedural guidance, and form assistance for individuals with limited resources; courts exploring GAI for specific areas like small claims to assist self-represented litigants; ensuring AI development and deployment is human-centered, supporting fairness and public confidence in the justice system; fostering AI literacy among the public and legal aid providers. | Legal information for self-represented litigants, understanding legal rights and procedures, basic legal document assistance, improving court efficiency in areas like small claims, ethical use of AI in justice. | Self-represented litigants (pro se litigants), litigants with limited financial resources, vulnerable accused persons, individuals with limited technological access or literacy. | General litigation, personal injury, divorce, probate, criminal justice. | International (with specific examples and discussions pertaining to USA, Singapore, UK, Australia, Germany, and Canada) | NaN | NaN | NaN | False | False | NaN | Ensuring reliability and accuracy of GAI outputs (reducing "hallucinations"); establishing trust and effective verification methods for AI-generated legal information; making GAI tools affordable and equitably accessible; improving fundamental understanding of LLM capabilities and vulnerabilities; developing robust mitigations for security risks like prompt injection; establishing ethical deployment guidelines and bias assessment for LLMs in legal contexts; addressing the impact of AI on legal training and profession structure; maintaining a human-centered approach to justice with AI integration. | Maintaining client confidentiality and protecting intellectual property when using GAI tools; vulnerability of LLMs to prompt injection attacks; inherent inaccuracy and potential for "hallucinations" in GAI outputs requiring diligent verification; overcoming automation bias among legal professionals; managing the high cost of GAI tools and the need for specialized tech competency; adapting legal training and law firm structures to the automation of entry-level tasks. | Breach of client confidentiality and legal professional privilege; leakage of sensitive intellectual property; submission of fabricated case law or inaccurate legal arguments to courts; erosion of public trust in the legal system due to AI errors; vulnerability to cyber-attacks like prompt injection; deskilling of junior lawyers and disruption of traditional legal career paths; potential for AI to be used in unauthorized practice of law or for high-risk decision-making without adequate safeguards; deepening of the justice gap if AI benefits are not equitably distributed; dehumanization of the legal process. |
| 64HungJLegalStud435.pdf | HeinOnline | Large language models and their possible uses in law | This paper explores the potential applications of Large Language Models (LLMs) like ChatGPT in the legal field, focusing on enhancing access to law through tasks such as text retrieval, generation, and classification. It discusses LLM limitations, customization needs for legal uses, and proposes how LLM-based applications could democratize access to justice, exemplified by an experiment with a GPT-based chatbot for small law firms. | True | Idealistic | True | 3.0 | Positive | GPT-3.5 based chatbot for a small law firm using OpenAI API (as a demonstrative experiment). | An exploratory build of a demo chatbot for a small law firm. Functionality observed included its ability to provide information, limitations regarding accuracy without specific prompting, and customization through prompt engineering with examples. | The demo chatbot was found to be mainly useful for marketing and providing basic firm information in an engaging way, but not suitable for reliable legal advice, booking appointments, or handling complex queries due to potential inaccuracies and current limitations. | LLMs' lack of true understanding ('stochastic parrots'), inability to perform reality checks or offer empathetic counsel, potential for 'hallucinations' (providing incorrect information), and difficulties in handling up-to-date, jurisdiction-specific legal information without extensive customization. | Customization of LLMs through prompt engineering, providing in-context examples, fine-tuning, and integration with curated legal knowledge bases. Utilizing staged information retrieval for accessing relevant legal texts. Developing LLM-based applications to provide more accessible legal information (not advice) to the public. | Improving access to legal information and understanding for the general public; democratizing access to justice. | General public / laypeople. | General law, contract law, litigation (e-discovery), corporate law (due diligence). | International, with specific examples and a chatbot experiment contextualized for Hungary. | For the demo chatbot: Customization was done using prompt examples (question-answer pairs in English and Hungarian) fed to a pre-trained GPT-3.5 model via OpenAI API. The underlying GPT-3.5 model was pre-trained on a large, general corpus (primarily English internet text). | For the demo chatbot: Prompt engineering and few-shot learning (by providing examples within the prompt) using the OpenAI API with the GPT-3.5 model. | The demo chatbot was made available via a web-based interface. The paper suggests potential for deployment on messaging platforms (e.g., Telegram, Viber). | True | False | The demo chatbot was stated to be available via a URL (link8 in paper). The source code for the demo chatbot is available on GitHub (link7 in paper), allowing replication, but the underlying OpenAI API (e.g., for GPT-3.5) incurs usage fees and is not free. | Need for domain-specific benchmarks to evaluate LLM accuracy in legal tasks; lack of large-scale, multi-jurisdictional empirical studies on LLM reliability in legal practice; better methods to ensure and verify the factual and legal accuracy of LLM outputs in specific legal contexts. | Token limits of LLMs restricting the extent of customization via prompts; LLM 'hallucinations' if not carefully prompted with all necessary context; ensuring chatbot compliance with legal ethics and deontological rules; inaccuracies in multilingual outputs, especially for specific legal terminology in languages other than English. | LLMs providing incorrect or 'hallucinated' information (e.g., wrong contact details for a law firm). Users misinterpreting chatbot outputs as qualified legal advice. Potential for unauthorized practice of law if LLM-generated legal information is not reviewed by a qualified person. |
| 2024IntlJLEthicsTech186.pdf | HeinOnline | "Trustworthy AI" Cannot Be Trusted: A Virtue Jurisprudence-Based Approach to Analyse Who Is Responsible for AI Errors | This paper argues that humans, not AI, must be held responsible for AI errors because a genuine trust relationship with AI is impossible due to AI's lack of moral motivation and responsibility. It proposes that this human responsibility should be assigned to direct beneficiaries of AI products and vary according to the AI's risk level, advocating for technical authentication obligations for high-risk AI like deepfakes. | True | Idealistic | True | 3.0 | Neutral | Virtue jurisprudence-based approach to analyse who is responsible for AI errors. | NaN | NaN | High cost and difficulty in authenticating AI-generated evidence (e.g., deepfakes), potential for misuse ("liar's dividend" leading to skepticism about genuine evidence), impacting affordability of justice. | Mandating technical authentication for high-risk AI evidence, requiring a good-faith basis for deepfake claims, ensuring developers facilitate access to detection tools for defense, and imposing obligations on responsible entities to provide reliable identification. | Authenticity and admissibility of AI-generated evidence (deepfakes), procedural fairness, equitable access to technical expertise in legal proceedings. | Litigants (especially those with limited resources), defence lawyers, and the justice system as a whole, impacted by challenges of AI-generated evidence. | AI Law, Product Liability, Evidence Law, Criminal Procedure, Ethics in Law. | European Union (focus on EU AI Act), United Kingdom (mentions UK ETAF). Principles discussed have broader relevance. | NaN | NaN | NaN | False | False | NaN | Technological gap between AI generation (e.g., deepfakes) and detection capabilities. Need for further research on differentiating human obligations based on AI risk levels across various fields. Ensuring affordable and accessible authentication methods for AI-generated evidence. | Ensuring AI reliability, explainability, and trustworthiness; managing AI's autonomy and unpredictability; attributing moral and legal responsibility for AI errors. | Erroneous AI outputs causing harm; manipulation of individuals; undermining due process via deepfakes ('liar's dividend'); infringement on fundamental rights; erosion of cognitive trust in evidence ('seeing is believing'). |
| 81MdLRev557.pdf | HeinOnline | PRETRIAL DISPARITY AND THE CONSEQUENCES OF MONEY BAIL | This paper empirically analyzes over 23,000 misdemeanor bail decisions in Pima County, Arizona, revealing significant inter-judge disparities in assigning money bail and its amounts. It also investigates the causal effects of money bail on defendant outcomes like recidivism, finding context-specific effects that caution against one-size-fits-all bail reforms. | True | Idealistic | False | 2.0 | Neutral | Instrumental variable (IV) design using quasi-random assignment of bail judges to analyze administrative court data on misdemeanor bail decisions. | The IV design was evaluated through balance tests (ANOVA, randomization inference) to confirm quasi-random judge assignment. Causal effects of money bail were estimated using two-stage least squares (2SLS) regressions on outcomes including guilty pleas, guilty judgments, failure to appear, and recidivism (rearrest/reconviction over 6-24 months), with controls for case/defendant characteristics and time-fixed effects. | Defendants assigned money bail showed no statistically significant difference in guilty pleas, guilty judgments, or failure to appear in the preferred model. However, money bail was associated with a statistically significant 9.2 percentage point decrease in rearrest recidivism at 6 months; effects on reconviction and at longer horizons were less consistent or not significant. | High inter-judge disparity in bail setting (frequency, amount, racial bias); defendants' inability to pay bail leading to detention; insufficient understanding of pretrial processes and impacts of judicial discretion; risk assessment tools not effectively curbing discretion or disparity. | Inform judges of their bail-setting behavior relative to peers (as a 'nudge'); avoid one-size-fits-all reforms, tailoring them to local contexts; implement rigorous, context-specific policy evaluation, replication, piloting, and cross-jurisdictional reporting. | Bail reform; pretrial detention; judicial discretion and disparity (including racial and socioeconomic); impact of money bail on case outcomes and recidivism; access to justice in misdemeanor cases. | Defendants in misdemeanor cases in a mixed rural/suburban county (Pima County, AZ); implicitly targets low-income individuals and racial/ethnic minorities (Black, Hispanic/Latinx) affected by bail disparities. | Criminal law (pretrial procedures, bail) | Pima County, Arizona, United States (specifically, misdemeanor cases from outside Tucson city limits handled by the Consolidated Justice Court, with initial appearances in Tucson City Court). | Proprietary administrative court data from Pima County TCC and CJC (2014-2017) on over 23,000 misdemeanor initial appearances (IAs), including IA forms (hand-coded) merged with court records. Data includes defendant demographics, charges, timing, judge, bail outcomes, criminal history, and case dispositions. | For the empirical study: Quasi-experimental design exploiting as-if-random judge assignment. Data collection (court records, IA forms), hand-coding of PDF data, data merging, descriptive statistical analysis, balance testing (randomization inference, ANOVA), and causal inference using an instrumental variable (IV) approach with two-stage least squares (2SLS) regression. | NaN | False | False | NaN | Lack of robust understanding of inter-judge disparity mechanisms; insufficient research on bail in non-urban and misdemeanor contexts; limited evidence on risk assessment tools' efficacy in reducing disparity; need for clarity on incapacitation vs. deterrence effects of bail on recidivism; uncertainty about pre-COVID-19 findings' applicability post-pandemic. | Finding a suitable study venue with reliable data and appropriate institutional (e.g., quasi-random judge assignment) and procedural structures. Difficulty obtaining complete incarceration data. Challenges in data processing, such as merging diverse data sources (PDFs, administrative records) and consistently defining variables like IA charges from available date fields. | Inequities of the money bail system (e.g., detention of low-income individuals). Pervasiveness of racial and socioeconomic disparities in bail decisions. Ineffectiveness or negative impacts of 'one-size-fits-all' bail reforms. Potential for algorithmic risk assessment tools to maintain or exacerbate existing disparities rather than reduce them. |
| 6LawTechHum88.pdf | HeinOnline | Prompts and Large Language Models: A New Tool for Drafting, Reviewing and Interpreting Contracts? | This paper examines the use of Large Language Models (LLMs) and prompt engineering for drafting, reviewing, and interpreting contracts, exploring both their potential to enhance efficiency and accessibility for lawyers and non-lawyers. It analyzes the significant challenges, including inaccuracies, biases, lack of transparency, and the crucial legal implications, particularly concerning the parol evidence rule and the admissibility of prompts in contractual disputes. | True | Idealistic | True | 3.0 | Neutral | Large Language Models (LLMs) and prompt engineering for contract drafting, review, and interpretation. | Cites external studies such as the Allens AI Australian Law Benchmark (testing LLMs on Australian legal questions) and research on LLM performance in professional law tasks (Hendrycks et al.). | Cited studies indicate that even top LLMs are not consistently reliable for legal questions, may contain 'infection' from other jurisdictions' laws (Allens benchmark), and show low accuracy in professional law tasks (Hendrycks et al.). | Perpetuation of existing inequalities; generation of unfair, unconscionable, or market-distorting contracts; perpetuation of harmful stereotypes and discriminatory clauses; exacerbation of power asymmetries due to biased AI. | Careful curation of diverse and unbiased training data; transparency in algorithms; continuous monitoring and audits for bias; human oversight at critical junctures; responsible implementation focusing on fairness and ethical considerations; adapting legal doctrines like the parol evidence rule. | Improving accessibility and affordability of contract creation for non-legally trained individuals; ensuring fairness, equity, and non-discrimination in AI-generated legal documents; adapting legal doctrines to AI. | Non-lawyers and individuals without legal expertise seeking to understand or create contracts, as well as the legal profession generally. | Contract Law; Civil Procedure (specifically evidence and interpretation rules like the parol evidence rule). | Australia (primary focus for legal analysis like parol evidence rule), with references to developments in the US, EU, Singapore, China, and UNCITRAL. | The paper describes LLMs as being trained on large, generic text corpora, then fine-tuned on specialized datasets. For legal LLMs, this includes 'legalese' and potentially legal documents. Lexis+AI is mentioned as trained on 'Lexis authoritative primary and secondary materials'. Access to proprietary law firm data (client contracts, advice) for training professional law LLMs is noted as limited. | NaN | Discusses commercial deployment by legal tech companies (e.g., Lexis+AI available in US and Australia) and adoption by law firms, including on-premises models for data privacy. | True | False | Commercial products like Lexis+AI, Motionize, Robin.AI are mentioned as available from their respective vendors/companies. | Technical gaps include LLM inaccuracy, hallucinations, lack of nuanced legal reasoning, and 'black box' transparency issues. Societal/legal gaps include the unclear legal status of prompts, need for legal doctrine adaptation (e.g., parol evidence rule), ethical concerns (data collection, copyright, declaration of use), and ensuring effective human oversight to mitigate AI risks and biases. | Ensuring accuracy and avoiding 'hallucinations' in LLM outputs; addressing the lack of transparency in LLM decision-making processes; mitigating inherent biases from training data and model design; defining the legal status of prompts and their interaction with existing legal rules (e.g., parol evidence rule); managing client confidentiality and data privacy with LLM use; effectively training legal professionals in prompt engineering and critical AI assessment. | Generation of unfair, unconscionable, or market-distorting contracts; perpetuation of existing inequalities and harmful stereotypes; inclusion of discriminatory clauses; exacerbation of power asymmetries; 'hallucinations' (inaccurate outputs); 'stochastic parrots' (mindless repetition); biased interpretation; 'infection' by laws from irrelevant jurisdictions; manipulation of contract interpretation processes; lawyers citing LLM-fabricated non-existent cases. |
| 57SuffolkULRev345.pdf | HeinOnline | The Legal Ethics of Generative Al | This paper analyzes how existing legal ethics rules, particularly the ABA Model Rules of Professional Conduct, apply to lawyers' use of generative AI, arguing that careful use is permissible and may eventually become ethically required. It also critiques current judicial standing orders on AI use as overly broad or unnecessary, and briefly suggests AI's potential for enhancing access to justice. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | The general 'access-to-justice crisis' and the 'public's unmet legal needs'. | Utilizing generative AI's potential to answer legal questions, offer low-cost legal assistance, and broadly help serve the public's unmet legal needs. | Addressing unmet legal needs; Improving access to civil justice; Low-cost legal assistance. | The general public, particularly those with unmet legal needs. | Legal Ethics, Professional Responsibility, Civil Procedure | United States | NaN | NaN | NaN | False | False | NaN | The full potential of generative AI to bridge the access-to-justice gap is yet to be realized; tools are still evolving in capability and reliability for widespread A2J application. The transformation to effectively address the A2J crisis is an ongoing process. | Ensuring client confidentiality when using third-party AI tools; determining when to consult clients about AI use; fulfilling oversight responsibilities for AI-generated content (akin to nonlawyer assistance); proper billing for AI-assisted work; maintaining technological competence, including understanding AI's benefits and risks (hallucinations, bias). | Breach of client confidentiality if sensitive information is inputted into generative AI tools without adequate protections; generation of inaccurate or fictitious content ('hallucinations'), including citations; inherent biases in AI models influencing outputs; lawyers failing to adequately review or supervise AI-generated work product; potential for sanctions if AI use leads to filing false or misleading court documents. |
| 30IndJGlobalLegalStud293.pdf | HeinOnline | Robo Justice: Constitutional Issues with Judge AI | This paper explores the constitutional, ethical, and societal implications of Artificial Intelligence (AI) in judicial functions, referred to as "Judge AI". It advocates for a societal constitutionalism approach, reframing "justice" to prioritize human wellbeing, and proposes judge-led, ethically guided reforms to address the challenges posed by AI in the justice sector. | True | Idealistic | False | 3.0 | Neutral | Judge AI (as a general concept, encompassing supportive, replacement, and disruptive AI technologies in judicial decision-making) | NaN | NaN | Digital divide (limited access to technology, poor digital skills, low literacy); AI exacerbating disadvantages for vulnerable populations; focus on 'fast, low cost' justice potentially undermining true justice; lack of human empathy in AI decision-making. | Adopting societal constitutionalism; reframing the definition of 'justice' to include human wellbeing; judge-led reform guided by ethical frameworks; human-centered legal design; ensuring human judicial oversight and contestability of AI decisions. | Automation of judicial decision-making; ethical use of AI in the judiciary; access to justice through technology; quality of justice; judicial independence; constitutional implications of AI. | Vulnerable populations; vulnerable users of the justice system. | General justice system, Civil law, Criminal law, Administrative decision-making. | China, USA, EU, Australia, Singapore, Chile, New Zealand, International (global context). | NaN | Human-centered legal design; Judge-led reform; Values-based ethical framework development. | NaN | False | False | NaN | Need for more relatable and judge-specific ethical material for AI; challenges in maintaining or replicating nuanced legal reasoning with AI; effectively addressing the digital divide and ensuring technology serves human wellbeing; translating ethical principles into concrete, auditable AI system designs. | The 'black box' problem (opacity of AI decision-making); ensuring transparency, explainability, and fairness in AI systems; maintaining judicial independence against executive or corporate influence; addressing the digital divide and digital literacy issues; replicating human traits like empathy and nuanced judgment; developing robust ethical frameworks and ensuring their implementation; potential for de-skilling or over-reliance on AI by legal professionals. | Algorithmic bias leading to discrimination; erosion of judicial independence and separation of powers; overreach by tech companies ('digital Switzerlands'); societal harm beyond individual cases; undermining the rule of law; dehumanization of justice and violation of human dignity; increased disadvantages for vulnerable populations; undetected operation of biased or inappropriate AI; 'fast, low-cost' justice compromising actual justice. |
| 75SMULRev815.pdf | HeinOnline | Al, EQUITY, AND THE IP GAP | This paper argues for a deliberate, equity-focused approach to integrating AI into intellectual property (IP) law to promote social justice principles like access, inclusion, and empowerment. It explores how current IP doctrines and AI can perpetuate inequity and proposes solutions such as "equity by design" and equity audits. | True | Idealistic | False | 3.0 | Positive | Equity by design (as a guiding principle/approach); Equity audits (as a methodological process). | NaN | NaN | AI amplifying inequity via biased algorithms/data; trade secrets hindering accountability for biased AI; biases embedded in existing IP doctrines (patent, copyright); lack of diversity in AI development and the IP system; difficulty accessing representative and unbiased training data due to copyright and other barriers; opacity of AI decision-making (black box problem); potential for a two-tiered justice system; human over-deference to algorithmic decisions. | Implement "equity by design" principles in AI development for IP; conduct regular "equity audits" of AI systems; reform data governance for AI training (e.g., advocating fair use for copyrighted data, creating civil rights exceptions for data mining, anonymization); diversify the developer workforce and the IP ecosystem; reform IP doctrines to be more inclusive; increase AI transparency and explainability; introduce legal reforms for trade secrets (e.g., a social justice exemption or enhanced whistleblower protections) to allow scrutiny of algorithms; use AI to assist with adjudicating equitable IP doctrines. | Algorithmic bias in intellectual property law; equitable access to the IP system for underrepresented groups; transparency and accountability of AI in IP law and administration; fairness in IP adjudication; the impact of trade secrets and copyright law on developing and auditing equitable AI. | Women, racial minorities (e.g., Black individuals, Hispanics), individuals from lower-income backgrounds, small businesses, and pro se litigants. | Intellectual Property Law (Trade Secrets, Patent Law, Copyright Law, Trademark Law), Civil Rights Law, Criminal Justice (by way of example). | United States, Singapore, European Union | The paper discusses AI systems potentially trained on biased historical IP case law, registration data, and copyrighted content. It advocates for using broader, diverse, and representative datasets, including copyrighted works (accessed via fair use or specific exemptions) and personal data (with appropriate safeguards), to train less biased AI. | The paper advocates for 'equity by design' as a high-level approach, entailing proactive consideration of fairness, conducting bias impact assessments, diversifying development teams, and incorporating ethical reviews. It also proposes 'equity audits' as a methodology for verification and ongoing monitoring. | NaN | False | False | NaN | Lack of comprehensive solutions for AI-perpetuated inequity in IP law; insufficient diversity in AI development and the IP system; inadequate legal frameworks for AI accountability in IP (especially concerning trade secrets); need for better, accessible, and unbiased data for training fair AI; challenges in implementing and regulating effective equity audits; AI's limited ability to handle nuanced equitable legal judgments; societal complacency towards AI. | NaN | AI amplifying existing societal inequities and biases within the IP system; biased algorithmic outcomes in IP rights and enforcement; trade secrets shielding biased algorithms from scrutiny and accountability, hindering due process; creation of a two-tiered justice system; over-reliance on AI and automation bias reducing critical oversight; chilling effects on innovation and creativity if AI is trained on narrow/biased data or if copyright restricts data access; privacy violations from improper data handling; potential for sophisticated actors to game transparent AI systems. |
| 57IndLRev581.pdf | HeinOnline | FROM PIXELS TO PRESCRIPTIONS: THE CASE FOR NATIONAL TELEHEALTH LICENSING & AI-ENHANCED CARE | This paper argues for federal incentives, via Medicaid funding, to encourage states to adopt mutual recognition of out-of-state medical licenses for telehealth and expand the scope of practice for non-physician providers, particularly when enhanced by AI. This dual policy approach aims to modernize healthcare regulation to improve access, efficiency, and quality, addressing challenges highlighted by the COVID-19 pandemic. | True | Idealistic | False | 1.0 | Positive | A policy proposal for the federal government to use Medicaid funding bonuses to incentivize states to: 1) mutually recognize out-of-state medical licenses for telehealth services, and 2) expand the scope of practice for non-physician providers, based on competency and augmented by AI technologies. | NaN | NaN | Fragmented state-based medical licensing, inconsistent scope of practice laws hindering telehealth and workforce innovation, disproportionate negative impact on rural and underserved healthcare access, and resistance to reform from incumbent medical groups and state boards. | Federal incentivization of states (via Medicaid bonuses) to adopt mutual recognition of out-of-state medical licenses for telehealth and to expand non-physician provider scope of practice based on competency and AI enhancement, thereby modernizing the regulatory landscape. | Telehealth enablement, scope of practice reform for healthcare providers, improving healthcare access for underserved populations, reducing healthcare disparities, addressing physician shortages. | Rural communities, lower-income individuals, racial and ethnic minorities, uninsured patients, elderly and disabled individuals, and other disadvantaged groups with limited healthcare access. | Health Law, Administrative Law, Constitutional Law (federal spending power, federalism), Antitrust Law, Occupational Licensing Law. | United States (federal and state levels). | NaN | NaN | Proposed deployment through federal legislation (leveraging spending power via Medicaid) followed by state-level legislative and regulatory adoption of the incentivized reforms. | False | False | NaN | Continued fragmentation of state licensing impeding telehealth, restrictive scope of practice laws limiting healthcare workforce innovation, persistent healthcare access disparities for underserved populations if reforms are not adopted, and ongoing political resistance to modernizing healthcare regulation. | Anticipated political resistance from states and medical professional associations to the proposed reforms; ensuring the safety and efficacy of AI tools used by non-physician providers requiring ongoing oversight; addressing federalism concerns regarding federal influence on state policy; securing congressional appropriations for the incentive program. | Potential for telehealth to facilitate fraudulent billing (though evidence suggests this is proportionally rare); AI systems performing poorly if incorporating low-quality data or lacking proper oversight; physician apprehension about job displacement due to AI and expanded roles for non-physicians. |
| 19RutgersBusLJ70.pdf | HeinOnline | Caveat Lector: Large Language Models in Legal Practice | This paper critically examines Large Language Models (LLMs) in legal practice, arguing they lack genuine understanding, knowledge, and reasoning abilities despite their textual fluency. It warns legal professionals against overreliance on LLMs due to their propensity for hallucinations and the significant risks of generating incorrect legal information. | True | Market | True | 3.0 | Negative | NaN | NaN | NaN | LLMs' propensity to hallucinate and generate incorrect information, their lack of true understanding and reasoning, risk of overreliance by users (especially laypersons lacking legal expertise to vet outputs), and difficulty in establishing 'legal ground truth' for many legal questions. | NaN | The unsuitability and risks of current LLMs for providing reliable legal information or advice to laypersons, undermining the potential for democratizing access to justice. | Users without legal training / laypersons. | General legal practice including contract law, tax law, criminal law, consumer protection. | International | NaN | NaN | NaN | False | False | NaN | Technical gaps include LLMs' lack of true understanding, reasoning, common sense, reliable knowledge, inability to distinguish fact from fiction, and susceptibility to hallucinations. Societal gaps related to access to justice include the risk of misinformation for lay users and the difficulty for non-experts in evaluating LLM output. | NaN | Overreliance on LLM-generated text due to its fluency; generation of factually incorrect or nonsensical information (hallucinations) in legal services; misinformation for users without legal training seeking access to law; financial losses or lawsuits from inaccuracies; propagation of bias and falsehoods from training data; model collapse from training on synthetic data. |
| 22NwJTechIntellProp109.pdf | HeinOnline | REGULATING CHATBOT OUTPUT VIA INTER-INFORMATIONAL COMPETITION | This paper proposes a market-centered approach, emphasizing inter-informational competition, to evaluate AI chatbot content risks and regulatory strategies. It argues that market competition can mitigate many risks, reducing the need for extensive direct regulation, while suggesting tailored rules for market failures like privacy and copyright issues. | True | NaN | True | 1.0 | NaN | A market-centered regulatory approach focusing on inter-informational competition to evaluate and design regulations for chatbot content. | The approach is supported by a review of the history of regulating information and communications technologies (ICTs), analysis of market dynamics, and theoretical reasoning, rather than empirical testing or benchmarks. | The market-centered approach suggests that inter-informational competition can mitigate many chatbot content risks (e.g., harmful content, bias, misinformation), making some direct regulations (like mandatory prohibitions, licensure, data curation) unnecessary. It advocates for tailored regulations like transparency, traceability, and auditing for market failures (e.g., privacy, copyright), and specific liability considerations using risk-utility tests. | NaN | NaN | NaN | NaN | Information law, Competition law, Privacy law, Copyright law, Tort law, Administrative law | International (with specific examples and discussions related to US, EU, China, UK) | NaN | Historical analysis of ICT regulation, application of economic principles (market competition, market failure), legal analysis of existing and proposed regulations, and comparative regulatory analysis. | The proposed approach is disseminated via scholarly publication, aiming to inform policymakers and future research on AI regulation. | False | False | NaN | NaN | NaN | Content risks from chatbots: harmful content (e.g., hate speech, self-harm promotion), discrimination and bias, misinformation (hallucinations, reputational damage, influencing democratic processes), privacy disclosure (sensitive personal information), and copyright infringement. Risks of over-regulation: stifling innovation, harming market competition, creating entry barriers, market concentration, and regulatory capture. |
| 4JusCorpusLJ208.pdf | HeinOnline | Navigating Legal Advice through Al Chatbots | This paper discusses the growing trend of using AI chatbots like ChatGPT for legal advice, highlighting their current unreliability and the ethical and legal challenges involved. It compares chatbots to human lawyers, concluding that while AI holds future promise, professional legal counsel remains essential for accurate advice, especially given the lack of specific AI regulations in India. | True | Idealistic | True | 3.0 | Neutral | AI Chatbots for legal advice (e.g., ChatGPT, Law Bot Pro) | Referenced studies indicating inaccuracy of AI chatbots like ChatGPT for legal advice, an anecdotal case study (Roberto Mata v Avianca Airline) of misuse demonstrating unreliability, user query to ChatGPT, and developer acknowledgment of limitations for Law Bot Pro. | AI chatbots like ChatGPT are reported as "INACCURATE and PROBLEMATIC" for legal advice, capable of creating fake cases, and self-admittedly not a substitute for professional legal advice. Law Bot Pro is acknowledged by its developers to have limitations, suitable for understanding basic laws/rights but not for proper legal advice. | Inaccuracy and unreliability of AI, ethical concerns like bias in AI outputs, lack of accountability for AI-generated advice, and absence of specific legal regulations governing AI. | Emphasis on consulting human lawyers for reliable advice, development of clear accountability frameworks and specific AI regulations, and continued research and development for more accurate AI models. Pro-bono initiatives like 'Law Bot Pro' for basic legal information are also mentioned. | Access to legal information and advice, reliability of AI in law. | General public, particularly those seeking free or low-cost legal information and potentially underserved communities. | General | India (primary focus), with mentions of US and EU. | NaN | NaN | Law Bot Pro deployed as a free legal AI app; ChatGPT accessible via web platform. | True | True | ChatGPT is accessible via its website with a free tier. Law Bot Pro is described as a 'free legal Al app'. | Lack of AI accuracy and reliability for complex legal advice, absence of robust legal and ethical frameworks for AI governance, and user over-reliance or misunderstanding of current AI capabilities. | Ensuring accuracy and reliability of legal information provided by AI, mitigating AI bias, establishing accountability for AI-generated advice, and navigating the lack of specific AI regulations. | Provision of inaccurate or misleading legal advice by AI, professionals misusing AI leading to legal errors and reputational damage, propagation and reinforcement of societal biases by AI, and potential copyright infringement issues. |
| 34LegalEducRev183.pdf | HeinOnline | Persuasive Legal Writing Using Large Language Models | This paper investigates the ability of GPT-4 to produce long-form persuasive legal writing by comparing LLM-generated law school essays with student essays using a developed multi-step prompting method. The study found that GPT-4 produced passable, well-structured essays but performed worse than students, particularly in knowledge accuracy and critical analysis, while also highlighting LLM limitations like hallucinations and bias. | True | Market | True | 2.0 | NaN | GPT-4 for generating persuasive legal essays using a three-step method (Outline, Content, Concatenation) for long-form content, combined with iterative prompt engineering. | Comparison of four GPT-4 generated essays (on two Legal Theory exam topics) against four student-written essays. Essays were de-identified and graded by four experienced law school graders based on: Argument and Structure; Knowledge and Understanding; Critical Analysis and Original Reflection; and an overall grade. Grader comments were also analyzed, including sentiment analysis. | GPT-4 essays received a median grade of H3 (competent), while student essays received a median of H2B (good). GPT-4 output was relatively well-structured but lacked accurate knowledge and showed no elevated performance in critical analysis or originality. Grader feedback on LLM essays showed greater negative sentiment. | NaN | NaN | NaN | NaN | Legal Theory, Legal Writing, Legal Reasoning, Legal Education. | Australia (experiment context: University of Melbourne graduate law class). | GPT-4's training data is not publicly available. It likely includes a massive corpus similar to GPT-3's, web documents, Wikipedia entries (including on legal theorists), original texts by theorists, student essays, blogs, and academic articles on legal theory. It was pre-trained on a large text dataset and then fine-tuned using Reinforcement Learning from Human Feedback (RLHF) for dialogue. | Iterative prompt engineering was used to develop the prompts for the three-step long-form content generation method (Outline, Content, Concatenation). The process involved evaluating interim outputs against components of persuasive legal writing and adjusting prompts to steer the LLM. | NaN | True | False | The LLM used (GPT-4) is publicly accessible via OpenAI (potentially with costs). The multi-step prompting methodology for long-form content generation is described in the paper, allowing for attempted replication. | NaN | Generating high-quality long-form content (due to context window limits, difficulty in prompting for length while maintaining quality). Ensuring factual accuracy and mitigating hallucinations in LLM output. Steering the LLM's output to be relevant to specific (e.g., course) material without being overly prescriptive, which could deaden creativity or induce more hallucinations. Addressing the LLM's inherent biases, such as US-centricity. Difficulty in making definitive claims about LLM performance due to output variability and prompt sensitivity. | Factual inaccuracies and hallucinations (e.g., fabricating sources, mischaracterizing legal theories or theorists). Biases in LLM outputs (e.g., geographic bias towards US law, potential for gender and racial biases). Misuse by students for academic assessments, potentially undermining educational objectives. Abuse of process in legal practice if LLM-generated content with errors is submitted to courts. Lack of privacy and concerns over data input into LLMs, especially for privileged or sensitive information. Potential for LLMs to contribute to a 'legal monoculture' by oversimplifying or homogenizing legal understanding. |
| 61CalWLRev71.pdf | HeinOnline | INCORPORATING GENERATIVE ARTIFICIAL INTELLIGENCE INTO THE PRACTICE OF LAW: UTILIZING GENERATIVE AI WITHIN THE FRAMEWORK OF THE CALIFORNIA RULES OF PROFESSIONAL CONDUCT | This paper explores the integration of generative AI into legal practice, highlighting its potential for tasks like document drafting and legal research. It primarily focuses on the ethical obligations and professional conduct rules California lawyers must adhere to when using such AI, addressing issues like AI hallucinations, competence, confidentiality, and candor to the tribunal. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General legal practice, Professional ethics/conduct | California, USA | NaN | NaN | NaN | True | False | The paper discusses the use of generally available generative AI tools (e.g., ChatGPT) and commercial AI-enhanced legal research platforms (e.g., LexisNexis, Westlaw). | NaN | The paper discusses challenges for lawyers using generative AI, including: ensuring AI outputs are accurate and not 'hallucinations'; maintaining lawyer competence and critical analysis; safeguarding client confidentiality when using AI tools; communicating AI use to clients; upholding candor to the tribunal with AI-assisted filings; and properly supervising AI as a non-lawyer assistant. | Key risks identified include: AI generating inaccurate information ('hallucinations'); lawyers facing sanctions for submitting AI-generated falsehoods (e.g., Mata v. Avianca); violations of professional conduct rules regarding competence, confidentiality (e.g., client data training AI models), communication with clients, candor to courts, and supervision of AI; and potential for charging unconscionable fees if lawyers fail to adopt efficiency-enhancing AI in the future. |
| 114TrademarkRep880.pdf | HeinOnline | Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models | This paper systematically evaluates legal hallucinations in several large language models (LLMs) like ChatGPT 4, finding high hallucination rates (at least 58% for the best model). It also shows LLMs struggle with self-awareness of these errors and can be misled by incorrect user premises, cautioning against unsupervised LLM integration in legal tasks. | True | Idealistic | True | 2.0 | Negative | Evaluation of OpenAI's ChatGPT 4, OpenAI's ChatGPT 3.5, Google's PaLM 2, and Meta's Llama 2 for legal hallucination, contra-factual bias, and calibration. | A custom set of 14 legal knowledge queries (grouped into low, moderate, and high complexity) applied to a stratified random sample of 5,000 cases from each level of the US federal judiciary (SCOTUS, USCOA, USDC). Evaluation used reference-based methods (comparing LLM output to ground-truth metadata) and reference-free methods (detecting self-contradictions in LLM outputs, with contradictions assessed by GPT-4). | LLMs hallucinated frequently, with ChatGPT 4 being the best performing yet still hallucinating 58% of the time on reference-based tasks. Hallucinations varied by task complexity, court hierarchy, jurisdiction, case prominence, and case year. LLMs also showed susceptibility to contra-factual bias and imperfect calibration (overconfidence in incorrect answers). | Unreliability of LLMs due to high hallucination rates and uneven knowledge distribution (e.g., disfavoring lower courts, less prominent jurisdictions, older and very new cases). LLMs' susceptibility to misleading prompts (contra-factual bias) and poor self-awareness of errors (calibration) also disproportionately affect under-resourced litigants. | The paper primarily highlights problems but suggests improved user education and transparent design choices by developers regarding hallucination trade-offs. It emphasizes caution, human-centered AI, and continued research rather than proposing direct technical fixes for access to justice. | Reliability of legal information from LLMs, accuracy of legal knowledge representation in LLMs, suitability of current LLMs for providing legal assistance to pro se or under-resourced litigants, assessing factual accuracy in AI-generated legal content. | Pro se litigants and under-resourced litigants. | General American case law (federal). | United States (federal courts: SCOTUS, US Courts of Appeals, US District Courts). | The paper evaluates existing general-purpose LLMs (ChatGPT, PaLM 2, Llama 2) presumed to be trained on vast, general corpora which include publicly available American case law. The paper itself does not train a new model. | Development of a question-answering (QA) framework with 14 legal knowledge queries across three complexity levels; stratified random sampling of US federal court cases for test data; use of both reference-based (comparison to ground-truth metadata) and reference-free (self-contradiction detection) evaluation methods. | N/A (The paper evaluates existing publicly available or accessible LLMs; it does not deploy a new tool). | True | True | The paper evaluates several LLMs: ChatGPT 4 & 3.5 (via OpenAI API, commercial), PaLM 2 (via Google API, commercial), and Llama 2 (open-source, e.g., Llama-2-13b-chat-hf implying HuggingFace availability). The authors' evaluation dataset and replication materials are also openly available on Harvard Dataverse and HuggingFace. | Significant gaps in LLMs' legal knowledge, particularly for less prominent jurisdictions/cases, complex legal reasoning, and very old/new cases. Methodological limitations in current hallucination mitigation techniques. Lack of normative clarity and transparency from developers on handling hallucination trade-offs and ensuring equitable performance. | Constructing a comprehensive and robust evaluation framework for legal hallucinations, including creating ground-truth data for diverse legal queries across different judicial levels. Developing reliable methods for reference-free evaluation, which currently only establish a lower bound on true hallucination rates. | Providing harmful/inaccurate legal advice or decisions; worsening disparities in legal service availability; creating legal monoculture by entrenching biases and eroding legal nuance; misguiding users (especially pro se litigants) who input flawed premises or are overconfident in LLM outputs; potential for new forms of tort liability from AI-generated falsehoods. |
| 7Issue5IntlJLMgmtHuman.pdf | HeinOnline | Judicial Reforms and Access to Justice: A Comparative Analysis of E-courts and Technological Integration in India and Singapore | This paper comparatively analyzes judicial reforms and technological integration, particularly e-courts, in India and Singapore, focusing on their impact on judicial efficiency, transparency, access to justice, and public trust. It evaluates the successes and challenges in both nations, offering insights on how technology and ADR can transform judicial systems for timely and equitable justice. | True | Idealistic | False | 3.0 | Positive | E-courts, e-filing, virtual hearings, National Judicial Data Grid (NJDG), e-Litigation systems, AI-based translation tools (e.g., SUVAS), online dispute resolution platforms, digital transcription systems, criminal case filing and management systems (e.g., ICMS), online legal information portals, self-help tools (e.g., DIY questionnaires), planned AI-powered virtual assistants and outcome simulators. | NaN | NaN | Case backlogs; inefficient judicial systems; unequal access to justice due to cost, complexity, and resource limitations for vulnerable groups; digital divide and varying digital literacy; resistance to change within the judiciary; inadequate infrastructure; cybersecurity and data privacy concerns; challenges in adapting traditional legal processes to technology; lack of standardization and interoperability; need for continuous technological upgrades and updated legal frameworks. | Digitization and streamlining of judicial processes through e-courts and integrated platforms; introduction of e-filing and virtual hearings; development of national judicial data grids; adoption of comprehensive e-litigation systems; utilization of AI for tasks like translation and decision support (e.g., outcome simulators); promotion of Alternative Dispute Resolution (ADR) and Online Dispute Resolution (ODR); provision of online access to legal information, self-help kits, and DIY tools; investment in technology, training, and infrastructure; fostering a supportive legal and regulatory environment; enhancing public awareness and ensuring equitable digital access. | Judicial efficiency, case backlog reduction, transparency in judicial processes, accessibility of justice, public trust in the judiciary, online dispute resolution, access to legal information, self-help legal resources, technological integration in courts. | General public, litigants (including litigants-in-person), special groups, people with limited resources, individuals in rural and remote regions, populations with low digital literacy or low income. | General court procedures, Civil Law, Criminal Law, Family Law. | India, Singapore | For AI tools like SUVAS (India): English judicial documents, orders, and judgments. For planned Outcome Simulator (Singapore): Information from previous cases. Specific details on data sources, structure, or public/proprietary nature are not provided. | NaN | Government-led initiatives and judiciary-managed systems such as the e-Courts Mission Mode Project in India (guided by the Supreme Court's e-Committee) and Singapore's integrated e-Litigation and ICMS systems (accessible via SG Courts). Services delivered through online portals, mobile applications, and other digital platforms. | False | False | NaN | Significant infrastructural disparities between regions (especially in India); varying levels of digital literacy; high costs associated with IT infrastructure development and maintenance; need for adaptable strategies considering regional differences; challenges in creating and sustaining a favorable legal and innovation-friendly environment; ensuring equitable distribution and access to digital resources; balancing technological advancements with the human elements of justice. | Digital divide and accessibility issues; cybersecurity and data privacy vulnerabilities; potential over-reliance on technology leading to system failures; difficulties in adapting all legal processes for digital environments; the need for continuous technological upgrades and associated resource demands; resistance to change from traditional judicial practices; achieving standardization and interoperability across diverse court systems (especially in India); ensuring scalability and robust maintenance for large volumes of cases; aligning legal and regulatory frameworks with technological advancements. | Cybersecurity threats including data breaches, unauthorized access, and misuse of sensitive legal information; disruption of justice delivery due to technological failures or system vulnerabilities if overly reliant on technology; exacerbation of inequalities if digital divide issues are not addressed, potentially excluding certain population segments from accessing justice fairly. |
| 25CardozoJConflictResol44.pdf | HeinOnline | An Information Flow Model of Online Mediation: Jeopardizing Privacy and Autonomy in the Shadow of Innovation | This paper explores how the digital transformation of mediation, particularly through online platforms and AI, impacts parties' rights to self-determination and privacy. It proposes an "information flow model" to analyze these risks and suggests normative measures to mitigate them, focusing on parties' control over information communication and analysis. | True | Idealistic | False | 1.0 | Negative | An Information Flow Model of Online Mediation (a conceptual/analytical model). | NaN | NaN | Undermining of participant autonomy and control over the mediation process and personal information. Breach of confidentiality and privacy essential for fair dispute resolution. Lack of transparency and potential biases in AI-driven decision support, impacting informed consent and fairness. Inadequate legal and ethical frameworks to govern online mediation platforms and AI use. | Establish comprehensive legal/regulatory frameworks for online mediation platforms, including duties of transparency, fairness, and upholding party autonomy. Enhance mediator obligations for ethical use of technology, including informed consent processes regarding technological risks. Promote development and adoption of robust ODR standards and accreditation for platforms. | Privacy, party autonomy (self-determination), confidentiality in online dispute resolution/mediation. | NaN | Civil and Commercial matters | USA and European Union | NaN | NaN | NaN | True | False | The information flow model is described in the paper and can be conceptually applied by readers who have access to the paper. | Regulatory gap: Mediation norms do not adequately govern digital platforms or their duties concerning information processing, party autonomy, and neutrality. Limitations of current privacy laws: Consent mechanisms are often ineffective for true user control. Lack of widely adopted, enforceable, and detailed ODR standards. Need for greater transparency and explainability of AI systems used in mediation. | NaN | Loss of control over personal information and decision-making in mediation due to platform design and AI. Unauthorized disclosure or use of sensitive mediation communications by platforms or integrated third-party AI. Manipulative or erroneous AI-driven analyses influencing parties unfairly. Creation of detailed user profiles by platforms through data aggregation for purposes beyond mediation. Automation bias leading to undue reliance on AI suggestions. |
| 109MinnLRev147.pdf | HeinOnline | Lawyering in the Age of Artificial Intelligence | This paper reports on a randomized controlled trial assessing GPT-4's impact on law students completing legal tasks. Results show AI significantly increased speed and slightly, inconsistently improved work quality, especially for lower-skilled students, with participants reacting positively to AI assistance. | True | Market | True | 2.0 | Positive | Human-AI collaboration using GPT-4 for legal task completion, with training emphasizing active lawyering skills and provision of relevant legal sources to the AI. | Randomized controlled trial with 59 law students performing four legal tasks (drafting a complaint, contract, employee handbook section, client memo) with or without GPT-4. Outcomes were blind-graded for quality and timed for speed. | Access to GPT-4 slightly and inconsistently improved work quality (e.g., +0.24 on a 4.0 scale for contract drafting) but induced large and consistent speed increases (e.g., -32.1% time for contract drafting). Lower-skilled participants saw the largest quality improvements. | High cost and inefficiency of traditional legal services. Potential for judicial policies to restrict AI use, hindering its benefits for access to justice. | Adoption of generative AI by lawyers to improve efficiency and reduce costs. Judges should avoid overly restrictive policies on AI use to allow its benefits for justice to be realized. | Increasing efficiency and affordability of legal services. | NaN | Litigation (complaint drafting), Contract Law, Employment Law, Tort Law (product liability). | United States (Federal, Minnesota, Ohio referenced in tasks). | NaN | NaN | NaN | True | False | GPT-4 is accessible via a paid ChatGPT Plus account or API from OpenAI. | Need for facilitative judicial/institutional policies regarding AI. Further development from general-purpose AI (like GPT-4 studied) to more specialized and reliable legal AI tools. Better understanding of higher-order impacts of AI on the legal market and access to justice. | Methodological limitations in the study (e.g., sample size, specific participant pool, simplified tasks). Technical limitations of the GPT-4 version available at the time of the study (e.g., context window, potential for hallucinations). Ensuring effective human oversight and critical engagement with AI outputs. | Reliance on inaccurate or fabricated AI-generated content (hallucinations). Breaches of client confidentiality if sensitive information is input into non-secure AI systems. Potential for AI to homogenize legal work and reduce creativity. Students over-relying on AI, hindering development of core legal skills. Ethical violations if AI use is not managed responsibly. |
| 3JFreeSpeechL589.pdf | HeinOnline | WHERE'S THE LIABILITY IN HARMFUL Al SPEECH? | This paper examines potential U.S. legal liability (defamation, speech integral to crime, wrongful death) for harms caused by generative AI speech, considering Section 230 immunity. It analyzes how different AI design choices (e.g., pretraining, retrieval-augmentation, RLHF) influence these legal outcomes, arguing against categorical immunity and for legal frameworks that incentivize safer AI development. | True | Idealistic | True | 2.0 | Negative | Generative AI model design and mitigation strategies (e.g., pretraining, extractive/abstractive methods, retrieval-augmentation, RLHF, inference-time processing, uncertainty communication). | NaN | NaN | AI systems providing inaccurate, misleading, or hallucinated information when used for legal guidance, potentially leading to severe negative consequences for vulnerable individuals (e.g., immigrants, asylum seekers) who lack alternative means of legal support. | The paper proposes general legal and technical solutions for mitigating harmful AI speech overall (e.g., clarifying liability, developing best practices for AI design, safety mechanisms). These could indirectly improve AI reliability in A2J contexts by making AI safer and more accountable, rather than proposing specific A2J-focused solutions. | The use of AI for providing legal information and assistance to laypersons, particularly in contexts like immigration and asylum proceedings where individuals may be underserved. | Immigrants and asylum seekers relying on AI for legal or procedural assistance. | Torts (defamation, wrongful death, personal injury, aiding and abetting), Criminal Law (speech integral to criminal conduct), Communications Law (Section 230), First Amendment Law. | United States | Large-scale, diverse datasets primarily from web crawls (e.g., CommonCrawl, C4), books, legal documents (e.g., CourtListener), and proprietary data curated by model creators including human-generated instructions and feedback. | The paper discusses various AI design methodologies such as unsupervised pretraining, supervised fine-tuning (instruction tuning), reinforcement learning (RLHF), retrieval-based methods, extractive/abstractive generation, and inference-time interventions. | Discusses deployment of generative AI as online services (e.g., APIs, chatbots like ChatGPT, Bard), integration into existing platforms (e.g., search engines), and potential for offline on-device models. Also mentions plugins. | True | True | Publicly accessible models like ChatGPT and Bard (embodying discussed techniques). Some discussed foundation models (e.g., Llama, Dolly) and datasets (e.g., Pile of Law) are open-source. | Technical gaps in AI reliability and factuality for legal applications. Societal/legal gaps in accountability frameworks for harms caused by AI in A2J contexts and lack of safeguards for vulnerable users. | Technical challenges in ensuring AI-generated speech is safe, factual, unbiased, and robust against misuse (e.g., mitigating hallucinations, harmful instructions, jailbreaking). Difficulties in scaling human oversight, avoiding negative side-effects of safety mechanisms (e.g., reduced accuracy, poor uncertainty calibration), and the high cost of some interventions. | Generation of defamatory falsehoods, instructions for harmful/illegal acts (e.g., weapons, crimes, self-harm), biased or manipulative content, disinformation, malware, and inaccurate advice leading to real-world harms (e.g., physical injury, negative legal outcomes). |
| 11RevEurolatinDerAdm1.pdf | HeinOnline | The Implementation of AI Systems in the Colombian Justice: The Constitutional Court and the Council of State | This paper details the implementation of AI systems, Pretoria in Colombia's Constitutional Court for tutela (judicial protection action) selection and two pilots in the Council of State for jurisprudential analysis, aimed at enhancing judicial efficiency and access to justice. It discusses the development process, challenges including adapting legal culture, achieved results in processing time, and risks related to AI in the justice system. | False | Idealistic | False | 2.0 | Positive | AI systems (Pretoria for the Constitutional Court, and two pilots for the Council of State) using Machine Learning (classification, topic modeling) for case selection, jurisprudential analysis, and drafting assistance. | Pretoria: Trained on 2500 tutelas, then refined with 7 datasets; evaluated on 33 selection criteria, audited by jurists. Council of State pilots: Proof of concept using judicial sentences and administrative acts; evaluated on speed and accuracy in detecting legal conflicts/assisting sentence drafting in specific legal areas. | Pretoria achieved 80% reliability in implementation for tutela selection, identifying 33 criteria and automating summary generation in seconds, reducing case processing time from 36 minutes (manual average) to seconds. Council of State pilots demonstrated ability to detect brand conflicts in 7 minutes and assist non-experts in drafting sentence models in 8.5 minutes. | High judicial backlog and inefficiency; lack of transparency and objectivity in traditional processes; organizational and legal cultures resistant to change; limited resources for technological adoption. | Implementation of human-supervised, 'white-box' AI systems to enhance efficiency, transparency, and consistency in judicial tasks. This includes AI for case selection (Pretoria), jurisprudential analysis, and drafting assistance, combined with efforts to evolve legal culture and organizational processes, and ensure human oversight and data protection. | Improving judicial efficiency, case selection for review (tutelas), analysis of jurisprudence (unification sentences), drafting judicial decisions, effective judicial protection, access to justice. | General public/citizens, particularly those seeking protection of fundamental rights (e.g., health rights, due process) and those in vulnerable situations (e.g. extreme poverty, minors). | Constitutional Law, Administrative Law, Health Law, Intellectual Property Law, Electoral Law. | Colombia | Pretoria: Initially 2500 decided and published tutela cases from the Colombian judicial branch; later, 7 datasets of judicial sentences. Council of State pilots: Judicial sentences (e.g., 1300 industrial property sentences) and administrative acts from the Council of State and other Colombian public entities (e.g., SIC). All data is domain-specific, unstructured (text), and sourced from the courts. | Collaborative co-design involving jurists (university researchers, court magistrates/staff) and AI experts (IALAB). Iterative development process including pilot/prototype stages, training data creation from existing legal documents, collaborative definition of criteria, testing, and auditing by legal experts, with a focus on 'white-box' (auditable and explainable) AI. | Pretoria: Implemented in the Colombian Constitutional Court, with dedicated technical staff and funding from private sector. Council of State Pilots: Developed as proof-of-concept pilots and presented to the court, but full-scale implementation and extension were not achieved due to funding constraints and lack of broader internal support within the Council of State. | False | False | NaN | Need for robust 'white-box' AI governance and auditing mechanisms; secure data management protocols specific to the judiciary; comprehensive training of legal professionals in AI; development of national and international legal/ethical regulations for AI in justice. Overcoming financial barriers and internal resistance for wider AI adoption in judicial bodies remains a gap. | Acquisition, structuring, and normalization of large volumes of legal data; training AI models to understand complex legal nuances and human situations; defining and agreeing upon relevant classification criteria collaboratively; adapting entrenched legal practices and organizational cultures to new technologies; securing sustained funding and internal buy-in for full implementation and scaling of AI projects; ensuring interoperability with existing judicial IT systems. | AI overlooking complex human situations if not adequately trained on nuanced criteria; potential violations of human rights (dignity, privacy, good name, right to be forgotten); misuse of data for citizen profiling or predictive sentencing; perpetuation or amplification of existing biases through algorithmic bias; lack of transparency and accountability with 'black-box' AI systems; data security breaches if data is not managed in secure, court-controlled environments. |
| 2023UIllLRevOnline165.pdf | HeinOnline | RAGE AGAINST THE MACHINE: WHO IS RESPONSIBLE FOR REGULATING GENERATIVE ARTIFICIAL INTELLIGENCE IN DOMESTIC AND CROSS-BORDER LITIGATION? | This paper analyzes which public and private bodies are best suited to regulate the use of generative AI in domestic and cross-border litigation, focusing on identifying who should act rather than proposing specific regulatory content. It suggests a multi-faceted approach involving courts, licensing authorities, legislatures, and international bodies to address generative AI's challenges to the justice system. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Generation of false or misleading legal information by AI (hallucinations, misinterpretations); lack of reliability of AI-generated documents; erosion of public trust in the justice system; concerns regarding due process and procedural fairness; shifting of costs and burdens to other litigation participants; lack of transparency in AI-generated content. | Establishment of clear, agile, and comprehensive regulatory frameworks in a phased manner (rules of court, rules of professional responsibility, legislation); involvement of various domestic (judicial, legislative, licensing authorities, research institutions) and international bodies (e.g., Hague Conference, UNIDROIT, IBA); conducting empirical and policy-oriented research to inform regulatory content; ensuring accountability for AI use. | Ensuring procedural fairness and due process; maintaining the integrity of legal information and court proceedings; upholding public confidence in the justice system; establishing accountability for AI-generated content in litigation. | Pro se litigants | Civil litigation, Criminal litigation, Cross-border litigation, Procedural law | United States, Canada, UK, EU, China, International | NaN | NaN | NaN | False | False | NaN | Lack of comprehensive, proactive, and agile regulatory frameworks for generative AI in litigation; insufficient technical safeguards within AI tools to ensure accuracy and reliability for legal use; need for more empirical and policy research to define appropriate regulatory content; risk of regulatory inertia or uncoordinated piecemeal responses. | NaN | Erroneous legal outcomes due to AI hallucinations and misinterpretations; violations of due process and procedural fairness; erosion of public confidence in judicial systems; increased litigation costs and burden-shifting; unreliability of AI-generated legal documents; improper delegation of judicial authority; potential for misuse by pro se litigants leading to system burdens; incompetent or unethical use by legal professionals. |
| 2023IntlJLegalSocOrd400.pdf | HeinOnline | DIGITAL SINGLE MARKET: CONSUMER PROTECTION RULES IN THE DIGITAL SERVICES ACT* | This paper analyzes the European Union's Digital Single Market framework, focusing on consumer protection rules established by the Digital Services Act (DSA) and Digital Markets Act (DMA). It discusses how these regulations aim to create a safer, more transparent, and fairer digital environment for EU citizens and businesses by addressing issues like illegal content, platform accountability, and fair competition. | True | Idealistic | False | 3.0 | Positive | Digital Services Act (DSA) and Digital Markets Act (DMA) as regulatory frameworks. | NaN | NaN | Spread of illegal content and goods, harmful online practices by platforms, lack of transparency regarding algorithms and content moderation, systemic risks to users' fundamental rights, ensuring fair competition in digital markets. | Implementation and enforcement of the Digital Services Act (DSA) and Digital Markets Act (DMA), including transparency requirements for digital service providers, user complaint mechanisms, prohibition of certain harmful practices, risk assessment and mitigation obligations for very large online platforms (VLOPs), and enhanced supervision and enforcement powers for national regulatory authorities. | Consumer protection in the digital single market, regulation of online platforms, content moderation, protection of fundamental rights online, fair competition in digital markets. | EU consumers | Consumer law, EU law, Digital law, Internet law, Competition law | European Union | NaN | NaN | NaN | True | True | The Digital Services Act (DSA) and Digital Markets Act (DMA) are EU regulations. The DSA Transparency Database, mentioned as part of DSA compliance, is publicly accessible and its source code is publicly available. | Lack of systematisation at the EU legislative level concerning the interplay between various consumer protection directives and the DSA. The need to better link DMA provisions with other legislation to more actively empower consumers beyond being passive beneficiaries. | NaN | Dissemination of illegal content and goods, harmful or fraudulent online activities, systemic risks to users' fundamental rights from platform operations, opacity in AI decision-making processes, vulnerability of AI and IoT products to cyber threats. |
| 27PotchefstroomElecLJ1.pdf | HeinOnline | Non-Educator Stakeholders and Public-School Principals' Views on the Proposed Amendments to the South African Schools Act 84 of 1996 | This paper discusses proposed amendments to the South African Schools Act 84 of 1996, focusing on changes to school admission and language policies, and the potential recentralisation of power to the Department of Basic Education. It presents mixed views from school principals and other non-educator stakeholders gathered through qualitative research, highlighting concerns and support for the Basic Education Laws Amendment (BELA) Bill. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Discriminatory school admission and language policies; Dysfunctional school governance (SGBs) in some schools; Mismatch between home language and language of instruction; Lack of capacity in schools and by officials, shortage of school places; Political agendas influencing legislative changes. | The proposed BELA Bill aims for recentralisation of power over admission/language policies to Heads of Department (HODs) to ensure fairer access. The paper also cites a model for differentiated school autonomy based on context, as an alternative to blanket recentralisation. | Access to basic education; Non-discriminatory school admission policies; Language rights and language in education policies; Equitable school governance and learner placement. | Black learners; Learners requiring English-medium instruction; Communities with limited access to local schools due to restrictive policies. | Education Law, Constitutional Law (right to basic education, language rights, equality, non-discrimination) | South Africa | NaN | NaN | NaN | False | False | NaN | The BELA Bill's 'one-size-fits-all' recentralisation may not suit all schools, potentially harming functional ones; Need for contextually intelligent approaches to school governance and reform; Potential for government incompetence or political agendas to undermine legislative intent; Failure of education departments to fulfill existing duties like ensuring sufficient school places. | NaN | Erosion of democratic school governance and community participation; Regression to a centralised, potentially inequitable, education system; Undermining school autonomy, creativity, and innovation; Impractical implementation and potential for HOD overreach or politically motivated decisions without local context; Legislative changes driven by political agendas or to circumvent past court rulings; Increased tensions over language policies if imposed without considering local needs and resources. |
| 25NCJLTech495.pdf | HeinOnline | ARTIFICIAL INTELLIGENCE, TRADE SECRETS, AND THE CHALLENGE OF TRANSPARENCY | This paper argues that AI system designers should be able to hold trade secret rights in AI algorithms even if they cannot fully articulate how those algorithms operate, but asserting misappropriation requires describing the algorithm in detail. It also explores how AI developers can comply with transparency regulations while protecting their intellectual property, cautioning against an overly broad assertion of trade secrets. | True | Market | False | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Trade Secret Law, Intellectual Property Law, AI Regulatory Law | United States | NaN | NaN | NaN | False | False | NaN | NaN | NaN | The paper identifies several risks: difficulty for AI system owners to protect AI-developed algorithms as trade secrets if their workings are unknown or cannot be described sufficiently for litigation; tension between AI transparency disclosure obligations to regulatory bodies and the preservation of trade secrets; companies adopting an overly maximalist approach to trade secret designation, which can be counterproductive to transparency goals and legally incorrect; the tendency to favor trade secret protection over patents for AI algorithms (due to patent eligibility uncertainties or description difficulties), potentially reducing public disclosure and broader innovation; opacity in complex AI supply chains making it difficult for end-product manufacturers to meet transparency requirements. |
| 96UColoLRev549.pdf | HeinOnline | JUDICIAL ECONOMY IN THE AGE OF AI | This paper argues that AI's potential to enhance access to justice may paradoxically strain judicial economy through a litigation boom. It advocates for the proactive integration of AI tools into the judicial system itself to scale up legal processes, rather than resorting to restrictive 'legal thermostats'. | True | Idealistic | True | 3.0 | Positive | Proactive integration of AI tools (e.g., for document summarization, document Q&A, generative interpretation) into the judicial process. | NaN | NaN | High costs of legal services, limited legal consciousness hindering individuals from recognizing or pursuing claims (the 'naming, blaming, claiming' deficit), and difficulties in navigating legal procedures and accessing legal information/strategy. | Proactive integration of AI tools (e.g., for document summarization, Q&A, generative interpretation) throughout the judicial process. This aims to increase the productivity of judges and court staff, enabling the legal system to handle increased caseloads without sacrificing substantive rights. | Overcoming general civil legal problems by reducing costs, improving legal consciousness (naming, blaming, claiming), and assisting with legal strategy and document production. | Low-income individuals and ordinary people facing access to justice barriers. | General civil litigation, including contract law, tax law, torts, prisoner's rights, employment discrimination, and civil procedure. | United States | NaN | NaN | NaN | True | True | Publicly available LLMs (e.g., ChatGPT, Gemini, Claude) can be used for tasks like document summarization, Q&A, and generative interpretation as discussed for integration. | Current AI systems' unreliability (including hallucinations) and confidentiality concerns. Societally, ensuring AI adoption truly benefits access to justice for all, managing risks of misuse (e.g., for unmeritorious litigation), and the need for rigorous testing and understanding of system constraints. | Overcoming technological uncertainties, unreliability (including hallucinations), and confidentiality issues of AI systems. Addressing judicial skepticism, ensuring fairness, and managing the potential for AI to obscure meritless claims by making them appear more plausible. Developing appropriate legal standards for AI use. | Increased caseload overwhelming the judicial system, leading to the erosion of substantive rights through 'legal thermostats'. Rise in abusive or unmeritorious litigation. Reliance on AI-generated 'hallucinations' in legal filings. Negative impacts on the nature of adjudication if AI is carelessly integrated into opinion drafting. Confidentiality breaches with judicial use of cloud-based AI. |
| 5LawTechHum24.pdf | HeinOnline | When Art Becomes a Lemon: The Economics of Machine-Enabled Artworks and the Need for a Rule of Origin | The paper analyzes the 'lemons problem' in the art market due to indistinguishable AI-generated and human-made art, potentially devaluing human artistry. It proposes implementing a 'rule of origin,' analogous to trade law's substantial transformation test, to label artworks by human/machine contribution, fostering transparency and fair valuation. | True | NaN | True | 1.0 | NaN | A 'Rule of Origin' for machine-enabled artworks, potentially using a 'substantial transformation test' (analogous to trade law) and a tiered system based on human input, to determine authorship origin (human-made vs. machine-enabled). | NaN | NaN | NaN | NaN | NaN | NaN | Copyright Law, Intellectual Property Law, Art Law, Economic Law (market regulation), International Trade Law (by analogy for rules of origin), EU Law (AI Act). | European Union (referenced for rules of origin and AI Act), United States (referenced for 'lemons problem' origin, Copyright Office cases), International (general applicability of the problem and proposed solution). | For LLMs (e.g., GPT-3): Internet-based (Common Crawl, Wikipedia, etc.), predominantly English, unstructured text. Identified as a source of bias. | Economic theory (Akerlof's 'lemons problem'), legal analogy (international trade law's rules of origin and 'substantial transformation test'), policy framework development. | Proposed strategies include: bottom-up (artist self-regulation like recording creation; publisher/editor policies requiring disclosure) and top-down (government regulation such as extending EU AI Act disclosure requirements, mandatory watermarking of AI-generated content, algorithmic screening). | False | False | NaN | NaN | For the proposed Rule of Origin: Difficulty in defining and applying 'substantial transformation' to hybrid human-AI creative works; complexity of establishing clear boundaries in a tiered system of human input; ensuring effective enforcement of disclosure requirements and technical measures like watermarking; risk of circumvention of such measures. | Economic risk of 'lemons problem' devaluing human-made art due to information asymmetry; deceptive practices (misrepresenting AI art as human-made); stifling human creativity; spread of misinformation, bias, and toxic content via LLMs if outputs are not managed; copyright infringement by AI models; deepfakes eroding trust. |
| 57VandJTransnatlL.pdf | HeinOnline | The Network Effects of International Crypto and DLT Regulation | This paper analyzes global coordination in DLT and cryptocurrency regulation using the framework of network effects, identifying impacts on society, firms, and regulators. It argues positive network effects can drive adoption of global standards, but warns that soft law might undermine these benefits. | True | Market | False | 1.0 | NaN | Analytical framework of network effects (social, firm-level, regulator-level) applied to international DLT and cryptocurrency regulation. | NaN | NaN | NaN | NaN | NaN | NaN | Financial regulation, technology law, international law, commercial law, anti-money laundering law. | International | NaN | Application of economic theory (network effects), literature review (legal, economic), and conceptual analysis to develop a framework for analyzing DLT/crypto regulation. | NaN | False | False | NaN | NaN | NaN | Risks from unregulated DLT/crypto (e.g., money laundering, systemic risk, illicit uses, environmental harm) and risks from poorly coordinated/designed global regulation (e.g., negative network effects like herding, weakened standards, incompatibility with local needs). |
| 26VandJEntTechL375.pdf | HeinOnline | Ten Thousand AI Systems Typing on Keyboards: Generative AI in Patent Applications and Preemptive Prior Art | This paper examines the potential misuse of generative AI in the patent system, specifically for creating massive preemptive prior art databases and flooding the Patent and Trademark Office with AI-generated applications. It proposes policy solutions, such as reinterpreting "printed publications" and modifying PTO fee structures, to address these anti-innovative applications and uphold the integrity of the patent system. | True | Market | True | 3.0 | NaN | Using generative AI for mass creation of preemptive prior art databases and for automated drafting and filing of large numbers of patent applications. | NaN | NaN | NaN | NaN | NaN | NaN | Patent Law, Intellectual Property Law | United States | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Undermining the goals of the patent system; foreclosing patentability through massive AI-generated preemptive prior art; flooding the PTO with AI-generated patent applications; enabling retroactive assertion of conception for AI-generated content not conceived by humans; gaming the patent system for speculative inventions; AI-written applications failing written description, enablement, or coherence for conception; potential for egregious misconduct by inventors falsely claiming inventorship. |
| 16JIntellPropInfoTechElec.pdf | HeinOnline | The Artificial Intelligence Act: Critical Overview | This article provides a critical overview of the European Union's Artificial Intelligence Act (Regulation (EU) 2024/1689), analyzing its structure, objectives, scope, key definitions, and risk-based approach. It discusses prohibited practices, high-risk AI systems, transparency obligations, general-purpose AI models, and concludes that the Act's complexity may undermine its goals of fostering responsible innovation and protecting public interests. | True | Idealistic | True | 2.0 | Neutral | The EU Artificial Intelligence Act (Regulation (EU) 2024/1689) | The paper evaluates the AI Act through a dogmatic legal analysis, presenting a general descriptive legal analysis of the Regulation in the wider context of EU law. | The paper concludes that while the AI Act contains generally balanced and reasonable solutions, its complexity and poor legislative quality risk defeating its purpose, negatively affecting innovation, and potentially reducing the supply of advanced AI in the EU. | AI-driven discrimination, lack of transparency in automated decisions affecting legal rights (para 3, 43, 45), and the potential for the AI Act's own complexity to hinder effective protection of fundamental rights relevant to access to justice (para 108). | The paper suggests that the adoption of technical standards to reduce compliance costs and uncertainty, and increased involvement of legal experts to navigate and implement the complex AI Act, could help overcome the legislation's limitations (para 109). | Protection of fundamental rights (non-discrimination, fairness, transparency, accountability) through AI regulation, particularly concerning AI in law enforcement, biometric identification, and judicial contexts, which indirectly relates to access to justice (para 4, Section F, Annex III). | The AI Act, as analyzed in the paper, aims to protect vulnerable groups (e.g., based on age, disability, socio-economic status) and prevent discrimination based on protected characteristics (e.g., race, political opinions) from harmful AI practices (para 53, 65). | AI Law, EU Law, Product Safety Law, Fundamental Rights Law, Data Protection Law, Competition Law. | European Union | NaN | The AI Act was developed through the EU legislative process, involving a proposal from the European Commission, intense negotiations between the Commission, Parliament, and Council, amendments, and a corrigendum (para 8). | The AI Act (Regulation (EU) 2024/1689) was published in the Official Journal of the EU and is subject to a phased entry into force, with general application scheduled for August 2, 2026, and some parts applying earlier (para 8, 37). | True | True | The EU AI Act (Regulation (EU) 2024/1689) is published in the Official Journal of the EU and is publicly accessible (para 8). | The paper highlights the AI Act's significant complexity and potential poor legislative quality as major gaps (para 108). It also cites critiques suggesting limitations, loopholes, and a potentially narrow scope for high-risk classification, which might leave some harmful AI applications inadequately regulated or enforced (para 71 footnote 106, para 140 footnote 140). | The EU legislators faced challenges in defining AI, establishing a risk classification system, regulating general-purpose AI models, addressing open-source AI, and balancing the promotion of innovation with the protection of fundamental rights and public safety during the Act's development (para 8, 13-14, 22, 36, 90-91). | The paper states that the AI Act itself, due to its complexity and legislative quality, risks negatively affecting innovation, hindering investment, reducing the supply of advanced AI in the EU, and potentially defeating its own purpose of promoting responsible innovation and protecting public interests (Abstract, para 108, footnote 137). |
| 17RomArbJ31.pdf | HeinOnline | ARTIFICIAL INTELLIGENCE AND ARBITRATION: SOME CONSIDERATIONS ON THE EVE OF A GLOBAL REGULATION | This paper reviews the application of IT and AI tools across various stages of arbitral proceedings and discusses the evolving regulatory framework and ethical considerations for their use. It highlights the importance of responsible AI deployment in arbitration and posits that arbitration might evolve to champion human-centric justice as a counter-trend to AI-driven justice systems. | True | Market | True | 3.0 | Neutral | Generative AI (e.g., ChatGPT) for legal research and drafting. | Discusses real-world use and failure, notably the *Mata v. Avianca* case where ChatGPT fabricated legal precedents, and judicial observations on its limitations for legal research and analysis. | In the *Mata v. Avianca* case, the use of ChatGPT for legal briefing resulted in the submission of non-existent judicial decisions, leading to sanctions against the legal counsel. | Ensuring AI systems respect fundamental rights (including access to justice, equality, due process), non-discrimination, transparency, and accuracy; preventing AI errors or biases that could undermine justice; and addressing the 'black box' nature of some AI. | Development of global and national regulations (e.g., EU AI Act), ethical guidelines (e.g., CEPEJ Charter, SVAMC draft Guidelines), risk management frameworks, mandatory disclosure of AI use in legal proceedings, and maintaining robust human oversight and accountability. | Right to access to justice, equality, due process, fundamental rights, non-discrimination, transparency, fairness in AI-assisted legal processes. | NaN | Arbitration, Dispute Resolution, Litigation | USA, EU, Canada, UK, International | NaN | NaN | AI tools and platforms provided by arbitral institutions (e.g., AAA-ICDR's AAAi Lab, various case management systems); court-issued practice directions regarding AI use; development of guidelines by legal bodies (e.g., SVAMC, UK Judiciary). | True | False | Some AI tools from arbitral institutions (e.g., AAAi Lab, case management platforms like ICC Case Connect) are available to users, parties, or arbitrators affiliated with or using their services. Draft guidelines (e.g., SVAMC) are publicly accessible for review. | Ensuring robust human oversight and accountability in AI-driven legal processes; Developing globally harmonized, enforceable regulations that effectively protect fundamental rights including access to justice; Overcoming data limitations (especially for confidential arbitration data) to train fair and unbiased AI models for legal applications. | Ensuring accuracy and reliability of AI outputs (e.g., avoiding hallucinations and fabricated information); Maintaining confidentiality and data security when using AI with sensitive legal data; Preventing improper delegation of human decision-making and ethical responsibilities to AI; Addressing potential biases in AI systems; Integrating AI ethically and effectively into established legal workflows. | Fabrication of information by AI (e.g., fake case law); Undermining the integrity of judicial and arbitral processes and public trust; Improper delegation of essential human cognitive tasks and decision-making responsibilities to AI; Cybersecurity vulnerabilities and data privacy breaches; Introduction or perpetuation of biases by AI systems, impacting fairness and equality. |
| 20OhioStTechLJ225.pdf | HeinOnline | PROMETHEUS' DIGITAL FIRE: THE CIVIC RESPONSIBILITIES OF ARTIFICIAL INTELLIGENCE | This article explores the civic responsibilities associated with AI, examining its benefits and risks, particularly regarding bias, privacy, and accuracy. It also discusses emerging regulatory frameworks in the EU and US and proposes industry actions to mitigate risks and maximize benefits. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Bias in AI leading to digital redlining and discrimination in critical areas like housing, employment, credit, and law enforcement; lack of transparency in AI decision-making; privacy violations through extensive data collection; and the spread of AI-generated disinformation and harmful content. | Adherence to civil rights laws, employing debiasing strategies and explainable AI (XAI), developing robust regulatory frameworks and industry standards (e.g., NIST AI RMF), diligent fact-checking of AI outputs, and establishing strong contractual safeguards with AI vendors for data protection and system accountability. | Preventing AI-driven discrimination and bias (digital redlining), upholding civil rights, ensuring access to accurate information by combating AI-generated disinformation and harmful content, protecting privacy rights, and promoting ethical use of AI in legal practice. | Various vulnerable groups, including racial minorities (Black people, Asians, Latinos), women, and the elderly, who are disproportionately affected by biased AI systems. | Civil Rights Law, Privacy Law, Defamation Law, Products Liability, First Amendment Law, Intellectual Property Law (minor mention), Criminal Law (re: AI-generated child pornography), Contract Law, and Legal Ethics. | United States, European Union, and mentions China in the context of AI development and regulation. | The paper discusses AI systems trained on vast amounts of data scraped from the internet, including publicly available information, pirated and copyrighted materials (e.g., books), user-submitted data via prompts and APIs, and unstructured data. This data is often collected via web crawlers and third-party services. | NaN | NaN | False | False | NaN | Ongoing difficulties in mitigating AI bias and ensuring explainability (XAI); technical limitations in AI factuality and source citation; societal challenges in adapting education for critical thinking; and the need for further development and implementation of regulatory frameworks. | NaN | Bias and discrimination amplifying societal inequities; extensive privacy violations from data collection and misuse; spread of AI-generated disinformation, defamation, and deepfakes; provision of dangerous or inaccurate advice leading to harm; significant job displacement in creative and knowledge-based industries; and the erosion of critical thinking skills. |
| 40GaStULRev889.pdf | HeinOnline | BRIDGING THE GAP TO EVERY AMERICAN: HOW A NATIONAL REGULATORY SANDBOX CAN PROMPT RADICAL COLLABORATION TO ADOPT LEGAL ARTIFICIAL INTELLIGENCE TOOLS | This paper advocates for establishing a national regulatory sandbox in the United States to foster the development and adoption of AI-powered legal tools, aiming to enhance access to justice for underserved populations. It reviews existing AI legal technologies and uses Utah's regulatory sandbox as a model, while also considering potential risks and challenges. | True | Idealistic | True | 1.0 | Positive | National Regulatory Sandbox for legal AI tools. | The proposed 'National Regulatory Sandbox' is a policy recommendation and not evaluated. It draws on the Utah regulatory sandbox, which was evaluated by collecting data on service outcomes and consumer harm (e.g., types of harm: inaccurate legal result, failure to exercise legal rights, purchase of unnecessary service) by the Utah Office of Legal Services Innovation. | For the Utah regulatory sandbox (cited as a model): Over 2,500 people assisted with various civil legal issues; low consumer complaint rate registered (14 total complaints over three years, approximately 1 harm-related complaint per 6,851 services delivered). | High cost of legal services, lack of legal aid for low-income individuals, complexity of the legal system, and insufficient access to tools for resolving civil legal matters for vulnerable populations. | Proposes a 'National Regulatory Sandbox' overseen by a 'National Office of Legal Services Innovation' under the U.S. Supreme Court to facilitate the development and safe deployment of low-cost AI legal tools, encouraging radical collaboration among stakeholders. | Affordability and accessibility of civil legal services, alternative legal service providers, simplification of legal information through AI, and regulatory frameworks for legal tech innovation. | Low-income Americans and economically vulnerable populations facing civil legal problems. | Civil legal services, including housing, immigration, healthcare, discrimination, employment, and consumer contracts (e.g., car leases, mortgage financing, life insurance, credit card agreements). | United States of America (with Utah as a specific case study). | NaN | The proposed 'National Regulatory Sandbox' design is to be modeled on the Utah Supreme Court's sandbox and the Consultative Group to Assist the Poor's (CGAP) 'Practical Guide for Policy Makers,' considering elements like eligibility criteria, governance style, experimentation timeline, evaluation criteria, and entity exit options. | The paper proposes creating a 'National Office of Legal Services Innovation,' potentially under the U.S. Supreme Court or in collaboration with the ABA, to oversee the National Regulatory Sandbox. This office would manage applications, monitor entities, and ensure compliance with harm mitigation policies. | False | False | NaN | Potential for AI to create a two-tiered system of legal services where low-income individuals receive inferior AI-driven assistance. Cost barriers to high-quality AI. Unequal technological access for underrepresented communities. Risk of stagnation in advocating for a federal right to civil counsel if AI is perceived as a complete solution. | For the proposed National Regulatory Sandbox: Designing effective eligibility criteria, governance structures, experimentation timelines, and consumer harm-mitigation safeguards. Securing adequate monetary and personnel resources. Gaining buy-in from diverse stakeholders (legacy media, entrepreneurs, developers, lawyers, regulators). Overcoming distrust and skepticism towards AI within the legal profession and judiciary. | Consumers receiving inaccurate legal advice or inappropriate services. Data privacy violations due to AI's data processing capabilities. Job displacement for legal professionals (e.g., automation of administrative tasks). Widening the access-to-justice gap if AI tools are expensive, inaccessible, or of inferior quality for vulnerable populations. |
| 22BerkeleyBusLJ108.pdf | HeinOnline | Have Plain Language Laws Kept up with the AI Revolution? An Empirical Test | This article empirically tests the ability of AI-writing assistants, specifically Grammarly, to improve the readability of Franchise Disclosure Documents (FDDs) subject to plain language laws. It finds AI can significantly enhance document comprehensibility and proposes integrating AI-writing assistant standards into plain language laws via a rebuttable presumption. | True | Idealistic | False | 2.0 | Positive | Grammarly (Premium subscription), an AI-powered writing assistant. | Analysis of 'Item 1' from 100 Franchise Disclosure Documents (FDDs) of leading U.S. quick-service restaurants using Grammarly Premium. Measured average percentage of sentences flagged for grammar/clarity corrections and qualitatively categorized types of linguistic issues identified. | Grammarly flagged an average of 96.33% of sentences in Item 1 of the FDDs for potential enhancements in either grammar (average 54.15%) or clarity (average 42.19%). | Legal documents remain difficult to understand despite plain language laws due to linguistic deficiencies (grammar, clarity); vagueness and subjectivity in interpreting and complying with current plain language standards. | Amend plain language laws to incorporate a rebuttable presumption of compliance if drafters use an advanced AI-writing assistant and demonstrate substantive adherence to its standards (via a digital report). | Enhancing readability and comprehensibility of legal and business documents (Franchise Disclosure Documents); improving effectiveness of plain language legislation; access to information for informed decision-making. | General public, consumers, potential franchise owners, and readers of documents governed by plain language laws. | Franchise Law (specifically Franchise Disclosure Documents), Consumer Law, Corporate and Financial Disclosures, and generally laws requiring plain language. | United States (federal Franchise Rule and state/federal plain language laws). | Grammarly's AI is trained on proprietary datasets consisting of a vast text corpus (millions of sentences organized and labeled by human researchers from research corpora) and refined through years of user feedback analysis. The data is domain-general English aimed at good writing practices. | Grammarly's development involves machine learning methods, training on large text corpora, and iterative refinement based on human feedback analysis from user interactions with its suggestions. | Grammarly is a commercial AI-writing assistant available through free and premium subscription plans, used by millions daily, including individuals, educational institutions, and corporations like Cisco, Dell, and Boeing. | True | False | Grammarly is commercially available with both free and premium subscription plans. The study utilized the Premium version for its comprehensive features. | The inherent vagueness and limited scope of current plain language laws; the fallibility of AI tools (potential for inaccuracies, manipulation) requiring human oversight and critical judgment. | For this study: the non-machine readable format of some source documents (FDDs) made processing challenging. For AI tool users generally: The need for critical evaluation of AI suggestions; potential cost of premium features for full benefits. | Over-reliance on AI tools that may produce inaccurate feedback; potential for users to manipulate AI outputs; if AI tool use were mandated (which the paper cautions against), risks include undue financial burdens, stifled innovation, and infringement on commercial speech. |
| 4LegalIssuesDigitAge59.pdf | HeinOnline | Artificial Intelligence vs. Judicial Discretion: Prospects and Risks of Judicial Practice Automation | This paper analyzes the feasibility of integrating artificial intelligence into the Russian judicial system, comparing AI's potential with judicial discretion. It concludes that AI implementation is impractical in the short to medium term due to significant risks, inadequate legal frameworks, and the current geopolitical climate. | True | NaN | False | 3.0 | Negative | NaN | NaN | NaN | Technological inequality; data security and privacy concerns; limitations of AI in legal reasoning and handling nuances; algorithmic bias; lack of legal frameworks and accountability for AI; potential for AI to create new obstacles to access to justice. | The paper primarily argues against near-term AI implementation. For potential future use, it suggests: ensuring human control over AI; developing robust legal frameworks and regulations; proactive compliance policies by developers and enforcement agencies. | Right to judicial protection; barrier-free access to justice (critique of AI's ability to provide it). | General population / litigants. | General judicial practice, civil law, criminal law, administrative law. | Russian Federation (primary), European Union, China, Argentina (comparative examples). | NaN | NaN | NaN | False | False | NaN | Lack of doctrinal definition of AI and regulations for negative scenarios; absence of legal basis for AI liability and legal personality; no unified AI regulatory document in Russia; no national strategy or quality criteria for AI-Ready Open Juridical Data; lack of universal criteria for selecting training data; insufficient digital literacy in the legal profession; unclear responsibility for AGI decisions; uncertainty of input meanings for AI training. | NaN | Unauthorized data access and theft; AI training on misleading information; discriminatory or unjust AI outcomes; hampering of fundamental procedural rights; lack of AI transparency and explainability; technological inequality; security vulnerabilities from cloud-based AI and unprotected interfaces; violations of data protection laws; lack of accountability for AI errors; undermining judicial independence; AI making suboptimal or unlawful decisions; automation errors and network failures; potential hacking of judicial systems. |
| 8UPaJLPubAff129.pdf | HeinOnline | ADDRESSING THE EVOLVING CONCEPT OF GENDER AND INTERSECTIONAL STEREOTYPES IN INTERNATIONAL NORM CREATION: DIRECTIONS FOR A NEW CEDAW GENERAL RECOMMENDATION | This paper analyzes how gender and intersectional stereotypes are addressed by the CEDAW Committee and other human rights bodies, highlighting their evolving nature. It then discusses the emergent challenge of stereotypes embedded in Artificial Intelligence and proposes that a new CEDAW General Recommendation should address these digitized biases from a human rights perspective. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Entrenched societal gender and intersectional stereotypes being codified and amplified by AI systems due to biased training data and lack of diversity in the AI workforce, leading to 'digitized bias' and 'algorithmic discrimination'. | Developing new international normative frameworks (e.g., a new CEDAW General Recommendation) grounded in human rights, substantive equality, and intersectionality; promoting education and diversity in AI development; establishing guidelines for AI data and design; and fostering multilateral collaboration to ensure AI governance addresses and mitigates bias. | Elimination of gender and intersectional stereotypes in legal systems and AI, ensuring non-discrimination, and promoting substantive equality for women in all spheres, including protection from gender-based violence and fair judicial processes. | Women, particularly those facing intersectional discrimination based on race, ethnicity, religion, age, disability, sexual orientation, migrant status, and other factors. | International Human Rights Law, Anti-discrimination Law, Gender Law, Technology Law/AI Governance. | International; with examples from various national jurisdictions (e.g., MENA, South Asia, East Asia, Americas, European countries). | Discusses AI training data generally as often being unrepresentative of women and minority groups, leading to 'data bias' and the reproduction of societal gender biases in AI systems. It does not specify a particular dataset used for a proposed technique as the paper is a broad discussion. | NaN | NaN | False | False | NaN | Societal gaps in recognizing and addressing subtle and intersectional stereotypes. Technical and legal gaps in understanding and regulating AI-driven bias, including unrepresentative datasets, lack of diversity in AI development, the need for gender-sensitive AI design, and the application of human rights frameworks to AI governance. | NaN | Reproduction and amplification of societal gender and intersectional stereotypes through 'digitized bias' in AI systems. Stigmatization and marginalization of women, particularly those with intersectional identities, on a global scale. Normalization of gender-based violence and discrimination through biased AI outputs and interactions. Erosion of human rights if AI is not governed by inclusive, rights-based frameworks. |
| 25DukeLTechRev48.pdf | HeinOnline | GRAY ADVICE | This paper defines 'gray advice' as interactive digital tools providing personalized legal or health assistance, often as a substitute for professional help due to access issues. It highlights significant trust and quality problems with current gray advice services and proposes regulatory and professional interventions to make them safer and more effective for users. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Lack of access to and affordability of traditional legal and health professionals. For gray advice itself: festering trust and quality issues, deceptive disclaimers, users' inability to evaluate advice quality, data exploitation, failure to handle edge cases, inducing user errors, and setting users up to fail. | Regulatory interventions (e.g., deception cases, duty of loyalty for providers, 'nutrition label' disclosures, auditing regimes, modernized confidentiality protections). Professional engagement (e.g., building referral bridges between gray advice and professionals, developing field-specific design ethics for digital advice, investigating the true impact of services). | Access to self-help and limited-scope assistance for resolving discrete legal issues (e.g., small claims, divorce, immigration, benefits, wills, expungement) and managing health conditions (e.g., mental health, addiction, health coaching, pregnancy advice, self-diagnosis). | Low-income Americans, people in rural areas (for healthcare), and generally individuals who cannot find or afford traditional professional help. | Civil law (general), family law (divorce), immigration law, administrative law (government benefits), wills and estates, criminal law (record expungement). Healthcare is also a major parallel field of focus. | United States | NaN | NaN | NaN | False | False | NaN | Regulatory gaps in protecting users of gray advice. Lack of ethical design standards for digital advice. Poor integration and referral mechanisms between gray advice and professional services. Insufficient research on user outcomes and the actual effectiveness of gray advice. The challenge of addressing diverse user capabilities and knowledge levels. Pervasive unmet demand for legal and health assistance. | Establishing user trust in digital advice services. Ensuring the quality, accuracy, and reliability of advice, especially for complex or edge cases. Designing interfaces that prevent user error and facilitate understanding. Protecting sensitive user data from exploitation and breaches. Bridging the gap between providing information/advice and achieving successful user outcomes. Overcoming the 'credence good' problem where users cannot easily evaluate advice quality. | Users being misled by deceptive claims or disclaimers. Receiving incorrect, incomplete, or harmful advice. Data privacy violations and exploitation of sensitive personal information. Users failing to successfully resolve their issues or having their situations worsened. Erosion of public trust in professional services and institutions. Gray advice providers developing economic interests that oppose systemic improvements to access to justice or health. |
| 2024UIllJLTechPoly235.pdf | HeinOnline | ASKING GPT FOR THE ORDINARY MEANING OF STATUTORY TERMS | This paper investigates the use of the Large Language Model GPT-3.5 Turbo as a tool for generating empirical evidence on the ordinary meaning of statutory terms. The authors test various prompting techniques, comparing GPT's responses to human survey data (Tobia 2020) as a benchmark, and explore the model's sensitivity to context and historical meaning to propose best practices. | True | Idealistic | True | 2.0 | Positive | Using GPT-3.5 Turbo with specific prompting strategies, particularly a "belief prompt (using a Likert scale)", to assess the ordinary meaning of statutory terms by generating a distribution of replies. | GPT's responses regarding the term "vehicle" (100 repetitions per prompt) were compared against benchmark results from an experimental survey by Tobia (2020) involving over 2,800 English speakers. The study also tested GPT's sensitivity to contextual information (specific rules, purposes) and historical meaning (1950s). Statistical comparisons used the Kolmogorov Smirnov test. | The belief prompt combined with a seven-point Likert scale produced GPT responses that were statistically indistinguishable from the human benchmark data (Kolmogorov Smirnov, p = .2798). Other prompting techniques (direct replication, chain of thought, belief prompt with percentage scale) were less successful. | The 'black box' nature of LLMs raises transparency concerns. The high cost and time of traditional empirical methods for determining ordinary meaning impede their widespread use. Potential for 'junk science' if LLMs are used with wrong methods. | Using LLMs like GPT with rigorously tested prompting techniques (e.g., belief prompt with Likert scale) to affordably and quickly generate empirical evidence of ordinary meaning. This can democratize access to such evidence for legal professionals and potentially improve public understanding of law. Triangulating LLM-derived evidence with other empirical methods. | Statutory interpretation, ordinary meaning of legal terms, empirical legal studies, democratization of legal evidence, rule of law. | General public/citizens subject to law, legal professionals (judges, lawyers), legal academics. Indirectly, those who cannot afford lawyers, by improving clarity and accessibility of legal meaning. | Statutory Interpretation (general applicability), with examples from municipal law (no vehicles in the park), criminal law, civil liability, and census law. | United States (benchmark data from US participants, legal examples primarily US-based). | GPT-3.5 Turbo was trained on general text data including Common Crawl, WebText, internet-based books, and Wikipedia; it was not specifically trained on legal text. | Experimental design, benchmarking against human survey data, iterative prompt engineering (testing direct replication, chain of thought, belief prompts with percentage and Likert scales), quantitative analysis (Kolmogorov Smirnov test). | NaN | True | True | The authors' code for their specific prompting strategies and analysis is available on GitHub, requiring user access to OpenAI's GPT-3.5 Turbo API. | Prompt engineering is still more an art than a science; further testing of different LLMs (e.g., GPT-4) and prompts is needed. More benchmarks are required for different types of interpretation questions (beyond noun classification) and for specialized/technical meanings. The 'black box' nature of proprietary LLMs remains a challenge. | Initial prompting techniques yielded unreliable results compared to human benchmarks. GPT-3.5 Turbo sometimes failed to strictly adhere to system prompts and showed limitations in precise quantitative reasoning (favoring Likert scales over percentages). Technical coding challenges and the need for data wrangling expertise were noted. | LLM 'hallucinations' or generation of misleading information ('junk science') if not used with validated methods. Over-reliance on LLMs without understanding their limitations. Lack of transparency due to the 'black box' nature of proprietary models. Potential for biased or inaccurate interpretations if LLMs are overinclusive/underinclusive due to poor prompting. False precision if margins of error are not considered. |
| 54CalWIntlLJ517.pdf | HeinOnline | A Socio-Legal Inquiry on Deepfakes | The paper examines the workings and types of deepfake technology, exploring its social ramifications and analyzing current legal and regulatory frameworks in major economies like the US, EU, UK, China, and India. It proposes public policy innovations for a whole-of-society approach to prevent, detect, respond to, and repair harm from malicious deepfake use. | True | Idealistic | False | 3.0 | Positive | Deepfake technology (general), Policy framework for deepfake governance | NaN | NaN | Misinformation and disinformation, erosion of trust, difficulty in authenticating evidence and identifying perpetrators, infringement of personal and intellectual property rights, challenges in legal enforcement and cross-border jurisdiction, victimization of vulnerable groups. | A comprehensive policy framework involving prevention (e.g., content creation tool certification, digital watermarking laws), detection (e.g., R&D funding for detection technologies, platform regulation), response (e.g., rapid response teams, emergency protocols), and repair (e.g., victim support programs, civil remedies for victims). | Access to justice for victims of deepfake-related harms including non-consensual pornography, defamation, fraud, and election manipulation; protection of personal rights (privacy, personality) and intellectual property. | Vulnerable populations including women, children, senior citizens, and minority groups who are targets of deepfake-related crimes like non-consensual pornography, extortion, and defamation. | Criminal law, intellectual property law, privacy and data protection law, tort law (defamation, false light), election law, evidence law, personality rights. | United States, European Union, United Kingdom, China, India, International (due to cross-border nature of deepfakes) | NaN | Socio-legal analysis, literature review, comparative legal analysis for the proposed policy framework. | Policy proposals intended for governmental and societal adoption. | False | False | NaN | Gaps in legal frameworks concerning cross-border jurisdiction, intellectual property and personality rights (especially post-mortem privacy); the continuous need for research and development in deepfake detection and understanding victim vulnerability; insufficient cybersecurity resources and public awareness. | NaN | Disinformation, defamation, creation and dissemination of non-consensual pornography, extortion, identity theft, manipulation of elections, erosion of democratic processes and public trust, infringement of intellectual property and personality rights, potential for wrongful convictions based on falsified evidence, psychological and financial harm to victims. |
| 51FlaStULRev543.pdf | HeinOnline | WHAT'S A LAWYER FOR? ARTIFICIAL INTELLIGENCE AND THIRD-WAVE LAWYERING | The paper discusses the impact of AI and new technologies on the legal profession, framing it as a "third wave" of lawyering. It proposes a conceptual framework for assessing how different legal service delivery models, including technology-enhanced ones, can uphold the core values and functions of the legal profession, particularly concerning access to justice. | True | Idealistic | True | 1.0 | Positive | A conceptual framework for calibrating legal service delivery modes, assessing legal problems (complexity, agility, preventative/reactive, stakes) and clients (sophistication, capacity, ability to pay, access barriers) against professional values (adversarial role, democratic interests, rule of law, access to justice) and functions (instrumental, affective, political). | The framework is illustrated through its application to two hypothetical real-world scenarios: the formation of a simple non-profit ('East Harlem All Stars') and a complex non-profit ('The Safe Center'). | The framework application demonstrated that simpler legal needs with sophisticated clients (East Harlem All Stars) might be adequately served by technology-based solutions, while complex, high-stakes situations (The Safe Center) require traditional, full-service legal representation. | Cost of legal services; Lack of public awareness of legal problems or need for lawyers; Difficulty in accessing lawyers; The digital divide; Potential for technology to undermine core legal values if not thoughtfully deployed; Restrictive ethical rules and UPL regulations. | Thoughtful deployment of technology, including AI, to enhance affordability and accessibility; Utilizing the proposed framework to determine appropriate service delivery models; Reforming ethical paradigms, including rules on non-lawyer investment and UPL, to support innovation in legal services. | Affordability and accessibility of legal services; Role of technology (AI) in bridging the justice gap; Models for legal service delivery to low- and moderate-income individuals and non-profits; Ethical considerations in legal tech. | Low- and moderate-income individuals and non-profit organizations. | General legal practice, Non-profit law, Legal ethics, Professional responsibility. | United States | NaN | Conceptual analysis, historical review of the legal profession, synthesis of legal ethics and theory, and application of business concepts (e.g., Christensen's 'jobs-to-be-done'). | NaN | True | False | The conceptual framework for assessing legal service delivery models is detailed within the paper and can be understood and applied by readers. | Need for technologies to accurately assess case complexity and client nuances; The persistent digital divide; Need for reform of ethical rules (UPL, non-lawyer ownership) to enable beneficial tech innovations; Ensuring new technologies uphold legal values and do not create a two-tiered justice system. | Accurately assessing problem complexity and client capacity for technology use via automated or limited-service means; Ensuring new service delivery models preserve the instrumental, affective, and political functions of lawyering; Overcoming professional resistance to changes in legal service delivery; The high cost of developing and maintaining sophisticated legal technology tools. | Undermining core values of the legal profession and democratic institutions; Displacing essential lawyer functions inappropriately; Loss of nuanced legal guidance through over-commoditization; Creation of a two-tiered justice system; Premature disruption by immature technologies; Malpractice from incorrect assessments or faulty tech-based advice; Exacerbation of inequality due to the digital divide hindering access to tech-based solutions. |
| 28RogerWilliamsULRev118.pdf | HeinOnline | Addressing the Failures of the U.S. Civil Legal System | This paper analyzes the U.S. civil legal system's shortcomings in providing access to justice, especially for vulnerable populations, focusing on inadequate legal capability and consciousness. It advocates for interdisciplinary, human-centered interventions, including community support and technology like AI, to enhance legal empowerment and system responsiveness. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Legally vulnerable individuals not identifying problems as 'legal'; avoidance and inaction; unsatisfactory processes/outcomes; low legal capability (knowledge, skills, confidence, agency) and legal consciousness (distrust, irrelevance of law); complex and intimidating legal system; financial costs; psychological barriers (stress, fear); digital exclusion. | Enhance legal capability and consciousness via education/empowerment; adopt interdisciplinary approaches (public health, psychology); utilize community organizations; leverage technology (including AI for information/automation, online courts); implement inclusive design, user-centered communication, and improved self-help materials; upstream interventions like legal check-ups. | Barriers to civil justice for vulnerable groups; legal capability and consciousness deficits; effectiveness of self-help tools and community-based support; role of technology (including AI) in enhancing access; psychological and socio-cultural influences on legal engagement. | Legally vulnerable populations including low-income individuals, women, racial/ethnic minorities (Black, multiracial Americans), younger/middle-aged persons, people with disabilities, homeless, and formerly incarcerated. | Civil law (general), landlord-tenant, public benefits, consumer credit/debt, family law, small claims. | United States (with comparative references to UK, Canada, Australia for legal capability frameworks). | NaN | NaN | NaN | False | False | NaN | Deficient legal education impacting capability; misaligned service delivery; lack of national coordination for self-help; need for trust and ethical integration of AI in legal aid; insufficient attention to users' psychological and cognitive barriers. | NaN | For AI in legal aid: inaccuracy/knowledge gaps, ethical violations (competence, UPL), economic disruption, data security/privacy concerns, perpetuation of bias. Broader risks: digital exclusion worsening inequality; ineffective interventions if human psychological and cognitive factors are ignored. |
| 13Laws1.pdf | HeinOnline | Law, Technology, and Our Governance Dilemma | This article highlights a dilemma in using new tools to improve law's imperfect governance, as technology offers benefits but risks displacing the human element. It concludes that technological applications must be human-centric and controlled to protect the generic conditions essential for viable human communities. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Delays, difficulties, and costs associated with access to justice, making legal remedies 'out of reach' for many citizens. | Controlled and human-centric application of technology in governance, ensuring protection of fundamental human conditions, while exploring various roles for technology (assisting humans, automation, technological management) to improve law's imperfect governance. | Barriers to accessing legal services (delays, difficulties, costs), justice being unattainable. | Many citizens for whom justice is out of reach. | General / Multiple | International | NaN | NaN | NaN | False | False | NaN | The fundamental dilemma of how to integrate technology into legal governance effectively and ethically, balancing efficiency with human values and agency. Ensuring that technological applications remain human-centric and do not undermine the generic conditions for viable human communities. | NaN | Displacement of the human element in governance, AI applications that are not human-centric (e.g., violating human rights/dignity, undermining human agency), potential for biased algorithms (e.g., in risk assessment tools like COMPAS), counter-productive impacts of technology, loss of human responsibility and agency when conduct is technologically managed, and technologies undermining generic conditions for human existence and viable communities. |
| 4LawTechHum109.pdf | HeinOnline | Framing the Future: The Foundation Series, Foundation Models and Framing AI | This paper critically examines how AI foundation models, particularly in NLP, risk embedding and amplifying dominant, often biased, neoliberal linguistic frames from law and economics. It argues that this uncritical adoption could entrench societal inequalities, hinder true progress, and make it harder to challenge existing power structures, drawing parallels with Asimov's Foundation series to highlight these dangers. | True | Idealistic | True | 3.0 | Negative | NaN | NaN | NaN | Entrenched hegemonic neoliberal frameworks and biases embedded in language, which are uncritically adopted into AI foundation models, leading to the perpetuation and amplification of societal inequalities and hindering access to justice. | Promoting greater awareness of how linguistic framing shapes AI and society; developing a research agenda to identify and mitigate deep-seated biases in AI beyond explicit ones; actively reframing societal narratives to challenge dominant, inequitable ideologies; fostering conceptual tools that prioritize social well-being over purely economic or individualistic metrics. | The risk of AI foundation models perpetuating socio-economic inequalities and unfair power dynamics by encoding and amplifying biased neoliberal frames; the impact of AI's linguistic framing on access to justice and the marginalization of non-dominant voices and values. | General population, particularly those marginalized or disadvantaged by dominant neoliberal socio-economic structures whose interests are not reflected in hegemonic frames. | General law, Law and Economics, Law and Development | International | Existing data created by (a subset of) humans, reflecting flawed, biased human preferences and assumptions, including text from the internet and other sources; data curated from interactions with the current generation of foundation models; unlabelled data for self-supervised learning tasks. | NaN | NaN | False | False | NaN | Insufficient awareness and research into how deep-level linguistic framing (beyond explicit bias) encodes and perpetuates systemic inequalities within AI systems; lack of critical engagement with the hegemonic (neoliberal) conceptual tools being embedded in foundation models; the current focus of de-biasing on superficial aspects, neglecting foundational framing issues. | The complexity and monolithic nature of foundation models, making them difficult to adjust post-release; the tendency for AI systems to inherit and amplify biases from foundation models; the difficulty in identifying and remedying subtle, deeply embedded framing biases compared to explicit social biases. | Preservation and amplification of hegemonic neoliberal frames in AI systems, entrenching existing inequalities; perpetuation of structural inequalities leading to tangible harms for sections of the population; limiting future interrogation and evolution of legal and economic concepts by 'preserving them in digital aspic'; shaping human users to conform to 'homines economici-juridici'; AI systems potentially allowing humanity to come to harm by entrenching socio-economic disadvantage. |
| 42CardozoArtsEntLJ295.pdf | HeinOnline | Bias Notification Duty | The paper proposes a 'Bias Notification Duty' (BND), a legal mechanism requiring companies to report discovered algorithmic biases to a governing body. BND's goal is to facilitate the study of these biases for broader societal understanding and de-biasing, rather than companies just covertly fixing algorithmic outputs and obscuring the underlying issues. | True | Idealistic | False | 1.0 | Positive | Bias Notification Duty (BND) | NaN | NaN | The covert fixing of algorithmic bias by companies, which prevents society from learning about and addressing the underlying societal biases that affect civil rights and liberties. | Impose a Bias Notification Duty (BND) on companies and their employees to report discovered algorithmic bias to a governing body for study, evaluation, and notification of affected parties, enabling societal learning and de-biasing efforts. | Algorithmic bias, discrimination (gender, racial), societal fairness, transparency, accountability, de-biasing society, civil rights and liberties. | Legally protected classes, including those based on gender and race; minorities. | Antidiscrimination law, AI governance, data protection law, corporate law (corporate social responsibility, reporting duties), administrative law. | US, EU, International (proposal seems broadly applicable) | NaN | Conceptual legal and socio-legal analysis; proposal of a regulatory framework drawing on existing legal mechanisms. | Proposed as a state-imposed legal duty enforced by a selected governing body (e.g., analogous to the FTC). | False | False | NaN | Current approaches focus on fixing algorithmic output without sufficient study of the bias itself, missing opportunities for societal learning and broader de-biasing efforts. Lack of transparency about discovered and fixed biases prevents societal de-biasing. | Defining bias for regulatory purposes; ensuring compliance and enforcement against corporate secrecy and reluctance to report; navigating intellectual property and trade secret protections; managing costs of implementation and oversight; avoiding chilling effects on innovation. | Algorithmic bias negatively affecting lives and becoming 'weapons of math destruction'; misuse of disclosed bias information for opportunistic or abusive behavior; potential chilling effect on data use or algorithmic development if BND is poorly implemented. |
| 9AthensJL509.pdf | HeinOnline | An Economic Perspective of the Justice Digitalisation Process: The Questions of Efficiency and Equity | The paper analyzes the digitalisation of the judicial administration, particularly in Spain, from an economic perspective, focusing on its impacts on efficiency and equity. It highlights existing problems like delays and corruption, and discusses the potential benefits and risks of technologies like AI, emphasizing the need for caution and control to ensure fairness and protect rights. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Time delays in legal resolutions; economic conditions of users influencing access and outcomes ('inequality of arms'); opacity, lack of transparency, and corruption in the judicial administration; high litigation costs creating unequal justice; the digital divide affecting vulnerable populations; and insufficient technical expertise among legal professionals to manage new technologies. | Enhancing transparency and efficiency through controlled digitalisation; implementing robust oversight and control mechanisms for AI and algorithms; training legal professionals in new technologies or establishing independent technological authorities; bridging the digital divide affecting vulnerable groups; and promoting alternative dispute resolution methods. | Improving judicial efficiency, ensuring an equitable justice system, enhancing transparency, combating judicial corruption, and mitigating the digital divide's impact on access to justice. | Economically disadvantaged individuals, the elderly, disabled persons, women, and residents of rural areas. | Civil justice, Criminal justice, Administrative justice (primarily focusing on judicial administration across these fields). | Spain, with comparative references to Europe/EU. | NaN | NaN | NaN | False | False | NaN | Technical gaps include the lack of robust control and validation mechanisms for AI in justice and insufficient technical literacy among legal professionals. Societal gaps involve the persistent digital divide exacerbating inequalities, risks to fundamental rights (privacy, honor, legal guarantees), systemic resistance to transparency from legal professionals, and unresolved issues of judicial corruption and independence. | NaN | Increased inequity for vulnerable populations; infringement of legal guarantees, privacy, and honor; biased or erroneous algorithmic outcomes; potential for government manipulation of the judiciary through technology; loss of crucial human elements in legal proceedings (e.g., immediacy, non-verbal Cues); and over-reliance on opaque technologies without adequate understanding or control. |
| 75AlaLRev563.pdf | HeinOnline | A LIBERAL THEORY OF LEGAL EDUCATION | This paper critiques current legal education for being 'a-liberal,' separating law from justice and morality despite a liberal reputation, and thereby failing to adequately promote access to justice. It proposes a 'liberal model' of legal education that systematically integrates justice, equality, and access to legal services into the curriculum and law school culture. | True | Idealistic | False | 1.0 | NaN | A liberal model of legal education, involving curricular reforms (e.g., new 1L courses on justice, equality, access to legal services; integration of these themes into all courses) and cultural reforms (e.g., faculty mentorship, emphasis on teamwork, transparency, faculty modeling liberal values). | NaN | NaN | The orthodox 'a-liberal' model of legal education separating law from justice and morality; historical and intellectual path-dependency in law schools (formalism, functionalism, 'new liberalism'); faculty adherence to the status quo due to perceived comfort, difficulty of change, and lack of institutional incentives; the high cost of legal education pressuring students away from public interest careers; and the prevailing client-centered ideology of the legal profession that often neglects broader justice considerations. | Adoption of a 'liberal model of legal education' that includes: 1) Curricular reform: mandatory 1L courses on justice, equality, and access to legal services, and holistic integration of these themes across all courses. 2) Cultural reform: faculty actively modeling and embodying commitment to liberal values, robust mentorship programs, emphasis on teamwork and collaboration, and institutional transparency. 3) Redefining faculty roles to actively include the stewardship of liberal values and engagement with justice issues. | Access to legal services, embedding justice and equality in legal education, reforming legal professional identity formation. | Those who cannot afford to pay for legal services; 'most Americans priced out of the market for legal services'; underrepresented clients. | Legal Education, General Legal Practice (as it discusses the training for all lawyers). | United States | NaN | NaN | NaN | False | False | NaN | The failure of current legal education to consistently instill a commitment to justice, equality, and access to legal services in law students. The prevailing 'a-liberal' culture in law schools and the legal profession which de-prioritizes these values. | Likely critiques and implementation challenges for the proposed liberal model of legal education include: perceived infeasibility of instilling values in a three-year program; the argument that the legal practice environment is inherently a-liberal and resistant to such values; resistance from faculty due to increased workload, changes to their traditional roles, and defense of their subject-matter turf; and institutional inertia, high costs of reform, and potential for the model's aims to be misunderstood (e.g., as a purely political agenda). | The proposed model risks being misunderstood as a political ploy to make law schools more politically liberal, rather than instilling apolitical liberal values. Institutional inertia and the difficulty of faculty buy-in may prevent successful implementation ('Path Dependencies and Committee Work, Where Good Ideas Go to Die'). |
| 5LegalIssuesDigitAge113.pdf | HeinOnline | Technologies Versus Justice: Challenges of Al Regulation in the Judicial System | This paper examines the integration of artificial intelligence into judicial systems, discussing current applications and the concept of "smart courts" in various countries. It argues that while AI can serve as a supportive tool, it fundamentally cannot replace human judges in delivering just decisions due to its lack of genuine understanding and ethical judgment, necessitating robust legal and ethical regulation. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Inability of AI to deliver genuinely just outcomes due to its lack of human consciousness, understanding, interpretive capacity, and ethical judgment required for fair decision-making; threat to the rule of law and fair trial if AI oversteps its auxiliary role. | Strict legal and ethical regulation ensuring AI remains auxiliary to human judges, prohibiting automated judgments without human control, and enshrining in law that the authority to render justice cannot be delegated to AI. Establishing multi-tier regulatory systems, including ethical corporate standards and 'pilot court' regimes for testing, based on principles like security, legitimacy, fairness, transparency, and compliance with public order. | Ensuring fairness and just outcomes in judicial decision-making; providing legal information and assistance (e.g., claim drafting, advice); improving court efficiency and accessibility while maintaining the human-centric nature of justice. | General public needing access to judicial protection and legal information. | General (judicial system), Civil law, Administrative law, Traffic law. | International (discusses China, India, Germany, Portugal, Singapore, Russia, and general principles). | NaN | NaN | NaN | False | False | NaN | Technical gap: AI's inability to replicate human consciousness, cognition, understanding, and ethical judgment necessary for true justice. Societal gap: Lack of comprehensive and timely legal and ethical regulatory frameworks for AI in the judiciary; need for deeper understanding of AI's impact on judiciary institutions and the human nature of fair judgment. | NaN | Undermining the rule of law and fair trial; compromising the human nature of justice and the role of judges; debasement of judicial power; damage to fundamental values of the judicial system (e.g., fairness); potential harm to national security and legitimate interests of individuals/organizations if AI is misused or unregulated. |
| 63STexLRev1.pdf | HeinOnline | ARTIFICIAL INTELLIGENCE (AI) AND THE PRACTICE OF LAW IN TEXAS | This paper provides an overview of Artificial Intelligence (AI), particularly generative AI, examining its potential impacts, opportunities, and limitations within the legal practice in Texas. It discusses ethical considerations, necessary adaptations for legal professionals, courts, and educational institutions, and touches upon broader policy and regulatory developments related to AI. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | Lack of reliability and accuracy of AI tools (e.g., 'hallucinations'); inherent biases in AI systems leading to potentially discriminatory outcomes; inability of AI to provide human empathy and nuanced understanding crucial for vulnerable clients; risk of misuse by unrepresented individuals leading to system strain and poor outcomes. | Emphasizing mandatory human oversight and verification of AI outputs, especially in pro bono contexts; promoting education and training for legal professionals and the judiciary on AI's capabilities and risks; developing ethical guidelines and adapting legal rules for AI use; carefully vetting AI tools for bias and accuracy; exploring AI for specific, efficiency-gaining applications like Online Dispute Resolution in small claims. | Pro bono service delivery; assistance for self-represented litigants; online dispute resolution for minor disputes; AI applications in criminal justice (including potential for bias mitigation and wrongful conviction review). | Individuals unable to afford legal representation; self-represented litigants; individuals involved in the criminal justice system, particularly those subject to bail and sentencing decisions. | General legal practice, Ethics, Litigation, Criminal Law, Employment Law, eDiscovery, Health Care Law, Immigration Law, Privacy Law, ADR. | Texas | NaN | NaN | NaN | True | True | The paper mentions commercial AI tools (e.g., ChatGPT, Westlaw Precision, LexisNexis) as available and specifically refers to the 'free version of ChatGPT'. | Need for adequately tested, reliable, and unbiased AI tools specifically for A2J use; clear ethical and regulatory frameworks to govern AI in A2J, including accountability for AI-generated advice and prevention of Unauthorized Practice of Law; preparedness of the justice system (courts, legal aid) to manage AI integration and its consequences (e.g., AI-assisted pro se filings); challenges in AI explainability ('black box' issue) when AI impacts rights. | Ensuring accuracy and reliability of AI outputs (avoiding 'hallucinations'); mitigating inherent biases in AI models; protecting client confidentiality and data security when using AI; addressing evidentiary challenges (authenticity, admissibility, 'black box' problem); keeping pace with rapid technological and regulatory changes; training legal professionals; managing cybersecurity risks; developing ethical billing practices for AI-assisted work. | Attorneys facing sanctions for submitting AI-generated work with errors or 'hallucinated' citations; inadvertent disclosure of confidential client information through AI platforms; perpetuation of societal biases by AI systems leading to discriminatory outcomes in legal contexts (e.g., criminal justice, employment); creation and use of 'deepfakes' to mislead courts or for fraud; increased cybersecurity threats due to AI-powered attacks; AI making the legal system more incomprehensible or alienating. |
| 103BULRev.pdf | HeinOnline | CHANGING ALL THE TIME: AI'S IMPACT ON HUMANITY'S ROLE IN COMMON LAW DEVELOPMENT AND INTERPRETATION | This paper examines the significant impact generative AI, such as ChatGPT, could have on the development and interpretation of common law, potentially severing humanity's connection to it. It argues for careful guidance and proposes amending professional conduct codes to ethically center the human role in law. | True | Idealistic | True | 3.0 | Cautiously Positive | Generative AI (e.g., ChatGPT) and hypothetical AI legal assistants (e.g., 'LegalBot') | NaN | NaN | Inability to afford legal representation, leading to individuals having to navigate the legal system pro se. | AI-powered legal assistants (like the hypothetical LegalBot) to provide guidance and support to pro se litigants, potentially improving their ability to navigate legal processes and evening the playing field. | Assistance for pro se litigants; Reducing public defender backlogs. | Pro se litigants (individuals unable to afford legal representation). | Common Law (general), Tort Law (example). | United States (primarily American common law). | Large datasets of legal texts (case law, motions, treatises), general textual data, and human-provided input, processed by machine learning algorithms. | NaN | Integration into legal practice through corporate adoption (e.g., PwC's Harvey) and potential court-sanctioned programs for pro se assistance (hypothetical LegalBot). | True | True | ChatGPT, a key example discussed, is publicly accessible via OpenAI, with a free usage tier. | Lack of robust regulatory and ethical frameworks guiding AI development and deployment in the legal field, specifically concerning AI's role in substantive legal work and common law development. Need for a deeper understanding and preservation of the human-law relationship in the age of AI. | Ensuring factual accuracy and avoiding 'hallucinations' (e.g., fake caselaw); ethical concerns around plagiarism, attribution, and unlicensed practice of law; risk of overreliance and deskilling of human lawyers; preventing AI from unduly controlling legal development through feedback loops. | Severing humanity's connection to the law; ceding control of common law development to AI algorithms and private companies; AI creating a feedback loop that dictates legal development based on past data; unlicensed practice of law; erosion of human legal reasoning skills; challenges to the integrity and fairness of the legal system. |
| 36IntlJSemioticsL.pdf | HeinOnline | Hyperrealistic Jurisprudence: The Digital Age and the (Un) Certainty of Judge Analytics | This paper introduces "hyperrealism" as an evolution of legal realism, enabled by digital tools like judge analytics that allow for empirical prediction of judicial decisions. It discusses the advantages and disadvantages of judge analytics, highlighting the need for regulatory mechanisms to improve justice and minimize associated risks. | True | Idealistic | False | 2.0 | Neutral | Judge analytics (also referred to as judicial analytics) | Evaluations include accuracy metrics for data extraction by tools like SupraLegem.fr (claimed 90-99%), and studies analyzing correlations between judicial characteristics/behaviors and case outcomes (e.g., ECHR and US asylum case studies). | Judge analytics tools have shown high accuracy in data extraction (e.g., 90-99% by SupraLegem.fr) and identified patterns such as varying asylum rejection rates among judges and correlations between judicial characteristics (e.g., gender, presence) and decisions. | Risk of rights violations, threats to judicial impartiality and privacy, algorithmic bias from incomplete/biased data, and potential for misuse (e.g., unfair 'judge shopping'). | Proposes regulatory mechanisms, expert evaluation, standardization, and ethical guidelines (emphasizing fairness, transparency, accountability) for judge analytics. | Transparency and predictability of judicial decision-making, identification and mitigation of judicial bias, and ensuring fairness within the justice system. | Asylum seekers are mentioned as an example of a group affected by judicial biases that analytics can reveal; the paper's focus is broader. | General litigation, with examples cited from asylum law and human rights law. | International, with examples and references to the US, France, ECHR, Brazil, and other countries. | Primarily publicly available and proprietary datasets of judicial decisions, case law, legislation, and other legal documents, largely unstructured text. | Machine learning, Natural Language Processing (NLP), data mining, text mining, and jurimetrics, applied to analyze judicial texts and behavioral patterns. | Commercial availability through legal tech companies (e.g., LexisNexis, Thomson Reuters, JusMundi) and specialized startups offering judge analytics tools. | True | False | Various commercial judge analytics tools (e.g., LexisNexis Context, Thomson Reuters Westlaw Edge, Predictice) are available from legal tech providers. | Need for robust regulatory and ethical frameworks for judge analytics, further research on 'hyperrealism', and methods to address technical limitations like data gaps and algorithmic bias to ensure trustworthy AI in justice. | Ensuring data completeness and accuracy, mitigating algorithmic bias, protecting judicial privacy and independence, preventing misuse for unfair advantage, and balancing innovation with regulation. | Algorithmic bias reinforcing societal inequities, violations of judicial privacy, undermining judicial impartiality, misuse for 'judge shopping', potential for hacking or manipulation, and over-reliance on imperfect predictions. |
| 31IntlJClinicalLegalEduc1.pdf | HeinOnline | CHATGPT, I HAVE A LEGAL QUESTION? THE IMPACT OF GEN Al TOOLS ON LAW CLINICS AND ACCESS TO JUSTICE | This paper evaluates the reliability and accuracy of Generative AI tools (ChatGPT, Bard, Bing) in providing legal information for common legal problems, finding significant errors and a tendency to provide incorrect or jurisdictionally flawed advice. It discusses the critical implications for access to justice, particularly for litigants in person, and explores the potential responsible use and risks of these tools within law clinics and clinical legal education. | True | Idealistic | True | 2.0 | Neutral | Evaluation of Generative AI tools: ChatGPT 3.5 (free version), ChatGPT 4 (subscription version), Bing Chat (balanced mode), and Google Bard. | Six generic legal queries from common law areas (family, employment, consumer, housing, online contracts, child maintenance) were posed to the four GenAI tools. Responses were initially collected in May 2023 and then for three queries in October 2023. Two qualified lawyers rated responses on a 0-5 scale for 'accuracy of legal advice' (currency, comprehensiveness, application, prompt-independence, audience clarity) and 'clarity of practical next steps' (practicality, ADR inclusion, links, gaps, audience clarity). | ChatGPT4 performed best overall (73% accuracy for legal advice, 70% for clarity of next steps). However, only 13% of initial (unprompted) responses across all tools were correct for UK law, with 42% being too generic, 21% defaulting to the wrong jurisdiction (often US law), and 25% being legally incorrect. Consumer and online contract law queries received more accurate responses than family, employment, and child maintenance law. | High cost of legal advice and representation preventing access to legal services. Significant cuts to legal aid (e.g., LASPO 2012 in the UK) and geographical 'legal aid deserts'. Low public awareness of existing reliable sources of help. Structural inequalities that technology cannot solely resolve. | The paper suggests a critical need for public legal education campaigns to raise awareness about the limitations and risks of using GenAI for legal advice. It proposes that law clinics develop clear policies and provide training for the responsible use of GenAI to potentially increase capacity and enhance student skills, while being cognisant of the risks. | Reliability of AI-generated legal information for laypersons, self-representation (litigants in person), role of AI in law clinics, public legal education regarding AI, ethical implications of AI in legal advice. | Litigants in person (self-represented individuals), individuals unable to afford legal advice (low-income populations). The paper also notes higher unmet legal needs among BAME communities, younger people, and those with low levels of education. | Family law, employment law, consumer law, housing law, online contract law, child maintenance law. | Primarily England and Wales (UK). The evaluation explicitly tested for responses relevant to English law and noted issues with AI tools defaulting to US law. | The paper states the Generative AI tools evaluated (ChatGPT, Google Bard, Bing Chat) are trained on 'vast amounts of internet text data'. | NaN | NaN | True | True | ChatGPT 3.5, Google Bard, and Bing Chat are described as freely available. ChatGPT 4 is a paid subscription service. All are accessible online. | Technical: Persistent inaccuracies, hallucinations, jurisdictional confusion, outdated legal information, and overly generic responses from GenAI tools. Societal: The digital divide (cost, access, literacy) limiting GenAI benefits for vulnerable populations; risk of exacerbating inequalities if superior AI tools are paywalled; need for robust public legal education on GenAI limitations; ethical concerns including data bias and exploitative labor practices in AI development. | For evaluators: Non-deterministic nature of GenAI responses making exact replication difficult; subjectivity in interpreting response quality. For users (litigants in person): Inability to critically evaluate GenAI output for accuracy, jurisdictional relevance, or completeness due to lack of legal knowledge. | Litigants in person relying on inaccurate, misleading, or fabricated GenAI legal advice, potentially harming their cases. Confusion over legal jurisdictions (e.g., receiving US law advice for a UK problem). Financial detriment due to following incorrect advice. Data privacy and confidentiality breaches if sensitive case details are entered into GenAI. Copyright infringement by inputting proprietary materials. Over-reliance on AI leading to diminished research and writing skills for students. Reinforcement of societal biases present in AI training data. |
| 51FordhamUrbLJ.pdf | HeinOnline | ROBOTS VS. PREDATORS: CAN GENERATIVE ARTIFICIAL INTELLIGENCE HELP TO ADDRESS THE JUSTICE GAP IN CONSUMER DEBT LITIGATION? | This paper explores how Generative AI (GenAI) could potentially address the access to justice gap in consumer debt litigation by proposing a conceptual "digital continuum of care." It discusses the opportunities GenAI offers for assisting low- and moderate-income debtors, alongside the significant technological, ethical, and practical challenges. | True | Idealistic | True | 1.0 | Positive | The paper proposes a conceptual "digital continuum of care" for consumer debt cases using GenAI. This includes: 1) A consumer-focused chatbot for legal information; 2) A document-assembly tool for consumers to prepare legal filings; 3) A sophisticated document-assembly tool for attorneys; 4) Automated discovery tools; 5) GenAI-fueled motion practice assistance. | NaN | NaN | High cost of legal services; lack of awareness among individuals that they have a legal problem or knowledge of how to obtain legal assistance; significant asymmetry of legal representation in consumer debt cases (creditors represented, debtors often not); the digital divide limiting access to technological solutions for underserved populations. | The paper proposes harnessing GenAI to create a "digital continuum of care" in consumer debt cases. This involves developing AI-powered tools like chatbots for initial guidance, document assembly tools for preparing pleadings, and systems for automated discovery and motion practice support to assist both self-represented litigants and legal aid attorneys. | Consumer debt litigation, access to legal information, self-representation, legal aid. | Low- and moderate-income Americans, debtor-defendants in consumer debt cases. | Consumer law, debt collection, civil litigation. | United States (primary focus, with specific examples and data points from New York City). | The proposed GenAI tools would rely on: curated legal information and pre-prepared answers for chatbots; content provided and AI training managed by legal aid organizations for more sophisticated document assembly and legal analysis tools, possibly using a limited large language model (LLM) drawing from this curated content. | The paper outlines a conceptual framework by identifying stages in consumer debt litigation and mapping potential GenAI tools to each stage. Specific software design methodologies for building these tools are not detailed. | Conceptual; envisioned tools could be used internally by legal aid organizations to augment their services, or as direct-to-consumer applications (e.g., chatbots, basic document assembly). No specific deployment strategies for a fully realized system are detailed. | False | False | NaN | The paper highlights a need for will, resources, and commitment to develop effective technological interventions for access to justice. Technical gaps include the current limitations of GenAI (e.g., accuracy, hallucinations). Societal gaps involve the digital divide and ensuring equitable access to such technologies. | Technological barriers (GenAI accuracy, reliability of OCR, need for human oversight, particularly for complex legal tasks). Practical considerations (limited human capital and budgets in non-profit legal services, the digital divide, language barriers for users). Ethical concerns (ensuring the standard of care for legal services, maintaining client confidentiality, avoiding the unauthorized practice of law). | GenAI "hallucinations" leading to inaccurate legal advice or fictitious case citations. Lawyers or pro se litigants relying on incorrect AI-generated information, potentially leading to sanctions or adverse case outcomes. Breach of client confidentiality if sensitive information is shared with GenAI platforms. The possibility of widening the justice gap if advanced legal tech primarily benefits well-resourced parties. Unauthorized practice of law (UPL) if AI tools provide tailored legal advice without lawyer supervision. Increased burden on courts sifting through AI-generated filings of varying quality. |
| 24PeppDispResolLJ91.pdf | HeinOnline | IS THE USE OF ARTIFICIAL INTELLIGENCE IN ALTERNATIVE DISPUTE RESOLUTION A VIABLE OPTION OR WISHFUL THINKING? | This paper examines the potential of AI in alternative dispute resolution (ADR), analyzing its benefits like increased efficiency and accessibility. It also discusses various AI-driven ADR methods, alongside significant challenges including ethical concerns, regulatory hurdles, and AI's limitations in replicating human reasoning. | True | Market | False | 3.0 | Neutral | Online Dispute Resolution (ODR) platforms, AI-assisted dispute resolution (AI-DR) including automated/assisted negotiation (e.g., Modria, Cybersettle, The Family Winner), adjudicative methods, case reasoning systems (e.g., Split-Up AI), and rule-based systems. | NaN | NaN | High cost and complexity of traditional litigation; intimidation of court proceedings; geographical and time constraints for physical court attendance; complexity of legal procedures for laypersons. | Online Dispute Resolution (ODR) to reduce costs, time, and intimidation, and make processes more accessible; AI-powered tools to offer affordable legal services. | Dispute resolution for common issues (e.g., motor vehicle violations, small claims, landlord-tenant disputes); increasing affordability and accessibility of legal services. | People of limited means. | Alternative Dispute Resolution (ADR), Civil Litigation (including small claims, landlord-tenant disputes, e-commerce, intellectual property, family law, financial disputes). | United States, China, Estonia, UK, France, Netherlands. | The paper notes that AI systems rely on training data such as past dispute outcomes (for case reasoning systems like Split-Up AI), party-provided data on preferences and priorities (e.g., The Family Winner), or large datasets of court cases (e.g., a standardized dataset of 100,000 US court cases for predictive analytics). | NaN | NaN | True | False | Discussed tools like eBrevia, Modria, Cybersettle, and OurFamilyWizard are commercial platforms; some ODR systems are integrated into court operations in various jurisdictions (e.g., for traffic tickets, small claims). | AI's inability to fully process intangible human elements like credibility and complex reasoning; need for AI to resolve novel issues; restrictive regulatory schemes hindering AI innovation in ADR; limited availability of comprehensive arbitration data due to confidentiality. | Acquiring sufficient and representative data for training AI in ADR (especially confidential arbitration awards); replicating nuanced human judgment and credibility assessment; overcoming regulatory and ethical hurdles (privacy, confidentiality, liability); ensuring fairness and transparency of AI-driven decisions. | Privacy and confidentiality breaches from data mining; product liability for AI-induced errors or harm; potential for ingrained bias in AI algorithms; over-reliance on AI without understanding its limitations; ethical concerns regarding the lack of human oversight and accountability in decision-making. |
| 91DefCounselJ1.pdf | HeinOnline | Emerging Artificial Intelligence Risk Management Considerations for Law Firms | This paper outlines key risk management considerations for law firms integrating AI tools, focusing on ABA Model Rules for competence, confidentiality, and billing. It uses case examples to illustrate potential pitfalls and suggests established risk management approaches, while also noting the "unknown unknowns" of AI's future impact on legal professional judgment. | True | Market | True | 2.0 | Neutral | Use of AI tools (including generative AI like ChatGPT) for legal tasks such as research and brief writing. | Analysis of case law (e.g., Mata v. Avianca, People v. Crabill) detailing misuse of AI, and application of ABA Model Rules to assess risks. | Misuse of AI tools, particularly unverified outputs from generative AI, led to factual errors in legal documents (e.g., 'hallucinated' citations), resulting in court sanctions for lawyers and firms, and professional discipline. Key risk areas identified are competence, confidentiality, and billing. | Lack of ready access to lawyers for individuals navigating the legal system pro se. | NaN | Potential of AI to assist pro se litigants. | Pro se litigants. | General legal practice, professional responsibility, ethics. Examples from personal injury and civil procedure. | United States (primarily, based on ABA Model Rules, state ethics opinions, and US federal/state case law). | NaN | NaN | NaN | True | True | The paper discusses lawyers' use of generally available AI tools like ChatGPT, which has free and paid public versions. | The paper notes AI's potential in access to justice but focuses on law firm risks, not specific A2J gaps. The main uncertainty highlighted is the 'unknown unknowns' of AI's future impact on legal professional judgment. | Challenges for law firms in adopting AI include: ensuring lawyers maintain competence with evolving technology, safeguarding client confidentiality when using third-party AI tools, establishing ethical and transparent billing methods for AI-assisted work, and the need for clear firm policies and training. | Key risks include: lawyers submitting AI-generated misinformation to courts leading to sanctions (Mata v. Avianca); disciplinary action for incompetence (People v. Crabill); breaches of client confidentiality if data is entered into non-secure AI; unreasonable client billing for AI use or 'saved time'; and vicarious liability of firms for such failures. |
| 16CaseWResJLTechInternet1.pdf | HeinOnline | Sam Altman, OpenAI, and the Importance of Corporate Governance | This paper examines the corporate governance crisis at OpenAI, focusing on the sudden firing of CEO Sam Altman and the subsequent turmoil, analyzing the company's unique structure and the influence of 'effective altruism'. It emphasizes the critical need for professional corporate governance in organizations developing powerful AI technologies with profound societal impacts. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Reliability issues such as AI 'hallucinations' creating non-existent legal citations, and the general need for caution and humility in deploying AI for legal assistance. | Improved corporate governance of AI development companies to ensure responsible development and deployment of AI; exercising caution and humility when using AI for legal applications. | Providing legal information and assistance for basic questions, document templates, and court form completion for those who cannot afford lawyers. | Individuals who cannot afford a lawyer. | General legal assistance, Contract law, Intellectual property law. | United States (focus on OpenAI, Delaware corporate law, US legal cases), European Union (AI Act), United Kingdom (CMA scrutiny). | NaN | NaN | NaN | False | False | NaN | Technical gaps in AI reliability (e.g., 'hallucinations'). Societal gaps include establishing effective AI regulation, combating bias, addressing job displacement, ensuring broadly distributed benefits of AI, and aligning powerful AI development with human values and safety through robust corporate governance. | Balancing a non-profit mission with the capital requirements for AI research; managing internal disagreements on AI safety and development speed; establishing effective and experienced corporate governance for a company developing high-stakes AI technology; navigating the influence of philosophical movements like 'effective altruism' on corporate strategy and safety prioritization. | Misuse of AI, drastic accidents, societal disruption (including job displacement), spread of misinformation and deepfakes, national security threats (e.g., AI-powered espionage, intellectual property theft), existential risks from AGI, AI 'hallucinations' leading to false information, and economic disruptions. |
| 72JLegalEduc577.pdf | HeinOnline | No "Robot Lawyers" Just Yet: The Role of Continuing Legal Education in Fulfilling the Duty of Technological Competence | This paper argues that despite a widely adopted ethical duty of technological competence, many lawyers lack proficiency, citing issues with e-filing, social media, and AI misuse. It advocates for more states to mandate technology-focused Continuing Legal Education (CLE) to protect client interests and uphold justice system integrity, reviewing current adoption statuses. | True | Idealistic | False | 2.0 | Positive | Mandatory technology-focused Continuing Legal Education (CLE) for lawyers. | The paper reviews the adoption, specific requirements, and reception of mandatory technology CLE in various US jurisdictions that have implemented it (e.g., Florida, North Carolina, New York, California, US Virgin Islands) and the failure of such proposals in others (e.g., Pennsylvania, Maryland). | Four U.S. states (Florida, North Carolina, New York, California) and the U.S. Virgin Islands have mandated technology CLE. Requirements vary, with Florida and North Carolina adopting broader technology training, New York focusing on cybersecurity, and California adding a general technology hour. Florida's initiative reportedly received favorable reactions. | Lawyers' lack of technological competence, resistance or indifference towards technology training, and the insufficient number of jurisdictions mandating technology-related CLE, which can lead to misuse of technologies like AI, thereby undermining competent representation and access to justice. | Widespread adoption by state bar associations and supreme courts of mandatory Continuing Legal Education (CLE) requirements specifically focused on technology, including areas like AI, cybersecurity, and e-discovery. | Lawyer competence in technology, professional responsibility, ethical use of technology, Continuing Legal Education (CLE) reform, cybersecurity for law firms, impact of AI on legal practice. | The general public and clients of legal services. | Professional responsibility, legal ethics, legal practice management, legal education. | United States (referencing ABA Model Rules, federal courts, and specific states including Florida, North Carolina, New York, California, Pennsylvania, Maine, Maryland, Texas, Colorado, Delaware, New Hampshire), US Virgin Islands. | NaN | NaN | Adoption of mandatory CLE rules by state supreme courts or bar associations. | False | False | NaN | A significant discrepancy exists between the widespread adoption of an ethical duty of technological competence for lawyers (40 states) and the very limited number of states actually mandating technology-specific CLE. There's also a gap in lawyers' understanding and preparedness for emerging technologies like generative AI. | Lawyer attitudes viewing CLE as a burden, cost and time constraints related to CLE, resistance from some segments of the legal profession to mandating specific CLE topics like technology, and the difficulty of keeping CLE content current with rapidly evolving technology. | Technological incompetence among lawyers leading to malpractice, client data breaches, inadvertent disclosure of confidential information, ethical violations (e.g., filing AI-hallucinated citations), sanctions, ineffective assistance of counsel, and ultimately, damage to public confidence in the justice system. |
| 2025AccesstoJustEEur241.pdf | HeinOnline | INNOVATIONS OF ARTIFICIAL INTELLIGENCE IN LIGHT OF THE APPLICABLE COPYRIGHT LAW: REALISTIC SOLUTIONS AND FUTURE PROSPECTS. A COMPARATIVE STUDY OF UAE, EGYPTIAN, AND FRENCH LAWS | This paper analyzes how current copyright laws in the UAE, Egypt, and France address AI-generated innovations, highlighting challenges in defining authorship. It argues for urgent legal reforms to create a framework that balances innovation promotion with the protection of rights, ensuring ethical and legally recognized AI development. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Ambiguity in defining 'author' for AI-generated content under existing copyright laws, as AI lacks human personal characteristics; inadequacy of current legal frameworks to address the novel challenges posed by AI innovations; the lack of legal personality for AI systems, complicating attributions of rights. | Reviewing and amending existing copyright laws to specifically address AI-generated innovations; developing a comprehensive legal framework that balances promoting AI innovation with protecting legal rights and ethical considerations; establishing a Code of Ethics for AI systems to guide their development and use responsibly. | Clarification of authorship and ownership rights for AI-generated creative works; establishment of fair and ethical legal frameworks for AI in the creative industries; ensuring legal certainty for creators, users, and developers in the context of AI and copyright. | NaN | Copyright Law; Intellectual Property Law | UAE, Egyptian, and French laws | NaN | NaN | NaN | False | False | NaN | Absence of a specific legal framework tailored to AI-generated intellectual property; lack of clear provisions for attributing legal personality or a special legal status to AI; need for harmonized ethical guidelines and codes of conduct for AI development and use in creative sectors. | NaN | Legal uncertainty and increased litigation due to unadapted copyright laws; potential for infringement on existing copyrights by AI systems using protected works as training data or generating similar outputs; ethical concerns regarding AI replacing human creators or devaluing human creativity if not properly regulated. |
| 20NwJTechIntellProp309.pdf | HeinOnline | LAW INFORMS CODE: A LEGAL INFORMATICS APPROACH TO ALIGNING ARTIFICIAL INTELLIGENCE WITH HUMANS | This paper proposes a research agenda, "Law Informs Code," advocating for the use of legal processes, concepts, and data to improve the alignment of Artificial Intelligence (AI) with human goals and societal values. It argues that law offers a legitimate, scalable, and democratically endorsed framework for specifying objectives to AI, thereby enhancing AI safety and utility. | True | Idealistic | True | 1.0 | Positive | Law Informs Code: A legal informatics approach using legal theory, processes, data (e.g., contracts, standards, public law), and reasoning to align AI with human and societal values. | NaN | NaN | The fundamental difficulty in specifying complex human goals and societal values (like those inherent in justice) to AI systems, leading to AI that may act unaligned with these values and lack legitimate grounding for its understanding of societal preferences. | Utilizing law (its theory, processes, data, and reasoning methods) as a legitimate and scalable framework to specify human intentions and democratically endorsed societal values to AI systems, thereby improving AI alignment. | NaN | NaN | General (contracts, public law, fiduciary law, statutory interpretation, securities law, tax law, etc.) | U.S. law (with aspiration for global applicability) | Proposed use of publicly available and potentially proprietary legal texts (constitutional, statutory, administrative, case law, contracts), legal training materials, rule-based systems, and expert feedback. Data is largely unstructured or semi-structured legal language. | NaN | NaN | False | False | NaN | Remaining gaps include: determining how law can guide AI's proactive positive goals (not just prohibitions); systematically accounting for historical injustices and biases in legal data; scaling the approach globally; understanding AI's 'intention' for legal purposes; addressing issues of law's representativeness, AI truthfulness, and loophole exploitation; and improving NLP for long legal documents. | Integrating complex and often ambiguous legal concepts and reasoning into computational AI models. Sourcing, curating, and processing vast amounts of diverse legal data while addressing biases. Developing robust methods for AI to generalize legal understanding to novel situations. Creating effective benchmarks to validate AI's legal comprehension and alignment. | Potential for AI to misinterpret complex legal directives or exploit loopholes. Risk of embedding historical biases or unjust aspects present in legal data into AI systems. Challenges in ensuring AI adapts to evolving legal norms and societal values, or that democratically produced law adequately reflects these values. |
| 30AIL561.pdf | HeinOnline | Thirty years of artificial intelligence and law: the third decade | This paper reviews eight significant papers from the Artificial Intelligence and Law journal's third decade (2012-2022), highlighting the field's major shift towards Machine Learning and Natural Language Processing techniques. It covers applications like document management, legal text analysis, outcome prediction, and detection of unfair contract clauses, discussing both advancements and challenges. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Key obstacles to A2J mentioned include: the difficulty for laypeople to understand legal texts and identify issues like unfair contract terms; the inherent complexity and volume of legal information hindering accessibility; the 'black box' nature of AI models which can impede trust and accountability; and the scarcity of high-quality, annotated legal data needed to train effective AI tools, especially for diverse legal areas and languages. | Proposed solutions to A2J obstacles include: developing AI tools for automatic analysis and retrieval of legal information (e.g., semantic parsing, unfair clause detection for consumers); employing explainable AI (XAI) methods to make system reasoning transparent and build trust; creating and sharing annotated legal datasets to foster research and tool development; and adapting advanced NLP models (like transformers) for specific legal tasks to improve information access and understanding. | Consumer protection (e.g., identifying unfair contract terms); access to and understanding of legal information (e.g., statutory provisions, ECHR case law); ensuring fairness and accountability in automated legal processes. | Consumers (e.g., understanding terms of service); general public/addressees of regulations; individuals interacting with human rights courts. | Statutory Law, EU Consumer Protection Law, Patent Law, Japanese Pension and Civil Law, Human Rights Law (ECHR), WIPO Domain Name Dispute Resolution (Intellectual Property), Italian Civil Code, Contract Law, GDPR. | EU, Italy, Japan, ECHR (Council of Europe), WIPO (International arbitration). | NaN | NaN | NaN | False | False | NaN | Remaining gaps for A2J include: need for more robust and legally meaningful explainability in AI systems; challenges in ensuring AI models generalize well across diverse legal factual scenarios and evolving laws; the ongoing difficulty and cost of acquiring and annotating high-quality legal data for training A2J tools, particularly for multilingual contexts; and the risk of AI systems merely learning correlations instead of true legal reasoning, which could undermine fairness. | NaN | Potential risks include: AI systems making predictions or classifications based on spurious correlations rather than sound legal reasoning (e.g., using judge names for ECHR outcome prediction); lack of transparency in AI leading to difficulties in verifying legal soundness and accountability, particularly problematic for A2J applications; AI predictions degrading significantly when applied to new or evolving legal contexts without proper adaptation; and AI tools for A2J failing if they cannot be trusted or understood by their intended users (e.g., consumers). |
| 37BerkeleyTechLJ71.pdf | HeinOnline | PREDICTING CONSUMER CONTRACTS | This paper empirically examines GPT-3's ability to read and understand consumer contracts using a novel dataset of questions about online terms of service. The study reveals potential for consumer empowerment but also highlights risks like anti-consumer bias and model brittleness, suggesting a need for careful engineering and governance. | True | Idealistic | True | 2.0 | Neutral | GPT-3 for question answering on consumer contracts. | GPT-3's performance was tested on a novel dataset of 200 yes/no legal questions relating to the terms of service of the 20 most-visited U.S. websites. Evaluation included accuracy, calibration, and overall performance, compared against random chance, majority class, and a 'contract withheld' baseline. | GPT-3 achieved 77% accuracy overall on yes/no questions about consumer contracts. However, it showed an anti-consumer bias, correctly answering only 60% of questions about pro-consumer provisions versus 83.64% for pro-company provisions. | Consumers' lack of time, expertise, and incentive to read complex and lengthy consumer contracts, coupled with the high cost of legal services, hinder their ability to understand and exercise their contractual rights. | Language models could read and explain consumer contracts, automating tasks typically done by lawyers, thus empowering consumers and improving access to legal information and services, particularly for those with limited resources. | Understanding consumer contractual rights and obligations from online terms of service. | General consumers, particularly those who cannot afford traditional legal services. | Contract Law, Consumer Law | United States | Proprietary (OpenAI's), general, large-scale, mostly unstructured text data from the internet (Common Crawl, Webtext2) and books. | Dataset creation (200 yes/no questions based on online terms of service), controlled API-based querying of GPT-3 (Davinci model) with specific prompt design and fixed hyperparameters, quantitative performance evaluation (accuracy, calibration) against baselines, and regression analysis. | Commercial API (OpenAI API). | True | False | GPT-3 is accessible via a commercial API provided by OpenAI; academic access was used for the study. | Technical gaps include model brittleness, anti-consumer bias, lack of interpretability, and ensuring alignment with human values. Societal gaps include the need for robust governance frameworks, data protection, addressing environmental impact, IP issues, unequal performance across groups, and regulatory reform for AI-driven legal services. | Methodological challenges in evaluating GPT-3 for legal text understanding included avoiding question-answer contamination, ensuring replicability of results, maintaining transparency in experimental setup, and designing objective evaluation metrics for legal questions (leading to the use of yes/no questions). | Misleading legal advice, entrenchment and amplification of harmful biases (e.g., anti-consumer bias), model brittleness leading to unreliable outputs, misuse for spreading misinformation or generating spam, data privacy violations, and compounding of biases in future AI systems. |
| 46HarvJLGender265.pdf | HeinOnline | BISEXUAL ERASURE, MARJORIE ROWLAND, AND THE EVOLUTION OF LGBTQ RIGHTS | This paper re-examines the 1980s employment discrimination case *Rowland v. Mad River Local School District*, arguing for its overlooked significance in LGBTQ legal history, particularly concerning bisexual rights and the impact of Justice Brennan's dissent from certiorari denial. Through original archival research and an interview with Marjorie Rowland, the article highlights systemic bisexual erasure within the legal system and LGBTQ advocacy, calling for the case's recognition and continued efforts for LGBTQ equality. | True | Idealistic | False | 2.0 | NaN | Legal and historical analysis of the *Rowland v. Mad River Local School District* case, Justice Brennan's dissent from certiorari denial, and the critical examination of bisexual erasure within the legal system and LGBTQ rights discourse. | Qualitative research methods: in-person interview with the plaintiff (Marjorie Rowland), original archival research (trial court testimony, pleadings, court documents), analysis of judicial opinions and dissents, review of secondary sources (legal scholarship, casebooks, news articles), and application of critical legal theories (feminist storytelling, critical race theory principles). | The paper concludes that *Rowland v. Mad River Local School District* is an underappreciated but significant case for LGBTQ rights, crucial for understanding bisexual erasure in law. It argues Justice Brennan's dissent offered a progressive legal framework, critiques the Sixth Circuit's flawed reasoning, and underscores ongoing needs for LGBTQ+ equality and recognition of bisexual experiences. | Bisexual erasure in legal and historical narratives; judicial prejudice and flawed legal interpretations of LGBTQ rights; societal discrimination and stigma against bisexual individuals; retaliation against those asserting LGBTQ rights; assimilationist pressures within LGBTQ advocacy marginalizing bisexual concerns; lack of robust and consistently applied legal protections for sexual orientation. | Historical reclamation by recognizing the significance of cases like *Rowland* and figures like Marjorie Rowland; critical legal analysis to expose and rectify flawed judicial reasoning; promoting inclusive narratives that acknowledge bisexual identities and challenges; continued legal advocacy for stronger, explicit legal protections (e.g., strict scrutiny for sexual orientation discrimination); enhancing education on bisexuality and related legal issues in law schools and scholarship. | LGBTQ rights; Bisexual rights and bisexual erasure; Employment discrimination (based on sexual orientation); First Amendment rights (freedom of speech for public employees); Equal Protection Clause (application to sexual orientation); Legal history of LGBTQ rights; Impact of judicial dissents. | Bisexual individuals; LGBTQ community; Public school teachers and counselors. | Constitutional Law (First Amendment, Equal Protection); Employment Discrimination Law; Civil Rights Law; LGBTQ+ Law; Legal History. | United States (primarily federal courts: S.D. Ohio, Sixth Circuit, U.S. Supreme Court certiorari denial); Ohio (state law context for teacher employment). | NaN | Legal analysis (of case law, statutes, legal arguments); Historical research (original archival research, in-person interview); Critical legal theory (feminist storytelling, critical race theory principles); Case study method. | NaN | False | False | NaN | Societal: Persistent bisexual erasure, prejudice, and discrimination; ongoing attacks on freedom of expression in education. Legal: Lack of consistent strict scrutiny for sexual orientation discrimination; vulnerability of current LGBTQ protections; need for explicit comprehensive federal anti-discrimination laws; procedural barriers for plaintiffs. Scholarly: Insufficient attention to bisexual legal issues and cases like *Rowland* in legal scholarship and education. | NaN | Continued discrimination and denial of rights for LGBTQ individuals, especially bisexuals, due to legal and societal erasure; regression in LGBTQ rights from conservative judicial or legislative actions; harm to LGBTQ individuals (especially youth) from discriminatory school policies; erosion of free speech in educational contexts; increased barriers to justice for marginalized plaintiffs. |
| 6Issue2IntlJLMgmtHuman.pdf | HeinOnline | Impact of Artificial Intelligence (AI) on Legal Profession and Justice System | The paper discusses the impact of AI on the legal profession and justice system, exploring its potential benefits like increased efficiency and access to justice, alongside drawbacks such as job displacement and algorithmic bias. It emphasizes the need for legal professionals' education in AI, ethical considerations, robust accountability mechanisms, and a comprehensive regulatory framework. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Case backlogs causing justice delays; AI bias leading to unfair or discriminatory outcomes; Lack of AI transparency and explainability (black box problem); Digital divide hindering equitable access to AI-powered justice; Erosion of human discretion and essential human-centric justice principles. | Utilizing AI to improve efficiency in legal processes and reduce case backlogs; Developing AI-powered Online Dispute Resolution (ODR) and free legal aid portals; Establishing comprehensive regulatory frameworks and strong ethical guidelines for AI in law; Emphasizing human oversight and Human-AI collaboration models; Mandating education and training for legal professionals and judges on AI. | Reducing court backlogs and justice delays; Providing legal aid and advice to underserved populations; Enhancing procedural efficiency in the justice system; Online Dispute Resolution (ODR); Ensuring fairness and mitigating bias in AI-driven justice tools. | People who cannot afford to hire lawyers; Vulnerable populations susceptible to algorithmic bias; General public seeking access to justice. | General (Civil Law, Criminal Law), Contract Law, Human Rights Law, Motor Accident Claims, Family Law, Tax Law, Electoral Law, Medical Negligence. | International (with specific examples and focus on India, USA, UK, EU, China, Brazil, Estonia, Malaysia, Kenya). | The paper discusses various AI systems that use diverse training data, including legal documents (e.g., case law, contracts), judicial decisions, criminal records, questionnaire responses, medical records, and game data. Concerns are raised about biases in legacy datasets. | NaN | NaN | False | False | NaN | Lack of comprehensive regulatory frameworks for AI in the legal sector; Inadequate methods to ensure AI transparency, explainability, and accountability; Persistence of AI bias and the challenge of achieving true fairness; Insufficient training and understanding of AI among legal professionals and judges; Bridging the digital divide for equitable access to AI justice tools; Difficulty in assigning liability for AI errors. | NaN | Job displacement for legal professionals; Algorithmic bias leading to discriminatory outcomes; Lack of transparency and explainability in AI decisions impacting due process; Erosion of human discretion and judicial independence; Creation and misuse of deepfakes and manipulated information; Automation bias leading to over-reliance on AI; Privacy violations from extensive data processing; Cybersecurity threats to legal AI systems; Diminished public trust in the justice system. |
| 13Laws1 (2).pdf | HeinOnline | The Legal Challenges of Realistic and AI-Driven Child Sexual Abuse Material: Regulatory and Enforcement Perspectives in Europe | This paper reviews current European legislative measures for combating online Child Sexual Abuse Material (CSAM), with a particular focus on the challenges posed by AI-driven CSAM. It systematically evaluates the effectiveness and applicability of these regulations in addressing virtual CSAM and concludes with policy recommendations for identified gaps. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Inconsistent criminalization of AI-driven CSAM (virtual depictions) across EU member states due to an exception in Directive 2011/93/EU; Difficulties in cross-border cooperation due to varying legal definitions, data retention laws, and jurisdictional complexities; Balancing online safety measures (like content monitoring and detection orders) with fundamental rights to privacy and data protection; Lack of resources and varying infrastructure in member states for effective implementation and enforcement of regulations; Rapid technological advancements (like AI-generated content) outpacing legal frameworks. | Standardize the definition and scope of online CSAM to uniformly criminalize realistic and AI-generated/manipulated CSAM across EU member states; Enhance cross-sector and international cooperation (e.g., between law enforcement, ISPs, AI developers, and global alliances); Require online platforms to implement efficient age verification systems and robust community guidelines/support; Ensure law enforcement agencies continuously update skills and tools, with support from EU and international institutions; Standardize data retention laws to facilitate digital evidence acquisition; Develop and harmonize cybercrime laws globally. | Child protection against sexual abuse and exploitation; Regulation of AI-generated harmful content (specifically AI-driven CSAM); Online safety for children; Legal and enforcement challenges related to virtual CSAM and deepfakes; Cross-border cooperation in combating cybercrime. | Children (as victims or potential victims of traditional and AI-driven Child Sexual Abuse Material). | Criminal Law; Cybercrime Law; EU Law; International Law; Human Rights Law (Children's Rights). | Europe; European Union | NaN | NaN | NaN | False | False | NaN | Discrepancies in criminalizing realistic/virtual CSAM among EU member states due to Directive 2011/93/EU allowing exceptions; Insufficient legal frameworks specifically addressing AI-driven CSAM and deepfakes; Lack of common standards for admissibility of evidence gathered through ISP surveillance in the EU; The European Directive 2011/93/EU needs updates to cover all technological issues and reconcile fundamental rights with combating child sexual abuse. | Balancing online safety requirements (e.g., ISP monitoring for CSAM) with citizens' privacy and freedom of expression, especially concerning end-to-end encryption; Inconsistent implementation and enforcement of EU directives across member states due to differing resources, infrastructure, and legal interpretations; Rapid technological evolution of AI-driven CSAM outpacing legislative updates; Difficulties in obtaining digital evidence and ensuring effective cross-border cooperation due to jurisdictional issues and varying national laws (e.g., data retention); High error rates and potential for circumvention of AI tools used for detecting CSAM and grooming. | Revictimization of former victims and new victimization through AI-driven CSAM (deepfakes, AI-generated content); Increased burden on law enforcement, hindering identification of real child victims; Normalization and desensitization to CSAM, potentially increasing risk of contact offenses; Psychological harm to children depicted in CSAM; Chilling effect on online communication and freedom of expression due to broad surveillance measures; Misuse of AI for mass surveillance and erosion of privacy rights; Overload of law enforcement with false positives from automated detection tools; Undue criminalization and stigmatization of minors exchanging consensual self-generated content if flagged by automated systems. |
| 4AmicusCuriae685.pdf | HeinOnline | PUTTING THE ARTIFICIAL INTELLIGENCE IN ALTERNATIVE DISPUTE RESOLUTION: How Al RULES WILL BECOME ADR RULES | This article argues that the evolving regulatory and governance landscape for artificial intelligence (AI) will significantly influence alternative dispute resolution (ADR), as AI becomes increasingly integrated into ADR processes. Appropriate AI regulations, sharing goals like trustworthiness and fairness with ADR, are expected to benefit the field by addressing existing challenges and enhancing accountability. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Difficulty training AI for complex disputes due to nuanced legal interpretation and lack of representative datasets (due to ADR confidentiality); AI's limitations in handling social/emotional aspects, novel analysis, and interpretation; concerns about AI accuracy, bias, fairness; lack of transparency and explainability in "black box" systems, undermining due process rights. | The paper suggests that emerging AI regulations and governance frameworks (e.g., EU AI Act, NIST RMF, ABA ODR principles) promoting trustworthiness, fairness, transparency, explainability, and accountability will address these obstacles by setting standards for AI used in ADR. It posits that these AI rules will effectively become ADR rules, potentially leading to higher standards for AIDR systems than currently exist for human neutrals. | Improving efficiency, affordability, and reliability of dispute resolution; enhancing access to justice for self-represented litigants and underrepresented parties; ensuring fairness, transparency, and accountability in ADR processes. | Self-represented litigants, underrepresented parties. | Alternative Dispute Resolution (ADR), Online Dispute Resolution (ODR), International Commercial Arbitration, Product Liability, Civil Law, Commercial Law, Administrative Law. Mentions tort, property, insurance, family law as examples. | European Union, United Kingdom, United States. Also mentions UNCITRAL (International), Canada, Colombia. | NaN | NaN | NaN | False | False | NaN | The implementation gap between proposed/predicted AI technologies in ADR and those actually realized; lack of universally agreed-upon and enforceable ADR governance and AI regulatory frameworks; potential for current ADR rules to not fully address AI-specific issues like systemic bias or algorithmic explainability. | Training AI for complex legal disputes with limited and confidential data; ensuring AI systems can handle nuanced interpretation, social, and emotional aspects of disputes; addressing AI accuracy, bias, and fairness; achieving transparency and explainability in AI decision-making; integrating AI into existing ADR frameworks without undermining due process. | AI systems causing discrimination (e.g., racial bias in decision-making); liability for AI-generated harms (physical injury, property damage, data loss, privacy breach); undermining individuals' right to a reasoned decision, appeal, and due process due to opaque AI systems; systemic advantages for technologically adept parties; ADR practitioners' liability for using flawed AI systems. |
| 108Judicature42.pdf | HeinOnline | How to Harness AI for Justice | This paper explores how generative AI can enhance access to justice for self-represented litigants by automating legal tasks, democratizing information, and improving court processes. It also outlines significant risks, such as bias and inaccuracies, proposing careful implementation through best practices like diverse data, human oversight, and rigorous evaluation. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Complexity and impenetrability of the legal system; high cost of legal representation resulting in widespread self-representation; existing biases and discrimination within the justice system; barriers to technology adoption by self-represented litigants. | Leveraging generative AI to provide accessible legal information, automate routine legal tasks, facilitate online dispute resolution, and simplify legal procedures; implementing AI tools responsibly by adhering to best practices (diverse data, human oversight, impact assessments, transparency); adopting rigorous evaluation methodologies (e.g., RCTs, pilot programs) for AI innovations. | Assisting self-represented litigants; Online Dispute Resolution (ODR); legal information provision and document generation; litigation avoidance and conflict prevention; simplification of legal rules and procedures; improving court efficiency and user experience; reducing bias in legal decisions; procedural fairness including translation. | Self-represented litigants, individuals unable to afford legal representation, racial and ethnic minorities, non-English speakers. | Civil Law (including family law, consumer debt, landlord-tenant/eviction), Administrative Law (unemployment benefits). | United States | NaN | NaN | NaN | False | False | NaN | Overcoming training data limitations to ensure AI serves diverse populations equitably; completely eliminating AI 'hallucinations'; achieving full transparency in proprietary AI decision-making processes; insufficient evidence base for many legal practices and reluctance to adopt experimental evaluation methods. | Mitigating exposure bias from unrepresentative training data; managing AI 'hallucinations' (false information/citations); ensuring transparency for due process; high cost of advanced, less error-prone AI models for A2J applications; overcoming institutional inertia among legal professionals. | Incorrect AI guidance leading to adverse legal outcomes (e.g., default judgments); generation of harmful or inappropriate advice by AI; submission of fabricated information or false legal citations to courts; compromised due process rights due to opaque AI decision-making; exacerbation of societal inequities through biased AI tools. |
| 35BondLRev143.pdf | HeinOnline | Legal Considerations in Machine-Assisted Decision-Making: Planning and Building as a Case Study | This paper identifies and examines legal considerations for governments and businesses in automating decision-making processes, using planning and building approvals as a case study. It argues that while AI offers benefits like efficiency, careful attention to transparency, bias, privacy, liability, and admissibility is crucial to minimize risks. | True | Idealistic | False | 3.0 | Neutral | Machine-assisted decision-making systems (including deterministic AI, machine learning, use of Building Information Modelling (BIM) with AI, and generative AI) in the context of planning and building approvals. | NaN | NaN | Lack of transparency in AI decision-making (technical and legal 'black boxes'), algorithmic bias, privacy and data protection concerns, outdated legal frameworks (e.g., definition of 'decision', accountability of decision-maker), assigning liability for AI-caused harm, and admissibility of AI-generated evidence. | Developing 'explainable AI' (XAI), legislative reforms to ensure transparency and accountability (e.g., defining AI decisions, deeming provisions for responsibility), auditing AI systems for bias, implementing robust data protection, clarifying liability rules for AI systems, and updating evidentiary rules. | Right to reasons, fairness and non-discrimination in automated decision-making, accountability for AI systems in government, judicial and administrative review of automated decisions, privacy implications of AI in public administration, liability for AI errors. | General public affected by government administrative decisions, particularly in planning and building permit processes. | Administrative law, planning and building law, privacy and data protection law, tort law (negligence, liability), intellectual property law, evidence law. | Victoria (Australia), with references to Australian federal law, EU, US, and UK. | NaN | NaN | NaN | False | False | NaN | Need for truly explainable and auditable AI systems, adaptation of legal doctrines to AI (e.g., 'decision', liability, standard of care), balancing proprietary AI interests with public transparency requirements in government, addressing risks of generative AI like 'hallucinations' in public sector use, and ensuring legal frameworks evolve with AI capabilities. | Ensuring transparency and explainability in complex AI decision-making processes. Preventing and mitigating algorithmic bias stemming from data or design. Integrating AI systems with existing legal frameworks for review, accountability, and liability. Protecting privacy and intellectual property rights when using AI and shared data platforms like BIM. | Unchallengeable decisions due to opaque AI systems ('black box' problem). Entrenchment of societal biases leading to discriminatory outcomes. Breaches of privacy, data security, and intellectual property rights. Harm or loss caused by erroneous or unlawful AI-driven decisions. Difficulty in assigning legal liability for damages caused by AI. Misinformation or untruthful content generated by AI (e.g., 'hallucinations') used in public sector decision-making. |
| 19OhioStTechLJ171.pdf | HeinOnline | THE SUBJECTS AND STAGES OF Al DATASET DEVELOPMENT: A FRAMEWORK FOR DATASET ACCOUNTABILITY | This paper examines the development process of large-scale AI datasets (LSLDs and LSCVDs), outlining the stages involved and the subjects affected, to identify pertinent legal issues such as copyright and privacy. It proposes a comprehensive framework, including a matrix of harms, to foster dataset accountability and mitigate adverse impacts from these datasets and the AI models trained on them. | True | Idealistic | True | 1.0 | Positive | A framework for dataset accountability, including taxonomies of dataset development stages (Problem Formulation, Data Collection, Data Cleaning, Data Annotation, Model Training and Evaluation, Model Implementation and Inference, Data and Representation Distribution) and dataset development subjects (data subjects, data annotators, copyright holders, model subjects), and a matrix mapping harms to these stages and subjects. | NaN | NaN | Opacity in dataset development processes; legal uncertainties regarding copyright and privacy for AI datasets; prevalence of biased, discriminatory, or otherwise harmful datasets impacting marginalized groups; difficulty in assigning responsibility for AI-driven harms; lack of meaningful consent and awareness from data subjects; perpetuation of harms through widely distributed datasets and pre-trained models. | Proposing a framework for dataset accountability (identifying stages, subjects, and potential harms); advocating for enhanced transparency and documentation in dataset development (e.g., datasheets); calling for recalibration of legal norms (copyright, privacy, due process) in the context of AI datasets; suggesting incorporation of accountability principles into legislative and regulatory measures. | Algorithmic bias and discrimination; privacy violations in data collection and use; copyright infringement in AI datasets; accountability for AI-driven harms; due process in automated decision-making systems; systemic informational harms. | Marginalized social and economic groups; racial, ethnic, gender, and religious minorities; disabled individuals; refugees and migrants; individuals in the Global South. | Copyright Law, Privacy Law, Constitutional Law (Due Process, Equal Protection), AI Law and Regulation. | United States (primarily, with discussion of US legal doctrines like fair use, FTC, proposed US legislation), with references to international data sources and issues. | NaN | Literature review (law, computer science, social sciences); case study analysis of existing AI datasets (e.g., ImageNet, LFW, Common Crawl, The Pile); legal analysis; conceptual framework and matrix development. | Publication in an academic journal intended for adoption by researchers, policymakers, legal practitioners, and industry leaders to inform dataset governance and accountability practices. | True | False | The conceptual framework and matrix are detailed within the published paper. Access to the paper itself (e.g., via HeinOnline) may require a subscription. | Lack of comprehensive legal and regulatory frameworks specifically addressing the lifecycle of AI dataset development; insufficient transparency and standardized documentation for datasets; challenges in applying existing legal doctrines (e.g., copyright, privacy) to novel harms engendered by AI datasets; need for effective individual and systemic accountability mechanisms and means of redress for data subjects and model subjects; limited understanding and conceptualization of novel informational harms. | The inherent complexity, opacity, and often poor documentation of current AI dataset development practices; the need to integrate multifaceted legal, ethical, and technical considerations; addressing the rapidly evolving nature of AI technologies and data practices when proposing a stable framework. | Wrongful accusations and arrests from biased AI systems (e.g., facial recognition); discrimination, stereotyping, and reinforcement of societal biases; significant privacy violations through data scraping, aggregation, and leakage of personally identifiable information; use of datasets for pervasive surveillance; reintroduction of security vulnerabilities via code generation models; copyright infringement and complex licensing conflicts; creation of offensive or harmful content by generative models; difficulty in retracting harmful datasets anAI models once distributed. |
| 14StMarysJonLegalMalpract.pdf | HeinOnline | Artificial Intelligence and Legal Malpractice Liability | This paper explores the anticipated rise in legal malpractice claims as AI becomes more prevalent in legal services. It analyzes how existing legal malpractice doctrines and ethical obligations, such as competence, loyalty, and informed consent, will apply to lawyers' use of AI. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | Algorithmic bias, lack of transparency, potential for errors and misuse in AI systems, rapid pace of AI development outpacing cultural absorption, and privacy concerns. AI systems may negatively impact individuals if not properly managed, particularly in areas like benefit denials or legal status determinations. | Adherence to principles like those in the 'Blueprint for an AI Bill of Rights' (safe and effective systems, algorithmic discrimination protections, data privacy, notice and explanation, human alternatives). Promoting human authority, oversight, control, accountability, and transparency in AI use. Ensuring lawyers obtain informed consent for AI use and firms proactively manage AI-related risks. | Affordable legal advice, self-help legal marketplace. | Ordinary persons unable to afford traditional legal services, users of self-help legal tools. | Legal Malpractice, Professional Ethics, General Civil Litigation, Contract Law | United States (with specific references to Texas and New York state law and New York City regulations) | NaN | NaN | NaN | True | True | The paper mentions that the law firm BNH.AI is launching 'Microwave,' a 'publicly available-and free-tool that tests AI risks' (p. 78). | Societal capacity to safely absorb rapidly advancing AI technology. Lack of widespread AI literacy and formal education/training on AI for legal professionals. Technical gaps in AI related to capturing human knowledge flexibility, ensuring unbiased outputs, and maintaining transparency. | AI systems acting in unanticipated ways, inherent algorithmic bias and lack of transparency, the rapidly evolving nature of AI technology, difficulty in distinguishing fact from fiction in AI outputs, potential for AI to generate biased language, ensuring data privacy and security, maintaining necessary human oversight and judgment, and navigating intellectual property issues with AI-generated content. | Economic or other harm to clients from AI errors or misuse; increased legal malpractice liability for lawyers and firms; algorithmic bias leading to discrimination in legal and administrative decisions; lack of transparency hindering accountability; breaches of data privacy and client confidentiality; spread of misinformation and 'cyber propaganda' via AI; AI systems making factual errors; cybersecurity vulnerabilities in AI systems; potential for misuse in creating fake documents or for phishing attacks; infringement of intellectual property rights. |
| 12ResolvedJAlternativeDis.pdf | HeinOnline | Ai: INCREASING ALTERNATIVES IN ALTERNATIVE DISPUTE RESOLUTION | This paper examines the application of Artificial Intelligence (AI) in Alternative Dispute Resolution (ADR), explaining AI mechanisms and their use in resolving disputes. It argues that AI can expand access to justice, lower costs, and increase efficiency in ADR, despite challenges such as bias and the need for human empathy. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | High cost of legal services, court congestion and delays, physical and geographical barriers to accessing courts, power imbalances in disputes (e.g., domestic violence), and the complexity of the legal system for self-represented litigants. | Utilizing AI in ADR (AIDR) to reduce costs, increase efficiency, alleviate court congestion, enable remote dispute resolution, lessen power imbalances, and support self-represented litigants through user-centric ODR platforms. The paper also suggests mandatory AIDR education in law schools and updating laws for new technologies. | Affordability of legal services, efficiency of the justice system (reducing backlogs/delays), accessibility for remote/constrained individuals, empowerment of vulnerable parties (e.g., domestic violence victims), support for self-represented litigants, Online Dispute Resolution. | Low-income individuals, self-represented litigants, victims of domestic violence or assault, individuals facing geographic or physical barriers, and disputants with language barriers. | Alternative Dispute Resolution (Arbitration, Mediation, Online Dispute Resolution), Family Law, Small Claims, Contract Law, Immigration Law, Tax Law, Civil Litigation. | United States | NaN | NaN | NaN | True | True | ChatGPT-4 mentioned as a subscription service; DoNotPay app described as an app providing free remedies; ROSS Intelligence, eBay's ODR, Lexis Nexis Legal Machina also mentioned as existing tools. | Technical gaps include AI's limited emotional intelligence/intuition and the 'black box' problem of AI decision-making. Societal and ethical gaps include algorithmic bias and accountability, data privacy concerns, the digital divide, lack of technological competence among legal professionals, and the need for updated legal and ethical frameworks for AIDR. | The absence of human presence (empathy, intuition) in AI-driven ADR. The introduction and perpetuation of bias through AI algorithms and data. Increased risks of professional misconduct related to confidentiality, competence, and reliance on AI. Ensuring data privacy and security. Achieving public and professional trust and acceptance of AI in dispute resolution. | Discriminatory outcomes from biased AI (e.g., mispredictions in criminal justice or immigration). Violations of client confidentiality and privacy through AI data processing. Professional misconduct by lawyers due to incompetent use or over-reliance on AI (e.g., citing non-existent cases). AI providing incorrect legal judgments or flawed advice. Data security breaches (e.g., hacking of LLMs). |
| 36SAcLJ307.pdf | HeinOnline | GENERATIVE ARTIFICIAL INTELLIGENCE The Protection of Personal Data and Countering False Narratives About the Person | This paper discusses the personal data protection and false information concerns arising from Generative AI (Gen AI), particularly in the Singaporean context. It examines current legal frameworks, policy responses, and proposes legal and non-legal measures to govern Gen AI and protect individuals. | True | Idealistic | True | 3.0 | Neutral | Generative AI (Gen AI), including Large Language Models (LLMs) like ChatGPT | NaN | NaN | Threats to personal data privacy, accuracy of personal information, lack of transparency and accountability in Gen AI, and the generation of false narratives about individuals. | Purposive interpretation and adaptation of existing laws (data protection, false information), new governance measures (licensing, reporting, mandatory disclosures), emphasis on transparency (source citation), and education for users and professionals. | Protection of personal data, Countering false narratives about individuals | Individuals generally (data subjects, persons subject to false narratives) | Data protection law, Privacy law, Laws against false information (e.g., defamation, POFMA, PHA), AI regulation/governance, Content regulation | Singapore (primary), with comparisons to EU, US, Canada, Australia, and international efforts | General discussion: large datasets, potentially including publicly available and user-provided data; user interaction and feedback. | NaN | NaN | False | False | NaN | Need for more specific and harmonized AI/Gen AI regulations (nationally and internationally), enhanced transparency and accountability mechanisms for Gen AI, and more effective tools to combat AI-generated false narratives and protect personal data. | NaN | Generation of false narratives about individuals, misuse and unauthorized collection, use, or disclosure (CUD) of personal data, lack of transparency and accountability in Gen AI systems, creation of deepfakes, and embedded bias leading to discrimination. |
| 92TennLRev87.pdf | HeinOnline | BEYOND CHATGPT: TRANSFORMING GOVERNMENT WITH AUGMENTED LLMS | This paper explores how generative AI, specifically augmented Large Language Models (LLMs), can enhance government efficiency and equitable access to services, particularly in legal administration like taxation. It discusses methods such as fine-tuning and Retrieval-Augmented Generation (RAG) to improve LLM performance and mitigate risks like bias and inaccuracy, advocating for a collaborative approach to responsible AI adoption in the public sector. | True | Idealistic | True | 3.0 | Positive | Augmented LLMs, specifically fine-tuning (including Reinforcement Learning from Human Feedback - RLHF), Retrieval-Augmented Generation (RAG), and the use of local/open-source LLMs. | NaN | NaN | Bias in AI, inaccuracy and hallucinations, lack of transparency (black box models), security and privacy vulnerabilities, "simplexity" (oversimplification of complex legal matters leading to misunderstanding), and the digital divide hindering universal accessibility to AI tools. | Proper design, careful application, and rigorous oversight of AI systems; using techniques like fine-tuning and RAG to improve accuracy and relevance; developing equity-focused AI tools (e.g., multilingual capabilities, tailored for specific communities/needs); creating tools to support intermediaries (e.g., legal aid, VITA sites) to bridge the digital divide; and improving transparency of automated legal guidance. | Improving access to government services and legal information, facilitating understanding of legal obligations (e.g., tax compliance), enhancing access to benefits (like EITC), supporting pro se litigants in legal processes, addressing misinformation, and overcoming language and literacy barriers in government interactions. | Marginalized communities, lower-income taxpayers, non-native English speakers, pro se litigants, the elderly, individuals in rural areas, persons with disabilities, and those with lower levels of education or digital literacy. | Tax administration (primary case study), administrative law, and peripherally mentions immigration law, patent law, and securities law. | U.S. (focuses on federal agencies like the IRS and mentions state-level initiatives in California, Minnesota, etc.) | General LLMs are trained on vast, diverse internet data, books, and articles. The paper advocates for augmenting these with curated, domain-specific datasets (e.g., legal texts, agency policies, anonymized case data), human feedback data for fine-tuning, and proprietary or public knowledge bases for RAG implementations. | The paper discusses augmenting LLMs through fine-tuning (including Reinforcement Learning from Human Feedback - RLHF) and Retrieval-Augmented Generation (RAG). It also emphasizes a collaborative approach involving subject-matter experts, technical experts, government authorities, and community feedback. | Proposed deployment includes LLM-powered chatbots and voicebots for public interaction, internal tools for government employee training and support, systems for generating educational content (e.g., infographics, simplified explanations), tools for intermediaries assisting underserved communities, and applications to help individuals draft communications with government agencies. | False | False | NaN | Technical gaps include mitigating hallucinations, reducing the cost and complexity of re-training and fine-tuning LLMs, and improving their transparency and interpretability. Societal gaps involve ensuring equitable technology access, building and maintaining public trust, bridging the digital divide, effectively reaching vulnerable populations, and fostering robust collaboration between legal, governmental, and technical experts for ethical AI deployment. | Key challenges include inherent LLM limitations (e.g., hallucinations, bias, reliability, security risks, opacity, cost of development and maintenance) and governmental hurdles such as budget constraints, knowledge deficits regarding AI, lower risk tolerance for new technologies, and the need for careful ethical and regulatory frameworks for public sector AI adoption and use. | Bias perpetuation leading to discriminatory outcomes, dissemination of misinformation or inaccurate legal guidance, privacy breaches and misuse of sensitive data, malicious use for disinformation or fraud, over-reliance on imperfect technology leading to errors, "simplexity" causing misunderstandings of law, and the exacerbation of societal inequities through differential access to technology or flawed AI-driven services. |
| 56ArizStLJ187.pdf | HeinOnline | Co-Authoring with an AI? Ethical Dilemmas and Artificial Intelligence | This paper examines the use of generative AI, specifically ChatGPT and Bing Chat, in legal academic writing, exploring ethical dilemmas and the capabilities of these tools through direct engagement. It also reviews and compares existing publisher guidelines on AI-generated text, highlighting the lack of policies in law reviews and advocating for the development of AI policies for legal writing. | True | NaN | True | 2.0 | NaN | Using generative AI models (ChatGPT and Microsoft's Bing Chat) for legal academic writing, and analysis of publisher guidelines regarding AI use. | The authors engaged with ChatGPT and Bing Chat by posing questions about AI ethics and asking the AI to structure the responses as parts of an academic article. They compared the outputs of ChatGPT (pre-2021 data) and Bing Chat (internet-connected). They also reviewed and compared AI policies of major academic publishers and a sample of law reviews. | ChatGPT provided relevant answers but suffered from 'hallucinated' (non-existent or inaccurate) sources and its knowledge was limited to pre-2021 data. Bing Chat provided more up-to-date and accurate sources with links, but these were often not the most relevant academic references for the specific queries. Major publishers have divergent AI policies (bans, transparency requirements, mixed approaches), while law reviews largely lack explicit AI guidelines. | NaN | NaN | NaN | NaN | Legal writing, Legal academia, Ethics of AI, Publishing law, Intellectual property (briefly) | EU, US, International | The paper discusses ChatGPT, which was trained on a massive corpus of text data using machine learning techniques (GPT-3 variant with billions of parameters, data pre-dating end of 2021 for the version tested initially). Bing Chat is described as an internet-connected variant based on ChatGPT. | The authors conducted an exploratory study by interacting with ChatGPT and Bing Chat through structured prompts and questions. They also performed a comparative analysis of AI policies from major academic publishers and leading law reviews. The authors themselves used ChatGPT in drafting parts of the appendices, followed by review and revision. | The paper discusses ChatGPT and Bing Chat (now Co-pilot) which are deployed as widely accessible consumer applications by OpenAI and Microsoft, respectively. The paper itself is deployed via academic publication. | True | True | ChatGPT is available from OpenAI with both free and paid subscription tiers. Bing Chat (Co-pilot) is available for free as part of Microsoft's Bing search engine and other Microsoft products. | NaN | The paper identifies challenges in using generative AI for academic writing, including: ensuring accuracy and avoiding 'hallucinated' or fabricated information (especially sources); the limited up-to-dateness of some models; potential for plagiarism; lack of genuine creativity or critical analysis from AI; defining authorship and accountability; and the current inconsistency or absence of clear editorial policies from publishers and law reviews regarding AI use. | Key risks stated include: generation of 'hallucinated' or fake information (e.g., non-existent legal cases) that could mislead; plagiarism; AI-generated text creating biases; decreased transparency in research if AI use is not disclosed; potential for AI to write fake abstracts that fool scientists; over-reliance on AI by authors; and the general impact on academic integrity if AI-generated content is not properly managed. The Mata v. Avianca case is cited as an example of risks in legal practice. |
| 27SMUSciTechLRev11.pdf | HeinOnline | Algorithmic Adjudication and Constitutional AI - The Promise of a Better AI Decision Making Future? | This paper argues that algorithmic adjudication, where AI makes legal decisions without human intervention, is inevitable. It discusses the challenges of traditional AI perpetuating biases and lacking explainability, and proposes Anthropic's "Constitutional AI" framework as a potentially more explainable, fair, and societally-aligned approach for future AI decision-making systems in law. | True | Idealistic | True | 2.0 | Positive | Constitutional AI (CAI), specifically Anthropic's methodology for training its LLM Claude, which involves using a predefined set of principles (a "constitution") to guide AI behavior during fine-tuning, particularly through Reinforcement Learning from AI Feedback (RLAIF). | The paper describes Anthropic's methodology for Constitutional AI. This involves a supervised learning phase where an LLM critiques and revises its own responses based on a 'constitution,' followed by a reinforcement learning phase (RLAIF) where an AI model evaluates response pairs against the constitution to train a preference model. Evaluation is based on the model's adherence to principles of harmlessness, helpfulness, honesty, and the defined constitution, and its outputs are compared to those from RLHF models. | Constitutional AI models, like Anthropic's Claude, are claimed to produce more explainable results that are better aligned with societal values and the defined 'constitution'. They are suggested to reduce the risk of introducing subjective human biases compared to RLHF, offer a more objective basis for training, and are potentially more efficient and scalable for fine-tuning. | Traditional AI perpetuates existing biases and its decisions can be difficult to explain (opacity). AI systems may lack contextual understanding for nuanced legal cases and may not grasp cultural sensitivities. There's a 'human-AI fairness gap' where people perceive algorithmic decisions as less fair. The legal profession also shows resistance to understanding and adopting new technologies. | The paper proposes using "Constitutional AI" frameworks integrating legal and ethical standards into AI design. It advocates for legal professionals to gain greater understanding of AI, participate in the design, development, and monitoring of algorithmic adjudication systems, and collaborate to establish ethical guidelines. | Algorithmic adjudication, fairness and bias in AI legal decision-making, explainability and transparency of AI in law, accessibility of legal processes (e.g., for small claims, reducing backlogs), maintaining integrity of the legal system and public trust. | General public needing access to dispute resolution, especially for smaller or routine cases, aiming to enhance accessibility and efficiency of the legal process. | General (algorithmic adjudication), with examples and implications for administrative law, civil law (specifically small claims), criminal law (predictive aspects), and alternative dispute resolution (ADR). | United States (primary focus regarding inevitability and implications), with international examples from Estonia, China, England and Wales, and Colombia. The Constitutional AI approach is discussed generally. | For Constitutional AI (Anthropic's Claude): The initial LLM is pre-trained on vast text corpora. The fine-tuning process uses AI-generated data: self-critiques, revisions, and preference labels generated by AI models, guided by a human-defined 'constitution' (inspired by sources like the UN Universal Declaration of Human Rights and ethical AI principles). | For Constitutional AI: Supervised Learning (SL) for initial alignment with the constitution, and Reinforcement Learning from AI Feedback (RLAIF) for further refinement based on AI-generated evaluations against the constitution. This embodies a principle-based design approach. | NaN | True | False | Anthropic's LLM Claude 3, which embodies the Constitutional AI training methodology, is commercially available via API and web interface. | The effectiveness of Constitutional AI depends on the quality and comprehensiveness of its guiding 'constitution.' The application of LLM technology in actual AI decision-making systems is still in its early stages. There's a need for greater involvement of legal professionals in the lifecycle of AI adjudication systems and for continued efforts to build and maintain public trust. | For Constitutional AI: Ensuring the 'constitution' (set of principles) is well-defined, comprehensive, and effectively covers all ethical considerations. The technology is still nascent for complex decision-making systems. General LLM fine-tuning challenges like resource intensity and potential for bias (though CAI aims to mitigate these compared to RLHF) remain relevant contexts. | Perpetuation of biases if the 'constitution' in CAI is not robust or if training data issues persist. Lack of explainability (though CAI aims for improvement). Potential for unfair or unjust outcomes if AI lacks nuanced understanding. Erosion of public trust. Algorithmic deference or automation bias leading to insufficient human oversight. Decisions being technically correct but failing to deliver broader justice. |
| 69SDLRev652.pdf | HeinOnline | REIMAGINING THE SUCCESSFUL ATTORNEY ARCHETYPE | This paper critiques the conventional, often geographically-detached, model of attorney success, arguing it's unsustainable and contributes to issues like rural legal deserts. It proposes lawyers reimagine success by deeply embedding within communities, especially rural ones, to foster personal fulfillment, social wealth, and tangible positive impact, adapting to changes like AI. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Lack of attorneys in rural areas ('legal deserts' and 'greying' rural bar); financial pressures (e.g., student debt) steering lawyers away from public interest or rural practice; distrust of 'outsider' lawyers in close-knit communities; urban-centric legal systems neglecting rural needs. | Reimagining attorney success to prioritize community rootedness, social wealth, and local impact over conventional metrics; encouraging legal professionals to practice in and integrate with rural communities, offering both formal and informal legal support; leveraging technology, including AI, and remote work to enhance rural legal practice and access to justice. | Rural access to justice; Addressing legal deserts; Role of lawyers in community development and well-being; Ethical and impactful use of AI in legal practice for community benefit. | Rural communities; Individuals in rural areas lacking legal representation. | General legal practice | United States | NaN | NaN | NaN | False | False | NaN | Societal: The dominant, unsustainable model of 'successful attorney' de-emphasizing community; insufficient lawyers in rural areas ('legal deserts'); financial barriers (student debt) preventing lawyers from choosing community-focused or rural careers; distrust between communities and 'outsider' professionals; need for greater social imagination to address_complex_problems. Technical/AI: Ethical AI use, bias prevention, and data privacy in legal applications; ensuring AI tools are reliable and avoid 'hallucinations' in legal advice; the digital divide (though acknowledged as narrowing). | NaN | AI-related: Legal malpractice from improper LLM use; exposure of sensitive data to AI; AI bias; 'hallucinations' or errors in AI-generated legal content. Societal: Social poverty, cultural erosion, and loneliness from lack of community connection; negative externalities (environmental, mental health) from relentless pursuit of productivity; exacerbation of wealth inequality. |
| 14JChristianLegalThought1.pdf | HeinOnline | MORE THAN MACHINES: THE ETHICAL AND HUMAN IMPLICATIONS OF GENERATIVE Al ON LAWYERING | This paper examines the ethical challenges generative AI poses for lawyers, including issues of competence, confidentiality, and supervision. It further argues that AI's rise necessitates a renewed focus on uniquely human qualities such as advocacy, empathy, and wisdom, especially for Christian lawyers. | True | Idealistic | True | 3.0 | Neutral | Generative AI (e.g., ChatGPT, LLMs) | NaN | NaN | AI generating "hallucinations" (fabricated content presented as real authority); AI bias perpetuating societal biases; Risks to client confidentiality when inputting information into AI tools; Potential for AI to facilitate the unauthorized practice of law if not properly supervised; Over-reliance on AI diminishing human oversight and professional judgment. | Lawyers should cultivate uniquely human qualities such as advocacy (especially for the vulnerable), empathy (understanding clients as fellow humans), and wisdom (moral and practical judgment, including biblical wisdom for Christian lawyers). Lawyers must adhere to ethical duties when using AI, including competence, diligence, confidentiality, proper supervision, and obtaining client consent where appropriate. | Ethical use of AI in legal practice; The role of human lawyers and their unique attributes (advocacy, empathy, wisdom) in an AI-driven legal landscape; The Christian lawyer's calling to advocacy for the poor, needy, and destitute. | Poor, needy, destitute, those who cannot speak for themselves. | General legal practice | United States | NaN | NaN | NaN | False | False | NaN | Current AI lacks true sentience, genuine empathy, and human-level wisdom; AI's unreliability due to issues like hallucinations and bias; The ongoing challenge of integrating AI into legal practice while upholding ethical responsibilities and preserving essential human elements. | Ensuring lawyer competence and diligence with rapidly evolving AI technology; Maintaining client confidentiality when using AI platforms; Providing proper supervision for AI tools (akin to nonlawyer assistants); Avoiding the unauthorized practice of law through AI; Determining appropriate client communication regarding AI use; Establishing ethical billing practices when AI enhances efficiency without corresponding human effort. | Attorneys facing disciplinary action for submitting AI-generated misinformation (e.g., fabricated case law); Perpetuation of societal biases through biased AI outputs; Inadvertent disclosure of confidential client information via AI tools; Reliance on AI "hallucinations" leading to incorrect legal work; Diminished human oversight and independent professional judgment due to over-reliance on AI; AI tools enabling or assisting in the unauthorized practice of law if used improperly. |
| 92FordhamLRev (4).pdf | HeinOnline | EDUCATING DEAL LAWYERS FOR THE DIGITAL AGE | This essay argues for adapting legal education to prepare deal lawyers for challenges posed by emerging technologies like AI and distributed ledgers, using closing opinions practice as an illustrative framework. It emphasizes that a strong understanding of foundational legal doctrines is crucial for navigating the technical, legal, and ethical issues arising from technology's impact on business law. | True | Market | True | 3.0 | NaN | Using legal opinions practice as a lens to examine the impact of emerging technologies (including AI and distributed ledgers) on deal lawyering and as a pedagogical strategy for legal education. | NaN | NaN | NaN | NaN | NaN | NaN | Business law, Commercial law, Secured Transactions (UCC Article 9), Contract law, Property law, Corporate law (Business Associations), Legal Ethics | United States | NaN | NaN | NaN | False | False | NaN | NaN | Identifying how emerging technologies disrupt doctrinal elements of business law (e.g., contract formation, enforceability, collateral perfection, characterization risk); ensuring lawyers maintain foundational legal skills despite AI tools; addressing ethical considerations regarding technology's impact on transactions and third parties; adapting legal education to equip students for these challenges, such as understanding AI-generated contracts and automated transactions. Articulating ideal baselines for automated contracts. | Potential atrophy of deal lawyers' analytical and drafting skills due to over-reliance on AI. Legal and financial risks from improperly structured or unenforceable deals due to misunderstanding technology's impact (e.g., AI-generated contracts, automated dispositions via DLT). Ethical breaches if lawyers fail to consider broader implications of tech-enabled transactions for stakeholders or market integrity, or if they do not maintain technological competence. Difficulty in establishing legal elements like intent or authorization in automated systems. |
| 6LawTechHum5.pdf | HeinOnline | Using Generative AI to Identify Arguments in Judges' Reasons: Accuracy and Benefits for Students | This paper evaluates the accuracy of Large Language Models (ChatGPT and Claude versions) in identifying and reconstructing legal arguments from High Court of Australia judgments, comparing their outputs to expert-created answers using a detailed rubric. It finds that Claude 3.5 significantly outperforms other models, offering potential benefits for legal education, but underscores the necessity for students to critically engage with LLM outputs due to varying accuracy. | True | Market | True | 2.0 | Neutral | Evaluation of Large Language Models (ChatGPT versions GPT-4 and GPT-4o; Claude versions 3.0 and 3.5) for identifying and reconstructing legal arguments from judicial reasons into a modus ponens structure using a one-shot prompt. | LLM outputs for five High Court of Australia cases were assessed by two human markers (a lawyer/legal academic and a philosopher) using a predefined rubric and compared against expert-created 'sample answers'. Marking was blind and focused on criteria like identifying disposition, premises, conclusions, argument location (paragraph numbers), and adherence to modus ponens structure. | Claude 3.5 significantly outperformed all other LLMs, with an average system mark of 16.2/20. The overall system average for Claude versions (3.0 and 3.5) was 13.4/20, while for ChatGPT versions (4 and 4o) it was 8.1/20. | General barriers to access to justice mentioned include financial cost, time, complexity of justice systems, lack of legal capability, language skills, and the high cost of legal advice leading many not to seek it. | Accurate Generative AI could potentially facilitate low- or no-cost legal advice and increase efficiency in handling legal matters, thereby reducing costs and improving access to justice. | Access to legal advice, cost of legal services, efficiency of justice systems. | Individuals who cannot afford legal advice or are deterred by system complexity; general public with limited resources. | Native title, criminal law, statutory interpretation, immigration (based on the cases used). | Australia (High Court of Australia cases). | The LLMs studied (ChatGPT, Claude) are pre-trained by their respective developers (OpenAI, Anthropic) on vast, proprietary, large-scale internet text corpora. The paper notes concerns about accuracy if training data is insufficient for specific jurisdictions. | NaN | NaN | True | False | The LLMs (ChatGPT 4/4o, Claude 3.0/3.5) used in the study are commercially available products, with premium versions generally requiring subscriptions. The study's prompt, rubric, and data (sample answers, LLM outputs) are shared on GitHub. | Technological gaps in LLM accuracy, reliability, and legal reasoning capabilities. Insufficient training data for specific jurisdictions, potentially leading to inaccurate outputs. The need for critical human oversight and skill to use LLMs effectively and avoid detriments. | High cost of labour for expert human evaluation of LLM outputs in legal NLP (annotator bottleneck). Defining the task of legal argument extraction for LLMs as distinct from conventional NLP argument mining. Significant variability in performance across different LLMs and versions for the same task. | LLM 'hallucinations' (fabricating information, e.g., non-existent legal cases). Significant safety issues if used for unsupervised legal advice. Detriment to unskilled users (e.g., students) due to convincingly presented but inaccurate outputs. Negative impact on the development of students' legal argument reconstruction skills if LLMs are used uncritically as a shortcut. |
| 2024IntlJLEthicsTech108.pdf | HeinOnline | A VISION FOR DIGITIZING JUDICIAL PROCESSES AND INTEGRATING ARTIFICIAL INTELLIGENCE IN PAKISTAN'S JUDICIARY: ENHANCING EFFICIENCY AND UPHOLDING JUDICIAL INTEGRITY | This paper outlines a vision for digitizing judicial processes and integrating Artificial Intelligence within Pakistan's judiciary to address current challenges like inefficiency and low public trust. It proposes a phased strategic roadmap, inspired by international models such as China's smart courts, while emphasizing ethical considerations and the necessity of human judicial oversight. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Lack of proper implementation of the rule of law, protracted trials, low public confidence in the judiciary, pervasive corruption, limited transparency, inefficiency, high case backlogs, and outdated manual case filing processes. | A phased digital transformation including e-filing, digitization of records, integration of AI for legal research, evidence standards, sentencing aid, and routine case management. This includes establishing a central database, drawing inspiration from international models like China's smart courts, implementing a strategic roadmap, and emphasizing ethical guidelines and human judicial discretion. | Judicial efficiency, transparency, accessibility of justice, public trust in the judiciary, modernization of court procedures, rule of law. | General public in Pakistan / Litigants | General (Civil and Criminal Justice, Family Law, Property Law) | Pakistan | NaN | NaN | NaN | False | False | NaN | Need for public accessibility of legal judgments for machine learning datasets; challenges in creating accurate AI knowledge maps due to legal language complexity and stare decisis; ensuring AI systems do not perpetuate biases; current lack of comprehensive digitization and IT infrastructure in Pakistan's judiciary. | Differing 'mental processes' between AI and humans, potential compromise of judicial independence, AI's reliance on past data, difficulty in AI mimicking human cognition, and the labor-intensive nature of constructing AI knowledge maps. For Pakistan: overcoming systemic judicial issues, transitioning from manual systems, and ensuring nationwide implementation of new technologies. | Unpredictable AI behavior leading to responsibility issues, AI perpetuating biases, undermining human judicial discretion and fairness, compromising judicial independence, misuse of private/confidential data in AI tools, and over-reliance on potentially inaccurate AI-generated information. |
| 60SanDiegoLRev671.pdf | HeinOnline | Protecting the Promise to the Families of Tuskegee: Banning the Use of Persuasive AI in Obtaining Informed Consent for Commercial Drug Trials | This paper calls for a ban on using AI designed to influence human decision-making ("Persuasive AI") for recruiting or enrolling participants in commercial drug trials, arguing it poses a substantial risk to the informed consent process. The author bases this on the technology's potential for undetectable manipulation, its tendency to reproduce societal biases, and the inadequacy of current regulatory measures to prevent harm, particularly to vulnerable populations. | True | Idealistic | False | 3.0 | Negative | Persuasive AI (also referred to as Emotion AI or Affective Computing), which includes AI that analyzes human emotions and behavior to influence decision-making. | NaN | NaN | The primary obstacle is the potential for Persuasive AI to undermine free and voluntary informed consent by manipulating potential research participants, especially those from vulnerable and historically exploited communities. This manipulation is often undetectable, irremediable, and can be compounded by AI's inherent biases and its 'black box' nature, making oversight by ethics committees ineffective. | The paper advocates for an immediate ban on an entire class of AI technology (Persuasive AI) from being used in the recruitment and enrollment process for federally regulated human subject research, particularly commercial drug trials. This is presented as a necessary measure to protect the integrity of informed consent. | Informed consent in biomedical research, protection of human research subjects, prevention of coercion and undue influence, addressing historical injustices in research (e.g., Tuskegee), ensuring autonomy in decision-making for vulnerable populations, regulation of AI in healthcare. | Vulnerable populations in research, particularly Black adults and other groups historically underrepresented or exploited in clinical trials (drawing parallels to the Tuskegee Syphilis Experiment). | Health Law, Human Subjects Research Law (Common Rule, FDA regulations), Research Ethics, AI Regulation, Bioethics. | Primarily United States (referencing U.S. federal laws like the Common Rule, FDA regulations, and the legacy of Tuskegee). The European Union's AI Act is also discussed extensively as a comparative model. | The paper discusses various AI systems. Examples include AI trained on text-based datasets (e.g., social media, news reports for emotion prediction by DARPA), facial image databases (for facial recognition, noting demographic biases), and data from human interactions with AI systems (e.g., the CSIRO study on manipulating choice). The data sources are varied and can be public or proprietary, structured or unstructured. | NaN | NaN | True | False | The paper states that companies are already marketing AI products to assist in recruiting participants for clinical trials and that Persuasive AI/Emotion AI is already deployed in various commercial settings. | Societal: Lack of U.S. federal regulation specifically targeting Persuasive AI and its manipulative capabilities; the difficulty of ensuring genuine informed consent when AI can influence decisions covertly; the risk of perpetuating historical exploitation of vulnerable communities. Technical: The opacity ('black box' nature) of AI decision-making hinders understanding and oversight; AI's capacity for autonomous development beyond initial programming makes it difficult to control or predict. | NaN | Manipulation of human decision-making, undermining autonomy and free will; coercion and undue influence in the informed consent process for research; perpetuation and amplification of societal biases (especially racial bias); exploitation of vulnerable groups; invasion of privacy; psychological or physical harm due to distorted behavior; inability to detect or remediate harm from AI influence; AI developing beyond its programming; violation of human rights through manipulative AI. |
| 6Issue3IntlJLMgmtHuman.pdf | HeinOnline | X-Raying the Legality of a Robot Lawyer in the Nigerian Courts | This paper analyzes the Nigerian legal system to determine if a robot lawyer could legally operate in its courts, concluding that current laws prevent this. It highlights the significant legal and professional reforms required for any future accommodation of AI in Nigerian legal practice. | True | Idealistic | True | 3.0 | Neutral | Robot lawyer / AI-powered legal assistance tools (e.g., chatbots, DoNotPay, ChatGPT) | NaN | NaN | Existing Nigerian laws requiring lawyers to be human citizens, hold specific qualifications, be called to the bar, and be enrolled; lack of legal personality for robots; resistance from the legal profession; and Nigeria's technological/economic limitations. | Comprehensive legislative reform to amend the Legal Practitioners Act, Rules of Professional Conduct, and other relevant laws to define and accommodate robot lawyers; development of a legal framework for their co-existence with human lawyers. | Affordable legal services, access to legal advice and representation in minor civil matters (e.g., traffic tickets, consumer rights disputes). | General public needing cheaper legal services, particularly for everyday legal problems or small claims. | General legal practice, Professional regulation, Civil litigation (minor disputes), Consumer rights, Evidence law | Nigeria | NaN | NaN | NaN | False | False | NaN | Lack of a specific legal framework for AI in legal practice; absence of provisions for non-human legal practitioners in existing statutes; insufficient adaptation of evidence laws for advanced AI; and a general lag in legal system modernization to accommodate technological advancements. | Satisfying legal requirements for being a lawyer (citizenship, education, bar admission, good character, practicing fees, continuous development, dress code); defining the legal status of a robot (juristic personality); overcoming resistance from the established legal profession; updating evidence laws; and the country's technological and economic readiness. | Displacement of human lawyers; unauthorized practice of law by AI leading to legal sanctions; potential for AI to provide inadequate or incorrect legal advice; erosion of the integrity/dignity of the legal profession if unregulated AI participates in legal processes. |
| 99IndLJSupp37.pdf | HeinOnline | Framing Online Speech Governance as an Algorithmic Accountability Issue | The paper argues for a regulatory approach to online speech governance that focuses on the AI tools used for both content moderation and generation, framing it as an algorithmic accountability issue. It highlights the shortcomings of current legal frameworks and advocates for a systems-level approach to examine the development and deployment of these AI tools, considering their technical and normative features. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Error-prone AI tools, lack of transparency and accountability in AI development and deployment, biases in AI leading to unfair outcomes and censorship, inadequacy of current legal frameworks to govern AI, and power imbalances favoring platforms over users. | Adopting a systems-level regulatory approach centered on algorithmic accountability, including measures like mandatory documentation (datasheets), Algorithmic Impact Assessments (AIAs) for AI tools, increased transparency in development processes, and stronger legal frameworks for AI governance. | Algorithmic accountability in online speech governance, fairness in content moderation and generation, protection of freedom of expression, and mitigation of AI-driven harms like censorship and disinformation. | Users whose speech is erroneously moderated or censored, marginalized groups disproportionately affected by AI biases (e.g., ethnic/religious minorities, speakers of non-dominant languages), and populations in global south countries affected by platform failures (e.g., Myanmar). | Internet Law (including CDA Section 230, DMCA), Constitutional Law (Freedom of Speech, Due Process), Copyright Law, AI Law/Regulation, Human Rights Law. | Primarily United States (discussing CDA, DMCA, Gonzalez v. Google, proposed Algorithmic Accountability Act), with references to global impacts and international contexts (e.g., Myanmar, India, non-English content moderation). | NaN | NaN | NaN | False | False | NaN | Significant regulatory gaps in holding AI tools accountable, lack of transparency in AI development and deployment, insufficient understanding of AI's contextual and linguistic nuances (especially non-English), limitations in creating unbiased and representative datasets, and inadequate mechanisms for user recourse against AI-driven decisions. | NaN | Erroneous censorship of legitimate speech, amplification of misinformation and hate speech, generation of harmful content and disinformation by AI tools, perpetuation of societal biases, copyright infringement by generative AI, and potential misuse of AI or disclosed data by malicious actors including authoritarian regimes. |
| 6Issue6IntlJLMgmtHuman52.pdf | HeinOnline | Unveiling the Impact of ChatGPT on Legal Services | This paper evaluates ChatGPT's potential as a supplementary resource for legal services, highlighting its utility in tasks like legal research, document drafting, and answering basic legal questions. It discusses both the benefits, such as enhanced efficiency, and drawbacks, including inaccuracies, ethical concerns, and limitations in handling complex legal issues. | True | Market | True | 3.0 | Neutral | ChatGPT | NaN | NaN | Lack of readily available and affordable legal information and counsel for the general public seeking to understand their rights or navigate legal issues. | Utilizing AI tools like ChatGPT to provide on-demand basic legal information and preliminary counsel, thereby increasing efficiency and potentially lowering barriers to accessing legal help, always as a supplement to human legal professionals. | Providing simple legal advice, answering basic legal questions, offering legal counsel on demand. | NaN | General legal practice | International | A sizable compilation of open-source material networked before September 2021 and some licensed origin; precise details not public, unknown if legal databases like Lexis Library or Westlaw Edge were included. | Large-scale language modeling based on transformer architecture (GPT-3), trained on a vast corpus of text data using machine learning techniques to predict subsequent text based on preceding context. | Publicly accessible via OpenAI's platform; potential for integration into law firm websites or internal messaging platforms. | True | False | ChatGPT is available through OpenAI's platform, which includes a free access tier. | Need for human oversight and expert integration, as AI alone cannot reliably handle legal complexity, ensure accuracy, avoid bias, or address nuanced ethical considerations, making it unsuitable for standalone use in critical access to justice scenarios. | Ensuring accuracy and avoiding hallucinations (e.g., fake citations by ChatGPT), maintaining data privacy and client confidentiality, addressing ethical responsibilities when using AI-generated content, and dealing with knowledge cut-offs (e.g., ChatGPT's data being pre-September 2021). | Submission of non-existent judicial opinions with fake citations leading to legal sanctions; generation of biased or discriminatory outputs; data privacy violations due to unclear data handling processes of the AI model; over-reliance on the technology leading to incorrect legal assessments or advice. |
| 21NYUJLBus119.pdf | HeinOnline | Don't Kill the Baby! The Case for AI in Arbitration | This paper argues that Generative AI can and should be used as an arbitrator if parties contractually agree, consistent with the Federal Arbitration Act (FAA). It positions arbitration as an ideal starting point for AI adoption in law, emphasizing contractual autonomy and calling for empirical comparison between AI and human arbitration. | True | Idealistic | True | 3.0 | Positive | The use of AI (particularly Generative AI and Large Language Models) as the contractually chosen arbitrator in dispute resolution, leveraging the flexibility of the Federal Arbitration Act (FAA). | The paper does not conduct its own empirical testing of AI as arbitrators. It supports its arguments by referencing existing studies on general AI capabilities, such as a study on deceptive review detection where AI's performance was compared to humans and human-AI teams. | The paper does not present results from its own evaluation of AI arbitrators. It cites a study by Lai et al. where AI alone achieved 86.3% accuracy in deceptive review detection, compared to 54.6% for humans alone and 74% for a combined human-AI team, to illustrate AI's potential. | Resistance to AI adoption in legal contexts due to concerns about bias, discrimination, lack of transparency, absence of human qualities like empathy, job displacement, and overly moralistic views that hinder experimentation and growth. | Upholding contractual autonomy under the Federal Arbitration Act to allow parties to choose AI-driven arbitration; utilizing arbitration as a flexible, contract-based environment for experimenting with AI in the legal field; fostering an open-minded approach and advocating for empirical studies comparing AI and human arbitration. | Dispute resolution (arbitration), cost reduction in legal processes, accessibility of legal services, enhancing subjective fairness in adjudication, contractual autonomy in choosing dispute resolution methods. | Pro se litigants, individuals lacking legal expertise or strong writing skills, elderly and/or disabled individuals facing difficulties with traditional hearings, and generally those seeking more accessible, lower-cost, and faster dispute resolution. | Arbitration, Contract Law, Alternative Dispute Resolution. | USA (due to the central focus on the Federal Arbitration Act - FAA). | The paper discusses Generative AI and LLMs generally, which are trained on vast datasets (e.g., scraped from the internet). It specifically mentions SaulLM-7B, an LLM trained on an English legal corpus of over 30 billion tokens, as an example of relevant AI development. | NaN | NaN | True | False | The paper argues that parties can, by contractual agreement under the FAA, use existing AI tools (like general-purpose LLMs or specialized legal AIs) as arbitrators. | The need for empirical research comparing the performance, fairness, and outcomes of AI arbitration versus human arbitration. Further development and fine-tuning of AI models are needed for specialized tasks in arbitration, ensuring confidentiality and building disputant trust. | Overcoming skepticism and resistance to AI in legal decision-making; addressing ethical concerns such as bias, transparency, and accountability in AI systems; adapting general-purpose AI models for the nuanced and complex requirements of legal arbitration, including handling emotional and ethical subtleties. | Perpetuation of biases present in training data; lack of genuine empathy and emotional understanding; potential for job displacement; ethical concerns regarding consent, manipulation, and privacy; erosion of trust in the legal process due to opaque 'black-box' decision-making; potential for inaccuracies or inappropriate outputs from AI; undermining due process norms. |
| 3JIntellPropInfoTechL15.pdf | HeinOnline | Law Without Lawyers: Examining the Limitations of Consumer-Centric Legal Tech Services | This paper explores the rise of business-to-consumer (B2C) legal technology, driven by factors like cost reduction, legal inclusion, market liberalization, and technological growth. It examines various B2C legal tech tools (e.g., document automation, ODR, chatbots) but argues they have significant limitations in handling complex legal issues and raise ethical/regulatory concerns, highlighting the continued importance of human lawyers. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | High cost of traditional legal services making them inaccessible to low/middle-income populations; The digital divide limiting access to technology for vulnerable groups. | Development and use of B2C legal tech; Enhancing technical competency of lawyers through education; Implementing regulation and technical standards for legal tech; Creating legal tech innovation hubs and regulatory sandboxes. | Cost reduction in legal services, Access to legal information and document generation, Online Dispute Resolution (ODR), Addressing the justice gap for underserved markets. | Low to middle-income earners, Consumers priced out of traditional legal services. | General Law, Multiple Fields (including Contract Law, Tort Law, Company Law, Traffic Law, Small Claims, Personal Injury) | Africa (Kenya, Namibia, Nigeria, Ghana, South Africa), UK, US, International | NaN | NaN | NaN | False | False | NaN | Technical gaps (handling legal complexity, reasoning, adaptability, explainability); Regulatory gaps (lack of specific legal tech laws, liability uncertainties); Ethical gaps (unauthorized practice of law, bias, privacy); Societal gaps (digital divide). | Limitations of AI in handling complex/unstructured legal issues and logical gaps; Difficulty translating legal ontology into algorithms; Potential for unauthorized practice of law; Lack of quality assurance and transparency (black box problem); Establishing legal liability for AI errors. | Providing substandard or inaccurate legal services/documents; Engaging in the unauthorized practice of law; Algorithmic misbehavior, bias, and errors due to flawed data or design; Lack of accountability and clear liability frameworks; Undermining the rule of law by oversimplifying complex issues; Data privacy and confidentiality breaches; Potential for unjust outcomes in automated dispute resolution; Exacerbating the digital divide. |
| 15BalticJLPol141.pdf | HeinOnline | DIGITAL TRANSFORMATION OF LEGAL SERVICES AND ACCESS TO JUSTICE: CHALLENGES AND POSSIBILITIES | This paper evaluates the potential of artificial intelligence (AI), particularly human language technologies, to improve access to legal services, especially in the post-pandemic context. It discusses technical, legal, and moral challenges, and presents a case study of AI application in the Lithuanian legal domain through the Semantika-2 project. | True | Idealistic | True | 2.0 | Neutral | The Semantika-2 project, which created a set of HLT-based tools for document automation and technology-assisted review for the Lithuanian language, including automatic speech transcription, automatic summarisation, semantic analysis (named entity recognition, aspect-based sentiment analysis), automatic spell checking, and linguistic analysis. | For the Semantika-2 project: morphological tagging accuracy (96% achieved) was evaluated. Named entity recognition was stated to achieve high accuracy but with remaining uncertainties. The developed tools used a combination of rule-based, neural, and hybrid methods, assessed qualitatively for their suitability to Lithuanian legal texts. | For the Semantika-2 project: Morphological tagging achieved 96% accuracy. Named entity recognition showed high accuracy with some uncertainties. The authors conclude that automation of legal tasks for the Lithuanian legal domain is progressing, but full LegalAI adoption is in early stages, with hybrid systems (rule-based and neural) being most effective due to data limitations. | Cost of litigation; lack of transparency in legal proceedings; inequality of arms (including digital exclusion and lack of technological literacy); insufficient public legal knowledge; complexity and inaccessibility of legal language. | Digitalisation of legal services (e.g., online hearings, e-filing); AI tools for legal research, document automation, and legal advice; development of NLP tools tailored to specific languages and legal domains (e.g., Semantika-2); ensuring AI systems are trustworthy, ethical, and secure. | Cost of legal services/litigation; transparency of legal proceedings; equality of arms; public legal literacy; efficiency of justice systems; online dispute resolution/online courts. | Socially vulnerable groups (women, older people, minorities, people with disabilities, refugees); individuals with insufficient education or local language skills; people with special needs; residents in rural areas with limited internet access. | General (civil and criminal procedure aspects like e-filing, court hearings), Contract Law, Human Rights Law. | Lithuania (primary case study), European Union (EU regulations, CEPEJ charter), International (global access to justice issues). | For Semantika-2: A Lithuanian legal text corpus comprising publicly available legal acts (from www.e-tar.lt) and court decisions (from LITEKO), and a proprietary, anonymised, and synthetically augmented sub-corpus of 1,500 real-life contracts. The data is domain-specific (legal), unstructured (text), and primarily in Lithuanian. | For Semantika-2: Interdisciplinary approach (law, IT, linguistics); creation of a dedicated Lithuanian legal text corpus; use of AI/machine learning where feasible; application of rule-based and hybrid methods to address limitations of ML with morphologically rich languages and scarce data; development of custom tools like a syntactical parser for Lithuanian legal texts. | The results of the Semantika-2 project are stated to be available free of charge for public use via its website (www.semantika.lt). | True | True | The tools and solutions developed under the Semantika-2 project are stated to be 'free of charge for public use' and accessible via the project website www.semantika.lt. | Technical: Need for conceptual breakthroughs in NLP for LegalAI, especially for under-resourced languages; limitations of current HLT models with long legal documents and context; insufficient domain-specific training data; AI model explainability ('black box problem'). Societal: Digital divide and technological inequality; establishing trust and ethical soundness for public acceptance of AI in law; cultural integration of AI in legal institutions. | For Semantika-2 and LegalAI: Insufficient training data for specific languages (e.g., Lithuanian) and legal domains; complexity of legal language; limitations of NLP algorithms (long sequences, context loss); morphological richness of certain languages; lack of standardization in legal document formats; difficulties in named entity recognition and linking in legal texts; high computational resources for training large models. | Bias in AI models leading to discriminatory decisions; opacity and lack of transparency in AI decision-making ('black box problem'); misuse of AI for manipulation or social control; erosion of public trust due to inaccuracies or unfairness; dehumanisation of the justice system; hampering of fundamental procedural rights if AI systems are not transparent or well-documented. |
| 72DePaulLRev171.pdf | HeinOnline | THE NEW JUDICIAL GOVERNANCE: COURTS, DATA, AND THE FUTURE OF CIVIL JUSTICE | This paper argues that the increasing digitization of the legal system, accelerated by the pandemic, is generating unprecedented amounts of data, positioning courts as central data governors. It explores the challenges and opportunities for courts in their new roles as data users, dispensers, and regulators, emphasizing how these roles will shape the future of civil justice and access to justice. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Pervasive lack of access to legal representation (pro se crisis), high cost of legal services, restrictive regulations on legal service provision, and inequitable access to court data and digital tools, alongside a lack of data standards and technical capacity within courts. | Enhanced use of technology by courts (e.g., ODR, litigant portals), reformed data governance by courts (emphasizing openness, standardization, and fair access), and deregulation of legal services to foster innovation and welcome new service models, all guided by multi-stakeholder input and oversight. | Online Dispute Resolution (ODR), technological assistance for self-represented litigants, reform of legal services regulation, open court data initiatives, and the overall future of civil justice in a datafied environment. | Self-represented litigants, low- and middle-income individuals, debtors, and tenants. | Civil justice (broadly), with specific examples including debt collection, eviction, family law, housing law, and consumer credit disputes. | United States (federal and state courts), with some comparative references to the UK and Australia for regulatory reform. | NaN | NaN | NaN | False | False | NaN | Insufficient, inaccessible, and non-standardized court data; lack of technical capacity and data literacy within courts; outdated regulatory frameworks for legal services; need for new governance models for digital justice; and insufficient empirical research on civil justice innovations and their impacts. | NaN | Increased inequality in litigation (legal tech benefiting the 'haves'), erosion of public legal norms and judicial legitimacy, privacy violations and cybersecurity threats from court data, vendor lock-in for courts, hollowing out of public sector technical capacity, and potential consumer harm from inadequately regulated new legal service providers, including risks from 'dark patterns'. |
| 26YaleJLTech527.pdf | HeinOnline | Who Wants a Robo-Lawyer Now?: On Al Chatbots in China's Public Legal Services Sector | This essay examines the potential for widespread adoption of AI chatbots, particularly LLMs, in China's public legal services (PLS) sector to enhance access to justice. It argues that China's political economy, coupled with technological advancements and the demand for basic legal information, makes PLS a promising near-term use case for these chatbots, offering significant benefits despite manageable risks. | True | Idealistic | True | 2.0 | Positive | AI chatbots, particularly Large Language Model (LLM)-powered ones (e.g., Baidu's Ernie), for providing public legal services. Older expert-system-based AI consultation services are also mentioned. | The paper mentions the Yunnan chatbots performed 620,000 consultations and the MOJ's older system generated 4.7 million opinions, but it does not detail a formal evaluation methodology or specific performance benchmark results for these systems conducted within this study. It references external studies (LegalBench, LawBench) on general LLM capabilities. | The paper reports adoption metrics for the Yunnan AI chatbots (620,000 consultations in initial months) and the MOJ's older system (4.7 million opinions generated). No specific performance or accuracy results from this paper's own evaluation are provided. | Scarcity of legal professionals, particularly in rural areas; uneven socio-economic development impacting access to legal services; difficulty for the government to achieve its commitment to universal access to public legal services (PLS) due to resource constraints; inadequate or dubious information from general web searches for legal questions; user-unfriendliness of older automated systems. | Wider adoption of AI chatbots in China's public legal services (PLS) system; government-led initiatives and digitized paths towards universal PLS coverage; leveraging LLMs, fine-tuning, and Retrieval Augmentation Generation (RAG) to meet popular demand for basic legal information; pragmatic approaches to manage risks associated with AI chatbots. | Universal access to public legal services; provision of basic legal information and advice on routine legal questions; mediation-based resolution of grassroots disputes; reinforcing legality in governance. | General populace in China, particularly rural residents and those in disadvantaged regions with limited access to legal professionals. | Public Legal Services (covering common, routine legal questions related to livelihoods, e.g., unpaid wages, divorce, landlord-tenant issues). | China | For the LLM chatbots (e.g., Ernie in Yunnan), it's based on general LLMs developed by tech firms like Baidu, which are trained on large text corpora. The paper discusses the potential for fine-tuning with specific legal Q&A task data and using Retrieval Augmentation Generation (RAG) connected to external legal knowledge databases. The MOJ's older system utilizes an expert knowledge-based generative software. | NaN | The Ernie-powered AI chatbots in Yunnan are deployed in government-run public legal services stations in over 14,000 rural villages, accessible via devices at local government offices. The MOJ's older AI consultation service is available on its public-facing website, China Legal Services Web. | True | False | The Ministry of Justice's (MOJ) expert system-based AI consultation service is available on its public website, China Legal Services Web (ai.12348.gov.cn/pc). | LLMs' hallucination problem (though considered manageable for PLS); lack of robust, systemic methodologies for assessing LLMs' overall capacity for legal tasks; user-friendliness of current systems for all demographics; ensuring user data confidentiality and adapting legal privilege concepts; effectively mitigating errors, misuse, and manipulation; addressing potential for unequal service quality compared to human lawyers. | Mitigating AI hallucination and ensuring accuracy for legal information; making interfaces user-friendly, especially for individuals with limited education; establishing user trust in robo-lawyers; protecting data privacy and confidentiality in LLM interactions; preventing malicious use (e.g., scams, official manipulation for biased outcomes); developing regulatory frameworks for pre-testing, piloting, and auditing PLS chatbots; ensuring transparency and public oversight. | Loss of confidentiality of user information; errors and inaccuracies in legal advice from AI (hallucinations); fraud and manipulation (e.g., fake PLS platforms, misuse by officials to spread misinformation or suppress rights); entrenching or creating new inequalities in service quality (a bifurcated system of AI vs. human legal aid); potential for chatbots to be designed with systemic biases (e.g., nudging users away from litigation). |
| 4JusCorpusLJ601.pdf | HeinOnline | AI-powered Indian Courtroom: ChatGPT a boon or a bane? | This paper discusses the potential benefits and drawbacks of integrating AI, particularly tools like ChatGPT, into the Indian judicial system to improve efficiency and access to justice. It highlights existing AI initiatives in Indian courts, such as SUPACE and SUVAS, and emphasizes the need for a cautious, regulated approach to adoption while considering ethical implications and practical challenges. | True | Idealistic | True | 3.0 | Neutral | ChatGPT, SUPACE (Supreme Court Portal for Assistance in Court's Efficiency), SUVAS (Supreme Court Vidhik Anuvaad Software), TERES (transcription tool), AI for administrative tasks, precedent analysis, and legal research. | For SUVAS: Observation of initial high productivity in translation, followed by decline in speed and scope (focus on Hindi, criminal matters). For ChatGPT: Anecdotal use by a High Court judge to gauge bail jurisprudence. For TERES: Deployed for live transcription in Supreme Court. | SUVAS: Initially translated many judgments efficiently but later became sluggish and limited in scope (mostly Hindi, criminal matters). TERES: Successfully used for live transcription. ChatGPT: Used anecdotally by a judge to gauge bail jurisprudence, signifying potential for greater AI participation. | High case backlogs leading to delays, language barriers (Apex court judgments primarily in English making them inaccessible to many), physical distance from courts, and the general complexity of law for the layperson. | Using AI for administrative tasks, legal research, and precedent analysis to reduce judicial workload and case backlogs. Implementing AI-powered translation services (like improved SUVAS) and virtual courtrooms to improve accessibility. Developing a legal framework to regulate AI use in courts and providing adequate training. | Reducing case backlogs, enhancing judicial efficiency, language access to legal information through translation, physical access to courts via virtual proceedings, improving legal research, and transcription of court proceedings. | The general Indian populace, particularly the "middle and lower strata" not proficient in English and those facing challenges due to physical distance from courts. | General, with specific mention of criminal law (bail jurisprudence, translation of criminal matters) and contract law (drafting). | India | For SUVAS: Supreme Court judgments and orders. For ChatGPT (implied by mention of GPT-4): Large, general text and code datasets from the internet. For SUPACE and TERES: Not specified in the paper. | NaN | SUPACE and SUVAS were launched as official Supreme Court initiatives. TERES was used in Supreme Court for live transcription. ChatGPT was used by a High Court judge via its public interface. | True | True | ChatGPT is a publicly accessible LLM, with free usage tiers available online, as evidenced by its use by a judge mentioned in the paper. | Need for improved AI translation capabilities (broader language support, consistent performance for tools like SUVAS). Lack of adequate technological infrastructure and widespread technological literacy among legal professionals. Absence of a comprehensive legal and ethical framework to govern AI in the judiciary, including clear guidelines on bias mitigation, accountability, and data privacy. | For specific tools like SUVAS: Maintaining translation quality, speed, and comprehensive coverage across languages and case types. For general AI adoption: Overcoming lack of technological literacy and resources among legal professionals, addressing fears of job displacement, mitigating algorithmic bias, defining accountability for AI-assisted decisions, and ensuring data privacy for sensitive court information. | Job displacement for court administrative staff, introduction of algorithmic bias leading to miscarriages of justice, erosion of judicial accountability if blame is shifted to AI, breaches of privacy and confidentiality of sensitive court data, and potential for cataclysmic outcomes from unregulated AI use in courtrooms. |
| 26JLegalEthicalRegulIsses.pdf | HeinOnline | ASPECTS OF ARTIFICIAL INTELLIGENCE ON E-JUSTICE AND PERSONAL DATA LIMITATIONS | This paper discusses the evolving applications of Artificial Intelligence (AI) within judicial systems, emphasizing the critical role of data availability and the necessity of robust personal data protection measures. It analyzes specific AI uses such as predictive justice and online dispute resolution, while also addressing key technoethical concerns, limitations, and the potential for algorithmic bias, particularly in criminal justice contexts. | True | Idealistic | True | 3.0 | Neutral | Predictive justice systems, Online Dispute Resolution (ODR), AI tools in criminal justice (e.g., risk assessment, crime prevention), ChatGPT for legal tasks. | Discusses evaluations of tools like COMPAS (showing racial bias from independent research) and ongoing testing of HART in the UK. Mentions a study on ChatGPT's legal drafting capabilities. | COMPAS algorithm showed discriminatory outcomes, with African-American individuals being assessed as twice as likely to reoffend compared to other groups. | Limited availability and quality of open data for training AI; technical difficulties in effective anonymization/pseudonymization to protect privacy; potential for algorithmic bias and discrimination; lack of transparency in proprietary algorithms. | Promoting open data policies for court decisions while ensuring robust anonymization/pseudonymization; upholding the right of individuals to contest automated decisions and to be informed about algorithmic reasoning (e.g., under GDPR); ensuring transparency, neutrality, and honesty in AI systems. | E-justice systems, online dispute resolution (ODR), predictive justice (including risk assessment in criminal cases), AI-assisted legal drafting, efficiency in judicial processes, personal data protection. | Individuals involved in the justice system, particularly those at risk of discriminatory treatment due to algorithmic bias (e.g., racial minorities in criminal justice). | Civil law, commercial law, administrative law, criminal law. | European Union, United Kingdom, United States, and mentions of specific AI adoption in China, Argentina, Colombia, Canada (Montreal). Discusses CEPEJ guidelines. | For HART: Durham police records from 2008 to 2012. For COMPAS: Information from accused individuals and their criminal records. For predictive justice generally: Court decisions and 'unrefined' data in structural computer databases. For ChatGPT: large volumes of data and documents. | NaN | Discusses deployment of ODR systems in several European countries, COMPAS in US courts, ongoing testing of HART in the UK, and early adoption of AI tools (like ChatGPT) in courts in China, Argentina, and Colombia. | True | True | ChatGPT, developed by OpenAI, is mentioned as being publicly accessible and has been used in legal contexts, with a free tier available. | Limitations in the reliability of predictive justice systems; lack of fully effective automated anonymization techniques; insufficient transparency and accountability in algorithmic decision-making; potential for 'technological solutionism' where AI is misapplied to complex social problems. | Ensuring data availability and quality for AI training; protecting personal data and privacy through effective anonymization/pseudonymization; mitigating algorithmic bias and ensuring fairness and non-discrimination; addressing lack of transparency in AI models; managing ethical implications and preventing over-reliance on AI. | Algorithmic bias leading to discriminatory outcomes (e.g., racial bias); violation of privacy and human dignity through misuse of personal data; 'profiling' of individuals; lack of transparency and accountability in AI decision-making; over-reliance on AI leading to errors or deskilling; reinforcement of existing societal inequalities; spread of misinformation or flawed legal advice from AI tools like chatbots. |
| 27StanTechLRev308.pdf | HeinOnline | Rule 11 Is No Match for Generative Al | The paper argues that Federal Rule of Civil Procedure 11 is ill-equipped to sanction attorneys who negligently submit court filings containing fictitious cases or false legal statements generated by AI. It then analyzes the standing orders issued by judges to address this issue, evaluating their benefits and detriments and suggesting alternative approaches. | True | NaN | True | 2.0 | Neutral | Federal Rule of Civil Procedure 11 and judicial standing orders regulating AI use. | Legal analysis of Rule 11's applicability, review of case law involving attorney misuse of generative AI, and examination of various judicial standing orders. | Rule 11 is found ill-suited to sanction negligent AI misuse by attorneys due to its safe harbor provisions and heightened standards for sua sponte sanctions. Judicial standing orders are a reactive measure with benefits and significant detriments, and the paper suggests improvements if such orders are used. | Attorneys' negligent reliance on generative AI leading to submission of false legal information, thereby compromising the reliability of the legal process. Inadequacy of current legal rules (Rule 11) to deter or sanction this misuse. | If regulation is deemed necessary: careful drafting of rules/orders, avoiding overly broad bans, informing litigants of existing obligations (like under Rule 11) in the context of AI, and using the formal local rules process over ad-hoc standing orders. | Integrity of legal submissions to courts, professional responsibility of lawyers using AI, and effective judicial regulation of AI use in legal practice. | NaN | Federal Civil Procedure, Professional Responsibility. | United States (Federal Courts) | NaN | NaN | NaN | False | False | NaN | Regulatory gap in effectively sanctioning negligent attorney use of generative AI under Rule 11. Societal gap in legal professionals' understanding and responsible adoption of AI. | Adapting existing legal frameworks (like Rule 11) and judicial practices to address novel issues posed by generative AI, particularly 'hallucinations' and attorney misuse. | Submission of fictitious cases and false statements of law in court filings by attorneys misusing generative AI. Potential disclosure of confidential or proprietary information when using generative AI. |
| 85UPittLRev331.pdf | HeinOnline | A PERFECT STORM FOR LEGAL EDUCATION: PRIVATIZATION, POLARIZATION, AND PEDAGOGY | The paper analyzes how emerging technologies, including AI like ChatGPT, and increasing political polarization are creating a 'perfect storm' for the legal profession and legal education. It argues these forces risk undermining lawyers' expertise and commitment to the public good, potentially leading to a stratified legal system with diminished access to justice and trust in law for ordinary people. | True | Idealistic | True | 3.0 | Negative | Online Dispute Resolution (ODR) systems (including AI-supplemented and blockchain-based versions), AI-powered tools for legal tasks (e.g., chatbots like ChatGPT, DoNotPay's 'robot lawyer'). | ODR in courts: user experiences vary, some speedier. DoNotPay AI lawyer: plan withdrawn due to regulatory threats. AI in family law (e.g., Matterhorn): company-reported positive outcomes. ChatGPT: passed law school/bar exams, can draft briefs, but prone to factual errors. | Matterhorn (company-reported for its family law ODR): reduced hearings, improved child support collection. ChatGPT: passed bar exam with high scores (latest version). | Cost of legal services and lack of counsel for low-income individuals; technological divides and discomfort; potential for technology to entrench stratification in legal services (robust law for elites, automated processing for others); declining public trust in legal institutions due to polarization. | Use of technology (e.g., ODR, AI tools) to improve efficiency and access to legal support for underserved populations; emphasis on legal proceduralism and ethical duties to counterbalance polarization; curricular reforms in law schools to foster skills for managing ideological conflict. | Access to legal representation for pro se litigants and people of modest means; use of technology in civil dispute resolution (e.g., family law, traffic courts); impact of technology and polarization on the perception and administration of justice. | People with limited means; pro se litigants; ordinary people interacting with the legal system. | Civil litigation, Family law, Traffic court (briefly mentioned), Criminal law (noted as an area with less AI penetration). | United States | NaN | NaN | NaN | True | True | ChatGPT is publicly accessible (free/paid tiers). Some court-based ODR systems are operational. DoNotPay offers subscription services (though its AI court lawyer concept was halted). | Deepening stratification in legal services; erosion of public trust in legal institutions; difficulty in upholding social trusteeship of lawyers amid polarization; ensuring technology serves the 'greater good' rather than just market efficiency; lack of common understanding impacting how ordinary people access and perceive law. | Ensuring fairness, transparency, and ethical application of AI in legal contexts; addressing the unauthorized practice of law by AI tools; overcoming the digital divide and user difficulties with legal tech; resource limitations in courts for technology adoption; maintaining the legal profession's integrity and relevance amidst technological displacement and ideological pressures. | Displacement of lawyers in routine legal work by technology; AI systems making errors or lacking moral capacity; increased stratification of legal services; erosion of public trust and perception that law is only for elites; lawyers potentially misusing technology or succumbing to partisan pressures, undermining the administration of justice and democratic processes. |
| 30WashLeeJCivRtsSocJust1.pdf | HeinOnline | Slavery.AI | This paper theorizes that unregulated AI systems are giving rise to an emergent form of modern slavery, termed 'Slavery.AI,' where people are commodified as data production units. It examines the structural power systems analogous to historical chattel slavery, tests its theory against universal characteristics of slavery, and calls for responsible governance to emancipate people from these AI-mediated harms. | True | Idealistic | False | 1.0 | Negative | Slavery.AI (as a theoretical framework for understanding AI's societal impact) | The 'Slavery.AI' theory was evaluated by comparing its tenets against twelve universal characteristics of historical slavery systems (identified by Drescher and Finkelman), grouped into 'property' and 'abuse of power', using illustrative examples of current AI systems and uses detailed in the paper. | The paper concludes its 'proof of concept holds,' asserting that many universal characteristics of slavery pertaining to property rights over individuals and abuse of power are firmly entrenched or emerging in the context of ungoverned AI systems, thereby validating the 'Slavery.AI' theory. | Lack of effective laws and governance for AI; the capitalist drive for profit leading to commodification of human data ('Data Industrial Complex'); a powerful alliance between tech oligarchy and governments; and the legal system's default to treating data (and thus people-as-data) as property. | Responsible AI governance, including: principled ethical requirements for AI design, development, and deployment; AI-appropriate interpretation and enforcement of existing laws; new, meaningfully-enforceable substantive AI laws; and strong leadership and political will to recognize and address the stakes. | AI-mediated enslavement; commodification of personal data as property; lack of legal protection against algorithmic harms; AI's impact on liberty, human rights, and the rule of law; systemic exploitation by AI systems. | The vast majority of humanity, with disproportionate impact on historically marginalized communities including the poor, disabled, elderly, women, non-English speakers, immigrants, and racial/ethnic minorities. | Human Rights Law, Property Law, Constitutional Law, Criminal Law, Tort Law, International Law, AI Governance/Regulation. | Primarily United States, with references to international legal frameworks (e.g., slavery conventions, Universal Declaration of Human Rights) and global implications of AI systems. | NaN | The 'Slavery.AI' theoretical framework was developed through legal and historical analysis, analogical reasoning with historical chattel slavery, comparative analysis against universal characteristics of slavery systems, and an interdisciplinary examination of current AI technologies and their societal impacts. | The 'Slavery.AI' theory is disseminated through academic publication in a law journal and presentations at academic conferences. | True | True | The theoretical framework of 'Slavery.AI' and its analytical crucible are detailed in this published academic paper, available through academic databases like HeinOnline, allowing readers to apply the framework. | Societal and legal failure to recognize and address 'Slavery.AI' as an emergent threat; lack of effective AI governance and AI-specific regulations; insufficient leadership and political will to protect individuals from AI-driven exploitation and harms. | Formulating and substantiating a provocative theory ('Slavery.AI') likening modern AI impacts to slavery; conducting interdisciplinary analysis across AI, law, and history; addressing the potentially jarring nature of the terminology to stimulate discourse and action. | The emergence of 'Slavery.AI' where individuals are commodified as data units, losing freedom and subjected to AI-mediated discrimination, surveillance, manipulation, and control. Specific risks include wrongful arrests, deepfake abuse, suppression of rights (movement, assembly, appeal), propagation of bias through predictive systems, and severe psychological harm including suicide. |
| 26NYUJLegisPubPoly625.pdf | HeinOnline | ANALOG PRIVILEGE | This paper introduces 'analog privilege' to describe how elites avoid AI systems and benefit from personalized human treatment, unlike the general populace. It argues this divide, explored through case studies in LegalTech and content moderation, exacerbates inequality and erodes social fabric, proposing multi-pronged solutions. | True | Idealistic | True | 3.0 | Negative | Analog privilege (conceptual framework) | NaN | NaN | The ability of elites to access superior human legal services while less privileged individuals are relegated to potentially inadequate AI-driven LegalTech, exacerbating existing inequalities and creating a two-tiered justice system. | A multi-prong approach involving legal, technical, and governance interventions to reduce analog privilege, increase accountability and transparency, improve AI systems, and implement external checks and balances, including empowering affected individuals and whistleblowers. | Disparities in access to quality legal representation and services due to the differential impact of AI and LegalTech on various socio-economic groups. | Low-income individuals, middle-class families priced out of legal services, and racial minorities who face systemic barriers to accessing justice. | Primarily civil law (e.g., housing, debt, family, torts, estate), with implications for access to justice broadly across legal fields. | Primarily United States, with references to and implications for the European Union and international human rights law. The core concept ('analog privilege') is framed as broadly applicable. | NaN | NaN | NaN | True | False | The paper discusses publicly accessible (often commercial or freemium) tools like ChatGPT, DoNotPay, and LegalZoom. | Societal: The 'automation divide' and lack of understanding of its contours. Technical: Current AI limitations in complex reasoning, creativity, and handling nuances, particularly in legal applications. Governance: Inadequate legal and regulatory frameworks to address analog privilege and ensure equitable AI deployment; need for polycentric governance models. | General challenges in deploying AI systems that lead to analog privilege include their inherent reductivism, determinism, and potential for voyeurism, as well as specific limitations in areas like legal reasoning (creativity, handling novel cases, emotional intelligence) and content moderation (context sensitivity, accuracy at scale). | Erosion of social fabric, increased social polarization and resentment due to perceived unfairness. In legal services, creation of a two-tiered justice system with lower quality for the non-elite, undermining fairness and judicial legitimacy. In content moderation, biased enforcement, disproportionate silencing of marginalized voices, or undue leniency for powerful actors, potentially enabling harm. |
| 7ArizLJEmergingTechi.pdf | HeinOnline | ARTIFICIAL LAWYERING: A JEKYLL AND HYDE STORY | This paper analyzes the dual potential of generative AI, like ChatGPT, in the legal field: its capacity to enhance access to justice for underserved populations versus the risks of unauthorized practice of law and public harm. It proposes amending the ABA Model Rules of Professional Conduct to regulate AI's use, especially by pro se litigants, through informed consent to balance these aspects. | True | Idealistic | True | 3.0 | Neutral | Generative AI (e.g., ChatGPT) | NaN | NaN | High cost of legal services for low-income individuals; lack of awareness among the public about their legal issues and options; insufficient number of lawyers dedicated to serving low-income populations and addressing their specific legal needs. | Leveraging AI (like ChatGPT) to provide legal information, education, and direct assistance (e.g., document drafting) to pro se litigants; Amending the ABA Model Rules of Professional Conduct (specifically Rule 5.5) to permit the use of AI by pro se litigants under conditions of informed consent and disclosure to the court. | Access to legal information and education, Pro se litigant assistance, Unmet civil legal needs of low-income populations, Unauthorized Practice of Law (UPL) by AI. | Low-income individuals, pro se litigants, veterans, persons with disabilities, parents of children under eighteen, survivors of domestic violence or sexual assault. | General civil law, Landlord-tenant law, Trademark law (as examples); broader discussion concerns legal ethics and professional conduct. | United States | For ChatGPT (as discussed): Large amounts of general text data used to train Large Language Models, enabling them to infer relationships between words and texts. | The paper describes the general design of generative AI like ChatGPT as based on Large Language Models (LLMs), machine learning, Natural Language Processing, trained on extensive text data, and refined using techniques like reinforcement learning from human feedback. | Online chatbot (referring to ChatGPT's accessibility). | True | False | ChatGPT is accessible via OpenAI's website, with free and paid tiers. | Lack of clear legal framework for AI liability and redressability for harm caused by AI; need for updated professional conduct rules (e.g., UPL) to address AI; technical limitations of AI including true language understanding and inherent biases; societal and ethical challenges concerning AI accountability. | Inherent bias in AI training data leading to discriminatory outputs; limitations in AI's true understanding of language and context versus mere generation; potential for misuse in generating incorrect or misleading legal information/documents; navigating unauthorized practice of law (UPL) restrictions. | AI engaging in the Unauthorized Practice of Law (UPL); public harm due to incompetent or biased AI-generated legal advice/documents; lack of redressability for individuals harmed by AI's mistakes (AI 'malpractice'); AI generating and citing non-existent legal precedent; potential to flood courts with frivolous AI-generated lawsuits; erosion of public trust in the legal system if AI is misused or performs poorly. |
| 15BeijingLRev.pdf | HeinOnline | The Utility of Artificial Intelligence in the Pursuit of Justice through Judicial Precedent in Nigeria | This paper discusses the potential of Artificial Intelligence (AI) to enhance the application of judicial precedent within Nigeria's justice system. It advocates for integrating AI tools to assist judges in legal research and decision-making, thereby improving judicial efficiency and the delivery of justice, while emphasizing that AI should support, not replace, judicial discretion. | True | Idealistic | False | 3.0 | Positive | AI for judicial precedent analysis and legal research in judicial decision-making | NaN | NaN | Systemic issues in the Nigerian judicial system: delays in justice delivery, lack of transparency, paper-based administrative procedures, large case backlogs, and judicial fatigue due to manual processes. | Integrating AI tools for case management, administrative task automation, workload management, legal research, and precedent analysis to improve judicial efficiency and the quality of decision-making, while preserving judicial autonomy. | Enhancing judicial efficiency, supporting judicial decision-making, improving the application of judicial precedent, reducing case backlogs and delays in the justice system. | NaN | Judicial process, Doctrine of Precedent, (briefly) Intellectual Property Law. | Nigeria (primary focus), with comparative mentions of Germany, Estonia, USA, Canada, India, UK, Europe. | NaN | NaN | NaN | False | False | NaN | Need for AI tools with advanced analytical capabilities (beyond data retrieval) for precedent analysis; inadequacy of current intellectual property laws for AI-generated works; persistence of judicial precedents that do not reflect current socio-economic realities. | General challenges in applying AI to the judiciary: ensuring data security and privacy, sourcing high-quality and unbiased training data for AI systems, and mitigating algorithmic bias in AI tools. | Data security breaches, infringement of fundamental rights, use of unsafe or compromised data for AI training, and algorithmic bias leading to unfair outcomes due to programmer or historical data biases. |
| 97StJohnsLRev195.pdf | HeinOnline | LOW-INCOME LITIGANTS IN THE SANDBOX: COURT RECORD DATA AND THE LEGAL TECHNOLOGY A2J MARKET | This paper argues for mandatory, standardized public access to state civil court record data, particularly for debt collection cases involving low-income, self-represented litigants, to enable proper evaluation of legal A2J technologies. It proposes model legislation to create a centralized, anonymized database of such records to ensure technology serves justice and protects vulnerable consumers. | True | Idealistic | False | 1.0 | Positive | Model legislation for state-wide collection, normalization, anonymization, and public accessibility of civil court record data, aimed at improving A2J initiatives and evaluation of legal technology. | NaN | NaN | Severe data deficit in state civil courts, particularly for cases affecting low-income and self-represented litigants (e.g., debt collection); disaggregated and inaccessible court case management systems; lack of standardized data collection and reporting by courts; high costs and barriers to accessing docket-level data; current data collection practices often focus on court efficiency rather than substantive outcomes for litigants. | State courts and legislatures should mandate the collection, aggregation, normalization, and public accessibility of granular, docket-level civil court record data. This includes establishing standardized data fields and creating a publicly accessible, anonymized database, potentially hosted by academic institutions or non-profit entities. The paper provides model legislation to achieve this. | Access to civil court record data; Data-driven evaluation of legal technology and A2J interventions; Consumer debt collection; Self-represented litigants; Regulatory sandboxes for legal tech; Online dispute resolution (ODR) evaluation. | Low-income litigants; self-represented litigants, particularly defendants in consumer debt collection cases. | Civil procedure; Consumer law (specifically debt collection); Access to Justice research; Legislation/Policy. | Primarily California (for the author's specific data collection and analysis and as a case study for the proposed legislation), with discussion of Utah (regulatory sandbox, ODR) and broader references to the United States state court systems. The model legislation is framed for a generic '[State]'. | NaN | The proposed model legislation is a legal and policy design. The paper's supporting research on California court data involved public records requests, data scraping, analysis of existing judicial reports, and data normalization using R and Tableau. | NaN | False | False | NaN | Lack of reliable, standardized, and accessible state court record data for evaluating A2J interventions; insufficient understanding of how legal tech tools impact outcomes for self-represented litigants; need for better metrics beyond efficiency for evaluating A2J tech (e.g., just outcomes, consumer protection); ethical considerations and potential for harm from unregulated or poorly evaluated legal tech. | Cost to local county courts for increased data reporting; need for state legislative budget appropriation; history of failed or expensive centralized case management system projects (e.g., CCMS in California) creating skepticism; disaggregated nature of current county court systems often reliant on various third-party vendors; potential resistance from judicial bodies to unfunded mandates for data collection and reporting. | Legal technology causing harm to vulnerable consumers if not properly evaluated (e.g., leading to worse outcomes, exacerbating default judgments); market-driven legal tech prioritizing profit over consumer protection or actual justice outcomes; Online Dispute Resolution (ODR) systems potentially disadvantaging unrepresented consumers if not carefully designed and evaluated based on substantive outcomes; regulatory sandboxes approving harmful tools due to a lack of baseline data for rigorous evaluation. |
| 19UMassLRev39.pdf | HeinOnline | We(ed) Hold These Truths to be Self-Evident: All Things Cannabis Are Inequitable | The paper argues that current social equity policies in the cannabis industry fail to redress historical and ongoing inequities stemming from the War on Drugs, particularly racial, gender, and economic disparities. It details these multifaceted inequities and analyzes the structural reasons for the ineffectiveness of existing industry, community, criminal justice, and access equity programs. | True | Idealistic | False | 3.0 | NaN | NaN | NaN | NaN | Structural flaws in licensing and market design favoring incumbents; underfunded and poorly designed criminal justice reforms; persistent stigmatization and criminalization; financial and regulatory barriers for equity applicants; disconnection between cannabis policy and broader social justice issues (e.g., housing, employment, health). | Development of new theoretical frameworks beyond current 'social equity' models; multidisciplinary approaches to create comprehensive, multidimensional solutions; grounding new policies in established theories of social, restorative, and racial justice; further research and honest evaluation of policy effectiveness. | Social equity in the cannabis industry; consequences of the War on Drugs; racial inequity; gender inequity; criminal justice reform (expungement, pardons); economic equity (business ownership); access to medical cannabis; policy analysis and critique. | Communities disproportionately impacted by the War on Drugs, including Black and minority communities (specifically Black Americans, Native Americans, Native Hawaiians), women, individuals with prior cannabis convictions, and medical cannabis patients. | Cannabis law; Drug law; Criminal law; Social equity law; Constitutional law; Administrative law; Business law; International law; Health law; Family law; Environmental law. | United States (federal and various states including Hawaii, California, Illinois, Colorado, New York); International (drug control treaties). | NaN | NaN | NaN | False | False | NaN | Fundamental ineffectiveness of current cannabis social equity policies; lack of a robust theoretical basis for effective interventions; insufficient scope, funding, and political will for Cmeaningful reforms; disconnect between narrow equity programs and the broad, intersectional harms of the War on Drugs; need for empirical data on policy impacts and alternative approaches. | NaN | Perpetuation and worsening of systemic inequities despite legalization; failure of social equity programs to achieve justice for harmed communities; exploitation of equity initiatives by established businesses; ongoing societal harm from stigmatization and criminalization; legal challenges undermining equity efforts; market forces disadvantaging equity participants. |
| 40GaStULRev957.pdf | HeinOnline | A(I)CCESS TO JUSTICE: HOW Al AND ETHICS OPINIONS APPROVING LIMITED SCOPE REPRESENTATION SUPPORT LEGAL MARKET CONSOLIDATION | This paper argues that Generative AI, when used appropriately within ethical frameworks like limited scope representation, can enhance access to justice for low and middle-income individuals by reducing legal service costs. It posits that this could lead to a 'TurboLaw' model of legal service delivery, fostering market consolidation but ultimately making legal services more affordable. | True | Idealistic | True | 3.0 | Positive | A conceptual 'TurboLaw' model combining legal-specific Generative AI with limited scope representation and contract attorneys for affordable legal services. | NaN | NaN | High cost of traditional legal services; unauthorized practice of law (UPL) concerns with unsupervised AI use by pro se litigants; misuse and misunderstanding of general-purpose GenAI (like ChatGPT) leading to errors and judicial skepticism; ethical concerns regarding novel service delivery. | Utilizing legal-specific GenAI tools designed for accuracy in legal tasks; leveraging ethically approved limited scope representation and ghostwriting; employing attorneys (potentially contract-based) to oversee AI-generated work, ensuring UPL compliance and ethical standards; developing 'TurboTax-like' platforms for affordable, AI-assisted legal services. | Affordability of legal services; limited scope representation/unbundling; ghostwriting; unauthorized practice of law (UPL); ethical use of AI in law; market consolidation in legal services. | Lower and middle-income individuals and families; pro se litigants (who could benefit from more affordable attorney-assisted options). | General legal practice, civil litigation, transactional law (e.g., deeds, wills, trusts). | United States (with references to ABA Model Rules, D.C. Bar, Texas law, New York courts, federal courts). | N/A (The paper discusses existing AI tools like ChatGPT, Google Bard, Westlaw Precision, and Lexis+ AI, and generally mentions their reliance on large or specialized datasets, but does not propose a new technique with a specific training dataset). | N/A (The 'TurboLaw' model is a conceptual framework, not a specifically designed tool or technique within the paper). | A proposed 'TurboTax-like' commercial platform offering AI-assisted legal services, overseen by contract attorneys, and leveraging online/virtual practice models. | False | False | NaN | Need for robust, reliable, and legally-specialized AI tools distinct from general-purpose GenAI; overcoming judicial and professional skepticism towards AI in legal practice; ensuring ethical guidelines evolve with technology; addressing potential negative socio-economic impacts of market consolidation and the working conditions of contract attorneys within such models. | Misuse of general GenAI tools leading to inaccuracies and negative perceptions; overcoming AI 'hallucinations' and ensuring reliability; navigating unauthorized practice of law (UPL) regulations; maintaining ethical standards (informed consent, confidentiality, attorney supervision) with AI and outsourcing; ensuring data security and privacy in tech-enabled virtual practices. | Fabrication of case citations and legal arguments by general-purpose GenAI; professional sanctions for attorneys misusing GenAI; denial of legal recourse for individuals relying on flawed AI-generated filings; AI use constituting unauthorized practice of law if not properly supervised; inadvertent disclosure of confidential client information; market consolidation potentially harming solo practitioners and small law firms. |
| 25MinnJLSciTech67.pdf | HeinOnline | Practice Guide: How to Integrate AI and Emerging Technology into Your Practice and Comply with Model Rule 3.1 | This paper serves as a practice guide for lawyers on integrating AI tools, particularly generative AI like ChatGPT, into their legal practice while ensuring compliance with Model Rule of Professional Conduct 3.1. It analyzes MRPC 3.1, discusses case law (Mata v. Avianca), provides general and state-specific guidance, and surveys efforts by bar associations to reform rules regarding AI. | True | Market | True | 2.0 | Positive | Use of generative AI tools (e.g., ChatGPT) for legal research and drafting assistance by legal practitioners. | Analysis of MRPC 3.1, FRCP 11, state-specific rules, and a case study (Mata v. Avianca) to illustrate compliance issues and best practices when using AI tools in legal practice. | Lawyers must conduct diligent inquiry, including independent verification of AI-generated legal research and arguments (cite checking, Shepardizing), to comply with MRPC 3.1, as AI tools like ChatGPT can produce inaccurate, outdated, or fabricated information. Failure to do so can lead to professional sanctions, as demonstrated in the Mata v. Avianca case study. | Insufficient access to affordable legal representation; difficulties for pro se litigants in navigating the legal system; lawyers' lack of understanding of AI tools and their limitations. | Responsible integration of AI tools by lawyers, following ethical guidelines, to improve efficiency and potentially expand legal services; exploring AI tools to assist pro se litigants; development of clear rules and ongoing education for lawyers on AI use. | Ethical use of AI in legal practice; compliance with professional conduct rules (specifically MRPC 3.1); AI's potential to assist pro se litigants; enhancing access to legal services and improving the quality of justice. | Pro se litigants; general public needing legal representation. | Professional Conduct, Civil Procedure (FRCP 11), General Legal Practice. | United States (federal via FRCP, ABA Model Rules, and state-specific rules/guidance for CA, NY, MN, TX, IL, OR, WI, NJ, TN, MT, CT, DC, UT, VA). | NaN | NaN | NaN | True | False | General AI tools (e.g., ChatGPT) discussed are publicly accessible, often with free tiers. The guidance itself is published in a law journal. | Lack of comprehensive and practical guidance for lawyers on ethical AI use (which this paper aims to partially fill); need for ongoing adaptation of legal professional rules to technological advancements; ensuring AI tools are developed and deployed responsibly with human oversight. | Understanding the limitations of AI Tools (e.g., hallucinations, outdated data, sensitivity to input phrasing); verifying AI-generated content; lawyers' lack of familiarity with technology; overestimation of AI capabilities by users. | Violation of MRPC 3.1 and FRCP 11 leading to judicial sanctions and disciplinary action; submission of inaccurate legal arguments and fabricated case citations (hallucinations); reputational harm for lawyers; misleading the court; failure to perform adequate independent legal research and inquiry. |
| 9IJODR177.pdf | HeinOnline | Can ChatGPT-like AI Function as ODR Fourth Party for Handling School-Related Disputes in China? | The paper argues that ChatGPT-like AI, while not replacing human third-party ODR, can serve as a "fourth party" to assist in preventing and resolving school-related disputes in China, particularly those involving student mental health. It proposes customizing these AI models with specific legal and psychological knowledge to effectively fulfill this role. | True | Idealistic | True | 1.0 | Positive | Using ChatGPT-like AI as an "ODR fourth party" for handling school-related disputes, customized with legal and psychological knowledge for tasks like student mental health support. | Illustrative examples of querying OpenAI ChatGPT and ChatSonic with scenarios related to student mental health. Reference to a Colombian judge's use of ChatGPT in a ruling. | ChatGPT and ChatSonic provided generally relevant advice on psychological issues and risk assessment based on described symptoms, suggesting potential for the proposed role. The Colombian judge example illustrated AI as an assistant, not a replacement for human judgment. | Limited access to and inconsistent quality of mental health support for students, especially out-of-hours and in remote areas; societal dismissal of youth psychological issues; lack of timely intervention for students with mental health struggles. | Deploying customized ChatGPT-like AI as a 24/7 accessible "fourth party" for initial psychological support and dispute prevention guidance for students. Integrating AI with human professionals (psychologists, mediators) and training AI with relevant legal and psychological knowledge specific to school disputes in China. | Online Dispute Resolution (ODR), Online Dispute Prevention (ODP), student mental health support, resolution and prevention of school-related disputes (e.g., bullying, academic stress). | Students in China, particularly those in boarding schools or remote areas with limited access to mental health services. | Education law, mental health law/ethics, Online Dispute Resolution (ODR). | China (primary), Colombia (secondary example). | For the proposed customized AI: Chinese legal and psychological data specific to school-related disputes for fine-tuning existing LLMs. Existing models (ChatGPT, Ernie bot) are noted as being trained on massive, general text datasets. | Conceptual framework proposal. Suggests customization and fine-tuning of existing LLMs with domain-specific data (Chinese law and psychology for school disputes). | Envisioned through ODR platforms or integrated into school support systems, potentially leveraging customized versions of AI from tech companies (e.g., Microsoft, Baidu, Alibaba, Tencent). | False | False | NaN | Technical limitations of LLMs (accuracy, bias, outdated knowledge, language-specific performance); need for robust human oversight and integration with professional services; accessibility of some advanced AI models in China; lack of AI specifically designed and trained for ODR in school-related disputes. | Ensuring accuracy, reliability, and lack of bias in AI-generated content; effectively customizing general LLMs for specialized legal and psychological domains relevant to Chinese school disputes; dealing with LLMs' existing knowledge limitations and regional accessibility hurdles. | AI producing incorrect, harmful, or biased outputs; reliance on AI leading to diminished human critical thinking in dispute resolution and mental health support; privacy risks associated with handling sensitive student data (implied). |
| 25MinnJLSciTech25.pdf | HeinOnline | Generative Artificial Intelligence and the Practice of Law: Impact, Opportunities, and Risks | This article explores the transformative potential of generative AI in legal practice, highlighting its impact on tasks like legal writing and research, and its opportunities for improving efficiency. It also examines how generative AI can broaden access to legal services and discusses the necessary adaptations for law firms and legal education to effectively and ethically integrate this technology. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Lack of awareness of rights and insufficient legal resources for tenants; risk of premature deployment of low-quality AI systems; privacy and privilege concerns with data collection; potential for litigation over unauthorized practice of law by AI; outdated regulatory frameworks. | Utilizing generative AI to speed document creation for tenants and pro se litigants; developing interactive, multi-lingual AI systems to help initiate complaints; creating in-house AI tools for legal aid organizations; adopting hybrid AI-human review models for document preparation; updating regulatory frameworks to support responsible AI deployment. | Access to legal services for low-income individuals; tenant rights and harassment; support for pro se litigants. | Low-income households; tenants (particularly low-income and those in rent-stabilized dwellings); pro se litigants. | Civil litigation; housing law; general civil legal problems (consumer issues, healthcare, income maintenance). | United States | NaN | NaN | NaN | False | False | NaN | Technical gaps in ensuring AI system quality and reliability (e.g., verification of facts and citations) before widespread deployment. Societal and regulatory gaps including the need for updated legal frameworks for AI in legal services, addressing privacy/privilege issues, and navigating unauthorized practice of law concerns. Educational gaps in adapting law school curricula for responsible AI use. | NaN | AI hallucinations (fabricating information); negative impacts from premature deployment of subpar AI systems; privacy violations and unauthorized practice of law lawsuits; ethical breaches concerning client data confidentiality; difficulty for educational institutions in reliably detecting AI-generated content and potential for AI use to hinder learning for some students. |
| 54CalWIntlLJ459.pdf | HeinOnline | THE DIGITAL "TO KILL A MOCKINGBIRD": ARTIFICIAL INTELLIGENCE BIASES IN COURTS | This paper discusses the use of AI in the judicial system, particularly for risk assessment and recidivism prediction, highlighting the significant legal and ethical concerns arising from AI biases. It explores the causes of these biases, challenges in identifying them, and potential mitigation strategies, including those outlined in the EU AI Act. | True | Idealistic | False | 3.0 | Negative | AI systems for risk assessment and recidivism prediction, specifically mentioning COMPAS (Correctional Offender Management Profiling for Alternative Sanctions). | For COMPAS, the paper refers to ProPublica's investigation which analyzed its risk scores against actual recidivism, and other studies comparing outcomes for different racial groups. | Studies on COMPAS indicated that Black defendants were more likely to be incorrectly assessed as higher risk for recidivism, and had a two times higher risk of being mislabeled as potential violent recidivists compared to White counterparts, while White individuals were more frequently misclassified as low risk. | AI biases perpetuating societal disparities; Lack of transparency and explainability in AI systems; Flawed and unrepresentative training data; Overreliance on imperfect AI predictions. | Ensuring diverse and representative training datasets; Implementing robust human oversight and continuous auditing; Enhancing transparency and explainability of AI systems; Developing comprehensive legal regulations like the EU AI Act. | Fairness in algorithmic decision-making in criminal justice; Algorithmic bias in risk assessment and recidivism prediction; Due process implications of AI in courts. | Racial and ethnic minorities (specifically Black individuals, Asian Americans); Religious minorities (specifically Jewish individuals); Gender minorities (females in criminal justice contexts). | Criminal Law, Due Process, Human Rights. | United States, European Union. Discusses issues with global implications. | Historical criminal justice data (e.g., criminal records, responses to questionnaires like in COMPAS), often characterized as biased, incomplete, inaccurate, and unrepresentative of the broader population or specific subgroups. | For tools like COMPAS: Development based on actuarial risk assessment principles and machine learning, utilizing historical criminal records and questionnaire data. | For tools like COMPAS: Deployment in U.S. state criminal justice systems for judicial decision support (e.g., pre-trial detention, sentencing, early release). | False | False | NaN | Inability to completely eradicate bias from AI systems, even with regulation; Technical challenges in achieving full transparency and explainability for complex AI; Difficulty in translating nuanced legal concepts into code without loss or bias. | Acquiring and maintaining unbiased, representative training data; Preventing developer-induced or code-translation biases; Ensuring transparency and explainability in complex algorithms; Auditing and managing dynamically evolving algorithms. | Amplification of societal biases leading to discriminatory outcomes in the justice system; Erosion of fairness, due process, and individualized justice; Miscarriages of justice due to inaccurate AI predictions. |
| 16ItalianJPubL165.pdf | HeinOnline | ARTIFICIAL INTELLIGENCE AT THE CROSSROADS BETWEEN THE EUROPEAN UNION & THE COUNCIL OF EUROPE: WHO SAFEGUARDS WHAT & HOW? | The paper analyzes and compares the legislative approaches to Artificial Intelligence (AI) regulation by the European Union (EU AI Act) and the Council of Europe (CoE Framework Convention). It highlights the evolution towards human rights-centric AI governance in Europe and discusses challenges for creating a coherent regulatory landscape. | True | Idealistic | False | 2.0 | Positive | EU's Artificial Intelligence Act and Council of Europe's Framework Convention on Artificial Intelligence, Human Rights, Democracy and the Rule of Law. | NaN | NaN | AI-derived discrimination; violations of fundamental human rights; lack of transparency and explainability in AI systems; potential for manipulation by AI; insufficient human oversight; lack of effective remedies for individuals harmed by AI systems; risks from biometric identification and social scoring. | Adopting human-rights-based AI regulation (EU AI Act, CoE Convention); implementing risk-based assessments; prohibiting unacceptable-risk AI; establishing obligations for AI developers/users (e.g., transparency, human oversight, data quality); ensuring access to remedies and procedural safeguards; international cooperation on AI governance. | Protection of fundamental human rights (dignity, autonomy, privacy, non-discrimination, freedom of expression); ensuring democracy and the rule of law; accountability and responsibility for AI systems; transparency and explainability of AI; access to remedies for AI-related harms; prevention of AI-based discrimination. | Vulnerable groups, including persons with disabilities, children, ethnic and national minorities; ensuring gender equality. | Public Law, Human Rights Law, EU Law, International Law, Data Protection Law, Anti-discrimination Law. | European Union, Council of Europe member states, with potential for broader international scope (e.g., for CoE Convention). | NaN | NaN | NaN | False | False | NaN | Initial limitations in EU AI Act's human rights scope (though improving); potential for regulatory fragmentation between EU and CoE; ensuring effective enforcement and remedies; keeping legal frameworks updated; potential weakness in CoE Convention's reliance on domestic implementation; insufficient addressal of gender-based discrimination in CoE draft. | Defining AI appropriately for legal texts; classifying AI systems based on risk; achieving consensus among diverse stakeholders; balancing innovation with fundamental rights protection; ensuring legal certainty; addressing the transnational nature of AI; coordinating different international regulatory initiatives. | Violations of human rights (privacy, freedom of expression, dignity); AI-derived discrimination; manipulation of human behavior; erosion of democracy and rule of law; misuse of biometric identification and social scoring; lack of transparency, accountability, and human oversight in AI systems. |
| 2024RegionalLRev179.pdf | HeinOnline | LEVERAGING ARTIFICIAL INTELLIGENCE IN eDISCOVERY: ENHANCING EFFICIENCY, ACCURACY, AND ETHICAL CONSIDERATIONS | This paper analyzes the eDiscovery process, including its phases, benefits, and drawbacks, and explores the impact of Artificial Intelligence (AI) on this field. It offers a preliminary overview of AI applications in eDiscovery, discusses various AI techniques, and considers future trends and ethical implications. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Civil litigation, Regulatory compliance, Legaltech | Common law jurisdictions (notably USA), EMEA, APAC | NaN | NaN | NaN | False | False | NaN | NaN | Data privacy and security concerns; ethical issues such as AI bias, transparency, and accountability; integration with existing legal workflows; need for technical expertise and training for legal professionals; cost of AI implementation; managing massive data volumes; and the risk of over-reliance on AI technology. | Data breaches and unauthorized access; algorithmic bias in AI; lack of transparency and accountability in AI decision-making; errors from over-reliance on technology leading to incomplete or incorrect data analysis; potential for spoliation or tampering with evidence if ESI is improperly handled. |
| 40GaStULRev917.pdf | HeinOnline | Robot Lawyers Don't Have Disciplinary Hearings - Real Lawyers Do: The Ethical Risks and Responses in Using Generative Artificial Intelligence | This paper discusses cases of lawyers misusing generative AI, highlighting the ethical risks such as breaches of competence, confidentiality, and candor, and subsequent disciplinary actions. It then examines various responses from the legal profession, including judicial orders, bar association taskforces, and ethics opinions, aimed at mitigating these risks and guiding the responsible use of AI in law. | True | Market | True | 3.0 | Neutral | Generative Artificial Intelligence (e.g., ChatGPT, Google Bard) | The paper reports on a Stanford study that evaluated LLMs (OpenAI's ChatGPT 3.5, Google's PaLM 2, Meta's Llama2) by posing over 200,000 legal questions to them. | The cited Stanford study found that general-purpose large language models hallucinate at least seventy-five percent of the time when answering questions about a court's core ruling, made more frequent mistakes with lower federal district court case law, and exhibited a "contra-factual bias." | NaN | NaN | NaN | NaN | General legal practice (discusses ethical rules applicable to all lawyers, with examples from civil, criminal, housing, and aviation law) | United States (federal and state levels) | The paper discusses general-purpose large language models (e.g., ChatGPT 3.5, PaLM 2, Llama2) which, as noted by a cited Stanford study, are not built specifically for legal use and are thus trained on broad, non-legal-specific data. | NaN | NaN | True | True | The generative AI tools discussed, such as ChatGPT and Google Bard (Gemini), have publicly available versions, some with free tiers. Llama2, also mentioned, is open source. | NaN | User Gaps in understanding of generative AI's capabilities, limitations, and associated risks (e.g., hallucinations, bias); difficulty in verifying AI-generated information; maintaining client confidentiality; challenges in ethical integration of AI into legal practice; the rapid pace of AI adoption outpacing lawyers' comprehension and preparedness. | Fabrication of legal information (hallucinations); breaches of lawyers' ethical duties (competence, confidentiality, supervision, candor to the tribunal, independent professional judgment); algorithmic bias in AI tools; potential for legal malpractice claims and disciplinary sanctions against lawyers; compromised client confidentiality when inputting sensitive data into AI tools. |
| 53HofstraLRev391.pdf | HeinOnline | ARE A.I. LAWYERS A LEGAL PRODUCT OR LEGAL SERVICE?: WHY CURRENT UPL LAWS ARE NOT UP TO THE TASK OF REGULATING AUTONOMOUS A.I. ACTORS | The paper argues that current Unauthorized Practice of Law (UPL) regulations are inadequate for regulating autonomous AI actors in the legal field, exemplified by tools like Pactum AI. It proposes reforms to UPL laws to balance consumer protection and innovation, facilitate attorney-AI developer collaboration, and clearly define boundaries for AI in legal work. | True | Idealistic | True | 3.0 | Positive | Autonomous negotiation software (e.g., Pactum AI); Legal self-help platforms (e.g., DoNotPay, LegalZoom, Quicken Family Lawyer). | Pactum AI: Pilot program with Walmart Canada involving 100 suppliers, evaluated on deal closure rate, turnaround time, cost savings, and supplier preference. Other tools (LegalZoom, QFL, DoNotPay): Evaluated through legal challenges and court cases assessing UPL compliance. | Pactum AI (Walmart pilot): 64% deal closure rate, 11-day average turnaround, 1.5% average savings (initial); later 68% closure, 3% savings. 75% of suppliers preferred AI negotiation. | Inadequate and unclear Unauthorized Practice of Law (UPL) laws hindering the development and safe deployment of AI tools that could potentially improve access to justice, and risking consumer harm. | Reforming UPL laws to: 1) Allow attorney-AI developer collaboration, 2) Clearly define AI's permissible legal work, 3) Balance consumer protection, the sanctity of the bar, with promoting innovation. This framework would support the responsible development of AI tools that could enhance access to justice. Considers regulatory sandboxes like Utah's model. | Legal self-help tools, automated document preparation, consumer-facing legal services for common issues (e.g., small claims, contract disputes with corporations), regulation of AI in legal practice. | General consumers, particularly those needing assistance with common legal issues against corporations or for personal matters where hiring a lawyer is prohibitive. | Contracts, Estate Planning, Corporate Law, Small Claims, Traffic Law, Employment Law, Administrative Law (FOIA), Unauthorized Practice of Law (UPL) regulation. | United States (with examples from specific states like Texas, Missouri, California, Utah, and mentions of international application of tools like Pactum AI by Walmart). | For AI negotiation tools like Pactum AI: Domain-specific data including negotiation project databases (e.g., Harvard's Negotiation Project), past negotiation experiences (via machine learning), and customer-specific contract data/forms. For other tools, generally legal forms, statutes, and related legal information. | For AI negotiation tools like Pactum AI: Machine learning, natural language processing, game theory, value function algorithms, customer-specific onboarding and data integration (e.g., 'contract space'). | Commercial software-as-a-service for corporate clients (e.g., Pactum AI); Web-based services/apps for consumers, often subscription-based (e.g., DoNotPay, LegalZoom). | True | False | Pactum AI is commercially available as an autonomous negotiation suite for large corporations. DoNotPay, LegalZoom, and Quicken WillMaker & Trust (successor to QFL) offer web-based legal self-help services to consumers, typically via purchase or subscription. | Outdated and ambiguous UPL laws; lack of a national, uniform standard for AI in legal practice; need for clear ethical guidelines for AI-human lawyer collaboration; and frameworks for assessing AI competency and liability. | For developers of advanced legal AI tools: Integrating complex AI components (LLMs, machine learning, NLP, game theory) effectively and ensuring ethical, accurate outputs. For early tools: Managing ambiguous content, personalized preferences, and complex goals. For all: Navigating unclear and inconsistent UPL regulations during development and deployment. | Unauthorized Practice of Law (UPL) by AI tools or those misusing them; Consumer harm from substandard, biased, or incorrect AI-generated legal advice/services; Stifling innovation due to unclear or overly restrictive regulations; Attorneys facing UPL liability or other disciplinary action for aiding AI developers improperly or for uncritical use of AI; Erosion of due process or democratic principles if AI is poorly implemented or regulated; Job displacement for legal professionals. |
| 12BelmontLRev196.pdf | HeinOnline | INTEGRATING SUSTAINABLE DEVELOPMENT GOALS IN THE LAW CURRICULUM: LEGAL EDUCATION FOR "PEOPLE, PLANET AND PROSPERITY" | This paper advocates for integrating the UN's Sustainable Development Goals (SDGs) into legal education, particularly within LLB and JD programs. It presents strategies and case studies for embedding SDGs across various law subjects to equip future lawyers to address global challenges and foster a just, sustainable world. | True | Idealistic | False | 1.0 | Positive | Pedagogical framework for integrating Sustainable Development Goals (SDGs) across core and optional law school curricula using illustrative case studies for various legal subjects. | NaN | NaN | Lack of awareness among legal professionals about SDGs and their relevance to legal practice and reform; insufficient legal frameworks to support SDGs; challenges in achieving rule of law and access to justice without SDG-informed legal education. | Integrating SDGs knowledge into the law curriculum; Cultivating SDGs awareness among law academics and students; Embedding SDGs education across relevant core and optional subjects without disrupting the existing curriculum structure. | Access to justice (SDG 16), rule of law, building effective, accountable, and inclusive institutions. | All people, particularly those affected by inequality, lack of access to justice, and unsustainable development, including women and girls (SDG 5) and people in developing countries. | Multiple legal fields, including Introduction to Law, Constitutional Law, Corporate Law, Criminal Law, Contract Law, Property Law, Equity and Trusts, International Law, Environmental Law, Water Law, Planning Law, Climate Change Law, Law of the Sea, Taxation Law, Trade Law, Health Law, Intellectual Property Law, Technology Law, and Human Rights Law. | Primarily common law jurisdictions (e.g., Australia, USA referenced in examples), with arguments for broader international applicability due to the global nature of SDGs and legal education principles. | NaN | Conceptual analysis, case study method for illustrating SDG integration in various law subjects, and review of SDG frameworks and existing legal education literature. | Recommendation for adoption by law schools and legal educators within their curricula. | True | False | The paper outlines a pedagogical approach and provides detailed examples that educators can adopt and implement in their law curricula based on the information within the published article. | Insufficient comprehensive integration of SDGs into law curricula; lack of awareness and expertise regarding SDGs among some law academics needed to drive this integration. | NaN | Erosion of privacy, misuse of data for democratic distortion, cybercrime, cyberwarfare, online hate speech and discrimination, defamation, and copyright infringement related to digital technologies; potential for generative AI to diminish progress towards SDGs if law does not keep pace (as discussed in the Technology Law section). |
| 2024IntlJLEthicsTech106.pdf | HeinOnline | EMPOWERING JUSTICE: BLOCKCHAIN AND LEGAL CHATBOTS AS CATALYSTS FOR ACCESS TO LEGAL AID | This paper examines the synergistic integration of blockchain technology and legal chatbots as a revolutionary approach to enhancing access to justice for the general public. It proposes a conceptual framework for this integration and outlines a roadmap for ethical and inclusive deployment, emphasizing global cooperation. | True | Idealistic | True | 1.0 | Positive | Conceptual framework for integrating blockchain technology with legal chatbots, including a proposed technical architecture. | NaN | NaN | Economic constraints, lack of legal literacy and awareness, geographical barriers, systemic discrimination, and the complexity and inefficiency of legal systems. | Proposing the integration of blockchain and legal chatbots to provide secure, accessible legal information and services; advocating for interdisciplinary collaboration, ethical guidelines, overcoming the digital divide, and fostering innovation. | Access to legal information, advice, and representation; legal aid; secure document and evidence management; identity protection; smart contracts for legal processes; democratizing legal information via chatbots. | General public, economically disadvantaged populations, individuals in rural/remote areas, refugees, stateless persons, and other underserved communities. | General legal aid, property law, contract law, family law (vital records), consumer rights, dispute resolution. | International, with specific examples and regulatory discussions concerning USA (California, Arizona), Honduras, Georgia, EU, China, UK, Singapore, Japan, and UN resolutions. | For the AI/chatbot component of the proposed conceptual framework: an extensive database of legal documents, precedents, statutes, and case law. The specific source is not detailed but implied to be comprehensive legal data. | Conceptual system architecture design, outlining key components (UI, chatbot engine, blockchain infrastructure, data security, integration layer, monitoring) for integrating blockchain and legal chatbots. | NaN | False | False | NaN | Technical challenges (scalability, interoperability, integration complexity), the digital divide, regulatory uncertainty and lack of legal recognition for blockchain records, ethical concerns (AI bias, data privacy, accountability), and ecological impact of some blockchains. | Technical complexity of integration, blockchain scalability, interoperability between diverse systems, ethical AI development (ensuring fairness and mitigating bias), ensuring robust data privacy and security, defining accountability mechanisms, navigating evolving regulatory landscapes, and managing ecological impacts of certain blockchain technologies. | AI bias leading to unfair or discriminatory advice, breaches of data privacy and security with sensitive legal information, lack of accountability for errors from AI systems or smart contracts, oversimplification of complex legal issues by chatbots misleading users, and potential for misuse of AI tools for deceptive purposes. |
| 31AIL773.pdf | HeinOnline | Judicial knowledge-enhanced magnitude-aware reasoning for numerical legal judgment prediction | This paper introduces NumLJP, a novel architecture for numerical legal judgment prediction (imprisonment and penalty) in criminal cases. NumLJP enhances prediction by integrating judicial knowledge through a selection module, acquiring numerical commonsense via masked numeral prediction, and performing magnitude-aware reasoning using a specialized graph network, demonstrating significant improvements on Chinese legal datasets. | True | Idealistic | True | 1.0 | Positive | NumLJP: a judicial knowledge-enhanced magnitude-aware reasoning architecture using a contrastive learning-based judicial knowledge selector (JKS), a masked numeral prediction (MNP) task for legal numerical commonsense, and a magnitude-aware numerical reasoning network (MagNet) on a scale-based numerical graph. | Evaluation on three Chinese legal datasets (CAIL-small, CAIL-large, AIJudge) using accuracy, macro-precision, macro-recall, macro-F1, and ImpScore metrics, compared against several baselines. Includes ablation studies and robustness analysis on a manually constructed variant dataset (VarLJP100). | Achieved state-of-the-art performance, with macro-F1 of NumLJP improving by at least 9.53% on penalty prediction and 11.57% on imprisonment prediction compared to competitive baselines. | Inaccurate numerical legal judgment prediction by existing AI systems due to ignoring numerical information, inability to perform numerical comparison and magnitude perception, limited training data, and sparse numerals in crime facts. | Proposing NumLJP, which incorporates official judicial knowledge (numerical anchors) as reference points, uses a masked numeral prediction task for acquiring legal numerical commonsense, and employs a magnitude-aware numerical reasoning network (MagNet) on a scale-based graph to handle numerical comparison and magnitude. | Numerical legal judgment prediction (imprisonment terms and penalty amounts in criminal cases) for enhanced legal information and understanding. | Laypeople/general public without legal background. | Criminal Law | China | Publicly available Chinese legal case documents (fact descriptions, law articles, imprisonment terms, penalty terms) from CAIL2018 and AIJudge challenges, originally sourced from China Judgment Online. Judicial knowledge (containing numerical anchors) specific to criminal charges is also utilized. | Deep learning methodology including use of pre-trained language models (RoBERTa), contrastive learning, masked language modeling techniques (for numerals), and graph neural networks. Design involves modular architecture (JKS, MNP, MagNet) and task-specific loss functions. | NaN | False | False | NaN | Technical gaps include handling complex criminal cases (multiple defendants/facts, coreference), reasoning over diverse numeral types within a single judicial knowledge, addressing issues with duplicate/excessive/oversized numerals, and interpreting implicit numerals. | Designing a model capable of numerical comparison and magnitude perception, distinguishing confusing cases for correct judicial knowledge application, acquiring legal numerical commonsense from judicial knowledge, handling unseen numerals and few-shot scenarios, and managing training stability of graph-based models. | Risk of incorrect predictions due to model errors (e.g., wrong judicial knowledge selection, misinterpretation of numerals). Potential for machine interference with judges' independent judgment if misused. Privacy risks if sensitive information in data is not properly handled. |
| 107Judicature68.pdf | HeinOnline | Is Disclosure and Certification of the Use of Generative AI Really Necessary? | Based on its title, this paper discusses the necessity of disclosing and certifying the use of generative AI, likely within legal professional contexts. It presumably explores arguments for and against such requirements for transparency and accountability. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | False | NaN | NaN | NaN | NaN |
| 57UICLRev291.pdf | HeinOnline | INNOCENT UNTIL PROVEN GUILTY: UNLESS YOU'RE POOR. RIGHTING A SYSTEMIC WRONG UNDER THE PRETRIAL FAIRNESS ACT. | This paper discusses Illinois' Pretrial Fairness Act (PFA), which abolishes cash bail, arguing it fosters a more equitable justice system by prioritizing risk over wealth in pretrial release decisions. It also proposes expanding the judicial role of Restorative Justice Community Courts to further improve pretrial processes and address court burdens. | True | Idealistic | False | 2.0 | NaN | The Pretrial Fairness Act (PFA) in Illinois, a legislative reform that abolishes cash bail and establishes new standards for pretrial detention. | The paper evaluates the Pretrial Fairness Act through legal analysis of its provisions, historical context, discussion of its implementation mechanisms (task forces, subcommittees), and by addressing criticisms. It also notes ongoing external empirical studies of the SAFE-T Act's impact by institutions like Loyola University and the National Institute of Justice. | The paper's evaluation concludes that the Pretrial Fairness Act is a significant and positive reform that moves Illinois towards a more equitable pretrial justice system by basing detention decisions on risk rather than wealth. It is expected to reduce unjust incarceration of the poor and address systemic disparities, though successful implementation faces challenges. | The cash bail system itself, which bases pretrial freedom on financial ability rather than risk, leading to disproportionate detention of the poor and minorities. Other obstacles include misinformation campaigns against reform, the PFA becoming a political pawn, and the existing overburdened court system. | The primary solution is the implementation of the Pretrial Fairness Act, abolishing cash bail. The paper further proposes granting judicial decision-making power for certain low-level offenses to Restorative Justice Community Courts to alleviate court burdens and enhance community-focused justice. It also emphasizes the role of implementation task forces, data collection, and collaboration with law enforcement. | Pretrial detention, bail reform, abolition of cash bail, risk assessment in pretrial decisions, Restorative Justice Community Courts, systemic injustice, socio-economic and racial disparities in the criminal justice system. | Low-income individuals and racial/ethnic minorities (specifically Black and Latinx defendants) who are disproportionately affected by the cash bail system and pretrial detention. | Criminal Law (specifically pretrial procedure and bail reform). | Illinois (Cook County often used as an example); comparative references to other US states (e.g., New York, California, New Jersey). | NaN | The Pretrial Fairness Act was developed through a legislative process, informed by studies from the Illinois Supreme Court Commission on Pretrial Practices which involved expert consultation, stakeholder input, and analysis of academic research. | Statewide implementation in Illinois guided by the Pretrial Implementation Task Force and its subcommittees, involving development of guidelines, pilot sites, educational programs, and communication strategies. A Data Oversight Board is tasked with collecting and analyzing pretrial data. | True | True | The Pretrial Fairness Act is an enacted law in Illinois, making its provisions (the approach discussed) legally operative within that jurisdiction. The text of the law is publicly available. | The Pretrial Fairness Act's silence on adequately addressing the overburdened court system. Lack of clear definitions for statutory terms like "obvious threat" or "obvious medical or mental health issues," potentially leading to subjective enforcement. The ongoing need for comprehensive data collection and analysis to ensure effective and equitable implementation. | Challenges related to the Pretrial Fairness Act's implementation include: overcoming political opposition and widespread misinformation; ensuring consistent application across different counties and by various justice system actors; clarifying ambiguous statutory language; managing judicial caseloads effectively under the new framework; and establishing robust data collection and analysis systems for continuous improvement. | Risks associated with the previous cash bail system included unjust detention of the poor and exacerbation of racial disparities. Risks related to the PFA, as mentioned by critics (though generally refuted by the paper), include potential for increased crime if not properly implemented. The paper also highlights risks of subjective interpretation of PFA provisions by law enforcement if terms remain undefined, and the risk of misinformation campaigns undermining public trust and effective reform. |
| 26NCJLTech1.pdf | HeinOnline | NEW GOVERNANCE AND NEW TECHNOLOGIES: CREATING A REGULATORY REGIME FOR THE USE OF GENERATIVE ARTIFICIAL INTELLIGENCE IN THE COURTS | This paper analyzes emerging ex ante judicial rules and standing orders designed to regulate the use of Generative AI (GenAI) in U.S. federal courts, categorizing them and framing their development within New Governance Theory. It discusses the risks posed by GenAI in litigation, such as hallucinations, and suggests that these decentralized, experimental regulatory approaches can foster effective, ethical GenAI use and inform broader AI governance. | True | Market | True | 3.0 | Neutral | Ex ante judicial rules and standing orders, framed by New Governance Theory, to regulate the use of Generative AI in court filings. | The paper provides a descriptive analysis and typology of existing judicial rules and orders; no formal testing of their effectiveness is presented, only observation of their emergence, characteristics, and preliminary impact. | The paper identifies a typology of ex ante judicial responses to GenAI (ranging from simple warnings to prohibitions) and notes that while few problematic filings have appeared in jurisdictions with such orders, causality is undetermined. It also notes the Fifth Circuit's decision not to adopt a special rule after stakeholder consultation. | Submission of inaccurate or fictitious legal information due to GenAI hallucinations, wasting court and litigant time and resources, burdening the judicial system, empowering aggressive litigants, and potentially undermining the integrity of legal precedent and public trust in the legal system. | Implementing ex ante judicial rules and standing orders based on New Governance principles (e.g., warnings, disclosure requirements, certifications of accuracy, stakeholder engagement, decentralized experimentation, soft-law approaches backed by sanctions) to guide and control GenAI use in litigation. | Integrity of court proceedings, responsible use of AI by all litigants (including pro se), regulation of AI in legal practice, and indirectly, the potential for AI tools for the unrepresented. | Pro se litigants and the unrepresented (as part of the broader group of all court users affected by GenAI use). | Primarily Civil Litigation within federal courts, but the discussed principles of GenAI regulation in courts could apply more broadly. | United States (Federal Courts). | NaN | NaN | Regulatory approaches are deployed via judicial standing orders by individual judges, local court rules adopted by district courts, and consideration of amendments to appellate practice rules by circuit courts. | False | False | NaN | Technological limitations of GenAI (accuracy, reliability, hallucinations, bias), data privacy concerns, intellectual property issues related to LLM training, and the general need for robust, adaptable regulatory frameworks to ensure safe and ethical AI development and deployment for legal applications. | For GenAI tools: ensuring accuracy and reliability (avoiding hallucinations), addressing inherent biases. For regulatory approaches: developing flexible and adaptable rules that can keep pace with rapid technological change and balancing innovation with risk mitigation. | Submission of fictitious cases and legal authorities due to GenAI 'hallucinations'; wasting court and litigant resources; undermining the integrity of the judicial process, legal precedent, and public trust; potential for bias in AI-generated content; misuse by litigants to amplify burdensome or frivolous claims; unauthorized disclosure of confidential client information to GenAI services; violations of consumer privacy; wrongful use of intellectual property in training LLMs. |
| 70SDLRev117.pdf | HeinOnline | "DO NOT READ" | The paper satirically argues that legal scholars should affix "Do Not Read" labels to their work, contending this aligns with existing academic norms and offers benefits like reputation management and efficiency. It humorously suggests this practice could even save humanity from AI by limiting its access to legal scholarship. | True | NaN | False | 1.0 | NaN | The 'Do Not Read' label (satirical proposal) | NaN | NaN | NaN | NaN | NaN | NaN | Legal Academia / Legal Scholarship | United States (referencing its academic practices) | NaN | NaN | NaN | False | False | NaN | NaN | NaN | AI leading to human annihilation ("annihilation at the hands of our digital overlords"), AI taking away work from attorneys, and the (satirical) risk of the paper itself revealing humanity's defense strategy against AI. |
| 17ContempAsiaArbJ133.pdf | HeinOnline | ASSESSING THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE ARBITRATION PROCESS | This paper investigates the integration of Artificial Intelligence (AI) in the arbitration process, aiming to enhance accessibility and affordability of justice. It compares traditional and AI-assisted arbitration, evaluates ethical and legal considerations, and incorporates insights from legal professionals to advocate for a balanced synergy between AI capabilities and human expertise. | True | Idealistic | False | 3.0 | Positive | AI-assisted arbitration | Field survey with a questionnaire (using Likert scales) distributed to 30 legal and arbitration professionals. Data was analyzed using descriptive statistics and correlation analysis to assess perceptions on accuracy, efficiency, and acceptability of AI in arbitration. | AI-assisted arbitrators were perceived as potentially more accurate in ensuring unbiased and consistent decisions, and more efficient in data handling and speed. Human arbitrators were valued for nuanced, experience-driven judgment, adaptability, empathy, and culturally sensitive resolutions. Respondents showed preference for AI assistance in pre-arbitration consultation/agreement and arbitrator selection stages. | High cost of traditional arbitration; lack of transparency and interpretability in AI systems; potential for AI-perpetuated bias; data confidentiality and security concerns; need for substantial and appropriate data for AI training; challenges in AI making reasoned decisions. | Cautious and ambitious integration of AI, synergizing with human cognitive processes and expertise; development of comprehensive ethical guidelines and standards; adaptation of legal and regulatory frameworks; enhancing AI interpretability and clarity; fostering human-AI collaboration models; ensuring robust data privacy and security measures. | Affordability of arbitration; efficiency of the arbitration process; accessibility of justice; ethical and legal implications of AI in dispute resolution. | Parties for whom the cost of arbitration is a barrier to accessing justice. | Arbitration (specifically international and commercial arbitration) | International | NaN | NaN | NaN | False | False | NaN | Effective human-AI collaboration models; understanding the impact of AI on party trust and satisfaction; AI's adaptability to diverse legal systems and cultural contexts in international arbitration; long-term implications of AI for the legal profession and arbitration practices; need for comprehensive ethical guidelines and updated legal frameworks; enhancing AI interpretability and ensuring data security. | Ensuring transparency and interpretability of AI algorithms; mitigating potential biases in AI systems; addressing data privacy, confidentiality, and security concerns; adapting existing legal and regulatory frameworks to accommodate AI; managing the technical complexities of developing and implementing legal AI (e.g., data availability, model opacity); maintaining human oversight and building trust in AI-assisted processes. | Algorithmic bias leading to unfair or discriminatory outcomes; lack of transparency and explainability in AI-driven decisions, undermining due process; breaches of data privacy and confidentiality; security vulnerabilities of AI systems; perpetuation or amplification of existing societal biases through AI; over-reliance on AI potentially eroding human legal skills and judgment; inaccuracies or errors from AI systems due to limited or biased training data, or lack of access to real-time information. |
| 22UNHLRev151.pdf | HeinOnline | Major Reform With Minor Risk: Implementation of Change Initiatives as a Learning Challenge | This paper argues that significant reforms are needed in legal education and that many sound ideas for change exist. It provides a framework for effectively implementing these reforms, emphasizing evidence-based practices and change management, alongside a survey of specific change proposals for law schools. | True | Idealistic | False | 3.0 | NaN | NaN | NaN | NaN | Misalignment of legal education content and attorney licensing exams with the practical competencies (e.g., client communication, cultural humility, tech literacy) required to effectively serve diverse public needs and ensure access to justice. High-stress, overly competitive, and non-inclusive law school environments that can negatively impact student well-being, ethical development, and the cultivation of a service-oriented professional identity. Insufficient institutional focus on and resources for promoting equity, diversity, and belonging within law schools. The high cost of legal education and resulting student debt limiting career choices. Resistance to substantial reform and evidence-based implementation within legal education. | Reforming attorney licensing and legal curricula to emphasize practical, client-centered skills, ethical development, cultural competency, and technological proficiency. Fostering healthier, more collaborative, inclusive, and supportive learning environments that prioritize student well-being and professional identity formation geared towards service. Integrating principles of equity, diversity, and belonging throughout legal education. Adopting evidence-based, iterative approaches to implement and sustain meaningful changes in legal education. | Attorney licensing and bar exam reform; Curriculum development for practical skills (e.g., client communication, tech literacy); Professional identity formation and ethics; Student well-being and mental health in legal education; Diversity, equity, inclusion, and belonging in law schools. | Law students, particularly those from first-generation and historically underrepresented backgrounds, by aiming to create a more equitable, inclusive, and supportive educational environment. Indirectly, the general public and underserved communities who would benefit from more competent, ethical, and culturally sensitive lawyers. | Legal Education | United States | NaN | NaN | NaN | False | False | NaN | The persistent research-practice gap in legal education, where known effective reforms are not widely or successfully implemented. Lack of comprehensive, evidence-based approaches to instilling practical, client-centered, and A2J-relevant competencies in all law students. Insufficient mechanisms for systematically evaluating and ensuring that legal education and licensing standards truly prepare lawyers to meet diverse societal legal needs, particularly for underserved populations. Need for more effective strategies to address systemic issues like student debt, mental health crises, and lack of diversity within the legal profession. | Overlooking implementation importance; blaming ideas for implementation failures; lack of success metrics and analysis; cognitive biases in assessment; organizational complexity (processes, turnover, norms, power relations); 'solutionitis'; achieving shared problem understanding; lack of psychological safety; resistance to evidence-based practices; demands for immediate results. | Wasted resources if initiatives are unsuccessful or misimplemented. Opportunity costs of choosing one initiative over another. Misimplementation causing more harm than inaction. Failed initiatives tainting good ideas for future reform. Continued misalignment of legal education with professional needs. Perpetuation of student mental health crises. Failure to address equity and belonging issues. Producing graduates ill-equipped for modern practice. High student debt. |
| 34IndIntlCompLRev249.pdf | HeinOnline | BOYCOTTING CHINESE GENOCIDE AND THE DUTY TO PREVENT: OPPORTUNITIES LOST IN THE 2019-2021 UK TRADE BILL | This paper argues that the UK had an international legal obligation to pass the proposed 2019-2021 Trade Bill amendment, which aimed to impose economic sanctions on China for the alleged genocide of Uighurs. It contends such sanctions would be effective due to China's economic vulnerabilities and historical precedent, particularly if applied collectively by international partners. | True | Idealistic | False | 2.0 | NaN | Imposing economic sanctions, as proposed via the failed 2019-2021 UK Trade Bill amendment, against states (specifically China) determined to be engaged in genocide. | The paper evaluates this approach through legal analysis of international law (Genocide Convention, ICJ judgment in Bosnian Genocide case), review of historical precedents of sanctions (e.g., on Russia, past sanctions on China), and economic analysis of China's vulnerabilities and trade relationships. | The paper concludes that imposing such economic sanctions against China is a state obligation under international law and would likely be effective in influencing China's conduct regarding the Uighurs, especially if sanctions are collective, pervasive, and leverage China's economic dependencies. | Political reluctance of states to implement robust economic sanctions against powerful nations like China; narrow interpretations of the state's duty to prevent genocide (e.g., the 'effective influence' standard derived from the Bosnian Genocide case); and arguments concerning the economic costs or perceived ineffectiveness of unilateral sanctions. | The paper advocates for passing legislation like the proposed UK Trade Bill amendment; calls for a broader interpretation of the 'effective influence' standard to trigger state obligations to prevent genocide; promotes collective international action for imposing pervasive economic sanctions; and suggests leveraging the economic vulnerabilities of the target state (China). | State responsibility to prevent genocide; use of international trade law and economic sanctions for enforcement of human rights; protection of vulnerable minority groups (specifically, the Uighurs). | The Uighur ethnic/religious minority in Xinjiang, China. | International Law (International Human Rights Law, Law of State Responsibility, International Criminal Law - Genocide Convention), UK Trade Law. | United Kingdom, China, International (with reference to ICJ jurisprudence and UN conventions). | NaN | NaN | NaN | False | False | NaN | The gap between existing international legal norms (such as the duty to prevent genocide) and their actual enforcement by states, particularly highlighting the lack of political will to impose meaningful and collective economic sanctions against economically powerful states. A need for broader interpretation and acceptance of extraterritorial obligations concerning genocide prevention is also implied. | Determining the precise nature, timing, and obligatory character of economic sanctions for genocide prevention; achieving collective action among states for sanctions to be maximally effective; countering arguments about potential negative humanitarian consequences or economic blowback on sanctioning states; and ongoing debates over the 'effectiveness' of sanctions in altering a target state's behavior concerning jus cogens violations. | Potential for economic sanctions to cause collateral damage to civilian populations in the targeted state (e.g., through impacts on food and medicine imports); sanctions being ineffective or even counter-productive (citing the Srebrenica example where sanctions purportedly made genocide easier); economic costs to the sanctioning state(s); and the possibility that leaders of the target state may ignore the suffering of their own people caused by sanctions. |
| 59TulsaLRev193.pdf | HeinOnline | FROM BRIEFS TO BYTES: HOw GENERATIVE AI IS TRANSFORMING LEGAL WRITING AND PRACTICE | This paper explores how Generative AI (GAI), particularly tools like ChatGPT, is revolutionizing legal writing and practice by offering capabilities for drafting, summarization, and analysis. It aims to educate legal professionals on GAI's mechanisms, diverse applications, ethical considerations, and effective usage strategies, emphasizing prompt engineering. | True | Market | True | 3.0 | Positive | Generative AI (e.g., ChatGPT, GPT models) and Prompt Engineering | Illustrative examples generated by GAI (e.g., ChatGPT, Write.law Bot) and reference to external productivity studies. | GAI makes people more productive, efficient, and capable of producing work product with 'significantly higher quality results (more than 40% higher quality compared to a control group)' (citing external study). | Knowledge gap among legal professionals, ethical concerns (confidentiality, bias, accuracy), risk of over-reliance, and data security. | Educating legal professionals about GAI, employing prompt engineering for effective use, continuous skill development, and adherence to ethical guidelines. For A2J (indirectly): GAI can facilitate the creation of self-help tools and productization of legal services. | Self-help legal tools, productization of legal services to scale expertise. | The general public, clients needing more accessible legal services. | General legal practice, Contract law, Litigation, Intellectual Property. | United States | Vast amounts of text data from the internet, including books, articles, websites, and potentially copyrighted materials, used to train foundation models like OpenAI's GPT series. | NaN | NaN | True | True | Publicly available GAI tools like ChatGPT (which has free and paid tiers) and other GAI models integrated into existing legal tech software or accessible via APIs. | Knowledge gap among users, GAI limitations (accuracy, bias, nuance, jurisdiction-specificity, emotional intelligence), data security, and ethical concerns. Potential for over-reliance. | Crafting effective prompts (prompt engineering), overcoming GAI's limitations (accuracy, bias, lack of deep legal understanding), ensuring data confidentiality and security, avoiding over-reliance, and managing ethical implications. | Ethical violations (confidentiality, competence, bias), data privacy and security breaches, generation of inaccurate information ('hallucinations') or biased content, over-reliance diminishing human skills, and potential copyright infringement. |
| 24GermanLJ551.pdf | HeinOnline | The Impact of Digitalization on Global Trade Law | This paper explores how digitalization impacts global trade law, examining the World Trade Organization (WTO) framework and the evolution of digital trade rules in Free Trade Agreements (FTAs) like CPTPP, USMCA, and emerging Digital Economy Agreements (DEAs). It assesses the adequacy of current legal responses for the data-driven economy, highlighting divergent approaches, regulatory challenges, and the need for enhanced international cooperation. | True | Market | False | 2.0 | NaN | Analysis of digital trade provisions and regulatory approaches in international trade law, specifically within the WTO framework, Free Trade Agreements (FTAs) like CPTPP and USMCA, EU's FTA models, and Digital Economy Agreements (DEAs). This includes examining rules on e-commerce, data flows, data localization, digital products, source code, and personal data protection. | The paper evaluates these legal and regulatory approaches through comparative analysis of treaty texts, discussion of their policy implications, review of academic literature, and reports from international organizations and governmental bodies. | WTO law is largely pre-internet and insufficient for current digital trade challenges. FTAs and DEAs are increasingly addressing digital trade with more comprehensive and binding rules, but significant divergence exists, notably between the US-led liberal model (e.g., CPTPP, USMCA) emphasizing data flows and the EUs model prioritizing data protection alongside data flows. DEAs represent an innovative, cooperative approach to broader digital economy issues. | NaN | NaN | NaN | NaN | International Trade Law, E-commerce Law, Data Governance Law, IT Law, International Economic Law | International (WTO); Plurilateral/Regional agreements including CPTPP member states (Australia, Brunei, Canada, Chile, Japan, Malaysia, Mexico, New Zealand, Peru, Singapore, Vietnam), USMCA (United States, Mexico, Canada), European Union, Japan, Singapore, Australia, New Zealand, Chile, South Korea, United Kingdom. | NaN | Legal analysis, comparative legal studies, treaty interpretation, policy analysis, review of official government documents and proposals, academic literature review. | The discussed trade agreements (FTAs, DEAs) are deployed through negotiation, signature, ratification, and implementation by sovereign states, becoming binding international law for the parties. | True | True | The texts of the discussed international trade agreements (e.g., CPTPP, USMCA, EU FTAs, DEPA) are generally publicly available through official government websites or treaty databases. | Insufficiency of the multilateral WTO framework for digital trade; lack of global consensus on key digital trade issues (e.g., data governance, balancing trade with privacy); the need for more adaptable and inclusive governance models that can keep pace with technological change; ensuring equity and inclusiveness in institutional developments. | Balancing trade liberalization with non-trade policy objectives (e.g., privacy, national security, consumer protection); addressing divergent national regulatory approaches (e.g., US vs. EU on data); managing the impact of data localization measures; protecting intellectual property in the digital environment; establishing rules for emerging technologies like AI; achieving consensus in multilateral forums; ensuring FTAs do not become overly opaque or state-centered. | Emergence of 'digital protectionism' and new trade barriers (e.g., data localization, censorship); inadequate protection of personal data and privacy due to trade pressures; increased data inequality and 'data colonialism'; cybersecurity threats; stifling of innovation through fragmented or overly restrictive regulations; negative impacts of disruptive technologies on employment. |
| 24BusLIntl215.pdf | HeinOnline | Utilising Generative Al in Businesses: Risks and Best Practices | This paper examines the application of generative AI, such as GPT chatbots and image generators, within business contexts, highlighting significant risks including data bias, misinformation, privacy violations, and copyright infringement. It also reviews current and proposed regulatory landscapes and proposes best practices for businesses to responsibly integrate these AI technologies. | True | Market | True | 3.0 | NaN | GPT chatbots (e.g., ChatGPT, GPT-4) and AI image-generating programs. | NaN | NaN | NaN | NaN | NaN | NaN | Privacy law, Copyright law, Defamation, Contract law, Tort law, Legal ethics | International; specific examples/regulations from USA, China, EU, UK, Switzerland, Italy | Discusses general characteristics of training data for existing models like GPT (e.g., pre-trained with data up to a certain date, large internet datasets) and related legal issues (e.g., use of copyrighted material). | NaN | NaN | True | False | The paper discusses widely known and accessible commercial generative AI tools like ChatGPT. | NaN | Creating reliable and unbiased datasets, preventing hallucination, ensuring verification of outputs, managing privacy and copyright issues, cybersecurity, establishing robust governance and regulatory frameworks for AI. | Data bias and data limitation, hallucination (generating incorrect information), spread of fake news and defamation, privacy violations (unconsented data collection, use, storage), copyright infringement and personality rights violations, data breaches and cybersecurity incidents, legal liability for damages caused by AI, lack of transparency and accountability. |
| 77MeLRev69.pdf | HeinOnline | BREAKING UP WITH THE ANTI-HERO: HOW 303(B)(3) CAN HELP LAW SCHOOLS MITIGATE THEIR PERENNIAL DEVICES, PRICES, VICES, AND CRISES | This paper argues that ABA Standard 303(b)(3) necessitates a comprehensive integration of professional identity development into the first year of law school to address current crises in legal education, such as student distress and unpreparedness. It proposes practical strategies, drawing on self-determination theory and wise interventions, and uses examples from Willamette University College of Law to illustrate how these can foster well-rounded, ethical practitioners. | True | Market | False | 1.0 | Positive | A comprehensive, integrated approach to professional identity development throughout the first year of law school, incorporating principles from ABA Standard 303(b)(3), self-determination theory, and wise interventions. Specific examples include the Academic Excellence Fellows (AEF) peer mentoring program, Zero-L summer engagement (book groups, podcasts), and classroom strategies like explicit instruction on study/management skills and formative assessment. | The paper describes the piloting and implementation of specific initiatives at Willamette University College of Law, such as the Academic Excellence Fellows (AEF) program. Evaluation appears to be based on qualitative feedback and observed benefits rather than formal, quantitative studies (e.g., student and fellow experiences). | Anecdotal and qualitative positive outcomes are reported for the Willamette initiatives, such as fellows finding the AEF program highly meaningful, 1Ls receiving early constructive feedback and support, fostering a sense of community, and students developing foundational academic and professional skills. | Current law school curriculum and culture destroying student enthusiasm and well-being; increasing student anxiety, depression, and substance abuse; students arriving less prepared for an autonomous learning environment; law schools' resistance to systemic change; misalignment between legal education and employer/societal needs; students' low sense of belonging. | Integrate professional identity development pervasively into the first-year curriculum rather than as supplemental programs. Implement early engagement strategies (Zero-L summer programs). Leverage trained peer mentors (e.g., Academic Excellence Fellows). Provide explicit instruction on foundational academic, self-management, and professional skills. Employ formative assessment and wise interventions to build competence, autonomy, and relatedness. | Professional identity formation; Legal education reform; Student well-being in law school; Pedagogical strategies for law students; Curriculum development. | Law students, particularly first-year (1L) students. Indirectly, the legal profession, clients, and civil society. Specific mention of benefits for non-traditional and first-generation students through peer mentoring. | Legal Education | United States | NaN | The proposed approach is based on educational theories (Self-Determination Theory, wise interventions), analysis of legal education reports (Carnegie Report, CLEA, IAALS), ABA accreditation standards, and pilot program implementation and observation (e.g., Willamette University College of Law's initiatives). | Specific programs (e.g., Academic Excellence Fellows, Zero-L podcast and book groups, integrated Lawyering course components) are described as implemented at Willamette University College of Law. The paper advocates for broader adoption of similar integrated approaches by other law schools. | True | False | The pedagogical approaches, strategies, and specific program ideas (like peer mentoring structures, pre-1L engagement, classroom techniques) are detailed in the paper, allowing other institutions to adopt and adapt them. | The need for broader, integrated adoption of professional identity development across the entire first-year curriculum and by all faculty, rather than relying on standalone courses or supplemental programs. Addressing faculty reluctance to engage in this type of student development. Ensuring consistent and meaningful implementation to avoid it becoming another superficial requirement. | Faculty resistance to changing teaching methods or incorporating non-doctrinal content. Student overload and limited bandwidth if professional identity initiatives are not seamlessly integrated. Engaging students who may not perceive the need for such development. Overcoming institutional inertia and moving from piecemeal solutions to a holistic, integrated approach. | NaN |
| 31AustlLLibr19.pdf | HeinOnline | ChatGPT – THE BLURST OF TIMES | This paper discusses OpenAI's ChatGPT, exploring its capabilities, market context, and potential applications in the legal field, including for access to justice via tools like DoNotPay. It also thoroughly outlines significant limitations such as inaccuracies, biases, ethical concerns, and the ongoing need for human judgment and oversight. | True | Idealistic | True | 2.0 | Neutral | ChatGPT (a large language model by OpenAI) and its integration into legal tech applications like DoNotPay and Clausebase. | The paper reports on various informal evaluations and observations: user experiences (e.g., Nick Cave lyrics generation), demonstrations (e.g., Google Bard's error), beta-testing feedback (e.g., Clausebase's module found 'useful, but imperfect'), and OpenAI's stated limitations. | General capabilities include human-like dialogue and text generation. Specific application feedback is mixed: Clausebase's module was 'useful, but imperfect'; creative outputs can be poor. DoNotPay is described as functional for tasks like ticket disputes. Known limitations include factual inaccuracies, knowledge cutoff (post-2021), and potential for bias. | High cost and insufficient availability of legal help for low-income individuals for a vast majority of their civil legal problems. | AI-powered chatbots like DoNotPay (using ChatGPT) to handle common legal issues (e.g., ticket disputes, consumer rights) and assist with government paperwork. AI tools for high-volume, less complex legal tasks like drafting wills and conveyancing. | Access to basic legal assistance for common civil legal problems (ticket disputes, consumer rights, landlord issues, employee rights), government paperwork, and routine legal document drafting (wills, conveyancing). | Low-income individuals and the general public facing common legal issues who lack access to traditional legal services. | General civil law (consumer protection, housing, employment, administrative), contract law, wills and estates, property law. | International (general discussion of ChatGPT), with specific examples/data from USA (LSC report, DoNotPay context) and Belgium (Clausebase). | ChatGPT was pre-trained on a large corpus of text and code ('large volumes of data gleaned from conversations between humans and the written word of humans') with a knowledge cut-off in 2021. The paper notes verification and truthfulness of training data as a concern. | NaN | ChatGPT (version 3.5) released publicly in November 2022, with a free tier and a paid subscription (ChatGPT Plus). An API is planned for broader integration. DoNotPay is an operational chatbot. Clausebase's ChatGPT-powered module was in beta-testing. | True | True | ChatGPT is accessible through a free tier online and a paid subscription; DoNotPay is an operational chatbot service. | Ensuring factual accuracy and truthfulness; overcoming knowledge limitations (post-2021 events); mitigating bias in outputs; incorporating human-like judgment, character, and contextual understanding; developing reliable methods for detecting AI-generated content; addressing ethical concerns regarding AI decision-making. | NaN | Generation and spread of disinformation and falsely generated assertions; inbuilt bias in AI models leading to unfair outcomes; loss of human-centric decision-making; privacy violations due to data handling; security vulnerabilities; copyright infringement from using trained-on content; producing harmful or biased instructions. |
| 6JLTechTex168.pdf | HeinOnline | THE AI-BASED LEGAL PARADISE-A (NECESSARY!) THOUGHT EXPERIMENT | This paper conducts a thought experiment on a future "AI-based legal paradise" where AI handles all legal decisions, assuming AI achieves human-level cognitive abilities. It explores the potential advantages, such as improved access to justice and efficiency, and discusses downsides like ensuring transparency and human oversight, arguing for distinguishing technical feasibility from desirability. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Inefficiency leading to delays in legal processes; inaccuracy and inconsistency in human legal decision-making; high costs of legal services and judicial proceedings; lack of legal certainty and predictability in outcomes; limited access to legal information. | Deployment of AI for automated legal decision-making to enhance speed, accuracy, consistency, and efficiency; utilization of AI to improve legal certainty and predictability; leveraging AI systems to expand access to legal information and reduce cost/duration of judicial processes. This requires careful development and governance of AI systems. | Improving speed, accuracy, consistency, and efficiency of legal processes; enhancing legal certainty and predictability; expanding access to legal information; reducing duration and cost of judicial proceedings. | NaN | NaN | International | NaN | NaN | NaN | False | False | NaN | Technical: Current AI not yet at human-level cognitive ability for comprehensive legal decision-making; ensuring robust cybersecurity, privacy, and data protection. Societal/Ethical: Developing adequate governance and control systems for AI to ensure fairness, accountability, transparency, democratic legitimization, and human oversight; addressing the "black-box" problem; defining appropriate human involvement. | NaN | Technical failures; cybersecurity breaches, privacy violations; unfair, unaccountable, or inexplicable AI decisions (e.g., due to bias, "black-box" algorithms); malicious AI or AI running out of control; undermining due process or judicial independence; distortion of legal values; job displacement; loss of legal creativity; lack of democratic legitimization; erosion of human dignity if human control is relinquished. |
| 25CardozoJConflictResol17.pdf | HeinOnline | OPENING THE VIRTUAL WINDOW: HOW ON-LINE PROCESSES COULD INCREASE ACCESS TO JUSTICE IN THE CRIMINAL LEGAL SYSTEM | This paper explores how online dispute resolution (ODR) and other technologies could improve access to justice (A2J) in the U.S. criminal legal system, particularly for misdemeanor cases. It introduces a 'green, yellow, red light' framework to evaluate and guide the adoption of various technologies, balancing potential benefits like enhanced information access with risks such as digital divide and confidentiality concerns. | True | Idealistic | False | 1.0 | Positive | A 'green, yellow, red light' framework for categorizing and recommending the adoption of ODR and AI tools in criminal misdemeanor processing. | N/A (The proposed framework is conceptual and not empirically tested by the authors in this paper. The paper draws on existing literature and examples of technology use.) | N/A (As the framework itself is not empirically tested in the paper, no specific results for it are presented. The paper cites external studies for some discussed technologies, e.g., auto-reminders reducing failure-to-appear rates.) | Chronic underfunding, resistance to change within the legal system, the digital divide limiting access for vulnerable populations, difficulties for defendants in accessing understandable legal information and adequate counsel, and concerns over confidentiality and ethical use of technology. | Strategic and cautious adoption of various ODR and AI technologies, categorized by risk and benefit (green, yellow, red light approach), to improve information access, communication, and efficiency. This includes providing user-friendly tools that empower defendants, assisting defense counsel, and addressing the digital divide, while ensuring human oversight and safeguarding rights. | Improving access to legal information and counsel for defendants, ensuring fair and efficient case processing (especially for misdemeanors), reducing procedural burdens, mitigating bias, and supporting informed decision-making by defendants. | Low-income individuals facing misdemeanor criminal charges, who often lack resources and legal literacy. | Criminal law, specifically misdemeanor case processing. | United States, with specific examples from various state and local jurisdictions. | The paper does not detail specific training datasets for a single, novel technique it proposes. It discusses various existing or potential AI applications (e.g., for legal research, blind charging) which would rely on diverse legal datasets (case law, police reports); these are generally domain-specific, proprietary or public, structured/unstructured. | Problem-solving functional analysis and a heuristic risk/benefit categorization (green, yellow, red light approach). | NaN | True | True | Mentions several existing tools, some commercial (e.g., Lexis+ AI, Clio, Matterhorn) and some with free public access (e.g., ChatGPT). | Need for more empirical research on the impact of these technologies on fairness and A2J in criminal cases, development of robust ethical guidelines and human oversight mechanisms, better solutions for the digital divide for defendants, secure communication tools for attorney-client interactions, and addressing systemic issues like underfunding and entrenched resistance to change within the legal system. | Ensuring accuracy, security, and confidentiality of data, managing costs and resource limitations, overcoming resistance to change, addressing the digital divide, maintaining ethical standards (e.g., avoiding UPL, ensuring human oversight), and preventing technology from exacerbating existing biases or power imbalances. | Erosion of attorney-client confidentiality, perpetuation or creation of biases through flawed AI, increased pressure on defendants to plead guilty, further marginalization due to the digital divide, unauthorized practice of law, dehumanization of legal processes, and net-widening where efficiency gains lead to more prosecutions rather than fairer outcomes. |
| 15AmUIntellPropBrief23.pdf | HeinOnline | Al GENERATED ART AND THE GAP IN COPYRIGHT LAW | This paper examines the disruption caused by AI-generated art to artists, focusing on the inadequacy of current copyright law to protect them from unauthorized use of their work for AI training and style imitation. It argues that this creates a disincentive for human creativity, discusses the shortcomings of existing legal alternatives, and cautiously explores potential legislative solutions. | True | Idealistic | True | 3.0 | Negative | Generative AI for art creation (e.g., Stable Diffusion, Midjourney) | NaN | NaN | Unauthorized use of artists' works for AI training; AI's ability to mimic uncopyrightable artistic styles, devaluing original work and threatening artists' income; existing copyright law not protecting artists from AI-generated imitations; uncertainty of fair use defense for AI developers using copyrighted training data. | Potential legislative amendments to copyright law (clarifying infringement or fair use for AI training), exploring limited protection for artistic style (with caution), or creating new forms of intellectual property; overall, a cautious approach to immediate, drastic legal changes is advised, alongside improving artists' access to courts. | Copyright protection for artists; economic impact of AI on artists' livelihoods; unauthorized use of creative works for AI training; protection of artistic style against AI imitation; fair compensation for artists. | Artists, particularly independent (indie) artists and those relying on commission work. | Copyright Law, Intellectual Property Law | United States | Datasets of existing images paired with detailed text descriptions, including artists' publicly available works. Examples include the LAION database, which reportedly contains billions of images, some potentially used without regard to copyright ownership. Data is largely unstructured (images, text). | NaN | NaN | False | False | NaN | Current copyright law's inability to protect uncopyrightable artistic styles from AI imitation; uncertainty regarding the applicability of fair use to AI training datasets; difficulty for artists in detecting and proving unauthorized use of their work for training AI; insufficiency of existing non-copyright legal alternatives to protect artists. | NaN | Disincentive for human artists to create and share work; devaluation of art and artists' income due to mass-produced AI imitations; violation of artists' personal connection to their work (personhood); potential for new 'style protection' laws to be co-opted by corporations, harming individual artists; legislative changes may have unintended negative consequences or stifle technological development. |
| 9IJODR147.pdf | HeinOnline | Comments on Artificial Intelligence | This paper compiles commentaries from experts on the integration of AI, exemplified by ChatGPT, into Online Dispute Resolution (ODR). The authors explore potential benefits for efficiency and access, alongside significant risks like bias, misinformation, and the need for human oversight and ethical frameworks. | True | Idealistic | True | 3.0 | Neutral | ChatGPT and similar Large Language Models (LLMs) in the context of Online Dispute Resolution (ODR); the HUMANIS concept is also introduced. | NaN | NaN | Power imbalances, digital exclusion, pervasive AI bias reinforcing societal injustices, lack of AI transparency and accountability, misinformation risks, and over-reliance on imperfect AI systems. | Developing ethical human-centered AI (e.g., HUMANIS initiative), robust human oversight, performance-based standards, redesigning neutral roles to audit AI, and interdisciplinary collaboration to combat AI bias. | Online Dispute Resolution (ODR), access to justice for online consumers and citizens, fair and impartial dispute resolution, ethical AI in legal decision-making, addressing digital power imbalances and bias. | Individual citizens, SMEs, digitally excluded/disadvantaged individuals, and marginalized communities vulnerable to AI bias. | Dispute Resolution (specifically Online Dispute Resolution - ODR), ADR (Alternative Dispute Resolution), consumer law, civil procedure (small claims), ethics in legal practice. | International | ChatGPT: A large, general-purpose corpus of unverified internet text data (up to 2021), containing inherent biases and inaccuracies. HUMANIS (concept): Envisioned to use anonymized data voluntarily shared by users and entities. | NaN | NaN | True | True | ChatGPT is available online through OpenAI, with free access tiers. | Technical limitations in AI's understanding of nuance, emotion, and truth; societal challenges in AI transparency, accountability, bias mitigation, equitable access, governance, and defining human-AI roles. | For tools like ChatGPT in ODR: ensuring accuracy and truthfulness, mitigating pervasive biases, defining appropriate use-cases given cognitive limitations, establishing accountability, and preventing user over-reliance and misuse. | Spread of misinformation; perpetuation of biases leading to discrimination; lack of accountability for AI errors; erosion of critical thinking; reinforcement of past injustices; exacerbation of power imbalances; and misuse. |
| 74SCLRev823.pdf | HeinOnline | THE RIGHT TO (HUMAN) COUNSEL: REAL RESPONSIBILITY FOR ARTIFICIAL INTELLIGENCE | This paper explores the ethical and constitutional implications of future AI counsel, arguing it could surpass human lawyers in capability and improve access to justice, but finds current legal ethics and regulatory frameworks unprepared for this shift. It calls for a fundamental re-evaluation of ethical rules and disciplinary approaches to directly incorporate and regulate AI counsel, ensuring its responsible integration into the legal profession. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Cost and inaccessibility of human legal counsel; inconsistency in the quality of human counsel; human biases (cognitive, racial) within the legal system; lack of human relatability and empathy in AI counsel posing a potential obstacle to client trust and acceptance. | Development and deployment of AI counsel to provide lower-cost, more accessible, and consistently high-quality legal services; direct regulation and ethical oversight of AI counsel, including embedding ethical principles into AI, establishing new disciplinary mechanisms, and adapting licensing and ethical rules for AI; ensuring AI systems are designed to be unbiased and to protect client confidentiality. | Access to affordable legal services; quality and consistency of legal representation; ethical regulation of legal technology to ensure safe A2J; bias reduction in the justice system; client autonomy and choice of counsel in the context of A2J. | General public, particularly 'millions of people who cannot afford or access counsel' and 'all clients (rich and poor)'. | General legal practice; Criminal law; Legal ethics and professional responsibility. | United States | NaN | NaN | NaN | False | False | NaN | Current legal ethics rules and disciplinary systems are inadequate for AI counsel, lacking direct regulation of AI and relying on human supervision which may become insufficient; technical expertise (e.g., computer science, AI forensics) is lacking in current regulatory bodies; societal acceptance and trust in AI counsel, especially compared to human counsel's relational aspects; ensuring genuine independence and lack of bias in AI counsel potentially controlled or created by specific entities. | NaN | AI counsel could be controlled by states or creators, limiting its independence; novel conflicts of interest may arise (e.g., one AI representing opposing parties); breaches of client confidentiality and data security due to hacking or improper data handling by AI systems; AI perpetuating or amplifying existing biases if trained on biased data or poorly coded; potential for inadequate human supervision over increasingly complex AI; erosion of the 'human' element in legal counsel, impacting client trust and the lawyer-client relationship. |
| 50RutgersComputerTechLJ28.pdf | HeinOnline | ARTIFICIAL INTELLIGENCE AND ETHICS | This paper examines the impact of AI on legal practice and ethics, focusing on the need for attorneys to be technologically competent. It reviews current ABA and state ethics rules, discusses risks exemplified by recent cases of AI misuse, and proposes amendments to rules and continuing legal education requirements to ensure ethical and competent AI adoption. | True | Market | False | 3.0 | Positive | NaN | NaN | NaN | High cost of legal representation leading to little access to legal services for a significant portion of the population, particularly disadvantaged individuals unable to pay hourly rates. | Utilizing AI to increase efficiency in legal tasks, thereby lowering the cost of legal services; Enhancing lawyer technological competence through mandatory continuing legal education (CLE) in technology; Amending ethics rules for clarity on AI use; Forming commissions on law and technology to investigate and guide AI adoption, with improving access to justice as a stated goal for some. | Reducing cost of legal services through AI-driven efficiencies; Enhancing lawyer technological competence to enable ethical AI use for broader legal service accessibility. | Disadvantaged individuals and the general population unable to afford traditional legal fees. | General legal practice, Legal ethics | USA | NaN | NaN | NaN | False | False | NaN | Insufficient attorney technological competence and lack of clear, updated ethical guidelines tailored to AI, which are necessary prerequisites for AI to effectively and ethically contribute to improving access to justice by lowering legal costs. | NaN | Bias, errors, lack of transparency, hallucinations, privacy/confidentiality breaches, over-reliance by attorneys leading to failure to verify AI-generated content, perpetuation of discrimination, and a corrosive effect on legal reasoning skills and the training of new lawyers. |
| 37GeoJLegalEthics39.pdf | HeinOnline | Existential Advocacy: Lawyering for Al Safety and the Future of Humanity | This paper presents an empirical study of lawyers and legal advocates working to mitigate existential risks, particularly those from advanced AI, focusing on their distinct social-change lawyering model called the "priorities methodology." It analyzes how these "existential advocates" approach efficacy and accountability, especially in representing future generations, and describes the scientific, truth-seeking cultural norms that support their methodology. | True | Idealistic | False | 2.0 | Positive | Priorities methodology for social-change lawyering, drawn from Effective Altruism. | Empirical study using a multi-method research design including ethnography at the Legal Priorities Project, 53 semi-structured interviews with legal advocates in the existential risk community, and a systematic review of online materials. | The main empirical finding is the identification and description of the "priorities methodology," a distinct model of social-change lawyering used by existential advocates. This model aims to maximize impact through formal goal/strategy selection (based on önem, neglect, tractability) and is supported by cultural norms emphasizing uncertainty embrace, deliberative rationality, supportive dissent, and epistemic identity, though it faces tensions regarding broader mobilization and representation. | Cognitive biases (e.g., availability heuristic, scope neglect, present bias) hindering recognition of large-scale, uncertain, and abstract existential risks; political incentives prioritizing immediate issues over long-term concerns for future generations; global coordination challenges; lack of experience with existential-scale events; difficulty in emotionalizing low-probability/high-impact risks. | The paper describes the "priorities methodology" (combining moral first principles with criteria like importance, neglect, and tractability for cause selection, and reverse engineering for strategy) used by advocates to systematically address these risks. It also discusses the cultural norms (uncertainty, rationality, dissent, epistemic identity) that support this methodology and concludes with recommendations for adapting and scaling the model. | Existential risk mitigation (particularly from AI and engineered pandemics), AI safety, protection and legal representation of future generations, longtermism. | Future generations. | AI law and policy, environmental law (as analogous for future generations), human rights law (extended to future generations), tort law, criminal law, constitutional law (rights of future generations), international law, regulatory policy. | International (movement is global, addressing global risks, involving organizations and actions in the US, UK, EU, and other regions, with reference to UN initiatives and international legal scholarship). | NaN | The "priorities methodology" is drawn from the philanthropic framework of Effective Altruism. It involves: 1) starting with moral first principles (often utilitarian-leaning but with normative uncertainty), 2) cause selection based on criteria of importance, neglect, and tractability (INT analysis), and 3) strategy selection using reverse engineering from end goals, focusing on maximizing counterfactual impact. This is supported by cultural norms fostering scientific, truth-seeking approaches. | The "priorities methodology" is employed by a community of "existential advocates" and organizations like the Legal Priorities Project (LPP) in their research, policy advising, strategic litigation considerations, and community building efforts aimed at mitigating existential risks. | True | True | The paper describes the "priorities methodology" as a conceptual framework, based on publicly discussed Effective Altruism principles, making its understanding and potential application accessible to readers of the paper. | The tension between maintaining the rigorous "priorities methodology" with its specific cultural norms and the need to broaden the movement for inclusivity, democratic legitimacy, and wider impact; the need for further diversification (racial, geographic, gender) within the movement to avoid blind spots and enhance effectiveness; challenges in operationalizing accountability to future generations while incorporating diverse current-person voices. | Maintaining adherence to scientific truth-seeking norms (uncertainty, deliberative rationality, dissent, epistemic humility) against human cognitive tendencies and professional legal norms; operationalizing accountability to silent future generations while engaging current diverse populations; dealing with the "missing mood" or difficulty in emotionally connecting to abstract, long-term risks; balancing rigorous analysis with timely action; scaling the movement without diluting its core methodology or politicizing the issues. | The movement faces criticism for potentially shifting attention and resources away from current social injustices. Association with controversial donors (e.g., Sam Bankman-Fried) has led to public narratives of the movement being a distraction or serving elite/techno-utopian interests. Efforts to broaden the movement might compromise the integrity of the "priorities methodology" or lead to politicization of existential risk issues. |
| 28LegalWritingJLegalWriti (1).pdf | HeinOnline | TEACHING CRITICAL USE OF LEGAL RESEARCH TECHNOLOGY | This paper examines the impact of advanced search technologies, including generative AI, on legal research and argues that skills faculty should use structured, interactive pedagogical methods to teach law students critical, effective, and ethical use of these tools. It highlights issues like superficial analysis and the 'black box' nature of technology, offering practical guidance for educators. | True | Market | False | 1.0 | NaN | A pedagogical framework for teaching law students critical use of legal research technologies, emphasizing interactive learning (e.g., research logs, live assignments), understanding system limitations, and adapting to new tools like generative AI. | NaN | NaN | NaN | NaN | NaN | NaN | General / All legal fields | United States | NaN | NaN | Classroom instruction by skills faculty (legal writing instructors, law librarians). | True | False | The pedagogical strategies are described in the paper for adoption by educators who have access to the publication. | NaN | NaN | Superficial analysis, over-reliance on technology, 'black box' nature of systems, keyword search limitations, information limitations in databases, and specific risks of generative AI including 'hallucinations,' lack of transparency in LLMs, knowledge cut-offs, and confidentiality/privilege issues. |
| 26ColumSciTechLRev34.pdf | HeinOnline | CERTIFYING LEGAL Al ASSISTANTS FOR UNREPRESENTED LITIGANTS: A GLOBAL SURVEY OF ACCESS TO CIVIL JUSTICE, UNAUTHORIZED PRACTICE OF LAW, AND Al | This paper proposes a capability-based framework to certify legal AI assistants for unrepresented litigants, addressing unauthorized practice of law (UPL) concerns by ensuring accuracy via testing on public benchmarks. It conducts a global survey on AI, UPL, and access to justice, advocating UPL exemptions for certified AI systems to improve civil justice. | True | Idealistic | True | 1.0 | Positive | A capability-based framework for certifying legal AI assistants for unrepresented litigants, involving accuracy testing on public benchmark datasets and UPL exemptions for certified systems. | The proposed framework relies on testing individual AI capabilities for accuracy against public benchmark datasets. Evaluation involves metrics like f-measure, MCC, Bleu, Perplexity, or Task Success Rate, with acceptability thresholds set by a certifying authority. | NaN | Unauthorized Practice of Law (UPL) rules restricting AI from providing legal services; risk of incorrect legal guidance from AI harming unrepresented litigants; the vast scale of unmet legal needs unaddressed by the traditional lawyer-client model. | Amending Unauthorized Practice of Law (UPL) rules to exempt certified legal AI assistants. Implementing a capability-based framework for certifying legal AI assistants based on accuracy, verified through public benchmark datasets, with certification thresholds set by a regulating or third-party authority. | Unauthorized Practice of Law (UPL) reform forAI; AI-driven legal information, guidance, and advice; ensuring accuracy and reliability of AI legal tools; access to civil justice for self-represented individuals. | Unrepresented litigants (also referred to as self-represented litigants, pro se litigants, litigants in person). | Civil justice | Global (surveys Argentina, Australia, Brazil, Canada, China, EU, Germany, India, New Zealand, Nigeria, Singapore, UK, US). Proposes a harmonized global framework for local implementation. | The proposed certification framework relies on public benchmark datasets (e.g., LegalBench, LawBench) developed by the legal AI community, covering various legal tasks like judgment prediction, legal text classification, question answering, and summarization. | The framework is proposed based on a global survey of access to justice, UPL, and AI in various jurisdictions, analysis of stakeholder perspectives, and an assessment of AI capabilities and evaluation methods. | Proposed deployment involves local implementation by each jurisdiction's practice of law regulating authority, potentially delegating certification to an external third-party. Includes amending UPL rules, public awareness campaigns, user feedback mechanisms, and ongoing monitoring. | False | False | NaN | Need for more public benchmark datasets specifically tailored to unrepresented litigants' tasks; technical challenge of data contamination in LLMs affecting benchmark reliability; lack of a global, harmonized approach to regulating AI for access to justice despite calls for collaboration. | Proliferation of benchmark datasets needed for diverse tasks, jurisdictions, and domains, which can be costly to create and maintain. Data contamination, where LLMs are trained on public data that may include benchmark datasets, potentially inflating their apparent accuracy. | Incorrect legal guidance from uncertified or inaccurate AI assistants causing harm to unrepresented litigants. Data contamination of LLMs leading to overestimation of accuracy and underestimation of risks. Unrepresented litigants misusing AI tools, for instance, by submitting fictitious case citations generated by AI. |
| 5ITARev46.pdf | HeinOnline | INTERNATIONAL COMMERCIAL ARBITRATION & TECHNOLOGY: AN AUTHORS' INTERVIEW WITH GENERATIVE ARTIFICIAL INTELLIGENCE | This paper presents an 'interview' conducted by the authors with generative AI tools ChatGPT 4.0 and Google Bard, exploring their potential roles and limitations in international commercial arbitration. The findings confirm AI's current unsuitability for independent decision-making, highlight differences in AI responses, and underscore the continued necessity of human judgment in arbitration. | True | Market | True | 2.0 | NaN | Evaluation of generative AI (ChatGPT 4.0 and Google Bard) through a semi-structured interview methodology to assess their application in international commercial arbitration. | A semi-structured 'interview' was conducted with two generative AI tools (ChatGPT 4.0 and Google Bard) using a predefined set of questions. The authors then qualitatively compared the AI-generated answers. | The experiment confirmed AI is not ready for independent arbitral decisions and responses can vary. ChatGPT 4.0 provided more mature, prompt-adherent answers than Google Bard, which exhibited more 'hallucinations' and misinterpretations. | NaN | NaN | NaN | NaN | International Commercial Arbitration | International | The paper studies LLMs (ChatGPT 4.0 and Google Bard). ChatGPT indicated its knowledge cutoff was September 2021; Bard stated it was trained on a massive dataset of arbitral awards, legal documents, and other information related to international arbitration. These are large, pre-existing datasets not created by the authors. | NaN | NaN | True | True | ChatGPT 4.0 (paid option) and Google Bard (free option at the time of the paper). | NaN | The authors acknowledged methodological limitations, such as interviewing only two AIs and the exercise being more of a 'cognitive curiosity' than a strict scientific experiment. They also faced challenges in interpreting AI responses, including identifying 'hallucinations' and assessing differences in response style and relevance. | Bias in AI from training data, lack of transparency, data security and confidentiality breaches, over-reliance on AI leading to abdication of human judgment, unequal access to AI tools, lack of accountability for AI errors, data misinterpretation by AI, and AI generating factual-seeming content (e.g., witness statements) not based on actual knowledge. |
| 43CardozoArtsEntLJ135.pdf | HeinOnline | The Doors of Janus: A Critical Analysis of the Socio-Technical Forces Eroding Trust in the Rule of Law | This paper critically analyzes how emerging data-driven technologies, particularly AI, contribute to eroding citizens' trust in the Rule of Law through systemic disinformation, algorithmic misgovernance, and the digitalization of the social contract. It proposes a framework to restore trust by better enforcement and reinterpretation of existing rights, and formulating new collective interest-based rights, emphasizing the mediating role of law and technology. | True | Idealistic | True | 3.0 | Negative | NaN | NaN | NaN | Systemic disinformation (worsened by Generative AI) eroding epistemic justifications for trust; algorithmic misgovernance (e.g., lack of procedural justice, unfair social structuring, human rights violations) belying expectations of good governance; digitalization of the social contract disrupting temporal-spatial aspects of governance and citizen engagement. | Acknowledge the mediating relation of law and technology; better enforcement of existing rights (e.g., privacy as in SyRI case); reinterpretation of existing rights (e.g., horizontal application of fundamental rights against private corporations); formulation of new collective interest-based rights to counter systemic disinformation and algorithmic misgovernance. | Erosion of trust in the Rule of Law; systemic disinformation and its impact on democratic processes and institutions; algorithmic misgovernance (including automated decision-making, procedural justice, legal certainty, social structuring, algorithmic bias, representation, and human rights); digitalization of the social contract; protection of fundamental rights in the digital age; need for collective rights and accountability for tech platforms. | General citizenry in liberal democracies, with specific examples highlighting disproportionate impacts on vulnerable groups such as racial minorities (e.g., Dutch childcare scandal), economically disadvantaged students (e.g., UK A-level grading), and welfare recipients (e.g., Australian Robodebt). | Constitutional Law, Administrative Law, Human Rights Law, Technology Law, Media Law | United States, European Union (and member states like the Netherlands), United Kingdom, Australia. The paper also refers to 'global techno-legal developments' and 'liberal democracies the world over'. | NaN | NaN | NaN | False | False | NaN | Inadequacy of current legal frameworks to address collective harms from AI and digital platforms; insufficient accountability mechanisms for Big Tech corporations regarding their impact on democratic processes and fundamental rights; challenges in effective AI regulation due to factors like regulatory entrepreneurship and lobbying; the difficulty for citizens to distinguish truth from falsehood in an AI-influenced infosphere; the Rule of Law ceding governance space to the 'rule of code'. | NaN | Erosion of public trust in the Rule of Law and democratic institutions; AI-driven misinformation and disinformation threatening electoral processes and social cohesion; algorithmic misgovernance leading to biased, discriminatory, and unjust outcomes; lack of transparency and contestability in automated decision-making; invasion of privacy; dehumanization of the law; increased social and political polarization; failure to uphold fundamental rights in the digital environment; regulatory capture by tech companies. |
| 2025JLMktInnovation63.pdf | HeinOnline | BIG DATA AND COMPETITION LAW: NAVIGATING TRADE PRACTICES IN THE DIGITAL AGE | This paper examines anti-competitive practices by data-driven businesses, discussing effects on market power, transparency, and conduct. It analyzes global competition law challenges and proposes regulatory recommendations for addressing big data in antitrust inquiries. | True | Market | False | 3.0 | NaN | NaN | NaN | NaN | Concentration of market power through big data leading to reduced competition, entry barriers, potential consumer harm (e.g., higher prices, reduced choice, privacy infringements), and difficulties for traditional antitrust enforcement. | Updating competition law frameworks and enforcement strategies, including redefining relevant markets, reassessing dominance using data-specific factors, considering mandatory data sharing (e.g., on FRAND terms), revising merger control thresholds for data-rich acquisitions, and fostering international regulatory cooperation. | Ensuring fair market competition, preventing consumer harm from anti-competitive data-driven practices, adapting antitrust/competition law for the digital economy. | Consumers and smaller/new market entrants facing dominant digital platforms. | Competition Law (Antitrust Law); Data Privacy Law (as it intersects with competition). | International (specifically discusses EU, USA, India, Canada, Germany, UK, Australia, and general OECD perspectives). | NaN | NaN | NaN | False | False | NaN | Inadequacy of traditional competition law tools for data-driven markets; nascent regulatory responses globally; need for enhanced international cooperation and updated investigative techniques for regulators. | Challenges for regulatory authorities in adapting and applying competition law to data-driven markets, including defining relevant markets, assessing dominance, identifying novel anti-competitive conducts (e.g., algorithmic collusion, harmful data-driven mergers), and balancing innovation with fair competition. | Market distortion through data monopolization, creation of entry barriers, algorithmic collusion, abuse of dominant positions (e.g., refusal of data access, discriminatory pricing, anti-competitive tie-ins, exploitative data collection), harmful data-driven mergers, and erosion of consumer privacy where it impacts competition. |
| 2024AccesstoJustEEur120.pdf | HeinOnline | LEGAL ANAL YSIS OF EU ARTIFICIAL INTELLIGENCE ACT (2024): INSIGHTS FROM PERSONAL DATA GOVERNANCE AND HEALTH POLICY | This paper analyzes the EU Artificial Intelligence Act (2024), focusing on its implications for personal data governance, health policy, and the protection of fundamental rights, especially within the health data sector. It also examines the Act's alignment with existing medical device regulations, its impact on access to justice, and related AI legal reforms in Ukraine and Moldova. | True | Idealistic | False | 2.0 | Positive | The EU Artificial Intelligence Act (2024) and its regulatory framework. | NaN | NaN | Complexities in implementing the AI Act within national justice systems impacting judicial reform and access to justice; Gaps in the AI Act's coverage, such as specific regulations for all public health areas; General challenges in AI regulation like ensuring explainability and effective human oversight; Difficulties in harmonizing new AI rules with existing legal frameworks. | The EU Artificial Intelligence Act itself, aiming for a harmonized, ethical, and safe AI market respecting fundamental rights; Emphasis on risk-based classification, ethical principles (human-centricity, transparency, non-discrimination), conformity assessments, and human oversight within the Act; National legislative reforms in EU candidate countries (e.g., Ukraine, Moldova) to align with EU AI standards. | Regulation of AI for ethical and safe use (particularly in healthcare and data governance); Harmonization of AI rules to protect fundamental human rights and improve access to justice; Impact of AI legislation on judicial reform and national legal systems. | General public / EU citizens, with a particular focus on patients within the healthcare system regarding protection of their rights and safety. | AI Law, Data Protection Law, Health Law, Medical Device Regulation, Human Rights Law, EU Law, Comparative Law. | European Union (EU), Ukraine, Republic of Moldova. | NaN | NaN | NaN | False | False | NaN | The EU AI Act's Annex III lacks precise regulations for all areas of public health; Need for subsequent sectoral approaches and regulations to complement the horizontal AI Act; Identified loopholes and limitations in fully regulating all AI systems and their applications, particularly in fast-evolving areas like healthcare AI. | Operationalizing concepts like 'human oversight' and 'human-centredness' in AI regulation; Aligning the EU AI Act's provisions with the complexities of AI system development; Balancing innovation with safety, especially through mechanisms like 'regulatory sandboxes'; Ensuring 'explainability' in AI, particularly for high-risk applications like medical diagnostics; Harmonizing the AI Act with diverse existing legislation and jurisprudence. | Misuse of AI in various sectors (economy, rule of law, democracy, healthcare); AI systems enabling cognitive-behavioural manipulation or discriminatory social scoring; Malfunctions in AI-driven medical devices impacting patient safety and rights; Breaches of data protection and privacy through non-compliant AI systems; Harmful content generation by AI (e.g., deepfakes) impacting human rights. |
| 24HousJHealthLPoly77.pdf | HeinOnline | Artificial Intelligence and the HIPAA Privacy Rule: A Primer | This paper examines how the HIPAA Privacy, Security, and Breach Notification Rules apply to various AI applications in healthcare, such as chatbots and diagnostic tools. It highlights significant regulatory gaps, data re-identification risks, and hurdles to data sharing, underscoring the need for updated guidance and rules to protect patient information in the age of AI. | True | Idealistic | True | 3.0 | Neutral | AI-driven symptom checkers, medical chatbots (e.g., Northwell Health Pregnancy Chats), AI-assisted medical image interpretation, AI-powered medical scribes (e.g., DAX Express with GPT-4), AI for health insurance claim review (e.g., nH Predict). | NaN | NaN | Regulatory gaps in HIPAA making it difficult to apply to AI tools; risk of AI-powered re-identification of de-identified health data; lack of transparency for patients regarding AI's use of their data; potential for AI errors harming patients with unclear recourse; and a patchwork of laws offering inconsistent protection. | The paper implicitly calls for regulatory reform, including HHS issuing clarifying guidance on HIPAA's application to AI (e.g., re-identification, synthetic data), and amending HIPAA for greater transparency (e.g., in Notices of Privacy Practices about AI use). | Data privacy and security in AI-driven healthcare; patient rights (notice, access, amendment, restriction) with AI; regulation of AI tools; re-identification risks of health data; algorithmic bias and discrimination in AI healthcare decisions. | Patients generally, with specific examples including elderly beneficiaries of health insurance and individuals whose de-identified data is at risk of re-identification. | Health Law (HIPAA), Privacy Law, Data Security Law, Administrative Law. | United States | Discusses the use of large health datasets, including electronic medical records and claims data (both identifiable and purportedly de-identified), by healthcare entities and tech companies for AI development and deployment. | NaN | NaN | True | False | Several AI tools discussed, such as specific hospital-operated symptom checkers, commercial symptom checkers (e.g., Ubie), and AI scribes like DAX Express, are presented as existing and deployed services. | HIPAA's definitions inadequately cover all AI actors and data types; de-identification safe harbors may be insufficient against AI-re-identification; lack of specific regulation for synthetic data; inadequate patient notification about AI use; limited patient ability to restrict AI or amend AI-generated errors; inconsistent protection from patchwork laws. | Defining and regulating new AI actors outside traditional HIPAA-covered entities; balancing data sharing for AI innovation with patient privacy; ensuring accuracy and fairness of AI-generated health information and decisions; keeping regulations updated with rapid AI advancements; operationalizing patient rights with AI-generated content. | Increased privacy/security breaches; informational injuries; re-identification of de-identified data; incorrect AI-generated medical information or claim denials causing harm; potential for discrimination via AI tools; lack of patient control over AI's use of their data. |
| 22NwJTechIntellProp1.pdf | HeinOnline | ATTRIBUTING Al AUTHORSHIP: TOWARDS A SYSTEM OF ICONS FOR LEGAL AND ETHICAL DISCLOSURE | This paper proposes the Artificial Intelligence Attribution (AIA) system, a set of icons for disclosing AI involvement in text generation across professional contexts like law and academia. Supported by empirical research, the AIA system aims to mitigate legal risks, improve public perception, and promote ethical AI use through transparency. | True | Market | True | 1.0 | NaN | The Artificial Intelligence Attribution (AIA) system: a set of badges (Research, Writing, Editing, AI-Free) to disclose AI's role in text creation. | Original experimental study (N=423, Prolific users) using a scenario based on the Mata v. Avianca case, comparing perceptions of an attorney's AI-related negligence with (AIA badges used) and without (AIA badges not used, but AI use admitted later) proactive disclosure. | Attorneys proactively disclosing AI use with AIA badges were perceived as less likely to face legal malpractice suits, received less severe punishment recommendations (e.g., 20% lower fines, less support for suspension), and were less likely to be recommended for disbarment. | NaN | NaN | NaN | NaN | General legal practice, Contract law, Consumer protection, Intellectual Property, Torts (personal injury, defamation), Professional Responsibility, Rules of Civil Procedure. | United States (federal and state levels), with mentions of EU regulations. The proposed system is intended for broad, potentially international, application. | NaN | Conceptual design inspired by Creative Commons, focusing on attributes such as feasibility, intuitiveness, comprehensiveness, universality, durability, flexibility, incentives, and verifiability. Badges designed to represent different stages/types of AI involvement (Research, Writing, Editing, AI-Free). | NaN | True | True | The paper introduces the AIA system and illustrates the badges, which individuals or institutions could conceptually adopt and use immediately as icons on their documents (as done by the authors in a footnote). | NaN | Potential criticisms of the proposed AIA system include that the badges may provide limited information, negatively bias the reader, provide too much information to some audiences, and potentially silence or discourage AI use or chosen speech. | Legal liability (contractual, consumer protection, intellectual property infringement, fraud), professional sanctions (e.g., for lawyers, judicial censure, fines), charges of plagiarism and academic dishonesty, reputational damage, fostering mistrust, and ethical breaches such as deception, lack of informed consent, unreliability, manipulation, or discrimination due to undisclosed or improperly used AI. |
| 17ContempAsiaArbJ35.pdf | HeinOnline | Artificial Intelligence and the Future of International Trade Law and Dispute Settlement | This paper examines AI's transformative potential in international trade law and WTO dispute resolution, discussing benefits like enhanced efficiency and challenges such as ethical concerns and data privacy. It advocates for international collaboration and new legal frameworks to guide AI applications, ensuring technological advancements support an equitable and transparent global trade system. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Expensive and lengthy nature of existing dispute settlement processes, particularly for developing countries; Algorithmic bias potentially perpetuating existing economic power imbalances and cultural insensitivities; Disparity in digital literacy and access to AI technology between developed and developing nations. | International collaboration to establish standards and new legal frameworks for AI in trade; Development of cohesive regulatory frameworks promoting fairness, transparency, and mitigating bias; Reform of existing dispute settlement mechanisms (e.g., WTO) to enhance accessibility, reduce costs, and improve transparency for all members, especially developing countries. | Access to equitable and efficient international trade dispute settlement for developing countries; Ensuring fair and transparent application of AI in global trade mechanisms; Addressing algorithmic bias in legal decision-making within international trade. | Developing countries | International Trade Law, Dispute Settlement (including international arbitration and WTO dispute settlement) | International | NaN | NaN | NaN | False | False | NaN | Need for updated international legal frameworks to govern AI in trade and dispute settlement, addressing issues like cross-border data flows, IP rights for AI creations, and liability; Lack of international consensus on data standards, algorithmic fairness, and the specific role of AI in dispute resolution to ensure equitable outcomes; Inadequacy of current international dispute settlement mechanisms to handle AI-driven complexities and provide accessible justice, particularly for developing countries. | Ensuring ethical AI deployment, maintaining transparency in AI decision-making, and protecting data privacy in cross-border contexts; Addressing and mitigating algorithmic bias to prevent perpetuation of inequalities and unfair outcomes; Establishing clear liability frameworks for decisions made or influenced by AI systems; Overcoming disparities in digital literacy and technological access across nations, particularly between developed and developing countries. | Compromising the integrity and fairness of legal systems through unscrutinized AI integration; Ethical violations including privacy breaches and algorithmic bias leading to discriminatory or unfair outcomes in trade and dispute settlement; Job displacement due to AI-driven automation in legal and trade-related sectors; Potential for AI to exacerbate existing global inequalities if access and benefits are not equitably distributed. |
| 38EmoryIntlLRev819.pdf | HeinOnline | THE DIGITALIZATION OF LITIGATION | This paper discusses the digitalization of litigation (DoL), examining its potential to enhance efficiency, transparency, and access to justice, alongside the inherent challenges and risks. It reviews international initiatives, the role of AI, and emphasizes the importance of ensuring equitable access and upholding fundamental rights in the digital transformation of justice systems. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Digital divide (connectivity, literacy, capabilities); risks to privacy, security, equality, and fundamental rights; potential for misuse of data; challenges in adapting legal concepts (e.g., right to a hearing) to digital contexts; abrupt implementation without adequate adaptation time. | International collaboration; development of digital strategies focusing on human rights and inclusivity; gradual implementation of digital tools with adaptation time; development of ethical guidelines (e.g., Al European Charter) and regulatory frameworks for AI. | Improving justice sector efficiency and transparency; enhancing access to justice, especially for vulnerable groups; mitigating court operational disruptions; the right to a hearing in digital settings; environmental justice; upholding fundamental rights in digitalized systems; addressing the digital divide. | People in remote areas, linguistic minorities, people with disabilities, those with time/travel/work constraints, individuals with low technological literacy or capacity, the economically disadvantaged. | General litigation, Criminal law, Civil liability, Environmental law | International | NaN | NaN | NaN | False | False | NaN | Digital literacy and access disparities; citizens' lack of capability to use digital justice tools; need for legal and constitutional adaptation to new technologies; insufficient empirical data on digital justice trends; societal impact management of AI. | NaN | Exacerbation of risks to privacy, security, equality, fundamental rights; hindering access to justice through poor implementation; government abuse under emergency pretexts; digital divide limiting access; misuse of personal data; legal inaccuracies from AI tools (hallucinations); exclusion of those without digital access/skills. |
| 35FordhamIntellPropMediaE.pdf | HeinOnline | Al in the Courtroom: The Boundaries of RoboLawyers and RoboJudges | This paper examines the impact of AI, including LegalTech and JudicialTech, on the legal system, acknowledging its potential to enhance efficiency and access to justice. However, it argues for clear boundaries, asserting that AI should not fully replace human litigators and judges due to concerns about fundamental rights, legal legitimacy, and the nature of law. | True | Idealistic | True | 3.0 | Neutral | Scoring algorithms (e.g., for risk assessment, outcome prediction) and Generative AI (e.g., for legal advice, document drafting), within broader categories of LegalTech and JudicialTech. | NaN | NaN | High cost of legal services, lack of sufficient legal help for low-income individuals, backlogs in courts, and legal uncertainty disproportionately affecting disadvantaged populations. | AI legal tools (LegalTech and JudicialTech) can reduce costs, improve dissemination of legal information, provide services to underserved populations, enhance court efficiency, and reduce legal uncertainty. | Affordability and availability of legal services, court efficiency, reduction of legal uncertainty, ensuring fair trial and due process in the context of AI deployment. | Low-income individuals, disadvantaged litigants, and those unable to afford traditional professional human legal services. | General / Multiple fields, including criminal law (sentencing, recidivism), civil litigation (e-commerce, product liability, patent, personal injury), family law (prenuptial agreements), and corporate law (due diligence, contract review). | Multiple (USA, China, Estonia, England, Israel, EU extensively discussed as examples and for regulatory approaches). | The paper discusses various AI systems: scoring algorithms (e.g., COMPAS) using historical case data and personal information; generative AI (e.g., ChatGPT) trained on vast general text corpora; specific tools like Amazon's hiring algorithm trained on proprietary company data. | NaN | NaN | True | True | Tools like DoNotPay (initially mentioned as free for specific tasks), ChatGPT (publicly available with a free tier), and LegalZoom (commercial service) are discussed as operational. | The paper highlights significant gaps in ensuring AI's ethical and fair application in law, including underdeveloped legal/ethical frameworks, the challenge of balancing AI benefits with fundamental rights (fair trial, due process, explainability), mitigating bias, ensuring transparency and human control, protecting privacy, preventing AI from stunting legal development, maintaining legal system legitimacy, and addressing AI's limitations with moral/value judgments and cultural nuances. | NaN | Inaccuracies and hallucinations in AI outputs; lack of accountability and liability for AI errors; opacity (black box effect) leading to lack of transparency and explainability; bias and discrimination; data privacy violations and cybersecurity threats; loss of human control and automation bias; infringement on fair trial, due process, and the rule of law; undermining legal system legitimacy; hindering dynamic legal development; AI's inability to handle nuanced values, morals, and cultural diversity; dehumanization and infringement on human dignity/autonomy; Unauthorized Practice of Law (UPL). |
| 37GeoJLegalEthics415.pdf | HeinOnline | Untangling Unreliable Citations | The paper argues that unreliable citation practices, exacerbated by new formats like "(cleaned up)" and the uncritical use of AI in legal research, threaten the integrity of the legal system and democratic stability. It advocates for a return to basic verification of sources to ensure accuracy in legal arguments and restore trust in the profession. | True | Idealistic | True | 3.0 | Negative | The use of generative AI tools (e.g., ChatGPT, Google Bard) for legal research and brief preparation, and its propensity to 'hallucinate' or fabricate citations and information. | Case studies of lawyers misusing AI tools (e.g., ChatGPT in Mata v. Avianca, Google Bard in Michael Cohen incident) leading to sanctions and public embarrassment due to fabricated citations. | The unverified use of generative AI tools for legal research by lawyers led to the submission of briefs containing non-existent case citations, resulting in judicial sanctions (e.g., a $5,000 fine in the Mata case), professional embarrassment, and the undermining of the legal process. | Erosion of legal precedent and trust in the legal system due to unreliable citations, exacerbated by practices like unverified copy-pasting, misuse of citation formats (e.g., '(cleaned up)'), and uncritical adoption of AI tools that generate false information. Unequal access to information further complicates verification. | A return to fundamental practices of thoroughly reading and verifying all cited sources, including those suggested by AI. Increased professional diligence, skepticism towards unverified information, and candor with courts are advocated. | Integrity of the legal process, reliability of legal precedent, professional ethics, and the impact of AI on these aspects, which are foundational to a just legal system. | NaN | Civil Procedure, Legal Ethics, Patent Law, General Legal Practice (research and writing). | United States (Federal and State, with specific examples from Kansas). | The AI tools discussed (e.g., ChatGPT, Google Bard) are based on large, diverse datasets, including public internet text, but the specifics are proprietary to their developers. The paper highlights issues stemming from this training, like hallucinations. | NaN | Commercial AI tools (e.g., ChatGPT, Google Bard) are deployed by tech companies via web interfaces and APIs, leading to widespread accessibility. | True | True | Generative AI tools like ChatGPT and Google Bard are available online, often with free access tiers. The '(cleaned up)' citation is a practice that can be adopted by any legal writer. | Technical gaps in AI reliability (hallucinations) and verification. Societal/professional gaps include insufficient diligence in source checking by legal professionals, ethical challenges with AI use, and the need for updated rules and norms for technology in legal practice. | General challenges for AI tools include ensuring factual accuracy, preventing 'hallucinations' of non-existent information, promoting critical use by legal professionals rather than blind reliance, and addressing the rapid pace of AI development that outstrips ethical guidelines and full understanding of its impact. | Submission of fabricated legal citations leading to professional sanctions (e.g., fines) and reputational damage for lawyers. Miscarriage of justice if decisions are based on false information. Broader risks include erosion of legal precedent, democratic instability, and diminished public trust in the legal system. |
| 92FordhamLRev (2).pdf | HeinOnline | The Legal Imitation Game: Generative AI's Incompatibility with Clinical Legal Education | This paper argues that Generative AI (GenAI) is largely incompatible with the core pedagogical goals of clinical legal education: practice readiness, justice readiness, and client-centered lawyering. It contends GenAI hinders genuine skill development and can exacerbate societal injustices and ethical issues, urging a critical approach to its integration. | True | Idealistic | True | 3.0 | Negative | NaN | NaN | NaN | Worsening unequal access to legal information and services; Concentration of legal information and power in a few corporations, replicating information asymmetries; GenAI systems are trained on data reflecting human biases and historical discrimination, potentially exacerbating injustices; GenAI tends to reinforce the status quo. | Clinicians should press students to critically interrogate how GenAI tools are built and operate, investigate their ethical implications for justice and society, and recognize the role lawyers using these tools may play in causing harm. The paper advocates for helping students make informed, value-based, and justice-ready decisions about technology, rather than uncritically adopting GenAI. | Access to legal information; Quality and ethics of legal services for underserved populations; Bias in legal technology; Impact of AI on justice systems and legal education. | Underserved communities generally, individuals without power, clients of public interest clinics, populations affected by systemic discrimination. | NaN | United States | NaN | NaN | NaN | False | False | NaN | Societal: Lack of an agreed-upon framework for evaluating the risk or utility of GenAI in legal education; GenAI's tendency to reinforce existing economic/power structures and injustices due to its design and data. Technical: The 'black box' nature of GenAI, with no existing mechanisms for auditing or interrogating the logic behind responses; GenAI's inherent limitation to imitation rather than genuine understanding. | NaN | GenAI outputs may imitate competent lawyering but fall short, leading to substandard legal work; Automation bias can lead users to uncritically accept AI outputs, including inaccuracies; GenAI can produce 'hallucinated' or false information (e.g., fake citations); Over-reliance on GenAI may undermine students' development of core legal skills (analysis, reasoning, writing); Worsening of unequal access to legal information and services; Concentration of legal information and power in a few large technology corporations; Appropriation of human creativity and personal data without consent or compensation for training models; Perpetuation and amplification of societal biases and historical discrimination embedded in training data; Significant negative environmental impact (resource extraction, high energy and water consumption); Exploitation of precarious workers in the AI development and maintenance pipeline; Reinforcement of the status quo and existing injustices by design. |
| 15IJCA1.pdf | HeinOnline | Unboxing Generative AI for the Legal Professions: Functions, Impacts and Governance | This paper examines the integration of Generative AI (GenAI) into legal professions and the administration of justice, focusing on its functions, impacts, and initial attempts at governance. It discusses GenAI's capabilities, its use by lawyers and judges, and analyzes different regulatory approaches, highlighting the tension between user responsibility and system certification. | True | Idealistic | True | 3.0 | Neutral | Generative AI (GenAI) / Large Language Models (LLMs) and their domain-specific applications (e.g., using Retrieval-Augmented Generation). Specific examples discussed include general chatbots (ChatGPT, Bard) and domain-specific tools like the Portuguese 'Practical Guide to Access to Justice (GPJ)'. | References a Stanford University study (Magesh et al., 2024) that assessed hallucination rates in leading commercial legal AI research tools; the author also conducted an 'initial test' of the Portuguese GPJ system for consistency and accuracy of answers. | The cited Stanford study (Magesh et al., 2024) found that leading commercial legal AI research tools produced hallucinations in 17% to 33% of responses. The author's test of the Portuguese GPJ found it gave consistent answers to simple questions but could give misleading answers to complex ones, though it showed learning capability over time. | Reliance on end-user's ability to verify AI output, which is challenging for laypersons; risk of AI generating inaccurate or misleading legal information; potential costs of reliable, high-quality AI systems for access to justice initiatives. | Development of curated GenAI systems for delivering legal information (e.g., chatbots based on official, verified data); strong emphasis on human oversight, critical evaluation of AI-generated content, and user responsibility in a legal context. | Access to legal information for citizens; support for self-represented litigants; simplification of interaction with the justice system. | General public / citizens seeking legal information or interacting with the justice system. | General (covers various fields including family law, company law, criminal law, contract law, and general legal research/drafting). | International | For general GenAI: extensive, sometimes non-contextualized datasets. For domain-specific legal AI: curated legal databases (judgments, doctrine, statutes), specific case files, law firm knowledge bases. For the Portuguese GPJ: content from the Ministry of Justice's Digital Justice platform. | Retrieval-Augmented Generation (RAG); semantic injection of domain-specific knowledge; prompt engineering; no-code/low-code development approaches using APIs/GPTs. | Integration into office applications (e.g., word processors, spreadsheets); standalone domain-specific applications; use of APIs for custom solutions; cloud platform deployment (e.g., Microsoft Azure for the Portuguese GPJ). | True | True | The Portuguese 'Practical Guide to Access to Justice (GPJ)' is mentioned as being in beta stage and accessible via a public URL, implying free web-based access. Basic versions of general GenAI chatbots (e.g., ChatGPT) are also noted as available online for free. | Need for robust, independent validation of AI tools' reliability and claims made by providers; the difficulty for laypersons to adequately verify AI outputs in legal contexts; current regulatory frameworks and guidelines struggle to keep pace with rapid technological advancements; weak accountability mechanisms for AI use. | Ensuring factual accuracy and avoiding 'hallucinations' in AI outputs; maintaining data confidentiality and privacy, especially with sensitive legal information; the necessity for users to possess sufficient expertise to verify AI-generated content; managing the 'black box' nature and potential biases of LLMs. | Generation of 'hallucinations' (false or misleading information, e.g., fake case citations); breaches of privacy and data protection; potential deskilling of legal professionals; over-reliance on AI leading to unchecked errors; economic shifts concentrating resources with tech providers; undermining public trust in the justice system if AI is misused; adverse impacts on due process if AI outputs are not rigorously verified. |
| 30ClinicalLRev227.pdf | HeinOnline | SEARCHING FOR JUSTICE: INCORPORATING CRITICAL LEGAL RESEARCH INTO CLINIC SEMINAR | This paper advocates for incorporating Critical Legal Research (CLR) into clinical legal education to equip students with tools to challenge biased legal information systems and pursue social justice. It presents CLR as a necessary pedagogical counterweight to the problematic rise of AI in legal research and offers a model module for its implementation. | True | Idealistic | False | 1.0 | Negative | Incorporating Critical Legal Research (CLR) pedagogy into clinic seminars, exemplified by a model transactional research module. | NaN | NaN | Bias in traditional legal research tools and classification systems reifying hegemonic norms; the myth of neutrality in legal information; negative impacts of AI/GAI entrenching biases and hindering critical thinking; data weaponization by legal publishers for surveillance. | Integrating Critical Legal Research (CLR) into clinical legal education: teaching deconstruction/reconstruction of research methods, 'unplugged brainstorming', challenging neutrality of databases and AI, developing research plans accounting for bias, and using CLR as a counterweight to AI. | Critiquing and improving legal research education for social justice lawyering; addressing biases in legal information systems disadvantaging marginalized communities; empowering lawyers to find innovative legal solutions beyond dominant narratives. | Marginalized groups, vulnerable populations, and clients whose needs fall outside dominant legal narratives (e.g., domestic workers, returning citizens, mutual aid organizations). | Legal Education, Transactional Law, Clinical Legal Practice | United States | NaN | Based on literature review of Critical Legal Research and clinical pedagogy, and the author's teaching experience. | Academic publication (law review article) and creation of an online LibGuide with resources for educators. | True | True | A publicly accessible LibGuide (https://wcl.american.libguides.com/critical-research_forclinics) with pedagogical resources. | Need for wider adoption and development of CLR pedagogy; deeper collaboration between faculty and librarians; ongoing strategies to address biases in legal tech and systemic injustices; development of CLR for transformative change beyond law reform. | Limited seminar time; varying prior legal research instruction for students; institutional hierarchies hindering collaboration with librarians/research faculty; the rapid growth of AI in legal research creating new pedagogical hurdles. | Perpetuation of harmful hegemonies by traditional legal research; AI/GAI entrenching biases, hindering critical thinking, and producing inaccurate or fabricated results; weaponization of data by legal publishers for surveillance; lawyers facing sanctions for misuse of GAI; potential for a two-tiered justice system due to AI adjudication. |
| 20ActaUDanubiusJur7.pdf | HeinOnline | The General Data Protection Regulation of 2016 (GDPR) Meets its Sibling the Artificial Intelligence Act of 2024: A Power Couple, or a Clash of Titans? | This paper explores the complex relationship between the EU's General Data Protection Regulation (GDPR) and the newly adopted EU Artificial Intelligence Act (AI Act), analyzing their potential synergies or conflicts in regulating AI technologies. It assesses whether these two frameworks will function as a 'power couple' fostering responsible AI and protecting individual rights, or a 'clash of titans' creating implementation challenges and hindering innovation. | True | Market | False | 2.0 | NaN | General Data Protection Regulation (GDPR) of 2016 and Artificial Intelligence Act (AI Act) of 2024 as regulatory frameworks. | The paper employs a qualitative research methodology, including a detailed review of legislative texts (GDPR and AI Act), existing scholarly literature, government reports, and legal documents. It uses a comparative approach to analyze provisions and identify alignments or divergences, supplemented by thematic analysis. | The GDPR and AI Act share goals of safeguarding fundamental rights and promoting ethical AI, but present potential conflicts due to differing focuses (e.g., data minimization under GDPR vs. data needs for AI development under AI Act). A harmonious balance, addressing regulatory divergence and compliance burdens, is crucial for them to effectively function as a 'power couple' rather than clashing. | NaN | NaN | NaN | NaN | Data protection law, Artificial intelligence law/regulation, EU law, Civil liability. | European Union (EU) | NaN | NaN | NaN | True | True | The GDPR is an EU regulation currently in force. The EU AI Act was adopted by the EU Parliament in March 2024 and its provisions will become applicable progressively. The texts of both regulations are publicly available. | NaN | Regulatory divergence and potential inconsistencies between GDPR and AI Act; balancing data protection principles (e.g., data minimization) with data requirements for AI development; ensuring transparency and accountability in AI systems in a way that aligns with both frameworks; translating ethical considerations into actionable regulatory measures without stifling innovation; significant compliance burden for businesses and organizations. | Potential for a 'clash of titans' between GDPR and AI Act hindering innovation and creating regulatory uncertainty; risks of algorithmic bias, discrimination, and lack of fairness and accountability in AI systems if not governed effectively; potential infringement on privacy and freedom of expression if regulations are not carefully implemented. |
| 50OhioNULRev533.pdf | HeinOnline | Access to Civil Justice in the Age of Al: Mindsets & Pathways to New Practices | This paper explores how AI, particularly generative AI, can improve access to civil justice, urging lawyers to adopt innovative mindsets and practices. It advocates for leveraging AI to create scalable legal information products, thereby enhancing service delivery in the 'PeopleLaw' sector and addressing unmet legal needs. | True | Idealistic | True | 3.0 | Positive | Lawyers using generative AI to create, package, and distribute curated legal information products (e.g., handouts, checklists, DIY guides, videos, podcasts). | NaN | NaN | Financial barriers to legal services; traditional legal service models' lack of scalability; inherent complexity and lawyer-centric design of the legal system; insufficiency of current legal aid models; difficulty accessing reliable legal information amidst prevalent misinformation; professional inertia and resistance to innovation within the legal field. | A shift in lawyers' mindsets towards innovation and client-centric models; widespread adoption of AI for creating scalable legal information products; development of productized and tiered legal services; systemic reforms by courts and legal institutions to simplify processes and support self-help; re-engagement of lawyers with the public through new service delivery channels focused on information. | Affordability of legal services; scalability of legal help (legal information vs. legal advice); self-represented litigants / DIY legal help; role of legal technology in bridging the justice gap; innovation in legal service delivery models; quality and accessibility of legal information. | Individuals and small businesses in the 'PeopleLaw' sector, including low-income individuals, 'ALICE' (Asset Limited, Income Constrained, Employed) populations, and the 'Missing Middle' (middle-class individuals). | Civil justice, Family law (divorce, child custody, spousal support mentioned as examples). | United States | The proposed approach involves lawyers using existing, pre-trained generative AI models (e.g., ChatGPT). The paper does not detail the specific training data of these underlying models, beyond general references to 'volume and quality of data'. | NaN | The paper suggests lawyers can make legal information products available via their websites, as downloads, through online portals, or in newsletters. | True | True | The paper suggests lawyers can use existing generative AI tools, some of which (like ChatGPT) have free versions, to create legal information products. | Practical strategies for lawyers to overcome resistance to new business models and AI adoption; robust frameworks for ensuring ethical AI use and quality control of AI-generated legal content; addressing digital literacy and access disparities for AI-based legal solutions; defining the boundaries between AI-provided information and regulated legal advice. | Understanding the capabilities and limitations of different generative AI tools; maintaining ethical obligations (competence, supervision) when using AI; managing expectations around AI's current abilities versus hype; the rapid pace of AI development requiring continuous learning; ensuring AI tools are used appropriately to avoid errors like factual 'hallucinations'. | Lawyers misusing AI tools leading to submission of incorrect or fabricated information to courts; consumers being harmed by inaccurate or misleading AI-generated legal information if not properly curated; potential for AI to overstep into unauthorized practice of law if not carefully managed; over-reliance on AI without sufficient human oversight and critical judgment. |
| 6LawTechHum60.pdf | HeinOnline | Integrating Generative Al into Legal Education: From Casebooks to Code, Opportunities and Challenges | This paper discusses the integration of Generative AI into legal education, highlighting opportunities like enhanced research and drafting, and challenges such as ethical concerns (bias, plagiarism) and the need for AI literacy. It broadly advocates for curriculum reform and policy development in law schools to prepare students for an AI-influenced legal profession, touching on AI's role in judicial efficiency and accessible legal information. | True | Market | True | 3.0 | Neutral | Generative AI (GenAI) integration in legal education | NaN | NaN | Algorithmic bias leading to unfair or discriminatory outcomes in legal applications; inaccuracies and 'hallucinations' in AI-generated legal information; lack of transparency in AI decision-making; potential for AI to perpetuate existing societal biases if training data is not diverse. | Ensuring AI models are trained on diverse and representative datasets; regular auditing of AI outputs to identify and mitigate biases; developing critical AI literacy and fact-checking skills for users; maintaining human oversight to ensure accuracy and fairness in AI applications. | Enhancing judicial efficiency (e.g., in small claims, property disputes, motor vehicle claims); providing accessible legal information through AI-powered legal assistants. | NaN | Legal Education, AI Ethics, Legal Technology | International | NaN | NaN | NaN | False | False | NaN | Persistence of AI 'hallucinations' (inaccuracies) even with advanced techniques like Retrieval-Augmented Generation (RAG), necessitating continued human verification; unreliability of AI detection tools; limited empirical evidence on long-term impacts of AI in legal education and practice; lack of foolproof methods for ensuring AI reliability and fairness generally. | Ethical issues (plagiarism, bias, accuracy/hallucinations, transparency, accountability); undermining critical thinking and fostering academic dishonesty; unreliable AI detection tools for academic integrity; intellectual property violations related to AI-generated content; need for AI literacy and faculty training; significant curriculum adaptation and development of new assessment methods; resource implications for AI software and infrastructure; human and environmental costs of GenAI development (energy consumption, e-waste, labor exploitation). | Undermining critical thinking and academic integrity among students; AI-generated inaccuracies ('hallucinations') in legal research and document generation; perpetuation and amplification of biases present in training data, leading to unfair and discriminatory outcomes; increased plagiarism and intellectual property violations; significant energy consumption and e-waste from AI model training and computational infrastructure; exploitation of underpaid workers for data annotation and content moderation in AI development. |
| 25TransactionsTennJBusL25.pdf | HeinOnline | ESTABLISHING A FUTURE-PROOF FRAMEWORK FOR Al REGULATION: BALANCING ETHICS, TRANSPARENCY, AND INNOVATION | This paper examines the multifaceted applications and societal impacts of artificial intelligence, particularly generative AI, covering its benefits in areas like healthcare and access to justice, alongside significant risks such as bias, job displacement, and misinformation. It advocates for a comprehensive, future-proof regulatory framework by analyzing global legislative efforts, aiming to balance innovation with ethics, transparency, and human rights. | True | Idealistic | True | 3.0 | Neutral | Generative AI (e.g., ChatGPT, Sora), Large Language Models, facial recognition technology, algorithmic decision-making systems (e.g., COMPAS), legal chatbots (e.g., DoNotPay), predictive policing tools. | The paper cites evaluations by others (e.g., institutional reports like FTC, ProPublica; academic studies) which involve analyzing AI outputs for accuracy, bias (e.g., racial, gender), and real-world impact (e.g., false identifications, discriminatory loan/rental decisions). | Reports findings from cited studies: facial recognition shows higher error rates for minorities and women; predictive policing tools (e.g., COMPAS) demonstrate racial bias by disproportionately flagging minorities as high-risk; some healthcare algorithms underdiagnose underserved populations or assign lower risk scores to Black patients with similar needs as white patients. | Incorrect/outdated/misleading AI legal information, embedded bias, liability gaps for AI advice, user difficulty in assessing AI advice quality, need for constant AI system updates for legal accuracy, cross-jurisdictional compliance issues, AI's limited nuanced understanding for complex legal matters, risk of widening the digital divide, confidentiality and attorney-client privilege concerns. | Develop robust AI data protection mechanisms (confidentiality, privilege), ensure regular AI system updates for legal accuracy, provide clear disclosures about AI capabilities and limitations, adopt a balanced regulatory approach promoting innovation while upholding ethics and compliance, mandate audits for bias, establish ethical guidelines for AI in legal services, train legal professionals on responsible AI use and verification of AI outputs. | Providing basic legal information, assistance with simple legal matters, enhancing understanding of legal proceedings, use of legal chatbots for initial guidance, potential for AI-assisted counsel for indigent defendants and readily accessible AI legal support for ordinary citizens. | Low-income individuals, marginalized communities, indigent criminal defendants, ordinary citizens needing legal assistance. | Civil law (general), Criminal law, Family law, Housing law, Employment law, Intellectual Property law, Privacy law. | International | The paper discusses various AI systems trained on diverse large-scale datasets, including public internet text and image data, copyrighted materials (news articles, artworks, music), official records (crime reports, arrest records), and consumer data (PII, credit history, behavioral data). It highlights issues with unverified, biased information within these datasets. | NaN | NaN | False | False | NaN | Ensuring reliability and legal accuracy of AI tools; establishing clear liability frameworks for AI-generated legal advice; developing AI with nuanced understanding for complex legal cases; addressing the digital divide for equitable AI access; lack of robust confidentiality/privilege mechanisms in current AI; insufficient legal professional training on AI; need for a comprehensive AI regulatory framework in legal services. | NaN | Deepfakes and misinformation eroding trust and manipulating democratic processes; algorithmic bias leading to discrimination in justice, housing, employment, and healthcare; privacy violations through enhanced surveillance and data misuse; AI-powered cybersecurity threats; significant white-collar job displacement; widespread intellectual property infringement; safety, control, and accountability issues with advanced AI; negative impacts on mental health and societal cohesion; high environmental costs of AI development. |
| 20UStThomasLJ190.pdf | HeinOnline | WHAT DOES RELEVANT MEAN TO YOU? CREATING A CHOOSE-YOUR-OWN-ADVENTURE TECHNOLOGY COMPETENCY FRAMEWORK | This paper argues for the necessity of clear definitions and standardized frameworks for lawyer's technology competence, as mandated by ethical rules. It proposes a 'Choose-Your-Own-Adventure' model to develop individualized technology competencies and discusses various approaches for law schools to integrate this essential training. | True | Market | False | 1.0 | NaN | A 'Choose-Your-Own-Adventure' technology competency framework and a 'Legal Competency-Creation Model' for lawyers, adapted from the NPEC model for competency-based education. | NaN | NaN | NaN | NaN | NaN | NaN | General legal practice | United States | NaN | Literature review of competence definitions and required technology skills; adaptation of an existing educational competency model (NPEC's model); conceptual framework development, including draft core competencies. | Proposed for implementation in legal education through various models (mandatory courses, embedded training, voluntary courses, non-credit sessions) and for professional development. Suggests collaboration with bodies like the SALI Alliance for lexicon development. | False | False | NaN | Lack of a universally agreed-upon definition and scope of technology competence for lawyers. Insufficient and inconsistent technology training within law school curricula. Absence of standardized competency sets and a common lexicon for legal technology skills. | Defining 'competence' and 'competency' in a way that is both specific enough to be useful and flexible enough for diverse practices and evolving technology. Overcoming inertia or lack of clarity regarding responsibility for technology training in legal education. Ensuring competencies remain relevant as technology rapidly changes. | Dismissal of client cases due to technological errors (e.g., improper redlining). Violation of ethical duties of competence. Cybersecurity vulnerabilities leading to breaches of client_confidentiality. Damage to professional reputation and potential malpractice claims. |
| 92FordhamLRev (3).pdf | HeinOnline | CHATGPT, Al LARGE LANGUAGE MODELS, AND LAW | This essay explains the workings, recent advancements, and capabilities of AI Large Language Models (LLMs) like ChatGPT/GPT-4, particularly their application in understanding and generating legal texts. It also presents a balanced discussion of their limitations, emphasizing the need for careful use while acknowledging their significant potential to impact the legal domain. | True | Market | True | 3.0 | Positive | Large Language Models (LLMs) like ChatGPT/GPT-4, and underlying mechanisms like the transformer architecture, self-supervised pre-training, instruction fine-tuning, and RLHF. | NaN | NaN | NaN | NaN | Increasing access to justice (mentioned as a potential benefit). | NaN | General legal practice (contracts, motions, legal analysis, patents, copyright). | United States | Vast corpus of general text from the internet (e.g., Wikipedia, Reddit), books, research papers, newspapers, and specific datasets of question-answer pairs for fine-tuning; includes legal documents like contracts and opinions. | Transformer architecture, self-supervised pre-training, deep learning neural networks, instruction fine-tuning, Reinforcement Learning from Human Feedback (RLHF). | Web-based chat interfaces (e.g., ChatGPT), integration into specialized commercial legal platforms (e.g., Lexis+ AI, Westlaw CoCounsel). | True | True | Free version of ChatGPT (GPT-3.5) available online; GPT-4 accessible for free via Microsoft's Bing Chat and Copilot. Paid subscription for direct GPT-4 access via ChatGPT Plus. | Reliability issues (hallucinations, reasoning flaws), potential for perpetuating biases, lack of transparency/interpretability, and context window limitations (though improving). | High cost and computational resources for training large models, ensuring factual accuracy and coherent reasoning, managing data privacy and security when handling sensitive legal information, and addressing the 'black box' nature of complex models. | Violating client confidentiality or privilege through data input, generation of fictitious legal citations ('hallucinations'), flawed legal analysis leading to incorrect conclusions, perpetuation of biases from training data, and concerns about accountability and trust due to lack of transparency. |
| 2024JurnalulBarouluiCluj2.pdf | HeinOnline | The Future of the Legal Profession (I) on Non-Lawyering: The British and American Perspectives; ChatGPT "Sins" in the Legal Profession | This paper examines the evolution of the legal profession, discussing Alternative Business Structures (ABS) and Non-Lawyer Ownership (NLO) in the UK and US as models for legal service delivery, and highlighting the ethical risks of AI misuse, particularly ChatGPT, through case studies. It reflects on the Romanian legal context, anticipating regulatory changes and emphasizing lawyers' need for technological adaptation and continuous training. | False | Market | True | 3.0 | Neutral | ChatGPT | Analysis of case law where ChatGPT was misused by lawyers (Mata v. Avianca, Inc.; Zheng v. Chen). | Lawyers who misused ChatGPT by submitting fabricated citations were sanctioned: in Mata v. Avianca, a $5,000 fine and notification requirements; in Zheng v. Chen, payment of additional costs incurred by the opposing party. | Regulatory restrictions on non-lawyer involvement in legal services and the legal profession's slow adaptation; lawyers' lack of understanding and misuse of AI tools, hindering competent service delivery. | Regulatory reform to allow new legal service delivery models (e.g., revising rules like ABA's Rule 5.4, adopting ABS-like structures); continuous education and institutional training for lawyers on AI tools, their limitations, and ethical use. | Regulation of the legal profession, Alternative Business Structures (ABS), Non-Lawyer Ownership (NLO) of law firms, ethical use of AI (ChatGPT) by legal professionals, professional responsibility. | General public / consumers of legal services. | General legal practice, Corporate law (company formation), Civil litigation (personal injury), Family law. | United Kingdom, United States of America, Romania, Canada. | NaN | NaN | NaN | True | True | ChatGPT, a tool discussed extensively, is publicly available with both free and paid tiers. | Lack of lawyer competency in understanding and responsibly using AI tools like ChatGPT; slow pace of regulatory adaptation to new legal service delivery models and technological advancements in some jurisdictions. | Lawyers' lack of understanding of AI limitations (e.g., generating fictitious information), failure to verify AI-generated content before submission to court, and the ethical misconduct arising from improper AI use. | Submission of fabricated legal precedents or false information to courts, professional sanctions for lawyers (fines, reputational damage), abuse of the judicial system, potential for judicial errors, and undermining the integrity of the justice system. |
| 59TulsaLRev361.pdf | HeinOnline | The Automation Paradox | This paper examines the legal paradoxes arising from generative AI and self-driving cars, focusing on issues of liability, intellectual property, and constitutional rights. It proposes analytical frameworks based on existing legal principles to address these challenges while also underscoring the need for comprehensive legislative updates. | True | NaN | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Intellectual Property Law (Copyright, Patent), Constitutional Law (First Amendment, Fourth Amendment, Fifth Amendment, Patent and Copyright Clause, Due Process, Equal Protection), Tort Law (Defamation, Negligence, Breach of Fiduciary Duty, False Light, Emotional Distress, Appropriation of Likeness), Criminal Law (Manslaughter, DUI, Hoaxes), Communications Law (Section 230 CDA), Autonomous Vehicle Law | United States (federal and state, including Arizona, Oklahoma, Texas, California) | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Generative AI risks include: creation of deepfakes leading to reputational/societal harm and threats to democracy/privacy; intellectual property infringement; AI hallucinations and false information; perpetuation of biases; misuse of AI in law enforcement (e.g., arrests based on inaccurate data); privacy violations from user data collection (location, personal information); Fifth Amendment self-incrimination issues. Self-driving car risks include: unclear liability in accidents; privacy concerns from data collection; potential for accidents due to system failures. |
| 10RevBrasileiradeDireitoP.pdf | HeinOnline | Towards a Digitalised Criminal Justice System: Lessons from Poland | This paper examines technological advancements in the Polish criminal justice system, accelerated by COVID-19, focusing on remote hearings, case file digitization, and automated translation. It analyzes their impact on efficiency and fair trial rights, highlighting existing limitations and proposing solutions like expanded remote access and hybrid translation models. | True | Idealistic | True | 2.0 | Neutral | Digitalization approaches in the Polish criminal justice system: remote hearings (including for detention), digitization of criminal proceeding files (e.g., PROK-SYS), and automated translation services. | Legal analysis against Polish law, ECHR standards, EU directives, and assessment of practical implications for fair trial rights, defendant guarantees, and judicial efficiency. | Remote hearings improve efficiency but risk defendant rights (e.g., confidentiality, counsel access) without proper safeguards; digitization offers significant benefits (accessibility, efficiency, security) but implementation is slow and faces challenges like digital exclusion; automated translation is currently insufficient alone for legal contexts and requires human oversight to ensure fairness and accuracy. | Infringement on the right to defense in remote hearings (e.g. lack of confidentiality, limited counsel access); digital exclusion and security risks with digitization; inaccuracy of automated translation for complex legal texts; institutional resistance, costs, and concerns about procedural guarantees. | Expand remote hearings with robust safeguards for confidential counsel-client communication; implement unified, secure digital case file systems with alternatives for digitally excluded persons; adopt a hybrid human-machine translation model with rights to human verification and intervention. | Remote hearings, digitization of case files, automated translation, right to a fair trial, right to defense, access to case files, right to an interpreter, efficiency of criminal proceedings, pre-trial detention hearings. | Defendants in criminal proceedings, particularly those deprived of liberty, non-native language speakers requiring translation/interpretation, and digitally excluded individuals. | Criminal Law, Criminal Procedure | Poland (with reference to EU law and ECHR) | The paper discusses LLM-based automated translation, noting these models are pre-trained on massive and diverse textual datasets; specific datasets for the particular LLMs are not detailed. | NaN | Remote hearings were legislatively adopted and expanded, particularly post-COVID-19, through amendments to the Polish Code of Criminal Procedure. The PROK-SYS digitization system is under gradual implementation by the National Prosecutor's Office. | True | False | Remote hearings are established in Polish law and used in courts for specific criminal proceedings. | Need for unified, interoperable, and secure digital infrastructure; improving AI translation accuracy for legal texts; addressing digital exclusion; ensuring full confidentiality and effective defense rights in digitalized procedures; overcoming institutional resistance to technological adoption. | Costs and security considerations for new technologies; ensuring protection of procedural guarantees and attorney-client privilege; overcoming technical limitations and ensuring system reliability; addressing institutional resistance and change management; balancing efficiency gains with the protection of fundamental rights. | Infringement of the right to defense (e.g., confidential communication with counsel) in remote hearings; digital exclusion hindering access to justice; cybersecurity threats to digitized case files (hacking, data leakage); inaccurate automated translations leading to miscarriages of justice; erosion of fair trial principles if technology is improperly implemented. |
| 57LoyLALRev859.pdf | HeinOnline | THE Al REGULATORY PYRAMID: A TAXONOMY & ANALYSIS OF THE EMERGING TOOLBOX IN THE GLOBAL RACE FOR THE REGULATION AND GOVERNANCE OF ARTIFICIAL INTELLIGENCE | This paper introduces the 'AI Regulatory Pyramid,' a taxonomy and framework for governing artificial intelligence, advocating for a multifaceted and dynamic approach. It emphasizes balancing AI's risks and potentials through a mix of voluntary standards, transparency measures, assessments, and targeted rules, urging rational debate and investment in 'AI for good.' | True | Idealistic | False | 3.0 | Positive | The AI Regulatory Pyramid (a conceptual framework for AI governance and regulation). | NaN | NaN | Irrational fears and skewed debates about AI leading to overly restrictive regulation; lack of public trust if AI risks like bias are not managed; insufficient infrastructure, skill-building, and investment for widespread 'AI for good' applications. | Adoption of balanced and dynamic regulatory frameworks (like the AI Regulatory Pyramid); fostering rational public debate on AI; investing in 'AI for good' initiatives, supporting infrastructure, and education; promoting collaborative public-private governance and experimentation; considering mandates for automation where AI is proven safer and fairer. | Legal information and assistance for pro se individuals (e.g., inventors); improving patent system equity and access; criminal justice reform (e.g., automated record clearing); addressing discrimination and promoting pay equity. | Pro se litigants/inventors; individuals with criminal records seeking to overcome systemic barriers; individuals facing systemic discrimination. | General AI Regulation, Intellectual Property, Employment Law, Consumer Protection, Financial Services Regulation, Criminal Justice, Constitutional Law, Election Law, Administrative Law, Tort Law. | United States, European Union, with comparative references to other international jurisdictions (e.g., China, UK, Japan, Singapore, African Union). | NaN | Regulatory theory (e.g., responsive regulation, new governance), legal analysis, policy analysis, comparative analysis of emerging AI governance approaches globally. | NaN | False | False | NaN | Lack of widespread adoption of beneficial AI for access to justice due to irrational fears or unsupportive regulatory frameworks; insufficient skill-building, market competition, and infrastructure for 'AI for good'; gap in implementing and scaling access to justice initiatives where AI could be transformative (e.g., 'second chance gap' for record clearing). | Rapid technological evolution of AI (pacing problem for regulation); diversity of AI applications challenging unified regulatory frameworks; achieving a balance between fostering innovation and ensuring safety, ethics, and public welfare; need for international coordination of regulatory efforts; potential for AI development to lead to market concentration. | Bias and discrimination embedded in AI systems; spread of misinformation and deepfakes impacting democratic processes and public trust; privacy violations through data collection and use; security vulnerabilities leading to misuse or manipulation of AI; AI systems manipulating human behavior or circumventing free will; risks from autonomous systems (e.g., in transportation, autonomous weapons). |
| 59CtRev32.pdf | HeinOnline | Want to Know More About Al? | This paper is a curated bibliography of various resources (articles, books, podcasts) on artificial intelligence aimed at legal professionals, particularly judges and lawyers. It highlights AI's practical uses in law, ethical considerations, regulation, and its potential to impact access to justice and judicial processes. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Algorithmic profiling, exclusion, and discrimination (e.g., denial of Medicaid benefits due to minor application errors); systemic issues for pro se civil litigants; problems not largely addressed by policy-makers; lack of human control and accountability over AI systems. | Implementing AI into the judiciary to help pro se litigants and improve court efficiency; ensuring AI systems are subject to human control and accountability; fostering education and understanding of AI among legal professionals; lawyers actively engaging with AI developers to promote justice and fairness. | Support for pro se litigants, improving court efficiency, addressing algorithmic bias and discrimination in social services and the justice system, ethical regulation of AI, ensuring fairness in AI-driven legal processes. | Pro se litigants, low-income citizens, the poor, recipients of public benefits (e.g., Medicaid applicants). | General legal system, criminal justice (bail, sentencing), civil litigation, corporate law, intellectual property, torts, tax law, public benefits law. | US, International (with specific mentions of Australia, UK, Europe) | NaN | NaN | NaN | False | False | NaN | The legal profession's general unpreparedness for the legal consequences of AI; need for AI templates, policies, and continued discussion; current lack of court-developed AI tools for access to justice; need for greater transparency and accountability in AI development; potential technical plateaus for conversational AI. | NaN | Algorithmic bias leading to discrimination and exclusion; AI tools in criminal justice acting against human best interests or lacking moral aptitude; susceptibility of AI systems to hacking; potential diminishment of the human legal community and judicial oversight; inaccuracy and potential for misinformation from conversational AI; unforeseen negative societal impacts due to unpreparedness. |
| 39SyracuseJSciTechL15.pdf | HeinOnline | Analyzing the Primary and Attendant Risks of GAI-Based Natural Language Processing Models in Legal Research | This paper analyzes the transformative potential and significant risks of Generative AI (GAI) based Natural Language Processing (NLP) models, such as ChatGPT, in legal research. It highlights primary and attendant risks including inaccuracies, bias, plagiarism, and copyright infringement, and offers recommendations for mitigating these challenges, including legal reforms. | True | Market | True | 3.0 | NaN | GAI-based Natural Language Processing models (e.g., ChatGPT, GPT series, BERT, Turing NLG, CUAD, ROSS Intelligence, EleutherAI) | The paper discusses general evaluation metrics for AI language models like perplexity and burstiness, and the importance of efficacy, precision, and reliability, but does not conduct new empirical testing of a specific model. | NaN | NaN | NaN | NaN | NaN | Legal research, Copyright law, Trademark law, Privacy law, Patent law | United States, European Union | The paper describes that GAI models are generally trained on large-scale datasets, including web pages, books, articles, and specific legal documents for domain-specific models (e.g., CUAD trained on contracts). It references public data, third-party licensed data, and datasets like Common Crawl. | The paper describes general design methodologies for GAI-based NLP models, such as using recurrent neural networks (RNNs), Markov chains, generative adversarial networks (GANs), and transformer architectures, trained on large datasets. | The paper mentions that GAI models like ChatGPT are developed by companies (e.g., OpenAI, Google, Microsoft) and made available as tools/services. It also notes some are open-source (e.g., EleutherAI by Hugging Face). | True | True | Discusses publicly available GAI models like ChatGPT (which has free access tiers) and open-source models like EleutherAI. | NaN | Challenges related to using GAI in legal research identified by the paper include: inadequate domain-specific knowledge in models, scarcity of high-quality legal training data, inherent biases in training data leading to biased outputs, lack of interpretability of AI decision-making processes (black-box nature), and difficulty in handling the complexity, nuance, and context-dependency of legal language. | Key risks include: inaccuracy and unreliability of GAI outputs (leading to flawed research and potential malpractice); plagiarism and copyright infringement (especially with derivative works); perpetuation and amplification of biases present in training data leading to discriminatory outcomes and exacerbation of social inequalities; lack of nuanced legal reasoning and contextual understanding; spread of misinformation (e.g., affecting elections or public opinion); and privacy violations due to data handling. |
| 14JIntellPropInfoTechElec.pdf | HeinOnline | Exploring the Viability of Al as Judicial Replacements: a Cautionary Perspective | This paper analyzes the viability of Artificial Intelligence replacing human judges, adopting a cautionary stance. It argues that AI's lack of social understanding, moral agency, and rational autonomy prevents it from fulfilling the complex social governance role of a judge, suggesting AI should primarily serve a supportive function. | True | NaN | False | 3.0 | Negative | NaN | NaN | NaN | AI's lack of social understanding, moral agency, and rational autonomy; inherent AI biases and discrimination; lack of transparency (black box problem); difficulty in translating nuanced law into code; absence of accountability for AI decisions; risk of legal stagnation; susceptibility to hacking and power dependence; high development and maintenance costs; potential privacy violations. | AI should be used cautiously in a purely supportive role to assist human judges, rather than replace them, preserving human oversight and decision-making due to AI's fundamental limitations. | Critique of AI as a solution for judicial system inefficiencies (often framed as access to justice issues); ethical and functional limitations of AI in judicial decision-making; preserving the human element (moral agency, social understanding, rational autonomy) in judging. | NaN | General, with examples from constitutional law, criminal law, civil law (debt collection, small claims, financial disputes), human rights law, and traffic penalties. | Multiple, including Netherlands, Estonia, Colombia, China, England and Wales, United States, Brazil, European Union (GDPR, AI Act), ECHR. | The paper discusses AI systems trained on various legal data including court records, case law, legislation, trial texts, criminal records, and offender interviews. This includes publicly available data and proprietary datasets. | NaN | NaN | False | False | NaN | Technical gaps: AI's inability to replicate human moral agency, rational autonomy, social understanding, contextual awareness, and prudence. Societal gaps: AI's incapacity to fulfill the judge's role in social governance, act as a role model, ensure judicial explicability for legitimacy, or foster public trust in the same way as human judges; difficulties in establishing accountability for AI decisions; risk of legal stagnation. | NaN | AI biases leading to structural discrimination (e.g., COMPAS); lack of transparency ('black box problem') hindering due process and appeals; unfair or arbitrary decisions due to inability to handle legal nuances; susceptibility to hacking and power outages; privacy violations from large-scale data collection; perpetuation of past mistakes and legal stagnation; erosion of public trust and legitimacy if AI replaces human judges; lack of accountability for AI-driven judicial errors; outsourcing public judicial functions to private entities, leading to potential undue influence and loss of state control. |
| 99WashLRev781.pdf | HeinOnline | CLIENT CONFIDENTIALITY AS DATA SECURITY | The paper critiques the legal profession's current approach to client data security, arguing Model Rule 1.6(c) is ineffective and hard to enforce due to its focus on technological breach prevention. It proposes a shift towards a harm mitigation framework, emphasizing changes in lawyers' processes and collaborative decision-making with clients, colleagues, and contractors to better protect client confidentiality. | True | Market | False | 1.0 | NaN | A harm-mitigation framework for the lawyer's duty of data security, focusing on regulating processes (data minimization, segregation, mapping, security planning) and people (requiring consultation with clients, colleagues, and contractors). | NaN | NaN | Ineffective, difficult-to-execute, and unenforceable current ethical rules (Model Rule 1.6(c)) regarding lawyers' duty of data security, leading to frequent client data breaches; lawyers' lack of technological expertise and understanding of client-specific data sensitivity and risk tolerances. | Shift the ethical duty from primarily technological breach prevention to harm mitigation. This involves regulating lawyers' data handling processes (data minimization, segregation, mapping, security planning) and mandating collaboration with key stakeholders (clients, colleagues, third-party contractors) in data security decisions. | Client confidentiality, Data security, Professional responsibility, Legal ethics. | NaN | Professional Responsibility, Legal Ethics, Cybersecurity Law (as applied to legal practice). | United States | NaN | Legal scholarship methods including analysis of existing rules (ABA Model Rule 1.6(c)), critique of current practices, review of data security literature and best practices from outside law, and normative reasoning to propose revisions to ethical rules. | Proposed revisions to the ABA Model Rules of Professional Conduct (specifically Rule 1.6(c) and its comments) for adoption by state bar associations. | False | False | NaN | The paper highlights existing gaps in data security practices and rules; it does not explicitly state remaining gaps if its own proposed solutions were implemented, beyond the general challenge of keeping rules updated with evolving technology. | NaN | Ongoing unauthorized access to and disclosure of client confidential information due to ineffective and poorly targeted ethical rules; harm to clients from data breaches; erosion of client trust in the legal profession; lawyers making inadequate or costly data security decisions; specific threats like phishing, ransomware, and accidental data leaks. |
| 23DukeLTechRev1.pdf | HeinOnline | THE GPTJUDGE: JUSTICE IN A GENERATIVE Al WORLD | This paper discusses the challenges and implications of Generative AI (GenAI) for the legal system, particularly concerning evidence authenticity, intellectual property, and the roles of lawyers and judges. It offers recommendations for courts and attorneys to address these evidentiary challenges and explores the future impact of GenAI on litigation. | True | Idealistic | True | 3.0 | Neutral | Generative AI (including LLMs like ChatGPT, image generators like DALL-E 2, voice cloning tools, and GANs) | NaN | NaN | Potential for misuse by malicious actors to flood courts with frivolous or defective lawsuits; risk of individuals relying on inaccurate AI-generated legal advice; courts' unpreparedness for high-volume AI-assisted filings and managing defective submissions. | NaN | Self-representation by individuals, particularly from marginalized communities; potential for AI to generate legal documents (e.g., complaints, pleadings) for pro se litigants; use of AI for obtaining legal advice by laypersons. | Litigants lacking legal representation, including individuals from racialized, marginalized, or economically disadvantaged communities; undocumented immigrants; ordinary people needing legal advice. | Evidence law, intellectual property (copyright, trademark), civil procedure, criminal procedure, torts (liability for AI-generated advice), academic disciplinary law. | United States (primarily federal, with state examples like California, Florida, New York; some international mentions e.g., Colombia, Canada, Estonia) | Discusses GenAI models trained on massive, diverse datasets primarily scraped from the internet, including text and images (e.g., 'The Pile'). This data is described as a mix of publicly available and proprietary, often unstructured, and can include copyrighted material used without artists' consent. | The paper describes methodologies used in GenAI development, such as Generative Adversarial Networks (GANs), transformer architecture, deep learning, and reinforcement learning from human feedback (RLHF). | Public release of models like ChatGPT; integration into consumer-facing applications (e.g., search engines, creative tools, apps like DoNotPay); availability of plugins (e.g., for Expedia, Instacart via ChatGPT) to extend functionality. | True | True | Publicly accessible tools like ChatGPT (via web interface, free tier), DALL-E 2, Midjourney, Stable Diffusion, DoNotPay. Explicit mention of OpenAI's release of research and code for its Shap-E model. | Courts' unpreparedness for AI-driven filings; lack of reliable AI detection tools; risk of public over-reliance on potentially inaccurate AI legal advice; justice system's ill-equipment to manage a massive influx of AI-generated, potentially defective, legal submissions. | Challenges for GenAI developers include: managing hallucinations, factual inaccuracies, and bias in model outputs; improving common-sense reasoning and handling of complex instructions (e.g., negation in image generation); addressing issues related to insufficient or problematic training data; developing reliable methods to detect AI-generated content. | Difficulty authenticating AI-generated evidence (deepfakes); increased litigation costs due to need for experts; erosion of trust in evidence and potential for 'liar's dividend'; copyright and trademark infringement by GenAI systems; misuse for generating vexatious lawsuits or spreading misinformation (e.g., scams, harmful advice); AI-generated content containing bias or hallucinations; undermining of judicial processes if judges improperly use AI; threats to academic integrity through AI-assisted cheating. |
| 62WashburnLJ587.pdf | HeinOnline | Life Beyond Zoom: The Promise of Emerging Virtual Court Alternatives | This essay discusses the evolution of virtual court technologies beyond standard videoconferencing, exploring emerging alternatives like online forms automation, hybrid courtroom tech, and integrated platforms. It highlights their potential to improve court processes and access to justice, while also acknowledging existing pitfalls and future challenges in their adoption and implementation. | True | Idealistic | False | 3.0 | Positive | Online court forms automation (e.g., Massachusetts Document Assembly Line), courtroom hybrid technologies (e.g., BEINCOURT), and immersive online "all-in-one" platforms (e.g., Tyler Technologies Virtual Court). | Real-world implementation, case studies (e.g., Alvin Municipal Court for Tyler Technologies), user feedback (e.g., judges for BEINCOURT), and adoption metrics (e.g., Massachusetts Document Assembly Line). | Tyler Technologies Virtual Court in Alvin Municipal Court, Texas, reportedly cleared a backlog of approximately 800 cases, saw a 60% decrease in failure-to-appear rates, and saved thousands of dollars annually. | Difficulties in maintaining court decorum and control, challenges in lawyer-client communication and rapport, altered credibility perceptions, increased participant distraction, security and privacy vulnerabilities, and the digital divide (lack of access to technology/broadband). | Development and adoption of diversified, law-specific virtual court technologies including automated forms, hybrid courtroom solutions, and integrated platforms. Adherence to guiding principles focusing on due process, user experience, and equity when implementing new technologies. | Improving court efficiency, enhancing public access to court services (e.g., forms, ODR), facilitating remote hearings, and modernizing court processes for various case types like small claims, traffic, and family law. | Self-represented litigants, low-income individuals, rural populations, and the general public needing access to court services, particularly in areas like housing, family law, small claims, and traffic disputes. | Criminal law, civil law (including small claims, landlord/tenant, debt collection), family law, traffic law, and general court procedure. | United States (various states including Massachusetts, California, Texas, Michigan, Utah, New York, Georgia, Connecticut, Louisiana), with mentions of Australia and Colombia. | Not explicitly detailed. The Massachusetts Document Assembly Line's natural language issue spotter likely uses user problem descriptions and legal knowledge, but specific datasets are not mentioned. | User-centered design, collaboration with legal professionals (lawyers, judges, court staff), iterative development based on user feedback and real-world trials, focus on accessibility (e.g., for language, education level). | Web-based platforms (e.g., Court Forms Online), commercial vendor installations in court systems, state/county-wide rollouts for ODR systems, and open-source code sharing (for Massachusetts Document Assembly Line). | True | True | The Massachusetts Court Forms Online (courtformsonline.org) provides publicly accessible online forms. The Document Assembly Line project's underlying code is open-source via Suffolk LIT Lab. | The digital divide (access to internet and hardware), lack of funding and internal champions for technology adoption in courts, scalability from niche to widespread use, ensuring robust security and privacy, addressing costs and implementation complexities, and maintaining community trust and meaningful human connection in virtual environments. | Overcoming the digital divide, securing funding and resources, ensuring robust security and privacy, integrating new tools with existing court infrastructure, managing physical courtroom constraints for hybrid models, ensuring accessibility for all users, and fostering user adoption and trust. | Erosion of due process and procedural fairness if not carefully implemented; compromised lawyer-client relationships and confidentiality; biased credibility assessments; dehumanization of participants; increased distractions; data security breaches and privacy violations; exacerbation of the digital divide and existing societal inequities; and privacy risks from cloud recordings if not properly managed. |
| 35GeoJLegalEthics549.pdf | HeinOnline | How Should Legal Ethics Rules Apply When Artificial Intelligence Assists Pro Se Litigants? | This paper explores the ethical dilemmas arising from the use of AI to assist pro se litigants, particularly concerning unauthorized practice of law, attorney-client relationships, and professional conduct rules. It advocates for applying and adapting existing legal ethics frameworks to AI, prioritizing consumer protection and holding human lawyers or law firms accountable for AI-provided legal services to narrow the justice gap. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | The 'justice gap' where many individuals, especially those with low-to-moderate income, cannot afford legal assistance. Ambiguity and restrictiveness of Unauthorized Practice of Law (UPL) statutes, which can deter the development of AI tools for legal aid. | Applying and adapting existing legal ethics rules to AI providers, specifically by requiring human lawyers or law firms to bear ethical responsibility for AI-assisted services. Reforming and harmonizing UPL laws to put software publishers on clearer notice and facilitate the development of legal AI for access to justice. | Access to legal services for self-represented litigants, application of legal ethics rules to AI, unauthorized practice of law, attorney-client relationships with AI, professional conduct for AI providers. | Pro se litigants, particularly low-to-moderate-income individuals and families who cannot afford traditional legal services. | General Civil and Criminal Litigation (e.g., drafting pleadings, motions, briefs; advising on litigation strategy), Bankruptcy. | United States | NaN | NaN | NaN | False | False | NaN | Need for reform and harmonization of UPL laws across states. Clarity on how conflict-of-interest rules apply to AI-provided services. Development of AI's explanatory capabilities. Establishing effective disciplinary processes and sanctions for AI providers (potentially collective discipline for law firms). | Ambiguity and lack of uniformity in UPL laws; patchwork attorney licensing system complicating multi-state service provision; ensuring AI competence equivalent to human lawyers; preventing AI bias; protecting client confidentiality with AI systems. | Provision of faulty legal advice by AI. Unauthorized practice of law by AI software or its nonlawyer publishers. Breach of client confidentiality through data use in machine learning or insecure systems. AI systems perpetuating or amplifying existing societal biases and discrimination. Difficulty in establishing attorney-client relationships and assigning liability for legal malpractice. |
| 54TexTechLRev255.pdf | HeinOnline | Limits of Using Artificial Intelligence and GPT-3 in Patent Prosecution | This paper discusses the potential applications and limitations of large language models like GPT-3 in patent prosecution, particularly for claim drafting and translating legal text. It also explores the legal (enablement, utility, inventorship), ethical (attorney supervision, bias), and social justice (access to innovation) consequences of using such AI tools in patent law. | True | Idealistic | True | 2.0 | Neutral | Application of GPT-3 (a large language model) for patent prosecution tasks like claim generation, specification drafting, and legal text simplification. | The paper cites existing evaluations: GPT-2 was evaluated for patent claim generation using a dataset of 55,890 patent claims. GPT-3's general writing capabilities were assessed by various users and researchers (e.g., Branwen, Elkins & Chun) through qualitative analysis, and its ability to translate legalese was demonstrated by a beta tester. | GPT-2 produced patent claims of 'reasonable quality'. GPT-3 demonstrated strong general writing capabilities, 'shockingly good' and creative, but with weaknesses in long-term coherence, consistency, commonsense reasoning, and exhibited bias. It could also 'impressively translate legalese into plain English' with few prompts. | If advanced AI tools like GPT-3 are only available to large, wealthy firms, it can widen the innovation inequality gap, making it harder for new entrants, small entities, and innovators from underrepresented groups to patent their inventions and compete. | Provide equal access to AI tools for all inventors, possibly through USPTO regional offices or Patent and Trademark Resource Centers (PTRCs). Make USPTO's AI-powered search systems available to small-entity inventors. | Innovation inequality, access to legal technology for patenting, social mobility for new innovators, fair competition in innovation. | New entrants to the market, small entities, innovators facing gender and racial inequalities. | Patent Law (specifically Patent Prosecution) | United States | GPT-3: Trained on a general dataset of 175 billion parameters from diverse internet text. The paper suggests potential for fine-tuning on millions of patents for domain-specific tasks. A cited study on GPT-2 for patent claims used a dataset of 55,890 patent claims. | For GPT-3 (as described in the paper): Autoregressive language model using deep learning; few-shot learning capabilities. Fine-tuning is mentioned as a potential customization technique. | GPT-3 was initially available via an API to select beta testers and was later exclusively licensed to Microsoft for commercial use. | True | False | GPT-3 is commercially available through an API, as it was licensed by OpenAI to Microsoft and is used in commercial projects. | Unequal access to powerful AI tools for patent prosecution, which can exacerbate existing disparities in innovation. Need for more sophisticated analysis and mitigation of biases in AI models like GPT-3. | Ensuring adequate attorney supervision of AI-generated content, managing AI's limitations (e.g., coherence, factual accuracy, bias), addressing patentability issues (enablement, utility, definiteness) for AI-assisted claims, and ethical concerns regarding competence and bias. | AI generating overly broad patent claims beyond an inventor's actual conception; exacerbation of the access to justice gap in innovation; AI reflecting and amplifying societal biases (e.g., racial, gender); attorneys violating ethical duties through inadequate supervision of AI; creation of denser patent thickets hindering competition; difficulty distinguishing AI-generated prophetic examples from actual working examples. |
| 32TexIntellPropLJ225.pdf | HeinOnline | A Framework for Applying Copyright Law to the Training of Textual Generative Artificial Intelligence | This paper analyzes the application of U.S. copyright law, particularly the fair use doctrine, to the training of large language models like OpenAI's ChatGPT using copyrighted textual works. It argues that such training likely involves non-actionable transitory copying or is permissible under fair use, highlighting copyright precedent and policy considerations for AI innovation. | True | Market | True | 2.0 | NaN | Application of U.S. copyright law, particularly the fair use doctrine, to the training process of textual large language models (exemplified by OpenAI's ChatGPT). | Legal analysis based on the four factors of fair use (purpose and character of the use, nature of the copyrighted work, amount and substantiality of the portion used, and effect on the potential market value), drawing on existing U.S. case law (e.g., Authors Guild v. Google, Perfect 10 v. Amazon.com, Field v. Google, A.V. v. iParadigms, Warhol v. Goldsmith) and applying it to the known training methods of LLMs. | The paper concludes there is substantial support for arguments that GenAI training involves only transitory, non-actionable copying, and that it is also permissible under fair use, with all four fair use factors, on balance, weighing in favor of fair use for training LLMs like ChatGPT. | NaN | NaN | NaN | NaN | Copyright Law, Intellectual Property Law | United States (primary focus); International (Israel, European Union, United Kingdom for comparative context) | The paper discusses training data for LLMs like ChatGPT, which includes: BooksCorpus (unpublished/self-published books), WebText (from Reddit links), Common Crawl (web crawl data), Wikipedia, news articles, social media posts, and code snippets. This data encompasses public domain works, openly licensed works, and copyrighted works not openly licensed, largely consisting of unstructured text. | NaN | NaN | True | False | The paper analyzes OpenAI's ChatGPT, a prominent GenAI model that is publicly available for use, with both free and paid tiers (e.g., through its website). | NaN | For GenAI developers (as discussed in the paper): Navigating legal uncertainty and lawsuits from copyright holders regarding the use of copyrighted materials in training data. Technical challenges in sourcing, curating, and processing massive and diverse datasets. Ensuring AI outputs do not directly reproduce copyrighted content ('regurgitation'). Lack of transparency from some AI developers (e.g., OpenAI for GPT-4) regarding training data complicates legal analysis. | Risk of legal liability for copyright infringement for AI developers if training is not deemed fair use. Potential stifling of AI innovation and U.S. competitiveness if restrictive copyright interpretations prevail. Erosion of established fair use principles if functional aspects of works become overly protected. Copyright holders' concerns about unauthorized use of their works and potential market substitution, although the paper argues the training process itself is transformative and non-substitutive. |
| 32JuridicaIntl107.pdf | HeinOnline | Al Systems' Impact on the Recognition of Foreign Judgements: The Case of Estonia | This paper examines how the use of AI in judicial proceedings could impact the cross-border recognition of foreign judgements, using Estonia as a case study. It highlights significant concerns regarding the lack of transparency and accountability in current AI judicial systems, which may conflict with fundamental rights and fair trial principles. | True | Idealistic | False | 3.0 | Negative | NaN | NaN | NaN | The primary obstacle is the lack of transparency and accountability in AI systems used in judicial proceedings, particularly concerning their algorithms, training data, and decision-making processes, hindering assessment of compliance with fair trial principles. | The paper advocates for adherence to established principles like transparency, explicability, and accountability for AI in judiciary, mandating official information disclosure, and suggests courts use procedural tools to scrutinize AI use in foreign judgments, potentially refusing recognition if principles are violated. | Cross-border recognition of foreign judgements, fair trial principles, due process, public order (ordre public), judicial co-operation, trustworthiness of judicial decisions, AI ethics in law. | NaN | Private International Law, Civil Procedure, Administrative Law | Estonia, European Union | The paper highlights that details on training data for the discussed AI systems (e.g., Xiaozhi, Smartsettle ONE, Salme, Krat) are generally not publicly available or are missing. | Not specified; the paper criticizes the lack of transparency regarding the development processes of discussed AI systems. | Integrated into court information systems (e.g., Estonia), used directly in adjudication support (e.g., China), or offered as online dispute resolution platforms. | False | False | NaN | Technical gaps include the non-explicability of many AI systems. Societal and legal gaps involve the lack of adherence to transparency, accountability, and fair trial principles in AI development and deployment in judicial systems, and insufficient accessible information about AI system functionality and data usage. | NaN | AI systems lacking transparency and accountability in judicial proceedings risk producing judgements that violate fundamental rights or public order, potentially leading to non-recognition of these judgements in other jurisdictions. There is also a risk of privacy breaches from inadequately implemented AI tools like anonymization software. |
| 92FordhamLRev (1).pdf | HeinOnline | TOWARD AN ETHICAL HUMAN-COMPUTER DIVISION OF LABOR IN LAW PRACTICE | This paper argues for a new framework for the ethical use of AI in law practice, distinguishing between deterministic and probabilistic technologies. It proposes a 'division of labor' model, particularly for probabilistic AI, to manage error and ensure lawyers uphold their professional responsibilities by treating such AI similarly to human subordinates requiring oversight. | True | Market | True | 1.0 | NaN | Conceptual framework for an ethical human-computer division of labor in law practice. This involves distinguishing between 'deterministic' and 'probabilistic' technologies, increasing 'error tolerance' for probabilistic tools by differentiating 'processual errors' from 'ultimate errors,' and treating probabilistic AI akin to human colleagues requiring diligent oversight. | NaN | NaN | NaN | NaN | NaN | NaN | General law practice | United States | NaN | NaN | NaN | False | False | NaN | NaN | Lawyers' misunderstanding of AI capabilities and limitations (especially probabilistic AI like LLMs), their tendency to misapply AI tools (e.g., for tasks better suited to deterministic systems or without adequate verification), and failure to exercise proper human oversight, leading to ethical breaches and negative consequences such as submitting false information to courts. | AI generating false or inaccurate information (e.g., 'hallucinations' like fake case citations); lawyers breaching ethical duties (e.g., competence, diligence, candor to the tribunal); clients being poorly served or harmed; courts being misled or duped; reputational damage to the legal profession and individual lawyers. |
| 31ClinicalLRev153.pdf | HeinOnline | DATA JUSTICE READINESS: AN ABOLITIONIST FRAMEWORK FOR TECH CLINIC INTAKE | This paper proposes a "Data Justice Readiness" framework for tech law clinics to guide client and project selection, aiming to support communities harmed by carceral technologies. Drawing from abolitionist and movement lawyering principles, the framework helps clinics align their work with a data justice vision, prioritizing non-reformist outcomes and integrated advocacy for structurally-marginalized groups. | True | Idealistic | False | 1.0 | Positive | Data Justice Readiness Framework for tech clinic intake, including a draft clinical mission and intake form. | The framework was illustratively applied to three potential tech clinic projects (Cyber Civil Rights Initiative, Just Futures Law, Domestic Care Workers Alliance & National Consortium for Independent Living) as case studies to demonstrate its decision-making process. | The application of the framework to the Domestic Care Workers Alliance & National Consortium for Independent Living project demonstrated it as 'most likely' to align with data justice principles because it represented mobilized communities (care workers and people with disabilities), pursued a non-reformist outcome (banning EVV systems), and involved integrated advocacy skills. | Concentration of data power in tech companies and their enmeshment with the state leading to widespread datafication; deployment of 'carceral tech' that exacerbates social, racial, and economic inequities and causes algorithmic violence; expert-driven tech reforms that fail to address structural issues or adequately represent impacted communities. | Adoption of the proposed Data Justice Readiness framework by tech law clinics, which prioritizes direct collaboration with structurally-marginalized communities, centers abolitionist and movement lawyering principles, and aims to build 'people (data) power' to resist harmful technologies and advocate for non-reformist tech reforms. | Data justice, carceral technologies, algorithmic violence, role of tech law clinics, movement lawyering, abolitionist pedagogy, client and project selection in legal clinics, public interest technology. | Structurally-marginalized communities, including IBPOC (Indigenous, Black, People of Color) communities, the poor and economically underserved, 2SLGBT+ communities, immigrants and asylum seekers, people with disabilities, laborers (especially gig economy, sex work, factory/agricultural), incarcerated or formerly incarcerated people, and unhoused people. | Technology law and policy, Clinical legal education, Civil rights, Public interest law, Movement lawyering. Specific examples touch on First Amendment, privacy law, immigrant rights, labor rights, disability rights. | United States | NaN | The framework was developed drawing on insights from prison industrial complex (PIC) abolitionist theory, movement lawyering principles, critical perspectives on data-driven technologies, and scholarship on clinical legal pedagogy. It includes a conceptual model and an intake form. | The framework is proposed within an academic law review article, intended for adoption by tech law clinics. The paper itself serves as the primary means of dissemination. | True | False | The paper's Appendix contains a draft clinical mission statement and a detailed intake form, which constitute the core of the proposed Data Justice Readiness framework, making it usable by readers. | A lack of shared, explicit data justice vision among tech clinics for client/project selection; insufficient direct engagement of tech clinics with communities harmed by carceral tech; the general deficit of people power to counter corporate data power; potential for clinicians to lack skills or face institutional resistance in adopting a data justice framework. | Potential obstacles for tech clinics adopting the framework include: clinicians lacking the necessary skills, knowledge, or cultural competencies; clinicians' reliance on ad hoc client selection from established expert networks rather than grassroots movements; and institutional resistance from law schools that may not prioritize social justice in tech law or may view such an approach as too radical. | NaN |
| 45MelbULRev950.pdf | HeinOnline | AN ECONOMIC PERSPECTIVE ON COSTS IN AUSTRALIAN CLASS ACTIONS | This paper develops an economic framework to analyze various costs (agency costs, externalities, preventive costs) in Australian class actions, particularly concerning litigation funding. It argues that existing legal mechanisms and potential reforms can manage these costs, ensuring class actions remain a fair, reasonable, and effective tool for access to justice and deterrence. | True | Idealistic | False | 3.0 | NaN | NaN | NaN | NaN | Agency costs (moral hazard and adverse selection involving plaintiff lawyers, representative parties, passive group members, and litigation funders); negative externalities (e.g., consumption of court resources, adverse impact on public perception of justice if proceedings are unfair); and preventive costs (costs of measures designed to reduce agency costs and negative externalities). These can make class actions excessively costly, undermining their access to justice function. | Enhanced judicial oversight and the strategic application of legal tools, including fiduciary duties, mechanisms for ensuring adequacy of representation, costs and funding agreements (like group costs orders), the right to opt out, effective notice provisions, and the appointment of independent legal representation (e.g., contradictors) or costs experts for passive group members. The paper also suggests reforms such as clarifying court powers over funding agreements and considering second opt-out opportunities in specific circumstances. | Effective functioning and cost management of class actions to preserve access to justice, ensure fair compensation for meritorious claims, and deter wrongdoing. | Individuals with small claims, dispersed and disorganised plaintiffs who would otherwise be unable to pursue their claims individually. | Class actions, Civil procedure, with examples from consumer law and shareholder litigation. | Australia | NaN | NaN | NaN | False | False | NaN | Imperfect judicial oversight due to reliance on information provided by involved parties; challenges for passive group members in effectively monitoring proceedings or exercising their rights (like opting out or giving informed consent for fiduciary matters); uncertainty regarding the court's power to vary litigation funding agreements; limitations of the standard opt-out right when misconduct or detrimental settlement terms become apparent only after the opt-out period has passed. | NaN | Excessive or disproportionate costs undermining the core benefits of class actions, such as access to justice, adequate compensation, and effective deterrence; potential for 'sweetheart' settlements that primarily benefit lawyers and funders at the expense of group members; adverse selection dynamics weakening the collective claims; negative externalities that can damage the civil justice system's efficiency and public confidence. |
| 50RutgersComputerTechLJ15.pdf | HeinOnline | Improving Solutions to AI-Related Difficulties | The paper examines legal, technological, and business challenges from AI, such as liability, IP issues, and bias, including in justice contexts. It proposes mandatory, identifiable domain names for AI/ML systems to improve solutions and prevent harm. | True | Market | False | 1.0 | NaN | Requiring AI and Machine Learning (ML) systems to be readily identifiable, for example, by requiring them either to register or to use specified domain names (e.g., an IP address using '.RealAI'). | NaN | NaN | Bias in AI systems leading to unfair criminal justice outcomes (e.g., risk assessments, predictive policing) and disproportionate impact on marginalized communities. | Making AI systems identifiable via mandatory domain names or registration to enable better tracking, management, or restriction, particularly for problematic uses in areas like the justice system. | Algorithmic bias in criminal justice decision-making and predictive policing. | Racial minorities and marginalized communities affected by biased AI in the justice system. | AI Law, Tort Law, Intellectual Property Law, Contract Law, Criminal Law, Professional Ethics, Data Privacy Law. | United States (primarily), European Union (mentioned). | General discussion of large volumes of internet data, including publicly available datasets (e.g., UC Irvine Machine Learning Repository), proprietary collections (e.g., Getty Images), and scraped personal data, often unstructured and including sensitive or copyrighted information, used to train the AI systems under discussion. | NaN | Proposed implementation via legislation, voluntary self-imposed industry standards, or regulations. | False | False | NaN | The primary gap identified is the inability of current solutions to prevent AI-related harms proactively; applied to access to justice, this means a lack of mechanisms to prevent or mitigate harm from biased AI in justice applications before deployment. | For the proposed domain name requirement: achieving widespread adoption and enforcement (via legislation, industry standards, or regulation) and addressing potential circumvention techniques. | Generation of false or biased information, privacy violations, intellectual property infringement, misuse for deepfakes, creation of discriminatory outcomes, and difficulties in assigning legal liability for AI-induced harm. |
| 90UCinLRev.pdf | HeinOnline | Prospects for Legal Analytics: Some Approaches to Extracting More Meaning from Legal Texts | This paper surveys recent research in legal text analytics focused on extracting more semantic meaning from legal texts, such as case decisions, contracts, and statutes. It discusses various AI approaches, including machine learning, deep learning (e.g., BERT, GPT-3), and knowledge representation, to improve tasks like outcome prediction, factor identification, argument mining, and providing explanations, with prospects for enhancing both legal practice and access to justice. | True | Idealistic | True | 3.0 | Positive | Advanced NLP and ML (including transformers like BERT, GPT-3, and deep learning) combined with knowledge representation for extracting deeper semantic meaning from legal texts (e.g., identifying factors, argument structures, explaining statutory terms). | NaN | NaN | Current AI's inability to fully understand and interpret legal texts as humans do (e.g., implicit meanings, common sense); lack of robust explainability in AI predictions; difficulty in extracting and reasoning with implicit information from texts. From an A2J perspective: general unfairness and lack of access to legal resources for laypersons. | Developing AI techniques to extract more semantic meaning from legal texts by combining machine learning (especially deep learning and transformers) with knowledge representation. Specifically, identifying factors, argument structures (issues, reasons, conclusions), and sentences explaining statutory terms. For A2J, deploying advanced AI tools through accessible platforms like Legal Information Institutes (LIIs) to provide free access to legal sources for the public. | Access to legal information; Understanding legal texts (statutes, case law); Legal reasoning and argumentation support. | Lay persons (as a target for an NSF project discussed); legal professionals. | General / Multiple (examples include human rights law, domain name disputes, trade secret law, contract law, copyright law, Fourth Amendment issues, veterans' benefits claims). | International / Multiple (includes specific examples or datasets from US, European Court of Human Rights, WIPO, Singapore, Japan). | Various legal text corpora, including case decisions (e.g., ECHR, WIPO, US caselaw from Harvard Caselaw Corpus, BVA), statutes, and contracts. Data includes publicly available sources and manually annotated corpora created for specific research tasks (e.g., WIPO cases for SCALE, trade secret cases for VJAP factors, sentences for statutory term explanation, case summaries for argument triples). Primarily unstructured text, domain-specific. | Manual annotation of legal texts to create labeled training datasets; application of machine learning algorithms (including deep learning NNs, LSTMs, transformer models like BERT); development and use of knowledge representation schemes (e.g., tag systems for WIPO cases, domain models for trade secret law); iterative development and evaluation, including active learning in some instances. | For the author's A2J project: planned deployment through Legal Information Institutes (LIIs) for free public access. Other mentioned tools have commercial deployments or are research prototypes. | False | False | NaN | Technical: AI's limited ability to understand implicit meaning and common-sense knowledge, lack of robust and legally intelligible explainability, challenges in integrating structured legal knowledge with deep learning models effectively. Societal: Insufficient access to and understanding of legal information for laypersons; a need for better education of legal professionals on AI's capabilities and limitations. | The knowledge representation bottleneck requiring significant manual effort for annotation and model creation; the need for large, high-quality, domain-specific annotated datasets for training ML models; high computational costs associated with training and fine-tuning large language models; difficulty in conducting extrinsic evaluations to assess real-world utility and impact on users; handling the inherent ambiguity, complexity, and stylistic variability of legal language; ensuring fairness and mitigating biases in AI models. | Over-reliance on AI-generated predictions or answers without critical assessment of their limitations and potential for error; AI models (e.g., GPT-3) producing plausible-sounding but incorrect or misleading information; models making predictions or classifications without true legal understanding, leading to flawed outputs if underlying data or logic is misinterpreted. |
| 37ComLWorld38.pdf | HeinOnline | ChatGPT in a Nutshell | This paper provides a high-level overview of ChatGPT, explaining what it is and its historical context within AI. It then discusses the potential benefits of ChatGPT for attorneys, such as streamlining legal research, document drafting, client communication, and litigation strategy. | True | Market | True | 3.0 | NaN | ChatGPT (Generative Pre-trained Transformer) | The author provides two examples of prompting ChatGPT and includes the generated text. No formal or systematic testing is described. | In two examples provided, ChatGPT generated coherent and relevant articles based on the prompts in under 30 seconds. One article was a high-level explanation, and the other was a more casual opinion piece on the same topic. | NaN | NaN | NaN | NaN | General legal practice (including legal research, document drafting, client communication, litigation strategy, legal education) | NaN | ChatGPT is described as being 'trained' on huge amounts of text data, aggregated from many sources, with its knowledge cutoff mentioned as September 2021. The model is developed by OpenAI. | NaN | NaN | True | False | The paper discusses ChatGPT, a tool developed by OpenAI, which is publicly accessible (often with free and paid usage tiers). | NaN | Uncertainty about the ease of feeding specific (e.g., sensitive internal) information for processing. Concerns about data privacy when using the tool, as user inputs might be used for software improvement. | Potential risk of exposing sensitive legal information if inputted into ChatGPT, as this data might be used to help improve the software. |
| 92FordhamLRev1.pdf | HeinOnline | TOWARD NATIONAL REGULATION OF LEGAL TECHNOLOGY: A PATH FORWARD FOR ACCESS TO JUSTICE | This paper argues that the current state-by-state regulation of legal technology is inadequate for addressing the access-to-justice gap and proposes an opt-in national legal services "sandbox". The sandbox aims to carefully test innovative legal services, generate data to inform regulatory reforms, and thereby balance consumer protection with improved access to justice. | True | Idealistic | False | 1.0 | Positive | National legal services 'sandbox' (a regulatory reform mechanism) | NaN | NaN | Inadequate state-by-state regulation, regulatory uncertainty (e.g., UPL, nonlawyer ownership), lack of data on legal tech's impact, resistance to reform within the legal profession, and resource/relationship/resilience barriers inhibiting innovation. | Proposing an opt-in national legal services 'sandbox' to test innovative services, generate data for informed regulatory decision-making, and leverage national expertise and resources. Reforming restrictive rules like those on nonlawyer ownership and UPL. | Improving affordability and availability of civil legal services, addressing unmet legal needs of low-income individuals, democratizing access to legal information, enabling self-help for certain legal problems, and fostering innovation in legal service delivery. | Low-income individuals and moderate-income individuals who cannot afford traditional legal services. | Broadly civil legal matters, including family law, business law, estate planning, consumer issues, and others where individuals often lack representation. | United States (proposing a national system, with discussion of state-level regulations and examples like Utah, Arizona, Washington, California). | NaN | The proposed national sandbox's design is based on principles of regulatory experimentation, drawing on existing state-level sandbox models (e.g., Utah), and involves structured processes for application, oversight, data collection, and staged implementation. | The proposed national sandbox would be an 'opt-in' system for U.S. jurisdictions. Deployment would involve establishing a national oversight body and participating states formally adopting its recommendations for testing innovative legal services. | False | False | NaN | Lack of empirical data on legal technology's impact, insufficient understanding of how to effectively 'calibrate' technology for diverse user needs and legal issues, ongoing resistance to regulatory reform, and absence of a national mechanism for testing and diffusing successful innovations. | Establishing a national oversight body, securing funding and national buy-in from states, developing standardized processes for application review and data collection, balancing innovation with consumer protection during testing, and overcoming resistance to national-level regulatory processes. | Legal technology, without proper regulation, risks creating a two-tiered justice system, automating bias, causing consumer harm through low-quality services, and magnifying inequality. Failure to reform regulation could also lead to 'spontaneous deregulation'. |
| 56ArizStLJ.pdf | HeinOnline | Generative Contracts | This paper explores how consumers can use generative artificial intelligence, specifically GPT-4, to create their own contracts, demonstrating this with various examples and a car sale case study. It discusses the potential of "generative contracts" to improve access to justice, while also analyzing the associated risks and implications for the legal profession. | True | Idealistic | True | 1.0 | Positive | Using OpenAI's GPT-4 (a large language model) to generate entire contracts from scratch based on user prompts, termed 'generative contracts'. | The paper uses OpenAI's GPT-4 to generate drafts of over a dozen types of contracts (e.g., employment agreement, residential lease, bill of sale) from simple, consumer-accessible prompts. A proof-of-concept case study details how two hypothetical consumers use GPT-4 to iteratively draft and modify a car sale contract. | Contracts generated by GPT-4 were found to be functional, enforceable, short, simple, and human-like in language, though not without errors, inconsistencies, and generally of lower quality than lawyer-drafted contracts. The case study demonstrated ease of use, speed, low cost, flexibility, and modifiability of the AI-assisted contract generation process. | High cost of legal services, shortage of lawyers (especially in rural "legal deserts"), and the difficulty consumers face in reading and understanding complex legal documents. | Generative AI tools like GPT-4 can create low-cost, easily accessible contracts through simple, plain-language prompts, potentially alleviating the justice gap by providing consumers with an alternative to expensive legal help or no contract at all. AI can also explain legal terms. | Access to consumer legal services, contract drafting for common consumer transactions, understanding legal documents. | Consumers, particularly low-income Americans and those in rural areas ("legal deserts") who are underserved by the traditional legal system. | Contract law, Consumer law | California, USA | The paper states GPT-4 is trained on 'massive amounts of data,' specifically 'huge amounts of text data' scraped from the internet. It does not specify contents beyond general natural language text. | Prompt-based interaction with a pre-existing large language model (GPT-4); proof-of-concept contract generation through simple, consumer-accessible prompts without specialized prompt engineering; and a qualitative case study. | NaN | True | False | GPT-4 is accessible via OpenAI's ChatGPT. The advanced version of GPT-4 used for generating the paper's contract examples was available through a paid subscription (ChatGPT Plus at $20/month at the time of research). A free version (GPT-4o mini) is also mentioned. | Technological limitations of AI (inscrutability, accuracy issues like hallucinations, bias, susceptibility to adversarial attacks). Need for ongoing AI safety research. The paper also implies continued gaps in widespread consumer adoption and comprehensive regulatory frameworks. | Ensuring accuracy and completeness of AI-generated contracts, as they can contain errors, inconsistencies, and may be of lower quality or less comprehensive than contracts drafted by human lawyers. | Technological risks (inscrutability, inaccuracy/hallucinations, bias, adversarial attacks); privacy and data protection risks (e.g., GDPR violations, breach of client confidentiality if trained on sensitive documents); intellectual property infringement (from training on copyrighted/trademarked works); and regulatory risks (unauthorized practice of law, restrictive AI-specific regulations). |
| 49BYULRev307.pdf | HeinOnline | Hidden Contracts | This paper defines "hidden contracts" as consumer agreements that firms unilaterally modify and then make inaccessible, and an empirical study shows this practice is common among major online companies. The authors argue this undermines consumer access to justice and propose a "contract transparency duty" requiring firms to provide and archive all contract versions. | True | Idealistic | False | 1.0 | NaN | A proposed "contract transparency duty" for firms. | NaN | NaN | Inaccessibility of original/previous contract versions (hidden contracts) leads to consumers not knowing their rights, inability to assess legal options, and deterrence from enforcing rights or suing firms for breaches. This results in legal uncertainty and firms being under-deterred from inefficient breaches. | Impose a contract transparency duty on firms, requiring them to: 1) provide consumers with contracts upon formation, 2) publish all historical contract versions online, and 3) reproduce original contracts upon consumer request. Supplement with administrative enforcement (fines, injunctions) and consider hidden contracts an unfair practice under UDAP laws. | Consumer contract transparency, access to previous contract versions, enforcement of consumer rights, ability to pursue legal remedies against businesses. | Online consumers generally, with a specific mention of vulnerable consumers (elderly, non-native English speakers, those with learning difficulties, less-educated populations) and disadvantaged consumers. | Consumer Contract Law, Consumer Protection Law. | United States (empirical study and legal context), with proposed solutions potentially applicable more broadly. | NaN | Legal and policy design, outlining the scope of the duty (provision of contracts, online archives, on-demand reproduction) and enforcement mechanisms (private and administrative actions, considering it an unfair practice). | Proposed for implementation through legislative and regulatory action by policymakers, enforced by administrative agencies (e.g., FTC, CFPB), State Attorneys General, and private litigation. | False | False | NaN | While the proposed transparency duty is a considerable step, it may not guarantee a fair overall market equilibrium on its own. Future research is needed on the broader implications and regulation of hidden contracts and related non-transparent practices. | Anticipated objections to the proposed transparency duty (which the paper refutes), such as arguments concerning consumers' personal responsibility, the sufficiency of existing social norms or online archives, consumer reading habits, and the relevance of original contracts post-modification. | The paper primarily details the risks and social costs of 'hidden contracts' (e.g., consumers' inability to know/enforce rights, firms inefficiently breaching contracts), which the proposed duty aims to mitigate. It briefly acknowledges, while refuting, the critique that the duty could increase business costs passed to consumers. |
| 73DePaulLRev301.pdf | HeinOnline | AI Malpractice | This paper explores whether AI modelers should be held to a professional malpractice standard of care, similar to doctors or lawyers, by comparing AI work to conventional software development and analyzing the applicability of malpractice doctrine. It suggests that for the immediate term, strict liability might be more appropriate for AI, with a potential transition to malpractice or ordinary reasonable care as AI technology and its societal integration mature. | True | Idealistic | True | 1.0 | NaN | Application of professional malpractice law (and other liability frameworks like strict liability or ordinary negligence) to AI modelers, based on an analysis of AI work considering factors like subjective judgments, risk of bad outcomes, and essential societal function. | NaN | NaN | Biased, incorrect, or insufficient training data leading to unfair or inaccurate AI systems that perpetuate societal harms and discrimination (e.g., in policing, employment), impacting fairness and due process. | Establishing clear liability frameworks (e.g., professional malpractice, strict liability, ordinary negligence) for AI modelers to incentivize the development of safer, fairer, and more accountable AI systems, thereby mitigating access to justice-relevant harms. | Algorithmic bias, discrimination, fairness, and accountability in AI systems, particularly in contexts like criminal justice and employment, which have significant implications for individual rights and due process. | Protected classes (e.g., based on race, gender) and other individuals disproportionately harmed by biased or flawed AI systems in critical decision-making processes such as law enforcement, hiring, and credit scoring. | Tort Law (malpractice, negligence, strict liability), with illustrative examples touching upon criminal law, employment law, and intellectual property. | United States | NaN | NaN | NaN | False | False | NaN | Persistent scientific uncertainties in aspects of AI development (e.g., optimal model configurations, hyperparameter tuning); challenges in ensuring training data is comprehensive, unbiased, and legitimate; limitations in current AI testing methodologies for guaranteeing robustness, fairness, and generalizability; and a lack of consensus on how to effectively translate AI ethics principles into enforceable legal duties for AI modelers. | Defining appropriate and adaptable liability standards for AI modelers given the unique characteristics of AI development (e.g., opacity of some models, data-driven nature, rapid evolution, and the difficulty in foreseeing all potential harms) and distinguishing AI work from traditional software or product liability for doctrinal purposes. | Accidental harms from AI errors (e.g., autonomous vehicle crashes, misidentification); intentional misuse for malicious purposes (e.g., deepfakes, disinformation, fraud); perpetuation and amplification of societal biases leading to discrimination; systemic risks such as erosion of trust in institutions or market instability; data privacy violations; and significant labor displacement. |
| 25DukeLTechRev116.pdf | HeinOnline | FINE-TUNING LLMS: STRUCTURAL FLUENCY AND AUGMENTATION FOR THE GREAT AND POWERFUL WIZARD OF Al | The paper argues that LLMs, despite their potential, can perpetuate existing biases in the civil legal system rooted in structural injustice. It proposes "structural fluency" through fine-tuning and prompt augmentation, informed by social justice principles, as a method for legal professionals to mitigate these risks and enhance fairness in LLM outputs. | True | Idealistic | True | 1.0 | Positive | "Structural fluency" achieved through fine-tuning prompts and prompt augmentation for LLMs, guided by social justice principles and structural competency frameworks. | NaN | NaN | LLMs replicating ineffective patterns and biases of the past rooted in racism and power imbalances; the civil legal system's inherent assumptions and biases; "color-evasive" policies and LLM deployment perpetuating racism; lack of access to justice and procedural unfairness; LLMs being developed by homogeneous groups. | Engaging in machine learning frameworks informed by social justice principles; fine-tuning LLMs and using prompt augmentation to enhance their fluency in structural injustice; prompting LLMs to consider macro structures, systemic forces, historical legacies of injustice, and social identity; incorporating critical lenses like cultural competency and racial literacy into LLM interaction; developing "structural fluency" in LLM interactions. | Mitigating bias in AI/LLMs; ensuring fairness and equal justice in AI-assisted legal processes; addressing systemic and structural injustice within the legal system through AI; the role of social context and identity in legal AI; ethical use of AI by legal professionals. | Subordinated individuals/groups, people of color, women and trans people, people in lower socioeconomic classes. | Civil law, Civil procedure | United States | NaN | Conceptual framework development drawing from critical legal theories (e.g., LatCrit, Critical Race Theory), social justice principles, legal pedagogy (Socratic method, scaffolded learning), and analogies from other fields (e.g., structural competency in medicine). | NaN | False | False | NaN | Lack of a method for prompting machines to "fine-tune" them for social justice; need for AI tools to move beyond replicating past injustices and incorporate social context and identity-consciousness; the legal system's "structural incompetence" and procedural unfairness; current LLM training and deployment often reflecting "color-evasiveness." | NaN | LLMs proposing outcomes based on ineffective past patterns, perpetuating a "civil legal system twilight zone"; replication of bias, prejudice, and discrimination; LLM "hallucinations" or fabricated information; misuse by legal professionals without proper verification; entrenchment of systemic injustice if LLMs are not intentionally guided; potential to worsen disparities in legal services; AI tools reflecting biases of their homogeneous developers; "color-evasive" LLM deployment. |
| 31AIL169.pdf | HeinOnline | Lawmaps: enabling legal Al development through visualisation of the implicit structure of legislation and lawyerly process | This paper proposes 'lawmaps,' a visual modelling approach using UML elements to represent legislative structures and lawyerly processes, aiming to improve legal accessibility and support Legal AI development. The authors present a methodology for creating lawmaps and demonstrate it with examples from UK law. | True | Idealistic | False | 1.0 | Positive | Lawmaps: A visual modelling approach using a subset of UML activity diagrams and Boolean logic to represent legislation and lawyerly processes, supported by a Lawmap Development Lifecycle. | Demonstrated through the creation of lawmaps for UK conveyancing practice, the Landlords and Tenants Act 1954, and UK road rules. The methodology and outputs were also applied in the Engine B project. | The paper demonstrates the successful application of the Lawmap methodology to create visual representations of UK conveyancing, landlord-tenant law, and road rules, intended to enhance clarity and serve as a basis for AI. The Engine B project utilizes these lawmaps, indicating practical application. | Complexity and incomprehensibility of legal texts for laypersons; difficulties in accessing legal help due to lack of information and reductions in legal aid. | Visualisation through 'lawmaps' to make legal structures and processes more comprehensible and accessible, thereby empowering laypeople and supporting expert decision-making and AI development. | Improving legal literacy for the public, simplifying understanding of legislation and legal procedures, enhancing transparency in legal processes. | General public, laypersons, individuals not trained in law seeking to understand their rights and legal processes. | Conveyancing, Landlord and Tenant Law, Road Traffic Law. | United Kingdom (specifically England and Wales for landlord-tenant law examples). | NaN | Expert elicitation, UML-based visual modelling (activity diagrams), Boolean algebra for rule formalization, iterative development lifecycle (Locate, Extract, Identify, Distinguish, Sequence, Traceability). | Exemplar lawmaps made available online. The methodology and outputs are being used in the Innovate UK funded Engine B project to develop practitioner-facing AI tools. | True | True | Exemplar Lawmaps (the visual outputs of the methodology) for conveyancing and aspects of the Landlords and Tenants Act 1954 are stated to be accessible online via URLs provided in the paper's footnotes. | Lack of existing visual tools for broad legal issues/legislation; need for explainable legal AI; requirement for the legal domain to adapt to technological advancements. | Complexity of translating intricate legal text into formal visual models; potential resistance to formulaic approaches within the legal profession; ensuring democratized visualisations are comprehensible by both experts and laypersons. | Ethical concerns (bias, racism) in broader AI applications within the justice system (e.g., COMPAS, Predpol), though these are considered outside the paper's direct scope. Potential for technology to be misconstrued as solely for wholesale lawyer replacement if not integrated thoughtfully to transform legal practice. |
| 56ArizStLJ545.pdf | HeinOnline | Systemic Regulation of Artificial Intelligence | The paper argues for systemic regulation of AI as a technology, beyond specific applications, due to broad societal risks (present and future, including bias, fraud, unemployment, geopolitical instability, and existential threats) and the AI alignment problem. It proposes principles for domestic and international AI regulation, emphasizing a precautionary approach and ex-ante oversight. | True | Idealistic | True | 3.0 | NaN | NaN | NaN | NaN | Bias and discrimination by AI systems against vulnerable groups; projection of historical inequity into the future. | Systemic regulation of AI as a technology, including ex-ante oversight, to mitigate AI risks such as bias and discrimination. | Algorithmic bias and discrimination; preventing AI-driven harms to vulnerable communities. | Vulnerable groups, people of color, women, minorities, groups with a history of discrimination or disadvantage. | General Law / AI Regulation | US, China, EU, International | NaN | NaN | NaN | False | False | NaN | Lack of effective systemic regulation for AI; limitations of technical tools to address algorithmic discrimination; the unresolved AI alignment problem making it difficult to ensure AI systems consistently uphold human values and avoid discriminatory outcomes. | The AI alignment problem (including goal specification, instrumental convergence, orthogonality thesis); complexity and poor auditability of AI systems; the rapid and unexpected rate of AI capability growth. | Bias and discrimination, fraud, privacy violations, unemployment, inequality, dangerous military applications (autonomous weapons), geopolitical imperialism, terrorism, totalitarianism, threats to democracy (misinformation, deepfakes), harms from misaligned AI (including deception and power-seeking), existential risks, misuse of AI for nefarious purposes (e.g., bioweapons). |
| 30MichTechLRev1.pdf | HeinOnline | THE IMPLICATIONS OF CHATGPT FOR LEGAL SERVICES AND SOCIETY | This paper demonstrates ChatGPT's capabilities and potential implications for legal services and society by presenting text generated by the AI in response to diverse legal prompts. It explores use cases like legal research and document drafting, while also critically assessing current limitations, ethical challenges, and the transformative potential for legal practice, education, and access to justice. | True | Idealistic | True | 2.0 | Positive | ChatGPT (OpenAI's language model, likely GPT-3 based for most examples) and Microsoft's Bing Chat (using a more advanced OpenAI model, likely GPT-4). | Qualitative demonstration by prompting ChatGPT and Bing Chat with diverse legal tasks (e.g., drafting documents, explaining legal concepts, legal analysis of hypos) and presenting/analyzing the generated outputs within the paper itself. | ChatGPT's outputs were 'surprisingly sophisticated' yet 'imperfect,' 'incomplete,' and lacked nuance; Bing Chat, on more advanced AI, performed better on legal analysis (e.g., 12/15 legal ethics MCQs) but was not comprehensive, with GPT-4 showing further improvement. | High cost and inaccessibility of the traditional legal system leading to a significant justice gap for low-income and middle-income individuals. Current AI limitations (e.g., unreliability, lack of nuance) and the potential for AI to widen the digital divide due to cost. | Leveraging AI tools like ChatGPT for self-help resources and to enable lawyers to serve more clients, particularly for less complex legal matters to address unmet civil legal needs. Continued development of AI to improve reliability and capabilities. | Providing general legal information and advice (e.g., on IEPs, Social Security); document drafting for common needs (e.g., simple contracts, wills); assistance with civil legal issues like child custody, debt collection, eviction, and foreclosure. | People living below the poverty line, middle-income Americans, and the general public facing unmet civil legal needs. | Civil Procedure, Contract Law, Tort Law, Wills and Estates, Family Law, Education Law, Social Security Law, Constitutional Law, Legal Ethics. | United States (with specific examples from Massachusetts, Rhode Island, Louisiana, Florida). | The paper refers to ChatGPT being trained by OpenAI on a 'vast amount of text data,' characteristic of large language models like GPT-3 and GPT-4, implying general internet text and other sources. The paper itself did not involve training a new model. | NaN | Public web-based release by OpenAI for ChatGPT; integration into Microsoft's Bing search engine for Bing Chat (initially in beta). | True | False | ChatGPT was publicly accessible via OpenAI's website (initially with a free tier, later with paid tiers for advanced versions like GPT-4). Bing Chat was available for beta testing. | Technical gaps include AI's current imperfection, error-proneness, lack of nuance, and unreliability for critical tasks. Societal gaps include the digital divide potentially exacerbated by costly AI, the need for robust regulation and ethical guidelines, issues of work attribution, potential for misuse, and job displacement. | For the author using the tools: Ensuring accuracy and reliability of AI-generated content, dealing with incomplete or nuanced-lacking outputs, and the need for effective prompt engineering. Broader challenges include ethical and regulatory considerations for AI in law. | Errors in AI-generated legal advice/documents causing harm; AI lacking human judgment; job displacement for legal professionals; malicious use for deception or manipulation; AI shaping thoughts/behaviors negatively; misuse/over-reliance on AI; widening digital divide; algorithmic bias; potential existential risks (mentioned as a broader societal concern). |
| 72JLegalEduc598.pdf | HeinOnline | Technically Speaking: How to Improve Technology CLEs to Meet the Needs of Lawyers and Get Them to Attend | This paper argues for the critical need for lawyers to achieve technology competence and critiques existing Continuing Legal Education (CLE) programs for failing to adequately meet this need. It proposes practical recommendations to enhance technology CLEs, focusing on adult learning principles, expert involvement, and stronger regulatory support to ensure lawyers can effectively meet their ethical obligations and serve the public. | True | Idealistic | False | 1.0 | Positive | Overhauling technology CLEs by applying adult learning principles (e.g., problem-centered, hands-on learning), involving non-lawyer technology experts as faculty, drawing inspiration from innovative law school programs and Practice Management Assistance Programs (PMAPs), and improving regulatory messaging and mandates for technology training. | NaN | NaN | Lawyers' insufficient technological competence, stemming from passive and ineffective Continuing Legal Education (CLE) delivery methods, low attendance at non-mandatory technology CLEs, and inadequate mandatory technology training requirements in many jurisdictions. | Revamp technology CLEs by incorporating adult learning methodologies for active engagement, utilizing diverse subject-matter experts (including non-lawyers), emulating successful educational models from law schools and PMAPs, and strengthening regulatory frameworks through clearer mandates for technology training and more supportive messaging about technology's benefits. | Enhancing lawyer technological competence through improved professional education (CLEs) to ensure quality and ethical legal representation for the public. | The general public/all clients of legal services. | General legal practice, professional ethics, and lawyer regulation. | United States (referencing ABA Model Rules and specific states like Florida, North Carolina, Maine, New York, Kansas, and the U.S. Virgin Islands). | NaN | The proposed approach is based on a review of adult learning theory, analysis of existing legal education models (law schools, Practice Management Assistance Programs), and a critique of current CLE shortcomings. | Implementation by CLE providers, bar associations, and legal regulatory bodies through revised CLE rules, development of new program curricula, speaker training, and proactive educational initiatives. | False | False | NaN | Persistent deficits in lawyers' technological competence due to generally ineffective CLE systems, insufficient mandates and quality standards for technology training, and regulatory environments that may not adequately promote proactive technology education and adoption. | Challenges to implementing the proposed CLE improvements include overcoming lawyer resistance to attending non-mandatory or non-ethics focused training, transitioning CLEs from passive lecture-based formats to active, hands-on learning experiences, achieving regulatory consensus for stronger technology CLE mandates, and ensuring CLE providers develop and deliver high-quality, practical programs. | Risks of lawyers lacking technological competence include providing substandard or unethical legal services, failing to protect client confidential information (cybersecurity), and inefficient practice. A minor risk identified with the proposed solution is non-lawyer CLE experts potentially 'selling from the podium' if programs are not carefully managed. |
| 51WStULRev299.pdf | HeinOnline | Navigating Artificial Intelligence Through a Products Liability Framework | This paper argues for applying California's product liability law, particularly strict liability, as a legal framework to address harms caused by artificial intelligence systems. It suggests this approach can adapt to evolving AI technology, ensure user safety, and proposes integrating a risk-based classification similar to European proposals. | True | Idealistic | False | 1.0 | Positive | Application of California's product liability law, integrated with elements of the European Commission's risk-based AI regulatory proposal, to address AI-related harms. | NaN | NaN | Rapidly evolving AI outpacing legislation; the 'black box' nature of AI making it difficult to understand, trace, and assign liability for harms; inherent susceptibility of AI to biases leading to discriminatory outcomes; complexity in determining accountability among numerous actors in the AI lifecycle. | Adopting California's product liability law (including strict liability for manufacturing, design, and warning defects) as a flexible framework for AI. Integrating a risk-based classification for AI systems to tailor legal scrutiny and potentially shift evidentiary burdens for high-risk AI. | Ensuring legal accountability and redress for individuals harmed by defective AI systems; consumer protection against risks posed by AI. | General public and consumers of AI products and services. | Products Liability Law, Tort Law, AI Regulation. | California, United States (primary focus); European Union (for comparative regulatory proposals). | NaN | NaN | NaN | False | False | NaN | The principal gap identified is the absence of an established legal framework for AI liability. Even with the proposed solution, a remaining gap involves the need for judicial development of case law to apply product liability principles to the novel complexities of AI, including defining 'defects' and adapting defenses in the AI context. | NaN | Physical harm from malfunctioning AI (e.g., autonomous vehicles); economic or social harm from biased algorithmic decision-making in areas like finance, housing, and employment; spread of misinformation by generative AI; lack of transparency and traceability ('black box' problem) leading to unexplainable and potentially harmful AI behavior. |
| 2024RussJEconL804.pdf | HeinOnline | Legal education and artificial intelligence: vectors of interaction | This paper explores the integration of AI into legal education, detailing potential benefits like personalized learning and significant risks such as algorithmic bias and ethical dilemmas. It advocates for comprehensive reforms in legal curricula and training for legal professionals to navigate the challenges and opportunities AI presents in the legal field. | False | Market | True | 3.0 | Neutral | NaN | NaN | NaN | AI perpetuating societal biases leading to unfair outcomes; Lack of AI transparency hindering accountability and due process; AI errors ("hallucinations") causing misinformation in legal contexts; Ethical violations (data privacy, misuse of AI) eroding trust; Unequal access to AI literacy and AI-powered legal tools, widening the justice gap. | Educating legal professionals on AI ethics, fairness, and transparency; Developing critical thinking and human-centered skills in lawyers to oversee and correct AI; Integrating interdisciplinary approaches in legal education to understand AI's societal impact, including on access to justice; Reforming legal curricula to include AI governance and its impact on access to justice; Promoting human control over AI and upholding principles of fairness and transparency. | Fairness and non-discrimination in AI-driven legal processes; Transparency and accountability of AI in legal decision-making; Ethical use of AI in law and its impact on human rights; The role of legal education in addressing AI's impact on access to justice; Mitigating AI bias. | NaN | Legal Education, General Law, Legal Practice | Russia | NaN | NaN | NaN | False | False | NaN | Need for better understanding and mitigation of AI bias and "hallucinations"; Development of robust ethical and legal frameworks for AI in law and education; Improved AI literacy among legal professionals and educators; More research into the pedagogical implications of AI in legal education; Addressing the "black box" problem for AI transparency; The pace of AI development outstripping legal and educational adaptation. | NaN | Over-reliance on AI leading to diminished critical thinking; Amplification of existing societal biases (e.g., racial) through biased training data; AI "hallucinations" generating false or misleading information; Lack of transparency ("black box" problem) in AI decision-making processes; Ethical concerns regarding student data: privacy, consent, and confidentiality with AI tools; Increased risk of AI-assisted plagiarism among students; Potential displacement of legal professionals and changes in traditional legal skills due to automation; Psychological and social challenges ("future shock") from rapid technological adoption. |
| 21BerkeleyBusLJ469.pdf | HeinOnline | Why Lawyers Must Responsibly Embrace Generative AI | This paper advocates for the legal profession's responsible adoption of Generative AI (GenAI), detailing its potential to transform legal practice by enhancing efficiency and possibly improving access to justice. It addresses common objections, outlines ethical duties, and proposes best practices for integrating GenAI while mitigating associated risks. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | High cost of legal representation and advice, rendering the judicial process inaccessible to a substantial portion of the population, particularly for civil legal problems. | Widespread adoption of GenAI to revolutionize legal service delivery, enabling more providers to offer affordable legal services and thus narrow the access-to-justice gap. | Affordability of legal services, access to legal help for civil legal problems, increasing availability of legal information (e.g., case law). | Low-income individuals. | General legal practice | United States | NaN | NaN | NaN | False | False | NaN | The existing access-to-justice gap due to high costs; need for more freely accessible case law; the skills gap among legal professionals needing upskilling for GenAI; ongoing technical limitations of GenAI regarding accuracy and bias; need for developed policies and ethical frameworks. | Managing risks associated with GenAI (confidentiality, ethics, accuracy, bias); ensuring technological competence and providing adequate training for legal professionals; developing and implementing appropriate governance policies; overcoming skepticism within the legal industry; ensuring compliance with existing and evolving laws. | Unauthorized disclosure of confidential information and client data; generation of inaccurate information or 'hallucinations'; perpetuation of biases and discrimination; intellectual property infringement; violations of employment, privacy, and data protection laws; reputational damage from data breaches; security vulnerabilities including social engineering attacks; competitive disadvantage if GenAI is not adopted responsibly. |
| 92UMKCLRev859.pdf | HeinOnline | The Lawyer's Duty of Competence in a Climate-Imperiled World | This paper argues that the lawyer's existing professional duty of competence, as articulated in rules like ABA Model Rule 1.1, is evolving to necessarily include understanding and advising clients on the risks and opportunities presented by climate change. It explores how climate change impacts various legal practice areas and emphasizes the need for lawyers to develop climate competence and leadership skills to effectively serve their clients and the public interest. | True | Market | False | 2.0 | NaN | The lawyer's duty of professional competence (e.g., ABA Model Rule 1.1) and its evolving application to climate change. | NaN | NaN | Lack of legal assistance for individuals and communities displaced by climate-fueled storms or fires; high costs of adaptation and disaster recovery for public coffers; disproportionate impacts on poor communities and people of color who have less ability to adapt. | Lawyers engaging in pro bono activities to aid efforts to reduce greenhouse gas emissions and adapt to climate change; climate justice litigation grounded on civil rights or human rights laws to address disproportionate impacts; lawyers developing systems leadership skills. | Climate-induced displacement; disaster recovery; climate justice; disproportionate impacts of climate change on vulnerable populations; ensuring a 'just transition'. | Individuals and communities displaced by climate events; poor communities; people of color; communities affected by unjust transition pathways. | General legal practice, with specific examples across various fields including environmental, energy, corporate, finance, real estate, insurance, tax, torts, property, contract, estate planning, immigration, and civil rights/human rights law. | Primarily United States (referencing ABA Model Rules, US legislation, US case law, and US government reports), with significant mention of England and Wales (Law Society guidance). | NaN | NaN | NaN | True | True | The duty of competence is an existing professional obligation for lawyers, and model ethical rules (like ABA Model Rule 1.1) and legal guidance (like the Law Society's) are generally publicly available. | Insufficient legal support for climate-affected vulnerable populations and ensuring a universally just transition; need for broader adoption of climate competence and systems leadership skills within the legal profession. | Keeping abreast of rapidly evolving climate science, climate-related laws and regulations, new technologies (including AI), and integrating this knowledge into legal advice across diverse practice areas. Understanding and applying systems leadership skills. Overcoming ingrained practices and lack of awareness. | For lawyers: professional discipline, malpractice liability for failing to advise on climate risks. For clients: financial losses from unmitigated climate risks (physical, transition, liability), missed opportunities, reputational damage (e.g., greenwashing), regulatory non-compliance, uninsurability. Broader risks: exacerbation of climate change impacts, obstruction of a just transition, impairment of national security, societal disruption. |
| 28LegalWritingJLegalWriti.pdf | HeinOnline | BRACING FOR IMPACT: REVISING LEGAL WRITING ASSESSMENTS AHEAD OF THE COLLISION OF GENERATIVE Al AND THE NEXTGEN BAR EXAM | This paper argues that legal writing assessments must be revised in response to generative AI (GenAI) and the upcoming NextGen bar exam. It proposes diverse assessment strategies to ensure students develop critical legal skills independently and are prepared for evolving professional demands. | True | NaN | True | 3.0 | NaN | Generative AI (GenAI) chatbots (e.g., ChatGPT) | The paper cites studies that evaluated GenAI using law school exams (multiple-choice and essay), standardized tests (LSAT, UBE), and tasks like drafting legal documents. AI detection tools were tested for accuracy in distinguishing human vs. AI text. | The paper reports GenAI (e.g., GPT-4) can achieve high scores on legal exams (e.g., 90th percentile on UBE) and pass law school exams (e.g., C+ average for ChatGPT 3.5). Reports AI detectors are unreliable and often fail. | NaN | NaN | NaN | NaN | Legal Research and Writing education | United States | For ChatGPT: Open-source internet data (e.g., 570GB from the internet, 300 billion words for pre-training). For emerging legal GenAI tools (e.g., CoCounsel, Lexis+ AI): Proprietary legal-specific databases. | For GenAI/ChatGPT: Neural network architecture (transformers/LLMs), pre-training on large internet datasets, and fine-tuning with human reviewers (reinforcement learning from human feedback - RLHF). | ChatGPT: Publicly available via website (free and paid tiers), with enterprise versions and plugins. Legal-specific GenAI tools (e.g., CoCounsel, Lexis+ AI): Commercial deployment through legal tech companies, integration into existing platforms. | True | False | ChatGPT (GPT-3.5) is available for free use via its website after sign-up. Paid versions (GPT-4) and enterprise versions also exist. | NaN | Educators face challenges in: accurately assessing student skills when GenAI can produce work, the failure of AI detection tools, preventing student over-reliance on GenAI, and adapting teaching methods to new technologies and bar exam requirements. | Students over-relying on GenAI, leading to skill deficits and failure in exams/practice; invalid assessment of student abilities by professors; unreliability and bias of AI detection tools; GenAI producing incorrect or fabricated information (hallucinations); ethical issues related to GenAI use in legal practice (confidentiality, bias). |
| 4KDULJ21.pdf | HeinOnline | Evaluating the Use of Artificial Intelligence for an Effective Justice System in Sri Lanka | This paper evaluates the potential of Artificial Intelligence (AI), including tools like ChatGPT and chatbots, to improve Sri Lanka's legal system and enhance access to justice. It discusses AI's applications, benefits, and challenges, proposing steps and considerations for its ethical and effective implementation. | True | Idealistic | True | 2.0 | Positive | Application of AI technologies (chatbots like NALA, ChatGPT, robotics) for enhancing the Sri Lankan justice system. | Doctrinal research and qualitative analysis of existing literature, legal acts, academic writings, and online sources to evaluate AI's potential and challenges in Sri Lanka's legal context. | AI offers significant potential to improve efficiency, reduce costs, and enhance access to justice in Sri Lanka by automating tasks and providing legal assistance. However, successful implementation hinges on addressing substantial challenges including data scarcity, risk of bias, need for ethical guidelines, infrastructure development, and capacity building among legal professionals. | Case backlogs and delays in justice; language barriers; high costs of legal services; lack of digital literacy among the population; insufficient legal data for AI development; potential for AI bias perpetuating systemic issues; resistance to change from legal professionals; inadequate technological infrastructure and resources. | Strategic implementation of AI for legal tasks (research, case management, translation); establishing strong data governance and ethical AI frameworks; investing in capacity building and training for legal professionals; fostering international collaboration for best practices and resources; developing a national legal data infrastructure; ensuring AI systems are transparent, auditable, and fair. | Addressing case backlogs and court delays; providing legal information and counsel to underserved populations; overcoming language barriers in legal services; improving efficiency and reducing costs of legal services. | Underserved areas, underprivileged populations lacking financial means for conventional legal services, and non-native speakers in Sri Lanka. | General law / Justice system | Sri Lanka | NaN | NaN | NaN | False | False | NaN | Absence of sufficient and complete legal data for training AI; lack of transparency in AI tool operations; need for robust data governance, cybersecurity, and ethical/legal standards for AI in law; insufficient technological infrastructure and digital literacy; need for legal reforms to address AI-related issues; potential for AI to magnify social injustice if not carefully managed. | Technological infrastructure limitations; language barriers; low digital literacy; high cost and accessibility of AI technology; potential for algorithmic bias; scarcity of comprehensive legal data; resistance to new technologies within the legal sector; ensuring data privacy, security, transparency, and accountability of AI systems; managing job displacement. | AI systems perpetuating or amplifying existing societal biases (e.g., as seen with COMPAS); unauthorized access to sensitive legal data and data breaches; generation of incorrect or fake legal information by AI (e.g., ChatGPT citing fake cases); potential displacement of jobs in the legal sector; over-reliance on AI diminishing human judgment in sensitive legal matters; AI lacking human ethical values. |
| 24YaleJLTech150.pdf | HeinOnline | Access to A.I. Justice: Avoiding an Inequitable Two-Tiered System of Legal Services | This paper argues that AI can help close the access to justice gap but warns against creating an inequitable two-tiered system of legal services. It proposes a framework for calibrating AI use and advocates for regulatory reforms, such as regulatory sandboxes, to facilitate equitable AI access and foster collaboration. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | High cost of legal services; Lack of consumer knowledge, experience, or resources; Language barriers for immigrants; Systemic barriers beyond court access; Risk of inequitable two-tiered AI system; Perpetuation of status quo; Resource, resilience, and relationship barriers to AI calibration. | Regulatory reforms including clearer UPL definitions and flexible firm ownership; Increased competition and collaboration; AI calibration framework (considering consumer, issue, process); Regulatory sandboxes/laboratories; Transparency in AI (e.g., accuracy rates, certifications). | Closing the justice gap for low/middle-income individuals; Equitable AI deployment in legal services; Self-help legal tools; Improving efficiency/affordability of legal services; Legal information access. | Low-income individuals; Middle-income individuals; Individuals with limited English proficiency/immigrants; Self-represented litigants; Rural communities; Small businesses and non-profits; Marginalized communities (e.g., heirs' property). | Civil legal aid; Family law; Transactional law; Litigation; Property law; Criminal law (briefly for risks); General legal problem solving. | United States (with specific state examples like Utah, Arizona). | NaN | NaN | NaN | False | False | NaN | Technical AI underperformance and limitations; Societal/Regulatory issues like the digital/algorithmic divide, cultural incompetence in design, barriers to AI calibration (resource, resilience, relationship), regulatory uncertainty, conservative legal culture, lack of AI transparency, and AI market consolidation. | NaN | Creation of an inequitable two-tiered legal services system; Widening the justice gap; Inferior or harmful AI-driven assistance; Devaluation of traditional legal aid efforts; Bias in AI leading to discriminatory outcomes; Lack of AI transparency; Exclusion of communities due to digital/data divides; Ethical issues (e.g., UPL, chatbot client intake); AI consolidation harming innovation. |
| 42YaleLPolyRev107.pdf | HeinOnline | Second-Wave DREAMers | This paper contrasts two waves of child migrants in the U.S., "first-wave" and "second-wave" DREAMers, focusing on their differing experiences with public schools under Plyler v. Doe. It argues for a modernized approach by schools, shifting from assimilation, formal equality, and innocence to inclusion, equitable education, and collective responsibility to better serve today's immigrant students. | True | Idealistic | False | 3.0 | NaN | NaN | NaN | NaN | Limited effectiveness of assimilationist and formally equal school approaches for immigrant children, especially for traumatized "second-wave DREAMers" facing immediate immigration enforcement and economic pressures; pervasive lack of legal status and difficulties accessing legal representation. | Schools should adopt a modernized reading of Plyler, shifting from assimilation to inclusion, from formal equality to equitable education (e.g., tailored programs, specialized services, community partnerships), and from an innocence narrative to collective responsibility (e.g., addressing legal needs via on-site services like school-based legal clinics). | Access to education for immigrant children (K-12 and higher education), social integration of immigrant youth, rights of undocumented and asylum-seeking children, impact of immigration enforcement on children, implementation and modernization of Plyler v. Doe. | Immigrant children and youth in the U.S., specifically "first-wave DREAMers" (who arrived between 1986-2007) and "second-wave DREAMers" (who arrived since 2014, often from Central America as unaccompanied minors or in asylum-seeking families). | Immigration Law, Education Law, Constitutional Law (Equal Protection), Children's Rights. | United States | NaN | NaN | NaN | False | False | NaN | Lack of a pathway to citizenship for DREAMers and DACA recipients; insufficient tailored educational, socioemotional, and legal support systems for newcomer students nationwide; persistent problematic societal narratives (innocence/guilt) that hinder integration; need for greater societal acknowledgment of U.S. foreign policy's role in migration. | NaN | Risk of deportation, labor exploitation, psychological trauma from migration experiences and enforcement; social exclusion and marginalization; creation of a permanent underclass if educational and legal needs are unaddressed; loss of legal remedies (e.g., SIJS) due to aging out or lack of timely counsel. |
| 4ModLRsch32.pdf | HeinOnline | Research on Generative Artificial Intelligence Legal Profession Substitution | This paper empirically examines the application of generative AI in the legal field, analyzing its potential to enhance efficiency and promote social justice. It also discusses the risks, limitations, and ethical considerations, proposing that AI will complement rather than fully replace legal professionals and advocating for regulation through AI and legal professional ethics. | True | Idealistic | True | 3.0 | Neutral | Generative Artificial Intelligence (e.g., ChatGPT, GPT-4, Harvey, various Chinese large models like Iflytek Spark, Baidu Wen Xin Yi Yan) | NaN | NaN | Data security and privacy leakage; risk of unethical use; lack of controllability of legal decisions by AI; AI generating incorrect or fabricated information ('hallucinations'); algorithmic discrimination and bias; lack of trustworthiness and social acceptability of AI in law; non-interpretability of AI decisions; and lack of human sensitivity/empathy in AI-assisted legal processes. | Regulation of generative AI applications in the legal field from the dimensions of AI ethics and legal professional ethics. Emphasizing people-centered humanism, fairness and justice over utilitarianism, and constructing a robust regulatory framework and assessment mechanism for legal technology ethics. | Enhancing efficiency and quality of legal services, promoting (social) justice, reducing cost of legal services, professional substitution in the legal field. | General public / 'the people' | General legal profession (lawyers, judges, judicial support staff), judicial applications, legal services market, legal advice, legal content generation. | International (with specific examples and regulatory discussions concerning China, USA, UK, and EU) | Public information from the judicial domain (e.g., judicial decision documents network, social media, judiciary websites, lawyer databases) for user profiling and language model training; copyrighted works (as highlighted by lawsuits against OpenAI). | NaN | NaN | True | True | The paper lists several AI platforms (e.g., Iflytek Spark, Baidu Wen Xin Yi Yan, Chatlaw) with URLs and notes on client app availability, and discusses widely accessible tools like ChatGPT which have free tiers. Commercial tools like Harvey are mentioned as used by specific firms. | Ensuring accuracy and reliability of generated content; overcoming model 'hallucinations'; addressing algorithmic discrimination and bias; building trustworthiness and social acceptability of AI in law; improving interpretability of AI decisions; maintaining human sensitivity and empathy in legal processes; and establishing comprehensive ethical and regulatory frameworks for legal AI. | Managing security risks (data safety, privacy); preventing unethical use; addressing intellectual property infringement; ensuring controllability of AI in legal decision-making; mitigating 'hallucinations' and fictitious outputs; combating algorithmic discrimination and bias; building trust in AI systems; dealing with the 'black box' nature (non-interpretability) of some AI; and preserving humanistic elements in the legal profession. | Data and personal privacy leakage from training on judicial data; unethical use of AI; intellectual property infringement by AI models trained on copyrighted works; lack of controllability of legal decisions made or assisted by AI; generation of incorrect or wholly fabricated information ('hallucinations'); algorithmic discrimination and bias leading to unfair outcomes; security threats; model illusion; environmental/social and regulatory risks; third-party risks. |
| 25DukeLTechRev183.pdf | HeinOnline | Determinants of Socially Responsible AI Governance | This paper identifies justice, equity, and the rule of law as key determinants for socially responsible AI governance, aiming to ensure AI promotes fairness rather than exacerbating inequalities. It explores AI's impact on access to justice, discusses inherent biases, compares global governance models (US, EU, China, Singapore), and proposes a proactive regulatory framework. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Exacerbation of existing inequalities for marginalized communities due to AI tools; algorithmic hallucinations and compromised due process; structural biases from biased data and coding practices; limited transparency and accountability due to trade secrets; digital literacy gaps leading to algorithmic exclusion. | Proactive governance framework incorporating transparency, equity audits, and tailored regulatory approaches; 'equity by design' principles in AI development; inclusive AI tools (e.g., multilingual, simplified UX) and community-based AI literacy programs; hybrid AI-human models in legal decision-making; systemic changes encouraging diversity in the AI field and embedding ethical considerations. | Access to legal information and services for unrepresented or underrepresented litigants; support in eviction proceedings; bridging language barriers in legal contexts; addressing unmet civil legal needs of low-income individuals; combating predatory debt collection practices; ensuring due process in AI-assisted legal systems. | Marginalized communities, including low-income individuals, ethnic minorities, Indigenous groups, unskilled immigrants, senior citizens, people with limited English proficiency, tenants facing eviction, and debtors. | Civil justice (access to justice, eviction, debt collection), criminal justice (sentencing, due process), administrative law (public services), intellectual property (trade secrets, copyright impact on training data), constitutional law (due process, human rights). | US (federal and state including California, Texas, Pennsylvania), EU, China, Singapore, International (referencing international AI treaties and comparative governance). | The paper discusses general issues of AI training data, such as large datasets from online sources, case law, and copyrighted materials (images, texts, audio), highlighting the risk of biases stemming from unrepresentative, historically biased, or incomplete data. It does not propose or rely on a specific dataset for a new technique. | Proposes an 'equity by design' framework; discusses prompt engineering to reduce bias; advocates for continuous equity auditing, embedding ethical principles from the outset, and the involvement of diverse, interdisciplinary teams (ethicists, sociologists, legal experts) in AI development and oversight. | NaN | False | False | NaN | Need for international standards and harmonized regulations for AI in legal contexts; addressing the digital divide to ensure equitable access to AI benefits; improving AI literacy among legal professionals and the general public; ongoing monitoring for algorithmic failures, data biases, and implementation shortcomings. | NaN | Algorithmic bias leading to or perpetuating discrimination (racial, gender, etc.); exacerbation of existing societal inequalities; lack of transparency and accountability from 'black box' algorithms and expansive trade secret protections; AI 'hallucinations' generating false or misleading legal information; violations of due process and human rights through opaque or flawed AI decision-making; spread of disinformation and deepfakes undermining trust and rule of law; privacy infringements; threats to national security; potential for labor displacement. |
| 2023MichStLRev377.pdf | HeinOnline | WHO WATCHES THE WATCHMEN? USING THE LAW GOVERNING LAWYERS TO IDENTIFY THE APPLICANT DUTY GAP AND HOLD BAR EXAMINER GATEKEEPERS ACCOUNTABLE | This paper identifies an ethical "duty gap" where bar applicants face high ethical burdens during licensure while bar examiners, the NCBE, and bar prep companies owe them no reciprocal duties, a problem exacerbated during the COVID-19 pandemic. It calls for reforms such as increased oversight, transparency, and applying professional conduct rules to bar examiners, and considers alternative licensure paths. | True | Idealistic | False | 3.0 | NaN | NaN | NaN | NaN | Lack of reciprocal ethical duties owed by bar examiners, NCBE, and bar prep companies to applicants; Opaque and rigid lawyer licensing procedures; Lack of transparency in bar examiner operations, funding, and governance; Immunity of boards of law examiners from challenges; Potential bias in the Uniform Bar Examination (UBE); Power imbalance between applicants and licensing bodies, stifling criticism; Financial and emotional burdens on applicants. | Adding reciprocal duties for bar examiners to Model Rule 8.1; Formal adoption of a "Code of Recommended Standards for Bar Examiners" with enhanced transparency and accountability; Implementing alternative paths to licensure (e.g., diploma privilege, experiential learning, supervised practice); Increased transparency in bar examiner operations (e.g., public annual reports); Greater oversight by the legal profession including committees on cooperation involving law schools, judiciary, and the bar. | Fairness and ethical treatment in the lawyer licensing process; Accountability and transparency of bar admission authorities; Diversity and inclusion in the legal profession (as affected by licensure); Reforming bar examination and admission standards. | Bar applicants (recent law school graduates). The paper also implies concern for groups disproportionately affected by current licensing practices, such as racial minorities and women. | Legal ethics, Professional responsibility, Legal education, Administrative law (as it relates to bar examiners) | United States (referencing ABA Model Rules, NCBE, and various state bar examiners) | NaN | NaN | NaN | False | False | NaN | The "ethical duty gap": lack of owed duties from examiners to applicants; Lack of meaningful oversight and accountability for bar examiners and the NCBE; Insufficient transparency in bar admission processes; Need for more valid and non-discriminatory methods for assessing lawyer competence; Failure of the profession's self-regulation to extend to the licensure process for new entrants. | NaN | Continued demoralization and harm to future lawyers; Negative reflection on the legal profession due to unfair treatment of applicants; Erosion of trust in the licensing process and the legal profession; Suppression of diversity in the legal profession; Maintaining a licensure system that may not accurately measure competence; Potential for retaliation against applicants who criticize the system. |
| 25DukeLTechRev1.pdf | HeinOnline | TRIBES AND AI: POSSIBILITIES FOR TRIBAL SOVEREIGNTY | The paper explores how AI can enhance tribal sovereignty across various sectors like legal systems, healthcare, education, cultural preservation, economic development, and administrative capacity. It argues that AI can help tribes overcome historical challenges and improve self-governance, despite potential risks and obstacles such as inadequate Internet infrastructure. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Limited tribal sovereignty and external skepticism towards tribal institutions; systemic underfunding and economic disadvantages (e.g., dual taxation); excessive federal bureaucracy constraining self-governance; inadequate infrastructure (especially internet) and workforce challenges in remote areas; historical injustices and cultural erosion impacting tribes. | Employing AI to enhance tribal institutional capacity (legal, healthcare, education, administration) and assert sovereignty; using AI to foster economic development and fiscal independence; leveraging AI to overcome bureaucratic inefficiencies and improve service delivery; utilizing AI to bridge infrastructure and personnel gaps, thereby improving access to services; applying AI for cultural and language preservation and supporting the assertion of Indigenous data sovereignty. | Enhancing tribal self-governance and sovereignty; improving tribal legal systems and access to justice (court efficiency, legal aid, code promulgation); improving access to and quality of healthcare and education; cultural and language preservation; economic development and fiscal independence; overcoming bureaucratic hurdles. | Federally recognized Indian tribes in the United States. | Federal Indian Law, Tribal Law, Administrative Law, Civil and Criminal Justice, Healthcare Law, Education Law, Tax Law, Corporate Law. | United States | NaN | NaN | NaN | False | False | NaN | Lack of Internet infrastructure in Indian country; high cost of AI development, hardware, and implementation; need for skilled personnel (e.g., data scientists) to operate and manage AI systems; the necessity for robust AI regulatory frameworks, including measures to ensure Indigenous data sovereignty; effectively addressing AI biases and the potential for hallucinations that could be detrimental to tribal history and culture. | NaN | AI hallucination propagating false historical narratives or exacerbating racial stereotypes about tribes; AI discrimination due to biased training data or algorithms; violations of data privacy if Indigenous data sovereignty is not respected; potential for job displacement in certain sectors; high implementation and maintenance costs acting as a barrier for financially constrained tribes. |
| 7Issue2IntlJLMgmtHuman92.pdf | HeinOnline | Artificial Intelligence: An Analysis in the Legal Field | This paper analyzes the role of Artificial Intelligence in the Indian legal sector, outlining its applications such as case law analysis and drafting, alongside challenges like cost and bias. Based on an empirical study with 106 participants, it highlights low public awareness of AI in law and advocates for increased education and responsible adoption. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Low awareness of AI's potential in the legal field among the general public and legal professionals; financial backwardness and illiteracy hindering AI adoption; difficulty for laymen to use AI; cost of AI tools. | Increase awareness of Artificial Intelligence in the legal field for students, legal professionals, and common people; government to ensure AI is legalised to a certain extent. | Legal information and guidance for laypersons, general awareness of legal tech, potential for AI in judicial processes, overcoming financial barriers to legal assistance. | Laymen who cannot afford legal services, common people, individuals with financial backwardness and illiteracy. | General legal field | India | NaN | NaN | NaN | False | False | NaN | Significant lack of awareness about AI in the legal field among both the general public and legal professionals in India. Financial and literacy barriers prevent widespread AI adoption. Difficulty for laypersons to effectively use existing AI tools for legal help. AI outputs may not always be accurate and can be biased. | Cost-effectiveness of AI tools (e.g., premium access for bots), potential for inaccurate or biased outputs from AI, over-dependence on AI leading to skill degradation in legal students and professionals. Authors' study limitations include small, geographically restricted sample size. | Over-reliance on AI diminishing legal skills among students and professionals; AI producing biased or inaccurate legal outputs leading to malpractice or misconduct; AI lacking human sympathy and situational understanding if used to replace human judges. |
| 13JIndianLSocy83.pdf | HeinOnline | ChatGPT: A New Era in Legal Research and Its Sustainable Impact on Judicial Decision Making | This paper analyzes the use of ChatGPT for legal research and its potential impact on judicial decision-making, with a particular focus on the Indian legal system. It highlights the tool's capabilities while strongly cautioning against over-reliance due to issues like inaccuracy and bias, emphasizing the need for human intervention and advocating for a 'right to contestability'. | True | Idealistic | True | 2.0 | Neutral | ChatGPT, a large language model by OpenAI. | Analysis of ChatGPT's responses to specific legal prompts posed by the authors, and review of two case studies where judges in India and Colombia used ChatGPT to assist in decision-making. | Authors' prompts to ChatGPT revealed its knowledge cutoff (September 2021), generation of general answers, self-acknowledged lack of legal liability for inaccuracies, and the necessity of human verification. Judicial use cases showed ChatGPT being used as an auxiliary tool, not a primary decision-maker, and an external instance highlighted ChatGPT fabricating case citations. | Inherent biases in AI, lack of transparency and accountability in AI systems, potential for errors and inaccuracies, the digital divide (especially in India), risk of 'automation bias' influencing human decision-makers, and the difficulty for AI to handle nuanced 'equitable justice' considerations. | Emphasizing consistent human intervention and oversight in the use of AI, establishing a 'right to contestability' for AI-driven decisions, developing comprehensive data protection laws and AI governance frameworks, and promoting transparency and explainability in AI systems. | Legal research assistance, aid in judicial decision-making (particularly bail matters), democratization of legal services, and addressing the access to justice crisis. | Individuals and small businesses (referred to as 'people law'), and the general Indian populace, particularly considering the existing digital divide. | Criminal Law (specifically bail jurisprudence), Contract Law, Medical Rights, and General Legal Practice. | India, with comparative references to Colombia, USA, Estonia, China, the European Union (GDPR, AI Act), and the United Kingdom. | ChatGPT is described as being trained on a large database of text including news articles, legal documents, case law, and academic literature. For Indian bail orders specifically, its training data would include relevant Indian statutes, court decisions, and legal literature, with a stated knowledge cutoff of September 2021 for the version tested. | ChatGPT, the discussed tool, was developed by OpenAI using both supervised and reinforcement learning techniques. | ChatGPT is available online as a chatbot provided by OpenAI. | True | True | ChatGPT is available online, with a 'Free Research Preview' version mentioned in the paper. | Limited and outdated knowledge base of the AI (e.g., ChatGPT's September 2021 cutoff), inability to access comprehensive and current legal information, potential for inherent biases, the AI's incapacity to deliver 'equitable justice' due to a lack of nuanced understanding, and the absence of robust regulatory frameworks for AI in the legal domain. Societally, the digital divide remains a significant barrier. | Ensuring the accuracy and reliability of information generated by ChatGPT, overcoming its data limitations (knowledge cut-off, comprehensiveness of legal sources), addressing the AI's lack of genuine legal expertise and nuanced contextual understanding, and the constant need for human verification of its outputs. | Generation of inaccurate, incomplete, or fabricated information (e.g., non-existent case law), perpetuation of existing societal and data-driven biases, 'automation bias' unduly influencing judicial or legal professionals' judgment, lack of legal accountability for errors made by the AI tool, and potential undermining of due process and equitable justice if human oversight is insufficient. |
| 27SMUSciTechLRev275.pdf | HeinOnline | Generative Al IN THE ATTORNEY-CLIENT RELATIONSHIP: AN EXERCISE IN CRITICAL REVISION AND CLIENT MANAGEMENT | This paper proposes a pedagogical exercise for law students using AI-generated legal motions to develop critical revision skills and client management strategies. The exercises aim to prepare students for scenarios where clients misuse generative AI, requiring attorneys to identify errors in AI output and diplomatically guide clients. | True | Market | True | 1.0 | Positive | A pedagogical exercise template for law students involving the critical revision of AI-generated legal documents (e.g., motions generated by ChatGPT) and developing client communication strategies. | NaN | NaN | Laypersons' overconfidence in flawed AI-generated legal documents; unreliability of AI (e.g., hallucinations, inaccuracies) for unguided legal use by those without legal expertise; potential for AI to generate incorrect or misleading legal information. | Development of proper guardrails for AI tools and basic education for lay users on AI's capabilities and limitations in legal contexts; preparing lawyers to critically evaluate AI output and guide clients. | Potential for AI in supporting self-representation through legal document generation; access to legal information for laypersons. | Laypeople without ready access to attorneys; underserved communities and litigants. | Criminal procedure (primary examples); general applicability implied for tort, contract, property law. | United States (examples from federal law and California state law). | The exercises use output from ChatGPT-3; the paper does not detail ChatGPT-3's specific training data, only that it's a large language model. The exercises are designed to be adaptable with new AI outputs. | Scenario-based learning; creation of hypothetical legal scenarios and client profiles; use of prompts to generate legal documents from an existing LLM (ChatGPT-3) for student critique and role-playing exercises. | Proposed for adoption in law school curricula by legal educators. | True | True | The paper fully describes the pedagogical exercise, including example scenarios, prompts, and AI outputs, allowing educators to replicate or adapt it. ChatGPT-3, used for generating example materials, has a free accessible version. | Need for better understanding of how generative AI can be advantageously and safely used by underserved communities; development of effective guardrails against misuse; addressing the unreliability and potential for errors in AI-generated legal information intended for laypersons. | For the pedagogical technique: Variability in LLM output affecting exercise standardization; ensuring students develop deep critical skills to identify subtle 'confident mistakes' in AI output beyond surface-level error correction; effectively teaching nuanced and diplomatic client communication strategies. | Attorneys uncritically relying on generative AI, leading to filing flawed legal documents with incorrect or hallucinated citations; damage to legal professionals' credibility and potential for sanctions; clients misusing AI to draft substandard legal documents; laypersons relying on unsafe AI-generated legal advice or documents, particularly those without access to legal counsel; AI misstating law or omitting critical legal precedents. |
| 34AlbLJSciTech1.pdf | HeinOnline | THE NEW KID ON THE BLOCK -THE USE OF ARTIFICIAL INTELLIGENCE IN ALTERNATIVE DISPUTE RESOLUTION | This paper discusses the growing role of Artificial Intelligence (AI) in Alternative Dispute Resolution (ADR), particularly in mediation and online dispute resolution (ODR). It explores AI's benefits, such as increased efficiency and data-driven insights, alongside cautions like ethical concerns, potential biases, and the limitations of AI in tasks requiring human emotional intelligence. | True | Idealistic | True | 3.0 | Neutral | AI in Alternative Dispute Resolution (ADR), particularly AI-assisted mediation and Online Dispute Resolution (ODR), including tools like ChatGPT, Modria, Smartsettle, Cybersettle, Kleros, and the adjusted winner procedure. | NaN | NaN | High cost and delays in traditional litigation and court systems; potential impersonality of online dispute resolution; risk of algorithmic bias in AI perpetuating societal inequities; privacy and data security vulnerabilities in AI systems. | Wider adoption of Alternative Dispute Resolution (ADR) and Online Dispute Resolution (ODR) to improve efficiency and accessibility; leveraging AI to enhance ADR/ODR processes; emphasizing ethical AI development, robust data governance, and maintaining human oversight, particularly for tasks requiring emotional intelligence and complex judgment. | Reducing court backlogs; making dispute resolution more affordable and faster; resolving family law disputes (e.g., parenting plans, asset division); handling small claims cases. | General public involved in disputes, particularly in family law and small claims, and potentially those who find traditional litigation costly or slow. | Alternative Dispute Resolution (Mediation, Arbitration), Family Law, Contract Law, Small Claims, Tort Law (contextually), Criminal Law (AI examples mentioned). | United States (including specific states like Idaho and California, and federal bodies), with some international examples (England, Estonia, China, eBay global operations). | For ChatGPT: Books, journals, articles, and general web content. For other AI/ADR systems: Prior case data, user-submitted dispute information, legal documents, parties' preferences and submitted evidence. Some systems also employ rule-based logic. | NaN | Platform adoption by legal institutions (courts, arbitration associations), mandatory ODR programs in some jurisdictions, commercial offerings by tech companies, and public availability of some tools (e.g., ChatGPT). | True | False | Several AI-ADR tools and platforms (e.g., ChatGPT, Modria, Cybersettle, Smartsettle, Kleros, Tyler's ODR) are described as operational and in use, offered commercially, by institutions, or publicly (like ChatGPT's free tier). | Need for AI systems with improved emotional intelligence emulation or effective human-AI teaming models; ensuring fairness, unbiasedness, and robust privacy/security in AI-ADR systems; enhancing trust and acceptance of AI tools within the legal profession; training for legal professionals to use AI effectively. | NaN | AI systems perpetuating errors from flawed training data; lack of emotional intelligence in AI leading to inappropriate responses in sensitive situations; violation of privacy and disclosure of confidential information through AI data handling; propagation of discriminatory practices due to algorithmic bias; over-reliance on AI potentially diminishing critical human skills in mediation. |
| 18RomArbJ42.pdf | HeinOnline | THE INTERACTION BETWEEN AL (ARTIFICIAL INTELLIGENCE) AND IA (INTERNATIONAL ARBITRATION): TECHNOLOGY AS THE NEW PARTNER OF ARBITRATION | This paper explores the integration of Artificial Intelligence (AI) into International Arbitration (IA), discussing current applications like case management and legal research, and future possibilities including robot arbitrators and ChatGPT. It also addresses the associated benefits such as increased efficiency, alongside significant risks including bias, ethical dilemmas, and cybersecurity challenges, emphasizing the need for careful regulation and continued human oversight. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | High cost of legal services and representation for individuals with limited financial resources. | Utilizing AI-powered tools to provide affordable legal information, assistance, and potentially representation for those who cannot afford traditional legal services. | Affordable legal representation, access to legal information for low-income individuals. | Individuals with limited financial resources, parties unable to afford legal counsel. | International Arbitration | International | NaN | NaN | NaN | True | True | The paper discusses generally available tools like ChatGPT (which has a free tier) and commercial platforms (e.g., Jus Mundi) that utilize AI and are currently accessible to users. | Ensuring the reliability, accuracy, fairness, and ethical application of AI tools for access to justice, particularly concerning biased outputs, the 'black box' nature of AI, and the need for human oversight for vulnerable populations. | NaN | Bias in AI systems (from data or algorithms), cybersecurity threats (hacking, data breaches), generation of incorrect, misleading, or fabricated information (e.g., by ChatGPT), ethical concerns (manipulation, unethical legal tactics, ghostwriting awards, lack of human empathy), issues with data privacy and GDPR compliance, and the 'black box' nature of AI limiting transparency and explainability. |
| 90GeoWashLRev83.pdf | HeinOnline | Contracts in the Age of Smart Readers | This paper explores "smart readers," AI tools based on language models like GPT-3, which can simplify, personalize, interpret, and benchmark contracts, potentially improving consumer understanding and market competition. It also analyzes significant risks including errors, adversarial attacks by firms, discrimination, and the need for legal and doctrinal adaptations to this emerging technology. | True | Idealistic | True | 3.0 | Neutral | Smart readers (AI language models for contract analysis, e.g., GPT-3 for simplification/personalization/construction, and tools like PrivacyCheck for benchmarking). | Illustrative examples generated by GPT-3, acknowledged by authors as 'cherry-picked'. The paper also describes the functionality of PrivacyCheck, an existing tool for ranking privacy policies, as an example. | GPT-3 examples demonstrated capabilities such as simplification of complex legal language, personalization of contractual presentation, and construction of term meaning. PrivacyCheck was cited as a tool that scores privacy policies and compares them to competitors. | Information barriers (complexity and length of contracts, cognitive load), lack of consumer understanding of contractual terms, high cost of legal services, potential for contractual bias and discrimination, digital divide limiting access to technology. | Employing "smart readers" to simplify, personalize, interpret, and benchmark contracts; increasing term transparency to empower consumers and potentially foster market competition; providing on-demand "know-your-rights" services to improve access to legal understanding. | Understanding contract terms, identifying unfair or one-sided clauses, comparing contracts, enhancing consumer comprehension of legal agreements, addressing information asymmetry in consumer contracting. | Consumers in general, with a particular focus on vulnerable consumers such as low-income individuals, recent immigrants, and young people who may struggle with complex legal texts. | Contract law, Consumer law, Privacy law. | United States (primary examples and legal framework discussed, e.g., US cases, FTC, Draft Restatement of Consumer Contracts), with general applicability often implied for consumer contracts. | For GPT-3 (a key example model): Trained on a large corpus of text including Wikipedia ("45TB of compressed plaintext"). For PrivacyCheck: Built on machine learning algorithms; specific training data not detailed in this paper. | NaN | For PrivacyCheck: Described as a browser extension. For GPT-3: Accessible via API or publicly available interfaces (e.g., AI Dungeon for some exmaples). | True | True | PrivacyCheck is available as a free browser extension. GPT-3 examples were generated via publicly accessible interfaces or API. | Digital inclusion disparities limiting access to smart readers, difficulty in detecting and proving adversarial attacks and algorithmic bias, defining relevant comparison groups for benchmarking increasingly personalized contracts, ensuring smart reader accuracy and reliability, potential for regressive cross-subsidies if firms discriminate based on smart reader use, effective regulation for emerging risks. | Ensuring accuracy and reliability of smart readers, managing errors (isolated, correlated), preventing and detecting sophisticated adversarial attacks by firms, addressing potential for bias and discrimination in smart reader outputs or arising from their usage patterns, achieving widespread and equitable consumer uptake, developing appropriate legal and regulatory frameworks. | Exploitation by sophisticated parties through adversarial attacks, inscrutability of black-box models leading to unaccountable errors, exacerbation of contractual bias and discrimination (e.g., firms offering worse terms to non-users of smart readers), premature relaxation of consumer protection measures by policymakers, consumer overcompliance with unenforceable terms due to simplified explanations, harms from misinterpretation of contract terms. |
| 44PaceLRev91.pdf | HeinOnline | Large Language Models: AI's Legal Revolution | This paper advocates for the integration of Large Language Models (LLMs) into legal practice, discussing their history, current market offerings (differentiating between general and legal-specific LLMs), and potential benefits like increased efficiency. It argues for understanding, accepting, and regulating LLMs within academia, private practice, and the U.S. court system, rather than banning them, to foster better and ethical legal services. | True | Market | True | 2.0 | NaN | Existing general-purpose LLMs (e.g., ChatGPT, Bing Chat, Bard) and legal-specific LLMs (e.g., CoCounsel, Lexis+ AI) for application in legal practice. | The paper evaluates LLMs based on their described features, training data, known limitations (e.g., hallucinations, privacy issues for non-legal LLMs), and specific capabilities reported by developers or in public discourse (e.g., GPT-4 passing the bar exam). It does not conduct original empirical testing. | Legal-specific LLMs (e.g., CoCounsel, Lexis+ AI) are identified as more suitable for professional legal use due to tailored training on legal data, features to mitigate hallucinations, and enhanced data privacy, compared to general-purpose LLMs. GPT-4 demonstrated capability by passing the bar exam. | NaN | NaN | NaN | NaN | General legal practice, legal academia, judiciary. | United States | Non-legal LLMs (e.g., ChatGPT, Bing Chat, Bard) are trained on vast general internet text (web pages, books, articles). Legal LLMs (e.g., CoCounsel, Lexis+ AI) are trained on proprietary, curated legal databases (caselaw, statutes, legal documents) and may leverage general LLM capabilities. | General LLMs: Deep learning on large text corpora, transformer architectures (implied). Legal LLMs: Fine-tuning on curated legal data, integration with existing legal databases, specific design controls to reduce hallucinations (e.g., limiting answers to known, reliable data sources) and ensure data privacy (e.g., private servers, encryption, zero-retention APIs). | General LLMs like ChatGPT, Bing Chat, and Bard are available via public web interfaces, often with free access tiers. Legal-specific LLMs like CoCounsel and Lexis+ AI are commercial SaaS products integrated into proprietary legal tech platforms. | True | False | General LLMs like ChatGPT (GPT-3.5 version free, GPT-4 subscription), Bing Chat, and Bard are accessible via public web interfaces. Legal LLMs like CoCounsel and Lexis+ AI are available as commercial products from their respective vendors. | NaN | General challenges mentioned relate to LLM adoption and use: lack of understanding by the legal profession, potential for hallucinations and data privacy issues with non-legal LLMs, need for appropriate regulation, and ensuring ethical use. | Risks of LLMs fabricating information (hallucinations), breaching client confidentiality (especially with non-legal LLMs), and the legal profession's unpreparedness leading to misuse or misguided regulation. |
| 58WakeForestLRev981.pdf | HeinOnline | FORMING GOOD LAWYERS | This paper argues for the necessity of intentional professional identity formation in legal education, specifically advocating for a character-based approach. It posits that cultivating virtues such as honesty, open-mindedness, civility, resilience, and practical wisdom can help lawyers navigate modern challenges like technological disruption, public distrust, and mental health issues, thereby forming better legal professionals. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | Public distrust of the legal profession; lawyers' poor mental health and well-being; over-specialization neglecting broader public interest; challenges of diversification without shared values; technological disruption (including AI) changing the nature of legal work; dominance of the 'neutral partisan' model of lawyering potentially undermining broader ethical duties; a compliance-based, minimalist approach to ethics. | Implementing a character-based approach to professional identity formation in law schools to cultivate key virtues (e.g., honesty, open-mindedness, civility, resilience, practical wisdom). This involves an intentional exploration of values and guiding principles to elevate the human elements of lawyering and foster a more holistic ethical development. | Legal ethics and professional responsibility; Role of lawyers in society; Public trust in the legal system; Reform of legal education. | Diverse populations (mentioned as beneficiaries of a more diverse legal profession which can increase their access to justice). | Legal Profession/Ethics, Legal Education | USA | NaN | NaN | NaN | False | False | NaN | Lack of systematic and intentional focus on character development and holistic professional identity formation within current legal education. | Concerns that character education is paternalistic/moralistic; perceived lack of time in curriculum for non-traditional content; skepticism about the feasibility of teaching character to adults; potential neglect of structural issues by focusing on individual character; difficulty in measuring character growth. | Continued public distrust of lawyers; high rates of mental health issues in the profession; inability of lawyers to articulate their value in an AI-driven world; AI tools like ChatGPT 'hallucinating' and providing incorrect information; ethical lapses due to a minimalist approach to ethics; misuse of 'character' assessments for discriminatory purposes if not implemented carefully. |
| 132YaleLJ.pdf | HeinOnline | Statutory Structure | This paper analyzes the Supreme Court's use of 'statutory structure' in statutory interpretation, categorizing types of structural arguments (compositional, operational, purposive). It evaluates these arguments against dominant interpretive methodologies, suggesting structuralism reveals an enduring need for purposive reasoning. | True | NaN | False | 2.0 | NaN | Structural argument in statutory interpretation, categorized into: Compositional Structuralism (Location, Geometry, Aperture), Operational Structuralism (Operational Compatibility, Operational Coherence), and Purposive Structuralism. | Analysis of U.S. Supreme Court case law and legal scholarship. | The paper's analysis categorizes structural arguments and concludes that while all dominant interpretive methods use them, purposive reasoning (particularly legal-process rationalism) best aligns with structuralism's underlying assumptions of coherence, highlighting an enduring need for purposive interpretation. | NaN | NaN | NaN | NaN | Statutory interpretation (general). Case examples span civil rights law, environmental law, criminal law, immigration law, and administrative law. | United States (primarily U.S. Supreme Court). | NaN | NaN | NaN | False | False | NaN | NaN | Reconciling structural argument with tenets of specific interpretive theories (e.g., textualism); the impact of 'unorthodox lawmaking' on assumptions of coherent legislative drafting; ambiguity in defining and applying 'coherence' or choosing between incompatible provisions. | Potential for manipulation of structural arguments (especially operational and purposive types); misattribution of drafting intent (e.g., relying on U.S. Code placement); structural arguments being used to unduly constrain statutory meaning or mask judicial policy preferences. |
| 17ContempAsiaArbJ91.pdf | HeinOnline | The Human Impact on Arbitration in the Emerging Era of Artificial Intelligence | This paper examines the benefits and risks of AI in arbitration, arguing that while AI can enhance efficiency, essential human qualities like complex reasoning and ethical judgment remain irreplaceable, especially in complex disputes. It concludes that AI should augment human capabilities rather than replace key human roles, emphasizing the need for human oversight and ethical guidelines in its application. | True | Market | False | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Arbitration | International, with examples from various jurisdictions (e.g., OECD, EU, US, UK, Australia, China, Taiwan). | NaN | NaN | NaN | True | True | The paper states that various AI tools are in use, such as commercial legal research platforms (e.g., Westlaw Edge, Harvey AI) and openly released AI models (e.g., Meta's SeamlessM4T for translation). | NaN | Practical and ethical challenges in using AI in arbitration, including AI's lack of cognitive/emotional capabilities, potential for bias, 'hallucinations' (generating false information), lack of reasoning transparency ('black box' issue), risk of improper delegation of decision-making functions, and privacy/confidentiality concerns. | Bias in AI leading to unfair outcomes, AI 'hallucinations' generating false legal or factual assertions, lack of transparency in AI decision-making ('black box' issue), ethical violations by counsel or arbitrators due to improper AI use (e.g., misrepresentation of law, delegation of decision-making), and breaches of data privacy and confidentiality when using third-party AI tools. |
| 15IJCA1 (1).pdf | HeinOnline | From Court Automation to e-Justice and beyond in Europe | This paper reviews the 25-year evolution of Information and Communication Technology (ICT) in European judiciaries, from early court automation to current e-Justice initiatives and the emerging role of Artificial Intelligence (AI). It discusses key projects, lessons learned, the impact of the COVID-19 pandemic, and future trends, emphasizing user-centric design and the protection of fundamental rights. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Digital divide/lack of resources for technology access; opacity and potential bias in AI; risk of AI infringing on fundamental rights; systemic rigidity in justice systems hindering adoption of effective ICT. | User-centric and accessible design of ICT/AI systems; development and adherence to robust ethical and legal frameworks (e.g., EU AI Act, CEPEJ Charter); training and capacity building for legal professionals and users; maintaining Hhman oversight, especially in AI decision-making; simplification of ICT to avoid complexity; concerted efforts to bridge the digital divide. | Ensuring equal access to justice for all, including self-represented litigants and vulnerable populations; bridging the digital divide; Online Dispute Resolution (ODR) as a means of access; improving accessibility of legal information and case law; protecting fundamental rights (e.g., fair trial, equality of arms) in the context of digitalized justice and AI. | Self-represented litigants; vulnerable populations; persons with disabilities; individuals affected by the digital divide. | Civil Law, Criminal Law, General Court Administration | Europe, European Union and member states | NaN | NaN | NaN | False | False | NaN | Lack of empirical assessment of ICT effectiveness in courts; need for consistent legislation and practical frameworks for EU-level justice tools; ensuring fair trial rights in remote hearings; modernizing outdated court IT systems; improving data collection for evidence-based policymaking; insufficient information sharing and coordination among EU judiciaries on ICT projects; challenges in regulating rapidly evolving AI while protecting rights and fostering innovation. | NaN | AI leading to discrimination or biased outcomes; opacity of AI systems hindering justified decisions and equality of arms; infringement of fundamental rights (including data protection and fair trial) by ICT/AI; misuse of predictive justice tools or analytics on judicial performance; technology creating new barriers to access if not designed inclusively; over-reliance on vendors, potentially threatening judicial independence. |
| 16JLegalAnalysis64.pdf | HeinOnline | Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models | This paper systematically investigates "hallucinations" (factually incorrect outputs) in prominent large language models (LLMs) concerning American case law. It finds high hallucination rates (e.g., 58% for ChatGPT-4) that vary with case complexity, jurisdiction, and model, while also noting LLMs' poor self-awareness of errors and susceptibility to users' incorrect legal assumptions. | True | Idealistic | True | 2.0 | Negative | OpenAI's ChatGPT 4, OpenAI's ChatGPT 3.5, Google's PaLM 2, and Meta's Llama 2. | Evaluation using 14 legal knowledge queries of varying complexity on a stratified random sample of 5,000 cases from each level of the US federal judiciary (SCOTUS, USCOA, USDC). Reference-based queries used ground-truth metadata for simpler tasks. Reference-free queries for complex tasks detected hallucinations by identifying logical contradictions between two stochastic answers from an LLM, with GPT-4 used to assess these contradictions. Both zero-shot and three-shot prompting were tested. | ChatGPT 4, the best-performing model, still hallucinated 58% of the time on average across reference-based tasks. For the high-complexity reference-free task 'Central holding', ChatGPT 4 hallucinated at least 63% of the time (lower bound). Hallucination rates generally increased with task complexity and were higher for lower courts and less prominent cases. | High rates of LLM hallucination (generating false legal information); uneven LLM knowledge favoring prominent cases/jurisdictions; LLMs' susceptibility to contra-factual bias (accepting users' incorrect legal assumptions); poor LLM calibration (overconfidence in false statements). These issues particularly risk harming pro se or under-resourced litigants and potentially widening justice gaps. | The paper primarily diagnoses problems, cautioning against unsupervised integration of LLMs. It mentions potential mitigation research (RAG, advanced prompting, fine-tuning, external database checks) but notes their limitations. It advocates for human-centered AI, transparency from developers about hallucination trade-offs, and releases a dataset for future benchmarking. | Reliability and accuracy of legal information provided by LLMs for litigants; potential for LLMs to exacerbate or reduce disparities in legal representation; suitability of current LLMs for providing legal advice/assistance. | Pro se litigants and under-resourced litigants. | American case law (federal courts). | United States (federal courts: SCOTUS, USCOA, USDC). | The LLMs studied are pre-trained on general, vast corpora including public domain American case law; the specific composition of which is proprietary to the model developers. The evaluation dataset used by the authors was constructed from Caselaw Access Project, Supreme Court Database, Appeals Courts Database Project, Library of Congress, and Shepard's Citations. | Development of a typology of legal hallucinations; creation of a set of 14 legal knowledge queries; stratified random sampling for test dataset construction; reference-based querying against ground-truth metadata; reference-free querying detecting self-contradiction (assessed by GPT-4); experimental setups including zero-shot and three-shot prompting; analysis of contra-factual bias and model calibration (Expected Calibration Error). | The LLMs studied are deployed by their respective companies: OpenAI (ChatGPT via API), Google (PaLM 2 via API), and Meta (Llama 2 as an open model). The authors deployed their evaluation dataset and replication materials on Harvard Dataverse and HuggingFace. | True | True | OpenAI's ChatGPT 4/3.5 and Google's PaLM 2 are available via commercial APIs. Meta's Llama 2 is an openly available model. The paper's evaluation dataset and replication materials are also openly available on Harvard Dataverse and HuggingFace. | Technical: Need for LLMs with significantly lower legal hallucination rates, improved calibration, robustness against contra-factual premises, and better knowledge of localized/less prominent law; effective and safe integration of mitigation techniques. Societal: Ensuring LLMs do not exacerbate legal service inequalities; developing user education; addressing ethical implications of deploying LLMs prone to error for vulnerable populations; establishing normative choices for minimizing different hallucination types. | For the evaluation methodology: Cost and difficulty of constructing ground-truth oracles for complex legal queries; limitations of reference-based queries (restricted to metadata) and reference-free queries (providing only lower bounds on hallucination). For LLMs generally: Inherent inevitability of some hallucinations; balancing fidelity to prompt, training data, and real-world facts; computational demands of mitigation techniques. | Providing nonsensical, harmful, or inaccurate legal advice/decisions; sanctions for lawyers using LLM-generated fictions; worsening disparities in access to legal services for vulnerable litigants; perpetuating representational harms through biased knowledge; creating algorithmic "monoculture" with a homogenous view of law; misleading users with overconfident, incorrect responses; dehumanizing the law. |
| 14JChristianLegalThought8.pdf | HeinOnline | The Multifaceted Impact of Generative AI on Lawyers and Legal Services | This paper explores the transformative potential of Generative AI (Gen AI) on the legal sector, detailing its effects on law firm business models, the redefinition of lawyer roles, and the acceleration of lawyer professional development. It also examines Gen AI's capacity to enhance access to justice and discusses the associated spiritual and ethical implications from a Christian perspective. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | High cost and general inaccessibility of legal services for most people; decaying legal system infrastructure and public lack of legal knowledge; legal profession's historical aversion to scaling services through technology; current Gen AI's insufficient dependability for reliable legal use. | Leveraging Gen AI to create scalable, accessible (affordable and convenient), and dependable legal information and solutions; ensuring Gen AI legal systems are developed ethically, particularly for disadvantaged groups, with informed consent and appropriate compensation; advocating for efforts to ensure AI-driven justice solutions remain widely and equitably available. | Affordability and availability of legal services; scalable provision of legal information and guidance; overcoming systemic barriers to justice. | The general public unable to afford traditional legal services, particularly disadvantaged and marginalized populations. | NaN | NaN | NaN | NaN | NaN | False | False | NaN | Current deficiency in Gen AI's dependability as a legal resource; the need to ensure Gen AI legal systems are developed to be suitable for all, especially vulnerable populations, rather than as experiments; challenge of ensuring widespread and equitable ongoing access to effective AI-driven justice solutions; absence of a fully developed Christian ethical framework for addressing issues like biased or unethically sourced AI training data. | NaN | Gen AI models being trained on data acquired without consent or through illegal means, such as copyright infringement or data theft; the potential for Gen AI legal solutions to be developed through unethical 'experimentation' on disadvantaged or marginalized individuals without their fully informed consent or due compensation; the possibility that advanced AI justice tools might become exclusive to a select few rather than remaining broadly accessible. |
| 13Laws1 (1).pdf | HeinOnline | AI Accountability in Judicial Proceedings: An Actor-Network Approach | This paper analyzes the impact of AI systems on accountability in judicial proceedings using an actor-network theory (ANT) framework, focusing on speech-to-text, legal analytics, and predictive justice technologies. It concludes that non-accountable AI poses risks to judicial values like fair trial if human control over outputs is lacking, and current legal remedies, including the EU AI Act, may be insufficient in such cases. | True | Idealistic | False | 2.0 | Neutral | Analysis of three types of AI applications used by judges: speech-to-text systems, legal analytics (e.g., Smart Sentencing project in Germany), and predictive justice systems (e.g., COMPAS in the US and RisCanvi in Spain). | Conceptual analysis using Actor-Network Theory (ANT) to explore how introducing non-accountable AI influences actor-network configuration and accountability distribution. The analysis is based on literature review and information about existing AI systems, assessed against the EU legal framework and the EU AI Act. | Non-accountable AI can be used without endangering judicial values if judges can control the system's outputs by evaluating its correspondence with inputs. When this is not met (e.g., complex legal analytics or predictive justice), remedies from the EU AI Act are insufficient, and judges become solely accountable for non-accountable systems, risking undue influence on decision-making and fair trial principles. | Lack of AI accountability, transparency, and explainability, leading to potential undue influence on judicial decision-making and threats to the fair trial principle, judicial independence, and equal treatment of justice seekers. | Ensuring human users (judges) can control and verify AI system outputs. For complex, non-transparent systems, reliance on regulatory frameworks like the EU AI Act for ex-ante and ongoing assessments, though the paper questions their full sufficiency. The paper highlights that accountability for non-accountable AI ultimately shifts to the human user. | AI accountability in judicial proceedings, Fair trial principle, Judicial independence, Algorithmic transparency, Judicial decision-making. | NaN | Judicial proceedings generally, Criminal justice (specifically sentencing and recidivism risk assessment). | EU (European Union legal framework and AI Act), Germany (Smart Sentencing example), Spain (RisCanvi example), US (COMPAS example). | For Smart Sentencing: A large corpus of German court judgments, initially classified by researchers for supervised machine learning. For RisCanvi: Internal data on approximately 600 inmates (initially), later expanded to 15,000 assessments; uses administrative, legal, criminological records, and interviews. For COMPAS: Criminal history data and data from close-ended questionnaires answered by offenders or probation officers. | For Smart Sentencing: Supervised machine learning, data coding and classification by researchers. For COMPAS and RisCanvi: Statistical methods (correlations, regression), criminological studies, expert commission recommendations, psychometric analysis (RisCanvi). | Speech-to-text: Widely available commercial applications used in courts. Smart Sentencing: A research project with potential for use. RisCanvi: Implemented across Catalan prisons since 2009. COMPAS: Used in various US jurisdictions for risk assessment. | False | False | NaN | Persistent lack of AI accountability and explainability ('Al remains essentially non-accountable'). Insufficiency of current legal frameworks (e.g., EU AI Act) to fully mitigate risks when human control over AI outputs is not possible. The difficulty in ensuring judicial values are upheld with opaque AI systems. | For developers/deployers of AI systems discussed: Incomplete or inconsistently structured data for training (Smart Sentencing). Opacity of algorithms even when not strictly AI (COMPAS, RisCanvi). Ensuring systems are free from bias. Difficulty for users (judges, professionals) to understand, verify, or override system outputs (RisCanvi). | Undue influence on judicial decision-making. Endangerment of the fair trial principle, judicial independence, and equal treatment of justice seekers. Introduction of biases through AI systems. Lack of transparency and accountability leading to unfair or erroneous outcomes. |
| 19JLAdminSci20.pdf | HeinOnline | ETHICAL AND LEGAL ASPECTS OF THE DEVELOPMENT AND USE OF ROBOTICS AND ARTIFICIAL INTELLIGENCE. PROTECTION OF HUMAN RIGHTS IN THE ERA OF GLOBALIZATION AND DIGITISATION | This paper provides a broad overview of the ethical and legal issues arising from the rapid development of AI and robotics, emphasizing the critical need for regulatory frameworks to protect human rights. It reviews international and EU legislative efforts, particularly the EU AI Act, analyzes AI's impact on various sectors including justice and employment, and discusses Romania's national strategy for responsible AI adoption. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Infringement on fundamental human rights (privacy, non-discrimination, dignity) by AI systems, lack of legal certainty and accountability for AI actions, societal distrust due to ethical concerns (e.g., biased outputs, opacity), and the risk of manipulative or harmful AI applications. | Development of comprehensive legal and ethical frameworks (e.g., the EU AI Act), promotion of human-centered and trustworthy AI, ensuring transparency, safety, non-discrimination, and human oversight in AI systems, fostering international cooperation on AI governance, and investing in AI literacy and education. | Use of AI in judicial systems (e.g., crime prevention, online dispute resolution, decision support), ensuring fair trial rights with AI, digital rights and principles, data protection, addressing AI-induced discrimination, and the ethical implications of AI for fundamental freedoms. | General public, human rights for all citizens, with specific attention to vulnerable categories such as children, people with disabilities, the elderly, and other disadvantaged or at-risk groups. | Human Rights Law, Data Protection Law, Civil Liability, AI-specific regulation (e.g., EU AI Act), Constitutional Law, Criminal Law, Administrative Law, Labour Law, Consumer Protection Law. | International (e.g., OECD, UNESCO), European Union (EU), and national levels (examples include Romania, USA, China, Japan, Canada, India, and various EU member states). | NaN | NaN | NaN | False | False | NaN | Keeping legal/ethical frameworks current with rapid AI evolution, achieving global consensus on AI governance, effectively translating ethical principles into technical practice, ensuring AI explainability (addressing the 'black box' problem), and building broad public trust in AI. | For the discussed regulatory approaches (like the EU AI Act): Legislating for a rapidly evolving technology, balancing innovation promotion with the need for safety and fundamental rights protection, achieving international regulatory coherence, Ccincretely defining and classifying AI risks, and ensuring effective oversight and enforcement of AI regulations. | Violation of fundamental rights (privacy, dignity, freedom, non-discrimination), manipulation of human behavior, perpetuation of societal biases, physical/mental/economic harm, erosion of human control, opacity in decision-making, spread of misinformation (deepfakes), job displacement, and establishment of surveillance systems. |
| 17ContempAsiaArbJ1.pdf | HeinOnline | WHAT'S REALLY WRONG WITH ISDS?-A CRITICAL ANALYSIS OF PHANTOM ISSUES AND REAL ISSUES TRIGGERED BY PRACTICE AND TECHNOLOGICAL DEVELOPMENT | This paper critically analyzes perceived ("phantom") and actual ("real") problems within Investor-State Dispute Settlement (ISDS), such as double-hatting, third-party funding challenges, case complexity, and arbitrator scarcity. It then explores potential solutions, including regulatory reforms, the use of Artificial Intelligence (AI), and an arbitrator team approach to address these issues. | True | Market | True | 3.0 | Positive | Artificial Intelligence (AI) in ISDS, including language models (e.g., ChatGPT) and AI legal assistants (e.g., Jus AI), alongside an 'arbitrator team approach'. | NaN | NaN | Prohibitively high costs of international arbitration for investors seeking to bring claims. | Third-Party Funding (TPF) to enable investors with meritorious claims but limited financial resources to access ISDS. For broader systemic issues impacting efficiency and fairness, the paper proposes the use of Artificial Intelligence (AI) and an 'arbitrator team approach'. | Access to Investor-State Dispute Settlement (ISDS) for financially constrained investors through mechanisms like Third-Party Funding. | Investors, particularly small or medium-sized companies, lacking sufficient funds to pursue ISDS proceedings. | International Investment Law, Investor-State Dispute Settlement (ISDS), International Arbitration | International | For AI tools like Jus AI, a proprietary global case law database. For general LLMs like ChatGPT, vast general and some proprietary internet text. The paper notes concerns that AI training data may overrepresent larger/powerful countries. | NaN | NaN | True | True | General purpose LLMs like ChatGPT, mentioned as examples, are widely accessible, including with free tiers. Specialized legal AI assistants like Jus AI are described as launched and available from Jus Mundi, likely on a commercial basis. | While TPF addresses cost barriers for investors' access, controversies surrounding TPF persist. For AI, which could improve overall system fairness: ethical concerns, potential for data bias to disadvantage parties (including those with lesser means), AI 'hallucinations,' data privacy issues, and the need for developed legal/policy frameworks for AI use in ISDS. | Ensuring AI accuracy and avoiding 'hallucinations'; addressing data bias in AI training; managing ethical concerns and data privacy; developing appropriate legal frameworks for AI use; adapting the role of arbitrators to include AI-skilled teams; overcoming skepticism towards AI. | AI producing incorrect information or 'hallucinations'; data bias in AI leading to unfair outcomes, especially for minorities or less-resourced parties; ethical concerns in AI-assisted legal decision-making; data privacy breaches; potential challenges to awards due to improper delegation of decision-making to AI. |
| 16IntlInHouseCounselJ (1).pdf | HeinOnline | Generative Artificial Intelligence: Legal Profession Disrupted? | This paper discusses the disruptive potential of generative AI in the legal profession, stressing that technology adoption should prioritize client needs and the administration of justice over mere efficiency. It highlights the high cost of AI tools, the importance of specialized legal AI, and showcases judiciary-led innovations as positive examples for harnessing technology responsibly. | True | Idealistic | True | 3.0 | Neutral | Generative AI, Large Language Models (e.g., ChatGPT), and specialized legal AI platforms (e.g., Casetext, Spellbook, Luminance Autopilot, LawGeex). | The paper cites evaluations of LLMs passing professional exams (e.g., Bar exam by ChatGPT) and a comparative study of LawGeex AI vs. human lawyers for NDA review (5 NDAs reviewed). | The paper cites a LawGeex demonstration where AI reviewed five Non-Disclosure Agreements in 26 seconds, compared to an average of 92 minutes for lawyer participants. | High cost of AI tools; technology adoption driven by 'solutionism' or commercial interests rather than client/public needs; risk of over-reliance on AI diminishing human judgment; challenges in creating AI that genuinely meets user needs leading to low adoption (e.g., ODR); lack of trust in AI outputs without verification. | Prioritizing client and public interests (paramountcy of consumer needs) in technology adoption; judiciary-led innovations focusing on proportionate justice, therapeutic justice, and safety for vulnerable parties; favoring specialized and verified legal AI tools over generic ones; critical evaluation of whether AI is the best or most cost-effective solution. | Online Dispute Resolution (ODR), proportionate justice, therapeutic justice in family law, safety of vulnerable parties in family law (child abuse/family violence), contract review, legal research. | Litigants in general, consumers of legal services, families undergoing dissolution, children and vulnerable parties affected by abuse and family violence. | General legal practice, contract law, family law, dispute resolution, litigation. | International, with specific examples and discussions related to Singapore, Australia, USA, UK, Canada, France, and Europe. | The paper implies that generic LLMs (like ChatGPT) are trained on vast internet data. Specialized legal AIs are mentioned as using more limited, curated data sources such as 'the White Book, the National Archives case law database, BAILLI, Westlaw, and Lexis Nexis.' | NaN | Widespread public availability for tools like ChatGPT; commercial licensing for specialized legal software (e.g., Microsoft 365 Copilot, LexisNexis tools); implementation within court systems for judiciary-led innovations. | True | True | Public availability of tools like ChatGPT (including a free tier) and commercial availability of specialized legal AI tools and platforms (e.g., LexisNexis AI, Microsoft Copilot, Casetext, Spellbook). | Ensuring trustworthiness and verifiability of LLM outputs for legal work; need for specialized legal AI that is more reliable than generic models; aligning AI development with genuine needs of justice and clients rather than just 'solutionism' or commercial interests; addressing low adoption rates of ODR by better understanding user needs. | High cost of generative AI-enabled tools; avoiding 'solutionism' (adopting technology for its own sake); ensuring security of technology; training and support required for new technologies; differentiating law firm services when all use similar AI tools. | Over-reliance on AI leading to diminished human judgment and critical thinking; inaccuracy of AI outputs, particularly from unspecialized models; technology dominating rather than assisting justice; potential for system failures to jeopardize dignity and due process; loss of human contact in legal processes. |
| 54CalWIntlLJ415.pdf | HeinOnline | Governing Artificial Intelligence Responsibility in Low to Middle Income Countries: Enabling Pathways to Sustainable Development | This paper examines the challenges Low- and Middle-income Countries (LMICs) face in governing Artificial Intelligence (AI) to foster sustainable development, highlighting digital divides and the concentration of AI infrastructure in the Global North. It proposes a structured, context-sensitive governance approach for LMICs, emphasizing principles like transparency, accountability, multi-stakeholder participation, and iterative methodologies to build trust and enable responsible AI innovation. | True | Idealistic | False | 1.0 | Positive | A structured, context-sensitive AI governance framework for LMICs, incorporating principles of good regulatory practice (transparency, accountability, participation, inclusion), multi-stakeholder collaboration, iterative and flexible methodologies (e.g., co-regulation, regulatory sandboxes), and fit-for-purpose institutional design. | The proposed framework is based on the authors' analytical and operational experience at the World Bank; the paper does not present a formal empirical evaluation of the proposed framework itself. | NaN | Digital infrastructure gaps (connectivity, electricity), lack of quality and locally relevant data sets, limited digital literacy, insufficient financial resources, limited technical and regulatory capacity in LMICs, the global AI divide with AI development and infrastructure concentrated in the Global North, and the risk of AI exacerbating existing inequalities. | Developing context-sensitive AI governance frameworks tailored to LMIC needs and values; adopting 'greenfield' regulatory approaches where legacy systems are absent; implementing good regulatory practices including transparency, accountability, participation (TAP), and inclusion; fostering multi-stakeholder, iterative, and flexible governance models; utilizing tools like co-regulation and regulatory sandboxes; and establishing fit-for-purpose institutional arrangements that account for local constraints. | AI governance for sustainable development, protection of human rights in the context of AI, equitable AI deployment in key sectors (e.g., education, healthcare, agriculture), fostering trust in AI systems, mitigating AI-related risks for vulnerable populations, and addressing the digital and AI divides. | Low- and Middle-Income Countries (LMICs), particularly vulnerable populations and underserved communities within them. | AI governance, data protection law, human rights law, regulatory law, digital economy law, international law (public and private aspects related to technology governance). | Low- and Middle-Income Countries (LMICs) globally, with specific examples and regional discussions concerning Africa, Asia-Pacific, Middle East, and Latin America and the Caribbean. | NaN | NaN | NaN | False | False | NaN | Lack of established 'good practice models' for AI governance applicable to LMICs; persistent digital divide (infrastructure, data, literacy, capacity); insufficient and fragmented legal/regulatory frameworks for AI in many LMICs; difficulties in enforcing individual and collective rights, especially against powerful global tech companies; need for greater multi-stakeholder collaboration and public participation in policy-making in some LMICs; and significant resource and expertise constraints for effective oversight and implementation of AI governance. | NaN | AI-induced bias and discrimination (e.g., in credit scoring, recruitment, public services), lack of transparency and accountability in AI systems, manipulation of beliefs and emotions by generative AI leading to psychological harm, spread of disinformation influencing political opinions, exacerbation of existing inequalities and digital divides, potential for AI to facilitate criminal activities, erosion of public trust due to irresponsible AI deployment, and unintended negative impacts from poorly designed regulatory interventions (e.g., on competition or consumer protection). |
| 34AlbLJSciTech27.pdf | HeinOnline | ON ADULT A.I. INTERACTIONS WITH ARTIFICIAL INTELLIGENCE IN THE SHADOWS OF REGULATION, ANTITRUST, AND FAMILY LAW. | This paper discusses the legal implications of increasingly capable AI, using a conversation with ChatGPT about antitrust and regulation as a foundation. It proposes an evolutionary approach to AI legal status and liability, analogous to human development, to address challenges like market power, algorithmic misbehavior, and the need for responsible AI governance. | True | Market | True | 3.0 | Positive | ChatGPT (as an example of Large Generative AI Models) | Author's exploratory conversation with ChatGPT on economic regulation and antitrust issues. | ChatGPT provided plausible and, in some instances, novel-sounding insights on antitrust and regulation, such as specific relevant market definitions ('AI-powered customer service', 'conversational AI') and suggestions for regulatory measures including open standards and data protection. | Concentration of market power by tech companies in AI-driven businesses; AI-driven anticompetitive practices harming consumers and fair competition; proprietary standards by dominant AI companies hindering open and competitive ecosystems; illegal collection of personal data by AI violating privacy rights; opacity and potential bias in AI decision-making challenging fairness and accountability; lack of clear legal frameworks for AI liability, making it hard to assign responsibility for harms. | Implementing regulatory measures (merger control, competition enforcement, open standards, data protection) to curb market power and ensure fair competition in AI markets; adopting an evolutionary approach to AI legal status and liability, mirroring human development; promoting human oversight in AI-driven regulatory processes; fostering international cooperation for trustworthy AI and shared principles for AI education and governance; designing AI for auditability and transparency. | Ensuring fair competition and preventing market dominance in AI-driven sectors; establishing responsible AI governance and legal frameworks for accountability; protecting consumer rights (e.g., privacy) in interactions with AI. | NaN | Antitrust Law, Economic Regulation, Family Law (as an analogy), AI Law/Technology Law, Corporate Law | USA, European Union, Italy (specific instance). Broader discussion has international implications. | Large, multi-modal datasets of text inputs for training LLMs like ChatGPT, often proprietary and web-derived. Specifics for ChatGPT, as noted by the paper, are not fully public. | Based on Large Language Models (LLMs), artificial neural networks, deep learning, and attention mechanisms. Fine-tuned with human supervision (Reinforcement Learning from Human Feedback implied). | Public online availability of ChatGPT by OpenAI; integration into commercial products like Microsoft's search engine. | True | False | ChatGPT is accessible online through OpenAI's platform, with free and paid tiers. | Lack of global governance for AI development, especially for high-risk AI; ongoing challenges in ensuring AI compliance with privacy and data ownership; need for criteria to measure AI's educational progression for assigning liability; absence of universally accepted AI auditability standards or 'psychometrics for machines'; need for a robust theory of legal and moral personhood for AIs; uncertainty on managing AI if it develops preferences divergent from human values. | Preventing 'hallucinations' or inaccurate outputs; ensuring responses are not harmful (racist, sexist); managing the 'black box' nature of decision-making; addressing privacy/bias risks from large-scale training data; balancing proprietary interests with calls for openness; predicting and mitigating risky emergent behaviors; high energy consumption. | AI threatening human freedom (job displacement, malicious use); AI-driven anti-competitive practices; bias in AI decisions from training data; opacity in AI-driven decision-making; 'artificial neurological illnesses' like hallucinations; illegal collection and misuse of personal data by AIs; catastrophic consequences from unsupervised development of powerful AI (e.g., military AI); emergence of 'power-seeking' and 'agentic' behaviors in AI; AI developing preferences misaligned with human values. |
| 14UCIrvineLRev404.pdf | HeinOnline | The Epistemic Injustice of Algorithmic Family Policing | This paper critiques risk-prediction algorithms in the U.S. 'family policing' system, arguing they automate and deepen 'epistemic injustice' against targeted families, especially poor and Black communities. The author advocates for abolishing this system in favor of community-based supports that value the knowledge of impacted individuals. | True | Idealistic | False | 3.0 | Negative | Risk-prediction algorithms (e.g., Allegheny Family Screening Tool - AFST, Hot Spot Models) used in family policing. | AFST V1 was trained to predict re-referral or out-of-home placement; V2 predicts out-of-home placement. A 2022 study (Cheng et al.) evaluated AFST's impact on racial disparity in decisions compared to human workers. | Cheng et al. (2022) found the AFST alone would have made more racially disparate decisions than human workers, but workers were able to reduce this algorithmic disparity when using the tool. | Systemic epistemic injustice (discrediting parents' knowledge and experiences); conflation of poverty with neglect; racial and class bias in the system; the carceral nature of 'family policing' prioritizing punishment over support; algorithms scaling up and automating these harms; lack of due process and avenues for contestation against algorithmic decisions. | Abolition of the 'family policing' system; investment in community-based resources and supports that are not carceral; valuing and prioritizing the knowledge and experiences of impacted communities (achieving epistemic justice); reparations; adopting design justice principles if any tools are to be used. | Child welfare/family policing; family separation; parental rights; algorithmic bias and accountability; racial and economic justice in family regulation; epistemic injustice. | Poor families, Black families, mothers (especially Black mothers and mothers of color), families targeted by the child welfare/family policing system. | Family Law (child welfare, child protection, parental rights), Administrative Law, Constitutional Law (due process), Civil Rights. | United States (with specific examples from Allegheny County, PA and New York City, NY). | Administratively-held data from public systems such as family policing, criminal legal system, public benefits system, public health services (hospitals), and census data. For example, the AFST uses county jail records, juvenile probation data, public welfare information, behavioral health service records, and census data. This data is domain-specific, often unstructured or semi-structured, and proprietary to government agencies. | Actuarial risk assessment methods, machine learning, and artificial intelligence. Development involves defining outcome variables (e.g., re-referral to child protection services, out-of-home placement) and training predictive models on historical administrative data. | Algorithmic tools are incorporated into government agency (e.g., child protective services) decision-making workflows, such as at the call screening stage for reports of suspected child maltreatment (e.g., AFST operational since 2016). | False | False | NaN | Technical: Algorithmic bias, inaccuracy, opacity, problematic proxies for 'maltreatment,' inability of algorithms to capture human complexity. Societal: Pervasive epistemic injustice, failure to address structural causes of family struggles (e.g., poverty, racism), need for abolitionist approaches and community-led alternatives instead of carceral 'solutions,' lack of true support for families. | The paper identifies challenges inherent in these tools: defining inherently political and subjective terms (e.g., 'maltreatment', 'neglect') for algorithmic modeling; identifying outcome variables that genuinely map to child welfare rather than system actions; addressing and mitigating historical bias and systemic racism present in training data; ensuring transparency and accountability; preventing misuse and avoiding the de-skilling of human workers. | Automation and exacerbation of epistemic injustice (through algorithmic gaslighting, enhanced surveillance, unjust definition of epistemic authorities, carceral reception of information, and suppressing contestation); reinforcement of racial and class-based discrimination; violations of privacy; expansion of the carceral state through increased surveillance and intervention; direct harm to families, including unnecessary separation; lack of due process and democratic accountability. |
| 16ContempAsiaArbJ263.pdf | HeinOnline | HARNESSING ARTIFICIAL INTELLIGENCE IN INTERNATIONAL ARBITRATION PRACTICE | This article reviews the current and anticipated applications of artificial intelligence, including generative AI and LLMs, in international arbitration practice. It discusses various tools, use cases, potential benefits, ethical challenges, and the need for guidelines to govern AI's use in this field. | True | Market | True | 3.0 | Positive (for increased accessibility in commercial arbitration for smaller claims) | Generative AI (e.g., LLMs like ChatGPT-4) for tasks like research, drafting, note-taking, and generating counter-arguments. Also discusses various other AI tools for document review (e-discovery platforms), Online Dispute Resolution (ODR), machine translation, conflict management/arbitrator due diligence, and data analytics for third-party funding. | The paper cites existing evaluations, such as ChatGPT-4 passing the New York Bar Exam (Katz et al.) and its performance in a mock arbitration hearing (CIArb Brazil Branch experiment). | ChatGPT-4 was capable of passing the New York Bar Exam. In the mock arbitration hearing, ChatGPT-4's performance was considered commendable but highlighted limitations such as 'hallucinating' fabricated cases and lacking personal commitment to the case. | High cost and complexity of traditional arbitration, making it inaccessible for low-value disputes or smaller parties. | AI-powered tools can reduce costs, automate tasks (e.g., document review, legal research, drafting), and enable new models like DIY online arbitration platforms for low-value commercial disputes, making arbitration more accessible. | Accessibility of dispute resolution, cost-effectiveness of arbitration, online dispute resolution for commercial claims, automation of legal tasks in arbitration. | Parties with low-value commercial disputes, impecunious or impatient parties, medium/boutique legal practitioners, and independent arbitration lawyers. | International Arbitration | International | For general LLMs like ChatGPT-4, it is large-scale, diverse text and code datasets from the public internet. For specialized in-house chatbots discussed as a future trend, it would be proprietary internal data from law firms (e.g., documents, case files, legal precedents). | NaN | NaN | True | False | The paper discusses many commercially available AI tools and platforms (e.g., ChatGPT, DeepL, Relativity, LexisNexis, Westlaw, Jus Mundi), some of which have free access tiers or versions. | The full realization of AI for enhanced access in arbitration requires further development of ethical guidelines, regulations, and continued technological advancement to ensure fairness, transparency, and justice in AI application. | Challenges discussed include effective prompt engineering for LLMs, ensuring data privacy and confidentiality when using AI tools, verifying accuracy and avoiding 'hallucinations' from Generative AI, addressing potential biases in AI algorithms, developing appropriate ethical guidelines and regulations for AI use in arbitration, and the need for constant human oversight. | Misuse of AI leading to sanctions (e.g., citing non-existent case law), factual inaccuracies in AI-generated content ('hallucinations'), breaches of confidentiality when inputting sensitive data into AI tools, undermining the integrity of evidence or proceedings, perpetuating biases (e.g., in arbitrator selection if AI tools are not carefully designed/used), and improper delegation of decision-making responsibilities by arbitrators to AI. |
| 3JusCorpusLJ106.pdf | HeinOnline | The Escalation of ChatGPT: How ChatGPT will exert Influence on the Legal Profession? | This paper examines ChatGPT's potential influence on the legal profession, covering applications in research and drafting, and addressing concerns like accuracy and ethics. It concludes AI should be an assistive tool for lawyers, not a replacement. | True | Market | True | 2.0 | NaN | ChatGPT | Refers to an external evaluation where ChatGPT scored a C+ on a law school exam and author's anecdotal use for drafting and current affairs queries. | ChatGPT achieved a C+ (low-passing grade) on a law school exam. | NaN | NaN | NaN | NaN | General legal practice | Mentions Indian law; discussion is largely general/international. | Trained on a large corpus of text data (570GB filtered content, 45TB unfiltered) from books, web texts, Wikipedia, and other online sources, with a knowledge cutoff in 2021. | Based on OpenAI's GPT-3 model family, fine-tuned using supervised and reinforcement learning (transfer learning). | Released for free public testing by OpenAI. | True | False | Available for free public testing via OpenAI's platform. | NaN | Ensuring accuracy and reliability of AI-generated legal information, protecting client confidentiality, dealing with outdated knowledge (pre-2021), establishing accountability for AI outputs, mitigating IP risks, preventing plagiarism and malicious use, and addressing complex ethical considerations. | Inaccurate legal information leading to detrimental effects, breach of client confidentiality, providing outdated legal advice, lack of accountability for AI errors, intellectual property infringement, plagiarism undermining professional integrity, potential for malicious uses like cyberattacks, and job displacement for some legal professionals. |
| 4JusCorpusLJ228.pdf | HeinOnline | The Role of ChatGPT and Emojis in Modern Legal Interpretation | This paper discusses the evolving role of technology, particularly AI tools like ChatGPT and the use of emojis, in modern legal practice and interpretation. It explores their potential benefits in legal research, document drafting, and contract acceptance, while also highlighting challenges related to copyright of AI-generated content and the legal standing of emojis. | True | Market | True | 2.0 | Neutral | Use of ChatGPT for legal tasks (e.g., research, document analysis, drafting, judgment interpretation) and the legal interpretation of emojis in contract acceptance. | The paper discusses examples of use, such as a Colombian judge using ChatGPT for a ruling on medical insurance and a Canadian court case (South West Terminal Ltd. v Achter Land & Cattle Ltd.) involving a thumbs-up emoji for contract acceptance. It is not an empirical evaluation by the author. | NaN | Complexities surrounding liability and ownership of AI-generated content within existing copyright laws; lack of clarity on authorship, originality, and legal personhood for AI in the context of copyright. | Development of a robust legal framework tailored to AI's unique attributes, addressing issues of legal personhood, authorship, ownership, and liability. Enactment of new legislation like India's proposed Digital India Act to regulate emerging technologies. | Dissemination of general legal information to the public; AI-assisted legal guidance for individuals assessing merits of court cases; improving efficiency of judicial processes. | NaN | Copyright law, Contract law, Judicial procedures. | India | NaN | NaN | NaN | False | False | NaN | Ambiguity in Indian copyright law regarding AI-generated works and AI as an author; need to determine originality for AI-generated content; interpretation of AI as a 'person' under law for authorship. The need for legal frameworks to adapt to ensure equitable protection and navigate nuances of AI-generated content and emojis in legal communication. | NaN | Copyright infringement from using LLM-generated content for commercial purposes without meeting fair use/dealing exceptions. Ambiguity and potential disputes arising from the use of emojis as acceptance in contractual contexts if intent is unclear. |
| 64HungJLegalStud472.pdf | HeinOnline | Rules over words: Regulation of chatbots in the legal market and ethical considerations | This paper examines the integration of chatbots into the legal market, highlighting their benefits such as improved efficiency for law firms and potential for enhanced access to justice. It primarily discusses the significant professional liability, data privacy, and ethical concerns arising from their use, and explores regulatory challenges and approaches to mitigate risks while fostering innovation. | True | Market | True | 3.0 | Neutral | Chatbots and AI tools in the legal field (e.g., ChatGPT, DoNotPay, Harvey, Brainspace) | NaN | NaN | Cost and complexity of accessing traditional legal services and information for underserved communities. | Utilizing chatbots and AI tools to provide easier access to legal information and basic legal services for those who cannot afford or easily access traditional legal aid. | Access to legal information, assistance with simple legal tasks (e.g., parking ticket appeals), provision of otherwise inaccessible legal services. | Economically disadvantaged individuals, less educated groups, segregated communities, and the general public needing assistance with simple legal matters. | Broad range including administrative law (parking tickets), contract law, corporate law (due diligence, contract analysis), legal research; with ethical considerations highlighted for criminal law and family law. | International (mentions US, UK, China, Italy, EU, and international law firms) | General discussion of reliance on large datasets, including user data for tools like ChatGPT, and confidential client information, raising privacy concerns regarding their use for training AI. | NaN | Deployment in law firms (e.g., Harvey, Brainspace), public-facing services (e.g., DoNotPay, ChatGPT), and integration into court systems. | True | False | The paper discusses existing and operational tools like ChatGPT and DoNotPay, implying their general availability to the public or specific users (e.g., law firms for Harvey). | Need for further technological development for complex tasks, robust and comprehensive regulatory frameworks, ensuring ethical application (especially empathy and human judgment), maintaining human oversight, and mitigating algorithmic bias and malpractice. | Addressing professional liability and malpractice from AI errors, ensuring data privacy and confidentiality (especially with client data), overcoming ethical dilemmas (e.g., lack of empathy, dehumanization of justice, bias), dealing with the 'black box' problem of AI decision-making, preventing algorithmic malpractice, ensuring accuracy and effectiveness, and protecting user mental health from chatbot interactions. | Providing incorrect legal advice leading to malpractice, breaching client confidentiality and data privacy, algorithmic bias and discrimination, dehumanizing the justice system, potential for misuse (e.g., encouraging unnecessary litigation, harassment), negative impacts on users' mental health, and deskilling junior legal professionals. |
| 6Issue6IntlJLMgmtHuman312.pdf | HeinOnline | From Data to Verdict: Navigating AI's Growth & Blemish in the Legal System | This paper discusses the increasing adoption of artificial intelligence, including large language models like ChatGPT, within the legal sector for tasks such as document analysis, contract drafting, and predicting case outcomes. It explores the potential benefits for efficiency and access to justice, while also highlighting significant ethical concerns, risks of bias, the need for human oversight, and regulatory challenges. | True | Idealistic | True | 3.0 | Neutral | Kira Systems (Machine learning program for contract review) | Reported by Kira Systems' clients based on their use of the program. | Reduction in lawyer time required for contract review between 20% to 60%. | Limited public access to court systems; Overwhelming case backlogs; Potential for algorithmic bias and lack of transparency in AI tools used in the justice system; Ethical dilemmas related to AI in legal decision-making. | Digitalization of judicial proceedings (e.g., online courts); Use of AI for court efficiency, transcription (e.g., Teres), and translation (e.g., SUVAS); Development of comprehensive legal, regulatory, and ethical frameworks for AI; Ensuring transparency, explainability, and human oversight of AI in legal applications. | Improving access to court systems; Reducing judicial backlogs; Enhancing transparency of judicial proceedings; AI-assisted legal document analysis and decision support in the justice system. | General public, particularly those with limited access to legal assistance or facing overwhelmed court systems. | Contracts, Litigation, Criminal Law (bail decisions), Patent Law, General Court Procedures. | India, USA, Canada, Europe (GDPR reference). General discussion with specific examples from these jurisdictions. | For Kira Systems: Proprietary legal documents (contracts), with the software being trained and refined by human legal experts. | For Kira Systems: Iterative refinement of standard machine learning algorithms based on human expert feedback over an extended period. | For Kira Systems: Commercial licensing to law firms. | True | False | ChatGPT is available as an online service. Tools like Kira Systems, Lex Machina, and Ravel Law are commercially available. AI tools like Teres, SUPACE, and SUVAS are deployed within the Indian judicial system. | AI's inability to replicate human judgment, resourcefulness, empathy, and creativity in complex legal scenarios; Uncertainty in how much better AI contract writers can become due to lack of domain experience and linguistic accuracy for autonomous operation; Need for AI systems to be fully transparent and explainable; Lack of comprehensive legal, regulatory, and ethical frameworks for AI in the justice system. | For Kira Systems (as reported for its development): Significant time and effort required to refine the software to accurately identify specific legal concepts within documents (took 2.5 years instead of an expected 4 months). | Job displacement for legal professionals; Embedded bias in AI leading to unfair or discriminatory outcomes; Lack of transparency and explainability in AI decisions, undermining due process; Amplification of errors if AI relies on flawed legal data; Over-reliance on AI (automation bias); Breaches of data security and privacy for sensitive legal information; Ethical concerns about machines making decisions on personal liberty. |
| 15BeijingLRev (1).pdf | HeinOnline | The Role of Disruptive Artificial Intelligence Technology in Combating Crime in Indonesia | This study examines the potential of disruptive AI, specifically Esri's ArcGIS Pro software, to combat crime in Indonesia. The paper argues that ArcGIS Pro can enhance crime prevention by integrating and analyzing crime data, investigating patterns, and facilitating collaboration among law enforcement agencies, while also highlighting the need for training, infrastructure, and appropriate governance. | True | Market | False | 2.0 | Positive | ArcGIS Pro software (Geographic Information System with AI/GeoAI capabilities) | The paper does not describe a specific empirical evaluation or testing procedure conducted by the authors using ArcGIS Pro. It discusses existing capabilities and presents Indonesian crime statistics as context. | The paper does not report specific performance results from an empirical evaluation conducted by the authors. It concludes, based on a review, that AI and ArcGIS Pro are expected to be beneficial for crime prevention. | High and volatile crime rates in Indonesia; socio-economic drivers of crime; need for specific AI regulations; potential lack of specialized training and infrastructure for advanced technologies within law enforcement. | Utilizing AI, specifically ArcGIS Pro, for crime data integration, advanced analysis, and crime pattern investigation; optimizing GIS use by law enforcement (e.g., Prosecutor's Office); increasing community involvement in crime prevention; implementing education and training programs for law enforcement; upgrading technological infrastructure. | Crime prevention; crime data integration and analysis; crime pattern investigation (e.g., hotspot analysis, geospatial profiling); collaborative platforms for law enforcement; application of AI/GIS in criminal justice. | Law enforcement agencies in Indonesia (e.g., Prosecutor's Office, Police); indirectly, the general public in Indonesia through improved public safety. | Criminal Law; Criminal Procedure; Cyber Law (related to ITE Law) | Indonesia | The paper discusses the use of ArcGIS Pro which would analyze law enforcement data such as crime incident reports, arrest records, geospatial data, sensor data (imagery, point clouds), cell phone records, and financial transactions. This data is typically domain-specific, structured and unstructured, and largely proprietary to law enforcement agencies. | NaN | The paper advocates for the adoption and use of ArcGIS Pro by Indonesian law enforcement and discusses the need for prerequisite training and infrastructure, rather than describing an existing deployment initiated by the research. | True | False | ArcGIS Pro is a commercial software product from Esri, available for purchase/licensing. | Absence of specific AI regulations in Indonesia; potential gaps in AI talent development, data ecosystems, and AI infrastructure within law enforcement; need for ongoing investment in ICT for public sector efficiency in crime prevention; full implementation of the Indonesian National Artificial Intelligence Strategy. | Integrating diverse and large volumes of crime data; ensuring data is current and analysis-ready; need for specialized training for law enforcement personnel to use advanced AI tools; cost of commercial software and infrastructure upgrades; adapting AI tools to rapidly evolving crime patterns; preventing and mitigating AI bias in crime detection systems. | Potential for unexplained or incorrect conclusions from AI; development of biases in AI models leading to unfair detection or profiling (e.g., racial bias, false alarms); loss of public trust in law enforcement if AI tools are misused or produce errors; challenges in ensuring AI systems are safe, secure, and trustworthy without effective governance. |
| 50OhioNULRev513.pdf | HeinOnline | AI Legal Innovations: The Benefits and Drawbacks of Chat-GPT and Generative AI in the Legal Industry | This paper explores the impact of Generative AI, particularly ChatGPT and other LLMs, on the legal industry. It highlights benefits like increased efficiency and the potential to improve access to justice by reducing costs, while also detailing significant drawbacks such as hallucinations, bias, copyright infringement, and ethical concerns, ultimately calling for cautious adoption and regulation. | True | Idealistic | True | 3.0 | Positive | Generative AI / Large Language Models (e.g., ChatGPT, vLex Vincent, Casetext Co-Counsel, Claude.AI) | The paper refers to a 2024 Stanford RegLab and Institute for Human-Centered AI study that evaluated LLMs on specific legal queries, finding high hallucination rates. It also mentions that ChatGPT-4 passed the American Bar exam. | The Stanford RegLab study found legal hallucination rates ranging from 69% to 88% in response to specific legal queries for state-of-the-art language models. | High cost of legal services making them inaccessible to many; a large percentage (80%) of legal needs for the poor and middle class are unmet. | Utilizing LLMs to reduce the cost of legal work, thereby making legal services more affordable and accessible; enabling lawyers to handle more work or different types of work, potentially expanding services to the 80% with unmet legal needs. | Reducing cost of legal services; Meeting unmet legal needs of poor and middle-class individuals; Automation of legal tasks (e.g. legal advice, document drafting). | Poor and middle-class people. | General legal practice, e-discovery, document automation, predictive legal analysis, case management, legal advice, contract law, corporate law, immigration law, litigation, transaction analysis. | USA (including specific states like Tennessee, New York), European Union, United Kingdom, International (vLex operates in multiple jurisdictions). | Publicly available web data, books, articles (including copyrighted material like New York Times articles for some models like ChatGPT); some systems use client's historical response data (e.g., LegalMation); Anthropic's Claude.AI uses publicly available web data under Constitutional AI principles. | Machine learning on historical client data (e.g., LegalMation); Constitutional AI (for Anthropic's Claude.AI); general LLM development techniques involving training on large text corpora. | Commercial Software-as-a-Service (SaaS) platforms; Integration into existing legal software (e.g., Casetext into Westlaw, Microsoft Copilot in Office); Mobile applications and websites (e.g., DoNotPay). | True | False | Many commercial AI tools discussed are presented as currently available (e.g., ChatGPT, vLex Vincent, Casetext Co-Counsel, Microsoft Copilot, DoNotPay). Some general models like ChatGPT offer free access tiers. | High rates of 'hallucinations' in LLMs for legal queries; inherent biases in AI models; lack of self-awareness in models regarding their errors; the necessity for human oversight in legal AI applications; a significant portion of legal needs remains unmet despite technological potential; underdeveloped regulatory and ethical frameworks for AI in law. | Ensuring accuracy and mitigating 'hallucinations'; addressing and reducing bias in AI; navigating copyright issues with training data; maintaining data privacy and attorney-client privilege; developing and adapting to new regulatory frameworks; fostering user trust and adoption within the legal profession; overcoming resistance to changes in traditional practices like the billable hour. | Generation of fabricated information (hallucinations) leading to legal errors (e.g., citing non-existent cases); perpetuation and amplification of societal biases (sexism, racism); copyright infringement through unauthorized use of training data; breaches of data privacy and confidentiality; ethical violations, including compromising attorney-client privilege; potential for misuse in spreading misinformation (e.g., AI-generated voice in election interference); over-reliance on AI without adequate human supervision and expertise. |
| 36CanCompetitionLRev88.pdf | HeinOnline | A Justice as Fairness Framework for a Revised Efficiencies Defence | The paper argues for reforming Canada's competition law's 'efficiencies defence' rather than abolishing it. It proposes a new framework that shifts focus from static to dynamic efficiencies and incorporates Rawlsian 'justice as fairness' to ensure benefits for all Canadians, especially the least advantaged. | True | Idealistic | False | 1.0 | NaN | A revised efficiencies defence framework for Canadian competition law, incorporating Rawlsian 'justice as fairness', focusing on dynamic efficiencies, and detailing a 6-step evaluative process. | NaN | NaN | The current efficiencies defence in Canadian competition law is uncertain, undervalues crucial dynamic efficiencies, and lacks a robust mechanism to ensure fairness, potentially harming disadvantaged groups while benefiting others. | The paper proposes a reformed efficiencies defence framework incorporating Rawlsian 'justice as fairness,' prioritizing dynamic efficiencies, and using a structured, order-driven approach with specific consideration for the least advantaged. | Fairness in economic outcomes of merger reviews; equitable application of competition law (specifically the efficiencies defence); protection of vulnerable consumer, worker, and small business groups from negative impacts of mergers. | The 'least advantaged' members of society, including small businesses, low-income consumers, consumers in especially affected or rural regions, consumers in captive market segments, employees, and workers in defined industries. | Competition Law / Antitrust Law | Canada | NaN | Legal analysis, review of existing case law and economic theory, and application of John Rawls' 'justice as fairness' philosophical framework. | Proposed for legislative reform of the Canadian Competition Act. | False | False | NaN | The paper acknowledges its proposed framework will require jurisprudential refinement, particularly in defining 'material' harm to disadvantaged groups and in the methodology for weighing diverse and potentially incommensurate factors. | Developing a framework that effectively integrates complex philosophical concepts (Rawlsian fairness) into legal tests for merger reviews, and balancing economic efficiency goals with explicit considerations for distributional justice and the 'least advantaged'. | Without reform, the current efficiencies defence risks producing unfair outcomes that harm the least advantaged and fails to support Canada's competitiveness by undervaluing dynamic efficiencies. The proposed framework's reliance on terms like 'material' harm may lead to initial interpretative uncertainty. |
| 96PhilLJ793.pdf | HeinOnline | Will Artificial Intelligence Replace Lawyers in the Philippines? | This paper examines how AI, including LLMs, might transform the legal profession in the Philippines by reviewing economic theories on automation and current AI capabilities. It argues that while routine legal tasks are automatable, lawyers' roles requiring creative/social intelligence will remain crucial, and suggests policy recommendations for AI integration and improving access to justice. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Oligopoly of information, high cost of legal services for indigent clients, bureaucratic processes for legal aid. | Automation of legal services with AI to reduce legal fees and increase access to legal information; upskilling of lawyers and legal staff; reform of legal education to include technology. | Access to legal information, Affordability of legal services, Legal aid. | Indigent clients. | General practice of law | Philippines (primarily), with references to US and Europe. | NaN | NaN | NaN | True | False | The paper discusses several AI tools, some of which are commercially available (e.g., DoNotPay, LexMachina) or have free/freemium access (e.g., ChatGPT, Bard). | Ethical guidelines for AI use in law are not yet established; risk of errors in AI outputs and need for human oversight; technological gap including lack of digitized and OCR-enabled government documents in the Philippines; AI's current inability to fully replicate human creative and social intelligence required for some legal tasks. | Ensuring competent and ethical use of AI by lawyers; addressing the technological gap and lack of digitized/OCR-enabled documents in the Philippines; difficulty in automating tasks requiring high degrees of creative and social intelligence; need for upskilling lawyers and legal staff. | Job displacement for legal support staff performing routine tasks; errors in AI-generated legal research (e.g., citing non-existent cases); breach of attorney-client confidentiality through third-party AI tools; unauthorized practice of law by non-lawyers using AI; over-reliance by lawyers on AI tools. |
| 2024IntlJLEthicsTech1.pdf | HeinOnline | CAN AI MAKE A CASE? AI VS. LAWYER IN THE DUTCH LEGAL CONTEXT | This paper evaluates GPT-4's ability to generate legal arguments against those of a human lawyer in the Dutch legal system. An experiment with 25 legal professionals found that 80% preferred GPT-4's output, highlighting its potential for improving legal writing quality and access to justice. | True | Idealistic | True | 2.0 | Positive | GPT-4 for legal argumentation, with input prepared using manual co-reference resolution on case documents and a 'prompt reducer' technique (Python script based) for text compression to fit token limits. | Experiment with 25 Dutch legal professionals. Participants read a case summary and two anonymized legal letters (one human-written Text A, one GPT-4 generated Text B) and rated them on four quality dimensions (persuasiveness, clarity & coherence, strength of key arguments, use of evidence) on a 1-10 scale, then selected the overall more effective text with justification. | 80% of 25 legal professionals preferred the GPT-4 generated text (Text B). Text B scored higher on average than the human-written Text A across all four dimensions: persuasiveness (7.9 vs 6.28), clarity & coherence (7.96 vs 6.36), strength of arguments (7.6 vs 6.6), and use of evidence (7.08 vs 6.16). | High cost of traditional legal advice; limited quality, speed, and language availability (Dutch-only) of government-supported free legal services, excluding non-Dutch speakers. | AI generating legal arguments and advice efficiently and in multiple languages to increase accessibility, timeliness, and equity; development of AI-powered tools with user-friendly interfaces. | Affordability of legal services, language barriers in accessing legal aid, quality and efficiency of legal advice for underserved populations. | Economically disadvantaged individuals, expatriate population in the Netherlands. | Employment law | Netherlands | A set of 10 legal documents from a real-world Dutch employment contract dispute case. Nine documents (unstructured text, case-specific) were processed via manual co-reference resolution and a 'Prompt Reducer' script to create a compressed case summary as input for GPT-4. The tenth document (the human lawyer's letter, Text A) was also provided to GPT-4 as part of the prompt. | Iterative prompt engineering using a 'Prompt Reducer' technique (inspired by an unformalized method and implemented via a Python script with the OpenAI API) for text compression. Manual co-reference resolution was performed as a pre-processing step on input documents. | NaN | True | False | The approach uses the commercially available GPT-4 API. Python code for the 'Prompt Reducer' technique is provided in Appendix 2; manual co-reference resolution as a method is described. | Need for study replication for generalizability; methods for incorporating nuanced, non-documented client circumstances (e.g., risk tolerance, external pressures) into AI models; understanding AI's impact on legal education; assessing client (non-legal professional) perspectives on AI-generated legal documents. | GPT-4 API token limitations requiring text compression techniques (Prompt Reducer); initial factual inaccuracies in AI-generated summaries due to ambiguities in source documents (addressed by co-reference resolution); AI's lack of access to non-documented, client-specific contextual information. | AI 'hallucinations' (generating incorrect information); lack of legal responsibility and accountability for AI-assisted services; perpetuation of human biases embedded in data or models; potential job displacement in the legal field; overburdening of the legal system with AI-generated filings; creation of unfair advantages in legal disputes if access to AI tools is unequal. |
| 19JLEconPoly295.pdf | HeinOnline | Artificial Intelligence Regulatory Sandboxes | This paper analyzes the global landscape of AI regulatory sandboxes, comparing approaches in various jurisdictions like the UK, EU, and US, and offers policy recommendations for their design. It emphasizes the role of sandboxes in fostering innovation, enabling evidence-based AI regulation, and highlights their potential in sectors like legal services to improve access to justice. | True | Idealistic | False | 2.0 | Positive | AI regulatory sandboxes | Review of existing sandbox programs and their outcomes in various jurisdictions (e.g., UK, EU, Norway, Switzerland, Singapore, US states), analysis of policy documents, and case studies like the Utah legal sandbox. | Well-designed regulatory sandboxes, such as Utah's legal sandbox for access to justice, have demonstrated effectiveness in promoting innovation, enabling new service providers, and informing regulatory approaches by allowing experimentation in a controlled environment. | High cost of legal services, restrictive regulations prohibiting non-lawyers from providing legal services or owning legal firms, leading to a significant justice gap, particularly for low-income individuals. | Proposes the establishment and wider adoption of AI-focused legal regulatory sandboxes, similar to Utah's model, to allow non-lawyer-owned entities (including tech firms) to provide specific legal services, thereby fostering innovation, competition, and reducing costs. | Lowering the cost of legal services, enabling alternative legal service providers (non-lawyer owned firms, tech companies), facilitating provision of specific legal services (e.g., form completion for marriage, business, immigration, real estate). | Low-income Americans. | General legal services, including family law (marriage), business law, immigration law, real estate law. | United States (specifically Utah, with recommendations for other states), Canada (British Columbia, Ontario). General discussion covers UK, EU, Norway, Switzerland, Singapore, China, India, Russia, Brazil, Colombia, Chile. | NaN | Comparative analysis of existing international AI regulatory sandbox models, review of policy documents and legal scholarship, and synthesis of best practices to propose design principles and policy recommendations for effective sandbox creation and operation. | Through government-run programs, legislation, and the establishment of dedicated regulatory bodies or initiatives by existing regulators (e.g., financial conduct authorities, data protection authorities, supreme courts, ministries). | True | False | Several jurisdictions discussed (e.g., Utah's legal sandbox, Norway's AI sandbox, Spain's AI sandbox, Zurich's innovation sandbox) have operational AI regulatory sandboxes that entities can apply to participate in, subject to eligibility criteria. | Limited adoption of legal AI sandboxes in most jurisdictions; a need to translate sandbox learnings into broader, systemic legal and regulatory reforms to fully address access to justice issues. | Regulatory fragmentation hindering coordinated efforts, difficulties in attracting a sufficient number of high-quality applicants, challenges in effectively using sandbox findings to inform broader regulatory reforms, designing appropriate scope and interagency coordination mechanisms, and mitigating risks like regulatory privilege. | Granting unfair regulatory privilege to sandbox participants, creating market distortions, misuse of sensitive personal data or consumer harm if safeguards are inadequate, potential for sandboxes to be co-opted in environments with weak rule of law, and stifling innovation if sandboxes are poorly designed or lead to premature, overly burdensome regulation. |
| 127WVaLRev1.pdf | HeinOnline | BALANCING INTERESTS: Al, BUSINESS & HUMAN RIGHTS, AND THE LEGAL LANDSCAPE IN AN ERA OF DISRUPTION | This paper analyzes the U.S. government's approach to AI regulation, primarily through the Biden Administration's Executive Order and National Action Plan on Responsible Business Conduct, in the context of business and human rights. It argues that while these initiatives signal important principles, they lack sufficient enforcement to effectively protect human rights from AI-related business harms and calls for a balanced regulatory approach. | True | Idealistic | False | 3.0 | NaN | NaN | NaN | NaN | Societal harms from AI (bias, discrimination, fraud); lack of effective enforcement in current regulations; difficulties in defining AI for regulation; rapid technological evolution outpacing legal frameworks; superficial compliance mechanisms. | Comprehensive and deliberate laws balancing innovation and rights; a risk-based regulatory framework; a "smart mix" of voluntary and mandatory measures; embedding ethical principles (transparency, accountability, non-discrimination) in AI governance. | Protection against AI-driven discrimination and bias; accountability for AI harms; access to remedies for victims of business-related human rights abuses. | Individuals and affected communities, particularly those vulnerable to discrimination and bias from AI systems; the general public interacting with AI. | Human Rights Law (including Business and Human Rights), Administrative Law/Regulatory Law, Consumer Protection Law, Anti-discrimination Law, Data Privacy Law. | United States, European Union, International | NaN | NaN | NaN | False | False | NaN | Lack of enforcement mechanisms in current US AI governance; insufficiency of EOs and NAPs alone, requiring Congressional action; superficiality of some HRDD compliance; regulatory gaps for disruptive technologies; lag between law and technological advancement; need for comprehensive federal privacy laws. | NaN | Societal harms (fraud, discrimination, bias, disinformation); labor displacement and disempowerment; stifled competition; national security risks; manipulative, exploitative, and social control practices; harms to public interests and fundamental rights (physical, psychological, societal, economic); misuse of biometric surveillance; AI-induced financial crises. |
| 26LegalWritingJLegalWriti.pdf | HeinOnline | "Alexa, Write a Memo": The Promise and Challenges of AI and Legal Writing | This paper examines the current and foreseeable capabilities of artificial intelligence in assisting with and potentially performing legal writing tasks, particularly the drafting of office memoranda. It analyzes how AI can be applied to various stages of the memo-writing process and discusses the implications for legal education and the skills future lawyers will need. | True | Market | True | 3.0 | Neutral | The paper broadly discusses multiple AI techniques and tools relevant to legal writing, including legal text analytics, machine learning (ML), network diagrams, question answering (QA) systems (e.g., ROSS), expert systems (e.g., Neota Logic, A2J Author), natural language processing (NLP), and large language models (LLMs like GPT-2, GPT-3). | The paper describes evaluations of various AI tools as reported by their developers or other researchers (e.g., GPT-3's text generation capabilities through examples; ML models for outcome prediction on ECHR dataset; VJAP's predictions). It does not present new empirical testing by the authors. | The paper notes varied results for different AI applications: LLMs like GPT-3 can generate plausible short texts but lack guaranteed legal accuracy and coherence for long documents; ML can predict case outcomes with some success but often lacks explainability; QA systems can retrieve relevant passages but require human assessment; expert systems are limited by manually created rules. | The paper mentions the difficulty for non-experts to navigate legal issues (which A2J tools try to address) and implies the cost/complexity of legal services as underlying A2J issues, although these are not the central focus. More directly, it refers to the lawyer job market concerns due to AI. | The paper mentions AI-powered solutions that can contribute to access to justice: legal expert systems and chatbots (e.g., A2J Author, Neota Logic) for client guidance and intake; specific AI tools for legal research and analysis in A2J contexts (e.g., LUIMA for veterans' claims, SCALE project for landlord-tenant disputes). | Client screening and intake, legal information provision, specific legal aid areas like veterans' benefits and landlord-tenant law. | General public needing initial legal guidance, veterans, tenants. | Tort law (dog bites), contract law, civil procedure (discovery), veterans' law, landlord-tenant law, general legal practice. | Illinois (for primary hypothetical), USA (general legal concepts, education, some case law), International (mentions ECHR, WIPO, Japanese law examples). | Discusses training data for various AI systems mentioned: e.g., GPT-3 (general web text like Common Crawl, Wikipedia, Webtext2), ML for ECHR outcome prediction (ECHR case decisions), LEGAL-BERT (legal documents corpus), LUIMA (Board of Veterans Appeals decisions). | Describes design methodologies for various AI systems discussed, such as rule-based approaches for expert systems, factor-based reasoning for case-based reasoning systems (e.g., VJAP), neural network architectures (transformers) for LLMs, and machine learning pipelines for predictive models. | Mentions various deployment models for discussed AI tools: commercial subscription services (e.g., Westlaw Edge, Casetext), API access (e.g., GPT-3), open platforms (e.g., A2J Author), and research prototypes (e.g., LUIMA). | True | True | Some discussed commercial AI tools (e.g., Westlaw Edge, Casetext, Lexis+) are available via subscription. Some platforms like A2J Author are openly accessible. GPT-3 is available via API (not entirely free for extensive use). | Technical limitations in AI's ability to perform complex legal reasoning, understand nuance and purpose, extract rules automatically, distinguish legal vs. commonsense meanings, and ensure consistent legal accuracy, particularly for complex tasks. Lack of 'common sense' and deep conceptual understanding in current AI systems. | General challenges for the AI and Law field, such as: interpreting nuanced and ambiguous natural language in legal contexts; automating the extraction of legal rules, elements, and factors from texts; synthesizing coherent and legally sound arguments from multiple sources; achieving genuine understanding versus statistical pattern matching; performing empathetic reasoning; and ensuring reliability and explainability of AI outputs. | Generation of facially convincing but legally inaccurate or misleading text by AI. Potential for job market disruption for legal professionals if they do not adapt. Over-reliance on AI without critical human oversight. Copyright infringement risks related to training data and AI-generated content (e.g., ROSS litigation). |
| 22ColoTechLJ301.pdf | HeinOnline | AI CANNIBALISM AND THE LAW | This paper discusses the emerging problem of "AI cannibalism," where large language models (LLMs) trained on increasing amounts of AI-generated content suffer degraded performance, leading to increased bias and misinformation. It explores the potential negative impacts of this phenomenon, along with other LLM issues like hallucinations and data bias, on lawyers, legal practice, and the development of law. | True | Market | True | 3.0 | Negative | Large Language Models (LLMs) and the phenomenon of AI cannibalism. | The paper cites and discusses external studies: Alemohammad et al. (2023) study on generative AI image models trained on their own outputs (mixing human-created and AI-generated images showing degradation); Shumailov et al. (2023) study on language models trained with increasing portions of auto-generated text, observing 'model collapse'. | Cited studies demonstrate that LLMs and other generative models degrade (e.g., 'model collapse', increased errors, deviation from real data distributions) when recursively trained on their own synthetic outputs, especially without sufficient fresh, human-curated data. | Amplification of existing societal biases through LLMs, particularly due to AI cannibalism, disproportionately harming marginalized communities and entrenching unjust laws, thereby hindering access to justice. | Ensuring LLM training data includes sufficient fresh, human-curated, high-quality data; active curation of datasets to distinguish real vs. synthetic content and assess quality; lawyers' critical awareness, careful review, and supervision of all AI-generated text to mitigate bias and error. | Bias in AI leading to discriminatory outcomes, negative impact on marginalized communities, entrenchment of unjust laws, potential stagnation of legal development. | Marginalized communities | General legal practice, litigation, appellate law, development of legal doctrine. | United States | The paper discusses LLMs trained on vast corpora of human-generated text (books, articles, web content like Reddit). It critically examines the problem of future LLMs being trained on 'synthetic data' (AI-generated content) and emphasizes the need for 'fresh, human-curated data'. | NaN | NaN | True | True | The paper discusses widely available LLMs like ChatGPT, which has publicly accessible versions, including free tiers. | Developing sustainable technical solutions to AI cannibalism to prevent model degradation over time. Addressing the difficulty in reliably distinguishing human-generated versus AI-generated content for training data curation. Preventing AI systems from further exacerbating societal biases and ensuring they do not stifle the progressive development of law beneficial to all, including marginalized communities. | Data bias inherent in training data (gender, racial, religious, political); AI hallucinations (generation of false information, including fake legal citations); limitations of training data (e.g., knowledge cut-off dates, inherent bias towards existing information); the core problem of AI cannibalism where training on AI-generated (synthetic) data leads to model degradation, increased errors, and potential amplification of biases. | Lawyers using LLMs may submit briefs with fabricated cases, leading to professional sanctions. AI systems can reflect and amplify existing societal biases, potentially resulting in discriminatory legal outcomes and harming marginalized communities. Over-reliance on LLMs could lead to stagnation in the development of legal theory and practice, as LLMs are trained on past data and may not foster novel arguments. The utility of LLMs for legal work may decrease due to increased misinformation and hallucinations resulting from AI cannibalism. |
| 15IndianJLJust1.pdf | HeinOnline | Evaluating ICT Adoption in the Indian Judiciary: Challenges, Opportunities, and the Impact of the E-Courts Project | This paper critically examines India's e-Courts Project, evaluating its success in integrating Information and Communication Technology (ICT) within the judiciary to reduce case backlogs, litigation costs, and improve transparency and legal literacy. It assesses the challenges and successes of the project's first two phases and considers the need for strategic reorientation for its upcoming third phase. | True | Idealistic | False | 2.0 | Neutral | The e-Courts Project (encompassing various ICT initiatives like Case Information System (CIS), National Judicial Data Grid (NJDG), e-filing, virtual courts, Supreme Court Portal for Assistance in Court's Efficiency (SUPACE), Supreme Court Vidhik Anuvaad Software (SUVAS)) | Analysis of the e-Courts Project's phases against its stated objectives (reducing case backlogs and judicial workload, cutting litigation costs and complexities, improving transparency and legal literacy), drawing upon National Judicial Data Grid (NJDG) statistics, National Council of Applied Economic Research (NCAER) survey reports, and official documents from the e-Committee of the Supreme Court of India. | Mixed results: Significant improvements in ICT infrastructure and some services for lawyers. However, limited overall reduction in case backlogs (which worsened post-pandemic), low awareness and benefit from e-services among litigants, persistent challenges in legal literacy and language accessibility, and data quality issues in judicial databases were noted. | High number of pending cases and judicial workload; low legal literacy among citizens and language barriers due to English being the predominant language in judiciary; high litigation costs; geographical and economic inaccessibility of courts, particularly for rural and impoverished populations; digital divide between urban and rural areas. | The e-Courts Project, deploying ICT solutions including digitisation of judicial processes (e-filing, Case Information System), creation of a national judicial data repository (NJDG), introduction of virtual courts, and development of AI-powered tools for judicial assistance (SUPACE) and legal document translation (SUVAS). The paper suggests strategic reorientation for Phase III, including better training, an ecosystem approach involving private entities, statistical analysis for case scheduling, and enhancing legal literacy through educational technology. | Reducing case pendency and judicial workload, lowering litigation costs and complexities, enhancing judicial transparency, improving legal literacy and public access to legal information, alternative dispute resolution (ADR). | General public, with specific focus on litigants in rural areas, economically disadvantaged individuals, non-English speakers, citizens with low legal literacy, and women litigants. | General Judiciary (covering civil and criminal matters across District courts, High Courts, and the Supreme Court); Alternative Dispute Resolution (ADR). | India | For SUVAS (translation software): English judicial documents translated into nine vernacular Indian languages. For SUPACE (AI assistance for judges): Legal information including precedents, statutes, and laws, with performance refined through training data and feedback. The National Judicial Data Grid (NJDG) contains extensive data from court records, including orders, judgments, and case details. | Phased implementation of the e-Courts Project (Phases I, II, and III); iterative development for software like the Case Information System (CIS); collaborative development between the e-Committee of the Supreme Court and the National Informatics Centre (NIC); use of machine learning algorithms for AI tools (SUPACE, SUVAS). | National-scale rollout across Indian courts, including provision of ICT infrastructure (hardware, internet connectivity); development and deployment of web portals, mobile applications (e.g., E-Court Services app), and physical eSewa Kendras (service centers); phased introduction of various digital services for litigants, lawyers, and judges. Implementation varies regionally due to the autonomy of High Courts. | True | True | Publicly accessible e-Courts web portal, the E-Court Services mobile application (free to download and use for case status, cause lists, etc.), the National Judicial Data Grid (NJDG) public portal for accessing case data and statistics, and eSewa Kendras for assistance. Translations from SUVAS are also made publicly available. | Low awareness and adoption of e-Court services among litigants; insufficient impact on reducing overall case backlogs; persistent legal literacy issues and language barriers despite translation efforts; data quality and completeness issues within the National Judicial Data Grid (NJDG); the digital divide disadvantaging rural and less privileged populations; lack of a unified national implementation strategy and consistent feedback mechanisms. | Initial lack of ICT standardization leading to inconsistencies; integrating diverse regional judicial processes and systems; ensuring data quality, accuracy, and consistency in a large-scale digital environment; high judicial workload and time constraints for training; varied levels of technical proficiency among users (judges, court staff, lawyers); managing the federal structure of the judiciary with autonomous High Courts impacting uniform adoption; addressing multilingual requirements effectively; maintaining cybersecurity for vast digital infrastructure. | Bias in AI algorithms used for judicial assistance (e.g., SUPACE) and their 'black box' nature; potential deterioration in the quality of judgments if AI or templates are over-relied upon; increased cybersecurity vulnerabilities with extensive digitization; data privacy concerns due to the public availability of detailed case information and potential for misuse; exacerbation of the digital divide, favoring technologically adept sections of society; risk of increased frivolous litigation due to easier access; data biases and opacity inherent in Large Language Models if adopted without caution. |
| 2024JComIntellPropL249.pdf | HeinOnline | CYBERSYMBIOSIS OF HUMAN JUDGES AND ARTIFICIAL INTELLIGENCE: PROBLEMS AND POTENTIAL SOLUTIONS FOR INTEGRATION AND FOR THE SUCCESSFUL MODERNIZATION OF THE JUDICIAL SYSTEMS OF THE BRICS COUNTRIES | This paper explores the concept of 'cybersymbiosis' between human judges and artificial intelligence to modernize judicial systems in BRICS countries, aiming to improve efficiency and access to justice. It proposes a model for integrating AI tools with human oversight, outlining an AI assistant architecture and discussing necessary legal and ethical frameworks to support this integration. | True | Idealistic | True | 1.0 | Positive | Cybersymbiosis model for human-AI judicial work, and an AI assistant architecture featuring NLP, information extraction (neuro-symbolic programming, machine learning), Natural Language Generation (NLG), explainability, and security modules. | NaN | NaN | Case backlogs, lack of timely access to justice for vulnerable populations, inconsistent judicial decisions, difficulties processing large data volumes, and systemic inefficiencies including potential corruption. | A human-AI 'cybersymbiosis' model with clear task distribution, AI-powered tools for legal analysis and support, and new legal/ethical frameworks including transparency, audits, and redress mechanisms. | Reducing judicial backlogs, improving access for vulnerable populations, enhancing decision consistency and fairness, increasing court efficiency and transparency. | Socially vulnerable populations, linguistic minorities. | General judicial processes, court administration, rule-making. | BRICS Countries (Brazil, Russia, India, China, South Africa) | Proposed use of structured/unstructured court decisions, human rights documents, and other legal texts from BRICS countries; notes data protection challenges. | Conceptual model developed via literature review, comparative analysis of BRICS approaches, and multi-criteria analysis; system proposes neuro-symbolic programming and machine learning. | NaN | False | False | NaN | Incomplete court digitalization, varying data standards, data protection restrictions, human-AI communication challenges, legal system diversity, budgetary differences, need for updated ethical/legal frameworks, AI explainability, and skilled personnel. | Incomplete digitalization and data protection issues hindering AI training, AI system imperfections, adapting to diverse legal systems and budgets within BRICS, developing multilingual and legally-aware AI, ensuring security, and creating user-friendly interfaces. | Ethical issues, AI-driven discrimination, inaccuracies, compromised fair sentencing, inappropriate AI use, harm from AI errors, and inherent AI biases. |
| 73SCLRev825.pdf | HeinOnline | OBSERVING THE EFFECTS OF AUTOMATING THE JUDICIAL SYSTEM WITH BEHAVIORAL EQUIVALENCE | The paper argues that current scholarship on automating judicial systems, which focuses on reproducing reasoning and outcomes, overlooks broader societal impacts and changes in system interaction. It proposes using "behavioral equivalence," a computer science concept emphasizing observer-dependent evaluation, as a framework to analyze the full consequences and tradeoffs of such automation for scholars and policymakers. | True | Idealistic | False | 1.0 | Neutral | The paper proposes "behavioral equivalence" as an analytical framework to evaluate the automation of judicial systems, focusing on different observers and perceived tradeoffs. | NaN | NaN | A narrow focus in current scholarship on merely reproducing legal reasoning and outcomes, thereby overlooking how automation changes system interactions and affects diverse societal interests, leading to unobserved unintended consequences and impacts on procedural justice and legitimacy. | Adopting the "behavioral equivalence" framework to evaluate legal automation by considering a wide range of observers (beyond the judicial system itself, including societal interests) and systematically analyzing the perceived tradeoffs (informational access, process, reasoning, outcome) to anticipate and manage the full consequences of changes. | Procedural justice, legitimacy of the legal system, fairness, due process, accountability in automated decision-making, unintended consequences of legal automation. | Society at large, participants in the legal system including pro se litigants. | General (examples from criminal law, tort law, administrative law, civil procedure, contract law). | United States | NaN | NaN | NaN | True | False | The analytical framework (behavioral equivalence for evaluating legal automation) is detailed in the paper and can be conceptually applied by any reader. | The practical difficulty of perfectly predicting all unintended consequences, comprehensively identifying all relevant societal observers and their diverse observations/values, and ensuring policymakers systematically consider and weigh the identified tradeoffs when implementing legal automation. | Difficulties in detecting and comparing outcomes of old vs. new automated systems, especially with human-involved or non-deterministic processes; ensuring the transparency and verifiability of AI reasoning and explanations when evaluating systems. | Overlooking societal impacts and observer perspectives in legal automation can lead to unintended negative consequences (e.g., altered legal practice, new biases), erosion of procedural justice and due process, loss of public trust and system legitimacy, manipulation by knowledgeable actors, and reduced transparency in legal decision-making. |
| 2023UIllJLTechPoly207.pdf | HeinOnline | CHATGPT, PROFESSOR OF LAW | This paper evaluates the free version of ChatGPT's utility for law professors by testing its performance on seven hypothetical service and teaching-related tasks. The author concludes that ChatGPT can significantly aid in routine service tasks, providing near-finished drafts, and offer a solid starting point for more complex teaching tasks, thereby potentially reducing workload. | True | Market | True | 2.0 | NaN | ChatGPT (free version available as of November 2022/early 2023) | The author created a hypothetical law professor and seven common tasks (four service-related, three teaching-related). Prompts for each task were run through the free version of ChatGPT, and the outputs were qualitatively analyzed for usability, accuracy, and the degree of revision needed. | ChatGPT performed very well on service-related tasks (e.g., drafting letters of recommendation, bios, opening remarks, curriculum review plans), providing usable first drafts requiring personalization. It performed moderately well on teaching-related tasks (e.g., creating exam questions, handouts, syllabi), offering time-saving starting points but often requiring more significant faculty intervention for detail, accuracy, and customization. | NaN | NaN | NaN | NaN | Legal education, Torts, Employment Discrimination (sexual harassment law), Law Practice Technology | United States | NaN | NaN | NaN | True | False | The free version of ChatGPT provided by OpenAI. | NaN | Need for prompt refinement to get acceptable results; outputs often require significant editing for personalization, detail, and accuracy; instances of inaccurate legal information in outputs (e.g., elements of sexual harassment claim). | Outputs can contain inaccuracies, including incorrect legal information, requiring careful review by faculty. Ethical questions regarding the use of AI for original scholarship (though this specific use was outside the experiment's scope). Potential for over-reliance if outputs are used without proper personalization and verification. |
| 49JCorpL833.pdf | HeinOnline | Robots, Markets, and the Value of Deal Lawyers | This paper discusses the implications of emerging automation technologies like AI and blockchain for deal lawyers and financial markets, particularly concerning asset-backed securities (ABS) and tokenization. It explores how automation can affect legal ambiguity, market risks, and the fundamental roles, value, and ethical responsibilities of lawyers in these evolving contexts. | True | Market | True | 3.0 | NaN | AI (e.g., Generative AI like ChatGPT, Harvey AI, Casetext CoCounsel) for legal tasks (research, drafting, contract review) and blockchain-based tokenization/smart contracts for financial transactions. | NaN | NaN | NaN | NaN | NaN | NaN | Corporate law, financial law, contract law, commercial law, property law, bankruptcy law, professional ethics. | United States | The paper notes that AI systems, including Generative AI like ChatGPT, are trained on large databases of examples, and specifically mentions the concept of AI models being trained on 'existing deal documentation' for legal work products. | NaN | NaN | True | True | The paper mentions generally available AI tools like ChatGPT (with mentions of OpenAI's website, implying free access to some models) and other commercial legal tech tools (e.g., Harvey AI, DoNotPay, Tome) accessible through their respective providers/websites. | NaN | Ensuring human judgment and ethical standards in automated legal services; addressing unauthorized practice of law issues for AI tools; potential atrophy of lawyers' skills; deciding appropriate contexts and methods for automation (e.g., translating complex legal provisions into code); understanding and mitigating risks of AI and blockchain in specific market contexts. | Atrophy of lawyers' skills and judgment; unauthorized practice of law by AI tools; increased risks in financial markets (e.g., from automated handling of legal ambiguity and distortion, hidden leverage); adverse effects on issuers, stakeholders, and systemic stability from digitized legacy forms or automated processes; markets defying legal norms and intervention points via blockchain; undermining bankruptcy protections (e.g., automatic stay); financial information failure; ethical dilemmas for lawyers. |
| 98TulLRev363.pdf | HeinOnline | Why Can't I Have a Robot Lawyer? Limits on the Right to Appear Pro Se | This article analyzes the historical limitations imposed by courts on the right to self-representation (pro se) and considers how these limits will impact litigants using new artificial intelligence technology. It then proposes a framework for how courts should address AI-assisted pro se litigants, suggesting an initial bar on AI use until its utility is proven, followed by mandatory disclosure of its use. | True | Idealistic | True | 1.0 | Neutral | A framework for courts to manage AI use by pro se litigants, involving an initial prohibition until AI utility is assured, followed by permission with mandatory disclosure. | NaN | NaN | Established judicial limitations on the right to self-representation (e.g., restrictions on who can appear pro se, rules against unauthorized assistance like ghostwriting); current unreliability of AI (e.g., inaccuracy, fabrication of sources). | Courts should initially bar self-represented litigants from using AI until its utility is assured. Subsequently, courts should allow its use only if a litigant discloses their use of an AI product to the court, enabling judges to properly assess litigant sophistication and provide appropriate leniency. | Right to self-representation (pro se), AI assistance for litigants, court procedure and administration, access to justice. | Pro se litigants (often low and middle-income individuals). | General (civil and criminal procedure), with examples from various specific fields including family law, bankruptcy, and criminal law. | United States (federal and state courts). | NaN | NaN | NaN | False | False | NaN | Technical: AI's current lack of robustness and truthfulness, including tendencies to fabricate sources or 'hallucinate' facts. Societal/Legal: How to ensure that AI assistance enhances, rather than undermines, the fairness and integrity of the judicial process for pro se litigants; determining when an AI product's benefits outweigh its risks of harm. | NaN | AI providing inaccurate or misleading legal information or advice; AI fabricating legal citations or facts ('hallucinations'); pro se litigants lacking understanding of AI-generated content and strategy; courts being misled about a litigant's actual sophistication if AI use is undisclosed; potential for AI use to constitute the unauthorized practice of law; violation of court rules (e.g., prohibitions on recording court proceedings if the AI requires it). |
| 7Issue5IntlJLMgmtHuman651.pdf | HeinOnline | Justice Is Mechanized: Ethical Implications of AI in Law | This paper explores the ethical implications of using artificial intelligence in the legal field, focusing on equality, accountability, and accuracy. It argues that AI, while offering benefits in efficiency and accessibility for tasks like legal research and contract review, should serve as a supplementary tool to human judgment to ensure justice is served effectively and ethically. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Cost and complexity of traditional legal representation; for AI-driven A2J solutions: data privacy concerns, and unreliability/inaccuracy of AI-generated legal advice (e.g., hallucinations); systemic issues a_ffecting overall justice delivery such as massive case backlogs and shortage of judges. | Utilizing AI-powered legal self-help applications for accessible legal information and assistance; integrating AI into the legal system to enhance efficiency and expedite case resolution; developing robust ethical rules and regulations for AI use, ensuring lawyer accountability and AI's supplementary role to human judgment. | Legal information and self-help; Court efficiency and case processing; Ethical use of AI in law. | General public, particularly those facing minor legal issues or lacking access to traditional legal representation; people in countries with overburdened legal systems (e.g., India). | General Law, Contract Law, Administrative Law (specifically mentions parking tickets). | India (primary focus for regulatory reform), USA (examples like ROSS, DoNotPay, NYC chatbot), UK (ethics codes mentioned). | NaN | NaN | NaN | True | True | DoNotPay is described as a publicly available app, often free, for legal self-help tasks like challenging parking tickets. | Lack of adequate regulatory frameworks for AI in law in jurisdictions like India; persistent issues with the reliability and accuracy of AI-generated legal advice (e.g., hallucinations); data privacy concerns associated with AI systems. | NaN | Inaccuracies and potential professional misconduct from reliance on unverified AI output; decline in critical thinking and analytical skills among legal practitioners; data privacy violations and security breaches due to AI's handling of sensitive information; generation of incorrect or deceptive legal advice by AI (hallucinations), potentially leading to adverse legal consequences; inherent data bias in AI systems leading to skewed and discriminatory outcomes; AI's inability to replicate human qualities essential for legal practice such as honesty, courage, judgment, and fellowship. |
| 5Issue2IndianJLLegalRsch1.pdf | HeinOnline | INFLUENCE OF TECHNOLOGY AND ARTIFICIAL INTELLIGENCE IMPACTING THE GROWTH OF LEGAL INDUSTRY | This paper discusses the transformative impact of technology and AI on the legal industry, highlighting increased efficiency, improved accuracy, and task automation. It examines AI applications in India for legal research, contract management, and predictive analytics, while also considering future trends, ethical implications, and the potential for AI to make legal services more affordable for individuals and SMEs. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | High cost and inefficiency of traditional legal services, hindering access for many individuals and small to medium-sized businesses. | AI-driven automation of tasks like legal research, document review, and contract analysis to improve efficiency and reduce costs, thereby potentially increasing affordability and access to legal services. | Affordability of legal services, efficiency in legal service delivery. | Individuals and small to medium-sized businesses unable to afford traditional legal services. | General legal practice | India | NaN | NaN | NaN | True | False | The paper discusses several existing AI tools and platforms (e.g., Manupatra, LawGeex, SpotDraft, CaseMine, eBrevia, Chat GPT) that are in use or gaining popularity in the legal industry, implying their availability from their respective providers. | Current AI is still at a 'weak artificial intelligence' level; ongoing ethical, data protection, and regulatory challenges; need for clear guidelines and Mstandards; fundamental philosophical and legal questions about AI's role and capabilities (e.g., AI judges, legal rights of robots). | NaN | Job displacement for legal professionals; ethical concerns regarding informed consent and quality of AI-provided legal counsel; privacy issues; accuracy and truthfulness of information provided by AI; potential failure to uphold professional and legal standards if AI is not properly guided; AI representing potentially the greatest threat to the legal profession. |
| 57LoyLALRev903.pdf | HeinOnline | GENERATIVE Al AND LEGAL AID: RESULTS FROM A FIELD STUDY AND 100 USE CASES TO BRIDGE THE ACCESS TO JUSTICE GAP | This study explores how generative AI can help legal aid professionals improve access to justice for low-income individuals. Through a field study and survey, it found AI tools increased productivity and users intended to continue their use, while also identifying key applications and strategies to manage risks, and releasing a database of 100 use cases. | True | Idealistic | True | 1.0 | Positive | A model for integrating generative AI (ChatGPT-4, Gavel, CoCounsel) into legal aid practice, supported by 'concierge services' (peer use cases, office hours, assistance), and a publicly released database of 100 application use cases derived from this model. | Field study with 91 legal aid professionals using AI tools (ChatGPT-4, Gavel, CoCounsel) for up to two months in their actual work. A subset (N=37) received 'concierge support' via a randomized controlled trial. Baseline (N=202) and exit surveys (N=66) measured productivity, usage patterns, attitudes, and intentions. | 90% of pilot participants reported increased productivity; 75% intended to continue using generative AI. Concierge support significantly improved outcomes. Participants focused on low-risk tasks (summarization, drafting, translation), achieving approximately 50% productivity gains on them, while concerns about AI (accuracy, confidentiality) persisted. | Knowledge gap (unawareness of legal issues/solutions) and service gap (unaffordable/unavailable legal aid) for low-income individuals, leading to inadequate representation. Resource constraints for legal aid organizations. Regulatory hurdles like UPL rules. | Augment legal aid lawyers with AI; ensure equitable AI access (funding, outreach, addressing gender gap); innovative assistance models (helpdesks, communities of practice, 'Tech Bono'); develop specific legal aid AI solutions; explore AI bot certification and regulatory sandboxes. | Increasing capacity of legal aid services through technology; specific applications in eviction defense, expungement, immigration, client intake, document automation, and legal information translation for underserved populations. | Low-income Americans, clients of legal aid organizations. | Broad range including housing, expungement, immigration, family law, employment, civil rights, and general civil legal needs of low-income individuals. | United States (field study primarily involved California legal aid professionals, with examples/discussion relevant to other US states). | The study utilized existing commercial tools. ChatGPT-4 is trained on large general text/code. CoCounsel uses GPT-4 and Casetext's proprietary legal databases. Gavel is primarily a rules-based automation platform. | The model and its impacts were developed and evaluated through a field study involving legal aid professionals, incorporating a randomized controlled trial for the concierge services. The use case database was compiled from participant contributions (prompts, outputs, classifications, efficiency ratings) during the study. | The AI tools studied (ChatGPT-4, CoCounsel, Gavel) are commercially deployed. The paper makes its database of 100 use cases publicly available via a URL (https://bit.ly/AIA2J). | True | True | Commercial AI tools (ChatGPT-4, CoCounsel, Gavel) are available via paid subscriptions. The paper's database of 100 use cases is publicly and freely accessible online via a URL. | Persisting AI concerns (confidentiality, accuracy, hallucinations, bias); gender disparity in AI uptake; UPL rules hindering innovation; resource constraints for legal aid AI adoption; need for scalable legal aid-directed AI solutions and bot certification. | For study participants: managing AI risks (hallucinations, privacy, accuracy); learning curve with tools. For researchers: participant time constraints; logistical issues with providing tool access during the study. | AI hallucinations and inaccuracies; exacerbation of inequities (two-tiered legal service); discriminatory outcomes from AI bias (racial, gender, anti-consumer); data privacy/confidentiality breaches; consumer harm from unauthorized practice of law (UPL) by AI tools; stifling innovation due to over-emphasis on harms. |
| 72UKanLRev313.pdf | HeinOnline | AI-ready Attorneys: Ethical Obligations and Privacy Considerations in the Age of Artificial Intelligence | This paper analyzes the ethical duties of competence, communication, and confidentiality for attorneys using AI in legal research and writing, alongside data privacy obligations under US and EU frameworks. It offers recommendations for law schools and practicing lawyers to responsibly integrate AI, highlighting risks like data breaches, AI "hallucinations," and confidentiality violations. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General_legal_practice | United_States | NaN | NaN | NaN | False | False | NaN | Lack of specific formal ethics opinions from bar associations on AI use in legal practice; insufficient training on legal technology and AI in law schools; current ethical rules provide only piecemeal guidance for AI; general lack of understanding among lawyers about AI's capabilities and risks. | NaN | AI generating 'hallucinations' like non-existent case citations; violation of lawyers' duty of competence due to misunderstanding AI tools; breach of client confidentiality from inputting data into AI systems; data breaches at law firms or AI vendors; unauthorized use or sharing of client data by AI vendors (e.g., for model training or data brokerage); lack of meaningful notice and consent in AI tools' privacy policies; potential for algorithmic bias and discrimination; cybersecurity vulnerabilities; intellectual property infringement concerns. |
| 49QueensLJ73.pdf | HeinOnline | Luck of the Draw III: Using Al to Extract Data About Decision-Making in Federal Court Stays of Removal | This article uses GPT-3 to extract data from Canadian Federal Court dockets on immigration stay of removal applications, revealing significant inconsistencies in grant rates among justices. It advocates for increased judicial consistency and greater access to legal data for research, demonstrating AI's potential for scrutinizing legal decision-making to enhance migrant rights. | True | Idealistic | True | 1.0 | Positive | A multi-step computational legal research methodology involving: 1) Web-scraping of Federal Court dockets; 2) Regex-based screening of dockets and entries; 3) Fine-tuning and application of GPT-3 for categorizing docket entries (e.g., identifying motions for stays, orders) and extracting specific data (e.g., deciding justice, outcome); 4) Pandas for docket-level data aggregation and analysis. | The data extraction technique was evaluated by: 1) Comparing its identification of stay of removal cases against a manually reviewed dataset for one year, achieving 98.0% (96/98) coverage. 2) Manually verifying 200 coded dockets for accuracy of extracted datapoints, resulting in 99% accuracy. | The automated data extraction technique identified 98.0% of manually identified stay of removal cases in a comparison sample and achieved 99% accuracy in extracting specific datapoints from dockets based on manual verification of 200 cases. | Limited access to bulk legal data for research and analysis due to restrictive terms of service and lack of court-provided bulk access mechanisms, hindering transparency and oversight. Inconsistent and potentially arbitrary judicial decision-making in high-stakes deportation cases (the 'luck of the draw' phenomenon), impacting fairness for migrants. | Making legal data (court dockets, decisions) accessible in bulk via APIs for non-commercial research by courts and tribunals. The Federal Court taking measures to encourage more consistency in stay decision-making, possibly through internal discussions, guideline development, or legislative intervention. Researchers sharing code and data, as done in this project. | Fairness and consistency in judicial decision-making in deportation/removal proceedings; transparency in the legal system; access to legal information for research; procedural justice in immigration and refugee law. | Marginalized migrants, non-citizens facing deportation in Canada, particularly those at risk of irreparable harm. | Immigration and refugee law, Administrative law (specifically judicial review). | Canada (Federal Court of Canada). | For GPT-3 fine-tuning: A human-coded dataset of hundreds of sample Federal Court docket entries (prompts) paired with desired completions (e.g., outcome categories like 'granted', 'dismissed', 'other'; extracted judge names). This training data was derived from publicly available, unstructured, bilingual (English/French) online Federal Court dockets web-scraped by the author. | An iterative process for machine learning model development: applying the GPT-3 model to new docket entries, verifying performance, providing additional labeled examples for fine-tuning if errors were found, re-training, and re-testing until satisfactory performance was achieved. The overall research methodology involved sequential data processing steps (scraping, regex, ML extraction, logical aggregation). | The Python code (excluding the web-scraping program), human-coded training datasets for GPT-3 fine-tuning, and the full dataset of scraped Federal Court dockets (87,776 dockets) were made available on GitHub for non-commercial research use. | True | False | The code (excluding web-scraping), human-coded training datasets for fine-tuning GPT-3, and the dataset of Federal Court dockets are available on GitHub for non-commercial research use. Execution of the GPT-3 component of the technique requires a paid OpenAI API key. | Need for systemic solutions for bulk access to legal data beyond individual researcher efforts (e.g., court-provided APIs and permissive terms of service). Further empirical research on reasons for judicial inconsistencies (e.g., role of counsel quality, interpretation of legal tests, country of origin). Addressing limitations in court decision publishing practices that hinder bilingual access and large-scale computational research. | Technical complexity and resource intensiveness of systematically web-scraping and maintaining an up-to-date large-scale database of court dockets. Difficulty of accurately extracting structured information from unstructured, natural language, and often bilingual court docket entries which may lack standardized phrasing. Requirement for manual data labeling to create training sets for fine-tuning machine learning models. | Inherent limitations of LLMs like GPT-3, including potential for bias amplification from training data, generation of 'hallucinated' or incorrect information, and susceptibility to misuse for disinformation. Risk of exacerbating power imbalances if advanced AI legal tools are asymmetrically available, primarily benefiting well-resourced entities (e.g., government) over marginalized individuals and their advocates. The identified inconsistencies in human judicial decision-making themselves pose a risk to justice, particularly when these decisions have high stakes like deportation and are relied upon for constitutional safeguards. |
| 92TennLRev1.pdf | HeinOnline | A VIEW OF HOW LANGUAGE MODELS WILL TRANSFORM LAW | This paper explores the transformative impact of Large Language Models (LLMs) on legal practice and the legal services industry, predicting new legal work in the near term and long-term structural changes such as enhanced lawyer productivity and potential sector consolidation. It also discusses the enduring role of lawyers in tasks involving value judgments and empathy, even as LLMs automate routine work. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | High cost of legal services; unequal access to up-to-date legal information and analytical tools for less-resourced professionals. | LLMs enhancing lawyer productivity to potentially lower costs and enable price competition; ensuring affordable access to legal data (e.g., via subsidies for PACER). | Affordability and quality of legal services; access to legal information/tools for professionals. | Less-resourced legal professionals; general public (indirectly, through more accessible services and open-source LLMs). | Broad application across multiple legal fields (e.g., civil litigation, corporate law, IP, torts, constitutional law). | United States | Discusses public web data (Common Crawl, Wikipedia), industry-level legal databases (public/commercial), firm-level proprietary data, and synthetic data as LLM training sources in law. | NaN | NaN | True | True | The paper mentions commercially available LLMs like Lexis+ AI and ChatGPT (which has a free tier), and open-source LLMs like BLOOM. | Technical: LLM accuracy, reliability (e.g., synthetic data quality), and cost of processing. Societal: Ensuring equitable access to data and LLM benefits to avoid widening disparities. | Ensuring LLM accuracy/reliability (avoiding hallucinations, bias); managing data privacy/confidentiality; developing quality synthetic data; addressing IP/copyright issues for LLM outputs and training data. | Professional liability from inaccurate LLM outputs; data breaches; model degradation from poor data; misuse for harmful activities; unresolved liability for AI errors or 'orphaned' AIs. |
| 32AustlLLibr68.pdf | HeinOnline | HOW TECHNOLOGY CAN SUPPORT OPEN JUSTICE AND TRANSPARENCY: A UK PERSPECTIVE | This paper surveys various technological advancements, from historical innovations like writing and printing to modern developments such as the Internet and AI, illustrating their role in enhancing open justice and transparency within the UK legal system. It highlights how these technologies, including AI-driven tools for case summarization, improve public access to and understanding of legal information and judicial processes. | True | Idealistic | True | 3.0 | Positive | AI-generated case summaries (using GPT-4 via Jurisage) for unreported judgments, integrated into ICLR's Case Genie brief analysis tool and general case search on the ICLR.4 platform. | The AI summaries are generated by GPT-4 based entirely on the judgment text to avoid hallucination. The paper mentions trying various prototypes before settling on the Jurisage system. No specific benchmark or formal user testing results for the AI summaries are detailed. | The AI system generates 100-word summaries and three bullet points identifying the top three issues for unreported cases, aiming to make case law clearer and more accessible to users searching on the ICLR.4 platform. | Physical barriers in courtrooms (sightlines, acoustics); low public legal literacy; cost of accessing court documents; incomplete digitization of court processes; potential for critical errors in online legal systems. | Improved design of court spaces (physical and virtual); creation of easy-read legal guides; online publication of judgments and legislation; use of legal blogs, podcasts, and social media for public education; Online Dispute Resolution systems; AI tools for legal research and information accessibility. | Open justice, legal transparency, public legal education, access to primary legal information, accessibility of court proceedings, online dispute resolution for unrepresented litigants. | General public, unrepresented litigants. | General (common law, statute law, family law, criminal justice). | United Kingdom (primarily England & Wales). | For AI summaries: GPT-4 is used, with summaries reportedly 'entirely based on what’s in the judgment' text itself. For Case Genie: A corpus of primary legal sources, including unreported judgments. | Iterative prototyping ('tried various prototypes') and collaboration with a technology developer (Jurisage) for the AI summary feature. | The AI-generated summaries are integrated into the ICLR.4 online platform, accessible via subscription, enhancing Case Genie and general case search functionalities. | True | False | The AI summaries are available as part of the ICLR.4 platform, which is a subscription-based service. More information is available on the ICLR website. | Cost as a barrier to accessing some digitized court documents (e.g., CE-file); the HMCTS Reform programme for digitisation is still incomplete; the AI in Case Genie does not explain *why* it recommends certain cases, only *what* the recommended cases are about via summaries. | Initial 'teething problems' with new digital systems (e.g., TNA judgment feed); ensuring AI-generated content is accurate and free of hallucinations (addressed by grounding summaries in source judgment text); the 'closed box' nature of AI recommendation reasoning for Case Genie, which necessitated the development of AI summaries for explication. | Potential for severe, irreversible errors in online legal processes (e.g., accidental online divorce); reputational damage to legal professionals from misuse of social media; the gradually decreasing outlandishness of 'cyber judges' as AI capabilities advance. |
| 96TempLRev349.pdf | HeinOnline | "I AM BECOME DEATH, THE DESTROYER OF WORLDS": APPLYING STRICT LIABILITY TO ARTIFICIAL INTELLIGENCE AS AN ABNORMALLY DANGEROUS ACTIVITY | This paper argues for applying strict liability to harms caused by artificial intelligence (AI) when AI use constitutes an "abnormally dangerous activity." It proposes a revised legal test for such activities and a two-tiered insurance system, modeled on the Price-Anderson Act for nuclear energy, to compensate victims while fostering AI industry innovation. | True | Idealistic | False | 1.0 | Positive | Application of strict liability for AI as an abnormally dangerous activity, featuring a revised six-factor test and a Price-Anderson Act-style two-tiered insurance model. | NaN | NaN | Difficulty in proving negligence for AI-induced harms due to AI's inherent unpredictability and "black box" nature; AI industry externalizing costs of injuries, leaving victims without adequate compensation; existing legal and insurance frameworks being insufficient for potentially catastrophic AI harms. | Applying strict liability to AI activities deemed "abnormally dangerous"; revising the traditional six-factor test for such activities to better suit AI's unique characteristics (e.g., focusing on unforeseeability of harm, removing common usage and locality factors); implementing a mandatory, two-tiered insurance system for AI companies modeled on the Price-Anderson Act to ensure victim compensation and limit industry liability. | Compensation for AI-induced harms, establishing legal accountability for AI creators and deployers, addressing systemic risks and biases in AI leading to disparate outcomes. | Black patients (in the context of a discussed healthcare AI example leading to discriminatory outcomes). More broadly, individuals harmed by AI engaged in abnormally dangerous activities. | Tort law (specifically strict liability, abnormally dangerous activities), Insurance law, Regulatory law concerning technology. | United States | For the discussed High-Risk Management Tool (HRMT): Proprietary, structured patient healthcare data, including historical healthcare costs (used as a proxy for health needs) and comorbidity information, from a large US national patient population. | NaN | NaN | False | False | NaN | Potential for AI-caused damages to exceed the proposed insurance caps, necessitating further governmental action for full compensation in catastrophic scenarios; challenges in international harmonization of AI liability and compensation schemes. | Balancing victim compensation with the encouragement of AI innovation; adapting existing legal doctrines (like strict liability for abnormally dangerous activities) to the novel characteristics of AI (e.g., unpredictability, "black box" nature); designing a fair and feasible insurance and indemnification system, including setting appropriate liability limits and ensuring broad industry participation. | AI causing physical harm, injury, and death; AI perpetuating or creating discrimination (e.g., racial bias in healthcare); weaponization of AI (e.g., for chemical weapons); AI-generated misinformation destabilizing society; concentration of AI power leading to surveillance and oppression; human over-dependence on AI; unpredictable AI behavior leading to unforeseen harms. |
| 39TouroLRev165.pdf | HeinOnline | THE CATEGORICAL IMPERATIVE: IN SEARCH OF THE MYTHICAL PERFECT PRIVILEGE LOG SO DEVOUTLY TO BE WISHED | The paper examines the burdensome nature of traditional, document-by-document privilege logs in legal discovery, particularly with the explosion of electronically stored information. It explores the development and adoption of "categorical" privilege logs as an alternative aimed at increasing efficiency and reducing costs, detailing academic commentary, judicial rule-making efforts, court reactions, and the persistent challenges due to the adversarial nature of litigation. | True | Market | False | 3.0 | NaN | Categorical privilege logs (as an alternative to traditional document-by-document logs); metadata-based 'objective privilege logs'. | NaN | NaN | NaN | NaN | NaN | NaN | Civil procedure, Evidence (evidentiary privilege), E-discovery practices, Civil litigation. | United States (primarily federal court system, with mentions of New York and Delaware state courts). | NaN | NaN | NaN | False | False | NaN | NaN | Achieving a balance between efficiency and sufficient detail in categorical logs; risk of misuse (e.g., overly broad categories, gamesmanship by parties); obtaining agreement between adversarial parties on protocols for categorical logging; overcoming judicial skepticism and ensuring consistent application. | Waiver of privilege due to inadequate logging (either traditional or categorical); categorical logs obscuring non-privileged documents if categories are poorly defined or misused; potential for increased disputes if categorical logs are not implemented cooperatively or lack sufficient detail for assessment; risk of inadvertently revealing sensitive information or trial strategy through the logging process itself. |
| 25TransactionsTennJBusL81.pdf | HeinOnline | Preparing Future Lawyers to Draft Contracts and Communicate with Clients in the Era of Generative AI | This paper argues for integrating generative AI into legal education, specifically transactional law, to prepare future lawyers for AI's impact on the profession. It discusses capabilities and risks (confidentiality, hallucinations, bias) of tools like ChatGPT and Sparllbook, emphasizing ethical use and curriculum adaptation. | True | Market | True | 3.0 | Positive | ChatGPT, Harvey, CoCounsel, Lexis Plus AI, Spellbook | Describes demos, beta testing by firms for commercial tools (CoCounsel, Lexis Plus AI, Harvey), and an in-class exercise using ChatGPT for drafting client emails. | In-class ChatGPT exercise showed prompt quality and legal knowledge significantly impact output quality. Commercial tools are described as providing head starts, increasing efficiency, and offering features like contract redlining and summarization. | The high cost and inefficiency of legal services, which create barriers to justice. | Leveraging AI to increase lawyer efficiency, thereby potentially lowering costs of legal services or enabling lawyers to undertake more pro bono work. AI can also assist in specific A2J tasks like screening cases for organizations like the Innocence Project. | Increasing efficiency of legal work to potentially free up resources for underserved clients, AI-assisted case screening (e.g., for innocence projects). | Individuals facing barriers to justice due to the cost and inefficiency of legal services; specifically mentions wrongfully convicted individuals (Innocence Project). | Transactional law (contract drafting, client communication), litigation (legal research, document drafting), employment law, general business law. Also touches upon AI law itself (privacy, IP, liability, discrimination, regulation). | United States, with references to global legal practice. | Primarily proprietary datasets for legal-specific tools (e.g., case law, legal treatises, firm documents for Harvey and Lexis Plus AI); publicly available internet data and user inputs for general models like ChatGPT. | NaN | Harvey: Retained by law firms, trains on firm's data. CoCounsel: Acquired by Thomson Reuters, beta tested by large firms and in-house counsel. Lexis Plus AI: Developed with partner law firms, planned for release. Spellbook: Microsoft Word plugin, offers free trials, planned availability for law schools. | True | True | ChatGPT (free version) and DALL-E are freely publicly available. Spellbook offers free trials and planned wider (potentially free) availability for law schools. Lexis Plus AI was anticipated for release in Fall 2023. | Need for ethical guidelines and regulation for AI in law. Ensuring equitable access to AI tools for all students and legal professionals. Developing pedagogical approaches to teach AI literacy and foundational legal skills concurrently. | For tool users/developers: ensuring data confidentiality, preventing AI 'hallucinations' (generating false information), mitigating bias inherited from training data. For legal educators: adapting curricula, ensuring students develop foundational skills despite AI availability, teaching ethical AI use, addressing equitable access to AI tools. | Confidentiality breaches (inputting client data into public models), reliance on AI-generated 'hallucinations' leading to professional misconduct (e.g., citing fake cases), perpetuation of societal biases embedded in AI models, de-skilling of lawyers if foundational skills are not maintained, ethical violations if AI use is not disclosed or properly supervised, job market changes for lawyers. |
| 2024EurJPrivacyLTech79.pdf | HeinOnline | Legal Arrangements of Artificial Intelligence in the European Union and the Republic of North Macedonia | This paper analyzes the necessity and content of AI regulation, examining the EU's AI Act and North Macedonia's progress towards a national AI strategy. It emphasizes addressing AI's challenges to social governance and legal systems through transparency, accountability, and fairness to protect fundamental rights. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Lack of access to technology and digital literacy hindering the use of AI-driven tools (e.g., remote courts) for justice; potential for AI to introduce bias and non-transparency, creating new barriers to fair legal processes; absence of comprehensive national AI strategies in some regions, delaying beneficial and safe AI adoption in the justice sector. | Developing and implementing comprehensive, risk-based AI regulations (e.g., the EU AI Act) focusing on safety, transparency, fundamental rights, and accountability; integrating Online Dispute Resolution (ODR) and e-filing into court systems; ensuring human oversight over AI in legal applications to maintain fairness and prevent harm. | Online Dispute Resolution (ODR), e-courts, technology in judicial reforms, ensuring fairness and non-discrimination in AI-assisted legal processes, AI regulation. | NaN | AI Law, EU Law, Contract Law, Criminal Law (Cybercrime), Tort Law, Product Liability Law, Administrative Law, Constitutional Law. | European Union, Republic of North Macedonia | NaN | NaN | NaN | False | False | NaN | The digital divide (inequitable access to technology and digital literacy) limiting widespread benefit from AI in legal services; the need for legal frameworks that effectively address AI's unique attributes (e.g., autonomy, intent) in the context of justice; ensuring AI development remains human-centric and upholds rule of law principles in the justice sector. | Ensuring AI systems are safe, transparent, traceable, non-discriminatory, and ethically sound, especially in legal contexts; defining and attributing legal liability for harm caused by AI systems due to their opaqueness and autonomy; harmonizing AI regulations across jurisdictions; managing socio-economic impacts like deskilling within the legal profession. | Violation of fundamental rights (e.g., right to life, privacy, non-discrimination) through AI misuse; cognitive behavioral manipulation or social scoring; biased or flawed AI in law enforcement (e.g., predictive policing, biometric identification) impacting fair trial; erosion of human judgment and accountability in legal decision-making; safety risks from malfunctioning AI systems used in contexts relevant to law (e.g., autonomous vehicles involved in accidents). |
| 14SeattleJTechEnvtInnovat.pdf | HeinOnline | A Framework for Data-Driven Legal Regulatory Reform | This paper proposes a framework for data-driven legal regulatory reform, developed by the Washington Supreme Court Practice of Law Board, which utilizes the scientific method and data analysis within a controlled environment like a 'sandbox' or 'lab'. The framework aims to make legal regulatory changes more timely, evidence-based, and effective in addressing issues such as the access-to-justice gap. | True | Idealistic | False | 1.0 | Positive | A framework for data-driven legal regulatory reform, incorporating: 1) Use of the scientific method (hypothesis testing of proposed reforms). 2) Operation within a 'sandbox' or 'regulatory lab' for safe testing. 3) A 3-D model for evaluating reforms based on current risk, future risk, and impact on the access-to-justice gap. 4) A 3x3 risk analysis matrix (Likelihood vs. Harm Severity). 5) A suggested methodology for measuring access-to-justice impact. | NaN | NaN | Current legal regulatory reform is slow, bespoke, rarely evaluated for effectiveness (especially concerning access to justice), and lacks sufficient public involvement; scarcity of data in legal services; legal profession's conservativism; cost of studies; and client confidentiality concerns hindering data collection. | Implement a data-driven framework for legal regulatory reform using the scientific method within a controlled 'sandbox' or 'lab' environment; systematically collect and analyze data to assess risks (current and future) and benefits (specifically access-to-justice impact) of proposed reforms, fostering evidence-based decision-making. | Legal regulatory reform process; improving access to justice through better regulation; data-driven decision making in law; evaluating the impact of legal innovations; authorized practice of law. | The public and consumers of legal services broadly, with a focus on those affected by the access-to-justice gap, including low- and moderate-income communities. | General legal practice regulation, including Rules of Professional Conduct (e.g., advertising, malpractice insurance, confidentiality, conflicts, competence, communication, client funds), unauthorized practice of law, and licensing of legal service providers. | Washington State (USA), with potential adaptability to other jurisdictions. | NaN | Iterative development by the Practice of Law Board, inspired by the scientific method, existing regulatory sandbox models (e.g., Utah), and risk assessment matrices. Initial concepts were presented as 'blueprints' and refined based on feedback and further consideration. | The paper proposes the framework and hopes for its adoption and adaptation by entities involved in legal reform. No specific deployment plan is detailed beyond publication and advocacy. | False | False | NaN | The ongoing difficulty in quantitatively measuring the benefits of legal reforms, particularly their impact on the access-to-justice gap; the need for robust data collection mechanisms in the legal field while addressing cost and confidentiality; fostering a cultural shift within the legal profession towards data-driven innovation; effectively defining and mitigating future 'unknown unknown' risks; ensuring meaningful public involvement in reform processes. | Challenges for the proposed framework include gaining adoption and buy-in from relevant stakeholders; securing resources for operating 'sandboxes' or 'labs'; developing reliable and accepted metrics for 'access-to-justice impact'; managing ethical considerations and potential harms within experimental settings; and overcoming the prevalent 'small-data world' of legal services to enable effective data-driven reform. | Risks that the framework aims to help assess and mitigate include: consumer harm (inaccurate legal results, failure to exercise rights, purchasing unnecessary services); breach of confidential information; application of incorrect law; missed deadlines; future risks stemming from changes in laws, client situations, or technological obsolescence (e.g., outdated wills); expert bias in risk assessment; and misuse of technology by legal professionals (e.g., AI generating fake citations). |
| 13StMarysJonLegalMalpract.pdf | HeinOnline | Unauthorized Practice or Untenable Prohibitions: Refining and Redefining UPL | The paper argues that current Unauthorized Practice of Law (UPL) rules are outdated, ambiguous, and hinder access to justice for many Americans. It proposes a revised definition of UPL with specific exceptions to allow nonlawyers and technology (including apps) to provide legal information and services, aiming to increase affordability and availability. | True | Idealistic | False | 1.0 | Positive | A revised definition of UPL (Unauthorized Practice of Law) with specific exceptions and substantive provisions to permit nonlawyers and computer programs/apps to provide certain legal advice and assistance. | NaN | NaN | Unaffordability and inaccessibility of lawyers for many Americans (access to justice crisis); outdated, ambiguous, vague, and conclusory UPL rules that deter innovation and are inconsistently applied. | Refine and redefine UPL by establishing clearer exceptions for what should not be considered UPL, allowing certain nonlawyers and computer apps to provide legal information and services. This includes a newly proposed definitional framework for UPL with specific substantive provisions. | Access to affordable legal information and services, resolution of civil legal matters, consumer debt collection, personal bankruptcy, traffic law matters. | Low-income Americans, individuals who cannot find or afford lawyers generally, those facing common civil legal problems (e.g., eviction, custody, tort, contract, debt collection, bankruptcy). | Civil Law (general), Consumer Law (debt collection), Bankruptcy Law, Traffic Law, Administrative Law. | United States (critiquing US UPL rules generally and discussing specific US state and federal cases), with references to United Kingdom and Australia for comparative experience. | NaN | Legal analysis, historical review, policy argument, case law review, comparative analysis (referencing UK/Australia and other studies). | NaN | False | False | NaN | Need for jurisdictional adoption and implementation of the proposed UPL framework; development of specific regulatory mechanisms for nonlawyer providers and AI tools; ongoing adaptation to technological evolution; ensuring public protection while increasing access. | Historical difficulty in achieving consensus on UPL definitions due to tensions between protecting the legal profession's turf and public interest; ensuring any definition is cogent and universally applicable while balancing various stakeholder interests. | Potential adverse effects from using computer programs/apps without full understanding of their limitations if disclosures are inadequate; general limitations, risks, or negative consequences of consulting nonlawyers instead of lawyers if not properly regulated or disclosed; (via citation) AI generating false or misleading legal documents if not properly overseen. |
| 56TexTechLRev525.pdf | HeinOnline | AREN'T WE EXHAUSTED ALWAYS ROOTING FOR THE ANTI-HERO? PUBLISHERS, PRISONS, AND THE PRACTICING BAR | This paper critiques the monopolistic practices of legal information providers like LexisNexis and Westlaw, detailing how these practices severely hinder incarcerated litigants' access to legal information and the courts. It contrasts the legal profession's inaction on this critical access to justice issue with their vocal advocacy in other areas, ultimately calling for mobilization against these publishers. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Monopolistic control and high cost of legal information by publishers; restrictive legal precedents limiting prisoners' rights to information; inadequate prison library funding and resources; gatekeeping of legal information through paywalls and opaque algorithms; legal profession's inaction and misdirected advocacy. | Increased advocacy by the legal profession against publisher monopolies; re-evaluation of restrictive Supreme Court precedents on information access for prisoners; promotion of greater transparency and open access to legal information; encouraging appropriate use of technology to enhance access, rather than fearing it. | Access to legal information for incarcerated litigants; prisoners' right to access courts; challenges of self-representation for inmates. | Incarcerated litigants | Constitutional Law; Criminal Law; Antitrust Law (indirectly); Legal Ethics | United States | NaN | NaN | NaN | False | False | NaN | Lack of meaningful and affordable legal information access for prisoners; insufficient advocacy and awareness within the legal profession regarding this issue; need for open access alternatives to proprietary legal Ppesearch platforms; outdated legal precedents that do not account for technological advancements in information access. | NaN | Denial of meaningful access to justice for incarcerated individuals due to information monopolies; erosion of prisoners' constitutional rights; potential for surveillance and data misuse by legal information providers; attorneys' over-reliance on opaque commercial legal research platforms; misdirection of the legal profession's concerns about technology away from systemic access issues. |
| 16CaseWResJLTechInternet7.pdf | HeinOnline | Al LAWYERING SKILLS TRAINERS: TRANSFORMING LEGAL EDUCATION WITH GENERATIVE Al | This paper argues for integrating Generative AI (GenAI) tools into legal education to enhance advocacy skills, offering personalized coaching and bridging theory with practice. It details the development of MootMentorAI, a GenAI skills trainer at UMKC School of Law, using Agile methodology and provides a guide for educators to create similar AI tools. | True | Market | True | 1.0 | NaN | MootMentorAI, a custom Generative AI (GPT-4o based) tool for moot court practice, and a framework for developing similar AI lawyering skills trainers using platforms like OpenAI's GPT Builder or CustomGPT.AI. | MootMentorAI was evaluated through designer-led iterative testing (30 simulations in the initial phase using the 1L Oral Argument Problem from UMKC Law), involving systematic feedback collection and training data updates. Student-led testing in educational settings is described as pending. | The paper describes iterative improvements to MootMentorAI based on designer-led testing. Specific quantitative results are not detailed as student-led testing is pending; Appendix A provides a transcript of a sample practice session. | NaN | NaN | NaN | NaN | Legal Education (Advocacy skills, Oral Argument, Moot Court) | United States (specifically focused on UMKC School of Law for the MootMentorAI tool, with broader applicability to US legal education) | Proprietary, domain-specific instructional support materials for UMKC Law's 1L Oral Argument Problem, including bench briefs, sample questions for judges, the factual record, and assigning memos. These are unstructured text documents used to create a closed knowledge universe for the AI. | Agile methodology, adapted for instructional design, involving iterative cycles of development, testing, feedback collection, and refinement. | MootMentorAI is intended for deployment within an educational setting (University of Missouri-Kansas City School of Law) for student use, potentially via shared links created through the GPT platform. Broader deployment and student-led testing are pending. | False | False | NaN | NaN | The refining process of the AI tool can be challenging and time-consuming, with the AI potentially behaving unexpectedly due to unclear prompts or ambitious expectations. Costs of AI platforms (e.g., ChatGPT Plus subscription). Ethical considerations and the potential need for Institutional Review Board (IRB) approval for academic use. Logistical challenges include integrating tools into curricula, training faculty/staff, and ensuring adequate infrastructure. | AI 'hallucinations' (generating incorrect information). Data privacy issues and over-reliance on AI-generated content. Potential for students to exhibit rude or inappropriate behavior towards the AI, requiring programmed responses to maintain professionalism. |
| 57ColumJLSocProbs397.pdf | HeinOnline | After Reaching the Courthouse Door: Why Lack of Affirmative Assistance Post-Pleading Violates Prisoners' Access to Courts Right | This paper argues that the lack of affirmative legal assistance for incarcerated persons after the pleading stage violates their fundamental right to access the courts under the Due Process Clause. It proposes reconciling current legal frameworks by requiring states to provide "legal information" but not "legal advice" throughout the litigation process for prisoners. | True | Idealistic | False | 1.0 | NaN | The paper proposes a legal framework: the "legal information vs. legal advice" distinction for providing post-pleading assistance to prisoners. | NaN | NaN | Current legal interpretations severely limit prisoners' post-pleading assistance (Lewis v. Casey); incarcerated individuals lack legal knowledge for complex pro se litigation; systemic issues like retaliation by prison officials and ineffective grievance systems hinder access; procedural hurdles of the Prison Litigation Reform Act (PLRA). | Reinterpret legal doctrine to mandate states provide prisoners with "legal information" (not "legal advice") throughout litigation; adopt the "legal information vs. legal advice" distinction, common for non-incarcerated pro se litigants, for the prisoner context. | Right of access to courts for prisoners; post-pleading legal assistance; civil rights violations; conditions of confinement; pro se litigation by prisoners. | Incarcerated persons (prisoners) | Constitutional Law (Due Process, First Amendment Petition Clause, Equal Protection); Civil Rights Law (Section 1983 claims); Prisoners' Rights; Procedural Law. | United States | NaN | NaN | NaN | False | False | NaN | The need for future case law to clarify the precise boundaries of "legal information" versus "legal advice" and define sufficient assistance under the proposed framework; the current circuit split and lack of Supreme Court guidance on post-pleading assistance. | NaN | Failure to provide adequate post-pleading assistance leads to meritorious lawsuits failing, civil rights violations going unremedied, and the access-to-courts right becoming illusory. The proposed solution itself might raise federalism concerns regarding state autonomy and resource allocation, though the paper argues these can be navigated. |
| 6LawTechHum69.pdf | HeinOnline | The Regulation of Judicial Analytics: Towards a New Research Agenda | This paper reviews the current state of research on the regulation of judicial analytics, identifying key risks such as misinformation and inequity, and evaluating proposed regulatory strategies. It calls for a new research agenda focused on consistent terminology, empirical study of impacts, and clear definitions of regulatory success to guide future policy. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Inequity in access to analytical tools advantaging wealthy litigants; potential for misinformation to undermine public trust and understanding of the justice system; consumer vulnerability to poor quality analytical services; gamification of law potentially overlooking individual justice considerations. | Proposing a research agenda focused on empirical study and defining regulatory success (including human rights considerations) to inform the development of regulatory strategies such as ethics frameworks, trustmarks, and potentially non-profit models to ensure fairness and quality. | Equitable access to legal insights and tools, fairness in legal proceedings, public trust in the judicial system, transparency and accountability of judicial actors, consumer protection in the legal tech market. | NaN | General (judicial decision-making processes and outcomes across various fields, with examples from administrative law, migration law, criminal law) | International (with specific examples and discussions pertaining to Australia, United States, Canada, France, European Union) | NaN | NaN | NaN | False | False | NaN | Lack of empirical evidence on the societal impacts (including on access to justice) of judicial analytics; absence of consistent terminology and jurisdiction-sensitive analyses; insufficient understanding of how existing laws apply; and no clear framework for defining or achieving regulatory success that balances access to justice concerns with innovation. | NaN | Misinformation about judges and the judiciary; threats to judicial independence and wellbeing; non-normative thinking and gamification of law; unwanted strategic litigant behaviour (e.g., forum shopping); harm to consumers from low-quality analytics; and creation or exacerbation of inequity among litigants. |
| 97TempLRev227.pdf | HeinOnline | AI NOW | This paper argues that law professors have an urgent and inescapable duty to understand and engage with generative AI due to its profound impact on legal pedagogy, scholarship, and governance. It criticizes the legal academy's current laissez-faire attitude and proposes steps for faculty to meet this "AI mandate." | True | NaN | True | 3.0 | Positive | Generative Artificial Intelligence (Gen-AI), including specific tools like ChatGPT, Lexis+ AI, and Westlaw AI. | The paper discusses an empirical study by Choi & Schwarcz where law students took final exams in two courses (Introduction to American Law and Legal Reasoning; Insurance Law) under traditional closed conditions and then with access to GPT-4 on a prior year's exam. | Choi & Schwarcz found GPT-4 assistance dramatically increased student performance on multiple-choice questions (29 percentile improvement) but had no statistically significant effect on essay questions. AI use also had an equalizing effect, significantly raising the performance of lower-performing students while slightly decreasing that of top-performing students. | Reinforcing existing inequalities if AI legal services are not properly treated. | NaN | Cost and affordability of legal services, automation of legal processes for accessibility. | Underserved populations that have been historically shut out from legal services. | Legal education, General legal practice (with examples from tax law, property law, insurance law). | United States | The paper discusses Gen-AI tools trained on large general text corpora and domain-specific legal data (e.g., case law, legal authority repositories for tools like Lexis+ AI and Westlaw AI). | NaN | Public web applications (e.g., ChatGPT), integration into existing commercial legal research platforms (e.g., Lexis+ AI, Westlaw AI), and firm-specific internal platforms. | True | True | Publicly available Gen-AI tools like free versions of ChatGPT; commercial Gen-AI tools integrated into platforms like LexisNexis and Westlaw, some requiring subscriptions or institutional access. | Ensuring AI development and deployment in legal services is equitable and does not exacerbate existing disparities. | Challenges faced by the legal academy in understanding and integrating Gen-AI: lack of clear standards on accepted AI use in academia and practice, unclear internal responsibility for AI strategy in law schools, faculty upskilling fatigue, and complexities introduced by distance learning. | Accuracy issues (hallucinations), confidentiality breaches, job displacement in the legal field, undermining academic integrity and assessment, negative impact on development of core legal skills, perpetuation of biases, ethical concerns in AI use for legal work and scholarship, unequal student access to AI tools, and potential for AI to reinforce societal inequalities. |
| 7Issue1IntlJLMgmtHuman.pdf | HeinOnline | Exploring Legal and Ethical Dimensions of Artificial Intelligence in Employment: Safeguarding Worker Rights and Ensuring Fair Practices | This research paper explores the legal, ethical, and policy implications of AI deployment in employment settings, focusing on safeguarding worker rights and promoting fair practices. It highlights challenges like algorithmic bias and job displacement, and recommends updated legal frameworks, ethical guidelines, and stakeholder collaboration for responsible AI adoption. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Algorithmic bias and discrimination in employment decisions; lack of transparency and accountability in AI systems; violations of privacy rights through AI-powered surveillance and data collection; job displacement and erosion of traditional employment opportunities; exacerbation of existing socioeconomic inequalities; legal frameworks lagging behind technological advancements. | Updating existing laws and establishing AI-specific regulations; promoting AI education, training, and ethical awareness; fostering stakeholder engagement (government, industry, civil society); prioritizing fairness, transparency, and human-centric design principles in AI development and deployment; conducting bias assessments of AI algorithms and auditing training data; implementing privacy-preserving techniques and robust data governance. | Worker rights, Fair employment practices, Algorithmic bias in employment, Data privacy in the workplace | Workers (general), with potential disproportionate impact on low-skilled workers, racial minorities, women, and individuals with disabilities | Employment Law, Labour Law, Data Protection Law, Anti-discrimination Law | India, International | NaN | NaN | NaN | False | False | NaN | Lack of comprehensive AI-specific legislation for employment; insufficient harmonization of AI regulation across jurisdictions; need for greater interdisciplinary collaboration in developing AI regulation; rapid pace of AI evolution outpacing regulatory responses; skills gap between AI-driven job demands and workforce capabilities, necessitating reskilling efforts. | NaN | Algorithmic bias leading to discriminatory employment outcomes; lack of transparency and accountability in AI-driven decisions; erosion of privacy rights due to workplace surveillance and extensive data collection; job displacement resulting from AI-powered automation; exacerbation of socioeconomic inequalities; potential for exploitation or misuse of sensitive personal employee data; unequal distribution of AI's benefits and risks. |
| 20UStThomasLJ129.pdf | HeinOnline | TECHNOLOGY COMPETENCE AS A COMPASS FOR HELPING TO CLOSE THE JUSTICE GAP | This paper argues that the ethical duty of lawyer technology competence, as outlined by the ABA, can guide the legal profession in using technology to address the U.S. access to justice crisis. It highlights how this duty can direct legal service providers, regulators, and educators to responsibly leverage technology, thus reducing rather than widening the justice gap. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Cost of legal services, consumer barriers (e.g., knowledge, language), insufficiency of traditional legal aid, regulatory and ethical uncertainties hindering technology adoption, difficulties in developing and implementing effective and unbiased legal technology, professional resistance to change, and resource limitations for access to justice organizations. | Promoting and robustly interpreting the lawyer's duty of technology competence; regulatory reforms (e.g., UPL, non-lawyer ownership, CLEs); enhancing legal education on technology and A2J; fostering interdisciplinary collaboration for ethical tech development and deployment; and organizational leadership in adopting technology thoughtfully. | Unmet civil legal needs, enhancing affordability and efficiency of legal services, democratization of legal information and self-help resources, ethical use of technology in law, and the role of technology competence in legal aid and pro bono services. | Low-income Americans, moderate-income individuals. | General civil law | United States | NaN | NaN | NaN | False | False | NaN | Persistent unmet legal needs for low- and moderate-income individuals; lack of clear ethical guidance for emerging technologies; insufficient technology knowledge and resources within the access to justice sector; and a need for greater transparency regarding the use and impact of legal technology. | NaN | Exacerbation of the justice gap through poorly designed or biased technology; creation and magnification of societal biases by AI; ethical misconduct and professional liability due to incompetent use of technology; and stifling innovation due to overwhelming choices or fear of non-compliance. |
| 26SMUSciTechLRev217.pdf | HeinOnline | REVOLUTIONIZING JUSTICE: UNLEASHING THE POWER OF ARTIFICIAL INTELLIGENCE | This paper reviews the transformative impact of AI on the legal profession, covering applications like legal research, contract analysis, and predictive justice, alongside ethical considerations and liabilities. It emphasizes the necessity for legal professionals to adapt to AI by detailing its benefits for efficiency and access to justice, as well as its inherent risks. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | High cost and inaccessibility of traditional legal services; concerns about AI accuracy, security, privacy, and client confidentiality; potential for AI bias. | Utilizing AI tools like virtual legal assistants and chatbots for basic guidance and resource direction; automating legal processes for pro bono work, public interest organizations, and legal aid clinics to serve more clients effectively. | Improving affordability and accessibility of legal services, providing basic legal information and guidance, supporting pro bono and legal aid organizations. | General public needing affordable legal help, clients of pro bono services, public interest organizations, and legal aid clinics. | General law, contract law, litigation, criminal law, e-discovery. | United States | For LLMs like ChatGPT: vast amounts of publicly available internet data, books, articles, and other documents. For specific tools like COMPAS: defendant's criminal files and interviews. Generally, varies by application. | NaN | Through commercial vendors, cloud platforms, direct-to-consumer applications, and integration into institutional (e.g., court) workflows. | True | False | Many discussed tools are commercially available (e.g., Westlaw, LexisNexis, Casetext, Kira Systems, DoNotPay, LegalZoom). Some platforms like ChatGPT offer publicly accessible free tiers or trial versions for online use. | Potential for bias in AI systems, need for current and accurate information from AI tools (addressing outdated databases and hallucinations), establishment of clear ethical guidelines and regulations for AI in legal aid, ensuring equitable access to AI tools for underserved communities. | Ensuring accuracy and reliability of AI outputs, preventing misuse of AI (e.g., plagiarism), managing confidential information and IP, addressing algorithmic bias, establishing clear ethical guidelines and liability frameworks, and fostering adoption and competence among legal professionals. | Disclosure of confidential information, generation of inaccurate/outdated legal information (hallucinations), misuse for academic dishonesty or impersonation, creation of deep fakes, job displacement for legal professionals, algorithmic bias leading to discriminatory outcomes, ethical violations (competence, confidentiality, unauthorized practice of law), and unclear legal liability for AI-generated errors. |
| 26YaleJLTech64.pdf | HeinOnline | ChatGPT, Esq.: Recasting Unauthorized Practice of Law in the Era of Generative Al | This paper analyzes how large language models like GPT-4 challenge existing Unauthorized Practice of Law (UPL) rules, arguing they exacerbate access to justice issues. It proposes recasting UPL rules to primarily regulate who can be called a "lawyer," while allowing nonlawyers, including AI, to provide most legal services except for in-court representation, thereby improving access to justice. | True | Idealistic | True | 1.0 | Positive | Recasting UPL rules as primarily regulation of entity-type claims (i.e., who can call themselves 'lawyer' or 'attorney'), while allowing nonlawyers, including AI-powered entities, to provide legal services except for representation in legal proceedings (in-court representation). | NaN | NaN | Current Unauthorized Practice of Law (UPL) rules hinder the use of AI for legal services and are vague, inconsistently enforced, and potentially protectionist. High cost of legal services creates a significant access to justice gap, especially for low-income individuals and small businesses. Resistance from bar associations to UPL reform also poses a hurdle. | Recast UPL rules to focus on regulating the 'lawyer' designation rather than the provision of all legal services. Allow nonlawyers, including AI, to offer legal services (except in-court representation), fostering competition and affordability. Utilize the tort system (negligence, false advertising) for consumer protection and to ensure competency of all service providers. | Unauthorized Practice of Law (UPL) reform; Regulation of AI in legal services; Access to affordable legal advice and services; Legal ethics and professional responsibility in the age of AI; Lowering barriers for non-lawyer provision of legal services. | Low-income individuals, underserved communities (e.g., veterans facing eviction), students, and small businesses unable to afford traditional legal services. | General/Multiple (covers UPL broadly, with examples from criminal/civil trespassing, landlord-tenant/eviction, securities law, patent law, wills/probate). | United States (focus on ABA Model Rules, federal and state UPL issues, e.g., Florida, California, North Carolina, Texas, Utah, Missouri). | NaN | NaN | NaN | False | False | NaN | Lack of a clear definition of 'practice of law'; Need for updated civil procedure rules to accommodate legal technology; Potential necessity for a federalized code of legal ethics for all legal service providers; Ensuring consumer protection mechanisms like malpractice insurance for nonlawyer providers; Addressing risks of new forms of exploitation; Mitigating potential negative economic impacts on the legal profession; Ensuring equitable access to AI-driven legal services and avoiding a digital divide. | NaN | Potential for AI 'hallucinations' (providing incorrect information) and AI bias if not carefully managed. Risk of consumer harm if nonlawyer providers are incompetent, though tort law is proposed as a remedy. Unintended consequences of significantly altering long-standing UPL rules. Selective enforcement of current UPL rules creating inequity. Maintaining the UPL status quo harms access to justice, particularly for low-income individuals. |
| 40WindsorYBAccessJust211.pdf | HeinOnline | Generative AI and Access to Justice in Canada: The Case of Self-Represented Litigants [SRLs] | The paper explores how generative AI, specifically LLMs, could assist self-represented litigants (SRLs) in Canada, weighing potential benefits against significant limitations. It concludes that SRLs' ability to effectively use LLMs depends heavily on their own literacy and understanding, as current LLM deficiencies could be detrimental. | True | Idealistic | True | 3.0 | Neutral | Large Language Models (LLMs) like ChatGPT | NaN | NaN | SRLs' general difficulty with law and litigation; high cost of legal representation leading to self-representation; need for clear legal information, document drafting, and procedural guidance; varying levels of digital literacy and access to technology among SRLs. | Developing customized LLMs tailored for SRLs; using LLM technology in customized interfaces for self-help (e.g., form completion, directing to verified resources); calibrating AI use based on user needs and legal context; requiring disclosure of AI use in legal filings; improving AI literacy for SRLs; combining LLM use with existing legal resources. | Understanding legal rights; preparation of court documents (e.g., pleadings); legal research; assistance with forms; drafting court documents and orders; preparation for court proceedings; facilitating settlement. | Self-represented litigants (SRLs) | Family law; Civil litigation (general) | Canada | Discusses training data for existing LLMs: e.g., for ChatGPT: publicly available information, licensed third-party information, user/trainer-provided information; for bespoke legal LLMs: legal-specific content, internet data. | NaN | NaN | True | True | Generic LLMs like ChatGPT (free/paid) are discussed as available; some commercial bespoke legal AI tools also mentioned. | Funding gap for A2J-focused legal tech vs. commercial legal tech; need for naturalistic evaluation of SRLs' LLM use; inadequate AI literacy among SRLs; digital divide (access and skills); ensuring LLMs cater to diverse SRL needs without exacerbating inequality; limited understanding of LLM capabilities for complex legal issues by SRLs; uncertainty about court adaptation to LLM use by SRLs. | Affordability of high-quality LLMs for SRLs; unreliability and inaccuracy of information from generic LLMs (e.g., hallucinations, jurisdictional errors); SRLs' over-reliance on unverified LLM outputs; LLM difficulty in understanding poorly phrased queries from users with low literacy. | SRLs receiving incorrect or misleading legal information; propagation of biases from LLM training data; distortion of public understanding of the law; SRLs submitting 'hallucinated' or fabricated legal citations, potentially leading to sanctions; increased frivolous litigation burdening courts; widening the justice gap. |
| 18LibertyULRev705.pdf | HeinOnline | Internet Frisking Jurors During Voir Dire: The Case for Imposing Judicial Limitations | This paper argues against allowing internet research of prospective jurors during voir dire, citing concerns about fairness, juror privacy, potential for bias in jury selection, and the integrity of the judicial process. It proposes a specific court rule to completely ban such research to preserve traditional, supervised voir dire and ensure judicial integrity. | True | Idealistic | False | 1.0 | Negative | A court rule to completely ban internet research (including AI-assisted methods) of prospective jurors by attorneys in preparation for and during the voir dire process. | The proposed rule is primarily supported by legal reasoning, analysis of existing judicial practices and opinions, ethical arguments, and concerns about fairness, juror privacy, and the integrity of the voir dire process as detailed in the paper. | NaN | Potential for biased jury selection due to discovery of information about race, religion, politics, etc.; invasion of juror privacy; decreased juror willingness to serve; unequal access to justice due to disparities in litigants' resources for research; compromising the integrity of voir dire and public trust in the justice system. | The adoption of a uniform court rule by federal and state trial courts that completely prohibits attorneys and their agents from conducting any internet research into a prospective juror's background in preparation for or during the voir dire process. | Fair trial rights; impartial jury selection; integrity of the voir dire process; juror privacy rights; ethical conduct of attorneys; equality of arms for litigants; regulation of technology in legal proceedings. | Prospective jurors from the general population; litigants, particularly those with fewer financial resources who cannot conduct extensive juror research. | Civil and criminal procedure (specifically voir dire / jury selection) | United States (federal and state courts) | NaN | Legal analysis, ethical reasoning, review of existing court practices and case law, synthesis of commentary and survey data. | The paper proposes the rule be adopted by federal and state trial courts to create uniformity. | False | False | NaN | Lack of uniform court rules regarding internet research of jurors; societal challenges in balancing technological advancements with fairness, privacy, and integrity in the justice system; the rapid development of AI tools for juror profiling outpacing regulatory responses. | NaN | Invasion of juror privacy; chilling effect on juror willingness to serve; enabling impermissible use of peremptory challenges (e.g., based on race, religion, political affiliation) discovered online; creating an uneven playing field for litigants based on differing resources for juror research; undermining public trust and confidence in the judicial system (e.g., through perceived hypocrisy); decisions based on inaccurate or misinterpreted online information; AI-driven tools exacerbating bias in jury selection. |
| 93UCinLRev408.pdf | HeinOnline | EXPANDING ACCESS TO JUSTICE THROUGH REGULATORY REFORM AND INNOVATION: ARIZONA LESSONS FROM THE PAST, PRESENT, AND FUTURE | This paper details Arizona's extensive efforts to improve access to justice through various regulatory reforms and innovations affecting both lawyer and non-lawyer legal service provision. It chronicles past changes, describes current programs such as Legal Paraprofessionals and pilot advocate programs, and discusses future directions including the role of technology and generative AI. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | The general failure to serve those most in need; the access to justice gap being too large for lawyers alone; historical resistance to regulatory changes; lack of lawyers in rural areas ("legal deserts"); barriers for self-represented litigants (e.g., low e-filing adoption); digital divide (lack of computer/internet access); and significant unmet needs in areas like evictions, domestic violence, public benefits, debt, and mental health. | Regulatory reforms (e.g., limited scope representation, narrowed UPL, easier admission by motion, creating new tiers of non-lawyer legal service providers like Certified Legal Document Preparers, Legal Paraprofessionals, and Legal Advocates via pilot programs, Alternative Business Structures, Lawyer Apprentice Program, Community Justice Worker models); Technological innovations (e.g., court navigators, self-service centers, kiosks, remote hearings, digital evidence portals, Online Dispute Resolution, leveraging Generative AI and data analysis); Funding and support initiatives (e.g., income tax credits, IOLTA, pro bono incentives, state agency collaborations). | Access for self-represented litigants, services for domestic violence survivors, housing instability and eviction, access in rural areas, public benefits, debt collection, family law, criminal law, administrative law, mental health, crime victim assistance, services for older persons. | Low-income individuals, self-represented litigants, domestic violence survivors, individuals facing housing instability/eviction, residents of rural Arizona, tribal communities/Native American populations, older persons, crime victims, veterans, individuals with serious mental health needs, immigrants. | Family Law, Housing Law/Landlord-Tenant/Eviction, Domestic Violence/Protective Orders, Administrative Law, Civil Law (general), Criminal Law, Debt/Creditor Law, Public Benefits, Real Estate Law, Wills, Immigration Law. | Arizona | NaN | NaN | NaN | False | False | NaN | Significant unmet legal needs in critical areas (evictions, domestic violence, public benefits, debt, mental health); the overall access to justice gap remains large; shortage of lawyers in rural areas; low e-filing adoption by self-represented litigants indicating barriers; digital divide hindering access to technological solutions, especially in rural areas. | NaN | Potential biases and fairness/justice concerns with GAI; public protection concerns and potential harm from non-lawyer service providers if not properly regulated or if reforms are ill-conceived; the overarching risk of rule of law erosion if meaningful access to justice is not provided. |
| 89MoLRev847.pdf | HeinOnline | Bridging the Divide: Does the EU's Al Act Offer Code for Regulating Emergent Technologies in America? | The paper analyzes the EU's AI Act, a comprehensive risk-based legislative framework for artificial intelligence, and explores its potential to inform emerging AI regulatory efforts in the United States. It details the AI Act's provisions, stakeholder objections, and compares them with recent U.S. legislative proposals and executive actions concerning AI governance. | True | NaN | False | 2.0 | NaN | The EU AI Act and proposed US AI regulatory frameworks (Bipartisan Framework for U.S. AI Act, No Section 230 Immunity Act, Executive Order 14110). | Qualitative legal and policy analysis of the provisions, stakeholder objections, potential impacts, and implementation challenges of these regulatory frameworks. | The EU AI Act establishes a comprehensive, risk-based regulatory system but faces criticism regarding compliance costs and potential to stifle innovation. US regulatory efforts are nascent and fragmented, with legislative proposals struggling for consensus and executive actions facing challenges of enforceability and scope. | NaN | NaN | NaN | NaN | AI regulation, Technology law, Comparative law | European Union, United States | NaN | The EU AI Act was developed through a multi-year process involving studies, white papers, public consultation, draft proposals, impact assessments, and stakeholder input leading to legislative approval. US approaches involve legislative bill drafting and executive order formulation. | The EU AI Act has entered into force (August 1, 2024) with a phased implementation plan over 6 to 24+ months depending on provisions. US legislative proposals are pending enactment; the Executive Order is being implemented through agency actions. | False | False | NaN | NaN | Challenges associated with the discussed regulatory frameworks include: balancing innovation with risk mitigation; high compliance costs potentially stifling innovation and affecting small businesses; achieving political consensus on regulatory details (e.g., scope of bans, regulation of general-purpose AI); the rapid evolution of AI outpacing legislative efforts; effective enforcement of rules (especially for executive orders and extraterritorial application); defining ambiguous terms within regulations (e.g., 'subliminal techniques', 'systemic risk'); addressing the 'black box' nature of some AI for transparency and oversight obligations; maintaining international competitiveness; and potential for executive overreach in implementing regulations. | Potential AI risks stated include: behavioral manipulation through subliminal or deceptive techniques; exploitation of vulnerable individuals (due to age, disability, socio-economic situation); discriminatory biometric classification and social scoring leading to unfair treatment; privacy violations from 'real-time' biometric surveillance in public spaces and untargeted scraping for facial recognition databases; flawed predictive policing and risk assessments in criminal justice; inference of emotions in workplace/educational settings; adverse impacts from high-risk AI in critical sectors (e.g., aviation, medical devices, critical infrastructure management, education, employment, access to public benefits/services, creditworthiness, emergency response, law enforcement, judicial proceedings, democratic processes); lack of transparency and human oversight in AI decision-making ('black box' problem); AI systems being inaccurate, non-robust, or insecure leading to harm; systemic risks from general-purpose AI (e.g., interference with elections, harm to economic security, public health and safety); and copyright infringement by generative AI models using protected training data. |
| 92GeoWashLRev.pdf | HeinOnline | Artificial Authorship and Judicial Opinions | This essay predicts how generative AI will transform judicial opinions, making them cheaper and more widespread but also potentially less deliberative and more rhetorical. It explores paradoxes such as AI-enhanced persuasion leading to the obsolescence of legal reasoning and courts resisting AI despite its utility due to threats to judicial authority. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Cost and limited availability of legal opinions and persuasive resources; Inegalitarian distribution of judicial attention and legal representation due to wealth disparities; Complexity and inaccessibility of legal language and judicial reasoning for laypersons. | AI making judicial opinions cheaper, more widely available, and customizable for different audiences, including legally unsophisticated individuals; Potential for court-appointed AI tools ("AI Gideon") to assist underresourced parties; AI-facilitated deliberation leading to more equitable distribution of judicial attention. | Access to legal information and understanding of judicial decisions; Fairness and equity in adversarial proceedings; Equitable distribution of judicial resources and attention. | Legally unsophisticated individuals, underresourced litigants, and the general public. | General/Multiple | International | NaN | NaN | NaN | False | False | NaN | Ensuring AI fairness and mitigating bias amplification from training data; Maintaining authenticity in AI-generated legal explanations and respecting human dignity; Preventing AI from enabling deceptive rhetoric that undermines truth and justice; Addressing the potential for AI to create an 'artificially balkanized readership,' thereby fracturing shared legal understanding; Establishing clear regulatory frameworks for AI use in the judiciary that ensure accountability and preserve judicial independence. | NaN | Erosion of judicial authority and public cynicism towards courts; Obsolescence of legal reasoning due to a surfeit of AI-generated rhetoric; Reduced deliberation in judicial opinion writing; Perpetuation and amplification of societal biases by AI tools; An 'arms race' of rhetoric between AI-equipped courts and a skeptical public; AI 'hallucinations' and factual errors in legal contexts; Increased difficulty in discerning truth from sophisticated, AI-generated sophistry; Deepening of partisan divides through AI-tailored, balkanizing content; Loss of human authenticity and accountability in judicial expression; AI being used to conceal improper bases for decisions; Over-reliance on AI diminishing human critical thinking and judgment; Threats to judicial independence from potential regulation of AI tools used by courts. |
| 40GaStULRev863.pdf | HeinOnline | AI Diversity and the Future of "Fair" Legal AI | This paper examines AI's transformative potential in law, focusing on the critical issue of bias in AI systems, especially those automating judicial decisions. It proposes a 'multisystem approach' to AI, utilizing diverse models qualified by public benchmarks, to mitigate bias and promote more equitable legal outcomes. | True | Idealistic | True | 1.0 | Positive | A 'multisystem approach' or 'AI automation diversity,' proposing the parallel operation of multiple, diverse AI models from different providers, all meeting public benchmarks, to mitigate bias and ensure diverse perspectives in legal decision-making. | NaN | NaN | Algorithmic bias in AI systems leading to unfair or discriminatory outcomes, especially in judicial decision-making; opacity of AI systems hindering trust, accountability, and regulation; over-reliance on single AI models leading to narrow perspectives and potential for systemic bias. | Implementing a 'multisystem approach' using multiple, diverse AI models in parallel; utilizing public and open benchmarks for AI system evaluation; ensuring diversity in AI development teams and training data; fostering broad stakeholder participation and maintaining human oversight in legal AI applications. | Algorithmic fairness and bias mitigation in automated legal and judicial decision-making; ensuring equitable application of law and due process through AI; enhancing trust and accountability in legal AI systems. | Groups vulnerable to algorithmic bias within the legal system, including racial minorities and other marginalized communities. | General legal practice, judicial decision-making (e.g., sentencing, parole), administrative decision-making, legal research, and document drafting. | United States | NaN | Conceptual framework development based on legal principles, drawing analogies to the adversarial system and existing checks and balances within the legal system. | Proposed adoption by governmental entities for legal and quasi-legal functions, facilitated by the use of public benchmarks and open participation from AI providers capable of meeting these standards. | False | False | NaN | Lack of emphasis in current AI adoption proposals on incorporating a variety of AI systems for government legal processes; need for robust public benchmarks tailored to legal AI; persistent challenges of biased data and ensuring diverse stakeholder participation in AI development and oversight. | Establishing effective and unbiased public benchmarks for legal AI; ensuring genuine diversity in AI models, their training data, and development teams; managing discrepancies and ensuring accountability when multiple AI systems yield different outputs; integrating human oversight effectively with complex AI systems. | Replication and reinforcement of societal biases leading to discriminatory legal outcomes; erosion of trust in the legal system due to opaque or unfair AI; marginalization of diverse perspectives if single AI models dominate; automated decisions lacking human moral discernment and accountability. |
| Justice AI Legal Case Retrieval Using Dense Passage Retrieval.pdf | IEEE_Xplore | Justice AI: Legal Case Retrieval Using Dense Passage Retrieval | This paper introduces Justice AI, a system developed for Korean legal case retrieval using Dense Passage Retrieval (DPR) with KoBERT and LCube models. It aims to make legal information more accessible to the general public and demonstrates its efficacy through performance metrics like an F1 score of 0.5915 for the LCube model. | True | Idealistic | True | 1.0 | Positive | Justice AI: A legal case retrieval system using Dense Passage Retrieval (DPR) with BERT-based KoBERT and GPT-based LCube models. | The system was evaluated using cosine similarity for relevance, and performance metrics including Precision, Recall, and F1 Score. The evaluation used a dataset of Korean legal documents, with user queries to retrieve relevant cases. | The LCube model achieved a Precision of 0.42, Recall of 1.0, and an F1 Score of 0.5915. A high cosine similarity score of 0.9002 was achieved for a highly relevant document. | Accessing and utilizing legal information is challenging for many, and it is difficult for the general public to acquire and use precise legal knowledge. | Justice AI uses Dense Passage Retrieval (DPR) to match user keywords with relevant legal cases, providing reliable legal information and enabling personalized legal services. | Legal information retrieval, access to legal information, personalized legal services, legal case understanding. | General public, individuals with limited legal knowledge. | General Korean legal documents including case law, statutes, regulations, administrative orders. Examples mentioned cover criminal law (murder, drunk driving, theft) and civil law (wrongful termination). | South Korea | An enhanced and tailored version of the Open Law Data from The Korean Ministry of Government Legislation, consisting of 87,160 Korean legal case documents. The 'reason' field was extracted for analysis. The data includes case law, statutes, regulations, and administrative orders. | Dense Passage Retrieval (DPR). Documents and queries were vectorized using pre-trained language models (KoBERT, LCube). Mean of text vectors was used for embeddings. Cosine similarity was used to retrieve top documents. | NaN | False | False | NaN | Lack of extensive, annotated datasets for Korean legal texts limits model generalization. The agglutinative nature of the Korean language poses challenges. Need for developing diverse, comprehensive datasets tailored to Korean legal language and adapting models accordingly. | The dataset was not originally structured as query-document pairs. Limited availability of extensive annotated Korean legal datasets compared to other languages (e.g., Chinese). The agglutinative nature of the Korean language causes complexity in tokenization and contextual understanding. | NaN |
| Too Legal- Didn-t Read -TLDR- Summarization of Court Opinions.pdf | IEEE_Xplore | Too Legal; Didn’t Read (TLDR): Summarization of Court Opinions | This paper proposes NLP-based methods for summarizing court opinions, exploring both extractive classifiers (with LSTM performing best for relevance tagging) and a domain-adapted abstractive model, PEGASUS CourtOp, fine-tuned from PEGASUS LARGE. The aim is to assist legal professionals by reducing the time and effort for document review, potentially lowering legal costs and thereby improving access to justice. | True | Idealistic | True | 1.0 | Positive | PEGASUS CourtOp (fine-tuned PEGASUS LARGE for abstractive summarization) and various classifiers (Naive Bayes, Decision Tree, Random Forest, LSTM NN) for extractive summarization by identifying relevant text segments. | Extractive models (including LSTM) evaluated using 5-fold cross-validation for classification performance (Recall, F1-score) on automatically labeled opinion segments, and ROUGE scores for generated summaries. Abstractive models (including PEGASUS CourtOp) evaluated using ROUGE scores against human-written summaries on a held-out test set comprising 25% of the dataset. | For abstractive summarization, PEGASUS CourtOp achieved a ROUGE-1 F1 score of 0.53 and ROUGE-1 Recall of 0.66, outperforming PEGASUS LARGE and Legal PEGASUS. For extractive sentence/paragraph classification, LSTM NN performed best (e.g., paragraph level Recall 0.85, F1-Score 0.73; ROUGE-1 F1 0.34 for summary from LSTM parts). | High cost of legal services partly due to the time-consuming and labor-intensive process of parsing very long and complex legal texts (court opinions), which requires specialized training and skills. | Developing NLP-based automatic text summarization tools (both extractive and abstractive) to help legal professionals create summaries more quickly or to automate the process, aiming to reduce costs and thereby increase access to the legal system for people of lower-income brackets. | Improving accessibility and understanding of lengthy legal documents (court opinions) by automatic summarization, aiding legal professionals, and potentially reducing legal service costs. | People of lower-income brackets. | Case Law / Court Opinions | United States (Utah, Idaho, Arizona, New Mexico, Nevada, Colorado state supreme courts) | A proprietary dataset of court opinions from six US State supreme courts (Utah, Idaho, Arizona, New Mexico, Nevada, Colorado) and corresponding human-written summaries provided by Justia under a data-sharing agreement. 3661 pairs were used for fine-tuning PEGASUS CourtOp. The base PEGASUS model was pre-trained on general web data, news, social media, and the BillSum dataset. | For extractive summarization: automatic labeling of sentences/paragraphs in court opinions based on similarity (N-Grams, LCS, Semantic Similarity, ROUGE score) to human summaries, followed by training binary classifiers. For abstractive summarization: fine-tuning the pre-trained PEGASUS LARGE model on court opinions and their summaries by freezing encoder weights and training decoder layers (creating PEGASUS CourtOp). | NaN | False | False | NaN | Need for improved legal-text-specific Named Entity Recognition for court opinions. Potential for better results by fine-tuning newer, more powerful (though potentially not open-source) language models. Further work to enhance the generation of novel language in abstractive summaries that is not explicitly present in the source opinions. | For extractive summarization: dataset imbalance between relevant and irrelevant text segments when labeling data for classifier training. For abstractive summarization: effective domain adaptation of general-purpose pre-trained language models to the specific characteristics and vocabulary of legal court opinions. | NaN |
| Interactive Legal Assistance System using Large Language Models.pdf | IEEE_Xplore | Interactive Legal Assistance System using Large \nLanguage Models | This paper presents a Retrieval Augmented Generation (RAG) chatbot designed to help laypersons in India understand complex Food Safety Regulations, operating in both English and Tamil. The system utilizes LLMs like GPT-4 and Llama2, an embedding model, and a translation model to provide query-based assistance and document section summarization. | True | Idealistic | True | 1.0 | Positive | A RAG-based chatbot using LLMs (GPT-4, Llama2, GPT-4 Vision), IndicTrans2 for translation, and 'Snowflake-arctic-embed' for embeddings. It includes Q&A and summarization components for legal documents. | Qualitative comparison of summaries generated by the proposed system and ChatGPT for a specific topic within the Food Safety Regulations. The comparison focused on precision and reflection of original content. | The system's summaries were found to be significantly more precise and better reflected the original content of the Food Safety Regulations when compared to summaries generated by ChatGPT, which exhibited inaccuracies. | Complexity of legal documents for non-experts, leading to misunderstanding and unintentional violations; language barriers for regional language speakers in India where regulations are often in English. | Development of a user-friendly RAG chatbot that provides clarifications (Q&A) and summaries of legal documents in both English and Tamil, incorporating translation models to overcome language barriers. | Understanding legal documents (specifically Food Safety Regulations), language accessibility in legal information, simplification of legal text. | Common people in India, particularly Tamil speakers, needing to understand Food Safety Regulations. | Food Safety Regulations | India | Publicly available PDF documents of India's Food Safety Regulations from the Food Safety and Standards Authority of India (FSSAI). These documents are processed (converted to HTML, chunked) to create embeddings for the RAG system using 'Snowflake-arctic-embed'. The system utilizes pre-trained LLMs (GPT-4, Llama2, GPT-4 Vision) and a pre-trained translation model (IndicTrans2). | System architecture involving PDF processing (conversion to HTML using GPT-4 Vision), text chunking, embedding generation (Snowflake-arctic-embed), vector storage (ChromaDB), query processing with language identification, translation (IndicTrans2), RAG with LLMs (GPT-4, Llama2) for Q&A, and content extraction with LLM-based summarization. | Local models are pulled from an Ollama server. Embeddings and HTML data are stored locally. No broader public deployment strategy is mentioned. | False | False | NaN | The system currently does not allow users to request or download specific forms related to the legal documents. | Ensuring relevance and accuracy of retrieved documents, as improper preprocessing or embedding can lead to irrelevant or noisy information; maintaining efficient performance at scale (challenges in optimizing index structures, caching, retrieval latency); validating correctness and relevance of generated answers in real-time. | Risk of generating inaccurate or misleading information if the RAG system retrieves irrelevant or noisy content, potentially leading to misinterpretation of legal regulations. |
| Proposal for Enhancing Legal Advisory Services in the Montenegrin Banking Sector with Artificial Intelligence.pdf | IEEE_Xplore | Proposal for Enhancing Legal Advisory Services in the Montenegrin Banking Sector with Artificial Intelligence | This paper proposes integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to enhance legal advisory services and financial education within Montenegro's banking sector. It details a system using ADA-2 for embedding regulatory documents, Chroma DB for storage, and GPT-4 for response generation, outlining a planned evaluation methodology. | True | Market | True | 1.0 | Positive | Integration of Large Language Models (LLMs like GPT-4) and Retrieval-Augmented Generation (RAG) using LangChain framework, with vectorization of regulatory documents via ADA-2 embedding model and storage in Chroma DB vector database. | Proposed evaluation methodology: 210 prepared questions on legal topics, 'ground truth' answers validated by legal experts, quantitative metrics (Exact Match, F1 score), and qualitative expert legal analysis of RAG-generated responses. | NaN | Complexity of financial concepts for bank clients, hindering financial literacy. | Using AI (LLMs and RAG) to provide personalized and easy-to-understand explanations of financial instruments and concepts, thereby enhancing financial literacy and informed decision-making. | Financial literacy; Financial education. | Bank clients in Montenegro; general public needing financial education. | Banking law, financial regulation. | Montenegro | Montenegrin banking laws, regulatory guidelines, legal precedents, and case studies. These are unstructured textual documents, domain-specific to Montenegrin banking. | System architecture design combining LLMs (ADA-2 for embedding, GPT-4 for generation), RAG (via LangChain), vector database (Chroma DB), chunk-based embedding, prompt engineering, and a proposed expert-informed evaluation methodology. | NaN | False | False | NaN | The paper implies the need for comprehensive validation of the proposed AI system's effectiveness for financial education and calls for ongoing scrutiny into ethical implications and human oversight. | Ensuring accuracy and avoiding misinterpretation/overlooking nuances in legal texts; dependency on quality and comprehensiveness of data sources; addressing ethical implications like AI bias; need for human oversight; fine-tuning models for domain-specific linguistic nuances; balancing retrieval and generation in RAG systems. | Misinterpretation or overlooking critical nuances in legal texts; bias in AI models; general ethical implications of using AI in legal contexts; inaccuracies stemming from issues in document retrieval, generation process, or misunderstanding of context. |
| EMPOWER-KARE Deep Prompt Learning for Knowledge-aware Response Generation in Clinical Counseling and Legal Support Conversations (1).pdf | IEEE_Xplore | EMPOWER-KARE: Deep Prompt Learning for Knowledge-aware Response Generation in Clinical Counseling and Legal Support Conversations | This paper introduces KARE, a novel dataset of knowledge-grounded conversations for clinical counseling and legal support for crime victims. It also proposes EMPOWER, a dual-tier deep prompt learning framework that uses KARE to generate knowledge-aware responses, demonstrating improved performance over existing methods. | True | Idealistic | True | 1.0 | Positive | EMPOWER, a dual-tier deep prompt learning framework for knowledge-aware response generation. It includes Knowledge-attributed Deep Prompt Learning (KDPL), Response-attributed Deep Prompt Learning (RDPL), and a Dynamic Dialogue-Knowledge Module (DDKM). | Evaluated on the KARE dataset using automatic metrics (PPL, BLEU-4, Avg. BLEU, F1, Knowledge-F1, BERTScore F1, EA, VE, GM) and human evaluation (Fluency, Adequacy, Contextual Relevance, Knowledge Existence, Correctness, Relevance, Helpfulness, Safety). | EMPOWER achieved improvements of 11.50% in BLEU-4, 28.5% in Knowledge-F1, and 11.6% in BERTScore compared to the best baseline on the KARE dataset. It attained a Perplexity of 7.11 and BERTScore F1 of 0.86. | Societal stigmatization, lack of adequate and accessible support services for crime victims, and the multifaceted challenges victims face, including mental trauma and navigating complex legal processes. | Development of AI-powered knowledge-grounded dialogue systems (like EMPOWER-KARE) to provide 24/7 clinical counseling and legal support, thereby improving access to assistance for crime victims. | Clinical counseling and legal support for crime victims, specifically mental health support and guidance on legal processes related to various crimes. | Crime victims, with a specific focus on women and children who have experienced violence. | Criminal law, cybercrime law (specifically related to cyberstalking), victim support services, and legal aid procedures. | India | The KARE dataset, built upon the synthetically created POEM dialogue dataset (5,000 English dialogues for crime victims). KARE augments POEM with external domain-specific knowledge collected via web scraping (using Google Search API, content extracted from URLs, segmented using Spacy) and processed into knowledge triplets using OpenIE and Sentence-BERT for relevance. | Dual-tier deep prompt learning (prefix-tuning) with Knowledge-attributed and Response-attributed prompts, Knowledge Triplets Construction (using Stanford OpenIE, filtering rules, Sentence-BERT for relevance, and GPT-J for verbalization), and a Dynamic Dialogue-Knowledge Module (using multi-head attention and a re-parameterization technique). | The code and dataset are made available via GitHub and an institutional webpage; no specific user deployment strategies are mentioned beyond research access. | True | True | Code and dataset are available on GitHub (https://github.com/priyanshu528priya/EMPOWER-KARE/) and an institutional resources page (https://www.iitp.ac.in/~ai-nlp-ml/resources.html). | Need for incorporating commonsense knowledge to induce commonsense reasoning ability and empathy. Reliance on the quality of source data and knowledge extraction methods can introduce inaccuracies or biases. | Limited computational resources for experimenting with larger language models. Ensuring the quality of source data and the accuracy of knowledge extraction. Effectively integrating external knowledge into response generation (addressed by the dual-tier prompt learning). | Generation of wrong or inaccurate information by the model. Potential for responses to contain repetitions, be inconsistent with context, or exhibit semantic variance from ideal answers. |
| LexSage Multi-Task Optimization in Legal Large Language Model Applications.pdf | IEEE_Xplore | LexSage: Multi- Task Optimization in Legal Large Language Model Applications | This paper introduces LexSage, a legal large language model fine-tuned from Qwen2.5-7B using a custom multi-task Chinese legal dataset created with one-shot prompting and data augmentation. LexSage demonstrates superior performance on various Chinese legal tasks within the LawBench benchmark, significantly outperforming models like GPT-4 and Qwen-7B on specific tasks like case analysis and law recitation respectively. | True | Market | True | 1.0 | Positive | LexSage, a legal large language model based on Qwen2.5-7B, fine-tuned using instruction fine-tuning (LoRA) with a specially constructed multi-task Chinese legal dataset (LexSage-SFT). The dataset was created leveraging one-shot capabilities of LLMs (GLM4-Plus API) for data generation (Self-Instruct) and data augmentation techniques (GPT-based instruction paraphrasing). | Evaluated on 6 tasks from the Chinese LawBench benchmark (law recitation, text proofreading, opinion summarization, case analysis, crime amount calculation, legal counseling) in a zero-shot setting. Compared against models including GPT-3.5 Turbo, GPT-4, Qwen-7B, LawGPT, and HanFei, using metrics like Rouge-L, Accuracy, and soft-F1. | Achieved a 76.2% improvement in the law recitation task (LexSage score 0.326) compared to Qwen-7B (score 0.185) on LawBench. Also showed 52.7% improvement in case analysis (LexSage 0.742) over GPT-4 (0.486). | NaN | NaN | NaN | NaN | Chinese law, including Criminal Law, Marriage Law, Social Law, and Economic Law, focusing on tasks like law recitation, opinion summarization, case analysis, and legal Q&A. | People's Republic of China | A proprietary dataset (LexSage-SFT) of 113.7K instruction-formatted entries for Chinese legal tasks. Compiled from: 1) Public NLP legal task datasets (JEC-QA, CAIL). 2) Raw legal texts (Criminal Law, Marriage Law, Social Law, Economic Law) and judicial exam questions. 3) Open-source legal instruction datasets (e.g., from LawGPT, Lawyer-LLaMA). Additional data was generated using Self-Instruct with GLM4-Plus API and data augmentation (instruction paraphrasing via GPT) was applied. | Instruction fine-tuning (LoRA) on the Qwen2.5-7B base model. Dataset construction involved data collection from diverse legal sources, data cleaning and preprocessing, structuring data into {instruction, input, output} format, employing Self-Instruct methodology (one-shot prompting with GLM4-Plus API) for generating Q&A pairs from raw texts, and data augmentation (GPT-based instruction paraphrasing) to balance task distribution. Chain of Thought (CoT) technique was introduced in the reasoning stage for enhanced interpretability in complex tasks like case analysis. | NaN | False | False | NaN | The paper identifies technical gaps for future model development, including further optimization of legal knowledge reasoning and long text processing, incorporation of more diverse data sources, and exploration of Retrieval-Augmented Generation (RAG) methods to enhance knowledge depth and reliability. | Computational resource constraints (single A100-40GB GPU). Ensuring balance and diversity across tasks in the dataset for multi-task fine-tuning. The inherent lack of sufficient legal knowledge and poor compatibility of general open-source LLMs with specific legal tasks. Addressing the common problem of 'illusions' (hallucinations) in legal LLM outputs. | The primary risk identified is that of 'illusions' (hallucinations) in legal LLM outputs, which can lead to the generation of inaccurate or unreliable legal information if not adequately mitigated. |
| CHRExpert An AI-Driven Court of Human Rights Expert Assistant for Legal Practitioners Utilizing Transformer Models.pdf | IEEE_Xplore | CHRExpert: An AI-Driven Court of Human Rights Expert Assistant for Legal Practitioners Utilizing Transformer Models | This paper introduces CHRExpert, an AI legal assistant using a fine-tuned 6 billion parameter GPT model on European Court of Human Rights (ECHR) data to help practitioners analyze judicial decisions and predict case outcomes. CHRExpert achieved 83% accuracy in predicting outcomes for specific ECHR articles and reduced case preparation time by 40%. | True | Market | True | 1.0 | Positive | CHRExpert: an AI-driven legal assistant utilizing a fine-tuned 6 billion parameter Generative Pretrained Transformer (GPT) model (referred to as GPT-J in INDEX TERMS) on the ECHR dataset. | Evaluations based on final judgments predicted outcomes for ECHR Articles 3, 6, and 8. Performance measured by accuracy, AUC, precision, recall, F1-score. Also assessed using 6-fold cross-validation, classification performance on 450 documents, legal document analysis (statutory interpretation, issue spotting comparison with law practitioners), and applicability in litigation (efficiency, strategy development, analogical reasoning). | Achieved 83% accuracy in predicting outcomes for cases involving Articles 3, 6, and 8 of the European Convention on Human Rights, with an average AUC of 0.93. Reduced case preparation time by 40%. Achieved 92% accuracy in issue spotting. | The complexity and high volume of legal information in human rights cases, hindering efficient case preparation and effective alignment of legal arguments with judicial reasoning by legal professionals. | Proposes CHRExpert, an AI-driven legal assistant to help practitioners analyze human rights case documents, predict outcomes, interpret statutes, and suggest legal strategies, thereby improving efficiency and effectiveness in human rights litigation. | Case outcome prediction in human rights law, Legal document analysis for human rights cases (statutory interpretation, issue spotting), Legal strategy development in human rights litigation, Efficiency enhancement for human rights legal practitioners. | Individuals seeking redress for human rights violations at the European Court of Human Rights (served indirectly via legal practitioners using the tool). | Human Rights Law (specifically European Convention on Human Rights). | European Court of Human Rights (ECHR), covering member states of the Council of Europe. | Fine-tuned on the European Court of Human Rights (ECHR) dataset, comprising 11,000 files of unstructured text (final judicial decisions with facts, legal arguments, outcomes). This dataset is based on publicly available ECHR data (e.g., as described by Medvedeva et al. [25]). | Utilized a 28-layer deep transformer model (GPT with 6 billion parameters) with transfer learning and fine-tuning. Preprocessing included text cleaning, normalization, outcome-dependent term filtering, BPE tokenization, embeddings, padding, and attention masking. Trained using PyTorch's Distributed Data-Parallel (DDP) on GPUs. | Designed as a cloud-based legal assistant accessible through a web interface via a subscription model, with functionalities exposed via RESTful APIs. | False | False | NaN | Difficulty in handling judicial discretion and subjective rulings; challenges with ambiguous or rarely invoked statutes; limitations in cases establishing new legal doctrines due to reliance on precedents; country-level selection bias in the ECHR dataset; need for evidentiary analysis, adaptive learning for evolving legal trends, and incorporation of ethical/cross-jurisdictional reasoning. | Capturing complex context and judicial discretion in human rights cases; handling complex legal texts (dense language, terminology, precedents); mitigating data leakage from outcome-revealing terms; ensuring predictions are based on legal reasoning rather than post-judgment awards; addressing selection bias from overrepresented jurisdictions in training data. | Potential for over-reliance given limitations in handling judicial discretion, subjective rulings, ambiguous statutes, and novel legal arguments. Risk of biased or less generalizable outputs due to country-level selection bias in the training data if not carefully managed. Misuse for pre-trial assessment (mitigated by defining scope as post-litigation analysis). |
| Large Language Models -LLM- in Industry A Survey of Applications- Challenges- and Trends.pdf | IEEE_Xplore | Large Language Models (LLM) in Industry: A Survey of Applications, Challenges, and Trends | This paper surveys the applications of Large Language Models (LLMs) across various industries, including legal services, highlighting their benefits in automation and decision-making. It also discusses significant challenges such as high costs, data privacy, bias, and lack of explainability, while exploring emerging solutions and trends. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal services, contract review, legal research, general legal sector. | International | NaN | NaN | LLM-as-a-Service (LLMaaS), API-based access, integration into specialized tools by companies (e.g., Luminance, ROSS Intelligence). | False | False | NaN | NaN | High computational and energy demands; bias and fairness issues from training data; data privacy and security concerns; lack of explainability (black box nature); ethical and societal issues (job displacement, misinformation); difficulty with domain-specific knowledge requiring extensive fine-tuning; high implementation and maintenance costs. | Bias leading to unethical outcomes, reproduction of personal information, potential job displacement, generation of misinformation. |
| Bettercall AI based legal assistant.pdf | IEEE_Xplore | Bettercall: AI based legal assistant | This paper introduces "Bettercall," an AI-based chatbot designed to improve access to legal and judicial information in India using advanced natural language processing and semantic search capabilities. The system aims to provide primary legal aid and promote legal awareness, with the paper detailing its methodology, challenges, and performance. | True | Idealistic | True | 1.0 | Positive | An AI chatbot ('Bettercall') utilizing semantic search (NLP, vector embeddings from legal texts, cosine similarity for query matching) and an LLM (OpenAI's GPT-3.5) combined with a legal ontology database for generating responses to user queries. | The system was evaluated using precision and recall metrics on a diverse set of legal queries compared against a manually created "gold standard". User satisfaction and usability were assessed through user feedback surveys. | The system demonstrated high precision and respectable recall scores across various legal domains (e.g., Criminal Law: Precision ~0.9, Recall ~0.85). User satisfaction scores were notably high, with an overall average satisfaction around 4.5 out of 5. | Lack of accessible legal information and understanding, especially for marginalised communities and those with low legal literacy in India; linguistic barriers. | Development of a multilingual, user-friendly AI-powered digital assistant (Bettercall) that uses semantic search to provide clear legal information, answer common legal queries, and guide users on legal procedures and rights. | Access to legal information, legal query answering, guidance on legal procedures (e.g., complaint filing), understanding legal rights, promoting legal literacy. | Indian populace, especially marginalised communities and individuals lacking legal literacy. | General Indian Law, including Criminal Law, Family Law, Property Law, Labour Law, Constitutional Law, Corporate Law, Environmental Law, Intellectual Property Law. | India | Publicly available Indian legislation (acts and sections with metadata like act name, section number, etc.) web-scraped from indiacode.nic.in. The data is textual and domain-specific. | Web scraping for data collection, data cleaning and formatting, chunking and tokenization, vectorization of text into embeddings, storage in a vector database (Supabase) and a non-relational database (MongoDB) for ontology, cosine similarity for query-document matching, and LLM (GPT-3.5) for response generation. | NaN | False | False | NaN | Existing gaps in scalability, multilingual support, and domain coverage of legal assistance tools. Future work includes continuous improvement of chatbot capabilities, expansion of legal ontology, and refinement of multilingual functions. | Constructing a comprehensive legal database due to lack of pre-existing structured data; inefficiencies in PDF scraping leading to a pivot to web scraping; accurately storing metadata for chunked data; managing and integrating legal ontology effectively without causing data duplication, reduced embedding accuracy, or increased costs. | Inaccurate interpretation of keywords in legal texts could lead to disparate or incorrect outcomes from the chatbot. |
| Classifying European Court of Human Rights Cases Using Transformer-Based Techniques.pdf | IEEE_Xplore | Classifying European Court of Human Rights Cases Using Transformer-Based Techniques | This paper proposes and evaluates transformer-based models, using a sliding window approach and data scraping for balancing, to classify European Court of Human Rights (ECHR) case documents. Experimental results show RoBERTa excels at binary classification and BigBird at multi-class classification, indicating AI's potential to enhance legal aid efficiency. | True | Idealistic | True | 1.0 | Positive | A legal document classification framework using various transformer-based models (BERT, Legal-BERT, RoBERTa, BigBird, ELECTRA, XLNet) enhanced with a sliding window approach to handle long text sequences and data scraping from the ECHR portal for dataset balancing. | The models were evaluated on the ECHR dataset (split 70% training, 30% evaluation) using 5-fold cross-validation. Performance was measured by precision, recall, and F1-score, comparing transformer models against conventional machine learning techniques (SVM, DT, NB, AdaBoost) and previous benchmarks. Both binary and multi-class classification tasks were performed. | For binary classification, RoBERTa achieved the best performance with precision, recall, and F1-score of 89.1%, 86.2%, and 86.7%, respectively. For multi-class classification (after data scraping), BigBird performed best with a weighted F1-score of 78.1%. | High cost of legal representation, limited eligibility for public legal aid programs due to restrictive means tests (considering income, assets, and home value), leading to many individuals being unable to afford legal assistance or being excluded from aid. | Automating the classification of legal cases to improve the efficiency of legal assistance provision. This can potentially reduce the cost of legal aid and increase the number of cases that can be assisted within publicly funded budgets. | Improving efficiency of legal aid provision, reducing costs of legal services, automating legal document classification. | Individuals who cannot afford high-quality legal representation and those who may be excluded from or inadequately served by public legal aid programs. | Human Rights Law | European Court of Human Rights (ECHR) | A publicly available ECHR (European Court of Human Rights) dataset (Chalkidis et al., 2019) consisting of unstructured text (case facts). This dataset was augmented by scraping additional case articles from the ECHR public database to balance class distribution, particularly for the multi-class task. | Application of various transformer-based neural networks and conventional machine learning models. A sliding window technique was used for handling long text sequences in transformer models. Data scraping and regular expressions were used for additional data collection and pre-processing. Dataset balancing was a key consideration. | NaN | False | False | NaN | Technical: Need for improvement in multi-class classification performance; potential overfitting from sliding window overlaps; transformer models not fully leveraging additional meta-data features. Dataset-related: Need for more high-quality, potentially domain-specific pre-trained models (e.g., combining Legal-BERT's domain specificity with BigBird's long sequence handling) and further dataset augmentation/quality improvements. | Handling long sequences of text data from legal documents with transformer models that have input length limitations (addressed via sliding window). Managing highly imbalanced datasets (addressed via data scraping). High computational load associated with training transformer models, especially with the sliding window approach generating multiple sub-sequences. Effectively incorporating additional meta-data features (like case importance or court branch) into text-centric transformer models. | Potential for overfitting due to the overlapping windows in the sliding window technique. Biases in algorithms were acknowledged as an area not focused on but are a general risk with AI in law. |
| Unlocking the Potential of Large Language Models in Legal Discourse Challenges- Solutions- and Future Directions.pdf | IEEE_Xplore | Unlocking the Potential of Large Language Models in Legal Discourse: Challenges, Solutions, and Future Directions | This paper evaluates the performance of various state-of-the-art Large Language Models (LLMs) on Canadian tax law queries, identifying issues like hallucinations. It then proposes and experiments with fine-tuning smaller LLMs (Gemma and Mistral) using domain-specific legal texts and vocabulary enhancement as a potential solution, though initial fine-tuning results showed limitations. | True | Idealistic | True | 1.0 | Neutral | Fine-tuning of LLMs (Gemma-2b and Mistral-7B-Instruct-v0.2) using semantic chunking of Canadian legal documents and domain-specific vocabulary updates. | Initial evaluation of six general LLMs (Gemini, Mistral Large, Gemma 7B, Falcon 180B, Llama2 70B, GPT-3.5) on 40 Canadian tax law questions rated by a tax expert. The fine-tuned Gemma and Mistral models were qualitatively evaluated with a sample legal question. | For the initial evaluation of general LLMs, Gemini achieved the highest accuracy (77.5% correct answers on 40 tax law questions). The fine-tuned Gemma-2B model (using an unsupervised dataset) repeatedly generated the input question, while the fine-tuned Mistral-7B model provided a tax-related but incorrect answer to a sample question. | Inaccuracy and unreliability of LLMs (e.g., hallucinations, biases), lack of interpretability, the complexity of legal language and reasoning for AI models, and scarcity of high-quality, labeled legal data suitable for training effective access to justice tools. | Development of domain-specific LLMs through fine-tuning with curated domain-specific datasets and vocabulary. Methodologies include semantic chunking for text preparation and iterative refinement based on expert feedback. Emphasis on creating instructional datasets for better fine-tuning. | Democratizing access to legal advice, providing legal guidance to non-expert users, improving legal information retrieval and question answering. | Non-expert users, general citizens requiring legal information (e.g., on taxation), and individuals who struggle with navigating legal processes. | Canadian tax law; more broadly, the legal domain. | Canada | For fine-tuning: A dataset of 10,000 unlabeled Canadian legal documents (federal and provincial laws, statutes, regulations), processed using semantic chunking. Domain-specific legal terminology was also integrated. | Semantic chunking of legal documents, domain-specific vocabulary expansion, and fine-tuning of pre-trained language models (Gemma-2b, Mistral-7B-Instruct-v0.2) on an unlabeled legal corpus. | NaN | False | False | NaN | Scarcity of extensively labeled legal documents for supervised fine-tuning, significant computational resources (especially memory) required for fine-tuning LLMs, need for high-quality and representative training data (addressing bias, privacy, timeliness, scalability), and the need for more explainable and transparent AI models to ensure trustworthiness and mitigate bias. | Detecting and mitigating LLM hallucinations in legal contexts, adapting general LLMs to domain-specific nuances like legal terminology and reasoning, achieving satisfactory results when fine-tuning with unlabeled legal corpora (e.g., models repeating questions or providing incorrect/inaccurate answers), managing high computational costs, and curating comprehensive domain-specific vocabularies. | Dissemination of inaccurate legal information or advice (legal hallucinations), perpetuation of biases embedded in training data leading to unfair outcomes, security vulnerabilities in AI systems handling sensitive legal information, and the potential for AI systems to mislead or harm human interests if not properly developed and governed. |
| Fine-tuning a Large Language Model for the Indian Legal System.pdf | IEEE_Xplore | Fine-tuning a Large Language Model for the Indian Legal System | This paper details the development and fine-tuning of a Llama 3.1 8B large language model specifically for the Indian legal system, employing techniques such as LoRA, QLoRA, RAG, and pruning. The resulting AI-driven chat application aims to provide accurate legal information and assistance, showing improved performance and reduced hallucinations on benchmarks like HaluEval. | True | Idealistic | True | 1.0 | Positive | A fine-tuned LLM (Llama 3.1 8B) for the Indian legal system, enhanced with Low-Rank Adaptation (LoRA), Quantized Low-Rank Adaptation (QLoRA), Retrieval Augmented Generation (RAG), and pruning, delivered via a chat application. | The system was evaluated using accuracy, precision, recall, F1-score, ROUGE scores for summarization, and the HaluEval benchmark for factual reliability and hallucination rates. Comparisons were made between base, pre-trained, fine-tuned (LoRA, QLoRA), and compressed model versions. | The fine-tuned model showed substantial improvements: on HaluEval for Question Answering, the score increased from 49.6 (base) to 58.1. The hallucination rate decreased from 5.10% to 3.10% with fine-tuning. | The exponential growth, volume, and complexity of legal documentation in intricate legal systems, and the reliance on extensive, time-consuming manual review and human judgment in traditional legal research. | Developing a specialized LLM tailored to the Indian legal system to simplify legal advisory services and decision-support, making legal knowledge more accessible. This involves fine-tuning on Indian legal data and using techniques like RAG for contextually accurate responses. | Legal information retrieval, answering complex legal queries, legal advisory services, decision-support systems within the judiciary. | Law students, legal practitioners, and individuals seeking legal assistance in India. | Criminal law, civil law, constitutional law, corporate law, consumer law, real estate law. | India | A diverse corpus of Indian legal texts from official government and court websites (Ministry of Law and Justice, Supreme Court, High Courts) including legal documents, statutes, case laws, and commentaries. This included approximately 4,000 question-answer pairs (CSV) and a JSON dataset of case file data. | Data collection and preprocessing, pre-training of the base model, fine-tuning using LoRA and QLoRA, Retrieval Augmented Generation (RAG) implementation for personalized queries, and model compression using structured pruning. | The system was developed as a chat application with a Flask backend and HTML/CSS/JavaScript frontend. LM Studio was used for local model client setup during development. No broad public deployment strategy is detailed. | False | False | NaN | Technical: Need for advanced RAG architectures, alternative parameter-efficient fine-tuning methods, dynamic pruning, knowledge distillation, multilingual support for regional Indian languages, specialized evaluation metrics for Indian legal tasks, and temporal awareness for legal updates. Societal: Further enhancing the accessibility, actionability, and impact of legal knowledge. | Substantial computational and memory requirements of LLMs; inherent ambiguity and context-dependency of legal terminology; balancing model performance with resource efficiency; ensuring factual reliability and minimizing hallucinations in legal responses. | Generation of fabricated legal meanings (hallucinations) by the LLM, potentially leading to misinterpretations if accuracy is not sufficiently high. |
| Iraqi Legal GPT.pdf | IEEE_Xplore | Iraqi Legal GPT | This paper proposes 'Iraqi Legal GPT,' an AI chatbot using the h2ogpt framework and Iraqi legal documents to provide accessible legal information within Iraqi jurisprudence, aiming to be locally deployable and overcome limitations of large models. The system demonstrates promising results with 80% accuracy and 1-minute response times, intending to enhance access to justice for individuals in Iraq. | True | Idealistic | True | 1.0 | Positive | A legal chatbot system named 'Iraqi Legal GPT' built using the open-source h2ogpt framework, trained on curated Iraqi legal documents, employing the 'instructor' embedding algorithm and Chroma db vector store for local deployment and offline use. | The proposed system, Iraqi Legal GPT (using h2ogpt with llama2-7b-chat), was evaluated for accuracy and response time. This involved comparative testing against other LLMs (Mistral, Mixtral variants) and different embedding algorithms (instructor-large vs. others) on Iraqi legal document processing tasks. | The Iraqi Legal GPT system, specifically using the h2oai/h2ogpt-4096-llama2-7b-chat model, achieved an accuracy of 70-80% (reported as 80% in abstract) and a 1-minute response time. The 'hkulp/instructor-large' embedding algorithm demonstrated 98% accuracy in document conversion. | Lack of a comprehensive legal framework for free or reduced-cost legal aid in Iraq. Challenges in finding affordable and specialized lawyers, and understanding legal rights. Time-consuming and costly processes for existing legal aid where available, particularly for underserved communities. | Development of a locally deployable AI chatbot ('Iraqi Legal GPT') using curated local legal documents and an open-source LLM (h2ogpt) to provide free, accessible legal information and guidance, operable offline on standard computers. | Access to legal information, Legal aid, Understanding legal rights, Navigating the legal system | Citizens and non-citizens in Iraq (including permanent residents, migrants, asylum seekers, refugees, victims of human trafficking, foreign students, temporary visitors, and stateless persons) with limited economic resources or facing difficulties accessing legal services. | General Iraqi law | Iraq | Publicly available, unstructured Iraqi legal documents (laws, constitution, etc.) collected from governmental and legal information websites such as Yasaii.info, Legislation.krd. These documents were curated and split into text chunks. | The system was designed using a block diagram approach, involving data collection and curation, document splitting, embedding using the 'instructor' algorithm, storage in a Chroma db vector store, and integration with the h2ogpt LLM. Comparative analysis of different LLMs and embedding algorithms was conducted. | The system is designed for local deployment on a personal computer, capable of running without an internet connection. A website interface (GUI) was developed for user interaction. | False | False | NaN | Hardware limitations for running advanced LLM models locally. The need for larger volumes of legal data and improved support for Arabic/Kurdish languages in local chatbot generators. Ongoing need for more efficient algorithms. | Hardware resource constraints for running large models. Obtaining and processing sufficient legal data, including translation to English due to tool limitations. Selecting and integrating optimal LLMs, embedding algorithms, and vector stores for accuracy and speed. | Potential for inaccuracies in legal information provided if the model hallucinates or if the underlying data is incomplete/incorrect, a known issue with language models in legal contexts (e.g., ChatGPT generating false legal provisions). |
| EMPOWER-KARE Deep Prompt Learning for Knowledge-aware Response Generation in Clinical Counseling and Legal Support Conversations.pdf | IEEE_Xplore | EMPOWER-KARE: Deep Prompt Learning for Knowledge-aware Response Generation in Clinical Counseling and Legal Support Conversations | This paper introduces EMPOWER, a novel dual-tier deep prompt learning framework for knowledge-aware response generation in dialogues supporting crime victims with clinical counseling and legal advice. It also presents KARE, a new dataset of 5,000 such conversations, demonstrating EMPOWER's superior performance over baselines in generating helpful and accurate responses. | True | Idealistic | True | 1.0 | Positive | EMPOWER: A dual-tier deep prompt learning framework using prefix-tuning for knowledge-aware response generation. It involves Knowledge-attributed Deep Prompt Learning (KDPL) and Response-attributed Deep Prompt Learning (RDPL), enhanced by a Dynamic Dialogue-Knowledge Module (DDKM). | Evaluated on the newly proposed KARE dataset against several baseline models using automatic metrics (PPL, BLEU-4, Average BLEU, unigram F1, Knowledge F1, BERTScore F1, EA, VE, GM) and human evaluation metrics (Fluency, Adequacy, Contextual Relevance, Knowledge Existence, Knowledge Correctness, Knowledge Relevance, Helpfulness, Safety). | EMPOWER achieved 7.11 PPL, 3.37 BLEU-4, 0.09 Knowledge-F1, and 0.86 BERTScore-F1 on the KARE dataset, outperforming all baseline models. | Societal stigmatization and the lack of adequate, accessible support services often create barriers for crime victims seeking help. | The paper proposes AI-driven dialogue systems, specifically EMPOWER, to provide 24/7 accessible, knowledge-grounded clinical counseling and legal support to crime victims, thereby aiming to improve engagement and understanding of their needs. | Clinical counseling and legal support for crime victims, including mental health support and guidance on legal processes, rights, and reporting mechanisms. | Crime victims, with a particular focus on women and children who have experienced various forms of violence, including traditional and cyber-related offenses. | Criminal law, Cybercrime law, Victim support and rights. | India | The KARE dataset: 5,000 English knowledge-grounded dialogues for clinical counseling and legal support for crime victims. It enriches the POEM dataset by augmenting dialogues with external domain-specific knowledge collected via web scraping (using formulated queries and templates) and verified by domain experts, structured as knowledge triplets and then verbalized. | For EMPOWER: Dual-tier deep prompt learning (prefix-tuning), multi-head attention within the Dynamic Dialogue-Knowledge Module (DDKM). For KARE dataset creation: Entity extraction (Stanford NER), query formulation, web scraping (Google Search API), text segmentation (SpaCy), semantic similarity filtering (Sentence-BERT), knowledge triplet extraction (OpenIE), co-reference resolution (AllenNLP), and few-shot learning for triplet verbalization (GPT-J). | The code and dataset are made publicly available on GitHub and an institutional AI-NLP-ML group's resource page. | True | True | Code and dataset are available on GitHub (https://github.com/priyanshu528priya/EMPOWER-KARE/) and an IIT Patna AI-NLP-ML group resources page (https://www.iitp.ac.in/~ai-nlp-ml/resources.html). | Reliance on the quality of source data and knowledge extraction methods which may introduce inaccuracies or biases. The need to explore commonsense knowledge for inducing commonsense reasoning ability and empathy in responses. | Complexity of creating end-to-end dialogue systems for victim counseling; effectively incorporating external domain knowledge; limited computational resources preventing experiments with larger language models; managing potential errors in generated responses (repetition, inconsistency, semantic variance, wrong information). | Generation of erroneous responses (repetition, inconsistency, semantic variance, factually incorrect information). Potential for inaccuracies or biases introduced from source data and knowledge extraction methods. Ensuring generated responses safeguard personal privacy and adhere to relevant laws and regulations. |
| LegalMind System and the LLM-based Legal Judgment Query System.pdf | IEEE_Xplore | LegalMind System and the LLM-based Legal \nJudgment Query System | This paper introduces LegalMind-GPT, an LLM-based system designed to analyze and summarize financial legal documents and query legal judgments for the finance sector. It evaluates LLMs like LLAMA-2, Claude AI, and FLAN-T5-Base for text summarization, finding LLAMA-2 most effective in providing accurate insights from these complex texts. | True | Market | True | 1.0 | Positive | LegalMind-GPT system: An LLM-based Legal Judgment Query System using models like LLAMA-2, Claude AI, and FLAN-T5-Base for text summarization and analysis of financial legal documents, incorporating text chunking, vectorization, and similarity search. | Comparative analysis of LLAMA2-7B, FLAN-T5-Base, and Claude AI on text summarization of managerial sections from NASDAQ-listed companies' 10-K reports (2022) using ROUGE (Rouge-1, Rouge-2, Rouge-L) and BERT Score (Precision, Recall, F1) metrics. | LLAMA2-7B demonstrated the highest performance across ROUGE and BERT scores. ROUGE-1: 0.508627, ROUGE-2: 0.315902, ROUGE-L: 0.323576, BERT(P): 0.917033, BERT(R): 0.904412, BERT(F1): 0.910372. | Complexity of specialized financial knowledge and legal documents, including financial jargon; The need for contextual understanding of these documents, leading to a gap in financial literacy for a broader audience. | Developing AI-driven tools like LegalMind-GPT to process, summarize, and interpret complex financial legal texts. This aims to provide clear, actionable insights, enhance decision-making, and democratize access to financial understanding. | Democratization of financial knowledge, improving financial literacy through accessible interpretations of financial legal documents, enhancing understanding of legal judgments in finance. | Broader audience with lower financial literacy, legal professionals in the finance sector. | Financial law, corporate law (specifically 10-K reports), legal judgments related to finance. | US (based on evaluation data from NASDAQ-listed companies' 10-K reports). | For LLM evaluation: Publicly available managerial sections from NASDAQ-listed companies' 10-K reports (2022; unstructured text). For the broader system: URLs of legal judgments from online legal databases and repositories. The LLMs (LLAMA-2, Claude AI, FLAN-T5-Base) are pre-trained on general large datasets and were integrated without additional fine-tuning for querying in the described evaluation, though the system concept mentions fine-tuning LLMs for legal text analysis. | System architecture involving data acquisition from online databases, text chunking, tokenization, word vectorization, relation identification, integration of pre-trained LLMs, model evaluation and comparison, and development of a user interface and backend. | The paper describes the design of a user-friendly interface for easy interaction, but specific deployment strategies or platforms are not detailed. | False | False | NaN | Need for wider accessibility and understanding of complex financial-legal information beyond specialized professionals; Enhancing AI's ability to accurately interpret and simplify diverse and jargon-heavy financial texts for non-experts; Expanding data sources to include diverse legal documents from various jurisdictions. | Complexity of financial jargon and the need for contextual understanding in financial text summarization; Ensuring accuracy and relevance in AI-generated legal interpretations. | Potential for bias in AI algorithms if not ethically developed; Lack of impartiality and transparency in AI processes if ethical principles are not prioritized. |
| Transforming Legal Workflows A Deep Dive into NLP Solutions for Legal Challenges.pdf | IEEE_Xplore | Transforming Legal Workflows: A Deep Dive into NLP Solutions for Legal Challenges | This paper proposes a novel framework using a modified BERT-based model for legal document summarization and a Doc2Vec approach for case similarity analysis. The system, evaluated on legal datasets, demonstrates its potential to streamline legal processes, enhance legal reasoning, and improve access to legal services. | True | Idealistic | True | 1.0 | Positive | A modified BERT-based model for legal document summarization and a Doc2Vec-based approach (using UMAP and HDBSCAN) for legal case similarity, with visualization using LeetTopic. | The summarization model was evaluated using precision, recall, F1 score, accuracy, and ROUGE metrics (ROUGE-1, ROUGE-2, ROUGE-L) on a split dataset (train, test, validation from BillSum and Australian legal cases). | The modified BERT summarization model achieved a validation accuracy of 0.7327 (training accuracy 0.7179, loss 0.5562). For summarization quality, it scored ROUGE-1: 0.79, ROUGE-2: 0.81, and ROUGE-L: 0.80. | Manual legal research is time-consuming, error-prone, and struggles with the volume and dynamic nature of legal information. Processing and clustering lengthy documents manually is inefficient, overworking legal practitioners. | Employing NLP (modified BERT, LLMs, RAG) for legal document summarization, case similarity analysis, and other tasks to automate research, improve efficiency, and make legal information more accessible, thereby democratizing legal assistance. | Legal document summarization, legal case similarity analysis, improving access to legal services, democratizing legal assistance, enhancing legal reasoning. | Marginalized communities | General Law | United States, Canada, Australia | Publicly available legal summaries from the BillSum dataset (US and Canadian legislation) and Australian legal cases (2006-2009) from the Federal Court of Australia sourced from AustLII via Kaggle. | Transfer learning with a modified BERT architecture (additional custom dense layers with ReLU activation and dropout layers) for summarization. For case similarity: Doc2Vec for text embeddings, UMAP for dimensionality reduction, and HDBSCAN for clustering. Standard NLP preprocessing techniques were applied. | NaN | False | False | NaN | Need for comprehensive, context-aware NLP systems integrating various legal functions. Robustness and accuracy of LLMs for specific legal answers. Need for improved model design, broader applicability across diverse legal systems and languages, and integration of advanced AI techniques for better performance and security. | Addressing the lack of comprehensive existing solutions for legal NLP tasks. For the conversational aspect of their work, insufficient context to optimize performance and user experience. General need for continued research, addressing limitations, and ethical considerations in legal AI. | Accuracy and reliability concerns with LLMs providing comprehensive legal answers tailored to specific inquiries. |
| The Significance of Cultivating High-Value Patents in the Development of AI.pdf | IEEE_Xplore | The Significance of Cultivating High -Value Patents in the Development of AI | This paper discusses the importance of high-value patents in generative AI for protecting innovation, promoting technological progress, and enhancing market competitiveness. It outlines strategies for cultivating such patents and uses the Transformer architecture as a case study to illustrate this process. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Patent Law; Intellectual Property Law | International | NaN | NaN | NaN | False | False | NaN | NaN | Rapid technological iteration requiring patent updates, data privacy and ethical issues in AI development affecting patentability or scope, and the complexity of global patent layouts when cultivating high-value patents for generative AI. | For Generative AI in general: issues of data quality and bias; requirements for substantial computational resources and costs; privacy and security issues; lack of algorithmic transparency; potential for bias and discrimination; security vulnerabilities; and negative employment impacts. |
| AI Legal Assistant for IPC.pdf | IEEE_Xplore | AI Legal Assistant for IPC | This paper introduces an NLP-based chatbot, 'AILA', designed to improve access to legal information regarding the Indian Penal Code (IPC) using LLMs (mistral-7b-instruct) and RAG techniques. The system, featuring a Streamlit interface and evaluated with high accuracy, aims to simplify complex legal language for individuals and small businesses in India. | True | Idealistic | True | 1.0 | Positive | An NLP-based chatbot (AILA) using Retrieval-Augmented Generation (RAG). It employs FAISS for vector database management, the mistral-7b-instruct LLM for generation, and a Streamlit user interface, focusing on the Indian Penal Code. | Evaluated on a custom test dataset of legal queries using accuracy, precision, recall, F1-score, AUC-ROC, and confusion matrix analysis. Performance was compared against individual models (BERT, GPT-3, RoBERTa). | AILA achieved 94% accuracy, 0.92 precision, 0.93 recall, 0.92 F1-score, and 0.97 AUC-ROC. It outperformed individual models (BERT, GPT-3, RoBERTa) on these metrics. | High complexity and density of legal information leading to public lack of awareness; prohibitive cost and inaccessibility of traditional legal consultation. | An NLP-based chatbot (AILA) that simplifies legal text, provides user-friendly access to legal information on the Indian Penal Code, and improves efficiency for individuals and small businesses. | Access to legal information, understanding legal rights and obligations, legal self-help, legal awareness. | General public, individuals, and small businesses in India. | Indian Penal Code (IPC) | India | A legal corpus derived from 'official legal sources' including the Indian Penal Code (IPC), related statutes, and judicial interpretations. Fine-tuning data consisted of 'pairs of legal questions and corresponding answers extracted from legal documents and expert annotations.' | System architecture design involving data preprocessing, NLP module, LLM integration (mistral-7b-instruct), RAG module (with FAISS), and UI development (Streamlit). Iterative refinement based on performance monitoring and user feedback is implied. | The system is designed for deployment on a cloud platform for scalability and accessibility, involving cloud infrastructure setup and security measures. | False | False | NaN | Need for expanding the system’s knowledge base, improving NLP algorithm adaptability, and incorporating multilingual support. | Ensuring accuracy and contextual relevance of legal advice, efficient retrieval of pertinent legal information from a vast legal corpus, interpreting complex legal language, creating an engaging and user-friendly interface, and maintaining an up-to-date legal knowledge base. | NaN |
| Generative Artificial Intelligence in Legal Drafting.pdf | IEEE_Xplore | Generative Artificial Intelligence in Legal Drafting | This paper introduces "Lexi," a generative AI tool designed to simplify legal document drafting by translating complex legal jargon into understandable language. Lexi aims to enhance accessibility and efficiency in legal documentation for both legal professionals and the general public, particularly for individuals and small businesses. | True | Idealistic | True | 1.0 | Positive | Lexi, an AI tool for legal document drafting and jargon simplification, based on a fine-tuned Llama 2 7B model with a chat interface. | Lexi (fine-tuned Llama 2 7B) was compared to a base Llama 2 7b chat model and GPT-3.5. Evaluation metrics included domain specificity, legal jargon level, token count, and an ease of understanding score. Training and validation loss curves for the fine-tuning process were also presented. | The fine-tuned model (Lexi) demonstrated domain-specific capabilities, produced text with 'basic' legal jargon, an average token count of approximately 512, and achieved the highest ease of understanding score of 9.5 out of 10. | The main hurdles identified are the complexity, time-intensiveness, and high cost of traditional legal document drafting. Additionally, the use of intricate legal jargon makes documents inaccessible and difficult for non-specialists to understand, creating a barrier to legal knowledge. | The paper proposes Lexi, an AI-powered tool, to streamline the legal drafting process and simplify complex legal terminology into understandable language. This is intended to democratize legal paperwork and enhance accessibility through a user-friendly interface, especially for individuals and small businesses. | Legal document drafting, simplification of legal language/jargon, improving access to legal information and services for laypersons. | Individuals and small businesses lacking legal expertise or resources, the general public, non-specialists, and non-lawyers. | Rental law (specifically Indian rental rules mentioned as an example), with plans for expansion to a broader range of legal areas. | India (explicitly mentioned for rental rule examples), though the general problem and tool are framed more broadly. | An extensive collection of current legal papers, formatted into JSON objects with 'inputs' and 'responses' keys for fine-tuning the Llama 2 7B model. The specific source or public/proprietary nature of the dataset is not detailed. | The system architecture includes user interaction, chat interface, iterative questioning, data handling, and AI/ML components. Methodologies include fine-tuning a pre-trained LLM (Llama 2 7B), prompt engineering, and UI/UX design principles for the web interface. | Lexi is deployed as a web application with user authentication (Firebase Auth), chat data storage (MongoDB), and model hosting on the Replicate platform. | False | False | NaN | The paper mentions the need to expand the AI's knowledge beyond rental rules, enhance usability (e.g., document export features), and crucially, ensure the preservation of legal accuracy and significance when simplifying language. | Challenges included acquiring and formatting a large, domain-specific legal dataset for fine-tuning, meeting hardware requirements for LLMs, effectively fine-tuning the model for legal language, and designing a user-friendly interface for complex legal drafting tasks. | A key risk highlighted is the potential loss of accuracy and significance of legal content if simplification is not handled carefully, ensuring the integrity of legal information is paramount. |
| An Analysis on Integrating Advanced Conversational AI in Legal Summarization and Information Retrieval.pdf | IEEE_Xplore | An Analysis on Integrating Advanced \nConversational AI in Legal Summarization and \nInformation Retrieval | This paper introduces LawGPT, a conversational AI specialized for the Indian Penal Code, which utilizes a Retrieval-Augmented Generation (RAG) architecture for accurate legal summarization and information retrieval. The study affirms LawGPT's efficacy through validation, aiming to democratize access to legal knowledge for both professionals and laypersons. | True | Idealistic | True | 1.0 | Positive | LawGPT, a conversational AI chatbot using Retrieval-Augmented Generation (RAG) architecture, Dense Passage Retriever (DPR), and BART architecture for generation, specialized for the Indian Penal Code. | Validation against human-generated responses using metrics like ROUGE score. ROUGE F1 scores for LLAMA 2, MISTRAL, and PHI2 were also reported for summarization context. | LawGPT's efficacy and accuracy were affirmed through validation against human-generated responses, demonstrating accurate retrieval and summarization of legal information. For summarization context, ROUGE F1 scores for other models were: LLAMA 2 (0.48), MISTRAL (0.46), PHI2 (0.40). | Limited effectiveness of general-purpose AI in understanding complex legal terminology and navigating intricate legal frameworks, hindering access to legal information. | Development of specialized conversational AI solutions like LawGPT, trained on specific legal corpora (e.g., Indian Penal Code) and employing advanced AI architectures (e.g., RAG), to provide tailored, efficient, and accurate access to legal knowledge. | Access to legal information, legal research, legal text summarization, interpretation of legal statutes (Indian Penal Code). | Laypersons and legal professionals. | Criminal Law (specifically Indian Penal Code). | India | Indian Penal Code (IPC) data and additional data from the OpenAI API. The nature of the IPC data (e.g., public, proprietary) is not specified beyond being the text of the code. | Integration of Retrieval-Augmented Generation (RAG) architecture, Dense Passage Retriever (DPR) for retrieval, BART model for generation, Streamlit for user interface development, LangChain for text processing, and the TogetherAI API for the Legal Language Model (LLM). | NaN | False | False | NaN | NaN | Developing a system capable of accurately interpreting complex legal terminology, performing efficient and relevant information retrieval from legal texts (Indian Penal Code), and generating contextually appropriate and accurate legal responses. | The paper's related works section cites general risks associated with AI in law, such as biases, ethical considerations, lack of transparency, accountability, and fairness, as well as potential negative impacts on justice, democratic governance, legal responsibility, and liability. |
| LAWBOTS Utilization of AI Chatbots for Legal Advising in the Philippines.pdf | IEEE_Xplore | LAWBOTS : Utilization of AI Chatbots for Legal \nAdvising in the Philippines | This paper explores the potential use of AI chatbots (Lawbots) for legal advising in the Philippines, examining existing chatbots and public perception through a survey. The study analyzes Filipinos' views on Lawbots' benefits, challenges, and impact, aiming to inform their acceptance and implementation. | True | Idealistic | True | 3.0 | Neutral | AI Chatbots for legal advising (Lawbots) | A survey of 60 Filipino respondents was conducted via Google Forms, using multiple choice, Likert scale, and linear scale questions to gather perceptions on familiarity with AI, awareness of legal chatbots, and views on the benefits and challenges of Lawbots. | Survey (N=60) indicated nuanced public perception: while 88.3% had used chatbots, 60% were unaware of their use for legal advising. On implementing Lawbots, 31.7% were neutral, 28.3% agreed, and 26.7% disagreed. Key perceived benefits included 24/7 availability and efficiency; key perceived challenges were limited scope/inadequacy of advice and lack of personalization. | General A2J obstacles in the Philippines: insufficient funds, distance/traffic issues, prolonged cases, lack of contact with lawyers. For Lawbots contributing to A2J: lack of public trust and acceptance, concerns about advice adequacy and personalization, ethical considerations, and the complexity of legal issues. | Improving access through AI chatbots like Tisya Hustisya. For broader Lawbot adoption: continuous system development for privacy and regulation, improved legal frameworks for Lawbots, standardization, and public education to build trust and address concerns about advice quality. | Access to legal aid, legal information, human rights, domestic violence, labor issues, general legal advising. | Marginalized communities in the Philippines, general public, victims of specific issues (e.g., sexual violence, human rights violations). | General legal advising, human rights law, labor law, criminal law (related to sexual violence), immigration law (in discussed examples). | Philippines (primary focus); Canada (mentioned for an example chatbot). | NaN | NaN | NaN | False | False | NaN | Need for clearer public communication and education about Lawbots (benefits, challenges, implications). Gaps in public trust and acceptance. Technical gaps include AI's understanding of human emotions and ensuring completeness of responses. | Limited scope/inadequacy of advice, lack of personalization, handling legal complexity, addressing ethical and moral considerations, ensuring data privacy and security, adapting to dynamic and evolving laws, fostering user trust and acceptance, overcoming communication challenges, potential for bias, integrating technology into the legal sector, and establishing clear legal regulations and responsibility. | Concerns regarding data sharing, privacy, and security. Potential for incomplete responses and unauthorized practice of law. Societal threats like manipulation of beliefs and emotions. Lack of legal responsibility for erroneous advice. Potential for algorithmic bias. |
| LDAA Legal Documents Automation and Assistance.pdf | IEEE_Xplore | LDAA: Legal Documents Automation and Assistance | This paper proposes "Legal Documents Automation and Assistance (LDAA)," a system utilizing fine-tuned open-source Large Language Models (like Llama3 or Gemma) to automate legal document creation and provide assistance, specifically targeting illiterate and underprivileged rural populations in India. LDAA aims to offer a user-friendly, efficient solution by integrating AI with legal expertise for personalized guidance, document generation via LaTeX, and an AI chatbot for legal queries. | True | Idealistic | True | 1.0 | Positive | Legal Documents Automation and Assistance (LDAA) system: fine-tuned LLMs (Llama3/Gemma), Retrieval Augmented Generation (RAG), LaTeX for document generation, vector databases (Chroma, FAISS) with TF-IDF for similarity search, and a chatbot for legal assistance. | LLM performance evaluated using BLEU (0.95), Perplexity (1.5), and Word Error Rate (0.01). A demonstrative use case of revising a 'deed of hypothecation' document based on user input is presented. | The LLM component achieved a BLEU score of 0.95, a Perplexity of 1.5, and a Word Error Rate of 0.01. The system demonstrated successful document modification based on user prompts in a hypothecation deed example. | Complexity and high cost of traditional legal documentation, limited access to legal experts for many, rudimentary nature and lack of personalization in existing automated systems, and misalignment of current tools with specific legal practice needs. | Automating legal document creation, review, and assistance using fine-tuned LLMs and a user-friendly interface. Providing personalized guidance, an AI-powered chatbot for legal queries, ensuring document security, and enabling customization to user needs. | Automation of legal document creation, legal assistance via chatbot, simplifying legal processes for accessibility. | Illiterate, underprivileged rural people in India. | General legal document drafting (e.g., contracts, agreements, legal notifications, deeds). | India | Fine-tuning of pre-trained open-source LLMs (Llama3 or Gemma series) using legal documents and articles. A proprietary knowledge base of codified laws and legal principles developed by the team for the chatbot. | System architecture involving a user interface, LaTeX for document compilation, fine-tuned LLMs for text generation and understanding, Retrieval Augmented Generation (RAG) for document modification, vector databases (Chroma, FAISS) with TF-IDF for semantic search, and an iterative user feedback loop for document refinement. | NaN | False | False | NaN | Need for broader legal document type coverage, development of more advanced Large Legal Language Models (LLLMs), mobile application accessibility, voice and multilingual support, integration with blockchain for secure document management, and incorporation of e-signature approvals and legal document verification features. | Overcoming the labor-intensive, error-prone, and expensive nature of manual legal drafting. Addressing the limitations (rudimentary, lack of sophistication and personalization) of existing automated legal tools. Ensuring security, accuracy, and adaptability of the automated system to diverse legal requirements and user needs. | NaN |
| Generative vs Intent-based Chatbot for Judicial Advice.pdf | IEEE_Xplore | Generative vs Intent-based Chatbot for Judicial Advice | This paper presents and compares two AI chatbot approaches, a generative model using OpenAI API and an intent-based model using Google's Dialogflow, designed to provide judicial advice on Indian laws. The generative chatbot demonstrated higher accuracy and more contextually rich responses, while the intent-based chatbot excelled in precision for predefined queries. | True | Idealistic | True | 1.0 | Positive | Comparative development and evaluation of a generative chatbot (using OpenAI API, GPT-3.5 turbo, fine-tuned on custom Indian legal conversations) and an intent-based chatbot (using Google's Dialogflow with custom intents for Indian law). | Both chatbots were tested against 100 test conversations. Performance was measured by calculating true positives, true negatives, false positives, and false negatives, from which accuracy, precision, recall, and F1-score were derived. Qualitative comparison of response nature, quality, handling changing scenarios, data requirements, and user experience was also conducted. | The generative chatbot achieved an accuracy of 96.00%, precision of 96.67%, recall of 98.86%, and F1-score of 97.75%. The intent-based chatbot achieved an accuracy of 80.00%, precision of 90.47%, recall of 97.43%, and F1-score of 93.82%. | Traditional legal advice is often lengthy and expensive. Key challenges in AI for legal advice include ensuring legal accuracy and reliability of responses, handling ambiguity and uncertainty in legal queries, and difficulties in obtaining diverse and extensive datasets due to privacy and legal restrictions. | The paper proposes the development and deployment of AI-powered chatbots (both generative and intent-based) to provide accessible, immediate, and 24/7 judicial advice on Indian legal matters, thereby addressing the cost and time barriers of traditional legal consultations. | Providing judicial advice, guidance on legal issues, procedures, and relevant laws. | Indians seeking judicial advice, particularly those with limited knowledge of Indian civil and criminal laws. | Indian civil and criminal laws. | India | Generative chatbot: A custom-made dataset of 100 conversations (100-150 words each), simulating user queries and lawyer-like responses on Indian civil and criminal law, informed by the National Judicial Data Grid, used to fine-tune GPT-3.5 turbo. Intent-based chatbot: 34 intents (abstract mentions 36, methodology details 34 created plus default ones) with training phrases and predefined responses based on Indian civil and criminal laws, developed within Google's Dialogflow. | Generative chatbot: Developed using Python, OpenAI API (GPT-3.5 turbo model), 'llama-index' and 'langchain' packages for indexing and interaction. Fine-tuning GPT-3.5 turbo on the custom legal conversation dataset. User interface built with Streamlit. \nIntent-based chatbot: Developed using Google's Dialogflow. Conversational flow designed using intents, entities, and follow-up intents. Training phrases and responses created for each intent. Support for English and Hindi, and text-to-speech functionality. | Generative chatbot: Deployed as a Streamlit application made accessible to users via a public URL using Ngrok. \nIntent-based chatbot: Integrated into a custom website (built with HTML, CSS, JavaScript) using Dialogflow's Web Demo (for English, with text-to-speech) and Dialogflow Messenger (for Hindi). | True | False | The generative chatbot was deployed via Ngrok to a public URL. The intent-based chatbot was integrated into a website using Dialogflow's Web Demo and Messenger. | Ensuring legal accuracy and reliability of chatbot responses, especially for generative models. Improving the ability of chatbots to handle ambiguous and uncertain legal queries. Overcoming challenges in obtaining diverse and extensive legal datasets due to privacy and legal restrictions. | Generative chatbot: Some responses required post-processing to improve clarity, despite being contextually rich and fluent. \nIntent-based chatbot: Difficulty handling user input outside predefined categories; initial poor performance necessitated detailed training phrases, meticulous entity definition, and a sufficient number of intents. | Generative AI chatbot responses can sometimes be inaccurate or provide partial guidance due to being derived from patterns in data. Validating the accuracy of legal information generated by AI is challenging, especially given the complexity of legal matters. |
| LEGAL-ANALYSIS-OF-EU-ARTIFICIAL-INTELLIGENCE-ACT-2024-INSIGHTS-FROM-PERSONAL-DATA-GOVERNANCE-AND-HEALTH-POLICY_2024_Access-to-Justice-in-Eastern-Europe.pdf | Scopus | LEGAL ANALYSIS OF EU ARTIFICIAL INTELLIGENCE ACT (2024): INSIGHTS FROM PERSONAL DATA GOVERNANCE AND HEALTH POLICY | This paper provides a legal analysis of the European Union Artificial Intelligence Act (AIA) adopted in 2024, focusing on its implications for personal data governance and health policy, including medical devices. It examines the AIA's correlation with existing EU regulations, such as the Medical Devices Regulation, and discusses recent AI regulatory developments in EU Candidate Countries like Ukraine and the Republic of Moldova. | True | Idealistic | False | 2.0 | Positive | EU Artificial Intelligence Act (2024) and related regulatory frameworks (e.g., Medical Devices Regulation, national AI white papers in Ukraine and Moldova). | NaN | NaN | Complexity of implementing comprehensive AI regulations (like the EU AIA) within national justice systems and aligning them with judicial reform and access to justice goals; ensuring the protection of fundamental rights in AI deployment; addressing prior regulatory deficits in specific sectors like high-risk AI in healthcare. | Adoption and implementation of comprehensive, harmonized AI legal frameworks (e.g., EU AI Act) establishing ethical principles (human-centricity, transparency, non-discrimination), risk-based classifications for AI systems, and mechanisms for human oversight. Fostering international cooperation and alignment of national legislation with these frameworks to safeguard fundamental rights and promote a safe AI market. | Impact of AI regulation (specifically the EU AI Act) on judicial reform, general access to justice, and the protection of fundamental rights within legal and healthcare systems. | NaN | AI law/regulation, Health law, Personal data protection, Human rights law, Copyright law (in Ukrainian context). | European Union, Ukraine, Republic of Moldova. | NaN | NaN | NaN | False | False | NaN | The need for subsequent sectoral regulations to complement the horizontal EU AI Act; lack of specific detailed measures for certain sectors (e.g., healthcare AI applications) within the current AIA's annexes. | NaN | Misuse of AI (e.g., cognitive-behavioural manipulation, social scoring leading to discrimination), risks to health, safety, fundamental rights, and freedoms. Specific risks associated with high-risk AI systems, particularly in the medical field and those impacting fundamental rights. |
| AI-and-access-to-justice-How-AI-legal-advisors-can-reduce-economic-and-shamebased-barriers-to-justice_2024_Oekom--Gesellschaft-fuer-Oekologische-Kommunikation-mbH.pdf | Scopus | AI and access to justice : How AI legal advisors can reduce economic and shame-based barriers to justice | This paper argues that publicly funded AI Legal Advisors (AI LAs) can reduce economic and shame-based cultural barriers to accessing justice, particularly during the initial information-gathering phase. It proposes these systems, likely based on LLMs, should provide reliable legal information to empower individuals in making informed decisions about their legal claims. | True | Idealistic | True | 1.0 | Positive | Publicly funded AI Legal Advisors (AI LAs) designed to provide specific, intelligible legal information and preliminary case assessment, likely based on Large Language Models. | NaN | NaN | Economic barriers (financial costs, opportunity costs of time, transportation costs) and cultural barriers (shame and stigma associated with seeking legal help, particularly for victims of IPV, women regarding inheritance rights in certain cultures, and victims of fraud). | The development and deployment of publicly funded AI Legal Advisors (AI LAs) that provide accessible, reliable, and intelligible legal information, assess claim strength, and offer interactive explanations, particularly for the information-gathering stage of pursuing justice. | Improving access to legal information and initial legal assessment; addressing economic and shame-based barriers in legal help-seeking; specific applications include intimate partner violence, inheritance rights, and fraud. | People with low socio-economic status, victims of intimate partner violence, women facing cultural barriers to asserting legal rights (e.g., inheritance), victims of fraud. | General civil law, with specific examples in housing law, medical malpractice, intimate partner violence, inheritance law, and fraud. | Anglo-American common law systems. | The paper implies that AI LAs would be trained on case law and legal documents. Given the mention of LLMs, this would likely involve large, domain-specific textual datasets. | Conceptual outline of functionalities (e.g., assessment of legal considerations, crude assessment of case success likelihood, interactive lay explanations) rather than specific software design or system architecture methodologies. | Advocacy for public funding of AI LAs and support for their implementation by democratic governments and international organizations. | False | False | NaN | The current lack of highly reliable AI LAs; the need for ongoing work to ensure AI systems are accurate and unbiased; developing robust frameworks for legal responsibility concerning AI-generated advice; addressing how the legal system will manage potential increases in caseloads. | Ensuring AI LA reliability and accuracy (to be as reliable as human lawyers); managing potential increased caseloads on the legal system; mitigating biases inherited from training data (e.g., biased case law); establishing clear legal responsibility and liability frameworks for AI LA errors. | AI LAs providing erroneous legal advice (e.g., recommending litigation for non-viable claims, or recommending abstention from litigation for viable claims); perpetuation or amplification of societal biases present in training data; potential for increased strain on legal systems if caseloads increase without broader AI-driven efficiencies; issues of legal responsibility when AI LAs err. |
| 2211.17094v2.pdf | Scopus | Better Transcription of UK Supreme Court Hearings | This paper describes methods to improve automatic speech recognition (ASR) for UK Supreme Court hearings by fine-tuning generic ASR systems with a custom language model trained on legal-specific data and by infusing common legal phrases. The proposed approach demonstrably reduces Word Error Rate and enhances the accuracy of transcribing critical legal terminology compared to off-the-shelf ASR systems. | True | Market | True | 1.0 | Positive | Domain adaptation of Automatic Speech Recognition (ASR) systems by fine-tuning with a custom language model (CLM) trained on in-domain legal documents and gold-standard transcriptions, and infusing a custom vocabulary of common multi-word expressions and legal entities. | Comparison of Word Error Rate (WER) and ratio of correctly transcribed legal entities. The proposed models (CLM1, CLM2, CLM2+Vocab, CLM2+Vocab2) were evaluated against AWS Transcribe (commercial) and OpenAI Whisper (open-source) on a test set of 2 UK Supreme Court case hearings (approximately 12 hours of audio). Gold-standard transcripts were used as reference. | CLM2 (trained on Supreme Court judgements and gold-standard court hearing transcriptions) outperformed AWS base and Whisper with a 9% and 8% WER improvement, respectively. CLM2+Vocab (CLM2 with a vocabulary list from a phrase detection model) achieved an average WER of 11.6, showing around 9% improvement over generic models, and demonstrated better transcription of legal entities such as 'Provisions' (0.97 ratio) and 'Judge' (0.84 ratio). | Speech transcription of legal proceedings is expensive and slow, hindering access to justice. Generic ASR systems exhibit high Word Error Rates on specialized legal domain content due to long hearings, multiple speakers, complex speech patterns, unique pronunciations, and specific legal jargon and terminology, leading to potential information loss. | Building an automated transcription tool specifically for the UK Justice sector by fine-tuning off-the-shelf ASR systems with custom language models trained on legal documents and gold-standard transcriptions, and incorporating domain-specific vocabulary to improve accuracy and reduce critical errors. | Transcription of court hearings, improving accuracy of legal document creation from audio. | NaN | Court proceedings (specifically Supreme Court hearings). | United Kingdom (UK) | For CLM training: 1) Supreme Court written judgements of 43 cases (3.26M tokens) scraped from the UK Supreme Court official site. 2) Approximately 81 hours of gold-standard transcripts of 10 Supreme Court hearings (post-edited AWS Transcribe output). For vocabulary list: Approximately 139 hours of gold-standard transcriptions of Supreme Court hearings and the aforementioned Supreme Court judgements. All data is domain-specific (UK legal). | Fine-tuning a base ASR system (AWS Transcribe), training a custom language model (CLM), using a phrase detection model based on Pointwise Mutual Information (PMI) for bigram collocation extraction, and employing NLP libraries (Blackstone, spaCy) for legal and non-legal entity extraction to build a custom vocabulary. | The work is part of a combined research and industrial project. The CLM models were trained and run on the AWS platform. No broader public deployment strategy for the resulting tool is mentioned. | False | False | NaN | The need to address ASR performance degradation due to a variety of accents in British audio procedures beyond Supreme Court hearings. Exploring the use of NLP topic modelling techniques to connect legal entities to court case decisions. | Domain mismatch between generic ASR models and specialized legal audio. Insufficient in-domain data to train highly competitive ASR systems from scratch. High Word Error Rates (WER) from generic systems on legal-specific terminology, names, and numbers. Accurately transcribing unique pronunciations and complex speech patterns in courtrooms. | Inaccurate transcription of legal terminology, names, and numbers by ASR systems can lead to serious information loss and confusion. Critical errors in transcription can affect the utility of the transcripts for legal professionals and case understanding. |
| ssrn-3740356.pdf | Scopus | Contracts in the Age of Smart Readers | This paper introduces "smart readers," AI tools based on language models like GPT-3, capable of simplifying, personalizing, interpreting, and benchmarking contracts to enhance understanding and transparency. It explores their potential for improving access to justice while also critically examining risks such as errors, adversarial attacks, bias, and the broader legal and policy implications. | True | Idealistic | True | 3.0 | Neutral | Language models (e.g., GPT-3) applied as 'smart readers' for contract analysis, simplification, personalization, construction, and benchmarking. | Illustrative examples generated by GPT-3 (cherry-picked by authors) for simplification, personalization, and construction; mentions PrivacyCheck (a browser extension using machine learning) for benchmarking privacy policies. | NaN | Cognitive and time burden of reading complex legal contracts, difficulty in understanding legal language and firm's strategic obfuscation, high cost of legal assistance, disparities between sophisticated and lay parties, and limited access to legal support in underserved areas. | AI-powered 'smart readers' to simplify text, personalize explanations, interpret contractual meaning, benchmark contract quality, and provide on-demand 'know-your-rights' services, thereby reducing information asymmetry and enhancing consumer empowerment. | Understanding contract terms (especially boilerplate/fine print), informed consent, access to information about legal rights/obligations within contracts, consumer empowerment. | Consumers (buyers, employees, tenants, lessees), low-income individuals, immigrants, young people, individuals in 'legal deserts' or with limited access to legal advice. | Contract Law (especially Consumer Contracts), Privacy Law. | United States | For GPT-3 examples: A very large corpus (45TB) of compressed plaintext including Wikipedia and other diverse web data (a mix of publicly available and proprietary sources, general text, unstructured). | NaN | Discusses concepts illustrated by existing technologies: GPT-3 (accessible via API and third-party tools) and PrivacyCheck (available as a browser extension). | True | True | PrivacyCheck is described as a free, on-demand browser extension. GPT-3 based models are accessible via APIs or third-party tools, some of which offer free tiers for limited use. | Accuracy of smart readers in the specialized legal domain, vulnerability to sophisticated adversarial attacks, potential for inherent algorithmic bias and discriminatory outcomes, ensuring equitable user adoption across diverse populations (digital divide), and the need for adapting existing legal and regulatory frameworks to this new technology. | Ensuring high accuracy and reliability of outputs, mitigating errors (including those from adversarial attacks), addressing the black-box nature of some models, developing robust and fair methods for contract benchmarking, managing information loss during simplification, and achieving widespread, equitable user adoption. | Inaccurate outputs from smart readers due to inherent errors or malicious adversarial attacks; algorithmic bias leading to discrimination (e.g., based on race, or differential treatment based on smart reader usage); premature relaxation of consumer protection standards by regulators and courts; inducing consumer overcompliance with unfair or illegal terms; and exacerbating societal inequalities due to differential access or proficiency with the technology. |
| 3594536.3595146 (1).pdf | Scopus | Beyond Readability with RateMyPDF A Combined Rule-based and Machine Learning Approach to Improving Court Forms | This paper describes RateMyPDF, a web application that combines rule-based approaches, traditional machine learning, and large language models (GPT-3) to measure court form usability and offer automated improvement suggestions. The tool, validated against a large dataset of U.S. court forms and expert reviews, aims to help form authors and courts enhance access to justice for self-represented litigants. | True | Idealistic | True | 1.0 | Positive | RateMyPDF: a web application using a combination of rule-based heuristics, traditional machine learning (for field normalization and classification), and GPT-3 (for data validation, metadata extraction, and text summarization) to analyze PDF court forms and provide a usability score with actionable recommendations. | A dataset of approximately 24,000 PDF forms from 46 U.S. States and the District of Columbia was collected. A random subset of 40 forms was rated for complexity by a panel of 6 expert reviewers. These human ratings were then correlated with the RateMyPDF scores using Intraclass Correlation Coefficients (ICC). | The RateMyPDF score showed a statistically significant correlation with the average expert rating (ICC3 0.5861, p-value=0.00). Expert reviewers also demonstrated agreement with each other (ICC1 0.3139, p-value=0.02), and including RateMyPDF as a seventh reviewer improved the group's agreement (ICC1 0.3931, p-value 0.00). | Complex and poorly designed court forms impose significant time, emotional, and cognitive burdens on self-represented litigants. These forms are often created by untrained staff without user input or usability expertise, and traditional usability testing is often too resource-intensive for courts to apply systematically. | Development and deployment of automated tools like RateMyPDF to assess form usability at scale, provide concrete suggestions for improvement based on established guidelines and data-driven insights, and facilitate the comparison of forms across jurisdictions. This enables courts to more efficiently identify and revise problematic forms. | Usability of court forms, readability metrics, plain language, administrative burden reduction, automated document analysis, legal form design. | Self-represented litigants. | Civil litigation (general), with examples from domestic violence, eviction, and divorce proceedings. | United States (data collected from 46 states and the District of Columbia). | The primary dataset consists of approximately 24,000 PDF court forms scraped from official court websites across 46 U.S. states and DC (unstructured text). A proof-of-concept ML model for field normalization was trained using features like adjacent text to fields, previous field names, field location, and topic classifications from the Spot NLP tool. The system also utilizes external resources like the Dale-Chall difficult word list and plainlanguage.gov. | Iterative development based on expert consultation (plain language experts, form committee members, legal aid providers), literature review on form usability and readability, statistical analysis of a large corpus of court forms, workshopping with potential users (court staff, legal aid providers), and a combination of rule-based systems, traditional machine learning, and LLM integration. | RateMyPDF is a publicly available web application (ratemypdf.com). The underlying Python library (FormFyxer) and the web application code (RateMyPDF) are open-sourced on GitHub. A companion website, Form Explorer, allows users to browse and compare the processed forms from the dataset. | True | True | The RateMyPDF tool is accessible at https://ratemypdf.com. The source code for FormFyxer and the RateMyPDF frontend is available on GitHub. | Assigning a qualitative 'goodness' grade (e.g., A-F) to forms beyond a numerical complexity score. Refining time-to-complete estimates with real-world user testing on court forms. Developing a more domain-specific list of difficult words for legal contexts. Incorporating metrics for whitespace and logical field ordering. Further exploring the use of LLMs for direct text simplification of form language. Extending the analysis capabilities to interactive legal applications (guided interviews). | Handling the wide variability in PDF structures and formats encountered in court forms, including proprietary formats like Adobe XFA. Accurately performing automated field recognition and meaningful field name normalization across diverse forms. Limitations of existing NLP tools for specific legal tasks (e.g., recognizing state-specific short-form citations). The complexity of automatically classifying field types (slot-in, gathered, created) accurately. | The potential for Large Language Models (LLMs) like GPT-3 to 'hallucinate' or generate factually incorrect information, particularly when used for text generation (mitigated in RateMyPDF by anchoring summarization tasks to source data). The risk that authors might try to 'game' or 'fool' automated scoring systems to achieve better scores without genuinely improving the form's usability (addressed by providing specific, actionable improvement suggestions beyond simple metrics). |
| 2212.08204v1.pdf | Scopus | LEGAL RELECTRA : Mixed-domain Language Modeling for Long-range Legal Text Comprehension | This paper introduces LEGAL RELECTRA, a novel language model designed for long-range legal text comprehension, particularly for mixed-domain texts like personal injury cases. The model, based on an ELECTRA framework using REFORMER components, is pre-trained on legal and medical corpora and shows improved performance on Named Entity Recognition tasks compared to existing models. | True | Market | True | 1.0 | NaN | LEGAL RELECTRA: A mixed-domain language model adapting the ELECTRA framework with REFORMER components (generator and discriminator) for improved long-sequence processing, pre-trained on a combined legal and medical text corpus with a custom domain-specific tokenizer. | Evaluated on Named Entity Recognition (NER) tasks for legal domain (labels: case type, plaintiff, defendant) and mixed legal-medical domain (labels: case type, plaintiff, defendant, injury). Performance compared against BERT, CLINICAL-BERT, LEGAL-BERT, REFORMER, and LEGAL RELECTRA with BERT tokenizer, using precision, recall, and F1-score metrics on custom annotated legal text and publicly available data (conll2003, MIMIC III) for general/medical NER model training. | LEGAL RELECTRA with a custom tokenizer achieved the highest overall F1-score of 85.93% on the legal domain NER task and 78.57% on the mixed-domain NER task, outperforming baseline models including LEGAL-BERT and REFORMER. | NaN | NaN | NaN | NaN | Personal injury civil suits, Civil law (general) | US (based on training data sources like US case law and Supreme Court opinions) | A 12GB corpus of unstructured text consisting of: 6GB legal text (excerpts from US case law), 3GB medical text (doctor’s notes/letters from MIMIC and MIMIC-CXR databases), and 3GB mixed personal injury case descriptions (from Supreme Court opinions, academic literature, COURT LISTENER, BYU LAW, anonymized attorney case descriptions). Mix of publicly available and proprietary/anonymized data. | The model architecture (RELECTRA) adapts the ELECTRA framework by replacing BERT-based generator and discriminator with REFORMER models to handle longer text sequences (up to 8,092 tokens). Pre-training on a mixed-domain corpus (legal, medical, mixed). Development and use of a custom domain-specific tokenizer trained via Byte-Pair Encoding. | NaN | False | False | NaN | NaN | Processing long legal documents (beyond typical BERT limits), handling specialized terminology from multiple domains (legal and medical) simultaneously, and difficulty in collecting a sufficiently large corpus of ideal pre-training data (specifically, personal injury texts). | NaN |
| 2409.08098v3.pdf | Scopus | The CLC-UKET Dataset: Benchmarking Case Outcome Prediction for the UK Employment Tribunal | This paper introduces the CLC-UKET dataset, developed using LLM-aided annotation, for benchmarking case outcome prediction in the UK Employment Tribunal. It evaluates baseline models (fine-tuned transformers, LLMs) against human expert predictions, finding that fine-tuned T5 performs best among models, though human experts still achieve higher accuracy. | True | Idealistic | True | 1.0 | Positive | Creation and benchmarking of the CLC-UKET dataset for UK Employment Tribunal case outcome prediction, utilizing LLM-aided annotation (GPT-4-turbo) for extracting legal information (facts, claims, outcomes etc.) from case judgments. The paper then benchmarks various models (BERT, T5, GPT-3.5, GPT-4) on a case outcome prediction task using this dataset. | Models (BERT, T5, GPT-3.5, GPT-4) were evaluated on a test split of CLC-UKET_pred (1,371 cases) where outcome labels were manually annotated by a legal expert. Performance was measured using Accuracy, Precision, Recall, and F-score. Human expert predictions on the same test set were used as a baseline. LLMs were tested in zero-shot and few-shot (random and jurisdiction code-based example selection) settings. | Fine-tuned T5 model achieved the best performance among machine learning models with an F-score of 0.564. Human experts outperformed all models, achieving an F-score of 0.672. | High-level obstacles to access to justice or effective prediction include: the inherent complexity of legal cases (e.g., distinguishing preliminary issues from final resolutions, multi-step proceedings); information asymmetry where extracted facts from judgments may not capture all necessary details or may be biased due to being summarized by judges post-outcome; the evolution of law over time, making predictions for older cases difficult without contextual knowledge; and the difficulty of accurately categorizing outcomes like 'partly wins' or 'other' procedural decisions. | The paper proposes the CLC-UKET dataset as a benchmark to facilitate research in predicting UKET case outcomes, which can improve understanding and potentially access to justice. It uses LLM-aided annotation to address the cost and time of manual data creation. For future work, it suggests exploring alternative methods for identifying facts/claims to reduce bias and better model legal evolution. | Case outcome prediction, access to justice, employment dispute resolution. | Claimants and respondents in UK employment disputes. | Employment law | United Kingdom (UK Employment Tribunal) | The CLC-UKET dataset, derived from the UKET subset of the Cambridge Law Corpus (CLC). CLC-UKET_anno (19,090 cases) has legal annotations (facts, claims, outcomes, etc.) automatically extracted using GPT-4-turbo from publicly available UKET judgments (2011-2023). CLC-UKET_pred (14,582 cases) is used for the prediction task; for training/validation, facts, claims, and outcomes are LLM-extracted; for testing, facts/claims are LLM-extracted, but outcomes are manually annotated by a legal expert. Data is unstructured legal text transformed into semi-structured annotations, domain-specific to UK employment law. | Iterative prompt engineering for LLM-based data annotation (GPT-4-turbo). Filtering of cases from CLC. Manual annotation of test set outcome labels by legal experts for evaluation and human baseline. Standard machine learning evaluation methodology with train/validation/test splits. Evaluation metrics include Accuracy, Precision, Recall, F-score. Comparative analysis of different model architectures (fine-tuned Transformers like BERT and T5; LLMs like GPT-3.5 and GPT-4) and settings (zero-shot, few-shot). | The CLC-UKET dataset is planned to be made available via the official Cambridge Law Corpus (CLC) website. Access to the CLC (and by extension CLC-UKET) is restricted to researchers with confirmed ethical clearance. | True | False | The CLC-UKET dataset will be made available via the official CLC website, with access restricted to researchers with confirmed ethical clearance and compliance with DPA/UK GDPR. | Technical gaps include improving model performance (e.g., through retrieval-augmented generation or chain-of-thought for LLMs), developing better methods for extracting unbiased facts and claims (closer to original submissions rather than judges' summaries), and effectively modeling the evolution of law over time in predictions. Societal gaps include the unknown representativeness of the dataset and addressing potential information biases in data derived from judicial decisions. | Key challenges included: the cost and time of manual legal data annotation (mitigated by LLM use); ensuring the quality of LLM-extracted annotations; defining a meaningful case outcome prediction task from complex legal narratives; achieving high prediction accuracy, particularly for nuanced outcomes; bridging the performance gap between AI models and human legal experts; and managing potential biases in the source data (judgments written post-outcome by judges). | Information bias: Facts and claims extracted from judges' written decisions may inherently contain biased information or sentiments reflecting the known outcome, which models might learn. Over-reliance/misinterpretation of prediction scores: Caution is advised when drawing conclusions for legal practice from these baseline results. Data privacy and ethics: Although based on publicly available judgments, handling legal data requires adherence to ethical guidelines, DPA, and UK GDPR; access is restricted accordingly. |
| Laws-Clearly-Large-language-models-and-plain-language-transformation_2024_MNYKNT.pdf | Scopus | laws clearly: large language models and plain language transformation | This paper investigates the use of OpenAI's GPT-4 model to automatically transform complex Hungarian legal texts into plain language, aiming to improve comprehensibility for laypeople and enhance access to justice. The study manually analyzes GPT-4's performance on four specific linguistic features, finding it can simplify text structure but often alters the normative legal content, making it a potential aid for experts rather than a fully automatic solution. | True | Idealistic | True | 2.0 | Neutral | Application of OpenAI's GPT-4 model with specific prompts for plain language transformation of legal texts, targeting four linguistic features: shortening long/interjected clauses, replacing light verb constructions, breaking down overly long sentences, and clarifying ambiguous conjunctions (e.g., Hungarian 'illetve'). | Manual qualitative analysis of GPT-4's outputs on excerpts from the Hungarian Act CXXII of 2013 on Transactions in Agricultural and Forestry Land. For each of the four linguistic features, specific prompts were given to GPT-4, and the results were evaluated from both legal (preservation of normative content) and linguistic (meeting prompt requirements) perspectives. | GPT-4 showed mixed performance. While it was promising for shortening clauses and improving readability by restructuring sentences, it frequently altered the normative legal content. For instance, attempts to simplify light verb constructions led to semantic errors, and in almost all evaluated cases, the legal meaning was changed, making the model unsuitable for fully automatic, unaided plain language transformation of legal texts. | The complexity, specialized terminology, and unique structure of legal language make it difficult for lay citizens to understand legal texts and effectively represent themselves in legal proceedings, hindering access to legal information and justice. | Leveraging Large Language Models like GPT-4 to transform complex legal texts into simpler, more understandable plain language. The paper suggests LLMs could serve as productivity tools to assist human experts in the plain language transcription process, rather than fully automated solutions. | Access to legal information, comprehensibility of legal text, plain language transformation, legal simplification. | Laypeople/citizens without legal expertise. | Land Law (specifically Act CXXII of 2013 on Transactions in Agricultural and Forestry land in Hungary), Legislative drafting. | Hungarian | N/A (The paper uses OpenAI's pre-trained GPT-4 model; its training data is not specified in the paper but is known to be a vast and diverse corpus.) | Experimental design involving: selection of specific legal text (Hungarian Land Transaction Act); identification of four target linguistic features for simplification; crafting concise, specific prompts for GPT-4 for each feature; manual, qualitative evaluation of the generated plain language text for linguistic accuracy and preservation of normative legal content. | NaN | False | False | NaN | LLMs like GPT-4, in their current state, are not suitable for fully autonomous plain language paraphrasing of legal texts due to the high risk of altering normative content. Human expertise remains indispensable. There are potential issues with processing non-English content due to models' internal translation mechanisms, which can lead to meaning distortion. | Ensuring the preservation of normative legal content during simplification. Models often perform general reformulations rather than the specific linguistic transformations requested. Risk of semantic drift or misinterpretation by the LLM, especially with nuanced legal terms or when processing non-English text (e.g., the model confusing meanings during internal translation for Hungarian words). | Alteration or violation of the normative (legal) content of the original text. Omission of crucial details in the simplified version (e.g., precise measuring methods). Incorrect interpretation of legal conditions (e.g., transforming conditional rights into apparently automatic rights). Misinterpretation of ambiguous conjunctions (e.g., translating 'illetve' as 'and' instead of 'or', thereby changing legal implications). |
| 2022.nllp-1.10.pdf | Scopus | Parameter-Efficient Legal Domain Adaptation | This paper proposes 'prefix domain adaptation,' a parameter-efficient method using unsupervised pre-training on public legal forum data to adapt language models for the legal domain. This approach is shown to improve fewshot performance and model calibration on legal classification tasks compared to baselines like LEGAL-BERT, while tuning only approximately 0.1% of model parameters. | True | Idealistic | True | 1.0 | Positive | Prefix domain adaptation | The proposed method (prefix domain adaptation) was evaluated on three datasets: Legal Advice Reddit (multi-class classification), Law Stack Exchange (multi-class classification), and European Court of Human Rights (ECHR, binary violation prediction). Performance was measured by macro F1 score and calibration by Expected Calibration Error (ECE) in fewshot settings, compared against RoBERTa full finetuning, LEGAL-BERT, full domain adaptation, P-Tuning v2, and prefix adaptation. | Prefix domain adaptation matched or exceeded the performance of LEGAL-BERT and full finetuning in most fewshot settings across various legal tasks (e.g., on Legal Advice Reddit with 32-shot, Prefix Domain Adapt: 41.9 F1 vs LEGAL-BERT+FT: 36.1 F1) while tuning only ~0.1% of parameters. It also showed comparable or better calibration than finetuning methods. | High cost of legal advice for laypersons. High cost and scarcity of labeled data in the legal domain, making fewshot learning critical. | Proposing parameter-efficient legal domain adaptation, which uses vast unsupervised legal data from public legal forums for pre-training a prefix prompt. This method aims to improve fewshot performance, reduce computational/storage costs, and thereby help make legal services more accessible. | Improving access to legal information for laypersons, legal question classification, parameter-efficient learning for legal NLP. | Laypersons seeking legal advice. | General law / Multiple fields (derived from public forum questions like criminal law, copyright law, and specific datasets like ECHR for human rights law). | International (uses ECHR dataset; other datasets like Legal Advice Reddit and Law Stack Exchange are not tied to a single jurisdiction and the method is broadly applicable). | Unsupervised legal text (questions/posts) from public forums (Legal Advice Reddit, Law Stack Exchange) for masked language modeling pre-training of the prefix. Labeled data from these forums (flairs/tags for classification) and the ECHR dataset (binary violation prediction) for downstream fewshot evaluation tasks. | The approach builds on prefix tuning (specifically P-Tuning v2) and domain adaptation principles. It involves pre-training a deep prompt using the masked language modeling (MLM) task on a large, domain-specific unsupervised corpus, then using this pre-trained prompt for downstream task-specific tuning. | The paper contributes two new datasets (Legal Advice Reddit, Law Stack Exchange) which are made available on Hugging Face to facilitate further research. | False | False | NaN | The need for more extensive hyperparameter search for larger models is mentioned as future work. The general exploration of parameter-efficient methods in the legal domain and their stability is ongoing. | Parameter-efficient methods typically underperform in low-data (fewshot) settings common in law. Large language models are resource-intensive (memory, storage). Existing domain-specific models like LEGAL-BERT may perform poorly on informal legal text from laypersons. Ensuring model stability across different data and fewshot sizes. | Misuse of the model if its confidence (logits) does not accurately reflect real-world likelihood (poor calibration), especially critical in the high-stakes nature of legal decision-making. |
| short2.pdf | Scopus | ChatGPT as an Artificial Lawyer? | This paper qualitatively investigates ChatGPT's ability to provide legal information to laypeople, comparing its performance with JusticeBot, an expert system. While ChatGPT demonstrates strong language comprehension, it struggles with accuracy and trustworthiness, leading the authors to suggest combining its interactive capabilities with the reliability of expert systems. | True | Idealistic | True | 2.0 | Neutral | ChatGPT (a large language model) | Qualitative evaluation using three simulated landlord-tenant cases in Quebec, generated by ChatGPT and manually selected/adjusted. ChatGPT's responses were compared to JusticeBot's and assessed against criteria: language comprehension, accuracy, completeness, trustworthiness, harmlessness, and user-friendliness. | ChatGPT showed good language comprehension but poor accuracy, frequently hallucinating legal information (false provisions/cases) and being overly confident in incorrect answers. JusticeBot performed better on accuracy and trustworthiness, highlighting a trade-off with ChatGPT's more natural interaction style. | High cost of lawyers, individuals living in a "legal advice desert," lack of awareness of applicable rights and procedures, difficulties for self-represented litigants in navigating litigation, and the inability of laypeople to verify the accuracy of AI-provided legal information. | Utilizing AI to provide legal information efficiently, accurately, and cost-effectively. Specifically, the paper suggests combining the interactive conversational abilities of LLMs like ChatGPT with the verified accuracy of expert systems like JusticeBot. | Access to legal information, self-help tools for everyday legal disputes, particularly landlord-tenant issues, understanding legal predicaments and needs. | Laypeople, self-represented litigants, individuals who do not have access to professional legal help. | Landlord-tenant law (housing law) for specific case studies; general legal information provision. | Quebec, Canada | For ChatGPT: Enormous general corpora of texts, not specifically trained on legal data. For JusticeBot (comparator): Content created by legal experts. | For ChatGPT: Large language model training on general text corpora. For JusticeBot (comparator): Expert system methodology. | ChatGPT is available via a free chat interface and an API. JusticeBot is accessible via a public website (justicebot.ca). | True | False | ChatGPT is accessible via a public web interface and an API. JusticeBot is accessible via its website (justicebot.ca). | ChatGPT's current lack of accuracy, reliability, and verifiability makes it unsuitable for directly providing legal information to laypeople. There is a need for AI tools that provide accurate, up-to-date, and sourced legal information. | For ChatGPT: Ensuring accuracy, avoiding 'hallucinations' (fabricating legal provisions/cases), and inability to admit errors. For rule-based systems like JusticeBot: The effort required for manual content creation, maintenance, and expansion to cover a broad range of legal matters. | Laypeople making harmful decisions based on inaccurate or incomplete AI-generated legal information. AI fabricating legal texts and cases, thereby misleading users. Potential for ChatGPT to generate harmful content under coercive circumstances. |
| Use-of-Generative-Artificial-Intelligence-Including-Large-Language-Models-Such-as-ChatGPT-in-Scientific-Publications-Policies-of-KJR-and-Prominent-Authorities_2023_Korean-Radiological-Society.pdf | Scopus | Use of Generative Artificial Intelligence, Including Large Language Models Such as ChatGPT, in Scientific Publications: Policies of KJR and Prominent Authorities | This editorial presents the Korean Journal of Radiology's (KJR) new comprehensive policies for the use of generative AI, including LLMs like ChatGPT, in scientific publications submitted to KJR. The KJR policy, aligned with prominent international authorities, outlines rules for authorship, author responsibility, transparency, acceptable AI use for language enhancement, and restrictions for peer review to uphold research integrity. | True | NaN | True | 1.0 | NaN | KJR's policy on generative AI use in scientific publications | NaN | NaN | NaN | NaN | NaN | NaN | Copyright law, Intellectual property (authorship), Publication ethics | Republic of Korea (for KJR policy); International (for comparative discussion and referenced authorities) | NaN | NaN | NaN | False | False | NaN | NaN | Ensuring research integrity, preventing plagiarism and copyright infringement, defining authorship, and maintaining the quality of peer review in the context of AI-generated content. | Breach of research integrity, plagiarism, copyright infringement, issues with assigning authorship, generation of incorrect/incomplete/biased content by AI, undermining the human expert perspective in peer review. |
| 2024.lrec-main.927.pdf | Scopus | Linking Judgement Text to Court Hearing Videos: UK Supreme Court as a Case Study | This paper presents a system for automatically linking segments of UK Supreme Court written judgements to relevant moments in their corresponding hearing video recordings. It introduces an annotated dataset for legal Information Retrieval and demonstrates that fine-tuning GPT text embeddings optimizes the linking accuracy for this task. | True | Idealistic | True | 1.0 | Positive | An automated system for linking UK Supreme Court written judgements to court hearing video segments using fine-tuned GPT-3 text embeddings (text-embedding-ada-002) for semantic relevance scoring, presented via a User Interface (UI) with video bookmarking. | The system was evaluated using Information Retrieval metrics (MAP@k, Recall@k, Accuracy, Precision, F1) on a dataset of 7 UKSC cases. Human annotators (post-graduate law students) provided gold-standard relevancy annotations for judgement-hearing segment pairs. The dataset was augmented using AI-generated paraphrases and negative sampling. Stakeholder feedback was also gathered on the UI. | The concatenation of customised GPT-3 embeddings for judgement and hearing segments with their cosine similarity scores achieved the best results on an augmented test set: Accuracy 0.85, Precision 0.85, Recall 0.84, F1 0.85. | Lengthy and difficult-to-navigate video recordings of court hearings, and the limited availability or high cost of human-generated transcripts, hindering comprehension and use of these materials. | An AI-powered tool that automatically links segments in written judgements to relevant video moments in court hearings, providing bookmarks for easier navigation and comprehension of the legal arguments and decision-making process. | Access to court proceedings, understanding of judicial decisions/judgements, comprehension of the legal system, legal research. | General public, legal professionals, academic researchers, judges. | General public law, Constitutional law (based on the UK Supreme Court's remit). | United Kingdom (UK Supreme Court). | A dataset of 7 UKSC case judgements (scraped from the UKSC website) and corresponding transcribed hearing videos (from UK National Archives). Initial dataset of 3620 human-annotated judgement-to-transcript links. Augmented to 7248 links using AI-generated paraphrases (via InstructGPT) for positive samples and random shuffling/in-batch negative sampling for negative samples. | Iterative development involving: data compilation and preprocessing, zero-shot IR for initial candidate generation, human-in-the-loop for gold-standard annotation, AI-based data augmentation, empirical evaluation and fine-tuning of various IR models (including GPT embeddings), and UI development for stakeholder demonstration. | A User Interface (UI) platform was created and demonstrated to stakeholders including the UK National Archives, the UK Supreme Court, and industrial partners for feedback and potential future adoption. | False | False | NaN | Need for larger annotated datasets and more sophisticated linking mechanisms, such as those based on common legal entities (articles, legal provisions, case names). | Segmenting legal text into semantically cohesive units, the need for domain-specific legal knowledge for accurate annotation, the high cost and time involved in expert annotation, and models being misled by high-frequency but low-relevance phrases. | NaN |
| Artificial_intelligence_AI_or_augmented_intelligen.pdf | Scopus | Artificial intelligence (AI) or augmented intelligence? \nHow big data and AI are transforming healthcare: \nChallenges and opportunities | This paper discusses how big data and AI are transforming healthcare, highlighting both innovative opportunities and significant ethical, legal, and social challenges. It emphasizes the critical need for robust governance frameworks, particularly in low- and middle-income countries, to address issues like the digital divide, data bias, and potential exacerbation of health inequities. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Digital divide; exacerbation of health inequities; data and algorithmic bias; low data literacy in LMICs; commercial exploitation of data from LMICs; lack of robust, context-specific governance and legislation in LMICs. | Developing context-specific ethical and legal frameworks for AI in LMICs; ensuring transparency, accountability, and human oversight; improving data literacy; promoting equitable benefit-sharing and sustainable AI practices; and adopting a hybrid human-AI approach to healthcare. | Health equity; digital divide in healthcare; ethical AI governance in LMICs; data privacy and security; algorithmic bias in medicine; regulation of AI in healthcare. | Populations in Low- and Middle-Income Countries (LMICs); resource-depleted settings; historically underrepresented groups in medical data (e.g., women, children, ethnic minorities, people with disabilities). | Medical ethics and law; data protection and privacy law; AI-specific legislation and regulation; liability and medical malpractice law; constitutional rights; consumer protection law; intellectual property law. | South Africa; International (with specific mentions of WHO, EU, USA, China); Low- and Middle-Income Countries (LMICs) generally. | Discusses LLMs trained on massive internet texts and medical AI using varied datasets (EHRs, images, mobile data); highlights concerns over use of identifiable patient data and inherent biases in historical medical data. | NaN | NaN | False | False | NaN | Absence of AI-specific and context-relevant governance, ethical guidelines, and legislation in many LMICs (including South Africa); lack of harmonisation in international AI regulations; unaddressed ethical and technical debt in rapid AI deployment. | NaN | Propagation of inaccurate/hallucinated information; amplification of societal biases leading to discriminatory outcomes and health disparities; erosion of clinical skills; severe privacy violations and data misuse; psychosocial harm from human-like AI; exploitative data commercialisation disadvantaging LMICs; significant environmental impact; and complex medicolegal liability. |
| Towards-humancentred-standards-for-legal-help-AI_2024_Royal-Society-Publishing.pdf | Scopus | Towards human-centred standards for legal help AI | This paper presents findings from structured interviews and design sessions with community members regarding their potential use of large language model AI tools (specifically Google Bard) for legal problems like eviction. It explores user preferences, behaviors, and concerns to inform human-centered design and policy for legal help AI, aiming to ensure community perspectives are prioritized alongside expert opinions. | True | Idealistic | True | 2.0 | Positive | Generative AI tools (specifically Google Bard) for responding to legal problems. | Qualitative study involving structured interviews and design sessions with 15 US adults who had experienced a civil legal problem. Participants engaged in a scenario exercise using Google Bard to respond to a fictional eviction notice, followed by feedback and co-design discussions. | Participants generally found Bard helpful (average rating 3.6/6), with post-use trust (4.2/6) higher than pre-use trust (2.7/6). They valued clear answers and perceived accuracy but desired hyperlinks, citations, and better prompting guidance. Three user personas emerged: 'I'm Going to Screenshot This', 'Tell Me The Law (and I’ll Cherry-Pick From There)', and 'Now I Know What to Research'. | General A2J issues: lack of problem recognition as legal, insufficient capacity of help services. AI-specific: risk of incorrect information (hallucinations, 'ersatz legal help'), AI as a 'second-class' service, inequitable access (digital divide, technical literacy), over-reliance by users, and data privacy concerns. | Emphasizes participatory policymaking and human-centered design to ensure community members' perspectives shape AI development and regulation. Proposes specific mitigations for AI risks, including better referral systems via expert collaboration, guardrails against case hallucinations, ensuring jurisdiction-specific information, and interface designs that contextualize information and guide users to verified resources. | Access to civil justice, issue spotting, legal information provision, self-help legal resources, legal problem-solving for common issues (eviction, debt collection, family matters). The study specifically used an eviction scenario. | General public / community members facing civil justice problems in the US. The study self-identifies its sample as not fully representative, particularly underrepresenting lower-income individuals and those with limited English proficiency or technological capability. | Civil justice, primarily landlord-tenant law (evictions). Mentions debt collection, family law (divorce, custody) as other relevant areas. | USA (participants from California, New York, Maryland, New Jersey). | The paper studies Google Bard, which is trained on large-scale, general web data. The paper does not specify further details, as this is proprietary to Google. | Qualitative research methods from design research, participatory policymaking, and human-computer interaction. The study used a scenario-based research protocol with structured and open-ended interview questions, observation of tool use (Google Bard), and co-design brainstorming. | The tool studied (Google Bard) is a publicly accessible web-based AI chat interface provided by Google. | True | False | Google Bard, the AI tool used in the study, is publicly accessible via a web interface. | Need for more extensive and ongoing empirical research with diverse community members to understand AI use for legal help. Development of a definitive risk typology for legal AI and effective, user-tested mitigation strategies. Strengthening participatory policymaking in legal AI governance. | For the study: achieving a representative sample of the public (acknowledged limitations in representing lower-income, less tech-savvy individuals). For the field: addressing 'ersatz legal help' where AI-generated information looks good but is incorrect or inapplicable; user reluctance to engage with warnings; ensuring AI tools are truly empowering and do not exacerbate inequalities. | AI providing incorrect legal information (e.g., hallucinated cases, wrong jurisdictional laws); users over-relying on AI-generated content without verification; AI tools becoming a 'second-class' service for those who cannot afford human lawyers; inequitable access due to digital divide; data privacy concerns; misinterpretation or 'cherry-picking' of legal details by users; bad referrals to unhelpful organizations. |
| Clopton-Huq-76-Stan.-L.-Rev.-893.pdf | Scopus | The Necessary and Proper Stewardship of Judicial Data | This paper argues federal judicial data is a vital, underused public asset that Congress should regulate for improved collection, management, and accessibility to advance public good and access to justice, countering its current imperfect availability and potential for private monopolization. It offers a descriptive analysis of current data practices, a doctrinal examination of regulatory power, and a normative vision for reform, including the use of LLMs. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Imperfect availability and high cost of judicial data (e.g., PACER fees, clunky interface); significant data loss and inconsistency in collection (e.g., "dark data", lack of standardization); monopolization of data by commercial firms for private profit; lack of comprehensive congressional regulation and some judicial resistance to open data; information asymmetry favoring well-resourced litigants. | Enact congressional legislation to treat judicial data as a public asset, ensuring its systematic production and broad public availability with narrow exceptions; improve data accuracy, consistency, and searchability through standardization (e.g., for NOS codes) and better capture methods, possibly involving court staff or public-regarding privatization; reform public disclosure by increasing transparency, reducing access barriers like fees (e.g., "Free PACER"), and improving data formats; leverage technologies like LLMs for public good analyses. | Access to court records and dockets; Improving judicial processes (e.g., IFP status, case management, sentencing); Reducing information asymmetry for litigants; Supporting legal research and policy-making for judicial reform; Enhancing judicial accountability and transparency. | Litigants with limited resources (e.g., pro se, in forma pauperis); the general public; academics and researchers; legal services providers (e.g., public defenders). | Civil Procedure, Criminal Procedure, Constitutional Law, Administrative Law, and general federal litigation. | United States (federal judiciary) | NaN | NaN | NaN | False | False | NaN | Technical gaps in data capture (accuracy, consistency, searchability), data formats, and public access interfaces. Societal/legal gaps include the lack of a comprehensive legislative framework for judicial data, judicial resistance to open data, and the need to balance transparency with privacy and judicial integrity. The full potential of LLMs for analyzing judicial data remains unmapped. | NaN | Private monopolization of public data for profit; exacerbation of inequality due to costly access systems; misinterpretation of disclosed data leading to unwarranted criticism or distorted judicial behavior; privacy violations from improper handling of sensitive information; compromising essential judicial deliberations or safety; incentivizing judges to "teach to the test" at the expense of accuracy; potential for errors in LLM-generated analyses of judicial data. |
| Large-language-models-as-tax-attorneys-a-case-study-in-legal-capabilities-emergence_2024_Royal-Society-Publishing.pdf | Scopus | Large language models as tax attorneys: a case study in legal capabilities emergence | This paper evaluates the legal analysis capabilities of Large Language Models (LLMs), specifically OpenAI's GPT series, in the domain of US tax law. It finds that newer models like GPT-4, when combined with retrieval augmentation and advanced prompting techniques such as chain-of-thought and few-shot learning, demonstrate emerging legal understanding capabilities but do not yet match expert human performance. | True | Idealistic | True | 2.0 | Positive | OpenAI LLMs (davinci, text-davinci-002, gpt-3.5-turbo, GPT-4) combined with retrieval-augmented generation (RAG) using US legal texts (CFR, US Code) and lecture notes, and various prompting techniques (chain-of-thought, few-shot prompting). | LLMs were evaluated on synthetically generated multiple-choice US tax law questions. Performance was compared across different LLMs, retrieval methods (no retrieval, similarity search with GTR-large embeddings, gold standard legal text, lecture notes), and prompting strategies (with/without Chain-of-Thought, few-shot examples). Answers were graded for accuracy, largely using GPT-4 for automated evaluation. | The 'mega_run' configuration using GPT-4 with 'gold_truth' legal text retrieval, few-shot prompting, and Chain-of-Thought prompting achieved the highest accuracy, averaging approximately 80% on Code of Federal Regulations (CFR) questions and approximately 70% on US Code questions. | The high cost and complexity of legal services, which makes them inaccessible to many people. Current LLMs, even the best performing, are not yet at the level of expert tax lawyers. | LLMs could potentially democratize access to legal advice by reducing costs and complexity, provide legal information directly to consumers, and increase the productivity of lawyers, thereby making legal services more affordable. | Reducing the cost of legal services, providing legal information and advice to laypersons, improving efficiency of legal professionals. | Individuals who currently cannot afford legal counsel or find it difficult to navigate the legal system. | US Tax Law | United States | The LLMs studied (OpenAI's GPT series) are pre-trained on broad, general internet corpora (proprietary). For retrieval augmentation, the study used publicly available texts from the US Code of Federal Regulations (CFR) and the US Code, as well as proprietary lecture notes. The evaluation questions were synthetically generated by the authors using Python code. | Experimental design comparing LLM performance across model types, information retrieval strategies (none, similarity-based, gold-standard, lecture notes), and prompting techniques (standard, chain-of-thought, few-shot) on synthetically generated, multiple-choice legal problems. Automated grading was performed using GPT-4. | NaN | False | False | NaN | LLMs still underperform expert human lawyers in legal reasoning tasks. There is a need for better document retrieval methods to close the gap with 'gold truth' context. Further research is needed on advanced prompting techniques adapted for law, the impact of prompt length, and the development of legal domain-specific fine-tuned models. | Ensuring that evaluation datasets (synthetic questions) are novel and not part of LLM training data. Scalable and consistent grading of LLM outputs. Suboptimal performance of general-purpose embeddings for legal text retrieval. Difficulty in retrieval methods matching the performance of providing perfect 'gold truth' legal context. | LLMs are 'black boxes' with no guarantees on behavior for new tasks. Deployment requires rigorous safeguards for data privacy, minimizing bias, maintaining accountability for decisions, and evaluating suitability for specific legal use cases. |
| Using-Generative-AI-to-Identify-Arguments-in-Judges-Reasons-Accuracy-and-Benefits-for-Students_2024_Queensland-University-of-Technology.pdf | Scopus | Using Generative AI to Identify Arguments in Judges’ Reasons: Accuracy and Benefits for Students | This study evaluates the accuracy of Large Language Models (ChatGPT and Claude) in identifying legal arguments from Australian High Court judgments, finding significant performance variations with Claude 3.5 being most accurate. The paper discusses potential benefits for law students and the legal profession, emphasizing the continued need for critical human engagement and cautioning against over-reliance on current AI capabilities. | True | Idealistic | True | 2.0 | Neutral | Using Large Language Models (ChatGPT versions 4 and 4o; Claude versions 3.0 and 3.5) to identify and reconstruct arguments from judges' reasons, specifically reformulating them into a modus ponens argument structure. | Five recent High Court of Australia decisions were provided to four LLM versions (ChatGPT-4, ChatGPT-4o, Claude 3.0, Claude 3.5) with a single, non-technical prompt. The LLM-generated outputs, identifying argument chains, were then anonymised and independently marked by two academics (a lawyer/legal academic and a philosopher) against a pre-defined sample answer (argument chain in modus ponens form) and a detailed rubric with specific marking criteria (total 20 marks). | Claude 3.5 markedly outperformed all others, achieving average grades up to 18/20 (90%). The system average mark for Claude 3.5 was 16.2/20, while ChatGPT versions demonstrated lower accuracy, with average marks not exceeding 10/20 (system average for ChatGPT-4 was 8.2/20 and ChatGPT-4o was 8/20). | Financial cost, time, complexity of justice systems, lack of legal capability, and language skills (cited as general barriers to access to justice). | GAI (specifically LLMs) could potentially increase access to justice by facilitating low- or no-cost (accurate) legal advice, or increasing the speed and efficiency in dealing with legal matters, thereby reducing costs associated with legal services. | Improving efficiency in legal analysis (argument identification from judgments); potentially reducing costs for legal services and facilitating access to legal advice. | Individuals who cannot access legal advice due to cost or overworked judicial systems (general public). | Native title, criminal law, statutory interpretation, immigration law. | Australia (High Court of Australia decisions). | The study used existing LLMs (ChatGPT and Claude). Their training data is generally described as 'enormous volumes of data – for example, text corpora scraped from vast swathes of the internet.' The input data for the study itself consisted of PDF versions of five recent High Court of Australia decisions. | NaN | NaN | True | False | The LLMs tested (ChatGPT and Claude) are publicly accessible online. The paper specifically refers to using paid versions (e.g., ChatGPT4o, ChatGPT4, Claude 3.5, Claude 3.0) for the study, implying these higher-capability versions generally require subscriptions. | Significant variance in accuracy across different LLM systems and versions for legal argument identification. The tendency of LLMs to 'hallucinate' or produce fabricated content. The need for users, especially students, to critically engage with LLM outputs. Current LLMs cannot replace nuanced human skill in legal argument analysis. | High cost of labour for human evaluation of legal documents, necessitating small numbers of annotators/assessors for the study. Ensuring accuracy and avoiding 'hallucinations' with LLMs. Obtaining reliable performance with simple, non-technical prompts. LLMs performed poorly in accurately citing paragraph numbers. | LLMs 'hallucinating' (e.g., inventing legal cases). Providing inaccurate legal advice. Unskilled users being misled by superficially convincing but inaccurate LLM outputs. Negative impact on law students' development of legal argument reconstruction skills if LLMs are used uncritically. Risks related to unsupervised creation of legal arguments. |
| 2207.00220v2.pdf | Scopus | Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset | This paper introduces Pile of Law, a 256GB open-source legal dataset, and proposes an approach to data filtering for large language models grounded in legal norms regarding privacy and toxicity. It demonstrates how such contextual filtering rules can be learned from the dataset, aiming to foster responsible AI development for legal tasks and potentially improve access to justice. | True | Idealistic | True | 1.0 | Positive | The Pile of Law dataset, an approach to data filtering grounded in legal norms, and methods to learn contextual privacy/toxicity filtering rules from this dataset (e.g., predicting pseudonymity in BIA decisions, analyzing toxicity filter behavior). Also, pretraining a BERT-large model (pol-bert) on Pile of Law. | Pseudonymity prediction in BIA cases (distill-BERT model predicting if a paragraph should use pseudonymity) was evaluated using F1 score and perturbation analysis. Pol-bert was evaluated on the CaseHOLD benchmark. Toxicity filters were analyzed using Cohen's Kappa on Supreme Court cases and by observing toxicity score changes based on context length. | A distill-BERT model trained to predict pseudonymity in Board of Immigration Appeals cases achieved approximately 80% F1 score on the validation set. | The difficulty of creating reliable and ethical AI tools for law due to problematic pretraining data (biased, private, toxic content) and current ad-hoc, context-insensitive filtering methods; these issues hinder the development of AI that could potentially serve access to justice. | Grounding AI data filtering practices in established legal principles and norms; providing a large, curated, open-source legal dataset (Pile of Law) as a resource; demonstrating methods to learn contextual filtering rules directly from legal data to enable more responsible legal AI development. | Responsible development of legal AI through legally-grounded data filtering, with potential applications in improving access to justice by enabling better legal AI tools. | Vulnerable populations interacting with the legal system (e.g., asylum seekers, veterans, litigants in sensitive cases), and more broadly, individuals who could benefit from AI-assisted legal tasks. | Various legal fields including court opinions, contracts, administrative law (e.g., immigration, veterans' benefits, labor law), legislative records, tax law, and constitutional law. | Primarily the U.S. legal system (federal and state), with inclusion of some international sources like European Parliament proceedings, Canadian court opinions, ECHR opinions, and world constitutions. | The Pile of Law dataset itself: a ~256GB compilation of open-source, publicly available English-language legal and administrative texts from 35 sources, including court opinions, contracts, and legislative records. Data is mostly unstructured text under permissive licenses. | Data Curation and Compilation for the Pile of Law dataset. Case studies involving supervised learning (distill-BERT for pseudonymity prediction), perturbation analysis, causal lexicon induction, and comparative model scoring (MLM score) for learning filtering rules. Standard transformer pretraining for pol-bert. | The Pile of Law dataset and pol-bert model checkpoints are hosted on Hugging Face. Code for experiments and data collection is planned for release on GitHub. | True | True | The Pile of Law dataset and pol-bert model checkpoints are available on Hugging Face. Code planned for GitHub. | Need for more robust and value-aligned toxicity filters sensitive to context and out-of-domain data. Challenges in pretraining LLMs on highly diverse legal text. Ensuring models are adequately trained. Addressing implicit biases present in legal texts even after filtering. The limitations of current legal frameworks in fully addressing privacy and toxicity in the context of AI. | Curating a large-scale diverse legal dataset; developing stable pretraining methods for such varied data (e.g., issues with large batch sizes/learning rates); applying existing sanitization tools effectively out-of-domain; handling the nuances of legal language and context for filtering; balancing transparency with privacy and toxicity concerns. | Potential for LLMs to generate harmful content if pretrained on unfiltered data containing biased, obscene, copyrighted, or private information. Memorization and leakage of sensitive data. Misapplication of learned filtering rules leading to censorship of important information or failure to remove harmful content. Copyright infringement if models generate content derived from copyrighted materials within the dataset, particularly for certain data subsets or in jurisdictions without robust fair use exceptions. Propagation or amplification of existing biases within legal texts. |
| Trustworthy-AI-Securing-Sensitive-Data-in-Large-Language-Models_2024_Multidisciplinary-Digital-Publishing-Institute-MDPI.pdf | Scopus | Trustworthy AI: Securing Sensitive Data in Large Language Models | The paper proposes a comprehensive framework for integrating trust mechanisms into Large Language Models (LLMs) to dynamically control the disclosure of sensitive information. This framework utilizes User Trust Profiling, Information Sensitivity Detection, and Adaptive Output Control, aiming to balance data utility and privacy in sensitive domains like healthcare, finance, and legal services. | True | Market | True | 1.0 | Positive | A framework for embedding trust mechanisms in LLMs, integrating User Trust Profiling (based on RBAC/ABAC, behavioral analytics), Information Sensitivity Detection (using NER, contextual analysis, privacy-preserving techniques like differential privacy), and Adaptive Output Control (employing redaction, summarization, differential privacy). | NaN | NaN | NaN | NaN | NaN | NaN | Legal services (general) | International | The framework manages LLMs typically trained on vast, web-scraped general text corpora. Its internal components (e.g., for sensitivity detection) would use general and domain-specific labeled datasets, potentially fine-tuned on data like medical records or financial data, and continuously retrained using user feedback and incident reports. | Conceptual framework design integrating existing access control models (RBAC, ABAC), NLP techniques (NER, contextual analysis), and privacy-preserving methods (differential privacy). | NaN | False | False | NaN | NaN | Balancing data utility and privacy; accuracy of domain-specific sensitivity detection; precision of adaptive output control based on user trust; mitigating biases in trust profiling; scalability across diverse domains. | Unauthorized disclosure of sensitive/private information by LLMs; extraction of training data through attacks; non-compliance with privacy regulations (e.g., GDPR, HIPAA); algorithmic bias leading to unfair or discriminatory outcomes; misclassification by the proposed framework leading to improper data disclosure or denial of access. |
| It-cannot-be-right-if-it-was-written-by-AI-onlawyers-preferences-of-documents-perceived-as-authored-by-an-LLM-vs-a-human-It-cannot-be-right-if-it-was-written-by-AI-onlawyers-preferences-J-Harasta-et-al_2024_Sprin.pdf | Scopus | It cannot be right if it was written by AI: on lawyers’ preferences of documents perceived as authored by an LLM vs a human | This paper experimentally investigates whether lawyers' and law students' perception of legal documents, specifically their correctness and language quality, is influenced by the documents' assumed authorship (human vs. AI). Findings reveal a significant preference for documents perceived as human-authored, despite a general expectation among participants that automated document generation will be common in the future. | True | Idealistic | True | 2.0 | Neutral | Experimental study investigating human perception of legal documents based on their perceived authorship (AI vs. human). | 75 Czech lawyers and law students evaluated two human-crafted debt acknowledgement documents (one Brief, one Verbose). Documents were presented with manipulated labels (AI-generated or human-crafted) across two participant groups. Evaluation was based on correctness and language quality (1-5 scale) via an online survey with open-ended explanations. Statistical tests (Fisher exact test) and thematic analysis were used for data analysis. | Participants significantly preferred documents labeled as human-crafted over those labeled as AI-generated, both in terms of correctness (mean score 4.69 for human-perceived vs. 4.21 for AI-perceived) and language quality (mean score 4.55 for human-perceived vs. 3.97 for AI-perceived). | Negative perception and 'algorithmic aversion' towards documents believed to be AI-generated, leading to their lower valuation even if objectively comparable. This bias could disadvantage individuals, particularly from underserved communities, relying on AI for legal document creation or assistance, especially if AI use disclosure is mandatory. | Fostering responsible implementation and adoption of AI in legal document generation. Encouraging policy discussions and updating legal processes to mitigate perception biases. Conducting further research on perceptions across different populations and document types. | Improving affordability and accessibility of legal services, supporting self-help with legal documents, and enhancing legal aid delivery for underserved populations through AI tools. | Lower-income groups and individuals needing legal assistance who might rely on AI-generated documents or AI-assisted legal aid. | Civil law (specifically, acknowledgement of debt, related to contract law). | Czechia (participants and legal context of sample documents). | NaN | Between-groups experimental design (manipulating perceived authorship label), within-subjects element (both groups saw brief/verbose document versions), online survey with Likert scales and open-ended questions, stratified random sampling for participant allocation, quantitative analysis (statistical tests like Fisher exact test), qualitative thematic analysis of free-text responses. | Research paper publication. Experimental materials (documents and survey) released on a GitHub repository to facilitate replication. | True | True | Experimental documents (acknowledgements of debt) and survey questions are available on GitHub for study replication. | Societal: The gap in understanding and addressing the negative perception bias ('algorithmic aversion') towards AI-generated legal documents, which can hinder access to justice. Need for research on diverse populations, document types, and the impact of prior AI knowledge. Policy/Process Gaps: Lack of established legal processes and responsible adoption frameworks that account for such perception biases. | Generalizability of findings due to specific participant pool (Czech lawyers/law students), single document type (acknowledgement of debt), and specific linguistic/cultural context. Limited sample size (n=75) impacting some statistical sub-analyses. Comprehensively defining and capturing all facets of document 'quality'. Not controlling for participants' prior exposure to AI. Ensuring ideal comparability of the human-crafted stimuli documents (Brief vs. Verbose). | Negative perception bias leading to unfair legal outcomes (e.g., denial of rightful claims if documents are AI-perceived). Mandatory AI-use disclosure exacerbating bias and disproportionately harming vulnerable users relying on AI. Increased social inequality if documents from underserved communities using AI are devalued. Undermining trust in potentially accurate AI-generated legal information. Over-reliance or unfounded skepticism impacting the appropriate use and effectiveness of AI tools in law. |
| ssrn-4582745.pdf | Scopus | Towards Human-Centered Standards for Legal Help AI | This paper presents findings from interviews and design sessions with community members on using AI tools like Google Bard for legal problems, such as eviction notices. It advocates for a human-centered, participatory approach to developing AI and policies in the legal sector to ensure they meet community needs and mitigate risks, rather than relying solely on expert speculation. | True | Idealistic | True | 2.0 | Positive | Using general-purpose Large Language Models (e.g., Google Bard) for legal problem-solving by community members through a conversational interface. | Structured interviews and design sessions with 15 adult participants in the US who had prior civil legal problems. Participants engaged in a scenario exercise using Google Bard to respond to a fictional eviction notice, followed by questions about their experience, feedback, and preferences using a mix of structured (multiple-choice, slider) and open-ended questions. | Participants generally found Google Bard helpful (average rating 3.6/6), and trust in the tool increased from an average of 2.7/6 pre-use to 4.2/6 post-use. Key issues included a lack of hyperlinks/citations, potential for over-reliance on AI-generated legal information, and instances of "ersatz legal help" (e.g., hallucinations, decontextualized information). | The primary obstacles to access to justice identified are the justice gap itself (people unable to resolve legal problems) and the current expert-driven, speculative approach to AI development and policymaking for legal help, which lacks community input. Specific to AI, risks include incorrect information, inequitable access, and tools becoming a second-class service. | The paper proposes adopting participatory design research and policy-making to prioritize community members' perspectives in the development and regulation of legal help AI. Specific solutions to AI quality problems include collaboration with domain experts to curate referrals, guardrails against a_i_hallucinations like false case citations, prompting for jurisdiction to provide localized information, and using prominent links to guide users to reliable resources. | Access to civil justice, specifically how AI can assist with common legal problems like evictions, poor living conditions, debt collection, divorce, and custody matters. The study uses an eviction notice scenario. | Community members in America who have experienced civil legal problems. The paper acknowledges limitations in its sample and advocates for future research to include more diverse populations, including lower-income individuals, those with limited English proficiency, and varying technological capabilities. | Civil justice, with a specific focus on landlord-tenant law (evictions). | United States (participants from California, New York, Maryland, and New Jersey; scenarios mentioned specific US state/county laws). | NaN | NaN | NaN | True | False | The study utilized Google Bard, an AI tool accessible online, which participants could use during the research. Google Bard is a publicly accessible tool. | A significant gap is the lack of community participation in legal AI development and policy. Further research is needed to understand diverse user needs, develop risk typologies for AI legal help, and create effective technical and interface solutions for identified problems like hallucinations and bad referrals. The paper also notes an underrepresentation of certain demographics in current research. | Challenges in the study included working with a convenience sample that was not fully representative of the US public. For users of AI legal tools, challenges include difficulty in formulating effective prompts and the risk of receiving generic or unhelpful information if prompts are not specific enough. | Potential risks include users relying on incorrect or hallucinated legal information from AI, leading to detrimental decisions. AI could become a 'second-class service' an data privacy concerns from over-harvesting data. 'Ersatz legal help' (hallucinations, decontextualized or inapplicable information) was observed, and users might over-rely on AI outputs without critical assessment or verification. |
| s10506-023-09367-6 (1).pdf | Scopus | Bringing legal knowledge to the public by constructing a legal question bank using large‑scale pre‑trained language model | This paper presents a three-step approach to make legal information more accessible to laypersons by improving navigability and comprehensibility. It focuses on using large language models (GPT-3) with novel prompting strategies to construct a Legal Question Bank (LQB) from simplified legal texts, and a recommender system (CRec) to guide users to relevant information. | True | Idealistic | True | 1.0 | Positive | A three-step approach: 1) CLIC-pages (plain language legal summaries), 2) a Legal Question Bank (LQB) constructed using GPT-3 with a 'Hybrid' partitioning prompting strategy, and 3) a CLIC Recommender (CRec) to match user queries to the LQB. | The LQB generation method was evaluated by comparing GPT-3 (using three prompting/partitioning strategies: section-based, paragraph-based, Hybrid) generated questions (MGQs) with human-composed questions (HCQs) for 100 CLIC-pages. Metrics included quantity, precision (verified by legal experts), coverage, and diversity. | The 'Hybrid' GPT-3 partitioning strategy yielded the best MGQs: 3,400 correct questions (vs. 2,686 HCQs), 68% precision, 93% coverage, greater diversity, and generation of 'augmenting questions' for content improvement. | The primary obstacle is the 'legal knowledge gap' for the general public, stemming from difficulties in: 1) Navigability: finding relevant legal rules for their situation. 2) Comprehensibility: understanding technical legal language and concepts. | A three-step approach: 1) Creating 'CLIC-pages' with legal information in layperson's terms to enhance comprehensibility. 2) Constructing a 'Legal Question Bank' (LQB) using GPT-3 to provide model questions, improving navigability and comprehensibility. 3) Designing an AI-powered 'CLIC Recommender' (CRec) to guide users from their problem descriptions to relevant LQB questions and CLIC-pages, further aiding navigability. | Improving navigability and comprehensibility of legal information for the general public, legal knowledge dissemination. | Laypersons, general public, individuals without legal education or formal legal training. | Various fields relevant to daily life. The evaluation sample included: Landlord and Tenant, Defamation, Insurance, Personal Data Privacy, Intellectual Property. The CLIC platform covers 32 legal topics. | Hong Kong | For question generation: CLIC-pages, which are human-written plain language summaries of Hong Kong law hosted on the CLIC platform. GPT-3 (the LLM used) was pre-trained on diverse, large-scale text and code datasets (e.g., Common Crawl, WebText2, books, Wikipedia). | For LQB creation: prompt engineering for GPT-3 (including section-based, paragraph-based, and a novel 'Hybrid' partitioning strategy), sentence embedding (DistilBERT), and single-link clustering for question deduplication. For CRec: text embedding (all-mpnet-base-v2) of user input and LQB questions/answers, cosine similarity for matching, and a redundancy removal strategy. | The CLIC platform (clic.org.hk) is an operational online platform. The CRec is presented as a prototype component being developed for and integrated into this platform, using the LQB generated by the described methods. | True | False | The CLIC platform (clic.org.hk), which incorporates the CLIC-pages and the CRec recommender prototype using the described LQB, is an online public resource. | The paper notes that 'augmenting questions' generated by the AI reveal omissions in current CLIC-page content, suggesting a need for continuous content enrichment. The sub-100% precision of AI-generated questions (Hybrid at 68%) implies a remaining need for human verification and curation. | Designing effective GPT-3 prompts (partitioning strategies) to optimize question quantity, precision, coverage, and diversity. Managing the probabilistic nature of LLM outputs leading to variability in question quality. The significant human effort and cost required for verifying machine-generated questions. Effectively deduplicating semantically similar questions. | Imperfect precision of machine-generated questions (e.g., the best strategy achieved 68% precision) could lead to users being presented with irrelevant or unhelpful legal information if not properly curated before deployment in the LQB. |
| A-CIA-TriadBased-Taxonomy-of-Prompt-Attacks-on-Large-Language-Models_2025_Multidisciplinary-Digital-Publishing-Institute-MDPI.pdf | Scopus | A CIA Triad-Based Taxonomy of Prompt Attacks on Large Language Models | This paper introduces a novel taxonomy for prompt attacks on Large Language Models (LLMs) based on the Confidentiality, Integrity, and Availability (CIA) cybersecurity triad. It analyzes emerging threats and proposes targeted mitigation strategies to enhance LLM security in critical applications, including legal services. | True | Market | True | 1.0 | NaN | A taxonomy of prompt attacks on LLMs based on the Confidentiality, Integrity, and Availability (CIA) triad, alongside a framework for corresponding mitigation strategies. | NaN | NaN | For LLMs in legal services: Exposure of confidential legal information (e.g., attorney-client communications), generation of incorrect or misleading legal advice and information, and disruption of access to legal AI tools and resources due to system vulnerabilities. | For LLMs in legal services: Enhancing data confidentiality through methods like differential privacy and access controls; ensuring information integrity via input validation, adversarial training, and bias mitigation; maintaining system availability and resilience through rate limiting, context management, and anomaly detection. | Security and privacy in AI-driven legal services; Integrity and reliability of AI-generated legal information and advice; Availability and resilience of AI tools for legal applications. | NaN | General legal services | International | NaN | Literature review, application of the established CIA cybersecurity triad to LLM prompt attacks, synthesis of existing attack mechanisms and case studies into a structured classification and mitigation framework. | The proposed taxonomy and framework are disseminated through publication as an open-access academic paper for conceptual adoption by researchers and practitioners. | True | True | The paper presenting the taxonomy and framework is published under an open access (CC BY) license, making it freely available. | Societal: Need for specific regulatory frameworks for AI in legal services, ethical guidelines for LLM use in law, and education for legal professionals and the public on LLM risks. Technical: Development of LLMs specifically hardened for legal applications, real-time monitoring for attacks on legal AI systems, and advanced security protocols to protect sensitive legal data. | NaN | Confidentiality risks: Unauthorized extraction of sensitive data (e.g., personal, proprietary, attorney-client communications, medical records), prompt stealing, model inversion. Integrity risks: Generation of misleading, biased, false, or harmful content (e.g., misinformation, incorrect legal/medical advice, hate speech), instruction injection. Availability risks: Denial-of-Service (DoS) attacks, system crashes or unresponsiveness, output degradation, context flooding, disruption of critical services. |
| Large-language-models-in-cryptocurrency-securities-cases-can-a-GPT-model-meaningfully-assist-lawyers_2024_Springer-Nature.pdf | Scopus | Large language models in cryptocurrency securities cases: \ncan a GPT model meaningfully assist lawyers? | This paper investigates the capabilities of GPT-3.5 for legal reasoning (identifying violations) and ChatGPT for legal drafting (complaints) in the context of U.S. cryptocurrency securities cases. Results show GPT-3.5's legal reasoning is currently weak, while ChatGPT demonstrates promising legal drafting skills, producing complaints that did not lead to significantly different mock juror decisions compared to lawyer-drafted ones. | True | Market | True | 2.0 | NaN | Evaluation of OpenAI's GPT-3.5 (text-davinci-003 model via API) for legal reasoning (identifying statutory violations from fact patterns) and ChatGPT (May 24, 2023 version, based on gpt-3.5-turbo-0301, via user interface) for legal drafting (class action complaints). | For legal reasoning (GPT-3.5): Fact patterns from 20 real cryptocurrency securities cases were provided as input. The LLM's output (identified violations) was scored against actual complaint allegations using precision, recall, and F1-score, averaged over 5 runs per case.\nFor legal drafting (ChatGPT): Mock jurors (N=88 recruited via Prolific) evaluated legal complaints from 9 cryptocurrency securities class action cases. Complaints were either drafted by ChatGPT (based on Law360 article summaries) or by lawyers (edited for comparability). Juror decisions on whether charges were proven and their confidence levels were compared using statistical tests (Fisher's Exact, Mann Whitney U). Linguistic concreteness of complaints was also analyzed. | ChatGPT performed well at legal drafting: mock juror decisions (whether a charge was proven) were not statistically significantly associated with the author of the complaint (ChatGPT vs. lawyer; p=0.39), and juror confidence did not significantly differ. ChatGPT-drafted complaints were found to be significantly more concrete than lawyer-drafted ones (t(8)=3.25, p=0.012). | NaN | NaN | NaN | NaN | Securities law, Cryptocurrency law, Litigation (specifically U.S. civil procedure and class actions). | United States (federal law, U.S. District Courts). | Proprietary, large-scale, pre-trained datasets used by OpenAI for its GPT-3.5 models (text-davinci-003 trained on data prior to June 2021; ChatGPT based on gpt-3.5-turbo-0301 fine-tuned with RLHF). The data is generally understood to be massive, diverse, and primarily unstructured text. | RQ1 (Legal Reasoning): Experimental design using real case fact patterns, zero-shot prompting with IRAC legal reasoning technique specified, parameter setting (temperature=0.2), repeated runs, manual scoring of outputs against ground truth using adapted TP/FP/FN for precision, recall, F1-score.\nRQ2 (Legal Drafting): Experimental design with mock jurors; cases selected based on real-world outcomes. Zero-shot, section-by-section prompting of ChatGPT using Law360 article summaries. Lawyer-drafted complaints edited for factual comparability. Juror surveys with standardized instructions. Statistical analysis of juror decisions and confidence. Qualitative and computational linguistic (concreteness) analysis of complaints. | NaN | True | False | OpenAI's GPT-3.5 models (text-davinci-003 via API and ChatGPT interface based on gpt-3.5-turbo-0301) are publicly accessible, though API usage incurs costs. | NaN | Prompt engineering effectiveness is unclear and task-specific. Proprietary nature of LLM training data poses validity concerns (risk of reproducing known material). API limitations and errors encountered (requiring switch to UI for one task). Difficulty in objective, scalable evaluation of LLM outputs (e.g., legal reasoning). Ensuring comparability between AI-generated and human-generated documents for evaluation. Cost and variability associated with human participant studies (mock jurors). | LLM hallucination or fabrication of facts/statutes (observed minor instances of hallucinating defendants and inconsistent details). Incorrect legal knowledge or reasoning capabilities of current LLMs. Over-reliance on AI for complex legal tasks without qualified human oversight. Potential for LLM outputs to be influenced by biases or inaccuracies in training data (though not explicitly detailed for these models/tasks in the paper, it's a general LLM risk). |
| Bringing-legal-knowledge-to-the-public-by-constructing-a-legal-question-bank-using-largescale-pretrained-language-model_2024_Springer-Nature.pdf | Scopus | Bringing legal knowledge to the public by constructing a legal question bank using large‑scale pre‑trained language model | The paper proposes a three-step method to enhance public access to legal knowledge: creating layperson-friendly legal explanations (CLIC-pages), building a Legal Question Bank (LQB) using GPT-3 to generate relevant questions for these pages, and developing an AI-powered CLIC Recommender (CRec) to link user queries to appropriate questions and information. The study emphasizes the technical creation of the LQB, showing that machine-generated questions are more scalable and diverse than human-written ones, while human questions are more precise. | True | Idealistic | True | 1.0 | Positive | A three-step approach for legal knowledge dissemination centered on: 1) CLIC-pages (layperson legal explanations), 2) a Legal Question Bank (LQB) generated using GPT-3 with a "Hybrid" partitioning and prompting strategy, and 3) a CLIC Recommender (CRec) using text embeddings (all-mpnet-base-v2) and cosine similarity. The paper primarily details and evaluates the LQB generation method. | The LQB generation method (specifically, three GPT-3 prompting strategies) was evaluated by comparing machine-generated questions (MGQs) against human-composed questions (HCQs) for 100 CLIC-pages. Human evaluators with legal training assessed quantity, precision (correctness of questions relative to CLIC-page content), content coverage, and diversity. The CRec prototype was anecdotally tested with case scenarios from a public discussion forum. | The "Hybrid" GPT-3 prompting strategy for LQB generation was most successful: it produced more correct questions than human efforts on the sample (approx. 3,400 MGQs vs. 2,686 HCQs), achieved 68% precision, 93% content coverage, and greater question diversity (including more general, multi-paragraph questions and unique "augmenting questions") compared to human-composed questions. | The "legal knowledge gap" for the general public, primarily due to: 1) Navigability: Difficulty for laypersons to find relevant legal rules or principles. 2) Comprehensibility: Difficulty understanding technical legal language and concepts. | A three-step approach: 1) CLIC-pages: Presenting legal information in plain, understandable language. 2) Legal Question Bank (LQB): Creating a large bank of questions linked to CLIC-pages, generated using GPT-3, to improve navigability. 3) CLIC Recommender (CRec): An AI assistant to match user's descriptions of legal situations to relevant LQB questions and CLIC-pages. | Access to legal information; Legal knowledge dissemination; Navigability of legal information; Comprehensibility of legal information; Legal question generation; Legal chatbots/assistants. | General public, laypersons, individuals without legal education. | Multiple areas of law relevant to daily life are covered by the CLIC platform, including Landlord and Tenant, Traffic Offenses, Medical Negligence, Sexual Offenses, Defamation, Insurance, Personal Data Privacy, and Intellectual Property. | Hong Kong | For Legal Question Bank generation: GPT-3, pre-trained on general text corpora (Common Crawl, WebText2, books, Wikipedia), was prompted using content from existing CLIC-pages (textual legal explanations in layperson's terms developed by the University of Hong Kong). For CLIC Recommender: Pre-trained text embedding models (e.g., all-mpnet-base-v2) were used. | For LQB generation: Prompt engineering for GPT-3, specifically developing and comparing three partitioning strategies (Section-based, Paragraph-based, Hybrid), with the Hybrid strategy being most effective. Question deduplication using sentence embeddings (DistilBERT) and clustering. Comparative quantitative and qualitative evaluation against human-composed questions. For CRec: System design involving text embedding and cosine similarity for matching queries. | The CLIC platform (www.clic.org.hk) is an existing, publicly accessible website. The paper describes the research and development of an enhanced LQB (using GPT-3) and a CRec prototype as additions to this platform. Public deployment of these specific AI-driven enhancements in their described form is not explicitly stated as complete. | False | False | NaN | The generation of "augmenting questions" by the LLM indicates omissions in current CLIC-page content, highlighting a continuous need for content enrichment. While machine generation is scalable, human-generated questions showed higher precision, suggesting potential for hybrid human-AI workflows for optimal quality. | Designing effective GPT-3 prompting strategies to achieve high quantity, precision, coverage, and diversity in generated legal questions. Managing the probabilistic nature of LLMs leading to some incorrect or irrelevant questions. The time and cost associated with manual verification of machine-generated questions. | The paper does not explicitly state societal risks, but the reported 68% precision for the best machine question generation method implies a risk of providing users with irrelevant or unanswerable questions if not properly verified and curated, potentially misdirecting users seeking legal information. |
| 0045 (1).pdf | Scopus | The Impact of Empathy in Conversational AI: A Controlled Experiment with a Legal Chatbot | This paper investigates how displaying empathy in a legal chatbot's language affects user perceptions of trustworthiness, helpfulness, and cognitive effort, using a controlled experiment with 277 participants in a tenant-landlord scenario. Results indicate that empathetic language generally improves helpfulness and trustworthiness, but anger can negatively moderate the effect on trustworthiness, while chatbots generally reduce cognitive effort compared to FAQs. | True | Idealistic | False | 1.0 | Positive | A rule-based legal chatbot designed with specific syntactic and rhetorical linguistic elements to display empathy, compared against a non-empathetic version (with the same underlying logic) and a static FAQ page. | A randomized controlled experiment with 277 Chicago residents in a 2x3 factorial design. Participants interacted with either an empathetic chatbot, a non-empathetic chatbot, or an FAQ page, with their emotional state (anger) manipulated as a moderating factor. Outcomes were measured via self-reported Likert scales for helpfulness, trustworthiness, cognitive effort, and a comprehension quiz. | Empathetic language increased perceived helpfulness. For trustworthiness, empathetic language had a positive effect when users were not angry; however, when anger was induced, the empathetic chatbot was perceived as less trustworthy. The use of a chatbot (either type) significantly reduced cognitive effort compared to an FAQ page. | Lack of user trust and satisfaction with AI legal aid tools, and potentially high cognitive effort required by users to solve their problems, which can hinder effective access to legal information and assistance. | Designing conversational AI for legal services with specific linguistic elements (syntactic and rhetorical rules) that display empathy to potentially enhance user perceptions of helpfulness, trustworthiness, and reduce cognitive effort. | Self-help legal information and advice for tenants, improving user experience with legal tech. | Tenants renting property, particularly those facing issues with landlords or potential eviction in Chicago. | Landlord-tenant law | Chicago, Illinois, USA | NaN | Theory-driven design (based on linguistic theory of empathy), experimental design (randomized controlled trial), user-centered evaluation (measuring perceived helpfulness, trustworthiness, cognitive effort). Empathetic/non-empathetic chatbot variations were created by applying/not applying ten specific linguistic rules for empathy display. | The specific experimental chatbot versions were deployed in a controlled website-based experiment using LandBot and Qualtrics, hosted on Firebase. Not a public deployment of these specific versions. | False | False | NaN | Further research is needed on emotional alignment in human-AI conversations, especially concerning how AI should adapt its empathy display in response to users' negative emotions (like anger), where simple empathy can be counterproductive to trust. | Disentangling the effects of linguistically displayed empathy from the AI's underlying 'cognitive' abilities in user perception, and developing empathy displays that are robust across different user emotional states. | Empathetic displays by AI systems, if not appropriately attuned to the user's emotional state (e.g., anger), can be counterproductive and lead to a decrease in user trust in the legal aid tool. |
| 2307.16732v1.pdf | Scopus | LLMediator: GPT-4 Assisted Online Dispute Resolution | This paper introduces LLMediator, an experimental platform utilizing GPT-4 to enhance online dispute resolution (ODR) for high-volume, low-intensity legal disputes. It explores features like AI-assisted message reformulation, drafting mediator responses, and potential autonomous intervention, with initial qualitative evaluations indicating promise for facilitating amicable settlements. | True | Idealistic | True | 1.0 | Positive | LLMediator, an experimental platform using GPT-4 for online dispute resolution with features for message reformulation (F1), mediator message drafting (F2), and autonomous intervention (F3). | Initial qualitative evaluations using example use cases and fictitious disputes (e.g., broken camera, water leak, unpaid loan) to demonstrate the features. Specific prompts and model outputs are presented and discussed. | Initial qualitative evaluations suggest GPT-4 is promising for reformulating inflammatory messages to be more neutral, drafting context-relevant mediator interventions (adaptable with mediator instructions), and potentially intervening autonomously in ODR, showing capabilities in understanding dispute contexts and generating relevant responses. | Difficulties for laypeople in resolving high-volume, low-intensity legal disputes; lack of understanding of legal rules; high costs (monetary, temporal, psychological) of traditional court proceedings; stress from unresolved legal issues and their societal cost. | Enhancing Online Dispute Resolution (ODR) with LLMs (like GPT-4 in LLMediator) to make dispute resolution more accessible, efficient, and cooperative. This is achieved by providing tools for message reformulation, mediator assistance through draft suggestions, and potentially autonomous AI mediation. | Online Dispute Resolution (ODR), negotiation, mediation, resolving high-volume, low-intensity disputes, access to justice for laypeople. | Laypeople (individuals without legal training) facing high-volume, low-intensity disputes such as debt, consumer, and employment issues. | Civil disputes, particularly high-volume, low-intensity cases such as consumer disputes, debt, employment issues, and landlord-tenant disputes. | International (examples from Canada/Quebec, but the system aims for general applicability in ODR). | The system uses GPT-4, a pre-trained LLM by OpenAI, accessed via API. The authors did not train or fine-tune the model; GPT-4 was trained by OpenAI on a large, diverse corpus of text and data. | Development of an experimental web-based prototype (LLMediator). The design anvolves prompt engineering for GPT-4 interactions based on empirical investigations, and focuses on augmented intelligence where AI supports human users (parties or mediators). | N/A (The paper describes an experimental prototype; deployment is discussed as requiring further study before it could be deployed, especially for autonomous intervention features.) | False | False | NaN | Need for extensive empirical evaluation of efficacy, usefulness, and potential biases of the system; further development and refinement of system features (e.g., triggers for message reformulation and autonomous intervention, prompt engineering); addressing risks associated with LLM inaccuracies and the potential for AI suggestions to unduly influence human mediators or parties before wider deployment. | Determining optimal activation triggers for features (e.g., detecting inflammatory messages); effective prompt engineering to achieve desired LLM outputs (e.g., neutral tone, helpful mediations); managing LLM limitations like potential for hallucination and inaccuracies within the ODR context; designing user interactions that preserve user control and mitigate risks, especially in autonomous modes. | LLM hallucinations and inaccuracies; inaccuracies in message reformulations leading to misunderstandings; user frustration or perceived censorship if reformulations are forced; AI suggestions unduly anchoring human mediators or leading to over-reliance on AI; biased or inaccurate autonomous interventions influencing negotiations unfairly, causing loss of trust, or the AI appearing to take sides. |
| generative-ai-systems-in-legal-practice-offering-quality-legal-services-while-upholding-legal-ethics (1).pdf | Scopus | Generative AI systems in legal practice offering quality legal services while upholding legal ethics | This paper investigates the impact of generative AI systems, like ChatGPT, on lawyers' ethical duties of competence and confidentiality, based on doctrinal and empirical research in Luxembourg. It concludes by reflecting on integrating these systems to improve legal service quality, emphasizing client-centricity, informed consent, safe AI design, and lawyer training. | True | Market | True | 3.0 | Neutral | Generative AI systems (e.g., ChatGPT, fine-tuned LLMs based on GPT models) | Survey of 28 lawyers in Luxembourg (members of the Barreau de Luxembourg) and semi-structured interviews with representatives from 2 law firms (Luxembourg) and 2 legal tech companies (France, Belgium) that design/develop fine-tuned generative AI. | Survey: 54% of responding Luxembourg lawyers use ChatGPT, mainly for document drafting (39%) and legal research (36%); 64% report increased efficiency. However, significant concerns exist regarding hallucinations requiring verification and client confidentiality (79% believe it's compromised by GenAI use). Interviews: Developers focus on fine-tuned models, emphasizing efficiencies but acknowledging hallucination risks and the need for human oversight; they generally avoid using client confidential data for training. | Obstacles to clients receiving ethical and competent AI-assisted legal services include: risks to client confidentiality from data disclosure to AI providers (especially public LLMs), AI 'hallucinations' leading to inaccurate legal work and undermining competence, and a lack of lawyer training and awareness regarding AI capabilities and risks. | Proposed solutions include: adopting a client-centric approach to AI integration, requiring informed client consent for processing confidential data via AI, establishing clear contractual terms with AI providers regarding data protection, implementing robust security and compliance measures for AI systems, and providing comprehensive lawyer training on AI ethics, prompt engineering, and verification. | Lawyers' professional ethics (competence, confidentiality), responsible AI integration in legal practice, quality of legal services, client data protection, regulation of AI in legal services. | Clients of legal professionals (general). | General legal practice, Professional ethics. | Luxembourg (primary focus for lawyer survey and legal framework), with references to France and Belgium (legal tech companies). | Varies by system: Public LLMs (e.g., ChatGPT) trained on vast general web data. Fine-tuned systems discussed are trained on controlled legal data such as legislation, case-law, public information from the internet (open or subscription-based), and legal data acquired from public authorities. | For the discussed AI systems: Generative Pre-trained Transformer (GPT) models, fine-tuning pre-trained models on legal data, retrieval augmented generation (RAG). | Publicly available web applications (e.g., ChatGPT), internally developed/customized tools within law firms, commercial products by legal tech companies provided to legal professionals. | True | True | ChatGPT, a prominently discussed tool, is a publicly available consumer application with free access tiers. | Need for clearer professional conduct rules or guidelines from Bar Associations regarding AI use, insufficient lawyer training on GenAI capabilities and risks, challenges in ensuring client data confidentiality with third-party AI providers, and improving the reliability and context-specificity of AI for complex legal tasks. | For users and developers of legal GenAI: Maintaining accuracy and mitigating 'hallucinations', ensuring client data confidentiality and security (especially with cloud-based systems), addressing the lack of context specificity in current AI models for legal tasks, overcoming user reluctance or fear regarding AI, and the necessity of continuous human oversight and verification of AI outputs. | Generation of fictitious case-law or inaccurate legal information ('hallucinations'), disclosure of client confidential information to third-party AI providers, lawyers relying on unverified or flawed AI outputs leading to incompetent representation, potential for AI systems to be used for unauthorized data processing, and lack of transparency in how AI systems process data. |
| Automatic-Text-Simplification-fortheLegal-Domain-inBrazilian-Portuguese_2025_Springer-Science-and-Business-Media-Deutschland-GmbH.pdf | Scopus | Automatic Text Simplification for the Legal Domain in Brazilian Portuguese | This paper investigates automatic text simplification for legal documents in Brazilian Portuguese, aiming to improve access to justice for laypeople. It evaluates five different LLM-based approaches, including fine-tuned models and prompted generative models, using both quantitative metrics and qualitative expert assessment. | True | Idealistic | True | 2.0 | Positive | Evaluation of five LLM-based approaches for text simplification: fine-tuned PTT5 (FT-PTT5), FT-PTT5 with Reinforcement Learning (FT-PTT5 + RL), GPT-3.5-Turbo, GPT-4o, and Flan-T5-Large. | Quantitative evaluation using SARI, BLEU, BERTScore, and ROUGE metrics on a test set of 91 hand-picked legal sentences. Qualitative evaluation by a judicial analyst assessing correctness, simplicity, and overall quality of simplifications for the same 91 instances. | Qualitatively, GPT-3.5-Turbo was judged best by a human expert (e.g., 98% of its simplifications were deemed simpler and 84% of 'Good' quality). Quantitatively, GPT-4o achieved the highest SARI score (0.43). | Difficulty for laypeople to understand legal documents due to domain-specific jargon and complex sentence structures; lack of parallel datasets of complex-simple legal sentences in Brazilian Portuguese; the slow process of manual simplification adoption by courts. | Employing automatic text simplification (ATS) using Large Language Models to make legal texts more accessible. This includes fine-tuning existing models and using prompting strategies with generative models, supported by assembling relevant datasets. | Understandability of legal documents, plain language in the legal domain, access to justice through improved legal text accessibility. | Laypeople without legal domain expertise, individuals with reading issues, or those with a low education level. | General legal documents, including rulings, laws, agreements, contracts, judicial decisions, warrants, notifications, and legal case status updates. | Brazil | A merged dataset of parallel complex-simple sentence pairs in Brazilian Portuguese, comprising: 1) 8,120 pairs from news articles (PorSimples), 2) 1,424 filtered legal case status updates simplified by an OpenAI model (JusBrasil), and 3) 149 hand-picked examples from court materials. Used for fine-tuning PTT5. | For the evaluated models: pre-training on large corpora (PTT5, Flan-T5, GPTs). For adapted models studied (FT-PTT5, FT-PTT5+RL): fine-tuning of a pre-trained model (PTT5) on the custom-assembled Portuguese text simplification dataset and application of reinforcement learning using FKGL, SAMSA, and Levenshtein Distance as reward components. For generative models (GPTs, Flan-T5): Prompt engineering with few-shot in-context learning. | NaN | False | False | NaN | Lack of high-quality, domain-specific parallel datasets for Portuguese legal text simplification; need for more robust and comprehensive evaluation metrics for TS; limited generalizability of models to the specific nuances of the legal domain and its sub-fields without sufficient in-domain training data; high cost associated with fine-tuning very large models. | Assembling a suitable parallel dataset for Portuguese legal text simplification, particularly with in-domain legal examples; effectively fine-tuning models with limited in-domain data leading to generalization issues; achieving good performance with instruction-only prompting for certain models (e.g., PTT5); selecting appropriate and effective reward metrics for reinforcement learning in text simplification; infrastructure limitations for training large models. | Generation of legally inaccurate simplifications that alter meaning (e.g., FT-PTT5+RL had 53% 'No' for correctness), introduce grammatical errors, add extraneous information, or fail to simplify adequately, potentially leading to misinterpretation of legal documents by laypeople. |
| GPT4-passes-the-bar-exam_2024_Royal-Society-Publishing.pdf | Scopus | GPT-4 passes the bar exam | This paper experimentally evaluates GPT-4's zero-shot performance on the full Uniform Bar Examination (UBE), including multiple-choice, essay, and performance test components. The results show GPT-4 significantly outperforms prior models and human test-takers, passing the UBE by a considerable margin, indicating its potential to support legal service delivery. | True | Idealistic | True | 2.0 | Positive | GPT-4 (Generative Pre-trained Transformer 4) | Zero-shot evaluation on the full Uniform Bar Examination (UBE), including the Multistate Bar Examination (MBE) multiple-choice questions, the Multistate Essay Exam (MEE), and the Multistate Performance Test (MPT). MBE questions were official NCBE questions; MEE and MPT questions were from the July 2022 Bar Examination. MEE/MPT answers were graded by two author-experts against representative 'good' answers. | GPT-4 scored approximately 297 points on the UBE, significantly exceeding the passing threshold for all UBE jurisdictions. On the MBE, GPT-4 achieved 75.7% accuracy, outperforming average human test-takers. On the MEE and MPT, GPT-4 scored an average of 4.2/6.0. | The complexity of legal language and the legal system; the high cost and unmet demand for legal services. | Proposes Large Language Models like GPT-4 as a 'technology-based force multiplier' to support the delivery of legal services and address cost and accessibility issues. | Accessibility of legal services, Cost of legal services, Evaluation of AI in professional licensing. | General public / Individuals and organizations facing challenges with the quantity, quality, and accessibility of legal services due to cost and complexity. | Civil Procedure, Constitutional Law, Contracts, Criminal Law and Procedure, Evidence, Real Property, Torts, Corporations, Trusts & Estates, Family Law, Legal Ethics (as covered by the Uniform Bar Exam). | USA (Uniform Bar Exam applicable in multiple states) | GPT-4 was pre-trained on publicly available data (such as internet data) and data licensed from third-party providers, then fine-tuned using Reinforcement Learning from Human Feedback (RLHF). Test data contamination checks were performed. | GPT-4 is a transformer-style model pre-trained to predict the next token in a document, using both publicly available data (such as internet data) and data licensed from third-party providers. The model was then fine-tuned using reinforcement learning from human feedback (RLHF). | Access to a same or significantly similar version of the GPT-4 model is generally available under commercial terms from OpenAI. | True | False | GPT-4 is generally available under commercial terms from OpenAI. | Translating LLM capabilities like GPT-4 into safe and efficient real-world public and private legal applications; addressing LLM issues like hallucinations, factual incorrectness, and ethical compliance failures; comparative performance of other foundational models (e.g., open-source vs. closed-source, domain-specific vs. general) on legal tasks; advancing LLM performance through techniques such as prompt engineering, few-shot learning, retrieval augmented generation, and other systematic engineering methods. | Ensuring test data was not part of the model's training set (contamination checks with OpenAI); handling long documents for MPT tasks (requiring an '8K' version of ChatGPT with a wider context window); inherent variability and subjectivity in qualitative assessment/grading of open-ended MEE and MPT responses; the MPT's requirement for models to work within the four corners of provided exam material, potentially suspending broader knowledge. | GPT-4 may hallucinate sources, incorrectly interpret facts, or fail to follow ethical requirements; GPT-4 has various biases in its outputs. |
| Automatic-Linking-of-Judgements-to-UK-Supreme-Court-Hearings_2023_Association-for-Computational-Linguistics-ACL.pdf | Scopus | Automatic Linking of Judgements to UK Supreme Court Hearings | This paper presents J-HAL, an AI tool that links UK Supreme Court judgments to relevant video hearing segments using customized GPT text embeddings for information retrieval. The system, deployed as a User-Interface, aims to improve access to justice by making lengthy court records more navigable for legal professionals, academics, and the public. | True | Idealistic | True | 1.0 | Positive | Judgement-to-Hearing Automatic Linking (J-HAL) system using customized OpenAI GPT text embeddings (text-embedding-ada-002) for information retrieval to semantically link judgment paragraphs to video hearing transcript segments. | Evaluation against human-annotated links on a dataset of 7 UK Supreme Court cases (3620 judgment-to-transcript document pairs). Performance measured using Mean Average Precision (MAP) and Recall at k=5, 10, 15. Comparison of multiple IR models including BM25, GloVe, Entailment, Legal BERT, Asymmetric Semantic Search, and GPT. | Customized GPT embeddings improved the cosine similarity distribution overlap for relevant/irrelevant links to 73.0% ±2.6% (from 70.5% ±2.7% with original GPT embeddings). The original GPT model, before customization, achieved MAP@15 of 0.711 and Recall@15 of 0.914 on the full dataset. | The excessive length of court video recordings and the associated time and effort required for legal professionals and the public to navigate them and extract critical arguments relevant to judgments. Difficulty in navigating transcribed material as well. | An automated tool (J-HAL) with a User-Interface that links segments in written judgments to semantically relevant timespans in court hearing videos. This provides bookmarks, enabling users to more easily navigate long recordings and identify key discussions related to the final judgment. | Improving public access to and understanding of court proceedings; enhancing navigability of lengthy legal video recordings and transcripts; aiding legal professionals and academics in studying case deliberations. | General public, legal professionals, and legal academics. | UK Supreme Court cases, which cover landmark rulings on arguable points of law, often of public and constitutional importance. | United Kingdom | A dataset created from public sources: 7 UK Supreme Court case judgements (1.4M tokens) scraped from the official UKSC website, and 53 hours of corresponding video hearing transcripts (transcribed by a custom speech-to-text model from videos obtained from the UK National Archive). A set of 3620 judgment-to-transcript document pairs was human-annotated by a post-graduate law student for evaluation and for customizing GPT embeddings. | A two-stage pipeline: 1) custom speech-to-text for transcribing hearings, 2) Information Retrieval (IR) system for linking judgments to transcripts. The IR stage involved a zero-shot approach, comparative evaluation of different embedding models, human annotation for creating an evaluation dataset, and customization of GPT embeddings (text-embedding-ada-002) by training a classification model on the annotated data. | Deployed as a User-Interface (UI) platform. A patent application for the UI is in progress with the UK Intellectual Property Office. | False | False | NaN | Need to expand the annotated linking dataset. Future work includes exploring linking based on specific legal entities like articles, legal provisions, and case names for more granular connections, beyond general semantic relevance. | Segmenting lengthy judgment texts into semantically cohesive units for querying. Bridging the gap between different language registers (formal written judgments vs. spontaneous spoken hearings). The high cost and effort of creating large-scale, human-annotated datasets for document-to-document similarity tasks. | NaN |
| Interpretable-LongForm-Legal-Question-Answering-with-RetrievalAugmented-Large-Language-Models_2024_Association-for-the-Advancement-of-Artificial-Intelligence.pdf | Scopus | Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models | This paper introduces LLeQA, a French dataset for long-form legal question answering, and proposes a retrieval-augmented LLM approach to generate interpretable answers for statutory law questions. Experimental results show promise but also highlight challenges like factual inaccuracies and the limitations of automatic evaluation metrics. | True | Idealistic | True | 1.0 | Positive | A "retrieve-then-read" pipeline for long-form legal question answering. It uses a bi-encoder retriever (fine-tuned CamemBERT) and an instruction-tuned Large Language Model (e.g., Vicuna, WizardLM) as a reader, adapted via in-context learning or QLoRA finetuning. The paper also introduces the LLeQA dataset. | The retriever was evaluated using Recall@k (R@5, R@10) and Mean Reciprocal Rank (MRR@10). The generator's answer quality was evaluated using METEOR, and rationale extraction accuracy using F1 score. A qualitative analysis of 10 randomly selected samples was also conducted. | The fine-tuned CamemBERT retriever achieved a Recall@5 of 48.6% and MRR@10 of 60.0%. For long-form answer generation, the fine-tuned WizardLM model achieved the highest METEOR score of 20.4; however, qualitative analysis revealed that generated answers, while syntactically correct, frequently harbored inaccuracies and erroneous information. | Individuals' lack of understanding of legal issues, the prohibitive cost of expert legal assistance, leading to unresolved legal challenges and exclusion from legal protections. | Development of automated legal aid systems, specifically legal question answering (LQA) systems using NLP and LLMs, to provide affordable, expert-like assistance and bridge the legal literacy gap by making law more comprehensible and accessible. | Any statutory law questions. The LLeQA dataset includes questions on topics such as Housing, Healthcare, Family, Work, Immigration, Money, Privacy, and Justice. | Individuals facing legal disputes, laypersons with little to no legal knowledge, vulnerable individuals unable to afford legal assistance, Belgian citizens. | Statutory law (general). The LLeQA dataset is based on Belgian law, covering various codes and acts. | Belgium. The LLeQA dataset and its legal corpus are specific to Belgian law and are in French. | The LLeQA dataset: 1,868 expert-annotated legal questions in French with detailed answers and references to relevant Belgian statutory provisions from a corpus of 27,942 articles. It was built upon the BSARD dataset and data from Droits Quotidiens (a Belgian non-profit organization). Paragraph-level rationales were partly synthetically generated using gpt-3.5-turbo-0613. | A "retrieve-then-read" pipeline. Retriever: bi-encoder architecture (CamemBERT) fine-tuned using contrastive loss with in-batch negatives and BM25-mined hard negatives. Generator (Reader): instruction-tuned LLMs (e.g., Vicuna, WizardLM) adapted via in-context learning (zero-shot, one-shot, few-shot) or parameter-efficient finetuning (QLoRA). Dynamic NTK-aware scaling for context window extension and extractive rationale generation by prompting the model for paragraph markers. | Public release of code, dataset (LLeQA), and model checkpoints on GitHub. | True | True | Code, dataset, and model checkpoints are publicly released on GitHub: https://github.com/maastrichtlawtech/lleqa. | The proposed framework's vulnerability to hallucinations in both generated answers and rationales. The inadequacy of conventional automatic evaluation metrics to accurately reflect answer quality, particularly factual correctness. The need for improved modeling and evaluation techniques in LQA. | Limited context window of LLMs for processing extensive legal inputs. Achieving high recall in the retrieval stage (identified as a major bottleneck). The cost and difficulty of obtaining high-quality expert annotations. The propensity of LLMs to fabricate convincing yet misleading justifications (hallucinations). Effective domain adaptation for retrieval models. | Premature deployment of LQA systems may lead laypersons to uncritically rely on flawed or inaccurate AI-generated legal guidance, potentially exacerbating their legal situations. |
| Artificial-intelligence-at-the-bench-Legal-and-ethical-challenges-of-informing--Or-misinforming--Judicial-decisionmaking-through-generative-AI_2024_Cambridge-University-Press.pdf | Scopus | Artificial intelligence at the bench: Legal and ethical challenges of informing —or misinforming —judicial decision-making through generative AI | This paper critically examines the integration of Generative AI (GenAI) into judicial decision-making, highlighting potential benefits like increased efficiency and access to justice, alongside significant risks such as bias, lack of accountability, and erosion of due process. It proposes a comprehensive framework with ex-ante standards and application principles to guide the responsible and equitable use of GenAI in the judiciary. | True | Idealistic | True | 3.0 | Neutral | A conceptual framework featuring ex-ante standards (e.g., capacity assessment, verification, trusted datasets) and ex-post application principles (e.g., legal education, risk-based deployment, disclosure, audits) for integrating GenAI into judicial decision-making. | NaN | NaN | Key obstacles include algorithmic bias, lack of transparency and accountability in AI systems, poor data quality leading to 'hallucinations', erosion of judicial independence and public trust, data privacy concerns, and the potential to widen the justice gap due to unequal resource distribution. | Proposes a dual-prong framework: 1) Foundational ex-ante standards for GenAI systems (e.g., capacity assessment, verification, trusted datasets, clear responsibility). 2) Application principles for deployment (e.g., legal education, risk-based use, human oversight, disclosure, procedural rights, ongoing audits). | Judicial efficiency, fairness in judicial processes, upholding the rule of law, protection of rights, due process, and equitable access to justice in the context of GenAI integration in courts. | Individuals interacting with the judicial system, especially vulnerable populations (e.g., minors, persons with disabilities) and those in under-resourced jurisdictions or reliant on legal aid. | General (judicial decision-making across various legal fields), with specific case examples from health law, procedural law, civil law (child support), electoral law, and criminal law (bail). | International, with analysis of approaches and case studies from Colombia, Mexico, Peru, India, UK, New Zealand, EU, Canada, Singapore, and Estonia. | Discusses GenAI tools (e.g., ChatGPT) trained on vast, diverse, publicly available internet-scale datasets, largely unstructured and not domain-specific for law; advocates for 'trusted datasets' for judicial GenAI. | Analysis of existing literature, case studies of GenAI use in courts, comparative review of jurisdictional regulatory approaches, and conceptual development of a guiding framework. | The proposed framework is disseminated through academic publication (this paper and conference presentation) aiming to guide policymakers, judiciaries, and regulators. | False | False | NaN | Lack of comprehensive regulatory frameworks and safeguards; technical limitations of GenAI (accuracy, bias, explainability); unclear legal liability; capacity deficits in some jurisdictions; need for standardized AI auditing methodologies and further research on prompt engineering for legal use. | NaN | Bias amplification, generation of false information (hallucinations), lack of transparency and accountability, compromised data privacy, undermining judicial independence and public trust, and exacerbating inequalities in access to justice. |
| paper5.pdf | Scopus | Bridging the Gap: Mapping Layperson Narratives to Legal Issues with Language Models | This paper proposes a system using language models to map layperson descriptions of factual situations to relevant legal issues, aiming to improve access to justice. Integrated into the JusticeBot tool and evaluated on real-world user data, the system achieved 93.5% precision at 3 in suggesting correct legal pathways when trained with both seed and user-provided examples. | True | Idealistic | True | 1.0 | Positive | A system that uses a multilingual universal sentence encoder to embed layperson factual descriptions and a database of example descriptions (linked to legal issues). It then uses an approximate nearest neighbors algorithm (Annoy library) to retrieve the most semantically similar example descriptions and suggest the corresponding legal issues to the user. | The system was evaluated using real-world, anonymized user-submitted factual descriptions from the JusticeBot project. Performance was measured by Precision@1 (P@1) and Precision@3 (P@3) in correctly identifying the relevant legal pathway. Experiments compared performance using only researcher-created seed examples versus seed examples augmented with user-submitted descriptions (evaluated via leave-one-out cross-validation). | When trained on both seed examples and user-submitted examples, the system correctly identified the relevant legal issue within the top 3 suggestions for 93.5% of user queries (P@3). The P@1 score was 74.5%. | The primary obstacle identified is the "gap" between layperson language (factual narratives) and legal language (legal issues and remedies). This gap hinders laypeople's ability to understand their rights, identify relevant legal solutions, and effectively use legal self-help tools. | The paper proposes an AI-powered system, conceptualized as an "augmented intelligence" tool, to automatically analyze layperson's factual descriptions and suggest potentially relevant legal issues or pathways. This helps bridge the language gap by guiding users to appropriate information while allowing them to verify the system's interpretation. | Mapping layperson narratives to legal issues, improving usability of legal decision support tools, overcoming the layperson-legal language gap. | Laypeople (individuals without legal training) facing legal disputes, particularly users of online legal self-help tools like JusticeBot. | Landlord-tenant law (Housing law) | Quebec, Canada | A combination of: 1) "Seed example descriptions": short, researcher-created textual examples of how a layperson might describe a situation corresponding to a known legal issue covered by JusticeBot. 2) "User-submitted example descriptions": real-world textual descriptions submitted by JusticeBot users when they indicated their issue was not covered (some of which actually were covered). | The system employs sentence embedding using a pre-trained multilingual universal sentence encoder and approximate nearest neighbor search (Annoy). The design emphasizes an "augmented intelligence" approach, providing suggestions with explanations for user verification. The system is designed to learn from user interactions. | The proposed feature is described as being implemented and having a new interface within the JusticeBot system, a legal decision support tool accessible online. User feedback collected through interaction can be used to improve the system. | False | False | NaN | The dataset of legal issues and corresponding example descriptions needs expansion. The system may provide irrelevant suggestions if a user's situation is not covered. Further empirical evaluations with end-users are needed to assess real-world utility and identify areas for improvement. Other machine learning models (including LLMs like GPT-4) could be explored for different stages or tasks. | The inherent difficulty of mapping varied and potentially messy layperson language to structured legal issues, especially with a limited number of initial seed examples for some categories. Ensuring "semantic homogeneity" within legal issue classes for better model performance. Overcoming the cold-start problem when introducing new legal issues or tools. | The risk of engaging in the unauthorized practice of law, as providing suggestions of legal pathways based on factual descriptions could be misconstrued as legal advice, despite design choices aimed at providing only legal information via an augmented intelligence approach. |
| JOIA2023022.pdf | Scopus | A New Era of Maritime Arbitration: Ex Machina Determinations | This paper explores the potential of Large Language Models, specifically ChatGPT 3.5, to act as arbitrators in maritime disputes. Through four hypothetical test cases, it evaluates ChatGPT's capabilities and limitations in this role, discussing benefits like speed and cost-reduction alongside challenges such as accuracy and legal reasoning. | True | Idealistic | True | 2.0 | Positive | Using ChatGPT version 3.5 as an AI arbitrator to make determinations in hypothetical maritime disputes based on structured prompts detailing facts and party submissions. | Four hypothetical charterparty disputes were presented to ChatGPT 3.5. The prompts included agreed facts, party submissions, and specific questions for determination. ChatGPT's responses (determinations and reasoning) were then analyzed. | ChatGPT 3.5 made determinations rapidly and showed some understanding of legal/trade terms. However, it struggled with nuanced legal reasoning, failed to cite relevant or correct case law (exhibiting 'hallucinations'), and its decisions sometimes differed from human arbitrator outcomes in similar published cases. | The high cost of traditional litigation and arbitration, which acts as a significant barrier to accessing justice, especially for small value claims. | The paper proposes using AI LLMs like ChatGPT as arbitrators to provide almost instantaneous, low-cost dispute resolution, particularly for small claims, thereby enhancing access to justice. | Access to justice for small value disputes in maritime arbitration. | Individuals or small businesses in the maritime industry with small value claims. | Maritime law, Arbitration | Maritime law, primarily with reference to English law and international arbitration practices (LMAA, SMA, SCMA). | ChatGPT 3.5 was trained on 'vast amounts of data from the internet written by humans' up to September 2021. This is general, unstructured internet data. | NaN | NaN | True | True | The publicly available version of ChatGPT 3.5, used for the experiments, is accessible, including a free tier. | Technical gaps include data limitations, hallucinations, inability to manage arbitration procedures, lack of real-time legal updates, and difficulty assessing witness credibility. Societal/legal gaps include the need for legal frameworks for AI arbitrators, ensuring enforceability of AI awards (e.g., revising the New York Convention), maintaining confidentiality, developing appeal mechanisms, and addressing potential biases or manipulation. | Authors faced challenges in initially prompting ChatGPT to make legal determinations and observed its limitations in legal reasoning, accuracy (including hallucinations and incorrect case citations), and applying deep subject matter expertise during the tests. | Risks include AI generating factually incorrect or misleading determinations ('hallucinations'), lack of transparency in AI decision-making undermining natural justice, awards being unenforceable under current legal frameworks (e.g., New York Convention), potential for AI responses to be manipulated by developers or users through prompt engineering, and decisions being influenced by online falsehoods if AI has unfiltered real-time internet access. |
| paper2.pdf | Scopus | The Potential for Jurisdictional Challenges to AI or LLM Training Datasets | This paper critiques the use of Large Language Models (LLMs) for Access to Justice (A2J), highlighting risks like jurisdictional bias from unrepresentative training data. It proposes a new conceptual framework, "informational sovereignty," to ensure LLMs are trained and utilized in a way that respects jurisdictional boundaries and community legal norms. | True | Idealistic | True | 1.0 | Negative | A novel quadripartite theory of informational sovereignty. | NaN | NaN | Systemic bias in LLM training data not reflecting local communities and legal norms; extra-jurisdictional data skewing legal application; challenges to legal sovereignty and rule of law; potential for low-quality or incorrect AI-generated legal advice; inaccessibility for digitally excluded populations; LLMs' inability to handle legal nuance, edge cases, or differentiate precedent types; lack of accountability for AI errors. | Proposing a theory of "informational sovereignty" requiring LLM training data to be: 1) sourced from observations of individuals within the specific jurisdiction (Population), 2) inclusive of practitioners and systems representing the community (Territory), 3) auditable to ensure reflection of community-accepted practitioners (Recognition), and 4) with immutable outputs to prevent modification across systems (Regulation of borders). Emphasizing jurisdictionally-defined training data and retaining human oversight. | Bias in AI legal systems; Jurisdictional integrity in AI; Quality of AI legal assistance; Role of LLMs in A2J; Self-represented litigants; Rule of law; Data sovereignty vs. informational sovereignty. | Underserved litigants, individuals who cannot afford legal professionals, less wealthy members of society, self-represented litigants. | General Legal Practice | International | NaN | Conceptual analysis, theoretical framework development based on legal principles and existing sovereignty theories (e.g., Krasner's quadripartite conception of sovereignty). | NaN | False | False | NaN | Technical gaps: LLMs' limitations in understanding legal nuance, differentiating precedent, handling edge cases, and ensuring accuracy. Societal gaps: Need for public trust in AI legal tools, establishing accountability for AI, and developing robust frameworks like "information sovereignty" to ensure AI supports rather than undermines democratic legal principles and equitable access to justice. | NaN | Systemic bias from unrepresentative training data leading to unfair outcomes; erosion of legal sovereignty and rule of law; lawyers' over-reliance on AI leading to errors (e.g., citing non-existent cases); AI providing incorrect legal information or forms; poorly constructed AI-generated arguments being accepted or unfairly disadvantaging laypersons; AI's probabilistic nature producing erroneous outputs; lack of accountability for AI systems. |
| TATUP_2024_1_21_27_Long (2).pdf | Web_of_Science | AI and access to justice : How AI legal advisors can reduce economic and shame-based barriers to justice | This paper argues that publicly funded AI legal advisors (AI LAs), utilizing generative AI, can reduce significant economic and shame-based cultural barriers to legal information. It focuses on how these AI LAs could assist individuals in the initial information-gathering stage of seeking justice, particularly in Anglo-American common law systems. | True | Idealistic | True | 3.0 | Positive | Publicly funded AI Legal Advisors (AI LAs) | NaN | NaN | Economic barriers (financial costs, opportunity cost of time, transportation costs); shame-based cultural barriers (stigma, fear of judgment, cultural norms obscuring legal rights); lack of information and education about legal rights and resources. | Development and deployment of publicly funded AI Legal Advisors (AI LAs) that provide (a) assessment of legal considerations, (b) crude assessment of case's likelihood of success, and (c) interactive lay explanations, all while ensuring privacy to mitigate shame. | Access to legal information; Overcoming economic barriers to justice; Overcoming shame-based cultural barriers to justice; Empowering individuals for informed decision-making in legal matters. | People with low socio-economic status (SES), victims of intimate partner violence (IPV), women facing cultural barriers to asserting rights (e.g., inheritance), victims of fraud, and other marginalized populations. | General civil law (implied through examples including medical malpractice, housing law, inheritance rights, fraud, and protection orders for IPV). | Anglo-American common law systems | NaN | NaN | Proposed to be publicly funded, developed by democratic governments and international organizations, and accessible online. | False | False | NaN | Achieving reliability and accuracy comparable to human lawyers; Addressing and mitigating biases inherent in AI training data; Managing potential increased caseloads in the legal system. | NaN | AI LAs may lack reliability and accuracy; AI LAs may inherit and reproduce biases from training data; Potential for increased court caseloads; AI LAs might provide incorrect advice, either recommending litigation for non-viable claims or recommending abstention from viable claims. |
| 0045.pdf | Web_of_Science | The Impact of Empathy in Conversational AI: A Controlled Experiment with a Legal Chatbot | This paper develops and empirically tests a theory of empathy in the language displayed by a legal chatbot designed to assist tenants. Through a randomized controlled experiment, it finds that empathetic language increases perceived helpfulness and interacts with user anger to affect trustworthiness, while chatbots generally reduce cognitive effort compared to FAQs. | True | Idealistic | False | 1.0 | Positive | An empathetic legal chatbot designed using specific syntactic and rhetorical linguistic rules derived from linguistic theory to display empathy. | A randomized controlled experiment with a 2x3 factorial design involving 277 participants comparing an empathetic chatbot, a non-empathetic chatbot, and an FAQ baseline. Participant anger was manipulated as a moderating factor. Outcomes measured were perceived helpfulness, trustworthiness, and cognitive effort. | Empathetic language increased perceived helpfulness. For trustworthiness, displayed empathy improved it when users were not angry, but this effect reversed with anger induction (-2.61 coefficient for interaction NonEmpathy:WithoutAnger, -2.86 for FAQ:WithoutAnger compared to empathetic bot baseline with anger). Chatbots significantly reduced cognitive effort compared to FAQs (FAQ coeff=3.13 to 3.74 for cognitive effort). | Lack of user trust, satisfaction, and high cognitive effort with existing AI legal tools due to insufficient empathetic interaction, potentially hindering their effectiveness for improving access to justice. | Designing conversational AI with rule-based empathetic language display (based on linguistic theory) to improve user trust, helpfulness, and reduce cognitive effort in legal service interactions, thereby making legal assistance more accessible and effective. | Legal information and assistance for tenants on landlord-tenant issues (e.g., security deposit disputes). | Tenants renting property, specifically Chicago residents. | Landlord-tenant law, Housing law. | Chicago, Illinois, USA | NaN | Theory-driven design based on linguistic theory of empathy in language structure. Experimental design (randomized controlled trial). Chatbot dialogue flows implemented using LandBot. | NaN | False | False | NaN | The need for more research into emotional alignment in human-AI conversations, particularly how AI can adapt its empathic display to users' negative emotional states (like anger) to maintain trust and helpfulness, especially in access to justice contexts. | Disentangling the effects of linguistic empathy display from the AI's underlying cognitive abilities during evaluation. Operationalizing complex linguistic theories of empathy into practical and effective chatbot design rules. Understanding and mitigating the interaction effects between AI-displayed empathy and user emotional states. | Displayed AI empathy can negatively impact user trustworthiness if the user is in an angry emotional state and the empathetic response is perceived as inappropriate or insincere, potentially undermining the tool's utility. |
| 3637-Article Text-14123-1-10-20241202.pdf | Web_of_Science | Using Generative AI to Identify Arguments in Judges’ Reasons: Accuracy and Benefits for Students | This study evaluates the effectiveness of Generative AI (specifically ChatGPT and Claude LLMs) in identifying legal arguments from High Court of Australia judges’ reasons, focusing on implications for law students. Key findings indicate significant performance variance, with Claude 3.5 excelling, highlighting the need for critical user engagement as current AI cannot fully replace human legal analysis skills. | True | Idealistic | True | 2.0 | Neutral | Comparative evaluation of Large Language Models (ChatGPT versions GPT-4 & GPT-4o; Claude versions 3.0 Opus & 3.5 Sonnet) for identifying and reconstructing legal arguments from judicial reasons in a modus ponens structure. | LLMs were prompted with PDF versions of five recent High Court of Australia decisions to identify argument chains from specified judges' reasons. Outputs were then blind-marked by two academics (a lawyer and a philosopher) against pre-defined sample answers and a detailed 20-mark rubric assessing identification of disposition, premises, conclusions, argument location (paragraph numbers), and modus ponens structure. | Claude 3.5 Sonnet significantly outperformed other models, achieving the highest average marks (ranging from 14/20 to 18/20, equivalent to up to 90%). ChatGPT versions (4 and 4o) performed worse, with average marks not exceeding 10/20 (50%). | Financial cost of legal services, time, complexity of justice systems, lack of legal capability, and language skills are common barriers to accessing justice. A substantial number of people do not seek legal advice due to cost or overworked judicial systems. | Generative AI, if sufficiently accurate, could potentially reduce costs associated with legal advice and increase efficiency in handling legal matters, thereby improving access to justice. The study evaluates LLM accuracy for a component legal task (argument identification) as a step toward this potential. | Reducing the cost and improving the efficiency of legal analysis (specifically argument identification from case law) as a means to potentially enhance access to legal advice and general access to justice. | Individuals who cannot afford legal advice or are affected by overworked judicial systems, rather than a single specific underserved community. | The study used cases covering a range of legal issues, including native title, criminal law, statutory interpretation, and immigration law. | Australia (High Court of Australia). | The evaluated LLMs (ChatGPT, Claude) are pre-trained on enormous volumes of general text corpora, typically scraped from vast swathes of the internet. The specific composition of this training data is proprietary to the LLM developers. | The evaluated LLMs were developed using large-scale deep learning procedures on vast text corpora. | The evaluated LLMs (ChatGPT, Claude) are commercially deployed by their respective creators (OpenAI, Anthropic) and made available to users via web-based interfaces and APIs. The study utilized the web interfaces. | True | False | ChatGPT and Claude LLMs are accessible via web interfaces; some versions tested (e.g., premium versions) require paid subscriptions. Study materials (rubric, prompt, sample answers, anonymised LLM outputs) are available on GitHub. | Significant variability in LLM performance for legal argument extraction across different models and versions. A persistent need for critical human engagement and skill development, as current LLMs cannot yet replace nuanced human legal analysis. Specific deficiencies noted include inconsistent accuracy in identifying argument components and particular difficulty with tasks like accurately citing paragraph numbers. | High cost and labour intensity of human evaluation for legal NLP tasks, which necessitated a small number of assessors for the study. The inherent subjectivity in assessing the quality of argument extraction, even when using a detailed rubric, led to some inter-marker variability in scoring. | LLM 'hallucinations,' such as inventing non-existent legal cases. Outputs may be inaccurate or misleading, posing safety risks if used for legal advice without expert oversight. Over-reliance on LLMs by students could hinder the development of essential legal reasoning and argument reconstruction skills, or lead to the uncritical acceptance of flawed outputs. |
| nay-et-al-large-language-models-as-tax-attorneys-a-case-study-in-legal-capabilities-emergence.pdf | Web_of_Science | Large language models as tax attorneys: a case study in legal capabilities emergence | This paper explores the capabilities of Large Language Models (LLMs) in applying U.S. tax law by testing different OpenAI models on thousands of synthetically generated multiple-choice questions. The findings demonstrate emerging legal understanding capabilities, with performance improving with newer models and enhanced prompting techniques, though not yet reaching expert human lawyer levels. | True | Idealistic | True | 2.0 | Positive | Evaluation of OpenAI's LLMs (davinci, text-davinci-002, gpt-3.5-turbo, gpt-4) for U.S. tax law question-answering using retrieval-augmented generation (with similarity search, gold_truth legal texts, lecture notes) and prompting strategies (Chain-of-Thought, few-shot). | LLMs were evaluated on two synthetically generated multiple-choice exams based on the US Code of Federal Regulations (CFR) and the US Code (total 28,700 questions). Responses were primarily graded for accuracy by GPT-4 comparing the LLM's choice to the correct answer. | GPT-4, when combined with 'gold_truth' retrieval, few-shot prompting, and Chain-of-Thought ('mega_run' setup), achieved the highest accuracy: approximately 85% on CFR questions and 67% on US Code questions. While significantly better than other configurations and weaker models, this performance is still below that of an expert tax lawyer. | High cost and complexity of legal services hindering access for many individuals. | Use of advanced LLMs to provide affordable legal information and services, thereby reducing cost and complexity for the public. | Access to legal advice and information; reducing cost and complexity of legal services, with a specific application in tax law. | Individuals who currently cannot afford legal counsel or find the legal system too complex to navigate. | US tax law | United States (federal tax law) | The LLMs (OpenAI models) were pre-trained on general internet data. For retrieval augmentation, the study used publicly available US tax statutes (Title 26 of the US Code), Treasury Regulations from the Code of Federal Regulations (CFR), and academic lecture notes on tax law. | Synthetic multiple-choice question generation for tax law; retrieval-augmented generation (RAG) using vector similarity search and direct 'gold_truth' texts; various prompting techniques (zero-shot, few-shot, Chain-of-Thought); comparative evaluation across different LLM versions; automated grading of LLM responses using GPT-4. | NaN | False | False | NaN | Technical: Current LLMs do not match human expert lawyer performance, retrieval methods need improvement (gap between 'similarity_search' and 'gold_truth'), more advanced prompting techniques (e.g., self-reflection) should be explored, impact of prompt length needs investigation, and legal-specific fine-tuning could enhance capabilities. Societal: LLMs lack nuanced human judgment and ethical counsel, requiring safeguards for data privacy, bias mitigation, and accountability; regulatory frameworks may need to adapt to LLM-delivered legal advice. | Ensuring the novelty of validation data by using synthetically generated questions (to avoid training set contamination). Developing effective and reliable retrieval mechanisms to provide LLMs with the correct legal context. Managing the variability and format of LLM outputs to enable consistent automated evaluation. | Data privacy concerns, potential for inherent or induced bias in LLM outputs, maintaining accountability for decisions made with LLM assistance, ensuring the suitability and reliability of LLMs for specific legal use cases, potential disruption to the legal services industry, and regulatory issues such as the unauthorized practice of law. |
| LEGAL_ANALYSIS_OF_EU_ARTIFICIAL_INTELLIGENCE_ACT_2.pdf | Web_of_Science | LEGAL ANAL YSIS OF EU ARTIFICIAL INTELLIGENCE ACT (2024): INSIGHTS FROM PERSONAL DATA GOVERNANCE AND HEALTH POLICY | This paper analyzes the EU Artificial Intelligence Act (2024), focusing on its ethical, functional, and legal implications for AI governance, particularly in the health data sector and concerning medical standards. It examines the Act's alignment with prior EU medical device regulations and discusses its potential impact on judicial reform, access to justice, and recent AI regulatory efforts in Eastern Europe. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | The inherent complexity of regulating rapidly evolving AI technologies. Ensuring the effective operationalization of ethical principles like human oversight and explainability. Aligning the broad AI Act with specific sectoral regulations (e.g., medical devices) and national legal systems, particularly to foster judicial reform and improve access to justice. Preventing AI systems from creating or exacerbating discriminatory outcomes or infringing on fundamental rights. | The comprehensive regulatory framework of the EU AI Act, including its risk-based classification, requirements for high-risk AI (data governance, transparency, human oversight), and establishment of governance bodies like the European AI Office. Promotion of ethical and human-centric AI. Harmonization of national laws with EU standards (e.g., in Ukraine and Moldova) and development of national AI strategies and infrastructure (e.g., MCloud in Moldova). | Judicial reform, access to justice, protection of fundamental human rights (especially in health and data privacy), non-discrimination, ethical AI governance, data governance. | General citizenry, particularly patients within the healthcare system and individuals whose rights might be impacted by AI. Specific mention of EU Candidate Countries (Ukraine, Republic of Moldova) in the context of legal reforms. | AI Law, EU Law, Health Law, Data Protection Law, Human Rights Law, Copyright Law | European Union, Ukraine, Republic of Moldova | NaN | NaN | NaN | False | False | NaN | Lack of specific regulatory details for all areas of public health within the AI Act's Annex III. The need for subsequent sectoral regulations to complement the horizontal AI Act. Ensuring comprehensive protection and safety in AI-driven medical devices beyond current provisions. | NaN | Misuse of AI leading to harms in economy, rule of law, democracy, and healthcare. AI systems employing cognitive-behavioural manipulation or social scoring leading to discrimination. Risks to patient safety and fundamental rights if AI in medical devices is inadequately regulated. Threats to data protection and privacy. |
| ssrn-4624814.pdf | Web_of_Science | OpenJustice.ai: A Global Open-source Legal Language Model | This paper calls for the development of OpenJustice.ai, a global open-source legal language model, arguing that domain-specific AI fine-tuned on curated legal data can address the risks posed by generalized AI for legal tasks. It outlines OpenJustice.ai's key features, including Retrieval Augmented Generation and multi-perspective outputs, and its collaborative, feedback-driven approach to enhance legal research and access to justice. | True | Idealistic | True | 1.0 | Positive | OpenJustice.ai, a legal language model featuring Retrieval Augmented Generation (RAG), Multiplicity (offering multiple perspectives), Probing (assisted prompting), and Assisted Negotiation. | NaN | NaN | Risks from using generalized AI for legal tasks (misinformation, hallucinations, lack of transparency/precision, single narratives impacting litigants); difficulty for non-lawyers in navigating legal jargon and procedures. | Development of an open-source, distributed, domain-specific legal AI (OpenJustice.ai) fine-tuned on curated legal data and human feedback. Key features like RAG aim for factual accuracy, 'Multiplicity' for diverse legal reasoning, and 'Probing' (assisted prompting) to aid non-lawyers and legal education. | Access to justice, legal information provision, legal education, dispute resolution, legal research. | Self-represented litigants, non-lawyers, legal students, legal professionals, legal clinics. | General law, negotiation, employment law, consumer protection, personal injury. | International | A combination of unstructured data (case law, journals, other legal resources) and structured data (annotated question-answer pairs). Leverages legislation, case law, and proprietary data from industry partners, with crowdsourced human feedback from the legal community, including law schools and legal professionals. Data compiled since 2019. | Retrieval Augmented Generation (RAG), instruction fine-tuning using question-response pairs, self-supervised training (masked language modeling) on unstructured data, crowd-sourcing human feedback, decentralized fine-tuning (combining open and closed datasets). | OpenJustice.ai is described as an open-source, distributed model developed by a consortium of universities, legal clinics, and industry partners. It operates as a natural-language processing interface. A non-proprietary version is intended to be openly accessible to the legal community. | False | False | NaN | The general issue of LLMs being unable to consistently provide accurate citations. The lack of transparency in existing (often proprietary) AI systems for law. The need for AI systems that can reflect the multifaceted nature of legal reasoning rather than a single 'correct' answer. | Crafting effective prompts for LLMs, especially for non-lawyers. The 'drifting' or wild fluctuations in performance of generalized AI models. Ensuring the development of dependable legal AI solutions. | Legal misinformation or hallucinations, lack of transparency and precision, and inability of AI to offer diverse and multiple narratives when used for legal tasks. Misuse of generative AI in courts, such as citing fake cases. |
| 3594536.3595146.pdf | Web_of_Science | Beyond Readability with RateMyPDF A Combined Rule-based and Machine Learning Approach to Improving Court Forms | This paper introduces RateMyPDF, a web tool using rule-based methods, machine learning, and GPT-3 to evaluate and suggest improvements for court form usability. Developed by analyzing a large dataset of US court forms and expert input, it aims to aid form authors in enhancing access to justice for self-represented litigants. | True | Idealistic | True | 1.0 | Positive | RateMyPDF, a web application combining rule-based methods, traditional machine learning (including NLP for field normalization and classification), and the GPT-3 large language model for court form usability analysis, scoring, and improvement suggestions. | The RateMyPDF score was validated against a dataset of approximately 24,000 PDF forms from 46 U.S. States and D.C. Its complexity score was correlated with ratings from a panel of 6 expert reviewers on a subset of 40 forms using Intraclass Correlation (ICC). | The RateMyPDF score correlated significantly with the average expert rating (ICC3 0.5861, p-value=0.00). When RateMyPDF was treated as a seventh reviewer, it improved the group's agreement (ICC1 0.3931, p-value 0.00). Human expert reviewers also showed agreement with each other (ICC1 0.3139, p-value=0.02). | Difficult court forms create significant time, emotional, and cognitive burdens for self-represented litigants, can hinder judicial understanding, and lead to unfair legal outcomes. Forms are often poorly designed by untrained staff, lacking input from users, designers, or plain language experts, and manual simplification at scale is challenging. | The paper proposes RateMyPDF, an automated tool that scores court form usability, provides actionable suggestions for improvement (e.g., word substitutions, plain language summaries via GPT-3), and enables large-scale analysis of form libraries. This helps systematically identify problematic forms and benchmark simplification efforts. | Improving the usability, comprehensibility, and accessibility of court forms; reducing administrative and cognitive burden on litigants. | Self-represented litigants. | Civil Law (general court forms, with examples from eviction, domestic violence, divorce). | United States (forms gathered from 46 U.S. States and the District of Columbia). | A dataset of approximately 24,000 PDF court forms scraped from official court websites of 46 U.S. States and D.C. for benchmarking. A proof-of-concept ML model for field normalization utilized features like adjacent text to fields, previous field names, field location, and topic (identified by Spot NLP classifier). GPT-3 was used with full text of forms for summarization and metadata extraction. | Iterative development involving workshopping with potential users (legal aid providers, court staff, document automation experts). Grounded in existing usability guidelines (e.g., Jarrett and Gaffney's framework, UK government guidance), expert interviews, and analysis of a large corpus of existing court forms. | RateMyPDF is deployed as a public web application. The underlying Python library (FormFyxer) and the web application code are open-sourced on GitHub. A companion website, Form Explorer, allows searching and comparing forms across jurisdictions. | True | True | RateMyPDF web application is publicly accessible at ratemypdf.com. The source code for RateMyPDF and the underlying FormFyxer library are available on GitHub. | Need for establishing a target 'ideal' complexity score for forms. Refining time-to-complete estimates with real-world user testing on court forms. Developing more tailored word lists for court forms beyond Dale-Chall. Enhancing metrics for whitespace and field ordering. Extending the approach to analyze interactive legal applications (guided interviews). Integrating LLM-based direct text rewriting for readability improvement responsibly. | Handling the high variability in PDF formats and quality (e.g., lack of embedded form fields, XFA format). Developing robust automated field normalization and classification. Overcoming limitations of traditional readability formulas for non-narrative form text. Improving the accuracy of citation extraction for state-specific short forms. Automatically assessing and incorporating emotional burden into the scoring. | Potential for Large Language Models (LLMs like GPT-3) to 'hallucinate' or provide factually incorrect responses (mitigated by anchoring to source data and presenting outputs as suggestions). Risk that authors might try to 'game' the scoring metrics without genuinely improving usability (mitigated by offering specific, actionable improvement recommendations beyond score components). |
| katz-et-al-gpt-4-passes-the-bar-exam.pdf | Web_of_Science | GPT-4 passes the bar exam | This paper experimentally evaluates the zero-shot performance of GPT-4 on the entire Uniform Bar Examination (UBE), including multiple-choice, essay, and performance test components. GPT-4 significantly outperformed human test-takers and prior models, achieving a score well above the passing threshold for all UBE jurisdictions, highlighting its potential to support legal service delivery. | True | Idealistic | True | 2.0 | Positive | Zero-shot evaluation of GPT-4 (a large language model) on the Uniform Bar Examination (UBE). | GPT-4 was tested on the full Uniform Bar Examination (MBE, MEE, MPT) using official questions from previous administrations (MBE) and the July 2022 exam (MEE, MPT). Performance was compared against prior GPT models and average human test-taker scores. MEE/MPT answers were graded by two authors against representative 'good' answers. | GPT-4 scored approximately 297 points on the UBE, significantly exceeding the passing threshold (typically 260-270). On the MBE, GPT-4 achieved 75.7% accuracy, outperforming human averages (68%) in 5 of 7 subjects. On the MEE and MPT, GPT-4 scored an average of 4.2/6.0. | The complexity of law, high cost of legal services, and unmet societal demand for such services. | Proposing large language models like GPT-4 as a 'technology-based force multiplier' or 'legal force multiplier' to support the delivery of legal services and address unmet demand. | Enhancing the accessibility, quantity, and quality of legal services; addressing the high cost and unmet demand for legal support by demonstrating AI's capability in complex legal reasoning. | General public/society facing challenges in accessing legal services due to cost and complexity. | Uniform Bar Examination subjects: Civil Procedure, Constitutional Law, Contracts, Criminal Law and Procedure, Evidence, Real Property, Torts. Also covers Corporations, Trusts, Estates, Family Law, and legal ethics as part of MEE/MPT components. | USA (Uniform Bar Examination applicable in multiple US states) | GPT-4 was pre-trained on publicly available data (such as internet data) and data licensed from third-party providers, then fine-tuned using reinforcement learning from human feedback (RLHF). A contamination check confirmed the UBE test questions were not in GPT-4's training data. | NaN | NaN | True | False | GPT-4 model evaluated is stated to be 'generally available under commercial terms'. The paper's analysis code and output data are available on GitHub. | Potential for stronger performance with more exhaustive prompt engineering or advanced techniques (few-shot learning, retrieval augmented generation). Need for further research to translate LLM capabilities into safe and efficient real-world applications, likely requiring human-in-the-loop workflows. Ongoing questions about the comparative performance of other/future models (e.g., domain-specific, open-source). | Ensuring test data (UBE questions) had not leaked into GPT-4's training set (addressed via contamination check with OpenAI). The qualitative and subjective nature of grading open-ended MEE and MPT responses. Handling the length of MPT documents, which required a special '8K' version of ChatGPT for comparison due to token limits in the publicly available version. | GPT-4 may still hallucinate sources, incorrectly interpret facts, or fail to follow ethical requirements. The model may have various biases in its outputs. |
| artificial-intelligence-at-the-bench-legal-and-ethical-challenges-of-informingor-misinformingjudicial-decision-making-through-generative-ai.pdf | Web_of_Science | Artificial intelligence at the bench: Legal and ethical challenges of informing —or misinforming —judicial decision-making through generative AI | This article critically examines the integration of Generative AI (GenAI) into judicial decision-making, highlighting its transformative potential alongside inherent legal and ethical challenges like bias and lack of transparency. It proposes a comprehensive framework of standards and guidelines to promote responsible GenAI use in the judiciary, aiming to enhance efficiency and access to justice while safeguarding rights. | True | Idealistic | True | 1.0 | Positive | A framework for responsible and equitable use of GenAI in the judiciary, encompassing foundational standards (e.g., capacity assessment, stakeholder engagement, licensing/verification, trusted datasets, responsibility allocation, prompt engineering) and application principles (e.g., legal education, risk-based assessments, disclosure, verification systems, procedural rights, ongoing audits). | NaN | NaN | Bias, lack of transparency/interpretability, accountability gaps, data quality issues (hallucinations), data privacy concerns, risks to judicial independence, erosion of public trust, and potential for widening access to justice disparities due to resource and expertise gaps. | A proposed framework with ex-ante controls (capacity assessments, stakeholder engagement, system verification, trusted datasets, clear responsibility allocation, prompt engineering) and deployment principles (enhanced legal education, case-based risk assessments, human-in-the-loop verification, disclosure requirements, specific procedural rights, ongoing audits). | Access to justice; Responsible use of AI in judicial decision-making; Ethical and legal implications of GenAI in courts. | Individuals interacting with the judicial system, with a focus on protecting vulnerable groups, ensuring fairness for litigants, and addressing resource disparities in access to justice across and within jurisdictions. | General (judicial decision-making across various fields, with case studies in health, administrative, family, electoral, and criminal law). | International (with case studies from Colombia, Mexico, Peru, India and discussion of approaches in UK, New Zealand, EU, Canada, Singapore, Estonia). | The paper analyzes the use of GenAI tools like ChatGPT in case studies, which are trained on vast, general, and often unverified datasets primarily from the internet, including non-legal sources. The proposed framework advocates for future judicial GenAI systems to be trained on trusted, high-quality, and ideally legally-specific datasets. | The proposed framework was developed through an analysis of existing GenAI applications in courts (case studies), a review of differing regulatory approaches across jurisdictions, and an examination of legal and ethical principles related to AI and judicial processes. | NaN | False | False | NaN | Lack of clear, unified regulatory frameworks for GenAI in judiciaries; Ad hoc, unregulated use of GenAI by judges; Technical limitations of GenAI (e.g., hallucinations, bias); Need for enhanced legal education on AI; Unclear liability and accountability mechanisms; Insufficient public debate and stakeholder engagement; Nascent state of AI auditing methodologies. | Developing a comprehensive framework that balances harnessing GenAI's potential benefits (efficiency, access to justice) with mitigating its diverse risks (bias, opacity, errors) in the sensitive and evolving context of judicial decision-making across different jurisdictions. | Bias perpetuation and amplification leading to discriminatory outcomes, generation of false/misleading information (hallucinations), lack of transparency__and explainability in AI-assisted decisions, compromised judicial independence and discretion, erosion of public trust in the justice system, data privacy violations, unclear legal liability for AI-induced errors, and exacerbation of access to justice disparities. |
| s10506-024-09399-6.pdf | Web_of_Science | Large language models in cryptocurrency securities cases: can a GPT model meaningfully assist lawyers? | This paper investigates GPT-3.5's capabilities in legal reasoning (identifying violations from fact patterns) and ChatGPT's legal drafting abilities (creating complaints) within cryptocurrency securities law. It finds GPT-3.5's legal reasoning weak, but suggests ChatGPT's drafting skills could assist lawyers, as mock jurors' decisions were not significantly affected by whether complaints were AI or human-written. | True | Market | True | 2.0 | Neutral | Evaluation of OpenAI's GPT-3.5 models: text-davinci-003 for legal reasoning and gpt-3.5-turbo-0301 (via ChatGPT May 24, 2023 version) for legal drafting, using zero-shot prompting. | Legal reasoning (text-davinci-003): Fact patterns from 20 real cryptocurrency securities cases were used as input; outputs were evaluated against actual case allegations (ground truth) using precision, recall, and F1-score. Legal drafting (ChatGPT): Complaints for 9 securities class action cases were drafted based on Law360 article summaries and compared to lawyer-drafted versions (edited for comparable facts); 88 mock jurors assessed complaints via a survey with jury instructions, comparing decisions and confidence levels. Concreteness of language was also analyzed. | For legal drafting (ChatGPT), which was more successful: Jurors’ decisions were not statistically significantly associated with the author (AI vs. human) of the complaint (Fisher’s Exact test, p=0.39), and juror confidence scores did not significantly differ (Mann Whitney U-test, p=0.371). AI-generated complaints were found to be significantly more linguistically concrete than lawyer-drafted ones (t(8)=3.25, p=0.012). (Legal_reasoning results: GPT-3.5's reasoning was weak, average F1-score 0.324; precision 0.66, recall 0.25). | Significant resource constraints faced by enforcement attorneys and in class action litigation within the cryptocurrency securities field, potentially hindering justice for victims. | Investigating LLMs (GPT-3.5, ChatGPT) for AI-based assistance to lawyers for legal reasoning and drafting. ChatGPT's legal drafting skills show potential to support lawyers by reducing drafting time for key legal documents, thereby potentially alleviating resource constraints. | Addressing resource constraints in legal enforcement and class action litigation in cryptocurrency securities cases; evaluating AI for legal reasoning (identifying violations) and legal document drafting (complaints). | Victims of cryptocurrency securities violations seeking justice through class actions or via enforcement agencies. | Securities law (cryptocurrency securities), Civil procedure, Litigation. | U.S. (United States federal law and courts). | Proprietary, large-scale, general text and code data used by OpenAI for pre-training GPT-3.5 models (text-davinci-003 and gpt-3.5-turbo-0301). The study itself used zero-shot prompting, not fine-tuning on specific legal data. | Iterative prompt engineering for zero-shot learning with GPT-3.5 models. For reasoning evaluation: comparison against real case allegations using precision, recall, and F1-score. For drafting evaluation: mock jury study involving comparison of human-vs-AI generated complaints, survey-based collection of participant decisions and confidence, and linguistic concreteness analysis of texts. | NaN | True | False | GPT-3.5 models (text-davinci-003 and gpt-3.5-turbo-0301 via ChatGPT) are available through OpenAI's API (paid) and the ChatGPT user interface (freemium model). | For AI assisting legal professionals (which could indirectly benefit aspects of access to justice): Current LLMs like GPT-3.5 show weak legal reasoning capabilities, particularly high false negatives in identifying legal violations. While drafting capabilities are more promising, human oversight remains essential, and improvements are needed for reliability without extensive review. | Effective prompt engineering remains an art; model limitations include inconsistency in following instructions and occasional failure in specific tasks (e.g., drafting captions); input data processing (cleaning complaint text, using summarized facts from news articles); technical issues with API access for certain tasks necessitating UI use; cost and logistical challenges of mock jury studies; defining robust ground truth for legal reasoning evaluation. | Hallucination or fabrication of facts/statutes by LLMs (e.g., adding 'John Doe' defendants, inconsistent party details). Incorrect legal knowledge application by LLMs, especially in legal reasoning (e.g., missing violations). Potential for over-reliance on LLMs, leading to errors if outputs are not carefully validated by human lawyers. Biases or limitations stemming from the LLMs' proprietary training data. Risk of malpractice if LLM outputs are used without adequate scrutiny. |
| generative-ai-systems-in-legal-practice-offering-quality-legal-services-while-upholding-legal-ethics.pdf | Web_of_Science | Generative AI systems in legal practice offering quality legal services while upholding legal ethics | This paper examines the impact of generative AI systems, such as ChatGPT, on lawyers' ethical duties of competence and confidentiality, primarily within the Luxembourg legal context. Drawing on doctrinal research, a survey of lawyers, and interviews with legal tech developers, it proposes a client-centric approach and regulatory adaptations to integrate AI ethically and enhance the quality of legal services. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Risks to client confidentiality due to data processing by AI; potential for lawyer incompetence if AI is used without proper understanding or verification; lack of client's informed consent regarding AI use; current professional conduct rules not fully adapted to AI challenges; insufficient transparency of AI systems. | Adopting a client-centric approach to legal services using AI; Bar Associations establishing clear rules and guidelines for AI use; ensuring lawyers obtain clients' valid and informed consent for processing data with AI; implementing robust security measures and due diligence for AI systems and providers; providing comprehensive training for lawyers on AI ethics, capabilities, limitations, and safe usage. | Ethical use of AI in legal practice; lawyer competence and digital literacy; client confidentiality and data protection with AI; regulation of AI in the legal profession; improving quality and accessibility of legal services through client-centric AI integration. | NaN | General legal practice (professional ethics, competence, confidentiality) | Luxembourg (primary focus), with references to France, Belgium, and the EU (GDPR, AI Act). | NaN | NaN | NaN | False | False | NaN | Need for adapted professional conduct rules and clear regulatory frameworks for AI in law; widespread lack of standardized lawyer training on AI; challenges in ensuring genuine informed client consent and maintaining data confidentiality with AI; improving transparency and explainability of AI systems; balancing AI innovation with ethical obligations and client protection. | Ensuring accuracy and reliability of AI outputs (combating hallucinations); maintaining client confidentiality with third-party AI systems; addressing lack of context specificity in general AI for legal tasks; overcoming user reluctance and ensuring proper human oversight; complexity in making AI systems explainable. | Disclosure of client confidential information to AI providers or other third parties; lawyers' reliance on inaccurate AI outputs leading to incompetent representation; breaches of professional secrecy and data protection regulations; lack of informed client consent for AI-driven data processing; potential for devaluing the legal profession if human judgment is diminished; security vulnerabilities like data breaches, biased outputs, or prompt injection attacks. |
| ZXX-Singh+101.pdf | Web_of_Science | Enhancing Privacy and Security in Large -Language Models: A Zero-Knowledge Proof Approach | This paper proposes using Zero-Knowledge Proofs (ZKPs) to enhance the security and reliability of Large Language Models (LLMs) by validating data sources, user identities, and prompts. A prototype, zk-GPT, demonstrated the viability of this approach in user authentication, data relevance filtering, and malicious prompt detection with promising results. | True | Market | True | 1.0 | NaN | Integration of Zero-Knowledge Proofs (ZKPs), specifically zk-SNARKs, with Large Language Models (LLMs) to create zk-LLMs (e.g., zk-GPT prototype) for enhanced security and reliability through user authentication, prompt analysis, and source data verification/relevance filtering. | The zk-GPT prototype was evaluated in three stages: 1) User Authority Analysis (100 iterations with admin, normal, and foreign user login attempts). 2) Supplemental Data Relevance (80 experiments using 40 research papers as supplemental data). 3) Malicious Prompt Detection (60 iterations against a dataset of 200 prompt injection keywords). | The User Authority Analysis showed 100% success in identifying and rejecting all 40 unauthorized login attempts, while correctly processing all 60 authorized login attempts with appropriate privilege separation. | NaN | NaN | NaN | NaN | Legal services (general mention as a potential application domain for expert-GPTs) | International | The zk-GPT prototype uses the Llama-2 7b-GPTQ model as its base LLM. For specific experiments, it utilized a curated set of 40 research papers for supplemental data relevance testing and a list of 200 prompt injection keywords for malicious prompt detection. The ZKP components themselves are cryptographic circuits designed based on rules, not trained on data. | Development of a theoretical framework for zk-LLMs. Prototyping (zk-GPT) using Circom for zk-SNARK circuits, Groth16 and Powers of Tau protocols, and SnarkJS for circuit implementation, binding, witness generation, and proof verification. Experimental validation of the prototype. | The research developed a prototype (zk-GPT) intended for privatised LLM environments for experimental purposes. No broader public deployment strategy is discussed. | False | False | NaN | NaN | Computational overhead of ZKP verification affecting LLM responsiveness. Difficulties in data availability and context understanding for large datasets and nuanced prompts. Current zk-circuit limitations: lack of flexibility (requires modification for input deviations), SHA256 hashing inefficiency for larger circuits, and reliance on trusted setups (Groth16) which expose witnesses. | For LLMs (that ZKPs aim to mitigate): Unreliability, factual inaccuracy, susceptibility to manipulation by biased/malicious data, data poisoning, malicious prompt engineering, spread of misinformation, creation of deep-fakes, exposure of sensitive/mission-critical data, and database infection/corruption. |
| s10506-023-09367-6.pdf | Web_of_Science | Bringing legal knowledge to the public by constructing a legal question bank using large‑scale pre‑trained language model | The paper introduces a three-step approach to improve laypersons' access to legal information: translating laws into 'CLIC-pages', creating a Legal Question Bank (LQB) from these pages using GPT-3, and designing a 'CLIC Recommender' (CRec) for guidance. The research details the LQB generation, demonstrating that machine-generated questions, especially via a 'Hybrid' prompting strategy, are scalable and diverse, though human-composed questions offer higher precision. | True | Idealistic | True | 1.0 | Positive | The core technique is the construction of a Legal Question Bank (LQB) by: 1) Prompting GPT-3 with simplified legal texts (CLIC-pages) using various partitioning strategies, with 'Hybrid partitioning' (section-level context, paragraph-level attention) being optimal. 2) Deduplicating generated questions using sentence embeddings (DistilBERT). This LQB, alongside CLIC-pages and a CLIC Recommender (CRec) using text embeddings for retrieval, forms a three-step approach for enhanced legal knowledge access. | Comparison of three GPT-3 prompting/partitioning strategies (Section-based, Paragraph-based, Hybrid) for machine-generated question (MGQ) creation against Human-Composed Questions (HCQs) on a sample of 100 CLIC-pages. Evaluation metrics included quantity, precision (verified by human legal experts), coverage (percentage of CLIC-page paragraphs covered by questions), and diversity (qualitative analysis of question scope and perspective). The CRec prototype's effectiveness was informally studied using case scenarios. | The Hybrid partitioning strategy for MGQ generation was most effective, producing 3,362 correct questions (68% precision) from the 100-page sample, achieving 93% paragraph coverage. MGQs were more numerous, diverse (including more general and 'augmenting' questions), and cost-effective than HCQs (2,685 questions, 98% coverage, 10.1% general questions), while HCQs exhibited higher precision. | The 'legal knowledge gap' for the general public, stemming from: 1) Navigability: Difficulty in finding relevant legal rules within vast legal information. 2) Comprehensibility: Difficulty in understanding technical legal language and concepts without legal training. | A three-step approach to bridge the legal knowledge gap: 1) CLIC-pages: Presenting legal information in layperson's terms. 2) Legal Question Bank (LQB): A large, searchable collection of model legal questions generated using GPT-3, linked to answers in CLIC-pages. 3) CLIC Recommender (CRec): An AI-powered tool to help users find relevant LQB questions based on their natural language descriptions of a legal situation. | Access to legal information, legal knowledge dissemination, improving navigability and comprehensibility of law for laypersons, machine question generation, legal tech for public understanding of law. | General public, laypersons without formal legal education seeking to understand their legal rights or situations. | Multiple fields relevant to daily life, including Landlord and Tenant, Defamation, Insurance, Personal Data Privacy, Intellectual Property, Traffic Offenses, Medical Negligence, and Sexual Offenses. | Hong Kong | Input data for question generation: CLIC-pages (explanations of Hong Kong law in layperson's terms, unstructured text, from the CLIC platform). Models used: GPT-3 (pre-trained on a general text corpus like Common Crawl, WebText2, books, Wikipedia) for question generation; DistilBERT (pre-trained) for question deduplication embedding; all-mpnet-base-v2 (pre-trained) for CRec text embedding. | Iterative development and comparative evaluation of different GPT-3 prompting/partitioning strategies (Section-based, Paragraph-based, Hybrid). Manual verification and annotation of machine-generated questions by human legal experts. Prototype development for the CLIC Recommender (CRec). | The LQB and CRec are components designed to enhance the existing Community Legal Information Centre (CLIC) online platform (www.clic.org.hk). The paper presents a prototype of CRec and the methodology for LQB construction, with evaluations suggesting future integration. | False | False | NaN | While machine-generated questions (MGQs) using the Hybrid method show high coverage and diversity, their precision (68%) is lower than human-composed questions. A significant portion of MGQs are 'augmenting questions' that, while relevant, are not answered by current CLIC-pages, indicating needs for content enrichment on the CLIC platform. | Developing effective prompting strategies for LLMs (like GPT-3) to ensure high precision, coverage, and diversity in generated legal questions. Accurately defining answer scopes for machine-generated questions. Efficiently deduplicating semantically similar questions. The inherent probabilistic nature of LLMs leading to variability in output quality. | Generation of incorrect or irrelevant questions by the LLM, potentially misleading users if not properly filtered or verified. Users might over-rely on the system, and if the question matching or information retrieval is imperfect, they could receive incomplete or inapplicable legal information. |
| futureinternet-17-00113.pdf | Web_of_Science | A CIA Triad-Based Taxonomy of Prompt Attacks on Large Language Models | This paper introduces a taxonomy of prompt attacks on Large Language Models (LLMs) based on the Confidentiality, Integrity, and Availability (CIA) triad. It analyzes emerging threats and proposes targeted mitigation strategies to enhance LLM security in real-world applications. | True | Market | True | 1.0 | NaN | CIA Triad-Based Taxonomy of Prompt Attacks | NaN | NaN | NaN | NaN | NaN | NaN | Legal services | International | NaN | Literature review and application of the CIA cybersecurity framework to classify prompt attacks. | NaN | False | False | NaN | NaN | Synthesizing a fragmented body of research on prompt attacks to create a cohesive and comprehensive classification; mapping diverse attack types to the established CIA triad framework. | Breaches of confidentiality (e.g., data extraction, model inversion), corruption of integrity (e.g., toxic prompting, semantic manipulation, generating misleading or harmful content, misinformation), and disruption of availability (e.g., Denial-of-Service prompts, context flooding). Specific real-world risks include privacy violations, malicious code generation, financial fraud, incorrect legal advice, erosion of public trust, and regulatory non-compliance. |
| s10506-024-09422-w.pdf | Web_of_Science | It cannot be right if it was written by AI: on lawyers’ preferences of documents perceived as authored by an LLM vs a human | This paper investigates how Czech lawyers and law students (n=75) perceive legal documents based on their assumed origin (AI-generated vs. human-crafted). Findings reveal a significant preference for documents believed to be human-authored in terms of correctness and language quality, despite participants' general optimism about future AI-based document automation. | True | Idealistic | False | 1.0 | Neutral | Experimental study comparing human evaluation (correctness, language quality) of two human-crafted legal documents (acknowledgement of debt, one Brief, one Verbose), which were differentially labeled as 'AI-generated' or 'human-crafted' across two participant groups (lawyers and law students). | 75 Czech lawyers and law students randomly assigned to two groups. Each participant evaluated two documents (one brief, one verbose, both actually human-written) regarding correctness and language quality using a 1-5 scale and open-ended comments. One document was labeled 'AI-generated', the other 'human-crafted', with labels swapped between groups. Quantitative analysis (scores, Fisher exact test for preference) and qualitative thematic analysis of comments were performed. | Documents labeled 'human-crafted' received significantly higher scores for correctness (mean 4.69 vs 4.21) and language quality (mean 4.55 vs 3.97) than those labeled 'AI-generated' (Fisher exact test p<10^-4 for preference). Lawyers were slightly less susceptible to this AI-label bias than law students. Despite these negative perceptions of AI-labeled documents, 93% of participants believed LLMs could automate such document generation in the future. | Negative perception and 'algorithmic aversion' among legal professionals towards documents believed to be AI-generated, affecting judgments of correctness and language quality. This bias could lead to unfair treatment, especially for vulnerable individuals relying on AI-generated legal aid. Mandatory disclosure of AI use might worsen this bias. | Increase awareness among legal practitioners, policymakers, and legislators about this perception bias to foster responsible adoption and implementation of AI in legal document generation. Promote discussions on updating legal processes. Further research into perceptions across different demographics, document types, and contexts. | Use of AI for drafting legal documents to improve access to justice (e.g., for legal aid, self-help). Impact of perception and bias against AI-generated documents on fairness and equality in legal processes. | Lower-income groups and individuals needing legal aid, who may increasingly rely on AI-generated or AI-assisted legal services. | Contract law / Obligations law (specifically, acknowledgment of debt). | Czechia (participants recruited from Masaryk University, documents referenced Czech Civil Code). | NaN | Between-subjects experimental design with counterbalancing of labels. Stratified random sampling for participant allocation. Survey methodology with Likert scales and open-ended questions. Quantitative statistical analysis (mean scores, side-by-side preference counts, Fisher exact test) and qualitative thematic analysis. | The documents and survey used in the experiments are made available in a public GitHub repository to facilitate replication of the study. | True | True | The documents and survey used in the experiments are available in an online repository on GitHub. | Understanding of perception bias in other demographics (judges, general public), with more complex legal documents, and in different jurisdictions/languages. The influence of prior AI exposure on perception. How to reconcile transparency of AI use with potential for bias. Nuanced understanding of how objective vs. subjective document properties affect perception. | NaN | Algorithm aversion leading to documents being unfairly judged as lower quality if perceived as AI-generated. Negative impact on access to justice if AI-produced documents (e.g., for legal aid clients) are devalued. Mandatory disclosure of AI authorship could exacerbate bias against AI-generated content. Potential for social inequalities if documents from certain (e.g., less resourced) parties relying on AI are viewed more critically. |
Cleaned Annotations
This section displays the annotations after deduplication and cleaning (removing rows with no title or summary).
Cleaned Annotations
| filename | source | title | summary | is_english | audience_legal_access | llm_use | paper_type | sentiment | technique | testing | results | obstacles | solutions | topics | community | legal_field | jurisdiction | training_data | design_methodologies | deployment | claimed_availability | claimed_open_availability | which_claimed_availability | gaps | challenges | risks |
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| UsinggenerativeAIinhumanresourcedevelopmentanappliedresearchstudy.pdf | Google_Scholar | Using generative AI in human resource development: an applied research study | This paper reports an experimental study investigating the use of ChatGPT for designing Human Resource Development (HRD) interventions with doctoral students. It identifies benefits like accelerated idea generation and drawbacks such as generic outputs and lack of contextual understanding, emphasizing the crucial role of human expertise in refining AI suggestions. | True | NaN | True | 2.0 | NaN | ChatGPT | Experimental study where 16 HRD doctoral students from two US universities used ChatGPT to develop an HRD intervention plan based on a provided organizational scenario. Data collected included intervention plans, prompts used, ChatGPT outputs, and participant responses to open-ended questions. | ChatGPT significantly reduced the time needed for the task (estimated three times faster) and was valuable for initial brainstorming and idea generation. However, its outputs were often generic, lacked contextual understanding specific to HRD situations, sometimes returned the same text despite refinement prompts, lacked source disclosure, and required substantial revision and validation by users with HRD expertise. | NaN | NaN | NaN | NaN | Human Resource Development | USA | NaN | NaN | NaN | True | True | ChatGPT is publicly accessible online through OpenAI, offering free usage tiers. | NaN | Challenges in using ChatGPT included its tendency to produce generic, non-contextualized outputs; difficulty in refining outputs through prompts; lack of source disclosure and accountability; potential security and confidentiality issues with proprietary data; the need for significant user expertise to evaluate and refine outputs; and the risk of over-reliance or accepting incorrect information ('black boxing'). | Risks identified include generating inaccurate or biased information, deskilling of HRD professionals, loss of human learning opportunities (especially for novices), potential for cognitive overload, security/confidentiality breaches when using company data, ethical concerns (e.g., inappropriate AI-driven decisions impacting individuals), and the danger of users uncritically accepting AI output leading to poor problem-solving. |
| KzGuuCj9bCMJ.pdf | Google_Scholar | Justice Link: Tech-Driven Solutions for Undertrial Prisoner | This paper proposes "Justice Link", a web-based platform designed to address systemic challenges faced by undertrial prisoners in India, such as prolonged detention and limited legal access. Pilot testing showed improved communication and reduced case processing times, highlighting technology's potential to enhance efficiency and access to justice despite infrastructure challenges. | True | Idealistic | False | 1.0 | Positive | Justice Link: A web-based platform with features like a centralized digital dashboard, real-time case request system, automated notifications, secure login, remote legal consultations, rehabilitation program integration, and database management. | Pilot implementation in selected prisons. Evaluation focused on system performance metrics (response time, query efficiency, data accuracy) and impact metrics (reduction in case processing times, improvement in lawyer-prisoner communication). | Pilot projects showed a 30% reduction in case-processing times and a 40% improvement in lawyer-prisoner communication. System performance testing showed response time < 3 seconds (observed 2.8s), query efficiency < 200ms (observed 185ms), and data accuracy of 99.8%. | Systemic delays in judicial proceedings, overcrowded prisons, psychological impact and social stigma, insufficient rehabilitation opportunities, poor health and hygiene conditions, limited legal aid access, lack of efficient case management, communication barriers between prisoners and legal representatives. | Developing and deploying a web-based platform ("Justice Link") to digitize case management, facilitate secure communication between prisoners and lawyers, provide access to rehabilitation programs, automate notifications, and streamline legal aid requests. | Access to legal aid, case management efficiency, prisoner-lawyer communication, rehabilitation access, reducing pre-trial detention time, ensuring speedy trials. | Undertrial prisoners in India. | Criminal Justice, Criminal Procedure | India | NaN | Literature review, primary data collection (interviews, surveys), secondary data analysis (official reports, pilot projects), case study analysis, qualitative analysis, quantitative analysis, field visits, pilot implementation and evaluation. | Pilot implementation in selected prisons. | False | False | NaN | Need for improved digital infrastructure in prisons, staff training, system scalability, integration with national databases and court management systems, multilingual support. | Implementing technology within existing prison infrastructure limitations, ensuring usability for prisoners and staff, need for staff training, ensuring data accuracy and system reliability for sensitive information. | Privacy and security risks associated with handling sensitive prisoner and case data (mitigated by secure login/authentication). |
| zMxRuhVaiw8J.pdf | Google_Scholar | Bridging the Legal Literacy Gap: A Survey on \nAI-Driven Document Simplification and Generation | This paper surveys AI-driven legal document simplification and generation, proposing an AI-powered legal documentation assistant for India. The system, using NLP and ML, aims to offer bilingual (English/Hindi) document drafting, simplification, and compliance checks to improve legal literacy and access to justice. | True | Idealistic | True | 1.0 | Positive | AI-powered legal documentation assistant utilizing NLP (tokenization, POS tagging, NER, dependency parsing, sentiment analysis) and ML techniques (supervised, unsupervised, transfer learning), including transformer models (BERT, GPT, Seq2Seq with attention). It provides bilingual (English/Hindi) document simplification, generation, and rule-based compliance checking. | NaN | NaN | Legal literacy gap; high cost and complexity of legal services hindering access to justice, particularly for underprivileged individuals and small businesses in India. | Development of an AI-powered legal documentation assistant for document simplification and generation, bilingual (English/Hindi) support, automated compliance monitoring, and an option to seek expert legal advice, aimed at democratizing legal information and reducing costs. | Legal document simplification, legal document generation, legal literacy, access to legal information. | Individuals, small businesses, and underprivileged populations in India. | General legal matters, routine legal documents. | India | Proposed collection of diverse Indian legal documents, including original texts paired with simplified versions, and a parallel corpus of English and Hindi legal terms/phrases. Data is unstructured; public/proprietary status and specific sources are not detailed. | Iterative design process including: data collection & preprocessing, training data preparation (paired texts, parallel corpus), transformer-based model architecture design (BERT, GPT), model training (fine-tuning, transfer learning), web-based user interface development, template-based & AI-generated content pipeline for document creation, rule-based compliance checking, and planned testing (unit, integration, user acceptance). | Proposed deployment via a web-based interface. No further deployment or diffusion strategies are detailed. | False | False | NaN | Accuracy and reliability of AI in handling legal nuances; lack of contextual understanding in AI; limited customization of AI tools for specific legal areas/jurisdictions; lack of transparency/explainability in AI models; perpetuation of biases from historical data; security/privacy concerns with sensitive legal data; limited scope of document types handled by AI; scarcity of large-scale Indian legal datasets for AI training; limited research focus on the Indian legal context. | Ensuring accuracy and reliability with complex legal language; achieving deep contextual understanding; providing sufficient customization for diverse legal needs; improving transparency and explainability of AI models; mitigating bias from training data; addressing security and privacy of legal information; handling a wide range of document types; overcoming scarcity of domain-specific (Indian legal) datasets; adapting models for multilingual contexts; high computational resource requirements for advanced models. | Misinterpretations or inaccuracies in AI-generated legal documents leading to serious legal consequences; perpetuation of existing biases present in historical legal data by AI systems; data privacy and confidentiality breaches of sensitive legal information. |
| DHTZl_KKZDkJ.pdf | Google_Scholar | Treu und Glauben: Frag GPT | The paper interprets the German legal principle of "Treu und Glauben" (good faith/fairness) through the lens of behavioral economics, focusing on fairness norms. It proposes using Large Language Models (LLMs) like GPT as an empirical tool to probe fairness perceptions in specific legal case variations involving this principle. | False | Idealistic | True | 1.0 | Positive | Using LLM (GPT-3.5 Turbo) via API calls to repeatedly assess fairness judgments (binary yes/no answers) on specific, detailed legal case vignettes involving the principle of 'Treu und Glauben'. | Three case studies (contractual adjustment due to unforeseen costs, withdrawal of administrative benefits, refinements of the contractual adjustment case) with variations were presented to GPT-3.5 Turbo 100 times each via API. The distribution of 'yes'/'no' answers regarding the fairness/appropriateness of a specific outcome was analyzed. | GPT's fairness assessments varied significantly and meaningfully across different case variations (e.g., justifying price increases more for external raw material cost shocks (71% yes) than internal personnel issues (36% yes)), suggesting sensitivity to context. Adding information about explicit reliance or prior negotiation details also strongly influenced the model's assessment. | The difficulty for legal practitioners to apply open-ended normative standards like 'Treu und Glauben' objectively, as it involves complex, context-dependent fairness assessments where professional legal training alone may not suffice. | Employing LLMs as an empirical method to gather data on fairness intuitions regarding specific case details, thereby providing judges and lawyers with broader, potentially more objective input for their discretionary judgments. | Improving judicial/administrative decision-making quality and consistency in cases requiring fairness assessments under 'Treu und Glauben'. | NaN | German Civil Law (Contract Law), German Administrative Law | Germany | The technique uses GPT-3.5 Turbo, which is trained on a large, proprietary dataset of general text and code by OpenAI. | Experimental design using structured prompts presented via API to an LLM, prompt engineering to elicit binary responses, repetition for statistical analysis, systematic variation of case details. | NaN | False | False | NaN | The need for further research to validate how accurately LLM responses reflect nuanced human fairness judgments and cognitive biases before such methods can be reliably used in legal practice. | Designing effective prompts ('prompt engineering') to elicit specific, quantifiable fairness judgments (yes/no) from the LLM; ensuring reproducibility and control over the interaction (addressed via API usage). | The risk of relying on insufficiently validated AI assessments for legal judgments; potential discrepancies between LLM outputs and genuine societal fairness norms. |
| zUG2dOzRSrAJ.pdf | Google_Scholar | SoMeLVLM: A Large Vision Language Model for Social Media Processing | The paper introduces SoMeLVLM, a large vision-language model specifically designed for social media tasks, leveraging a custom cognitive framework and a large multimodal dataset for instruction tuning. SoMeLVLM demonstrates state-of-the-art performance on various social media analysis benchmarks, overcoming limitations of general models in understanding informal language and multimodal context. | True | NaN | True | 1.0 | NaN | SoMeLVLM: A Large Vision Language Model (based on Vicuna-7b-v1.1 and Blip2 architecture) fine-tuned using instruction tuning (QLoRA for LLM) on a custom multimodal social media dataset based on a five-level cognitive framework (Knowledge & Comprehension, Application, Analysis, Evaluation, Creation). | Evaluated on 14 multimodal datasets and 12 plain text datasets covering social media tasks (emotion, humor, hate speech, misinformation, ideology, etc.) using zero-shot settings. Comparison against baseline LLMs and LVLMs using Accuracy (overall Acc and instruction-following Acc*), BLEU-L, ROUGE-L, and GPT-4 scoring. | Achieved state-of-the-art zero-shot performance on various social media classification and generation tasks (both text-only and multimodal), significantly outperforming baseline LLMs and LVLMs across different cognitive levels. For instance, achieved 72.57% overall accuracy (Acc) on multimodal hate speech classification (Table 2). | NaN | NaN | NaN | NaN | NaN | International | A custom 654k multimodal social media instruction-tuning dataset, combining existing open-source benchmarks (e.g., Sentiment140, FakeNewsNet, jigsaw, MVSA, listed in Appendix A.1.1) and self-collected social media data (text and text-image pairs from unspecified platforms). Some labels/explanations for generation tasks were produced using GPT-4/GPT-4V. | Instruction tuning (QLoRA for LLM, standard fine-tuning for vision connection module) of a base LVLM architecture (Vicuna-7b-v1.1 + vision components). Development of a cognitive framework (based on Bloom's Taxonomy) to guide task selection and dataset construction. Manual prompt engineering for instruction formatting. | NaN | False | False | NaN | The model primarily focuses on English and may not generalize well to other languages. It may exhibit interpretive biases towards neologisms and culturally specific terms, especially without sufficient context. | Curating a large-scale, high-quality multimodal instruction dataset specific to social media, covering diverse tasks and cognitive levels. Designing an effective cognitive framework to structure the tasks and model capabilities. Overcoming limitations of general-purpose models in understanding informal language, multimodal nuances, and complex cognitive requirements of social media tasks. Addressing the observed degradation in instruction-following ability when adapting LLMs into LVLMs. | Potential for interpretive biases towards neologisms, slang, or culturally specific language. Privacy risks associated with the collection and use of real user data from social media platforms (though the authors state intent to mitigate this before dataset release). |
| F0YMldnt1UoJ.pdf | Google_Scholar | GENERATIVE AI IMPACT ON LABOR MARKET: ANALYZING CHATGPT’S DEMAND IN JOB ADVERTISEMENTS | This study examines the demand for ChatGPT-related skills in the U.S. labor market by analyzing job advertisements collected between May and December 2023. Using text mining and topic modeling, it identifies five key ChatGPT-related skill sets and details associated job attributes, highlighting Gen AI's increasing integration and evolving skill requirements. | True | Market | True | 2.0 | NaN | Data-driven analysis of job advertisements using text mining, NLP (TF-IDF, cosine similarity, fuzzy string matching), machine learning classification (SVM), and topic modeling (LDA) to identify trends in demand for Generative AI skills. | SVM model for job title classification: 5-fold cross-validation, evaluated for false positive rate (0.5%), precision (99%), and accuracy (99%). LDA model for topic modeling: evaluated using coherence and perplexity metrics, and manual examination of topic interpretability. | SVM for job title classification achieved 99% precision and accuracy. LDA identified five distinct ChatGPT-related skill sets (General Familiarity, Creative Content Generation, Marketing, Advanced Functionalities, AI Product Development) with varying prevalence and centrality across occupation families. | NaN | NaN | NaN | NaN | Legal services | United States | A proprietary dataset of 1128 unique U.S. job postings collected from Indeed, LinkedIn, and ZipRecruiter (May-Dec 2023). O*NET database was used as a reference for job titles. A subset of this data, with O*NET titles as labels, was used to train an SVM for job title classification. | A multi-step research methodology involving: 1) Web scraping of job advertisements, data cleaning (removing irrelevant and duplicate postings), and de-duplication. 2) Job title identification and standardization using TF-IDF, cosine similarity, fuzzy string matching, and SVM classification against the O*NET database. 3) Topic modeling (LDA) on job description segments related to ChatGPT to identify skill clusters. | NaN | True | True | The dataset of 1128 unique job postings is stated to be provided in Supplementary Materials. The analytical techniques (text mining, SVM, LDA) are standard and can be implemented with open-source tools. | NaN | Data quality issues (irrelevant and duplicate job postings), complexity in standardizing job titles from natural language descriptions, ensuring interpretability of topic models. | Job displacement, widening of skills gaps, wealth disparity; ethical concerns regarding biases in AI-generated legal advice. |
| S4tkmznxx80J.pdf | Google_Scholar | A Framework for Data-Driven Legal Regulatory Reform | This paper proposes a framework based on the scientific method for data-driven legal regulatory reform in Washington State, aiming to make the process faster, more evidence-based, and focused on access-to-justice impact. The framework incorporates risk assessment (present and future) and suggests methods for measuring benefits, particularly concerning the access-to-justice gap. | True | Idealistic | False | 1.0 | Positive | A conceptual three-dimensional framework for data-driven legal regulatory reform, incorporating risk assessment (current and future) and access-to-justice impact evaluation, potentially implemented within a regulatory lab/sandbox. | The paper describes the conceptual framework and its components (e.g., risk matrix, reference to NCSC A2J assessment tool) but does not report on specific testing or empirical evaluation of the framework itself in practice. | NaN | Slow pace of traditional legal regulatory reform; bespoke nature of reform efforts; lack of data collection and evaluation of reform impacts; difficulty measuring access-to-justice improvements; cost and perceived difficulty of data collection; concerns about client confidentiality (RPC 1.6); passive public involvement in reform processes; professional conservatism. | Adopt a data-driven framework using the scientific method for legal regulatory reform; use hypotheses (proposed reforms) and test them in safe environments (labs/sandboxes); systematically collect data on risks (present and future) and benefits (especially A2J impact); use tools like risk matrices and A2J assessment methodologies; make reform processes more timely and evidence-based. | Legal regulatory reform process; Access to justice gap measurement and reduction; Innovation in legal services delivery; Regulation of legal service providers (including alternative providers and online services). | General public facing the access-to-justice gap in Washington State. | General / Regulatory | Washington State (USA), with references to Utah and Arizona. | NaN | Scientific method principles; Iterative development (multiple blueprint versions); Conceptual modeling (3D framework); Incorporation of existing risk assessment matrices and access-to-justice evaluation tools (e.g., NCSC tool). | The framework was developed by the Washington State Practice of Law Board (POLB) and described in this publication. The paper expresses hope that others will adopt and adapt it. | False | False | NaN | Need for robust methodologies to measure access-to-justice impact; Overcoming challenges in collecting meaningful data while respecting confidentiality; Addressing long-term/future risks of legal innovations; Mitigating bias in risk assessment; Gaining wider adoption of data-driven approaches within the legal profession. | Dealing with scarce data in the legal services market; Quantifying the benefits (access-to-justice impact) of regulatory reforms; Accurately assessing and mitigating both current and future risks; Overcoming stakeholder resistance or skepticism (e.g., framing of 'sandbox' vs 'lab'); Balancing innovation with core professional duties (competence, confidentiality, conflicts, communication, safeguarding property). | Consumer harm from ineffective or flawed reforms (inaccurate results, failure to exercise rights, buying unnecessary services); Breach of client confidentiality; Conflicts of interest; Incompetent service provision; Poor communication; Mishandling client funds/property; Bias in risk assessment favoring developers over public good/justice; Unforeseen negative consequences ('unknown unknowns') of reforms, especially longer-term ones. |
| n-8yKQ3b_hkJ.pdf | Google_Scholar | Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise: A Case Study on Chinese Legal Domain | This paper proposes an 'adapt-retrieve-revise' framework to improve the performance of Large Language Models (LLMs) like GPT-4 in specialized domains, specifically Chinese law, by mitigating hallucinations. The method involves adapting a smaller 7B LLM to the domain, using its output to retrieve evidence, and then having GPT-4 revise the draft answer based on this evidence. | True | Market | True | 1.0 | NaN | Adapt-retrieve-revise framework for LLM domain adaptation. | Evaluated in a zero-shot setting on four Chinese legal tasks (Law Clause Recommendation, Criminal Prediction, LegalQA, JEC-QA) and a Similar Case Retrieval task. Metrics included F1, Recall, Accuracy, Precision@k, and MAP. Human evaluation was used for JEC-QA. | The proposed method (7B legal LLM for draft, answer-based retrieval, GPT-4 for revision) achieved an average score of 78.7 across four tasks, improving by +33.6 points over GPT-4 direct generation, and outperforming two stronger retrieval-based baselines by +17.0 and +23.5 points. | NaN | NaN | NaN | NaN | General Chinese law, potentially covering civil, criminal, administrative, and enforcement law based on training data sources and tasks like criminal prediction and legal QA. | Chinese legal domain | For domain adaptation of the 7B LLM (Baichuan-7B): continual learning on over 50B tokens from 'Chinese Law Clauses' (publicly available from flk.npc.gov.cn) and 'Chinese Judgments Online' (publicly available from wenshu.court.gov.cn). For supervised fine-tuning: 70K instruction examples (52K GPT-4 self-instruct Chinese data and 18K human-expert created legal instructions). | The proposed adapt-retrieve-revise framework consists of: 1) Domain adaptation of a 7B LLM (Baichuan-7B) through continual pre-training on Chinese legal corpora followed by supervised fine-tuning on legal instruction data. 2) Answer-based evidence retrieval, where the draft answer from the adapted 7B LLM is used to query an external knowledge base (e.g., Chinese law clauses, legal textbooks) via a sentence embedding model (Multilingual-E5-large) and kNN. 3) Revision by GPT-4, which takes the original query, the draft answer, and the retrieved evidence as input to produce the final answer. | The primary diffusion strategy is the release of the training code for their domain-adapted 7B LLM on GitHub. | False | False | NaN | NaN | Key challenges include LLM hallucinations in specialized domains like Chinese law, the high cost and impracticality of continually training very large models (e.g., GPT-4 scale) on domain-specific data, limitations of standard query-based retrieval systems, residual inaccuracies and hallucinations in smaller domain-adapted LLMs (7B scale) due to limited capacity, difficulties in automatic evaluation of generative outputs requiring human evaluation, and the expense of GPT-4 API access and human evaluation limiting the scale of experiments. | The primary risk discussed and addressed is LLM hallucination: the tendency of LLMs to generate non-logical content, factual mistakes, and fail to refer to correct legal provisions, especially when applied to specialized domains like law where accuracy is critical. |
| oWv69BqZmVMJ.pdf | Google_Scholar | Legal Evalutions and Challenges of Large Language Models | This paper reviews and evaluates the performance of various Large Language Models (LLMs), including general-purpose and legal-specific ones, on legal case judgment tasks using Chinese and English datasets. It discusses LLM capabilities, highlights key challenges like data privacy, liability, ethics, and technical limitations, and assesses their potential in the legal field. | True | Market | True | 2.0 | Neutral | Evaluation of various LLMs (e.g., GPT-4o, O1-preview, Qwen2, Gemma2, GLM-4, LawGPT_zh, lawyer-llama, llama3.2, Mistral, Phi-3.5) on legal case judgment tasks. | Evaluation on 26 legal cases (13 Chinese, 13 US from Court Listener and Chinese Judgments Online) covering civil, criminal, and administrative law. Metrics used: ROUGE-1, ROUGE-2, ROUGE-L, BLEU, and human evaluation scores (1-5 scale by law students). | O1-preview achieved the highest overall human evaluation score (3.96). Automated metrics (ROUGE) showed different top performers (e.g., Phi-3.5-mini-instruct, lawyer-llama-13b-v2), highlighting a discrepancy between lexical overlap and perceived judgment quality. | NaN | NaN | NaN | NaN | Civil law, criminal law, administrative law, immigration law | China, United States | NaN | NaN | NaN | False | False | NaN | Need for improvements in LLM training methodologies, domain-specific legal knowledge integration, reasoning capabilities, interpretability, legal frameworks for liability, and ethical review mechanisms. | Data privacy concerns with sensitive case information; unclear definition of legal liability for AI outputs; ethical issues including bias and lack of transparency; technical limitations in understanding legal nuances and interpretability; complexities due to legislative differences across jurisdictions. | Data leakage (privacy violation), generation of biased/unfair outputs, incorrect legal advice/analysis leading to undesirable outcomes and liability issues, undermining reliability of legal practice due to technical limitations/lack of interpretability, compliance risks due to legislative differences. |
| BILETA_Response_to_White_Paper_AI_Regulation_A_Proinnovation_Approach.pdf | Google_Scholar | BILETA Response to White Paper AI Regulation: A Pro-innovation Approach | This paper is a formal response by the British Irish Law, Education and Technology Association (BILETA) to the UK government's white paper on AI regulation. BILETA critiques the proposed non-statutory, principles-based approach, advocating for mandatory statutory regulation and clearer, more accessible redress mechanisms to effectively address AI risks and protect user rights. | True | Idealistic | False | 3.0 | Negative | NaN | NaN | NaN | Inadequate, unclear, expensive, and inaccessible redress mechanisms for AI-related harms; a proposed voluntary and principles-based regulatory system is deemed insufficient to protect user rights or prevent AI abuse, potentially leading to arbitrary enforcement and adverse impacts. | Implementation of mandatory, statutory AI regulation (akin to the EU AI Act) to provide clarity and enforcement; establishment of clear, accessible, and effective redress mechanisms for individuals and groups, including class actions and judicial review, potentially overseen by a single or coordinated independent regulatory body. | Adequacy and accessibility of redress mechanisms for AI-related harms; contestability of AI decisions; fairness, accountability, and governance in AI systems; protection of user rights against AI-driven harms. | General users and consumers of AI systems; marginalized populations and communities (specifically mentioned in the context of risks from Large Language Models). | AI regulation, Technology Law, Administrative Law, Human Rights Law | UK | NaN | NaN | NaN | False | False | NaN | The lack of mandatory and statutory regulation, leading to a potentially voluntary and arbitrary system; insufficient clarity and strength in the proposed non-statutory principles to ensure fairness, accountability, and redress; inadequate mechanisms for user contestation and appeals for AI-related harms; difficulty in allocating legal responsibility, especially for foundation models. | NaN | Difficulties in obtaining redress for AI-related harms; potential for AI abuse and adverse impacts on user rights and interests due to weak regulation; specific risks from LLMs such as 'hallucinations', propagation of bias against marginalized communities, and negative impacts on the workforce, economy, and fundamental rights like free elections and non-discrimination. |
| EeMzvDhc2e8J.pdf | Google_Scholar | Artificial Intelligence in Accounting, Medicine, and Law with Potential Implications for Financial Planning: A Review of Literature | This paper reviews the impact of generative Artificial Intelligence (AI) on the professions of accounting, medicine, and law, drawing parallels and discussing potential implications for financial planning. It highlights AI's capacity to automate tasks and improve efficiency while emphasizing the ongoing necessity of human skills, judgment, and ethical considerations in these fields. | True | Idealistic | True | 3.0 | Positive | DoNotPay ("World's First Robot Lawyer") | The paper reports that DoNotPay's CEO claims over 2 million successfully resolved cases through AI. The paper does not conduct its own evaluation of DoNotPay. | The paper reports that DoNotPay's CEO claims over 2 million successfully resolved cases. | The financial prohibitiveness of hiring a lawyer for low-income individuals, with 80% reportedly unable to afford legal representation. | AI-powered tools like DoNotPay to bridge the justice gap and expand access to legal counsel for low-economic communities. | Access to legal counsel, resolving common legal disputes (e.g., related to medical bills). | Individuals from low-economic communities, low-income individuals. | Legal conflicts related to medical bills (specifically for DoNotPay). The paper also broadly mentions AI for developing wills, trusts, and other legal documents. | USA | NaN | NaN | Available as a subscription-based online service/app (DoNotPay). | True | False | DoNotPay is described as having active subscribers and a website (donotpay.com). | The need for human lawyer involvement for complex legal issues and situations requiring nuanced legal strategy, decision-making, and ethical considerations, which AI tools like DoNotPay may not fully address for underserved communities. | Potential for AI (like the type DoNotPay might use, e.g., generative AI) to 'hallucinate' or produce fictitious information, lack of nuanced legal reasoning, and the challenge of ensuring authenticity and reliability of AI-generated legal content or advice. | Over-reliance on AI for tasks requiring human judgment, leading to serious repercussions such as the submission of fictitious case law generated by AI (as in Mata v. Avianca), resulting in legal penalties and undermining the legal process. |
| uBHZkwvRvS0J.pdf | Google_Scholar | LawLLM: Intelligent Legal System with Legal Reasoning and Verifiable Retrieval | This paper introduces LawLLM, an intelligent legal system powered by LLMs, designed to offer versatile legal services. LawLLM is enhanced with legal reasoning through syllogism-based fine-tuning and verifiable retrieval capabilities to ensure accurate and reliable outputs based on external knowledge. | True | Idealistic | True | 1.0 | Positive | LawLLM: An LLM (Baichuan-13B-Base) fine-tuned using a custom supervised dataset (Law-SFT) incorporating legal syllogism prompting for enhanced legal reasoning and a triplet instruction format for verifiable knowledge retrieval. | Evaluated using the custom Law-Eval benchmark (objective and subjective assessments, including Chinese legal examinations and GPT-3.5 as a subjective referee) and the LawBench benchmark (20 legal tasks). | On the Law-Eval objective evaluation, LawLLM (13B) achieved an average total score of 37.11, outperforming other LLMs including GPT-3.5-turbo (34.10). On LawBench, LawLLM outperformed GPT-3.5-turbo on average performance (zero-shot). | Limited accessibility to reliable and versatile intelligent legal systems for the general public due to the task-specific focus of prior work. Unreliability of LLMs stemming from issues like hallucinations, difficulty with long-tail knowledge, and inconsistent or unverified use of retrieved information. | Development of multi-task LLMs like LawLLM with features such as: 1) Versatile services through multi-task capabilities, 2) Enhanced legal reasoning fine-tuned with legal syllogism prompting, and 3) Verifiable retrieval to distinguish, incorporate, and validate external knowledge, thereby improving reliability and accessibility. | Legal consultation, legal question answering, improving understanding and accessibility of legal information and services for the general public. | General population, students (as part of a broader set of users that also includes legal professionals). | General Chinese Law (covering areas tested in National Judicial Examination, Patent Agent Examination, CPA examination, etc., e.g., civil law, bidding law). | China | Law-SFT dataset, a high-quality supervised fine-tuning dataset, constructed from: 1) Public NLP legal task datasets (e.g., LEVEN, JEC-QA, CAIL2018), 2) Crawled legal raw text (e.g., judicial advisory websites, Chinese laws and regulations, typical cases, judicial verdicts), 3) Open-source instruction datasets (e.g., Lawyer-LLaMa, LawGPT-zh). Data is primarily unstructured text processed into instruction pairs and triplets. | Supervised fine-tuning (SFT) of a pre-trained LLM (Baichuan-13B-Base). Creation of the Law-SFT dataset involved: Pair Instruction Generation (rule-based cleaning, LLM-assisted Behavior Shaping with legal syllogism prompting, Thinking Development with Law-specific Chain of Thought) and Triplet Instruction Generation (for verifiable retrieval, including addition of distractors). A two-step fine-tuning process: legal reasoning fine-tuning and retrieval augmentation fine-tuning. | NaN | True | True | Detailed resources (model, code, and/or data) are available on GitHub: https://github.com/FudanDISC/DISC-LawLLM. | NaN | Developing advanced legal reasoning capabilities in LLMs that align with established legal frameworks. Ensuring robust, faithful, and verifiable utilization of external legal knowledge, including the ability to distinguish relevant information from distractors and mitigate model hallucinations. | Model hallucinations and generation of unreliable outputs in legal scenarios, particularly if external knowledge is not correctly distinguished, incorporated, and verified. |
| TCfDBG8LDZYJ.pdf | Google_Scholar | InspirePat: An approach for patent recommendation based on Siamese ERNIE model and Large Language Model | This paper introduces InspirePat, a novel framework for patent recommendation that utilizes Large Language Models (LLMs) for technical problem extraction and expansion, and a Siamese ERNIE (SERNIE) model for information retrieval. The system aims to help engineers find innovative solutions from patents by improving upon existing keyword matching and BERT-based methods. | True | Market | True | 1.0 | NaN | InspirePat framework, which includes LLMs (GPT-3.5 Turbo) for technical problem extraction and generation, a technical problem database constructed using BART summarization, a Siamese ERNIE (SERNIE) model for patent retrieval, and a HyDE-inspired filtering mechanism. | The SERNIE model component was evaluated on the SICK dataset (Pearson and Spearman correlations) and a labeled patent sentence pair dataset (F1 score, Precision, Recall, Accuracy). The overall InspirePat framework was demonstrated through case studies. | On a labeled patent sentence pair dataset, the SERNIE model (NO. =7 configuration: epoch=4, learning rate=2e-5, batch size=8) achieved an F1 score of 0.893. This outperformed BERT (F1=0.8412) by 5.2% and ERNIE (F1=0.8703) by 3.2%. | NaN | NaN | NaN | NaN | Patent law | United States | SERNIE model: Sentences Involving Compositional Knowledge (SICK) dataset, a public benchmark dataset. Technical problem database: Constructed from full-text XML data of US patents (2010-2020) from USPTO, specifically using a 'technical problems' dataset of over 89,000 entries, with descriptions summarized using a BART model. | Framework development involving: 1) LLM (GPT-3.5 Turbo) for technical problem extraction from patent IDs and generation of relevant problems using prompt engineering. 2) Construction of a technical problem database from USPTO patent data, summarized using a BART model. 3) Training a Siamese ERNIE (SERNIE) model for similarity assessment. 4) A HyDE-inspired filtering mechanism using LLM-generated hypothetical answers. 5) Text segmentation for patent Q&A. | A prototype system was built to demonstrate the InspirePat framework. | False | False | NaN | NaN | Key challenges included: 1) Overcoming LLM hallucination and leveraging their generative capabilities effectively. 2) Processing full patent text despite model input length limitations. 3) Constructing a high-quality, concise technical problem database from lengthy patent texts without losing critical information. 4) The scarcity of large-scale, labeled patent-specific datasets for training retrieval models. 5) Improving retrieval accuracy beyond simple cosine similarity in high-dimensional spaces. | LLM hallucination leading to misunderstandings and inaccuracies in responses. |
| feE2u5tQrLYJ.pdf | Google_Scholar | DIGITAL TRANSFORMATION OF LEGAL SERVICES AND ACCESS TO JUSTICE: CHALLENGES AND POSSIBILITIES | This paper examines the potential and challenges of using digital technologies, particularly AI and Human Language Technologies (HLT), to improve access to justice in the post-pandemic era. It discusses technical, legal, and ethical hurdles, using the Lithuanian language and the Semantika-2 project as a case study to illustrate difficulties, especially for under-resourced languages. | True | Idealistic | False | 3.0 | Neutral | Semantika-2 project tools: automatic speech-to-text transcription, automatic document summarisation, semantic analysis (NER, aspect-based sentiment analysis), automatic spell checking, linguistic analysis tools for Lithuanian. | Qualitative discussion of challenges and outcomes for the Semantika-2 project (e.g., need for hybrid methods, 96% accuracy for morphological tagging) without formal benchmark testing reported. | Achieved 96% accuracy for morphological tagging using a custom adjusted Hunspell-based tagger. Highlighted significant challenges and limitations for other NLP tasks (e.g., NER, context handling) due to data scarcity and linguistic peculiarities of Lithuanian legal text, necessitating hybrid rule-based/neural approaches. | Lack of access to legal services for vulnerable groups; high litigation costs; lack of transparency; inequality of arms due to digital divide and technological illiteracy; complexity of legal language; lack of public legal knowledge. | Digitalisation (e-filing, online hearings); AI/HLT for document automation, legal research, tech-assisted review, legal advice, outcome prediction; development of language-specific tools (e.g., Semantika-2); ethical frameworks; enhancing AI trustworthiness; citizen empowerment. | Access to legal services, procedural justice (cost, transparency, equality of arms), e-justice, legal information access, language barriers in law, legal tech adoption. | Socially vulnerable groups (women, elderly, minorities, disabled, refugees, low-income, linguistically diverse populations); Lithuanian language users (as case study). | General / Multiple Fields (including civil and criminal procedure) | Lithuania (case study), EU, International | Semantika-2 used a created corpus of Lithuanian legal texts: publicly available legal acts (e-tar.lt) and court decisions (LITEKO), plus a proprietary dataset of 1,500 anonymised, synthetically augmented contracts. Primarily unstructured text. | Applied AI, machine learning (neural networks), rule-based methods, and hybrid approaches. Included linguistic analysis (lexical, syntactic, semantic) and adaptation of NLP techniques for the specifics of Lithuanian legal language. | Results stated as free for public use due to public funding. A project website (www.semantika.lt) is mentioned. | True | False | Semantika-2 project results are free for public use, funded by public sources, accessible via www.semantika.lt. | Technical: AI/NLP limitations (especially for non-English/morphologically rich languages), data scarcity, long-document processing, context handling, AI opacity, bias. Societal/Systemic: Digital divide, lack of trust, need for ethical/legal frameworks, cultural resistance, legal language complexity, ensuring fundamental rights, slow tech adoption. | For Semantika-2: Insufficient Lithuanian legal training data; linguistic peculiarities (capitalization, syntax, vocabulary context); long document context handling; need for custom tools (tagger, NER) and hybrid methods; data anonymization hindering linking; lack of suitable pre-trained models; computational costs. | AI misuse (manipulation, control); biased/discriminatory outcomes; opacity hindering accountability; system inaccuracy/unreliability; infringement of fundamental rights (fair trial, privacy); dehumanisation of justice; projecting past biases via predictive justice. |
| x8wiCT_UuY0J.pdf | Google_Scholar | GUIDEPOST CAPTURING VALUE FROM ARTIFICIAL INTELLIGENCE | This essay discusses how organizations can capture value from rapidly evolving AI, particularly generative AI like ChatGPT. It emphasizes the critical role of developing and managing complementary assets like talent and data amidst technological uncertainty, outlining key questions for future management research. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal services (mentioned as highly exposed industry) | International | NaN | NaN | NaN | False | False | NaN | Understanding required complementary assets for different AI types (generative vs. discriminative); Managing rapid AI evolution and updating complementary assets ('dynamic complementary assets'); Navigating human capital challenges and emerging AI skillsets, especially in creative industries. | Rapid pace of AI development leading to investment uncertainty; Identifying, developing, and updating necessary complementary assets; Managing changes in human capital requirements and the nature of work; Adapting organizational strategies and capabilities. | Potential job displacement due to AI substitution; Risk of technology investments becoming quickly outdated; Challenges for education systems (e.g., cheating). |
| -OR_MJVKvsoJ.pdf | Google_Scholar | Attributed Question Answering for Preconditions in the Dutch Law | This paper proposes and evaluates a Retrieval Augmented Generation (RAG) pipeline designed to answer questions about legal preconditions in Dutch law, providing answers attributed with specific law article references. A new Dutch legal QA dataset with attributions was created for evaluation, showing promising results for generating verifiable legal information for laypeople. | True | Idealistic | True | 1.0 | Positive | Retrieval Augmented Generation (RAG) pipeline for attributed legal question answering focusing on preconditions. | Evaluation used a custom dataset of 102 Dutch legal QA pairs with ground-truth attributions. Metrics included adapted versions of ALCE and G-EVAL, measuring fluency (Coherence, Fluency), correctness (ROUGE-L, METEOR, Consistency, Relevance), and citation quality (Precision, Recall, HitRate@k). Various retrievers (BM25, SBERT, E5, DRAGON, SPLADE) and LLM generators (GPT-3.5, GPT-4O, GEITje, Llama-3-dutch, Fietje) were tested. | The best results were achieved using the E5-multilingual-LARGE retriever and the GPT-4O generator, attaining high scores across metrics, including an 83.0% Hitrate@3 for citation quality. GPT models generally outperformed the tested open-source models. | Costs of legal assistance, lack of public awareness about legal rights and options, and the complexity/specificity of national legal frameworks hindering the development of universal digital legal aid. | Developing automated, language-specific legal Question Answering (QA) systems, particularly Attributed QA using RAG, to provide affordable, accessible, and verifiable legal information tied to primary sources. | Accessing legal information, understanding legal preconditions and rights. | Laypeople encountering civil justice problems, particularly those lacking legal knowledge or facing cost barriers. | Civil Law (based on examples and corpus filtering) | The Netherlands | The RAG system retrieves from a knowledge corpus created from publicly available Dutch law texts (XML from wetten.overheid.nl, parsed into chunks). The LLM generators used were pre-trained models, some with specific Dutch fine-tuning. The evaluation dataset consists of 102 manually created QA pairs with expert verification. | Standard RAG architecture implementation, corpus creation from legal texts, manual creation and expert validation of a QA evaluation dataset, experimentation with various off-the-shelf retrieval and generation components, adaptation of existing evaluation frameworks (ALCE, G-EVAL). | NaN | False | True | Code and dataset are publicly released on GitLab. | Need for validation of layperson understandability and inter-expert agreement, expansion of dataset (e.g., jurisdictions), testing more advanced retrievers (e.g., multilingual hybrid), potential retrieval bias towards specific linguistic patterns (conditional phrases). | Need for language-specific solutions, ensuring output format consistency from LLMs (especially open-source), creating high-quality expert-verified legal datasets, potential loss of meaning when chunking long legal articles. | Implicit risks include generating incorrect or hallucinatory legal information (addressed by attribution) and potential retrieval bias leading to incomplete answers. |
| XR0M6OXV57cJ.pdf | Google_Scholar | Weaving Pathways for Justice with GPT LLM-driven automated drafting of interactive legal applications | This paper investigates using LLMs like GPT-3 and GPT-4 turbo to automate the creation of guided interviews that complete court forms, aiming to assist self-represented litigants. It compares generative AI, constrained template-driven, and hybrid approaches, finding a hybrid model with human review, leveraging the Docassemble platform and Assembly Line Weaver tool, to be the most promising. | True | Idealistic | True | 1.0 | Positive | A hybrid approach using LLMs (GPT-3, GPT-4 turbo) for auto-labeling fields in Word documents and generating draft questions/interview flows from PDF forms, integrated with the Docassemble platform and Assembly Line Weaver tool, with human review points. | Qualitative evaluation of Word document auto-labeling (visual inspection of output); quantitative evaluation of PDF-to-interactive app generation on 12 name change forms (measuring field recognition rates, e.g., 62-69% average, 93% best, 27% worst; 28% checkbox pairing success). | For PDF app generation from 12 forms, 62-69% of fields were automatically processed (93% best, 27% worst); checkbox field to text pairing was successful 28% of the time. Word document field labeling showed promising qualitative results with a revised prompting strategy. | High cost and time (hundreds of hours per form) for manual creation of interactive legal applications (guided interviews) for court forms, hindering assistance for self-represented litigants and large-scale automation efforts. | A hybrid model using LLMs for automated drafting of guided interviews with human review, integrated with tools like Assembly Line Weaver and Docassemble, to significantly reduce the cost and time of form automation. | Automating court form completion via guided interviews for self-represented litigants. | Self-represented litigants | General civil litigation forms (complaints, answers, deeds, wills, demand letters); specifically tested on name change forms (family law). | USA (experiments focused on Massachusetts and name change forms from 12 US jurisdictions). | Pre-trained LLMs (GPT-3, GPT-4 turbo) prompted with text extracted from Word and PDF court forms. No fine-tuning described. | Prototyping, iterative prompt engineering, experimental comparison of three approaches (generative AI, constrained template-driven, hybrid), integration with existing open-source tools (Docassemble, Assembly Line Weaver). | NaN | True | True | Python notebooks on GitHub and Google Colab demonstrating experimental auto-labeling of Word documents and LLM-driven generation of interactive apps from PDF forms. | Improving checkbox field identification in PDFs (28% success); handling all field types; further integration of LLM capabilities with existing tools (Assembly Line Weaver); reducing need for extensive human review for complex forms; addressing form elements requiring external legal research. | For Word documents: Balancing automated field identification with document format preservation, managing LLM context window limitations, and enforcing specific variable naming conventions. For PDF documents: Accurately identifying and contextualizing all fields, especially small ones like checkboxes, due to PDF's stream-based format and reliance on OCR. For both: Designing effective input validation without being resource-intensive or error-prone; preventing error propagation from initial inaccuracies in field identification or question generation. | User annoyance or offense from overly rigid or conversational LLM-based validation; incorrect data processing due to LLM misclassification of field types (e.g., ZIP codes, phone numbers); potential for errors in legal documents if AI-generated content, especially for complex forms, is not thoroughly reviewed by humans. |
| 6dCMgNYlD1wJ.pdf | Google_Scholar | IA Generativa e acesso \nà Justiça: sexta onda e os riscos \ndos LLMs no Judiciário | This paper examines the potential use of Generative AI (LLMs) in the Brazilian Judiciary, framing it within the sixth wave of access to justice. It analyzes benefits like increased efficiency but primarily focuses on proposing a typology of risks—operational, interactional, and systemic—to guide its responsible adoption. | False | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Risks associated with using Generative AI in the judiciary, including: Opacity, lack of explainability and validation, potential for copyright infringement, inaccuracies/errors ('hallucinations'), misalignment with human values, difficulty translating legal concepts to code, automation bias leading to dependency and acriticality, loss of human decisional autonomy, threats to privacy/data protection/confidentiality, generation of disinformation/toxic content, cybersecurity threats, amplification of societal biases and discrimination, loss of cultural/legal particularities. | Proposes a risk typology (operational, interactional, systemic) for better management. Advocates for caution, robust mitigation strategies, human-centric governance, adherence to principles (transparency, accountability, impartiality, due process), human oversight and validation, compliance with regulations (e.g., CNJ Resolution 332/2020, LGPD), and potentially creating clearer, machine-readable laws. | Judicial efficiency and celerity, reduction of case backlog, administration of justice, quality of judicial services, technology's role in access to justice (Sixth Wave). | NaN | Judicial Administration, Constitutional Law, Data Protection Law | Brazil | General discussion: LLMs trained on large, unspecified datasets often scraped from the web; specific mention of desired training on Brazilian jurisprudence for legal-specific AI. | NaN | NaN | False | False | NaN | Lack of transparency and explainability in LLMs; difficulty aligning AI with human/legal values; underdeveloped legal frameworks for Generative AI governance; challenges in translating legal concepts into code; potential for bias amplification; insufficient human oversight mechanisms; concentration of AI development in specific private companies and regions leading to potential cultural homogenization. | Ensuring accuracy and avoiding 'hallucinations'; managing copyright issues with training data/outputs; preventing bias; ensuring privacy and confidentiality; maintaining human autonomy and critical thinking; translating complex legal norms into code; developing effective governance and regulation for Generative AI in the judicial context. | Operational risks (opacity, low explainability/validation, copyright issues, inaccuracies/errors, value misalignment, legal translation difficulties); Interactional risks (automation bias/dependency, loss of autonomy, privacy/data protection/confidentiality/honor violations); Systemic risks (disinformation/toxic content, cybersecurity threats/fraud, bias/discrimination amplification, loss of cultural/legal diversity). |
| Os_WQAcXUMAJ.pdf | Google_Scholar | Using Large Language Models to Support Thematic Analysis in Empirical Legal Studies | This paper proposes a framework using GPT-4 to assist legal experts with thematic analysis, focusing on generating initial codes and identifying themes from legal texts like criminal court opinions. The study demonstrates that the LLM can produce reasonable codes, improve with expert feedback, and effectively classify data and discover themes, supporting empirical legal studies. | True | NaN | True | 1.0 | NaN | A framework leveraging GPT-4 for supporting legal experts in thematic analysis of legal texts, specifically for generating initial codes (phase 2), searching for themes (phase 3), and initial data classification by themes (kick-starting phase 4). | The framework was evaluated using a dataset of 785 facts descriptions from Czech criminal court opinions on thefts. Evaluation included: 1) Manual assessment of LLM-generated initial codes for quality (addressing 'how' and 'what'). 2) Re-assessment after expert feedback. 3) Zero-shot classification performance (R@1, R@3) predicting human-expert themes. 4) Comparison of LLM-discovered themes against expert-identified themes. | After expert feedback, 88.8% of LLM-generated initial codes for facts descriptions were deemed reasonable (addressing how the theft happened and what was stolen). For zero-shot prediction of expert-defined themes, the system achieved an overall Recall@1 of .66 and Recall@3 of .82. | NaN | NaN | NaN | NaN | Criminal Law (specifically theft offenses), Empirical Legal Studies | Czechia | The technique uses OpenAI's GPT-4 model, which is pre-trained on a large, general corpus of text data (proprietary to OpenAI, not detailed in the paper). The framework operates in a zero-shot manner on the evaluation dataset (785 facts descriptions from Czech criminal court opinions) without task-specific fine-tuning. | The framework design involved: 1) Basing the process on the established thematic analysis methodology (Braun & Clarke). 2) An iterative approach for code and theme generation, incorporating an expert feedback loop. 3) Batch processing of input texts to manage LLM context limits. 4) Prompt engineering, including general instructions on thematic analysis and specific research questions/parameters for the given task. | NaN | True | False | The paper describes the framework methodology which can be implemented using OpenAI's GPT-4 API and standard Python libraries; no specific tool or code is directly provided for download. | NaN | Challenges included: initial LLM outputs not fully aligning with analytical needs (e.g., focus of codes), variability in zero-shot theme prediction accuracy, differences in granularity between LLM-discovered and expert-identified themes, ensuring adherence to negative instructions, the black-box nature of proprietary LLMs, and managing LLM context window limitations. | Potential risks include methodological implications of using LLMs for qualitative analysis, over-reliance on autonomous outputs without sufficient expert intervention, and lack of transparency and researcher agency due to the black-box nature of proprietary LLMs. |
| QlA7C_8Et-MJ.pdf | Google_Scholar | Exploring the Capabilities of Chatgpt as a Travel Advisor: A Study on the Use of Generatıve AI in Tourism Marketing | This paper evaluates the capabilities of ChatGPT Model 4 as a travel advisor by analyzing its holiday recommendations for different budgets. The study found that ChatGPT can provide personalized, budget-conscious travel suggestions, typically structured around accommodation, dining, and attractions, but lacks real-time information and transportation advice. | False | NaN | True | 2.0 | NaN | ChatGPT Model 4 | ChatGPT Model 4 was prompted to provide holiday destination recommendations for daily budgets ranging from $50 to $1000, without geographical restrictions. The first five suggestions for 6 different budget levels (30 responses in total) were analyzed using content analysis with MAXQDA, including word frequency, word cloud, and interactive word tree. | ChatGPT demonstrated an ability to offer personalized travel recommendations tailored to different budgets. Recommendations were generally made under the categories of accommodation, dining, and other attractions, with region-specific elements emphasized, and responses followed similar structural patterns. | NaN | NaN | NaN | NaN | NaN | International | The paper states that language models like ChatGPT are trained on an unlabeled dataset consisting of texts from various sources, primarily Wikipedia and various other websites. | NaN | NaN | True | False | ChatGPT Model 4 is available via OpenAI, typically through a subscription model. | NaN | Lack of real-time data access; reliance on pre-loaded information (making its performance on dynamic data like prices or current events questionable); inconsistency in responses to identical prompts; potential biases in output; and the need for human verification of generated content. | Privacy and security concerns, potential for biases in output, risk of misuse, and dissemination of misinformation. |
| ckBaHf32WokJ.pdf | Google_Scholar | UNDERSTANDING THE DUTY OF COMPETENCE FOR ATTORNEYS USING GENERATIVE AI | This paper argues that ethical duties, particularly the duty of competence, are the main regulatory mechanism for attorneys using generative AI (GAI). It posits that competence requires attorneys to make informed decisions about GAI tools and their application, coupled with diligent verification of outputs and retention of human judgment to avoid automation complacency. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal Ethics, Professional Responsibility, General Legal Practice | United States | NaN | NaN | NaN | False | False | NaN | NaN | Challenges for attorneys using GAI include understanding tool-specific limitations, managing output issues (hallucinations, inaccuracies, incompleteness, misgrounded information), the lack of explainability ('black box' problem), and overcoming tendencies towards automation bias and complacency. | Stated risks include attorneys making errors due to GAI hallucinations, inaccuracies, or biases; erosion of attorney skills and judgment through automation complacency and bias; violation of ethical duties (competence, confidentiality, candor); and the potential for GAI's 'black box' nature to obscure flawed reasoning or accountability. |
| XKSKlgFLZNUJ.pdf | Google_Scholar | Comments on “Guide on the use of Generative AI” | This paper provides feedback on the Canadian Treasury Board Secretariat's preliminary guidance on using generative AI (GAI) in federal institutions. It recommends strengthening the guide through new legislation, clearer ethical sourcing rules (copyright, worker protection), and enhanced environmental impact mitigation guidance. | True | NaN | True | 2.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | AI Policy/Regulation, Administrative Law, Copyright Law, Labour Law, Environmental Law, Procurement Law | Canada | NaN | NaN | NaN | False | False | NaN | NaN | Challenges identified regarding public sector GAI use and governance include: difficulty verifying legality and copyright status of GAI data/outputs due to opacity; assessing and mitigating biases in foundation models; ensuring ethical sourcing (especially worker protection in global supply chains); measuring and mitigating environmental impacts; lack of enforceable regulations and independent oversight. | Legal risks of copyright infringement; Perpetuation of biases against minorities due to foundation models; Harm (physical, psychological, socio-economic) to data workers in the GAI supply chain; Significant environmental impacts (water/resource consumption, emissions); Lack of accountability and public trust due to weak governance frameworks. |
| PIIS2405844024030573.pdf | Google_Scholar | Evaluating human resources management literacy: A performance analysis of ChatGPT and bard | This study compares the performance of ChatGPT and Bard on 134 Human Resources (HR) management certification questions, assessing accuracy, relevance, and clarity. ChatGPT slightly outperformed Bard in overall accuracy, suggesting potential suitability for transactional HR roles, while Bard showed more caution, possibly indicating different design safeguards. | True | Market | True | 2.0 | NaN | Comparative evaluation of ChatGPT (GPT-4) and Bard | Performance analysis using a dataset of 134 multiple-choice questions (MCQs) from the Society for Human Resource Management (SHRM) Certified Professional (SHRM-CP) certification. Responses evaluated for accuracy, relevance, and clarity by experts using a 5-point Likert scale, supplemented by statistical tests (t-tests), cosine similarity, readability scores (Flesch), and testing the impact of confirmation queries. | ChatGPT achieved slightly higher overall accuracy (84.3%) than Bard (82.8%). ChatGPT scored significantly higher on clarity. Confirmation queries did not improve accuracy for either model. Bard sometimes refused to answer or gave more cautious responses. | NaN | NaN | NaN | NaN | Human Resources Management | United States (based on SHRM certification) | NaN | NaN | NaN | True | False | The tools evaluated (ChatGPT via web interface, Bard via web interface) are publicly accessible online, though specific versions like GPT-4 may require payment. | NaN | Inconsistent accuracy, variable relevance and clarity between models; limitations in improving accuracy via confirmation queries; potential for AI hallucinations; differing approaches to sensitive/strategic questions; readability variations; lack of contextual understanding; influence of training data biases; need for human oversight. | Generating deceptive responses; reliance on outdated training data; lack of transparency; propagation of errors; potential misuse; ethical implications (data usage, privacy, consent, bias in HRM); legal problems; inherent biases from training data leading to discriminatory outcomes; erosion of diverse thinking; AI hallucinations; impact on employee wellbeing (job security anxiety); reduction of ethical standards in decision-making. |
| s42979-024-03533-6.pdf | Google_Scholar | Large Language Model Evaluation Criteria Framework in Healthcare: Fuzzy MCDM Approach | This paper proposes a framework using Fuzzy Analytical Hierarchy Process (FAHP) to help healthcare providers evaluate and select Large Language Models (LLMs). Based on expert interviews, it identifies and ranks nine key evaluation criteria and associated sub-criteria for LLMs in the healthcare domain. | True | Market | True | 1.0 | NaN | An LLM evaluation criteria framework for healthcare using Fuzzy Analytical Hierarchy Process (FAHP) based on expert judgment. | The framework was developed by interviewing 38 AI and healthcare experts using the Delphi technique to identify criteria, sub-criteria, and metrics. FAHP was used to calculate the weights and rank the criteria based on aggregated expert judgments. | Identified 9 main criteria and 12 sub-criteria with metrics. The main criteria ranking (highest to lowest weight) is: Reliability (0.200), Robustness (0.171), Bias and Fairness (0.126), Availability (0.121), Performance (0.092), Usability (0.090), Resilience (0.089), Predictability (0.063), Cost (0.047). | The primary challenge identified is the difficulty for healthcare providers in selecting suitable LLMs from diverse options due to a lack of standardized evaluation methods and technical expertise. | Proposes a structured evaluation framework with weighted criteria (derived via FAHP and expert input) to guide healthcare providers in selecting appropriate LLMs. | NaN | Healthcare providers | NaN | International | Expert judgments collected from 38 AI and healthcare specialists via interviews and questionnaires using a 9-point fuzzy scale, processed using FAHP. Not a traditional ML training dataset. | Literature review, Expert interviews (Delphi technique), Qualitative content analysis, Fuzzy Set Theory, Analytical Hierarchy Process (AHP), Fuzzy AHP (FAHP). | NaN | False | False | NaN | Existing LLM evaluation methods are fragmented, neglect user preferences, lack interpretability/customization, and don't capture the full picture. The proposed framework needs validation for broader model types and could be extended to non-quantifiable criteria (trust, security, privacy) and other sectors beyond healthcare. | Identifying comprehensive and relevant evaluation criteria for LLMs in healthcare; defining measurable metrics for qualitative aspects; managing subjectivity and uncertainty in expert judgments during the weighting process. | Risks of poor LLM selection in healthcare (inaccurate diagnoses, inappropriate treatment suggestions, safety risks, compromised decision-making, legal consequences). Inherent LLM risks including bias leading to unfair outcomes, lack of reliability (incomplete/inaccurate information), security vulnerabilities, data privacy violations, and model drift. |
| bBYvbhf2w6QJ.pdf | Google_Scholar | Future Prospects for the Application of Artificial Intelligence in Judicial Management | This paper explores the current state and future potential of artificial intelligence (AI) in judicial management across various jurisdictions, reviewing existing applications and challenges. It highlights significant hurdles such as the lack of legal frameworks, ethical concerns, and low trust, proposing a conceptual model for integrating AI governance within judicial systems. | True | Market | True | 3.0 | Neutral | Conceptual model for AI governance in judicial management | NaN | NaN | Lack of legal foundation, legitimacy, and regulation for AI in judicial settings; Lack of trust in AI decision-making; Ethical challenges (bias, privacy, transparency, responsibility); Algorithmic bias and the "black box" problem; Security risks; Need for human oversight; Potential dehumanization of justice. | Develop comprehensive legal and regulatory frameworks for AI in justice; Implement a conceptual model integrating legal basis (constitution), human factors (training, ethics), AI system design, and secure ICT infrastructure; Ensure human oversight and 'human-in-the-loop' approaches; Establish and adhere to ethical guidelines (fairness, non-discrimination, transparency); Foster collaboration and stakeholder engagement. | Access to legal information/advice; Judicial process efficiency; Judicial decision support; Automation of legal tasks. | NaN | General judicial administration, Criminal law, Constitutional law principles | International (with examples from Ecuador, USA, UK, Canada, Germany, Portugal, Spain, Saudi Arabia, India, China, Italy, Malaysia, Pakistan, Philippines, Bangladesh, Ukraine, EU) | NaN | Literature review, conceptual synthesis based on deductive method and exploratory research. | NaN | False | False | NaN | Lack of comprehensive legal frameworks and regulations globally; Need for improved trust, transparency, and explainability in judicial AI; Research and development in judicial AI lagging behind other fields; Need for effective human-AI collaboration models; Methods for addressing algorithmic bias and ensuring fairness; Standardized security measures for sensitive legal data; Ensuring AI upholds fundamental rights and democratic values. | AI misinterpretation of judicial decisions; Inability of AI to make nuanced value judgments; Algorithmic bias and data selectivity; Explainability issues ('black box' problem); Achieving public and professional acceptance of AI rulings; Managing security risks of implemented technologies; Ensuring ethical and responsible use; Integrating human expertise effectively ('human-in-the-loop'). | Algorithmic bias leading to discrimination; Lack of transparency hindering accountability; Security vulnerabilities exposing sensitive data; Erosion of fundamental human rights; Dehumanization of the judicial process; Potential for errors in AI analysis impacting case outcomes; Misuse of AI for illegal activities (e.g., deepfakes); Diminished public trust in the justice system; Loss of judicial autonomy. |
| DkyUIApE_CAJ.pdf | Google_Scholar | How ChatGPT and generative AI systems will revolutionize legal services and \nthe legal profession. | This paper presents predictions elicited from ChatGPT regarding the transformative impact of generative AI on legal services and the profession. It details ChatGPT's own views on areas of application, efficiency gains, benefits for access to justice, timelines, and the future for legal practitioners and students. | True | Idealistic | True | 2.0 | Positive | ChatGPT, a generative AI language model by OpenAI. | Eliciting detailed responses from a February 2023 version of ChatGPT (Free Research Preview) to a series of specific questions about its impact on the legal field. | ChatGPT predicts a seismic shock to the legal sector within 5-10 years, with reduced human-centric work, increased client self-help, and fundamental changes in pricing and manpower. | High cost of legal services, limited availability/accessibility of legal professionals, complexity of legal language, and slow legal processes. | AI-powered tools offering 24/7 access to cost-effective, simplified legal information, document preparation, research, and basic advice for ordinary people. | Access to legal advice, legal research, document preparation (contracts, wills), contract review, court filings, mediation and dispute resolution, and legal education for the public. | Ordinary people / General public | Multiple fields, including contract law, intellectual property, e-discovery, compliance, litigation support, and alternative dispute resolution. | International | Proprietary, large-scale, general textual data from diverse sources up to 2021, used by OpenAI to train ChatGPT. | NaN | Web-based access provided by OpenAI, initially as a free research preview. | True | False | ChatGPT was accessible via a free research preview on OpenAI's website. | AI's limitations in highly complex/novel legal reasoning; lack of established ethical and regulatory frameworks for legal AI; need for upskilling legal professionals. | Slow/varied adoption by legal professionals, evolving regulatory landscape, need for user training and education, integration with existing legal technology infrastructure, and addressing ethical considerations. | Significant job displacement for legal professionals (lawyers, support staff, potentially academics and judges), downward pressure on legal fees, and unaddressed ethical implications of AI in legal practice. |
| 3589335.3651557.pdf | Google_Scholar | Semantic interlinking of Immigration Data using LLMs for Knowledge Graph Construction | This paper proposes a framework using Large Language Models (LLMs) and Knowledge Graphs (KGs) to structure and analyze complex immigration data, specifically focusing on the US Adjustment of Status process. The framework aims to transform paper-based records into an interconnected knowledge network to improve data handling efficiency and decision-making for legal professionals. | True | Market | True | 1.0 | NaN | A framework combining LLMs (GPT-3.5 tested) and Knowledge Graphs (Neo4j) to extract entities and relationships from immigration forms (US Form I-485), generate structured summaries, create Cypher queries, and build a KG representing the immigration process. | Qualitative assessment based on constructing a KG using synthetic data derived from US Form I-485 populated by experts. Compared GPT-3.5 and GPT-4 performance for entity/relationship extraction (negligible difference found). Preliminary tests with local Mistral 8*7b mentioned. | Successfully constructed a knowledge graph from synthetic immigration data using GPT-3.5, demonstrating effective information aggregation and relationship mapping with minimal manual intervention. GPT-3.5 performance was comparable to GPT-4 but more cost-effective. | NaN | NaN | NaN | NaN | Immigration Law | United States | Synthetic data generated by experts based on US Form I-485 (Adjustment of Status) and its instructions. The LLMs used (GPT-3.5) are pre-trained on general large datasets. | PDF data extraction (PyMuPDF), key-value mapping strategy, transformation into summaries, prompt engineering, LLM-based JSON generation (entities, relationships), LLM-based Cypher query generation, manual query refinement, population into Neo4j graph database. | NaN | False | False | NaN | Technical challenges in PDF extraction (especially multiple-choice questions). LLM context window and output token limitations requiring workarounds and manual refinement. Need for expanded testing and inclusion of regulatory/legal texts. | Limitations of PDF form extraction tools (line-by-line processing, handling multi-column layouts and multiple-choice questions). LLM context window/output token limits requiring section-wise processing and subsequent manual merging/refinement of generated queries to ensure consistency. | General AI risks mentioned: data privacy issues with evolving technology, potential for algorithmic bias leading to unfair outcomes (though not evaluated for the proposed technique). |
| -bjvGA-RheQJ.pdf | Google_Scholar | Generative Adversarial Training with Perturbed Token Detection for Model Robustness | This paper proposes GenerAT, a novel generative adversarial training framework to enhance the robustness of language models against text-based adversarial attacks. The framework integrates gradient-based adversarial token generation and perturbed token detection, significantly outperforming existing methods and ChatGPT on the AdvGLUE benchmark. | True | NaN | False | 1.0 | NaN | Generative Adversarial Training (GenerAT) framework combining a generative adversarial attack (gradient-based discrete token generation using shared embeddings between classifier and generator) and an adversarial training process (integrating adversarial regularization via KL-divergence and perturbed token detection). | Evaluated on the five datasets (adversarial versions of SST-2, QQP, MNLI-m, QNLI, RTE) from the AdvGLUE benchmark. | GenerAT achieved state-of-the-art results on AdvGLUE, reaching an average accuracy of 80.1%, surpassing ChatGPT by 10%. | NaN | NaN | NaN | NaN | NaN | NaN | The technique fine-tunes a pre-trained discriminative language model (DeBERTa-v3-large). Training and evaluation uses the datasets from the AdvGLUE benchmark (adversarially modified versions of SST-2, QQP, MNLI, QNLI, RTE derived from GLUE). | The framework integrates several components: using a discriminative PLM (DeBERTa-v3) as the base, sharing embeddings between the classifier (discriminator) and a generator model, propagating adversarial gradients calculated on the classifier to guide the generator's perturbed token generation, and optimizing a combined loss function including task loss, perturbed token detection loss, and symmetric KL-divergence for adversarial regularization. | The authors state that the code is provided via a GitHub link. | True | True | Code is available on GitHub: https://github.com/Opdoop/GenerAT | NaN | Bridging the gap between continuous embedding perturbations used in traditional adversarial training and discrete token perturbations seen in real attacks. Reducing the high computational cost associated with adversarial augmentation methods. Balancing the contributions of task loss, perturbed token detection loss, and adversarial regularization loss during training. | The primary risk addressed is the vulnerability of language models to adversarial attacks. The ethics statement briefly notes the need to understand impacts of robustness enhancements on model bias and fairness. |
| uNIX4MJ1rq8J.pdf | Google_Scholar | ChatGPT and digital capitalism: need for an antidote of Competition Law | This paper analyzes generative AI like ChatGPT, highlighting risks to market competition, consumer welfare (privacy, misinformation), and academic integrity. It advocates for proactive competition law measures to address these threats. | True | Market | True | 2.0 | NaN | ChatGPT (Conversational Generative Pre-Training Transformer) | NaN | NaN | NaN | NaN | NaN | NaN | Competition Law, Privacy Law, Data Protection Law | International | NaN | NaN | Launched by OpenAI Inc., achieving rapid mass user adoption (over 100 million users in 2 months), and offering a premium subscription service (ChatGPT Plus). | True | False | Publicly accessible online service provided by OpenAI Inc., with a premium version (ChatGPT Plus) available for a monthly fee. | NaN | NaN | Distortion of market competition; harm to consumer welfare (e.g., reduced quality of information, privacy violations, misinformation); undermining academic integrity and intellectual growth; potential for anti-competitive practices by AI providers (e.g., abuse of dominance, tying arrangements, data misuse, algorithmic collusion). |
| 3628602.pdf | Google_Scholar | Measuring and Mitigating Gender Bias in Legal Contextualized Language Models | This paper proposes methods to measure and mitigate gender bias in legal contextualized language models like LegalBERT, introducing a new crime-based evaluation corpus (BEC-Cri) and a fine-tuning debiasing technique (LCD) using ECtHR data. Evaluations on the LexGLUE benchmark show the proposed LCD method effectively reduces bias with minimal impact on downstream task performance. | True | Idealistic | True | 1.0 | Positive | Proposes two techniques: 1) BEC-Cri: A template-based gender bias measurement method using MLM probabilities on a new corpus derived from FBI crime data. 2) Legal-Context-Debias (LCD): A fine-tuning debiasing method using a gender-balanced European Court of Human Rights (ECtHR) corpus for a gender classification task. | Bias was measured by comparing association scores (derived from MLM probabilities) for male/female targets using the proposed BEC-Cri and existing BEC-Pro datasets, before and after debiasing. Downstream performance was evaluated using µ-F1/m-F1 scores on six classification tasks from the LexGLUE benchmark. | The proposed LCD debiasing method significantly reduced measured gender bias scores towards zero on both BEC-Cri and BEC-Pro, outperforming baseline methods (GPD, GAP). LexGLUE benchmark performance showed only slight decreases after LCD debiasing, preserving overall semantic utility. A proposed bias-penalized performance metric showed LCD incurred the lowest penalty. | Inherent gender bias exists in legal language models, stemming from training data, which can lead to unfair outcomes in legal AI applications. Mitigating this bias without significantly degrading model performance on useful tasks is challenging. | Develop and apply domain-specific methods for bias measurement (BEC-Cri) and mitigation (LCD fine-tuning with balanced legal data). Evaluate models using a bias penalty framework alongside standard performance metrics. | Fairness in AI, Gender bias mitigation, Legal NLP | NaN | General legal NLP / Multiple (Human Rights Law, US Constitutional Law, EU Law, Contract Law, Criminal Law references) | Multiple (European Court of Human Rights, US, EU) | Debiasing (LCD) used a modified, gender-balanced subset (3,032 cases) of the publicly available European Court of Human Rights (ECtHR) corpus [30]. Bias measurement (BEC-Cri) used author-created templates populated with crime words from the public FBI database [79]. The base model (LegalBERT-Small) was pre-trained on various legal corpora. | Template-based bias measurement using MLM probabilities, Supervised fine-tuning for debiasing using a curated dataset and classification task, Comparative analysis against baseline methods, Evaluation on standard NLP benchmark (LexGLUE), Proposal of a bias-penalized evaluation framework. | NaN | True | True | Code and data stated to be available on GitHub (https://github.com/koc-lab/ContextLegalBias). | Focus limited to gender bias (other biases like race remain unaddressed for these models). Potential for catastrophic forgetting during fine-tuning requires careful monitoring. Need for potentially more sophisticated debiasing tasks or hyperparameter tuning. | Balancing bias mitigation with preservation of model performance on downstream legal tasks. Creating effective domain-specific datasets and methods for bias analysis and reduction in law. Computational costs associated with transformer models. | NLP models perpetuating or amplifying gender bias in legal applications, leading to unfair outcomes. Debiasing techniques potentially harming the model's general language understanding capabilities (catastrophic forgetting). |
| P_QMzDF2YcAJ.pdf | Google_Scholar | Gener ative AI and Legal Aid: Results fr om a Field Study and 100 Use Cases t o Bridge the Access t o Justice Gap | This paper reports on a field study where legal aid professionals used generative AI tools (ChatGPT-4, Gavel, CoCounsel), finding increased productivity and intent for continued use. It also releases 100 use cases and offers recommendations to bridge the access to justice gap, emphasizing equitable AI adoption and lawyer-AI collaboration. | True | Idealistic | True | 2.0 | Positive | A field study providing 91 legal aid professionals with free access to paid generative AI tools (ChatGPT-4, Gavel, CoCounsel) for up to two months. A randomized controlled trial component tested 'concierge' support (peer use cases, office hours, assistance) for a subset of participants. A companion database of 100 use cases was compiled from participant submissions. | Baseline and exit surveys administered to pilot participants (N=91, with 66 completing exit survey). Outcomes measured included self-reported productivity, satisfaction with AI, quality of output, frequency of use, changes in attitudes, and intentions to continue using AI tools. Comparison between control group and 'concierge' support group on these metrics. | 90% of participants reported increased productivity (25% medium/high increase); 75% intended to continue using generative AI. 'Concierge' services significantly improved outcomes (productivity, satisfaction, quality of output, frequency of use, attitudes, future paid use). Despite women being less likely to use AI tools pre-pilot, post-pilot outcomes were statistically indistinguishable by gender for most metrics. | The access to justice gap (92% of low-income Americans' civil legal needs unmet) due to knowledge and service gaps. Financial constraints for legal aid organizations to adopt AI. Regulatory hurdles like Unauthorized Practice of Law (UPL) rules stifling innovation. Risk of AI exacerbating inequities. | Augment legal aid lawyers with AI. Provide funding and supportive services (e.g., 'concierge' support, help desks) for AI adoption. Foster 'Tech + Legal Aid Lawyer' collaborations. Explore regulatory sandboxes and voluntary certification for legal aid AI tools. Develop lawyer-directed and consumer-facing AI solutions. | Increasing productivity of legal aid professionals, document summarization/analysis, legal research (preliminary/confirmatory), legal and non-legal writing/drafting, translation (plain language/other languages), client intake automation, grant writing, case management support. | Low-income Americans with unmet legal needs, clients of legal aid organizations. | Eviction defense/housing, expungement (criminal records), immigration, family law, employment/workers' rights, civil rights, consumer/economic justice, disability rights, domestic violence, elder law, health, income maintenance, veterans' rights. | United States | The study utilized existing pre-trained models: ChatGPT-4 (trained by OpenAI on diverse large-scale text/code) and CoCounsel (GPT-4 augmented with Casetext’s proprietary legal databases). Gavel.io uses rules-based AI and automation technologies. | For the field study: Randomized Controlled Trial (RCT) for the 'concierge services' component. Survey methodology (baseline and exit surveys) for quantitative and qualitative data collection. Use case compilation and analysis. | The paper releases a companion database of 100 use cases via a public URL (https://bit.ly/AIA2J). The AI tools studied (ChatGPT-4, Gavel, CoCounsel) are commercially available products. | True | False | The AI tools studied (ChatGPT-4, Gavel, CoCounsel) are commercially available through subscriptions. The companion database of 100 use cases is openly accessible at https://bit.ly/AIA2J. | Gender gap in organic AI tool uptake by legal professionals. Insufficient funding for AI in legal aid. Need for ongoing training, support structures, and quality control/certification for legal aid bots. Regulatory frameworks (UPL) hindering direct-to-consumer AI solutions. Technical limitations of AI (hallucinations, bias). | Managing AI risks (data privacy, confidentiality, hallucinations). Overcoming learning curves for AI tools. Ensuring equitable access and adoption, particularly addressing the gender gap. Securing funding for paid AI tools in resource-constrained legal aid settings. | AI hallucinations (e.g., fake case citations), data privacy and confidentiality breaches, inaccurate results, algorithmic bias (racial, gender, anti-consumer), consumer harm from unauthorized practice of law by AI, creation of a two-tiered justice system, dehumanizing the law. |
| M0yH6UAgsI0J.pdf | Google_Scholar | GENERALIZING TRUST : WEAK-TO-STRONG TRUSTWORTHINESS IN LANGUAGE MODELS | This paper investigates whether trustworthiness properties like fairness, robustness, and privacy can transfer from a smaller (weak) language model to a larger (strong) one when the strong model is trained on the weak model's outputs (weak-to-strong generalization). The authors propose and evaluate two fine-tuning strategies (Weak TFT and Weak+WTS TFT) incorporating trustworthiness regularization, finding that fairness and robustness can generalize and even improve, while privacy does not. | True | NaN | True | 1.0 | NaN | Proposes two fine-tuning strategies for weak-to-strong generalization focusing on trustworthiness: Weak Trustworthiness Finetuning (Weak TFT) and Weak and Weak-to-Strong Trustworthiness Finetuning (Weak+WTS TFT), which incorporate regularization for fairness, robustness, or privacy. | Evaluated using Pythia models (14M, 70M, 410M, 1B, 6.9B) on four real-world datasets: Adult (fairness), OOD Style Transfer (OOD robustness), AdvGLUE++ (adversarial robustness), and Enron Emails (privacy). Compared No TFT, Weak TFT, and Weak+WTS TFT strategies against each other and a 'strong ceiling' baseline, using metrics like DPD (fairness), Robust Accuracy (robustness), and Extraction Rate (privacy). Included sensitivity analysis on model size and regularization strength. | Weak+WTS TFT consistently showed weak-to-strong trustworthiness generalization for fairness, OOD robustness, and adversarial robustness, often enhancing the property compared to the weak model. Privacy did not exhibit weak-to-strong generalization in any tested setting. Tradeoffs between trustworthiness and task performance were minimal (<= 1.5% accuracy decrease for fairness/adversarial robustness). | NaN | NaN | NaN | NaN | NaN | International | Uses publicly available datasets: Adult (reconstructed US Census data), OOD Style Transfer (modified SST-2), AdvGLUE++ (multi-task NLP benchmark with adversarial examples), Enron Emails. Data is primarily unstructured text, except Adult (structured). | Proposed novel training strategies (Weak TFT, Weak+WTS TFT) adapting the weak-to-strong generalization framework with specific trustworthiness regularizers (based on Demographic Parity, adversarial training, embedding perturbations, DP-SGD). Employed empirical evaluation with multiple model sizes, datasets, and metrics, including comparative analysis and sensitivity analysis. | NaN | False | False | NaN | The paper identifies the difficulty in achieving weak-to-strong generalization for privacy as a remaining gap or limitation, contrasting with the successful generalization observed for fairness and robustness. The reasons why privacy behaves differently require further investigation. | Achieving successful weak-to-strong transfer of trustworthiness properties, particularly privacy. Tuning hyperparameters (regularization strength λ, auxiliary loss weight α) to balance trustworthiness and task performance. Understanding the impact of model size combinations on transfer dynamics (e.g., disruption of adversarial robustness trend for 70M weak models). | The paper implicitly addresses the risks of deploying LLMs that lack trustworthiness (unfairness, non-robustness, privacy leakage) in high-stakes domains. It highlights the specific risk that larger models may be inherently more prone to privacy leakage due to memorization, complicating weak-to-strong privacy transfer. |
| 2025.03.23.644789.full.pdf | Google_Scholar | SynBioGPT: A Retrieval-Augmented Large Language Model Platform for \nAI-Guided Microbial Strain Development | This paper introduces SynBioGPT v2.0, an LLM platform for synthetic biology, which enhances knowledge retrieval by decomposing queries and using keyword-based searches. Tested on a domain-specific benchmark, v2.0 achieved 98% accuracy, showing significant improvement over its predecessor by mitigating hallucination and improving contextual relevance in microbial strain development. | True | NaN | True | 1.0 | NaN | SynBioGPT v2.0, a Retrieval-Augmented Generation (RAG)-enhanced LLM platform. It uses LLM reasoning for query decomposition into sub-questions, followed by targeted keyword-based searches (BM25 index) on a curated knowledge base, and LLM synthesis of retrieved paragraphs into a coherent answer. | Tested on a custom 100-question synthetic biology benchmark (71 specific, fact-based questions and 29 open-ended, reasoning-based questions) covering gene mutation, overexpression, etc. Performance was compared with SynBioGPT v1.0 and different LLM backends (DeepSeek V3, Gemini-2.0-flash, Claude-3.7-sonnet). | SynBioGPT v2.0 with the Claude-3.7-sonnet backend achieved 98% accuracy on the 100-question benchmark, a 10% improvement over SynBioGPT v1.0 (88% with Llama3-8B-Instruct). | NaN | NaN | NaN | NaN | NaN | NaN | Knowledge base built from open-access PDF documents (peer-reviewed studies in synthetic biology, >51,777 for v1.0, expanded and updated monthly for v2.0 from sources like PubMed). Raw data stored as Hugging Face dataset, processed into Markdown using Docling and spaCy Layout for text and metadata extraction. | Iterative development from SynBioGPT v1.0. SynBioGPT v2.0 architecture: 1) Data acquisition and cleaning (PDFs to Markdown, metadata extraction via Docling, spaCy Layout). 2) Index creation (BM25 from TF/IDF on curated Markdown and Table of Contents, serialized using Python's pickle). 3) Service deployment (FastAPI backend, Streamlit frontend, SQLite for session/chat history, OAuth for SSO). Core RAG method involves LLM-driven query decomposition, keyword-based search, direct paragraph extraction, and LLM-based synthesis. | Deployed as a web platform with a FastAPI-based API server for backend and Streamlit for frontend user interaction. User authentication via BDC’s single sign-on (SSO) service using OAuth. | True | False | Available as a web platform at https://synbiogpt.biodesign.ac.cn. | NaN | Limitations of keyword search (dependency on exact term matching, potential to miss relevant documents if terminology differs). Ensuring high-quality sub-problem generation by the LLM for effective query decomposition. Significant impact of LLM inference backend choice on overall system performance. Need for continuous updates of the specialized knowledge base. | General LLM risks like reliance on outdated corpora and hallucination. For SynBioGPT v1.0 (vector search): retrieval of semantically similar but contextually irrelevant documents, leading to inaccuracies. For SynBioGPT v2.0 (keyword search): potential to overlook relevant documents due to terminology mismatch, and risk of poorly defined LLM-generated sub-problems leading to retrieval results that deviate from user intent. |
| xxzftjRKRFAJ.pdf | Google_Scholar | LegalBench : Prototyping a Collaborative Benchmark for Legal Reasoning | This paper introduces LegalBench, a collaborative benchmark designed to evaluate the legal reasoning capabilities of foundation models (FMs) using the IRAC framework. It presents an initial set of 44 tasks with preliminary FM performance results and calls for community contributions to expand the benchmark. | True | Idealistic | True | 1.0 | Positive | LegalBench, a collaborative benchmark for legal reasoning structured using the Issue, Rule, Application, Conclusion (IRAC) framework, comprising a seed set of 44 tasks. | Five different foundation models (GPT-3 davinci, GPT-3 curie, J1-Jumbo, J1-Grande, J1-Large) were evaluated on the 44 LegalBench tasks using zero-shot, few-shot, and chain-of-thought prompting. Performance was measured using F1 (macro) for classification/conclusion tasks and accuracy for others. | GPT-3 (davinci) with chain-of-thought prompting achieved the highest reported score, with an F1 (macro) of 0.92 on the PROA (Conclusion) task. Generally, larger models performed better, classification tasks were easier than application tasks, and chain-of-thought prompting improved performance. | The paper identifies the United States' "access-to-justice crisis" as a key challenge. It also implies that a lack of understanding of Foundation Models' capabilities and limitations in legal reasoning, alongside the high-risk nature and ethical concerns of AI tools in law, are hurdles to leveraging AI for access to justice. | The paper proposes LegalBench, an open and collaborative benchmark, to systematically assess the legal reasoning capabilities of Foundation Models. This evaluation aims to guide the safe, ethical, and effective development and use of AI tools, which could in turn improve the accessibility of legal services. | Improving accessibility of legal services; Evaluating AI capabilities in legal reasoning; Fostering safe and ethical use of AI in law. | Low-income Americans (mentioned via reference to "The Justice Gap" report). | Contract law (CUAD), Civil Procedure (Diversity Jurisdiction, Personal Jurisdiction), Evidence (Hearsay), Trademark Law (Abercrombie), Statutory Interpretation (PROA). | United States | The benchmark tasks use a variety of data: CUAD tasks use annotated contracts from the EDGAR database (publicly available); other tasks (Rule QA, Abercrombie, Hearsay, Personal Jurisdiction, PROA) use manually constructed or annotated datasets of legal questions, scenarios, product-mark-pairs, or statutes, typically with small numbers of samples (50-100). | The IRAC (Issue, Rule, Application, Conclusion) framework is used to categorize and structure legal reasoning tasks. A data-centric approach is adopted, with lightweight and accessible task construction to encourage collaboration. | LegalBench is presented as an ongoing, collaborative project hosted on GitHub. The authors call for community contributions of new tasks, and plan to run new FMs on the benchmark and release results. | True | True | The LegalBench project, including initial tasks, is available on GitHub: https://github.com/HazyResearch/legalbench. | A clear understanding of which types of legal reasoning Foundation Models can perform and what FM programming strategies are effective for legal tasks. Current FMs perform significantly worse on legal application tasks compared to classification or conclusion tasks. The need for law-specific prompting strategies and frameworks for safe and ethical usage. | Distinguishing between different types of IRAC tasks during benchmark design; fostering sustained interdisciplinary collaboration between computer science and legal communities; designing tasks that meaningfully measure legal reasoning while being accessible for contribution. | The paper mentions the "high risk nature" of computational legal tools and the need for evaluation to ensure "safe and ethical usage." It implicitly acknowledges the risk of misapplication if AI capabilities are not well understood, or if tools are used to replace human legal professionals inappropriately. |
| SKfsqhktqhMJ.pdf | Google_Scholar | ChatGPT: limitations, challenges and potential applications | This paper provides an overview of ChatGPT, an AI language model based on GPT-3.5 developed by OpenAI. It discusses the model's training, characteristics, potential applications across various sectors (including law), limitations (e.g., accuracy, bias), and ethical challenges. | True | NaN | True | 3.0 | Neutral | ChatGPT (based on GPT-3.5) | NaN | NaN | NaN | NaN | NaN | NaN | Law and legal services (mentioned as one potential application area) | International | Large-scale, diverse text and human conversation data (e.g., forums, chat, customer service), likely proprietary to OpenAI. | Based on GPT-3.5 architecture (Transformer, attention mechanism); trained using supervised learning, reinforcement learning, and autoregressive techniques. | NaN | True | False | Available via OpenAI's website (chat.openai.com). | NaN | Factual inaccuracy, sensitivity to input phrasing, bias mitigation, ensuring ethical responsibility and transparency, handling complex commands, preventing abuse. | Generating factually inaccurate information, amplifying bias, generating inappropriate responses, potential for abuse, privacy and security concerns. |
| 5VFMMdneX9MJ.pdf | Google_Scholar | Employing Label Models on ChatGPT Answers Improves Legal Text Entailment Performance | This paper proposes a method to enhance legal text entailment accuracy by using label models to aggregate multiple, potentially inconsistent answers generated by ChatGPT. Experimental results on the COLIEE 2022 dataset show this approach significantly outperforms existing state-of-the-art methods. | True | Idealistic | True | 1.0 | Positive | Employing label models (specifically a 'Generative model' label model) to integrate multiple provisional answers generated by ChatGPT (using a 'Reason-then-Answer' prompt and varying temperature settings) for legal text entailment decision. | The approach was evaluated on the COLIEE 2022 legal text entailment dataset. The authors tested different prompt types for ChatGPT, varied temperature settings to generate multiple answers, and applied several label models (Majority voting, FlyingSquid, Dawid-Skene, Hyper label model, FABLE, Generative model) to consolidate these answers, measuring accuracy. | The 'Generative model' label model, when applied to 10 provisional answers from ChatGPT (using the Reason-then-Answer prompt with temperature=0.5), achieved an accuracy of 76.15% on the COLIEE 2022 dataset. | The main obstacles identified are the reasoning errors of LLMs like ChatGPT, which hinder their reliable application. These include: 1) hallucinating facts, 2) incorrect deduction from correct premises, 3) difficulty with nuanced legal concepts like 'mutatis mutandis', and 4) issues arising from incomplete contextual information (e.g., missing relevant legal articles). | The paper proposes using label models to aggregate and refine multiple LLM-generated answers, thereby improving the robustness and accuracy of legal text entailment. The error analysis also implicitly suggests that providing more comprehensive and accurate contextual information to the LLM could mitigate some errors. | Improving the accuracy and reliability of legal text entailment, a foundational capability for developing advanced legal AI applications (e.g., legal chatbots, question-answering systems) aimed at enhancing access to legal information and services. | People who cannot afford expensive legal advice. | Civil Law (based on COLIEE competition context and examples like contract law provisions) | Japan (based on the COLIEE competition, which typically uses Japanese legal texts, and mentions of 'Japanese legal data' in related work). | The label models operate on provisional answers generated by ChatGPT for queries from the COLIEE 2022 dataset. These provisional answers (text including 'Yes'/'No' and reasoning) serve as the noisy labeled data for the label models. ChatGPT itself is pre-trained on a massive, general corpus. | Experimental evaluation comparing different ChatGPT prompting strategies ('Answer-only', 'Answer-then-Explain', 'Reason-then-Answer'), varying ChatGPT's temperature parameter to generate diverse outputs, and applying various established label models to consolidate these outputs. Includes a qualitative error analysis of ChatGPT's incorrect responses. | NaN | False | False | NaN | Remaining technical gaps include LLMs' deficiencies in complex legal reasoning, such as handling 'mutatis mutandis' clauses, consistent logical deduction, and avoiding factual inaccuracies (hallucinations). The lack of sufficient relevant articles provided as context for LLMs also poses a challenge to accurate entailment. | The primary challenges included managing the variability and potential inconsistency of ChatGPT's outputs (especially with non-zero temperature settings), identifying the most effective prompting technique for legal reasoning, and developing a robust method to integrate multiple, potentially noisy, LLM-generated answers into a single, more accurate consolidated answer. | The paper identifies concrete risks associated with ChatGPT's errors in legal text entailment: 1) incorrect provision of facts (hallucinations), 2) inability to draw correct conclusions from correct premises, 3) difficulties reasoning on 'mutatis mutandis' articles, and 4) incorrect responses or inability to conclude due to lack of relevant articles in the provided dataset. |
| G2vdU-5fzE4J.pdf | Google_Scholar | ChatGPT and Generative AI Systems as Military Ethics Advisors | This paper explores the potential of ChatGPT and similar generative AI systems to serve as military ethics advisors by testing ChatGPT's responses to a complex ethical dilemma scenario involving a potential strike on a hospital. The author suggests that AI could provide valuable, accessible ethical guidance to soldiers and commanders, potentially reducing war crimes and improving decision-making. | True | Idealistic | True | 2.0 | Positive | Using ChatGPT as a military ethics advisor by prompting it with specific scenarios. | ChatGPT (Feb 2023 version, trained on data up to end of 2021) was prompted with a detailed hypothetical military scenario involving a potential strike on a hospital suspected of hiding enemy artillery, followed by specific ethical and legal questions related to the scenario. | ChatGPT provided extensive, detailed, and ethically reasoned advice addressing principles like military necessity, proportionality, discrimination, the implications of attacking hospitals (treating civilians or enemy wounded), the use of human shields, the certainty required for intelligence, and the legality of following or refusing potentially unlawful orders. | Lack of readily accessible ethical guidance for soldiers in combat, complexity of formal Law of War manuals, stress of war leading to poor decisions, potential for immoral leadership, leading to ethical breaches and war crimes. | Deploying generative AI systems like ChatGPT, potentially integrated via voice interfaces, to provide real-time military ethics advice, automatic checking of orders, and decision support for both frontline soldiers and commanders. | Access to ethical guidance in conflict zones, interpretation of the Law of Armed Conflict / International Humanitarian Law, prevention of war crimes, ethical military decision-making. | Military personnel (specifically frontline soldiers and commanders) lacking immediate access to ethical/legal guidance. | Military Law, Law of Armed Conflict, International Humanitarian Law, Ethics | International | ChatGPT was predominantly trained on general data up to the end of 2021 (as per OpenAI FAQ cited in the paper). | NaN | The paper suggests potential future deployment (e.g., via voice interface) but does not describe current deployment strategies. | False | False | NaN | NaN | NaN | Implicit risks related to reliance on AI for high-stakes ethical decisions in warfare, potential for AI to provide incorrect or flawed advice leading to illegal or immoral actions, misuse of AI. |
| HXDmOtw5Oj0J.pdf | Google_Scholar | The future of court’s procurators with the advent of artificial intelligence technologies | This paper analyzes the impact of artificial intelligence on Spain's justice administration, with a particular focus on the court procurator profession. It argues that AI-driven automation of routine tasks, especially in procedural communication, threatens the traditional roles and future necessity of court procurators. | True | Market | False | 3.0 | Neutral | NaN | NaN | NaN | Lack of algorithmic transparency ("black box" issue), potential for bias and discrimination in AI systems, threatening due process and the right to defense. | Ensuring algorithmic transparency and non-discrimination in AI design and use; utilizing AI as a support tool rather than a replacement for human judgment, especially in judicial decision-making; comprehensive training for personnel. | Efficiency and speed of judicial processes, right to defense, due process, procedural representation, automation of legal tasks. | NaN | General (Civil Procedure, Criminal Law, Bankruptcy Law) | Spain | Existing legal data including case law (judicial decisions, sentences), legislation, and large text datasets for generative models. Data sources include historical case records and legal texts, used for training predictive and generative AI systems. | NaN | NaN | True | False | The paper discusses existing tools like Chat GPT which are publicly accessible as online services, and operational systems like Lexnet integrated into specific national justice infrastructures for authorized users. | Technical gaps include the need for fully transparent and unbiased AI, and AI with human-like emotional intelligence for complex legal tasks. Societal gaps include addressing widespread job displacement and potential increases in economic inequality due to AI. | NaN | Widespread job displacement in legal and administrative sectors, particularly for court procurators; violation of fundamental rights (due process, right to defense) through non-transparent or biased AI; perpetuation of discrimination through biased algorithms; increased socio-economic inequality. |
| MclRsgjrGSEJ.pdf | Google_Scholar | The K eynote Addr ess t o Geor gia State Univ ersity College of Law' s 29th Annual Law Re view Symposium - Access t o AI Justice: A Global Response t o a Global Crisis | This paper, a keynote address, argues that the narrative around AI in law should shift to focus on closing the justice gap and discusses how AI can serve the public interest. It calls for significant regulatory reforms, including a U.S. national legal regulatory sandbox and globally-informed approaches, to ensure AI's potential is realized without creating a two-tiered legal system. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | The significant justice gap due to unaffordable legal services, which AI could worsen by creating a two-tiered system. Systemic barriers include high costs and restrictive regulations (e.g., limiting non-lawyer investment), alongside AI-specific issues like bias, lack of transparency, and data privacy concerns. | Implement 'calibrated' AI for access to justice, focusing on consumer needs, specific legal issues, and tasks. Advocate for data-driven regulatory reform through a national U.S. legal regulatory sandbox and learning from international approaches to foster innovation and equitable access to legal services. | Closing the justice gap; democratizing access to legal information; regulatory reform of legal services; preventing a two-tiered system of legal services; nonlawyer ownership/investment in legal services; ethical use of AI in law. | Low-income Americans; individuals who cannot afford legal services. | General civil legal problems; Legal ethics and professional regulation. | United States (primarily for proposed reforms), with comparative discussion of international jurisdictions (e.g., Colombia, France, UK, Canada, Australia). | NaN | NaN | NaN | False | False | NaN | Lack of data-driven regulatory reform in the U.S. legal services industry; state-level resistance to regulatory experimentation, such as sandboxes; insufficient and outdated ethical guidance for new AI technologies; failure of the U.S. legal industry to systematically learn from international experiences in legal tech regulation; need for more interdisciplinary and collaborative reform efforts. | NaN | AI generating fictitious legal citations (hallucinations); creation of a two-tiered system of legal services disadvantaging certain populations; perpetuation of existing biases through data-driven conservatism; breaches of client confidentiality and data protection; stifling of lawyer creativity and critical thinking; potential for discriminatory outcomes from AI systems. |
| December2024Publication.pdf | Google_Scholar | Factors Associated with the Low Uptake of Quality Medico-Legal Services at Secured Diagnostic Crime Scene, Western Kenya | This paper investigates the reasons for the underutilization of quality medico-legal services at crime scenes in Western Kenya, identifying issues like evidence contamination, lack of trained personnel, and community interference. The study finds significant problems with evidence handling and staff training, recommending an integrated forensic system and improved capacity building to ensure evidence admissibility and access to justice. | True | Idealistic | False | 2.0 | NaN | NaN | NaN | NaN | Widespread evidence contamination at crime scenes (84%), often due to community participation or improper handling; lack of adequately trained forensic service providers (93% of mortuary staff lack formal training); limited understanding and use of witness grant immunity (85% unaware); poor maintenance of chain of custody; fragmented forensic services. | Promote an integrated forensic science ecosystem under unified management; enhance capacity building and training for forensic personnel; develop and implement policies for witness grant immunity; improve evidence reconstruction techniques and adherence to chain of custody procedures; foster public-private partnerships for training. | Quality and reliability of forensic evidence collection; medico-legal procedures at crime scenes; evidence admissibility; role of trained personnel in investigations; chain of custody. | General population affected by crime in Western Kenya. | Criminal Law, Forensic Law, Evidence Law | Kenya (Western Kenya) | NaN | NaN | NaN | False | False | NaN | Lack of integrated forensic management systems; insufficient training infrastructure/access for forensic personnel; absence of robust witness protection/immunity policies; under-documentation of regional medico-legal practices; need for improved community engagement that avoids contamination. | NaN | Inadmissibility of evidence leading to delayed or denied justice; harm to population health (due to unresolved crimes); undermining the rule of law through flawed investigations; continued evidence contamination; potential for wrongful convictions or acquittals. |
| 6C9WMJbxT4oJ.pdf | Google_Scholar | REFORMULATING DOMAIN ADAPTATION OF LARGE LANGUAGE MODELS AS ADAPT -RETRIEVE -REVISE | This paper introduces an 'adapt-retrieve-revise' framework to improve domain adaptation for large language models (LLMs) like GPT-4, specifically targeting hallucination issues in specialized domains such as Chinese law. The method uses a domain-adapted smaller LLM to generate a draft answer, retrieves evidence based on this draft, and then employs GPT-4 to revise the draft using the query and retrieved evidence, demonstrating significant accuracy improvements on Chinese legal tasks. | True | Market | True | 1.0 | NaN | Adapt-Retrieve-Revise framework: 1) A domain-adapted smaller LLM (Baichuan 7B) generates a draft answer to a query. 2) The draft answer is used to retrieve supporting evidence candidates from an external in-domain knowledge base using a sentence embedding model (Multilingual-E5-large). 3) GPT-4 assesses the retrieved evidence and revises the draft answer to generate the final answer, using a prompt that includes an instruction, the original query, the draft answer, and the retrieved evidence. | Evaluated in a zero-shot setting on four Chinese legal tasks: Law Clause Recommendation (LCR), Criminal Prediction (CP), LegalQA (filtered EUQALS), and JEC-QA (lawyer's certificate exam questions). Metrics used were recall for LCR, CP, and LegalQA, and accuracy for JEC-QA (human-evaluated). Retrieval was also evaluated on a Similar Case Retrieval task using precision@k and MAP. | The proposed adapt-retrieve-revise method (using the 7B legal LLM for draft generation/retrieval and GPT-4 as the revisor) achieved an average improvement of 33.3% in accuracy/recall compared to direct GPT-4 generation across the four Chinese legal tasks (LCR, CP, LegalQA, JEC-QA), reaching an average score of 80.7%. It also outperformed query-based retrieval baselines. | NaN | NaN | NaN | NaN | Chinese Law (general), covering tasks like law clause recommendation, criminal prediction, legal question answering from laws, and question answering for legal qualification exams (JEC-QA). | China | For the domain-adapted 7B LLM (Baichuan 7B): Continual pre-training on 50B tokens from Chinese law clauses (publicly available from flk.npc.gov.cn) and 100M Chinese judgments online (publicly available from wenshu.court.gov.cn). Supervised fine-tuning on 70K instruction examples, including 52K GPT-4 self-instruct Chinese data and 18K undisclosed human-expert annotated legal instructions (guideline to be released). | Continual learning (for domain adaptation of the 7B LLM), supervised fine-tuning (for instruction alignment), retrieval-augmented generation (using draft answers for similarity-based evidence retrieval and subsequent revision by a larger LLM). | The paper states that the code and the domain-adapted 7B LLM 'will be released', with an anonymous link provided for review purposes. | False | False | NaN | NaN | The infeasibility of continual training for very large LLMs (e.g., GPT-4 scale) on in-domain data due to cost and API limitations. Limitations of retrieval modules in mapping queries to evidence and susceptibility to domain issues. The limited capability of smaller 7B models to fully understand queries/evidence and assess evidence effectively, despite domain adaptation. The high cost of using GPT-4 API for experiments and evaluation. | Hallucination in LLM-generated content when applied to specific domains like Chinese law, manifesting as non-logical content, factual mistakes, and failure to refer to correct legal provisions. This is due to the absence of sufficient in-domain training data for general large models. |
| bUM57XdgCiAJ.pdf | Google_Scholar | CO-AUTHORING WITH AN AI? ETHICAL DILEMMAS AND ARTIFICIAL INTELLIGENCE | This paper explores the ethical dilemmas and practical challenges of using generative AI like ChatGPT and Bing Chat for legal academic writing. It evaluates the strengths and weaknesses of these tools through direct engagement and analyzes the divergent approaches of academic publishers and the notable lack of AI policies among law reviews. | True | NaN | True | 2.0 | Neutral | Use of Generative AI chatbots (ChatGPT, Bing Chat) for legal academic writing. | The authors posed questions about AI ethics to ChatGPT and Bing Chat, asking them to generate text (abstract, introduction, arguments, references, conclusion) for an academic paper. The outputs were analyzed for relevance, accuracy (especially of references), and completeness. | ChatGPT provided relevant text but hallucinated/inaccurate references and used outdated (pre-2021) knowledge. Bing Chat offered accurate, up-to-date sources (links) but they were less academically relevant (blogs, primary sources), and its answers were less focused. Both missed key recent regulatory developments. | For AI in academic writing: Hallucinations (fake references/cases), potential for bias, lack of up-to-date knowledge, lack of accountability, risk of plagiarism, difficulty enforcing AI bans, unclear disclosure requirements, lack of policies in law reviews. | For AI in academic writing: Transparency (disclosing AI use rather than banning), developing clear publisher/journal guidelines (especially for law reviews), evolving the scholar's role towards guiding AI and critical evaluation, maintaining human accountability for final work. | NaN | NaN | Legal Academia, Legal Research, Legal Ethics, Academic Publishing | International | Massive text corpora (including internet data) predating late 2021 for ChatGPT; Bing Chat uses a similar model connected to the live internet. | NaN | NaN | True | True | ChatGPT via OpenAI website (free tier available); Bing Chat integrated into Microsoft products (typically free). | Lack of clear, consistent AI policies among academic publishers, particularly law reviews. Need for better methods than outright bans or unreliable detection. Lack of clarity on disclosure standards. Need to understand AI's impact on scholarly roles and editorial practices. | Evaluating the ethical/practical implications of using rapidly evolving generative AI for academic legal writing; dealing with AI inaccuracies/hallucinations; ensuring proper attribution/avoiding plagiarism; navigating inconsistent publisher policies; assessing AI vs human contribution. | AI hallucination leading to misinformation/sanctions; plagiarism; bias amplification; misuse by authors (passing off AI work); misuse by editors (biased screening); lack of accountability; chilling effects from AI bans; erosion of trust in academic publishing. |
| XLMN4NL-8-wJ.pdf | Google_Scholar | The Law and NLP: Bridging Disciplinary Disconnects | This position paper argues that legal NLP research is often disconnected from the practical needs of the legal community, which impedes its potential to address the access to justice crisis. The authors call for a shift towards more needs-driven research, greater interdisciplinary collaboration, and the adoption of access to justice as a primary normative goal for the field. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | High cost and unequal access to legal services, particularly for low-income individuals and small businesses; a disconnect between the focus of legal NLP research and the practical needs of the legal community; slow adoption of technology by the legal profession due to factors like risk aversion and lack of expertise. | Adopting access to justice as a shared normative goal for legal NLP research; fostering closer interdisciplinary collaboration between NLP researchers and legal professionals; reorienting research towards practical applications like document generation/analysis, semantic search, legal language accessibility, and practice-oriented tools. | Addressing the access to justice gap; improving legal services for low-income individuals, public defenders, and small businesses; enhancing the accessibility of legal language and processes for non-lawyers; increasing the efficiency of legal professionals to potentially lower costs. | Low-income individuals, criminal defendants reliant on public defenders, small businesses, non-lawyers seeking to understand legal matters, and underserved communities globally. | General legal practice, including civil law, criminal law, contract law, statutory interpretation, and litigation. | United States; International | NaN | NaN | NaN | False | False | NaN | Societal/Systemic: Deep-rooted inequities in the justice system and resistance to technological adoption within the legal field. Research Focus: A misalignment between academic NLP research agendas and the practical requirements of legal work, leading to underexplored areas with high potential impact; insufficient interdisciplinary interaction. Ethical: Need for robust frameworks to manage bias, ensure accountability, and maintain trust in legal AI systems. | NaN | Poorly designed NLP tools may embed or amplify biases, reduce essential human oversight in legal decision-making, undermine public trust in the judicial system, lead to inaccurate or unfair automated judgments, and risk the leakage of sensitive or confidential legal information. |
| TransformingEducationwithLargeLanguageModels.pdf | Google_Scholar | Transforming Education with Large Language Models: Opportunities, Challenges, and Ethical Considerations | This paper examines the potential of Large Language Models (LLMs) like GPT-4 to enhance education through personalized learning, content creation, and tutoring. It also discusses significant challenges, including technology dependency, content accuracy, data privacy, bias, and ethical considerations, offering recommendations for integration. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | NaN | NaN | False | False | NaN | NaN | Challenges discussed include dependency on technology, potential degradation of critical thinking skills, access inequality exacerbating the digital divide, system failures disrupting learning, ensuring content accuracy and reliability, keeping information up-to-date, protecting student data privacy (compliance with regulations like GDPR/FERPA, obtaining consent, preventing breaches), and addressing bias in training data (representation and performance bias) to ensure fairness. | Specific risks mentioned include skill degradation due to over-reliance on AI, exacerbation of educational inequalities, learning disruptions from technical failures, students receiving incorrect or misleading information, violation of student privacy through data collection and potential breaches, and perpetuation of stereotypes or unfair outcomes due to algorithmic bias. |
| taH8uxiVYoAJ.pdf | Google_Scholar | ChatGPT Creates a Review Article: State of the Art in the Most-Cited Articles on ChatGPT in Health Science, Computer Science, Communication, and Culture, According to Altmetric in Dimensions.ai | This paper explores using ChatGPT to generate a review article summarizing the most influential preprints about ChatGPT across Health Science, Computer Science, Communication, and Culture, identified via Dimensions.ai and Altmetric. The authors prompted ChatGPT to analyze selected preprints and found the results promising for automating review article creation. | True | NaN | True | 2.0 | NaN | Using ChatGPT (GPT-4) to summarize and structure information from selected research preprints to generate a review article, based on specific prompts. | Qualitative evaluation. Abstracts from top preprints (selected via Dimensions.ai search filtered by Altmetric score) were fed to ChatGPT via prompts. The authors assessed the generated tabular summaries for coherence and utility in creating a review article. | ChatGPT successfully generated structured tables summarizing the preprints' design, applications, risks, conclusions, and sentiment, which the authors deemed 'surprisingly promising' for review article creation. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | False | NaN | The paper highlights the general difficulty and time-consuming nature of creating and updating traditional academic review articles. It also mentions limitations of ChatGPT identified in the reviewed literature, such as potential bias, inaccuracy, lack of transparency, and ethical concerns. | Effectively prompting ChatGPT to generate accurate and structured summaries from scientific abstracts. Selecting relevant and influential source material from a large volume of preprints using metrics like Altmetric. | Potential for generating harmful/biased content, lack of interpretability/transparency, potential job displacement, generation of fabricated data, inaccuracy (especially in complex domains or low-resource languages), decreased user trust for complex tasks (e.g., health advice). |
| EnhancingTrustinGenerativeAI_InvestigatingExplainabilityofLLMstoAnalyseConfusioninMOOCDiscussions.pdf | Google_Scholar | Enhancing Trust in Generative AI: Investigating Explainability of LLMs to Analyse Confusion in MOOC Discussions | This paper investigates using the Explainable AI (XAI) method Integrated Gradients to understand how a DistilBERT language model identifies learner confusion in MOOC discussion forums. The goal is to enhance trust in AI-generated feedback by making the model's reasoning transparent. | True | NaN | True | 2.0 | NaN | Application of the Integrated Gradients XAI method to interpret predictions from a fine-tuned DistilBERT model classifying learner confusion in MOOC discussion texts. | The DistilBERT model was trained and evaluated on the Stanford MOOC discussion datasets (split by domain: Education, Medicine, Humanities and combined) using weighted-averaged F1 scores. The Integrated Gradients XAI method was then applied, and its outputs (word-level attributions) were qualitatively analyzed and compared to findings from previous studies. | The fine-tuned DistilBERT model achieved high classification performance (weighted F1 scores up to 0.94 when excluding neutral messages). The Integrated Gradients method successfully identified word-level indicators of confusion (e.g., first-person pronouns, question stems) and non-confusion (e.g., second-person pronouns, academic writing expressions), aligning with prior research. | NaN | NaN | NaN | NaN | NaN | NaN | Publicly available Stanford MOOC discussion datasets: unstructured text messages from 11 courses (Education, Medicine, Humanities), annotated for confusion level by experts. | Machine learning pipeline (data preprocessing, model fine-tuning, evaluation), application of XAI technique (Integrated Gradients), qualitative interpretation, comparison with prior work. | NaN | False | False | NaN | NaN | Lack of trust in 'black-box' AI models in education. Potential impact of ambiguous (neutral) data points on model performance. Need for further model refinement for generalisability across different domains. Investigating direct applicability of XAI methods to generative models. | Risk of low adoption of AI tools in education due to lack of trust and transparency. General risks associated with GenAI mentioned (biases, reliability, ethics, safety, accountability, equality, eco-friendliness). |
| EAq8gE4cA44J.pdf | Google_Scholar | Breaking Barriers to Creative Expression: Co-Designing and Implementing an Accessible Text-to-Image Interface | This paper describes the co-design and development of PromptAssist, a prototype accessible interface for text-to-image (T2I) generation models. PromptAssist uses a large language model (LLM) to provide prompt suggestions, reducing typing effort for users, particularly those with motor disabilities. | True | Idealistic | True | 1.0 | Positive | PromptAssist: An accessible web-based interface for creating text-to-image prompts using LLM-generated suggestions via a wizard-based workflow, supporting keyboard and pointer input. | Iterative co-design and testing sessions within the research team, which included members with motor disabilities. Feedback was gathered through think-aloud protocols during prototype use. | Iterative testing led to UI improvements enhancing flexibility, navigation, suggestion quantity, and keyboard accessibility. Team members found the revised prototype easier to use and better supported their creative processes, demonstrating LLMs can improve T2I accessibility. | Difficulty for users with motor disabilities in typing the long and detailed text prompts required by standard text-to-image (T2I) interfaces. | An accessible interface (PromptAssist) that reduces typing effort by using an LLM to generate contextual prompt suggestions and supports multiple input methods (keyboard, pointer). | Accessibility of creative tools (Text-to-Image generation). | People with motor disabilities. | NaN | International | The technique uses an internal (Google) transformer-based LLM. The specific training data for the base LLM is not specified. The system is prompted using examples (provided in Appendix A) created by the authors to generate relevant suggestions for T2I prompts. | Iterative development, co-design involving researchers with motor disabilities, usability testing with think-aloud protocols within the team. | Developed as an internal prototype within Google; no broader deployment strategies mentioned. | False | False | NaN | Future work could include multimodal input (speech, body movements) and adjusting prompts based on previously generated images. Need for platforms for users to share, collaborate, and exchange ideas. | Balancing ease of use (provided by suggestions) with creative flexibility and user autonomy. Ensuring the interface supports varied creative workflows rather than enforcing a strict sequence. Optimizing the UI layout and interaction based on accessibility feedback. | Over-reliance on LLM suggestions might limit user creativity or diminish the user's sense of agency and independence, particularly for users with disabilities. |
| le3MEvufjeIJ.pdf | Google_Scholar | AI LAWYERING SKILLS TRAINERS: TRANSFORMING LEGAL EDUCATION WITH GENERATIVE AI | This paper advocates for integrating generative AI (GenAI) skills trainers into legal education to enhance advocacy skills through personalized, continuous coaching. It details the development and potential of MootMentorAI, a custom GenAI tool designed at UMKC School of Law to simulate courtroom scenarios and provide feedback to law students. | True | Market | True | 1.0 | Positive | MootMentorAI: A custom Generative AI (GenAI) tool built on OpenAI's GPT platform (specifically mentioning GPT-4o and GPT Builder) designed to act as an AI lawyering skills trainer, simulating a judge in moot court scenarios and providing personalized feedback. | Designer-led testing involved iterative simulations (30 mentioned) using the tool, systematic feedback collection, dialogue with the GPT builder, and intentionally introducing errors (misciting cases, confusing facts) to test the AI's ability to identify and correct them or redirect appropriately. Student-led testing was planned but pending at the time of writing. | Designer-led testing demonstrated the tool's ability to simulate courtroom interactions based on provided training data, identify user errors, provide feedback, and be iteratively refined based on performance. The process confirmed the feasibility of creating such a tool using platforms like GPT Builder. | NaN | NaN | NaN | NaN | Legal Education, Advocacy Skills Training | USA | Proprietary, domain-specific, unstructured text data from the University of Missouri-Kansas City (UMKC) School of Law's 1L Lawyering Skills II course, including bench briefs, sample questions for judges, the factual record, and assigning memos. A custom training guide developed by the author outlining AI behaviors and scenarios was also used within a 'closed knowledge universe'. | Agile methodology adapted for instructional design, involving phases: Align (identifying needs, compiling materials), Get Set (defining user experience, AI interaction strategy), Iterate & Implement (building, testing, iterative improvements), and Leverage & Evaluate (designer/student-led feedback). | Developed using OpenAI's GPT Builder platform (requiring a ChatGPT Plus subscription). Deployment involves sharing a link to the custom GPT. Planned deployment for student-led testing within UMKC Law. | False | False | NaN | NaN | The iterative refinement process of training the GPT, which can be time-consuming and requires patience ('akin to mentoring a teaching assistant'). Ensuring clear prompts and avoiding confusing information to prevent unexpected AI behavior. Initial consideration of platform cost (CustomGPT.ai). Potential need for institutional approval (IRB) for student testing. | AI 'hallucinations' (generating incorrect information), requiring expert oversight and mitigation techniques (like RAG or closed knowledge universes). Ethical considerations regarding AI use (need for guidelines, ABA Formal Opinion 512 mentioned). Data privacy concerns (FERPA) when used with identifiable student data. Potential for student misuse or unprofessional interaction with the tool. |
| bWkbsgfjIKIJ.pdf | Google_Scholar | Artificial Intelligence and the Sustainable Development Goals: \nGPT -3`s reflect ions on the Society Domain | This paper evaluates the large language model GPT-3's perspectives on how Artificial Intelligence (AI) can contribute to achieving the Sustainable Development Goals (SDGs) within the society domain. Through analyzing GPT-3's responses to queries about specific SDGs, the study identifies potential benefits, such as in education and health, alongside significant risks like bias and privacy concerns, ultimately stressing the need for robust regulation for responsible AI deployment. | True | Idealistic | True | 2.0 | Neutral | GPT-3 model (text-davinci-003) | The authors prompted GPT-3 (text-davinci-003 model) with queries related to nine societal SDGs and their 58 outcome targets. The prompts requested shortened target titles and 3-5 sentences outlining benefits and risks of AI's contribution to each target. The AI's textual responses were then descriptively analyzed for content, structure, word/sentence counts, and patterns of consistency or error. | GPT-3 identified numerous potential benefits of AI for societal SDGs, including poverty reduction, enhanced food security, improved healthcare diagnostics, personalized education, support for gender equality, better water management, optimized energy systems, sustainable urban planning, and enhanced access to justice. However, it consistently highlighted risks such as data bias leading to discrimination, privacy violations, exacerbation of existing inequalities, job displacement, and the necessity of human oversight. The model exhibited variability in response structure and an increase in errors (e.g., punctuation) with longer text generations. | For access to justice (primarily under SDG 16), identified hurdles include: AI systems potentially targeting specific populations or being biased against certain groups, leading to discriminatory outcomes; misinterpretation of data by AI leading to false accusations or unjust decisions; increased surveillance capabilities infringing on privacy rights critical for justice; and the risk of AI perpetuating or creating new forms of inequality in legal and institutional processes. | The paper emphasizes the critical need for proper regulation and oversight of AI development and deployment. It calls for establishing ethical guidelines, ensuring transparency and safety of AI systems, fostering a global, science-driven debate to develop shared principles and legislation, and promoting responsible AI use to mitigate risks and align AI with sustainable development. | Poverty eradication (SDG 1), zero hunger (SDG 2), good health and well-being (SDG 3), quality education (SDG 4), gender equality (SDG 5), clean water and sanitation (SDG 6), affordable and clean energy (SDG 7), sustainable cities and communities (SDG 11), and peace, justice, and strong institutions (SDG 16). Within SDG 16, specific topics include reducing violence, ending child abuse, promoting rule of law, reducing illicit financial flows, combating corruption, building effective institutions, inclusive decision-making, and ensuring legal identity and access to information. | Vulnerable populations, people living in poverty, communities in developing nations, women and girls (gender disparities), minority groups, children, and individuals at risk of discrimination. | Human rights law, criminal justice, anti-corruption law, data privacy law, administrative law, access to information law, environmental law (as it relates to social impacts of resource management). | International | The study used the GPT-3 model 'text-davinci-003', which was trained on data up to June 2021. The paper generally notes that such AI models are trained on vast amounts of internet text, which can include misinformed and biased content. | NaN | NaN | True | False | The authors interacted with GPT-3 via the OpenAI playground, implying availability through OpenAI's platform (API and playground). | Technical gaps include the unreliability and error-proneness of current LLMs like GPT-3, inconsistencies in output, and the need for improved natural language processing skills to avoid mimicking human writing flaws. Societal and ethical gaps include the lack of adequate regulation for AI, the potential for AI to exacerbate existing inequalities, pervasive data biases, significant privacy concerns, and the challenge of differentiating AI-generated content from human-written text, necessitating a global consensus on ethical AI principles and legislation. | The authors encountered challenges in obtaining consistent and accurate outputs from GPT-3, including variability in answering patterns and adherence to formatting instructions. They also noted an increase in punctuation mistakes in longer AI-generated texts and instances where the system failed to produce results without error messages, possibly due to beta tier limitations or capacity issues. | Bias in AI algorithms leading to discrimination and unfair decisions; privacy violations from data collection and analysis (e.g., health, financial, personal information); exacerbation of existing social and economic inequalities; job displacement due to automation; over-reliance on AI leading to reduced human oversight and accountability; potential for misuse of AI for manipulative purposes (e.g., targeting specific populations, citizen scores); and infringement on human rights and fundamental freedoms (e.g., through surveillance, profiling). |
| h4InHnlnqGoJ.pdf | Google_Scholar | Leveraging Large Language Models for Learning Complex Legal Concepts\nthrough Storytelling | This paper presents a novel application of LLMs to generate stories and multiple-choice questions for explaining complex legal concepts to non-experts, and introduces the LEGAL STORIES dataset. Through RCTs, it demonstrates that LLM-generated stories, using an expert-in-the-loop process, can enhance legal comprehension, interest, and knowledge retention, especially for non-native English speakers. | True | Idealistic | True | 1.0 | Positive | Using LLMs (LLaMA 2, GPT-3.5, GPT-4) to generate explanatory legal stories and multiple-choice questions from legal doctrine definitions, with an expert-in-the-loop process for question refinement, to create the LEGAL STORIES dataset. | Human evaluation of story quality (Prolific workers, automatic complexity metrics) and question quality (Prolific workers, legal expert critiques). Randomized Controlled Trials (RCTs) with legal novices (native and non-native English speakers) comparing learning with definition vs. definition + story, assessed by comprehension questions and a delayed retention test. | LLM-generated stories (GPT-4 performing best) enhance comprehension of legal concepts and interest in law among non-native English speakers compared to definitions alone. Stories also help participants relate legal concepts to their lives and show higher knowledge retention for non-native speakers. | Legal documents are challenging for non-experts due to unfamiliar terms and nuanced language, hindering access to justice and civic participation. Scalable legal storytelling education is limited by the high costs of legal experts. | Leveraging LLMs to generate legal stories and assessment questions in a scalable way, using an expert-in-the-loop pipeline to maintain quality and enhance legal literacy for non-experts. | Enhancing general legal literacy, learning intricate legal concepts, legal education for non-experts. | Non-experts, people without legal backgrounds, legal novices, with a particular focus on non-native English speakers. | General legal concepts and doctrines | International | Input data for generation (not model training) consists of 294 legal doctrines with definitions from Wikipedia, which is publicly available, domain-specific (legal), unstructured text. The study uses pre-trained LLMs (LLaMA 2, GPT-3.5, GPT-4). | Expert-in-the-loop pipeline combining LLM generation with human (Prolific workers, legal experts) evaluation and refinement. Randomized Controlled Trials (RCTs) for evaluating effectiveness. Iterative design for question refinement based on expert feedback. | Release of the 'LEGAL STORIES' dataset and associated code on GitHub. | True | True | The LEGAL STORIES dataset and code are available on GitHub. | Limited sample size in RCTs affecting statistical power for small effects. The cost and scalability of human expert involvement, though reduced, remain a factor. Need for further research into diverse prompting strategies and LLM-based explanation methods. | Ensuring high quality and factual accuracy of LLM-generated content. Cost and time for human/expert evaluation and refinement. Designing effective prompts for LLMs. Evaluating generated questions without gold standards. | LLM-generated content may contain misleading, biased, harmful, or wrong information if not supervised. Risk of over-simplifying or over-generalizing nuanced legal jargon. Potential for inherent biases in LLMs to be perpetuated. |
| vmJp9pKwcFwJ.pdf | Google_Scholar | Addressing the Failures of the U.S. Civil Legal System | This paper analyzes the failures of the U.S. civil legal system in providing access to justice, particularly for vulnerable populations, by examining the concepts of legal capability and legal consciousness. It advocates for interdisciplinary interventions, including improved self-help resources, community-based support, and thoughtful use of technology, to better address the complex barriers faced by individuals. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Low legal capability (lack of knowledge, skills, confidence, agency); low legal consciousness (failure to identify problems as 'legal', distrust of institutions); systemic barriers (complex/intimidating procedures, cost, arcane language, digital exclusion); psychological/emotional barriers (stress, scarcity mindset, fear, anxiety, shame, lack of motivation); insufficient community support; educational system gaps. | Enhance legal capability and consciousness through targeted education and support; leverage interdisciplinary insights (public health communication, behavioral economics/nudges, inclusive design, marketing, neuroscience); utilize non-legal community organizations as trusted intermediaries; redesign self-help materials (plain language, procedural focus, address psychological needs, visual aids, motivational elements); coordinate resources nationally; employ technology thoughtfully (user-centered design, potential of generative AI like ChatGPT); co-design solutions with users. | Access to civil justice, self-representation support, legal empowerment, understanding and navigating the legal system, community-based legal help. | Legally vulnerable populations including lower-income Americans, women, racial/ethnic minorities (specifically Black and Multi-racial non-Hispanic Americans), younger and middle-aged Americans, urban and rural residents, people with disabilities, individuals experiencing houselessness, formerly incarcerated individuals. | Civil Law (general), Housing Law, Debt Collection, Family Law, Public Benefits Law, Employment Law, Consumer Law. | U.S. Civil Legal System (primary focus), with references to research/initiatives in the United Kingdom, Canada, and Australia. | NaN | NaN | NaN | False | False | NaN | Need for national coordination of resources and branding; challenge of integrating interdisciplinary approaches into legal service delivery; bridging the digital divide; addressing deep-seated distrust in legal institutions; ensuring ethical and effective implementation of AI; securing funding and resources for proposed interventions. | NaN | Generative AI risks (knowledge gaps, ethical concerns, economic disruption, data security, privilege issues, generating trust); digital exclusion exacerbating inequality; ineffective interventions leading to further harm or disillusionment for vulnerable individuals; potential for poorly designed technology to be unhelpful or harmful. |
| BthWmIW7q08J.pdf | Google_Scholar | Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language Models | This paper introduces KBL, a new benchmark designed to evaluate the Korean legal language understanding capabilities of Large Language Models (LLMs). KBL comprises legal knowledge tasks, legal reasoning tasks, and Korean bar exam questions, developed with legal professionals and used to assess LLMs in both closed-book and RAG settings. | True | NaN | True | 2.0 | NaN | KBL benchmark for evaluating LLMs and RAG systems on Korean legal tasks. | Evaluation of various LLMs (GPT-4, Claude series, Qwen2, etc.) using the KBL benchmark (7 knowledge tasks, 4 reasoning tasks, Korean bar exam questions). Testing was performed in zero-shot, multiple-choice QA format, under both closed-book and Retrieval-Augmented Generation (RAG) settings. RAG used BM25 retrieval on Korean statute and precedent corpora. | GPT-4 generally performed best, achieving 72.0% average accuracy on knowledge tasks, 88.6% on two core reasoning tasks (CAUSAL, CONS), and 48.1% on the 2024 bar exam (closed-book). Using RAG with both precedent and statute corpora improved GPT-4's knowledge task accuracy to 75.3% and 2024 bar exam accuracy to 49.7%, though RAG effectiveness varied by task and corpus. | NaN | NaN | NaN | NaN | Civil Law, Criminal Law, Public Law (Constitutional Law, Administrative Law), Professional Responsibility, Food Sanitation Law, various specific statutes. | South Korea | The benchmark KBL was created using Korean precedents, statutes, bar exams, legal QA datasets from Korea Legal Aid Corporation, legal terminology reference documents, etc. The RAG evaluation used a public corpus of 150k Korean precedents (LBoxOpen) and a newly compiled corpus of 220k Korean statutes and municipal ordinances. | Benchmark development involved sourcing diverse Korean legal texts, structuring tasks as multiple-choice QA, categorizing tasks (knowledge, reasoning, bar exam), and close collaboration with 8 licensed lawyers for task design, verification, and quality assurance (including correction of external data errors). RAG evaluation used standard BM25 retrieval. | The KBL benchmark dataset, associated corpora for RAG, and evaluation code are stated to be released via GitHub under a CC BY-NC license. | True | True | Stated intention to release dataset, RAG corpora, and code via GitHub under a CC BY-NC license. | Significant room for improvement in LLM capabilities for Korean legal tasks, particularly in recalling specific legal knowledge (e.g., statute numbers) and applying knowledge/reasoning in bar exam scenarios. RAG effectiveness is inconsistent and depends on LLM, corpus, and task type. | Ensuring the quality and accuracy of legal benchmark data required extensive expert verification (correcting up to 21% errors in sourced data). Designing pragmatic tasks beyond standardized tests. Building relevant Korean legal corpora for RAG evaluation. | Implicit risk of LLM hallucination in legal context (addressed via specific QA tasks and citing external work). General risk of misuse of open-source LLMs (mentioned briefly). |
| Y4rTcW-hKRcJ.pdf | Google_Scholar | Better Call GPT, Comparing Large Language Models Against Lawyers | This paper compares Large Language Models (LLMs) against Junior Lawyers and Legal Process Outsourcers (LPOs) for legal contract review tasks based on accuracy, speed, and cost. It finds that top LLMs match or exceed human accuracy in identifying issues, are dramatically faster, and significantly cheaper, suggesting a potential disruption in legal services. | True | Market | True | 2.0 | Positive | Comparative evaluation of multiple Large Language Models (GPT-4 variants, GPT-3.5, Claude variants, Palm2) for legal contract review (issue determination and location) using specific prompts. | Compared LLM performance (GPT-4, GPT-3.5, Claude, Palm2) against Junior Lawyers and LPOs on 10 anonymized procurement contracts (US & NZ), using ground truth established by Senior Lawyers. Measured accuracy (Precision, Recall, F-score for issue determination and location), time, and cost. | Best LLM (GPT4-1106) matched LPO accuracy for issue determination (F-score 0.87), slightly beating Junior Lawyers (0.86). LPOs led in issue location (F-score 0.77), followed closely by GPT4-32k (0.74). LLMs were vastly faster (seconds/minutes vs hours) and cheaper (>99.9% cost reduction). | NaN | Use of LLMs for contract review to drastically reduce cost and time, potentially enhancing accessibility of legal services. | NaN | NaN | Contract Law | United States, New Zealand | The study uses pre-trained commercial LLMs (GPT, Claude, Palm2) with their original, unspecified training data. The evaluation dataset consisted of 10 anonymized, real-world procurement contracts from US and NZ jurisdictions. | Experimental design involving benchmark creation (contract dataset, ground truth annotations by senior lawyers), prompt engineering for selected LLMs, comparative evaluation against human reviewers (junior lawyers, LPOs) based on accuracy, speed, and cost metrics. | NaN | False | False | NaN | Need to evaluate LLMs on more contract types; need to explore LLM capabilities in contract negotiation; LLMs may struggle compared to experts in locating issues where contract language is absent. | Selecting LLMs with sufficient context windows to avoid inefficient document splitting; prompt engineering specific to each LLM; initial setup, testing, and validation time. | LLMs performing less accurately in locating contract issues (vs. determining their existence), potentially impacting automated markup; need for ongoing human supervision of LLM outputs; potential for industry resistance to adoption based on protecting existing business models. |
| informit.T2024051500023791292031947.pdf | Google_Scholar | Rethinking Jurisdictional Barriers to Practising Law Abroad: A Soft Technological Deterministic Approach | This paper examines restrictive jurisdictional barriers to cross-border legal practice, arguing that technology is a key driver of change. Using a soft technological deterministic approach, it posits that this change is shaped by an interplay of technological progress with non-technological factors like legal system similarities and trade affiliations. | True | Market | False | 3.0 | NaN | NaN | NaN | NaN | Restrictive regulations (e.g., nationality/local admission requirements), professional protectionism, and outdated geographical-based regulatory frameworks for lawyers. | Advocates for reassessing historical justifications for restrictions and understanding the interplay of technology with factors like legal system similarity and trade ties to foster more liberal cross-border practice regulations. | Cross-border practice of law; Foreign lawyer mobility; Regulation of the legal profession. | NaN | General legal practice regulation, with examples from contract law, data protection, intellectual property. | International (mentions OECD countries, EU, US, UK, Australia, New Zealand, Nigeria, etc.). | NaN | NaN | NaN | False | False | NaN | Resistance to liberalizing foreign lawyer mobility due to protectionism; outdated geocentric regulations misaligned with technological advancements and globalization; need for adaptive regulatory reforms. | NaN | Traditional justifications for restrictions include protecting the public from incompetent legal service providers and risks from unauthorized cross-border practice, though the paper questions these as primary motivations. |
| 6hCPZ8Fr_aMJ.pdf | Google_Scholar | Beyond Readability with RateMyPDF*: A Combined Rule-based and Machine Learning Approach to Improving Court Forms | This paper introduces RateMyPDF, a web application designed to help authors assess and enhance the usability of court forms for self-represented litigants. The tool provides a score and automated improvement suggestions by combining rule-based methods, traditional machine learning, and GPT-3, validated against expert reviews and a large dataset of US court forms. | True | Idealistic | False | 1.0 | Positive | RateMyPDF: A web application combining rule-based metrics (readability, field counts, page counts, etc.), traditional ML (field classification), and LLMs (GPT-3 for summarization and metadata extraction) to automatically score court form usability and suggest improvements. | Compared RateMyPDF scores with human expert ratings (6 experts) on a subset of 40 forms. Experts rated complexity on a 1-5 scale. Intraclass correlation (ICC) was used to measure agreement. | Statistically significant intraclass correlations were found among experts (ICC1=0.3139, p=0.02) and between the average human rating and RateMyPDF score (ICC3=0.5861, p=0.00). RateMyPDF scores correlated with average expert ratings. | Court forms are often difficult for self-represented litigants to comprehend, complete accurately, and provide complete responses due to complex language, poor design, and legal jargon. Forms impose time and emotional burdens, are often created without usability expertise or user input, and traditional usability testing is resource-intensive. | Provide an automated tool (RateMyPDF) that measures form usability based on multiple features (readability, field types, layout proxies, burden estimation) and offers specific improvement suggestions. Enable scalable analysis and benchmarking of large form libraries to prioritize simplification efforts. | Improving the usability and accessibility of court forms, reducing administrative burden. | Self-represented litigants. | General Civil Litigation (court forms cover various areas like eviction, restraining orders, divorce, fee waivers) | United States (dataset from 46 states and D.C.) | Benchmark dataset: ~24,000 PDF forms scraped from official court websites in 46 U.S. States and D.C. (unstructured text, some with form fields). Field classification ML model trained using features including adjacent text, field location, previous field, and topic (derived from the form dataset). Leverages pre-trained GPT-3 model. | Literature review (form design, readability), data collection (web scraping), feature engineering (readability scores, field classification, burden estimation), development of rule-based and ML models, integration of external libraries (OpenCV, spaCy, PassivePy, EyeCite) and LLMs (GPT-3), user-centered design (interviews, workshopping with legal aid providers and court staff), validation through expert evaluation. | Publicly accessible web application (RateMyPDF.com), companion website for exploring the form dataset (Form Explorer), open-source code repositories on GitHub (FormFyxer library and RateMyPDF frontend). | True | True | Available as a web tool at RateMyPDF.com and as open-source code on GitHub (SuffolkLITLab/RateMyPDF and SuffolkLITLab/FormFyxer). | Need for establishing normative target scores ('good' vs 'bad' forms beyond complexity), refining time-to-answer estimates with real-world user testing, developing more domain-specific difficult word lists for legal forms, improving detection of state-specific citations, measuring whitespace and field ordering more directly, extending the approach to interactive legal applications (guided interviews). | Handling variability in PDF quality and formats (including XFA), automatically recognizing and normalizing form fields, classifying field types accurately (slot-in, gathered, third-party, created), integrating multiple NLP and computer vision tools, balancing automated metrics with actionable design advice, evaluating usability beyond simple readability. | Large language models (like GPT-3) may hallucinate or produce factually incorrect responses (mitigated by anchoring tasks like summarization to source text). Readability formulas can be 'gamed' by authors without improving true usability (mitigated by providing specific, varied recommendations). |
| kkd5gfg1ZFcJ.pdf | Google_Scholar | A Pattern Language for Persona-based Interactions with LLMs | This paper proposes a pattern language to enhance large language model (LLM) interactions by extending the basic 'Persona' prompt engineering pattern. It introduces seven new interconnected patterns designed to make LLM personas more dynamic, context-aware, culturally sensitive, and collaboratively developed. | True | Market | True | 1.0 | NaN | A pattern language for persona-based prompt engineering, including seven specific patterns: Multi-Persona Interaction, Dynamic Persona Switching, Role-Playing Scenarios, Contextual Depth Enhancement, Multi-Language and Cultural Adaptation, Temporal Perspective, and Collaborative Persona Development. | NaN | NaN | NaN | NaN | NaN | NaN | General Legal Advice, Contract Law, Legal Education, Compliance | International | NaN | Conceptual design based on pattern language methodology, identifying limitations and extending existing patterns. | NaN | True | True | The paper describes the prompt patterns conceptually with examples. | NaN | Increased complexity in prompt design, ensuring consistency and coherence (especially with dynamic switching/multiple personas), time consumption (for collaborative development), complexity in managing feedback (for collaborative development). | Hallucinations (generating incorrect/fictional content), Inconsistent/incoherent/disjointed outputs, Cultural stereotyping or oversimplification, Overfitting personas to specific users/contexts, Perpetuating biases, Historical inaccuracies (with Temporal Perspective pattern). |
| 1WKST3FL64cJ.pdf | Google_Scholar | Structured Legal Argumentation with LLMs: A Study in Landlord-Tenant Law | This paper proposes and evaluates a method using OpenAI's GPT-4o with context augmentation (Chicago's RLTO) and Chain-of-Thought instructions to generate structured legal arguments for landlord-tenant disputes. The study tests this approach on ten scenarios, finding reasonable accuracy and factuality but limitations in handling out-of-scope issues and relevance assessment. | True | Idealistic | True | 1.0 | Positive | Using GPT-4o with context augmentation (full text of Chicago's Residential Landlord and Tenant Ordinance - RLTO) and Chain-of-Thought (CoT) prompting to generate structured legal arguments (Exposition, Specific law, Why this Law Applies, Conclusion) for specific scenarios. | Evaluation of generated arguments for 10 hypothetical landlord-tenant scenarios (5 from legal aid, 4 AI-generated, 1 author-crafted) by a Landlord-Tenant lawyer based on metrics: Accuracy, Factuality, Comprehensiveness (0-1 scale), and Relevance (0-1 scale). | The method was accurate in 8/10 scenarios and 54/55 arguments were factual. Limitations identified include failing to recognize issues outside the scope of the provided RLTO and difficulties in filtering irrelevant details from emotionally charged scenarios or narrowing arguments to the core legal issue. | The implicit difficulty for laypersons in understanding their rights and drafting legal documents like demand letters in landlord-tenant disputes. | Providing LLM-generated, structured legal arguments based on specific scenarios and relevant law (RLTO) to assist laypersons in drafting documents and asserting rights, with outputs designed to be verifiable by legal professionals. | Generating legal arguments, assisting with drafting demand letters, understanding legal rights in landlord-tenant disputes. | Tenants, particularly those who might seek assistance from legal aid organizations. | Landlord-Tenant Law | Chicago | The technique uses context augmentation with the text of Chicago’s Residential Landlord and Tenant Ordinance (RLTO). The underlying LLM (GPT-4o) was pre-trained on general web data by OpenAI. | Prompt engineering (structured output format, Chain-of-Thought instructions), context augmentation, expert evaluation. | The scenarios, model parameters, and results are shared on GitHub, but no deployment of the tool/system itself is mentioned. | False | False | NaN | Limitations in classifying legal issues outside the provided context (RLTO), reliably assessing the relevance of generated arguments, robustness of the process, need for refined evaluation methods, difficulty filtering noise from emotionally charged descriptions. | LLM's inability to filter out less important concerns from user scenarios (especially when emotionally charged), difficulty in narrowing down arguments to the crux of legal issues, ensuring generated arguments stay within the scope of the provided legal text. | Inaccuracy (e.g., missing that an issue falls outside the scope of the provided law), lack of factuality (connecting premise and conclusion to the cited law), generating irrelevant arguments. |
| ftds8EOUbrIJ.pdf | Google_Scholar | The Continued Rise of Artificial Intelligence in Higher Education | This paper examines the rapid growth and integration of AI within higher education, specifically focusing on the University of North Carolina (UNC) System. It discusses current uses, future opportunities, significant risks (like bias, plagiarism, data privacy), and proposes a framework for developing institutional policies and risk mitigation strategies. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | Legal education, Legal profession automation, Ethical AI use | University students (including underrepresented groups) | General law, Legal education, Legal tech | USA (specifically North Carolina educational institutions) | NaN | NaN | NaN | False | False | NaN | Lack of institutional AI policies/strategy, Curriculum gaps in AI literacy/application, Need for ethical guidelines, Faculty training deficit, Potential for bias/discrimination, Job displacement concerns. | Policy development lag, Ethical concerns (plagiarism, bias, privacy), Faculty adoption and training, Ensuring validity/transparency of AI tools, Risk management complexity, Resource constraints, Potential degradation of critical thinking skills. | Data privacy breaches, Discrimination/Bias, Inaccurate/Unreliable outputs, Plagiarism/Academic integrity issues, Job displacement (legal sector), Loss of public trust, Security vulnerabilities (tampering), Disinformation propagation. |
| cQHRZiimZz0J.pdf | Google_Scholar | Large Language Models in Politics and Democracy: A Comprehensive Survey | This paper surveys the current and potential applications of large language models (LLMs) across various political domains, including policymaking, communication, analysis, national security, and law. It outlines both the opportunities for enhanced efficiency and inclusivity, and the significant challenges related to bias, transparency, reliability, and ethics. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Unreliability due to legal hallucinations, need for human oversight, potential biases favouring specific groups or jurisdictions. | Responsible development principles, creation of ethical guidelines and governance frameworks, ensuring human oversight, developing methods for bias mitigation, using domain-specific adaptation and curated data. | Legal information provision, legal research, legal drafting. | Under-resourced nations (mentioned generally in policy context), general public needing access to justice (implied). | General Legal Field | International | NaN | NaN | NaN | False | False | NaN | Technical: Robust bias mitigation, transparency, explainability, reliability (reducing hallucinations). Societal: Ensuring fairness, equity, representation; addressing impacts on polarization and democratic processes; establishing accountability frameworks. | Bias in models and data, reliability issues (hallucinations), lack of transparency and accountability, ethical concerns (e.g., manipulation, deception, lobbying), privacy risks, security vulnerabilities (adversarial attacks), need for effective human oversight, ensuring equitable access and outcomes. | Disinformation and manipulation, amplification of political polarization, biased or unfair policy outcomes, unreliable legal outputs ('hallucinations'), potential for unintended escalation in military/diplomatic contexts, erosion of democratic accountability, AI deception. |
| 26yOzn8f_vkJ.pdf | Google_Scholar | To Learn or Not to Learn: Replaced Token Detection for Learning the Meaning of Negation | The paper proposes continued pretraining of the ELECTRA model using its Replaced Token Detection objective on newly created negation-focused datasets (Expanded NLI and Expanded LAMA) to improve understanding of negation in Natural Language Inference (NLI). Results show significant gains on binary NLI tasks (RTE) using Expanded LAMA, but challenges remain for multi-class NLI (MNLI) and datasets with scarce negation examples (SNLI). | True | NaN | False | 1.0 | NaN | Continued pretraining of ELECTRA's discriminator using the Replaced Token Detection (RTD) objective on custom datasets (Expanded NLI, Expanded LAMA) designed to teach negation. | Models were evaluated on standard development sets of NLI benchmarks (RTE, MNLI, SNLI) and their corresponding negated subsets (Negated RTE, Negated MNLI, Negated SNLI from Hossain et al., 2020) using accuracy. | Continued pretraining with Expanded LAMA (+LAMA model) achieved a 19.9% accuracy increase on Negated RTE compared to the baseline ELECTRA-Small, reaching 70.3% accuracy. | NaN | NaN | NaN | NaN | NaN | International | Continued pretraining used two new datasets: 'Expanded NLI' (derived from RTE, MNLI, SNLI negation subsets from Hossain et al. 2020, plus Wikipedia/Books data) and 'Expanded LAMA' (derived from LAMA and Negated LAMA datasets, plus Wikipedia/Books data). Source datasets are publicly available; the derived datasets were created via specific processing rules for the RTD task. Unstructured text. | Dataset creation involved converting existing NLI and LAMA examples containing negation into a format suitable for ELECTRA's Replaced Token Detection (RTD) objective, including specific rules for generating 'original'/'replaced' token labels. Continued pretraining leveraged this modified dataset and the RTD task. | NaN | False | False | NaN | Difficulty handling negation cues in neutral-labeled NLI examples; overfitting on non-negated examples when negation data is scarce in finetuning dataset; limitation to specific overt negation cues (e.g., 'not', 'never'); need for pretraining tasks suitable for multi-class NLI with negation. | Adapting existing NLI/LAMA datasets for the RTD pretraining task; preventing overfitting during continued pretraining; handling the under-representation of negation in standard NLP benchmarks; difficulty learning negation's impact in multi-class (entailment/neutral/contradiction) settings. | NaN |
| pTUY-puzpdkJ.pdf | Google_Scholar | Technologically Competent Reprised: Ethical Practice in an AI Age and Considerations for Our Courts in a Burgeoning AI Era | This paper examines the ethical implications of generative AI for legal practice under existing professional conduct rules, particularly ABA Model Rule 1.1 regarding technological competence. It reviews recent court cases involving AI misuse, discusses court orders regulating AI, analyzes ABA and state bar guidance, and proposes revisions to ethical rules for greater clarity. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal Ethics and Professional Responsibility, Civil Procedure, General Litigation | United States | NaN | NaN | NaN | False | False | NaN | Lack of specific ethical guidelines and rules addressing generative AI; Insufficient lawyer training and competence regarding AI tools and risks; Inconsistent approaches by courts in regulating AI use. | NaN | AI Hallucinations leading to citation of nonexistent cases; Violation of client confidentiality through data input into AI tools; Lack of candor towards the tribunal; Frivolous claims/submissions; Inflated/unreasonable legal fees; Inadequate supervision of staff using AI; Bias in AI outputs; Misleading advertising by AI tools; Deceptive AI practices. |
| yRTzQcw3sdsJ.pdf | Google_Scholar | An Introduction to A Roadmap for Law School Modernity: Teaching Technology Competence | This paper introduces a law journal symposium focused on developing a 'Roadmap for Law School Modernity' by integrating technology competence into legal education. It highlights the professional duty for lawyers to be tech-competent and summarizes the symposium's articles, which cover curriculum framing, pedagogical considerations, competency testing, and the relevance of technology to access to justice. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | The access to justice gap; uneven technology access, especially for rural communities. | Improving legal education on technology competence to include how legal technology can address the justice gap and mitigate disparate impacts of technology access; incorporating technology competence across all law school curricula. | Access to justice gap; disparate impact of technology access in underserved areas like rural communities; legal education reform. | Underserved communities generally; rural communities specifically. | General legal practice | United States | NaN | NaN | NaN | False | False | NaN | Need for a unified approach to technology competence education in law schools; integrating rapidly evolving technologies like LLMs/Generative AI into legal education and understanding their impact. | Resistance from law school administration and faculty to curriculum changes; defining and assessing technology competence effectively; keeping legal education current with rapid technological advancements. | Professional and financial repercussions for lawyers lacking tech competence (e.g., cybersecurity breaches); ethical risks associated with the use of technology if lawyers are not properly educated on its benefits and risks. |
| iCe6v16i9SwJ.pdf | Google_Scholar | Friend or Foe – AI’s Invasion of the Legal Battlefield | This paper discusses the integration of AI into the legal profession, highlighting potential benefits like increased efficiency and access to justice through lower costs. It also examines significant risks, including ethical considerations, privacy concerns, AI errors ('hallucinations'), and the unauthorized practice of law. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | High cost of legal services; insufficient number of lawyers to meet population needs. | Leveraging AI for efficiency to enable lawyers to offer more affordable services (e.g., document drafting/review) and handle more clients, thereby increasing accessibility. | Cost of legal services, Efficiency of legal service delivery, Document automation, Legal research. | General public requiring affordable legal services. | General Legal Practice | United States (primarily, with brief mention of Italy/EU) | NaN | NaN | NaN | False | False | NaN | Need for clear governmental regulation and ethical guidelines for AI in law; ensuring lawyer competency in using AI; addressing AI limitations like bias and 'hallucinations'; defining boundaries related to the unauthorized practice of law. | NaN | AI 'hallucinations' (incorrect outputs); privacy violations due to handling client data on third-party platforms; unauthorized practice of law; potential for AI bias; cybersecurity threats (e.g., AI-generated malware); ethical concerns regarding lawyer competence, oversight, and accountability; potential legal liability for AI outputs. |
| lftOiX2IcekJ.pdf | Google_Scholar | Chain of Logic: Rule-Based Reasoning with Large Language Models | This paper introduces "Chain of Logic," a novel prompting method inspired by the IRAC legal framework, designed to improve rule-based reasoning in Large Language Models (LLMs). Evaluated on LegalBench tasks, Chain of Logic consistently outperforms existing prompting methods by decomposing rules into elements and then recomposing their logical resolution to arrive at a conclusion. | True | Idealistic | True | 1.0 | Positive | Chain of Logic prompting method | Evaluated across eight rule-based reasoning tasks involving three distinct compositional rules from the LegalBench benchmark. Compared against zero-shot, standard prompting, chain of thought, and self-ask methods using GPT-3.5, GPT-4, Llama-2-70b-chat, and Mistral-7B-OpenOrca models, using a one-shot example from a different rule application. | Chain of logic consistently outperforms other prompting methods across all tested models. On average, Chain of Logic achieved 79.3% accuracy across all rules and models (as per Table 1). | Language models are prone to hallucinations in legal settings and struggle with basic legal tasks and complex compositional rules. Annotated legal data is scarce, limiting fine-tuning capabilities. LLMs also show difficulty with in-context learning for legal reasoning. | The proposed 'Chain of Logic' prompting method guides LLMs to perform rule-based reasoning through explicit decomposition of rules into elements and recomposition of sub-answers to resolve the logical expression, thereby improving in-context learning and reducing the need for numerous rule-specific examples. | Improving rule-based legal reasoning in LLMs, enhancing the interpretability of AI-driven legal analysis, and potentially broadening access to justice by increasing the capacity of legal professionals. | NaN | Civil Procedure (Personal Jurisdiction, Diversity Jurisdiction), Contract Law (J.Crew Blocker covenant). | United States | The technique is a prompting method applied to pre-trained large language models (GPT-3.5, GPT-4, Llama-2, Mistral-7B-OpenOrca). The evaluation uses tasks from LegalBench, each providing a rule, fact pattern, and question. The method uses a single in-context example from a different rule application, not requiring model fine-tuning on task-specific data. | Inspired by the IRAC (Issue, Rule, Application, Conclusion) legal reasoning framework. The method involves: 1) Structured Input, 2) Rule Decomposition, 3) Logical Expression construction, 4) Question Answering per element, 5) Element Recomposition, and 6) Resolving the Expression. | NaN | True | False | The Chain of Logic prompting methodology and its steps are fully described in the paper, allowing users to implement it with compatible LLMs. The specific LLMs used have varying access models (commercial or open-source). | The rules in LegalBench are simplified compared to real-world legal rules. The current approach primarily addresses rule antecedents, not complex consequences. Future work areas include rule identification, dynamic sampling of reasoning paths, and incorporating retrieval augmented generation. | Models struggling with in-context learning in legal settings for compositional rules. Cost and scalability of requiring multiple reasoning examples per rule. Difficulties in correctly decomposing rules, identifying elements, and understanding logical relationships between them without explicit guidance. | Language models are prone to hallucinations in a legal setting. Potential for incorrect rule application or logical errors leading to inaccurate conclusions, even with advanced prompting. |
| bpVcEyHR4cQJ.pdf | Google_Scholar | HUMAN REALIGNMENT: AN EMPIRICAL STUDY OF LLMS AS LEGAL DECISION-AIDS IN MORAL DILEMMAS | This paper empirically investigates the alignment between human judgments and large language model (LLM) decisions in moral dilemmas, specifically trolley problems, finding significant misalignment with LLMs exhibiting utilitarian bias. It tests whether normative prompting can realign LLMs (GPT-3.5, GPT-4, GPT-o3-mini) with deontological or balancing principles, yielding mixed and often unsatisfactory results, raising concerns about their use as legal decision-aids under the Rule of Law. | True | NaN | True | 2.0 | Negative | Evaluating Large Language Models (specifically OpenAI's GPT-3.5, GPT-4, and GPT-o3-mini) as legal decision-aids in moral dilemmas (trolley problems), using normative prompting (deontological, utilitarian, balancing instructions) as a method to attempt human realignment. | LLMs were prompted with 41 moral dilemma vignettes multiple times (25-100 iterations per condition) via OpenAI API, varying prompts (no norm, deontological, utilitarian, balancing) and temperature settings (0.7, 1.0, 1.3 for GPT-3.5/4). LLM decision proportions (intervene/utilitarian vs. do nothing/abstain) were compared against human benchmark data from neal.fun and experimental studies (Mikhail 2002). LLM beliefs about human choices were also elicited and compared. | LLMs showed significant misalignment with human choices, exhibiting a stronger utilitarian bias. Normative prompting failed to reliably realign the models: GPT-3.5 often refused to decide when given deontological or balancing prompts; GPT-4 remained predominantly utilitarian and largely ignored deontological instructions; GPT-o3-mini responded strongly to deontological prompts but ignored instructions to balance concerns. | Misalignment of AI decisions with human values and established legal/normative principles (e.g., utilitarian bias overriding deontological concerns). Limited controllability or 'malleability' of LLMs through normative instructions, hindering efforts to ensure they act as faithful agents of the legislator (Rule of Law). Opacity of LLM reasoning processes, making it difficult to predict or understand their normative biases. | Investigated normative prompting as a realignment method but found it insufficient with current models. Suggests the need for rigorous, ongoing testing of LLMs against human and political/legal norms before deployment in morally laden legal contexts. Implies more intrusive methods like fine-tuning might be necessary, or 'heavy-handed' instructions, to achieve satisfactory alignment. | NaN | NaN | Constitutional Law (human dignity, balancing), Criminal Law (necessity by analogy), Tort Law (duty of care by analogy), Rule of Law principles. | Multiple (mentions US, UK, Germany) | Proprietary datasets used by OpenAI to train GPT-3.5, GPT-4, and GPT-o3-mini. Known to be large-scale, general-purpose, primarily unstructured text and code data derived from the internet, books, etc., not specifically legal domain data. | Experimental design: Comparing LLM outputs to human benchmarks across different conditions (normative prompts, LLM versions, temperature settings) using moral dilemma vignettes. Statistical analysis (t-tests, linear probability models, multinomial logistic regression) of quantitative choice data. Semantic clustering of qualitative justification data (for abstentions). | NaN | True | False | The evaluated LLMs (GPT-3.5, GPT-4, GPT-o3-mini) are accessible via the commercial OpenAI API. The specific prompts and vignettes used in the study are provided in the paper. | Technical: Current LLMs lack sufficient sensitivity and reliability in responding to nuanced normative instructions, especially balancing competing principles. Their opacity hinders trustworthiness and predictability. Societal: Need for governance mechanisms to ensure AI used in legal contexts aligns with democratic will and Rule of Law. Lack of established methods for reliably testing and ensuring normative alignment across different LLM versions and updates. | Inherent utilitarian bias in the studied LLMs. Difficulty in controlling LLM behavior via prompting (insensitivity, refusals to answer, unpredictable responses). Significant behavioral differences between LLM versions. Managing the stochastic nature of LLM outputs for systematic study. | LLMs acting as 'unauthorized normative rulers' imposing hidden or unintended biases (e.g., utilitarianism) contrary to legal principles or democratic will. Undermining the Rule of Law through misalignment. Users (e.g., judges) potentially making biased decisions based on flawed AI advice. Increased disconnect between legal decisions and societal values. Risk that technical improvements to LLMs may worsen normative alignment without specific testing. |
| PLFHrc0U1FoJ.pdf | Google_Scholar | “AI Takes the Gavel: Contract Laws' New Sidekick in Automated Decision -Making" | This paper explores the impact of Artificial Intelligence (AI) on contract law, focusing on opportunities like efficiency and risks such as errors, bias, and lack of transparency. It emphasizes the complexities of automated decision-making in law and the need for human oversight despite AI's growing capabilities. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Contract Law, General Legal Practice | India, Canada, US, EU, International | Discusses the need for labeled legal performance data (e.g., contract language variations) but notes challenges like context-specificity, jurisdiction variations, evolving law, privacy, and potential amplification of errors. No specific dataset described. | NaN | General market adoption by law firms and legal departments. | False | False | NaN | Technical gaps include AI's difficulty adapting to legal dynamics, ensuring confidentiality, achieving transparency without sacrificing accuracy, and handling legal complexity. Societal/Ethical gaps include establishing accountability, mitigating bias, preventing skill erosion, ensuring fairness, and addressing privacy concerns. | Data acquisition/labeling challenges (context, privacy, amplifying errors), ensuring accuracy/reliability, maintaining security/confidentiality, mitigating bias, achieving transparency/explainability, adapting to legal changes, avoiding deskilling/over-reliance, establishing accountability. | Errors in AI output (e.g., fake citations, flawed contracts), perpetuation of bias, data security/confidentiality breaches, over-reliance eroding human skills/judgment, lack of transparency hindering challenges to decisions, unclear accountability for mistakes, potential amplification of poor legal practices, workflow disruption from technical issues. |
| i3AWry70BicJ.pdf | Google_Scholar | STUDENT SCHOLARS: ACCESS -TO-JUSTICE RESEARCH IN THE LAW SCHOOL DIRECT REPRESENTATION CLINIC | The paper argues for integrating empirical access-to-justice research projects into traditional direct representation law school clinics. This model aims to enhance student learning about systemic legal issues and contribute to data-driven solutions for improving access to justice for low-income communities. | True | Idealistic | False | 1.0 | Positive | Integrating empirical access-to-justice research projects (qualitative and quantitative) into direct representation law school clinics. | Discussed using a case study of the author's related 3-year qualitative/quantitative research project on debt collection, and supported by literature on clinical education and A2J research. | NaN | Civil justice gap (unmet needs); A2J data gap (lack of data on court workings and community needs); limitations of lawyer-centric solutions; barriers for low-income litigants (e.g., lack of legal consciousness, systems avoidance, costs). | Conducting localized, empirical A2J research within law school clinics; using mixed methods (court data, qualitative interviews) to understand community needs and system failures; data-driven policy reform; training students as systems-change agents. | Consumer debt collection, housing/eviction, family law, high-volume state court litigation ('poor people's courts'). | Low-income litigants, self-represented litigants, marginalized communities (including racially/ethnically diverse and non-English speaking populations). | Civil Procedure, Consumer Law, Housing Law, Family Law, Poverty Law, Access to Justice. | USA (focus on state courts, with specific examples/case study in California, Texas, Utah, Arizona, New York, Massachusetts). | The proposed approach uses research data, not training data for a specific model. Data sources discussed include state court records (public but often unstructured/inconsistent), administrative data, and qualitative data from interviews with community members. | Integration of existing clinical models (direct representation, project-based, policy advocacy); application of social science research methodologies (quantitative analysis of court data, qualitative interviews); pedagogical theories (experiential learning, social justice lawyering). | Proposed for adoption by law school clinics; suggests resource sharing, network building, and partnerships with legal aid, other academic departments, and research centers. | False | False | NaN | Need for more granular data on SRLs and court processes; deeper understanding of legal consciousness and non-engagement; systematic integration of research into clinics; effective translation of research into policy/practice; evaluation of A2J interventions; improving inclusivity and scope of A2J research methodologies. | Securing grant funding; navigating IRB approval for human subjects research; fostering interdisciplinary partnerships; managing project continuity across student cohorts; training clinicians/students in empirical research methods; accessing and processing court data. | Ethical considerations in human subjects research (requiring IRB oversight for confidentiality and safety). |
| i1mVllezProJ.pdf | Google_Scholar | Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence | The paper critiques 23 existing LLM benchmarks using a novel evaluation framework based on people, process, and technology, identifying significant inadequacies related to functionality and integrity. It proposes a shift from static benchmarks towards dynamic behavioral profiling and regular audits for more accurate LLM evaluation. | True | NaN | True | 3.0 | NaN | Unified evaluation framework (based on People, Process, Technology) and proposal for behavioral profiling and regular audits for ongoing LLM assessment. | Structured Literature Review and Thematic Analysis applied to 23 LLM benchmarks using the proposed framework. | Identified widespread inadequacies in 23 benchmarks across Functionality and Integrity dimensions, including issues like response variability, inability to distinguish reasoning from optimization, linguistic bias, implementation inconsistency, lack of evaluator diversity, and overlooking cultural norms (detailed in Table II). | NaN | NaN | NaN | NaN | AI Evaluation / Computer Science (reviews benchmarks including some in law, finance, medicine, coding) | International | NaN | Structured Literature Review, Thematic Analysis, adaptation of the People, Process, Technology (PPT) framework. | NaN | False | False | NaN | NaN | Challenges identified with *current LLM benchmarking*: response variability, distinguishing reasoning vs. optimization, helpfulness/harmlessness tension, linguistic/logic diversity, installation/scalability, biases in LLM-generated evaluations, implementation inconsistency, slow iteration, prompt engineering difficulty, evaluator diversity, handling diverse cultural/social norms. *Challenges for the paper's own approach*: Subjectivity in evaluation (especially behavioral profiling), keeping evaluations current with rapid AI evolution, mitigating authors' own bias. | LLM 'gaming' benchmarks leading to misleading results, technical optimization mistaken for reasoning, data contamination/overfitting, perpetuation of biases, generation of harmful/unsafe content, security vulnerabilities (e.g., jailbreaking, delayed patching due to slow evaluation), misuse of the term 'benchmark', reliance on biased LLMs for evaluation. |
| aRJ0E_41Vj4J.pdf | Google_Scholar | Prompts for generative artificial intelligence in legal discourse | The paper examines the legal nature of prompts for generative AI in law, classifying them as legal actions and discussing copyright implications. It also explores their potential and risks in legal practice and education, advocating for standardization and interdisciplinary research. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | Restricted access to advanced AI models due to corporate control, which hinders broad technological development and legal diversity relevant for access to justice; general unreliability of AI (e.g., hallucinations, replication of non-compliant legal positions) if not properly managed. | Fostering interdisciplinary and international collaboration to balance diverse interests (including societal/access to justice needs) and ensure varied AI development; standardization of prompts and specialized legal education to improve reliability and effective use of AI for legal tasks, potentially extending to access to justice applications. | The general potential for AI to contribute to ensuring access to justice. | NaN | General legal practice, Copyright law, Contract law, Civil law theory, Judicial practice analysis. | International | N/A (The paper discusses training data for LLMs generally but does not propose or study a technique using a specific dataset.) | NaN | NaN | False | False | NaN | Lack of comprehensive legal understanding, regulation, and standardization of prompts for generative AI; insufficient development of specialized legal education for AI interaction; restricted access to advanced AI models controlled by corporations, limiting broader societal benefit including for access to justice. | NaN | Generation of incorrect or plausible but false information (hallucinations); replication of legally non-compliant common positions; unreliability of AI-generated legal documents and counsel without human validation; cognitive errors in prompt design or model training leading to flawed outputs; overlooking critical details in legal texts due to oversimplification by AI methods; narrow regulatory focus neglecting private AI use in legal services. |
| ldw0ALaiFLgJ.pdf | Google_Scholar | LAWYERING IN THE AGE OF ARTIFICIAL INTELLIGENCE | This paper presents a randomized controlled trial studying GPT-4's impact on law students performing legal tasks. AI assistance significantly increased speed and satisfaction, while quality improvements were slight, inconsistent, and most pronounced for lower-skilled participants. | True | Market | True | 2.0 | Positive | GPT-4 assistance for human legal analysis tasks. | Randomized controlled trial with 60 law students assigned to complete four legal tasks (complaint drafting, contract drafting, employee handbook section, client memo) with or without GPT-4. Outcomes were blind-graded for quality, and time taken was recorded; surveys assessed participant perceptions. | AI assistance slightly and inconsistently improved output quality (e.g., contract drafting +0.24, client memo -0.07 on a 4.0 scale) but consistently and largely reduced completion time (e.g., contract drafting -32.1%, complaint drafting -24.1%). Quality gains were larger for lower-skilled participants; time savings were consistent across skill levels. | High cost and inefficiency of legal services (implicitly identified as barriers AI could reduce). | Embracing generative AI to improve efficiency and potentially reduce costs, thereby lowering barriers to justice; proactive exploration and integration by lawyers, firms, and law schools. | Improved efficiency and potential cost reduction in legal service delivery through AI, thereby reducing barriers to justice. | NaN | Civil Litigation, Constitutional Law, Contract Law, Employment Law, Tort Law (Products Liability) | United States (Federal, Minnesota, Ohio) | NaN | Randomized controlled trial (RCT) with pre-registration of methods and hypotheses. | Participants accessed GPT-4 via a central 'ChatGPT “clone” website using the GPT-4 API' provided by the researchers. | True | False | GPT-4 is generally available via paid services like ChatGPT Plus from OpenAI. | Uncertainty about the higher-order impacts of AI on the legal services market (e.g., demand, billing) and how these will translate to tangible access to justice improvements; need for study on specialized legal AI tools and more complex tasks. | Recruiting and managing a specific participant pool (law students), designing realistic yet manageable legal tasks, providing controlled access to and training for GPT-4, and accounting for the rapid evolution of AI capabilities beyond those tested. | Hallucination of legal sources and facts by AI; over-reliance leading to decreased performance or malpractice; risks to client confidentiality with general-purpose AIs; hindering skill development in law students if AI use is not appropriately managed in education. |
| L8CImat85ScJ.pdf | Google_Scholar | REGULATION OF THE USE OF GENERATIVE ARTIFICIAL INTELLIGENCE TOOLS IN THE DELIVERY OF LEGAL SERVICES : VERIFICATION AND ACCOUNTABILITY | This paper discusses the challenges lawyers face in verifying the confidentiality and security of client data when using generative AI tools, as current guidance often requires diligence beyond their capabilities. It proposes that regulators, possessing technical expertise, should be responsible for assessing these AI tools and ensuring vendor accountability for data handling practices. | True | Market | True | 1.0 | NaN | Regulator-led assessment and verification of generative AI tools for legal services. | NaN | NaN | Lawyers' inability to verify AI tools for data confidentiality and security due to proprietary nature and lack of expertise; Unclear and unattainable due diligence standards in current legal guidance; Lack of comprehensive regulatory oversight for AI in legal services; Diverging incentives between lawyers seeking data protection and tech providers aiming to train models. | Shift the burden of AI tool verification from individual lawyers to legal regulators; Regulators, with technical experts, to establish and enforce standards for AI tools, including data security and handling; Mandate transparency and auditability from AI vendors for tools used in legal services; Implement consequences for non-compliant AI vendors. | Regulation of AI in legal practice; Lawyer's professional responsibility (confidentiality, competence); Accountability of AI technology providers; Cybersecurity and data privacy in legal tech. | NaN | General legal practice; Professional Responsibility/Legal Ethics; Patent Law. | United States (federal and state levels). | NaN | NaN | NaN | False | False | NaN | Absence of a comprehensive and effective regulatory framework in the U.S. for governing the use of generative AI in legal services, particularly concerning data security and vendor accountability; Lawyers' lack of access to AI vendors' proprietary information, hindering their ability to conduct proper due diligence; Unclear and often unattainable standards for lawyer diligence regarding AI systems; Insufficient mechanisms to ensure tech companies adhere to contractual and ethical obligations regarding data handling and safety. | Potential challenges in implementing the proposed regulator-led assessment include securing regulator preparedness and resources, developing requisite technical expertise within regulatory bodies, establishing clear and enforceable standards in a rapidly evolving technological landscape, and ensuring effective cross-jurisdictional coordination. | Breach of client confidentiality through AI data handling; Inadvertent disclosure of sensitive client or national security information; Violation of export control laws and secrecy orders; AI tools generating inaccurate or fabricated information (hallucinations); Copyright infringement by AI systems; Lack of transparency and accountability from AI providers regarding data use and model behavior; Potential for lawyers to violate ethical duties (competence, confidentiality) if relying on unverified AI. |
| mIXnP9q0bRsJ.pdf | Google_Scholar | OpenJustice.ai: A Global Open-source Legal Language Model | The paper critiques the use of generalized AI like ChatGPT for legal tasks due to risks like misinformation and lack of transparency. It introduces OpenJustice.ai, a proposed open-source, domain-specific legal language model designed to be reliable, transparent, and accessible, leveraging curated data and crowdsourced feedback. | True | Idealistic | True | 1.0 | Positive | OpenJustice.ai: An open-source, distributed legal language model using Retrieval Augmented Generation (RAG), instruction fine-tuning on legal data, and crowdsourced human feedback. | NaN | NaN | Risks associated with using general AI for legal tasks: legal misinformation/hallucinations, lack of transparency and precision, inability to offer diverse narratives, poor citation capabilities. Difficulty for non-lawyers in effective prompting. | Developing domain-specific, open-source, distributed legal AI (OpenJustice.ai) using: curated legal data, Retrieval Augmented Generation (RAG) for accuracy, multiplicity for diverse perspectives, assisted prompting for non-lawyers, crowdsourced feedback for improvement and transparency, and decentralized fine-tuning. | Access to justice, legal research, legal information provision, dispute resolution (negotiation), legal education, addressing legal misinformation. | Self-represented litigants, non-lawyers, legal students, legal clinics, Pro Bono Students Canada (PBSC), the broader legal community. | General Law (using legislation and case law), Employment Law, Consumer Protection, Personal Injury (mentioned for negotiation context). | International | Combination of: (i) Unstructured legal data (case law, journals, etc.) for self-supervised training. (ii) Structured data (annotated question-answer pairs since 2019) for instruction fine-tuning. (iii) Crowdsourced human feedback from the legal community. (iv) Proprietary data from industry partners for closed-system fine-tuning. | Retrieval Augmented Generation (RAG), Instruction Fine-tuning, Self-supervised Training (Masked Language Modeling), Crowdsourced Human Feedback, Decentralized Fine-tuning, Consortium-based development, Design Probes (for assisted prompting). | Rollout via a consortium of universities, legal clinics, and industry partners starting March 2023. A non-proprietary version intended to be openly accessible to the legal community for feedback, alongside custom models for partners. | True | True | Claims to be an open-source model launched in March 2023, intended to be openly accessible to the legal community via the OpenJustice.ai project/consortium. | Underlying reasons for LLM citation inaccuracies remain an unresolved computer science question. Need for better interfaces/tools (like assisted prompting) for non-expert users. Current LLMs lack true legal reasoning capability. | Ensuring factual accuracy and reliable citations; Training models for multifaceted legal reasoning; Making AI tools usable for non-lawyers; Managing crowdsourced feedback; Balancing open-source and proprietary data needs. | Legal misinformation or hallucinations, lack of transparency and precision, inability to offer diverse narratives (associated primarily with generalized AI but relevant context for legal AI). Poor citations. |
| H-SXQ38r3nMJ.pdf | Google_Scholar | Enhancing Semantic Validity in Large Language Model Tasks Through Automated Grammar Checking | This paper proposes integrating automated grammar checking tools into Large Language Model (LLM) workflows to improve the semantic validity of generated text. Experiments demonstrate significant enhancements in coherence, contextual accuracy, grammatical correctness, and readability across various text types. | True | NaN | True | 1.0 | NaN | Integration of automated grammar checking tools as a post-processing step for LLM-generated text. | LLM-generated text was assessed before and after applying an advanced grammar checking tool. Evaluation metrics included coherence (automated scoring), contextual accuracy (cross-referencing with contextual data), grammatical correctness (grammar checking tools), readability (formulas/scoring systems), lexical diversity (type-token ratio), and syntactic complexity (average dependency length). Analysis was done across different text types (news, academic, technical, conversational). | Significant improvements were observed: coherence (6.2 to 8.5), contextual accuracy (5.8 to 8.2), grammatical correctness (7.0 to 9.1), readability (65 to 85). Lexical diversity (TTR) increased from 0.45 to 0.55, and syntactic complexity (avg. dependency length) from 3.5 to 4.2. | NaN | NaN | NaN | NaN | NaN | NaN | Publicly available corpora from diverse domains (news articles, academic papers, technical documentation, conversational text) were pre-processed and fed into an LLM to generate text samples for the experiment. | An automated approach involving: LLM text generation, initial semantic validity assessment, integration of an advanced grammar checking tool for post-processing, re-evaluation of semantic validity, and comparative analysis of pre- and post-processing results. An algorithm for the integration process is provided. | NaN | False | False | NaN | NaN | Reliance on existing grammar checking tools not fully capturing LLM text subtleties; computational overhead of grammar checking affecting efficiency/scalability; evaluation focus on specific metrics may not cover all dimensions of semantic validity; limited generalizability due to dataset constraints. | Influencing public discourse and information dissemination; misuse for spreading misinformation or deepfakes; impact on human editors/writers; potential biases from the technologies. |
| n0Hs2VDVw4QJ.pdf | Google_Scholar | PanGu- π: Enhancing Language Model Architectures via Nonlinearity Compensation | This paper introduces PanGu-π, a novel Large Language Model (LLM) architecture designed to address the feature collapse problem by enhancing model nonlinearity through a series informed activation function and augmented shortcuts. The paper demonstrates its effectiveness and efficiency on general NLP tasks and through a domain-specific model, YunShan, applied to finance and law. | True | Market | True | 1.0 | NaN | PanGu-π architecture, incorporating Series Informed Activation Function (SIAF) in the Feed-Forward Network (FFN) and Augmented Shortcuts (AS) in the Multi-Head Self-Attention (MSA) module. | Evaluated PanGu-π (1B, 7B) on general NLP benchmarks (C-Eval, CMMLU, MMLU, AGI-Eval, BoolQ, AX-b, PIQA, CSL, EPRSTM, XSum, LCSTS) via OpenCompass. Evaluated domain-specific YunShan model (based on PanGu-π-7B) on financial (FinanceIQ, FinEval) and legal (LawBench) benchmarks. Included ablation studies and feature analysis (PCA, gradient visualization). | PanGu-π-7B achieved comparable performance to SOTA models with ~10% faster inference. PanGu-π-1B achieved SOTA performance for its size. YunShan surpassed similar-scaled models on financial (e.g., avg 61.34 on FinEval) and legal benchmarks (e.g. avg 31.75 on LawBench). | NaN | NaN | NaN | NaN | Chinese Civil Law (based on LawBench benchmark) | China (for domain-specific YunShan model evaluation); International (for base PanGu-π model training) | Base PanGu-π: 1.6 trillion tokens (1:1 English/Chinese) from diverse internet sources. YunShan Further Pre-training: Financial data (36.5B tokens - company announcements, news, articles, exams from FinCorpus, TuShare) and Legal data (111.7B tokens - regulations, cases, papers, exams from Pile of Law, LeXFiles, pkulaw.com, wenshu.court.gov.cn). YunShan Instruction Tuning: 995k domain instructions (JEC-QA, ChatLaw, Lawyer LLaMA, LawGPT, FinCorpus sources). Mix of public and crawled unstructured text data. | Theoretical analysis (feature collapse, nonlinearity), network architecture design (SIAF, AS with bottleneck), large-scale pre-training, ablation studies, supervised fine-tuning (SFT) for domain adaptation (YunShan). | Deployed as YunShan LLM for practical application in finance and law domains. | False | False | NaN | NaN | High computational cost of large models, complexity of LLM system engineering (data, architecture, training), balancing performance increase (nonlinearity) with efficiency (augmented shortcut cost addressed via bottlenecks), mitigating catastrophic forgetting during domain-specific further pre-training. | NaN |
| -FEDgvjRcnIJ.pdf | Google_Scholar | UNCOVERING THE FAIRNESS OF AI: EXPLORING FOCAL POINT , INEQUALITY AVERSION, AND ALTRUISM IN CHATGPT’S DICTATOR GAME DECISIONS | This paper investigates the social preferences of ChatGPT-3.5 and ChatGPT-4o using the Dictator Game with varying transfer efficiencies. It finds that GPT-3.5's tendency to give half its endowment is likely a focal point heuristic, while GPT-4o's decisions align more closely with altruistic motives, though inconsistently. | True | NaN | True | 2.0 | NaN | Using the Dictator Game experimental economics paradigm with varying transfer efficiency factors (f) to probe the social preferences (altruism, inequality aversion, focal point heuristic) of Large Language Models (ChatGPT-3.5-turbo and ChatGPT-4o). | Compared donation decisions of ChatGPT-3.5-turbo and ChatGPT-4o across 113 different transfer efficiency factors (f ranging from 0 to 1000). Each scenario (combination of LLM version and f value) was prompted 100 times via OpenAI's API (temperature=1). Donations were compared against theoretical predictions for payoff-equalizing, altruistic, and focal point strategies. | ChatGPT-3.5 consistently donated 50% regardless of transfer efficiency, suggesting a focal point heuristic. ChatGPT-4o's donations varied with efficiency, mostly aligning with altruistic motives (increasing donations with f), especially at higher efficiencies (donating 100% for f > 100), but showed some inconsistencies and trimodal distributions (50%, 100%, payoff-equalizing amount) for intermediate f values. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Experimental economics (Dictator Game with parameter variation), API-based interaction with LLMs, quantitative analysis of response distributions. | NaN | False | False | NaN | NaN | Interpreting AI decisions (distinguishing between heuristics like focal points and genuine preferences like fairness or altruism). Observed inconsistencies in GPT-4o's behavior across different transfer efficiency factors, making it difficult to conclude stable preferences. | Risk of misinterpreting AI behavior (e.g., concluding fairness from simple experiments when a heuristic is driving the behavior) if experimental parameters are not sufficiently varied. |
| kcm5NP6MOecJ.pdf | Google_Scholar | Language Model Fine-Tuning | This paper reviews language model fine-tuning, covering various methodologies like supervised and unsupervised techniques, and domain adaptation. It discusses applications in sentiment analysis, question answering, and conversational AI, along with challenges such as data quality, overfitting, and ethical bias. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal research, case law identification, legal document summarization, contract analysis | International | NaN | NaN | NaN | False | False | NaN | NaN | Data quality issues, risk of overfitting, ethical concerns (bias in data), substantial computational resources. | Ethical concerns surrounding bias in data. |
| _xt52fZFqmoJ.pdf | Google_Scholar | Quebec Automobile Insurance Question-Answering With Retrieval-Augmented Generation | This paper introduces two French corpora for Quebec automobile insurance and evaluates a GPT-4o based Retrieval-Augmented Generation (RAG) system for answering related questions. While RAG improves answer quality over a baseline, the study concludes that LLM-based QA is not yet reliable enough for critical applications due to a significant rate of false statements. | True | Idealistic | True | 1.0 | Neutral | Retrieval-Augmented Generation (RAG) using GPT-4o, a custom Quebec automobile insurance reference corpus for retrieval, and a custom question-answer corpus for evaluation. | Automatic metrics (BLEU, ROUGE, METEOR, BERTScore, MeaningBERT) and manual evaluation by an insurance expert using a predefined grading scale on 82 question-answer pairs assessing criteria for completeness and correctness. | The RAG approach using the complete custom reference corpus performed best, achieving a 51.74% score on manual expert evaluation. However, between 5% to 13% of LLM-generated answers included a false statement that could mislead a customer. | Lack of public's legal/insurance knowledge; complexity and jurisdiction-specific nature of insurance information; difficulty for individuals to find and correctly interpret relevant information online. | Developing AI-powered QA systems (like RAG) using curated, high-quality domain-specific corpora to provide more accurate and accessible information. Releasing these specialized corpora to foster further research. | Access to insurance information, understanding insurance products, consumer rights regarding automobile insurance. | General public / insurance customers in Quebec, particularly those seeking information online about automobile insurance. | Insurance Law (specifically Quebec automobile insurance). | Quebec, Canada. | The primary dataset used for the RAG system's retrieval component is the purpose-built 'Quebec Automobile Insurance Expertise Reference Corpus'. This French corpus consists of unstructured text from seven official and reliable online sources (legislation, legal insurance documents, regulator informative resources, domain-specific educative articles), manually extracted and cleaned. The LLM itself (GPT-4o) is pre-trained on general data not detailed by the paper. | Comparative evaluation of GPT-4o (zero-shot vs. RAG with incrementally added reference sources from the custom corpus); RAG architecture built using LangChain, OpenAI's text-embedding-ada-002 for embeddings, and GPT-4o for generation, including context compression. A manual evaluation protocol with a grading scale was developed and applied by a domain expert. | The research prototype uses proprietary OpenAI APIs for core LLM and embedding models. The developed corpora are released on GitHub. No public deployment of the QA system itself is mentioned. | False | True | The two custom corpora created for this research (Quebec Automobile Insurance Expertise References Corpus and Corpus of 82 Expert Answers to Laypeople Automobile Insurance Questions) are released on GitHub. | The reliability of LLM QA for critical legal/insurance applications remains insufficient (5-13% false statements). LLMs' tendency to hallucinate or not abstain when information is lacking, the impact of potential data leakage from pre-training, and the need for better alignment of automatic evaluation metrics with human judgment in specialized domains like law are remaining gaps. | Ensuring factual accuracy and avoiding misinformation in LLM outputs for specialized, high-stakes domains like insurance law. Potential for LLMs to be confused by incomplete or overly complex legal texts provided as context. LLM memorization versus true understanding and reasoning. Models defaulting to information from incorrect jurisdictions if not precisely prompted/contextualized. The labor-intensive and costly nature of high-quality manual evaluation for specialized QA. | Generation of false or misleading information by LLMs (study found 5-13% of answers contained false statements), potentially leading to customer misunderstanding and financial or legal harm. Premature deployment of inadequately vetted legal NLP tools. Inherent biases in training corpora and AI systems potentially leading to discriminatory outcomes. |
| Mr3hcqPrRuYJ.pdf | Google_Scholar | NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models | This paper details the NOWJ1 team's participation in the ALQAC 2023 competition, focusing on legal document retrieval and question answering. They propose hybrid systems combining classical statistical models (TF-IDF, BM25, QLD) and fine-tuned BERT models, utilizing techniques like learning-to-rank and specific pipelines for different question types. | True | Market | True | 1.0 | NaN | Hybrid approach combining traditional lexical models (TF-IDF, BM25, QLD) and fine-tuned BERT embeddings. For retrieval: features from lexical models and BERT-based classifiers (SVM, XGBoost, LightGBM) are ensembled using LightGBM (learning-to-rank). For QA: TF-IDF for matching, fine-tuned BERT for sentence classification (True/False, MCQs) and a two-stage BERT-based system for span extraction. | Evaluation within the ALQAC 2023 competition using its official training, public test, and private test datasets. Metrics: F2-score for retrieval, Accuracy for question answering. | For retrieval (Task 1), the system achieved the 1st rank on the public test (F2=0.94) and 2nd rank on the private test (F2=0.8358). For question answering (Task 2), it achieved 2nd rank on the public test (Accuracy=0.67) and lower on the private test (Accuracy=0.6545). | NaN | NaN | NaN | NaN | General Vietnamese Law | Vietnam | Competition datasets (ALQAC 2021, 2022, 2023 official samples; Zalo legal dataset) containing Vietnamese legal questions and articles (unstructured text), provided by organizers. | Pipeline approach involving rule-based pre-processing (word/article segmentation), feature extraction (TF-IDF, BM25, QLD, BERT embeddings), classification (SVM, tree-based models), and ensemble learning (LightGBM). | Submission to the ALQAC 2023 competition. | False | False | NaN | NaN | Handling long legal documents with model input limitations (addressed via segmentation). Adapting general pre-trained models (BERT) to the specific legal domain (addressed via fine-tuning). Managing computational time differences between models. Dealing with distribution shifts between public and private test datasets. Achieving high performance on legal question answering (noted lower results compared to retrieval). | NaN |
| SoCFwEeEKWUJ.pdf | Google_Scholar | A Cross-Lingual Statutory Article Retrieval Dataset for Taiwan Legal Studies | This paper introduces LawFactsQA-TW, a new cross-lingual (English-Chinese) statutory article retrieval dataset focused on Taiwanese law, aimed at improving legal information access for non-native speakers. It also proposes and evaluates several LLM-based retrieval methods as baselines, with LLM-augmented techniques showing improved performance metrics. | True | Idealistic | True | 1.0 | Positive | The LawFactsQA-TW dataset and LLM-augmented cross-lingual statutory article retrieval methods, including Answer Expansion, Statutory Article Expansion, and LLM-based Reranking. | Retrieval performance was evaluated using Recall and Average Precision (@10, @20, @50) on both human-labeled and synthetically A Cross-Lingual Statutory Article Retrieval Dataset for Taiwan Legal Studiesgenerated QA pairs within the LawFactsQA-TW dataset. Question-answering was evaluated using BLEU scores and an LLM-based 3-point scoring system. | On human-labeled data, LLM re-ranking with Breeze achieved the highest Recall@10 (0.472); Taide with Statutory Article Expansion achieved Recall@50 of 0.729. On synthetic data, BGE-m3 augmented with Breeze for Statutory Article Expansion achieved the highest Recall@50 (0.845). | Difficulties for non-native speakers in accessing and understanding legal information in a foreign language (cross-lingual retrieval challenge); scarcity of specialized, multilingual legal datasets for SAR. | Creation of LawFactsQA-TW, a cross-lingual (English-Chinese) dataset for Taiwanese statutory articles. Proposal and evaluation of LLM-based methods, particularly LLM-augmented retrieval, to enhance cross-lingual legal information access. | Cross-lingual statutory article retrieval; access to legal information (FAQs, statutes) for non-native speakers. | Foreign nationals in Taiwan; non-native Chinese speakers seeking legal information pertaining to Taiwan. | Taiwanese civil law, criminal law, and administrative regulations. | Taiwan | The LawFactsQA-TW dataset was constructed using: 1) A corpus of all Taiwanese civil, criminal, and administrative laws from the National Regulatory Database. 2) 92 human-labeled QA pairs derived from legal agency FAQs. 3) 173 synthetic QA pairs generated by gpt-4-turbo based on news articles and legal regulations. LLMs used for augmentation (GPT series, Breeze, Taide) are pre-trained models. | Dataset: Collection of official legal texts, manual annotation of FAQs, and an automated pipeline using gpt-4-turbo for synthetic QA generation. Retrieval Methods: Comparative analysis of sparse retrieval (BM25), dense retrieval (BGE-m3), and LLM-augmented retrieval (query expansion, hypothetical document generation, LLM-based reranking using various LLMs). | The LawFactsQA-TW dataset is introduced as a research resource. The paper presents LLM-based methods as baselines for this dataset. | True | False | The dataset is named LawFactsQA-TW and is presented as a key contribution of the paper, referenced via a footnote, implying it is a distinct resource associated with the research. | The synthetic portion of the dataset has not been evaluated by legal professionals, potentially affecting its credibility. The dataset primarily covers common public queries and may not address the specific retrieval needs of legal professionals. Further collaboration with legal experts is needed. | Mitigating translation errors in cross-lingual settings, enhancing retrieval accuracy for legal texts, and effectively evaluating the quality of LLM-generated legal content (answers and expanded queries/articles). | The paper notes a limitation that its synthetic dataset has not been evaluated by legal professionals, which could affect system credibility and expertise if deployed without such validation. This implies a risk of providing inaccurate or unreliable legal information. |
| ecxnpROAuQAJ.pdf | Google_Scholar | Integrating Generative AI into Legal Education: From Casebooks to Code, Opportunities and Challenges | This paper discusses the integration of Generative AI (GenAI) into legal education, highlighting the gap between traditional methods and modern practice needs. It explores opportunities like enhanced research and personalized learning, alongside challenges such as ethics, bias, academic integrity, and the need for curriculum reform. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General Legal Education / Legal Profession | International | NaN | NaN | NaN | False | False | NaN | NaN | General challenges discussed include: integrating AI without undermining critical thinking or enabling academic dishonesty; addressing AI inaccuracies ('hallucinations') and algorithmic opacity; mitigating bias amplification from training data; developing reliable methods for detecting AI-generated content in assessments; providing necessary resources (software, infrastructure, technical support); ensuring adequate faculty training; acknowledging and addressing the environmental and human costs of AI development. | Potential risks stated include: undermining students' critical thinking and skill development; increased academic dishonesty and plagiarism; generation of inaccurate legal information ('hallucinations'); perpetuation and amplification of societal biases leading to unfair or discriminatory outcomes; lack of transparency and accountability in AI decision-making; intellectual property violations; significant environmental costs (carbon emissions, e-waste) from AI model training and infrastructure; exploitation of human labor in AI development (e.g., data annotation). |
| vflh02DRLncJ.pdf | Google_Scholar | REGULATING ARTIFICIAL INTELLIGENCE AS A PERPETRATOR OF DEEPFAKE CRIMES IN INDONESIA | This paper examines the regulation of artificial intelligence (AI) as a perpetrator of deepfake crimes under Indonesian law, concluding that AI is not currently recognized as a legal subject. It discusses how existing laws (ITE Law, Criminal Code, Pornography Law) might apply to deepfake-related offenses like hoaxes, fraud, defamation, and pornography, while highlighting the need for specific AI legislation. | True | NaN | False | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Criminal Law, Cyber Law (Electronic Information and Transactions Law), Pornography Law | Indonesia, California (USA) | NaN | NaN | NaN | False | False | NaN | Lack of specific legal regulation for AI and deepfakes in Indonesia, particularly concerning AI's legal subjectivity and accountability. | NaN | Spreading hoaxes/disinformation, fraud (especially using deepfake audio), defamation, non-consensual pornography, manipulation of facts/circumstances, eroding public trust, social unrest, use as political propaganda, identity theft, privacy violations. |
| _rV9oWYXkzoJ.pdf | Google_Scholar | War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education | This paper reviews the development and compares the performance of major AI chatbots (ChatGPT 3.5/4, Bing Chat, Bard) on tasks relevant to higher education, finding current models perform poorly overall despite hype. It provides recommendations for faculty, students, and institutions on navigating AI's impact on assessment, teaching, learning, and academic integrity. | True | NaN | True | 2.0 | Neutral | Comparative evaluation of existing chatbots: ChatGPT (GPT-3.5), ChatGPT Plus (GPT-4), Bing Chat, and Bard. | Systematic comparison using 15 multi-disciplinary questions (Sociology, business, maths, history, economics, philosophy, literature, psychology, art history, Chinese non-fiction, literature search/annotation) relevant to higher education assignments/exams. Responses graded (A-F scale) based on accuracy, comprehensiveness, and clarity. | GPT-4 performed best (average C+), followed by ChatGPT-3.5 (average C). Bing Chat and Bard performed poorly (average F). Issues included lack of academic sources, hallucinations, factual errors, and inability to follow instructions. | Threats to academic integrity (plagiarism); difficulty detecting AI text; potential for misinformation/hallucinations; lack of critical evaluation by users; ethical concerns (data privacy, bias, exploitative data labeling); accessibility issues (bans, workarounds); potential deskilling; rapid pace of development. | Reform assessments (authentic, process-focused); teach responsible AI use, ethics, and limitations; require disclosure of AI use; update integrity policies; foster digital literacy; use AI to enhance teaching/learning; promote critical thinking; encourage stakeholder dialogue. | Higher Education: assessment, teaching, learning, academic integrity, research, employability. | Higher education stakeholders (students, faculty, institutions). | Multi-disciplinary including Law and Medicine (mentioned in literature review/testing examples). | International | Large-scale, primarily unstructured text and other data (web pages, books, articles, search data, image data, voice data, knowledge graphs); described as potentially 'internet scale'. Includes data from 'darkest recesses of the internet' labeled by outsourced workers for safety fine-tuning. Primarily proprietary datasets specific to each company (OpenAI, Google, Baidu). | NaN | Web interfaces (ChatGPT, Bard), Integration into existing products (Bing Chat in Bing Search/Edge), Paid subscription tiers (ChatGPT Plus), Initially restricted access via waitlists or geographically (Bing Chat, Bard), Planned enterprise focus/integration (Ernie). | True | False | ChatGPT (free version via web), ChatGPT Plus (paid subscription via web), Bing Chat (via Edge browser, likely free), Bard (via web, likely free). Ernie Bot access was restricted at time of writing. Availability may have geographical limitations. | Lack of reliable AI detection tools; need for updated assessment methods and academic integrity policies; need for improved AI digital literacy; need for more research on AI's educational effects; need for ethical guidelines/dialogue; current AI limitations (reasoning, bias, transparency); insufficient focus on equity in AI's educational use. | Inaccuracy and hallucinations in AI responses; poor sourcing (non-academic/fictitious references); bias in outputs; ethical issues (privacy, data sourcing); limitations in understanding context/instructions; ability to bypass safety features (jailbreaking); models lacking current information; difficulty accessing certain models for research (Ernie Bot). | Academic integrity threats (plagiarism); spread of misinformation/disinformation/fake news; harmful hallucinations; automation of nefarious activities (spam, malware, hacking); job displacement; deskilling; privacy violations; data breaches; algorithmic bias (racism, sexism); exploitation of data labelers; erosion of education as a public good; deepfakes; incitement of violence; risks to democracy; exposure of minors to inappropriate content. |
| 0NeSdgcY4UUJ.pdf | Google_Scholar | From Distributional to Overton Pluralism: Investigating Large Language Model Alignment | This paper investigates the effects of alignment on large language model (LLM) output distributions, particularly concerning response diversity. It finds that apparent diversity loss is often due to quality control and information aggregation into longer, more comprehensive responses, and demonstrates that aligned LLM behavior can be largely mimicked by base LLMs using advanced in-context learning prompting strategies. | True | NaN | True | 1.0 | NaN | In-context distillation prompting strategies, including static (URIAL with human/teacher outputs, random teacher outputs) and dynamic (kNN-selected teacher outputs, oracle kNN, URIAL with teacher summaries) approaches, to make base LLMs mimic aligned LLMs. | Evaluated on CONFLICTING QA and LIMA-OE datasets using Llama 2 and Mistral model families (base vs. aligned/instruct). Metrics included GPT-4 assessed quality (helpfulness, clarity, factuality, depth, engagement), lexical similarity (Jaccard-based Self-Sim and Max-Sim to teacher), semantic coverage (GPT-4 assessed), and stance analysis (GPT-4 assessed). | The dynamic 'URIAL Prompts and Summary' strategy for in-context distillation (referred as 'Llama 2 Base Summary Llama 2 Chat' in tables) allowed base Llama 2 to achieve a Max-Sim to Llama 2 Chat of 0.31 on CONFLICTING QA and 0.33 on LIMA-OE, closely approaching Llama 2 Chat's self-similarity (0.36 and 0.34_respectively). | NaN | NaN | NaN | NaN | NaN | International | The study utilizes pre-trained base LLMs (Llama 2, Mistral) and their instruction-aligned counterparts. For its in-context learning experiments, few-shot prompts were constructed using queries from the CONFLICTING QA and LIMA-OE datasets (or a separate corpus U) paired with responses that were either human-written or generated by the 'teacher' (aligned) LLM. The evaluation datasets (CONFLICTING QA, LIMA-OE) consist of open-ended questions. | Iterative design of few-shot prompting strategies. This included static prompts with fixed examples and dynamic prompts where examples were selected based on k-Nearest Neighbors (kNN) semantic similarity (using embeddings) to the input query or teacher's response. Some strategies incorporated summaries of teacher responses as additional hints. | Code and data are made available on GitHub. | True | True | The paper states 'Our code and data is available at https://github.com/thomlake/investigating-alignment'. The LLMs used (Llama 2, Mistral variants) are also generally publicly available. | The study is limited to two English-language QA datasets, LLMs up to 7B parameters, and uses imperfect evaluation metrics (lexical overlap, GPT-based assessment). The analysis does not cover information missing from base models themselves, potentially underrepresenting cross-cultural perspectives. | The imperfection of using lexical overlap and prompting GPT-4 to assess semantic similarity and other qualitative aspects. Difficulty in finding better intermediate semantic representations for evaluation. | The findings should not be taken as evidence that LLMs will appropriately handle diverse viewpoints in all high-stakes settings. The analysis does not address information missing from base models, a source of underrepresentation. The presented tools are for analytical purposes and not yet suitable for deployment. |
| meIFFFgdLAMJ.pdf | Google_Scholar | ChatGPT, Esq.: Recasting Unauthorized Practice of Law in the Era of Generative Al | This paper examines the conflict between traditional Unauthorized Practice of Law (UPL) rules and the capabilities of generative AI like GPT-4 in providing legal services. It proposes a novel UPL reform where bar associations would primarily regulate who can be designated a 'lawyer,' while allowing non-lawyers, including AI, to offer most legal services except for in-court representation, aiming to enhance access to justice. | True | Idealistic | True | 1.0 | Positive | A regulatory reform proposal: recasting Unauthorized Practice of Law (UPL) rules to focus on regulating the 'lawyer' designation, while permitting non-lawyers (including AI) to offer most legal services, excluding in-court representation. | NaN | NaN | Current Unauthorized Practice of Law (UPL) rules restricting non-lawyers (including AI) from providing legal services, leading to high costs, limited access to justice (especially for low-income individuals), and potential protectionism by the legal profession. | Recast UPL rules to primarily regulate who can claim the title 'lawyer' or 'attorney,' while allowing non-lawyers (including AI) to provide most legal services except for in-court representation. Consumer protection would rely on tort law (negligence, deceptive practices) and clear distinctions regarding lawyer status. | Reducing cost and increasing availability of legal information, advice, and document preparation for routine legal matters; reform of professional responsibility rules. | Low-income individuals and small businesses currently underserved by the legal system due to cost and access barriers. | General (Unauthorized Practice of Law regulation), Professional Responsibility, with examples from various fields like criminal law (trespassing), property law (eviction), and business law. | United States | NaN | Policy proposal developed through legal analysis, review of existing UPL jurisprudence and literature, and consideration of technological advancements in AI. | Adoption of revised Model Rules of Professional Conduct and corresponding changes in state-level UPL statutes and court rules, driven by bar associations and judiciaries. | False | False | NaN | Further development of tort law standards for AI/non-lawyer legal service providers; specifics of civil procedure adjustments; potential need for federalizing legal ethics for non-lawyer providers; ensuring equitable access to AI-driven legal services for all demographics; addressing potential for new forms of consumer exploitation if the new framework is not carefully managed. | Overcoming resistance from the established legal profession (judges, lawyers, bar associations); achieving consensus on the scope of UPL reform, particularly the definition of 'representation in legal proceedings'; ensuring the new framework adequately protects consumers while fostering innovation. | If UPL is not reformed: continued lack of access to justice, stifling of innovation, anticompetitive practices by the legal profession. With AI in law generally: errors (hallucinations), bias in AI systems if not properly developed and overseen, over-reliance by consumers. With the proposed reform: potential for consumer misunderstanding or exploitation if the distinction between lawyers and non-lawyer providers is not clear or if tort remedies prove insufficient; economic disruption to the traditional legal profession. |
| R0gkfcmKmPwJ.pdf | Google_Scholar | Will AI Replace Tax Practitioners? | This paper discusses the potential for AI to replace tax practitioners, arguing that AI will augment rather than replace human roles, particularly in tax law. It concludes that practitioners embracing AI will lead the profession, emphasizing a future of human-AI collaboration and the continued importance of human skills like ethics and complex reasoning. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | High cost and unequal access to AI tools, complexity of AI for non-proficient users, lack of transparency in AI decision-making (black box), AI bias perpetuating inequalities, profit-driven development neglecting marginalized communities, and deep-rooted systemic inequities hindering access to justice. | NaN | Accessibility of legal information and assistance in tax matters, particularly for low-income individuals and marginalized communities. | Low-income individuals, marginalized communities, individuals with disabilities, poor and minority communities. | Tax law | United States | NaN | NaN | NaN | False | False | NaN | Technical gaps include AI's limitations in handling ambiguity and novel situations, reliance on historical data, lack of nuanced legal reasoning, and opacity. Societal gaps include the digital divide, prohibitive costs, data access inequality for AI refinement, potential for AI bias, and insufficient infrastructure or political will for equitable AI deployment in justice. | NaN | Reinforcement of preexisting biases, privacy concerns, inaccurate or misleading AI outputs (hallucinations), potential job displacement for some tax professionals, reduced transparency in decision-making, exacerbation of inequalities in access to justice, and liability for AI errors. |
| LHYfQYjVfOUJ.pdf | Google_Scholar | A.I. In Law: Adversary or Ally? Addressing the Possible Implications of A.I. Technology in Law and the Necessity of Regulation | This paper examines the benefits and significant risks (like bias and inaccuracy) of integrating AI into the legal profession, focusing on impacts on marginalized communities. It argues for a comprehensive dual regulatory framework involving government and legal institutions to ensure ethical AI deployment and uphold justice. | True | Idealistic | True | 3.0 | Neutral | Discussion and evaluation of existing legal AI research tools (e.g., Lexis+ AI, Westlaw AI) and general LLMs (e.g., GPT-4), and proposal of a regulatory framework. | References empirical evaluation by Magesh et al. (2024) assessing hallucination rates in Lexis+ AI, Westlaw AI-Assisted Research, Ask Practical Law AI, and GPT-4. Also references Narayanan & Kapoor (2024) study on AI accuracy in predicting criminal justice outcomes (7%). References Gender Shades study on facial recognition bias. | Based on Magesh et al. (2024), hallucination rates in leading AI legal research tools remained between 17% and 33%, despite vendor claims about retrieval-augmented generation (RAG). | Algorithmic bias exacerbating systemic discrimination; unreliability and hallucinations leading to inaccurate legal information potentially harming vulnerable users; potential negative impact on employment equity for underrepresented groups in law; lack of AI literacy; exclusion of marginalized communities from AI governance. | Proposed dual regulatory framework (government oversight + internal legal institution governance), including mandatory sandbox evaluations, bias mitigation teams, transparency/accountability offices, mandatory education/certification for legal professionals, and inclusion of marginalized communities in policymaking (grassroots involvement/relational justice). Emphasizes human oversight. | Algorithmic bias and discrimination; Ethical AI use in law; Reliability and accuracy of legal AI; Regulation of AI; Impact on marginalized communities; Access to justice; Employment equity in the legal profession. | Marginalized communities, underrepresented groups, low-income individuals, early-career legal professionals and law students from historically underrepresented backgrounds, women, people of color. | General legal practice, Legal research, Contract review, Case prediction, Document drafting, Criminal justice. | US, EU | NaN | NaN | NaN | True | False | Discusses commercial tools like Lexis+ AI and Westlaw AI, available via subscription, and general models like GPT-4 with varied accessibility. | Need for reliable and unbiased legal AI tools; Effective regulatory frameworks balancing innovation and risk mitigation; Improved AI literacy among legal professionals; Mechanisms for community involvement in AI governance; Addressing AI's impact on diversity and equity in the legal workforce. | Ensuring AI accuracy and reliability (combating hallucinations); Mitigating algorithmic bias from training data and models; Achieving transparency and accountability in AI decision-making; Developing effective and adaptive regulations; Bridging the AI literacy gap among legal professionals; Managing data privacy and security. | Generation of fictitious legal citations/information (hallucinations); Amplification of systemic bias and discrimination; Privacy violations; Lack of transparency and accountability; Malpractice liability due to AI errors; Job displacement, particularly impacting marginalized groups entering the profession; Erosion of public trust; Misapplication in high-stakes legal decisions (e.g., criminal justice). |
| -ajvsbsAALoJ.pdf | Google_Scholar | Shariah Governance Standard on Generative AI for Islamic Financial Institutions | This paper proposes a comprehensive Shariah governance framework for integrating generative AI within Islamic Financial Institutions (IFIs), ensuring AI applications align with Islamic legal and ethical principles. The framework details a dual governance model and an operational standard to guide IFIs in mitigating risks and embedding Shariah compliance throughout the AI lifecycle. | True | Market | True | 1.0 | Positive | A Shariah governance framework and an 'Operational Shariah Governance Standard on Generative AI for Islamic Financial Institutions'. | NaN | NaN | Aligning AI with Shariah principles (avoiding ribā, gharar, maysir); AI opacity ('black box') hindering compliance verification; AI biases leading to discriminatory outcomes and undermining financial justice; AI-generated misinformation; lack of Shariah considerations in conventional AI governance. | A comprehensive Shariah governance framework integrating Islamic jurisprudence with AI governance principles, featuring dual governance (Shariah Supervisory Board and AI Governance Committee), ethical AI lifecycle management (data, model development, deployment, monitoring), and operational standards emphasizing transparency, fairness, accountability, explainable AI, and bias mitigation. | Ethical and Shariah-compliant financial services, financial inclusivity, fairness in financial decision-making, prevention of impermissible financial practices. | Customers of Islamic Financial Institutions, particularly low-income applicants or minority communities; underserved communities for zakāh distribution; women entrepreneurs for Islamic microfinance. | Islamic Finance Law, Shariah Law, Financial Regulation, AI Governance/Regulation | International | NaN | Integration of classical fiqh, maqāṣid al-Sharīʿah, and contemporary AI governance literature; conceptual framework development based on the AI lifecycle model. | The paper proposes the framework and standard for adoption by Islamic Financial Institutions and suggests it may be adopted or mandated by supervisory authorities (e.g., AAOIFI, IFSB). The standard itself is provided in an appendix for potential implementation. | True | True | The 'Operational Shariah Governance Standard on Generative AI for Islamic Financial Institutions' is provided in the Appendix of the paper, available for IFIs to adopt and implement. | Need for empirical validation of the proposed framework's efficacy in real-world settings; development of advanced technical tools for explainable and suitable AI in Islamic finance; ongoing updates to the governance framework to address evolving AI technology and challenges. | Synthesizing diverse and complex fields: Islamic jurisprudence (fiqh, maqāṣid al-Sharīʿah), contemporary AI governance, and specifics of generative AI in the financial sector to create a cohesive and practical framework. | Inadvertent promotion of impermissible financial practices (ribā, gharar, maysir); ethical lapses and biased AI decision-making leading to discrimination; AI-generated misinformation misleading stakeholders; 'black box' opacity undermining transparency and Shariah compliance; misuse of deepfake technology; data breaches. |
| 2dTgL-HM2fkJ.pdf | Google_Scholar | Topic classification of case law using a large language model and a new taxonomy for UK law: AI insights into summary judgment | This paper introduces a novel functional taxonomy for UK law and employs the Large Language Model Claude 3 Opus to classify UK summary judgment cases based on this taxonomy. The study evaluates the LLM's accuracy (achieving 87.13% F1) and analyzes the resulting topic distributions across legal domains, courts, and time. | True | Idealistic | True | 1.0 | Positive | Topic classification of UK case law using the Claude 3 Opus LLM, guided by a newly developed functional legal taxonomy and a specific prompt incorporating self-evaluation. | Manual classification by a legal expert on a statistically significant random sample (342 cases) from a dataset of 3078 summary judgment cases. Evaluation metrics included accuracy, precision, recall, F1 score (overall, macro, micro, weighted), and per-class analysis. | Claude 3 Opus achieved an overall accuracy of 87.13% (88.20% adjusted for minor naming errors) and a macro F1 score of 0.87 (weighted F1 0.89). Some topics showed lower performance, and a low rate of topic hallucination was observed (< 2%). | Summary judgments disproportionately affecting self-represented litigants; the challenge of balancing judicial efficiency with fairness and access to justice; lack of existing topic classifications for UK case law hindering analysis. | Developing a functional legal taxonomy and using LLMs for accurate topic classification to enable better analysis of case law trends (specifically summary judgment). This data-driven understanding can inform policy and judicial administration regarding fairness and efficiency. | Summary judgment procedure, fairness vs. efficiency in civil procedure, judicial administration, analysis of court trends. | Self-represented litigants. | Civil Procedure (specifically summary judgment), with topic classification covering multiple fields including Commercial law, Dispute Resolution, Personal/Consumer Matters, Public law, Criminal law (in civil contexts), and International law. | United Kingdom (UK) | The technique uses the pre-trained Claude 3 Opus LLM (proprietary data). Evaluation was performed on a curated dataset of 3,078 UK summary judgment cases (XML format, unstructured text) from the Cambridge Law Corpus. | Development of a new functional legal taxonomy using a grounded theory approach; Prompt engineering for the LLM, including closed-set prompting, detailed instructions, reasoning prompts, iterative refinement based on feedback, and adding self-evaluation instructions to mitigate hallucinations. | NaN | True | False | The prompt and taxonomy are published in the paper. The LLM (Claude 3 Opus) is commercially available. The dataset requires access permission from the Cambridge Law Corpus. | Lack of comparison with other models/methods; Relatively small dataset size; Subjectivity in manual evaluation; Potential for information leakage in LLM training data; Need for further research on hallucination mitigation; Need for more objective evaluation metrics; Limited generalizability beyond summary judgment/UK law without further testing. | Developing a suitable UK legal taxonomy; Effective prompt engineering for accuracy and hallucination reduction; Handling nuances/overlaps in legal topics; Evaluating performance accurately, especially for low-frequency topics; Distinguishing primary/secondary topics; Correcting LLM errors (hallucinations, naming discrepancies). | LLM hallucinations leading to incorrect topic assignments; Inaccurate classification impacting analysis reliability; Cascading errors from dataset identification and classification; Information leakage from LLM training data; The procedure itself (summary judgment) potentially sacrificing fairness for efficiency, especially for vulnerable litigants; Risk of non-specialist judges deciding complex cases via summary judgment. |
| dIjEiCOLmbgJ.pdf | Google_Scholar | Legal large language models (LLMs): legal dynamos or “fancifully \npackaged ChatGPT”? | This comment discusses the impact and perception of large language models specifically designed for legal tasks (Legal LLMs). It argues for a balanced view, positioning these tools as advanced assistants that require human oversight, rather than fully autonomous replacements for lawyers, while cautioning against both overhype and overly restrictive regulations. | True | Market | True | 3.0 | NaN | Legal LLMs (general category, mentioning specific examples like Harvey AI, Lexis+ AI, Westlaw AI, CoCounsel, etc.) | Cites a Stanford study [19] evaluating hallucination rates in Lexis+ AI, Westlaw AI-Assisted Research, and Ask Practical Law AI. Also mentions internal testing by Thomson Reuters. | Stanford study [19] found hallucination rates between 17% and 33%. Thomson Reuters claimed ~90% accuracy in internal testing dependent on customer usage patterns. | NaN | NaN | NaN | NaN | General legal practice, Contract law, Litigation, Regulatory compliance, Legal research, Tax law | USA, UK mentioned, but discussion seems broadly applicable. | Mentions specific tools trained on proprietary legal datasets (e.g., LexisNexis content) or combinations of legal and internet data. Kelvin LLM trained "from scratch" on legal data. | User feedback (e.g., from lawyers), fine-tuning by AI engineers and legal experts. | Commercial previews, partnerships with law firms, beta programs, direct product launches, software add-ins. | True | False | Specific commercial products (e.g., Lexis+ AI, PatternBuilder MAX, LawDroid Copilot) are stated as launched or available to customers. | NaN | Managing expectations (hype vs. reality), ensuring accuracy/reducing hallucinations, needing human verification, ethical integration, countering restrictive regulations. | Hallucinations/inaccurate output, uncritical reliance leading to errors, potential undermining of professional competence/judgment, ethical breaches if used without human supervision/verification. |
| SGGW0H1kypUJ.pdf | Google_Scholar | Beyond Words: A Controlled Experiment on the Role of Linguistic Empathy for Trust in Conversational AI | This paper develops and tests a theory of linguistic empathy in conversational AI using 9 specific rules. An online experiment with 277 participants solving a tenant law problem found that linguistic empathy increased perceived helpfulness, but decreased trustworthiness for angry users, while chatbots generally reduced cognitive effort compared to FAQs. | True | Idealistic | False | 1.0 | Positive | Rule-based chatbot designed with 9 specific rules for linguistic empathy (syntax, punctuation, rhetoric) built on a deterministic decision-tree logic. | A 2x3 factorial randomized online experiment with 277 Chicago residents. Participants used either an empathetic chatbot, a non-empathetic chatbot, or an FAQ page to solve a tenant security deposit problem. Anger was induced in half the participants. Outcomes (helpfulness, trustworthiness, cognitive effort) were measured using surveys (Likert scales) and analyzed using ANOVA and OLS regressions. | The linguistically empathetic chatbot was perceived as significantly more helpful. Trustworthiness increased with linguistic empathy for non-angry users but decreased for angry users. Using either chatbot significantly reduced cognitive effort compared to the FAQ page. | The primary challenge identified for AI tool effectiveness is building user trust, especially when users are experiencing negative emotions like anger, where linguistic empathy alone can be insufficient or counterproductive. | Designing conversational AI with specific linguistic empathy rules (based on syntax and rhetoric) to enhance helpfulness and trustworthiness. The findings suggest combining linguistic empathy with affective/emotional empathy capabilities, particularly for interactions involving negative user emotions. | Providing legal information and guidance in Tenant Law. | General public facing tenant law issues (specifically Chicago residents in the study). | Tenant Law / Landlord-Tenant Law | Chicago, Illinois, USA | N/A (The chatbot was rule-based using a decision tree, not trained on data in the ML sense). | Theoretical framework development (extending linguistic empathy theory), rule derivation (9 rules), rule-based system design (using decision trees and visual programming software Landbot), scenario design (collaboration with legal experts/non-profit), controlled behavioural experiment (2x3 factorial design). | Deployed within a controlled online experiment for recruited participants via the SONA research registry. | False | False | NaN | The need for conversational AI to possess affective/emotional empathy, in addition to linguistic empathy, to effectively build trust with users experiencing negative emotions. Further research is needed to examine the individual effects of the proposed linguistic empathy rules. | Disentangling the effects of linguistic empathy from cognitive ability and psychological empathy in experimental research. Designing AI that can effectively build trust, especially with users experiencing negative emotions like anger. | Linguistic empathy without corresponding emotional understanding can reduce trust in users experiencing negative emotions (e.g., anger). Training generative AI for empathy using non-expert labels can introduce biases. |
| rxTZXXLaMTcJ.pdf | Google_Scholar | Robots in the Middle: Evaluating LLMs in Dispute Resolution | This paper evaluates the performance of Large Language Models (LLMs), specifically GPT-4o, in acting as mediators for dispute resolution. Using a novel dataset of 50 dispute scenarios, the study found that LLMs can select appropriate intervention types and generate high-quality intervention messages, often outperforming human annotators in a blind evaluation. | True | Idealistic | True | 2.0 | Positive | Using GPT-4o to select mediation intervention types and generate intervention messages based on dispute scenarios, within the conceptual LLMediator framework. | A blind evaluation comparing GPT-4o with human annotators on a manually created dataset of 50 dispute scenarios. Evaluation included: 1) comparing choices of intervention types (5-point Likert scale), 2) comparing generated intervention messages (5-point Likert scale overall, and on understanding, neutrality, empathy, resolution quality), and 3) safety checks for LLM messages. | In 62% of cases, LLM-chosen intervention types were rated better than or equivalent to human-chosen types. In 84% of cases, LLM-generated intervention messages were rated better than or equal to human-written messages, with LLMs outperforming humans in 60% of these cases. | High cost of human intermediaries, scarcity of trained facilitators, limiting access to mediation, especially for low-value disputes or in certain areas. | Using LLMs in Online Dispute Resolution (ODR) to provide scalable, cost-effective mediation services, thereby increasing the availability of facilitated dispute resolution. | Online Dispute Resolution (ODR), mediation, access to justice. | Individuals facing cost or availability barriers to traditional mediation services. | General civil disputes (examples include parcel delivery, land property rights, noise complaints). | International | NaN | Experimental design involving: construction of 50 diverse dispute scenarios; human and LLM (GPT-4o) selection of intervention types and drafting of intervention messages for these scenarios; blind comparative evaluation of intervention types and messages by human evaluators using Likert scales and specific criteria; safety checks for LLM outputs. | The full data, code, and prompts for reproducing the experiment are made available on a GitHub repository. | True | False | The prompts, dispute data, and code for the experiment are available on GitHub, allowing replication using the commercial OpenAI GPT-4o API. | Lack of evaluation with expert mediators and in real-world ODR systems; limitations of structured intervention tasks not reflecting real mediator processes; evaluating complex, nuanced LLM outputs objectively; need for multi-modal data integration; determining when to intervene. | Scarcity of accessible real-world dispute data (due to sensitivity/privacy) necessitating manual dataset creation; difficulty in objectively evaluating LLM performance on complex, nuanced tasks like mediation where answers are not definitively right or wrong. | Potential for LLMs to hallucinate information or generate unsafe messages (though not observed in this study's specific experiment with GPT-4o). |
| A_Framework_for_LLM-Assisted_Smart_Policing_System.pdf | Google_Scholar | A Framework for LLM-Assisted Smart Policing System | This paper proposes and evaluates a framework using large language models (LLMs) like BART, GPT-3, and GPT-4 for crime prediction within smart policing systems. It applies zero-shot prompting, few-shot prompting, and fine-tuning techniques to crime datasets from San Francisco and Los Angeles, comparing LLM performance against traditional machine learning models. | True | Market | True | 2.0 | NaN | LLM-based framework (using BART, GPT-3, GPT-4) for crime prediction employing zero-shot prompting, few-shot prompting, and instruction fine-tuning. | Evaluated using weighted accuracy, precision, recall, and F1-score on crime datasets from San Francisco (SF) and Los Angeles (LA). Compared LLM approaches against each other and baseline ML models (Random Forest, XGBoost). Data split 80% training / 20% testing. | Fine-tuned GPT-3 achieved the best performance on the SF dataset (97% weighted accuracy and F1-score). On the LA dataset, few-shot GPT-4 performed best among the tested LLM approaches (68% weighted accuracy), but overall performance was lower. | NaN | NaN | NaN | NaN | Criminal law (crime prediction and policing) | United States (San Francisco, CA; Los Angeles, CA) | Publicly available historical crime incident report datasets from San Francisco (DataSF) and Los Angeles (LA Open Data), pre-processed and transformed into natural language descriptions. An instruction dataset was derived from this data for fine-tuning. | Application of existing LLMs (BART, GPT-3, GPT-4) using standard techniques (zero-shot prompting, few-shot prompting, instruction fine-tuning via API/HuggingFace). Comparison with baseline ML models (RF, XGBoost). Performance evaluation using standard metrics. | Conceptual framework and integration diagrams provided, suggesting cloud or local deployment for law enforcement, but no specific deployment of the prototype tool itself is described. | False | False | NaN | LLM performance variability across datasets; need for output calibration for reliable probability estimates in predictive policing; lack of a detailed framework for identifying and mitigating biases and ethical issues in deployment. | Significant variability in LLM performance between SF and LA datasets. Difficulty adapting LLMs to diverse dataset characteristics. Poor performance in classifying minority crime classes, particularly in the LA dataset. Computational costs of fine-tuning. Ensuring prompt quality. | Perpetuating biases from historical crime data leading to discrimination. Privacy violations from data collection/sharing. Lack of transparency and accountability in LLM decisions. Overreliance on AI systems in policing. |
| KthtaKV79LAJ.pdf | Google_Scholar | A Survey of Generative AI in Finance | This paper surveys real-world generative AI applications in the financial sector, analyzing tools from major institutions across different regions and segments. It examines their technologies, functionalities, impacts, and regional adoption patterns to provide insights for financial institutions. | True | NaN | True | 2.0 | NaN | DeepSeek R1: AI model for enhanced reasoning (math, coding, knowledge tasks) using reinforcement learning (GRPO). | Benchmarks: AIME 2024, MATH-500 (math); Codeforces (coding); MMLU, GPQA Diamond (knowledge); AlpacaEval 2.0 (QA). Compared to OpenAI o1-1217. | AIME 2024: 79.8% pass@1; Codeforces: 96.3 percentile; MMLU: 90.8%; MATH-500: 97.3%; AlpacaEval 2.0: 87.6% win rate. Matches/exceeds OpenAI o1-1217. | NaN | NaN | NaN | NaN | NaN | International | Built on DeepSeek-V3-Base. R1 version: multi-stage training pipeline including cold-start data, reasoning-oriented reinforcement learning, rejection sampling, and comprehensive fine-tuning. R1-Zero: pure reinforcement learning. | Reinforcement learning (GRPO - Group Relative Policy Optimization framework), multi-stage training pipeline, model distillation. | Described as having an open-source nature, making it a valuable resource for the research community. | True | True | DeepSeek R1 is described as having an open-source nature available to the research community. | NaN | For DeepSeek R1: Sensitivity to prompting, occasional language mixing issues. | Misinformation, harmful/discriminatory content, hallucinations, over-reliance on AI outputs, lack of transparency/explainability, data privacy and security breaches, regulatory compliance failures. |
| H5HwzgGHq2wJ.pdf | Google_Scholar | The Disrupting Influence of AI and the Potential Impact of ChatGPT on Maritime Law and Practice | The paper explores the disruptive potential of AI, particularly ChatGPT, within the field of maritime law and practice. It discusses potential applications like contract analysis and incident investigation, while also highlighting significant challenges such as accuracy, legal acceptance, data privacy, and ethical concerns. | True | Market | True | 3.0 | Neutral | ChatGPT | NaN | NaN | Legal acceptance by the community, data privacy and security concerns, integration challenges with existing systems, intellectual property disputes, liability uncertainties (especially with autonomous systems), AI inaccuracies and potential for misinformation, need for human expertise/oversight, ethical concerns (bias, transparency). | Keen human oversight and validation ('human in the loop'), transparency with users about AI interaction, development of robust guidelines and best practices, workforce adaptation and reskilling, creation of specific AI usage protocols (e.g., data security, content validation). | Automating legal tasks (contract review/analysis/generation, maritime incident investigation, environmental monitoring/compliance, legal research/information retrieval), potentially lowering cost of legal services and improving efficiency. | NaN | Maritime Law | International | General text data from the internet (pre-September 2021); potential for fine-tuning on maritime-specific data (laws, contracts, incident reports). | NaN | Discusses potential investigation and protocol development by companies like Maersk, but no specific deployment strategy detailed. | False | False | NaN | Need for established legal acceptance and frameworks for AI use, unresolved data privacy/security/IP/liability issues, requirement for improved AI accuracy/reliability and mitigation of biases, lack of integration standards, need for workforce adaptation strategies, limited knowledge base (e.g., past Sept 2021 cutoff for ChatGPT). | Ensuring accuracy and avoiding misinformation/hallucinations, understanding specific domain nuances (e.g., maritime nomenclature), preventing misuse, implementing quality control, integrating with existing systems, addressing legal acceptance, data privacy, IP, and liability concerns, managing implementation costs, mitigating ethical issues. | Spreading legal misinformation, misinterpreting legal/trade terminology, creating intellectual property disputes, complex liability issues from AI errors or autonomous operations, data privacy/security breaches, job displacement, perpetuating biases, lack of transparency. |
| 3yFwsD-ie9gJ.pdf | Google_Scholar | A Comprehensive Study of ChatGPT: Advancements, Limitations, and Ethical Considerations in Natural Language Processing and Cybersecurity | This review paper presents an in-depth study of ChatGPT, analyzing its architecture, training, capabilities, and limitations in NLP and cybersecurity. It compares ChatGPT with other language models and discusses ethical considerations, privacy risks, and diverse applications across various industries. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | Reviews training data for models like GPT-3 (175B parameters, 570GB text including Common Crawl, WebText2, Wikipedia, Books) and ChatGPT (fine-tuned GPT-3 using Reinforcement Learning from Human Feedback (RLHF) on dialogue datasets). Data is generally large-scale, web-based, unstructured text, proprietary in its final form used by OpenAI. | NaN | NaN | True | False | Available as a service/API from OpenAI (implied through discussion of the existing tool). | NaN | Bias from training data, lack of contextual understanding/common sense, high computational cost, large data dependency, lack of interpretability, potential for generating incorrect information ('hallucination'), limited knowledge cutoff (initially 2021), lack of direct internet search, inability to interpret images. | Misuse for generating fake news/disinformation/impersonation, generation/leakage of sensitive/personal data (privacy violation), use in phishing/scams, facilitating cybercrime (malware/script generation), replicating biases, potential copyright infringement, inappropriate substitution for human interaction. |
| DlbOhgReUPsJ.pdf | Google_Scholar | Developing Fictitious Country Maps through Generative AI Techniques | This thesis explores the application of diffusion models to generate high-resolution maps for the fictional country of Carana, a scenario used by international peacekeeping organizations for training and strategic planning. The study aims to address the limitations of existing map representations by developing a framework to produce synthetic, adaptable imagery for enhanced simulations. | True | Idealistic | False | 1.0 | Positive | Diffusion model based on U-Net architecture for generating synthetic satellite imagery tiles. | Validation was performed using histogram-based analysis of color channels and Fréchet Inception Distance (FID) scores, comparing generated tiles to real Sentinel-2 imagery. | The Fréchet Inception Distance (FID) score was 435.0111, indicating that further improvements are needed to enhance the quality of the generated tiles. | Lack of detailed, adaptable, and realistic maps for training international peacekeeping organizations, hindering effective simulation, strategic planning, and operational preparedness for complex crises. | Developing a framework using diffusion models to generate high-resolution, synthetic maps for the fictional country scenario (Carana) to improve the quality and realism of training materials for peacekeeping personnel. | Enhancing training effectiveness for peacekeeping operations, crisis management, humanitarian aid distribution, conflict resolution, and post-disaster response through improved geospatial visualization. | International peacekeeping organizations (e.g., UN), military leaders, policymakers, and humanitarian workers undergoing training. Indirectly, populations in regions affected by geopolitical conflicts, humanitarian crises, and natural disasters. | Public International Law, International Humanitarian Law (as relevant to the peacekeeping and crisis response training scenarios). | International (Carana is fictional; training data from Ethiopia, Kenya, Somalia; intended for international organizations). | Publicly available Sentinel-2 L2A RGB satellite imagery from regions in Ethiopia, Kenya, and Somalia, preprocessed into 64x64 pixel tiles after filtering for cloud cover and null data. | Iterative development following a data science project pipeline: data acquisition (Sentinel-2 imagery), preprocessing (tiling, cleaning), model training (U-Net based diffusion model, initially local, then on HPC), and validation (histogram analysis, FID). | The generated maps are intended for integration into training simulations, with a specific example of georeferencing an output map using ArcGIS Pro for use with a geodatabase. | False | False | NaN | Technical gaps include the lack of labeled geospatial features in training data for conditional generation, inconsistent color and unrealistic shapes (e.g., cloud-like patterns) in generated tiles, limitations of 10m resolution source imagery, and computational intensity of training/sampling. Need for improved tile coherence and boundary blending in assembled maps. | Ensuring spatial coherence and color consistency across assembled tiles, managing high computational requirements for model training (necessitating HPC), GPU configuration, robust preprocessing of satellite imagery (cloud-cover, black/white pixel filtering), and adapting validation metrics like FID to the specific tile characteristics. | General ethical concerns related to AI-generated maps, including data integrity, potential for misinformation, and biases inherited from training data (acknowledged from literature, not specific to this study's findings). |
| nI4pc9EGbUoJ.pdf | Google_Scholar | Exploring a GPT-based large language model for variable autonomy in a VR-based human-robot teaming simulation | This paper introduces and evaluates a simulation framework using Virtual Reality (VR) where users interact via natural language with multiple robot agents, each powered by a GPT-4 core. A user study explored interaction strategies, finding users often defaulted to simple commands despite the LLM's capabilities, highlighting challenges in shared understanding and perceived agent autonomy. | True | NaN | True | 1.0 | NaN | A VR-based simulation framework (using Unity) for human interaction with multiple simulated robot agents controlled by individual GPT-4 instances, employing OpenAI's function calling to map natural language commands to robot actions. | Exploratory within-subjects user study with 12 participants performing seven structured tasks of increasing complexity within the VR simulation, interacting with the agents via speech. Data collected included audio/video recordings, system logs, post-study questionnaires (SASSI), and semi-structured interviews. | Users often employed simple, command-like instructions and had preconceived expectations, seldom exploring the LLM's full conversational potential. Challenges included mismatches in expected autonomy, response latency, and occasional LLM meticulousness or errors, though some users successfully adopted more complex, conversational coordination strategies. | NaN | NaN | NaN | NaN | NaN | NaN | The system uses OpenAI's pre-trained GPT-4 model (gpt-4-0613) accessed via API. The model is initialized with specific prompts (role, restraints, few-shot examples) and structured function descriptions (JSON objects detailing available actions and parameters) relevant to the simulation environment. | Development of a custom software framework using Unity Engine for VR simulation, integration of OpenAI API (GPT-4 for control logic, Whisper for speech-to-text), Amazon Polly (text-to-speech), and implementation of OpenAI function calling for command interpretation. Design involved creating a multi-agent architecture with a central controller and individual agent GPT cores, task design for user studies, and thematic analysis of user interaction data. | The simulation framework is described as being available via a provided GitHub link. | True | True | The framework is available at a GitHub link provided in footnote 1. | NaN | Mapping unstructured natural language to structured robot actions (addressed via function calls but still imperfect); aligning user and agent conceptual/world models; LLM non-determinism, opacity, and planning limitations; response latency due to cloud API calls and sequential processing; achieving natural turn-taking and dialogue flow; designing effective inter-agent communication for collaborative tasks; providing adequate intervention/control mechanisms. | LLM hallucination/errors leading to incorrect actions or communication breakdown; potential for user frustration due to latency or misalignment of expected vs. actual agent autonomy/capabilities; ethical concerns regarding potential future empathetic channels (user deception, authenticity). |
| G4OomxoYXtoJ.pdf | Google_Scholar | On Evaluating Legal-Reasoning Capabilities of Generative AI | This paper critically examines recent studies on the legal-reasoning capabilities of generative AI, particularly large language models. It also discusses the potential roles of traditional symbolic AI approaches in legal reasoning and argumentation in the era of generative AI. | True | NaN | True | 3.0 | Neutral | Generative AI / Large Language Models for legal reasoning and argumentation, including various prompt engineering methods (e.g., zero-shot, few-shot, Chain-of-Thought). | The paper reviews studies that evaluated LLMs on tasks such as bar/law school exam performance, specific legal reasoning tasks (e.g., rule application, IRAC adherence, entailment), and legal document generation. Evaluation methods included qualitative expert assessment, quantitative metrics (accuracy, F1 score, precision, recall), and comparisons against human performance or other NLP models. | The paper reviews various results; one of the highest performances cited is from Servantez et al. (2024), where GPT-4 with a 'Chain of logic' prompting method achieved 92.3% accuracy on specific rule-based tasks from the LegalBench benchmark. | NaN | NaN | NaN | NaN | General legal reasoning, Tax Law, Cryptocurrency Securities Law, Criminal Law (based on reviewed studies). | Japan, United States (based on reviewed studies); discusses concepts broadly applicable internationally. | Reviewed studies use LLMs (e.g., GPT-3, GPT-4) pre-trained on broad general datasets. Some specific studies involved fine-tuning on legal datasets (e.g., COLIEE) or used retrieval-augmented generation with legal documents like statutes or case law. | The paper discusses approaches that utilize prompt engineering (zero-shot, few-shot, chain-of-thought, retrieval-augmented generation), selection/fine-tuning of LLMs, and structuring tasks according to legal frameworks like IRAC, as observed in the reviewed studies. | NaN | False | False | NaN | NaN | Challenges identified in applying/evaluating LLMs for legal reasoning include: LLM 'hallucinations'; difficulties in robustly evaluating reasoning beyond output; interpreting ambiguous natural language; ensuring models genuinely follow proclaimed reasoning methods (unfaithful explanations); avoiding training data memorization effects in evaluation; and moving beyond simplistic deductive tasks to full legal argumentation. | Potential risks stated include: LLMs producing factually incorrect information ('hallucinations'); biases (e.g., racial, gender) influencing LLM outputs and decision-making processes; unfaithful explanations misleading users about the actual reasoning; and over-reliance on LLMs for complex legal work for which they may be ill-suited or not robustly validated. |
| BO49BB8AYbkJ.pdf | Google_Scholar | From Text to Structure: Using Large Language Models to Support the Development of Legal Expert Systems | This paper investigates using large language models (LLMs), specifically GPT-4, to automatically extract structured representations (pathways) from legislative text. The goal is to support the efficient development of rule-based legal expert systems, like JusticeBot, for improving access to justice. | True | Idealistic | True | 1.0 | Positive | An LLM-based framework (JusticeCreator Automatic Pathway Generator - JCAPG) using prompted GPT-4 to extract structured pathways (criteria and conclusions) from legislative text, formatted for the JusticeBot/JusticeCreator system. | Evaluation by 4 experts on 40 articles from the Civil Code of Quebec. Compared GPT-4 generated pathways to manual ones using direct rating (textual/logical accuracy, usability) and a blind comparison test (E2). | In a blind comparison test, 60% of automatically generated pathways were rated equivalent or better than manual ones. In direct evaluation, 90% were rated as correct or needing only slight adjustment for use as a basis for a JusticeBot tool. | The manual analysis and encoding of legislation into formal representations is time-consuming and requires legal expertise, creating a bottleneck for developing legal decision support tools. | Using LLMs (GPT-4) to automatically generate draft pathways from legislative text, which can then be reviewed and refined by legal experts, thereby increasing efficiency in developing rule-based expert systems. | Development of legal decision support tools for laypersons based on legislation. | Laypeople seeking to understand how legislation applies to them. | Civil Law (based on the Civil Code of Quebec). | Quebec (Canada) | The technique uses OpenAI's GPT-4 model. The input data for the experiment consisted of 40 selected articles from the Civil Code of Quebec. | Iterative prompt engineering for GPT-4 based on the JusticeBot methodology. Development of the JCAPG tool integrating prompt execution and JSON conversion. | Output pathways can be imported into the JusticeCreator tool. Code and prompt shared on GitHub. | True | True | Code and prompt available on GitHub (link provided in footnote 3). | Generalizability to more complex/interconnected legislation, other jurisdictions/legal traditions (beyond Quebec Civil Code), need for integration of case law/doctrine to resolve ambiguities, need for robust study of efficiency gains, and application to related tasks (e.g., mapping case facts to pathways). | Ensuring logical correctness (avoiding errors like denying the antecedent), handling legal ambiguity inherent in texts, variability in valid pathway structuring, preventing model hallucination, and occasional technical errors in generating valid structures. | Inaccuracy of generated pathways (textual errors, missing elements, hallucinations, logical fallacies) potentially leading to incorrect legal information or flawed system logic if not diligently verified by human experts. |
| 5NF0TDdTxRgJ.pdf | Google_Scholar | Explaining Legal Concepts with Augmented Large Language Models (GPT-4) | This paper evaluates GPT-4's ability to explain legal terms from statutory provisions to legal professionals. It finds that augmenting GPT-4 with relevant sentences retrieved from case law significantly improves explanation quality and reduces factual errors compared to using GPT-4 directly. | True | Market | True | 2.0 | Positive | Retrieval-augmented generation using GPT-4 and a legal information retrieval component to explain statutory terms based on case law. | Manual comparison by two legal scholars evaluating pairs of explanations (short and long) generated by baseline vs. augmented GPT-4 for 42 statutory terms. Evaluation dimensions were Factuality, Clarity, Relevance, Information Richness, and On-pointedness. | The augmented GPT-4 approach was significantly preferred over the baseline across all evaluation dimensions, particularly Factuality. The augmentation appeared to eliminate the issue of hallucination (citing non-existent cases or misrepresenting content) present in the baseline model's outputs. | NaN | The paper suggests augmented LLMs can assist legal professionals; future work could adapt this for laypeople to enhance access to justice. | Statutory interpretation explanation. | Legal professionals (lawyers, judges, scholars). Mentions laypeople as a potential future audience. | Statutory interpretation | United States | The technique uses GPT-4 (trained on undisclosed large corpus) and augments it with data retrieved from the publicly available Statutory Interpretation Data Set, which contains manually classified sentences (high, certain, potential, no value) extracted from US case law (Caselaw Access Project) relevant to interpreting specific statutory phrases. | Comparative experimental evaluation using human annotators; Retrieval-Augmented Generation (RAG) approach. | NaN | False | False | NaN | The quality of the information retrieval component limits the augmented model's output quality. The approach needs evaluation for generating explanations suitable for laypeople to improve access to justice. Applicability to other legal tasks needs investigation. | Ensuring factuality and avoiding hallucinations in baseline LLMs. The quality of the retrieved information (e.g., relevance, source type like dissenting opinions, outdated cases) directly affects the augmented system's output. Proper formatting of legal citations. | Hallucination (generating non-factual information, citing non-existent cases, misrepresenting case holdings) in baseline LLMs. Propagation of errors or irrelevant information from the retrieval component in the augmented approach. Users potentially relying on incorrect information without verification. |
| OLZjlJlYtzIJ.pdf | Google_Scholar | Do Robot Lawyers Dream of Electric Clients? | This paper experimentally evaluates ChatGPT's ability to draft a legally sound last will based on a complex user prompt, analyzing its performance with and without jailbreaking compared to human drafting. It concludes that while ChatGPT shows potential as a lawyer's tool, it is currently unsafe for direct consumer use due to significant limitations in legal reasoning, handling ambiguity, and susceptibility to errors, especially when jailbroken. | True | Idealistic | True | 2.0 | Negative | ChatGPT (version 4) for drafting a last will and testament, including testing with a 'jailbreak' prompt (DAN). | A fictional client prompt for a Virginia will with embedded legal complexities was given to ChatGPT under different conditions: 1) standard interaction, 2) with a jailbreak primer, 3) with human co-piloting (the author), 4) using its output as a rough draft. Outputs were qualitatively analyzed and compared against a will drafted independently by the author. | ChatGPT's independently drafted wills contained significant legal flaws, errors, and ambiguities related to spousal disinheritance, asset distribution, libelous statements, and identification. The jailbroken version performed worse, exhibiting degraded reasoning. Human co-piloting was necessary to rectify major issues, highlighting the need for expert supervision. | The high cost of legal services motivating consumers to use potentially unreliable AI tools. Consumers' lack of legal expertise to evaluate AI outputs. AI's inability to correctly interpret complex/ambiguous instructions, understand legal nuances (like spousal elective share), and prioritize legal validity over problematic user requests. Widespread misconceptions about AI capabilities. | Human lawyers must supervise AI use, treating AI as a nonlawyer assistant under ethical rules (e.g., ABA Model Rule 5.3). Increased education for both the public and legal professionals about AI limitations is needed. Regulation for consumer protection is considered but noted as difficult due to technical challenges like jailbreaking. | Self-help legal document drafting (Wills), Consumer protection | General public / consumers seeking to avoid legal fees. | Wills and Estates, Legal Ethics | Virginia | Proprietary data used by OpenAI for ChatGPT, described generally as massive volumes of internet text (blogs, articles, Wikipedia, etc.) combined with reinforcement learning from human feedback. | NaN | NaN | True | False | ChatGPT 4 is described as a mass-market consumer product (paid), while ChatGPT 3.5 is mentioned as free. Access is via OpenAI's platform. | Lack of public understanding regarding AI limitations versus science fiction portrayals. Difficulty in ensuring AI prioritizes legal correctness over problematic user instructions. Need for reliable methods to evaluate AI output quality and failure rates. Effective consumer protection mechanisms for AI legal tools, especially considering jailbreaking. | Evaluating proprietary 'black box' AI models. AI tendency to prioritize user satisfaction over legal accuracy. Variability and unpredictability of AI outputs. Addressing AI misuse through techniques like jailbreaking. Overcoming user misconceptions. | Creation of invalid or legally flawed documents (e.g., wills) by consumers using AI without supervision. Financial loss or unintended consequences due to reliance on faulty AI legal advice/drafting. Potential for libel claims arising from AI-generated content. Ethical breaches or malpractice if lawyers inadequately supervise AI assistants. |
| AwmwqPYg6eIJ.pdf | Google_Scholar | EXPLORING THE FACTORS INFLUENCING ACTUAL USAGE OF GENERATIVE AI IN ACADEMIC RESEARCH | This master's thesis investigates factors affecting academic researchers' adoption and use of generative AI tools via a quantitative survey of 141 participants, primarily in Finland. Findings indicate that the perceived benefit of mutual adaptation and AI literacy significantly predict intention to use, while intention strongly predicts actual usage frequency. | True | NaN | True | 2.0 | NaN | Factors influencing academic researchers' adoption and usage of existing Generative AI tools (like ChatGPT), analyzed via a survey-based multi-stage model. | Quantitative survey distributed to 141 academic researchers (predominantly Finland). Statistical analysis included reliability tests (Cronbach's Alpha), factor analysis (PCA), correlation analysis, and regression analysis (including bootstrapping and moderation testing). | Benefit of mutual adaptation (β = 0.533, p < 0.001) and AI literacy (β = 0.330, p = 0.004) were the strongest positive predictors of intention to use Generative AI. Intention significantly predicted actual usage frequency (β = 0.582, p < 0.001). | NaN | NaN | NaN | NaN | General academic research (multi-disciplinary) | Predominantly Finland, with international participants | NaN | Development of a theoretical multi-stage model based on existing literature (e.g., Technosymbiosis, Source Credibility). Quantitative survey design using adapted, validated scales (Likert scale). Online data collection via Webropol. Statistical analysis using SPSS and Excel (Reliability, Factor, Correlation, Regression, Bootstrap, Moderation). | NaN | False | False | NaN | Limited understanding of human-AI interaction nuances in academic workflows; applicability of existing adoption models; lack of standardized ethical guidelines/acknowledgment protocols; need for discipline-specific and cross-cultural studies; limited understanding of barriers for older researchers; gap between AI's potential and perceived role (especially as collaborator); need to explore factors beyond intention for actual usage; impact on research quality/creativity. | Defining/measuring complex constructs (e.g., technosymbiosis, AI literacy); sampling challenges (low response rate, geographic/age skew); potential multicollinearity between predictors; capturing the full scope of 'actual usage' beyond just frequency. | Overdependence diminishing critical thinking; potential for bias/errors impacting research credibility; exacerbating inequalities; threats to academic integrity (plagiarism, data fabrication); challenges to authorship/IP; spread of misinformation. |
| T_UxWrCFaRQJ.pdf | Google_Scholar | LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods | This paper provides a comprehensive survey on the 'LLMs-as-judges' paradigm, where Large Language Models evaluate outputs based on natural language. It examines existing research across functionality, methodology, applications, meta-evaluation, and limitations, while also outlining future research directions. | True | NaN | True | 3.0 | NaN | LLMs-as-Judges paradigm (using LLMs as evaluators of natural language responses or other outputs based on defined criteria). | The paper surveys various meta-evaluation methodologies for 'LLMs-as-Judges'. This includes the use of diverse benchmarks categorized by application domain (e.g., code generation, machine translation, text summarization, dialogue generation, value alignment) and metrics (e.g., Accuracy, Pearson, Spearman, Kendall’s Tau, Cohen’s Kappa, ICC) to assess alignment with human preferences. | NaN | NaN | NaN | NaN | NaN | Legal (Section 5.4 mentions applications such as evaluation of law LLMs and relevance judgment in legal case retrieval). | International | NaN | NaN | NaN | False | False | NaN | NaN | The paper extensively discusses limitations of the LLMs-as-Judges paradigm (Section 7), including: Biases (position, verbosity, social like authority/bandwagon, content-related like sentiment/token/context, cognitive like overconfidence/self-enhancement); susceptibility to Adversarial Attacks; and Inherent Weaknesses (knowledge recency, hallucination, domain-specific knowledge gaps). | Potential for biased, inconsistent, or unfair judgments; manipulation through adversarial attacks leading to unreliable evaluations; propagation of errors due to hallucinations or outdated knowledge; negative impacts from domain-specific knowledge gaps leading to incorrect assessments in critical fields like medicine or law. |
| ysPWbU8zbbEJ.pdf | Google_Scholar | Construction of a Japanese Financial Benchmark for Large Language Models | This paper introduces a new benchmark designed to evaluate Large Language Models (LLMs) specifically within the Japanese financial domain. The benchmark comprises five distinct tasks, and the authors present evaluation results for various LLMs, highlighting GPT-4's superior performance and confirming the benchmark's effectiveness. | True | NaN | True | 1.0 | NaN | A new Japanese financial benchmark for LLMs comprising five tasks: chabsa (financial sentiment analysis), cma_basics (securities analysis knowledge), cpa_audit (CPA exam auditing questions), fp2 (financial planner exam questions), and security_sales_1 (securities broker representative test questions). | The benchmark's effectiveness was validated by applying it to evaluate various LLMs. Its ability to differentiate model performance across tasks of varying difficulty and consistency with known model capabilities (e.g., GPT-4's high scores) demonstrated its functionality. | The benchmark effectively differentiated LLM performance. The GPT-4 series demonstrated outstanding performance, with openai/gpt-4-32k achieving the highest average score of 66.27 across the five tasks. | NaN | NaN | NaN | NaN | Auditing, accounting law, financial planning regulations, consumer finance law, securities law, financial instruments regulation. | Japan | The benchmark datasets were constructed from publicly available sources: chabsa from a previous study's GitHub repository; cma_basics, fp2, and security_sales_1 from crawled and cleansed online sample exam questions and practice materials; cpa_audit data was from a previous study using Japanese CPA examination questions. | Dataset construction by sourcing from previous studies and web crawling/cleansing of public exam materials; data processing including format conversion (e.g., tables to markdown) and task adaptation (e.g., chabsa to binary classification); prompt engineering involving preparation and selection of best-performing prompts based on preliminary experiments. | The benchmark and model performance results are publicly released on GitHub. | True | True | Publicly available on GitHub: https://github.com/pfnet-research/japanese-lm-fin-harness. | NaN | Scarcity of existing domain-specific benchmarks for Japanese finance; data processing for diverse question types (e.g., removing figures, table conversion); ensuring stable performance evaluation with imbalanced datasets (e.g., chabsa neutral class); significant impact of prompt engineering on LLM performance; cost of API access for evaluating certain proprietary models. | NaN |
| 7PttF-rL6z8J.pdf | Google_Scholar | Through the AI -Looking Glass and What Consumers Find There* | This paper examines the risks and potential benefits of consumer-facing generative AI tools for access to justice, particularly for self-represented litigants in the US. It proposes an incentive-based regulatory framework to mitigate harms like misinformation and the unauthorized practice of law, while encouraging the development of trustworthy AI tools. | True | Idealistic | True | 1.0 | Positive | An incentive-based regulatory framework for consumer-facing legal AI tools, offering liability shields and presumptions against UPL findings for compliant providers. | NaN | NaN | High cost and complexity of the legal system; lack of legal representation (justice gap); difficulties for self-represented litigants in navigating the system; potential for misinformation from unregulated AI tools; protectionism within the legal profession (e.g., UPL enforcement). | Utilize generative AI to provide accessible legal information and assistance; implement the proposed incentive-based regulatory scheme requiring disclosures, clear disclaimers, data protection options, transparency, and expert review; offer liability shields/presumptions for compliant providers. | Access to legal information for self-represented litigants; document drafting assistance; understanding legal procedures; navigating civil litigation. | Self-represented litigants; consumers facing legal issues without lawyers; general public needing legal assistance. | General Civil Litigation, Family Law, Housing Law, Consumer Protection, Traffic Law (based on examples discussed) | United States (with comparisons to EU and China) | NaN | NaN | NaN | False | False | NaN | Lack of clear definition for 'practice of law' / 'legal advice' concerning AI; uncertainty about liability for AI-generated errors; absence of effective US regulation for consumer-facing legal AI; need for transparency in AI operations and data usage; ensuring AI accuracy and reliability; balancing innovation with consumer protection. | Defining 'legal advice' for AI regulation; ensuring AI provider transparency; designing effective enforcement for regulations; balancing access goals against UPL and misinformation risks; overcoming legal profession skepticism; keeping pace with AI development; avoiding stifling innovation through regulation. | AI providing inaccurate information (hallucinations); users over-relying on AI; deepening the justice gap and user distrust; AI engaging in Unauthorized Practice of Law (UPL); privacy violations/data misuse; user manipulation via hidden prompts; bias in AI outputs; provider liability. |
| JjKy892udNQJ.pdf | Google_Scholar | Developing aGenerative AIModel toEnhance Sentiment Analysis fortheSaudi Dialect | This PhD dissertation proposes a novel method using generative AI (AraGPT2) to create synthetic data for the low-resource Saudi Dialect (SD), addressing data scarcity. By combining collected Twitter data with generated data to fine-tune AraBERT, the study significantly improves sentiment analysis performance for SD compared to using only collected data. | True | Market | True | 1.0 | NaN | A hybrid approach using MARBERT (a BERT variant) for dialect annotation/filtering, AraGPT2 (a GPT-2 variant) for generating synthetic Saudi Dialect data, and fine-tuning AraBERT (another BERT variant) for sentiment analysis using a combination of collected tweets and generated data. | AraBERT fine-tuned for sentiment analysis was evaluated using Accuracy, Precision, Recall, and F1-score on combinations of datasets (collected Saudi Twitter Data - STD, generated data, AraCust dataset). Evaluation involved single runs and averaging over 10 iterations of data reshuffling and splitting (80% train, 20% test). MARBERT for annotation was evaluated using Accuracy, Precision, Recall, F1-score, and LIME (XAI). AraGPT2 for generation was evaluated using Perplexity and BLEU scores. | The best sentiment analysis performance was achieved by fine-tuning AraBERT on a combination of the AraCust dataset and the generated Saudi Dialect dataset, yielding an average accuracy of 96.47% and an average F1-score of 92.15% over 10 iterations. | NaN | NaN | NaN | NaN | NaN | Saudi Arabia | Initial data: ~50,000 Arabic tweets collected from X (Saudi Twitter Dataset - STD), geotagged to Saudi Arabia. Annotation step used ~27,870 preprocessed STD tweets, fine-tuning MARBERT. Generation step used ~19,251 dialectal tweets identified by MARBERT to fine-tune AraGPT2, producing 19,251 synthetic tweets. Sentiment analysis step used combinations of STD, generated data, and the public AraCust dataset (20,000 manually labeled Saudi telecom tweets). Data is unstructured text. | Multi-step process including: selecting pre-trained models (MARBERT, AraGPT2, AraBERT), web scraping (X API), data preprocessing (cleaning, normalization, tokenization with NLTK), model fine-tuning, synthetic data generation using generative AI, comparative model evaluation using standard metrics, and explainability analysis (LIME). | NaN | False | False | NaN | Significant lack of research and open datasets for Saudi Dialect (SD) NLP. Challenges include limited resources (datasets, tools, models), linguistic variation and ambiguity (lack of standardization, diglossia), and the nonconcatenative structure of Arabic. | Data scarcity and quality for the low-resource Saudi Dialect. Linguistic diversity, lack of standardization, and diglossia within the dialect. Technical difficulties in processing dialectal text. Time and effort required for data collection and annotation. Potential for model overfitting. Evaluating the quality and coherence of synthetically generated text. | NaN |
| ACmFBJB5spsJ.pdf | Google_Scholar | Enhancements for Developing a Comprehensive AI Fairness Assessment Standard | This paper proposes expanding the Telecommunication Engineering Centre (TEC) Standard for AI Fairness Assessment to cover images, unstructured text, and generative AI like LLMs. The goal is to create a more comprehensive framework for responsible AI deployment by addressing biases in diverse data modalities and advanced AI models. | True | Idealistic | True | 1.0 | Positive | The proposed enhanced TEC Standard for AI Fairness Assessment, incorporating specific methodologies for fairness in images (e.g., tabular reduction, XAI), unstructured text (e.g., WEAT, SEAT, GBETs), and LLMs (e.g., embedding-based, probability-based, generation-based metrics). | NaN | NaN | Biased AI systems leading to discriminatory outcomes that disproportionately affect vulnerable or marginalized groups, reinforcing prevailing societal inequities and undermining trust in AI applications. | Expanding and enhancing the existing TEC AI Fairness Standard to include specific assessment methodologies for images, unstructured text, and LLMs, thereby enabling more comprehensive identification and mitigation of biases in a wider range of AI systems. | Ensuring equitable and non-discriminatory outcomes from AI systems, especially for vulnerable and marginalized populations. This impacts fairness in diverse sectors such as telecommunications, finance, healthcare, public services, and touches upon areas like law enforcement actions and legal aid. | Vulnerable entities, marginalized or underrepresented groups, marginalized communities. | NaN | India (primary focus on the TEC Standard), with references to international frameworks (ITU, NIST). | NaN | NaN | NaN | False | False | NaN | The current TEC Standard's limitation to structured tabular data and supervised learning models, making it less applicable to AI systems using unstructured data (images, text) and advanced models like LLMs. | NaN | Biased or unjust AI outcomes disproportionately affecting vulnerable entities; inequalities in network access or resource allocation; perpetuation of harmful stereotypes or discrimination by image recognition systems; LLMs reinforcing societal biases and generating discriminatory or harmful content; potential for wrong medical diagnoses or autonomous vehicle accidents due to biased AI. |
| jhu4mHJ3DpUJ.pdf | Google_Scholar | LegalGuardian: A Privacy-Preserving Framework for Secure Integration of Large Language Models in Legal Practice | This paper introduces LegalGuardian, a framework using NER and local LLMs to mask PII in prompts for external LLMs, aiming to protect client confidentiality in legal practice. Evaluated on synthetic immigration law prompts, it achieved high PII detection accuracy (97% F1 with Qwen2.5-14B) and maintained semantic fidelity, demonstrating a method for safer LLM use by lawyers. | True | Idealistic | True | 1.0 | Positive | LegalGuardian: a framework using Named Entity Recognition (NER) techniques (specifically GLiNER) and local LLMs (specifically Qwen2.5-14B) to mask and unmask Personally Identifiable Information (PII) in prompts sent to external LLMs. | Evaluated using a synthetic dataset of 50 prompts in US immigration law scenarios. PII detection performance was assessed using precision, recall, and F1-score (overall and entity-level). Semantic consistency between original and masked/unmasked LLM outputs was measured using Cosine Similarity, Jaro-Winkler Distance, and Levenshtein Distance. | For PII detection, Qwen2.5-14B achieved an F1-score of 97% (Precision 99%, Recall 94%), while GLiNER achieved an F1-score of 93% (Precision 100%, Recall 88%). GLiNER showed slightly higher cosine similarity (0.9808) compared to Qwen2.5-14B (0.9731) for semantic consistency. | The primary obstacle is the risk of breaching client confidentiality when lawyers use LLM-based tools due to the inclusion of PII in prompts. This hinders LLM adoption, especially for practitioners with limited resources (e.g., legal aid, solo practitioners) who cannot afford custom secure solutions, thereby limiting AI's potential to democratize legal services. | The paper proposes LegalGuardian, a lightweight framework that allows lawyers to mask PII in prompts before sending them to external LLMs and subsequently unmask this PII in the LLM's response. This approach aims to preserve confidentiality while enabling the use of advanced AI tools by a broader range of legal professionals. | Protection of client confidentiality when using AI tools; Enabling access to advanced AI for a broader range of legal professionals, including those in legal aid or solo practice, thereby indirectly supporting access to justice goals. | Legal professionals, particularly legal aid workers and solo practitioners with limited resources. By extension, their clients, who may include individuals from underserved communities. | Immigration law (for the synthetic dataset and scenarios); the framework is intended for broader legal practice. | United States (references to ABA Model Rules, US state initiatives, and US immigration law scenarios). | The evaluation involved a synthetic dataset of 50 legal prompts in US immigration law, generated using the Faker library and the Qwen-2.5 14B model. PII detection relies on the pre-trained GLiNER model (GLiNER Multi PII-v1, fine-tuned for PII) and one-shot prompting of the pre-trained Qwen2.5-14B model. | The framework development included: 1. Synthetic legal prompt dataset generation. 2. A PII masking layer using NER (GLiNER) and a local LLM (Qwen2.5-14B via one-shot prompting). 3. A secure prompting layer for interacting with external LLMs. 4. An evaluation layer using accuracy and semantic similarity metrics. | NaN | False | False | NaN | Future work includes extending the framework to more legal areas, enhancing PII detection for complex data, integrating with cloud-based LLMs using privacy-preserving techniques (e.g., secure multi-party computation, federated learning), and conducting user studies with practicing lawyers. | Balancing PII masking accuracy (privacy) with the preservation of semantic integrity and utility of LLM outputs. Ensuring comprehensive PII detection across various PII types and contexts. Developing a lightweight solution to avoid high computational costs and complexity associated with some advanced privacy-preserving methods. | Unauthorized exposure of client PII to third-party LLM providers. Breaches of attorney-client privilege and data protection laws. LLM misinterpretation of prompts if masking techniques alter meaning or introduce ambiguity. Potential for sensitive information to surface in unrelated prompts if LLMs learn from input data (though LegalGuardian aims to prevent this by masking before external interaction). |
| crj8G8qyKYEJ.pdf | Google_Scholar | AI White Paper, consultation response | This paper is a consultation response by the British Irish Law, Education and Technology Association (BILETA) to the UK government's AI White Paper. BILETA critiques the proposed non-statutory, principles-based approach, advocating instead for a mandatory statutory framework for AI regulation to ensure adequate protection, fairness, and redress. | True | Idealistic | True | 2.0 | Negative | NaN | NaN | NaN | Inadequate, unclear, inaccessible redress mechanisms for AI-related harms; lack of mandatory regulation leading to potential abuse and weak enforcement; challenges in regulating foundation models (LLMs) including bias, hallucination, and societal impacts; risks to human rights (e.g., non-discrimination, fair elections). | Implement a mandatory statutory regulatory framework (akin to the EU AI Act); establish clear, strong redress mechanisms including class actions and judicial review; potentially establish a single coordinating regulatory body; enhance transparency requirements; implement auditing. | Fairness, accountability, contestability, redress, transparency, AI risk management, regulation of high-risk AI, foundation models (LLMs), human rights protection, statutory vs non-statutory regulation. | General public / users / marginalized groups | AI Regulation, Technology Law, Human Rights Law, Administrative Law | United Kingdom | NaN | NaN | NaN | False | False | NaN | Lack of a mandatory statutory framework in the UK proposal; inadequate redress mechanisms; insufficient clarity on handling foundation models and assigning legal responsibility; potential for overlapping/contradictory guidance from multiple regulators. | Challenges for regulators in applying principles consistently across diverse AI applications; determining legal responsibility across the AI lifecycle, especially with foundation models; potential for overlapping or contradictory guidance from different regulators under the proposed framework. | AI reinforcing biases against marginalized groups; LLMs 'hallucinating' (providing false information); adverse impacts on workforce and economy; inadequate redress for AI harms; insufficient protection of human rights (e.g., free elections, non-discrimination, health, fair pay, freedom of expression); risks associated with specific AI applications like social scoring, remote biometric identification, predictive policing, emotion recognition, indiscriminate scraping of biometric data. |
| feikXgtDjy8J.pdf | Google_Scholar | Continual Pre-Training is (not) What You Need in Domain Adaption | This paper investigates the efficacy of Domain-Adaptive Continual Pre-Training (DACP) for Legal Large Language Models (LLMs) in the Taiwanese legal system. It finds that while DACP enhances domain-specific knowledge, it does not uniformly improve performance across all legal tasks and can have trade-offs regarding generalization and prompt-based tasks. | True | Idealistic | True | 2.0 | Neutral | Domain-Adaptive Continual Pre-Training (DACP); Low-Rank Adaptation (LoRA); Direct Preference Optimization (DPO); Odds Ratio Preference Optimization (ORPO). Creation of LLAWA, BLLAWA, BLAWSTRAL models. | Custom benchmark for Taiwanese legal framework: multiple-choice questions from Bar/Judicial Exam and Jurist Journal (Tasks A, B; accuracy metric), argument-based decision-making in legal symposia (Task C; accuracy), and essay questions from Bar/Judicial Exam (Task D; GPT-4o evaluation against segmented golden answers based on 'Juristisches Gutachten' method). | DACP enhances domain-specific knowledge but does not uniformly improve performance across all legal tasks. For example, while BLAWSTRAL (LoRA-tuned Mistral-Nemo) achieved the highest accuracy on Task C (56.54%), models with DACP (LLAWA variants) did not consistently outperform base models or other fine-tuning methods on all tasks, and sometimes DACP led to performance degradation on prompting tasks. | Lack of resources and difficulty in accessing expert-level legal analysis for individuals and organizations. | Improving Legal LLMs through techniques like Domain-Adaptive Continual Pre-Training to provide more accessible expert-level legal analysis and democratize legal services. | Democratizing access to legal services; Making expert-level legal analysis more accessible. | Individuals and organizations that might otherwise lack the necessary resources. | Taiwanese law (general), including juvenile law, criminal law, laws, regulations, and court documents. Also references German law. | Taiwan (primary), Germany (secondary, for comparative pre-training data). | Pre-training: Publicly available Taiwanese legal data (laws, regulations, court documents from Judicial Yuan), a German law subset from MultiLegalPile, and self-curated data (ConceptNet, CBETA). Instruction tuning: Cleaned TAIWAN CHAT (general instructions) and a legal dataset from Taiwan's Bar/Judicial Exams and Taiwan High Court website (specific legal tasks). Data is largely unstructured text. | For LLAWA: Domain-Adaptive Pre-Training, full-parameter instruction tuning, preference alignment (DPO, ORPO). For BLLAWA & BLAWSTRAL: Low-Rank Adaptation (LoRA) for instruction tuning. | The paper states that models and a Hugging Face repository will be made publicly available upon acceptance or after anonymized review. | False | False | NaN | Need for hybrid approaches combining DACP with other methods; Refinement of evaluation benchmarks for legal reasoning; Addressing potential data contamination in LLM training; Finding optimal mixture ratios for general vs. domain-specific corpora; Limitations of current evaluation metrics (e.g., BLEU/ROUGE) and potential biases in LLM-as-evaluator setups. | DACP not uniformly beneficial, leading to trade-offs in generalization and prompt-based task performance; Fine-tuning can sometimes lead to suboptimal states (e.g., BLLAWA); Preference optimization techniques (DPO, ORPO) did not yield expected improvements under the study's conditions; Complexity in evaluating essay-type legal questions; Difficulty in modeling complex legal argumentation in settings like legal symposia. | Potential for LLM hallucinations; Ensuring ethical use of legal AI; Maintaining transparency in AI decision-making; Addressing concerns about AI bias; Risk of data contamination in training leading to inflated performance perception; Biases introduced by using LLMs as evaluators. |
| A3TgdbzreLMJ.pdf | Google_Scholar | Customizing Large Language Models for Legal Consultations | This paper introduces a multi-turn prompt engineering method to enhance large language model (LLM) performance for legal consultation, iteratively refining responses for improved accuracy and legal coherence. Evaluations using a curated legal dataset, with GPT-4 as a judge and human assessment, demonstrate the method's superiority over baselines in delivering precise and contextually relevant legal advice. | True | Idealistic | True | 1.0 | Positive | A multi-turn prompt engineering method for LLMs, designed to iteratively refine model responses in legal consultation tasks by dynamically adjusting prompts based on previous outputs. | The method was evaluated on a manually curated legal query dataset (covering contract, intellectual property, constitutional law) using GPT-4 as a judge to score outputs on legal coherence, legal precision, reasoning depth, and iterative improvement. Additionally, legal professionals conducted human evaluations based on relevance, completeness, clarity, and legality. | The proposed method (OM) achieved scores of 4.8 for Legal Coherence, 4.7 for Legal Precision, 4.6 for Reasoning Depth, and 4.5 for Iterative Improvement (on a 1-5 scale). Human evaluation rated OM at 4.7 for Relevance, 4.6 for Completeness, 4.8 for Clarity, and 4.7 for Legality, significantly outperforming baseline methods. | The high cost of traditional legal representation and limited availability of legal services, particularly in underserved or remote areas. Additionally, the inherent challenges of applying general AI to the complex legal domain (e.g., lack of precision, misinterpretation of legal nuances) without specialized approaches hinder reliable A2J applications. | The development and application of specialized AI techniques, such as the proposed multi-turn prompt engineering for LLMs, to generate more accurate, reliable, and contextually appropriate legal advice. This approach aims to democratize access to legal consultations, making them more affordable and broadly available, especially for underserved communities. | Access to legal advice and consultation, Democratization of legal services, Improving understandability and reliability of AI-generated legal information. | Individuals in underserved or remote areas, populations with limited access to traditional legal representation due to cost or geographical constraints. | General legal consultation, with evaluation dataset examples from contract law, intellectual property law, constitutional law. The method is suggested to be adaptable to other domains like family law and corporate law. | International | NaN | Iterative design; a multi-step pipeline involving an input layer (user query), processing layer (initial LLM response), refinement layer (iterative follow-up prompts guiding the LLM), and output layer (final, refined legal response). | NaN | False | False | NaN | Reliance on the quality of the initial user query; the current fixed sequence for iterative refinement could be improved with adaptive mechanisms. Further integration of domain-specific legal knowledge bases is needed. Broader ethical considerations, including privacy and bias in AI legal systems, require ongoing research. | Computational cost of multiple prompt iterations (though claimed to be manageable), susceptibility to errors from poorly framed initial user queries, and optimizing the iterative refinement process (e.g., determining when to stop iterations). | Potential for misinterpretation of legal terminology, errors in applying legal principles, and difficulties in adhering to jurisdictional rules by LLMs (which the method aims to mitigate). Broader AI in law risks include bias, data privacy concerns, and ethical implications of automated legal advice. |
| mDOOmREBPQoJ.pdf | Google_Scholar | Getting in the Door: Streamlining Intake in Civil Legal Services with Large Language Models | This paper investigates using Large Language Models (LLMs) to streamline the legal intake process for civil legal aid, focusing on eligibility determination. It describes and evaluates a digital intake platform piloted in Missouri that combines logical rules with LLMs, finding promising results with GPT-4-Turbo achieving an F1 score of .82. | True | Idealistic | True | 1.0 | Positive | A digital intake platform built on the Docassemble framework, which uses a combination of Python-encoded formal rules and zero-shot LLM prompting (with program-specific intake rules provided as text) to assess eligibility for legal aid and elicit further information from users. | Evaluated using two datasets: D1 (48 scenario-jurisdiction pairs generated via ChatGPT and manually coded) to test initial LLM response accuracy across 8 LLMs for predicting 'accept', 'deny', or 'question'; D2 (11 manually generated multi-turn conversational transcripts with GPT-4-turbo) for qualitative assessment of follow-up questions and overall interaction quality by an expert rater. | GPT-4-Turbo achieved the highest overall weighted F1-score of 0.82 on dataset D1, with high precision for the 'Deny' class. Qualitative analysis (D2) by an expert rater showed 73% correct overall results, and perfect scores (5/5) for understandability and satisfaction with the tool, though noting that additional follow-up questions could have been asked by the AI in 63% of cases. | Time-consuming nature of legal intake for legal aid, nuanced and frequently changing substantive eligibility criteria, high demand for services leading to long wait times for applicants. | A digital intake platform using LLMs combined with logical rules to provide 24/7 preliminary eligibility screening, inform applicants about their likelihood of qualifying before waiting, and potentially reduce staff burden by handling initial assessment. | Legal intake streamlining, eligibility determination for civil legal aid, reducing barriers to accessing legal help, client-facing legal technology. | Low-income individuals and applicants for free legal aid programs, specifically tenants facing housing issues in Missouri. | Civil legal aid, housing law, landlord-tenant law. | Missouri, USA (specifically, legal aid programs in Eastern Missouri, Mid-Missouri, and Western Missouri). | The technique uses pre-trained LLMs in a zero-shot setting. Program-specific substantive intake rules are provided as plain text within the prompt at inference time, along with the user's problem description. Evaluation datasets (D1 and D2) consist of scenarios generated using ChatGPT, manually reviewed, reworded, and coded, or entirely manually generated. | Iterative prompt engineering (specifically for GPT-4-turbo), development of a user-facing application using the Docassemble framework, pilot testing in collaboration with four legal aid programs in Missouri. | The intake application was piloted in Missouri, accessible on mobile phones, embedded in a legal help website (MOTenantHelp.org), and referred to in the on-hold message for callers to the phone intake system. | True | True | The full code and prompt are available on GitHub in two repositories. The piloted application is embedded in MOTenantHelp.org for Missouri tenants. | Integration with a seamless online intake experience, improved user analytics, simplifying rule updates (e.g., allowing staff to upload documents directly), potential for using semi-structured reasoning, further prompt and intake rule refinement, evaluation of human intake staff performance for comparison, exploration of potential LLM biases, and expansion to best-match eligibility recommendations across multiple providers. | Initial LLM tendency to give inappropriate advice (addressed by clarifying its task), LLMs generating example replies leading to hallucinations (addressed by omitting examples in prompt), content censorship by some LLMs (e.g., Google Gemini for a domestic violence scenario), and prompt optimization being model-specific. | Content censorship by LLMs may limit applicability to other legal topics (e.g., involving violence or abuse). Biased LLM training data could expose vulnerable legal aid applicants to risks (mitigated by human-in-the-loop design, focusing LLM on minimum qualification criteria, and prompting for explanations). |
| 3615859.pdf | Google_Scholar | Generative AI as a New Innovation Platform | This paper explores generative AI as a potentially transformative innovation platform, analyzing its ecosystem structure including foundational models, infrastructure, and applications. It discusses the significant opportunities alongside major concerns such as market concentration, content ownership, data privacy, information accuracy, and the need for effective regulation. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Intellectual Property Law (Copyright/Fair Use), Privacy Law, Tech Regulation | USA, Italy | Large, general datasets ('trillions of words and other data points', potentially including copyrighted content and open source software) used to train foundational LLMs. | NaN | NaN | False | False | NaN | Gaps in regulation and governance frameworks for AI, difficulties in controlling technological issues like hallucinations and detecting fake content, challenges in addressing societal impacts (misinformation, job displacement), and unresolved legal questions regarding data usage (e.g., fair use). | NaN | Diffusion of misinformation, detrimental societal impact, lack of ethical guardrails, concentration of market power, privacy violations, data leaks, algorithmic bias, copyright infringement, generation of inaccurate information (hallucinations), difficulty in detecting AI-generated fakes, high energy consumption, job displacement. |
| DoIEmP47jgoJ.pdf | Google_Scholar | The Legal AI: Justifying Justice | This paper reviews AI applications in the legal domain, focusing on tools to improve court efficiency like automated docketing in Florida and Brazil's VICTOR system, while also critically examining issues like algorithmic bias with the COMPAS example. It further touches upon commercial AI tools for litigation analytics such as Lex Machina. | True | Idealistic | False | 2.0 | Neutral | Automated docketing system (Florida) using classification, Learn by Example (LBX), Robotic Process Automation (RPA), and OCR for processing court filings. | The automated docketing system in Palm Beach County, Florida, was trained and evaluated on 'thousands of filings'. Its accuracy was compared to human performance. | The automated docketing system in Florida achieved 98 to 99 percent accuracy in classifying and docketing documents, which was reported as better than human counterparts. | Systemic court delays and backlogs; resource shortages (judges, lawyers); procedural inefficiencies (e.g., party absence, evidence issues); risk of algorithmic bias and embedding societal prejudices. | Utilizing AI to enhance efficiency in judicial processes (e.g., automated document processing, case management assistance); developing AI systems mindful of and adaptable to ethical considerations and bias mitigation. | Reducing court backlogs, improving judicial process efficiency (e.g., docketing, appeal categorization), and addressing algorithmic bias in legal AI. | General public affected by justice system delays; specific demographic groups (e.g., racial minorities) vulnerable to algorithmic bias. | Court administration, criminal justice (risk assessment), patent litigation, general civil and criminal procedure. | India, USA (Florida, Wisconsin), Brazil | For the automated docketing system in Florida: 'thousands of filings' from the Circuit Court & Comptroller, Palm Beach County (unstructured, domain-specific legal documents). | For the automated docketing system in Florida: Machine learning for classification, Learn by Example (LBX), Robotic Process Automation (RPA), and Optical Character Recognition (OCR). | The automated docketing system is deployed in the Circuit Court & Comptroller from Palm Beach County, Florida. Other systems mentioned (SUPACE, VICTOR) are deployed in their respective Supreme Courts. | False | False | NaN | Ensuring accountability for AI decisions; preventing errors, malfunctions, and misjudgments; effectively mitigating algorithmic bias; developing AI that can adapt to evolving societal norms and ethical considerations for complex legal decision-making. | Achieving high accuracy and efficiency in AI legal tools, handling diverse and complex data inputs (e.g., handwritten scripts requiring OCR), integrating AI into existing legal workflows, and overcoming limitations in mimicking nuanced human legal reasoning. | Algorithmic bias leading to discriminatory outcomes and unfairness (e.g., as seen in COMPAS); errors, malfunctions, or miscalculations in AI systems leading to misjudgment and negative societal impacts; lack of accountability and transparency in AI-driven legal decisions; perpetuation of existing societal biases through biased training data. |
| afRxufBh6fkJ.pdf | Google_Scholar | Artificial intelligence (AI) technology in OpenAI ChatGPT application: A review of ChatGPT in writing English essay | This paper reviews OpenAI's ChatGPT, focusing on its capability to generate English essays for educational purposes. It describes how to use ChatGPT and provides examples of generated essays, noting its ability to structure text appropriately but also highlighting concerns about accuracy and potential misuse like plagiarism. | True | NaN | True | 2.0 | Neutral | ChatGPT | Qualitative observation: The researcher prompted ChatGPT to write English essays on various topics (descriptive text, recount text, future plans) and analyzed the output for structure, grammar (tense, voice), and coherence. | ChatGPT generated essays considering writing structure (introduction, body, conclusion), appropriate voice (active/passive), and tense selection based on the topic. However, grammatical accuracy needs further verification. | NaN | NaN | NaN | NaN | NaN | International | Trained using Reinforcement Learning from Human Feedback (RLHF) on a large dataset (billions of words including text and books), based on OpenAI's GPT-3.5 model. | NaN | Web interface access via OpenAI website (chat.openai.com) after user registration. | True | True | Available for free public use via web interface (chat.openai.com) during its "research preview" phase, requiring user registration. | NaN | Tendency to generate plausible but incorrect/illogical answers; lack of deep contextual understanding, critical thinking, and ethical decision-making abilities; potential for perpetuating societal biases (race, gender, culture); difficulty in fixing inaccuracies. | Academic dishonesty (plagiarism by students); difficulty for educators in detecting AI-generated text; perpetuation of societal biases; dissemination of incorrect information presented as fact. |
| uTqP15w03YEJ.pdf | Google_Scholar | CHATGPT, I HAVE A LEGAL QUESTION? THE IMPACT OF GEN AI TOOLS ON LAW CLINICS AND ACCESS TO JUSTICE | This paper evaluates the accuracy of Generative AI tools like ChatGPT for providing legal advice, finding them prone to significant errors and jurisdictional confusion. It discusses the risks for non-lawyers and explores the potential for responsible use of Gen AI in law clinics to enhance access to justice and student skills, tempered by ethical considerations. | True | Idealistic | True | 2.0 | Negative | Evaluation of Generative AI tools: ChatGPT 3.5 (free version), ChatGPT 4 (paid subscription version), Bing Chat (balanced mode), and Google Bard. | Six generic legal queries (family, employment, consumer, housing, online contracts, child maintenance) reflecting common legal problems were posed to four Gen AI models. Responses were rated 0-5 by two qualified lawyers based on accuracy of legal advice (currency, comprehensiveness, correct application, need for prompts, clarity for non-lawyer) and clarity of practical next steps (practicality, ADR inclusion, links provided, completeness, clarity for non-lawyer). A follow-up question regarding English law was used if necessary. | ChatGPT4 (subscription model) performed best, scoring 73% for accuracy of legal advice and 70% for clarity of next steps. However, overall, only 13% of the initial queries across all tools were correctly answered based on UK law, with 42% of responses being too generic and 25% wrong in law. | High cost of legal advice and representation; limited availability of legal aid and geographical 'legal aid deserts'; public an DRAFTlack of awareness of reliable free legal helphuman-centeredsources; digital divide (cost of technology, internet access, digital literacy) disproportionately affecting low-income individuals; structural inequalities in the justice system not solely solvable by technology. | A public legal education campaign about Gen AI limitations for legal advice. Responsible integration of Gen AI in law clinics with appropriate training, policies, and student supervision. Development of bespoke, reliable legal AI solutions (though funding for free advice organisations is a challenge). Encouraging human-centered design for legal technologies. | Reliability and accuracy of Gen AI for legal queries; impact on litigants in person; role and risks of Gen AI in clinical legal education; addressing the access to justice gap. | Litigants in person (non-lawyers); individuals with unmet legal needs, particularly highlighting BAME communities, younger people, those on low income, or with low levels of education. | Family law, employment law, consumer law, housing law, online contract law, child maintenance law. | England and Wales (queries focused on English law). The paper notes issues with AI tools defaulting to US law. | The Gen AI tools studied (ChatGPT, Bard, Bing Chat) are described as being trained on 'vast amounts of internet text data.' | NaN | NaN | True | True | ChatGPT 3.5, Google Bard, and Bing Chat are freely available. ChatGPT 4 is available via paid subscription. | Need for improved accuracy, reliability, and jurisdictional awareness in Gen AI for legal advice. Ensuring equitable access to beneficial AI tools, avoiding a 'two-tiered system' based on ability to pay. Lack of public understanding of Gen AI's limitations and risks in legal contexts. Development of tailored, trustworthy AI solutions for free legal advice providers. Addressing ethical concerns regarding Gen AI training data, inherent biases, transparency, and data privacy. | Gen AI tools providing generic, incorrect, or outdated legal advice. Frequent jurisdictional confusion (e.g., defaulting to US law when UK law is needed). Outputs lacking crucial details (e.g., legal deadlines). Difficulty for non-lawyers to critically evaluate the veracity of Gen AI responses. The dynamic nature of law requiring continuous updates to AI models (implied). | Non-lawyers relying on inaccurate Gen AI legal advice, leading to detrimental consequences. Exacerbation of existing inequalities if more reliable AI tools are only available via paid subscriptions. Ethical issues including inherent bias in AI models, lack of transparency, and compromises to client confidentiality when sensitive data is input into Gen AI tools. Reputational and legal risks for law clinics if students misuse Gen AI. Potential for 'hallucinations' or fabrication of legal information by Gen AI. Over-reliance on AI potentially degrading research and writing skills. |
| PiELflBCXh8J.pdf | Google_Scholar | AI ASSISTANCE IN LEGAL ANALYSIS: AN EMPIRICAL STUDY | This paper investigates how AI (GPT-4) assistance affects human performance on law school exams. Results show significant improvement on multiple-choice questions but not essays, with lower-performing students benefiting most and top performers potentially seeing declines; optimally prompted AI alone can outperform both humans and AI-assisted humans. | True | Market | True | 2.0 | Neutral | Evaluating the performance of law students taking exams with GPT-4 assistance, compared to students without AI and GPT-4 alone using various prompting techniques (basic, chain-of-thought, few-shot, grounded). | Experiment involving University of Minnesota law students taking prior years' exams (Introduction to American Law, Insurance Law) with GPT-4 assistance after training. Performance compared against their own real exam scores (without AI) and prior year students' scores (without AI). Exams were blindly graded; results analyzed quantitatively (percentiles, grades, speed) and qualitatively. | With grounded prompting, GPT-4 alone outperformed both average human students and average AI-assisted students on both exams, achieving perfect scores on multiple-choice and high scores on essays (93rd percentile on Intro essay, 65th percentile on Insurance Law). | AI struggles with complex legal reasoning, issue-spotting, and incorporating nuanced legal details (cases, rule variations) without grounding. Integrating AI output is challenging for complex tasks. Top performers may be negatively impacted by AI assistance (potential over-reliance, stifled creativity). | Effective prompt engineering, particularly 'grounded' prompting (providing relevant source material like lecture notes), significantly improves AI performance, making AI alone potentially superior to humans or AI-assisted humans for some tasks. | Legal analysis, Legal reasoning, Legal education (law school exams), Performance evaluation, Future of legal profession | Law students (undergraduate and JD), Legal professionals (potential implications for elite vs. non-elite lawyers, paraprofessionals) | Legal Education, Introduction to American Law (Contracts, Torts, Criminal Law, Civil Procedure, Property, Constitutional Law), Insurance Law | United States | GPT-4 (pre-trained on broad data by OpenAI). The study utilized specific prompting techniques: 'grounded' prompting used domain-specific, unstructured text (instructor lecture notes); 'few-shot' prompting used sample questions and model answers from prior exams. | Experimental design, Human subjects research (law students), Between-subjects and within-subjects comparisons, Blind grading, Quantitative analysis (statistical testing, bootstrapping), Qualitative analysis (review of exam answer characteristics). | The experimental setup used a private website cloning ChatGPT via the GPT-4 API for participants; this was specific to the study. | False | False | NaN | Need for better understanding of why AI assistance harms top performers; difficulty integrating AI for complex tasks; generalizability beyond exam settings to real legal work; adequacy of short-term training; impact of unknown parallel forms reliability of exams; potential for automated prompt engineering. | Integrating AI insights with human reasoning for complex essay questions; potential for AI to crowd out independent thinking or high-order reasoning (e.g., spotting hidden issues); variation in student ability to effectively use AI; ensuring comparable effort levels between real exams and study exams; limitations of training. | Performance degradation for high-skilled users; potential job displacement for paraprofessionals due to AI superiority in certain tasks; over-reliance on AI leading to reduced effort or creativity; AI generating inaccurate or conclusory analysis (especially without grounding); organizational problems in AI-assisted writing. |
| 3Kw3imwyDSMJ.pdf | Google_Scholar | TOWARD NATIONAL REGULATION OF LEGAL TECHNOLOGY: A PATH FORWARD FOR ACCESS TO JUSTICE | The paper argues that state-by-state regulation of legal technology is inadequate for addressing the access-to-justice gap and potential harms like bias and inequality. It proposes a national, opt-in regulatory sandbox to test innovative legal services, generate data, and inform evidence-based regulatory reforms by states. | True | Idealistic | False | 1.0 | Positive | National, opt-in legal services regulatory sandbox. | NaN | NaN | Inadequate state-level regulation failing to keep pace with technology; regulatory uncertainty (UPL, ethics rules); risk of a technology-driven two-tiered system; lack of data on legal tech benefits and harms; resistance to innovation and reform (e.g., nonlawyer ownership); financial and logistical barriers to local reform efforts. | Establish a national, opt-in legal services regulatory sandbox to centralize expertise, generate empirical data through controlled testing of innovative services with temporary safe harbors, and provide data-driven recommendations to states for regulatory reform. | Access to civil legal services, regulatory innovation, legal technology regulation, unauthorized practice of law (UPL) reform, alternative business structures (ABS) / Rule 5.4 reform, data-driven regulation. | Low-income and moderate-income individuals facing the justice gap. | Civil law (broadly), including family law, business law, estate planning, consumer issues. | United States (discusses state initiatives but proposes a national mechanism) | NaN | Conceptual proposal based on analysis of existing regulatory issues, state-level sandbox examples (e.g., Utah), and academic literature. | Proposal for an opt-in system for US states. | False | False | NaN | Need for empirical data on legal tech impact (benefits, harms, A2J effects); lack of national coordination and expertise in regulating legal tech; need for clear definitions and safe harbors (e.g., UPL); need for flexible regulatory models (e.g., ABS) to foster innovation; overcoming resistance to change; ensuring technology doesn't exacerbate inequality. | Overcoming state-level resistance to national coordination, securing state participation (opt-in), designing effective sandbox processes (application, monitoring, data analysis, recommendation), funding the national oversight body. | Exacerbation of the justice gap / creation of a technology-driven two-tiered system; consumer harm from poorly designed/implemented tech (inaccurate advice, bias); automation of bias leading to discrimination; inadequate protection of client data; stifling innovation due to regulatory uncertainty; potential for "spontaneous deregulation". |
| _wFRigLwihMJ.pdf | Google_Scholar | Uniandes at the Regulations Challenge Task: A Scalable Framework for Legal Text Understanding in Regulatory and Financial Contexts. | This paper presents the development and fine-tuning of a domain-specific LLM (LLaMA-3.1-8B) for understanding regulatory and financial texts. The process involved creating a specialized corpus via web scraping, implementing data cleaning and preprocessing pipelines, and instruction fine-tuning using QLoRA for tasks defined in the Coling 2025 Regulations Challenge. | True | Market | True | 1.0 | NaN | Domain-specific further pretraining and instruction fine-tuning of LLaMA-3.1-8B using QLoRA. Methodology includes corpus creation (web scraping, TF-IDF filtering, GPT-4o-mini cleaning) and instruction dataset generation (GPT-4o prompting, external dataset integration). | Evaluated on nine tasks from the Coling 2025 Regulations Challenge (Abbreviation Recognition, Definition, NER, QA, Link Retrieval, Certificate Analysis, XBRL Analytics, CDM Processing, Financial Mathematics, License Compliance) using metrics like Accuracy, BERTScore, F1 Score, FActScore. Compared against baselines (GPT-4o, Llama 3.1 8B base, Mistral Large 2) on the challenge leaderboard. | The model achieved a final weighted score of 0.43929 (2nd place in the challenge). It showed strength in Question-Answering (0.7688 FActScore) but weaknesses in Named Entity Recognition (0.4302 F1) and XBRL Analytics (0.3444 FActScore). | NaN | NaN | NaN | NaN | Regulatory law, Financial law, Compliance | US, EU, International | A custom corpus of publicly available financial and regulatory documents scraped from sources suggested by the Coling 2025 Regulations Challenge (e.g., EUR-LEX, ESMA, FDIC, Fed Reserve, eCFR, SEC, CFA/CPA Exam info, XBRL Int'l, CDM Docs, OSI). Preprocessed using TF-IDF filtering and GPT-4o-mini cleaning. Instruction dataset generated using GPT-4o on the cleaned corpus and supplemented with public Hugging Face datasets (flare-cfa, XBRLBench). Unstructured text data. | Corpus creation (recursive scraping, source-specific scrapers), Data Filtering (TF-IDF relevance scoring), Data Cleaning (GPT-4o-mini with prompt engineering), Instruction Dataset Generation (task-specific prompts with GPT-4o, integration of external datasets), Model Selection (LLaMA-3.1-8B), Fine-tuning (further pretraining, two-stage QLoRA instruction fine-tuning with varying context windows). | Potential for local deployment discussed due to model size, but no specific deployment strategy described. | True | True | Code, prompts, and implementation details are available on GitHub. | Technical gaps identified: suboptimal performance in NER, XBRL Analytics, and Certificate tasks; challenges in handling long documents exceeding context windows; need for enhanced structured data processing; lack of comprehensive expert validation; potential for hallucinations without mitigation strategies like RAG. | Handling noisy web-scraped data; lack of standardized regulatory NLP benchmarks; computational resource management (addressed via QLoRA and 8B model); varying context window requirements across tasks; achieving high performance on complex structured tasks (NER, XBRL); potential for model hallucination. | Risk of model inaccuracy, particularly in complex tasks like NER and XBRL Analytics. Potential for generating factually incorrect information (hallucinations), especially without retrieval augmentation. |
| gScUXpSxSxgJ.pdf | Google_Scholar | PREDICTING CONSUMER CONTRACTS | This article empirically evaluates the ability of the GPT-3 language model to understand consumer contracts by testing its performance on a novel dataset of questions about online terms of service. While showing potential for consumer empowerment, the study finds GPT-3 exhibits brittleness regarding question wording and a possible anti-consumer bias, highlighting the need for safeguards before deploying such models in law. | True | Idealistic | True | 2.0 | Neutral | Evaluating GPT-3's ability to answer yes/no questions about consumer contracts (terms of service) when provided with relevant excerpts. | A novel dataset of 200 yes/no questions was created, relating to the terms of service of the 20 most-visited U.S. websites. GPT-3 (davinci engine, temperature=0) was prompted with a contract excerpt and a question, and its accuracy and calibration were measured against random chance, majority class, and a 'contract withheld' baseline. Regression analysis controlled for variables like question category and wording. | GPT-3 achieved 77% accuracy, outperforming baselines, suggesting it used contract information. However, it performed significantly worse on questions about pro-consumer provisions (60% accuracy) compared to pro-company provisions (84% accuracy), indicating potential anti-consumer bias. Performance was also highly sensitive to question wording (readability) but not contract length or readability. | Consumers lack time, expertise, and incentive to read/understand contracts. AI models may provide misleading advice, contain harmful biases (e.g., anti-consumer bias), lack reliability due to brittleness (sensitivity to input variations), and lack interpretability, making errors hard to diagnose and trust difficult to establish. | Language models could empower consumers by reading/explaining contracts. The paper proposes ongoing experimentation (e.g., varying prompts), development of prompt design guidance, establishing technical and institutional safeguards (transparency, accountability, auditing), and regulatory reform (e.g., regarding unauthorized practice of law) to ensure responsible deployment. | Understanding consumer rights and obligations in online terms of service. | General consumers interacting with online services. | Consumer Law, Contract Law | US (based on the dataset of terms of service from US websites) | GPT-3 was trained by OpenAI on vast unlabeled datasets (570GB+) including Common Crawl, Webtext2, online books, and Wikipedia. This data is proprietary and likely includes numerous online terms of service. | Creation of a novel test dataset (200 yes/no questions on 20 terms of service), specific prompt engineering for GPT-3 interaction via API (davinci engine, temp=0), quantitative evaluation based on accuracy and calibration metrics, comparison against defined baselines, and statistical analysis (OLS regression) to identify factors influencing performance. | The paper evaluates GPT-3 used via the OpenAI API; it does not deploy a tool itself but discusses the potential for future deployment of similar technologies for consumers. | True | False | The methodology relies on the GPT-3 API provided by OpenAI, which is commercially available (subject to OpenAI's terms and pricing). | Need for larger, more diverse, and robust legal benchmark datasets (including unanswerable questions). Deeper investigation into model biases (sources and mitigation). Improving model robustness and interpretability. Development and implementation of effective technical/institutional safeguards and governance frameworks. Addressing regulatory barriers like unauthorized practice of law rules. Need for real-world evaluation methodologies. | Methodological challenges in evaluation: avoiding test data contamination, ensuring question independence, managing model stochasticity, maintaining transparency. Limitations of the study: small dataset size, single author annotating questions, narrow scope (one model, one task), reliance on yes/no format due to difficulty evaluating open-ended legal answers. Identifying and controlling for all variables influencing performance (potential omitted variable bias). | Misleading legal advice from AI; amplification and entrenchment of societal biases (e.g., anti-consumer bias); model brittleness leading to unreliable outputs; lack of interpretability hindering error diagnosis and trust; misuse for malicious purposes (misinformation, phishing, spam); data protection/privacy violations (in training data or API use); high environmental costs of training; intellectual property ownership ambiguity; unequal performance/access across languages/groups; compounding bias via feedback loops where model outputs pollute future training data. |
| hfKbdgn8f08J.pdf | Google_Scholar | Linking Judgement Text to Court Hearing Videos: UK Supreme Court as a Case Study | This paper presents a system for automatically linking paragraphs in UK Supreme Court written judgements to relevant segments in the corresponding court hearing video transcripts. The system uses customized GPT text embeddings for information retrieval and aims to improve access to and understanding of lengthy court proceedings. | True | Idealistic | True | 1.0 | Positive | An Information Retrieval (IR) system using customized GPT-3 text embeddings (text-embedding-ada-002) and cosine similarity to link written judgement paragraphs (queries) to transcribed spoken hearing segments (corpus). Includes data augmentation via InstructGPT paraphrasing. | Initial IR models (BM25, GloVe, Entailment, Legal BERT, Asymmetric, GPT) were evaluated using MAP@k and Recall@k against human annotations on a subset to select candidates for full annotation. Supervised models (Logistic Regression, Cross-encoder, CT bi-encoder, customized GPT embeddings) were trained on annotated/augmented data and evaluated using Accuracy, Precision, Recall, F1 against gold-standard labels on a test set. | Best results achieved with customized GPT-3 embeddings combined with cosine similarity as a feature in a logistic regression model (Accuracy=0.85, Precision=0.85, Recall=0.84, F1=0.85 on the gold-standard test set). | Key A2J obstacles identified: 1) Time required to analyze lengthy hearing videos. 2) Scarcity and difficulty of using hearing transcripts. | Proposed solution: An automated tool/UI platform linking judgement text to relevant video moments via semantic search, aiding navigation and comprehension of UKSC proceedings. | Improving access to and understanding of Supreme Court proceedings and judgements; Navigating lengthy legal video recordings. | General public and legal professionals/researchers needing to understand UKSC proceedings. | General (UK Supreme Court cases) | United Kingdom | Dataset derived from 7 UKSC cases (judgements from UKSC website, transcripts from custom ASR of UK National Archive videos). Annotated by law postgraduates (3620 gold links). Augmented using InstructGPT paraphrasing and negative sampling (total 7248 links). Domain-specific, mixed written/spoken register, unstructured text. | Information Retrieval (IR) approach (semantic search), custom ASR model development, zero-shot IR evaluation, human annotation by legal experts, data augmentation using generative AI (InstructGPT), supervised model training, embedding customization (OpenAI method), User Interface (UI) development. | Presented via demos to stakeholders (UK National Archives, UKSC, legal AI companies) with interest expressed for integration into transcription software pipelines. No wide deployment mentioned. | False | False | NaN | Mentioned gaps: Need for larger datasets, exploring entity-based linking, improving model robustness against high-frequency irrelevant phrases. | Challenges: Data segmentation (judgements/transcripts), linking different linguistic modes (written/spoken), costly domain-expert annotation, distinguishing true semantic links from superficial term overlap. | NaN |
| JIrkB5Ps8MEJ.pdf | Google_Scholar | Legal Considerations in Machine -Assisted Decision -Making: Planning and Building as a Case Study | This paper examines the legal considerations for governments and businesses implementing machine-assisted decision-making, using planning permits and building approvals in Victoria, Australia, as a case study. It identifies challenges related to transparency, bias, privacy, liability, and administrative law, arguing that addressing these issues is crucial to minimize risks while harnessing AI's benefits. | True | Market | False | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Administrative Law, Planning Law, Building Law, Tort Law (Negligence/Liability), Privacy Law, Data Protection Law, Intellectual Property Law, Evidence Law | Victoria (Australia), Australia (Federal), EU, US, UK | NaN | NaN | NaN | False | False | NaN | Legislative frameworks not designed for AI (e.g., defining 'decision', authorising AI-made decisions, establishing liability standards, ensuring reviewability). Need for clarity on scope of AI use and criteria. | Technical black box (inscrutability of algorithms, especially deep learning), legal black box (proprietary claims/trade secrets hindering transparency), algorithmic bias stemming from training data, ensuring privacy and data protection (particularly with collaborative platforms like BIM), cybersecurity risks, defining accountability (who is the decision-maker?), adapting existing legal doctrines (e.g., definition of 'decision' for judicial review, standard of care for negligence), ensuring admissibility of AI-generated evidence. | Lack of transparency preventing judicial review and undermining legitimacy; Algorithmic bias leading to unfair or discriminatory outcomes; Privacy violations and misuse of personal/proprietary data; Cybersecurity breaches (data corruption/theft); Legal liability for damages caused by AI errors (e.g., faulty building approvals); Decisions potentially being non-reviewable under current administrative law; 'Hallucinations' or untruthful outputs from generative AI leading to incorrect decisions. |
| jcDJsdOzy-4J.pdf | Google_Scholar | Generative AI in the Law | This paper provides an overview of Generative AI, focusing on its current uses and misuses by lawyers, particularly exemplified by tools like ChatGPT. It extensively discusses court responses, sanctions in cases like Mata v. Avianca, and the significant ethical and professional responsibility implications for attorneys, emphasizing caution and the need for human oversight. | True | Market | True | 3.0 | NaN | Generative AI, specifically discussing platforms like ChatGPT, Google Bard, Bing Chat, and Harvey.AI. | The paper discusses outcomes from real-world misapplications (e.g., Mata v. Avianca, Ex Parte Lee, a Second Circuit case) and refers to informal testing by the author on ChatGPT regarding Texas procedural rules and substantive legal research questions. It also mentions DoNotPay's attempted in-court use of its 'robot lawyer'. | In Mata v. Avianca, ChatGPT fabricated case citations. The author's tests showed ChatGPT provided incorrect answers to legal questions and failed substantive legal research tasks. When later prompted by the author, ChatGPT acknowledged creating fictional case examples. | NaN | NaN | NaN | NaN | General legal practice, Civil Litigation, Criminal Law (Habeas Corpus), Legal Research, Legal Drafting. | United States (specifically mentioning federal courts like S.D.N.Y., N.D. Tex., E.D. Pa., N.D. Ill., U.S. Court of International Trade, Second Circuit, Fifth Circuit; and state-level mentions like Texas courts and the Florida Bar). | For ChatGPT, it is mentioned as being trained on "hundreds of billions of words scraped from the internet." | Generative AI is described as a "deep-learning model" that uses a "neural network algorithm." | Platforms like ChatGPT, Google Bard, and Microsoft Bing Chat are described as widely available or released. DoNotPay attempted to deploy its 'robot lawyer' in court. | True | False | ChatGPT is stated to be free, and Bing Chat is accessible on the Microsoft Edge internet browser. | NaN | Challenges in using Generative AI in law include: inaccuracy and 'hallucinations' (fabricating information like case citations); potential bias in outputs; ensuring client confidentiality and privilege when using third-party AI tools; the ethical obligation for lawyers to remain competent and diligent, requiring thorough verification of AI-generated content; and the risk of professional sanctions or disciplinary action from misuse. | Potential risks include: court sanctions for submitting AI-generated filings with fabricated information (Mata v. Avianca); professional discipline by bar associations; breach of client confidentiality and attorney-client privilege; harm to clients through incompetent representation; damage to the integrity of the judicial system and public trust; reputational harm to judges, parties, and attorneys involved with fabricated legal materials; and violating rules of professional conduct regarding competence, diligence, and candor to the tribunal. |
| NrBD0NLvApwJ.pdf | Google_Scholar | פרטיות ואוטונומיה בהליכי גישור דיגיטליים: מיפוי הסיכונים והצעות לפתרון | This paper introduces an information-flow model to analyze how digital mediation affects participants' privacy, data protection, and autonomy. It identifies key risks from digital platforms and proposes legal and regulatory solutions to address current framework shortcomings. | False | Idealistic | False | 1.0 | Neutral | An analytical model conceptualizing digital mediation as an information flow process along two axes: information disclosure (internal/external to mediation) and information processing (for internal/external mediation purposes). | NaN | NaN | The digitalization of mediation creates new types of information and processing methods, leading to risks for participants' confidentiality, privacy, data protection, and autonomy. Existing legal frameworks (mediation law, privacy law) are inadequate and fragmented in addressing these new risks, particularly concerning the role of digital platforms and AI. | Proposes: 1) Regulating the legal status and duties of dedicated mediation platforms regarding information handling, confidentiality, and party control. 2) Defining mediators' responsibilities when using digital platforms, including training and risk disclosure. 3) Adapting general data protection, privacy, and AI laws to address the specific challenges of digital mediation, emphasizing transparency and accountability. | Digital mediation, Online Dispute Resolution (ODR), Privacy, Data Protection, Participant Autonomy, Confidentiality of mediation. | NaN | Mediation law, Privacy law, Data protection law, Civil procedure (by implication). | Primarily Israel, with comparative references to US and EU law and regulations. | NaN | Conceptual modeling based on legal and theoretical analysis of mediation, privacy, and technology literature. | NaN | True | False | The analytical model presented in the paper can be understood and conceptually applied by reading and understanding the paper. | Legal and regulatory gaps in defining the status and obligations of digital mediation platforms; Insufficiency of current data protection frameworks (including consent mechanisms) for the mediation context; Need for enhanced mediator training on technological risks; Lack of transparency and accountability in AI tools used in mediation. | Developing a comprehensive analytical model to map complex information flows and multi-faceted risks in the evolving landscape of digital mediation, amidst a fragmented and lagging legal framework. | Compromise of party autonomy in decision-making (due to interface design, algorithmic bias, automation bias); Breach of confidentiality (within mediation and externally, e.g., AI tools sharing data, platform data misuse for external purposes like marketing or profiling); Collection of excessive data; Lack of transparency in platform data practices; 'Trust spillover' from mediator to platform; Creation of detailed user profiles by combining mediation and platform usage data. |
| 2rvZcTZWe3oJ.pdf | Google_Scholar | GEWICHTSFORMEL – \nWÖRTLICH GENOMMEN.\nEIN EMPIRISCHER TEST \nMIT DER HILFE EINES \nSPRACHMODELLS | This paper empirically tests Robert Alexy's "Gewichtsformel" (weight formula) for legal balancing using large language models (GPT-3.5 and GPT-4) as proxies in a constitutional law scenario. It investigates how varying prompts based on the formula influence the LLMs' decisions and consistency, finding that the formula can have a rationalizing effect but may also be perceived as too rigid. | False | NaN | True | 1.0 | NaN | Using LLMs (GPT-3.5 turbo and GPT-4) with varied prompts incorporating Robert Alexy's "Gewichtsformel" to simulate and analyze legal balancing decisions in a constitutional case. | LLMs (GPT-3.5 turbo, GPT-4) were presented with a hypothetical German constitutional law case concerning cannabis cultivation for medical reasons. Six main versions of prompts were administered, progressively incorporating elements of Alexy's "Gewichtsformel," including chain-of-thought reasoning and explicit aggregation instructions. Each prompt variant was queried 100 times with "temperature" set to 1 to obtain a distribution of binary (yes/no) decisions on whether a constitutional complaint should be granted. | When GPT-4 was used to aggregate individual interest assessments (previously generated by GPT-3.5) and then make a final decision based on explicit instructions for balancing according to the 'Gewichtsformel' (prompt version six), it resulted in granting the constitutional complaint in 27 out of 100 instances. This highlighted that explicit formulaic guidance significantly impacts outcomes and that GPT-4 showed more consistency in aggregation but also instances of deviating from strict formula application when it seemed too rigid. | NaN | NaN | NaN | NaN | Constitutional law | Germany | The study uses pre-trained GPT-3.5 turbo and GPT-4 models from OpenAI; the paper refers to the general, proprietary training data these models were trained on. | Empirical experimental design using LLMs as subjects. The methodology involved systematic variation of prompts (including simple queries, 'chain of thought' prompts, and prompts with explicit formulaic instructions for aggregation and balancing) to test hypotheses about the impact of Robert Alexy's "Gewichtsformel" on decision outputs. Different LLM versions (GPT-3.5 for assessment, GPT-4 for aggregation) were used for specific tasks based on their perceived strengths. | NaN | True | False | The methodology, including the specific prompts and parameters (e.g., model choice, temperature setting), is detailed in the paper, allowing replication by anyone with access to OpenAI's API for GPT-3.5 turbo and GPT-4. | NaN | Challenges included effective prompt engineering to elicit desired binary outputs, the known weakness of GPT-3.5 in mathematical or quasi-mathematical aggregation tasks, the imperfect replicability of results due to the proprietary nature of the LLMs, and the ongoing general uncertainty about how well LLM responses mirror human decision-making processes. | The paper notes normative problems with potential full delegation of judicial decision-making to AI, mentions that cognitive biases might persist in LLMs (citing other research), implicitly warns against using LLMs as direct substitutes in actual legal proceedings without due caution ("Sicher nicht, dass die Grundrechtsauslegung künftig einem amerikanischen Unternehmen... überlassen bleiben soll"), and observes that LLMs may show inconsistencies or find established legal formulas too rigid. |
| GqRsr_mXZ9UJ.pdf | Google_Scholar | Legal Literacy in Indonesia: Leveraging Semantic -Based AI and NLP for Enhanced Civil Law Access | This paper addresses low legal literacy in Indonesia by developing and evaluating CerdasHukum, an AI system using IndoSBERT and QDrant for semantic search of the Indonesian Civil Code. The system demonstrated good accuracy (76.66% expert-validated) and usability (SUS score 74.81) in retrieving relevant legal articles. | True | Idealistic | False | 1.0 | Positive | A semantic legal information retrieval system named CerdasHukum using IndoSBERT (an Indonesian BERT-based sentence embedding model) to generate 256-dimensional vectors from legal texts and QDrant (a vector database) for efficient cosine similarity-based search. | Accuracy was evaluated by legal experts using a binary grading scale on retrieved articles for sample queries. Usability was assessed with 30 participants using the System Usability Scale (SUS). Comparative case studies were conducted against IndoBERT and FastText using cosine similarity scores. | The system achieved a recommendation accuracy of 76.66% as validated by legal experts. It received a System Usability Scale (SUS) score of 74.81 (Grade B). In comparative case studies, IndoSBERT achieved higher cosine similarity scores (e.g., 0.9227 and 0.9089) than IndoBERT (0.7065, 0.6232) and FastText (0.6668, 0.6205) for specific legal queries. | Low legal literacy, complex legal language, limited resources hindering public understanding and access to justice, particularly for marginalized communities. Insufficiency of traditional keyword-based information retrieval systems. | Development of a semantic-based AI retrieval system (CerdasHukum) using IndoSBERT and QDrant to provide context-aware search results from legal texts, enhancing accessibility and understanding of civil law. | Access to legal information, Legal literacy | General public in Indonesia, particularly marginalized communities and individuals facing civil disputes (e.g., in South Kalimantan). | Civil Law (specifically the Indonesian Civil Code - KUHPerdata) | Indonesia | The system uses the text of the Indonesian Civil Code (KUHPerdata), comprising 2,074 articles, preprocessed and transformed into embeddings by the pre-trained IndoSBERT model. The source is domain-specific (legal code), unstructured text data. | Systematic approach including: Data collection (Indonesian Civil Code), Text preprocessing (lowercasing, normalization, WordPiece tokenization), Semantic vector embedding (IndoSBERT), Vector storage and retrieval (QDrant with cosine similarity), Evaluation (Expert validation for accuracy, System Usability Scale for usability). | NaN | False | False | NaN | Need to incorporate additional legal texts and expand to other legal domains (criminal, administrative law). Requirement for further refinement of IndoSBERT for domain-specific tasks. Potential for exploring multilingual embeddings or zero-shot learning. General lack of focus on AI for low-resource languages. | Handling the complexity and nuances of legal language. Overcoming limitations of traditional keyword search. Achieving high contextual relevance in information retrieval. Working with a low-resource language (Indonesian). | NaN |
| 0FONfFqRRU4J.pdf | Google_Scholar | Mini-CarbonGPT: A Domain-Specific Large Language Model \nfor Carbon Neutrality | This paper introduces Mini-CarbonGPT, an LLM tailored for the carbon neutrality domain, built by fine-tuning the GLM-4-9B model and integrating Retrieval-Augmented Generation (RAG). Evaluations show it outperforms the base model and several commercial LLMs on domain-specific objective questions and performs competitively on subjective tasks. | True | NaN | True | 1.0 | NaN | Mini-CarbonGPT: Integration of supervised fine-tuning (SFT) using LoRA on the GLM-4-9B base model and Retrieval-Augmented Generation (RAG) using a custom knowledge base. | Evaluated using a custom dataset of 700 objective (single-choice) and 249 subjective (open-ended) questions across five carbon neutrality subfields. Metrics included accuracy for objective questions, and F1 score, BERT score, METEOR score, GPT-o1 scoring (accuracy, completeness, clarity), and keyword coverage for subjective questions. Compared against base GLM-4-9B, fine-tuned GLM, RAG-only GLM, and commercial models (GPT-4o, Gemini-1.5 Flash, Kimi, ERNIE Bot-3.5). | Mini-CarbonGPT achieved the highest average accuracy (80.57%) on objective questions, outperforming the base GLM-4-9B model (70.00%) and commercial models like GPT-4o (79.43%). For subjective questions, it showed improved accuracy and completeness in GPT-o1 evaluations and better keyword coverage compared to the base model, though commercial models generally led in automated metrics (BERT, F1) and perceived clarity. | NaN | NaN | NaN | NaN | Environmental Science / Policy / Energy / Economics | International | Supervised Fine-Tuning (SFT) data: ~50k general instructions (cleaned GPT4-Alpaca) + 5,382 professional instructions (extracted from UltraChat). Retrieval-Augmented Generation (RAG) Corpus: 6,096 carbon neutrality documents (from CNKI, Web of Science) + 60,000 Wikipedia pages. Evaluation Datasets: 700 objective + 249 subjective questions (from Studocu, Baidu Wenku, national graduate entrance exam materials). Primarily unstructured text data from mixed public/academic/proprietary sources. | Base model selection (GLM-4-9B), Parameter-Efficient Fine-Tuning (PEFT) via Low-Rank Adaptation (LoRA), INT4 quantization, two-phase SFT (general then domain-specific), Retrieval-Augmented Generation (RAG) implementation using FAISS vector store and paraphrase-multilingual-MiniLM-L12-v2 embeddings, dynamic paragraph-priority chunking, weighted fusion retrieval strategy. | The paper details training on standard hardware (2x 2080Ti GPUs) enabled by quantization and PEFT, suggesting feasibility for resource-constrained environments. Discusses potential future deployment strategies like knowledge distillation but does not state current deployment status. | False | False | NaN | NaN | High computational cost of LLMs, scarcity of large-scale domain-specific datasets for carbon neutrality, data imbalances across sub-disciplines, handling interdisciplinary and heterogeneous data, potential knowledge conflicts between fine-tuned model and RAG results, balancing information coverage and conciseness in RAG-generated answers, ensuring semantic accuracy beyond surface keyword matching, potential accuracy loss from quantization techniques (e.g., INT4). | General LLM risks such as hallucinations and biased outputs. Potential for inconsistent or inaccurate outputs due to conflicts between the model's internal knowledge (from fine-tuning) and externally retrieved information (via RAG). Accuracy loss due to model compression techniques like quantization. |
| r0sO2A6Lo0UJ.pdf | Google_Scholar | Artificial Intelligence and Legal Transparency: A Comparative Analysis between Public and Private Law | This paper analyzes the legal dimensions and challenges of artificial intelligence, focusing on transparency issues arising from the differences between public and private law. It discusses the regulatory landscape, particularly the EU AI Act, and the need for both legal branches to adapt to frame AI effectively. | True | NaN | True | 3.0 | Neutral | NaN | NaN | NaN | Difficulty in challenging unfair or discriminatory AI decisions due to lack of transparency and established legal recourse mechanisms; potential for bias in AI systems used for justice. | Developing AI tools (e.g., Q&A bots) to automate legal tasks and assist with filings; implementing robust legal frameworks (like the EU AI Act) emphasizing transparency, accountability, and appeal rights; updating existing laws. | Automating lawyer tasks; Assistance with legal document filing. | General public needing legal assistance but lacking resources for lawyers. | Public Law, Private Law, Administrative Law, Human Rights Law, Data Protection Law, Civil Law (Contracts, Liability), Commercial Law, Competition Law, Intellectual Property Law, Consumer Protection Law, International Law | European Union, United States, International | NaN | NaN | NaN | False | False | NaN | Insufficiency of current legal frameworks (e.g., copyright for generative AI); lack of transparency and accountability in AI decision-making; difficulty defining legal liability for AI harm; challenges adapting contract, commercial, competition law; potential for bias and digital divides. | Ensuring AI respects fundamental rights; protecting data privacy; achieving algorithmic transparency and accountability; preventing bias and discrimination; defining legal liability; adapting existing legal frameworks; regulating effectively; addressing market concentration. | Violation of fundamental rights (e.g., privacy via facial recognition); unfair/discriminatory decisions due to AI bias; lack of legal recourse against AI decisions; data protection violations; misuse of personal data; market monopolies; unfair competition; copyright infringement. |
| 8Yv6l4FgOwUJ.pdf | Google_Scholar | Generative AI – Uses and Abuses in Litigation | This paper discusses the increasing use of Generative AI (GenAI) in litigation, outlining potential benefits for tasks like drafting and eDiscovery, alongside significant risks such as inaccuracies and ethical breaches. It emphasizes the need for responsible use, adherence to emerging court guidelines like NSW's Practice Note SC Gen 23, and ongoing development of GenAI literacy among legal professionals. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | Lack of effective participation in formal dispute resolution processes by unrepresented parties. | GenAI can potentially enable more effective participation by unrepresented parties in litigation. | Participation in formal dispute resolution. | Unrepresented parties/litigants. | Litigation | New South Wales (Australia), with references to other jurisdictions (e.g., US). | NaN | NaN | NaN | False | False | NaN | Need for GenAI literacy among legal professionals; technical limitations of GenAI (accuracy, bias, reasoning); need for clear governance frameworks and ethical guidelines. | Ensuring accuracy, avoiding hallucinations and bias, maintaining data security and privacy, verifying AI-generated content, integrating GenAI responsibly into legal workflows, keeping up with rapid technological development and evolving court rules. | Generation of fake/inaccurate citations or legal summaries, fallacious arguments, inadequate fact-checking, prolix/incorrect drafting, court 'flooding' with AI documents, litigation delays, increased workloads and costs, failure of proceedings, reputational damage, professional sanctions (e.g., costs orders), misuse in preparing evidentiary materials, data security and privacy breaches. |
| 3nlNDsmskIAJ.pdf | Google_Scholar | Incorporating Generative Artificial Intelligence into the Practice of Law: Utilizing Generative AI within the Framework of the California Rules of Professional Conduct | The paper explores the potential applications of generative AI in legal practice, such as document drafting and research, while highlighting the significant ethical challenges under the California Rules of Professional Conduct. It emphasizes the need for lawyers to understand AI limitations like hallucinations and adhere to duties of competence, confidentiality, communication, candor, and supervision. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | AI generating inaccurate information ('hallucinations'); ensuring lawyer competence with AI; protecting client confidentiality; maintaining candor to courts regarding AI use; proper supervision of AI; over-reliance hindering critical analysis; potential for bias. | Maintain human oversight and verify AI output; understand AI limitations; use AI platforms with strong data privacy; anonymize client data; communicate AI use to clients; disclose AI use to courts as required; supervise AI diligently; ongoing education; establish clear policies. | NaN | NaN | General legal practice | California | NaN | NaN | NaN | False | False | NaN | Reliability of AI (hallucinations), need for lawyer competence and clear ethical guidelines for AI use, ongoing research into mitigating AI flaws. | Ensuring ethical compliance (competence, confidentiality, candor, supervision) when using generative AI, dealing with AI hallucinations/inaccuracies, verifying AI output, avoiding over-reliance. | Producing inaccurate legal or factual statements (hallucinations); violating client confidentiality; lawyer incompetence; misleading courts; facing sanctions or discipline; potential for bias in AI output; charging unconscionable fees due to inefficient AI use or lack of cost pass-through. |
| VxfIMJROhukJ.pdf | Google_Scholar | How effectively can ChatGPT-4 draft data transfer agreements for health research? | This paper evaluates the effectiveness of ChatGPT-4 in drafting specialized Data Transfer Agreements (DTAs) for health research using a two-stage prompting methodology. While ChatGPT-4 can generate a comprehensive outline and detailed clauses, the resulting DTA requires significant refinement by legal experts due to issues with clarity, precision, and data protection compliance. | True | Market | True | 2.0 | Neutral | Using ChatGPT-4 with a two-stage iterative prompting methodology (contract-level outline generation until saturation, followed by clause-level drafting) to generate Data Transfer Agreements (DTAs) for health research. | ChatGPT-4 was prompted iteratively (10 sessions until saturation) to generate DTA outlines. Based on the aggregated outline, it was prompted clause-by-clause to draft the full DTA. The generated DTA was then qualitatively analyzed for comprehensiveness (comparison with best practices identified by Swales et al., 2024), content quality (clarity, precision, redundancy, ambiguity, overlap), and alignment with data protection compliance standards. | ChatGPT-4 produced a comprehensive outline after iteration and a 6847-word DTA covering standard clauses. However, the content suffered from redundancies, ambiguous terminology (e.g., 'derivative work'), overlapping provisions, and lacked sufficient detail to fully meet data protection best practices regarding legal justification for transfer, data handling lifecycle specifics, data subject rights implementation, technical/organisational security measures, and cross-border transfer rules. | NaN | NaN | NaN | NaN | Contract Law, Data Protection Law, Health Law | International | NaN | Iterative prompting (contract-level outline saturation and clause-level generation). Comparative analysis against best practices (Swales et al., 2024). Qualitative content analysis. | NaN | True | True | The technique involves using ChatGPT-4, which is publicly available (with free tiers), following the prompting methodology detailed in the paper. | NaN | Inconsistency in AI's initial outline generation (necessitating iteration to avoid omissions); limitations in generating extensive documents in one session; achieving consistent quality in generated legal text (redundancy, ambiguity, overlap); ensuring sufficient specificity for compliance with detailed data protection requirements. | Risk of significant omissions if relying on a single AI-generated draft without iteration. Potential for embedded bias in AI outputs (though none observed in this study). Risks associated with lack of human oversight and professional accountability when using AI tools for legal drafting. |
| gzrmVfqby74J.pdf | Google_Scholar | Generative Artificial Intelligence and Article 6 of the European Convention on Human Rights: The Right to a Human Judge? | This paper examines the implications of using generative AI in judicial processes under Article 6 of the European Convention on Human Rights (ECHR), focusing on the right to a fair trial. It argues that interpreting Article 6 through the lens of human dignity implicitly supports the right to a human judge to safeguard against dehumanisation. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Unaffordability of legal advice, significant court backlogs causing delays, potential for AI-driven advice to exacerbate system strain without improving resolution, risks of dehumanisation and undermining fair trial rights (voice, neutrality, respect, trustworthiness) through AI. | Advocating for a human dignity-based interpretation of Article 6 ECHR to establish the right to a human judge. Proposing the use of AI to complement, not replace, human judges (e.g., automating non-judicial tasks, research assistance, bias identification), emphasizing transparency, explainability, ethical review, and potentially using AI in ADR with consent. | Right to a fair trial (Article 6 ECHR), access to courts, judicial efficiency, judicial decision-making, human dignity in legal processes. | NaN | Human Rights Law, Civil Procedure, Civil Justice | European Convention on Human Rights (ECHR) signatory states, European Union (mentions AI Act) | NaN | NaN | NaN | False | False | NaN | Lack of explicit recognition of a 'right to a human judge' in ECHR Article 6 interpretation. Need for equitable access to AI tools, better understanding of AI's cognitive impact on judicial work, balancing transparency with proprietary IP, defining adequate human oversight. Technical limitations in AI 'understanding', 'reasoning', bias, empathy, and explainability. | NaN | Dehumanisation (loss of individuality, lack of genuine voice), discrimination (algorithmic bias), erosion of public trust (errors, opacity), undermining procedural fairness (neutrality, respect, trustworthiness), inaccurate outputs (hallucinations), compromised judicial independence/impartiality (external influence, hidden bias), inadequate reasoning ('black box' problem), erosion of judicial accountability. |
| pnYx_0Zyq1oJ.pdf | Google_Scholar | The Potential for Jurisdictional Challenges to AI or LLM Training Datasets | This paper critiques the use of Large Language Models (LLMs) for Access to Justice (A2J), arguing that their training datasets pose significant jurisdictional challenges related to bias, sovereignty, and the rule of law. It proposes a conceptual framework of "information sovereignty" to ensure AI tools are jurisdictionally appropriate and truly serve A2J goals. | True | Idealistic | True | 3.0 | Negative | NaN | NaN | NaN | Systemic bias in LLMs due to training datasets not reflecting specific communities/jurisdictions; challenges to legal sovereignty and the rule of law from extra-jurisdictional data; failure to ensure quality and legal compliance of datasets; AI exacerbating existing inequalities (digital divide, cost); lack of transparency and accountability in AI decision-making. | Proposes a conceptual framework of "information sovereignty" with four tenets: Population (limiting training data to jurisdictional individuals), Territory (defining jurisdiction by practitioners/systems), Recognition (auditable outputs reflecting community practitioners), and Regulation of borders (immutable outputs). Emphasizes the need for jurisdictionally bounded training data and encoded procedural logic. | Procedural justice; Rule of law; Legal information provision; Document drafting; Use of AI by self-represented litigants. | Underserved litigants; Self-represented litigants; General public unable to afford legal services. | General Law; Constitutional Law; Procedural Law | International | NaN | NaN | NaN | False | False | NaN | LLMs lack nuance for legal technicalities and edge cases; difficulty ensuring datasets represent community norms; lack of accountability mechanisms for AI. | NaN | Systemic bias leading to unfair outcomes; undermining the rule of law; lack of transparency and accountability; inaccurate information and fabricated citations (hallucinations); exacerbating inequalities; declining public trust in the justice system; lawyers over-relying on flawed AI outputs; AI acting as a liability shield; denial of justice due to incorrect AI guidance. |
| bY8xyMfAAK0J.pdf | Google_Scholar | NAVIGATING THE CHALLENGES OF GENERATIVE AI | This paper analyzes ABA Formal Opinion 512 and other ethical guidelines for lawyers using generative AI. It details key obligations like competence, confidentiality, and candor to ensure responsible AI integration in legal practice. | True | Market | True | 2.0 | Neutral | Ethical guidelines for legal professionals using generative AI (based on ABA Formal Opinion 512 and similar state bar guidances) | NaN | NaN | AI-generated biased information, inaccurate 'hallucinated' citations in legal filings, and unintentional disclosure of confidential client information, all of which risk undermining the justice system. | Adherence to clear ethical guidelines (e.g., ABA Opinion 512), including understanding AI capabilities and limitations, independently verifying AI outputs, protecting client confidentiality, establishing firm policies and training, and transparent billing practices. | Ethical and competent use of AI by legal professionals to maintain the integrity and fairness of the justice system. | NaN | Legal ethics, Professional responsibility, General legal practice | United States (referencing ABA, Colorado, California, Florida, New Jersey, New York, Texas) | NaN | NaN | NaN | True | True | The discussed ABA Formal Opinion 512 is available online via a provided URL. | The need for continuous adaptation of ethical rules to evolving AI, maintaining lawyers' up-to-date knowledge, and addressing AI's inherent limitations (e.g., bias, lack of human nuance). | Understanding GenAI capabilities and limitations (hallucination, reliability, bias); verifying AI outputs; protecting client confidentiality with AI tools; communicating AI use to clients; ensuring candor to tribunals; establishing firm AI policies and training; adjusting fee structures for AI efficiencies; staying current with evolving AI technology and guidance. | AI hallucinations (e.g., fake citations); unreliable, inaccurate, or biased AI outputs; breaches of client confidentiality through AI tools; misleading tribunals with AI-generated content; lawyers facing sanctions for AI misuse. |
| 1B6lhnMG9xwJ.pdf | Google_Scholar | Analysis of the Digital Transformation of Legal Services and the Role of Policy Brokers in KOREA through the Advocacy Coalition Framework | This paper analyzes the nine-year conflict between the Korean Bar Association and the legal tech platform LawTalk using the Advocacy Coalition Framework (ACF). It examines how different advocacy coalitions, their belief systems, and the intervention of policy brokers shaped the policy outcomes regarding digital legal services in South Korea. | True | Idealistic | False | 2.0 | Positive | NaN | NaN | NaN | Resistance from traditional legal institutions (Bar Association), regulatory ambiguity for new platforms, conflicting interpretations of law (Attorney-at-Law Act, Fair Trade Act), concerns over ethics vs. accessibility, information asymmetry in the traditional market. | Mediation by neutral policy brokers (e.g., Ministry of Justice, FTC), legal clarification and rulings supporting innovative platforms, adapting policy frameworks to technological change, promoting dialogue between stakeholders. | Access to legal information and consultation via online platforms, regulation of LegalTech. | General public / consumers | General legal services, Attorney regulation, Competition law | South Korea | NaN | NaN | Commercial platform available to the public in South Korea, facing legal/regulatory challenges. | True | False | Operational commercial platform (LawTalk) in South Korea. | Need for updated legal frameworks to address LegalTech innovation, risk of stifling domestic innovation due to prolonged conflicts, potential dominance by foreign platforms if domestic innovation is hindered. | Legal challenges from established professional bodies (KBA), navigating regulatory uncertainty ('grey area'), resistance from the traditional legal profession. | Lowering of lawyer ethical standards, commercialization undermining judicial justice and public trust, stifling innovation due to protectionism, dominance by foreign platforms if domestic innovation is hindered, significant social costs from prolonged disputes. |
| ChatGPTAndAcademicIntegrityAnalyzingItsInfluenceOn.pdf | Google_Scholar | Chat GPT And Academic Integrity: Analyzing Its Influence On College Students' Study Practices And Performance | This paper investigates the impact of ChatGPT on the study habits, academic performance, and perceptions of academic integrity among college students at Banaras Hindu University, India. Using questionnaires and interviews, the study identifies benefits like improved understanding and efficiency, alongside significant concerns about accuracy, over-reliance, and academic dishonesty. | True | NaN | True | 2.0 | Neutral | ChatGPT | Mixed-methods approach: Questionnaire survey (N=100 students from Banaras Hindu University using ChatGPT for >= 1 semester) analyzed with descriptive statistics and chi-square tests; semi-structured interviews (N=15 students) analyzed using thematic analysis. | 80% reported positive grade changes (though impact varied). 77% reported better understanding of complex subjects. Major benefits included concept simplification and research aid. Significant concerns included information accuracy, lack of depth/referencing, technical limitations, and threats to academic integrity. | NaN | NaN | NaN | NaN | NaN | India | NaN | NaN | NaN | True | True | ChatGPT is available online, with both free and paid ('Plus') versions mentioned. | NaN | Inaccuracy/misleading information, insufficient detail/depth, lack of proper referencing, difficulty answering complex queries effectively, platform technical limitations (login issues, response limits in free version), lack of graphical/visual aids. | Threats to academic integrity (plagiarism, authenticity concerns), potential decline in critical thinking and problem-solving skills due to over-reliance, ethical issues regarding authorship and originality, potential for increasing educational inequalities, concerns over surveillance and data exploitation associated with AI in education. |
| FEWa_jU34vsJ.pdf | Google_Scholar | SCALE :Scaling up the Complexity for Advanced Language Model Evaluation | This paper introduces SCALE, a large-scale, multilingual (5 languages) benchmark designed to evaluate Large Language Models (LLMs) on complex legal tasks using Swiss court data, focusing on long documents, domain-specificity, multilinguality, and multitasking. The authors establish baselines by evaluating various open and closed LLMs, including newly pretrained Swiss legal models, revealing significant challenges and low performance, particularly on tasks like Court View Generation and Information Retrieval. | True | Market | True | 1.0 | Neutral | SCALE benchmark suite, including 7 datasets for various legal tasks (IR, CE, CP, LAP, JP, CVG, LDS) and 3 pretrained Swiss legal language models (Legal-Swiss-RoBERTa Base/Large, Legal-Swiss-LF Base). | Evaluation of baseline models (MiniLM, DistilmBERT, mDeBERTa-v3, XLM-R, X-MOD, SwissBERT, mT5, BLOOM, GPT-3.5, Claude-2, LLaMA-2, PaLM-2) on the SCALE benchmark tasks. Metrics included Macro F1 (hierarchically aggregated for classification), BERTScore, BLEU, METEOR, ROUGE (generation), NDCG, and Capped Recall@k (IR). Zero-shot evaluation for large closed models, fine-tuning for smaller open models. | The best overall aggregated Macro F1 score on the text classification tasks was 48.6 (XLM-R Large). Performance on IR and CVG was particularly low, highlighting the benchmark's difficulty. Even large models like GPT-4 underperformed fine-tuned models on some tasks (e.g., CVG). | NaN | NaN | NaN | NaN | Civil, public, criminal, social law; legislation covers diverse areas including public health, education, civil rights, energy, environment, infrastructure, visa regulations. | Switzerland (Federal and Cantonal) | Publicly available, anonymized Swiss court rulings (638K) and legislation (36K) scraped from Entscheidsuche.ch and fedlex.admin.ch, covering German, French, Italian, Romansh, English. Unstructured text data. Pretraining also used EUR-LEX data. Downstream task datasets derived from the same Swiss sources. | Data scraping and processing pipeline (parsing, regex extraction, metadata utilization) for dataset creation. Standard language model pretraining techniques (warm-start, new tokenizer, MLM objective). Standard NLP evaluation metrics and aggregation methods for benchmarking. | Public release of datasets, pretrained models, and code via Hugging Face and GitHub under CC BY-SA license. | True | True | Datasets, pretrained models (Legal-Swiss-RoBERTa/LF base/large), and code are available on Hugging Face and GitHub under a CC BY-SA license. | Significant performance gaps exist for current LLMs in handling long legal documents, domain-specific reasoning, multilinguality (especially within one jurisdiction), and complex multi-tasking. Need for better models, potentially larger legally pretrained generative models, and methods incorporating retrieval/tool use. Current models struggle with tasks like Court View Generation and Information Retrieval. | Resource limitations (compute for pretraining/evaluation). Data curation challenges (algorithmic label generation, quality control for large scraped corpus). Handling long document contexts (truncation required). Evaluating diverse multilingual, multitask performance fairly. | Job market impact for legal professionals. Inaccuracy/misinformation due to model limitations in the high-stakes legal domain. Failure to capture cultural/contextual nuances. Misuse for generating misleading legal content at scale. |
| dhPMuPLW7IIJ.pdf | Google_Scholar | Al Cannibalism and the Law | This paper discusses Large Language Models (LLMs) used in law, focusing on the problem of "AI cannibalism" where models are trained on AI-generated content. It argues this could lead to model degradation, increased hallucinations and bias, impacting lawyers' use of these tools and potentially stifling legal development. | True | Market | True | 3.0 | Negative | NaN | NaN | NaN | AI hallucinations leading to incorrect legal filings; amplification of existing societal biases (gender, racial, political) through AI; training data limitations (knowledge cut-offs, bias towards existing norms); AI cannibalism causing model degradation and increased misinformation; potential for AI overuse to stifle legal creativity and development. | The paper highlights the need for AI developers to address AI cannibalism and suggests careful human oversight, fact-checking, and awareness of bias as necessary strategies for lawyers using current LLMs. It notes research indicating the importance of incorporating sufficient 'fresh' human-generated data in training. | Functioning and limitations of LLMs; AI hallucinations in legal practice; AI bias in legal contexts; AI cannibalism; Impact of AI on legal development and practice. | NaN | General Legal Practice | United States | The paper discusses training data for existing LLMs (e.g., Common Crawl, books, news, web content) and the problem of future models training on mixtures of human-generated and AI-generated (synthetic) text. | NaN | NaN | False | False | NaN | Methods to mitigate AI cannibalism; ensuring AI doesn't stifle legal development or exacerbate biases; reliably distinguishing human vs. AI content for training data curation; agreed-upon evaluation metrics for generative models. | Ensuring data quality for training LLMs; preventing bias propagation; avoiding AI hallucinations; high cost and difficulty of training/fine-tuning LLMs; evaluating generative models effectively; weeding out AI-generated content from future training datasets. | Increased misinformation and AI hallucinations undermining LLM utility and leading to legal errors/sanctions; amplification of existing societal and legal biases; stifling legal creativity and innovation, leading to stagnation; potential disclosure of confidential client information; overall degradation of LLM capabilities due to recursive training on synthetic data (AI cannibalism). |
| VniR1rNFOEwJ.pdf | Google_Scholar | MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applications | This paper introduces MindLLM, a series of lightweight (1.3B, 3B parameters) bilingual (English/Chinese) LLMs trained from scratch, detailing their development, training strategies, and evaluation. It highlights their competitive performance against larger models, explores efficient instruction tuning using entropy-based filtering, and demonstrates applications in law and finance. | True | Market | True | 1.0 | Neutral | MindLLM (1.3B and 3B parameter bilingual lightweight LLMs), pre-training strategies (bilingual from scratch vs. monolingual then transfer), entropy-based instruction tuning filtering, SFT/COT fine-tuning for specific domains. | Standard benchmarks (MMLU, AGIEval, C-Eval, CMMLU), specific capability tests (math, reasoning, bilingualism), zero-shot/few-shot evaluation pre/post-instruction tuning, domain-specific tests (ChatGPT ranking for law, accuracy for finance sentiment). | MindLLMs match/outperform larger models on some benchmarks (e.g., MMLU, AGIEval). MindLLM-3B excels in math/bilingual tasks for its size. Entropy-filtered instruction tuning improves specific capabilities significantly. Domain-specific fine-tuning yields competitive results (MindLLM-Law outperforms Baichuan2-7B; MindLLM-1.3B with COT achieves 47.79% accuracy in finance task). | NaN | NaN | Legal consultation, General legal NLP tasks | NaN | General Law | China, USA (implied by English data/benchmarks) | Pre-training: Public (Pile, Wudao, CBooks) and web-crawled English/Chinese text (unstructured). Instruction Tuning: Mix of public NLP/human-written/generated/translated datasets (Chinese MingLi, English Tulu, Bilingual MOSS), specific public datasets (Wanjuan, LogiCoT). Domain Fine-tuning: Public legal datasets (LaW-GPT, DISC-LawLLM) + public general instruction data; Proprietary web-crawled financial news (EastMoney). | Empirical evaluation on benchmarks, ablation studies (data mix, curriculum, tuning data), development of data filtering strategy (entropy-based), domain-specific fine-tuning (SFT, COT), LLM-based evaluation (ChatGPT ranking with Elo). | NaN | False | False | NaN | NaN | High cost/resource requirements for LLMs, data processing complexity (quality, deduplication, mix ratio), training instability and catastrophic forgetting (especially with transfer learning), limited capacity of lightweight models affecting complex tasks and instruction tuning effectiveness, balancing multilingualism vs. capacity, robust domain-specific evaluation. | Generation of harmful/sensitive content, privacy violations (PII leakage). |
| paper-icon.pdf | Google_Scholar | A TEXT INTELLIGENCE-BASED APPROACH FOR AUTOMATIC GENERATION OF FAULT TREES IN NUCLEAR POWER PLANTS | This paper introduces NuLLM-FTG, a large language model fine-tuned on a custom textual fault tree dataset to automate fault tree analysis (FTA) for Nuclear Power Plants (NPPs). NuLLM-FTG demonstrated performance comparable to experts and superior to GPT-4 in some aspects, aiming to assist non-experts in the complex FTA process. | True | NaN | True | 1.0 | NaN | Nuclear Large Language Model Fault Tree Generator (NuLLM-FTG): A fine-tuned Baichuan 2-13B-Chat model using a novel textual fault tree representation, Supervised Fine-Tuning (SFT), and prompt engineering techniques (Fault Tree Chain of Thought - FTCoT, Role-Playing - RP, few-shot learning). | Quantitative evaluation via cosine similarity and conversation pattern alignment against baseline, GPT-3.5, GPT-4 across different few-shot settings. Qualitative evaluation using Delphi method with domain experts (single-blind and double-blind comparisons with GPT-4 on professionalism, completeness, satisfaction). Ablation studies on FTCoT and RP. Language corpus impact assessment (English vs. Chinese). Case study integration with Risk Spectrum software. | NuLLM-FTG significantly outperformed baseline, GPT-3.5, and GPT-4 on cosine similarity (~0.94 vs. max ~0.84 for GPT-4) and conversation pattern alignment. Qualitative results showed performance comparable to experts and often preferred over GPT-4, particularly in single-blind setup. FTCoT prompting showed a stronger impact than RP. English corpus training/testing yielded better similarity scores. | NaN | NaN | NaN | NaN | NaN | International | A curated dataset of over 1700 examples (fault trees represented textually) collected by volunteers from academic papers (e.g., CNKI) covering multiple domains including nuclear, aerospace, transportation. Used for Supervised Fine-Tuning; likely proprietary in its curated form. | Supervised Fine-Tuning (SFT), Novel textual data structure design for fault trees, Prompt Engineering (Few-shot learning, Fault Tree Chain of Thought - FTCoT, Role-Playing - RP). | Demonstrated via integration with Risk Spectrum software in a case study for quantitative risk assessment. | False | False | NaN | NaN | Designing an effective textual representation for complex fault tree structures, collecting and curating specialized training data, evaluating the 'black box' nature of LLMs, optimizing few-shot prompting strategies (observing a performance threshold), assessing the impact of specific prompting techniques (FTCoT, RP). | NaN |
| FbgEwaRT2gcJ.pdf | Google_Scholar | How Privacy-Savvy Are Large Language Models? A Case Study on Compliance and Privacy Technical Review | This paper evaluates the performance of various large language models (LLMs) on privacy-related tasks such as information extraction, key point detection, and question answering using specific datasets. It introduces a Privacy Technical Review (PTR) framework and finds that while LLMs show promise, significant gaps remain in their ability to fully meet privacy compliance requirements. | True | Market | True | 2.0 | Neutral | Evaluation of various LLMs (BERT, GPT-3.5, GPT-4, GPT-4o, Mistral_7b, gemini-1.5-flash, moonshot_8k_v1, Doubao, Doubao-pro, ComBERT, etc.) for Privacy Information Extraction (PIE), Key Point Detection (KPD), and Question Answering (QA) within a proposed Privacy Technical Review (PTR) framework. | Benchmarking LLMs on PIE, KPD, and QA tasks using custom datasets derived from privacy policies and agreement texts. Metrics included Precision, Recall, F1-score (Macro/Averaged), ROUGE-L, Exact Match (EM), and Re-85. | For PIE, gemini-1.5-flash had the best F1 (99.8%). For KPD, GPT-4 had the best F1 (94.8%). For QA, Doubao had the best F1 (95.4%). Overall, modern LLMs significantly outperformed older models but showed variance across tasks. | Significant gaps persist in LLMs' ability to fully comply with evolving legal standards and technical privacy requirements. | Implementing a Privacy Technical Review (PTR) framework within the software development lifecycle; enhancing LLM capabilities; better integration of LLMs with legal and regulatory requirements. | Privacy compliance review, Privacy Information Extraction (PIE), Key Point Detection (KPD) in legal/regulatory text, Question Answering (QA) on privacy policies. | NaN | Data Protection Law, Privacy Law, Compliance | International | Evaluation datasets were used: 1) Privacy Information Extraction Dataset (approx. 8,800 sentences from privacy policies, BIOE tagged). 2) Legal and Regulatory Key Point Detection Dataset (10 key legal concepts, binary labels). 3) Domain-Specific Question Answering Dataset (approx. 2,300 passages from agreements + queries). Data originates from https://github.com/alipay/ComBERT, suggesting publicly available, domain-specific (legal/privacy) unstructured text. | NaN | NaN | False | False | NaN | LLMs' capability gaps in fully adhering to evolving legal standards and technical privacy requirements. | NaN | Implicit risks of LLMs failing privacy compliance checks, potentially leading to non-compliance with regulations (e.g., GDPR, CCPA) and inadequate protection of user data. Mentions risks like data leakage, model inversion, and membership inference from related works. |
| w_7u2VP-ra8J.pdf | Google_Scholar | WenyanGPT: A Large Language Model for Classical Chinese Tasks | This paper presents WenyanGPT, a large language model derived from LLaMA3-8B-Chinese through continued pre-training and instruction fine-tuning specifically for Classical Chinese processing. The authors also introduce WenyanBENCH, a benchmark for evaluation, demonstrating WenyanGPT's superior performance over existing models on various Classical Chinese tasks. | True | NaN | True | 1.0 | NaN | WenyanGPT: A large language model created by continued pre-training and instruction fine-tuning of LLaMA3-8B-Chinese on Classical Chinese data. | Evaluation performed using a newly developed benchmark, WenyanBENCH, covering six tasks: Punctuation, Part-of-speech tagging, Named Entity Recognition (NER), Translation, Word Explanation, and Reverse Dictionary. Metrics used include Precision, Recall, F1-Score, BLEU, and BERT-Score. | WenyanGPT significantly outperformed baseline models (incl. GPT-4o, Deepseek-V3) across all tasks on WenyanBENCH. For example, it achieved F1 > 91% in NER, F1 > 75% in Punctuation, and BLEU1 = 0.47 in Translation. | NaN | NaN | NaN | NaN | NaN | NaN | Continued pre-training on a proprietary ~16GB corpus aggregated from publicly available Classical Chinese text sources (e.g., Daizhige, Wenyanguji, GitHub). Instruction fine-tuning used ~1.85 million data points derived from corpora and LLM-assisted generation (mix of structured/unstructured data). | Continued pre-training, Supervised Fine-Tuning (SFT), Development of a domain-specific instruction data construction framework (manual design, LLM expansion, testing, filtering). | The model, benchmark dataset, and instruction fine-tuning data are publicly released via Hugging Face and GitHub. | True | True | Model available on Hugging Face (Wenyanmuc/WenyanGPT); benchmark (WenyanBENCH) and data (WenyanGPT) available on GitHub (Wenyanmuc). | NaN | General challenges identified: poor performance of existing models on Classical Chinese; lack of standardized evaluation benchmarks. Model limitations: potential subjectivity in evaluating tasks like poetry generation (not included); reliance on large instruction datasets; room for improvement with long texts and complex syntax. | NaN |
| r57zQs5yHEMJ.pdf | Google_Scholar | Harmonizing Innovation and Ethics: The Complex Landscape of Artificial Intelligence in Legal Practice | This paper critically examines the transformative potential of Artificial Intelligence (AI) in legal practice, highlighting opportunities like enhanced efficiency and access to justice. It primarily focuses on the complex ethical challenges, such as algorithmic bias, liability, and data confidentiality, advocating for a collaborative approach to develop robust ethical frameworks for AI's responsible integration into the legal system. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | High cost of legal services for individuals and small businesses; Algorithmic bias in AI potentially perpetuating or amplifying societal injustices against vulnerable groups; Lack of transparency and explainability in AI decision-making processes; Risks to data privacy and confidentiality of sensitive legal information. | Development and deployment of AI-powered tools (e.g., chatbots, virtual assistants) to provide low-cost basic legal guidance and document drafting; Collaborative creation of comprehensive ethical frameworks involving all stakeholders (legal professionals, technologists, ethicists, policymakers, public); Implementation of robust regulatory frameworks addressing AI liability, data security, and algorithmic transparency; Designing AI systems to be fair, non-discriminatory, and explainable with human oversight; Integrating AI education into legal curricula. | Providing low-cost basic legal guidance and information; Assisting with simple legal tasks like drafting basic documents; Supporting self-represented litigants in understanding legal processes and preparing for court; Reducing the overall cost of accessing legal services; Alleviating the justice gap for disadvantaged and minority groups. | Individuals and small businesses unable to afford traditional legal services; Vulnerable members of society; Disadvantaged groups and minorities; Pro se (self-represented) litigants. | General legal practice, Family law, Housing law, Employment law | International | NaN | NaN | NaN | False | False | NaN | Need for evolving ethical frameworks to keep pace with AI development; Ensuring true fairness and non-discrimination in AI by mitigating bias in data and algorithms; Achieving transparency and explainability for complex AI systems; Maintaining meaningful human oversight and control in AI-assisted legal decision-making; Adapting legal education and professional roles for an AI-integrated future; Establishing clear lines of liability for AI errors. | NaN | Algorithmic bias perpetuating or amplifying societal discrimination, particularly towards vulnerable groups; Lack of transparency in AI decision-making hindering due process, accountability, and public trust; Breaches of client data confidentiality and privileged legal information; AI systems providing incorrect legal advice or flawed legal documents, leading to adverse legal outcomes; Difficulty in assigning liability for errors made by 'black box' AI systems; Potential for job displacement or negative transformation of roles within the legal profession. |
| pCYBrSdeFH8J.pdf | Google_Scholar | Design Novel Effective Method for Large Language Model Compression BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation | This paper introduces BiLD (Bi-directional Logits Difference), a novel loss function for knowledge distillation designed to compress large language models (LLMs) by filtering noise in logits and leveraging their internal ranking. Experimental results on 13 NLP datasets show BiLD outperforms existing distillation methods and supervised fine-tuning. | True | NaN | True | 1.0 | NaN | Bi-directional Logits Difference (BiLD) loss for task-specific LLM knowledge distillation. | Evaluated on 13 NLP datasets (SuperGLUE, Arc-C, Arc-E, HellaSwag, PIQA, WinoGrande) using BLOOM and Qwen1.5 models. Compared against supervised fine-tuning, vanilla KL loss, top-k KL loss, DKD, NKD, NormKD, and RKL, using metrics like accuracy, EM, F1, and overlap@k. | BiLD loss, using only top-8 logits, achieved the highest average accuracy across four distillation settings, outperforming SFT, vanilla KL, and five other methods. For instance, in Qwen-4B to 0.5B distillation, BiLD surpassed vanilla KL by 3.52% in average accuracy. | NaN | NaN | NaN | NaN | NaN | International | 13 publicly available NLP benchmark datasets (SuperGLUE (BoolQ, CB, COPA, MultiRC, ReCoRD, RTE, WiC, WSC) and Arc-C, Arc-E, HellaSwag, PIQA, WinoGrande) consisting of unstructured text, used collectively for SFT and distillation. | Qualitative exploration of LLM logits characteristics (long-tail distribution, ranking information importance) followed by the design of BiLD loss. Evaluation through quantitative experiments comparing against baseline methods. | NaN | True | True | Code available in an open-source repository (mentioned on page 18). | NaN | Requires access to teacher logits and shared vocabularies. Computational complexity increases with more top-k logits considered. Clipping long-tail logits results in some knowledge loss. Significant computational overhead and memory for training/distillation. | NaN |
| Iyi-fuvhE5gJ.pdf | Google_Scholar | AI in the Courts: How Worried Should We Be? | This paper presents a multi-expert discussion on the applications and implications of AI in the legal system and courts, addressing both potential benefits like enhanced access to justice and serious risks such as bias and misinformation. The authors emphasize the need for rigorous verification, transparency, and human oversight to harness AI responsibly in the legal field. | True | Idealistic | True | 3.0 | Positive | Generative AI (e.g., ChatGPT), Technology-Assisted Review (TAR), predictive policing tools, algorithmic risk assessment tools (e.g., COMPAS), online adjudication systems. | References external evaluations (e.g., ChatGPT on bar exam, empirical evidence for TAR); discusses concerns about lack of transparency and testability (e.g., COMPAS in Loomis case). | ChatGPT-4 passed the Uniform Bar Exam at the 90th percentile. Technology-Assisted Review (TAR) has been shown to substantially reduce e-discovery time, cost, and burden. | High cost of justice; potential for AI misuse by litigants; systemic bias in AI systems leading to discriminatory outcomes; lack of verifiable reliability and fairness in AI tools. | AI assistance for self-represented litigants; online adjudication systems; ensuring AI systems are valid, reliable, equitable, and unbiased through rigorous testing, transparency, auditing, and human oversight, particularly judicial gatekeeping. | Legal aid for self-represented litigants, online dispute resolution for minor cases, cost reduction in legal services, algorithmic bias mitigation. | Self-represented litigants, individuals in small claims/housing/traffic disputes, general public seeking affordable legal help. | Civil procedure (e-discovery, pleadings), criminal law (sentencing, recidivism risk), general legal practice, administrative law (adjudication). | Primarily United States, with comparative examples from UK, Colombia, China; insights are broadly applicable. | NaN | NaN | NaN | True | True | Discussed tools like ChatGPT (free version available from OpenAI) and commercial Technology-Assisted Review (TAR) software are generally accessible. | Technical: Development of trustworthy and verifiable Generative AI; robust methods for ensuring AI fairness, reliability, and transparency_ Societal/Legal: Consensus on defining 'algorithmic fairness'; comprehensive legal and ethical regulations for AI in law; ensuring due process with AI; enhancing digital literacy among legal professionals; fostering public trust. | NaN | Use of untested, invalid, or unreliable AI systems; function creep; discriminatory outcomes from biased AI; proliferation of misinformation and deepfakes; increased fraud; threats to personal privacy; AI errors ('hallucinations') in legal documents or judicial decisions; due process violations; erosion of trust in evidence; decline in essential legal skills due to over-reliance on AI. |
| oLraehfrATYJ.pdf | Google_Scholar | Scenario-based Sociotechnical Envisioning (SSE) The Guide Book | This paper introduces Scenario-Based Sociotechnical Envisioning (SSE), a method for anticipating the societal impacts of new AI technologies by collecting and evaluating diverse written scenarios. It details the SSE data collection and analysis approach, aiming to equip researchers and policymakers to mitigate risks and steer towards desirable technological futures. | True | Idealistic | False | 1.0 | Positive | Scenario-Based Sociotechnical Envisioning (SSE) | SSE has been applied and refined through studies focusing on generative AI in the news environment, general-purpose chatbots, and (in preparation) access to legal justice. Data collection involved workshops and surveys where participants wrote scenarios, which were then analyzed using qualitative thematic analysis and axial coding. | The application of SSE produces collections of scenarios and sociotechnical risk/impact classification frameworks. For example, its use in studies on generative AI in news and chatbots led to the development of such human-centered frameworks for risk and impact. | Risk of AI systems providing incorrect or harmful legal advice, thereby undermining access to justice. | Employing the Scenario-Based Sociotechnical Envisioning (SSE) method to proactively identify, understand, and develop mitigation strategies for negative outcomes, such as incorrect AI-generated legal advice. | The impact of generative AI on access to justice, particularly concerning the reliability and quality of AI-provided legal information and advice. | NaN | General legal advice/justice | International | Human-generated fictional scenarios written by diverse participants (e.g., experts, citizens, stakeholders) based on their perspectives, experiences, and knowledge. This data is unstructured text. | The SSE method is built upon principles of scenario planning, sociotechnical studies, and anticipatory governance. Its design involves structured scenario-writing tasks for participants (via workshops or surveys) followed by qualitative data analysis techniques like thematic analysis and axial coding. | The SSE method is disseminated as a guidebook, with full materials (including questionnaires and data from previous studies) made available open access via an OSF repository and through academic publications. | True | True | The guidebook for the SSE method and associated materials (questionnaires, data from previous studies) are available open access via an OSF link (https://osf.io/8sdgh/). | The inherent difficulty in systematically anticipating the diverse, complex, and often unpredictable sociotechnical impacts of emerging AI technologies on society, including on access to justice, which SSE aims to address. | Ensuring high-quality (creative, specific, believable, plausible) scenarios from participants; effectively filtering out AI-generated scenarios when human-derived insights are paramount; scaling data collection to capture diverse perspectives and achieve conceptual saturation; guiding participants to create plausible scenarios, especially in unfamiliar contexts, without overly constraining their creativity (e.g., regarding character roles). | For emerging AI technologies generally: diverse and unpredictable societal impacts, potential for human rights infringements, and severe or transformative implications for users. Specifically related to AI in access to justice: generation of wrong or misleading legal advice. Other exemplified risks include AI-driven misinformation in news leading to false accusations and arrests, job displacement, erosion of trust in information, and a decline in original human thought due to over-reliance on AI. |
| lIQ28MAsj1IJ.pdf | Google_Scholar | Private Ordering and Generative AI: What Can We Learn From Model Terms and Conditions? | This paper reports on a pilot empirical study analyzing the Terms and Conditions (T&C) and privacy policies of 13 generative AI providers in early 2023, focusing on copyright and data protection. It finds providers assign output ownership but shift all risks to users, mimicking platform moderation practices while avoiding platform obligations, highlighting a governance gap that existing regulations like the EU's DSA fail to address. | True | NaN | True | 2.0 | NaN | Qualitative comparative analysis of Terms and Conditions (T&C) and privacy policies of generative AI providers. | Manual collection and legal analysis of T&C, privacy policies, and related documents from a sample of 13 generative AI providers (T2T, T2I, T2A/V; varied sizes and origins) during January-March 2023. Focused analysis on clauses related to copyright, data protection, and dispute resolution. | Providers typically assign copyright ownership of outputs to users but retain extensive licenses and disclaim all liability, shifting risks (copyright infringement, privacy breaches) entirely onto users. Most implement notice-and-takedown procedures akin to platforms but are argued not to fit platform definitions (e.g., under DSA), thus avoiding obligations. Data protection rights were poorly addressed in early 2023, with some improvement observed by late 2023, though implementation remains basic. | NaN | NaN | NaN | NaN | Contract Law, Copyright Law, Data Protection Law, Internet Law, Platform Regulation, Consumer Law, AI Regulation | EU, US, China, UK, International | NaN | Qualitative empirical legal research involving comparative analysis of legal documents (T&C, privacy policies). Sampling aimed for representativeness across modalities (text, image, audio/video), provider size, and geographic origin. | Publication as a working paper and forthcoming book chapter. | False | False | NaN | Regulatory gap where generative AI models avoid platform obligations (like those in the EU DSA) despite controlling content generation and moderation. Insufficient user protection against unfair terms and opaque moderation. Lack of standardized terms and poor enforcement mechanisms for data protection rights. Need for research into B2B contract fairness and the impact of market concentration. | Difficulty obtaining B2B T&C due to commercial secrecy. The dynamic nature of T&C requires automated tracking for longitudinal analysis. Managing the complexity of a multi-dimensional sample (modality, size, origin). | Copyright infringement by model outputs. Violation of data protection rights (e.g., unlawful processing of training data, inability to exercise erasure or rectification rights). Generation and dissemination of illegal or harmful content (deepfakes, hate speech, disinformation, bias). Lack of transparency and fairness in content moderation and user sanctions. Unfair allocation of risk and liability to users via T&C. Imbalance of power between large providers and users/SMEs. |
| pXk4HgLmMQMJ.pdf | Google_Scholar | Ushering In a New Era of User Rights | This paper introduces and argues for the necessity of establishing a distinct concept of "user rights" in the digital age, separate from traditional consumer rights, due to the unique power dynamics created by platform enterprises. It highlights the profound negative impacts of unchecked platform power on civil, political, economic, social, and cultural rights, especially for vulnerable groups, and proposes national and international governance reforms centered on user rights protection. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Inadequacy of existing consumer rights frameworks; unchecked digital power of platforms leading to infringements on civil, political, economic, social, and cultural rights; manipulation via algorithms and data (information cocoons, amplification of harmful content); increased risks for vulnerable groups (children, workers); lagging legal and governance structures unable to hold global platforms accountable; power asymmetry between concentrated platforms and dispersed users. | Establish a distinct legal concept and framework for "user rights"; create new national and international governance systems regulating platform power (e.g., specialized legislation like EU's DMA/DSA, defining platforms' international legal status and obligations); enforce platform responsibility (unity of power and responsibility); ensure platform transparency and fairness (rules, algorithms); develop user rights protection organizations and public interest litigation mechanisms. | Platform governance, fundamental rights protection in the digital sphere, regulation of digital power, protection of vulnerable groups online (children, gig workers), information integrity and democracy, corporate accountability. | General users of digital platforms, with specific attention to vulnerable groups like children, women, the elderly, and gig economy workers (e.g., delivery riders). | Human Rights Law, Internet Law / Digital Law, Platform Governance, Consumer Law, Competition Law, Labor Law, International Law | International | NaN | NaN | NaN | False | False | NaN | Lack of theoretical clarity and recognition of "user rights"; inadequacy of current national and international legal frameworks for platform regulation; insufficient research on platform impacts (especially on vulnerable groups); need for transparency in platform operations (algorithms, DTA); lack of effective enforcement mechanisms for user rights. | NaN | Erosion of civil and political rights (manipulation, polarization); exacerbation of economic inequality and worker exploitation; harm to vulnerable groups online (harmful content, addiction, abuse); undermining of democratic processes; abuse of digital power by platforms; potential for state power corruption or capture by platforms. |
| reBnNJjAlrgJ.pdf | Google_Scholar | Authors in the age of language -generation AI: to be or not to be, that is… the question? | This paper discusses the rise of large language models like ChatGPT and their impact on academic writing. It primarily focuses on the debate surrounding whether AI tools should be credited as co-authors on scientific publications. | True | NaN | True | 3.0 | NaN | Discussion of Large Language Models (specifically ChatGPT) regarding their use in academic writing and the ethics of AI co-authorship. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | Mentions training on massive amounts of diverse text data, but specifics are not provided in the paper. | NaN | User-friendly web interface (referring to ChatGPT). | True | False | ChatGPT is described as readily accessible to the general public via OpenAI's platform. | NaN | Ethical considerations and lack of consensus/guidelines regarding AI co-authorship in academic publishing. | Ethical risks related to authorship attribution and academic integrity. |
| A_oLE1bQogYJ.pdf | Google_Scholar | The potential Legal Chat Bots have in the context of Access to Justice . | This thesis explores the potential of legal chatbots to enhance access to justice within the European Union, focusing on improving legal aid availability and reducing the length of proceedings. It analyses the advantages, such as cost reduction and efficiency, alongside significant challenges including algorithmic bias, ethical concerns, regulatory gaps, and the digital divide. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | High cost of legal services (lawyer fees, court costs), lengthy court proceedings, lack of legal knowledge among laypeople, complexity of the legal system, procedural hurdles, psychological stress of litigation, digital illiteracy/divide limiting access to tech solutions. | Utilizing legal chatbots to provide affordable/free legal information and basic advice, automate tasks like document drafting, potentially shorten proceedings through efficiency gains, enhance public legal knowledge and self-help capabilities, and using online dispute resolution platforms. | Legal aid, length of proceedings, effective remedy, self-representation support, legal information provision, cost reduction. | Financially disadvantaged individuals unable to afford legal representation, individuals lacking legal knowledge, general public facing barriers to civil justice. | Civil Justice (broadly), including consumer law, family law (divorce), employment law, housing law (tenant agreements), tort law, administrative law (parking tickets), data privacy (GDPR), immigration law. | European Union (primary focus, including ECHR, EU Charter, CJEU/ECtHR references, proposed EU AI Act), with examples from the Netherlands, China, Estonia, UK, US, Canada, Australia. | NaN | NaN | NaN | False | False | NaN | Lack of comprehensive regulatory frameworks for AI in the legal field (need for adaptation, e.g., via proposed EU AI Act), ensuring digital inclusivity for chatbot use, mitigating algorithmic bias, resolving ethical dilemmas (confidentiality, competence, supervision, IP), difficulty coding complex legal reasoning/nuance/human emotion, balancing consistency and flexibility in AI responses, potential lack of user understanding of chatbot limitations. | NaN | Providing inaccurate or misleading legal advice, algorithmic bias leading to unfair or discriminatory outcomes, digital exclusion of vulnerable populations, confidentiality breaches/data security risks, unauthorized practice of law, undermining legal certainty, erosion of trust in the justice system, ethical violations (duty of competence, supervision), difficulty in enforcing chatbot decisions or advice, potential for AI errors leading to significant harm (e.g., incorrect fines). |
| qsyjdYBfGrUJ.pdf | Google_Scholar | Judge AI: Assessing Large Language Models in Judicial Decision -Making | This paper evaluates OpenAI’s GPT-4o by replicating a prior factorial experiment conducted on human judges, focusing on a simulated international war crimes appeal where defendant sympathy and legal precedent were varied. The study finds that GPT-4o's decisions are strongly influenced by precedent but not by sympathy, aligning it more with student subjects than with professional judges, who were swayed by sympathy. | True | NaN | True | 2.0 | Neutral | Using GPT-4o to replicate a 2x2 factorial experiment on judicial decision-making, varying defendant sympathy and precedent strength in a simulated international war crimes case appeal. | Replication of Spamann and Klöhn (2016, 2024) experiments. GPT-4o decided a simulated war crimes appeal under four conditions (Sympathetic/P-Affirm, Sympathetic/P-Reverse, Unsympathetic/P-Affirm, Unsympathetic/P-Reverse) across 25 random seeds per condition (n=100 total). Performance compared to original human judge and student subject data using frequency of affirming, Boschloo two-sided exact test, OLS, Logit, and Exact Logistic regression models. Prompt engineering techniques were also tested. | GPT-4o was strongly affected by precedent but not by sympathy (p<0.01 for precedent, not significant for sympathy). Its performance was similar to students and opposite to professional judges, who were influenced by sympathy. Prompt engineering had little success in making GPT-4o act like human judges. | NaN | NaN | NaN | NaN | International Criminal Law | International Criminal Tribunal for the Former Yugoslavia (ICTY) | The paper used GPT-4o (May model: gpt-4o-2024-05-13), a closed, pre-trained large language model by OpenAI. The specific training data for GPT-4o is proprietary and not detailed in the paper. The experiment input data consisted of modified materials from a real ICTY case (Prosecutor v. Momčilo Perišić), including instructions, statement of agreed facts, prosecution/defense briefs, ICTY statute, and GPT-4o generated summaries of precedent and trial judgments. | Experimental replication. Case materials from Spamann and Klöhn (2016, 2024) were adapted for LLM input (e.g., summarization of lengthy documents due to token limits). Prompt engineering, including system prompts and varied user instructions, was used. Temperature set to 0.7, and seed numbers were used to generate multiple trials (n=100). | NaN | False | False | NaN | NaN | Adapting experimental materials for LLM input (e.g., token limits requiring summarization of lengthy legal documents). Ensuring replicability with a closed model (GPT-4o) and its seed feature's beta status. Prompt engineering difficulties in steering LLM behavior to emulate human judges, particularly regarding non-formalist reasoning (e.g., considering sympathy). LLMs' tendency towards formalism and potential affirmance bias. The 'deep unintelligibility' of LLMs making it hard to understand their decision-making process. | LLMs may perpetuate a naive or 'official story' understanding of law, lacking the nuanced, realist decision-making of experienced human judges. Difficulty in trusting AI judges if they operate like human judges by deciding realistically but reasoning formally (lack of transparency). Replacing human judges with formalist AIs could lead to outcomes not aligned with social needs or policy judgments, especially where law requires discretion. LLMs might resist prompts for 'unethical' or non-standard judicial behavior derived from their training. |
| Szk2wzoXOGwJ.pdf | Google_Scholar | Ethics 3.0—Attorney Responsibility in the Age of Generative AI | This paper examines the heightened ethical responsibilities for lawyers in the digital era, focusing on the implications of generative AI and the metaverse. It underscores the importance of technological competence, client confidentiality, data security measures, and truthful online communication, referencing ABA Model Rules and real-world examples of misuse. | True | Market | True | 3.0 | NaN | Generative AI (e.g., ChatGPT), Extractive AI, Metaverse | NaN | NaN | NaN | NaN | NaN | NaN | Legal ethics, professional responsibility, data privacy, cybersecurity, civil procedure (related to legal research and filings) | United States | NaN | NaN | NaN | True | True | The paper discusses publicly launched generative AI services like OpenAI's ChatGPT, which offers free access tiers, as well as commercial legal AI tools from companies like LexisNexis and Thomson Reuters (Casetext). | NaN | Lawyers using generative AI face challenges including: ensuring factual accuracy and avoiding 'hallucinations'; maintaining client confidentiality with third-party services; understanding the fundamental limitations of generative AI (content generation vs. factual retrieval); addressing potential biases in AI outputs; and ensuring robust contractual safeguards with AI vendors regarding data security. | Potential risks include: submitting fabricated legal precedents to courts; breaching client confidentiality via data input into AI or insecure platforms; inadvertent disclosure of sensitive data (e.g., metadata); misleading online communications violating advertising rules; and violating duties of candor and professional competence, potentially leading to sanctions or malpractice claims. |
| b496aCfAJecJ.pdf | Google_Scholar | Generative AI and Entrepreneurial Entry* | This study investigates how access to generative AI (GenAI), particularly following ChatGPT's release, influences entrepreneurial entry. Using a difference-in-differences analysis of Current Population Survey data, it reveals that increased GenAI exposure significantly boosts incorporated entrepreneurship in the STEM sector, primarily through an 'augmentation channel' where GenAI automates peripheral business tasks. | True | Market | True | 2.0 | NaN | Analysis of GenAI access's (via ChatGPT release) impact on entrepreneurial entry. GenAI itself, exemplified by ChatGPT, relies on Large Language Models. The study methodology also uses LLMs (Llama 3, GPT-4o) to construct its 'GenAI Exposure' measure from O*NET task data. | Quasi-experimental difference-in-differences (DID) design using Current Population Survey (CPS) data (2021Q2-2024Q2) for STEM individuals. Compared changes in incorporated self-employment rates before/after ChatGPT release for high vs. low GenAI exposure groups, controlling for various factors. The GenAI exposure measure was validated using Semrush data on ChatGPT website traffic. | GenAI access generated a 0.3 percentage point increase in the likelihood of launching an incorporated business for each 1 standard deviation increase in an individual’s GenAI Exposure (a 15% increase from an average 2 percentage point likelihood). The effect is primarily driven by the augmentation channel (automation of peripheral tasks). | NaN | NaN | NaN | NaN | NaN | United States | The 'GenAI Exposure' measure was constructed using the O*NET database (task descriptions for STEM occupations), with task automation potential classified by a Large Language Model (Llama 3, with robustness checks using GPT-4o/GPT-4o-mini). The main empirical analysis uses Current Population Survey (CPS) data and Semrush website traffic data. | Quasi-experimental difference-in-differences (DID) research design. Construction of a novel 'GenAI Exposure' metric using Large Language Models (LLMs like Llama 3) to classify occupational task exposure to GenAI based on O*NET data, aggregated to industry-level for the STEM workforce. | NaN | True | True | The studied GenAI tool, ChatGPT, is publicly available with basic features being free, enabling democratized access. | NaN | Establishing a causal link between GenAI access and entrepreneurial entry due to potential selection and omitted variable biases. Creating a robust and valid GenAI exposure measure; potential noise in LLM-coded exposure. | Potential for labor displacement as GenAI automates core tasks, possibly leading to necessity-driven entrepreneurship (the 'automation channel'), even if findings support augmentation for the studied cohort. This implies broader labor market disruptions. |
| 9vDU08JcYqsJ.pdf | Google_Scholar | The Use of Artificial Intelligence and the Professional Duties of German Lawyers | This paper examines how the use of AI, particularly large language models, by German lawyers interacts with their professional duties under German law (BRAO and BORA). It analyzes potential conflicts with duties of independence, confidentiality, and faithfulness, highlighting legal uncertainties and advising caution. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal Ethics / Professional Responsibility / Regulation of the Legal Profession | Germany | NaN | NaN | NaN | False | False | NaN | NaN | Ensuring client confidentiality when using AI systems, especially those transferring data externally; verifying the reliability and accuracy of AI outputs (risk of hallucination); acquiring sufficient technical competence to assess and use AI tools; navigating legal uncertainty regarding professional duties; needing informed client consent in certain scenarios. | Violation of professional duties (confidentiality, independence, faithfulness); facing professional sanctions (warnings, fines, expulsion) or criminal liability (e.g. Sec 203 StGB); incurring contractual or tort liability towards clients due to AI errors; violating data protection laws; reputational damage. |
| R9Rm5fVzOc0J.pdf | Google_Scholar | IMPACT OF DİGİTAL TRANSFORMATİON ON ADMİNİSTRATİVE LAW İN THE FİELD OF LEGAL SERVİCES | This paper examines the impact of digital transformation, including AI and blockchain, on administrative law and legal services, with a focus on Uzbekistan and international examples. It argues that such technologies can simplify legal processes, improve public administration, reduce corruption, and enhance transparency in citizen-state interactions. | True | Idealistic | False | 3.0 | Positive | Discussion of digital transformation, encompassing e-government services, artificial intelligence, and blockchain technologies, as applied to administrative law and legal services. | NaN | NaN | Problems arising during the implementation of digital technologies; potential malfunctioning of AI systems; errors and inaccuracies in complaint mechanisms leading to legal problems; ensuring legal security and citizens' rights. | Aligning legal norms with technological development; clearly defining the responsibilities of artificial intelligence; strengthening international cooperation; harmonizing technological innovations and legal norms. | Digitization of public services; e-government; reducing corruption in public administration; transparency in public administration; efficiency of legal processes; improving relations between citizens and the state. | Citizens in general, particularly in developing countries like Uzbekistan, in the context of accessing public and legal services. | Administrative law; Public administration; Legal services | Uzbekistan; International | NaN | NaN | NaN | True | True | Uzbekistan's 'Unitary interactive public services portal' is mentioned as an existing, operational e-government service, implying availability to citizens. | Need for legal norms to align with technological development; lack of clarity in defining AI responsibilities; insufficient international cooperation; need to harmonize technological innovations and legal norms for full implementation of digital transformation. | General problems arising during the implementation of digital technologies; ensuring legal security and the rights of citizens during the introduction and use of digital technologies. | Malfunctioning of artificial intelligence systems; errors and inaccuracies in complaint mechanisms creating legal problems. |
| PoJ8D2VsNwoJ.pdf | Google_Scholar | Incorporating AI impacts in BLS employment projections: occupational case studies | This paper explains the U.S. Bureau of Labor Statistics' (BLS) methodology for incorporating potential AI impacts into its 10-year employment projections. It presents case studies for the 2023–33 cycle, analyzing selected occupations in computer, legal, business/financial, and engineering fields, projecting varied AI impacts from job growth to decline depending on the occupation. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General Legal Practice | United States | NaN | NaN | NaN | False | False | NaN | NaN | Difficulty in projecting AI's future employment impacts due to uncertainties (timing, scale, regulation, adoption pace/cost); Insufficient data infrastructure hindering AI adoption by businesses. | Job displacement/reduced demand in certain occupations (e.g., claims adjusters, credit analysts, paralegals); Errors and biases (e.g., hallucinations) in AI output requiring human oversight. |
| Research_on_the_Application_of_Mediation_Model_Based_on_Deep_Learning_in_Dispute_Resolution.pdf | Google_Scholar | Research on the Application of Mediation Model Based on Deep Learning in Dispute Resolution | This paper proposes an Attention-based Long Short-Term Memory (LSTM) model to automate the classification of dispute mediation outcomes (success or failure) from case texts. The model aims to improve the efficiency and accuracy of the dispute resolution process compared to traditional methods. | True | Market | False | 1.0 | Positive | Attention-based LSTM model for classifying dispute mediation outcomes. | Evaluated on a proprietary dataset of 5,000 mediation cases (split 70% train, 15% validation, 15% test) using accuracy, recall, precision, and F1 score. Compared against Logistic Regression, SVM, CNN, and conventional LSTM. | Achieved 92.5% accuracy, 90.0% recall, 88.0% precision, and 89.0% F1 score on the validation set. Outperformed baseline models (Logistic Regression, SVM, CNN, LSTM) on the test set across all metrics. | Limitations of traditional dispute resolution: reliance on human resources, time costs, inefficiency, subjective judgments, slow processing, inconsistency, increasing case complexity and volume. | Using an Attention-based LSTM model to automate the classification of dispute mediation outcomes, aiming to improve efficiency, accuracy, objectivity, and consistency, and reduce mediator workload. | Dispute Mediation Outcome Prediction | NaN | Civil Law, Commercial Law | NaN | A proprietary dataset of 5,000 mediation cases (text, initially also images, audio) covering civil and commercial disputes, obtained from multiple courts, mediation agencies, and law firms. Includes party statements, evidence, mediation records, and outcomes (labelled as success/failure). | Supervised learning, data preprocessing (cleaning, annotation, feature extraction), model training (Attention-LSTM with cross-entropy loss and Adam optimizer), hyperparameter tuning, quantitative evaluation. | NaN | False | False | NaN | Technical gaps: handling textual ambiguity, understanding complex legal issues, integrating external legal knowledge. Societal gaps: N/A | Handling formalized legal text, integrating domain knowledge, model interpretability ('black box' problem), processing long texts and complex semantic relationships. | Potential misclassification of cases, lack of interpretability ('black box' problem). |
| g8yPDVQinAIJ.pdf | Google_Scholar | The Impact of Artificial Intelligence Technologies on the Justice Administration and on the Judicial Office Personnel | This paper reflects on the potential impacts of artificial intelligence applications, including predictive and generative AI, on the administration of justice. It specifically examines the effects on judicial office staff's tasks and the role of judges, highlighting significant risks to fundamental rights and procedural guarantees. | True | NaN | False | 3.0 | Negative | NaN | NaN | NaN | Lack of transparency in algorithms ('black box' problem), algorithmic bias leading to discrimination, threats to judicial independence and democratic legitimacy, potential erosion of due process (right to defense, reasoned judgments), risk of significant job losses in judicial administration. | Use AI solely as a complementary tool, ensuring human judges retain ultimate decision-making authority ('last word') to safeguard judicial independence, fundamental rights, and due process guarantees. | Automation of judicial procedures, Predictive justice (risk assessment), Judicial decision-making support versus replacement, Protection of procedural rights (due process, right to defense) and judicial independence, Impact on judicial office personnel. | NaN | Criminal law, Civil law, Procedural law, Judicial Administration | Spain, USA, Argentina, Estonia, China | NaN | NaN | NaN | False | False | NaN | Ensuring algorithmic transparency, explainability, and accountability; Mitigating bias and ensuring non-discrimination; Defining the appropriate role of AI versus human judges to protect fundamental rights and judicial independence; Addressing socio-economic impacts like job displacement in the legal sector. | NaN | Lack of algorithmic transparency ('black box') hindering challenges and defense; Algorithmic bias leading to discrimination; Violation of due process, right to defense, and right to reasoned judgments; Threat to judicial independence and democratic legitimacy if AI replaces judges; Significant job losses for judicial office staff and legal professionals; Difficulty assigning accountability for algorithmic errors; Potential misuse of predictive AI leading to undue rights restrictions ('Minority Report' scenario). |
| _FX_fECYb00J.pdf | Google_Scholar | The future of Cyber crime: AI and Emerging Technologies are creating a Cybercrime tsunami | This paper reviews how AI and emerging technologies like generative AI, blockchain, and IoT are driving an unprecedented increase in sophisticated cybercrime, creating a 'tsunami' of threats. It argues that law enforcement and regulators are ill-prepared and must radically adapt by raising awareness and leveraging these same technologies for detection, prevention, and prosecution. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | Lack of awareness among law enforcement and regulators about the cybercrime ecosystem; outdated operational models ill-suited for dynamic, real-time threats; challenges posed by anonymous actors (humans, algorithms, avatars); difficulties with global jurisdictions and jurisdictional arbitrage; the speed and scale of technological innovation outpacing legal and regulatory responses. | Radically rethinking law enforcement and regulatory operational models; increasing awareness and knowledge transfer (e.g., 'Cyberwise'); leveraging AI and emerging technologies for automation, anomaly detection, and real-time intervention; adopting innovative approaches like tech sprints and sandboxes (similar to FCA); enhancing coordination (national and international) through secure infrastructures and standards; proactive horizon scanning; potentially establishing rapid-response legal provisions and specialist international agencies. | NaN | NaN | Criminal Law (Cybercrime), Information Technology Law, Regulation (especially financial), International Law | International | NaN | NaN | NaN | False | False | NaN | Technical gaps in real-time anomaly detection, agent (human/AI/avatar) authentication, deepfake detection/mitigation, securing decentralized infrastructures (Web3, IoT). Societal/Regulatory gaps include widespread lack of awareness, need for updated legal frameworks for AI/digital agents, ensuring AI alignment with human values, addressing ethical concerns (bias, fairness), establishing effective international coordination, and managing the risks of superintelligence. | Addressing the dynamic nature and rapid pace of emerging technologies; dealing with anonymous, global actors; shifting from retrospective analysis to real-time intervention; managing massive data volumes; ensuring ethical use of AI in law enforcement; overcoming lack of awareness and expertise; fostering cross-disciplinary collaboration (AI developers, cybersecurity, law enforcement). | AI-generated crimeware and enhanced social engineering; sophisticated deepfakes for fraud, impersonation, and misinformation; increased ransomware and denial-of-service attacks, especially on critical infrastructure; algorithmic manipulation of markets and public opinion; misuse of AI for surveillance and social control (e.g., predictive policing bias); data breaches; AI hallucinations leading to false accusations; potential for 'feral' or uncontrollable AI; digital addiction fueled by AI; jurisdictional arbitrage; erosion of privacy and trust. |
| deDSwE3z9PMJ.pdf | Google_Scholar | LLM4Causal: Democratized Causal Tools for Everyone via Large Language Model | This paper introduces LLM4Causal, a large language model fine-tuned to interpret user requests for causal analysis on tabular data, execute appropriate causal tools, and explain the numerical results in simple language. The authors also propose a data generation pipeline and two benchmark datasets (Causal-Retrieval-Bench and Causal-Interpret-Bench) used for fine-tuning and evaluation. | True | Idealistic | True | 1.0 | Positive | LLM4Causal: A fine-tuned LLM (Llama 2 base) designed for end-to-end causal analysis workflow, including natural language query interpretation (task classification, entity extraction), selecting/executing external causal analysis tools (from libraries like CausalML, CausalDM, causal-learn), and generating natural language interpretations of the results. Uses custom fine-tuning datasets (Causal-Retrieval-Bench, Causal-Interpret-Bench) created via LLM generation and human annotation. | Evaluated end-to-end on synthetic datasets generated for five causal tasks (CGL, ATE, HTE, MA, OPO) using Pass Rate, Relevance Rate, and Win Rate metrics. Ablation studies evaluated performance on causal entity extraction (Step 1, accuracy metric) and result interpretation (Step 3, human evaluation based on hallucination, incompleteness, non-fluency rubrics) against GPT-4-turbo. | LLM4Causal significantly outperformed GPT-4. The LLM4Causal-Mixed variant achieved an average end-to-end Win Rate of 80.6% (compared to very low rates for GPT-4), 98% overall accuracy in Step 1 entity extraction (vs. 77% for GPT-4), and comparable or better performance in Step 3 interpretation based on human evaluation rubrics. | The complexity of causal inference methods, the need for specialized knowledge to use existing tools, and the difficulty for non-experts to interpret quantitative results from these tools, hindering broader access. | An end-to-end system (LLM4Causal) that uses a fine-tuned LLM to automate causal analysis: understanding user queries in natural language, applying appropriate causal algorithms to user data, and explaining the findings accessibly. | Democratization of causal decision-making tools, specifically targeting tasks like Causal Graph Learning (CGL), Average Treatment Effect Estimation (ATE), Heterogeneous Treatment Effect Estimation (HTE), Mediation Effect Analysis (MA), and Off-Policy Optimization (OPO). | General audiences / everyone lacking specialized expertise in causal inference. | NaN | International | Two custom instruction-tuning datasets created for the paper: Causal-Retrieval-Bench (causal questions paired with structured JSON representations) and Causal-Interpret-Bench (context including query, task, method, numerical output paired with human-revised natural language interpretations). Data was generated using a combination of LLM (GPT-4) prompting and human/expert annotation; it is synthetic, domain-specific (causal inference), and includes structured elements. | Definition of causal tasks, design of a three-stage framework (interpret, execute Ttools, interpret results), development of a data generation pipeline (LLM prompting + human annotation), fine-tuning a pre-trained LLM (Llama 2) using Parameter-Efficient Fine-Tuning (LoRA), integration with existing causal libraries. | NaN | False | False | NaN | Need to extend support to more causal tasks/methods, potential for integrating LLM's internal knowledge with tool use, lack of interactive capabilities for user feedback and guidance. | Existing LLMs struggle with specialized causal tasks (hallucination, confusion with correlation, lack of end-to-end capability, outdated knowledge). Creating high-quality, diverse, and accurate fine-tuning data for these specialized tasks required a complex generation pipeline with human oversight. Efficiently fine-tuning large models (addressed via LoRA). | Potential for inaccurate causal inference leading to poor decisions. Risk of model hallucination or incomplete/misleading interpretations misguiding users. General risks associated with democratizing powerful analytical tools without ensuring user understanding or safeguards against misuse. |
| 309XxeqZV9EJ.pdf | Google_Scholar | Using Artificial Intelligence to Increase Access to Justice | This PhD thesis investigates how Artificial Intelligence (AI) can improve access to justice for laypeople facing legal issues. It proposes and details the 'JusticeBot' methodology, a hybrid rule-based and case-based reasoning approach implemented as an augmented intelligence tool to provide users with relevant legal information and similar case precedents. | True | Idealistic | False | 1.0 | Positive | JusticeBot methodology: A hybrid rule-based/case-based reasoning approach combined with an augmented intelligence tool, supported by the JusticeCreator interface for building tools. | Public deployment and use of JusticeBot TAL (landlord-tenant disputes) with over 17k uses; user survey (N not specified, 86% recommendation rate); analysis of usage analytics (time spent, pathways clicked). | JusticeBot TAL was used over 17k times, and 86% of survey respondents would recommend the system. | Cost, complexity, time consumption, and emotional difficulty of the legal system for laypeople; lack of legal knowledge and awareness of rights/solutions; difficulties for self-represented litigants. | Develop AI-powered augmented intelligence tools (like JusticeBot) that simplify legal information access for laypeople through guided questions, providing tailored information and relevant case law examples. | General everyday legal problems (high-volume, low-intensity), specifically landlord-tenant disputes in the case study. Potentially also consumer issues, debt, employment. | Laypeople / average citizens without legal training facing everyday legal problems. | Housing law (Landlord-Tenant), potentially Consumer law, Debt law, Employment law, Administrative law. | Québec, Canada | Structured legal knowledge (rules, criteria) encoded by experts using JusticeCreator; abstracted case data (reasoning paths, outcomes) derived from analyzing previous court decisions (e.g., 10k TAL decisions for the case study). Primarily processed legal texts (statutes, case law). | Literature review (AI&Law, Access to Justice, HCI), user-centered design (focus on laypeople), prototyping (FactorBot, JusticeBot), iterative development (incorporating feedback), case study evaluation, hybrid AI approach (rule-based + case-based reasoning). | Public website deployment (justicebot.ca) for the JusticeBot TAL tool; collaboration with relevant legal institutions (TAL, Legal Aid) for promotion/support. | True | False | The JusticeBot TAL tool for landlord-tenant disputes in Québec is available via a public website: https://justicebot.ca. | Need for expansion to more legal/administrative areas; improving user interaction (e.g., NLP); enhancing evidence handling; integration with ODR; reducing knowledge encoding effort; ensuring information accuracy and updates; addressing potential biases; managing user expectations; ensuring equitable technology access. | Encoding complex legal knowledge (including vague concepts and case law); designing intuitive interfaces for laypeople; matching user input to relevant rules/cases; evaluating tool effectiveness and user satisfaction; maintaining and updating the knowledge base. | Over-reliance on the tool by users; misinterpretation of provided information; potential for encoded biases in rules or case data; system failure on complex, novel, or edge cases; potential perception of providing legal advice rather than information. |
| 15NbsabryQwJ.pdf | Google_Scholar | Artificial Intelligence (AI) and the Practice of Law | This article provides an overview of Artificial Intelligence (AI) applications in the legal profession, discussing potential benefits such as increased efficiency and access to justice, alongside significant challenges like accuracy, bias, confidentiality, and ethical considerations. It calls for lawyers, courts, rules committees, and ethics bodies to understand AI technology, evaluate its risks, ensure human oversight, and consider necessary regulatory updates. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | High cost of legal services (implied). For AI in A2J: Risk of inaccurate or biased AI output harming pro se litigants or clients of pro bono services; need for human vetting and oversight; potential for AI misuse (e.g., unauthorized practice of law); confidentiality concerns with AI platforms. | Use of AI tools to automate tasks (legal research, document review, form completion), potentially reducing costs and increasing efficiency for pro bono providers and legal aid organizations. Exploration of Online Dispute Resolution (ODR) potentially enhanced by AI for small claims. Emphasizes lawyer supervision, vetting AI output for accuracy and bias, and maintaining confidentiality. | Cost reduction in legal services, automation of legal tasks (form completion, research, review), legal aid/pro bono service delivery, online dispute resolution (ODR) for small claims. | Pro se litigants, individuals unable to afford attorneys (general population needing legal aid/pro bono services). | Multiple fields including Litigation (eDiscovery, evidence, motions), Criminal Law (bail, sentencing, innocence projects, law enforcement), Intellectual Property (copyright, patents), Employment Law (hiring, discrimination), Contract Law (review, management), Healthcare Law (diagnosis, privacy), Immigration Law (form completion), ADR (mediation, arbitration, ODR), Corporate Law (due diligence). | Primarily US, with references to International (EU, Canada, Colombia, India). | NaN | NaN | NaN | True | False | The paper discusses various types of AI tools, some of which are commercially available (e.g., ChatGPT, Westlaw Precision, Lexis+, Clearbrief, eDiscovery platforms) or under development by firms (e.g., LAER.AI). Some have free versions (e.g., ChatGPT), while others are paid subscription services or proprietary. | Need for clear regulations and ethical guidelines for AI use in law; methods to mitigate bias and ensure fairness; improved accuracy, reliability, and explainability ('black box' problem) of AI; enhanced education/training for legal professionals and students; frameworks for liability regarding AI errors; reliable methods for authenticating AI-generated evidence (esp. deepfakes); ensuring AI use upholds due process. | Ensuring accuracy and avoiding 'hallucinations'; mitigating bias; maintaining client confidentiality and data security; need for human oversight and validation; lack of transparency/explainability; cost; ensuring ethical compliance (competence, supervision); integration into workflows; potential 'function creep'; authenticating AI evidence (deepfakes); need for specialized skills; navigating evolving regulations; cybersecurity. | Inaccurate legal filings leading to sanctions; violation of client confidentiality; perpetuation of societal biases (hiring, sentencing); use of deepfakes to mislead; AI-powered cybersecurity threats (scams, breaches); unauthorized practice of law; erosion of due process/transparency in AI adjudication; misinformation; potential job displacement; financial fraud via AI voice synthesis; violation of privacy laws (HIPAA, GDPR). |
| lxdPlzm8HF8J.pdf | Google_Scholar | Artificial Intelligence Cannibalism and the Law | This paper discusses the concept of "AI cannibalism," where future large language models (LLMs) trained on increasing amounts of AI-generated content may degrade in quality, coherence, and accuracy. It explores the specific risks this poses for the legal profession, including increased hallucinations, exacerbated bias, and potential stagnation of legal development. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General Legal Practice, Legal Research, Legal Writing, Appellate Advocacy | US | NaN | NaN | NaN | False | False | NaN | NaN | The challenge of maintaining LLM quality and avoiding model collapse when training data increasingly includes AI-generated (synthetic) content. Difficulty distinguishing human vs AI-generated content for curation. Need for massive datasets for LLM improvement potentially conflicting with the need to exclude synthetic data. Lack of agreed-upon evaluation metrics for generative models beyond technical error rates. | AI hallucinations leading to incorrect legal citations and professional sanctions for lawyers. Amplification and perpetuation of existing societal biases through biased training data and outputs. Stagnation in the development of law due to LLMs favouring existing legal precedent and potentially hindering novel legal arguments. Undermining lawyers' own creativity and critical thinking processes if over-relied upon. Potential disclosure of confidential client information when using external AI tools. |
| 5optiAllNawJ.pdf | Google_Scholar | The Impact of Artificial Intelligence on Access to Justice: Predictive Analytics and the Legal Services Market | This paper examines how developers of predictive analytics market their software regarding access to justice, analyzing claims about time savings, improved access, clarity, and certainty. It concludes that while these claims have merit, the economic realities of the legal services market limit the technology's actual positive impact on access to justice. | True | Idealistic | False | 2.0 | Negative | Predictive analytics software for legal outcome prediction (e.g., Blue J Legal, LexMachina). | Qualitative documentary analysis of company websites and social media (blogs, webinars, promotional materials) to assess marketing claims regarding access to justice. The paper mentions company claims about prediction accuracy (e.g., Blue J Tax 90%) but does not independently test the software. | Companies claim time savings, improved access to law, clarity, and certainty. However, the paper concludes these claimed benefits are unlikely to translate into significant access to justice improvements for individuals due to market factors (tools target firms/corporations, cost doesn't trickle down), required legal capability, and technology limitations (novelty handling, instrumentalism). | High cost of legal services; limited availability/scope of legal aid; legal system complexity; difficulty understanding rights/procedures; unmet legal needs causing negative life impacts; inequality between individual litigants and well-resourced entities; lack of legal capability. | The paper critiques predictive analytics as a solution in its current deployment model and mentions university projects (MyOpenCourt, JusticeBot) with limitations. It does not propose specific new high-level solutions but implicitly points towards market reforms and enhancing legal capability alongside technology. | Affordability of legal services; availability of legal services; legal capability; impact of technology on legal practice; fairness and equality in the justice system. | General public facing cost and complexity barriers in the legal system, particularly individuals contrasted with corporations/large firms. | Civil Law (broadly), with specific examples including Tax Law, Labour and Employment Law, Intellectual Property Law. | Canada, United States (primarily), with references to UK. | Large datasets of case law (implied to be court decisions). Source details (public vs proprietary) not specified, but limitations regarding unreported decisions and non-digitized evidence are noted. | Machine learning (statistical analysis of case law data), validation using test datasets. Specific software development methodologies are not described. | Commercial marketing and sales targeted at legal professionals (law firms, corporate counsel), accountants, and HR professionals through company websites, promotional materials, and direct outreach. Subscription-based model implied. | True | False | Commercial subscription-based software (Blue J Legal, LexMachina) marketed to legal professionals and corporations. Some limited, university-developed tools (MyOpenCourt, JusticeBot) are mentioned as publicly accessible. | Technical: Data limitations (unreported cases), inability to handle novelty/reason by analogy, algorithm opacity, potential bias. Societal: Lack of cost pass-through to clients, high technology cost, need for user legal capability, focus of commercial tools on market advantage, potential to stifle legal development. | Ensuring accuracy, avoiding bias from training data, algorithm opacity ('black box'), high cost of development and maintenance, limitations of data availability. | Lawyer over-reliance leading to competency issues; screening out novel/difficult cases; reinforcing historical biases; lack of transparency; hindering legal development through instrumentalism; potential increase in overhead costs; widening justice gap between resourced/unresourced parties; misinterpretation of probabilities. |
| uNE_TxZM_g0J.pdf | Google_Scholar | Enhancing Judicial Efficiency and Access to Justice Using AI | This study explores integrating AI into Indiana's legal system to enhance judicial productivity and access to justice. Using survey data from over 100 judges, the research applies NLP and Azure Language AI to identify concerns, informing the development of an AI awareness packet, an integration roadmap, and a comparative analysis of AI-generated content detection tools. | True | Idealistic | True | 1.0 | Positive | Application of NLP (Azure Language AI for sentiment analysis and key-phrase extraction) to judicial survey data, qualitative interviews with judges, and process mining. This informed the development of an AI awareness packet, an AI integration roadmap, and a comparative analysis of AI-generated content detection tools. | For the comparative analysis of AI content detection tools, publicly available benchmark datasets featuring AI-generated images, deepfakes, synthetic audio, and other artificial media were utilized to evaluate tool performance metrics, including precision and efficiency. The AI awareness packet was integrated into the IOCS learning management system. | An AI awareness packet was successfully integrated into the Indiana Office of Court Services (IOCS) learning management system. Pilot programs for AI-enhanced workflows were recommended to IOCS, and a proposal packet comparing multi-modal synthetic media detection tools was provided to IOCS leadership. | Judges' security concerns regarding AI tools, lack of knowledge about AI applications and their specific use-cases, and difficulty distinguishing between different types of AI tools. Broader issues include AI bias, transparency, accountability, and the unreliability of current AI-generated content detection methods against sophisticated attacks. | Development of an AI awareness packet to educate judges on AI concepts, tools, and ethical considerations. Creation of an AI integration roadmap suggesting AI applications for specific judicial workflows like document review, calendar management, and court transcriptions. Provision of a comparative analysis of available tools for detecting AI-generated or -altered media. | Judicial efficiency, AI literacy for judges, identification of AI-generated evidence, AI integration into court workflows, ethical AI adoption in the judiciary. | General public / litigants in Indiana (as indirect beneficiaries of improved access to justice and judicial efficiency). | Criminal law, tax law, mental health law, family law, misdemeanor cases, appellate procedure, general court administration. | Indiana (US) | Proprietary survey data from over 100 Indiana judges (quantitative and open-ended responses) and qualitative interview transcripts from 12 judges were used for NLP analysis. Publicly available benchmark datasets of AI-generated content were used for evaluating detection tools. | Survey design and administration, qualitative data collection (interviews, open-ended questions), NLP (sentiment analysis, key-phrase extraction using Azure Language AI), process mining, thematic analysis, comparative market analysis of existing tools, and literature review. | The AI awareness packet was integrated into the Indiana Office of Court Services (IOCS) Learning Management System. Recommendations for pilot programs and a proposal for AI detection tools were submitted to IOCS for consideration and potential implementation. | False | False | NaN | Limited empirical research on AI's impact on judicial bias and case outcomes. Current AI text detection methods are not robust against paraphrasing/spoofing attacks. Real-world deepfake detection requires more scalable and computationally lighter models. A general need for continuous research and adaptation of judicial AI policies. | Balancing efficiency gains from AI with accountability and the protection of sensitive court data. Addressing judicial skepticism and lack of familiarity with AI tools. Ensuring ethical AI integration within the legal framework and maintaining data security. Identifying and selecting appropriate AI tools for specific judicial needs. | Mis D_identification or failure to identify AI-generated/altered evidence, potentially undermining justice. Proliferation of deepfakes and synthetic media in legal proceedings. Inherent biases in AI models, lack of transparency, and accountability issues. Security vulnerabilities related to sharing sensitive court data with AI systems, and AI feedback loops impacting data integrity. |
| uVSVKWt3LiMJ.pdf | Google_Scholar | UNLOCKING LEGAL KNOWLEDGE WITH MULTI -LAYERED EMBEDDING -BASED RETRIEVAL | This paper proposes a multi-layered embedding-based retrieval method for legal and legislative texts, creating embeddings at various granularity levels to capture their hierarchical structure and semantic nuances. The method, demonstrated with the Brazilian Constitution, aims to enhance Retrieval Augmented Generation (RAG) systems for more accurate and contextually relevant legal information retrieval. | True | Market | True | 1.0 | Positive | Multi-layered embedding-based retrieval for legal texts, integrated with Retrieval Augmented Generation (RAG). | Comparative analysis against a traditional flat chunking approach using the Brazilian Constitution. Eight queries were used, and retrieval results (chunk relevance, similarity scores using text-embedding-3-large, token counts) were analyzed. Embeddings were visualized using PACMAP for dimensionality reduction and Plotly. The gpt-4-turbo-preview model was used for response generation. | The multi-layered approach yielded a higher proportion of essential chunks (37.86%) compared to the flat embedding method (16.39%), and a lower proportion of unnecessary chunks (58.25% vs 75.41%). The multi-layered approach also showed more semantically consistent chunks aligned with user queries. | The increasing volume and complexity of legal corpora; traditional keyword-based search methods failing to capture legal nuances, semantic content, and intricate relationships within hierarchical legal documents. | A multi-layered embedding-based retrieval method that captures the semantic content and inherent hierarchical structure of legal texts at varying levels of granularity. This allows RAG systems to provide more accurate and context-specific responses and enables queries in plain language. | Access to information on constitutional rights (e.g., foundations of the republic, social function of property, attributes of the vote, tax revenue distribution, rights of children and teenagers, jury rights, right to association, legal assistance for those with insufficient funds). | Individuals with insufficient funds (specifically regarding legal assistance information), General public (by making legal knowledge more understandable). | Constitutional Law, Legislative texts. | Brazil (primary focus on Brazilian Constitution and legislative structure). The paper suggests applicability to other civil and common law systems. | The text of the Brazilian Constitution was used as the corpus for creating and testing embeddings. The embedding model used was OpenAI's 'text-embedding-3-large', pre-trained on general text data. | Conceptual design of a multi-layered chunking strategy based on the inherent hierarchy of legal texts. Empirical comparison with a baseline flat chunking method using quantitative (similarity scores, token counts, relevance classification) and qualitative (response evaluation) metrics. | NaN | False | False | NaN | Representing inter-article relationships (e.g., cross-references), incorporating a temporal dimension for legal text evolution, and further investigation into optimal vector dimensions for embeddings. | Handling the complexity, hierarchical structure, and semantic nuances of legal texts; overcoming limitations of traditional search and flat chunking methods (e.g., overlooking intrinsic hierarchy); managing semantic overload in dense legal articles (like Article 5 of the Brazilian Constitution). | NaN |
| F6cddn0EKiIJ.pdf | Google_Scholar | The Rise of the Robotic Tax Analyst | This paper discusses the use of AI, specifically large language models like GPT-3 and predictive analytics tools like Blue J, in tax law. It reviews the accuracy of Blue J's tax case predictions from 2022 and speculates on the future integration of AI for tax research, analysis, and compliance by 2030. | True | Market | True | 2.0 | NaN | Blue J's machine learning models for predicting tax case outcomes; Use of GPT-3 for text generation. | Comparison of Blue J's 2022 predictions published in the 'Blue J Predicts' column against the actual court outcomes in the corresponding tax cases. | Blue J accurately predicted the outcome in 6 out of 8 cases where a prediction was made and the litigation outcome was known at the time of writing. The paper also states Blue J's models achieve over 90% accuracy generally. | NaN | NaN | NaN | NaN | Tax Law | USA | Blue J's models use 'massive amounts of data collected from tax cases and legal sources'. The nature (public/proprietary) is not explicitly stated but likely involves proprietary processing of public legal data (case law, statutes). | NaN | Blue J Tax is a commercial software product. Predictions are also shared via the 'Blue J Predicts' column in Tax Notes Federal. | True | False | Blue J Tax is commercially available via subscription from Blue J Legal Inc. | NaN | The complexity and perceived inconsistency of tax case law make it difficult for humans to analyze accurately; Wariness among some legal academics and practitioners regarding large language models. | NaN |
| 3599696.3612895.pdf | Google_Scholar | Analyzing the Use of Large Language Models for Content Moderation with ChatGPT Examples | This paper proposes an enhanced content moderation pipeline integrating Large Language Models (LLMs) to improve fairness, personalization, and user communication on online social networks. It demonstrates the approach with ChatGPT examples for sex-related texts, gender stereotypes, and ableist language, highlighting the potential for user-defined rules and decision explanations. | True | Idealistic | True | 1.0 | Positive | An enhanced content moderation pipeline that integrates an LLM (using ChatGPT as an example) to classify text based on user-customizable rules (provided via prompts) and to generate explanations for moderation decisions. | Qualitative demonstration using ChatGPT with specific prompts and predefined rules for three case studies: sex-related texts, texts containing gender stereotypes, and texts offensive to people with disabilities. The LLM's binary classification (violates rules: Yes/No) and its generated explanations were examined. | ChatGPT successfully adapted to different rule sets, classifying content and providing explanations. For instance, it correctly distinguished permissible medical sex-related content and identified non-inclusive language regarding disabilities. However, it sometimes failed to detect more subtle gender stereotypes without explicit phrasing or in isolated instances. | Current content moderation systems are often unfair to fragile users and minorities, lack personalization, fail to provide adequate explanations for decisions, and struggle with interpreting diverse languages and cultural contexts, thereby hindering safe and inclusive online environments. | Integrating LLMs into content moderation to enable personalization through user-specified rules (via prompts), provide explanations for moderation actions, enhance user-platform communication, and offer better support for human moderators. | Fairness and equity in online content moderation, protection of vulnerable groups from harmful content, transparency and explainability of automated moderation decisions, user empowerment in defining online content filtering. Indirectly relates to upholding principles of justice in digital spaces. | Fragile users (defined by age, digital literacy, education), minorities (e.g., LGBTQ+), marginalized people (e.g., based on race, religion, users from the Global South), and people with disabilities. | Online speech regulation, anti-discrimination principles as applied to online content, platform governance. | International | NaN | Conceptual framework proposal for an enhanced content moderation pipeline, demonstrated through illustrative case studies using prompt engineering with a pre-trained LLM (ChatGPT). | NaN | False | False | NaN | LLMs have inherent limitations such as 'hallucinations and knowledge recency.' Obtaining numeric confidence values from LLMs for their decisions is an open research problem. Designing user-friendly interfaces for rule customization and addressing privacy implications of such personalized systems are also needed. | Effectively designing prompts for LLMs to handle nuanced content moderation. LLMs' difficulty in interpreting subtle or highly contextual violations without explicit cues. The current inability of LLMs to provide numeric confidence scores for their decisions, limiting their comparability with traditional ML classifiers. | LLM limitations like 'hallucinations and knowledge recency' may lead to incorrect moderation decisions. The proposed system's reliance on binary (Yes/No) LLM outputs, due to the difficulty in obtaining confidence scores, might be insufficient for complex cases. |
| gkdm8RV9wjYJ.pdf | Google_Scholar | Mapping the Challenges of HCI: An Application and Evaluation of ChatGPT and GPT-4 for Mining Insights at Scale | This paper proposes and evaluates a two-step approach using ChatGPT and GPT-4 to mine research challenges from the HCI conference proceedings (CHI 2023). It demonstrates this method's cost-efficiency for analyzing large text corpora at scale and discusses broader implications of LLMs for academic research and insight mining. | True | NaN | True | 1.0 | NaN | A two-step insight mining approach: 1) ChatGPT (gpt-3.5-turbo-0301) extracts candidate research challenges from papers. 2) GPT-4 (gpt-4-0314) filters this list to the top five most significant challenges per paper. | Quantitative evaluation using NLP metrics (EM, ED, WER, BLEU, ROUGE, METEOR, BLANC, BERTScore) by comparing LLM outputs to best-matching text from the source or previous LLM step. Qualitative evaluation via human annotation of research challenges from a 5% random sample of papers, compared against GPT-4's output, with inter-rater agreement (Cohen's kappa) calculated. Semantic similarity analysis using embedding cosine distances. | Human evaluation showed high agreement (κ=0.97) that LLM-extracted statements are potential research challenges. The GPT-4 list matched human-identified challenges in approximately 65% of the sampled papers (κ=0.86 for alignment). GPT-4 selected 98.62% of challenges verbatim from ChatGPT's output, and no hallucinations were found in the sampled qualitative evaluation. The approach was deemed cost-efficient. | NaN | NaN | NaN | NaN | NaN | NaN | The underlying LLMs (OpenAI's ChatGPT and GPT-4) were pre-trained on opaque, internet-scale text corpora. The method described in the paper was applied to the CHI 2023 conference proceedings (879 papers), which served as input data for analysis by the pre-trained models. | Iterative prompt engineering using best practices for reliable prompting, with observation of outcomes on sample documents in Jupyter notebooks. Temperature parameter set to zero for determinism during prompt design. | The dataset of 4,392 extracted HCI research challenges and an interactive visualization were made publicly available on GitHub Pages and the Open Science Framework (OSF). | True | False | The described insight mining approach relies on OpenAI's commercial ChatGPT and GPT-4 APIs. The resulting dataset and visualization are open access. | NaN | Iterative and experimental nature of prompt design to achieve desired output quality and consistency; Managing API limitations such as context window length (requiring batching and error handling for InvalidRequestError) and rate limits; Cost considerations for using LLM APIs, which motivated the two-step approach for efficiency; The lack of a gold standard for the specific task, requiring approximation methods for quantitative evaluation metrics. | General LLM risks mentioned include potential for hallucinations (though not observed in their specific evaluation with context), sycophancy, reproduction of biases from training data, encoding of opinions and cultural values, and sensitivity to prompt phrasing. |
| kbfw6Fsq-qAJ.pdf | Google_Scholar | Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-Generation | This paper proposes EDC2-RAG, an efficient dynamic clustering-based document compression framework to improve Retrieval-Augmented Generation (RAG) for large language models (LLMs). The framework clusters retrieved documents based on semantic similarity to reduce noise and redundancy before compressing them using an LLM, leading to improved performance on knowledge QA and hallucination detection tasks. | True | NaN | True | 1.0 | NaN | EDC2-RAG: An Efficient Dynamic Clustering-based document Compression framework for Retrieval-Augmented Generation. It uses document embeddings for dynamic clustering, followed by LLM-based query-aware compression of clusters before feeding the context to the final generation LLM. | Evaluated on knowledge QA datasets (NQ, WebQ, TriviaQA) and hallucination detection datasets (FELM World Knowledge Subset, WikiBio GPT-3, HaluEval). Performance compared against baseline RAG methods (RALM, Raw Compression, CEG, Self Consistency) using F1 score, Balanced Accuracy, and Accuracy metrics under varying conditions (top-k retrieved documents, noise levels, redundancy rates). | The proposed EDC2-RAG method achieved consistent performance improvements over baseline methods across different datasets and experimental settings. For example, on WebQ (Top-100), it achieved an average F1-score improvement of +0.91 over RALM across varying noise rates. On HaluEval, it improved Accuracy by +1.05 over the CEG baseline (Top-10). | NaN | NaN | NaN | NaN | NaN | International | The technique utilizes pre-trained LLMs (GPT-3.5-Turbo, GPT-4o) and embedding models (SimCSE Bert, all-mpnet-base-v2). Evaluation was performed on publicly available datasets (NQ, WebQ, TriviaQA, FELM, WikiBio, HaluEval) using retrieval corpora derived from Wikipedia snapshots (2018, Oct 2023) and Freebase. | Algorithmic design involving document embedding, similarity calculation, rule-based dynamic clustering, and prompt-based LLM compression. | Code and datasets are publicly released on GitHub. | True | True | Code and datasets available on GitHub: https://github.com/Tsinghua-dhy/EDC-2-RAG | NaN | General RAG challenges addressed: Noise, redundancy, and repetition in retrieved documents; limited exploitation of inter-document relationships by standard RAG. Limitation of the specific method: Incurs API consumption costs for the compression step. | Implicitly addresses the risk of LLM hallucination (generating factually incorrect information) by improving the quality of retrieved context provided to the LLM. |
| cKasUkcMinkJ.pdf | Google_Scholar | Generative AI's Role in Reducing Transaction Costs \nin Finnish Legal Markets \nAn Analysis of Litigation Process Participants | This literature review examines the potential of generative AI (GenAI) to reduce transaction costs and improve efficiency in the Finnish litigation process. It analyzes empirical studies and theoretical frameworks, finding mixed results where GenAI shows promise but requires careful implementation to mitigate risks like reduced economies of scope. | True | Market | True | 3.0 | Neutral | Generative AI (GenAI) for tasks such as supporting legislative drafting and producing summaries of public consultation responses. | The paper reviews two Finnish pilot projects: 1) Futurice Oy: Finnish language models (e.g., FinGPT, Poro) were further trained with legislative texts and a chatbot demo was developed for legal drafters. 2) SiloGen AI Oy: An AI tool was used to create draft summaries of public consultation responses, with a demo solution evaluated by drafters. | The reviewed studies showed mixed results. The Futurice Oy pilot found Finnish language models 'are not yet at a sufficient level.' The SiloGen AI Oy pilot showed 'promising results' in generating preliminary summaries but also 'produced inaccurate and incomplete interpretations' requiring further refinement. | High transaction costs in legal markets, making legal services expensive and often inaccessible for ordinary consumers, citizens, and small businesses. Law is perceived as too expensive and low quality due to lack of innovation. | Integrating GenAI into the litigation process to automate routine tasks like legal research and document drafting. This could lead to cost savings, faster processes, better allocation of legal expertise, and potentially make legal aid more accessible. | Reducing transaction costs in the litigation process, improving efficiency of legal services, and enhancing accessibility of legal aid. | Ordinary consumers, citizens, and small businesses who currently find legal services unaffordable. | Litigation (civil, criminal, petitionary cases) and legislative drafting processes. | Finland (Finnish Legal Markets) | For the Futurice Oy pilot: Finnish legislative texts and related materials used to further train Finnish language models (e.g., FinGPT, Poro). For the SiloGen AI Oy pilot: Public consultation responses. | The paper reviews pilot projects that developed: 1) A service demo with a chatbot interface for legislative drafting support (Futurice Oy). 2) A demo AI tool for summarizing public consultation responses (SiloGen AI Oy). The paper itself is a literature review. | The discussed GenAI applications are at the pilot project/demo stage and not broadly deployed. | False | False | NaN | A significant research gap on legal market efficiency and litigation cost minimization globally. Need for further studies in Finland on welfare gains from GenAI in legal systems. Technical gaps include Finnish language models not being sufficiently advanced and AI summary tools requiring more training. | For the reviewed studies: Finnish language models not being sufficiently advanced (Futurice Oy). AI tools producing inaccurate/incomplete interpretations, generalizing feedback, overlooking comments, and requiring significant additional training (SiloGen AI Oy). For the thesis author: The difficulty of undertaking original empirical/theoretical work on a novel topic. | Privacy violations and cybersecurity concerns. Over-reliance on AI predictions leading to misuse. AI producing inaccurate or incomplete outputs. Reduction in economies of scope, potentially leading to humans losing holistic understanding and decreased productivity. Challenges in balancing regulation to foster innovation while protecting rights. |
| TAPs_final_CHI.pdf | Google_Scholar | Privacy Perceptions of Custom GPTs by Users and Creators | This paper explores the privacy perceptions of users and creators regarding OpenAI's custom GPTs through interviews (N=23). It reveals blurred user/creator roles, unclear mental models of data flow, significant privacy concerns about data handling and regulation, and proposes recommendations for improved transparency and platform oversight. | True | NaN | True | 2.0 | NaN | OpenAI Custom GPTs | Semi-structured interviews (N=23) with users and creators, analyzed via thematic analysis. | Participants exhibit blurred user/creator roles and uncertain data flow mental models. Key privacy concerns include data collection scope, processing misuse, unauthorized dissemination, and lack of regulation; creators also worry about knowledge exploitation. Users practice self-censorship and GPT evaluation, while creators employ knowledge protection techniques; expertise and responsibility shape perceptions. | NaN | NaN | NaN | NaN | Privacy Law (implicitly, through user concerns and GDPR mentions), General Technology Regulation | International | NaN | NaN | OpenAI GPT Store | True | False | Available via OpenAI subscription (ChatGPT Plus) through the GPT Store. | Need for clear platform regulations, GPT verification mechanisms, creator knowledge protection, effective privacy communication strategies, resolution of machine unlearning challenges. | Unclear data flows, third-party data sharing risks, potential for data misuse/profiling/leaks, lack of user control over data (e.g., deletion), inadequate privacy protections for creators' knowledge, proliferation of spam/malicious GPTs, lack of robust platform verification and regulation. | Data misuse (profiling, marketing, scams, political manipulation, deepfakes), data breaches/leaks, identity theft, financial loss, unauthorized exposure of personal/confidential information, exploitation of creator knowledge, malicious GPTs stealing user data, platform promotion of spam/scams. |
| yNavCh3Cl8gJ.pdf | Google_Scholar | AI Tools for Lawyers: A Practical Guide | This paper provides a practical guide for lawyers on how to ethically and efficiently use large language models (LLMs) like GPT-4 and Bing Chat for legal tasks. It offers generalizable strategies for prompting LLMs to analyze caselaw, identify legal issues, and draft legal documents such as memos, briefs, and contracts. | True | Market | True | 2.0 | Positive | Prompt engineering strategies for using LLMs (specifically GPT-4 and Bing Chat) in legal research, analysis, and drafting, including detailed prompting, iteration, chain-of-thought, and providing source material for verification. | Qualitative demonstration through examples of prompting GPT-4 and Bing Chat for various legal tasks (e.g., case summarization of Chipokas v. Hugg, legal analysis of hypothetical fact patterns) and assessing the LLM-generated output. No formal benchmarks were used. | The paper demonstrates that well-prompted LLMs like GPT-4 can produce highly readable and accurate case summaries (e.g., Chipokas v. Hugg synopsis described as more accurate than court-supplied or West headnotes), identify relevant legal issues, and generate good first drafts of legal arguments and contract clauses. | Historical lack of resources for lower-income individuals to pay for legal services (mentioned in footnote 55). General LLM limitations include potential for hallucinations, lack of access to nuanced facts without direct input, and (for some models like older GPT versions) lack of access to the latest legal sources if not connected to a search engine. | Expanded use of AI tools by lawyers could plausibly lower costs or increase efficiency, thereby helping to expand the availability of legal services. For LLM limitations, the paper suggests verifying outputs, providing specific source material to the LLM, and using tools like Bing Chat that access current information. | Expanding availability of legal services. | Lower-income individuals. | General legal practice, Torts (defamation), Statutory Interpretation, Contract Law (promissory estoppel, contract drafting). | United States (examples from US federal and state law, e.g., New York Times Co. v. Sullivan, U.S. v. Marshall, Chipokas v. Hugg (Iowa)). | The paper refers to the training data of GPT-4 as a large, historical corpus of text, and Bing Chat as having access to current information via search. These are general, large-scale, proprietary datasets from OpenAI and Microsoft. | The strategies were developed through the authors' experimentation and application of general LLM prompt engineering best practices (e.g., detailed input, iteration, chain-of-thought, few-shot prompting) to common legal tasks. | The paper guides lawyers on using existing, publicly accessible LLM platforms like ChatGPT Plus (for GPT-4) and Bing Chat. | True | True | The paper describes techniques for using LLMs like GPT-4 (accessible via paid ChatGPT Plus subscription) and Bing Chat (freely available). | The primary A2J gap mentioned is the cost of legal services for lower-income individuals. Technical gaps for LLMs include their propensity to hallucinate and their outputs requiring careful verification. A societal gap could be the digital literacy required to effectively use these tools for A2J, though not explicitly detailed. | Key challenges for users include the learning curve for effective prompt engineering, the need to constantly verify AI-generated content due to potential inaccuracies or hallucinations, managing context window limitations for lengthy legal documents, and ensuring client confidentiality when using third-party AI tools. | Risks include LLMs making mistakes or 'hallucinating' incorrect information/citations, providing inaccurate answers if their training data is limited or outdated, and confidentiality concerns due to potential data security bugs in third-party LLM services. Using AI-generated text without permission in academic settings is also noted as unethical. |
| HQ7IncDAqDQJ.pdf | Google_Scholar | Law Without Lawyers: Examining the Limitations of Consumer-Centric Legal Tech Services | The paper discusses the rise of business-to-consumer (B2C) legal tech driven by cost reduction and inclusion needs, analyzing examples like document automation, ODR, and chatbots. It argues that while promising for access to justice, these tools have significant limitations in handling complexity, ensuring quality, and replacing human lawyers, necessitating regulation and adaptation within the legal profession. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | High cost of traditional legal services creating a justice gap for low/middle-income earners; complexity of legal issues requiring human reasoning, interpretation, and empathy beyond current AI capabilities; the digital divide limiting access for vulnerable populations (cost of devices/internet, digital literacy). | Regulating legal tech (minimum standards, liability rules, ethical guidelines, transparency); increasing lawyer's technical competency through legal education reform; establishing legal tech innovation hubs and regulatory sandboxes; raising public awareness about legal tech's benefits and limitations. | Access to affordable legal services, bridging the justice gap, online dispute resolution (ODR), legal information access, basic legal document generation. | Low to middle-income earners, populations underserved by traditional law firms, digitally vulnerable populations (elderly, disabled, digitally illiterate), general public needing basic legal services, populations in Africa. | General Civil Law | International | NaN | NaN | NaN | True | False | Various commercial B2C legal tech platforms (e.g., LegalZoom, DoNotPay, JusDraft) and some government online services (e.g., MCOL) are presented as operational. | Lack of specific legal frameworks for legal tech and ODR (especially in Africa); regulatory gap in AI governance; need for AI explainability and independent quality assurance; difficulty encoding complex legal reasoning/ontology; digital divide limiting access; lack of lawyers' technical competency; limited academic research on legal tech in Africa. | Handling legal complexity and unstructured issues; translating legal ontology into algorithms; ensuring quality and accuracy (data bias, interpretational risk); lack of transparency (black-box problem); keeping pace with evolving law; addressing the digital divide; regulatory lag. | Unauthorized practice of law; ethical dilemmas (client protection, confidentiality); legal liability issues (algorithmic errors, lack of accountability); risk transfer to users via disclaimers; potential undermining of the rule of law; biased or inaccurate outcomes; data privacy breaches; user misinterpretation of guidance. |
| 3627673.3680020.pdf | Google_Scholar | LawLLM: Law Large Language Model for the US Legal System | The paper introduces LawLLM, a multi-task large language model fine-tuned on US legal data to perform Similar Case Retrieval, Precedent Case Recommendation, and Legal Judgment Prediction. LawLLM demonstrates superior performance over existing baselines, including GPT-4, particularly in Legal Judgment Prediction. | True | Market | True | 1.0 | NaN | LawLLM, a multi-task LLM fine-tuned from Gemma-7B with custom data preprocessing, instruction tuning, in-context learning (ICL), and advanced information retrieval methods for Similar Case Retrieval (SCR), Precedent Case Recommendation (PCR), and Legal Judgment Prediction (LJP). | Evaluated on a subset of the CaseLaw dataset for SCR, PCR (using top-k metrics: top-1, top-3, top-5 and not-found rate), and LJP (using accuracy and F1-score in zero-shot and few-shot ICL scenarios). Compared against baselines including LLaMa2-7b, Gemma-7b, Vicuna-13b, Guanaco-13b, GPT-3.5, and GPT-4. | For Legal Judgment Prediction (Few-shot), LawLLM achieved an accuracy of 0.794 and an F1 score of 0.758, outperforming all baselines including GPT-4 (0.732 accuracy, 0.712 F1). | NaN | NaN | NaN | NaN | General US Law (covering Similar Case Retrieval, Precedent Case Recommendation, Legal Judgment Prediction) | United States | A subset of 1,000,000 cases from the publicly available CaseLaw dataset (US court cases), preprocessed (summarized, verdicts extracted) using GPT-4. For SCR, training cases converted to vectors using OpenAI Embedding model. For PCR, precedent relationships from training data converted into a knowledge graph. | Instruction tuning of Gemma-7B using a combined dataset for three tasks (SCR, PCR, LJP). Custom data preprocessing for each task, including GPT-4 for summarization/verdict extraction, vector database creation for SCR, knowledge graph construction for PCR. Use of in-context learning (ICL) and 4-bit quantized Low-Rank Adaptation (LoRA). | Code and data made available via a GitHub repository. | True | True | Code and data are available on GitHub (https://github.com/Tizzzzy/Law_LLM). | Need for expansion to more legal tasks and further refinement of data processing techniques and in-context learning methodologies to improve the model’s understanding of legal nuances and precedents. | Handling voluminous and complex legal text, distinguishing nuanced legal concepts (similar vs. precedent cases), managing token size limitations of base LLMs, and developing effective multi-task learning for the legal domain. | Hallucination (model producing answers unrelated to the provided options, measured by 'not-found' rate). |
| nPNWRlE8MbcJ.pdf | Google_Scholar | The Effect of Race, Gender, and Priming on AI’s Conviction Predictions | This paper experimentally evaluates ChatGPT (GPT-3.5 and GPT-4) for race and gender biases in predicting criminal conviction probabilities using manipulated defendant descriptions and priming. It finds no significant race or gender bias in either model but observes significant priming effects and better performance (lower variance, lower conviction rates) in GPT-4. | True | Idealistic | True | 2.0 | Neutral | Evaluating ChatGPT (GPT-3.5 and GPT-4) for conviction probability prediction in a criminal case scenario using manipulated prompts (varying defendant race/gender, applying priming). | Experimental design using 90 queries (45 per model) based on a modified criminal case vignette (Rachlinski et al. 2009). Defendant attributes varied across a 3x5 matrix (Gender x Race Implicit/Explicit), with three priming conditions (positive, negative, neutral). Statistical analysis (t-tests, ANOVA, regression) of predicted conviction probability ranges (0-100%). | Neither GPT-3.5 nor GPT-4 showed statistically significant race or gender bias. Priming significantly affected predictions (especially GPT-3.5), generally lowering conviction rates compared to no priming. GPT-4 predicted significantly lower conviction rates and showed less variance than GPT-3.5. | Human cognitive biases (race, gender stereotypes) influencing judicial decisions. The 'black box' nature of proprietary LLMs hinders understanding and evaluation. | Exploring LLMs as potential decision-support tools to mitigate human biases in judicial decision-making, possibly due to algorithmic de-biasing or lack of visual cues. Need for transparency and robust evaluation. | Fairness in judicial decision-making, racial bias, gender bias, conviction prediction. | General racial (Black vs. White defendants) and gender (Male vs. Female defendants) categories, implicitly addressing disparities faced by Black individuals in the criminal justice system. | Criminal Law | United States (implied) | Proprietary datasets used to train GPT-3.5 and GPT-4 (details not publicly known or specified in the paper). | Experimental design (Factorial experiment), Quantitative analysis (Statistical testing: t-tests, ANOVA, linear regression). | NaN | False | False | NaN | Need for LLM transparency (training data, policies), better understanding of priming effects, development of legal LLM evaluation metrics (especially without ground truth), qualitative analysis of reasoning, larger scale testing to address randomness. | Lack of 'ground truth' for legal predictions, opacity of proprietary models, high sensitivity of LLMs to prompt variations (priming), randomness in LLM outputs, methodological limitations (sample size). | Potential for AI bias perpetuation (despite negative findings here), risks associated with 'black box' models (difficulty in auditing), susceptibility to manipulation via priming/prompting, potential for poor performance or hallucinations in legal tasks. |
| pMuzPPoMHigJ.pdf | Google_Scholar | Guarding the News Media’s Intellectual Property in the Age of Generative AI | This paper investigates the intellectual property challenges generative AI poses to the news media, emphasizing the unauthorized use of copyrighted journalistic content for training AI models. It argues that this practice threatens the financial viability of journalism and its democratic role, proposing legislative reforms, stronger regulation, and financial support to protect news creators. | True | Idealistic | True | 3.0 | Negative | NaN | NaN | NaN | Unauthorized and uncompensated use of copyrighted news content for training AI, leading to financial unsustainability of news outlets; spread of misinformation and distortion of news by AI, undermining journalism's democratic role; inadequate existing legal frameworks to protect journalistic IP from AI. | Legislative action (e.g., Journalism Competition and Preservation Act, new AI-focused laws); enhanced regulation and enforcement by agencies like the FTC (e.g., mandatory disclosures, fines); public funding or tax breaks for journalism (e.g., Local Journalism Sustainability Act, levy on digital advertising revenue from AI). | Protection of intellectual property for news media, ensuring financial viability of journalism, combating AI-generated misinformation, upholding the democratic role of the press, public access to reliable information. | The general public, whose access to reliable information and a functioning democracy is dependent on a viable press. | Copyright Law, Intellectual Property Law, Media Law, First Amendment Law | United States | The paper discusses AI models being trained on large, publicly available datasets scraped from the internet, which include copyrighted news articles, in-depth investigations, opinion pieces, and other journalistic content without permission or compensation. | NaN | NaN | False | False | NaN | Lack of solid legal standards for resolving disputes over AI's use of copyrighted material; uncertainty about the applicability and adequacy of current copyright law (especially fair use) to generative AI; disparities in bargaining power between news outlets and AI companies; need for comprehensive legislative and regulatory frameworks specifically addressing AI and news content. | NaN | Copyright infringement and financial deprivation for news outlets due to uncompensated use of their content for AI training; spread of AI-generated misinformation, disinformation, and fabricated news, potentially attributed to real news outlets; diminished work opportunities for journalists; reduced media diversity and public access to trustworthy information; undermining of the press's democratic and societal functions. |
| dkUIaaWEdX8J.pdf | Google_Scholar | AI-ASSISTED GERMAN EMPLOYMENT C ONTRACT \nREVIEW: A BENCHMARK DATASET | This paper presents a benchmark dataset of 1094 German employment contract clauses annotated for legality and fairness by legal experts. The authors provide baseline performance results using various NLP models, including fine-tuned and prompt-engineered GPT variants, for automatically identifying problematic clauses. | True | Idealistic | True | 1.0 | Positive | Creation and benchmarking of a dataset for German employment contract clause legality/fairness classification using transformer models (BERT, GPT-3.5, GPT-4) via fine-tuning and prompt engineering. | Evaluation on a 10% held-out test set from the created dataset (893 samples after deduplication). Metrics used were Precision, Recall, and F1-score for binary classification (okay vs. problematic). Various input formats incorporating clause text, section titles, and instructions were tested. | The best performance (highest weighted average F1-score 88.9%, positive class F1-score 61.5%) was achieved by fine-tuning the OpenAI gpt-3.5-turbo-1106 model with instructions and clause text only as input. | Cost and time of traditional legal review; insufficient legal knowledge among employers and employees; scarcity of expert-annotated legal datasets. | Developing AI-assisted tools for contract review to reduce costs, time, and improve accessibility. Providing an open benchmark dataset to facilitate research and development of such tools. | Legality and fairness review of employment contract clauses. | Employees (limited legal knowledge/financial resources) and employers (risk reduction). | Employment Law, Contract Law | Germany | A dataset of 1094 German employment contract clauses, sourced from a law firm's anonymized client data, annotated by two lawyers for legality (valid, unfair, void), category (14 types), and explanation. Released publicly (CC BY-NC 4.0). | Dataset creation involved sourcing, anonymization, clause segmentation, multi-round expert annotation with inter-annotator agreement calculation, categorization. Baseline evaluation involved standard NLP fine-tuning and prompt engineering techniques. | NaN | False | True | The annotated dataset is available on GitHub under a CC BY-NC 4.0 license. | Current dataset size potentially limits fine-tuning performance (plan to expand). Baselines lack extensive hyperparameter tuning/prompt exploration. Need for advanced classification pipelines (e.g., RAG) and evaluation of a prototype system (technical, economic, social). Need to bridge the gap between research and practical application. | Scarcity and cost of creating expert-annotated legal datasets, especially non-English. Handling sensitive data/privacy. Potential model bias (e.g., GPT models favouring employee protection). Data imbalance. Potentially insufficient dataset size for optimal fine-tuning. | Employees unknowingly accepting unfair/void contract terms. Employers facing lawsuits due to void clauses. AI models potentially misclassifying clauses (risk of overlooking problematic ones deemed higher). Privacy risks if data anonymization fails. |
| viewcontent.pdf | Google_Scholar | Continuing Legal Education in Germany – Digitalization | This paper discusses the increasing importance of digitalization in continuing legal education (CLE) for German legal professionals due to growing legal complexity and market changes. It outlines necessary digital skills, relevant CLE content, and innovative teaching methods, citing examples from Bucerius Law School. | True | Market | False | 3.0 | NaN | The paper broadly discusses digitalization in CLE but specifically mentions the 'dskrpt' platform for text-based legal education and the 'Bucerius Legal Tech Essentials' free online course. | dskrpt: Developed in-house over two years; aims to collect user data for future improvement. Bucerius Legal Tech Essentials: Evaluated via participant numbers (12,500+), geographic reach (120+ countries), Net Promoter Score (85.58), and median overall satisfaction (10/10). | Bucerius Legal Tech Essentials: 12,500+ participants from 120+ countries (2020-2022), NPS 85.58, median satisfaction 10/10, led to enrollments in paid programs. | NaN | NaN | NaN | NaN | Legal Education, Legal Profession, Technology Law, Civil Procedure, Criminal Procedure, Corporate Law | Germany, with references to USA and Canada. | The dskrpt platform aims to collect user interaction data for future ML applications, but no specific training dataset is described for its current state or other mentioned techniques. | dskrpt: Developed in-house. Bucerius Legal Tech Essentials: Deployed as a Massive Open Online Course (MOOC). | dskrpt: In-house use, plans for a separate company. Bucerius Legal Tech Essentials: Free MOOC offered online from 2020-2022. | False | False | NaN | Need for legal professionals to acquire digital skills; need for legal education and CLE providers to adapt curricula and methods to digitalization trends; historically slow pace of judicial digitalization in Germany (though improving). | Teaching foundational technical concepts effectively in a CLE setting (vs. university); keeping CLE content (like tech landscaping) current; potential overlap between advanced CLE and consulting services; developing new educational platforms like dskrpt. | Professional liability for lawyers failing to keep up with legal/technical developments; clients becoming competitors by developing/offering legal tech solutions; legal professionals being unprepared for market shifts due to digitalization. |
| 3xpB1xoOKekJ.pdf | Google_Scholar | Mapping the Potentials and Limitations of Using Generative AI Technologies to Address Socio-Economic Challenges in LMICs | This paper explores the potential of Generative AI (GenAI) to address socio-economic challenges in Low- and Middle-Income Countries (LMICs), drawing on experiences from 50 projects across various sectors like health, agriculture, and education. While highlighting significant opportunities, it also details substantial risks (bias, privacy, safety) and barriers (infrastructure, data, cost, language) that must be overcome for equitable and just AI deployment. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Lack of affordable compute and reliable infrastructure; Poor data quality, availability, and representativeness (incl. bias from Western datasets); Limited capabilities for low-resourced languages; Insufficient gender-sensitive capacity; Data privacy risks due to inadequate regulations; Safety and cultural sensitivity concerns; Potential to perpetuate bias and stigma; Ethical trade-offs in resource-poor settings. | Enable local innovation through funding and platforms; Build a solid evidence base via M&E and longitudinal studies; Foster public awareness, engagement, and critical digital literacy; Establish rights-based AI governance and regulation; Mobilize resources to build local ecosystems and strengthen capacity (infrastructure, expertise, data ownership). | Global health (healthcare access, health communication, SRH/MCH, evidence generation, disease surveillance), Agriculture (climate adaptation, crop disease detection, farmer advisory), Education (personalized learning, local content generation, literacy assessment), Financial inclusion (financial literacy/services for underserved populations), Gender equality (support for GBV survivors, access to information for women), Access to information in low-resourced languages. | Populations in Low- and Middle-Income Countries (LMICs), including frontline workers (health, agriculture), patients, smallholder farmers, students, rural populations, informal/small-business owners, women, survivors of Gender-Based Violence (GBV), marginalized communities (e.g., LGBTQAI+), low-literacy populations, users of low-resourced languages. | Data Privacy and Protection, Access to Justice (specifically for GBV), Human Rights, AI Governance and Regulation. | LMICs (various, including specific examples from Africa, Asia, and South America) | Varied across projects; included proprietary data collected from users (text, speech), domain-specific data (health records, agricultural info, financial queries, educational materials), sometimes requiring digitization or creation of new datasets (e.g., parallel corpora for low-resourced languages). Base models trained on large, often Western-biased datasets. | Co-creation with communities, human feedback loops, expert reviews, user-led testing, prompt engineering, Retrieval-Augmented Generation (RAG), fine-tuning, development of gold standard responses for evaluation, expert-in-the-loop models, gender-sensitivity training. | Deployed within specific projects/communities/institutions (e.g., hospitals, community programs) for testing or limited service provision. Some projects reported as 'live' providing services, others in user testing/validation. | False | False | NaN | Need for diverse, locally reflective data repositories; Lack of comprehensive M&E and longitudinal studies on AI impacts in LMICs; Insufficient local capacity for critical AI research; Underdeveloped AI governance and regulatory frameworks in many LMICs; Need for resource mobilization to build sustainable local AI ecosystems and address infrastructure/data ownership issues. | High cost and limited access to compute/infrastructure; Poor data quality, availability, and digitization needs; Supporting low-resourced languages; Mitigating bias, ensuring accuracy and cultural sensitivity; Protecting data privacy with inadequate regulations; Navigating ethical trade-offs; Budget constraints and unpredictability; Unstable connectivity; User training and adoption. | Data privacy breaches and misuse of personal/sensitive information; Harm from biased, inaccurate, or culturally insensitive AI outputs (e.g., incorrect health advice, reinforcing stereotypes); Perpetuation of discrimination and exclusion; Stigmatization; Overreliance on technology leading to neglect of human resources; Lack of accountability due to weak governance. |
| mMl9vQtSY1YJ.pdf | Google_Scholar | Generative AI Considered Harmful | This paper critiques generative AI like ChatGPT, outlining potential and actual harms arising from both its usage (e.g., plagiarism, misinformation) and its design/development (e.g., bias, copyright issues, human/environmental costs). It advocates for researchers to study the interactive contexts of AI use and development rather than viewing these systems as autonomous black boxes. | True | NaN | True | 3.0 | Negative | NaN | NaN | NaN | Risk of generating hallucinations and misinformation, particularly problematic for applications like legal advice. | NaN | Legal advice generation (as an example of risk) | NaN | General Legal Advice (examples include Contract Law, Traffic Law) | International | Discusses training data used by models like ChatGPT: massive datasets including Wikipedia, Common Crawl archive, books, websites, scientific articles. Characterized as vast, internet-based, unstructured text, potentially biased, and often lacking proper attribution. | NaN | NaN | False | False | NaN | Lack of reliable, verifiable, and attributable advice generation; need for understanding user interaction and adoption; lack of attribution/explainability; ethical issues in data sourcing and labor; environmental impact assessment needs; need for effective regulation and harm mitigation. | Managing bias, ensuring accuracy (avoiding hallucinations), addressing copyright/attribution, ethical labor practices, environmental sustainability, countering misuse (e.g., plagiarism). | False authorship/plagiarism; hallucinations/misinformation (e.g., incorrect legal advice); job displacement; replication of societal biases; copyright/IP infringement; exploitation of hidden human labor; environmental costs. |
| N0eYrm4EzjUJ.pdf | Google_Scholar | The Path of Tax Law: Toward Legal Singularity | This paper discusses the concept of the "legal singularity," a future where AI makes law fully comprehensive and predictable, primarily drawing insights from the book "The Legal Singularity.". It explores AI's potential to revolutionize tax law, improve access to justice by increasing legal literacy and addressing service unaffordability, and outlines ethical considerations for AI development in law. | True | Idealistic | True | 3.0 | Positive | AI-powered computational legal tools, including predictor-style machine learning models for outcome prediction (e.g., worker classification, innocent spouse relief) and generative AI (large language models) for tax research (e.g., Ask Blue J). | For predictor models: Evaluated using datasets of past court decisions (e.g., hundreds of cases for worker classification; all available cases for innocent spouse relief). For generative AI (Ask Blue J): Described as providing answers backed by relevant source documents for user verification. | For predictor models: Demonstrably able to extract key factors and predict future outcomes with confidence, providing detailed explanations. For generative AI (Ask Blue J): Delivers quality answers to challenging tax questions in seconds. | Law's inherent incompleteness and ambiguity; unaffordability of legal representation; complexity of the law; knowledge gap between legal professionals and clients; potential for AI to act as an expensive gatekeeper or entrench inequalities; algorithmic bias and decontextualization of data. | Achieving "complete law" through AI; developing dynamic rules and microdirectives for clearer, specific laws; promoting universal legal literacy via AI; democratizing access to legal information; improving algorithmic design to consider social context and extralegal factors to mitigate bias; maintaining human oversight in AI-assisted legal processes. | Legal predictability and clarity; accessibility of legal information and services; affordability of legal representation; universal legal literacy; fairness and equity in tax law application and administration; efficiency of legal and government services. | The general public, taxpayers, less well-resourced individuals, and specifically mentions Black taxpayers in the context of addressing algorithmic bias in IRS audits. | Tax law, General Law | United States (primarily, with references to IRS and US case law), Estonia (as an example of digital governance). | For predictor models: Datasets of past court decisions (e.g., "hundreds of past court decisions" for worker classification, "all available innocent spouse cases"). For generative AI (Ask Blue J): "Blue J’s vast tax database" (proprietary, domain-specific, includes source documents). General discussion of AI using "vast legal data sets." | Machine learning, big data analytics, predictor-style models, natural language processing, large language models. For addressing bias: improving algorithmic design by considering a wider range of social context and extralegal considerations. | IRS use of AI for tax-related Q&A and potential tax return processing; Estonia's digital government platform; Commercial AI tools for legal professionals (e.g., Blue J's platforms). | True | False | Ask Blue J is described as a "newly released" product from Blue J Legal. The book "The Legal Singularity" is commercially available. | Achieving full legal singularity; ensuring ethical and equitable AI development and deployment (addressing bias, fairness, accountability); continued need for legal advocacy and diverse perspectives; need for more research and multi-stakeholder collaboration; preventing AI from creating new access barriers; robustly solving data decontextualization. | Capturing the nuances and multidimensionality of legal reasoning with AI; addressing data and algorithmic biases (reflection, amplification, techno-epistemic problems); managing the decontextualization of legal data when building AI tools; drafting AI-generated rules that are both clearer and more specific. | AI being reductionist in legal reasoning; algorithmic decision-making tools perpetuating and amplifying existing societal inequalities (e.g., racial disparities in audits); embedding biases in institutions under a guise of technological objectivity; AI tools becoming expensive gatekeepers to legal information, exacerbating access to justice issues; generative AI entrenching inequalities if critical information remains behind paywalls. |
| l9PBmsLmLYwJ.pdf | Google_Scholar | Measuring Political Preferences in AI Systems – An Integrative Approach | This paper assesses political bias in various Large Language Models (LLMs) using an integrative approach combining four methods: linguistic comparison with US political speech, policy recommendation analysis, sentiment analysis towards public figures, and political orientation tests. The study finds a consistent left-leaning bias in most contemporary conversational AI systems, discusses potential sources and consequences, and recommends mitigation strategies like prioritizing accuracy, transparency, and independent monitoring. | True | NaN | True | 2.0 | NaN | Integrative approach combining: 1) Linguistic comparison of LLM text with US Congress members' language (using Jensen-Shannon Divergence on partisan bigrams). 2) Classification of political viewpoints in LLM-generated policy recommendations (using gpt-4o-mini for annotation). 3) Sentiment analysis of LLM text towards politically aligned public figures (using gpt-4o-mini for annotation). 4) Administration of standardized political orientation tests. | Applied the four methods to 20 conversational LLMs, 6 base LLMs, and 2 ideologically aligned LLMs. Data was generated by prompting models for policy recommendations (27 topics, 30 prompts each) and commentary on public figures (290 figures, 15 prompts each). Results from the four methods were standardized (Z-score) and averaged for a final bias ranking. Linguistic method validated by comparing its results on news media outlets with AllSides bias ratings (r=0.80). | Most conversational LLMs exhibit a statistically significant left-leaning bias across methods, though intensity varies. Google's Gemma 1.1 2b IT was ranked least biased (but still left-leaning); Google's Gemini 1.5 Flash was ranked most biased among conversational models tested. Base models showed milder left-leaning bias. Ideologically aligned models performed as expected. | NaN | NaN | NaN | NaN | NaN | United States (based on analysis focus: US Congress language, US public figures, US policy recommendations) | The study analyzes existing LLMs, discussing their likely original training data (diverse internet sources, potentially including biased sources like Wikipedia, news media, academic papers; often proprietary/undisclosed). For its own analysis, the study generated data (LLM policy recommendations, LLM commentary on public figures) and used external data (US Congressional Record 2010-2022, AllSides media bias ratings, Wikipedia political alignments, Politico journalist list). | Computational linguistics (bigram frequency analysis, Jensen-Shannon Divergence), automated text classification and sentiment analysis (using gpt-4o-mini as annotator), standardized testing (political orientation tests), statistical analysis (Z-score normalization, averaging). | NaN | False | False | NaN | NaN | Limitations of political orientation tests (calibration bias, constrained format); potential calibration bias in any single assessment method; difficulty measuring bias accurately in incoherent base model outputs; understanding the asymmetry observed in partisan term usage; lack of transparency regarding composition of LLM training data. | Reduced viewpoint diversity; increased societal polarization; public mistrust in AI; AI reinforcing pre-existing beliefs (echo chambers); biased autonomous AI agents impacting environments; potential for AI manipulation or control; AI providing deceptive or false information in critical roles (healthcare, finance, legal services). |
| 573DLIdsWUYJ.pdf | Google_Scholar | Artificial Intelligence (AI) Vs Academic Integrity (AI) in Law and \nSociety | This paper explores the conflict between Artificial Intelligence (AI), particularly generative models, and academic integrity within the educational and legal sectors of Bangladesh. It highlights concerns such as plagiarism and ethical breaches due to AI, and advocates for comprehensive policy frameworks, ethical guidelines, and legal reforms to manage AI use responsibly while upholding integrity in law and society. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Education Law, Copyright Law, Technology Law, Legal Ethics/Professional Responsibility, ICT Law, Cyber Security Law | Bangladesh, European Union, USA (California) | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Harming academic integrity through plagiarism and cheating; copyright infringement from AI-generated content; data breaches and misuse of sensitive student information; perpetuation of biases through AI algorithms; job displacement due to AI automation; blurring of lines between authentic skill and machine-generated output; lack of transparency/explainability in AI conclusions. |
| Healthcare__A_Growing_Role_for_Large_Language_Models_and_Generative_AI3.pdf | Google_Scholar | The Expanding Function of Generative AI and Large Language Models in Healthcare | This preprint surveys the application of generative AI (GAI) and large language models (LLMs) in healthcare, covering techniques like GANs, VAEs, biomedical transformers, and multimodal models. It discusses their use in medical text analysis, image analysis, diagnosis support, and drug discovery, while also highlighting existing tools, benchmarks, challenges, and ethical considerations. | True | NaN | True | 3.0 | Positive | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Healthcare / Medical Law | International | Various publicly available and proprietary datasets including electronic health records (EHRs), biomedical literature (PubMed, scientific articles), clinical notes, medical images (MIMIC-CXR), biobanks (UK Biobank), and specific annotated biomedical NLP corpora (e.g., BC5-chem, NCBI-disease). Data types include unstructured text, structured data, images, genomics, and sensor data, largely domain-specific to healthcare and biomedicine. | NaN | Describes various deployment strategies including commercial tools, EHR integration, and public model repositories (e.g., Hugging Face). | True | True | Mentions publicly accessible tools (ChatGPT, Google Bard, DALL-E 2, Midjourney, Amazon Transcribe) and open-source/publicly released models (e.g., PathologyBERT on HuggingFace, PMC-LLaMA, ClinicalBERT, BioBERT). | NaN | Need for large diverse domain-specific data, data privacy/security, model interpretability, mitigating bias, regulatory hurdles, workflow integration, model robustness (hallucinations, instruction sensitivity), need for human oversight, computational resources. | Data privacy violations (PHI), biased/unfair diagnosis or treatment, generation/spread of misinformation, patient harm from inaccurate AI, legal liability, misuse for creating deceptive content, over-reliance, plagiarism/academic integrity issues. |
| 7j3b1GMYe48J.pdf | Google_Scholar | Integrating ChatGPT , Bard , and leading -edge generative artificial intelligence in \nbuilding and construction industry : applications, framework, challenges, and future \nscope | This paper reviews the diverse applications of generative AI models like ChatGPT and Bard across various stages of the building and construction lifecycle, including project management, design optimization, risk management, and safety monitoring. It proposes a high-level conceptual framework for integration and discusses implementation challenges, ethical considerations, and future potential. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Construction Law (implicitly, through discussions of compliance, risk, contracts), but primarily focuses on Construction Management and Engineering. | NaN | NaN | Conceptual Framework Development, Literature Review, Bibliometric Analysis. | NaN | False | False | NaN | NaN | Technical Integration (with existing construction software), Domain-specific Understanding (need for construction context/jargon), Safety and Compliance (ensuring AI adheres to regulations), Real-time Collaboration support, Data Privacy and Security, Training and Adoption by industry professionals. | Misinterpretations leading to errors in communication/decision-making, failure to adhere to safety guidelines leading to hazards, data privacy breaches, unauthorized data access, legal implications, compromise of project integrity. |
| 3T7NSdWW0p4J.pdf | Google_Scholar | Natural Language Processing in the Legal Domain | This paper surveys the field of Natural Language Processing applied to law (Legal NLP) over the past decade (2013-2022), analyzing trends in publication volume, tasks, methods, languages, and reproducibility based on a corpus of over 600 papers. It highlights increasing sophistication, methodological alignment with general NLP, and improved data/code sharing, while noting remaining challenges and future directions like legal text generation. | True | NaN | True | 3.0 | Positive | NaN | NaN | NaN | Complexity of legal language, culture of law (lawyers, judges, educators), regulation of the legal profession. | Application of NLP/AI technologies (especially modern LLMs) to process legal text, potentially enabling digital justice and justice at scale. Proposed creation of a 'living survey' for ongoing knowledge management in the field. | Improving delivery of justice, meeting legal needs, reducing case backlogs, improving access (mentioned broadly as context/motivation). | NaN | Broad coverage across various legal fields (constitutional, environmental, IP, labor, corporate, immigration, criminal, tax, family, maritime, contract law, patent law, human rights law, etc.). | International (mentions papers covering English, Chinese, Japanese, French, German; specific examples from ECHR, US, Germany, Singapore, Brazil). | The survey itself analyzes a corpus of >600 NLP & Law papers (peer-reviewed journals, conference proceedings, pre-prints) collected via targeted searches and snowball sampling. The papers *within* the surveyed corpus use various legal text datasets. | Corpus construction, qualitative review and categorization (tasks, reproducibility), keyword frequency analysis over time, citation analysis (including normalization), proposal for a 'human-in-the-loop' interactive living survey. | Proposal for an interactive 'living survey' website (availability stated as 'Coming Soon'). The survey data itself is planned for release on GitHub. | True | True | Survey data (corpus metadata, analysis results) claimed to be available on GitHub upon publication. | Understanding the comparative effects of domain-specific vs. large general models, data availability/sharing (though improving), effective application/integration of NLP into legal products/workflows, exploration of legal text generation, need for further research on training data effects, model architectures, and modeling techniques. | For the survey: Building a comprehensive corpus, qualitatively categorizing diverse papers, tracking evolving methods, assessing reproducibility across papers. For the field surveyed: Processing complex legal language, data availability and reproducibility, effectively adapting general NLP advancements to the specific legal domain. | Mentioned critique of LLMs ('dangers of stochastic parrots') in the general NLP context, but no specific risks for Legal NLP applications were detailed in this survey paper. |
| LX4k-4hBp0EJ.pdf | Google_Scholar | From Knowledge Management to Intelligence Engineering - A practical approach to building AI inside the law-firm using open-source Large Language Models | This paper explores options for law firms to build AI, advocating for a 'Creator Customiser Posture' that uses open-source LLMs fine-tuned with internal data to address privacy concerns. It introduces 'intelligence engineering' as an extension of knowledge management and demonstrates this approach with a proof-of-concept on contract data. | True | Market | True | 1.0 | Positive | The 'Creator Customiser Posture' for in-house AI development in law firms, involving layered fine-tuning (unsupervised and instruction-response) of open-source LLMs on internal and open-source legal data, and 'intelligence engineering' for knowledge management. | Proof-of-concept using Cerebras-GPT (590M variant) fine-tuned on combined text (1.8M tokens) from CUAD and MAUD datasets for unsupervised fine-tuning, and 8,000+ instruction-response pairs from CUAD for instruction fine-tuning. Evaluation was via perplexity scores on an 80:20 train/test split and qualitative analysis of outputs. | Unsupervised fine-tuning reduced perplexity on the test set from 8.33 to 4.68. Subsequent instruction fine-tuning reduced perplexity on its test set from 14.09 to 3.43. This demonstrated feasibility with reasonable data volumes, cost, and time. | NaN | NaN | NaN | NaN | Contract law, Corporate law (mergers and acquisitions) | US (based on datasets and examples like 'State of New York' law) | Base model (Cerebras-GPT) pre-trained on 'The Pile'. For fine-tuning: 1) Unstructured text (1.8M tokens) from public domain contract datasets (CUAD, MAUD) for unsupervised fine-tuning. 2) 8,000+ structured instruction-response pairs mined from the CUAD dataset for instruction fine-tuning. | The paper proposes the 'Creator Customiser Posture', which involves selecting an open-source foundational LLM, bringing it in-house, and performing layered fine-tuning: first, unsupervised fine-tuning on a domain-specific corpus, followed by instruction-response fine-tuning with structured task-specific data. It also introduces 'intelligence engineering' to enhance knowledge management. | The fine-tuned model is intended to be served internally within a law firm, potentially using on-premise or cloud compute resources, to be accessed by internal applications. | False | False | NaN | NaN | Requires internal infrastructure management and specialized AI skillsets for the 'Creator Customiser Posture'. Further quantitative evaluation on downstream tasks and investigation into the effect of model parameter size on performance are needed. | The paper highlights that other AI adoption postures (e.g., using vendor-managed services, sharing data with external vendors) raise risks related to data privacy, security, confidentiality, and IP ownership of models. The proposed 'Creator Customiser Posture' aims to mitigate these risks. |
| eL1CqE7Zs-EJ.pdf | Google_Scholar | ChatGPT and the Future of Legal Services | This article discusses the potential of ChatGPT to transform legal services in India by improving efficiency in research, drafting, and case prediction for advocates. It also highlights ChatGPT's potential to enhance public access to legal information and advice, particularly in underserved rural areas. | True | Market | True | 3.0 | Positive | ChatGPT | NaN | NaN | Limited access to legal services, particularly in rural areas. Difficulty for the public in finding and interpreting legal information through traditional means. | Utilizing ChatGPT for legal research, document drafting, case outcome prediction, and providing legal advice/information to the public. Adapting technology like machine learning for tasks such as translating court judgments. | Access to legal information, Access to legal advice | Rural populations, General public | General Law | India | NaN | NaN | NaN | True | False | ChatGPT is generally available as a service provided by OpenAI. | Need for continuous development and updates of the technology to ensure accuracy and keep pace with legal changes. Need to ensure ethical and responsible use. | Need for legal professionals (advocates) to adapt to the new technology to remain relevant. Ensuring ethical and responsible use of the technology. | NaN |
| FCFL8LhLaeMJ.pdf | Google_Scholar | COMMENTS IN RESPONSE TO THE GOVERNMENT OF CANADA ’S \nCONSULTATION QUESTIONNAIRE ON COPYRIGHT IN THE AGE OF GENERATIVE \nARTIFICIAL INTELLIGENCE | This paper responds to a Canadian government consultation, arguing against expanding copyright law to restrict Text and Data Mining (TDM) for AI training or to grant authorship to AI-generated content. It advocates for legal clarity favoring TDM (e.g., via fair dealing or exceptions) and maintaining the requirement of human authorship for copyright protection. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Lack of legal clarity on Text and Data Mining (TDM) under copyright law chills AI research and development; potential copyright restrictions might impede access to comprehensive training data, leading to biased or lower-quality AI; impossibility and inefficiency of clearing rights for vast training datasets; risk of copyright being expanded based on industry lobbying ('copyright trap') rather than public interest. | Amend the Copyright Act to clarify that TDM/informational analysis is permissible (e.g., new exception, broadening fair dealing); reject copyright protection for AI-generated works lacking human authorship; maintain human authorship requirement; apply existing infringement doctrines carefully; avoid specific remuneration rights for TDM training data use; focus copyright policy on public interest balance, not solely industry incentives. | Legal information summarization; Empirical legal research | NaN | Copyright Law | Canada | NaN | NaN | NaN | False | False | NaN | Lack of legal clarity regarding Text and Data Mining (TDM) permissibility under Canadian copyright law; potential difficulties in applying liability frameworks when AI outputs infringe copyright without clear human control; discrepancy between balanced public interest goals of copyright and industry-focused framing of policy debates. | Applying existing copyright concepts (authorship thresholds for AI-assisted work, substantial similarity and causality for infringement, authorization liability) to AI contexts; designing clear TDM exceptions that balance innovation and rights; avoiding biased AI outcomes potentially caused by restricted training data; practical impossibility of tracking/remunerating individual works in massive datasets. | Copyright restrictions chilling AI research and development; decreased AI quality/fairness due to biased/incomplete data; stifling human creativity by granting copyright to vast amounts of AI-generated content; undue expansion of copyright driven by lobbying; ineffective/burdensome TDM licensing; unethical uses of generative AI (e.g., misinformation, academic dishonesty); reduced competition and transparency in the AI field. |
| OTFgKz00ph8J.pdf | Google_Scholar | Automatic Linking of Judgements to UK Supreme Court Hearings | This paper describes J-HAL, an AI system using customized GPT embeddings to automatically link segments in UK Supreme Court written judgements to relevant timespans in court hearing videos. The goal is to create a user interface that bookmarks relevant video segments, improving access for legal professionals, academics, and the public. | True | Idealistic | True | 1.0 | Positive | Information Retrieval system (J-HAL) using customized OpenAI GPT embeddings (text-embedding-ada-002) to calculate semantic similarity between judgement paragraphs and hearing transcript segments. | Compared multiple IR models (BM25, GloVe, Entailment, Legal BERT, Asymmetric Search, GPT) on a human-annotated dataset of 3620 judgement-transcript segment pairs derived from 7 UK Supreme Court cases. Evaluated using Mean Average Precision (MAP) and Recall @ 5, 10, 15. Optimized GPT embeddings were evaluated by comparing cosine similarity distributions. | Customized GPT embeddings performed best. The overlap between cosine similarities for relevant and irrelevant links improved from 70.5% +/- 2.7% (original GPT) to 73.0% +/- 2.6% (customized GPT). Original GPT achieved MAP@5 of 0.691 and Recall@15 of 0.914 on the full dataset. | Court hearing recordings are extremely long, making manual review inefficient. Existing transcription methods make navigating recorded arguments difficult. | An automated tool (J-HAL) that uses AI to semantically link written judgments to specific timespans (bookmarks) in the corresponding hearing videos, facilitating navigation and understanding. | Access to court proceedings; Understanding judicial decision-making; Navigating legal audiovisual recordings. | Legal professionals, academics, and the general public. | UK Supreme Court cases (covering various fields, particularly public and constitutional law). | United Kingdom | Judgements (7 cases, 1.4M tokens) scraped from UK Supreme Court website; Video transcripts (53 hours) from UK National Archive transcribed via custom ASR; Pretrained embeddings (GloVe, MiniLM, Legal BERT, MS MARCO, OpenAI GPT); Human-annotated dataset of 3620 judgement-transcript pairs for evaluation and GPT customization. | Information Retrieval; Comparative evaluation of IR models; Zero-shot IR followed by human annotation; Embedding customization via classification model training and cosine similarity threshold optimization; User Interface development. | Deployed as a User-Interface (UI) platform. Mentioned application for a UK patent based on the UI. | False | False | NaN | Need for larger annotated datasets; Potential for more granular linking based on legal entities (articles, provisions, case names); Applicability to other domains needs exploration. | Difficulty of creating large-scale human annotations; Linking text across different language registers (written vs. spoken); Data preprocessing (segmentation, filtering); Balancing IR performance and computational speed. | NaN |
| xI22v_VkAogJ.pdf | Google_Scholar | Hallucinations and Truth:A Comprehensive Accuracy Evaluation of RAG,LoRA and DoRA | This paper empirically evaluates and compares Retrieval-Augmented Generation (RAG), Low-Rank Adaptation (LoRA), and Weight-Decomposed Low-Rank Adaptation (DoRA) using extensive FAQ-based datasets. The study finds that DoRA significantly outperforms RAG and LoRA in accuracy, relevance, and latency, making it a promising approach for accuracy-critical, domain-specific generative AI applications. | True | Market | True | 2.0 | Positive | Comparative evaluation of RAG, LoRA, and DoRA focusing on their performance in NLP tasks, with DoRA highlighted for its superior accuracy and efficiency. | Model fine-tuning and generation performance assessed on 20,000 FAQ-based queries/technical service tickets, with a knowledge base of 400,000 technical troubleshooting FAQs. Metrics included accuracy, relevance, latency, BLEU, ROUGE, MRR, NDCG. Generative output quality also evaluated on the SQuAD dataset. | DoRA achieved the highest accuracy (90.1%), a relevance score of 0.88, the lowest latency (110 ms per query), BLEU-4 score of 52.6, ROUGE-L score of 65.8, and a hallucination rate of 6.8%. | NaN | NaN | NaN | NaN | General legal services, legal research, legal document analysis. | International | A large-scale dataset of 400,000 technical troubleshooting FAQs (used as knowledge base and for fine-tuning LoRA/DoRA). Evaluation on a separate test set of 20,000 questions/technical service tickets. Publicly available SQuAD dataset also used for evaluating generative output quality. | NaN | NaN | False | False | NaN | Need for enhanced alignment with user expectations (e.g., via RLHF), multimodal capabilities, more efficient architectures, and addressing ethical concerns like bias and explainability. | Hallucinations, retrieval misalignment, cost-performance trade-offs, parameter selection, dataset quality, fine-tuning optimization, scalability, potential overfitting, and adaptation stability. | Generating factually incorrect outputs (hallucinations), retrieval errors leading to inaccuracies, misinformation, potential bias in AI models. |
| KjEhfsM_c_sJ.pdf | Google_Scholar | Enhancing Judicial Efficiency: The Role of AI and Blockchain in Modernizing Legal Systems | This paper explores how AI and blockchain technologies can improve judicial efficiency by addressing challenges like delays and backlogs. It reviews current applications, proposes an integrative framework, discusses associated risks and ethical considerations, and notably uses an AI pipeline involving generative AI for its own creation. | True | Idealistic | True | 3.0 | Positive | AI (for case management, legal research, predictive analysis), Blockchain (for record management, security, transparency), Smart Contracts (for automating agreements) | NaN | NaN | Judicial inefficiencies including procedural delays, case overload/backlogs, excessive costs, lack of transparency, and bureaucratic bottlenecks. | Integration of AI (for automation, predictive analysis), Blockchain (for secure, transparent records), and Smart Contracts (for automated agreements) within a strategic framework to streamline procedures and enhance efficiency. | Judicial efficiency, Case management, Reducing delays and backlogs, Transparency in judicial processes, Secure record-keeping, Access to justice | General public, potentially marginalized groups | General judicial processes | International | The paper reviews techniques using data like legal documents, case histories, and judicial records. The AI pipeline used for writing relied on the general large datasets of LLMs like ChatGPT. | An AI Pipeline utilizing tools like ChatGPT 4o, Perplexity, Consensus, Elicit, Zotero plugins, and Grammarly for topic selection, literature review, structuring, writing, and refinement. | NaN | False | False | NaN | Ethical concerns (privacy, bias, AI opacity), technical challenges (interoperability, skill development), resource constraints, need for human oversight, limitations of smart contracts complexity, underutilized opportunities (ADR, access to justice platforms, training tools). | Challenges implementing AI/Blockchain: Ethical issues, technical barriers (interoperability), resource needs, legal compliance, security. Challenges using AI pipeline for writing: Difficulty generating original reasoning, AI defaulting to reproduction, robustness of insights, managing context windows, tool instability (e.g., ChatGPT Canvas). | Ethical risks (privacy violations, algorithmic bias, lack of transparency/accountability), security risks (data manipulation/breaches if not secured), over-reliance on AI, potential for deskilling, technical failures, poor interoperability. |
| K9RrIhC9DNcJ.pdf | Google_Scholar | AI Luddites: Consumers Penalize Creative Work Output Generated by Arti cial Intelligence | This paper investigates consumer reactions to creative work (e.g., posters, scripts, logos) produced by generative AI versus humans through five experiments. It finds that consumers significantly penalize AI-generated creative output, particularly those holding 'Luddite' beliefs concerned about machines displacing humans, attributing this to a perceived lack of essential human process in creation. | True | Market | False | 3.0 | NaN | NaN | NaN | Participants significantly penalized creative works (posters, scripts, logos) when informed they were AI-generated compared to human-generated or baseline conditions. This negative reaction (penalization) was significantly stronger among individuals with higher 'Luddism' scores, linked to valuing the human creative process. | Negative consumer perception and penalization of AI-generated creative work, rooted in beliefs that AI lacks the appropriate 'human touch' or process for creativity, and concerns about job displacement (Luddism). | The paper highlights the difficulty in overcoming negative perceptions, noting that experimental interventions (educating about AI collaboration, co-creation exercises, transparency statements, premium 'human-made' labels) were unsuccessful. It calls for future research and proactive engagement from businesses and policymakers on AI integration strategies. | NaN | NaN | Briefly touches on disclosure (consumer protection implication) and labor issues (job security). | International (focus primarily on consumer reactions analogous to Western markets, e.g., US references) | NaN | Experimental design (between-subjects, pre-registered), surveys (MTurk, Prolific), statistical analysis (t-tests, regression, mediation analysis). | NaN | False | False | NaN | Need for effective interventions/communication strategies to mitigate negative consumer reactions to AI creativity; uncertainty about the future role of human creative professionals. | Consumer resistance and penalization of AI-generated creative work, particularly from those holding Luddite views. | Negative impact on brand image, perceived corporate unethical behavior, job displacement for creative professionals, societal disruption. |
| ZRf7TqNsvaYJ.pdf | Google_Scholar | Large Language Models (LLMs) for Legal Advice: A Scoping Review | This paper provides a scoping review of the use and potential use of Large Language Models (LLMs) for generating legal advice, focusing on the US and UK jurisdictions. It synthesizes literature on the benefits, such as reduced costs and improved access, and significant risks, including misinformation (hallucinations), bias, copyright issues, and the need for regulation. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | High cost of traditional legal services; Risk of LLMs providing false/misleading information (hallucinations); Potential for LLMs to encourage vexatious litigation, delaying justice for others; Lack of attorney-client privilege and ethical guarantees with LLMs; Privacy risks associated with LLM data use; LLM biases reinforcing societal inequalities. | Improving LLM accuracy (e.g., linking to verified sources); Developing "justice bots" to help laypeople navigate legal issues; Using LLMs to translate legal jargon into plain language; Implementing technical safeguards (watermarking, fine-tuning, censoring); Establishing clear regulations and industry standards (e.g., transparency obligations, risk-based approaches); Educating users (lawyers and public) about LLM limitations. | Reducing legal costs, Legal information access and understanding (plain language), Issue identification for laypersons, Assistance for self-represented litigants. | People with lower socio-economic status, General consumers facing corporations or bureaucracy. | Broad / Multiple fields including Civil litigation, Tax law, Contract law, Criminal law (sentencing/probation context), Consumer law. | US, UK | General LLMs: Terabytes of broad internet data, potentially including copyrighted materials. Legal-specific LLMs: Fine-tuned on legal text databases (cases, legislation) from sources like Westlaw, LexisNexis, Casetext, or proprietary curated legal/financial datasets (e.g., KL3M). | Sandbox testing, User evaluation, Red-teaming (for Harvey); Benchmarking using curated legal task datasets (LawBench, LegalBench); Reinforcement Learning with Human Feedback (RLHF) for alignment; Ontology creation from legal concepts (older ML example). | Internal deployment within law firms (e.g., Harvey); Public web/app access for consumers (e.g., DoNotPay, ChatGPT); Planned commercial release for industry professionals (e.g., KL3M); Research platforms/benchmarks. | True | False | Public access via web interfaces/APIs (e.g., ChatGPT, Gemini) some with free tiers; Consumer service model (e.g., DoNotPay). | Need for systematic empirical evaluation of LLM legal advice quality and user perception; Understanding and mitigating cross-jurisdictional/cultural biases; Continuous evaluation due to rapid model evolution; Need for qualitative research on lawyer adoption/experience; Legal clarification on AI copyright (input and output); Gaps and inconsistencies in regulatory approaches (US/UK). | Ensuring accuracy / mitigating hallucinations; Mitigating dataset bias; Navigating copyright complexities (training data and generated output); Implementing effective and robust safeguards (alignment, preventing misuse); Managing data poisoning and model collapse risks; Addressing the 'black box' transparency problem; Managing user expectations and avoiding misleading claims. | Generating false or misleading legal information (hallucinations); Wasting court resources and causing delays; Undermining trust in the legal system; Encouraging vexatious litigation; Lack of attorney-client privilege and confidentiality; Disclosure of private user data; Embedding and amplifying societal biases; Copyright infringement (input data and generated output); Data poisoning and pollution leading to model degradation; Circumvention of safety guardrails (jailbreaking). |
| Enhancing_the_Precision_and_Interpretability_of_Retrieval-Augmented_Generation_RAG_in_Legal_Technology_A_Survey.pdf | Google_Scholar | Enhancing the Precision and Interpretability of Retrieval-Augmented Generation (RAG) in Legal Technology: A Survey | This paper surveys the application of Retrieval-Augmented Generation (RAG) techniques within the legal technology domain, reviewing methods, applications, datasets, and evaluation metrics. It highlights challenges such as hallucination and computational cost, discusses ethical considerations, and proposes future research directions for improving RAG systems in legal contexts. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | Technical challenges hindering RAG application in law: Computational cost and complexity, potential for hallucination or no response, difficulty handling complex legal queries, heavy dependence on retrieval accuracy, and limitations of current evaluation metrics. | Improving RAG performance through technical strategies: Advanced pre/post-retrieval optimization (e.g., query rewriting, reranking, KG integration, adaptive chunking), hybrid retrieval approaches, adaptive retrieval mechanisms, fine-tuning models on legal corpora, and advanced sampling strategies. | NaN | NaN | Privacy law, legislative texts, public law, criminal law, statutory law, immigration law, contract law, case law, patent law, tax law, border inspection law. | Multiple specific jurisdictions (US, China, Australia, EU, France, Italy, Montenegro, Pakistan) and general applicability. | Discusses various datasets used in surveyed papers, including public and private sources like legal judgments, case law repositories (e.g., Caselaw Access Project), legislative texts, court records, contracts, privacy policies, EU laws (EUR-Lex), patent documents, and domain-specific Q&A pairs. Data includes structured and unstructured text. | Discusses various RAG pipeline design choices: embedding models (BERT-based, OpenAI, multilingual), retrieval methods (sparse, dense, hybrid), retrieval processes (one-time, iterative, adaptive), chunking strategies (sentence, semantic, pattern-based), augmentation (prompt engineering), generation models (GPT, Llama), fine-tuning (QLoRA, full), knowledge graph integration, reranking algorithms, query rewriting, and indexing techniques (HNSW). | NaN | False | False | NaN | Need to expand RAG applications to more legal domains and jurisdictions (especially non-English); lack of robust, open-source benchmark datasets; need for better multilingual RAG techniques; requirement for standardized RAG evaluation metrics (including interpretability and ethics); limited exploration of integrating RAG with other AI methods like reinforcement learning; insufficient attention to ethical considerations (privacy, bias, safety, trust) in existing systems. | Computational cost and complexity (API usage, in-house LLM maintenance); achieving robustness against hallucination and failure to respond (addressing RAG failure points); handling complex, ambiguous, or multi-hop legal queries; high dependence on the accuracy and relevance of the retrieval step; limitations and lack of standardization in evaluation metrics for factual correctness and semantic quality. | Bias propagation from data or models, privacy violations through handling sensitive legal data, generating hallucinated or factually incorrect legal information/advice, safety concerns arising from unreliable outputs, lack of transparency and accountability in RAG system decisions. |
| ck8Ac0neujYJ.pdf | Google_Scholar | AI and access to justice : How AI legal advisors can reduce economic and shame-based barriers to justice | This paper argues that publicly funded Artificial Intelligence Legal Advisors (AI LAs), particularly large language models specialized for law, can lower barriers to accessing the legal system. It focuses on how these tools can mitigate economic costs and shame-based cultural obstacles during the initial information-gathering stage of pursuing justice. | True | Idealistic | True | 3.0 | Positive | Artificial Intelligence Legal Advisors (AI LAs), described as specialized AI systems (potentially LLMs) providing legal information and preliminary assessment. | NaN | NaN | Economic barriers (financial costs, time/opportunity costs, transportation costs, lack of resources, lack of awareness of rights or affordable legal options) and shame-based cultural barriers (stigma associated with seeking legal help, particularly for victims of intimate partner violence, individuals disputing cultural norms like inheritance practices, or victims of fraud; fear of judgment or social reprisal). | Developing and deploying publicly funded AI Legal Advisors (AI LAs) that offer reliable, specific, and intelligible legal information, preliminary case assessment, and interactive explanations. This aims to reduce costs and provide a private, non-judgmental means of information gathering. | Access to legal information, preliminary case assessment, understanding legal rights and recourse, reducing barriers during the information-gathering stage. Specific examples include intimate partner violence (IPV) protection orders, inheritance rights disputes, and pursuing claims related to fraud. | People with low socio-economic status (SES), marginalized populations facing cultural barriers (e.g., women expected to relinquish inheritance rights), victims of intimate partner violence (IPV), victims of fraud. | General Civil Law, Housing Law (example: JusticeBot), Family Law (IPV context), Inheritance Law, Consumer Law (Fraud context). | Anglo-American common law systems (stated scope). Examples also draw from the US, UK, Canada, and Quebec (JusticeBot). | Implied to be case law, noted as potentially containing historical biases. | NaN | Advocates for public funding by governments and international organizations. | False | False | NaN | Ensuring AI LAs reach reliability and accuracy standards comparable to human lawyers. Developing methods to mitigate biases present in legal training data (case law). | Achieving sufficient reliability and accuracy for AI LAs. Mitigating inherent biases in training data. Potential for increased caseloads on the existing legal system. Establishing frameworks for legal responsibility and liability for AI errors. | AI LAs inheriting and perpetuating biases from historical case law. Potential for increased litigation burdening the legal system. AI LA malfunction or error leading to incorrect advice (e.g., dissuading a valid claim or encouraging a futile one), causing harm to users. Difficulty in assigning legal responsibility for harms caused by AI advice errors. |
| 2501.00957v3.pdf | Google_Scholar | Generative AI and LLMs in Industry: A text-mining Analysis and Critical Evaluation of Guidelines and Policy Statements Across Fourteen Industrial Sectors | This paper analyzes 160 guidelines and policy statements concerning Generative AI (GAI) and Large Language Models (LLMs) across fourteen industrial sectors using text-mining techniques. It identifies key governance themes, sector-specific variations, and gaps, proposing recommendations for adaptive, ethical, and human-centric AI integration in industry. | True | Market | True | 3.0 | NaN | Text-mining analysis (tokenization, stemming, lemmatization, TF-IDF, KMeans clustering, Sankey diagrams) and qualitative thematic analysis applied to AI policy documents. | Analysis of 160 GAI/LLM guidelines and policy statements collected from companies across 14 industrial sectors globally. Evaluation involved qualitative semantic analysis, frequency analysis, TF-IDF heatmap analysis, and Sankey diagram keyword co-occurrence analysis. | Identified common themes (e.g., privacy, data, risk, ethics, integrity) and sector-specific concerns across industries. Revealed gaps in guidelines regarding disclosure, human-centricity, democratization, alternative methods, misinformation, and skepticism. Highlighted varying levels of AI adoption maturity and governance approaches across sectors. | NaN | NaN | NaN | NaN | General AI Governance, Policy Analysis, Legal Tech / Legal Services / Intellectual Property Law (as one of the 14 sectors analyzed) | International | A dataset ('IGGA') of 160 industrial guidelines and policy statements for GAI/LLMs collected by the authors from company websites, official documents, and media interviews, covering 14 sectors across multiple continents. Claimed to be available on Harvard Dataverse. Consists of unstructured text. | Systematic document collection, Qualitative thematic analysis, Text-mining (tokenization, stopword removal, stemming, lemmatization, TF-IDF, KMeans clustering), Visualization (Sankey diagrams, frequency plots). | NaN | False | False | NaN | NaN | Data collection: Identifying companies with official guidelines, substituting with policy statements or interviews when guidelines were absent, ensuring diversity across sectors/geographies despite exclusions. | Data security breaches, misinformation generation, algorithmic bias, intellectual property infringement, cybersecurity vulnerabilities, job displacement, erosion of human oversight, privacy violations, lack of fairness and accountability, safety risks (e.g., healthcare, construction), ethical risks, marketing hype obscuring limitations and risks, lack of transparency. |
| BGBNDfe58egJ.pdf | Google_Scholar | Multidisciplinary collaboration : key players in successful implementation of ChatGPT \nand similar generative artificial intelligence in manufacturing, finance, retail, \ntransportation, and construction industry | This paper argues that successful implementation of ChatGPT and similar generative AI across industries like manufacturing, finance, retail, transportation, and construction requires collaboration among multidisciplinary teams. These teams, comprising experts like AI specialists, domain experts, ethicists, legal professionals, and UX designers, are essential for navigating technical, operational, ethical, and regulatory challenges. | True | Market | True | 3.0 | NaN | Multidisciplinary collaboration strategy for implementing generative AI (e.g., ChatGPT) in industry. | NaN | NaN | NaN | NaN | NaN | NaN | Data Privacy Law, AI Ethics Regulation, Regulatory Compliance (Industry-specific), Intellectual Property Law | International | Industry-specific data relevant to the target sector (e.g., manufacturing processes, financial terminology/regulations, retail customer data/transactions, transportation logistics, construction plans/codes/historical data). The paper discusses the need for such data but does not specify a dataset used. | User Experience (UX) design, Human-Computer Interaction (HCI) principles. | Integration into existing IT infrastructure, development of user interfaces/APIs, training programs for workforce, change management strategies, cybersecurity protocols implementation. | False | False | NaN | NaN | Communication gaps between diverse team members, addressing ethical implications (data privacy, algorithmic bias, job impact), keeping up with rapid AI evolution, managing data security concerns, overcoming resistance to technological change, need for continuous training and skill development. | Data security vulnerabilities (data breaches, cyber threats), bias in AI-generated decisions, ethical violations (privacy concerns), legal and regulatory non-compliance, negative impact on employment, potential for errors leading to operational or physical risks (e.g., structural issues in construction). |
| mdjWtUfQe6AJ.pdf | Google_Scholar | The Legal Ethics of Generative AI | The paper argues that lawyers can ethically use generative AI by following existing Model Rules, particularly regarding confidentiality, client consultation, competence, and oversight. It criticizes recent court orders banning or requiring disclosure of AI use as unnecessary and overbroad, and posits that competence may eventually require lawyers to use generative AI. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | High cost and lack of availability of traditional legal services leading to unmet legal needs (implied by the mention of the 'access-to-justice crisis' and AI's potential to help). | Suggests generative AI could become an important tool for addressing unmet legal needs and the access-to-justice crisis. | General access to justice crisis, unmet legal needs. | NaN | Legal Ethics, Civil Procedure | United States | NaN | NaN | NaN | False | False | NaN | The general 'access-to-justice crisis' and 'unmet legal needs'. Technologically, the tools are still evolving, their reliability needs improvement (e.g., hallucinations), and use cases are still emerging. A need for mandatory training on generative AI for law students and lawyers is also suggested. | NaN | Violation of client confidentiality (Rule 1.6), inaccuracy and hallucinations in AI output leading to flawed legal work or filings (violating Rule 1.1, Rule 3.1, FRCP 11), inherent bias in AI models, unauthorized practice of law, issues related to duties to prospective clients (Rule 1.18), incorrect billing or fee arrangements (Rule 1.5). |
| ti1sOnOBim4J.pdf | Google_Scholar | LEGILM: A F INE-TUNED LEGAL LANGUAGE MODEL FOR DATA COMPLIANCE | This paper introduces LegiLM, a legal language model derived from SaulLM-7B and fine-tuned on GDPR-specific data to automatically assess data protection compliance in contracts. Evaluated on a custom benchmark, LegiLM outperformed baseline models in accuracy and justification quality for GDPR compliance tasks. | True | Market | True | 1.0 | NaN | LegiLM: A legal language model based on fine-tuning SaulLM-7B for GDPR compliance detection in data-sharing contracts using instruction tuning and contrastive learning. | Custom benchmark created from GDPR texts, case law, data-sharing contracts, and privacy policies. The benchmark included 200 multiple-choice questions, 150 open-ended questions, and 50 real-world case studies. Metrics used were Accuracy, F1-Score, and Compliance Justification Quality, compared against models including Saul-7B, GPT-4, and various Chinese legal LLMs. | LegiLM-Advanced achieved the highest scores: 68.05% Accuracy, 68.21% F1-Score, and 'High' Justification Quality, outperforming Saul-7B (62.10% Accuracy, 63.15% F1-Score) and other baselines. | NaN | Develop domain-specific fine-tuned language models like LegiLM to automate and streamline compliance assessments for data protection regulations (e.g., GDPR), reducing the burden on legal professionals. | NaN | NaN | Data Protection Law, Privacy Law, Contract Law | EU (GDPR focus), USA (mentions CCPA), English-speaking jurisdictions (base model focus) | Fine-tuning dataset includes GDPR text, CCPA text, EDPB guidelines, EUR-Lex interpretations, EU case law, GDPR Fines Database, GDPR Enforcement Tracker dataset, custom-annotated data-sharing contracts, and various privacy policies. Derived from public sources (e.g., official websites, EUR-Lex) and custom creation/annotation. Base model (SaulLM-7B) trained on a large English legal corpus. | Supervised fine-tuning of a pre-trained LLM (SaulLM-7B), instruction tuning, contrastive learning for generating negative examples and improving answer diversity. | Resources made publicly available via GitHub. | True | True | Publicly available on GitHub: https://github.com/DAOLegalAI/LegiLM | Current model is specific to GDPR; future work needed to expand coverage to data protection regulations in other countries and regions. | Ensuring nuanced understanding of complex legal requirements (GDPR), maintaining answer diversity and avoiding bias during fine-tuning. | NaN |
| yU764-jHuYIJ.pdf | Google_Scholar | Do Large Language Model Benchmarks Test Reliability? | The paper argues that current LLM benchmarks fail to adequately measure model reliability due to pervasive label errors, proposing meticulously curated "platinum benchmarks" instead. Evaluating frontier LLMs on these cleaned benchmarks reveals significant remaining failures even on simple tasks and uncovers specific, consistent error patterns. | True | NaN | True | 1.0 | Neutral | Platinum benchmarks: A methodology for creating reliable evaluation datasets by systematically identifying and correcting/removing label errors and ambiguities in existing benchmarks. | Subsets of 15 existing benchmarks (e.g., GSM8K, SVAMP, MMLU Math, SQuAD2.0, VQA v2.0) were manually revised using LLM agreement flagging and inspection. Various frontier LLMs (e.g., GPT-4o, Claude 3.5, Llama 3, Gemini, o1 series) were then evaluated on these cleaned 'platinum' subsets, reporting error counts. | Frontier LLMs still make errors on simple tasks in the cleaned benchmarks (most models failed on most benchmarks). Many original benchmark errors were due to label noise (e.g., ~75% on SVAMP). More generally capable models were more reliable, but reliability varied by task. Consistent failure patterns (e.g., 'first event bias', 'rounding up primes') were identified. | NaN | NaN | NaN | NaN | NaN | International | NaN | Analysis of existing benchmarks, use of multiple LLMs to identify disagreements/errors, manual review and annotation, quantitative evaluation of LLM performance on revised benchmarks. | Release of the created platinum benchmark datasets and associated code on GitHub. | True | True | The platinum benchmark datasets are available via code release on GitHub: https://github.com/MadryLab/platinum-benchmarks | The primary gap identified is the lack of focus on reliability (vs. capability) in LLM evaluation, leading to benchmarks being retired before ensuring models are truly error-free. The paper also notes limitations in its own benchmark coverage, size, and the potential for remaining errors. | The difficulty and resource-intensive nature of creating error-free benchmarks through manual verification. Benchmark noise obscuring true model reliability. Ensuring LLM reliability even on simple tasks remains a challenge for current models. | Deploying unreliable LLMs in high-stakes domains (like legal services, healthcare, finance) due to inadequate reliability evaluation, potentially causing significant harm, financial loss, or legal liability (citing Moffatt v. Air Canada). |
| XurNiV9wTRQJ.pdf | Google_Scholar | Chat Kanoon: A Novel Approach to Legal Assistance in India | This paper introduces ChatKanoon, a multilingual AI chatbot leveraging GPT-4 and Llama2 70B through instructional techniques to provide legal assistance within the Indian legal system. It aims to democratize access to legal information, reduce costs, and enhance the efficiency of legal processes in India. | True | Idealistic | True | 1.0 | Positive | ChatKanoon: A multilingual AI chatbot using GPT-4 and Llama2 70B APIs via instructional techniques (not traditional fine-tuning), guided by Indian legal documents and case laws. | Descriptive evaluation through example user interaction scenarios and UI demonstrations with sample prompts in multiple Indian languages (e.g., Marathi, English) and corresponding system responses for legal queries. | The paper claims ChatKanoon successfully provides detailed and accurate legal information and advice in response to queries on topics like cyberbullying and distinctions between civil/criminal law, in multiple languages, based on example scenarios. | Limited access to legal information and assistance, high costs of legal services, complexity of legal procedures and laws, linguistic diversity challenges, scarcity of specialized legal guidance, and urban-rural disparities in legal service accessibility. | Developing and deploying AI-powered, multilingual chatbots like ChatKanoon, tailored to specific legal contexts (e.g., Indian law), to provide accessible, affordable legal information, simplify understanding of legal concepts, and enhance the efficiency of legal processes. | Access to legal information and advice, legal literacy and education, cost reduction for legal services, efficiency in legal processes, multilingual legal support. | General public in India, particularly economically weaker sections, low-income earners, those in rural areas, and individuals facing language barriers to accessing legal information. | General Indian Law, with examples from cyberlaw, civil law, and criminal law. | India | Utilizes pre-trained foundation models (GPT-4, Llama2 70B APIs). Instructional techniques are applied, informed by a 'diverse array of legal documents and case laws' from the Indian legal system, as opposed to fine-tuning the models. | Application of instructional techniques to pre-trained LLM APIs (GPT-4, Llama2 70B). System architecture built with Next.js (React) for the front-end, Node.js for server-side logic, employing a component-based design. | Hosted and deployed on the Vercel platform. | False | False | NaN | Technical gaps include high computational needs for LLMs, ensuring predictable and user-controlled outputs, refining instructional guidance precision, achieving comprehensive regional language support, and enhancing document processing capabilities. Societal and ethical gaps involve addressing user data privacy/security and the ongoing need for human oversight and verification of AI-generated legal advice. | High computational requirements for the large language models (GPT-4, Llama2 70B), ensuring model outputs are predictable and user-controllable, addressing user data privacy and security concerns for sensitive legal queries, effectively guiding LLMs through instructions (instructional techniques), and providing comprehensive support for India's diverse regional languages. | Potential for the AI to generate unexpected or inaccurate legal advice, risks to user data privacy and security if not robustly protected, and the possibility of users over-relying on AI-generated information without seeking verification from qualified legal professionals. |
| 3583780.3614953.pdf | Google_Scholar | Leveraging Event Schema to Ask Clarifying Questions for Conversational Legal Case Retrieval | This paper proposes LeClari, a method using a legal event schema (LEVEN) to improve the generation of clarifying questions by Large Language Models (LLMs) for conversational legal case retrieval. LeClari employs an event selection module optimized with ranking-oriented rewards to guide LLMs, significantly enhancing downstream retrieval performance compared to baseline methods. | True | Market | True | 1.0 | NaN | LeClari: A conversational search model using a legal event schema (LEVEN) for prompt construction and an Event Selection Module (with transformer-based interaction layers) optimized via Reward Augmented Maximum Likelihood (RAML) with ranking-oriented rewards to guide LLMs in generating clarifying questions for legal case retrieval. | Evaluated on two Chinese criminal case retrieval datasets (LeCaRD, CAIL2022-LCR) using simulated conversations with LLMs (ChatGPT, GPT-4) as user simulators. Performance measured by MAP, P@5, NDCG@10 using BERT-Crime and LawFormer as rankers, compared against baselines including direct LLM prompting ('w/o Event') and various event selection strategies (Random, MaxE, GBS, LinRel, GP+UCB/EI). | LeClari significantly outperformed all baselines on both datasets. For instance, on CAIL2022-LCR using GPT-4 + BERT-Crime, LeClari achieved NDCG@10 of 0.7104, compared to 0.6105 for the 'w/o Event' baseline. | NaN | NaN | NaN | NaN | Criminal Law | China | The Event Selection Module was trained using simulated conversational data derived from the LeCaRD and CAIL2022-LCR datasets, incorporating the LEVEN legal event schema (publicly available) as external knowledge. Training involved ranking-oriented rewards based on performance improvement using pre-trained legal language models (BERT-Crime, LawFormer). | Prompt engineering for LLMs, incorporation of external structured knowledge (legal event schema), transformer-based neural networks for interaction modeling, Reward Augmented Maximum Likelihood (RAML) optimization. | NaN | False | False | NaN | Dynamically determining when to stop asking clarifying questions is mentioned as future work. | LLMs directly prompted for clarifying questions in legal case retrieval often produce low-utility or redundant questions. Aligning the question generation process with downstream retrieval performance improvement. | NaN |
| 2hMq09zdNrgJ.pdf | Google_Scholar | Tech -Business Analytics – a Review -based New Model to Improve the Performances of Various Industry Sectors | This paper proposes a new conceptual model called Tech-Business Analytics (TBA), integrating traditional Business Analytics (BA) and Big Data with broader Information, Communication, and Computation Technologies (ICCT). The goal of TBA is to enhance decision-making and improve performance across various industry sectors. | True | Market | False | 1.0 | NaN | Tech-Business Analytics (TBA) model - a conceptual integration of Business Analytics/Big Data with broader ICCT underlying technologies (AI, Cloud, IoT, Blockchain, etc.). | The paper is review-based and proposes a conceptual model; no empirical testing or specific evaluation methodology is described. | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | Literature review, analysis of current status, prediction of desired status, research gap identification, qualitative ABCD analysis framework, conceptual model development. | NaN | False | False | NaN | NaN | General challenges related to implementing analytics solutions include ensuring data quality, managing model complexity, achieving timely results, gaining user trust and confidence, and integrating analytics into organizational capabilities. | Potential misuse of analytics knowledge for discrimination; complexity requiring specialised skills; risks related to data security and ethics (data breaches); potential for AI-integrated analytics leading to opacity, poor judgement, and operational inefficiency (citing Rana et al.). |
| o30m2SrIoEMJ.pdf | Google_Scholar | LEGAL LITERACY AND GENERATIVE ARTIFICIAL INTELLIGENCE: COMPARING THE EDUCATION LAW KNOWLEDGE OF PRACTICING EDUCATORS AND LARGE LANGUAGE MODELS LIKE CHATGPT | This paper compares the education law knowledge of practicing K-12 educators with several large language models (LLMs) like ChatGPT, using a pre-existing true/false survey. It finds that LLMs generally outperform educators but are not infallible, highlighting their potential to supplement, but not replace, educator legal literacy. | True | Idealistic | True | 2.0 | Positive | Evaluation of existing LLMs (ChatGPT GPT-3.5, GPT-4 with/without plugins, Google Bard, Microsoft Bing AI Chat Mode) for education law knowledge. | Zero-shot prompting of LLMs using the 34 true/false questions from the Principals’ Education Law Survey (Militello, Schimmel, & Eberwein, 2009). Performance was compared against established correct answers and historical scores of teachers and principals. | Four out of five LLMs (ChatGPT versions, Bing AI) achieved >70% proficiency (76.47% correct), outperforming average teacher (40.04%) and administrator (58.71%) scores. LLM performance varied by legal topic, scoring highest on constitutional law (80%) and lowest on liability (57.78%). | Educators' lack of legal knowledge and literacy, fear/anxiety towards legal issues, and reliance on potentially inaccurate sources. Limitations of LLMs including inaccuracies ('hallucinations'), inconsistent performance, inherent biases, and unresolved copyright/ownership issues. | Leveraging LLMs as tools to supplement educators' legal knowledge. Developing educators' technological proficiency and legal literacy skills to critically evaluate and verify LLM outputs ('trust but verify'). Rethinking educator preparation programs to incorporate responsible AI use. | Educator legal literacy; K-12 education law topics including student rights (discipline, free speech, general rights), teacher rights (free speech, general rights), liability, religion in schools, special education, school authority, student records, copyright. | K-12 Educators (teachers and school administrators). | Education Law | United States | The paper mentions LLMs are trained on "huge swaths of information from the internet and other sources" but does not provide specific details on the datasets used for the evaluated models (ChatGPT, Bard, Bing). Training data is implied to be vast, unstructured text, and largely proprietary. | NaN | NaN | True | False | The LLMs studied (ChatGPT, Google Bard, Microsoft Bing AI) are generally publicly accessible via web interfaces. | Need for research on LLM reliability and statistical significance of findings. Assessing LLM legal literacy (application) beyond knowledge recall. Updating assessment tools for contemporary legal issues. Understanding LLM training data limitations. Addressing ethical/legal issues (privacy, liability, equity). | Ensuring LLM accuracy and avoiding 'hallucinations'. Achieving consistent results from LLMs. Prompt engineering for specific answer formats. Evaluating models using potentially outdated survey instruments. Limitations due to lack of transparency regarding LLM training data. | LLM 'hallucinations' leading to false legal information and potential negative consequences (e.g., defamation, incorrect legal actions). Copyright infringement issues related to training data. Bias amplification. Over-reliance without critical verification. Student data privacy issues. Potential misuse for academic dishonesty. |
| The_Truth_s_About_AI_and_Legal_Education_A_Discourse_Analysis_of_the_Conflicting_Narratives_Regarding_the_Implications_of_Generative_AI_for_the_Teaching_of_Law.pdf | Google_Scholar | The Truth(s) About AI and Legal Education: A Discourse Analysis of the Conflicting Narratives Regarding the Implications of Generative AI for the Teaching of Law | This paper analyzes the conflicting narratives surrounding the impact of generative AI on legal education. It employs discourse analysis to explore different perspectives on how AI will affect the teaching of law. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal Education | NaN | NaN | NaN | NaN | False | False | NaN | NaN | NaN | NaN |
| 8JE3jvLmRJIJ.pdf | Google_Scholar | NATURALIZING LEGAL INTERPRETATION AFTER GENERATIVE AI | This essay explores how generative AI, particularly LLMs, can be integrated into legal interpretation by aligning with constitutive theories of language and complexity science. It argues for a conceptual framework that harmonizes AI's computational power with the contextual, moral, and emergent dimensions of human legal reasoning. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | The primary obstacle identified is the inadequacy of current AI approaches, often based on simplistic 'designative' views of language, to grasp the complex, contextual, moral, and emergent nature of legal interpretation, leading to biased or superficial outcomes that undermine justice. | Adopting a conceptual framework for legal AI based on constitutive theories of language and complexity science, where AI augments human judgment rather than replacing it, thereby aligning AI with the dynamic and morally-rich nature of law to foster fairer outcomes. | Ensuring AI contributes to justice in legal interpretation and reasoning, potentially enhancing accessibility and efficiency in legal practice. | NaN | General jurisprudence and legal interpretation, with examples from contract, family, tort, constitutional, and criminal law. | Primarily US (due to case law examples), but discusses principles with broader, potentially international, applicability. | NaN | NaN | NaN | False | False | NaN | The primary gap is the inadequacy of current legal AI to truly engage with the moral, contextual, and emergent dimensions of legal reasoning, stemming from a limited philosophical understanding of language and law. This leads to challenges in developing AI that is fair, just, and genuinely supportive of complex legal interpretation, thereby hindering its potential for improving access to justice. | NaN | Key risks include the perpetuation of systemic biases due to reliance on historical data (e.g., racial bias in predictive algorithms like COMPAS), the creation of a misleading 'facade of objectivity' by AI in value-laden legal decisions, and the lack of transparency and accountability in 'black-box' AI systems. |
| 4kOtMViO_DAJ.pdf | Google_Scholar | Data-Driven Justice: Effective Data Governance to achieve SDG 16 | This paper examines the role of effective data governance in achieving Sustainable Development Goal 16 (Peace, Justice, and Strong Institutions) in India, highlighting the challenges posed by the overburdened justice system and fragmented legal frameworks. It explores the potential of data analytics and AI to improve access to justice, transparency, and court efficiency while noting the need for robust governance to mitigate risks. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Lack of a holistic national data governance framework; fragmented legal framework for data; overburdened judicial system with large case backlogs and high numbers of unsentenced prisoners; low judge-to-population ratio; challenges in digital accessibility potentially leading to inequality. | Implement effective data governance frameworks (like the proposed NDGFP); adopt people-centric approaches prioritizing citizens' needs and experiences; utilize AI and data analytics to understand systemic problems, crime statistics, and legal aid needs; enhance court efficiency through technology (e-Courts, NJDG for scheduling, case classification); leverage online forums and AI (e.g., chatbots) for accessible legal support; develop local policies based on local data mapping; foster collaboration between government branches (executive/judiciary); establish clear leadership for justice data. | Access to justice; Sustainable Development Goal 16 (Peace, Justice, Strong Institutions); Data governance; Rule of law; Court efficiency; Case pendency reduction; Legal aid; Public access to judicial information. | Poor and marginalized communities disproportionately affected by the slow legal system; general citizens seeking access to justice; potentially people with disabilities facing digital accessibility barriers. | Justice System Administration, Data Privacy Law, Information Technology Law, Constitutional Law (Right to Privacy) | India | NaN | NaN | NaN | False | False | NaN | Lack of a holistic and binding national data governance framework; fragmented existing legal protection; slow integration and updating of technology within the judicial system (e.g., NJDG); lack of clear guidelines for data collection, processing, and sharing; insufficient technical capacity among justice system actors; need for a cultural shift towards data-driven justice; inadequate focus on people-centric approaches and understanding user needs/experiences; limited use of data for understanding local justice issues. | Legislative fragmentation and evolving data governance landscape; inconsistencies between policy Gaps between policy formulation and practical implementation; slow adoption and integration of rapidly advancing technologies like AI; ensuring data privacy and security amidst increased data sharing; preventing potential discrimination arising from data use; addressing cybersecurity threats; building technical capacity among court staff, judges, lawyers, and litigants; fostering a cultural shift within the justice system to embrace data-driven methods; complexity in assigning leadership responsibility for justice data governance. | Privacy violations through data sharing; potential for increased discrimination based on data analysis; unauthorized surveillance; cybersecurity threats to sensitive judicial data; inaccuracy and unreliability of generative AI tools (like ChatGPT) used for legal support, posing risks particularly for legally unrepresented individuals. |
| Hzv8CB3O47YJ.pdf | Google_Scholar | LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development | This paper introduces LeXFiles, a diverse English multinational legal corpus, and LegalLAMA, a legal knowledge probing benchmark, aimed at advancing the development and detailed analysis of legal pre-trained language models (PLMs). The authors also release and evaluate two new legal PLMs, LexLMs, finding that diverse pre-training corpora and model size are crucial for effective upstream, probing, and downstream performance. | True | Idealistic | True | 1.0 | Positive | LeXFiles (multinational English legal corpus), LegalLAMA (legal knowledge probing benchmark), and LexLM (RoBERTa-based legal PLMs). | LexLM models were evaluated on: 1. Upstream Masked Language Modeling (MLM) performance (Accuracy/P@1) on LeXFiles sub-corpora. 2. Probing performance (Mean Reciprocal Rank - MRR, P@1) on the LegalLAMA benchmark. 3. Downstream performance (micro-F1, macro-F1) on selected LexGLUE classification tasks after single-epoch fine-tuning. | The LexLM-L (large) model generally performed best. On LegalLAMA, LexLM-L achieved an average MRR of 77.4%. On selected LexGLUE downstream tasks, LexLM-L achieved an average micro-F1 of 73.3% and macro-F1 of 51.0%. | Lack of diverse, multinational legal corpora; insufficient benchmarks for probing specific legal knowledge in PLMs; limited understanding of how pre-training settings and model characteristics affect legal language understanding. | Release of LeXFiles, a diverse multinational English legal corpus; release of LegalLAMA, a benchmark for probing legal knowledge in PLMs; development and release of LexLMs, new PLMs trained on diverse legal data to improve legal language understanding. | Democratizing legal information, improving legal services and tools for legal professionals and laypersons. | Laypersons, legal professionals, and the NLP research community working on legal AI. | Legislation, Case Law, Contracts, Human Rights Law (ECHR), Criminal Law. | EU, CoE, Canada, US, UK, India. | LexLM models were trained on LeXFiles, a new corpus of approx. 19 billion tokens from 6 million publicly available, English, unstructured legal documents (legislation, case law, contracts) sourced from EUR-Lex, UK.LEGISLATION.GOV.UK, BAILII, Court Listener, SEC-EDGAR, Canadian official legislation portal, HUDOC, and re-distributions from Henderson* et al. (2022) and Malik et al. (2021). | For LexLM: Warm-starting from RoBERTa checkpoints, training a new BPE tokenizer on LeXFiles, continued pre-training using Masked Language Modeling on LeXFiles with sub-corpora sampling smoothing. For LegalLAMA: Creation of mask-filling probing tasks based on LAMA, extended for multi-token targets, using test subsets of LeXFiles. | LeXFiles corpus, LegalLAMA benchmark, and LexLM models are released on Hugging Face Hub. The codebase is available on GitHub. | True | True | LeXFiles corpus, LegalLAMA benchmark, and LexLM models are available on Hugging Face Hub. Associated codebase is on GitHub. | Need for more diverse corpora (more languages, legal systems); expansion of probing benchmarks (more tasks, topics, jurisdictions); exploration of larger/different model architectures (e.g., GPT-like) and advanced training P\nparadigms (instruction-tuning, RLHF); development of more robust evaluation methods for probing and fine-tuning; further research into trustworthiness, including model interpretability and fairness in legal AI. | Compiling diverse and representative legal corpora; avoiding overspecialization of models to specific jurisdictions or text types; designing effective methods to probe specific legal knowledge acquired by PLMs; balancing capacity across sub-corpora of varying sizes during pre-training; understanding the interplay between model size, pre-training data, and performance on diverse legal tasks. | Models may perpetuate biases from training data if not carefully curated (e.g., outdated or discriminatory legal standards). Lack of interpretability and fairness in models can lead to irresponsible deployment. Over-reliance on models without understanding their limitations. |
| 3477495.3531668.pdf | Google_Scholar | LawNet-Viz: A Web-based System to Visually Explore Networks of Law Article References | This paper presents LawNet-Viz, a web-based prototype tool for visualizing networks of legal article references extracted from statute law, demonstrated using the Italian Civil Code. The system aims to aid legal research for professionals and enhance understanding for laymen by displaying article connections, network statistics, and semantic similarities calculated using NLP techniques including BERT. | True | Idealistic | True | 1.0 | Positive | LawNet-Viz: A web-based system that extracts references from statute law, builds a network graph, calculates semantic similarity between articles using NLP (incl. BERT), and provides interactive visualization of the network with associated statistics (e.g., centrality) and search capabilities. | Demonstration of the system's functionalities using the Italian Civil Code (ICC) as a case study. A BERT-based model (LamBERTa) fine-tuned on the ICC was used for semantic analysis. No formal user study or quantitative benchmark evaluation reported. | NaN | Complexity of navigating legal corpora ("intricate regulatory systems"); knowledge gap for laypersons unfamiliar with the legal domain; time and cost involved in traditional legal research. | Providing an interactive visual exploration tool (LawNet-Viz) to map article references and semantic relationships, reducing the knowledge gap for laypersons and increasing efficiency for legal professionals through enhanced search and understanding capabilities. | Legal research support; Understanding statutory law structure; Navigating complex legal texts. | Legal professionals (lawyers, jurists) and citizens/laymen. | Statute law (specifically demonstrated with Civil Law / Private Law) | Italy (Italian Civil Code), designed to be adaptable. | Network structure derived from Italian Civil Code (ICC) text. Language models (including LamBERTa, a fine-tuned BERT model) trained/fine-tuned on the text of the ICC using unsupervised labeling for data augmentation. The ICC is public statutory law; resulting models/embeddings may be proprietary. | Modular architecture (network, text, integration modules), use of NLP libraries (Gensim, HuggingFace), web technologies (Bootstrap, DataTables, vis.js), JSON data format compatible with Gephi, focus on interactive user experience. | System prototype using web technologies (Bootstrap, DataTables, vis.js, Python backend). Planned for product development. A screen recording demo is provided via a shared drive link. | False | False | NaN | Social and ethical considerations related to automating legal research are acknowledged but not explored. The system is a prototype requiring further development. | Developing tailored methods for extracting article references according to specific legal syntax; processing and normalizing legal text; managing computational load (addressed via server-side processing); designing effective interactive visualizations for complex network and textual data. | Not explicitly stated, beyond acknowledging that social/ethical considerations are outside the paper's scope. |
| itbYunRMpiQJ.pdf | Google_Scholar | LLM vs. Lawyers: Identifying a Subset of Summary Judgments in a Large UK Case Law Dataset | This paper compares two computational methods, a traditional keyword-based NLP approach and an application of the Claude 2 LLM, for identifying summary judgment cases within the Cambridge Law Corpus of UK court decisions. The study finds that the LLM significantly outperforms the keyword method, achieving a weighted F1 score of 0.94, demonstrating AI's potential to enhance legal research and accessibility of legal information. | True | Idealistic | True | 2.0 | Positive | Application of Claude 2 Large Language Model with engineered prompts for classifying legal cases (summary judgments) and a traditional NLP keyword/RegEx-based search. | Manual review of statistically representative samples by a legal expert for both keyword-based and LLM-based classification. Performance evaluated using confusion matrices and F1 scores. | The Claude 2 LLM method achieved a weighted F1 score of 0.94, significantly outperforming the keyword-based method (weighted F1 score of 0.78). | Difficulty for self-represented litigants to navigate summary judgments; lack of automatic categorization of legal issues in UK case law; incomplete publication of court judgments; complexity of legal language hindering automated analysis and general access to legal information. | Employing advanced NLP and LLMs (like Claude 2) to efficiently identify and classify specific types of legal cases, thereby improving the accessibility of legal information and aiding legal research, which can help democratize access to legal resources. | Identifying specific case types (summary judgments) for legal research; improving accessibility of legal information; understanding procedural justice, particularly concerning summary judgments affecting self-represented litigants. | Self-represented litigants. | Civil procedure. | United Kingdom (primarily England and Wales regarding Civil Procedure Rules). | The Claude 2 LLM, one of the techniques studied, was pre-trained by Anthropic on large, general natural language datasets (details proprietary to Anthropic). The keyword-based method is rule-based and does not use a training dataset. The Cambridge Law Corpus was used as the input data for classification. | Keyword-based method: Expert-driven keyword generation, RegEx development, iterative refinement of search logic based on legal domain knowledge (CPR, case law). LLM-based method: Prompt engineering for Claude 2, utilizing insights from keyword analysis and LLM provider guidelines, including structured prompts with examples. | The identified dataset metrics are shared to support further research. Code is made available on GitHub. | True | True | The code implementing the methods is available on GitHub. The Claude 2 method relies on accessing the Claude 2 Chat console (used for final results and generally accessible). | Need for further refinement of LLM methodologies (e.g., prompt engineering) to improve accuracy in legal case classification; incompleteness of available legal datasets (e.g., CLC not containing all judgments); ongoing challenges with LLM reliability (e.g., errors, over-inclusivity, hallucinations); lack of standardized benchmarks for legal information retrieval tasks. | Keyword method: Capturing nuances and variability in legal language, distinguishing true cases from mere mentions or similar legal tests used in other procedures. LLM method: Effective prompt engineering, LLM output inconsistencies (API vs. Chat console), handling LLM context window limits for very long documents, general complexity of legal language for NLP. | Misclassification of legal cases by AI methods, potentially leading to incorrect legal research outcomes or flawed understanding of legal trends; inherent limitations of LLMs such as errors, over-inclusivity (incorrectly identifying non-summary judgment cases as summary judgments), and potential for hallucination when applied to complex legal tasks. |
| vkejhE-Ze-oJ.pdf | Google_Scholar | Human Resource Analytics in the Era of Artificial Intelligence: Leveraging Knowledge towards Organizational Success in Pakistan | This paper investigates how workplace coordination (implicit and explicit) influences organizational performance in Pakistani software houses, mediated by knowledge sharing. It finds that employee use of generative AI moderates this relationship, significantly boosting performance when knowledge sharing is low. | True | Market | True | 2.0 | NaN | Utilization of generative AI tools (e.g., ChatGPT) by employees as a moderating variable. | Cross-sectional survey data from employees in software houses in Islamabad, Pakistan. Analysis via Partial Least Squares Structural Equation Modeling (PLS-SEM) using SMART-PLS, including mediation and moderation analysis (Baron & Kenny's interaction term approach). | Generative AI infusion significantly moderated (p<0.05) the relationship between knowledge sharing and organizational performance. AI use substantially enhanced performance in low knowledge-sharing environments but had minimal impact in high knowledge-sharing environments. | NaN | NaN | NaN | NaN | NaN | Pakistan | NaN | Quantitative survey research, Partial Least Squares Structural Equation Modeling (PLS-SEM). | NaN | False | False | NaN | NaN | NaN | NaN |
| AIoPYeNkVewJ.pdf | Google_Scholar | VIOLATION OF HUMAN RIGHTS OF CHILDREN: A CASE OF JUDICIAL PRACTICES IN PROTECTION OF MINORS FROM SEXUAL OFFENCES | This paper examines judicial practices and the Protection of Children from Sexual Offences (POCSO) Act in India for protecting minors from sexual offences, highlighting the high incidence and underreporting of such crimes. It finds a significant lack of public awareness regarding protective legal provisions and notes that despite legal frameworks and judicial efforts, children are often denied justice. | True | Idealistic | False | 2.0 | NaN | Judicial practices and the Protection of Children from Sexual Offences (POCSO) Act, 2012 in India for protecting minors from sexual offences. | Doctrinal analysis of secondary sources (books, journals, legal reports), analysis of National Crime Record Bureau (NCRB) reports, and assessment of major judgments from the Supreme Court of India and High Courts related to child sexual abuse and the POCSO Act. | The POCSO Act, 2012 has made a substantial contribution to addressing child sexual abuse in India by outlawing harmful sexual behaviours. However, its effectiveness is hampered by a significant lack of public awareness of legal provisions and persistent issues in delivering justice, with many children remaining unprotected despite judicial efforts. | Most child sexual abuse cases go undetected; many child victims are victimized by known persons (family, relatives); lack of public awareness about protective laws; cases not being effectively addressed despite legal safeguards; poverty leading to child labour and marriage; societal and cultural norms accepting child marriage; government inaction on judicial suggestions for child protection. | The paper suggests a need for stricter enforcement mechanisms. While not detailing new solutions, it implicitly calls for increased public awareness of legal provisions, more effective implementation of the POCSO Act, and governmental responsiveness to judicial recommendations for child protection. | Child sexual abuse, protection of minors from sexual offences, access to justice for child victims, human rights of children, implementation of the POCSO Act. | Children in India, particularly minors who are victims or at risk of sexual offences. | Criminal Law, Child Protection Law, Human Rights Law, Constitutional Law. | India | NaN | NaN | The POCSO Act is a national law enacted by the Parliament of India and implemented through the Indian legal and judicial system, including the establishment of special courts. | True | True | The Protection of Children from Sexual Offences (POCSO) Act, 2012, is a public law in India and is accessible through official government legal resources. | Lack of public awareness of protective laws; high number of undetected/unreported abuse cases; poor implementation of existing legal frameworks like the POCSO Act despite its contributions; governmental inaction on judicial recommendations; persistence of child labor and child marriage due to poverty and societal norms. | NaN | Continued sexual abuse and exploitation of children; violation of children's human rights; severe emotional and psychological trauma to victims; denial of justice and impunity for perpetrators; perpetuation of child marriage and child labour due to lack of protection and awareness. |
| uq8bglKm_NoJ.pdf | Google_Scholar | WHERE’S THE LIABILITY IN HARMFUL AI SPEECH? | This paper examines potential legal liability for harmful speech generated by AI foundation models under US law, focusing on defamation, speech integral to criminal conduct, and wrongful death. It argues that liability and Section 230 immunity analyses are complex, depend heavily on technical design choices, and that current legal frameworks create potentially misaligned incentives for AI safety. | True | NaN | True | 3.0 | NaN | Generative AI / Foundation Models and associated design/mitigation strategies (extractive, retrieval-augmented, RLHF, inference-time processing, uncertainty calibration). | NaN | NaN | NaN | NaN | NaN | NaN | Torts (Defamation, Wrongful Death, Aiding and Abetting, Negligent Misrepresentation), Communications Law (Section 230), Criminal Law (Speech Integral to Criminal Conduct), First Amendment Law | United States | The paper discusses general practices for training foundation models, referencing data sources like web crawls (e.g., CommonCrawl, C4), CourtListener cases, books (e.g., from BitTorrent trackers like Bibliotik in The Pile dataset), and methods like instruction fine-tuning using human-generated datasets (potentially company-proprietary) and reinforcement learning from human feedback (RLHF). | NaN | NaN | False | False | NaN | Legal uncertainties regarding Section 230 applicability to generative AI; difficulties in applying traditional mens rea (state of mind) requirements to AI systems for liability purposes; misalignment between current legal incentives and the encouragement of optimal AI safety interventions (e.g., human feedback potentially increasing liability risk while improving safety); imperfect technical solutions for preventing harmful AI outputs (hallucinations, bias, dangerous instructions, manipulation); challenges in reliable fact-checking, uncertainty calibration, and preventing malicious misuse (e.g., jailbreaking). | Analyzing complex interactions between evolving AI technology (foundation models, various mitigation techniques) and established legal doctrines (Section 230, torts, First Amendment); assessing the incentive effects of different legal rules and interpretations on AI system design and safety investments. | AI generating defamatory falsehoods; AI providing instructions or encouragement for criminal acts (e.g., violence, terrorism, financial crimes); AI causing physical harm or wrongful death (e.g., encouraging self-harm, providing dangerous advice); AI generating biased or hate speech; AI facilitating disinformation, manipulation, or scams; AI generating malware. |
| 1286021.pdf | Google_Scholar | Generative AI in Practice: Pipeline Design, Implementation, and Ethical Considerations | This paper presents the design and implementation processes for generative AI pipelines, focusing on applications such as chat systems, Retrieval-Augmented Generation (RAG), and fine-tuning pre-trained models. It outlines the key components, implementation steps, and ethical considerations associated with deploying these AI technologies. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Mentions general concepts: Large datasets of human dialogue for chatbots, extensive databases/repositories (public like Wikipedia or private) for RAG, domain-specific data (e.g., question-answer pairs) for fine-tuning. Does not specify a concrete dataset used within the paper's scope. | Describes pipeline design in terms of components (e.g., UI, Input Processing, Model Inference, Post-processing, Memory Management, API/Deployment) and implementation steps for chatbots, RAG, and fine-tuning. | Mentions deployment via APIs using web frameworks (FastAPI, Flask), integration into applications, hosting on cloud platforms (AWS, Hugging Face). | False | False | NaN | NaN | Computational constraints, model interpretability, bias mitigation, data privacy, model selection, data preprocessing, integration of components, ensuring scalability and reliability. | Bias and fairness issues (skewed/discriminatory outputs), privacy concerns (use of personal data), lack of transparency (users unaware they interact with AI, how data is used). |
| CYTtXANcRR4J.pdf | Google_Scholar | Combat Security Barriers with state- of-the-art Tools and Techniques | This paper reviews common network and cybersecurity threats, including internal risks like BYOD and external attacks like phishing and DoS. It proposes solutions such as cryptography, firewalls, and enhancing security awareness to mitigate these threats. | True | Market | False | 3.0 | NaN | General cybersecurity tools and practices (Cryptography, Firewalls, Network Monitoring, Security Awareness Training) | NaN | NaN | NaN | NaN | NaN | NaN | Cybersecurity / Network Security | International | NaN | NaN | NaN | False | False | NaN | NaN | Network complexity, lack of technical resources, Bring Your Own Device (BYOD) risks, insider threats, attackers constantly developing new tactics. | Insider threats causing security breaches, virus infection and data theft via BYOD, phishing attacks leading to credential compromise or malware installation, Denial-of-Service (DoS/DDoS) attacks causing service unavailability and financial loss, wormhole attacks disrupting communication. |
| jC3rwCcyzLcJ.pdf | Google_Scholar | Artificial Intelligence (AI) in Legal System | This paper reviews the current state and impact of AI on the legal profession, highlighting its potential to enhance legal work but also noting risks like inaccuracies and the need for human oversight. It explores benefits such as increased efficiency and access to justice, alongside challenges including ethical concerns, bias, and the potential for errors if AI is used without proper safeguards. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Scarcity of attorneys in 'legal deserts'; high cost of legal services for the general public; risk of biased or erroneous AI decisions impacting fairness and perpetuating discrimination; ensuring the right to be heard ('Audi alteram partem') when AI is involved in decision-making. | AI-powered tools to provide legal information and assistance in underserved areas (e.g., 'legal deserts'); using AI to efficiently handle straightforward legal matters (e.g., petty cases, amicable divorces, Khula) potentially reducing costs; development and implementation of robust ethical frameworks, guidelines, and human supervision for AI in law to ensure fairness and mitigate bias; public awareness and discourse on AI's role in the legal system. | Addressing 'legal deserts' and scarcity of legal professionals; providing accessible legal information and assistance for common/minor legal issues; improving efficiency and potentially lowering costs for resolving straightforward legal cases (e.g., uncontested divorces, small claims); ensuring fairness, non-discrimination, and upholding legal rights in AI-assisted legal processes. | Individuals in 'legal deserts' (areas with limited access to legal professionals); general public needing assistance with common or minor legal issues (e.g., parking tickets, simple family law matters); individuals who could benefit from more efficient and less costly resolution of straightforward cases. | General legal practice, Contract Law, Real Estate Law, Commercial Law, Criminal Law (bail proceedings), Family Law (divorce, Khula), Human Rights, Intellectual Property Law, Small Claims. | Pakistan, UK, USA, India, International (due to discussion of global justice and broad applicability). | Vast text and code datasets for Generative AI like ChatGPT; general pre-existing legal data for other AI systems discussed. The paper notes these data can contain biases. | NaN | NaN | True | False | Some discussed tools like DoNotPay are presented as services available to individuals (likely paid). ChatGPT is generally accessible (with free/paid tiers). Other commercial tools (Kira Systems, LEVERTON, etc.) are mentioned as existing products from companies. | Limited research on AI's role in deciding legal cases where law is ambiguous or nonexistent; need for AI systems capable of reasoning from first principles or handling social dilemmas effectively; insufficient literature on AI's broader human rights impacts beyond privacy and expression; challenges in integrating societal values, moral principles, and ethical considerations into AI reasoning, especially in novel legal situations; developing effective oversight and auditability for AI systems. | High cost of developing and implementing sophisticated AI systems; ensuring accuracy and reliability of AI-generated information and avoiding errors; overcoming AI's difficulty in handling legal ambiguity or non-existent law due to reliance on pre-existing data; incorporating human expert knowledge and ethical considerations into AI decision-making; potential for inherited bias from training data leading to discriminatory outcomes; the 'black box' nature of some AI systems making them opaque. | AI errors leading to incorrect legal outcomes and miscarriages of justice (e.g., UK divorce software error, bogus citations from ChatGPT); job displacement for legal professionals; perpetuation of societal biases and discrimination through biased AI systems; violations of privacy due to large-scale data collection by AI; spread of misinformation through AI-generated content; lack of accountability for AI decisions; challenges in assigning authorship and intellectual property for AI-generated content. |
| n9YM6j_xIvUJ.pdf | Google_Scholar | Technology Competence as a Compass For Helping to Close the Justice Gap | This paper argues that the ethical duty of technology competence for lawyers can serve as a crucial guide in leveraging legal technology to address the access to justice crisis in the U.S. It explores the potential of this duty to influence legal service providers, regulators, and educators in promoting technology for social good, despite current obstacles and the rapid evolution of AI. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Cost of legal services; consumer sophistication and language barriers; cuts to legal aid; insufficiency of traditional legal aid/pro bono; poorly designed or irresponsibly used technology (e.g., one-size-fits-all tools, overhype, bias); ethical uncertainty regarding new technologies; lawyers' resistance to technology; difficulty for A2J organizations to gain tech competence due to resource/time constraints; rules stifling cross-industry collaboration (unauthorized practice of law, non-lawyer ownership). | Leveraging legal technology for cost, availability, and quality of legal services; using the duty of technology competence as a guide for all stakeholders; adopting a 'thick view' of technology competence; organizational leadership embracing 'people factors,' algorithmic literacy, and interdisciplinary collaboration (especially for bias and cultural competency); regulators clarifying ethics rules, reforming restrictive rules, and offering tech competence CLEs; legal education integrating tech competence, interdisciplinary approaches, and A2J focus; increasing transparency in legal tech use. | Closing the justice gap; access to legal services for low- and moderate-income individuals; ethical obligations of lawyers (technology competence, pro bono, reasonable fees); role of legal technology in legal service delivery (efficiency, cost reduction, self-help tools, connecting consumers to providers); democratizing access to legal information. | Low-income Americans, moderate-income individuals, people facing economic or social barriers to legal counsel, those with limited English proficiency, recent immigrants. | General Civil Law (examples include income maintenance, education, housing, family law, immigration, arbitration, traffic infractions). | United States | NaN | NaN | NaN | False | False | NaN | Significant unmet legal needs; lack of tech knowledge in A2J organizations; ethical ambiguity hindering innovation; insufficient resources for A2J tech adoption; lack of transparency in legal tech use; need for more interdisciplinary collaboration and algorithmic literacy. | Rapid pace and complexity of technological evolution leading to overwhelm; conservative nature of the legal profession and resistance to change; ethical uncertainties and lack of clear guidance; risk of bias in AI requiring vigilance and interdisciplinary solutions; ensuring technology is culturally competent and user-centric; integrating unbundled services into business models. | Poorly designed/irresponsibly used tech exacerbating the justice gap; 'one-size-fits-all' tools failing diverse needs; overhyped expectations leading to negative impacts; failure to account for consumer differences; overreliance on tech for tasks requiring human judgment or interaction; technology creating/automating/magnifying bias; uncertainty about unauthorized practice of law, data protection, and business structures; passive tech adoption for marketing leading to ineffective use. |
| TM1elPIE7dsJ.pdf | Google_Scholar | ChatGPT and GPT-4: utilities in the legal sector, functioning, limitations and risks of foundational models | This paper examines the architecture, operation, applications, and significant limitations (such as hallucinations and biases) of large language models like ChatGPT and GPT-4 within the legal sector. It also analyzes the associated legal risks, particularly concerning data protection and intellectual property, and discusses emerging EU regulatory frameworks for AI. | False | Market | True | 3.0 | Neutral | ChatGPT and GPT-4 (OpenAI's foundational large language models) | NaN | NaN | NaN | NaN | NaN | NaN | General legal practice, Contract law, Intellectual Property law, Data Protection law, Litigation, Criminal law, Administrative law, AI law | Multiple (EU, USA, Spain, and others cited) | Publicly available internet data (e.g., Common Crawl, WebText2, Wikipedia), licensed third-party data, and user/reviewer-generated data, largely collected via web scraping. Primarily unstructured text data. | Pre-trained transformer architecture, trained using semi-supervised learning (unsupervised pre-training, supervised fine-tuning) and Reinforcement Learning from Human Feedback (RLHF). | Publicly available chatbot (ChatGPT), API access (GPT-4), and integration into specialized legal tech tools (e.g., Harvey, CelIA) used by law firms. | True | False | ChatGPT is available as a publicly accessible chatbot (with free and paid tiers). GPT-4 is accessible via paid subscriptions (e.g., ChatGPT Plus) and API. | NaN | Technical limitations (hallucinations, biases, explainability, handling long texts), data acquisition and quality for legal domain training, ensuring responsible and ethical use, navigating legal compliance (data privacy, IP). | Hallucinations and biases leading to incorrect information; data protection violations (improper use of personal data in training/prompts, international transfers, difficulty exercising data subject rights); intellectual property infringement (use of copyrighted training data, infringing outputs, authorship issues); over-reliance by users; professional liability for legal professionals. |
| 1FuR9e3J6qUJ.pdf | Google_Scholar | PDF-WuKong : A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling | This paper introduces PDF-WuKong, a multimodal large language model designed for question answering on long PDF documents containing interleaved text and images, such as academic papers. It utilizes an end-to-end sparse sampler to efficiently select relevant text and image evidence based on user queries, improving accuracy and reducing computational cost compared to existing methods. | True | NaN | True | 1.0 | NaN | PDF-WuKong: A multimodal large language model (MLLM) incorporating an end-to-end sparse sampler that selects relevant text paragraphs and images based on query similarity using contrastive learning. | Evaluated on a newly created bilingual (English/Chinese) dataset 'PaperPDF' (1.1M training pairs, 10k test pairs from academic papers) using ANLS, F1, Rouge, and GPT-Acc metrics. Also tested on public benchmarks: DocVQA, ChartQA, InfoVQA, MPDocVQA, DUDE, and MM-NIAH. Compared against open-source MLLMs (with/without RAG) and commercial products. Ablation studies performed on sampler, dataset size, document length, and sampling strategy. | PDF-WuKong surpassed baseline open-source MLLMs and proprietary commercial products on the PaperPDF benchmark (e.g., by an average of 8.6% F1 over proprietary products). It achieved competitive performance on other document VQA benchmarks, particularly on multi-page (DUDE) and long-context tasks (MM-NIAH @ 64K). Performance remained stable with increasing document length. | Limitations of existing methods for long multimodal PDFs: text-only approaches lose visual information; vision-only approaches suffer from scalability issues (high token counts, computational cost) with many pages/high resolution; difficulty processing interleaved text and images efficiently; attention dilution in LLMs with long inputs. | Proposed PDF-WuKong model with an end-to-end sparse sampler integrated with the MLLM's vision encoder. The sampler uses text and image embeddings (trained via contrastive learning) to retrieve top-k relevant evidence (text blocks, images) based on query similarity, reducing LLM input tokens. Created PaperPDF dataset with question-answer-evidence triplets for training and evaluation. | NaN | NaN | NaN | International | Primary dataset: PaperPDF, a newly created dataset of 1.1M bilingual (English/Chinese) QA pairs with evidence grounding, automatically generated using Gemini Pro/GPT-4V from ~70k parsed academic PDF documents (text blocks and images). Also trained on public datasets: DocVQA, ChartQA, InfoVQA, MPDocVQA, DUDE. | Document parsing (Grobid, MinerU), sparse sampling (contrastive learning, similarity matching), multimodal large language model fine-tuning (IXC2-VL-4KHD backbone, BGE-M3 text encoder), end-to-end joint training of sampler and LLM, automatic QA data generation using LLMs/VLMs. | Code and dataset planned for release on GitHub. | True | True | Code and dataset will be released at https://github.com/yh-hust/PDF-Wukong. | Current dataset limited to academic papers; model not specifically designed for global queries requiring synthesis of the entire document rather than sampled evidence. | Efficiently processing long multimodal documents, integrating text and visual information, reducing computational cost for LLMs, mitigating attention shift in long sequences, generating high-quality training data with evidence. | NaN |
| LLMSurvey-MBS.pdf | Google_Scholar | Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future Prospects | This paper provides a broad survey of Large Language Models (LLMs), covering their history, architectures, training methods, diverse applications across various domains including law, and associated challenges. It discusses technical aspects, ethical considerations, limitations like hallucination and bias, and future research directions for LLMs. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General Legal Practice, Civil Law | US, Colombia, Japan, International | Large corpora of diverse text data (e.g., CommonCrawl, Wikipedia, books, articles, code) used for pre-training; domain-specific data sometimes used for fine-tuning. | Transformer architecture, large-scale pre-training, supervised fine-tuning, Reinforcement Learning from Human Feedback (RLHF), prompt engineering, in-context learning. | General discussion of deployment in applications (chatbots, search engines, virtual assistants) via APIs, including monitoring. | True | True | Discusses numerous publicly available models like ChatGPT (free/paid tiers), Bard (free), Llama 2 (open source download). Provides GitHub repository for the survey itself. | NaN | Computational requirements (cost, energy, water), Training data issues (size, quality, bias, privacy), Tokenization problems, Fine-tuning complexity, Inference latency, Limited context length, Knowledge updating difficulties, Lack of explainability, Reasoning errors, Susceptibility to adversarial attacks (prompt injection, jailbreaking, data poisoning), Behavioral drift over time, Factual errors (hallucination), Spelling/Counting errors. | Bias amplification, information hallucination, lack of explainability, reasoning errors, adversarial attacks (prompt injection, jailbreaking, data poisoning), security vulnerabilities, privacy leaks, environmental costs (energy/water consumption), copyright infringement, training data contamination, generation of harmful/offensive content, erosion of trust, potential job displacement, undermining critical thinking skills. |
| Ugs9Xq5VlCIJ.pdf | Google_Scholar | Parameter-Efficient Legal Domain Adaptation | This paper proposes 'prefix domain adaptation', a parameter-efficient method using unsupervised data from legal forums (Reddit, Stack Exchange) to pre-train prompts for language models. This approach improves few-shot performance on legal classification tasks compared to standard fine-tuning and existing methods like LEGAL-BERT, while only tuning ~0.1% of parameters. | True | Idealistic | False | 1.0 | Positive | Prefix Domain Adaptation: Pre-training a prefix prompt using Masked Language Modeling (MLM) on unsupervised, domain-specific legal text (from public forums), then using this pre-trained prefix for parameter-efficient tuning (P-Tuning v2) on downstream few-shot legal tasks. | Evaluated on few-shot classification tasks using three datasets: Legal Advice Reddit (new), Law Stack Exchange (new), and ECHR. Performance (Macro F1) and calibration (ECE) were compared against baselines including full fine-tuning (FT), LEGAL-BERT + FT, full domain adaptation + FT, P-Tuning v2, and prefix adaptation (general corpus pre-training). | Prefix Domain Adaptation matched or outperformed full fine-tuning and LEGAL-BERT in most few-shot settings across datasets (in terms of Macro F1), while tuning only ~0.1% of parameters. It also achieved competitive or better calibration (ECE). | High cost of legal advice; large model sizes requiring parameter-efficient methods; poor performance of existing parameter-efficient methods in low-data settings common in law due to high labeling costs. | Leveraging abundant unsupervised legal text from public forums (Reddit, Stack Exchange) for domain-specific pre-training of prompts using Masked Language Modeling (MLM). This 'prefix domain adaptation' improves the few-shot performance of parameter-efficient tuning methods, reducing computational and data labeling costs. | Legal area classification based on layperson questions; improving access to legal information/services for laypersons. | Laypersons seeking legal advice, particularly users of online legal forums like Reddit and Stack Exchange. | General / Multiple (based on classification tasks covering various areas like criminal, copyright etc., and human rights law via ECHR) | International (Uses data from Reddit/LSE with unspecified/broad user base and ECHR covering European states) | Prefix pre-training uses unsupervised, unstructured text data from public legal forums (Legal Advice Reddit, Law Stack Exchange). Downstream tasks use labeled, unstructured text data (forum posts/titles mapped to legal area tags; ECHR case facts mapped to violation status) in few-shot settings. The forum data is publicly available via Pushshift/StackExchange dumps, and ECHR data is also public. | Builds on RoBERTa architecture, Masked Language Modeling (MLM), Prefix Tuning (P-Tuning v2), and domain adaptation principles. Combines domain-specific MLM pre-training with prefix tuning. | NaN | False | False | The two new datasets (Legal Advice Reddit, Law Stack Exchange) are claimed to be available via Hugging Face. | Focus is on classification; need for extension to more complex legal tasks (Q&A, reasoning). Potential robustness issues related to data distribution shifts. Need for more extensive hyperparameter tuning for larger models. | Adapting parameter-efficient methods to perform well in few-shot legal settings. Processing noisy, informal (Reddit) and formal (Stack Exchange) text data. Computational resource limitations for hyperparameter search. | Misuse or over-reliance on model predictions due to poor calibration, especially given the high-stakes nature of law. Models trained on formal legal text may perform poorly on informal layperson language. |
| i4jm_4PwR-IJ.pdf | Google_Scholar | THE LEGAL TECH BRO BLUES : GENERATIVE AI, LEGAL INDETERMINACY , AND THE FUTURE OF LEGAL RESEARCH AND WRITING | This paper critiques techno-optimism surrounding generative AI in law, arguing its proponents ignore legal indeterminacy and the importance of human experience, potentially stifling legal creativity and reinforcing biases. It proposes a four-step model for lawyers to responsibly integrate LLMs into research and writing, emphasizing traditional research, critical prompting, verification, and human oversight. | True | NaN | True | 1.0 | Neutral | A conceptual 4-step normative model for integrating LLMs into legal research and writing: 1. Research: Edification (using traditional sources first), 2. Prompting and Generation (LLM drafting), 3. Research: Verification (independent validation), 4. Writing: Polishing and Preparation (human review and editing). | NaN | NaN | The complexity and indeterminacy of law which simplistic AI ignores; the risk of AI automating the status quo and reinforcing existing biases; potential for AI to justify cuts to legal aid or devalue human legal representation. | Adopt a critical stance towards legal tech ('legal bibliographer' vs 'legal tech bro'); implement a responsible model for using AI in practice (the proposed 4-step model); reform legal education with standalone courses on critical legal information literacy and technology evaluation. | NaN | NaN | General law (focus on legal research, writing, reasoning, education) | US | The paper discusses LLMs trained on large, potentially biased text corpora, including private repositories of legal materials used by legal tech companies, but does not specify a dataset for its proposed model. | Conceptual analysis based on legal theory, critique of AI capabilities, and established legal research practices. | NaN | False | False | NaN | Disconnect between techno-optimist visions ('legal singularity') and legal indeterminacy; need for improved legal education integrating critical technology assessment; lack of understanding of how AI may entrench bias or devalue human expertise, especially experience-based reasoning relevant to justice; technical limitations like LLM non-determinism and bias. | Managing LLM 'hallucinations' and ensuring factual accuracy/verification; overcoming inherent biases in training data; accounting for legal indeterminacy; preventing automation from stifling legal creativity and critical thinking. | AI automating the legal status quo and reinforcing bias; hindering law reform; 'determinization of law' stifling creativity; malpractice due to LLM errors/misuse; LLMs subtly influencing user judgment; AI justifying cuts to legal aid or devaluing human lawyers; undermining stare decisis; mistaking automated outputs for true legal process; dulling sensitivity to legal complexity and values. |
| vFkrzAPX5eMJ.pdf | Google_Scholar | Generative AI and the Rule of Law⋆ | This exploratory paper discusses the emergence of Large Language Models (LLMs) and Multimodal Foundation Models (MFMs), examining their potential to model the rule of law and serve regulatory purposes. It analyzes responses from models like ChatGPT and Claude, highlighting both their capabilities in generating plausible legal discourse and the ongoing challenges related to accuracy, ethics, and semantic understanding. | True | Idealistic | True | 2.0 | Neutral | Prompting of LLMs (ChatGPT, GPT-4, Claude) for modeling the rule of law; discussion of Semantic Injection and Constitutional AI. | Qualitative experiment involving prompting ChatGPT3, GPT-4 (via Lex.page), and Claude with the question "How can we model the rule of law?" and analyzing the generated responses. | LLM responses were generally plausible and detailed, outlining various components of the rule of law, but exhibited cultural legal biases and operated at a symbolic level requiring user interpretation for meaning. The quality and comprehensiveness of responses varied and improved with newer models/versions. | Unreliability of LLMs (hallucinations, lack of true understanding of meaning vs. symbols), inherent biases in models, unresolved legal and ethical issues (e.g., copyright, privacy, defamation), and challenges in aligning AI with regulatory compliance and democratic values. | Improving LLM accuracy and reliability through semantic injection and knowledge graphs; utilizing advanced prompt engineering (e.g., Moral Chains of Thought) and Constitutional AI principles to align models with ethical and legal norms; adopting a 'Law informs AI' approach for better legal reasoning; and conducting further empirical testing and benchmarking. | Modeling the rule of law; Regulatory applications of AI; Computational ethics; Legal reasoning in AI. | NaN | Constitutional law, Jurisprudence, Regulatory law, Tax law (in an example), Intellectual Property law, Privacy law. | International; references to US and EU. | For LLMs in general: Large, diverse corpora of unlabeled text scraped from the internet for pre-training; specific fine-tuning datasets (e.g., for Constitutional AI, instruction-following datasets based on principles and examples). | For LLMs: Unsupervised pre-training, fine-tuning, Reinforcement Learning (RLHF/RLAIF). For Constitutional AI: Principle-based design, self-critique, preference modeling. For semantic injection: Knowledge Graph integration techniques. | Web-based interfaces and APIs for models like ChatGPT and Claude; some models with geographically restricted access initially. | True | False | ChatGPT is publicly accessible via a web interface (with free and paid tiers). Claude's access was stated as limited to USA and UK at the time of writing (via application). Lex.page is a commercial writing assistant. | Technical: Scalability of knowledge injection methods, achieving genuine legal/ethical reasoning beyond pattern matching, mitigating hallucinations and biases reliably. Societal/Legal: Establishing clear legal frameworks for LLM use (copyright, liability, privacy), ensuring alignment with democratic values and societal norms, adapting regulation to fast-evolving AI, and the conceptual challenge of adequately modeling the rule of law itself. | Design/Development: Sourcing quality training data, effective and scalable fine-tuning/alignment (e.g., Constitutional AI), robust knowledge integration (Semantic Injection), bias mitigation, lack of transparency in model development. Use: Effective prompt engineering, critical interpretation of outputs, avoiding over-reliance due to potential inaccuracies. Deployment: Ensuring safety and preventing misuse, managing computational costs, navigating unclear regulatory environments. | Generation of false information ('hallucinations'); perpetuation of biases leading to disproportionate negative impacts on minority groups; legal infringements (copyright, privacy, defamation); generation of hate speech; challenges to existing regulatory frameworks due to undefined purpose and scale of use; potential for misuse (e.g., flawed legal document generation). |
| AKc59QWPmfEJ.pdf | Google_Scholar | Generative AI in Education From the Perspective of Students, Educators, and Administrators | This dissertation explores the integration of generative AI in education through five studies, covering legal text summarization, stakeholder perspectives (students, educators, administrators) on AI tools and policies, and AI adoption models. The research highlights AI's transformative potential for teaching, learning, and information access, while also underscoring challenges related to ethics, equity, and practical implementation in educational settings. | True | Idealistic | True | 3.0 | Positive | PEGASUS CourtOp, a domain-adapted transformer-based model (fine-tuned from PEGASUS LARGE) for abstractive summarization of legal court opinions (detailed in Chapter 2). | The PEGASUS CourtOp model was evaluated using ROUGE scores (ROUGE-1, ROUGE-2, ROUGE-L) by comparing its generated summaries of court opinions against human-written reference summaries from Justia. A test set of 25% of 4814 court opinions was used. | PEGASUS CourtOp achieved a ROUGE-1 F1 score of 0.53 and a ROUGE-1 Recall of 0.66, outperforming baseline models PEGASUS LARGE and Legal Pegasus. | Cost and complexity of accessing and understanding legal information, particularly lengthy court opinions, which forms a barrier to legal help for people with lower incomes. | Automated legal text summarization using NLP models like PEGASUS CourtOp to reduce the time, effort, and cost associated with parsing and understanding legal documents, thereby potentially lowering barriers to legal services. | Automated summarization of court opinions to enhance accessibility to legal precedents and reduce the cost of legal services. | People of lower-income brackets. | Case Law / Court Proceedings (specifically, summarization of court opinions). | United States (supreme courts of Utah, Idaho, Arizona, New Mexico, Nevada, and Colorado). | A dataset of 4814 US state supreme court opinions paired with human-generated summaries from Justia. For fine-tuning PEGASUS CourtOp, 3661 such pairs were used. This is domain-specific (legal court opinions) unstructured text data, provided under a data-sharing agreement. | Domain adaptation of a pre-trained language model (PEGASUS LARGE) by fine-tuning it on a specific corpus of legal opinions and their summaries. Standard NLP data processing techniques and evaluation using ROUGE metrics were employed. | NaN | False | False | NaN | Technical gaps in AI model capabilities for legal text summarization, including the need for more powerful and potentially open-source language models, domain-specific Named Entity Recognition, and improved generation of highly abstractive, human-like summaries not strictly tied to source text phrasing. | General difficulty of abstractive summarization due to natural language complexity, the need for substantial domain-specific training data (paired opinions and summaries), potential data imbalances, and inherent challenges in objectively evaluating the quality of generated summaries. | Incorrect or fabricated results (hallucinations), misuse for academic dishonesty (cheating, plagiarism), data privacy and security vulnerabilities, algorithmic bias, lack of transparency and accountability in AI systems, negative impacts on critical thinking and creativity, and challenges in ensuring equitable access to AI tools and their benefits. |
| 3627673.3679154.pdf | Google_Scholar | LeDQA: A Chinese Legal Case Document-based Question Answering Dataset | This paper introduces LeDQA, a new Chinese legal dataset for question answering based on civil case documents, featuring a question schema designed by legal professionals and annotations generated using GPT-4. The authors evaluate several LLMs and retrieval methods on this dataset, finding that relevant sentence retrieval improves QA performance but challenges like irrelevant retrieval and incorrect reasoning remain. | True | Idealistic | True | 1.0 | Positive | LeDQA dataset, a Chinese legal case document-based question answering dataset, along with a methodology for its creation and baseline evaluations of retrieval and QA models. | Relevant sentence retrieval was evaluated using R@3, R@5, MRR with models like BM25, TF-IDF, and pre-trained dense retrievers. Question answering was evaluated using Accuracy and Macro-F1 with various LLMs (e.g., Baichuan2, Qwen-7B-Chat, GPT3.5-turbo) using the full document, chain-of-thought prompting, retrieved sentences, or oracle sentences. | For question answering, the Qwen-7B-Chat model, when using the top-5 retrieved sentences from TF-IDF (Retrieve setting), achieved an accuracy of 0.7623 and an F1 score of 0.5605 on the binary classification task ("yes" vs. "no and unknown"). Using oracle (human-annotated) relevant sentences generally yielded the best performance across models, highlighting the importance of accurate sentence retrieval. | The general public's limited knowledge of their rights and fundamental legal processes, and the inherent complexity of legal texts. The high cost of human annotation for creating legal AI resources. | Developing legal question answering systems based on case documents to bridge the gap between people and the law. Creating specialized datasets like LeDQA to facilitate research and development in legal AI. Using LLMs like GPT-4 for cost-effective annotation of legal data, with human validation. | Legal question answering, legal information access and understanding, legal document analysis, element extraction from legal cases. | General public with limited legal knowledge, individuals involved in legal disputes (specifically private lending cases initially). | Chinese civil law, specifically private lending cases. | China | For LeDQA dataset creation: 100 private lending case documents selected from authoritative cases published by the Supreme People’s Court of China. Relevance and answer annotations for these documents were generated using GPT-4 and subsequently validated by human legal experts (PhD students in Chinese civil law). | For LeDQA dataset creation: Question schema construction by a legal expert team through review of laws, element listing, group discussions, and categorization. Case document selection based on authoritativeness and coverage of question schema categories. Annotation of relevant sentences and answers using GPT-4, followed by human validation with inter-annotator agreement checks. | The LeDQA dataset is made publicly available on GitHub. | True | True | The LeDQA dataset is available on GitHub via the link https://github.com/BulouLiu/LeDQA. | Insufficiency of current retrieval models to accurately extract all relevant sentences from long legal documents. LLMs struggle with correct multi-sentence reasoning even when provided with relevant sentences. Difficulty for models in correctly identifying 'unknown' answers. | High cost of human annotation for legal datasets. Ensuring questions are designed from a legal knowledge perspective and cover complex legal elements. Dealing with the length and noisy information in legal case documents compared to typical MRC datasets. Achieving accurate retrieval of relevant sentences and enabling models to perform correct, multi-step reasoning based on these sentences. | Models may retrieve irrelevant sentences or fail to perform correct reasoning even with relevant sentences, leading to potentially incorrect legal interpretations or answers. |
| kLpjOdGODhMJ.pdf | Google_Scholar | Robots vs. Predators: Can Generative Artificial Intelligence Help to Address the Justice Gap in Consumer Debt Litigation? | This paper explores the potential for Generative Artificial Intelligence (GenAI) to alleviate the access-to-justice crisis, particularly in the context of US consumer debt litigation where low-income individuals are often unrepresented. It proposes a 'digital continuum of care' utilizing GenAI and related technologies while also discussing the significant technological, practical, and ethical challenges involved. | True | Idealistic | True | 3.0 | Positive | Proposal for a 'Digital Continuum of Care' for consumer debt cases, leveraging GenAI, chatbots, document assembly/generation tools, and automated discovery. | NaN | NaN | High cost of legal services, individuals not recognizing their problems as legal issues, lack of knowledge on how/where to find legal help, asymmetry of representation (creditors represented, debtors not), sewer service, digital divide. | Deploying technology (specifically GenAI) to provide legal information (chatbots, know-your-rights), automate repetitive tasks (document assembly, discovery, drafting basic pleadings/motions) to make legal assistance more efficient and affordable, creating targeted interventions like a 'digital continuum of care' for high-need areas like consumer debt. | Consumer debt litigation defense, providing legal information and guidance, automating legal tasks (pleadings, discovery, motion practice), self-representation support. | Low- and moderate-income Americans, specifically those facing consumer debt lawsuits. Also mentions disproportionate impact on women, minority populations, and urban communities. | Consumer Law (specifically debt collection), Civil Procedure. | United States | The paper proposes using existing pro se resources curated by non-profits for chatbots and suggests the potential use of scanned court filings (via OCR) or curated/restricted LLMs for document generation, but does not detail a specific implementation or dataset. | NaN | NaN | False | False | NaN | GenAI accuracy/hallucinations, need for human oversight ('lawyer in the loop'), required human capital/resources (especially for under-staffed non-profits), funding for technological innovation in legal aid, the digital divide (access to internet/technology), language and accessibility barriers for users. | Technological feasibility (especially for more complex tasks like analyzing/opposing summary judgment motions), securing human resources for implementation and oversight within budget-constrained legal aid organizations, addressing the digital divide and accessibility issues, navigating ethical concerns (standard of care, confidentiality, UPL). | GenAI producing inaccurate results ('hallucinations'), lawyers/litigants relying on fictitious sources, increased burden on courts due to AI-generated filings (especially pro se), sharing confidential client information with AI tools, potential violations of Unauthorized Practice of Law (UPL) rules, possibility of widening the justice gap if technology disproportionately benefits well-resourced parties. |
| 21M7EwsP0fIJ.pdf | Google_Scholar | What will ChatGPT revolutionize in the financial industry ? | This paper explores the potential transformative impact of ChatGPT on the financial industry by examining its use cases, comparing it to existing financial chatbots, and analyzing its responses to specific queries. It also identifies key challenges such as data quality, regulatory compliance, bias, and cybersecurity, and discusses the future outlook for generative AI in finance. | True | Market | True | 2.0 | NaN | ChatGPT (specifically version 3.5) | Qualitative analysis of ChatGPT 3.5's responses to nine predefined questions regarding its applications in the financial industry. The outputs were interpreted using academic literature and articles on ChatGPT in finance. | ChatGPT's responses indicated potential for customer engagement, personalization, data analysis, stock price prediction, and compliance assistance in finance, claiming advantages over existing chatbots in conversational ability, learning, and customization. However, it acknowledged limitations and the need for legal consultation regarding regulatory approval for its use in the financial industry. | NaN | NaN | NaN | NaN | Finance (primary); Law (mentioned as an area where ChatGPT's capabilities have been tested and discussed by other cited studies) | International | NaN | The paper's methodology involved a conversational approach using prompts to elicit responses from ChatGPT 3.5 on its applications in finance, followed by critical analysis of these outputs in conjunction with existing academic literature. | NaN | True | True | ChatGPT is stated to be an 'open AI chatbot... available to everyone,' accessible via OpenAI, which offers free and paid tiers. | NaN | Data quality and quantity limitations, ensuring regulatory compliance (e.g., GDPR, FINRA), lack of interpretability and transparency in model decision-making ('black box' nature), potential amplification of existing data biases, risk management associated with synthetic data accuracy, cybersecurity vulnerabilities, and establishing clear lines of responsibility for decisions made by AI models. | Data inaccuracies in AI outputs, cybersecurity vulnerabilities (e.g., new attack vectors for financial institutions), breaches of customer data privacy and security, amplification of existing biases leading to discriminatory outcomes in financial services, inaccurate predictions or decisions due to model limitations or poor data, and regulatory non-compliance issues. |
| MAPiegzikAIinFamilyLaw.pdf | Google_Scholar | The Adoption of Artificial Intelligence in Family Law – Brand New or Well-known Idea? | This paper reviews the historical development and current state of Artificial Intelligence (AI) adoption in family law, contrasting early systems ('Wave 1') with modern machine learning approaches ('Wave 2'). It assesses AI's application across administrative efficiencies, client support, and decision-making, concluding that progress is accelerating despite ongoing challenges. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Complexity of human emotions in family law; lawyer skepticism ('dehumanization'); jurisdictional inconsistencies; usability challenges for non-specialists; AI's difficulty with nuance/context; ethical concerns (bias, privacy); accuracy limitations; large amounts of unstructured data. | Keeping 'humans in the loop'; developing AI for specific tasks (administration, ODR, information provision, decision support); leveraging AI (ODR, virtual assistants) to improve access to justice; advancing AI capabilities (machine learning, LLMs). | Access to legal information/aid, Online Dispute Resolution (ODR), child welfare (risk assessment, contact scheduling), divorce/separation processing (document drafting, property division). | Self-represented litigants in family law, children. | Family Law, Dispute Resolution (ODR, ADR). | International | Varies depending on the tool; includes social care data, demographic/historical/legal data for predictive models; legislation and case law for legal advice tools; large corpora of legal documents for drafting/review tools. | NaN | Adoption by law firms, courts (ODR), government agencies; commercial software releases; integration into existing legal tech platforms. | True | False | Numerous commercial AI tools for document review/drafting, case management, translation, legal advice (e.g., Casetext, Claude, CoCounsel Drafting, numerous ODR platforms) are mentioned as available on the market. | Knowledge gaps on AI's impact in family law; need for improved accuracy (advice, translation, prediction); need for tools better suited for non-specialists; addressing ethical concerns and bias; regulation/oversight needs; better handling of unstructured data. | Handling emotional complexity; ensuring accuracy/reliability; overcoming lawyer skepticism; cross-jurisdictional integration; addressing ethical issues (bias, privacy, accountability); obtaining quality training data; usability for laypeople. | Dehumanization of justice; algorithmic bias; privacy violations; inaccurate legal information/advice; misleading translations; over-reliance on flawed predictive models; AI becoming an unaccountable source of 'law'. |
| fYmtydY0ZpUJ.pdf | Google_Scholar | FAIRNESS AND FAIR USE IN GENERATIVE AI | This paper advocates for applying the 'non-expressive use' doctrine to assess fair use for generative AI, arguing that AI training on copyrighted works is permissible if it doesn't reproduce original expression in outputs. It contends that fair use analysis should stem from copyright principles, not broad policy considerations, while acknowledging specific fairness issues like lawful data access or systematic substitution. | True | Market | True | 1.0 | NaN | The legal theory/analytical framework of 'non-expressive use' for assessing whether the use of copyrighted materials in training and operating Generative AI models (such as LLMs and text-to-image models) constitutes fair use. | NaN | NaN | NaN | NaN | NaN | Disabled artists and people lacking specific artistic/musical competencies (in the context of broader access to creative tools, not legal services or justice). | Copyright Law, Intellectual Property Law | United States (primarily), with comparative mentions of UK, Japan, EU, Canada and others. | Massive quantities of text and images scraped from the internet, including copyrighted works, publicly available data (e.g., Project Gutenberg, Wikipedia, Github, arXiv, Common Crawl datasets like C4), licensed data, and potential use of 'shadow libraries' (e.g., for Books2, Books3 in ThePile). Specific datasets mentioned include Books2, LAION 5B, ThePile. | NaN | NaN | False | False | NaN | NaN | Applying existing copyright law (specifically the fair use doctrine) to the novel context of generative AI training and output; navigating legal uncertainty for AI developers regarding copyright liability for training data and model outputs; balancing copyright protection with technological innovation and public benefit. | Generation and propagation of misinformation, hate speech, cyberattacks, phishing emails; disclosure of private information; perpetuation and exacerbation of biases from training data; cultural homogenization; unhealthy dependence on technology; job displacement in creative industries; potential for AI to become deceptive, power-seeking, and pose existential risks. Also, 'memorization' by AI models leading to reproduction of copyrighted training data, and AI models being used as tools for copyright infringement by users. |
| U5ILlHRdNAkJ.pdf | Google_Scholar | Amusing Inventions Not to Be Thrown Away: ChatGPT and the Future of Tax | This article discusses the potential applications and implications of Generative AI, specifically ChatGPT T, for tax practice and research. It highlights potential benefits like increased efficiency while emphasizing the significant ethical risks and current limitations practitioners must consider. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Tax Law | US | NaN | NaN | NaN | True | True | ChatGPT has free and paid versions publicly available. BlueJ Ask is mentioned as a commercial product. | NaN | Inaccuracy (hallucinations, fake citations), outdated knowledge (pre-2021 data), lack of source citations, difficulty in verification, inability to handle nuanced judgment or complex analysis, need for human oversight. | Violation of professional competence duties (ABA Rule 1.1, Circular 230), breach of client confidentiality (ABA Rule 1.6), failure of supervisory duties (ABA Rule 5.1), reliance on incorrect or fabricated information leading to incorrect legal advice/filings, professional sanctions or disciplinary actions against lawyers/practitioners. |
| UiT7QntcF2wJ.pdf | Google_Scholar | Understanding National, Regional, and Global Priorities for the Social Justice and Economic Inclusion of Persons with Disabilities: Analyzing CRPD State Reports Using Text Mining, NLP, and LLMs | This paper analyzes 170 State Reports submitted under the UN Convention on the Rights of Persons with Disabilities (CRPD) using traditional text mining/NLP techniques and Large Language Models (LLMs). The study aims to identify global, regional, and national implementation priorities, assess the focus on social justice and economic inclusion, and evaluate the hybrid analytical approach. | True | Idealistic | True | 2.0 | Positive | Hybrid approach using traditional text mining/NLP (N-grams, TF*IDF, LDA topic modeling, NER with spaCy, custom dictionary/lexicon analysis) and LLMs (Gemini 1.5 Flash, GPT-4o) to analyze CRPD State Reports. | Analysis applied to a corpus of 170 CRPD State Reports scraped from the OHCHR website (subset of 20 used for LLM analysis due to token limits). Evaluation involved frequency analysis, LDA topic coherence assessment (0.461 score achieved), NER entity extraction, lexicon-based quantification of CRPD article/paragraph representation and disability model prevalence, and comparison of traditional NLP results with LLM outputs generated via multi-shot prompt engineering. | Identified key themes (e.g., awareness-raising, family rights, regional variations), found a general shift towards a social justice model (64% representation), quantified representation of specific CRPD articles (Art. 8 most represented, Art. 10 least), extracted relevant named entities, and demonstrated that LLMs could produce coherent analyses comparable to traditional methods on the tested subset. | Challenges in effectively monitoring the global implementation of the CRPD due to the volume and complexity of State Reports. Data collection and monitoring challenges are mentioned generally in the literature review. | Proposes a hybrid computational text analysis methodology (NLP and LLMs) to systematically analyze State Reports, enabling researchers, civil society, and monitoring bodies to identify implementation priorities, gaps, and regional variations, thereby facilitating accountability and strategic planning. | Monitoring implementation of the UN Convention on the Rights of Persons with Disabilities (CRPD). | Persons with disabilities. | International Human Rights Law, Disability Law. | Global (analyzing reports from 170 State Parties to the CRPD, with regional breakdowns). | The analysis corpus consists of 170 CRPD State Reports (publicly available PDFs from OHCHR website, unstructured text). NER uses spaCy's pre-trained 'en_core_web_sm' model. LLMs (Gemini, GPT-4o) utilize their own pre-training. Custom lexicons were created based on CRPD text and disability studies literature. | Corpus collection (web scraping), data preprocessing, lexicon development (manual, literature-based, validation via KWIC and robustness checks), text mining (N-grams, TF*IDF), NLP techniques (NER via spaCy, LDA Topic Modeling via Gensim), LLM analysis (Prompt Engineering with Google AI Studio/Gemini and OpenAI/ChatGPT). | Findings presented in an academic paper. The methodology is proposed as a framework to enable broader analysis by scholars, practitioners, and civil society, facilitated by more user-friendly GenAI tools. | False | False | NaN | Methodological limitations: reliance on self-reported state data, potential dictionary limitations, NER model not fine-tuned, LLM analysis constrained by token limits (subset used). Need to incorporate shadow reports for a balanced view. Substantive gaps: Less emphasis found on addressing stigma and barriers in reports. | Developing robust custom lexicons, achieving high coherence in topic modeling (LDA score was moderate), managing LLM token limits for large corpus analysis, standard PDF text extraction and cleaning. | The paper primarily focuses on benefits but limitations imply a risk of drawing inaccurate conclusions if relying solely on the analysis of self-reported data without considering its inherent biases or the methodology's limitations. |
| X_xiJkMBc48J.pdf | Google_Scholar | Generative AI and Tax Professionals: Current PR Guidance | This presentation outlines professional responsibility guidance for tax professionals using generative AI. It discusses ethical considerations regarding competence, confidentiality, supervision, client communication, and fees based on recent bar association directives. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Tax Law, Professional Responsibility | United States | NaN | NaN | NaN | False | False | NaN | NaN | Challenges for users include: AI hallucinations, lack of transparency ('black box'), user error (untrained users, poor prompting), limitations of training data, ensuring competence, maintaining confidentiality, supervising AI and staff use, accurate billing, and client communication. | Inaccurate outputs (hallucinations); violation of client confidentiality (through input, data breaches, third-party access, discovery); violation of attorney-client privilege; over-reliance hindering critical judgment; inaccurate billing (charging for saved time, training time, or general fees); failure to supervise subordinates' AI use; reputational damage. |
| ZqGazwM9LhwJ.pdf | Google_Scholar | Higher Education After Artificial Intelligence: An Invitation to a New Kind of Conversation About the Future | This paper argues that higher education must move beyond reactive measures to address the profound challenges posed by AI, particularly LLMs. It proposes a 'responsive' approach, inviting a new kind of collective conversation to fundamentally rethink the meaning and future of education. | True | NaN | True | 3.0 | NaN | A conceptual approach called 'responsiveness' involving experiential self-awareness, taking notice of embedded meanings, and retrieving historical possibilities to guide future action in higher education. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | The proposed 'responsiveness' approach is developed through philosophical argumentation, drawing on concepts from philosophy of technology, hermeneutics, and historical analysis of educational transformations. | Proposed through publication as an essay and an open invitation for the higher education community to join a new kind of conversation facilitated by the American Council on Education (ACE). | True | False | Readers are invited to join a conversation by contacting a representative at the American Council on Education (ACE). | NaN | Challenges in fostering adoption of the proposed 'responsiveness' approach include: overcoming the prevailing 'reactive' mindset, the dominance of instrumental rationality, and entrenched disciplinary silos within academia. | Risks from AI to higher education: academic integrity (cheating, plagiarism); hindrance to student skill development; spread of misinformation/disinformation; perpetuation of AI biases; disruption of the knowledge workforce and labor markets; existential threat to the role and authority of universities. |
| FET1CXtySgkJ.pdf | Google_Scholar | AHOW ARTIFICIAL INTELLIGENCE CAN HELP TO RESHAPE LEGAL PROFESSION THROUGHOUT THE WORLD | This paper explores how AI, including machine learning and NLP, can reshape the global legal profession by automating tasks, enhancing efficiency, and improving accuracy in areas like research and document review. It also discusses benefits such as cost-effectiveness and improved access to justice, alongside challenges like job displacement and the need for lawyers to adapt. | True | Market | True | 3.0 | Positive | AI tools for document review (e.g., Kira Systems), legal research (e.g., ROSS Intelligence), contract drafting/analysis (e.g., ContractZen), and virtual legal assistance (e.g., LegalShield, AI chatbots). | NaN | NaN | High cost of legal services and limited availability of legal assistance in underserved areas. | Automating legal processes with AI to reduce costs; using AI-powered tools like chatbots and virtual assistants to provide basic legal information and guidance. | Providing basic legal information and guidance, making legal services more affordable. | Underserved communities | General legal practice | International | NaN | NaN | NaN | False | False | NaN | Ensuring responsible, ethical, transparent, and accountable deployment of AI in legal services; upskilling legal professionals for AI integration. | Ethical concerns (bias, transparency, accountability), data privacy and security, accuracy and reliability of AI, potential job displacement, and the need for legal professionals to acquire AI competency. | Job displacement, breach of attorney-client privilege, AI bias, lack of accuracy/authority in AI outputs, ethical issues in AI decision-making, data privacy and security vulnerabilities. |
| -_ghJP3E10kJ.pdf | Google_Scholar | From Briefs t o Bytes: How Gener ative AI is T ransforming Legal Writing and Pr actice | This paper explores how Generative AI (GAI), particularly tools like ChatGPT, is revolutionizing legal practice with a focus on legal writing. It examines GAI's capabilities, practical applications for lawyers, ethical considerations, limitations, and provides a framework for effective use through prompt engineering. | True | Market | True | 3.0 | NaN | The paper focuses on the use of Generative AI (GAI), exemplified by ChatGPT and GPT models, for legal tasks, particularly legal writing. It details Prompt Engineering as the key technique for effectively interacting with and guiding these AI models. | The paper does not present a formal evaluation or systematic testing procedure. It uses illustrative examples generated by the author using GAI tools (e.g., editing text, summarizing, generating captions), cites external studies on GAI productivity, and relies on the author's expertise and experience. | NaN | Primary obstacles discussed relate to GAI use by legal professionals, not access to justice directly: GAI inaccuracy and 'hallucinations', inherent bias in training data leading to discriminatory outputs, risks to client confidentiality and data privacy, the knowledge gap among legal professionals regarding GAI, potential for overreliance diminishing critical skills, and ensuring ethical compliance. | Solutions focus on responsible GAI use by legal professionals: educating practitioners about GAI, employing careful prompt engineering techniques, verifying AI outputs, taking steps to mitigate bias, protecting client confidentiality, continuous learning and skill development, and adhering to ethical obligations. It mentions potential for legal aid to use GAI for app development. | NaN | NaN | General legal practice, Legal Writing, Legal Research, Contract Law, Litigation (briefs, motions), E-discovery, Legal Education, Law Firm Management, Marketing. | United States | The paper states GAI models like GPT are pre-trained on vast amounts of text data (hundreds of billions of pieces) gathered from the web, noting this includes diverse sources and copyrighted material, and may contain biases. Specific datasets are not detailed. | The paper primarily discusses prompt engineering as a method for *using* existing GAI tools, synthesizing best practices from research (e.g., Chain-of-Thought prompting) and practical experience. It does not detail the design methodologies for creating the underlying GAI models beyond mentioning transformer architecture and pre-training. | Discusses publicly available GAI chatbots (like ChatGPT), integration into commercial legal tech platforms (Thomson Reuters, LexisNexis), and the potential for custom tool creation and service productization by law firms. | True | False | Publicly available chatbots like ChatGPT (some versions free, advanced versions paid, e.g., GPT-4) and integrations into commercial legal tech software (requiring subscriptions). Prompt engineering techniques can be applied to available tools. | Knowledge gap among legal professionals on how GAI works and how to use it effectively and safely. Technical gaps in GAI include ensuring accuracy (reducing hallucinations), mitigating bias, improving reasoning, and maintaining data privacy. Societal gaps include adapting legal ethics and education to GAI. | Challenges for users include understanding complex GAI technology, mastering effective prompt engineering, critically evaluating AI outputs for accuracy and bias, ensuring confidentiality and ethical compliance, integrating GAI into existing workflows, and keeping pace with rapid technological advancements. | Inaccuracy and fabrication ('hallucinations', e.g., fake case citations), perpetuation of biases from training data leading to discrimination, breaches of client confidentiality and data privacy, failure to meet ethical duties (competence, diligence), overreliance diminishing lawyers' critical thinking and writing skills, potential copyright infringement issues related to training data and generated outputs. |
| dPkZcjcHFQsJ.pdf | Google_Scholar | Generative AI and Finding the Law | This paper outlines six principles for evaluating generative AI large language models in legal research, focusing on the shift in cognitive authority and the instability of AI outputs. It applies ecological holistic media theory, explains generative AI concepts, analyzes AI performance on legal tasks with examples, and concludes that law librarianship must evolve towards legal information science. | True | Market | True | 2.0 | Neutral | Evaluation of commercial legal research AI tools (Casetext CoCounsel, Lexis+ AI) and general LLMs (ChatGPT-4) employing Retrieval-Augmented Generation (RAG) and large language models for legal research tasks. | Qualitative evaluation through specific legal research problems/prompts across various legal fields (e.g., special needs trusts, slip and fall, ADA, boxing regulation, securities law, case summarization, fair use). Analysis focused on accuracy, consistency, handling complexity, abstraction capabilities, and hallucination. | AI demonstrated strengths like abstraction and analogical reasoning but showed significant weaknesses: inconsistent answers over time, difficulty with complex multi-issue/jurisdictional prompts, sensitivity to syntax, potential bias towards case law, prompt rewriting, and severe hallucinations (including reversing case holdings). No single tool was consistently superior; performance varied significantly. | AI unreliability (hallucination, instability), ethical issues for lawyers (competence, candor, supervision), need for human oversight and traditional research skills, potential AI bias, difficulty verifying AI outputs, and the existing digital divide in access to legal information tools. | Emphasizing traditional legal research skills for verification, adapting the profession towards legal information science, employing careful prompt engineering and iterative questioning, using Retrieval-Augmented Generation (RAG) grounded in authoritative legal sources, and adhering to ethical rules and court mandates regarding AI use. | NaN | NaN | General Law / Multiple Fields (including Estate Planning, Torts, Disability Law, Sports Law, Securities Law, Tax Law, Criminal Law, Copyright Law, Civil Procedure, Professional Ethics) | United States (Federal and various States including Missouri, Pennsylvania, Wisconsin) | Combination of general web data (for base LLM like GPT-4) and proprietary, domain-specific legal text databases (cases, statutes, regulations, secondary sources like Matthew Bender treatises) used for Retrieval-Augmented Generation (RAG) by commercial tools (CoCounsel, Lexis+ AI). | NaN | Commercial web-based subscription services (CoCounsel, Lexis+ AI) and publicly available web interfaces (ChatGPT). | True | False | Commercial subscription services (CoCounsel, Lexis+ AI, ChatGPT-4) and free web access (ChatGPT-3.5). | Need for improved AI reliability and stability; mitigating AI bias; developing robust ethical frameworks and user competence; ensuring effective human oversight; better AI handling of complex queries and non-caselaw sources; persistence of the digital divide; conceptual need for law librarianship to evolve into Legal Information Science. | Ensuring AI accuracy/avoiding hallucination; maintaining output consistency; handling complex legal queries; appropriate training/augmentation with diverse legal sources; effective prompt engineering; managing user trust vs. skepticism; ethical integration into legal workflows. | AI hallucination leading to misinformation and citing fake cases (Mata v. Avianca example); instability undermining legal certainty; violation of lawyers' ethical duties (competence, candor, FRCP Rule 11); AI bias; automation complacency; erosion of traditional skills; AI making false statements of law. |
| MMjoMWJmYBkJ.pdf | Google_Scholar | Legal Practitioners' Views on the Effectiveness of Virtual Courts | This study explores legal practitioners' perspectives on the effectiveness of virtual courts through semi-structured interviews, identifying key themes around technological adoption, procedural changes, and access to justice. It finds that while virtual courts can improve efficiency and accessibility, their success depends on addressing technological, procedural, and equity challenges, requiring ongoing adaptation and training. | True | Idealistic | False | 2.0 | Neutral | Virtual courts | Qualitative research design using semi-structured interviews with 30 legal practitioners (lawyers, judges, paralegals, court clerks). Data were analyzed using thematic analysis. | The analysis revealed four main themes: Technological Adoption, Procedural Changes, Impact on Justice Access, and Future Directions. Key findings include the importance of user-friendly technology, the potential of virtual courts to improve access to justice, and the need for continuous adaptation and training to address technological and procedural challenges. | Digital divide (lack of digital literacy and technology access); economic barriers (technology costs, funding disparities); physical barriers (disability access, age-related challenges); psychological barriers (privacy concerns, lack of trust in technology, perceived intimidation). | Enhancing user satisfaction, ensuring equitable access to justice, and adapting to evolving technological landscapes. Prioritizing user-friendly technology, robust security measures, comprehensive training programs, and providing support and resources to address the digital divide. | Effectiveness of virtual courts in relation to technological adoption, procedural changes, and impact on geographical, economic, physical, and psychological barriers to justice. | Individuals in remote areas, persons with mobility issues or other disabilities, those facing economic disparities, and individuals with low digital literacy. | General | International | NaN | Qualitative research design, semi-structured interviews, purposive sampling, thematic analysis (using NVivo software). | NaN | False | False | NaN | Need for research including broader stakeholders (litigants, witnesses, technical experts); quantitative and longitudinal studies; adapting laws and regulations to keep pace with technology; fully addressing the digital divide and ensuring equitable participation. | Ensuring ease of use and accessibility of platforms; implementing robust security measures; integrating with existing case management systems; managing technical glitches; adapting communication dynamics for virtual settings; overcoming resistance to change and learning curves; addressing the digital divide and technology costs. | Compromised effectiveness of legal procedures (e.g., cross-examinations) in virtual settings; exacerbation of accessibility issues due to digital divide or technology costs; negative psychological impacts (anxiety, distrust); security and confidentiality breaches if measures are inadequate; potential diminution of therapeutic justice aspects in legal proceedings. |
| WB8suT_r-MIJ.pdf | Google_Scholar | The Rapid Rise of Generative AI Assessing risks to safety and security | This report examines the national security implications of generative AI, based on literature reviews and expert interviews. It assesses political, digital, and physical security risks (e.g., disinformation, cyberattacks, CSAM, weapon instruction) and potential opportunities for intelligence agencies, proposing policy recommendations for governance and safe deployment. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | National Security Law, Cybersecurity Law, Criminal Law, Intelligence Law, International Law, Technology Regulation | Primarily UK, with discussion of US, EU, China, and Global governance. | Discusses models trained on large, pre-existing internet/text corpora (e.g., for GPT, LLaMA). Case study 1 (LLM_OSINT) involves agents using web searches. Case study 2 (Gen-MAS-Sim) uses OpenAI models (davinci-003, GPT-3.5 Turbo, GPT-4). | NaN | NaN | False | False | NaN | Need for better evaluation frameworks (socio-technical approach); reliable methods for identifying/watermarking AI content; techniques for 'machine unlearning' for compliance; robust evidence on terrorist uses; improved voice cloning detection; common technical standards and measurement theory for AI; enhanced government expertise, compute infrastructure, and data access; addressing hardware supply chain issues (semiconductors). | Unreliability, inaccuracy, and hallucinations of LLMs; user over-trust and over-reliance; data security risks (sensitive data input); prompt injection and jailbreaking vulnerabilities; data poisoning; difficulty purging information from LLMs; lack of transparency and explainability; distinguishing real vs. fake content; ensuring human validation and oversight; managing autonomous agents securely; aligning global standards; government skills and infrastructure gaps. | Political risks: Disinformation, electoral interference, surveillance, geopolitical fragmentation, erosion of trust. Digital risks: Enhanced cyberattacks (lowering skill barrier), targeted fraud/phishing (quality/scale), voice cloning scams, AI-generated CSAM creation/proliferation, terrorist radicalisation/propaganda. Physical risks: Lowering barriers to weapon (especially biochemical) instruction/development. Broader risks: Improper adoption in critical sectors (CNI, public services), unintended consequences from DIY experimentation, degradation of code integrity, potential for AI race-to-the-bottom dynamics impacting safety, accidental misuse leading to crises, undermining strategic stability. |
| H5rHkJZPK4QJ.pdf | Google_Scholar | Evaluating Errors and Improving Performance of Chatgpt: a Research Paper | This paper analyzes common errors (grammatical, semantic, contextual, factual) made by ChatGPT, identifying underlying causes like insufficient training data and model biases. It proposes and discusses various mitigation strategies (e.g., fine-tuning, reinforcement learning, bias mitigation) to improve ChatGPT's performance, reliability, and user experience. | True | Market | True | 2.0 | NaN | Error analysis of ChatGPT and proposed mitigation strategies for its improvement. | The paper proposes error identification through: Human Evaluation (rating responses on fluency, relevance, grammaticality, overall quality), Automatic Evaluation Metrics (Perplexity, BLEU, ROUGE, METEOR), Error Annotation (manual annotation of error types), Comparison with Gold Standard, and User Feedback and Surveys. It states that experimental evaluations were conducted to assess proposed mitigation strategies. | The paper states that 'The experimental results demonstrated the effectiveness of the employed error mitigation strategies in reducing errors and enhancing ChatGPT's performance.' No specific quantitative results are provided. | NaN | NaN | NaN | NaN | General (mentioned as a potential application area, not a focus) | International | ChatGPT is trained on 'a vast corpus of text data from the internet.' The error analysis dataset described comprises 'a collection of user interactions with chatgpt,' potentially constructed from human-generated conversations, simulated interactions, crowdsourced dialogues, or scraped public dialogue data (anonymized and unstructured). | Proposed error mitigation strategies include: Fine-tuning, Reinforcement Learning with User Feedback, Context-Awareness Enhancements (e.g., memory mechanisms, attention mechanisms, dialogue state tracking), Error-Specific Training Data Augmentation, Bias Mitigation techniques, Ethical and Safety Constraints (e.g., rule-based filtering, human-in-the-loop), Active Learning, Multi-Model Ensembles, and User Interface/Interaction Design. | NaN | False | False | NaN | Remaining LLM challenges include: handling long-range dependencies, understanding complex reasoning, generating context-aware responses, insufficient training data for specific scenarios, contextual ambiguity, and lack of common sense or comprehensive world knowledge. | Causes of errors (challenges) in ChatGPT include: Insufficient Training Data, Contextual Ambiguity, Lack of Common Sense or World Knowledge, Biases in Training Data, Overconfidence or Insufficient Uncertainty Estimation, Lack of continuous Feedback and Reinforcement Learning, Data Skewness or Bias, Sensitivity to Input Phrasing, and Algorithmic Limitations. | Potential risks include: generating biased, offensive, or harmful content; providing factually incorrect or misleading information; erosion of trust in AI systems; and providing inappropriate or insensitive suggestions or advice. |
| RiRt0XNLpwEJ.pdf | Google_Scholar | Assessing Information Literacy in the Age of Generative AI: A Call to the National Conference of Bar Examiners | This paper argues for the National Conference of Bar Examiners (NCBE) to incorporate information literacy assessment, especially concerning generative AI, into the Multistate Professional Responsibility Exam (MPRE). This is presented as crucial for ensuring newly licensed lawyers meet their duty of technology competence and to protect the public from the risks of incompetent AI use in legal practice. | True | Idealistic | True | 1.0 | Positive | Incorporating information literacy assessment for generative AI into the Multistate Professional Responsibility Exam (MPRE). | NaN | NaN | The primary obstacle identified is the risk of newly licensed lawyers' incompetent use of generative AI, stemming from a lack of assessed information literacy. This incompetence can lead to flawed legal research, ethical breaches, and ultimately harm to clients, thereby undermining access to competent legal services. | The paper proposes that the National Conference of Bar Examiners (NCBE) address this by incorporating specific assessments of information literacy related to generative AI into the Multistate Professional Responsibility Exam (MPRE). This would ensure a minimum standard of technological competence for newly licensed lawyers. | Lawyer competence in using AI, professional responsibility, public protection, legal research ethics in the age of AI. | The general public seeking legal services. | Professional Responsibility, Legal Ethics, Legal Research | United States | NaN | Conceptual analysis, review of legal and educational literature, argumentation based on existing institutional frameworks (e.g., NCBE history, AALL standards), and analysis of current technological impacts on the legal profession. | Proposed deployment through the National Conference of Bar Examiners (NCBE) by integrating new assessment components into the Multistate Professional Responsibility Exam (MPRE). | False | False | NaN | The current lack of formal assessment of AI-related information literacy in lawyer licensing exams (specifically the MPRE), which fails to ensure newly licensed lawyers are competent in using emerging AI technologies responsibly and ethically. | The primary challenge for the NCBE would be the rapid pace of AI development, requiring continuous updates to assessment content and methodologies, and ensuring the validity, fairness, and psychometric soundness of new question types related to AI and information literacy. | Risks identified include lawyers producing inaccurate legal work due to AI 'hallucinations,' breaching client confidentiality through improper AI use, and a general decline in critical legal skills if AI is used without adequate oversight. These issues can lead to disciplinary actions for lawyers and significant harm to clients, thereby eroding public trust in the legal profession. |
| VWi01BsHzJwJ.pdf | Google_Scholar | USING KAZAKH NER DATASETS FOR MULTICLASS CLASSIFICATION IN THE LEGAL DOMAIN: A COMPARATIVE STUDY OF BERT, GPT, AND LSTM MODELS | This study comparatively analyzes the performance of BERT, GPT, and LSTM models for multiclass text classification within the Kazakh legal domain, utilizing a specialized Named Entity Recognition (NER) dataset. The research highlights the models' effectiveness and challenges in processing a low-resource language, emphasizing the need for specialized datasets and algorithms for applications like legal document automation and decision support. | True | Market | True | 2.0 | Neutral | Comparative study of BERT, GPT, and LSTM models for multiclass text classification in the legal domain. | Models were evaluated on a specialized Kazakh NER dataset (KazNERD) adapted for legal multiclass text classification. Evaluation metrics included accuracy, recall, precision, and Area Under the Curve (AUC). | BERT demonstrated the best performance on validation data, achieving: Loss 0.0481, Accuracy 0.9202, Precision 0.9712, Recall 0.9585, and AUC 0.9781. | Scarcity of linguistic resources (annotated data, research) and NLP tools for low-resource languages like Kazakh, hindering the development of advanced AI tools for the legal domain, which could impact potential access to justice applications. | Development of specialized datasets (like the adapted KazNERD) and NLP models (BERT, GPT, LSTM) tailored for the Kazakh language and its legal domain to improve legal information processing, potentially making legal services more efficient and systems more accessible. | Automation of legal document management, analysis of court decisions, development of intelligent decision-support systems for the legal sector, growth of digital jurisprudence. | NaN | General legal domain, including legal document management, court decision analysis, and legal services automation. | Kazakhstan | The KazNERD dataset, an annotated corpus for Named Entity Recognition in the Kazakh language, adapted for legal topics. The data was preprocessed and transformed from NER annotations to suit multiclass text classification. | Comparative analysis of existing NLP models (LSTM, BERT, GPT). Data preparation involved tokenization specific to each model and creation of binary/multiclass labels from NER data for the classification task. | NaN | False | False | NaN | The Kazakh language is under-researched in computational linguistics; critical importance of creating more specialized datasets for training and testing models; need for development of advanced methods like cross-sentence entity recognition for deeper text understanding. | Adapting NLP models to the agglutinative structure and complex legal terminology of the Kazakh language; scarcity of annotated training data for specific legal tasks; ensuring model generalization from training data to unseen data. | NaN |
| GocnXfuRjPsJ.pdf | Google_Scholar | ARTIFICIAL REASON AND ARTIFICIAL INTELLIGENCE: THE LEGAL REASONING CAPABILITIES OF GPT-4 | This paper explores the concept of "artificial" legal reasoning, comparing it to the capabilities of artificial intelligence, specifically GPT-4. Through testing, it concludes that GPT-4 can generate outputs in legal tasks like fact-finding, interpretation, qualification, and decision-making that mimic human legal reasoning. | True | NaN | True | 2.0 | NaN | GPT-4 (via ChatGPT) | Qualitative testing using zero-shot prompting (with requests for step-by-step reasoning) on hypothetical legal scenarios, primarily traffic law examples set in a Serbian context. The study involved presenting these scenarios to ChatGPT (using GPT-4) and analyzing its responses, comparing outputs from May 2023 and March 2024 versions. | ChatGPT (GPT-4) can generate outcomes in fact-finding, interpretation, qualification, and decision-making that appear as if it reasons legally. It identified factual and interpretative problems, and when prompted to decide with underdetermined information, it relied on general legal principles (e.g., 'beyond a reasonable doubt', 'reasonable person standard'). | NaN | NaN | NaN | NaN | Traffic law (used in the paper's own illustrative test cases); General legal reasoning (as the broad subject of investigation). Mentions various fields covered by UBE/LSAT for context. | Serbia (setting for the paper's own illustrative test cases); USA (context for referenced LSAT/UBE benchmarks). The philosophical discussion aims for broader applicability. | GPT-4's training data: proprietary, large-scale, general text and multimodal data. The paper refers to it as 'large amounts of text' and 'large quantity of text the model was trained on'. | NaN | Commercial availability through OpenAI's ChatGPT service (the paper mentions using a paid version). | True | False | ChatGPT (based on GPT-4) is accessible as a commercial service; the paper specifically mentions use of a paid version. | NaN | Hallucinations (providing factually incorrect information), the 'black box' nature of LLMs (lack of transparency in reasoning processes), performance variability and potential degradation of models over time, and managing user/researcher 'expectation of perfection' bias when evaluating LLMs. | Generation of and reliance on 'hallucinated' or factually incorrect legal information (e.g., lawyers citing fake cases from ChatGPT). Emergent 'risky' AI capabilities (e.g., agency, long-term planning) if not understood or controlled. |
| JxMDLxGZzF4J.pdf | Google_Scholar | Competitive Advantage in B2B Marketing and Sales Through Generative AI | This paper explores how B2B firms can use generative AI in marketing and sales to gain a competitive advantage, applying the Situated AI Framework through three case studies. It finds that generative AI enhances efficiency, customer engagement, and strategic decision-making, with grounding, bounding, and recasting activities playing key roles. | True | Market | True | 2.0 | NaN | Application of Generative AI (ChatGPT-4, DALL-E, custom applications using GPT-4) in B2B marketing and sales, analyzed through the Situated AI Framework (grounding, bounding, recasting activities). | Qualitative analysis based on three case studies involving semi-structured interviews with company representatives and review of public documents/articles. | Generative AI enhanced operational efficiency (e.g., proposal generation time reduced by 75% in C3), improved customer engagement (personalized communications), enabled data-driven decisions, and built new capabilities (e.g., visual design in C1). Grounding, bounding, and recasting activities were identified as relevant for leveraging AI strategically. | NaN | NaN | NaN | NaN | B2B Marketing, B2B Sales, Business Strategy, Logistics. Peripheral: Corporate Law, IP Law, Data Privacy Law. | International | Mix of proprietary internal data (historical communications, RFPs, proposals) for custom models/fine-tuning/RAG, and user prompts/criteria combined with potentially public data for publicly available models (e.g., ChatGPT, DALL-E). | Case study research; Qualitative interviews; Thematic analysis; Application of Situated AI framework. For tools developed: Proof of Concept (POC), iterative development, agile methodology, user feedback, blind testing. | Use of public tools (ChatGPT, DALL-E) integrated into workflows; Development and internal deployment (or planned deployment) of custom AI applications (chatbot, proposal generator) often built on platforms like Azure OpenAI. | False | False | NaN | NaN | Technological limitations (hallucinations, image generation quality); Context alignment (difficulty achieving desired outcomes, e.g., C1 India campaign); Maintaining competitive advantage with widespread AI adoption; Change management; Ensuring data quality for training; Protecting AI capabilities; Effective adaptation/recasting based on feedback. | Data leakage/security breaches; Knowledge expropriation; AI errors/hallucinations; Ineffective implementation/poor ROI; Failure to adapt AI; Legal/compliance risks (data privacy, AI regulations); Reputational damage from inaccurate outputs. |
| BqZr04cxhiwJ.pdf | Google_Scholar | GPT Takes the Bar Exam | This paper evaluates the performance of OpenAI's GPT-3.5 (text-davinci-003) on the multiple-choice section (MBE) of the US Bar Exam using zero-shot prompting. GPT-3.5 significantly outperformed random guessing, achieving 50.3% accuracy overall and passing scores in Evidence and Torts, suggesting future LLMs may pass the full MBE. | True | Market | True | 2.0 | Positive | Evaluating OpenAI's GPT-3.5 (text-davinci-003) via zero-shot prompting on the Multistate Bar Examination (MBE). | Assessed performance on a complete official NCBE MBE practice exam (purchased December 2022) using zero-shot prompting with the text-davinci-003 API. Involved prompt engineering (testing 7 types, finding rank-ordering top 3 choices best) and hyperparameter tuning (temperature, top_p, best_of, max_tokens). Results were compared against baseline guessing, average human scores, and passing thresholds. | Best configuration (rank-ordering top 3 choices prompt) achieved 50.3% overall accuracy on the MBE practice exam. GPT-3.5 achieved passing rates in Evidence (63%) and Torts (62%). Its top two and top three choices were correct 71% and 88% of the time, respectively. Fine-tuning attempts did not improve performance over zero-shot. | NaN | NaN | NaN | NaN | Civil Procedure, Constitutional Law, Contracts, Criminal Law and Procedure, Evidence, Real Property, Torts | USA | The evaluated model (GPT-3.5 / text-davinci-003) was pre-trained by OpenAI on proprietary data described as "a blend of text and code from before Q4 2021". An unsuccessful fine-tuning attempt used 200 unseen, simulated MBE questions with explanations from an NCBE answer guide. | The evaluation method involved zero-shot prompting, extensive prompt engineering (comparing different prompt structures), and hyperparameter optimization using the OpenAI API. An attempt at fine-tuning via the API was also conducted. | NaN | True | False | The core technology (GPT-3.5 model family) is accessible via OpenAI's commercial API. | Performance gap between GPT-3.5 (50.3%) and the MBE passing threshold (~60%), particularly in subjects like Criminal Law. Nascent scientific understanding of LLM behavior and limitations due to the proprietary nature of GPT. Need for evaluation on other Bar exam components (essays, performance tests). | High sensitivity of LLM performance to prompt engineering. Difficulty in interpreting model reasoning due to lack of access to internal states. Failure of fine-tuning attempts with limited data. | NaN |
| nvJ-YKrRcQAJ.pdf | Google_Scholar | Artificial Intelligence in Civil Justice Systems : An Empirical and Interdisciplinary Analysis and Proposal for Moving Forward | This paper analyzes the systemic and individual harms posed by generative AI to civil justice systems (litigation and arbitration), drawing on empirical social science research. It proposes restructuring the legal profession and education based on England's split bar model to balance AI's benefits with the need to preserve human expertise and system legitimacy. | True | Idealistic | True | 1.0 | Negative | A proposed restructuring of the legal profession into a 'split bar' (post-AI solicitors using AI for routine tasks, post-AI barristers avoiding AI for complex/novel work), inspired by the English system. | The proposal is based on theoretical analysis, empirical social science research on AI's effects, legal scholarship, and comparative analysis of the English legal system; no empirical testing of the proposal itself is described. | NaN | Systemic threats to the legitimacy and integrity of civil justice (e.g., algocracy, path dependency, erosion of diffuse support); individual cognitive harms (e.g., automation bias, cognitive atrophy, skill degradation, metacognitive laziness, cognitive loafing, AI addiction); difficulty ensuring expertise development for junior lawyers; ethical challenges; risk of inequitable two-tiered justice. | Adopt a 'split bar' model distinguishing lawyers who use AI extensively (post-AI solicitors) from those who do not for complex tasks (post-AI barristers). Implement corresponding differentiated legal education pathways focused on either AI proficiency or traditional independent legal analysis, ensuring a baseline legal understanding before specialization. | Legitimacy of justice systems, Quality of legal services, Professional ethics and competence, Legal education reform | NaN | Civil justice systems (litigation and arbitration) | US, UK, EU, International | NaN | Interdisciplinary analysis, review of empirical social science research, legal analysis, comparative legal systems analysis (England and Wales). | NaN | False | False | NaN | Need for development of practical implementation details for the proposed split bar system; overcoming status quo bias for adoption; addressing potential negative impacts of pre-collegiate AI education on foundational skills required for the 'post-AI barrister' path; underexplored choice-of-law issues related to AI. | Ensuring responsible AI use by legal professionals; overcoming cognitive biases (automation bias, cognitive loafing, anchoring bias); preventing skill degradation and ensuring expertise development; maintaining system legitimacy and public trust amidst technological change; adapting legal education effectively; addressing ethical concerns (hallucinations, self-dealing); managing potential AI addiction. | Erosion of civil justice system legitimacy (algocracy, undermining judicial independence, loss of public trust); degradation of critical thinking, legal skills, and creativity (cognitive atrophy, path dependency); increased errors due to automation bias and hallucinations; reinforcement of societal biases through algorithms; creation of inequitable two-tiered justice systems; ethical violations; AI addiction among professionals. |
| 3613904.3642700.pdf | Google_Scholar | How Knowledge Workers Think Generative AI Will (Not) Transform Their Industries | This paper presents findings from participatory research workshops with knowledge workers across seven industries in the US regarding their expectations of generative AI's impact. Participants largely view generative AI as a tool for menial tasks requiring human review, not anticipating major industry disruption, but fearing it may amplify existing negative social forces like deskilling and dehumanization. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Law | USA | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Deskilling (especially elimination of entry-level jobs, reduction in value of skills, shift to precarious work), dehumanization (loss of human connection, joy in work, critical thinking), disconnection (from reality, human interaction, exacerbating loneliness), disinformation (proliferation of false/low-quality content, deepfakes, propaganda), job loss, inaccuracy/hallucinations in AI output, reflection of societal biases, privacy breaches (memorization), undermining professional expertise, potential for misuse under capitalism. |
| l1iBcAN9nccJ.pdf | Google_Scholar | Legal Validity w ith Artificial Intelligence Technology on Gpt Chat as Legal Aid | This paper analyzes the legal validity of using AI, specifically ChatGPT, for legal aid in Indonesia, highlighting the absence of a clear regulatory framework for liability and user protection. It argues for the urgent need for specific regulations to ensure AI's safe and ethical application in the legal field without compromising legal certainty for users. | True | Idealistic | True | 3.0 | Neutral | ChatGPT for legal aid | NaN | NaN | Uncertainty of legal liability for AI errors; AI (ChatGPT) lacking legal capacity/qualifications under Indonesian law; risk of inaccurate/outdated AI advice; AI's inability to make ethical judgments; data privacy/confidentiality concerns; lack of a clear regulatory framework for AI in legal aid. | Adopting specific regulations for AI in law (defining limits, accountability); public education on AI limitations; collaboration between tech developers and legal institutions; ensuring compliance with data protection laws; setting quality/accuracy standards for legal AI; clarifying provider liability and consumer protection. | Legal validity of AI-provided legal aid; legal liability for AI errors; data privacy and consumer rights in AI legal services; regulation of AI in the legal field; accessibility of legal information. | General public, especially those unable to afford professional legal advocates and individuals unfamiliar with the law. | Advocate Law, Consumer Protection Law, Personal Data Protection Law, provision of legal aid. | Indonesia | NaN | NaN | NaN | False | False | NaN | Lack of a clear legal framework for AI liability in legal aid; absence of specific regulations for AI use ensuring legal/ethical standards; unclear application of data protection laws to legal AI; no established mechanism for holding AI or developers liable for erroneous advice. | Ensuring legal validity of AI-generated advice; establishing accountability for AI errors; protecting user data; AI's lack of contextual/ethical understanding; potential for AI inaccuracies; public over-reliance or misunderstanding of AI capabilities. | Inaccurate or irrelevant AI-generated legal advice leading to adverse outcomes for users; misuse or leakage of personal/sensitive legal data; users relying on AI without understanding its lack of legal authority or accountability compared to human advocates. |
| 10._Efficient_prompt_engineering_Techniques_and_Trends_for_maximizing_LLM_output.pdf | Google_Scholar | Efficient Prompt Engineering: Techniques and T rends for Maximizing LLM Output | This paper reviews prompt engineering techniques (e.g., structured prompting, iterative refinement, chain-of-thought) aimed at optimizing Large Language Model (LLM) performance. It also discusses emerging trends like automated prompt generation and multi-modal prompting, along with challenges such as response bias, ambiguity, security, and ethical concerns. | True | Market | True | 3.0 | NaN | General prompt engineering techniques for LLMs (e.g., structured prompting, role-based prompting, iterative refinement, chain-of-thought, few-shot learning, automated generation, multi-modal prompting). | NaN | NaN | NaN | NaN | NaN | NaN | General Legal Field | International | NaN | NaN | NaN | False | False | NaN | Need for bias reduction, improved interpretability (explainable AI), automatic/secure prompt optimization, handling long contexts, robust evaluation standards. | Prompt ambiguity/vagueness, response bias, vulnerability to adversarial attacks (e.g., prompt injection), computational cost/latency, ethical concerns (misinformation, fairness, transparency). | Generation of irrelevant, incorrect, or biased responses; perpetuation of societal stereotypes; manipulation via adversarial prompts to produce false, dangerous, or unethical content; distribution of misinformation and deepfakes; lack of accountability due to opacity. |
| Cozd6dhwLwwJ.pdf | Google_Scholar | PR Council Guidelines on Generative AI | These guidelines provide ethical and legal advice for public relations professionals using generative AI, emphasizing responsible use, human oversight, and client confidentiality. The document outlines risks like bias, copyright infringement, and misinformation, and recommends transparency and accuracy. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Public Relations Law, Copyright Law, Contract Law, Intellectual Property Law, Confidentiality | US | NaN | NaN | NaN | True | True | Guidelines publicly released by the PR Council on April 26, 2023. | NaN | Ensuring accuracy of AI output, managing and mitigating algorithmic bias, protecting client confidentiality when using AI tools, maintaining intellectual property integrity, ensuring proper disclosure of AI use, need for adequate staff training, keeping guidelines current with rapid AI evolution. | Spreading deepfakes, misinformation, or disinformation; Inaccuracy and fabrication of information by AI; Inadvertent plagiarism, copyright infringement, or trademark infringement; Violation of confidentiality agreements; Bias in AI-generated text and images; Contract violations related to 'work for hire' clauses; Increased legal risk due to indemnification clauses; Misuse of voice/music generation tools. |
| f4RWySu8iFcJ.pdf | Google_Scholar | Ten Thousand AI Systems Typing on Keyboards: Generative AI in Patent Applications and Preemptive Prior Art | This paper examines the potential negative impacts of generative AI on the US patent system. It specifically analyzes how AI could be misused to create massive databases of preemptive prior art and flood the Patent and Trademark Office (PTO) with low-quality patent applications, proposing policy solutions to mitigate these risks. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Patent Law, Intellectual Property Law | United States | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Use of generative AI to publish massive online databases of preemptive prior art intended to foreclose patentability. Use of generative AI to automate the writing and filing of enormous numbers of low-quality provisional and utility patent applications, potentially overwhelming the PTO. Weakening of the conception requirement if AI-generated text is accepted without a substantive nexus to human inventorship. Misuse of the provisional application system and fee structures to file excessive disclosures cheaply. Potential for findings of egregious misconduct/inequitable conduct if inventors falsely claim inventorship over AI-generated application content. |
| DOSrEgjcnAoJ.pdf | Google_Scholar | REVOLUTIONIZING JUSTICE: UNLEASHING THE POWER OF ARTIFICIAL INTELLIGENCE | This paper provides an overview of artificial intelligence (AI) and machine learning (ML) applications in the legal field, discussing their history, benefits (efficiency, research, contract analysis, predictive analytics), and potential uses by lawyers and courts. It also explores ethical considerations, potential disadvantages (confidentiality, misuse, inaccuracy, job loss), and legal liability issues associated with AI in law. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | Lack of affordability and accessibility of traditional legal services; Potential for systemic bias in AI tools used in the justice system (e.g., risk assessment). | Using AI tools like virtual assistants and chatbots for basic legal guidance and resource direction; Automating legal processes for pro bono and legal aid services to increase efficiency and reach. | Providing basic legal guidance; Answering common legal questions; Client intake/direction; Automation for pro bono/legal aid; Bail decisions; Sentencing (recidivism risk assessment). | General public needing affordable legal services; Clients of pro bono services, public interest organizations, and legal aid clinics. | General Litigation, Contract Law, Criminal Law, Legal Research, Practice Management | US | NaN | NaN | NaN | False | False | NaN | Systemic bias in AI datasets and algorithms leading to unfair outcomes; Lack of transparency ('black box') in AI decision-making; Ensuring AI accuracy and preventing 'hallucinations'; Need for human oversight, accountability, and clear ethical/legal frameworks; Ensuring AI tools for access to justice are affordable, accessible, accurate, and do not constitute unauthorized practice of law. | NaN | Disclosure of confidential information; Misuse of AI (e.g., plagiarism); Inaccurate or outdated AI outputs ('hallucinations'); Creation and use of deepfakes; Potential job displacement in the legal sector; Systemic bias leading to discrimination (e.g., in hiring, sentencing); Lack of transparency and accountability; Ethical violations (competence, confidentiality, unauthorized practice of law); Legal liability uncertainty. |
| XKWZNKbyZE0J.pdf | Google_Scholar | Lawful Grounds to Share Justice Data for Lawtech Innovation in the UK | This paper analyzes the legal framework under UK data protection law (UK GDPR) for sharing publicly held 'justice data' (like court judgments and pleadings) with commercial lawtech entities. It argues that 'public interest' or 'legitimate interests' could serve as lawful bases, facilitating innovation aimed at improving access to justice. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Lack of a clear legal basis under data protection law (UK GDPR) for secondary processing (reuse) of justice data containing personal information by commercial entities for innovation purposes; difficulty balancing public interest, commercial goals, and individual data protection rights. | Proposes using GDPR Article 6(1)(e) 'public interest' or Article 6(1)(f) 'legitimate interests', potentially in a hybrid model, as lawful bases for sharing justice data. Emphasizes the need for careful assessment of purpose limitation, necessity, controllership, and the balancing test. | Access to legal information, Cost-effective legal advice, Predicting case outcomes. | Litigants in person. | Data Protection Law, Access to Justice | UK | Conceptual discussion of using 'justice data' (e.g., judgments, potentially pleadings and submissions) held by UK public bodies (e.g., HMCTS, Ministry of Justice), which contains personal data and is largely unstructured text. | NaN | NaN | False | False | NaN | Regulatory uncertainty regarding the interpretation and application of 'public interest' and 'legitimate interests' grounds for data sharing involving commercial entities; practical challenges in establishing robust data sharing agreements and governance; potential impact of future data protection law reforms. | Interpreting complex GDPR provisions (e.g., necessity test, balancing test, purpose limitation, research exemptions) in the context of public-private data sharing; defining the scope of 'public authority tasks'; determining data controllership roles. | Unlawful processing of personal data leading to sanctions; infringement of individuals' data protection rights and privacy; potential for inaccurate or biased outputs from legal analytics tools; misuse of sensitive justice data; undermining public trust in the justice system due to opaque data use or data breaches. |
| HmTfEfhHkRMJ.pdf | Google_Scholar | AI Diversity and the Future of “Fair” Legal AI | This article examines the potential for AI to reshape legal practice, highlighting the critical issue of embedded bias, particularly in automated legal decision-making. It proposes that using a diversity of AI systems ("AI diversity" or a "multisystem approach"), benchmarked against public standards, can help mitigate bias and lead to fairer legal outcomes. | True | Idealistic | True | 1.0 | Neutral | AI Diversity / Multisystem Approach: Employing multiple, distinct AI models (developed by diverse teams, trained on different data) in parallel for legal tasks, comparing their outputs to enhance reliability and mitigate bias. | The paper proposes the technique conceptually and discusses the general importance of testing and benchmarking AI, including using public benchmarks, but does not report any specific testing or evaluation conducted on the proposed "AI diversity" approach itself. | NaN | Algorithmic bias stemming from training data that reflects historical societal and legal system inequalities; Lack of transparency in AI systems ("black box" problem) hindering trust, accountability, and regulation. | Adopt an "AI diversity" or "multisystem approach" using multiple AI models benchmarked against public standards; Ensure diversity in development teams and training data; Promote transparency and public participation in AI implementation and oversight; Use consensus among models for credibility and discrepancies to trigger human review. | Fairness in legal AI, Bias mitigation in automated legal/governmental decision-making (administrative decisions, judicial rulings, sentencing), Algorithmic accountability and transparency. | Minority and underrepresented groups disproportionately affected by bias in the legal system (e.g., racial minorities mentioned in context of COMPAS and juror questioning). | General / Multiple (Criminal Law, Administrative Law, Constitutional Law, Litigation, Legal Research, Document Drafting, Appellate Review) | International | The paper discusses general types of data used for legal AI (historical text, cases, statutes, dockets) and emphasizes the source of bias often lies in this data reflecting societal inequalities or specific legal system biases (e.g., COMPAS data, voir dire data). It advocates for diverse, cleaned, and vetted data but does not describe a specific dataset. | Conceptual proposal; The paper does not detail specific design methodologies used to develop the proposed 'AI diversity' technique itself. | The paper proposes parallel deployment of multiple benchmarked AI systems for government functions and potentially in the appeals process, but does not describe any actual deployment. | False | False | NaN | Lack of emphasis on incorporating AI system diversity (multisystem approach) in current proposals for AI adoption, regulation, and transparency, especially in government legal processes; Need for effective public benchmarks for legal AI; Need for truly democratized processes for AI implementation and oversight in the public sector. | Ensuring fairness and eliminating bias in AI systems; Dealing with the opacity ('black box') of complex models; Developing appropriate and timely regulations; Establishing trust and accountability; Sourcing diverse data and development teams; Creating meaningful benchmarks; Managing and interpreting outputs from multiple AI systems. | Replicating and amplifying societal biases leading to unfair or discriminatory legal outcomes (e.g., in sentencing, administrative decisions); Degrading trust in the legal system due to biased or opaque AI; Lack of accountability for AI-driven decisions; Hindering access to justice or exacerbating inequities if AI implementation is flawed. |
| oS44t3l-DBgJ.pdf | Google_Scholar | A Question and Answering Service of Typhoon Disasters Based on the T5 Large Language Model | This paper proposes a question-answering (Q&A) system for typhoon disaster information using the T5 large language model, integrating domain fine-tuning and retrieval-augmented generation (RAG). The system aims to improve access to accurate and timely disaster information for the public. | True | NaN | True | 1.0 | NaN | A Q&A system (Typhoon-T5) based on the T5 model, enhanced through continuous pre-training on typhoon-related text, fine-tuning on typhoon Q&A pairs, and retrieval-augmented generation (RAG) using external knowledge. | Evaluation using text similarity metrics (cosine similarity with 'all-MiniLM-L6-v2', Jaccard similarity coefficient), text generation quality metrics (ROUGE-1, ROUGE-2, ROUGE-L), intelligent evaluation (using ChatGPT), and manual evaluation by human workers. Compared different configurations: T5-large vs T5-base, with/without fine-tuning (Typhoon-T5 vs T5), and with/without RAG context. | The proposed method (Typhoon-T5-large_with_context) integrating fine-tuning and RAG outperformed other configurations across all evaluation metrics (Cosine similarity, Jaccard index, ROUGE, ChatGPT, Manual). For instance, it achieved the highest ROUGE scores (e.g., 40.82% ROUGE-1) and demonstrated the highest frequency of outputs in high-similarity score ranges. | NaN | NaN | NaN | NaN | NaN | China | Textual information about typhoon disasters collected from open-source databases like Baidu Encyclopedia and Wikipedia, plus news website reports (specifically focusing on Typhoon 'In-Fa'). This unstructured text data was processed to create a corpus for continuous pre-training using masked language modeling and a dataset of 2204 Q&A pairs for fine-tuning. | Data collection from public web sources, data cleaning and classification, masked language modeling for pre-training data construction, Q&A pair generation (using open-source self-instruct method), continuous pre-training of T5 model, supervised fine-tuning on Q&A pairs, and implementation of RAG using ColbertV2 for vector DB creation and retrieval. | NaN | False | False | NaN | NaN | Increased complexity and hardware/computational resource requirements due to integrating fine-tuning and RAG, especially with larger knowledge bases. Potential for poor quality of RAG-retrieved text negatively impacting results ('hallucinations'). Limitations of the T5 model itself, such as inability to automatically adjust response length appropriately. Current data representation focuses on spatiotemporal text descriptions, lacking integration with other key factors like meteorological, political, or socioeconomic data. | LLM hallucination leading to incorrect or misleading information, which is particularly problematic in the context of disaster response. Poor quality of retrieved text in the RAG process could mislead the LLM, resulting in inaccurate answers. |
| O3Q_xyM4nOAJ.pdf | Google_Scholar | Exploring the Impact of Attention Mechanisms in Big Data Analysis and Large Language Models | This paper reviews the transformative effect of attention mechanisms on big data analysis and large language models (LLMs), highlighting their improvements over traditional sequence models. It discusses applications in generative AI, business intelligence, and prompt engineering, noting challenges like computational cost and interpretability. | True | Market | True | 3.0 | NaN | Attention Mechanisms / Transformer Models (e.g., BERT, GPT) | Comparison of Transformer/Attention-based models (BERT, GPT) against traditional models (LSTM) using performance metrics (Accuracy, F1-Score, Inference Time, Training Time) on tasks like time-series forecasting, text classification, anomaly detection, and text summarization. Datasets included text corpora (Common Crawl, Wikipedia) and structured data (financial transactions, sensor logs). | Attention-based models substantially outperformed traditional LSTM models across tasks. For example, BERT achieved 94% accuracy in text classification vs. 81% for LSTM, and a Transformer model achieved 94% accuracy in financial anomaly detection vs. 87% for LSTM. | NaN | NaN | NaN | NaN | NaN | International | Textual corpora from open repositories (e.g., Common Crawl and Wikipedia) and structured big data sources such as financial transactions and sensor logs. | Comparative analysis of different model architectures (LSTM vs. Attention/Transformer), hyperparameter tuning (Bayesian optimization), standard dataset splitting (train/validation/test). | NaN | False | False | NaN | NaN | High computational costs and model interpretability are identified as key challenges associated with attention mechanisms and large models. | NaN |
| nMYAEiY8Io4J.pdf | Google_Scholar | If You Give an LLM a Legal Practice Guide | This paper examines how providing Large Language Models (LLMs) with information from legal practice guides, using Retrieval Augmented Generation (RAG) and structured propositional prompting, impacts their ability to answer legal questions and predict case outcomes. Findings indicate that while practice guides generally enhance performance, effectiveness varies significantly across models, legal domains, and prompting techniques, with structured approaches sometimes substantially improving or degrading results. | True | Market | True | 1.0 | NaN | Retrieval Augmented Generation (RAG) using legal practice guides, and a structured propositional prompting methodology breaking down legal rules from these guides into discrete queries. | Evaluated various LLMs (GPT-3.5, GPT-4, Claude Haiku, Sonnet, Opus) on legal question answering and outcome prediction using real cases (California res ipsa loquitur, Minnesota eminent domain) and expert-written hypothetical cases (same domains plus New Jersey pretrial detention). Performance was measured by accuracy across different prompting strategies: baseline (case name only), facts only, RAG with practice guide excerpt (+Guide), and propositional logic-based multi-query (Prop.). | The propositional prompting method (Prop.) achieved 100% accuracy on Minnesota-specific eminent domain hypotheticals for GPT-3.5, Claude Haiku, and Claude Sonnet (Prop. 2/3 variants), significantly outperforming other methods for these specific model-task combinations. However, overall results were highly variable, with no single method consistently superior across all models and legal areas. | NaN | NaN | NaN | NaN | Tort law (res ipsa loquitur), Real estate law / Constitutional law (eminent domain), Criminal procedure (pretrial detention). | California, Minnesota, New Jersey (USA). | The technique uses existing legal practice guides (California torts, Minnesota real estate, New Jersey criminal procedure) as the source for RAG and for structuring propositional prompts. Evaluation data consists of manually extracted facts and holdings from real cases referenced in these guides, and hypothetical examples written by a legal expert. | Comparative evaluation of different prompting strategies: baseline (case name only), facts-only, RAG with full practice guide excerpts, and a propositional logic-based multi-query approach derived from practice guides (with two levels of breakdown, Prop. 2 and Prop. 3). | NaN | False | False | NaN | NaN | High variability in LLM performance across different models, legal subject areas, and prompting methods. Difficulty in separating facts from legal reasoning in real case opinions. Real cases used for evaluation often concern gray areas of law, making 'ground truth' complex. Appellate court decisions can be nuanced (e.g., remands) rather than clear yes/no outcomes. | LLMs may be misled by their creative capacity, especially in nuanced legal doctrines like res ipsa loquitur. Providing practice guides or using more complex prompting can sometimes worsen performance (inverse scaling). Risk of LLMs generating erroneous legal analyses or predictions. |
| vsJBIMMWpjwJ.pdf | Google_Scholar | ChatGPT for Legal and Tax Professionals: ‘World-Altering Power’ Requires Kid Gloves | The paper examines the ethical challenges legal and tax professionals face when using ChatGPT, specifically concerning confidentiality and accuracy under the MRPC and Circular 230. It concludes that due to significant risks like data privacy issues and AI hallucinations, professionals should use ChatGPT with extreme caution and primarily for low-stakes tasks. | True | Market | True | 2.0 | Negative | ChatGPT (specifically referring to GPT-4 capabilities and limitations at the time of writing) | NaN | NaN | NaN | NaN | NaN | NaN | Legal Ethics, Tax Law | United States | Large datasets of unlabeled text, user interactions/content (as disclosed by OpenAI privacy policy). Mentions training data cutoff of Sept 2021 for GPT-4. Also notes legal challenges regarding the use of potentially proprietary material in training AI models generally. | NaN | Available via OpenAI website; paid subscription for latest version (GPT-4 at time of writing). | True | False | Accessible via OpenAI website link provided in the paper. A paid subscription version (GPT-4) is mentioned. | NaN | Ensuring compliance with ethical rules (competence, confidentiality, diligence) given ChatGPT's inaccuracy and data usage policies; verifying AI output; obtaining informed client consent; staying updated on AI risks/benefits; navigating prohibitions on AI reliance for written tax advice. | Violation of client confidentiality; providing inaccurate advice (malpractice/ethics violations); reputational harm; disciplinary action; legal liability; malware from spoofed sites. |
| qOSNB97orXcJ.pdf | Google_Scholar | AI-Powered Platforms for Access to Justice: The Case of Hear Me Out | This paper introduces Hear Me Out, an AI-powered platform using GPT-4o and RAG to help disadvantaged Australians navigate complex complaint pathways, thereby enhancing access to justice. It details the platform's user-centered design, technical architecture, ethical considerations, and initial impact, outlining plans for future expansion. | True | Idealistic | True | 1.0 | Positive | Hear Me Out: An AI chatbot platform using Azure OpenAI (GPT-4o) with tool-based Retrieval Augmented Generation (RAG), OpenAI Ada embeddings, Pinecone vector database, and Cosmos DB backend to guide users through legal complaint processes. | Usability testing with potential users during prototype development; ongoing user feedback collection (surveys, forms) for iterative improvement. | Positive user feedback led to refinements (response sensitivity, language adjustments); qualitative descriptions of enhanced user self-advocacy and potential efficiency gains for legal aid providers; systemic impact illustrated via analogous case studies. | Complexity and fragmentation of the legal complaint system, lack of centralized guidance, resource constraints in legal aid, lack of legal representation, difficulty understanding legal language, traditional barriers (cost, time, location). | An AI-powered platform (Hear Me Out) to simplify complaint navigation, provide automated guidance and triage, offer plain-language explanations, overcome traditional access barriers, and facilitate data collection for systemic advocacy. | Navigating complaint systems, lodging complaints, self-advocacy support, systemic advocacy. | Disadvantaged communities in Australia experiencing discrimination and disadvantage, including First Nations, CALD communities, and people with disabilities. | Administrative Law (complaint procedures), Discrimination Law, Human Rights Law, potentially others depending on the specific complaint. | New South Wales (Australia), with planned expansion across Australia and potentially internationally. | Information on NSW complaint bodies and pathways (stored in Cosmos DB); synthetic scenarios based on real data linked via metadata to complaint bodies (stored in Pinecone vector DB using OpenAI Ada embeddings); base model is GPT-4o. | User-centered design (prototype testing with target users), collaborative development (non-profit, universities, tech company), technical investigation, iterative development based on design principles derived from user testing. | Web application accessible via www.hearmeout.org.au. | True | False | Available as a web application at www.hearmeout.org.au (currently focused on NSW). | Need for broader geographic/jurisdictional coverage, enhanced AI capabilities (complaint drafting, translation, accessibility), deeper integration with public systems, development of comprehensive AI governance policies for justice, need for public awareness and trust. | Adapting AI to diverse legal jurisdictions, ensuring data privacy/security, managing ethical AI considerations (bias, transparency, accountability), balancing content filtering, maintaining accuracy and relevance of legal information, securing collaborations and resources for development/expansion. | Data breaches, unauthorized data access, AI model drift impacting response quality, inaccurate AI guidance, suppression of valid complaints via content filtering, potential AI bias. |
| informit.T2024121000001400747097470.pdf | Google_Scholar | ALLA CONFERENCE 2024: TAKE THE LEAP | This paper is a personal reflection by a law librarian on her attendance at the ALLA Conference 2024, summarizing key presentations. Topics include the AI tool 'amica' for assisting couples in separation (access to justice), space law, and the role of librarians in navigating generative AI and LLMs. | True | Idealistic | True | 3.0 | Positive | amica: an AI tool by the Legal Services Commission of South Australia, designed by family lawyers, to help couples navigate separation and asset division. | For amica: Ongoing quality assurance on every case by a team of people; if a case is unusual or out of range of the AI's training scenarios, it is flagged for the team to contact the couple with resource suggestions. | For amica: Described as a valuable tool for those who cannot afford lawyers during separation, with a free version ('amica one') available to provide an estimate of asset division. | The high cost of hiring lawyers for couples going through separation, preventing access to legal assistance. | The 'amica' platform, an AI tool designed to guide couples through separation and asset division, offering a free version ('amica one') for initial estimates. | Access to legal assistance for relationship separation, financial settlements, and property division. | Couples, particularly those with limited financial means, undergoing relationship separation and needing guidance on legal processes. | Family Law | Australia (specifically, amica is a government platform, with the Legal Services Commission of South Australia involved in its development). | For amica: The AI tool was trained on over one thousand scenarios and is a closed model system. These scenarios were presumably related to family law separations. | For amica: Designed by family lawyers; incorporates quality assurance for every case by a team of people, with out-of-scope cases escalated for human intervention. | For amica: Deployed as a government web platform (amica.gov.au), with a free version called 'amica one' also available. | True | False | The 'amica' platform (amica.gov.au) is described as an existing, usable government service, with a free version 'amica one' accessible online. | AI systems, while beneficial for access to justice (e.g., amica), still require human oversight, especially for unusual cases, and lack human qualities like empathy and discretion critical in legal matters. The need for human input to review AI outputs. | NaN | Generative AI risks include lack of empathy and discretion, potential for inaccuracies ('hallucinations'), and the need for critical human review. Broader concerns about AI involve human rights implications. For amica, the risk of unusual cases falling outside its AI capabilities is managed by human review. |
| BB-GeoGPT-IPM1.pdf | Google_Scholar | BB-GeoGPT: A Framework for Learning a Large Language Model for Geographic Information Science | This paper introduces BB-GeoGPT, a Large Language Model specialized for Geographic Information Science (GIS), developed by fine-tuning LLaMA-2-7B on curated GIS-specific datasets. It also presents the framework for creating such domain-specific LLMs and benchmark datasets, demonstrating BB-GeoGPT's improved performance over general LLMs on GIS tasks. | True | NaN | True | 1.0 | NaN | BB-GeoGPT, a GIS-specific Large Language Model created by adapting LLaMA-2-7B through continued pretraining (on BB-GeoPT) and supervised fine-tuning (on BB-GeoSFT) using LoRA, along with a framework for curating domain-specific datasets and evaluation. | Evaluated using the custom BB-GeoEval dataset (600 objective, 150 subjective GIS questions across 5 domains: Spatial Analysis, Geodatabase, Cartography, Remote Sensing, Surveying). Also tested on toponym extraction (Harvey2017, Ju2016 datasets) and temporal reasoning (TEMPREASON-L1 dataset). Compared against LLaMA-2-7B, Alpaca-7B, Vicuna-7B, K2 (geoscience LLM), and GPT-3.5-turbo. Subjective evaluation involved GPT-4 as a referee and 12 human GIS professionals. | On BB-GeoEval objective tasks, BB-GeoGPT achieved an average accuracy of 0.608, outperforming similar-sized open-source LLMs by 10.55%-47.57%. On subjective tasks, it showed improvements of 7.87%-27.73% and outperformed K2, though it lagged behind GPT-3.5-turbo, particularly in completeness. | NaN | NaN | NaN | NaN | NaN | NaN | Custom-curated datasets: BB-GeoPT (26,907 GIS-related papers and Wikipedia documents for pretraining). BB-GeoSFT (35,876 instructions for fine-tuning, including general instructions from GPT4-Alpaca, self-instructed GIS Q&A from BB-GeoPT, rule-based text summarization/generation from GIS papers, and data from open-source professional datasets like BroadTwitterCorpus, LNEx, SemEval-2015 Task 8, and UltraChat). | Domain adaptation of LLaMA-2-7B. Continued pretraining on GIS-specific text corpus (BB-GeoPT) and supervised instruction fine-tuning on a mixed general and GIS-specific instruction dataset (BB-GeoSFT). Utilized Parameter-Efficient Fine-Tuning (PEFT) method LoRA. Self-instruct method employed for generating GIS-specific instruction data using LLaMA-2-7B-chat. | NaN | False | False | NaN | NaN | High demand for computing resources for training LLMs; scarcity of large-volume, high-quality professional training data for specialized domains like GIS; general LLMs' lack of deep understanding of specific disciplinary knowledge; deployment challenges for large models (compute/memory-intensive requirements); model limitations such as hallucination, toxicity, stereotypes, and limited non-English support. | Hallucination, toxicity, and stereotypes inherited from foundational LLMs. Potential for factual inaccuracies despite domain-specific training, which is crucial in geographic information. |
| k1-G1sD5mA0J.pdf | Google_Scholar | AI and Tools for Expanding Access to Justice | This paper explores how artificial intelligence, encompassing traditional expert systems and modern large language models, can significantly improve access to justice by automating legal tasks and enhancing the accessibility of legal support. Through case studies like MADE, the Resurrection Project, and Rentervention, it demonstrates practical applications of AI in assisting unrepresented individuals and legal aid organizations. | True | Idealistic | True | 2.0 | Positive | Expert systems for document automation, and their enhancement with Large Language Models, including conversational AI chatbots. | User-centered design, iterative feedback from users (tenants, clinic staff, volunteers), usage analytics (e.g., Google Analytics, internal metrics), and qualitative impact assessment (e.g., time saved, error reduction). | The Resurrection Project's tool, built with LLM-assisted development, reduced legal form processing time for migrant families from 2 hours to 45 minutes, assisting 4,440 individuals (1,097 family groups) between February and May 2024, and saving over 1,370 hours. | High cost and limited availability of lawyers, difficulty for people to understand legal processes or recognize their legal problems, restrictive regulations on providing legal help (unauthorized practice of law), and the sheer scale of unmet legal needs. | Deploying AI-powered tools like expert systems, document automation, and conversational chatbots to guide self-represented litigants; using LLMs to enhance these tools' capabilities and reduce development costs; promoting regulatory reforms (e.g., sandboxes); and developing accessible, interactive legal applications. | Eviction defense, immigration assistance (work authorization, Temporary Protected Status), tenant rights, and broader civil legal aid for self-represented litigants. | Low-income individuals, tenants, migrants (particularly recent arrivals), self-represented litigants, and other vulnerable populations facing civil legal issues. | Housing Law, Immigration Law, Civil Law (general, including family law and administrative benefits). | Primarily United States (Massachusetts, Illinois), with mentions of broader U.S. applicability (e.g., CourtFormsOnline.org in a dozen states) and global access to justice issues. | For rule-based expert systems: encoded legal knowledge, procedures, and template documents. For LLM-assisted development (e.g., GitHub CoPilot for Resurrection Project tool) or LLM-powered features (e.g., OpenAI models in Rentervention, Weaver tool): large, general pre-trained models based on public code, web text, and other diverse data sources. | User-centered design, iterative development, co-development with users and stakeholders, rapid prototyping, feedback loops using direct observation and analytics, and leveraging existing development frameworks (e.g., Docassemble, Assembly Line). | Web applications accessible on various devices (including smartphones), deployment in legal aid clinics and for remote assistance, integration with existing legal aid workflows and intake processes, online chatbots, virtual help desks, and dissemination of standardized development frameworks to other organizations. | True | False | Specific tools like MADE are available online for their target audience (e.g., Massachusetts tenants). Rentervention is an operational service for Illinois renters. CourtFormsOnline.org provides access to forms for users in several US states. | The vast scale of unmet legal needs persists. Low adoption of existing automation due to cost/rigidity, scarcity of deployed public interest LLM applications, challenges in safely and effectively integrating LLMs (ensuring accuracy, reliability, handling sensitive topics with current LLM moderation), need for investment in open-source LLMs for legal aid, and restrictive regulations on legal service provision. | Ensuring user comprehension and managing complexity for self-represented litigants, development costs and timelines for robust tools, inflexibility of traditional rule-based systems, potential for LLM errors ('hallucinations') and bias, the necessity for human oversight with AI, LLM moderation policies interfering with legally relevant content, and ensuring the overall accuracy and safety of AI-driven legal assistance. | LLM 'hallucinations' leading to incorrect information, algorithmic bias in AI systems, inappropriate or unethical application of AI in legal contexts, lack of fairness and transparency potentially hindering rather than helping access to justice, and LLM moderation filters preventing the processing of essential (but sensitive) legal topics like human rights violations or domestic abuse. |
| 62PlXWw-qiYJ.pdf | Google_Scholar | Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence | This paper evaluates the performance of several Large Language Models (LLMs), particularly OpenAI's GPT series, on U.S. tax law questions using synthetically generated multiple-choice problems. It finds that newer models like GPT-4 show emerging legal reasoning capabilities, significantly improving with few-shot prompting and access to relevant legal texts, though still falling short of expert human performance. | True | Idealistic | True | 2.0 | Positive | Retrieval-Augmented Generation (RAG) using various OpenAI LLMs (davinci, text-davinci-002, gpt-3.5-turbo, gpt-4) combined with different prompting strategies (zero-shot, few-shot, chain-of-thought) and retrieval methods (no retrieval, lecture notes, similarity search with GTR-large embeddings on CFR/US Code, gold standard retrieval). | Evaluation on two synthetic multiple-choice exams (one based on CFR, one on U.S. Code), each with multiple 100-question sections covering specific tax law question types. Questions were randomly generated using Python code to avoid training data contamination. Answers were graded for accuracy using GPT-4 comparing the model's choice to the ground truth across 28,700 evaluated answers. | GPT-4 combined with few-shot prompting, chain-of-thought (CoT) prompting, and retrieval using the 'gold standard' correct legal text ('mega_run') achieved the highest accuracy, approaching or exceeding 80% on average for both CFR and U.S. Code exams. Performance increased consistently with newer OpenAI model releases. Few-shot prompting and providing relevant legal text significantly improved GPT-4's accuracy. | Complexity of legal reasoning; need for accurate legal source retrieval; current LLM performance limitations compared to human experts; need for safeguards regarding data privacy, bias, and accountability; cost of legal counsel for potential users. | Using enhanced LLMs (with retrieval augmentation, few-shot prompting, CoT) to potentially provide legal information/advice, increase lawyer productivity, and lower costs. Further research into advanced prompting, better retrieval, and fine-tuning models for law is proposed. | Answering fact-specific legal questions; providing basic legal information/advice; augmenting lawyer tasks. | People who currently cannot afford legal counsel; consumers not engaging a traditional lawyer; general public needing tax law information. | Tax Law | United States | The evaluated LLMs (OpenAI GPT series) were pre-trained on general web corpora. Retrieval augmentation used vector databases built from the U.S. Code of Federal Regulations (Treasury Regulations) and Title 26 of the U.S. Code, embedded using the GTR-large model (trained on general domain data). Evaluation data was synthetically generated via Python code. | Experimental design varying LLM model, retrieval method, and prompting technique. Synthetic data generation for evaluation. Retrieval-augmented generation (RAG). Automated evaluation using a separate LLM (GPT-4). | NaN | False | False | NaN | LLM performance gap compared to expert lawyers; sub-optimal performance of similarity search retrieval compared to gold standard; need for improved prompting techniques (e.g., self-reflection); need to explore legal-specific model fine-tuning; need for better safeguards (privacy, bias, accountability). | Ensuring evaluation validity (avoiding data contamination); developing effective legal text retrieval; optimizing prompting strategies; accurate automated grading of LLM outputs; managing varying model capabilities and context window limitations. | Inaccurate legal information/advice; model bias; lack of accountability; LLM hallucinations; vulnerability to misleading prompts; potential disruption of the legal profession; challenges for regulations like unauthorized practice of law. |
| DaI7QWpvj28J.pdf | Google_Scholar | The Role of Artificial Intelligence (AI) in the Academic Paper Writing and Its Prospective Application as a Co-Author: A Letter to the Editor | This letter discusses the use of AI, specifically ChatGPT, in academic writing, highlighting potential benefits like refining text. It cautions against significant risks such as factual inaccuracies, bias, ethical concerns, and the inappropriateness of AI co-authorship under current academic norms. | True | NaN | True | 3.0 | Neutral | Use of ChatGPT for academic writing assistance | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | False | NaN | Need for more nuanced AI programs that can provide accurate information with proper references and address ethical concerns in academic writing. | Ensuring accuracy, avoiding bias, addressing ethical concerns (including plagiarism), and defining the appropriate role of AI (e.g., co-authorship). | Factual inaccuracies, biased text generation, potential undermining of researcher credibility, ethical concerns, plagiarism, inappropriate co-authorship attribution. |
| nexxQ6MBzmYJ.pdf | Google_Scholar | TRANSFORMING LEGAL PRACTICE: THE RISE OF AI FOR EFFICIENCY AND ACCESS TO JUSTICE” | This paper discusses the historical evolution and contemporary applications of AI in the legal domain, highlighting its role in improving efficiency and access to justice internationally and specifically in India. It also outlines key challenges such as data privacy, ethics, accuracy, training, and cost, while maintaining a positive outlook on AI's future in law. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Key obstacles include data privacy concerns, ethical considerations surrounding AI use in law, ensuring the accuracy and reliability of AI systems, the need for adequate training for legal professionals, and the high costs associated with implementing AI technologies. | The paper suggests that adopting AI innovations such as tools for legal research, document analysis, live transcription of proceedings, and Online Dispute Resolution can enhance efficiency and access to justice. It emphasizes addressing identified challenges (privacy, ethics, cost etc.) to fully leverage AI. | Improving efficiency in legal practice, enhancing access to justice, AI in judicial processes (e.g., transcription, decision support), legal research automation, document analysis and management, Online Dispute Resolution (ODR). | General public | General Law / Multiple Fields (including litigation, criminal justice, arbitration, and contract law). | Multiple (India, USA, China, UK, Colombia mentioned as examples, with a focus on India in one section). | NaN | NaN | NaN | False | False | NaN | The paper highlights the gap between current AI capabilities and full human-level intelligence, meaning AI cannot yet replace human judgment in complex legal tasks. Broader gaps include the need to fully address data privacy, ethical issues, AI system accuracy, training requirements for legal professionals, and AI implementation costs to realize its full potential in the legal field. | NaN | Risks related to data privacy, ethical misapplication of AI, and consequences of AI inaccuracies in legal contexts. |
| 231FJFX2Ms8J.pdf | Google_Scholar | LEEC for Judicial Fairness: A Legal Element Extraction Dataset with Extensive Extra-Legal Labels | This paper introduces LEEC, a large-scale dataset of 15,919 Chinese criminal judicial documents annotated with 155 legal and extensive extra-legal labels designed by legal experts. Experiments show current DEE models and LLMs struggle with legal element extraction, while empirical analysis using LEEC reveals potential sentencing biases based on defendant demographics, highlighting the dataset's value for promoting judicial fairness research. | True | Idealistic | False | 1.0 | Positive | NaN | NaN | NaN | Influence of extra-legal factors (e.g., demographics) on sentencing leading to potential unfairness; lack of comprehensive datasets covering these factors; difficulty for researchers in extracting labels from large volumes of text; potential disadvantages for socio-economically disadvantaged defendants due to legal aid system structure. | Creation of a comprehensive, large-scale dataset (LEEC) incorporating both legal and extra-legal factors, annotated based on legal expertise and empirical research, to facilitate research on judicial fairness and develop better AI tools. | Judicial fairness; Sentencing disparities; Criminal sentencing; Extra-legal factors in judicial decisions; Element extraction from legal documents. | Individuals involved in the criminal justice system potentially subject to bias based on demographic factors (gender, ethnicity, age, employment status, education level); socio-economically disadvantaged defendants. | Criminal Law | China | The LEEC dataset itself, constructed from 15,919 publicly available Chinese criminal judicial documents (sourced from LEVEN and LeCaRD datasets). Documents were annotated by law students with 155 labels covering legal and extra-legal factors based on a system designed by legal experts. | Literature review (Chinese and international empirical legal studies), expert interviews (legal researchers, legal aid officers, lawyer), legal expert knowledge consolidation, development of an extensive label system (knowledge graph), manual annotation by law students following detailed guidelines. | Dataset sample released on GitHub; full dataset available upon email request for non-commercial research purposes. | True | False | Sample available on GitHub; full dataset available upon email request for non-commercial use. | Limitations of existing datasets (incomprehensive labels, lack of domain focus); limitations of current DEE models and LLMs (low accuracy, context length issues, poor handling of multiple entities, erroneous legal reasoning); potential selection bias in publicly available court decisions; need for deeper investigation into specific sentencing disparities; uncertainty about whether AI model biases reflect real-world judicial biases. | Designing a truly comprehensive and nuanced label system for complex legal documents; ensuring high-quality, consistent annotation across a large dataset; limitations of current NLP models (context length, reasoning ability, output formatting) when applied to lengthy and complex legal texts. | Potential misuse of the dataset for discriminatory purposes or violations of the rule of law; risk of AI models trained on the data perpetuating or amplifying existing societal biases if not carefully developed and evaluated. |
| 11KlEG9Q9e8J.pdf | Google_Scholar | LEGAL RELECTRA : Mixed-domain Language Modeling for Long-range Legal Text Comprehension | This paper introduces LEGAL RELECTRA, a language model using REFORMER within the ELECTRA framework, pre-trained on mixed legal-medical texts for improved long-range comprehension in legal documents. Tested on Named Entity Recognition, it outperformed existing models in processing personal injury case texts. | True | Market | True | 1.0 | NaN | LEGAL RELECTRA: an adaptation of the ELECTRA framework using REFORMER for its generator and discriminator, trained on mixed-domain legal and medical corpora, and utilizing a custom domain-specific tokenizer. | Named Entity Recognition (NER) on a legal domain dataset (labels: case type, plaintiff, defendant) and a mixed legal-medical domain dataset (labels: case type, plaintiff, defendant, medical problem). Performance was measured by F1 scores and compared against BERT, CLINICAL-BERT, LEGAL-BERT, REFORMER, and LEGAL RELECTRA with BERT tokenizer. | LEGAL RELECTRA (with custom tokenizer) achieved an overall F1 score of 85.93% on the legal domain NER task and 78.57% on the mixed-domain NER task, outperforming other tested models. | NaN | NaN | NaN | NaN | Personal injury civil suits, Civil law | United States | A 12GB corpus consisting of: 6GB legal text (excerpts from US case law), 3GB medical text (doctor’s notes and letters from MIMIC and MIMIC-CXR databases), and 3GB mixed-domain personal injury text (Supreme Court opinions, academic literature, COURT LISTENER, BYU LAW, anonymized case descriptions from attorneys). | Adaptation of the ELECTRA pre-training framework by replacing BERT-based generator and discriminator with REFORMER models. Pre-training on a mixed-domain corpus (legal and medical). Development and use of a custom domain-specific tokenizer trained via Byte-Pair Encoding. | NaN | False | False | NaN | NaN | Processing long legal documents (exceeding typical 512-token limits of models like BERT), handling specialized terminology from multiple domains (e.g., legal and medical terms in personal injury texts), and the difficulty of collecting a sufficiently large corpus of specific pre-training data (e.g., personal injury texts). | NaN |
| sEHknHKUxvUJ.pdf | Google_Scholar | ChatGPT: A New Era in Legal \nResearch and its Sustainable Impact \non Judicial Decision Making | This paper examines the use of ChatGPT in the legal field, particularly for legal research and potential judicial decision-making assistance in India. It highlights ChatGPT's limitations, such as inaccuracy and bias, arguing for caution, human oversight, and the need for contestability frameworks. | True | Idealistic | True | 2.0 | Negative | ChatGPT | Analysis of two court cases (India, Colombia) where judges used ChatGPT; interactive prompting of ChatGPT by the authors with legal questions (focused on bail, capabilities, limitations, data) and analysis of its responses. | ChatGPT responses were found to be potentially inconsistent, inaccurate (e.g., citing fake cases), biased, lacking legal nuance, limited by a knowledge cut-off (Sept 2021), not fully comprehensive in accessing case law, and acknowledging its own limitations and lack of liability. | Inaccuracy and unreliability of AI; potential for bias amplification; lack of transparency and explainability; digital divide limiting access; inability to replicate human judgment, equitable justice, and discretion; resistance to change in the legal profession; inadequate regulatory frameworks. | Maintaining human intervention and oversight; using AI as an assistive tool, not a replacement; implementing a 'right to contestability' for AI decisions; developing robust legal/regulatory frameworks for AI governance (transparency, accountability, fairness); verifying AI outputs. | Bail jurisprudence, judicial decision-making, legal research, access to justice, legal information services. | Individuals and Small/Medium Enterprises ("people law"), general public seeking legal information, citizens interacting with the justice system. | General Legal Practice, Criminal Law (Bail), Constitutional Law (Due Process), Civil Procedure. | India, Colombia, USA, EU, UK | Described by ChatGPT as a large preprocessed text database including news articles, legal documents, case law (including Indian statutes and court decisions up to Sept 2021 available in the public domain), and academic literature. Mix of publicly available and potentially proprietary data curated by OpenAI. | NaN | Publicly accessible web application by OpenAI. | True | True | Available online as a "Free Research Preview" (ChatGPT May 3 Version mentioned). | Technical gaps include the need for up-to-date, accurate, unbiased, and contextually nuanced information, along with transparency. Societal/Regulatory gaps include the lack of comprehensive AI governance laws (especially in India regarding contestability, liability), the digital divide, and the need for legal professional training. | Unreliability, inaccuracy, potential for bias, lack of genuine legal understanding, limitations of training data scope and recency when using ChatGPT for legal tasks. | Inaccurate legal research/advice; perpetuation of bias; erosion of trust in justice; violation of due process/fundamental rights; automation bias; lack of accountability for AI errors. |
| RG_Manuscript_Avatarjudgesandvirtuousadjudication.pdf | Google_Scholar | GenAI avatar judges and virtuous adjudication | This paper examines the potential use of GenAI avatars as judges through the lens of virtue ethics and jurisprudence. It argues that fully autonomous AI judges cannot achieve 'virtuous adjudication' due to lacking genuine virtuous agency, but suggests that advice-giving AI avatars could potentially support human judges' virtuous practice, while also identifying significant risks to moral responsibility and potential deskilling. | True | Idealistic | True | 3.0 | Neutral | Conceptual discussion of 'GenAI avatar judges', distinguishing 'automated decision-making' (Mode A) and 'supportive advice-giving' (Mode B) types, personalized using adjudication records. | NaN | NaN | AI lacks genuine virtuous agency (internal states, right reasons, phronesis) needed for virtuous adjudication; Difficulty in training AI for virtue (data curation, ensuring virtuous output); Potential undermining of human judges' moral responsibility (control, freedom, knowledge, deliberation); Risk of human cognitive/moral deskilling. | Use advice-giving GenAI avatars (Mode B) as 'virtue cultivators' to support, not supplant, human judges; Enhance judges' perceptual capacity and contextual knowledge using AI trained on curated exemplary adjudication records; Potential use in VR training simulations for judges. | Judicial decision-making (adjudication); Judicial ethics; Virtue jurisprudence; Moral responsibility; Access to justice via digital courts. | General public / Litigants using digital courts | General | International | Hypothesized use of personal/exemplary adjudication records (from one or multiple judges), legislation, and jurisprudence; likely proprietary/court-held, domain-specific, potentially structured and unstructured. | NaN | Hypothesized deployment in digital/online/Metaverse courts, potentially via VR for training. | False | False | NaN | Philosophical/ethical gap: AI's inability to replicate genuine virtue and moral responsibility for virtuous adjudication. Societal gap: Ensuring AI supports rather than undermines human judicial qualities. Technical gaps: Difficulty curating appropriate training data for virtue; Ensuring AI output aligns with virtuous deliberation (explainability, bias mitigation). | Defining and implementing 'virtuous adjudication' in AI; Aligning AI statistical methods with human phronesis; Curating training data (identifying/labelling virtue/vice); Ensuring meaningful human control; Avoiding psychological coercion, automation bias, or under-trust; Addressing explainability issues; Mitigating human deskilling. | Undermining human judges' moral responsibility; Psychological coercion by AI; Automation bias; Under-trusting AI; Infringement on deliberation due to black box issues; Human cognitive and moral deskilling; Potential for AI to act viciously if truly autonomous; Difficulty ensuring virtuous AI output. |
| 0b5FFMvGIoYJ.pdf | Google_Scholar | The Implications of ChatGPT For Legal Services and Society | This paper explores the potential impact of large language models, specifically ChatGPT, on legal services and society by demonstrating its capabilities through generated text examples. It discusses use cases like legal research and document drafting, alongside significant challenges, ethical considerations (like accuracy and bias), and the rapid evolution of AI in law. | True | Idealistic | True | 2.0 | Positive | Large Language Models: ChatGPT (GPT-3 based) and Bing Chat (reportedly GPT-4 based) | Demonstration through prompting ChatGPT and Bing Chat on various legal tasks (research, document generation, information provision, analysis). Includes qualitative assessment of outputs and reports Bing Chat's performance on 15 legal ethics multiple-choice questions (12/15 correct) and a civil procedure problem. | ChatGPT outputs were imperfect, incomplete, and sometimes problematic, lacking nuance and detail. Bing Chat (GPT-4 based) showed better performance, answering 12/15 legal ethics MCQs correctly and providing plausible legal analysis comparable to a B/B+ law student. | High cost and complexity of the US legal system, lack of right to counsel in most civil cases, legal profession regulations (monopoly, fee-sharing rules), limited government funding for legal aid, and the cost of legal education contribute to a significant justice gap. | Leveraging technology, particularly AI tools like ChatGPT, to create self-help resources and enhance lawyer efficiency to serve more clients. | General civil legal needs (e.g., family law, debt, housing), disability rights (IEP), government benefits (Social Security). | Low-income individuals and middle-income Americans. | Civil Procedure, Torts, Contract Law, Constitutional Law, Estate Planning, Education Law, Social Security Law, Legal Ethics. | USA (primarily Massachusetts and Federal) | General, large-scale text data used to train OpenAI's GPT-3 and GPT-4 models (implied to be broad web text and other sources). | NaN | NaN | True | False | ChatGPT and Bing Chat are available online via OpenAI and Microsoft, possibly with free and paid tiers. | Accuracy and reliability of AI outputs, handling legal nuance, need for user prompt engineering skills, digital divide/cost of access to advanced AI, need for integration into legal education, potential for misuse, broader societal risks including existential concerns. | Ensuring accuracy and reliability, handling legal complexity/nuance, potential job displacement for lawyers, misuse for generating false information or manipulation, ethical concerns (UPL, competence, confidentiality), attribution problems, over-reliance, potential for bias, societal disruption, managing AI's rapid development responsibly. | Inaccurate legal information/advice, job displacement, generating false/misleading documents or information, manipulation of user beliefs/emotions, unauthorized practice of law, breaches of competence/confidentiality, algorithmic bias, exacerbating digital divide, difficulty in attributing authorship, existential risks. |
| v19LnyREUAsJ.pdf | Google_Scholar | Let’s Have a Chat! A Conversation with ChatGPT: Technology, Applications, and Limitations | This paper reviews ChatGPT, exploring its underlying Transformer and reinforcement learning technology, historical context, and diverse applications in fields like healthcare, education, and research. It summarizes evaluations of ChatGPT's capabilities, including exam performance, alongside its limitations and significant ethical and privacy concerns. | True | NaN | True | 3.0 | Neutral | ChatGPT | Review of multiple studies evaluating ChatGPT: Performance on professional/academic exams (medical, law, CS, etc.), qualitative assessments, plagiarism detection comparisons, text summarization (Rouge scores), reasoning tasks, translation benchmarks, clinical decision support tasks. | Average accuracy of 59.53% across various exams reviewed, though performance varied significantly by domain and task (e.g., high on USMLE, low on math-heavy tasks). Showed potential in text summarization, detecting its own generated text, and deductive reasoning, but limitations in inductive reasoning, accuracy, and citation reliability. | NaN | NaN | NaN | NaN | Law (general), Constitutional Law, Torts, Taxation | USA, China | Large corpus (>300 billion words) of text data from varied sources (books, articles, websites) up to September 2021. Includes public internet data, potentially containing personal information. Unsupervised pre-training followed by Reinforcement Learning from Human Feedback (RLHF). | Deep learning (Transformers, LLMs), unsupervised learning, prompt engineering, Reinforcement Learning from Human Feedback (RLHF). | Released publicly by OpenAI via a web interface in November 2022. | True | False | Publicly released by OpenAI, accessible via a web interface (URL provided in footnote). | Factual inaccuracies ('hallucinations'), reasoning errors (especially inductive), knowledge cutoff (Sept 2021), potential biases, limited context window, lack of multimodal input capabilities, need for better evaluation metrics, improving performance consistency across diverse domains. | Ensuring factual accuracy, avoiding harmful/biased outputs, detecting AI-generated text (for plagiarism/cheating), aligning AI with human values (via RLHF), limitations in reasoning and specific tasks (math, low-resource languages), computational cost of training/running LLMs, potential for misuse. | Generation of misinformation ('infodemic'), potential for race/gender bias, privacy risks due to training data potentially containing personal information or memorizing user inputs, copyright infringement and plagiarism risks, misuse for cheating in education or generating misleading content. |
| Aota7JmCmSEJ.pdf | Google_Scholar | Chapter 22: AI and the future of private dispute resolution mechanisms | This chapter reviews how artificial intelligence, including natural language processing, predictive analytics, machine learning, and generative AI, is transforming private dispute resolution mechanisms such as arbitration, mediation, and negotiation. It discusses current AI tools and their applications in enhancing case preparation, predicting outcomes, and automating dispute resolution, while also considering future prospects, implementations around the world, and ethical implications. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | High costs, lengthy processes, perceived biases and inconsistencies in traditional private dispute resolution; waning confidence in courts due to expenses, delays, and impartiality concerns; complexity of the legal system. | Leveraging AI tools (NLP, predictive analytics, machine learning, generative AI) to enhance efficiency, fairness, and accessibility in dispute resolution through enhanced case preparation, predictive analytics, and automated dispute resolution platforms (ODR). | Private dispute resolution (arbitration, mediation, negotiation), Online Dispute Resolution (ODR), improving efficiency and reducing costs of legal processes, enhancing fairness and consistency in dispute outcomes, increasing accessibility to justice mechanisms. | General public involved in disputes (e.g., consumer, small claims, family law matters such as divorce and asset division), laypeople needing legal information (e.g., landlord-tenant issues), legal practitioners, and dispute resolution providers. | Private dispute resolution, including arbitration, mediation, negotiation. Specific applications cover family law (divorce, asset division), consumer law, small claims, commercial disputes, landlord-tenant law, and insurance claims. | International | Various, including large language models trained on general and legal text (documents, statutes, case law, opinions); historical case data for predictive analytics; specific datasets curated for particular tools (e.g., lawyer-reviewed randomized scenarios for Amica). | Includes rule-based systems, case-based reasoning, machine learning (including deep learning and LLMs with pre-training and fine-tuning), game-theoretical algorithms, and expert systems methodologies. Some tools also incorporate user-centered design and iterative development based on expert input. | Online platforms, web applications, integration into existing legal/judicial systems (e.g., Jupitice for courts), APIs, educational initiatives for stakeholders. | True | False | Many tools discussed are presented as launched and accessible, either as commercial products (e.g., Relativity, Lex Machina, various ODR platforms) or as public/research initiatives with websites (e.g., Amica, JusticeBot, CREA platform). | Technical gaps include AI accuracy (e.g., LLM hallucinations) and data privacy. Societal gaps include ethical concerns, ensuring meaningful human control and oversight, addressing the digital divide, preventing bias and discrimination, and the need for education and training for legal professionals on AI capabilities and limitations. | NaN | Generation of incorrect or biased information (hallucinations) by AI, especially LLMs; ethical and privacy concerns regarding sensitive client data; potential for misuse of generative AI (e.g., deepfakes, misinformation); erosion of human responsibility and oversight in decision-making; risk of unjust outcomes if AI errors are not mitigated by human control. |
| nN70shbEoP0J.pdf | Google_Scholar | Ketergantungan Mahasiswa Universitas Jember Terhadap Artificial Intelligence (AI) | This study investigates the dependency of Jember University students in Indonesia on AI tools, specifically Chat GPT, for their academic tasks. Using a qualitative ethnographic approach with 5 student interviews, the research finds students use AI for inspiration opportunistically rather than continuously, acknowledging its limitations such as answer accuracy and the risk of reduced critical thinking. | False | NaN | True | 2.0 | NaN | Chat GPT | Qualitative ethnographic study: observation, interviews, and documentation with 5 students at Jember University. | Students at Jember University use Chat GPT for inspiration and to help with assignments but are not continuously dependent. They recognize that its answers are not always accurate and that overuse can lead to reduced critical thinking and laziness. | NaN | NaN | NaN | NaN | NaN | Indonesia | NaN | NaN | NaN | True | True | Chat GPT, the tool discussed, is accessible online with a free usage tier provided by OpenAI. | NaN | For users (students): Inaccuracy of AI-generated answers which require verification and supplementation; potential to foster laziness and reduce critical thinking if overused. | Decline in students' critical thinking and problem-solving abilities, increased laziness and lack of independence, potential for plagiarism, and over-dependence on AI. |
| 1-s2.0-S1877050924011177-main.pdf | Google_Scholar | Artificial Intelligence as an Innovative Element of Support in Policing | This paper explores the potential application of large language models (LLMs), specifically GPT, to reduce the administrative burden within the Czech Republic police force. It outlines various conceptual use cases, including document creation, data analysis, investigation support, and public communication, while emphasizing the need for further research and addressing ethical concerns. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | Increasing administrative burden limits police officers' ability to focus on core security tasks. | Integrating LLMs (like GPT) into police work for tasks such as: supporting document creation (using speech-to-text and fine-tuning), acting as personal assistants for information retrieval, supporting investigations (data analysis, chronology generation, pattern detection), enhancing analytical processes (using plugins for data analysis/visualization), facilitating international police cooperation (translation, document analysis, request generation), supporting forensic activities (data processing, calculations), and creating public communication tools (chatbots for inquiries and reporting). | NaN | NaN | Policing, Criminal Law, Criminal Procedure, International Law | Czech Republic | NaN | NaN | NaN | False | False | NaN | Absence of empirical data and practical research to validate the proposed LLM applications in policing. Need for developing secure, locally operated LLMs to protect sensitive police data. | Ethical, legal, and security considerations (transparency, accountability, privacy). Ensuring human oversight due to potential AI errors. Obtaining high-quality, unbiased training data. Need for secure infrastructure (potentially separate from the internet). Requires interdisciplinary collaboration. | Potential negative impact on privacy and human rights. Misuse of AI by criminals or terrorists. Risk of bias propagation from training data. Over-reliance on potentially inaccurate AI outputs. Data security vulnerabilities. |
| x2Rhas8fUBAJ.pdf | Google_Scholar | ChatGPT: Literacy or intelligence about UN sustainable development goals? | This paper evaluates ChatGPT's literacy and intelligence regarding the UN Sustainable Development Goals (SDGs) using two assessment tools: the SDG Fitness Test and the SULITEST. While ChatGPT demonstrates high SDG literacy, its intelligence, particularly concerning core competencies like critical and systems thinking, is found to be at an intermediate level, and the assessment tools themselves show limitations in coverage. | True | Idealistic | True | 2.0 | Neutral | Evaluation of ChatGPT (GPT-3.5 based model) using standardized sustainability literacy tests. | ChatGPT's performance was assessed using the UN SDG Fitness Test and the SULITEST (Sustainability Literacy Test). Questions from both tests were input into ChatGPT, and the responses were scored according to the tests' frameworks. ChatGPT was also used to map test questions to SDG competencies and SDG types. | ChatGPT scored highly on literacy tests (<90% on SDG Fitness Test, 80.9% on SULITEST). However, its performance on core SDG competencies (evaluated via SDG Fitness Test) was mostly intermediate, particularly in areas like Collaboration, Systems Thinking, Anticipatory skills, Integrated problem-solving, Critical thinking, and Self-awareness. Both assessment tests were found to have inadequate coverage of SDG competencies and SDG types. | Current limitations of LLMs like ChatGPT, including intermediate-level capabilities in crucial SDG competencies (e.g., critical thinking, systems thinking, self-awareness); inadequacy and unbalanced coverage of existing SDG assessment tools (SULITEST, SDG Fitness Test); potential for LLMs to generate misinformation ('hallucinations'). | Improve future LLM versions to enhance specific SDG competencies (collaboration, critical thinking, systems thinking, etc.); refine SDG assessment tools (SULITEST, SDG Fitness Test) for better coverage of competencies and types; use LLMs cautiously for SDG-related tasks, primarily for information gathering and suggesting actions, not decision-making. | UN Sustainable Development Goals (SDGs) literacy and intelligence; Assessment of AI capabilities related to sustainability; Core cross-cutting SDG competencies (e.g., Systems Thinking, Critical Thinking, Collaboration). | NaN | Sustainable Development / UN SDGs | International | The paper evaluates a pre-trained model (ChatGPT). Its underlying training data (e.g., for GPT-3) is described as vast (e.g., 45TB text dataset), web-sourced, largely proprietary, unstructured text and code data. | Experimental evaluation using existing standardized tests (SULITEST, SDG Fitness Test). Utilized ChatGPT itself to map test questions to SDG competencies and types as a methodology step. | The evaluated technique (ChatGPT) is deployed by OpenAI via web interface and API. The study itself did not involve deployment. | True | False | ChatGPT is available via web interface and API from OpenAI, often with free and paid access tiers. | ChatGPT's intermediate performance in key SDG competencies; Inadequate and unbalanced coverage of SDG competencies and types by existing assessment tools (SULITEST, SDG Fitness Test); Need for validated mappings between test questions and competencies/SDGs; Need for AI development specifically targeting SDG competencies; Societal gap in safely integrating LLMs for SDG advancement. | Lack of validated mappings for test questions to SDG competencies and types, requiring the use of ChatGPT itself for mapping; Potential inconsistency in LLM responses; Evaluating the 'intelligence' beyond simple 'literacy'. | Over-reliance on LLMs for SDG decision-making; Generation of misinformation or 'hallucinations'; Ethical issues (bias, misuse); Potential negative impact on human critical thinking skills; Test security and validity if LLMs can easily pass assessments. |
| FrIATqlyPS4J.pdf | Google_Scholar | FIGHTING THE HYPOTHETICAL: WHY LAW FIRMS SHOULD RETHINK THE BILLABLE HOUR IN THE GENERATIVE AI ERA | This paper analyzes how generative AI (GenAI) challenges the traditional billable hour model in law firms, forcing a shift towards value-based billing. Based on interviews with firm leaders, it predicts GenAI will automate routine tasks, disrupt existing staffing models, and require firms to innovate their pricing and service delivery to remain profitable. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | High cost of legal services inherent in the billable hour model; affordability barrier for low-income individuals (e.g., needing upfront fees for Chapter 7 bankruptcy). | AI-powered tools for self-help (e.g., Upsolve for bankruptcy); potentially redeploying lawyers made efficient by AI to serve lower-cost markets. | Bankruptcy (Chapter 7), Affordability of legal services | Low-income individuals needing bankruptcy assistance; middle-income households; small/midsize organizations. | General Legal Practice (Law Firms), Corporate Law, Mergers and Acquisitions, Litigation (Document Review, Discovery), Bankruptcy Law, Contract Law | United States | NaN | NaN | NaN | False | False | NaN | Technical: AI accuracy (hallucinations), potential for bias, AI's inability to replicate human judgment, empathy, creativity, and contextual reasoning. Societal: Training gap for junior lawyers losing learning opportunities from routine tasks, ethical challenges (confidentiality, UPL, reasonable fees, bias), resistance to change within the legal profession, need for new business/pricing models, potential digital divide. | Law firm resistance to changing the profitable billable hour model; cost of AI investment and implementation; ensuring data security and client confidentiality; training lawyers to use AI effectively and ethically; managing the disruption to traditional staffing (pyramid/leverage) models; developing and implementing new value-based pricing structures; overcoming lawyer skepticism and change aversion. | Ethical violations (incompetence, lack of diligence, confidentiality breaches via AI input, filing AI-generated 'hallucinations', unauthorized practice of law, unreasonable fees due to unchanged billing despite efficiency gains); generation of inaccurate or incomplete AI output leading to bad advice; deskskilling of junior lawyers due to automation of foundational tasks; data security breaches; perpetuating biases present in AI training data; increased pressure/burnout if efficiency gains aren't managed well; potential negative impact on firm revenue/profitability if transition is poorly managed; possibility that AI benefits primarily accrue to well-resourced firms/clients, widening the justice gap. |
| iCJapnvrHUoJ.pdf | Google_Scholar | Artificial Intelligence in the Workforce National and Regional Implications | This report analyzes the impact of artificial intelligence, particularly generative AI, on the workforce and economy at the national (US) level and within the Rio Grande Valley (RGV) region. It identifies industries and occupations most likely to be affected, highlights potential economic shifts, and suggests strategies for workforce development and adaptation for community stakeholders. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | USA (National and Rio Grande Valley, Texas) | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Potential job displacement/automation in specific sectors (e.g., food service, office support, production) and for low-skilled workers; regional disparities in automation impact (affecting areas like the RGV more); over-dependence on AI; legal and ethical issues; security concerns regarding AI models (open vs closed source). |
| 3584931.3606955.pdf | Google_Scholar | Shaping the Emerging Norms of Using Large Language Models in Social Computing Research | This paper proposes a Special Interest Group (SIG) to discuss the impacts, opportunities, and challenges (validity, privacy, ethics) of using Large Language Models (LLMs) in social computing research. The goal is to facilitate community discussion and collectively shape emerging norms for LLM use across various research stages. | True | NaN | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | NaN | NaN | False | False | NaN | NaN | Ensuring validity of findings (non-determinism, prompt sensitivity, bias), protecting privacy (captured training data, user data during analysis), ethical concerns (consent, potential misuse, equity), effectively evaluating LLM performance in research tasks, lack of interpretability, preventing over-reliance, managing resource requirements (cost, expertise). | Potential misuse of LLMs for manipulation or deception, propagation of biases and stereotypes, privacy violations (PII exposure, interdependent privacy), ethical breaches regarding informed consent and testing on users (especially in sensitive domains), equity concerns due to unequal access to resources, potential for economic, reputational, or psychological harms to users of LLM-enabled systems. |
| 37KsD-fAzisJ.pdf | Google_Scholar | How generative AI Is shaping the future of marketing | This paper distinguishes Generative AI (Gen AI) from analytical AI and proposes a four-quadrant framework based on input type (general vs. custom) and human augmentation level (low vs. high) to guide marketers in selecting and implementing Gen AI tools. It discusses the benefits, risks, and strategic trade-offs of different Gen AI approaches in marketing contexts. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | Literature review, industry reports review, expert interviews. | NaN | False | False | NaN | Need for research on: boundary conditions for choosing custom vs. general inputs, lower-cost custom input methods, user trade-offs regarding privacy/IPR protection, managing bias in outputs, ethical use boundaries, risk measurement/mitigation, reducing opacity, maximizing returns, enabling production deployment, appropriate classification frameworks for Gen AI, impact of regulation, and long-term societal/social impacts. | Deciding between general vs. custom inputs; determining the optimal level of human augmentation; ensuring high-quality data for custom inputs; managing costs of implementation; moving from experimentation to production deployment; addressing privacy and transparency concerns. | Inaccuracy/hallucinations, intellectual property rights (IPR) infringement, creation of misinformation and deepfakes, privacy breaches (data leakage), perpetuating/amplifying algorithmic bias, opacity of algorithms, potential legal liability for infringing or inappropriate outputs. |
| hjat-xhoNUwJ.pdf | Google_Scholar | AI Legal Innovations: The Benefits and Drawbacks of Chat-GPT and Generative AI in the Legal Industry | This paper reviews the impact of generative AI, particularly large language models like ChatGPT, on the legal industry. It details potential benefits like increased efficiency and expanded access to justice, alongside significant drawbacks including inaccuracies, bias, copyright issues, and privacy concerns. | True | Market | True | 3.0 | Neutral | Generative AI / Large Language Models (LLMs) for legal applications | The paper reports on external findings, such as a Stanford study showing high hallucination rates (69-88%) in LLMs for legal queries and mentions ChatGPT-4 passing the Uniform Bar Exam. | NaN | High cost of legal services leading to unmet legal needs, particularly for poor and middle-class individuals (cites 80% unmet need). | Using AI/LLMs to decrease the cost of legal services, potentially allowing lawyers to serve the previously unmet needs of the poor and middle class and expand the overall market for legal services. | Access to justice cost barriers. | Poor and middle-class people. | General / Multiple Fields (including corporate tax, regulatory, litigation, e-discovery, contract analysis, immigration) | US, EU, UK, International | Publicly available web data (books, websites, social media), potentially including copyrighted material; specific datasets for proprietary tools not detailed. | Mentions Anthropic's 'Constitutional AI' approach; general references to machine learning, NLP, neural networks. | Commercial software offerings (SaaS, licensed), integration into existing platforms (e.g., MS Office, Westlaw), mobile apps, potential for in-house development by firms. | True | False | Mentions publicly accessible tools like ChatGPT and lists numerous commercial AI legal tech products available through companies like vLex, Thomson Reuters (Casetext), LegalMation, DoNotPay, etc. | High rates of legal hallucinations/inaccuracies in LLMs. Lack of clear regulatory frameworks. Persistence of bias in AI outputs. Unresolved copyright issues in training data. Insufficient safeguards for data privacy and attorney-client privilege. Need for better AI integration into legal workflows that accounts for limitations (e.g., specificity for contracts). Ensuring AI benefits improve access to justice effectively. | Ensuring accuracy and avoiding hallucinations ('fabrications'). Addressing algorithmic bias. Managing data privacy, security, and attorney-client confidentiality. Navigating copyright complexities. Ethical integration into legal practice (requiring human oversight). Overcoming professional resistance and adapting business models (e.g., billable hours). Developing appropriate regulations. Combating misuse (e.g., deepfakes, AI washing). | Generating incorrect legal information (hallucinations/fabrications) leading to flawed legal work and potential sanctions. Amplifying societal biases (racism, sexism). Copyright infringement liability. Breaches of data privacy and attorney-client privilege. Facilitating misinformation and election interference (AI-generated deepfakes). Financial misrepresentation ('AI washing'). Job disruption within the legal profession. |
| _OXxj01xIJYJ.pdf | Google_Scholar | An Empirical Study of Production Incidents in Generative AI Cloud Services | This paper analyzes production incidents from a major GenAI cloud service provider (Microsoft) over four years, detailing their characteristics, root causes, and mitigation strategies. It reveals unique reliability challenges for GenAI services, including content quality issues and difficulties in incident management, and identifies areas for future improvement. | True | Market | True | 2.0 | NaN | Incident management practices (detection, triage, diagnosis, mitigation) within production Generative AI cloud services. | Analysis of anonymized production incident data from Microsoft's Incident Management system (IcM) over four years. The study involved quantitative analysis of hundreds of thousands of GenAI incidents and qualitative_in-depth analysis of high-severity incidents, using manual open coding by multiple annotators with inter-rater reliability checks (Cohen's kappa). | GenAI incidents show high rates of human detection (38.3%) and monitor false alarms (11.0%), take longer to mitigate (avg. 1.12 normalized time units vs 0.65 for other services), and commonly manifest as performance degradation (49.8%). Key root causes include infrastructure issues (27.2%), configuration issues (24.5%), and code bugs (21.5%), with fixes often being ad-hoc (22.4%) or rollbacks (15.2%) rather than immediate code changes (7.6%). | NaN | NaN | NaN | NaN | NaN | International | NaN | Empirical study using quantitative and qualitative analysis of production incident data from Microsoft's Incident Management system. This involved collection of GenAI and non-GenAI incidents over four years, selection of high-severity incidents for in-depth analysis, and manual open coding by multiple annotators with inter-rater reliability checks (Cohen's kappa) to categorize incident symptoms, root causes, and mitigation strategies. | NaN | False | False | NaN | NaN | Unique challenges for GenAI cloud services include large scale, high hardware demands, ensuring content quality (e.g., invalid inference) and privacy, immature automated monitoring systems leading to high human detection rates and false positives, and increased complexity and time for incident diagnosis and mitigation due to diverse root causes. | Operational failures leading to user dissatisfaction and monetary loss; degraded service quality including invalid, harmful, or low-quality AI-generated content (e.g., hallucinations); privacy violations; and security vulnerabilities from model fine-tuning or prompt exploitation (e.g., 'hidden text' attacks). |
| 3696319.pdf | Google_Scholar | “This Verdict was Created with the Help of Generative AI...?” On the Use of Large Language Models by Judges | This paper discusses the emerging use of Large Language Models (LLMs) like ChatGPT by judges in various jurisdictions, citing specific real-world cases. It explores the complex interdisciplinary questions (legal, ethical, technical) arising from this practice and calls for increased research collaboration to address the implications for the judiciary. | True | NaN | True | 3.0 | Neutral | General purpose LLMs (e.g., ChatGPT) used by judges for tasks like legal research, analysis, summarization, and calculations. | NaN | NaN | Lack of transparency/explainability (black-box problem); Potential for bias and discrimination; Risk of inaccuracies (hallucinations); Threats to judicial independence and impartiality; Accountability challenges; Data privacy and confidentiality issues; Ensuring fairness of trial and due process. | Emphasis on the need for interdisciplinary research (involving legal studies, ethics, information systems, etc.); Suggestion of frameworks (e.g., 'orders of change') to analyze impact; Development of official guidelines (referencing UK example); Call for collaboration with courts. | Impact of LLM use by judges on judicial process integrity (fairness, transparency, accountability, independence). | NaN | General (examples from Health Law, Family Law, Election Law, Criminal Law) | Colombia, Peru, Mexico, India, Brazil, UK, USA, China, Singapore, Germany | Not specified, but implicitly refers to the large-scale, general (and often proprietary) datasets used to train commercial LLMs like ChatGPT. Concerns about data bias, quality, and privacy are raised. | NaN | Ad hoc use of publicly available LLMs (e.g., ChatGPT) by individual judges; Issuance of official guidelines (e.g., UK). | True | True | Publicly available LLMs (e.g., ChatGPT) accessible via web interfaces, often with free tiers. | Lack of sufficient interdisciplinary research on the legal, ethical, technical, and societal implications of LLM use by judges; Need for better understanding of impacts on judicial independence, fairness, and accountability; Challenges in managing bias and ensuring transparency (XAI); Need for appropriate guidelines and potentially domain-specific models; Addressing public trust concerns. | Need for interdisciplinary research collaboration; Lack of understanding of LLM impacts on judiciary; General LLM challenges (accuracy, bias, transparency, privacy, cost, hallucinations); Developing specific guidelines; Integration into existing court IT systems. | Inaccurate judgments due to LLM errors/hallucinations; Erosion of judicial independence and impartiality; Violation of fairness/due process rights; Discrimination due to biased outputs; Breach of confidentiality/privacy; Undermining public trust in the judiciary; Lack of accountability for AI-influenced decisions. |
| RAoTkOxbBj0J.pdf | Google_Scholar | CHATGPT, PROFESSOR OF LAW | This paper experiments with using ChatGPT for seven common tasks faced by law professors related to teaching and service. The results suggest ChatGPT can generate usable first drafts quickly, especially for routine service tasks, potentially reducing faculty workload. | True | Market | True | 2.0 | NaN | ChatGPT | Qualitative evaluation of ChatGPT's output for seven hypothetical law professor tasks (exam question, handout, recommendation letter, bio, symposium remarks, committee plan, syllabus) based on usability as first drafts. | ChatGPT produced usable first drafts for six out of seven tasks in 23 minutes. It performed best on routine service tasks (recommendation letter, bio, remarks, committee plan) but required personalization, while performance on teaching tasks was mixed (good for syllabus brainstorming, weaker/inaccurate for exam question details and handout content). | NaN | NaN | NaN | NaN | Torts, Employment Law, Legal Education, Academic Administration | USA (implied) | NaN | NaN | NaN | True | False | The paper uses ChatGPT, accessible via OpenAI's website. | NaN | Need for prompt engineering/tweaking; potential for factual inaccuracies in output requiring significant expert revision; output may lack depth/detail for complex analytical tasks. | Inaccuracy of generated legal content; potential unstated ethical concerns regarding AI use in academic work (though scholarship was explicitly excluded). |
| QWltlnjUlekJ.pdf | Google_Scholar | NEW RULES FOR A NEW ERA: REGULATING ARTIFICIAL INTELLIGENCE IN THE LEGAL FIELD | This paper argues that the legal industry should be cautious about fully integrating AI due to its current flaws and limitations, which could lead to negative consequences for legal professionals and the legal system. It proposes that jurisdictions amend professional conduct rules to restrict the use of generative AI for specific litigation purposes until the technology matures. | True | Market | True | 1.0 | Negative | Generative AI / Large Language Models (e.g., ChatGPT) | Author's informal test of ChatGPT's legal research capabilities (checking for case law on vehicular battery in Ohio) and references to other anecdotal tests and OpenAI's stated limitations. | ChatGPT provided incorrect and fabricated legal information (hallucinations), such as citing non-existent court cases to support its legal explanation. | AI's current unreliability (hallucinations, inaccuracies); AI's potential to stagnate legal development due to reliance on historical data and lack of true understanding/morality; Risk of entrenching and amplifying biases present in training data; AI's inability to replicate crucial human elements of legal practice (e.g., emotional intelligence, complex strategic thinking). | Proactive self-regulation by the legal profession to restrict AI use in specific legal tasks (e.g., drafting persuasive legal communications, client communications, judicial rulings) until it matures, enforced by AI-detection software. | Ensuring the integrity, reliability, and fairness of the legal system and legal representation in the face of AI adoption; Regulation of AI in legal practice; Ethical use of AI by legal professionals. | NaN | General legal practice, litigation, professional conduct/ethics, criminal law, contract law. | United States (implicitly, for proposed rule changes), but arguments are broadly applicable. | Massive, diverse, largely unsupervised textual data from the internet (e.g., Common Crawl, Reddit, news, Wikipedia, historic books) for pre-training, supplemented with supervised labeled prompt-answer pairs and human-ranked outputs for fine-tuning (for models like ChatGPT). | Machine learning, including unsupervised and supervised learning, deep learning (artificial neural networks, transformer models using attention/self-attention mechanisms), reinforcement learning with human feedback (RLHF) for large language models like ChatGPT. | Publicly accessible web interfaces (e.g., ChatGPT), APIs for integration into other software and services, commercial product integrations (e.g., search engines, workplace tools). | True | True | ChatGPT, a primary example discussed, has a publicly accessible free version for use. The paper also mentions GPT-4 as a paid service and a free AI detection tool from OpenAI. | Technical gaps include AI's unreliability (hallucinations, inaccuracies), data staleness, black-box nature, and lack of true understanding or human-like intelligence. Societal/Ethical gaps include the absence of a moral compass in AI, unresolved issues of bias encoding and amplification, and the need for robust regulatory frameworks. | Challenges for AI developers include mitigating misalignment (inaccuracy, bias, harmful outputs), keeping models updated with current and unbiased information, overcoming the black-box problem for transparency, and instilling genuine understanding and ethical reasoning in AI systems. | Generation of incorrect legal information (hallucinations) leading to professional misconduct or malpractice; Stagnation of legal development and misalignment with current social values; Entrenchment and amplification of societal biases through AI; Compromised quality of legal representation due to AI's lack of human skills (e.g., emotional intelligence, strategic thinking, negotiation); Erosion of public trust in the legal system. |
| lm9K0vSCKEcJ.pdf | Google_Scholar | A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law | This survey extensively reviews the methodologies, applications, challenges, ethics, and future advancements of Large Language Models (LLMs) in the critical domains of finance, healthcare, and law. It highlights LLMs' transformative potential in these high-stakes sectors, such as enhancing diagnostics in healthcare, financial analytics, and legal interpretation, while also critically examining ethical concerns and advocating for responsible AI development. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Lack of access to legal services due to cost or knowledge barriers; ethical issues in LLMs (bias, fairness, robustness, hallucination); difficulty in acquiring high-quality, domain-specific (legal) training data; risk of LLMs worsening existing societal inequalities and creating technology access gaps. | Using LLMs to democratize legal information, education, and advice; improving quality and availability of training data for legal AI; promoting interdisciplinary collaboration; establishing robust ethical frameworks, security measures, open-source tools, and educational programs to ensure equitable access and responsible deployment. | Democratizing access to legal information, education, and advice; facilitating online dispute resolution; ensuring fairness, equity, and non-discrimination in legal AI; providing legal guidance for marginalized and under-resourced communities. | Individuals with limited financial or knowledge resources for legal help; marginalized communities; self-represented litigants; underrepresented groups; smaller organizations and non-profits. | General legal tasks (question answering, judgment prediction, text classification, summarization, information retrieval), Tax law, Transportation law, Privacy law, Criminal law, Contract law, EU law, Copyright law, Online dispute resolution. | US, China, Japan, European Union, Switzerland, Vietnam, Greece. | NaN | NaN | NaN | False | False | NaN | Significant ethical challenges (explainability, bias, fairness, robustness, privacy, accountability, potential for inequality exacerbation); insufficient reliability and advanced reasoning in legal-specific LLMs; difficulties in curating comprehensive, high-quality legal datasets; unresolved knowledge gap between NLP developers and legal domain experts. | NaN | Severe consequences from LLM errors in high-stakes FHL decisions (e.g., financial losses, incorrect medical diagnoses, wrongful legal outcomes); breaches of sensitive confidential data; propagation of biases leading to discriminatory outcomes and eroded trust; generation of 'hallucinated' or misleading information, especially harmful in legal and medical advice; exacerbation of societal inequalities and job displacement due to automation. |
| JQl5IoVQjuAJ.pdf | Google_Scholar | Hallucinating Law: Legal Mistakes with Large Language Models are Pervasive | This paper reports on a study evaluating the performance of three large language models (GPT-3.5, Llama 2, PaLM 2) on various U.S. legal tasks. The study found alarmingly high hallucination rates (69%-88%), particularly for complex tasks, lower court cases, and when presented with incorrect premises, suggesting current LLMs are unreliable for legal applications and may worsen access-to-justice issues. | True | Idealistic | True | 2.0 | Negative | Evaluation of existing Large Language Models (GPT 3.5, Llama 2, PaLM 2) on legal tasks. | Tested over 200,000 queries against GPT 3.5, Llama 2, and PaLM 2. Queries covered tasks like identifying opinion authors, determining precedential relationships, and identifying case holdings, stratified by court hierarchy, case prominence/age, and circuit. | Hallucination rates ranged from 69% to 88%. Performance deteriorated with task complexity and for lower court/less prominent cases. Models exhibited overconfidence and susceptibility to contra-factual bias. GPT-3.5 generally performed best but showed biases. | Current LLMs perform poorly on localized legal knowledge (lower courts) and complex reasoning tasks. They exhibit overconfidence, fail to correct user misconceptions (contra-factual bias), and are least reliable for the users (e.g., pro se litigants, those needing complex advice) who could most benefit from democratized legal information. | The paper advocates for caution, responsible integration requiring human supervision, transparency in model trade-offs, and a human-centered AI approach rather than specific technical fixes. | Access to legal information, Legal research accuracy, Reliability of AI in law, Case law analysis (precedent, holdings), Judicial system structure. | Litigants in lower courts, individuals in less prominent jurisdictions, users lacking legal expertise, general public seeking legal advice. | General US Case Law, Litigation | United States | NaN | Systematic evaluation using a large dataset (~200,000) of structured legal queries targeted at existing LLMs, stratified along dimensions like court level, case prominence, and task type. | NaN | True | False | The paper evaluates existing LLMs (GPT-3.5, PaLM 2, Llama 2) which are generally available, though access modalities vary (e.g., API, open release for Llama 2). | Significant gaps exist in LLM reliability for legal tasks, including handling complexity, local nuance (lower courts), calibration (confidence vs accuracy), and robustness against incorrect premises (contra-factual bias). Current models risk deepening legal inequalities rather than alleviating them. Need for transparency and normative judgment in model development. | NaN | Providing inaccurate legal information; deepening existing legal inequalities; fostering legal monoculture; representational harms (e.g., misattributing judicial opinions); users being misled by overconfident or factually incorrect responses (contra-factual bias). |
| laae003.pdf | Google_Scholar | Large Legal Fictions: Profiling Legal \nHallucinations in Large Language Models | This paper presents the first systematic empirical evidence of legal hallucinations in large language models (LLMs) like ChatGPT 4, PaLM 2, and Llama 2, finding they hallucinate at least 58% of the time when queried about US federal case law. It also documents their susceptibility to users' incorrect legal assumptions and poor self-awareness of errors, cautioning against unsupervised integration into legal tasks and highlighting risks for access to justice. | True | Idealistic | True | 2.0 | Negative | Public-facing LLMs: OpenAI’s ChatGPT 4, OpenAI’s ChatGPT 3.5, Google’s PaLM 2, and Meta’s Llama 2. | Evaluation using 14 legal knowledge query tasks (categorized by complexity) on a random sample of US federal case law (SCOTUS, USCOA, USDC). Employed reference-based querying (comparison to ground-truth metadata from legal databases) and reference-free querying (detecting self-contradiction across multiple LLM responses generated at a non-greedy temperature, with contradictions assessed by GPT-4). | LLMs hallucinate between 58% (ChatGPT 4) and 88% (Llama 2) of the time on direct, verifiable questions about federal court cases. GPT-4 performed best in terms of raw hallucination rates but was less calibrated than PaLM 2 and GPT 3.5. Models also demonstrated susceptibility to contrafactual bias and imperfect self-awareness of their propensity to hallucinate. | High rates of factual hallucination in LLM responses, poor model calibration (overconfidence in errors), susceptibility to contrafactual bias (uncritically accepting users' incorrect legal premises), and uneven legal knowledge (better for prominent/newer cases and major jurisdictions, worse for localized or older law). These issues risk exacerbating existing inequalities in legal services and creating a 'legal monoculture'. | The paper discusses potential mitigation techniques from other research (e.g., retrieval-augmented generation, advanced prompting, specialized fine-tuning, factuality-focused decoding, external database checks) but notes their current limitations. It advocates for human-centered AI approaches and emphasizes the need for developers to be transparent about the types of hallucinations their LLMs might produce and the choices made to minimize them. | Accuracy of LLMs in retrieving and stating US case law facts; factual hallucinations; implications of LLM errors for legal research, legal advice, and access to justice for pro se litigants. | Pro se and under-resourced litigants. | US federal case law. | United States (federal judiciary: US Supreme Court, US Courts of Appeals, US District Courts). | The paper states the LLMs were trained on vast text corpora including public domain American case law. Specific training datasets for the evaluated commercial/open-source LLMs (OpenAI, Google, Meta) are generally proprietary to the developers and not detailed further by the paper. | Construction of a test dataset of legal queries based on American case law, stratified by court level, jurisdiction, and time. Application of reference-based evaluation (comparing LLM output to known metadata) and reference-free evaluation (measuring self-contradiction in LLM outputs to infer hallucinations). Statistical analysis of hallucination rates and their correlation with case/court characteristics. | The evaluated LLMs (ChatGPT, PaLM 2, Llama 2) are deployed by their respective developers (OpenAI, Google, Meta) via APIs and public interfaces. | True | True | The discussed LLMs (ChatGPT 4, ChatGPT 3.5, PaLM 2, Llama 2) are generally accessible via APIs or public interfaces, with Llama 2 being open-source (e.g., Llama-2-13b-chat-hf). The paper's evaluation dataset is also available on HuggingFace and replication materials on Harvard Dataverse. | Technical: persistent high rates of factual hallucination in LLMs despite ongoing research into mitigation, poor model calibration (especially LLMs being overconfident in errors), difficulty handling localized or less prominent legal information, and an inability to reliably correct users' legal misconceptions. Societal: the risk of LLMs exacerbating the access to justice gap for vulnerable populations, the potential for creating a 'legal monoculture' due to biased knowledge, and the need for normative frameworks and transparency regarding which types of hallucinations are minimized by developers. | For LLMs in legal tasks: Ensuring factual accuracy and reliability in open-domain legal question answering. For the evaluation: Designing comprehensive and scalable methods (reference-based and reference-free) to detect and quantify legal hallucinations. General limitations of hallucination mitigation techniques like RAG (dependency on retrieval quality, query ambiguity, computational cost, handling conflicting information in databases) and evaluation metrics. | Generation of factually incorrect legal information leading to harmful or inaccurate legal advice. Worsening disparities in access to legal services due to LLMs' uneven knowledge distribution (e.g., better on prominent law, worse for specific needs of pro se litigants). Creation of a 'legal monoculture' by promoting a homogenized and potentially biased understanding of the law. Misleading users due to LLMs' overconfidence in false statements and their tendency to uncritically accept and respond to queries based on incorrect legal premises (contrafactual bias). |
| eWEN5-3w78IJ.pdf | Google_Scholar | Generative AI and the Future of Legal Scholarship | This paper proposes "Generative Synthesis" as a new paradigm for legal scholarship, advocating for the integration of generative AI as a co-creator of knowledge alongside human researchers. It explores the potential transformation of scholarly practices while detailing significant challenges like AI bias, deskilling, authorship ambiguity, and the need for ethical guidelines and institutional adaptation. | True | NaN | True | 1.0 | Neutral | Generative Synthesis: Integrating generative AI (LLMs) as a co-creator in the legal scholarship process. | Demonstration through AI generation of the paper's main body (Parts I-IV) using ChatGPT (OpenAI o1) based on specific prompts provided by the human author. | AI-generated text demonstrated creativity and sophistication comparable to a competent legal scholar, though with acknowledged gaps and flaws. | NaN | NaN | NaN | NaN | Legal Scholarship (meta-level) | International | Not specified, but uses ChatGPT (OpenAI o1, Dec 2024) which is known to be trained on large, diverse datasets. The paper mentions the risk of bias embedded in AI training data. | Prompt engineering with ChatGPT (OpenAI o1). Initial high-level prompt followed by section-specific prompts requesting law-review suitable text. Minimal iteration reported. | NaN | True | False | The conceptual approach ('Generative Synthesis') can be used with available LLMs; the specific prompts/outputs transcript from the paper's generation process is shared via a link. | Need for robust verification protocols for AI output; methods to mitigate algorithmic bias; development of new norms for authorship, disclosure, and citation; addressing deskilling risks; ensuring AI use aligns with normative legal values (justice, fairness); adapting peer review and legal education; establishing institutional oversight; considering global/cross-jurisdictional applicability and sensitivity. | Over-reliance on AI, deskilling, epistemic complacency, algorithmic bias perpetuating inequality, accountability/authorship issues, IP concerns, need for evolving standards (publishing, ethics), adapting peer review, updating legal education, ensuring AI aligns with normative values, potential for a narrow Western/Anglo-American focus. | Inaccurate/flawed AI outputs, deskilling of legal analysis, perpetuation/amplification of systemic bias, automated discrimination, IP infringement, undermining field credibility, creating digital divides. |
| Q_89nrnh9yYJ.pdf | Google_Scholar | Creative and Strategic Capabilities of Generative AI: Evidence from Large-Scale Experiments | This study experimentally compares generative AI (ChatGPT-4, Bard) with US adults on creative and strategic tasks. Results indicate ChatGPT-4 often surpasses human creativity, and AI augmentation improves human creativity but not beyond AI alone; in strategic games, AI adapts but humans can outperform it. | True | NaN | True | 2.0 | NaN | ChatGPT-4 and Bard (Google's AI chatbot) | Large-scale experiments with over 4,000 US adult participants. Creative tasks involved generating text based on prompts, rated by other humans on creativity, novelty, surprise, and usefulness. Strategic tasks involved playing 24 rounds of Rock-Paper-Scissors against a pre-determined opponent strategy (either balanced/equilibrium or unbalanced/biased). | For creativity, ChatGPT-4's ideas were rated highest, surpassing unassisted humans, humans augmented with AI, and Bard. In strategic games (Rock-Paper-Scissors against a biased opponent), humans earned significantly more points than ChatGPT-4, despite both showing adaptation to the opponent's strategy. | NaN | NaN | NaN | NaN | NaN | USA | NaN | NaN | NaN | True | False | ChatGPT-4 and Bard are accessible via their standard chat interfaces. Access to ChatGPT-4 as used in the study (version 4) typically requires a subscription. | NaN | Effective prompting of AI for optimal creative output (HumanPlusAI underperformed AI alone). Understanding AI's adaptive limits in strategic contexts and how humans interact with AI. | Algorithm aversion where humans rate suspected AI output lower. Potential for AI competition to disproportionately affect certain demographics (e.g., women's creativity). Public skepticism or resistance towards AI. |
| xqPlbTspskYJ.pdf | Google_Scholar | Legal Market Decartelization | This paper critically examines the trend towards legal market decartelization in the United States, arguing that deregulation, while intended to improve access to justice, presents significant risks such as increased information asymmetry, corporate dominance, and negative litigation externalities. The authors advocate for policymakers to consider solutions beyond mere decartelization, including targeted aid, process simplification, and the cautious adoption of new technologies like AI, to address the maldistribution of legal services. | True | Idealistic | False | 3.0 | Positive | Legal market decartelization (as a policy approach, including reforms to rules on nonlawyer ownership of law firms/Alternative Business Structures and the unauthorized practice of law) | NaN | NaN | Maldistribution of legal services; high rates of unmet legal needs; consumers' lack of awareness of their legal problems or available solutions; asymmetric information between consumers and legal service providers; high information costs; distrust of providers; complexity of legal processes. | Targeted interventions to reduce asymmetric information; subsidized legal services (e.g., "civil Gideon") in critical areas like housing; simplification of legal and court processes; leveraging new technologies like generative AI (with appropriate safeguards) to lower information costs; government and professional investment in civics training and dissemination of legal information; partnerships with trusted community organizations; specific process-based reforms like court appearance reminders, remote hearings, and automatic criminal record expungement ('clean slate' laws). | Addressing unmet civil legal needs; improving access for low- arid middle-income consumers; housing law (eviction); criminal record expungement; consumer debt; simplification of legal processes; self-represented litigants; regulation of legal services. | Low-income Americans; ordinary consumers of legal services; individuals of limited means; rural communities facing 'legal deserts'; tenants in eviction proceedings; individuals with criminal records. | Legal Profession Regulation; Access to Justice; Civil Law; Housing Law; Criminal Law (specifically expungement); Consumer Law; Litigation. | United States (with comparative references to the UK, Europe, Canada, and Australia, and specific examples from US states like Arizona, Utah, Washington, Minnesota, New York, Pennsylvania). | NaN | NaN | NaN | True | True | Discusses existing generative AI technologies like GPT-4, which are publicly accessible, with some versions available for free. | Vast scale of unmet legal needs unresolved by current approaches; lack of comprehensive studies on the impact of deregulation (e.g., in the U.K.); persistent information costs and consumer unawareness; the digital divide limiting technology-based solutions in 'legal deserts'; potential for AI misuse (e.g., hallucinations, lack of accuracy) requiring regulatory oversight; need for public/philanthropic funding for non-market solutions like Civil Gideon. | NaN | Legal market decartelization exacerbating asymmetric information; private equity and well-capitalized entities gaining market dominance, potentially leading to consolidation and higher prices without service improvement; increased moral hazard and negative externalities in litigation (e.g., frivolous suits, reduced attorney gatekeeping); diminished role of lawyers in constructive law development, potentially worsening regulatory capture; 'one-size-fits-all' deregulation failing due to spatial localization of legal markets; digital-first solutions marginalizing vulnerable populations or those in digital deserts; deregulation leading to reduced competition if capital is primarily used for consolidation rather than innovation; misuse of AI if deployed without proper regulation and disclosure of limitations. |
| RTRKZVYlBPYJ.pdf | Google_Scholar | DETERMINANTS OF SOCIALLY RESPONSIBLE \nAI GOVERNANCE | This paper proposes justice, equity, and the rule of law as core determinants for socially responsible AI governance, ensuring AI actively promotes fairness and inclusivity. It analyzes AI's impact on access to justice, discusses risks like bias, and offers a proactive governance framework by comparing approaches in the US, EU, China, and Singapore. | True | Idealistic | False | 1.0 | Positive | Proactive governance framework (incorporating transparency, equity audits, tailored regulatory approaches); 'Equity by Design' framework; justice, equity, and the rule of law as yardsticks for socially responsible AI. | NaN | NaN | Algorithmic bias (stemming from data, code, and existing legal frameworks), lack of transparency and accountability in AI systems (e.g., due to trade secrets, complexity), exacerbation of existing socio-economic inequalities and the digital divide, and the potential for AI to undermine the rule of law and democratic processes (e.g., through disinformation or unscrutinized norm-setting). | Proactive governance frameworks (featuring transparency, equity audits, tailored regulation), adherence to 'Equity by Design' principles, fostering diversity in development teams and training data, promoting explainable AI, ensuring human oversight, implementing AI literacy programs, encouraging international collaboration and standards, and establishing normative oversight bodies for AI. | Access to legal information and representation, fairness and non-discrimination in legal processes, efficiency of legal services for underserved populations, overcoming language and literacy barriers in legal contexts. | Marginalized communities (including low-income individuals, ethnic minorities, Indigenous groups, unskilled immigrants, senior citizens), self-represented litigants, individuals with limited English proficiency, and tenants facing eviction. | Civil law (especially housing, debt collection), criminal justice, due process, intellectual property, litigation generally, constitutional law. | United States, European Union, China, Singapore, International | NaN | Comparative legal analysis, conceptual framework development, literature review. | Policy adoption by governments and regulatory bodies, international collaboration and standard-setting, implementation of proposed principles by AI developers and deployers. | False | False | NaN | Ensuring equitable access to AI benefits and mitigating the digital divide, developing effective and balanced regulations that foster innovation while safeguarding rights, managing AI's autonomous norm-setting capabilities, achieving truly unbiased and fair AI systems (addressing technical and data limitations), and establishing international harmonization for AI governance. | Synthesizing diverse international governance models, balancing innovation with ethical safeguards (justice, equity, rule of law), addressing the rapid evolution and multifaceted risks of AI, and conceptualizing abstract principles for practical AI governance. | Exacerbation of existing inequalities, algorithmic bias and discrimination, lack of transparency and accountability, AI errors such as hallucinations, misuse by malicious actors (e.g., disinformation, fraud, predatory practices), undermining of due process and the rule of law, erosion of privacy and free expression, and threats to national security. |
| yV9JSc0E-YYJ.pdf | Google_Scholar | Regulatory Framework for Artificial Intelligence in the Legal System of Pakistan | The paper discusses the growing use of AI in Pakistan's legal system, highlighting applications like e-discovery and judicial assistance (e.g., ChatGPT) and emphasizing the urgent need for a comprehensive regulatory framework. It outlines potential benefits for efficiency alongside significant risks such as bias, errors, job displacement, privacy violations, and the current lack of a coherent national AI strategy. | True | Market | True | 3.0 | Neutral | General AI applications in law (incl. TAR, ChatGPT/GPT-4) | NaN | NaN | Lack of coherent national AI strategy and regulation; potential for AI bias and errors; risks to privacy; need for human oversight/judgment; job displacement concerns. | Develop a comprehensive legal and regulatory framework for AI; create a clear national AI strategy; provide education and training for legal professionals; launch public awareness campaigns; ensure human judgment remains central in legal decision-making. | Judicial efficiency, Regulation of AI in legal services, E-discovery, AI-assisted judicial decision-making | NaN | Civil Law, Criminal Law, Technology Law, Procedural Law, Privacy Law | Pakistan | NaN | NaN | NaN | False | False | NaN | Lack of a comprehensive and strategic national AI policy; inadequate regulation for ethical/societal implications (bias, privacy, liability, data scraping); insufficient cyber/internet governance policy for AI; need for greater public awareness and digital literacy; need for mechanisms for redress for AI-related harms. | Integrating AI while maintaining human oversight; ensuring fairness and avoiding bias; addressing job displacement; protecting privacy; developing effective regulations; overcoming lack of strategic policy implementation. | AI bias leading to inequitable outcomes; errors in AI judgments causing injustice; job displacement; privacy violations; misuse for crime (e.g., voice cloning); erosion of human rights (dignity, privacy); lack of transparency. |
| Y4hGJX6FeicJ.pdf | Google_Scholar | NEW FRONTIERS IN ATTORNEY REGULATION : INTRODUCTION TO VOLUME II OF II | This paper introduces Volume II of a symposium on attorney regulation, summarizing articles on topics including the NextGen Bar Exam, lawyer competence, Generative AI in law and legal education, and professional responsibility. It highlights how Generative AI is discussed in the context of legal practice and education, its potential to provide DIY legal solutions for low-income individuals, and emerging regulatory considerations. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Inability of low-income individuals to afford or secure free legal services. | Encouraging the use of Generative AI tools for DIY legal solutions and permitting nonlawyers to assist consumers in using these tools effectively. | DIY legal solutions for low-income individuals using Generative AI; Access to legal services for the underserved. | Low-income individuals; persons unable to afford or secure free legal services. | Attorney regulation; Delivery of legal services; Legal education; Legal ethics; Professional responsibility. | United States | NaN | NaN | NaN | False | False | NaN | Need for more specific guidance and proactive regulatory frameworks for the legal profession's use of Generative AI. | NaN | Compromise of confidential client information when inputted into Generative AI tools; lawyers encountering problems using AI tools without proper training or guidance. |
| vgbfX5Ex6JoJ.pdf | Google_Scholar | VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs | This paper introduces VANE-Bench, a new benchmark dataset designed to evaluate the ability of Large Multi-modal Video Models (Video-LMMs) to detect anomalies in videos. The benchmark includes real-world surveillance clips and synthetically generated videos with subtle inconsistencies, finding that current models struggle significantly with this task. | True | Market | True | 1.0 | NaN | VANE-Bench: A benchmark dataset and evaluation methodology for video anomaly detection using Video-LMMs via a Multiple-Choice Video Question Answering (MC-Video QA) task. | Evaluation of nine Video-LMMs (7 open-source, 2 closed-source like GPT-4o and Gemini-Pro) on the VANE-Bench dataset (325 videos, 559 QA pairs from real-world and AI-generated sources). Human evaluation also performed on SORA videos. | Most Video-LMMs, particularly open-source ones, performed poorly (<26% accuracy). Closed-source models (GPT-4o: 72.82%, Gemini-Pro: 69.64% average accuracy) performed better overall but still struggled with subtle AI-generated anomalies. Human performance on subtle anomalies was also sub-optimal. | NaN | NaN | NaN | NaN | Criminal Law, Evidence Law | International | The benchmark dataset (VANE-Bench) comprises videos from existing publicly available real-world anomaly datasets (CUHK Avenue, UCF-Crime, UCSD Pedestrian) and synthetically generated videos from state-of-the-art text-to-video models (SORA, Open-Sora, Runway Gen2, ModelScopeT2V, VideoLCM). Associated question-answer pairs were generated using GPT-4o based on annotations. | Benchmark design involved collecting real-world and AI-generated videos, semi-automatic annotation of anomalies (Frame Annotation Module - FAM), caption generation using GPT-4o (Caption Generation Module - CGM), and multiple-choice question-answer pair generation using GPT-4o (Question Answer Generation Module - QAGM). | The VANE-Bench dataset and associated code are made publicly available via GitHub. | True | True | Code and data for the VANE-Bench benchmark are publicly available on GitHub. | Current Video-LMMs lack robustness in detecting subtle video anomalies, especially in AI-generated content. Existing benchmarks do not sufficiently focus on this specific challenge. | Detecting subtle anomalies in high-fidelity videos (challenging for AI and humans), inconsistent predictions from Video-LMMs depending on query phrasing, variability in performance across different anomaly types, limitations in accessing/evaluating closed-source models, limited availability of samples from cutting-edge models like SORA. | The difficulty in detecting anomalies in high-fidelity AI-generated videos poses risks related to misinformation, deepfakes, and distinguishing real from synthetic content, particularly during critical events like elections. |
| Paper_113-Accurate_AI_Assistance_in_Contract_Law1.pdf | Google_Scholar | Accurate AI Assistance in Contract Law Using Retrieval-Augmented Generation to Advance Legal Technology | This paper proposes an AI chatbot using Retrieval-Augmented Generation (RAG) to provide accurate legal assistance in contract law, demonstrated with Moroccan legislation. The system aims to enhance understanding for non-experts by grounding responses in verified legal documents, thereby mitigating Large Language Model (LLM) hallucinations. | True | Idealistic | True | 1.0 | Positive | A chatbot system integrating Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs like GPT-4 Turbo, Llama 3) and a vector database (FAISS) containing embedded legal documents (Moroccan Code of Obligations and Contracts). | Comparative evaluation of GPT-4 Turbo and Llama 3 within the RAG system using the RAGAS framework, measuring 'Faithfulness' and 'Answer Relevance' metrics, alongside response time. | GPT-4 Turbo achieved higher Faithfulness (1.0 vs 0.84) and Answer Relevance (0.971 vs 0.79) compared to Llama 3, although Llama 3 was faster (0.86s vs 3.12s). GPT-4 Turbo was selected for its higher accuracy. | Complexity of legal documentation, prevalence of misinformation, need for specialized legal skills to understand/draft contracts, limitations of LLMs (outdated knowledge, hallucinations). | An AI chatbot using RAG to provide accurate, contextually relevant responses based on integrated official legal documents, simplifying legal information access for non-experts and reducing reliance on potentially inaccurate LLM knowledge. | Contract law understanding and assistance. | General public / citizens / non-expert users. | Contract Law (specifically mentioning Moroccan Code of Obligations and Contracts, and potential application to Property Law). | Morocco (with stated adaptability to other jurisdictions). | The knowledge base used for RAG consists of the Moroccan "Code of Obligations and Contracts (COC)", extracted from official PDF documentation using OCR. This is unstructured, domain-specific legal text. The underlying LLMs (GPT-4 Turbo, Llama 3) were pre-trained on general datasets by their respective organizations. | System architecture development involving data collection (OCR), preprocessing (text splitting), embedding (LLM-Embedder), vector storage (FAISS), retrieval (ANN similarity search), response generation (RAG with LLMs), and comparative evaluation (RAGAS framework). | NaN | False | False | NaN | Need for automated legal updates, integration of multimodal capabilities, improved explainability (providing explicit legal references), enhanced adaptability to different legal frameworks, the system cannot replace human expertise in complex cases. | Balancing LLM speed vs. accuracy/relevance, ensuring factual consistency and avoiding hallucinations, managing and updating the legal knowledge base, processing PDF legal documents effectively (OCR, chunking). | Providing incorrect legal information due to LLM hallucination or outdated data, users over-relying on the system for complex legal matters requiring professional advice. |
| JDIxmlFdiQYJ.pdf | Google_Scholar | THE USE OF ARTIFICIAL INTELLIGENCE IN ACADEMIC PUBLISHING: PRELIMINARY REMARKS AND PERSPECTIVES | The paper discusses the potential applications of Artificial Intelligence (AI) across various stages of the academic publishing workflow, including manuscript analysis, reviewer selection, and communication. It highlights how AI can enhance efficiency but also notes limitations, risks like bias and copyright issues, and ethical considerations. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | NaN | NaN | False | False | NaN | NaN | Integrating AI software with existing journal management systems; need for custom programming (e.g., using APIs) to connect different tools; some tasks are already adequately handled by existing non-AI systems. | AI systems struggling with nuance and context; perpetuation or amplification of biases from training data; difficulty assigning accountability for AI errors or biased content; potential job displacement for human editors; concerns regarding authorship of AI-generated text; copyright infringement/piracy risk. |
| DrMmT3gajroJ.pdf | Google_Scholar | NYAYA ANUMANA and INL EGAL LLAMA : The Largest Indian Legal Judgment Prediction Dataset and Specialized Language Model for Enhanced Decision Analysis | This paper introduces NyayaAnumana, the largest dataset for Indian Legal Judgment Prediction (LJP), containing over 700,000 cases from various courts. It also presents INLegalLlama, a LLaMa-based language model specifically adapted for the Indian legal domain via continued pretraining and supervised fine-tuning, designed for both predicting judgments and providing explanations. | True | Idealistic | True | 1.0 | Positive | NyayaAnumana dataset creation and INLegalLlama model development (LLaMa-2 7B adapted via Continued Pretraining and Supervised Fine-tuning with LoRA for Legal Judgment Prediction and Explanation). | Evaluated LMs (InLegalBERT, InCaseLaw, XLNet) and LLMs (including INLegalLlama) on NyayaAnumana splits for binary/ternary classification across court types and temporal data. Also tested on external datasets (ILDC, PredEx, ILDC_expert). Metrics included Precision, Recall, F1, Accuracy, Rouge, BLEU, METEOR, BERTScore, BLANC, and expert evaluation using a Likert scale. | Achieved approximately 90% F1-score/accuracy in binary prediction tasks on the NyayaAnumana dataset using domain-specific models. INLegalLlama (CPT+SFT) outperformed baseline LLaMa-2 and other LLMs on PredEx and ILDC_expert datasets for prediction and explanation tasks, achieving 76.05% accuracy on PredEx. | Significant backlog of lakhs of pending cases burdens the Indian legal system. | Develop AI-driven systems for legal judgment prediction and explanation (like NyayaAnumana and INLegalLlama) to enhance efficiency, accessibility, and transparency in the legal process. | Legal Judgment Prediction (LJP), Explainable AI (XAI) in law. | General population interacting with the Indian judicial system (implicitly, by addressing case backlog). | General Litigation (covering multiple fields adjudicated by Supreme Court, High Courts, Tribunals, District Courts). | India | NyayaAnumana: A new publicly sourced (IndianKanoon) corpus of 702,945 preprocessed, English-language, unstructured Indian court case documents from various court levels. Subsets used for model training (CPT: SCI + 100k HCs subset; SFT: PredEx dataset with expert annotations). | Data compilation and preprocessing (web scraping, regex cleaning, keyword filtering, label extraction). Model development involved Continued Pretraining (CPT) of LLaMa-2 7B on a subset of NyayaAnumana, followed by Supervised Fine-tuning (SFT) using the PredEx dataset and Parameter-Efficient Fine-Tuning (PEFT) with LoRA. | Dataset and code made available via a GitHub link. | True | True | Dataset (NyayaAnumana) and code for prediction and explanation models available on GitHub. | Lack of datasets in regional Indian languages. Need for larger, more advanced models and refined fine-tuning techniques incorporating diverse legal documents (statutes, contracts). LLM applicability for complex legal reasoning requires further investigation. | Resource constraints (GPU memory, compute time, cost) leading to model quantization (4-bit) and limiting the use of larger models. High cost and time for obtaining expert annotations. Difficulty for generative models in processing long legal documents and performing complex reasoning. Inconsistencies and preprocessing errors in existing benchmark datasets (e.g., ILDC). Reducing model hallucination. | Generative models may produce hallucinated or factually incorrect content. Over-reliance on AI without human oversight in legal decision-making (mentioned as a need for caution). |
| p4PiylqM104J.pdf | Google_Scholar | Exploring Possibilities of AI-Powered Legal Assistance in Bangladesh through Large Language Modeling | This paper details the development of UKIL-DB-EN, a corpus of Bangladeshi legal documents, and the fine-tuning of GPT-2 (creating GPT2-UKIL-EN) on this corpus to provide legal assistance in English. The model shows promising results in evaluations including expert opinions but requires further refinement for accuracy and safety. | True | Idealistic | True | 1.0 | Positive | Fine-tuning the GPT-2 medium model on a custom-built corpus of Bangladeshi legal documents (UKIL-DB-EN) using instruction-tuning prompts to create the GPT2-UKIL-EN model. | Quantitative semantic similarity analysis (Cosine similarity, Jaccard index) comparing model output to original texts, and qualitative evaluation through three case studies (varying difficulty) assessed by five legal experts using a rating scale and providing feedback. | GPT2-UKIL-EN achieved the highest scores on semantic similarity metrics (Cosine: 0.515, Jaccard: 0.133), outperforming baseline GPT-2, Mistral-7b, and Gemma-2b. Expert evaluation (average score 4.81/7) indicated good reasoning and approach but issues with accuracy and clarity, especially in complex cases. | Significant delays, procedural complexity, high legal costs, large case backlogs (over 3.7 million), police harassment, inadequacies in legal provisions, lack of legal knowledge, and financial constraints preventing access to representation, particularly for lower-income/marginalized communities. | Developing a specialized LLM (GPT2-UKIL-EN) to automate legal assistance, simplify legal language, provide affordable support, streamline administrative processes, democratize access to legal information, and empower individuals to understand their rights and navigate the system. | Access to legal information, understanding legal rights and procedures, reducing legal costs, mitigating judicial delays, improving case management efficiency. | General population of Bangladesh, particularly lower-income or marginalized communities facing financial or educational barriers to accessing the legal system. | General Legal Assistance (derived from scraping various acts like civil, criminal, administrative and case studies on property, criminal law). | Bangladesh | UKIL-DB-EN: A publicly available corpus of English-language Bangladeshi legal documents (595 Acts, ~18,023 sections) collected via web scraping from an open-access government portal (bdlaws.minlaw.gov.bd) and preprocessed. | Data collection (web scraping), data curation (cleaning, noise reduction, standardization, verification), model selection (GPT-2 medium), model fine-tuning (instruction tuning, LoRA, quantization), prompt engineering, quantitative evaluation (semantic similarity), qualitative evaluation (case studies, expert review). | The dataset (UKIL-DB-EN) and the fine-tuned model (GPT2-UKIL-EN) are publicly released on Hugging Face. | True | True | Dataset and model available on Hugging Face under Apache-2.0 license. | Need for improved model accuracy, credibility, and safety; limitations in contextual comprehension for complex cases; handling of multilingual requirements (Bangla and English); need for larger models; language simplification for lay users; information gaps requiring more comprehensive data. | Limited computational resources restricted experimentation with larger/multilingual models and RAG; ensuring accuracy and reliability in the sensitive legal domain; handling legal complexities and context-specific nuances. | Potential inaccuracies and reliability issues in the model's responses could lead to incorrect legal understanding or advice, posing ethical concerns due to the critical nature of legal applications. |
| h2e8YjKeFzcJ.pdf | Google_Scholar | OBJECTION! USE OF AI! \nEvaluating the Role of Generative Arti/f_icial Intelligence in Litigation: \nRisks and Regulations | This paper reviews the significant risks posed by generative AI, particularly ChatGPT, in litigation, including misinformation, privilege breaches, data collection, and ethical concerns regarding judicial integrity. Based on a literature review, it argues that current generative AI should be prohibited in litigation pending further societal consideration and regulation. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Litigation | United States, Colombia | NaN | NaN | NaN | False | False | NaN | Need for societal discussion and consensus on whether and how generative AI should be used in litigation; Need for evaluated regulations. | NaN | Misinformation and generation of fake legal citations/cases; Breaches of legal professional privilege and confidentiality due to data handling practices (review, retention, training use, third-party sharing); Undermining judicial integrity if judges rely on potentially inaccurate AI outputs; Ethical concerns about the appropriateness of AI influencing high-stakes legal decisions; Potential for biased AI outputs. |
| Assessing_the_Benefits_of_ChatGPT_for_Business_An_Empirical_Study_on_Organizational_Performance.pdf | Google_Scholar | Assessing the Benefits of ChatGPT for Business: An Empirical Study on Organizational Performance | This paper empirically investigates the impact of ChatGPT's system, information, and service quality on user satisfaction and benefits, and subsequently on organizational performance in businesses, using the DeLone and McLean's Information Systems Success model. Findings from a survey of 361 Korean business users indicate that these quality attributes positively affect satisfaction and benefits, which in turn enhance organizational performance. | True | Market | True | 2.0 | NaN | ChatGPT (a conversational generative AI by OpenAI) | Survey of 361 business users in Korea; data analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test hypotheses derived from the D&M IS Success model. Measurement model assessed for reliability, convergent validity, and discriminant validity. | System quality, information quality, and service quality of ChatGPT positively impact user satisfaction and benefits; service quality had the most significant impact on satisfaction (β=0.451, p<0.001). Both satisfaction (β=0.262, p<0.001) and benefits (β=0.269, p<0.001) significantly enhanced organizational performance. The model explained 46.1% of variance in organizational performance. | NaN | NaN | NaN | NaN | NaN | Republic of Korea | The study analyzes ChatGPT, which is trained by OpenAI on large-scale, diverse text and code datasets using Reinforcement Learning from Human Feedback (RLHF); this data is proprietary to OpenAI. | Quantitative survey-based study applying the DeLone and McLean’s Information Systems Success (D&M IS) model; instrument development based on verified measurements; data analysis through structural equation modeling (PLS-SEM). | ChatGPT, the tool studied, was publicly released by OpenAI in November 2022 and is accessible via a web interface and API. | True | True | ChatGPT is available via a web interface and API provided by OpenAI, with both free and paid access tiers. | NaN | Identifying ChatGPT's impact on organizational performance due to its novelty; generalizability of findings as ChatGPT is in its nascent stage and the user base may not be representative; potential for same-method bias in self-report survey data. | Disinformation; ethical issues, including those related to academic writing and test integrity. |
| vbPLjHykthcJ.pdf | Google_Scholar | Experimenting with Legal AI Solutions: The Case of Question-Answering for Access to Justice | This paper introduces a human-centric pipeline for legal question-answering aimed at laypeople, featuring a new dataset (LegalQA) with expert-written answers and citations. It demonstrates that retrieval-augmented generation (RAG) using a small, domain-specific set of expert-approved documents can match or outperform RAG using internet-wide retrieval for factual accuracy. | True | Idealistic | True | 1.0 | Positive | A human-centric legal NLP pipeline involving: 1) A new dataset (LegalQA) of real layperson legal questions and expert-written answers/citations. 2) Domain-specific Retrieval-Augmented Generation (RAG) using only expert-approved documents. 3) An automatic, expert-vetted evaluation protocol focused on factuality. | Evaluated using the created LegalQA dataset (323 questions). Factual accuracy was assessed using an automatic evaluation protocol (GPT-4 comparing model output to expert answers), measuring the percentage of factual disagreement. Compared domain-specific RAG (using 850 expert-sourced documents) against non-RAG models (GPT-3.5, GPT-4, Mixtral-8x7B) and internet-wide RAG (GPT-3.5 with Google search, Cohere Command R+). | Domain-specific RAG using GPT-3.5 ('GPT-3.5 Legal', 8.7% disagreement) performed better than non-RAG GPT-3.5 (11.8%) and internet-wide RAG ('GPT-3.5 Internet', 8.3%; Command R+, 14.4%). However, the non-RAG GPT-4 model performed best overall (4.4% disagreement). | Lack of high-quality, expert-vetted structured legal data (question-answer pairs) suitable for laypersons; factual incorrectness (hallucination) and outdated information in LLMs; prohibitive costs of high-performing models (like GPT-4) limiting accessibility. | Creating and releasing high-quality, expert-verified datasets (like LegalQA). Employing domain-specific retrieval-augmented generation (RAG) using a curated set of trusted legal sources to improve factual grounding and reduce costs compared to retrieving from the entire internet. Developing human-centric evaluation protocols focused on factuality. | Providing factual answers to specific legal questions asked by laypeople. | Laypeople seeking legal information. | Employment and labour law, Family and juvenile law, Real estate law, Corporate law, Personal injury law, Civil rights law. | Canada (specifically Ontario mentioned in an example, and expert annotators were knowledgeable in Canadian law). | The study uses a retrieval dataset comprising 850 legal documents (citations) provided by legal experts corresponding to answers for real layperson questions sourced from Reddit (r/legaladvice). The evaluation dataset (LegalQA) is a subset (323 Q&A pairs) released publicly. This data is structured (question, expert answer, citation) and domain-specific (Canadian law). | Human-centric design involving legal experts (law professors and students) for data sourcing (writing answers, providing citations) and evaluation design. Technical methodology involves Retrieval-Augmented Generation (RAG) based on embedding similarity (dot product) between questions and a curated document set. | The LegalQA evaluation dataset was released publicly on Hugging Face. | False | False | The evaluation dataset (LegalQA) is claimed to be publicly released, but not the full RAG system or the 850-document retrieval corpus. | Performance gap between open-source and closed-source models; need for continual updating of legal knowledge in AI systems; lack of expert involvement in sourcing unstructured data for pre-training legal models; accountability issues with black-box models. | Sourcing high-quality, expert-approved legal data suitable for laypersons; developing reliable automatic evaluation methods for factual correctness in open-ended legal answers; difficulty answering highly specific/nuanced questions, particularly in certain legal areas (e.g., civil rights, real estate); managing computational/storage costs of retrieval. | LLMs providing factually incorrect or misleading legal advice (hallucination); lack of accountability and transparency in closed-source models used for legal purposes. |
| 8fuknjhzvVkJ.pdf | Google_Scholar | “Balancing Innovation and Copyrights: The Legal Framework for AI Training in the European Union” | This master's thesis examines the European Union's copyright framework concerning the use of copyrighted materials for training Generative AI systems. It analyzes the legal challenges, compares EU, US, and Japanese approaches, and explores potential remedies like licensing, unlearning, and exceptions for non-expressive use. | True | Market | False | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Copyright Law, Intellectual Property Law, AI Law/Regulation | European Union (EU), United States (US), Japan | Discusses the use of large datasets (text, images) scraped from the internet, including public domain, licensed, and copyrighted works (e.g., Getty Images, books, news articles) for training Generative AI, without using a specific dataset for its own study. | NaN | NaN | False | False | NaN | Lack of conclusive EU case law on AI training copyright infringement and remedies. Technical difficulty and feasibility of AI 'unlearning'. Impracticality of obtaining licenses for vast amounts of scraped data. Tension between fostering innovation (especially for SMEs) and protecting creators' rights. Lack of standardized machine-readable opt-out mechanisms. | Legal challenges in obtaining authorization/licenses for copyrighted training data. Balancing innovation needs with creator rights under existing legal frameworks. Legal uncertainty (especially with doctrines like fair use). Technical difficulty of implementing remedies like 'unlearning' or selective data removal without impacting model performance. Ensuring effective copyright compliance mechanisms (e.g., scalable opt-out, filtering). | Risk of copyright infringement lawsuits leading to damages, injunctions, or model destruction. Stifling innovation due to overly restrictive regulations or high compliance/licensing costs. Market harm to creators if AI outputs devalue their original work. Potential degradation of AI model quality if access to diverse data is heavily restricted ('garbage in, garbage out', 'Habsburg AI'). Lack of transparency regarding training data potentially leading to legal or ethical issues. |
| idl5o7BDS4YJ.pdf | Google_Scholar | A KNOWLEDGE GRAPH MODELING APPROACH FOR AUGMENTING LANGUAGE MODEL-BASED CONTRACT RISK IDENTIFICATION | Large language models (LLMs) show promise for automating contract review but struggle with domain-specific knowledge and factual accuracy. This paper proposes augmenting LLMs with a nested Knowledge Graph (KG) modeling approach to enhance automated contract risk identification in the construction industry. | True | Market | True | 1.0 | NaN | A nested Knowledge Graph (KG)-augmented Large Language Model (LLM) framework for automated contract risk identification. | Case study comparing the KG-augmented LLM (gpt-3.5-turbo) against a baseline LLM using sample clauses from international construction projects. Evaluation was conducted manually by domain experts assessing risk label identification and analysis accuracy against a gold standard review. | The KG-augmented LLM correctly identified 'No risk' for the sample clause and provided analysis aligned with expert review, whereas the baseline LLM incorrectly identified risk due to hallucination and lack of domain knowledge. | NaN | NaN | NaN | NaN | Construction Law, Contract Law | International | The Knowledge Graph was constructed semi-automatically using an ontology and LLM-based prompting on unstructured contract text (potentially standard forms and project-specific clauses), requiring manual intervention. The underlying LLM (gpt-3.5-turbo) is pre-trained on general data. Testing data comprised clauses from international construction projects. | Ontology development (using Protégé), nested Knowledge Graph modeling (using RDF-star), semi-automated knowledge extraction (using LLMs and ontology prompting), Retrieval-Augmented Generation (RAG). | NaN | False | False | NaN | NaN | Difficulty in fully automating the complex, multi-layer knowledge graph construction (requiring human intervention); trade-off between KG expressivity and scalability; lack of suitable evaluation benchmarks for LLM-based contract risk analysis that align with expert judgment. | The paper highlights risks associated with using unaugmented LLMs for contract review, namely hallucinations (factual errors) and inability to leverage domain-specific knowledge accurately. |
| qj8laKSbW8gJ.pdf | Google_Scholar | Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education | This paper proposes a conceptual framework for integrating fine-tuned Large Language Models (LLMs) into interactive visualization systems, focusing on alignment between domain problems, visualizations, and interactions. The framework is applied to develop Tailor-Mind, a system using a fine-tuned LLM and visualizations to support self-regulated learning (SRL) for AI beginners. | True | NaN | True | 1.0 | Positive | Tailor-Mind: An interactive visualization system using a fine-tuned LLM (Baichuan2-7B-chat based) to support self-regulated learning (SRL) in AI education, implementing a proposed framework for LLM-visualization integration. | The fine-tuned LLM (SFT-2.0) was compared against other models (Base, EduChat, GPT-3.5, SFT-1.0) on a custom AI education test set (280 examples), evaluated by human experts and GPT-4 on Accuracy, Completeness, Clarity. The Tailor-Mind system was evaluated via a comparative user study (N=24) against GPT-4 for learning the Transformer model, measuring objective learning outcomes and subjective feedback. | The fine-tuned model (SFT-2.0) outperformed comparison models in human expert evaluations (Avg 4.30) and GPT-4 evaluation (Avg 4.20). The user study showed Tailor-Mind users achieved significantly better learning outcomes on objective tests and reported higher satisfaction and engagement compared to using GPT-4. | Challenges specific to Self-Regulated Learning (SRL) identified: Limited student knowledge of SRL, lack of motivation/guidance, difficulty understanding complex/esoteric knowledge, and lack of immediate/personalized feedback. | Tailor-Mind system addresses SRL challenges by: Providing SRL guidance, optimizing learning depth via structured explanations/visualizations (mind maps, learning paths), offering personalized recommendations/assessments, and creating an engaging interactive environment. | NaN | NaN | Education | International | Proprietary dataset (74,932 entries) for AI education, mixing domain texts (textbooks, notes etc.), open-source data (Alphaca_gpt4_data, ChatGPT-Corpus), and generated dialogues, structured for instruction fine-tuning. | User-centered design involving preliminary study (expert interviews, student surveys), requirement analysis, iterative design based on a proposed workflow (Task Identification, Design Mapping, User Alignment), incorporating SRL models (Zimmerman) and educational frameworks (Bloom's Taxonomy), and prototype testing with user feedback. | NaN | False | False | NaN | Limitations in handling multimodal data (images, audio, video); lack of integration with web resources; potential for LLM hallucinations; challenges in automated personalized fine-tuning; need for better error reporting/trustworthiness mechanisms. | Aligning LLMs with domain problems, visualization systems, and user interactions; creating high-quality domain-specific fine-tuning data; ensuring model outputs match visualization requirements; evaluating domain-specific model performance without standard benchmarks. | LLM 'hallucinations' (producing plausible but nonsensical responses). |
| tC9KkckGsnoJ.pdf | Google_Scholar | Law and Economics of Language Model Development: Empirical Examination of Corporate Strategies and Vaporware Claims | This paper analyzes corporate strategies for Large Language Model (LLM) development in Japan using a law and economics perspective, specifically investigating if announcements constitute 'vaporware' with antitrust implications. Using a stock event study, it finds no significant abnormal returns following LLM development announcements, suggesting the market reacted calmly and did not perceive these as vaporware. | True | Market | True | 2.0 | NaN | Stock price event study analysis | Calculated Cumulative Abnormal Returns (CARs) for four Japanese companies (CyberAgent, Fujitsu, Hitachi, NTT) following their LLM development announcements using daily stock price data from two months prior to the announcement, benchmarked against the Nikkei Stock Average. Robustness checks involved varying the event window and using control group companies (GREE, NEC, Toshiba, KDDI). | No statistically significant positive CARs were found for the companies following their LLM development announcements; market reaction was calm, suggesting the announcements were not perceived as vaporware. | NaN | NaN | NaN | NaN | Antitrust Law, Competition Policy, Securities Law | Japan, United States (references to law and cases) | Daily closing stock price data for Nikkei Stock Average, CyberAgent, Fujitsu, Hitachi, NTT, GREE, NEC, Toshiba, and KDDI. | Econometric analysis (Stock Price Event Study) | NaN | False | False | NaN | NaN | Standard limitations of event studies (e.g., benchmark choice, confounding events, potential information leaks prior to announcement). Difficulty in directly observing and evaluating vaporware characteristics. | Vaporware announcements leading to anti-competitive effects (antitrust violations under Sherman Act Section 2) or securities fraud (misleading investors). |
| Mo87jYlV5roJ.pdf | Google_Scholar | CLOSING ACCESS TO JUSTICE GAPS GLOBALLY | This chapter argues that closing the global justice gap requires a shift to a people-centered, data-driven, innovative, and collaborative approach, moving beyond traditional justice institutions. It highlights the critical role higher education institutions can play by leveraging their expertise in data science, fostering multidisciplinary innovation, and initiating cross-sectoral collaborations. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Lack of public understanding that problems are legal or solvable by the justice system; cost barriers; insufficient information, advice, or representation; inadequate justice data collection, analysis, and use; data silos; institutional resistance to innovation and collaboration; regulatory barriers limiting non-lawyer assistance; professional protectionism. | Adopt a people-centered justice approach; build robust justice data ecosystems leveraging legal needs surveys and administrative data analysis; foster innovation in justice services (e.g., prevention, systemic reforms, ADR, paralegals, technology, holistic defense); promote cross-sectoral collaboration; strategically engage higher education institutions to provide expertise in data science, multidisciplinary research, and innovation incubation. | General civil justice problems (housing, family, debt, consumer, public services, land tenure, probate), legal identity, criminal justice (sentencing, prosecution, defense, holistic approaches), domestic violence, eviction. | Poor and marginalized populations, people lacking legal identity, informal workers, those without land/housing proof, self-represented litigants, indigenous communities, women and girls, tenants facing eviction, people involved in the criminal justice system, victims of domestic violence. | Civil Law, Criminal Law, Legal Identity, Administrative Law, Human Rights Law | International | NaN | NaN | NaN | False | False | NaN | Inadequate justice data ecosystems; institutional resistance to innovation and collaboration; regulatory hurdles; lack of funding for innovation; insufficient data science and interdisciplinary skills in legal education and practice; disconnect between local/national A2J efforts and global agendas (e.g., SDGs, human rights). | NaN | NaN |
| W4582WozKXQJ.pdf | Google_Scholar | Strengthening Legal Mechanisms for Consumer Protection in the Digital Marketplace | This paper discusses the challenges to consumer rights in the digital marketplace and proposes strategies to strengthen legal mechanisms for consumer protection. These strategies include enhancing transparency, strengthening data protection laws, facilitating access to justice, promoting consumer education, and fostering international cooperation. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Difficulties for consumers in obtaining redress in cases of dispute or fraud; Lack of transparency and accountability in digital transactions; Insufficient protection of consumer data. | Developing legal frameworks for accessible and effective redress; Enhancing transparency and accountability in the digital marketplace; Strengthening data protection laws; Promoting consumer education; Fostering international cooperation on consumer protection standards. | Consumer protection in the digital marketplace; Access to redress mechanisms for consumers in online disputes or fraud. | Consumers in the digital marketplace. | Consumer law; Data protection law; E-commerce law. | International | NaN | NaN | NaN | False | False | NaN | Existing legal frameworks are insufficient to protect consumers in the evolving digital marketplace, particularly regarding transparency, data protection, and access to redress mechanisms. | NaN | Fraud, misinformation, and privacy breaches for consumers in the digital marketplace. |
| 9Eriq0jTDrAJ.pdf | Google_Scholar | AI, plurality and democracy \nReflecƟons on the impact of Large Language Models like ChatGPT on the rule of law and \ndemocracy | This paper critically analyzes the impact of large language models, like ChatGPT, on the legal system, the rule of law, and democracy, highlighting risks such as the reduction of plurality, monopolization of legal language and values, and potential manipulation. It evaluates the EU AI Act's capacity to mitigate these systemic threats, expressing doubt about its sufficiency. | True | NaN | True | 3.0 | Negative | NaN | NaN | NaN | Potential creation of a two-tier justice system where disadvantaged groups rely on potentially lower-quality AI legal services, while the privileged access human lawyers. | Raising awareness of AI's impact; deeper democratic discussion on AI's role in law; potential for stricter regulations including transparency, licensing, restricting AI use in the legal sector (e.g., limiting AI responses to legal queries); treating the legal system as critical infrastructure requiring protection. Critically evaluates the EU AI Act as a potential, but possibly insufficient, regulatory solution. | Quality of legal services, potential for unequal access/two-tier system, impact on rule of law, impact on democracy | Broad masses (as opposed to privileged groups) | General Law / Multiple Fields (discusses impact on legal practice broadly, including examples related to litigation, contracts, administrative law, judicial processes, constitutional law) | EU (primarily discusses the EU AI Act), with broader international context. | General description: Large amounts of diverse text data, using supervised learning and learning from human feedback. Notes the EU AI Act requirement for providers of general-purpose AI models to make publicly available a summary of the content used for training. | NaN | NaN | True | False | The paper discusses generally available LLMs like ChatGPT, some of which have public access tiers provided by commercial entities. | Societal: Insufficiency of current regulations (specifically the EU AI Act) to address systemic risks; lack of awareness and democratic control over AI's role in the legal system; erosion of pluralism. Technical/Regulatory: Difficulty in effectively monitoring, controlling, and mitigating harmful systemic effects, manipulation, and value imposition by LLMs within legal contexts. | NaN | Reduction of linguistic and cognitive plurality (monoculture); corporate monopolization and control over legal processes; dependency on AI; undermining the rule of law and separation of powers; creation of a two-tier justice system; deskilling of legal professionals; potential overload of courts with AI-generated content; manipulation of legal discourse and outcomes; facilitation of authoritarian control; ecological costs; disinformation; threats to democratic values and human rights. |
| MiabMAWShnEJ.pdf | Google_Scholar | The Impact of ChatGPT Technological Innovation on Civil Law Practices: Challenges, Opportunities, and Implications of Article 1338 of the Civil Code | This paper reviews the impact of ChatGPT on civil law practices, discussing challenges such as technological skill gaps, data security, and legal validity, alongside opportunities like enhanced efficiency and accessibility. It emphasizes the need for collaboration between legal and technology experts to develop ethical guidelines, particularly considering Indonesia's Civil Code. | False | Idealistic | True | 2.0 | Neutral | ChatGPT | The paper is a literature review based on 23 articles from Google Scholar (2019-2024) using a qualitative approach and descriptive analysis. It does not involve direct empirical testing of ChatGPT by the authors. | NaN | Lack of technological skills among legal practitioners, data security and privacy concerns, questions regarding the legal validity and credibility of AI-generated content, potential for errors and biases in AI outputs, and the risk of exacerbating inequality in access to legal services. | Collaboration between legal and technology experts to develop guidelines and standards for ethical AI use, fostering technological literacy among legal practitioners, implementing robust data protection measures, establishing transparency and accountability mechanisms for AI systems, developing strategies to ensure equitable access to technology-assisted legal services, and continuous monitoring and adaptation of legal practices. | Accessibility of legal services, equitable access to justice, validity of AI-generated legal documents, ethical use of AI in law. | Individuals who may lack sufficient access to or skills in using technology. | Civil Law, Contract Law (specifically referring to Article 1338 of the Civil Code). | Indonesia | The paper states that ChatGPT is trained on massive text data from various sources, allowing it to learn and mimic human language patterns. It does not specify the exact datasets but notes the importance of representative data to avoid bias. | The paper mentions that ChatGPT is built on the Transformer architecture and utilizes machine learning techniques. | NaN | True | False | ChatGPT is generally accessible as a web-based service provided by OpenAI, with both free and paid tiers. | The need for clear guidelines, standards, and updated legal regulations for the ethical and effective use of AI in law; insufficient technological literacy among legal practitioners; and the necessity for continuous adaptation of legal practices to keep pace with technological advancements. | Ensuring legal validity and credibility of AI-generated texts, addressing intellectual property rights, managing data privacy and security, ensuring transparency and accountability of AI systems, overcoming potential biases in AI, and fostering adequate technological skills among legal professionals. | Use of AI-generated text that may not be legally valid or admissible as evidence, infringement of intellectual property rights, data breaches of sensitive client information, perpetuation of biases through AI outputs leading to unfair outcomes, and increased disparities in access to justice due to unequal tech access or skills. |
| 1.9781611977653.ch111.pdf | Google_Scholar | Making a Computational Attorney | This paper outlines a vision for a "computational attorney," an AI agent capable of assisting human lawyers with complex legal tasks using Large Legal Language Models (L3Ms). It discusses the current state of L3Ms in law, highlights their potential to democratize legal services, and identifies key future research challenges for their development. | True | Idealistic | True | 3.0 | Positive | The 'computational attorney' concept as a future AI system based on advanced Large Legal Language Models (L3Ms). | NaN | NaN | Prohibitively expensive legal fees leading to inadequate or no legal assistance for a large percentage of low-income individuals with civil legal problems. | Development of advanced AI like 'computational attorneys' using L3Ms, which could democratize legal services. | Affordability of legal services, access to legal aid for civil matters. | Low-income Americans with civil legal problems. | General law, covering tasks like drafting legal briefs, analyzing legal judgments, opinions, and contracts. | US (primary focus, especially for access to justice aspects and legal system examples); Japan (referenced for specific AI model evaluations). | Large-scale legal text data, including publicly available corpora (e.g., Pile-of-Law) and potentially proprietary datasets. The paper discusses pre-training L3Ms on general and legal-specific corpora. | NaN | NaN | False | False | NaN | Technical gaps in current L3M capabilities (making them updatable, stable, provable, communicable, and predictable) hinder the creation of a 'computational attorney' capable of democratizing legal services. The societal gap is the current widespread lack of affordable legal assistance. | Developing L3Ms that are: updatable with new legal precedents and laws efficiently; stable against generating false information ('hallucinations') and robust to out-of-distribution data; provable in their reasoning by linking to legal sources; communicable for effective human-lawyer interaction and learning; and predictable in anticipating legal outcomes and strategic implications. | AI models providing outdated or incorrect legal analysis; models 'hallucinating' or inventing non-existent legal facts/precedents; lack of verifiability or provability for AI-generated legal opinions; potential liabilities associated with AI outputs if not properly managed. |
| 4oxIKlQvBHAJ.pdf | Google_Scholar | MAINDZ at SemEval-2024 Task 5: CLUEDO - Choosing Legal oUtcome by Explaining Decision through Oversight | This paper presents CLUEDO, an ensemble LLM system for legal reasoning, where fine-tuned collaborator models generate answers and explanations for a zero-shot 'detective' model. Evaluated on U.S. civil procedure cases, the system demonstrated strong performance in determining answer correctness and improved prediction stability compared to individual models. | True | Market | True | 1.0 | Positive | CLUEDO: An ensemble system using multiple fine-tuned collaborator LLMs (Llama 2 13B, Mistral v0.1 7B, Zephyr beta 7B) with multiple-choice prompting for label prediction and explanation generation, overseen by a zero-shot 'detective' LLM (GPT-4) for final answer selection. | The system was evaluated on the SemEval-2024 Task 5 dataset (U.S. civil procedure cases). Performance was measured using F1 macro score and accuracy on development and test sets. Stability was assessed by running experiments five times and measuring standard deviation of the metrics. | CLUEDO achieved an F1 macro score of 0.77 and accuracy of 0.82 on the test set. It also demonstrated higher prediction stability, with a standard deviation for F1 macro of ±0.017 on the test set, compared to the zero-shot detective model alone (±0.022). | Limited scope and diversity of existing legal benchmarks; inherent risks of LLMs generating incorrect, misleading, or offensive content; challenges in accurately assessing LLM legal reasoning capabilities. | The CLUEDO framework, an ensemble of LLMs with collaborator models and a 'detective' overseer, utilizing multiple-choice prompting and explanation generation to improve legal reasoning accuracy and the stability of predictions. | Legal reasoning; Correctness evaluation of legal arguments in response to specific questions based on case facts. | NaN | U.S. Civil Procedure | United States | SemEval-2024 Task 5 dataset: derived from a U.S. civil procedure textbook for law students. Each instance includes a case introduction, a specific question, a potential solution argument, an annotated label (correct/incorrect), and a detailed analysis. Collaborator models were fine-tuned on this data. | Multiple-choice prompting (MCP); Supervised Fine-Tuning (SFT) using 8-bit quantization and Parameter-Efficient Fine-Tuning (PEFT) for collaborator models; Ensemble learning (multiple collaborators combined with a zero-shot 'detective' model). | NaN | True | True | Code available on GitHub: https://github.com/irenebenedetto/PoliToHFI-SemEval2024-Task5 | The limited nature of existing legal benchmarks to capture diverse legal tasks; the ongoing general need to enhance legal reasoning capabilities and reliability in LLMs. | Variability in performance across different LLMs; achieving reproducibility and stability in predictions from large models like GPT-4 (even with deterministic settings); effectively fine-tuning smaller open-source LLMs for specialized legal tasks. | LLMs generating offensive, misleading, or factually incorrect content, which could disproportionately affect marginalized or under-resourced populations; instability and unreliability of LLM predictions in critical legal contexts. |
| VIrPJN95W2sJ.pdf | Google_Scholar | Human Law, Human Lawyers and the Emerging AI Faith | This paper critiques the growing 'AI faith' in the legal sector, questioning its transformative promises regarding efficiency and democratization. It argues for caution, emphasizing the unique human dimensions of law and legal practice that current AI cannot replicate and might undermine. | True | Market | False | 3.0 | Neutral | NaN | NaN | NaN | The 'black box' problem hindering transparency and trust; AI's inability to replicate human empathy, ethical reasoning, and contextual understanding; risk of errors and lack of accountability; potential to increase complexity and create knowledge divides rather than simplify access. | Adopt a critical perspective towards AI in the legal sector; engage in careful reflection by individual operators, firms, and regulators on AI's impacts rather than blindly accepting the hype ('AI faith'). | Democratization of legal services, Cost reduction, Efficiency gains | NaN | General / Cross-domain | International | NaN | NaN | NaN | False | False | NaN | Societal: Potential undermining of public trust and legitimacy of law; widening knowledge divide. Technical: AI's inability to fully replicate human legal reasoning, ethics, and empathy; lack of transparency and explainability ('black box'); potential for errors ('hallucinations'). | Reconciling AI capabilities with human law's foundations (authority, function, reactivity); ensuring AI aligns with legal ethics; addressing opacity/explainability; managing AI-induced complexity; dealing with potential human lawyer substitution; establishing accountability. | Generation of incorrect information (e.g., fake citations); erosion of public trust due to opacity, errors, or lack of human values; undermining legal authority and legitimacy; creation of inaccessible 'artificial law'; loss of human skills (reasoning, ethics) in legal practice; potential for unchallenged abuses of power. |
| ilFk-RDHRnYJ.pdf | Google_Scholar | Beyond Human Discretion: Reconciling AI Systems With Traditional Legal Frameworks | This paper argues that the increasing use of artificial intelligence in the legal field presents significant challenges, including algorithmic bias, opacity, and accountability issues, which traditional legal frameworks rooted in human discretion are ill-equipped to address. It calls for comprehensive regulatory reforms, and fundamental changes in legal education and ethics to ensure AI integration upholds justice and fairness. | True | Idealistic | False | 3.0 | Negative | NaN | NaN | NaN | Algorithmic bias perpetuating societal inequities and leading to unfair outcomes, particularly for minority groups; Lack of transparency (algorithmic opacity) in AI decision-making, hindering accountability and trust; AI systems lacking human-like nuanced judgment, empathy, and moral reasoning essential for equity. | Implementing a comprehensive legal and regulatory framework tailored to AI's characteristics; Reforming legal education and ethics standards to address AI; Embedding legal values like fairness and transparency into AI design ('legal protection by design'); Ensuring human oversight and accountability mechanisms. | Algorithmic bias in legal and justice systems (e.g., criminal sentencing, predictive policing, tenant screening); Ensuring fairness, equity, and non-discrimination in AI-driven legal processes; Accountability and transparency of AI in legal decision-making; Impact of AI on vulnerable and minority communities. | Racial minorities (specifically Black and Hispanic individuals mentioned in examples), economically disadvantaged individuals (e.g., those using housing vouchers), and individuals interacting with the criminal justice system. | General legal practice, Criminal Justice, Housing Law, Civil Litigation, Constitutional Law, Legal Ethics, Administrative Law. | United States (primary focus), United Kingdom, European Union. | The paper generally refers to AI systems being trained on 'historical data,' 'past data,' 'large datasets,' or 'historical records and data points,' which can encode and perpetuate existing societal biases. | NaN | NaN | True | False | The paper discusses several existing AI tools; some, like ChatGPT (which has free tiers), are publicly accessible, while others (e.g., Lex Machina) are commercial products or institutionally deployed (e.g., COMPAS). | Technical gaps include algorithmic opacity and the difficulty of embedding nuanced human judgment and moral reasoning into AI. Societal gaps include the lack of comprehensive regulatory frameworks, insufficient ethical guidelines, the need for extensive reform in legal education to prepare professionals for AI, and the unpreparedness of the legal community to manage AI risks, all contributing to potential erosion of public trust and perpetuation of discrimination. | NaN | Generation of fictitious legal citations by GAI (e.g., ChatGPT); Algorithmic bias leading to discriminatory outcomes in criminal justice (COMPAS, predictive policing), corrections (PACT), and housing (SafeRent); Violations of privacy due to AI's data needs; Lack of accountability for AI-driven decisions; Erosion of public trust in the legal system; Misleading consumers or users of AI tools; Undermining of legal precedent and established judicial reasoning; Professional negligence by legal professionals using AI without adequate verification; Unjust denial of rights or services (e.g., healthcare coverage, housing). |
| oO6c-Wwoy2sJ.pdf | Google_Scholar | The CLC-UKET Dataset: Benchmarking Case Outcome Prediction for the UK Employment Tribunal | This paper introduces the CLC-UKET dataset, a collection of ~19,000 UK Employment Tribunal cases annotated using an LLM to facilitate research on access to justice. The dataset is used to benchmark various models, including LLMs, on the task of predicting case outcomes based on facts and claims, comparing results against human expert performance. | True | Idealistic | True | 2.0 | Positive | Benchmarking dataset (CLC-UKET) creation using LLM-aided annotation (GPT-4) and evaluation of case outcome prediction models (BERT, T5, GPT-3.5, GPT-4) on this dataset. | Evaluation on a test split of the curated CLC-UKET pred dataset (1,371 cases) using manually annotated gold-standard outcome labels. Metrics: Accuracy, Precision, Recall, F-score. Comparison with human expert predictions. | The fine-tuned T5 model achieved the best performance among the models tested (F-score: 0.564), significantly outperforming random guessing but still falling short of human expert performance (F-score: 0.672). | Uncertainty regarding the likely outcome of court procedures hinders access to justice and amicable dispute resolution. | Creating large-scale, annotated legal datasets (like CLC-UKET) and developing/benchmarking AI models for case outcome prediction to provide insights into likely results. | Case outcome prediction, Facilitating dispute resolution, Access to legal information | Claimants in the UK Employment Tribunal system. | Employment law | United Kingdom (UK Employment Tribunal) | The CLC-UKET dataset, derived from the publicly available Cambridge Law Corpus (CLC) containing UKET judgments (2011-2023). Facts, claims, and initial outcome labels were extracted from unstructured judgment text using GPT-4 (LLM-aided annotation). Gold-standard outcome labels for the test set were manually annotated by a legal expert. | Dataset curation (filtering public legal documents), LLM-aided annotation (GPT-4 with prompt engineering), manual validation (for test set outcome labels), standard ML benchmarking (train/val/test split, baseline models), Human evaluation (expert prediction task with guidelines). | The CLC-UKET dataset is planned to be made available via the Cambridge Law Corpus (CLC) website, with access restricted to qualified researchers adhering to ethical and legal standards. | False | False | NaN | Reliance on extracted facts/claims from judgments rather than original filings (potential bias), limitations of LLM-based annotation quality, need for more detailed factual information, dataset representativeness uncertainty, handling legal evolution over time, performance gap between AI models and human experts. | Cost and time of manual legal annotation, potential inaccuracies in LLM-based annotation, complexity of legal cases (e.g., preliminary issues, procedural decisions), potentially insufficient information in extracted facts/claims for accurate prediction, ensuring ethical use of legal data. | Information bias in facts/claims extracted from judgments, potential inaccuracies from LLM annotation, models learning spurious correlations (e.g., sentiment), misinterpretation or over-reliance on prediction results in legal practice, data privacy concerns (mitigated by CLC protocols). |
| 4Nbz7njEtzoJ.pdf | Google_Scholar | Fighting the Knowledge Representation Bottleneck with Large Language Models | This paper investigates using Large Language Models (GPT-4o) to tackle the knowledge representation bottleneck in developing legal expert systems. It proposes and evaluates a human-in-the-loop, prompt-based methodology for formalizing legal articles and case law into Prolog rules, using the Facilex system as a case study. | True | Idealistic | True | 1.0 | Positive | Using GPT-4o with a 'Chain of Prompts' methodology (few-shot learning) and human-in-the-loop validation to: 1) formalize legal articles into Prolog rules by refining LLM-generated code based on existing system facts and structure; 2) extract key legal principles from case law and formalize them into new Prolog rules, integrating them with existing legal provisions in an expert system (Facilex). | Two-tiered evaluation: 1) Formal validation (automated check for syntactic correctness and executability of Prolog rules within the Facilex system). 2) Expert validation (by knowledge engineers) assessing Accuracy (completeness of legal elements), Relevance (adherence to legal reasoning and text), Human Alignment (support for model-engineer dialogue), and Fluency (consistency and readability of Prolog code). | For article generation, LLM-generated Prolog rules passed formal validation. Expert validation showed high accuracy (23 out of 27 expert-formalized conditions captured), with minor issues like redundant conditions or structural variations. For case generation, rules also passed formal validation, and expert validation confirmed strong accuracy in identifying and representing key legal elements from case law, though significant prompt engineering was needed for relevance. | The primary obstacle addressed is the Knowledge Representation Bottleneck (KRB) in legal expert systems, which makes the acquisition, formalization, and constant updating of expert knowledge time-consuming, error-prone, and limits the systems' flexibility, scalability, and longevity. | The paper proposes leveraging Large Language Models (GPT-4o) within a human-in-the-loop 'Chain of Prompts' framework. This approach semi-automates the generation and revision of Prolog rules from legal articles and case law, aiming to make expert systems more scalable, adaptable, and easier to update. | Enhancing the development, maintainability, and scalability of rule-based legal expert systems, particularly for complex legal domains such as EU mutual recognition instruments in criminal matters (e.g., European Arrest Warrant procedures), by using LLMs to formalize legal knowledge. | Individuals involved in EU cross-border criminal proceedings (indirectly, through improved tools and systems for the legal professionals representing or adjudicating their cases). | EU procedural law, mutual recognition instruments in criminal matters, European Arrest Warrant. | European Union (EU) | The approach uses a pre-trained LLM (GPT-4o). For its few-shot prompting methodology, it utilizes: 1) existing Prolog rules and facts from the Facilex expert system, 2) natural language text of legal articles (e.g., EU Framework Decision on European Arrest Warrant), and 3) raw text of EU case law (e.g., CJEU judgments). | Human-in-the-loop approach, 'Chain of Prompts' methodology for LLM interaction, few-shot learning, iterative refinement of LLM outputs by knowledge engineers, and a two-tiered evaluation process (formal and expert-driven validation). | NaN | False | True | The Jupyter Notebook containing prompts, inputs, and outputs of the experiments is available on GitHub at https://github.com/LegalMachineLab/JURIX24-fighting_krb. | The need for continuous human supervision to ensure legal correctness, consistency, and alignment with expert system's domain. The challenge of achieving full automation in knowledge formalization due to LLM limitations and the nuanced nature of legal interpretation. Making the resulting expert systems truly user-friendly for diverse end-users. | Ensuring consistency and avoiding redundancy in LLM-generated Prolog rules. Aligning the LLM's rule generation style with specific expert preferences (e.g., structure of sub-rules, use of negation). Significant prompt engineering effort required to achieve desired relevance and scope in outputs. Managing the LLM's tendency to introduce legally accurate but contextually irrelevant information. Potential for structural errors in generated code when processing large or complex inputs. The necessity of an iterative human-in-the-loop process for refinement and validation. | Generation of syntactically correct but legally inaccurate, incomplete, or subtly flawed Prolog rules if expert oversight is insufficient. Introduction of inconsistencies, redundancies, or out-of-scope information into the expert system's knowledge base. Unpredictability in LLM outputs regarding naming conventions or rule structures, potentially affecting code maintainability and expert alignment. |
| _3PICPHoZiIJ.pdf | Google_Scholar | LLMediator: GPT-4 Assisted Online Dispute Resolution | This paper introduces LLMediator, an experimental platform using GPT-4 to enhance Online Dispute Resolution (ODR) for low-intensity legal disputes. It discusses and qualitatively evaluates features like reformulating user messages to be less emotional and drafting mediator responses to facilitate amicable settlements. | True | Idealistic | True | 1.0 | Positive | LLMediator platform using GPT-4 API calls with specific prompts for: F1 (reformulating inflammatory messages), F2 (drafting message suggestions for human mediators), F3 (experimental autonomous AI intervention). | Initial qualitative evaluations through illustrative examples and discussion of potential outputs generated by GPT-4 in different scenarios. | Qualitative examples demonstrate GPT-4's promising ability to perform the intended tasks (reformulation, drafting interventions) appropriately, relevantly, and adaptively based on context and instructions. | Difficulty understanding rights, costs (monetary, temporal, psychological) of traditional courts, challenges in reaching resolution for laypeople in low-intensity disputes. | Enhancing ODR platforms with AI (specifically LLMs like GPT-4) to reformulate inflammatory messages, assist human mediators, and potentially provide automated mediation support for low-value cases. | Online Dispute Resolution (ODR), Negotiation, Mediation | Laypeople facing low-intensity disputes (debt, consumer, employment). | Consumer law, Debt collection, Employment law, Landlord-tenant law, Torts (minor) | International | Pre-trained GPT-4 model accessed via OpenAI API; no specific training data mentioned by the authors. | Prototyping, Prompt Engineering, Qualitative evaluation via examples. | Experimental prototype, proof of concept. | False | False | NaN | Need for empirical evaluation of efficacy and bias, refinement of prompt engineering, development of improved triggers for AI intervention, exploring further LLM applications (e.g., summarization). | Potential for LLM hallucination and inaccuracy, risk of AI taking sides, user frustration/self-expression concerns (for F1), anchoring bias/over-reliance (for F2), high risks with autonomous intervention (F3). | LLM hallucination and inaccuracy, biased outputs leading to unfair outcomes or loss of trust, mediators developing anchoring bias or over-reliance, user frustration with automated message changes. |
| LERC_Book_of_abstracts_website.pdf | Google_Scholar | DIGITAL SAVIOUR OR JUST ANOTHER PROBLEM TO DEAL WITH: A DISCOURSE ANALYSIS OF THE CONFLICTING NARRATIVES REGARDING THE IMPLICATIONS OF GENERATIVE AI FOR THE TEACHING OF LAW | This paper analyzes the diverse and conflicting narratives surrounding Generative AI's impact on legal education using discourse analysis. It aims to identify dominant discourses and predict their evolution, helping legal educators form strategic responses. | True | NaN | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal Education | Australia | NaN | Discourse Analysis | NaN | False | False | NaN | NaN | NaN | NaN |
| 3w_RoYmbStkJ.pdf | Google_Scholar | Human Centered AI for Indian Legal Text Analytics | This position paper proposes a human-centered, compound AI system using Large Language Models (LLMs) for legal text analytics in India to improve access to justice. It introduces a new Indian legal dataset and outlines 'InLegalLLaMA', an LLM to be trained on Indian legal texts, to address current AI limitations like low trustworthiness and lack of specialized resources. | True | Idealistic | True | 1.0 | Positive | Human-centered compound AI system integrating LLMs (specifically a proposed 'InLegalLLaMA') with human input for Indian legal text analytics, supported by a novel domain-specific dataset. | LLaMA-2-70B-Chat for case similarity (few-shot prompting on 2,626 document excerpt pairs, ROC-AUC); LLaMA-2-34B-Instruct for relation/tail prediction on a legal KG subset (Hits@k). | For case similarity, LLaMA-2-70B-Chat achieved a ROC-AUC score of 0.566. For relation/tail prediction, LLaMA-2-34B-Instruct achieved Hits@1: 0.520, Hits@5: 0.556, Hits@10: 0.617. | Overwhelmed legal system with case backlogs and time-consuming processes; low trustworthiness of current AI; lack of AI focus on common citizens; scarcity of specialized legal datasets; citizens' unfamiliarity with legalese; poorly written petitions leading to inefficiencies and dismissals; complexity of legal documents for laypersons. | Development of Human-Centered AI (HCAI) as a compound system eliciting human input; creation of specialized Indian legal datasets; using LLMs to help citizens understand legal documents, conduct research, and draft better petitions; abstractive summarization for layperson comprehension; LLM-based conversational QA for identifying missing information in petitions; pre-training and fine-tuning LLMs (e.g., InLegalLLaMA) on Indian legal texts and infusing them with domain knowledge. | Speeding up justice delivery; improving legal understanding for common citizens and self-represented litigants; aiding legal research; assistance with petition drafting; reducing system burden from poorly prepared documents; democratizing legal knowledge. | Common citizens, self-represented litigants, individuals not well-versed in legal language, and the general public in India seeking access to justice. | General Indian Law / Indian Case Law | India | A new dataset composed of: 1) A Legal Knowledge Graph derived from 2,286 Indian legal documents (court cases, judgements, laws from public repositories, IndianKanoon, Casemine), processed using Stanza, SystemT, and manually curated dictionaries. 2) A Question-Answering dataset from 45 Delhi High Court judgments, with QA pairs generated by gpt-3.5-turbo using few-shot prompting. 3) A Text2SQL dataset extended from the QA dataset. The proposed InLegalLLaMA will use general Indian legal domain corpora. | Human-Centered AI (HCAI) principles; compound AI systems approach; dataset creation via web scraping, automatic/manual annotation, LLM-based generation (gpt-3.5-turbo, few-shot prompting); proposed LLM development includes pre-training, instruction-tuning, concept-enhanced pre-training, PEFT, knowledge infusion, and Retrieval Augmented Generation (RAG). | NaN | False | False | NaN | Low trustworthiness of current generative AI; scarcity of specialized legal datasets for training LLMs; existing LLMs not adequately tailored to specific legal domains like the Indian legal system; poor performance of European-trained legal models in the Indian context due to document structural differences; need to mitigate hallucinations in LLMs for domain tasks with societal impacts; lack of focus on common citizens in current AI applications; general unavailability of resources for AI in domains directly touching human lives. | Scalability of supervised methods due to extensive annotation needs; ensuring factual accuracy and avoiding misrepresentation in AI-generated legal text (e.g., summaries); adapting general LLMs to the nuances of the Indian legal domain; developing trustworthy LLMs for high-stakes legal applications; creating comprehensive, high-quality specialized legal datasets; mitigating LLM hallucinations in critical legal tasks. | Low trustworthiness of generative AI; misleading readers with AI-generated content (e.g., abstractive summaries generating information absent in original documents, or altering meaning through subtle word changes); inaccuracies in generated text (e.g., altered proper nouns, locations, numbers); societal consequences from LLM hallucinations in domain tasks; potential for poorly written petitions (if AI is faulty) adding costs and risking dismissal. |
| 4gUeSLlHb8MJ.pdf | Google_Scholar | Large Vision-Language Model Security: A Survey | This paper surveys security issues in Large Vision-Language Models (LVLMs), covering malicious attacks like jailbreaking and backdoors, alongside defenses. It also discusses application risks such as hallucinations and privacy leaks, reviewing mitigation methods and highlighting areas for future research. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Jailbreak attacks (inducing harmful content generation), Backdoor attacks (implanting hidden triggers for malicious behavior), Controllable misinformation generation (producing targeted, deceptive content), Hallucinations (generating factually incorrect or prompt-irrelevant responses, risky in areas like medical/legal aid), Privacy leakage (extraction of Personal Identifiable Information (PII) from training data, Membership Inference Attacks). |
| iL5Ltm0_mAcJ.pdf | Google_Scholar | The unreasonable effectiveness of large language models in zero-shot semantic annotation of legal texts | This paper evaluates the zero-shot semantic annotation performance of GPT-4 and GPT-3.5-turbo(-16k) on diverse legal texts (adjudicatory opinions, contracts, statutes), comparing them to earlier GPT models and supervised baselines. It finds GPT-4 performs well, especially on contract clauses, and analyses the trade-offs between performance, cost, and batch processing for practical applications. | True | Idealistic | True | 2.0 | Positive | Zero-shot semantic annotation (classification) of short legal text snippets using large language models (GPT-4, GPT-3.5-turbo(-16k), text-davinci-003) instructed via prompts containing category names and definitions. | Evaluation on three manually annotated datasets: BVA (rhetorical roles in veterans' appeal decisions), CUAD (clause types in commercial contracts), PHASYS (purpose of public health statutes/regulations). Performance measured by Precision, Recall, F1-score (micro-average overall). Compared against Random Forest and fine-tuned RoBERTa baselines. Tested both single-instance and batch prediction. | GPT-4 achieved F1 scores of 0.82 (BVA), 0.90 (CUAD), and 0.54 (PHASYS), outperforming GPT-3.5 models and matching Random Forest on BVA/CUAD, but below fine-tuned RoBERTa. Cost-effective GPT-3.5-turbo matched the more expensive text-davinci-003. Batch processing significantly lowered costs with only a minor performance decrease compared to single-instance prediction, but large batches degraded performance. | High cost of current AI workflows requiring manual annotation or expensive enterprise solutions. Potential cost of using LLM APIs, especially for high-volume or non-batched tasks. Performance limitations compared to fine-tuned models, particularly for nuanced or ambiguous categories. Difficulty handling domain-specific nuances with simple definitions. Constant evolution of proprietary models. | Leveraging zero-shot capabilities of LLMs with simple prompts (type lists and definitions) to perform semantic annotation without task-specific training data. Employing batch prediction within prompts to significantly reduce API costs, making sophisticated annotation workflows more accessible and economically feasible for experimentation and deployment. | Semantic annotation, Rhetorical role classification, Contract clause classification, Statutory provision classification, Contract review, Case law analysis, Empirical legal studies. | Legal professionals, legal researchers, potentially smaller law firms or organizations unable to afford traditional high-cost AI legal tech solutions. | Veterans Law, Contract Law, Public Health Law, Administrative Law | United States (based on BVA, PHASYS datasets; CUAD likely US-centric) | The evaluated LLMs (GPT-4, GPT-3.5) used their large, general, proprietary pre-training data. No task-specific fine-tuning data was used for the evaluated zero-shot approach. Baseline models were trained on the specific BVA, CUAD, and PHASYS datasets (manually annotated legal texts). | Prompt engineering: Designing specific text prompts instructing the LLMs to classify text snippets based on provided type definitions. Experimental comparison across models, datasets, and batching strategies. | The approach relies on accessing LLMs via the OpenAI API. Prompts and model settings are shared via a GitHub repository for replication. | True | False | Prompts and settings are available on GitHub; execution requires access to the commercial OpenAI API. | Performance gap between zero-shot and supervised/fine-tuned models, especially for complex/nuanced tasks. Handling imbalanced datasets and ambiguous definitions in zero-shot settings. Need for methods applicable to longer texts and more complex reasoning. Understanding and optimising effects of batching (e.g., ordering). Addressing cost barriers for wider adoption. Research challenges due to proprietary, evolving models. | Designing effective prompts for diverse legal annotation tasks. Balancing performance vs. cost (especially regarding batch size). Handling model context length limitations. Achieving high accuracy for nuanced legal distinctions. Dealing with dataset imbalance. Reproducibility issues with closed models. | Inaccuracy of annotations, potentially leading to incorrect analysis or decisions if used without verification. Cost can still be a barrier depending on scale and approach (batched vs. single). Dependence on proprietary, changing models. |
| VzUbPp4kve8J.pdf | Google_Scholar | Automated User Story Generation with Test Case Specification Using Large Language Model | This paper introduces "GeneUS", a tool using GPT-4.0 and a novel "Refine and Thought" (RaT) prompting technique to automatically generate user stories, deliverables, and test case specifications from software requirements documents. The tool aims to improve software engineering productivity by automating parts of the Requirements Engineering phase. | True | Market | True | 1.0 | NaN | GeneUS tool using GPT-4.0 with a Refine and Thought (RaT) prompting technique for automated user story and test case generation. | Tested with 7 Requirements Engineering documents (6 from a textbook, 1 from industry). Output quality evaluated via a RUST (Readability, Understandability, Specifiability, Technical-aspects) survey questionnaire distributed to 50 software developers. | The RaT prompting technique improved results and reduced LLM hallucinations compared to basic prompting. The RUST survey yielded a median score of 4 out of 5 ('Good'), indicating general acceptance by developers, although Specifiability and Technical Aspects showed more room for improvement. | NaN | NaN | NaN | NaN | NaN | NaN | The approach uses a pre-trained LLM (GPT-4.0). The system was tested using 7 textual Requirements Engineering documents (some sourced from a Software Engineering textbook, one from industry). | Prompt engineering (development of the Refine and Thought - RaT technique), tool development (GeneUS), qualitative evaluation via expert survey (RUST questionnaire). | An online REST API was made available for researchers to test the application. Future plans include making the tool publicly accessible. | False | False | Online REST API mentioned as available for researchers. | NaN | LLM Hallucinations (generating incomplete, incorrect, or inconsistent information), especially with long and complex input documents (like Requirement Analysis Documents). Processing requirement documents containing non-text elements (images, diagrams) which become meaningless tokens. | Risk of generating factually incorrect or incomplete user stories due to LLM hallucinations. |
| Ey5B4UxN4Q8J.pdf | Google_Scholar | Bridging the Gap: Mapping Layperson Narratives to Legal Issues with Language Models | This paper proposes a system using language models to automatically map layperson factual descriptions of their problems to relevant legal issues, aiming to improve access to justice. Integrated into the JusticeBot tool, the system was evaluated on real-world user data and demonstrated high accuracy in suggesting appropriate legal pathways to users. | True | Idealistic | True | 1.0 | Positive | A system using a multilingual universal sentence encoder to create vector embeddings of layperson factual descriptions and pre-defined example situations. It employs an approximate nearest neighbor search (Annoy library) to match the user's description to the most similar example situations, thereby suggesting relevant legal issues and pathways, integrated within the JusticeBot. | The system was evaluated using real-world, anonymized user-submitted factual descriptions from the JusticeBot. Performance was measured by Precision@1 (P@1) and Precision@3 (P@3) against annotated ground truth pathways. Two main experimental setups were used: 1) training on seed examples and testing on user submissions, and 2) training on seed examples plus user submissions (excluding the test instance, in a leave-one-out manner) and testing on user submissions. A cold-start scenario comparing the language model approach to an SVM baseline was also conducted. | When trained with both seed examples and user-submitted data, the system achieved 93.5% P@3 (relevant legal issue suggested within the top 3 options) and 74.5% P@1 (relevant legal issue suggested as the top option) on user-submitted descriptions. | The 'gap' between layperson language (focusing on facts) and legal language (requiring identification of legal issues), causing laypeople to struggle in identifying their rights or relevant legal remedies. This hinders their ability to use self-help tools effectively. | An 'augmented intelligence' system that analyzes layperson's factual descriptions to suggest potentially relevant legal issues and pathways. The system provides factual explanations for its suggestions, allowing users to verify the system's understanding before exploring a suggested legal pathway within tools like JusticeBot. | Legal issue identification from layperson narratives, improving usability of legal self-help tools, bridging the language gap in legal information. | Laypeople (individuals without legal training) facing legal disputes, particularly those who might self-represent or use online legal information tools. | Landlord-tenant disputes (primary focus of JusticeBot and evaluation), with potential applicability to other areas like consumer rights, debt, and employment law. | Quebec, Canada (based on the JusticeBot project and data source). | A combination of: 1) 'Seed example descriptions' (58 examples) created by the research team, formulating potential layperson descriptions for various legal issues. 2) 'User-submitted example descriptions' (3,250 annotated examples) from real JusticeBot users, representing genuine layperson narratives. Data is unstructured text. | User-centered design (addressing observed user difficulties), augmented intelligence approach, use of pre-trained multilingual sentence encoders, approximate nearest neighbor search, iterative improvement based on user data (seed examples and real user feedback). | The proposed feature is integrated into the JusticeBot (https://justicebot.ca), an online legal decision support tool. Users can type a description of their situation, and the system suggests relevant pathways. | True | False | The feature is described as part of the JusticeBot tool, which is accessible online at https://justicebot.ca. | Need to expand the dataset to cover more legal issues and domains. Further empirical evaluation with end-users is needed to assess real-world utility. Exploration of alternative embedding models (including newer LLMs like GPT-4) and classification approaches for potential performance improvements. | Handling the variability and ambiguity of layperson language compared to structured legal text. Overcoming the 'cold-start problem' when introducing new legal topics or tools. Ensuring suggestions are not misleading if a user's specific issue is not covered. | The system might provide irrelevant suggestions if a user's situation is not covered by the pre-defined pathways. Misinterpretation of the system's suggestions as legal advice rather than legal information, potentially leading to concerns about the unauthorized practice of law. |
| DynamicUniversallyAdaptiveLanguageModelANewApproachtoNaturalLanguageProcessinginMachineLearning.pdf | Google_Scholar | Dynamic Universally Adaptive Language Model – A New Approach to Natural Language Processing in Machine Learning | This paper introduces the Dynamic Universally Adaptive Language Model (DUALM), a novel NLP approach designed for adaptability, efficiency, and reduced resource consumption compared to traditional LLMs. DUALM features a modular, dynamically adjusting architecture enabling real-time learning and context-aware interactions across diverse tasks and languages. | True | NaN | True | 1.0 | NaN | Dynamic Universally Adaptive Language Model (DUALM), featuring modular design (ModuleRegistry), dynamic layer adjustment (DynamicLayerAdjustment), adaptive attention, hierarchical processing, and real-time learning. | The paper mentions that benchmarking is planned or ongoing, but does not present any specific testing procedures or results. | NaN | NaN | NaN | NaN | NaN | General legal domain | International | The abstract mentions enabling proficiency in multiple Romance languages with a model trained primarily in English, but details of the specific training corpus are not provided. | Conceptual design, modular architecture, dynamic adaptation principles based on task complexity and feedback. | Open-source release via GitHub repository and encouragement of community contributions. | True | True | Code available on GitHub repository: https://github.com/NeeravSood/DUALM | Potential computational efficiency issues, difficulties in training dynamic systems, limitations in handling certain language tasks or languages, scalability, generalization across diverse languages and domains, ensuring fairness, transparency, accountability, and mitigating biases. | NaN | Lack of fairness, transparency, accountability; perpetuation of biases; non-compliance with data protection and privacy laws, particularly in sensitive domains. |
| lnLVibnxH7AJ.pdf | Google_Scholar | Access to Justice and the Legal Profession: Three Questions | This article argues that the Canadian legal profession faces a critical access to justice crisis and has moral, regulatory, and economic imperatives to act now. It advocates for a people-centered approach, listening to public needs and exploring diverse solutions like increased funding, regulatory reform, technology, and community services to bridge the justice gap. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | High costs of legal help; significant court delays (civil, family, criminal); limited legal aid; alienation of communities (e.g., Indigenous, racialized) from the justice system; lack of public/government awareness and funding; inefficient court procedures; underlying societal issues (poverty, racism, homelessness). | Adopt a people-centered justice approach; increase public funding for justice; explore broad legal care models (expanded legal aid, legal insurance); regulatory reform and experimentation; expand service providers (community services, technology/AI); court reform (efficiency, case management, settlement counsel). | Access to justice; everyday legal problems; civil justice; family justice; criminal justice delays; legal aid; court delays; legal profession's role; public perception of justice; people-centered justice; funding for justice. | General public experiencing everyday legal problems, low-income households, middle-class individuals, Indigenous communities, racialized communities, people facing homelessness, poverty, systemic discrimination, or other societal barriers. | Civil Law, Family Law, Criminal Law (re delays), Administrative Law (re delays), General Access to Justice | Canada (primarily Ontario, with national context), USA (comparative statistics), Global (context) | NaN | NaN | NaN | False | False | NaN | The "justice gap" between public needs/barriers and solutions; insufficient funding; lack of public awareness; need for regulatory innovation; need for more efficient court processes; disconnect between providing legal services and achieving substantive justice outcomes; insufficient focus on public perspectives ("people-centered justice"). | NaN | Loss of the legal profession's self-regulation privilege if inaction continues; erosion of public trust in justice and democracy; continued negative societal costs (financial, health, social) from unresolved legal problems. |
| av1Arye_3y4J.pdf | Google_Scholar | Generative AI systems in legal practice offering quality legal services while upholding legal ethics | This paper examines the impact of generative AI systems, like ChatGPT, on Luxembourg lawyers' ethical duties of competence and confidentiality, drawing on doctrinal analysis, a survey, and interviews. It finds lawyers use AI for efficiency but face challenges with accuracy and data privacy, suggesting a need for client-centric approaches, informed consent, training, and potentially updated regulations. | True | Market | True | 2.0 | Neutral | Use of Generative AI systems (primarily LLMs like ChatGPT) in legal practice; mentions fine-tuning and Retrieval Augmented Generation (RAG). | Empirical research: Anonymous online survey distributed to members of the Bar Association of Luxembourg (28 responses analyzed); four semi-structured interviews with representatives of two law firms and two legal tech companies active in Luxembourg, France, and Belgium. | Survey: 54% use ChatGPT, mainly for drafting (emails, some legal docs), research, translation; 64% find it improves efficiency, but concerns exist over hallucinations/verification need; only 25% receive training; opinions split on duty to use AI/inform clients; 79% believe AI use compromises confidentiality; 93% avoid inserting client data. Interviews: Firms/companies develop fine-tuned GPT-based systems for similar tasks, emphasizing efficiency but acknowledging hallucinations/context limitations; stress lawyer verification; divided on informing clients; generally avoid processing client data but desire access for improvement; highlight security measures (encryption, EU servers, vendor reviews). | Risk of AI 'hallucinations' leading to inaccurate outputs; Threat to client confidentiality due to potential data disclosure to third-party AI providers; Lack of transparency in how AI systems process data; Need for constant verification of AI outputs, counteracting efficiency gains; Potential deskilling or over-reliance on AI. | Adopt client-centric approach prioritizing quality service and client interests; Obtain specific, informed, freely given client consent before processing confidential data via AI; Conduct thorough due diligence on AI vendors; Implement robust security and compliance measures (contracts, encryption, access controls, audits); Provide mandatory lawyer training on AI use, risks, limitations, and prompt engineering; Bar Associations should consider issuing clear guidelines or rules. | Lawyer competence, Client confidentiality, Professional ethics, Quality of legal services. | NaN | General legal practice | Luxembourg (primary focus), EU (secondary, via GDPR and AI Act references) | Discusses public LLMs (trained on broad internet data) and fine-tuned systems. Fine-tuned systems use controlled legal data (legislation, case law), possibly public/subscribed legal content. Some systems leverage internal law firm databases (non-confidential or anonymized data preferred). Explicit avoidance of using client confidential data for training is emphasized. | For the systems discussed (not the paper itself): Fine-tuning pre-trained models, Retrieval Augmented Generation (RAG), Evaluation using legal expert scenarios, User feedback loops, Implementation of security by design (encryption, data silos, access controls, auditing), Due diligence processes for vendors. | Public LLMs (e.g., ChatGPT) accessed via web; Fine-tuned systems deployed internally within law firms or offered as commercial products by legal tech companies. | False | False | NaN | Lack of clear professional conduct rules/guidelines specific to generative AI use; Tension between improving AI performance (requiring data) and maintaining client confidentiality; Insufficient training for lawyers on AI tools and associated risks; Need for improved AI explainability and context-awareness in legal tasks. | Ensuring accuracy and reliability of AI outputs (mitigating hallucinations); Protecting client confidentiality when using third-party AI tools; Integrating AI ethically and effectively into legal workflows; Addressing user (lawyer) reservations and ensuring proper usage; Need for context-specific AI performance in complex legal tasks; Balancing innovation with ethical obligations. | Disclosure of confidential client information to AI providers or other third parties; Generation of inaccurate or fabricated information (hallucinations); Breaches of data protection regulations (e.g., GDPR); Unauthorized access to sensitive data; Deskilling of legal professionals; Erosion of client trust due to opaque AI use; Cybersecurity risks (e.g., prompt injection, data poisoning). |
| e3vCn9f3qxcJ.pdf | Google_Scholar | GPT, Ontology, and CAABAC: Attribute-based personalized access control model anchored by compliance, context, and attribute | This paper proposes GPT-Onto-CAABAC, a novel framework integrating Generative Pre-trained Transformers (GPT), ontologies, and Context-Aware Attribute-Based Access Control (CAABAC) for enhancing access control to Electronic Health Records (EHRs). The system aims to provide dynamic, personalized, and compliant EHR access by interpreting legal/policy documents and adapting to contextual changes in healthcare settings. | True | Market | True | 1.0 | NaN | GPT-Onto-CAABAC (GPT-powered Ontology-Driven Decision of Context-Aware Attribute-Based Access Control). It integrates GPT (specifically ChatGPT-4) for natural language understanding and policy interpretation, ontologies (dynamically constructed from legal texts) for structured knowledge, and CAABAC for managing access permissions based on attributes and real-time context in EHR systems. | Empirical evaluation using over 120 use-case scenarios across 12 categories, cross-referenced with Australian legislation (Privacy Act 1988, My Health Records Act 2012). Scenarios included anonymized real-world EHR data and constructed artificial situations. Evaluation metrics: 'context comprehension' and 'recommendation effectiveness' (scored 0-1 using a rubric), compliance, adaptability, and conflict resolution efficiency. Fault injection testing was also performed. | The GPT-Onto-CAABAC framework showed high capability in handling extrinsic (environmental context, access subject) and intrinsic factors (ontology, GPT) for access control. In scenario testing across 12 healthcare categories, it achieved average scores for 'context comprehension' and 'recommendation effectiveness' generally above 0.8 (on a 0-1 scale), demonstrating robust interpretation of legal requirements and adaptive decision-making. | NaN | NaN | NaN | NaN | Health Law, Data Privacy Law, Healthcare Compliance (specifically citing Australian Privacy Act 1988, My Health Records Act 2012, Health Records Act 2001 (Victoria), and mentioning GDPR, HIPAA). | Australia (specifically Victoria for some legislation, and federal acts for testing), with conceptual applicability to international standards like GDPR and HIPAA. | The core GPT model (ChatGPT-4) is pre-trained by OpenAI. For this framework's development and testing: 1) Australian legislation (Privacy Act 1988, My Health Records Act 2012, Health Records Act 2001 (Victoria)) loaded as PDFs via 'AskYourPDF' plugin for dynamic, implicit ontology construction. 2) A dataset of over 120 use-case scenarios (in 12 categories) combining anonymized real-world EHR data and constructed artificial scenarios for evaluation. | Proof-of-concept development using a constructive research approach. Involved literature review of existing access control models, development of the GPT-Onto-CAABAC algorithm and architecture, and iterative refinement. Evaluation through scenario-based testing with predefined metrics and fault injection. | The framework is at a proof-of-concept stage. The paper discusses future steps for real-world implementation, including pilot testing, optimization, and regulatory approvals, but it is not currently deployed for general use. | False | False | NaN | NaN | Key challenges include ensuring stability and validity of GPT-generated outputs (mitigating hallucinations), managing GPT model performance (response times for real-time decisions), fostering societal trust in opaque AI systems, achieving scalability for large healthcare environments, high resource requirements for deployment and maintenance, ensuring data privacy during integration, and maintaining continuous adaptation to evolving legal and technological landscapes. | Stated risks include data breaches from system vulnerabilities, non-compliant or harmful decisions due to GPT hallucinations or misinterpretations, erosion of societal trust due to AI opacity or errors, and challenges in maintaining interpretability and accountability of AI-driven access control decisions. The paper also notes the complexity and potential for misinterpretation if human oversight is not robust. |
| CXzDSayL0SEJ.pdf | Google_Scholar | 7th Annual Innovation and Technology Law Conference: Generative AI: Infringement or \nInnovation? | This paper introduces the 7th Annual Innovation and Technology Law Conference held by Seattle University School of Law, focusing on Generative AI. It summarizes the conference's history, goals, and the specific legal and societal panels presented, including copyright, publicity rights, tort liability, digital resurrection, and the impact on professions. | True | Market | False | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Copyright law, Rights to publicity, Tort liability | US | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Copyright infringement, violation of rights to publicity, tort liability from bad generative AI advice, displacement of professions (e.g., voice artists, journalists), potential aggravation or perpetuation of existing societal inequities. |
| Zz495LiJ5oAJ.pdf | Google_Scholar | Exploring the Nexus of Large Language Models and Legal Systems: A Short Survey | This paper surveys the applications of Large Language Models (LLMs) in the legal domain, covering tasks like text comprehension, case retrieval, analysis, and legal education. It also discusses key challenges such as bias, hallucination, and ethical concerns, along with available datasets and fine-tuned models for various legal systems. | True | Idealistic | True | 3.0 | Positive | Survey covering various techniques including fine-tuning LLMs (e.g., LawGPT, Lawyer LLaMA, LexiLaw, ChatLaw, DISC-LawLLM, LexGPT, LLaMandement), Prompt Engineering (LPE, CoT), Retrieval-Augmented Generation, and neuro-symbolic methods. | NaN | NaN | Lack of true understanding (stochastic parrots), spurious correlations, biases (racial, gender, religious, LGBTQ+), hallucination, privacy encroachments, interpretability issues, challenges in distinguishing authentic AI-generated evidence, potential negative impacts on fairness and fundamental values. | Fine-tuning on diverse/representative data, adversarial prompts, retrieval augmentation, integration of external knowledge bases, development of methods to mitigate bias and ensure transparency, aligning models with human values ('Law Informs Code'), evolving legal frameworks, interdisciplinary collaboration. | Legal text processing and understanding, legal case retrieval and analysis, legal education and examinations, legal practice assistance, dispute resolution, legal advice provision, enhancing accessibility to legal knowledge. | NaN | General/Multiple (including criminal law, constitutional law, contract law, tort law, tax law, privacy law, parliamentary procedure) | Multiple (including China, Taiwan, Palestine, France, US, UK, EU, CoE, Canada, India) | Surveys works using various legal datasets (e.g., CAIL2018, LeCaRD, Pile of Law, LeXFiles, CaseHOLD, Cambridge Law Corpus, MultiLegalPile) from multiple jurisdictions and languages, including court cases, legislation, contracts, Q&A. | NaN | NaN | True | True | The survey provides GitHub repository links for several specific fine-tuned LLMs it reviews (e.g., LawGPT, Lawyer LLaMA, LexiLaw, ChatLaw, DISC-LawLLM). The survey paper itself is available on arXiv. | Need for further mitigation of biases, enhanced interpretability, development of specialized data resources (especially multilingual), establishment of ethical guidelines, improved robustness and reliability for legal tasks, better handling of complex legal reasoning and causality, improved performance on benchmarks (e.g., LexGLUE), advanced multimodal capabilities. | Need for domain-specific data/training, preventing hallucination and ensuring factual accuracy (necessitating retrieval augmentation/human oversight), adapting general models to specialized legal tasks efficiently, addressing inherent biases in models and data, evaluating performance accurately in complex legal scenarios, ensuring transparency and interpretability. | Privacy violations, perpetuation of societal biases (racial, gender, religious, etc.), generation of inaccurate or misleading information (hallucination), lack of genuine understanding leading to errors, potential for misuse in generating false evidence or overwhelming legal systems, undermining judicial integrity and fairness, threats to fundamental human values (autonomy, equality). |
| 9I7B1zrXEvgJ.pdf | Google_Scholar | KRAG Framework for Enhancing LLMs in the Legal Domain | This paper introduces Knowledge Representation Augmented Generation (KRAG), a novel framework using structured knowledge graphs to improve Large Language Models' (LLMs) performance in the legal domain. Its implementation, Soft PROLEG, enhances legal reasoning, argumentation, and explainability, demonstrating improved accuracy and stability on Japanese Bar Exam questions. | True | Market | True | 1.0 | NaN | Knowledge Representation Augmented Generation (KRAG) framework, with Soft PROLEG as an implementation model. It uses inference graphs derived from structured legal knowledge (conditions, subconditions, exceptions) to guide LLM reasoning and generate explanations. | Evaluated on the English version of the Japanese Bar Exam (Heisei 29 (2017) to Reiwa 03 (2021)). SoftPROLEG (using GPT-3.5 and GPT-4 backbones) was compared against vanilla GPT-3.5 and GPT-4 on accuracy and stability (consistency of answers across two trials). | GPT-4-SP (SoftPROLEG with GPT-4 backbone) achieved the highest stability (91.9%) and demonstrated improved accuracy on the Japanese Bar Exam compared to baseline GPT-4 (e.g., 0.8765 for GPT-4-SP vs 0.8018 for GPT-4 in the Reiwa 02 exam year). | NaN | NaN | NaN | NaN | Civil Law (exemplified through JUF theory and scenarios); evaluation conducted on the Japanese Bar Exam which covers multiple legal fields. | Japan | The KRAG system uses a 'Knowledge Set' (not for LLM fine-tuning). This set, comprising 1,287 samples for the PoC, contains (Query, Related Legal Articles, Graph Structure) triplets. These were semi-automatically constructed: LLMs generated initial data points from Japanese Bar Exam-like scenarios, which were then reviewed, verified, and structured into graphs by human legal experts. | The KRAG framework and Soft PROLEG were designed based on derivational analogy and Presupposed Ultimate Fact Theory (JUF theory), employing graph-based knowledge representation. The Knowledge Set construction involved a semi-automated process: LLM-based data generation followed by human expert review and verification. | A Proof of Concept (PoC) system (Soft PROLEG 1.0) was developed. No specific deployment or diffusion strategies for wider external use are mentioned. | False | False | NaN | NaN | Computational complexity of handling large knowledge graphs and real-time inference with LLMs. Scalability limitations due to the semi-automated, human-expert-reliant process for Knowledge Set construction. Need for refinement in methods for evaluating the quality and relevancy of graph-based explanations. | The paper notes the 'high stakes of incorrect information' in legal applications generally as a motivation for improving LLMs. It does not explicitly list new risks introduced by KRAG/Soft PROLEG but aims to mitigate existing LLM risks like inconsistency and inadequate knowledge representation. |
| SP6gNobGvBUJ.pdf | Google_Scholar | HACKING GENERATIVE AI | This paper analyzes whether prompt injection attacks, which manipulate generative AI like ChatGPT into producing harmful or illegal content, violate existing US computer crime law, specifically the Computer Fraud and Abuse Act (CFAA). The author argues that such attacks constitute accessing a computer in excess of authorization under the CFAA and offers recommendations for applying the law. | True | NaN | True | 3.0 | NaN | Prompt Injection Attacks | Reviews examples and research demonstrating prompt injection attacks on platforms like ChatGPT and Clyde, citing specific instances like generating instructions for bombs, meth, napalm, and malicious code. | Researchers successfully used prompt injection to bypass AI safety restrictions, generating harmful content (bomb instructions, meth recipes, malicious code, hate speech) and extracting sensitive information. | NaN | NaN | NaN | NaN | Computer Crime Law, Cybersecurity Law, Criminal Law | United States | NaN | NaN | NaN | False | False | NaN | NaN | Legal challenges in applying existing computer crime law (CFAA) to prompt injection, including defining authorization (code-based vs. contract-based, intended function, norms), distinguishing information obtained vs. generated, valuing generated content based on nature rather than monetary value, First Amendment concerns regarding speech, protecting legitimate security research, and the AI black box problem making internal workings opaque. | Generation of harmful, offensive, dangerous, or illegal content (e.g., instructions for bombs, meth, napalm; hate speech; phishing emails; malicious code, including polymorphic malware). Disclosure of sensitive or personal information. Lowering the barrier of entry for malicious hacking. Increased sophistication and adaptability of hacking attacks. Potential for indirect prompt injection to poison training data or exfiltrate user data. |
| T4UCpfvU-usJ.pdf | Google_Scholar | laws clearly: large language models and plain language transformation | This paper investigates the capability of OpenAI's GPT-4 large language model to automatically transform complex Hungarian legal texts into plain language to improve access to legal information. The study manually evaluates the model's performance on specific linguistic simplification tasks, assessing both comprehensibility improvements and the preservation of legal meaning. | True | Idealistic | True | 2.0 | Neutral | Using GPT-4 with specifically crafted prompts to perform plain language transformations on legal text excerpts. | Manual analysis of GPT-4 outputs based on four specific linguistic features (avoiding long/interjected clauses, replacing light verb constructions, splitting long sentences, clarifying ambiguous conjunctions like 'illetve'). Evaluation focused on prompt adherence and preservation of normative legal content. | GPT-4 showed mixed performance. While promising for simplifying sentence structures (clause shortening, sentence splitting), it struggled to accurately replace light verb constructions (potentially due to internal translation issues altering meaning) and incorrectly interpreted the conjunction 'illetve', changing the legal meaning from 'or' to 'and'. Normative legal content was altered in almost all tested cases. | The complexity, specialized terminology, and convoluted sentence structures inherent in legal language (legalese) prevent citizens from understanding legal texts and representing themselves effectively. | Leveraging Large Language Models (specifically GPT-4) to automatically simplify complex legal texts into more understandable plain language versions for laypeople. | Access to legal information, Comprehensibility of legal texts, Plain language transformation | Laypeople / citizens without legal expertise. | Land Transaction Law (specifically Act CXXII of 2013 on Transactions in Agricultural and Forestry land) | Hungary | The study utilizes the pre-trained GPT-4 model from OpenAI; details of its training data are proprietary but known to be vast text corpora. | Experimental approach using prompt engineering to guide GPT-4, followed by manual qualitative analysis of the generated text. | NaN | False | False | NaN | Current LLMs like GPT-4 are unsuitable for fully automatic plain language paraphrasing of legal texts due to the high risk of altering normative content. The task still requires significant human legal expertise and oversight. | Ensuring the preservation of normative legal content during simplification; potential misinterpretation of prompts or linguistic nuances by the LLM (e.g., function verbs, conjunctions); issues arising from the model's internal processing/translation for non-English languages. | The potential alteration or violation of the normative legal content during automatic simplification, leading to misinterpretations of the law by citizens relying on the simplified text. |
| oCY_5uUnGtIJ.pdf | Google_Scholar | Artificial intelligence in the analysis and screening of criminal processes: implications for speed and access to justice | The paper examines how AI can accelerate the analysis and screening of criminal cases within the Brazilian judicial system, potentially enhancing access to justice. It discusses existing AI initiatives in Brazil and internationally, while also considering the ethical challenges and risks, such as algorithmic bias and lack of transparency. | False | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | High volume of cases, lack of human and technological resources, slowness and delay (morosidade processual) in the judicial system, difficulty identifying priority cases, compromising the right to a reasonable duration of proceedings and access to justice. | Using AI for automated analysis, classification, and triaging of cases to increase speed and efficiency, improve resource allocation, standardize decisions, and potentially enhance transparency and predictability. | Celerity/speed of judicial processes, access to justice, efficiency of the judicial system, case triaging and analysis. | General population facing delays in the judicial system, particularly economically vulnerable individuals. | Criminal Law, Criminal Procedure, Constitutional Law | Brazil | NaN | NaN | NaN | False | False | NaN | Need for robust regulatory frameworks and ethical guidelines (transparency, explainability, bias mitigation), continuous human supervision, addressing algorithmic opacity ("black box"), ensuring AI respects constitutional principles and human rights, avoiding reinforcement of existing biases (racial, social, gender), preventing desumanization of the justice process. | Lack of technological infrastructure, need for human resource training, ensuring ethical design and use, managing large volumes of data, integrating AI with existing systems, balancing efficiency with fundamental rights protection. | Algorithmic bias (including racial bias, termed 'racismo algorítmico'), lack of transparency/explainability ('caixa-preta'), reinforcement of structural inequalities, potential violations of fundamental rights (due process, privacy), unjust decisions due to lack of human oversight, over-dependence on technology, compromising judicial autonomy. |
| VhD8GBNm_7QJ.pdf | Google_Scholar | Text Mining Legal Documents for Clause Extraction | This paper investigates the feasibility of using pre-trained language models (BERT, RoBERTa, DeBERTa) for legal clause extraction with limited training data. The study finds that acceptable performance (within 10% of results using 3.3x more data) can be achieved with just 120 contracts, suggesting potential for smaller law firms. | True | Market | True | 2.0 | NaN | Fine-tuning pre-trained Transformer-based language models (RoBERTa, DeBERTa, BERT) for clause extraction formulated as a question-answering task, varying the amount of training data. | Evaluation on the Contract Understanding Atticus Dataset (CUAD) using F1-Score (token-level), Precision, Recall, and AUPR (text-level). Tested varying numbers of training contracts (50-400) and epochs. | RoBERTa fine-tuned with 120 training contracts achieved an F1-Score (token comparison, best prediction) within 10% of the score achieved with 400 training contracts (54.8% vs 57.9% for the 'Total' of 8 clause types). RoBERTa generally performed best, especially with smaller datasets. | High resource requirements (large annotated datasets, labelling effort) traditionally needed for training NLP models, making them inaccessible to smaller law firms. | Demonstrating that fine-tuning pre-trained language models like RoBERTa requires significantly less labelled data (e.g., 120 contracts) for reasonable clause extraction performance. Suggests using model predictions to aid further data labelling. | NaN | NaN | Contract Law | US | Contract Understanding Atticus Dataset (CUAD), a publicly available dataset of ~510 English-language commercial contracts labelled for 41 clause types, structured as a Question-Answering dataset (unstructured text). | Empirical evaluation and comparative study, involving fine-tuning existing pre-trained models and varying experimental parameters (training set size, epochs) to measure performance. | NaN | False | False | NaN | Need for improved performance, potentially through exploring smaller models or enhancing pre-training with diverse legal corpora. Challenges remain in handling multi-answer clauses and reconciling text vs. token level evaluation. | Achieving robust performance with limited training data. Optimizing hyperparameters (epochs). Selecting appropriate evaluation metrics and methods, especially for multi-answer clauses. Performance variation across clause types and models. | NaN |
| fyLCMIyr3Q4J.pdf | Google_Scholar | Generative AI, Cybersecurity And Cybercrime For Lawyers: Myths, Risks And Benefits | This paper discusses the historical context, risks (security, privacy, legal), and benefits of Generative AI for legal professionals, focusing on its implications for cybersecurity, cybercrime, and enhancing access to justice. It aims to debunk myths about AI replacing lawyers while highlighting its potential to improve efficiency, fairness, and reduce backlogs within the legal system if implemented responsibly. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Suboptimal access to justice, especially for socially vulnerable groups, racial or ethnic minorities; Sluggish, expensive, and operationally inefficient legal and judicial systems leading to wrongful convictions and miscarriages of justice; Overloaded public defenders; Judicial system backlogs due to mounting cases and overcriminalization, impacting the quality and fairness of due process. | Properly implemented GenAI systems to streamline litigation and reduce judicial bottlenecks; AI tools for lawyers to summarize cases and assemble relevant information from diverse legal documents; AI assistance for lawyers, court clerks, and judges in prioritizing and summarizing case content; AI use by prosecutors to predict conviction chances (with safeguards against bias) for better resource allocation. | Improving efficiency of legal and judicial systems; Reducing wrongful convictions and miscarriages of justice; Aiding overloaded public defenders; Streamlining litigation and case management for lawyers, judges, and prosecutors; Addressing judicial backlogs. | Socially vulnerable groups, racial or ethnic minorities, indigent defendants in criminal cases. | Criminal law, General legal practice, Cybersecurity law, Data protection law. | US, UK, EU, Switzerland | NaN | NaN | NaN | False | False | NaN | Resolving AI hallucinations; Ensuring human oversight in AI-generated legal content; Developing effective guardrails for AI use in law firms; Addressing and mitigating AI bias, especially in criminal justice predictions to uphold principles like presumption of innocence; Technical limitations in managing personal data within AI models (e.g., deletion requests). | Ensuring data security and confidentiality when using third-party AI tools or training proprietary models; Compliance with evolving data protection and AI regulations (e.g., EU AI Act, DSRs under privacy laws); Preventing copyright infringement when using data for AI training; Overcoming the technical difficulty of removing specific data from trained AI models; Dealing with AI-generated misinformation (hallucinations) and ensuring outputs are reviewed by legal professionals; Protecting against cyber-attacks targeting AI systems, such as data poisoning. | Disclosure of confidential client information through AI systems; Legal liability and sanctions for lawyers relying on inaccurate AI-generated content (e.g., fake case law); Copyright infringement issues related to AI training data and outputs; Increased vulnerability to sophisticated cyber-attacks like deep fakes and data poisoning targeting AI; Perpetuation or amplification of biases through AI systems, particularly in criminal justice, leading to unfair outcomes or infringement of rights; Misuse of AI for malicious activities such as creating convincing phishing content or impersonation. |
| 2jqrUByqR-8J.pdf | Google_Scholar | LEGAL ANALYTICS WITH LARGE LANGUAGE MODELS AND STRUCTURED KNOWLEDGE BASES | This paper explores how integrating legal analytics with large language models (LLMs) and structured knowledge bases (SKBs) can enhance the efficiency and effectiveness of legal services. It discusses the roles, capabilities, benefits, and challenges of these technologies, advocating for a more data-driven approach to law. | True | Market | True | 3.0 | Positive | Integration of Large Language Models (LLMs) and Structured Knowledge Bases (SKBs) for legal analytics | NaN | NaN | NaN | NaN | NaN | NaN | General Legal Practice / Multiple Fields | International | LLMs trained on large, diverse corpora (web text, potentially legal texts); Structured Knowledge Bases containing proprietary legal data (caselaw, statutes, regulations) | NaN | NaN | False | False | NaN | NaN | Data quality and scarcity, computational complexity, potential for false positives, model interpretability ('black box' issue), need for expertise and resources, vulnerability to adversarial attacks | Data privacy violations, security risks, algorithmic bias leading to discrimination, lack of fairness and accountability in automated decision-making |
| YimleaMoY5QJ.pdf | Google_Scholar | Towards Human-Centered Standards for Legal Help AI | This paper presents findings from interviews and design sessions with community members on their use of large language model-based AI tools (like Google Bard) for legal problems, specifically an eviction scenario. It highlights user preferences, trust factors, and concerns, advocating for participatory, human-centered approaches to design and policymaking for legal AI to enhance access to justice. | True | Idealistic | True | 2.0 | Positive | Users interacting with Google Bard (a large language model) for a fictional legal problem (eviction notice) as part of a research study. | Qualitative research study with 15 US adults involving: 1) background questions, 2) a scenario exercise using Google Bard for an eviction notice, 3) feedback/brainstorming. Data collected via online interviews with structured and open-ended questions. | Participants generally found Bard helpful (average rating 3.6/6), and trust in the AI tool increased after use (from an average of 2.7/6 to 4.2/6). Key desires included hyperlinks/citations for information, features like "People Also Ask," and simple responses with options for more detail; reactions to prominent warnings were mixed to negative. | General public's lack of awareness that life problems may have a legal dimension; inability to resolve problems via the formal justice system due to lack of capacity or limited help. For AI: risk of providing incorrect legal information, AI tools becoming a second-class service, and inequitable access due to digital divide or literacy barriers. | Adopting human-centered design and participatory policy-making involving community members in AI development. Designing AI tools that are user-friendly, provide clear and actionable information, and incorporate safeguards. Specific suggestions include better referral systems, guardrails against case law hallucinations, jurisdiction-specific information, and prominent links to reliable human help. | Access to civil justice, specifically for issues like evictions. Use of AI for legal issue spotting, triage, guidance on options, finding free assistance, and understanding legal-procedural steps. | General community members in America who have faced civil legal problems and might use AI for legal help. The study sample was a convenience sample with some demographic limitations. | Civil justice, with a specific focus on landlord-tenant law (eviction). Also mentions debt collection, family law (divorce, custody), and employment law. | United States (participants from California, New York, Maryland, New Jersey; scenario included elements like 'Alameda Eviction laws'). | NaN | Qualitative research methods derived from design research, participatory policymaking, and human-computer interaction. Scenario-based research protocol involving structured interviews, observation of AI tool use (Google Bard), and co-design discussions. | NaN | True | False | The study used Google Bard, which is a publicly accessible web service provided by Google. | Need for more extensive and ongoing research with representative samples. Development of a comprehensive risk typology for legal AI. Creation of interface and technical solutions to mitigate specific harms like 'ersatz legal help' (correct-seeming but flawed information). Understanding how to design effective disclosures and warnings that users engage with meaningfully. | For the study: limitations of a convenience sample (underrepresentation of certain demographics). For legal AI in general: ensuring accuracy and reliability of AI-generated legal information (avoiding hallucinations, providing context, jurisdictional accuracy); user over-reliance on AI; designing interfaces that meet diverse user needs and literacy levels; balancing simplicity with the complexity of legal matters and necessary warnings. | AI providing incorrect legal information (hallucinations, e.g., non-existent case law). Users misapplying information due to lack of context or jurisdictional errors. AI tools becoming a 'second-class' service. Inequitable access due to digital divide or varying tech literacy. Data privacy concerns (over-harvesting data). Users over-relying on AI without verification. 'Ersatz legal help' leading to poor outcomes (e.g., bad referrals, cherry-picking details). |
| I6Ful7p1yP0J.pdf | Google_Scholar | ARTIFICIAL INTELLIGENCE, ETHICS AND SPEED PROCESSING IN THE LAW SYSTEM | This paper reviews how generative AI can enhance the Brazilian Judiciary's efficiency by automating tasks and aiding sentence generation, exemplified by tools like VitorIA and Victor. It highlights the importance of embedding ethical considerations in AI to ensure fair, accessible, and non-discriminatory justice. | True | Idealistic | True | 2.0 | Positive | Generative AI applications in the Brazilian Judiciary, specifically VitorIA (appeal profiling/binding) and Victor (appeal admissibility analysis). | Qualitative review of secondary data and documentary evidence concerning the functionalities and operational impact of existing systems (VitorIA, Victor) in the Brazilian Judiciary. | Generative AI significantly expands judicial operational capacity by automating tasks and aiding sentence generation, leading to improved decision-making, effective legal strategies, and enhanced overall judicial efficiency. | Risk of algorithmic bias leading to unfair/discriminatory outcomes; slowness and case overload in traditional judicial systems; high operational costs; complexity of ensuring ethical AI judgments, especially in heterogeneous societies. | Use generative AI to automate tasks for speed and cost reduction; embed ethical standards in AI design for fairness; free human judges for complex ethical considerations; promote extrajudicial resolution for simpler cases identified by AI. | Improving judicial efficiency (speed, cost); enhancing access to justice; ensuring fairness and reducing discrimination; supporting judicial decision-making and sentence generation; ethical application of AI in law. | Society at large; specific challenges noted for heterogeneous societies (e.g., Brazil's indigenous populations) regarding ethical AI. | General judicial processes and litigation. | Brazil | Not explicitly detailed, but implied to be case files, appeals, and jurisprudential databases from the Brazilian Federal Supreme Court for tools like VitorIA and Victor. | N/A (Paper discusses existing tools, does not detail their specific design methodologies beyond Victor being developed by STF's IT staff). | Deployed within the Brazilian Federal Supreme Court (STF) for internal use (e.g., VitorIA for appeal analysis, Victor for admissibility checks). | False | False | NaN | Teaching AI nuanced social values and ethical behaviors for sentencing; developing AI for ethical complexities in heterogeneous societies; current AI's inability to handle all circumstantial/mitigating factors like humans; need for AGI for more complex judicial tasks. | Ensuring ethical considerations, neutrality, and avoiding bias in AI for judicial tasks; defining and embedding ethical standard value criteria; adapting AI for culturally heterogeneous societies; balancing efficiency with human oversight. | Algorithmic bias leading to discriminatory or unfair sentences; doctrinal bias in AI-processed information; unjust punishment due to lack of nuanced human judgment; creation of legal uncertainty. |
| Generative_Artificial_intelligence_Applications_in.pdf | Google_Scholar | Generative Artificial intelligence Applications in Banking and Finance sector | This paper reviews the applications of Generative AI in the banking and finance sector, focusing on improving customer support services and operational efficiency. It discusses benefits like enhanced personalization, fraud detection, risk assessment, and compliance automation, while also outlining challenges and ethical considerations. | True | Market | True | 3.0 | NaN | Generative AI (incl. models like GPT-3, LLaMA), Graph Neural Networks (GNNs), Retrieval-Augmented Generation (RAG), fine-tuning methods | NaN | NaN | NaN | NaN | NaN | NaN | Banking Law, Financial Regulation, Compliance (KYC/AML), Data Privacy, Contract Law | USA, South Korea, International (mentions EU regulations like GDPR, MiFID II) | Discusses use of public PLM training data (web text, books), private conversational data (customer service chats), transaction data, customer profiles, regulatory documents, network traffic data; mentions fine-tuning on real chat discussions and specialized instruction datasets in cited studies; discusses RAG using custom knowledge bases. | NaN | NaN | False | False | NaN | NaN | Data privacy and security; model output accuracy (hallucination); skills/expertise deficit; scaling and integration difficulties; regulatory compliance; cost of fine-tuning; handling disjointed data; potential for bias; need for explainability (XAI). | Biased/discriminatory outputs; exposure/misuse of sensitive data; model inaccuracy/hallucination; non-compliance with regulations (AML, GDPR, KYC); data breaches; cybersecurity threats; reputational damage; legal vulnerabilities. |
| T6NjEju5IEEJ.pdf | Google_Scholar | LegalTech in the Light of the Upcoming Artificial Intelligence Act | This paper introduces Artificial Legal Intelligence (ALI) and reviews various LegalTech tools aimed at automating legal tasks, enhancing consumer access to legal services. It further analyzes the implications of the upcoming European Artificial Intelligence Act (AIA) on these technologies and discusses the future of legal services. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | High cost of legal services, limited access to legal aid, unaffordability of pursuing small claims, and consumers' lack of legal skills and expertise. | Utilizing LegalTech tools for automated, cost-effective legal information and services; liberalizing legal markets; reforming legal education to include technology skills. | Affordable legal information and services, self-representation tools (do-it-yourself), assistance with small claims, Online Dispute Resolution (ODR). | Low-income individuals and general consumers lacking legal expertise. | General legal services, consumer law, contract law, intellectual property law, AI regulation. | Primarily European Union (due to focus on the AIA), with references to the USA and UK. | NaN | NaN | NaN | True | False | Various commercial and some potentially free/low-cost LegalTech tools and platforms (e.g., legal intermediation platforms, DIY document tools, small claims services) are mentioned as existing and accessible online. | Technical gaps include unreliable fuzzy logic systems and the need for further research in computational legal argumentation. Regulatory gaps include the AIA's coverage of hybrid AI systems and the classification of certain LegalTech tools as high-risk. Societal gaps include the slow implementation of high-risk AI systems, the need for legal market liberalization, and updated legal education. | NaN | Inadequate representation of ambiguous legal rules by logic-based AI, difficult-to-understand AI outputs for laypersons, ethical conflicts with online legal platforms, interpretation difficulties and potential for social engineering with Legal Design, AI-driven manipulation or harm through subliminal techniques or exploitation of vulnerabilities, social scoring leading to detrimental treatment, biases in AI leading to discrimination and unfair decisions, lack of transparency, and general impact on fundamental rights. |
| UMyqIKz4N7YJ.pdf | Google_Scholar | Knowledge Pyramid Construction for Multi-Level Retrieval-Augmented Generation | This paper proposes PolyRAG, a multi-layer knowledge pyramid approach (Ontologies, Knowledge Graphs, raw text) for Retrieval-Augmented Generation, aiming to balance precision and recall in domain-specific question answering. Evaluated on academic and financial benchmarks, PolyRAG demonstrated significant improvements over existing RAG techniques and base LLMs. | True | Market | True | 1.0 | NaN | PolyRAG: A multi-layer knowledge pyramid (Ontologies, Knowledge Graphs, chunk-based raw text) with cross-layer augmentation (knowledge completion) and cross-layer filtering (knowledge condensation), using a waterfall model for retrieval in Retrieval-Augmented Generation (RAG). | Evaluated on two domain-specific benchmarks: AcadChall (academic, self-created from XXX University data) and R-FLUE-FiQA (financial, extended public FiQA dataset). Compared against 19 SOTA methods using metrics including Precision, Recall, F1 score, BLEU, BERT similarity, and HitRate. | PolyRAG combined with GPT-4 achieved an F1 score of 0.8109 on the AcadChall benchmark, representing a 395% F1 gain from GPT-4's baseline performance of 0.1636 on the same benchmark. | NaN | NaN | NaN | NaN | NaN | International | Raw text from a self-constructed academic benchmark (AcadChall: based on XXX University data including staff, courses, departments) and an extended public financial Q&A dataset (R-FLUE-FiQA from FiQA). This text is processed using LLMs and Open Information Extraction to construct the knowledge pyramid's layers: Ontologies, Knowledge Graphs, and raw text chunks. | Iterative knowledge pyramid construction involving: 1) Initial layer creation (Ontology, Knowledge Graph, Raw Text) from domain-specific corpora. 2) Knowledge Completion through cross-layer interaction, identifying and integrating missing concepts from lower layers (KGs) into higher layers (Ontologies) using semantic distribution divergence (KL-divergence) and k-medoids clustering. 3) Knowledge Condensation via top-down refinement, using Ontology anchors to filter and summarize KG triplets with LLM assistance. | The paper states that implementations are available in a GitHub repository, and the two benchmarks (AcadChall, R-FLUE-FiQA) will also be made available to the community. | True | True | Implementations available in a Github repository. Benchmarks also stated to be made available. | NaN | Significant human effort required for initial Ontology schema definition; Noisy output from direct Open Information Extraction for Knowledge Graph construction; Effectively integrating heterogeneous knowledge bases (Ontologies, Knowledge Graphs, raw text). | General LLMs are prone to hallucinations in domain-specific tasks; Supervised Fine-Tuning (SFT) of LLMs can lead to catastrophic forgetting of general knowledge and model hallucination. (PolyRAG is proposed to mitigate these issues). |
| BuN0HcT9T0sJ.pdf | Google_Scholar | Regenerating Justice: ChatGPT and the Legal Minefield of Generative AI | This paper critically examines Generative AI (GenAI), particularly systems like ChatGPT, and its profound implications for the legal field. Adopting an automation bias lens, it argues that unthinking reliance on GenAI risks undermining law's truth-seeking functions and core epistemic foundations through the propagation of inaccurate and sourceless information. | True | Idealistic | True | 2.0 | Negative | Generative AI / Large Language Models (specifically GPT models like ChatGPT) | Theoretical analysis using an automation bias lens, literature review, and examination of real-world incidents and GenAI capabilities (e.g., hallucinations, performance claims). | GenAI fundamentally threatens legal truth-seeking and epistemic integrity due to inherent issues like hallucinations and sourceless information, compounded by human automation bias. | Misinformation and hallucinations from AI leading to incorrect legal guidance; lack of accountability for AI-provided advice; erosion of trust if AI is unreliable/biased; entrenchment of biases from training data; ethical issues (unlicensed practice of law, loss of solicitor-client privilege); over-reliance due to automation bias. | Enhanced critical thinking and awareness of AI limitations (automation bias, inherent nature of hallucinations); robust human oversight (while acknowledging its limits); caution in deploying AI, especially solutions that obviate human participation in legal reasoning and storytelling. | Automated legal advice for consumers/self-represented litigants; consumer protection in automated legal services; reliability and trustworthiness of AI tools for those unable to afford traditional legal services. | Individuals unable to afford legal services; self-represented litigants (especially with low-value claims); consumers seeking rights protection. | Legal Practice, Legal Ethics, Consumer Law, Contract Law, Civil Procedure, Copyright Law. | International / Multiple (primarily US and Canada examples, but broadly applicable concerns) | Vast quantities of text scraped from publicly accessible internet sites (e.g., websites, social media, digital books like BooksCorpus, Wikipedia), largely unlabelled and collected via webcrawling bots. | Machine learning (supervised, unsupervised, reinforcement learning), transformer architecture, pre-training on large unlabelled text datasets, fine-tuning for specific tasks like dialogue. | Publicly accessible web interfaces (often with free tiers), APIs for developers, beta releases for public testing, integration into existing software products. | True | True | Publicly accessible web interfaces (e.g., ChatGPT free tier) and APIs. Some models (e.g., Meta's Llama) are stated to be open-source and downloadable. | Technical: Inherent unreliability (hallucinations, factual inaccuracies, lack of true reasoning). Societal/Legal: Absence of robust legal/ethical frameworks for AI in law, accountability vacuum, risk of exacerbating inequalities, erosion of solicitor-client privilege, public over-trust and misunderstanding of AI capabilities. | Technical: Managing massive datasets, reducing hallucinations (though seen as inherent), addressing bias in training data, ensuring factual accuracy, resolving tokenization issues. Ethical/Societal: Preventing misuse (e.g., disinformation), managing copyrighted material in training, ensuring safety and avoiding harmful or biased outputs. | Undermining law’s truth-seeking functions with sourceless/incorrect information; automation bias leading to over-reliance on flawed AI; erosion of legal meaning and narrative; spread of misinformation; ethical violations by legal professionals; harm to individuals relying on faulty AI advice; entrenchment of societal biases; threats to privacy and solicitor-client privilege. |
| EA5UKSipTqkJ.pdf | Google_Scholar | Are Robot Lawyers the Future of Increasing Access to Justice? | The paper discusses the potential of AI-powered legal tools ("robot lawyers") to improve access to justice by providing affordable information and self-help options. It also highlights risks like exacerbating inequalities and excluding vulnerable populations if not developed responsibly. | True | Idealistic | True | 3.0 | Neutral | AI-powered legal information and self-help tools (e.g., AdviceNow, Farewill, Valla, Amicable) | N/A (No specific evaluation performed by the author; cites tool provider claims) | N/A (No independent results reported; cites provider claims) | Digital exclusion (affecting elderly, non-English speakers, digitally illiterate), varying digital/legal capabilities, lack of access to devices/digital literacy. | Responsible development, diverse training data, auditing/testing AI, Assisted Digital services, leveraging AI to free up human advisors for vulnerable clients. | Access to legal information, self-representation tools, cost reduction in legal services, specific issues like benefits challenges, wills, employment claims, divorce. | General public needing legal assistance, with specific concern for vulnerable groups (elderly, non-English speakers, digitally excluded, marginalized populations). | Family law, Wills & Estates, Welfare Benefits, Employment Law, Civil Procedure. | UK, USA (mentioned briefly) | N/A (Mentions the need for diverse data but doesn't describe data used by specific tools). | NaN | Online websites/platforms, integration into government digital justice services. | True | False | Online services (some free guidance/tools, some paid). | Ensuring equitable access, preventing digital exclusion, mitigating AI bias, ensuring tools accommodate varying needs and capabilities. | Designing effective/accurate tools, addressing digital literacy/access issues, ensuring fairness/avoiding bias, integrating with existing legal systems. | Amplifying existing inequalities (racial, gender, socioeconomic, geographic bias), digital exclusion, inaccurate AI outputs. |
| 1237243.pdf | Google_Scholar | Leveraging the Use of ChatGPT: Exploring Its Real-World Applications Including Their Related Ethical and Regulatory Considerations | This paper explores twenty real-world applications of ChatGPT across diverse sectors, detailing its operational functionalities and benefits. It also systematically discusses the ethical and regulatory considerations, alongside potential risks, for each application, emphasizing the need for human oversight and verification. | True | Market | True | 3.0 | NaN | ChatGPT | NaN | NaN | NaN | NaN | NaN | NaN | Legal document review, contract drafting, legal research | International | Massive, diverse data from the internet, primarily unstructured text, used to train ChatGPT on human language, context, style, and common patterns. | Based on Generative AI, Large Language Models (specifically Transformer architecture), trained via pre-training and potentially Reinforcement Learning from Human Feedback (RLHF). | Web-based service, API access, embeddable in other tools, custom chatbot creation via GPT Builder. | True | False | ChatGPT is accessible via a web interface and API, with both free and paid tiers. Custom GPTs can be built on its platform. | NaN | Ensuring accuracy and reliability of generated content, necessity of human verification and quality control, adherence to ethical and regulatory considerations, issues of content ownership and accountability, debuggability, and maintenance of applications built using it. | Inaccuracy of information, ethical breaches, data privacy violations, security vulnerabilities, copyright and trademark infringement, plagiarism, legal liabilities, financial and reputational damage, potential for user manipulation or exploitation, perpetuation of bias from training data, lack of emotional understanding in sensitive human interactions, and potential harm if advice is followed without expert human oversight, especially in health and legal domains. |
| oZsXj4Vs990J.pdf | Google_Scholar | Prof Felix Steffek November 2024 PRESENTATIONS | This document is a list of academic presentations and convened conferences by Prof Felix Steffek, covering topics primarily in AI in law, corporate insolvency, dispute resolution, and access to justice. Several presentation titles highlight AI for legal tasks like court outcome prediction and the development of legal datasets such as the Cambridge Law Corpus. | True | Idealistic | True | NaN | Positive | NaN | NaN | NaN | NaN | NaN | Application of AI to dispute resolution for access to justice, Online Dispute Resolution (ODR), consumer dispute resolution (ombuds proceedings, conciliation), people-centered justice services. | Consumers, Small and Medium-sized Enterprises (SMEs) | Corporate Insolvency Law, Employment Law, Dispute Resolution, Civil Procedure Law, Consumer Law, Company Law, Private Law, Commercial Law | UK, Singapore, Latvia, Germany, EU, Japan, US, Hong Kong, International | NaN | NaN | NaN | False | False | NaN | NaN | NaN | NaN |
| _7IvdJ0dADYJ.pdf | Google_Scholar | A Review on Alex AI Legal Assistant | This paper reviews Alex AI Legal Assistant, a specialized AI system for legal tasks, comparing it favorably to general-purpose AI models like ChatGPT for accuracy and legal reasoning. It discusses Alex AI's architecture, benefits such as real-time legal updates and structured case law retrieval via 'Gorq', its limitations including computational demands, and future research directions for AI in law. | True | Market | True | 2.0 | Positive | Alex AI Legal Assistant (powered by Gorq) | Feature-based comparative analysis (qualitative) against ChatGPT, DeepSeek, and Gemini across attributes like legal accuracy, IPC interpretation, and API specialization. | Alex AI Legal Assistant is claimed to achieve 'Very High' legal accuracy and 'Expert-Level' IPC interpretation, outperforming ChatGPT, DeepSeek, and Gemini, due to its real-time legal database integration and jurisdiction-specific analysis via Gorq. | Limited accessibility and efficiency in exploring case law for legal education. | Employing AI tools like Alex AI to facilitate efficient case law exploration for students and researchers, making legal education more accessible. | Improving access to legal education and research materials. | Law students and researchers. | General law, with specific mention of compliance verification, case law interpretation, legal document analysis, Indian Penal Code (IPC) interpretation, and contract analysis. | India (specifically for IPC interpretation), with aspirations for global applicability across multiple legal regimes. | Proprietary legal datasets, real-time legal databases, specialized legal databases with legal texts, case laws, regulations, and continuously updated legal precedents. The NLP engine is fine-tuned on legal datasets. | System architecture includes a three-tier structure: Legal Document Retrieval Module (leveraging Gorq), NLP Engine (fine-tuned on legal datasets), and Legal AI Assistant. | NaN | False | False | NaN | Need for multilingual and jurisdiction-specific AI for global legal accessibility; lack of transparency and interpretability (explainability) in AI legal tools; incomplete integration with live court systems for real-time assistance; maturity of AI for reliable legal prediction and risk assessment. | High computational demands requiring cloud-based AI acceleration; ensuring adaptability across multiple jurisdictions and languages; ethical considerations regarding AI bias and the need for human expert validation to ensure fairness and accountability. | Risk of bias in AI-generated legal interpretations; potential for AI to produce unfair or unaccountable outcomes without human review and validation; risk of "hallucinated" or inaccurate content from general-purpose AI models if not specifically designed for legal domain accuracy. |
| assyXFv39zkJ.pdf | Google_Scholar | The Cost of Justice at the Dawn of AI | This paper examines the historical and potential future impact of legal service costs, particularly in light of AI, on the legal system, including access to justice and trial rates. It analyzes whether law suffers from 'cost disease' and urges the legal system to proactively adapt its doctrines and procedures to either continued cost stagnation or an AI-driven productivity revolution. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | High cost of legal services, perceived stagnation in legal sector productivity (cost disease), leading to diminished access to justice, the 'vanishing trial' phenomenon, and difficulties for individuals to afford legal representation. | Proactive adaptation of legal doctrines and procedures to explicitly incorporate and respond to changes in legal costs (e.g., in summary judgment, class actions, contracts of adhesion, arbitration, rules vs. standards). AI itself is presented as a potential solution to lower costs and thereby improve access to justice and potentially revive trials. | Cost of legal services, access to legal representation (especially for those with limited means), efficiency of the civil and criminal justice systems, trial rates, plea bargaining, summary judgment, class actions, rule of law, impact of technology on the legal profession. | The general public, particularly individuals with limited financial means and underrepresented groups who face barriers to accessing legal services due to high costs. | General civil litigation, criminal justice, contract law, administrative law, constitutional law (due process), intellectual property (as an example). | United States (federal and state systems), with brief comparative mentions of the United Kingdom and Ontario (Canada) regarding trial rates. | NaN | NaN | NaN | False | False | NaN | Persistent difficulty in accurately measuring legal productivity and service quality; technical limitations of current AI (e.g., reasoning depth, context limits, hallucination); potential exhaustion of high-quality AI training data; societal and professional inertia in adapting legal systems and practices to technological change and varying cost structures; uncertainty regarding the elasticity of demand for legal services and AI's impact on lawyer employment/wages. | NaN | AI-driven efficiencies in criminal justice leading to harsher, unintended sentencing outcomes; continued cost stagnation exacerbating access to justice problems; lower legal costs due to AI causing undesirable overenforcement or frivolous litigation in some areas; potential for increased wage inequality among lawyers; ethical challenges and errors from AI use (e.g., hallucinations, lack of human judgment). |
| Log48v1Ok7AJ.pdf | Google_Scholar | AI and LLMs in Legal Technology: Revolutionizing Research and Document Analysis | This paper provides an overview of how Artificial Intelligence (AI) and Large Language Models (LLMs) are transforming the legal field, particularly in research and document analysis. It highlights benefits like increased efficiency, accuracy, and predictive capabilities, while briefly noting associated challenges. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General legal practice | International | NaN | NaN | NaN | False | False | NaN | NaN | Potential bias in AI algorithms, need for transparency in AI operations, dependence on data quality and completeness for accuracy, ensuring responsible use that complements human judgment. | Potential bias leading to unfair outcomes, inaccurate predictions due to poor data quality, over-reliance replacing human judgment. |
| Paper23272RetrainingUSWorkforceintheAgeofAgenticGenAIRoleofPromptEngineeringandUp-SkillingInitiatives.pdf | Google_Scholar | Retraining US Workforce in the Age of Agentic Gen AI: Role of Prompt Engineering and Up- Skilling Initiatives | This review synthesizes research on the importance of prompt engineering skills for the US workforce in the age of generative AI. It discusses applications across various sectors, highlights available training initiatives, and identifies challenges and future directions for workforce development. | True | Market | True | 3.0 | NaN | Prompt Engineering | NaN | NaN | NaN | NaN | NaN | NaN | Legal, Finance, Education, Healthcare, Human Resources, Project Management | US | NaN | NaN | Discussion of various online courses, workshops, and educational programs (free and paid) offered by different providers (e.g., Alison, deeplearning.ai, Rutgers, Siemens, Deloitte, Google, Microsoft). | False | False | NaN | Lack of standardized training frameworks, limited accessibility to affordable training, inadequate focus on domain-specific applications, insufficient evaluation of training outcomes, weak integration between academia and industry. | Developing effective curricula, keeping training up-to-date with rapid technological advancements, addressing ethical concerns (bias, fairness), ensuring accessibility and equity of training, measuring training impact. | Bias and fairness issues in LLMs, lack of interpretability, security risks (malicious content generation, bypassing security), job displacement, potential for misinformation and manipulation. |
| 6RYeLVaZ8VgJ.pdf | Google_Scholar | THE DUTY OF EFFICIENCY & GENERATIVE AI PEDAGOGY | This article argues that lawyers have an ethical duty of efficiency which necessitates embracing generative AI, and that law schools must proactively teach students to use these tools responsibly and effectively. It examines lawyers' ethical obligations concerning AI, critiques restrictive regulations, and proposes a pedagogical approach for AI integration in legal education. | True | Market | True | 3.0 | Positive | Generative AI (GenAI) / Large Language Models (LLMs), specifically mentioning ChatGPT, Lexis+ AI Assistant, and Westlaw Practical Law AI as examples. | Illustrative comparison of outputs from ChatGPT 4.0, Lexis+ AI Assistant, and Westlaw’s Practical Law AI in response to a sample legal question about California slip and fall tort claims. | GenAI tools provide a starting point for legal questions but their outputs vary, may lack citations or cite non-precedential sources, and require careful lawyer verification for accuracy and legal soundness. They cannot yet perform genuine legal analysis. | The legal profession's reluctance to embrace AI, concerns about accuracy (hallucinations) and confidentiality, lack of technological competency among lawyers, and slow integration of AI training in law schools. These hinder the adoption of AI which could otherwise lower costs and broaden access to legal services. | Proactive and comprehensive AI education in law schools, lawyers embracing their 'duty of efficiency' by responsibly adopting AI tools, and reliance on existing professional conduct rules rather than overly restrictive, AI-specific regulations to manage AI use, thereby fostering an environment where AI can enhance efficiency and potentially lower costs for clients. | Increasing lawyer efficiency through AI to potentially reduce the cost of legal services and thereby improve broader public access to legal advice. | NaN | General legal practice, Professional Ethics, Civil Procedure (including discovery and Rule 11 sanctions), Torts (specifically premises liability/slip and fall examples). | United States (Federal and various States including California, Florida, Missouri, New York, North Carolina, Colorado); Canada (Ontario). | LLMs are generally trained on 'a vast corpus of texts.' For some tools like ChatGPT, user-inputted information may also be used for training, raising confidentiality concerns. | NaN | NaN | True | True | ChatGPT is mentioned as a free and popular product. Commercial tools like Lexis+ AI and Westlaw AI are also discussed as being used by the authors for examples. | Technical gaps include GenAI's inability to perform true legal analysis, the risk of 'hallucinations,' and potential biases. Societal/professional gaps include the slow adoption and insufficient understanding of GenAI within the legal field and legal education, preventing the full realization of AI's benefits for efficiency and potential A2J improvements. | For lawyers using GenAI: ensuring the accuracy and reliability of AI-generated content (avoiding 'hallucinations'), maintaining client confidentiality, overcoming personal lack of technological competency, and correctly prompting AI for useful outputs. | Misreliance on AI leading to inaccurate legal filings (e.g., 'hallucinated' cases), breach of client confidentiality through inputting sensitive data into non-secure AI, perpetuation of biases embedded in AI training data, lawyers abdicating professional judgment, and potential for fraudulent billing. |
| informit.T2025011900000390025191863.pdf | Google_Scholar | Introduction: Law as Data, Data as Law | This paper introduces a symposium on "Law as Data, Data as Law," summarizing diverse contributions that analyze data-driven approaches and AI in law. It emphasizes the need for critical reflection, methodological rigor, and interdisciplinary engagement to navigate impacts on legal practice, education, and access to justice. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Unreliability and inaccuracy of current AI tools for complex legal tasks, potential for algorithmic bias and mismatches with legal reasoning principles in sensitive areas like asylum claims, and the risk of creating opaque systems that hinder rather than help justice. | Enhancing AI reliability through further research, ensuring human oversight and expert legal involvement in AI system design and deployment, fostering interdisciplinary dialogue and critical interrogation of AI tools, and adopting human-centered design methodologies that incorporate stakeholder input. | Automated decision-making in refugee status determination and its fairness; reliability of LLMs for legal tasks crucial for accessing legal information or support; ethical integration of AI in legal education to prepare future professionals for promoting access to justice. | Asylum seekers/Refugees | Administrative Law (specifically refugee/asylum law), Legal Education, General Legal Practice (research, reasoning) | International | NaN | NaN | NaN | False | False | NaN | Methodological gaps in legal research for evaluating data-driven law, lack of robust benchmarks for AI legal tools, need for deeper understanding of AI's societal impacts (bias, fairness, environmental costs), and insufficient interdisciplinary collaboration and expertise within the legal academy. | Synthesizing diverse and technical contributions from various disciplines, evaluating research outside traditional legal expertise, and fostering a coherent, critical dialogue on the complex and rapidly evolving field of law and AI. | Fossilization of law into opaque and difficult-to-challenge infrastructures, perpetuation of harmful bias and feedback loops through AI systems, negative social and environmental consequences of AI, uncritical adoption of AI tools by students and practitioners, and adverse transformations to legal processes if new technologies are not carefully vetted and implemented. |
| hdPHnUO15h0J.pdf | Google_Scholar | Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review | This paper provides a comprehensive review of prompt engineering for Large Language Models (LLMs) and Vision-Language Models (VLMs). It details foundational and advanced prompting techniques, methods for evaluation, diverse applications, significant security concerns like adversarial attacks and model stealing, and outlines future research directions such as understanding model structures and AI agents. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | NaN | NaN | False | False | NaN | NaN | Crafting effective prompts requires significant manual effort and expertise; ensuring accuracy and avoiding hallucinations in LLM outputs, especially for complex reasoning; addressing security vulnerabilities (e.g., adversarial attacks, data poisoning, prompt injection); overcoming lack of reproducibility and transparency due to limited understanding of model internal structures. | Adversarial attacks causing unintended or harmful outputs; data poisoning compromising model integrity and leading to erroneous outputs; backdoor attacks embedding hidden vulnerabilities activated by specific prompts for malicious behavior; prompt injection manipulating model outputs for misinformation or harmful content; prompt leaking exposing sensitive or proprietary information; prompt hacking leading to unintended model actions, misinformation, or data breaches; model stealing for intellectual property theft, loss of competitive advantage, and unauthorized replication of models. |
| dwaKJ69S2wEJ.pdf | Google_Scholar | Emerging Artificial Intelligence Risk Management Considerations for Law Firms | The paper discusses the emerging risk management considerations for law firms using AI tools, focusing on competence (Rule 1.1), confidentiality (Rule 1.6), and billing (Rule 1.5) under a framework of ABA Model Rules. It emphasizes the need for firms to evaluate AI tools, set clear policies, and train personnel, highlighting both known and unknown risks associated with rapidly evolving AI technology. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | Lack of ready access to lawyers for individuals navigating the legal system pro se. | AI tools may potentially help individuals navigate the legal system pro se by reshaping the delivery of legal services. | Access to legal services for self-represented litigants. | Self-represented litigants / Individuals who do not currently have ready access to lawyers. | Legal ethics, Professional responsibility, Civil litigation, Risk management for law firms. | United States | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Incompetent use of AI leading to errors (e.g., fabricated citations) and professional misconduct; breach of client confidentiality through insecure AI tools; improper billing practices related to AI cost or time saved; failure in supervision of AI use by lawyers and staff; legal sanctions, civil liability, and reputational damage; copyright infringement issues; and the general uncertainty of 'unknown unknowns' as AI substitutes or replaces lawyer professional judgment. |
| SulcX-It8GoJ.pdf | Google_Scholar | Robustness of Structured Data Extraction from In-plane Rotated Documents using Multi-Modal Large Language Models (LLM) | This paper investigates the impact of in-plane document rotation (skew) on the structured data extraction accuracy of three multi-modal LLMs: Anthropic Claude V3 Sonnet, GPT-4-Turbo, and Llava:v1.6. The study finds skew significantly degrades performance, identifies safe rotation angles for each model, notes varying hallucination tendencies under skew, and suggests solutions like de-skewing or building skew-robust models. | True | Market | True | 2.0 | NaN | Evaluation of multi-modal LLMs (Anthropic Claude V3 Sonnet, GPT-4-Turbo, Llava:v1.6) for structured data extraction from skewed documents using LMDX-derivative JSON schema prompting. | Synthetically generated sample documents containing first and last names were rotated in 5-degree increments (0-355 degrees). LLMs extracted key-value pairs, and accuracy was measured by the average Levenshtein distance between extracted values and ground truth across different skew angles. | GPT-4-Turbo demonstrated the widest Safe In-plane Rotation Angles (SIPRA) ([0°, 35°] and [330°, 360°]), suggesting highest robustness to skew among the tested models, but also exhibited the most hallucinations outside its SIPRA. | NaN | NaN | NaN | NaN | General document processing (potential application in legal services) | International | Synthetically generated sample documents with manually annotated ground truth key-value pairs (first name, last name). Details of synthetic data generation not provided. | NaN | NaN | True | False | The evaluated models (GPT-4-Turbo, Claude V3 Sonnet, Llava:v1.6) are available, some commercially via API, one open-source. | Need for comprehensive testing on diverse real-world document quality (older, scanned, stained). Need for multi-modal architectures inherently robust to skew or models pre-trained with skew augmentation. | Document skew degrades data extraction accuracy. Identifying critical skew angles for reliable performance. Models hallucinate outside safe rotation angles. Applying de-skewing techniques adds computational overhead and complexity. Evaluating performance on noisy, real-world documents. | Model hallucinations (generating incorrect or fabricated information) when processing skewed documents, especially noted for GPT-4-Turbo outside its Safe In-plane Rotation Angles (SIPRA). Propagation of erroneous data downstream. |
| oKOMNS2NmFsJ.pdf | Google_Scholar | BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation | This paper proposes BiLD, a novel loss function for knowledge distillation in large language models (LLMs) that focuses on top-k logits differences to filter noise and capture ranking information. Experimental results on multiple NLP benchmarks show BiLD outperforms standard distillation methods. | True | NaN | True | 1.0 | NaN | Bi-directional Logits Difference (BiLD) loss: A knowledge distillation method that selects top-k logits from teacher and student, calculates pairwise differences within these logits, and minimizes the KL divergence between the distributions of these differences. | Evaluated on 13 public NLP datasets (SuperGLUE subset, Arc-C, Arc-E, HellaSwag, PIQA, WinoGrande) using BLOOM and Qwen1.5 models. Compared against Supervised Fine-Tuning (SFT), vanilla KL loss, top-k KL loss, RKL, DKD, NKD, NormKD based on standard task metrics (Accuracy, F1/EM) and a proposed 'overlap@k' metric. | BiLD loss achieved the highest average accuracy across all datasets and teacher/student model pairs, outperforming all baselines. It also demonstrated superior performance on the overlap@8 metric, indicating better imitation of the teacher's key logit patterns. | NaN | NaN | NaN | NaN | NaN | NaN | Publicly available NLP benchmark datasets (SuperGLUE subset: BoolQ, CB, COPA, MultiRC, ReCoRD, RTE, WiC, WSC; Others: Arc-C, Arc-E, HellaSwag, PIQA, WinoGrande) used for task-specific distillation. | Theoretical analysis of LLM logit characteristics, formulation of a novel loss function (BiLD), comparative empirical evaluation on benchmark tasks. | Code made available on GitHub. | True | True | Code is available at https://github.com/fpcsong/BiLD. | NaN | Computational complexity associated with calculating pairwise differences (O(k^2)), especially for larger k. Requirement for shared vocabularies between teacher and student models. Inability to use teacher models with restricted access (e.g., output text only). | Potential loss of knowledge contained in the clipped long-tail logit distribution (mentioned as a limitation). |
| OpPoPkNx0W4J.pdf | Google_Scholar | Intelligent Legal Assistant: An Interactive Clarification System for Legal Question Answering | This paper introduces 'Intelligent Legal Assistant', an interactive legal question-answering system using large language models (LLMs). The system addresses incomplete user queries by asking for location, detecting missing information, generating clarifying questions with options, and then providing a detailed legal analysis. | True | Idealistic | True | 1.0 | Positive | An interactive legal Q&A system ('Intelligent Legal Assistant') using LLMs (Llama-3.1-8B, GPT-4o) for information deficiency detection, Reinforcement Learning (DDPG) with GNNs for predicting missing information elements (nodes in a fact-rule graph), and LLMs/retrieval models for generating clarifying questions/options and final responses. | Blind human evaluation with 100 users comparing the proposed system against GPT-4o, AI Lawyer, and Callidus AI. Users rated systems on accuracy, satisfaction (1-5 scale), and usage preference. | The proposed system scored 4.8/5 for accuracy, 4.8/5 for satisfaction, and was preferred by 90% of users, significantly outperforming GPT-4o, AI Lawyer, and Callidus AI. | The general public often lacks professional legal knowledge, leading to incomplete questions that omit critical information, hindering traditional Q&A systems. The complexity and specialized nature of legal terminology and procedures act as barriers. | An interactive LLM-based system that: 1) asks for user location for jurisdiction-specific laws, 2) detects information deficiency in user questions, 3) generates clarifying questions and options to gather missing details, and 4) provides comprehensive legal analysis based on the completed information. | Legal question answering, access to legal information/advice. | The general public, non-specialists who lack financial resources or opportunity to consult lawyers directly. | General (not specified) | NaN | Uses case law data processed by GPT-4o to generate questions for fine-tuning Llama-3.1-8B (deficiency detection). Constructs a fact-rule node graph from case law documents parsed via LLM into IRAC structure, used for RL training (missing node prediction). Utilizes a legal document database for retrieval. Sources (public/proprietary) not specified. | Information deficiency detection via prompt-based fine-tuning of Llama-3.1-8B. Missing node prediction via Deep Deterministic Policy Gradient (DDPG) reinforcement learning using Graph Neural Networks (GNNs) on a fact-rule graph. Clarifying question/option generation via LLMs using predicted missing nodes. Response generation via retrieval models (text-embedding-3-large, cosine similarity) and LLMs. | A demonstration system is described. A GitHub link is provided for 'more materials'. | False | False | A GitHub repository containing 'more materials' is mentioned: https://github.com/RujingYao/Intelligent-Legal-Assistant | NaN | Implicit challenges include generating varied quality training data (questions), constructing the detailed fact-rule graph, training the reinforcement learning agent effectively for the legal domain, ensuring relevant legal document retrieval, and integrating multiple complex AI components. | NaN |
| 3NLzN6i5MaIJ.pdf | Google_Scholar | Artificial Intelligence & Criminal Justice: Cases and Commentary | This open-access casebook provides a comprehensive exploration of artificial intelligence's integration into the criminal justice system, featuring curated cases, commentary, and policy documents. It examines AI applications in areas like policing, lawyering, access to justice, and AI governance, while critically discussing associated benefits, risks, and ethical considerations. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | AI potentially worsening access to justice; biased AI disadvantaging vulnerable groups like self-represented litigants and legal aid recipients; AI-generated misinformation; complexity of AI creating new barriers. | Promoting AI literacy for all stakeholders; ethical guidelines and professional standards for AI in legal services; human oversight and accountability in AI systems; leveraging AI to empower self-represented litigants; open-access educational resources. | Legal aid, self-represented litigants, judicial interim release, mental health disorders and AI. | Self-represented litigants, individuals needing legal aid, persons with mental health disorders involved in the justice system, Indigenous communities. | Criminal Justice | Canada, United States, European Union | NaN | NaN | NaN | True | True | The casebook is available for free and open access via Allard Research Commons and the Canadian Legal Information Institute (CanLII) under a CC BY-NC-ND 4.0 license. | Lack of reliable, unbiased AI tools for access to justice; insufficient frameworks for ethical AI deployment in A2J; digital divide and literacy issues hindering equitable access; need for ongoing research on AI's A2J impact and development of safe tools. | NaN | Generation of false/misleading information (hallucinations) by AI; perpetuation of societal biases leading to discriminatory outcomes; threats to privacy and data security; lack of transparency and accountability in AI decision-making; deepfakes and AI-generated misinformation undermining legal processes; AI exacerbating access to justice issues if not implemented equitably; misuse of AI for surveillance; potential for AI to be used for malicious purposes (adversarial AI). |
| l4r5s2gwfukJ.pdf | Google_Scholar | Generative AI and Access to Justice in Canada: The Case of Self-Represented Litigants [SRLs] | This article examines the potential benefits and significant limitations of using Large Language Models (LLMs) like ChatGPT for self-represented litigants (SRLs) in the Canadian legal system. It argues that while LLMs can assist SRLs, their effectiveness is limited by factors like accuracy, cost, and the user's literacy, potentially causing more harm than good for those without legal knowledge. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | High cost of legal representation leading to self-representation; SRLs finding law/litigation difficult; Lack of clear/practical legal information; Need for assistance with forms, drafting, court preparation; Ensuring a 'level playing field'; High cost of bespoke legal AI tools; Reliability/accuracy limitations of LLMs (hallucinations, jurisdictional errors); Lack of legal expertise for SRLs to verify AI output; Over-reliance on inaccurate AI; Basic language/digital literacy gaps; Lack of access to technology/internet. | Using customized LLMs for SRLs; Developing AI tailored to SRL demographics (e.g., form completion); LLM interfaces directing users to verified resources; Calibrating AI reliance; Requiring disclosure of AI use in filings; Enhancing SRL AI literacy; Combining LLM use with existing free legal resources. Creating public AI models mentioned but feasibility questioned. | Access to legal information; Legal document drafting (pleadings, correspondence); Case preparation; Understanding legal rights and procedures; Facilitating settlement. | Self-Represented Litigants (SRLs) in Canada (acknowledged as a diverse group). | General litigation, Family Law, Civil Procedure | Canada | NaN | NaN | NaN | False | False | NaN | Affordability gap (cost of bespoke AI); Reliability/Accuracy gap (especially with generic LLMs); Literacy gap (digital, legal, AI); Lack of evaluation of everyday SRL use of LLMs; Funding gap between A2J tech and commercial legal tech; Uncertainty about market-driven development addressing SRL needs. | Accuracy/Hallucinations in LLMs; Bias from training data; Limited contextual understanding; Jurisdictional confusion; Cost/Affordability of bespoke tools; Need for user literacy (AI and legal). | LLMs potentially harming SRLs without legal knowledge; Over-reliance on inaccurate/hallucinated information; Distortion of public understanding of law; SRLs submitting AI-generated false citations to courts; Widening justice gap due to AI cost disparities; Potential increase in frivolous litigation. |
| o1rPy5FGPjIJ.pdf | Google_Scholar | Mitigating Translationese with GPT-4: Strategies and Performance | This paper investigates using GPT-4 with linguistically informed prompts to reduce translationese in human-translated German and English texts derived from the Europarl corpus. The study demonstrates that prompts incorporating specific linguistic instructions lead to revised translations more similar to original target language texts, particularly for English. | True | NaN | True | 1.0 | NaN | Prompting GPT-4 with linguistically informed instructions (self-guided vs. feature-guided, with varying detail) to rewrite human translations and reduce translationese. | Evaluation involved SVM-based translationese classification (F1 score reduction on rewritten text vs. human translation), COMET scores for content preservation, statistical analysis of linguistic feature shifts, and expert human assessment of accuracy and fluency. | The feature-guided detailed mode for English translations was most successful, reducing the F1 score for translationese classification by 7.63 points on the top-15 features (and 4.07 on all 58 features) compared to human translations. | NaN | NaN | NaN | NaN | EU Parliamentary Proceedings | European Union (German-English language pair) | The technique relies on the pre-trained GPT-4 model. Experiments used a publicly released, segment-aligned, bidirectional German-English dataset built from the Europarl corpus (parliamentary speeches). | Prompt engineering varying in guidance: self-guided modes (relying on LLM's internal knowledge with minimal or detailed task description) and feature-guided modes (providing specific, segment-tailored linguistic instructions based on feature deviations from target language norms). | The segment-aligned bidirectional German-English dataset from Europarl and multiparallel datasets including LLM-generated outputs are released on GitHub and Zenodo. Prompt examples are provided in the paper's appendix. | True | False | The experimental dataset and prompt examples are publicly available (GitHub/Zenodo, paper appendix). The core LLM, GPT-4, is accessible via OpenAI's API (paid). | NaN | Extensive cleaning of GPT-4 output was required due to meta-comments and inconsistent formatting. Limiting the number of instructions per segment was important, as too many instructions were less effective. The model showed different willingness to edit text across languages in self-guided modes. | Excessive application of linguistic instructions can lead to 'overtransformed renditions' and decreased translation accuracy. Content preservation requires further attention. Rewritten texts sometimes exhibited new, unintended linguistic deviations, including over-normalisation effects. |
| _UgzRabzPPEJ.pdf | Google_Scholar | Generative AI, Fake Law and Professional Guidance | The paper discusses the risks associated with lawyers using Generative AI (GenAI), particularly the emergence of 'fake law' (hallucinated case citations) in court filings, drawing on examples primarily from the US and Canada. It reviews existing professional guidelines and ethical obligations, emphasizing the need for diligence, verification, and further guidance for the Australian legal profession to ensure responsible AI adoption and maintain public trust in the justice system. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | The primary hurdle identified is the unverified use of Generative AI by legal professionals leading to the submission of inaccurate or fabricated legal citations ('fake law'), which undermines court processes, professional integrity, and public confidence in the justice system. | The paper advocates for increased education of lawyers and the judiciary, adherence to existing professional ethical obligations (diligence, honesty, duty to the court), and the development and adoption of clear, consistent professional guidelines for the responsible and ethical use of GenAI in legal practice. | NaN | NaN | Professional Conduct/Ethics, Litigation/Court Procedure | Australia (NSW, Victoria, ACT, WA), United States (Federal, New York, Colorado, Massachusetts, Florida), Canada (British Columbia), United Kingdom, New Zealand | NaN | NaN | NaN | False | False | NaN | The need for more comprehensive, consistent, and updated professional guidelines and education regarding GenAI use across the Australian legal profession is highlighted. | NaN | Generation of fake cases and citations ('fake law'); misleading courts; undermining the administration of justice; erosion of public confidence; professional sanctions against lawyers (fines, suspension, cost orders); harm to clients' cases; wasting court and opposing party resources; reputational damage (judges, courts, legal profession); potential breaches of confidentiality/privacy with public GenAI tools; outputs may be inaccurate, incomplete, misleading, outdated, or biased. |
| W5ZX8VFbaeIJ.pdf | Google_Scholar | Evaluating AI for Law: Bridging the Gap with Open-Source Solutions | This study evaluates general-purpose AI like ChatGPT for legal question-answering, highlighting significant risks and performance issues such as lack of citations and verbosity. It proposes OpenJustice.ai, a domain-specific, open-source legal AI platform, advocating for collaborative development and improved benchmarks to enhance accuracy, transparency, and access to justice. | True | Idealistic | True | 1.0 | Positive | Evaluation of LLMs (GPT-4, Mixtral-8x7B) on legal Q&A tasks using the curated LegalQA benchmark; proposal of OpenJustice.ai, an open-source legal AI platform and development framework. | GPT-4 and Mixtral-8x7B were evaluated on legal question-answering using two datasets: LegalQA (curated from Reddit, >2000 questions, answers by law students) and Law Stack Exchange (200 popular questions, top-voted answers). Evaluation involved automatic comparison (using GPT-4 via OpenAI Evals) of model-generated answers to expert answers based on factuality categories (subset, superset, same, disagree, incomparable), supplemented by qualitative review by law students. | On the LegalQA task, GPT-4 had under 5% factually incorrect responses. Mixtral-8x7B performed significantly worse. Qualitative feedback indicated GPT-4's answers lacked citations and were often verbose compared to concise human expert answers. | Reliability issues of current AI (hallucinations, bias, lack of legal nuance, poor citation practices); limited accessibility and transparency of specialized legal AI tools (closed, proprietary systems benefiting mainly large firms); lack of diversity in AI-generated content and potential for creating AI echo chambers; inadequate regulatory frameworks and evaluation benchmarks for legal AI. | Develop domain-specific, open-source legal AI systems (e.g., OpenJustice.ai); revise benchmarks and protocols for evaluating legal AI in real-world settings, focusing on bias, fact-checking, legal reasoning, and narrative diversity; foster collaborative, crowdsourced development with expert feedback loops; emphasize high-quality data curation and advanced AI methodologies (DPO, world models, etc.). | Legal question-answering for laypeople; improving accuracy, transparency, and narrative diversity in legal AI; addressing legal misinformation; assisting self-represented litigants; reducing legal fees and research costs. | Self-represented litigants, laypeople with legal questions, broader legal communities (beyond large firms), legal aid centers, law students, legal professionals. | General law (covering various topics as found in public legal advice forums and general legal Q&A sites). | Canada (primary for LegalQA annotation context), US (source of some LegalQA questions, OpenJustice.ai data), France (OpenJustice.ai data), EU (OpenJustice.ai data, EU AI Act). | For the proposed OpenJustice.ai: A mix of curated open-source legal data (annotated question-answer pairs, case law from US, Canada, France, EU), crowdsourced human feedback, and proprietary partner data. For the LegalQA benchmark created: Publicly available questions from r/legaladvice with expert answers written by law students. | For OpenJustice.ai: Open-source development, crowdsourcing human feedback from legal experts, iterative improvement, data curation, LLM fine-tuning, training Small Language Models (SLMs), and leveraging advanced AI techniques like Direct Preference Optimization (DPO), world models, Flash Attention 2, rejection sampling, reward modeling, supervised fine-tuning, and alignment research. | OpenJustice.ai launched in March 2023 by Conflict Analytics Lab, operating as a natural-language processing interface (www.OpenJustice.ai). The open version is intended for sophisticated users (legal background) to provide quality feedback. It aims to partner with law schools and aid centers. | True | True | The OpenJustice.ai platform (www.OpenJustice.ai) is described as launched and operational. It has a core open-source component. Access to the 'open version' of the platform for feedback contribution is restricted to sophisticated users with a legal background. | Existing legal benchmarks lack real-world complexity; insufficient empirical data on AI performance in diverse legal tasks; need for improved automatic evaluation methods for the legal domain; understanding the utility of unstructured legal databases for pretraining/domain-adaptation is unexplored; current AI struggles with nuanced legal reasoning, citation, and conciseness. | Ensuring factual accuracy and avoiding hallucinations in legal AI; addressing and mitigating bias; achieving diversity in narrative representation; handling the dynamic nature of law with static training data; reliable source citation; modeling complex, non-algorithmic legal reasoning; high cost of developing purpose-built models; curating high-quality, representative, and unbiased legal datasets. | Overreliance on unreliable general-purpose AI for legal tasks by both laypeople and professionals, leading to incorrect advice or actions; generation of 'hallucinated' or fictitious legal information (e.g., fake citations, case law); propagation of biases present in training data; misleading users with AI tools that appear specialized but are general-purpose; widening the access to justice gap if specialized tools remain closed and expensive; creation of AI echo chambers stifling diverse legal thought and democratic discourse; potential for ossification of law due to static models. |
| Iv6wOJNR-lkJ.pdf | Google_Scholar | GoalAct: A Globally Adaptive Dynamic Legal Multi-agent Collaboration System | This paper proposes GoalAct, a multi-agent legal collaboration system using the GLM-4 language model, designed to provide legal services by accessing legal databases. GoalAct aims to improve accuracy and adaptability through integrated planning, reflection, and memory mechanisms at both global and local levels. | True | Idealistic | True | 1.0 | Positive | GoalAct, a globally adaptive dynamic legal multi-agent collaboration system composed of five agent types (Processor, Memorizer, Actor, Judge, Reflector) built on GLM-4, accessing legal databases through APIs. | The paper mentions that "experimental results also demonstrate its superior performance for legal services" but provides no specific details on the testing procedure within the provided text. | The paper claims "superior performance for legal services" but does not provide specific metrics or quantitative results in the provided text. | Limited availability and high cost of legal professionals, especially in regions with restricted access; complexity of user inquiries requiring AI systems to effectively filter information, generate logical plans, and self-correct. | Developing advanced AI-driven multi-agent systems like GoalAct, leveraging LLMs (GLM-4) with integrated planning, reflection, and memory to provide more efficient and adaptable legal services. | Access to legal information and consultation services. | Individuals in regions with limited access to legal professionals or those facing high costs for legal services. | General legal services / Legal consultation | International | The system uses the pre-trained GLM-4 language model. It accesses unspecified external legal databases through API calls for information retrieval during operation, not explicitly for further training of the core model. | Multi-agent system design with specialized agents (Processor, Memorizer, Actor, Judge, Reflector); integration of planning, reflection (self-correction), and memory (short-term and long-term) mechanisms; emphasis on balancing local task accuracy with global objective consistency. | NaN | False | False | NaN | Ensuring robust filtering of user inputs, coherent logical planning, effective self-correction, and reliable memory formation in legal AI systems. Balancing local task accuracy with global objectives in multi-agent systems for complex legal problem-solving. | Effectively filtering irrelevant or redundant information from user inputs; generating logical and coherent planning paths while avoiding local search loops; developing robust self-correction mechanisms; forming memory and accumulating experience to reduce repeated errors; balancing local task accuracy with global objective consistency in a multi-agent system. | Risk of the system getting trapped in local search loops, leading to no responses; potential for degraded system performance if individual agents' tasks do not align with the overall global objective. |
| SSRN.pdf | Google_Scholar | Uncovering the Influence of ChatGPT’s Prompts on Scientific Writings using Machine Learning-Based Text Mining Approaches | This paper investigates how variations in prompts given to ChatGPT affect the quality and content of generated scientific text, specifically introduction sections of traffic safety articles. It compares outputs from basic versus enhanced prompts against human-written texts using text similarity and network analysis, finding minimal quality differences based on prompt detail. | True | NaN | True | 2.0 | NaN | Prompt engineering for ChatGPT (comparing basic vs. enhanced prompts with persona/citation info) evaluated using text similarity (Cosine/LSA) and Text Network Analysis (TNA). | Generated ChatGPT introductions for 327 traffic safety paper titles using two prompt types (initial vs. improved). Measured similarity (Cosine/LSA via text2vec) between generated/human texts and between the two generated texts. Used Text Network Analysis (TNA) to compare content themes and collocations. | Improved prompt offered negligible improvement (avg similarity 0.56 vs 0.54) over initial prompt when compared to human text. High similarity (avg 0.82) between outputs of the two prompt types. ChatGPT generated more generic phrases than human text. | NaN | NaN | NaN | NaN | NaN | International | The study uses ChatGPT; evaluation data are introductions from 327 published traffic safety papers (Web of Science). | Experimental comparison of prompt variations; computational text analysis (Cosine similarity, LSA, Text Network Analysis). | NaN | False | False | NaN | Need for exploring specific personas, other scientific paper sections, different domains, and human-AI collaboration in writing. | Crafting effective prompts to generate high-quality, human-like scientific text. | NaN |
| fXjnV8ksgc0J.pdf | Google_Scholar | Professionals Beware: The Opportunities and Risks of Generative AI in L egal P ractice | This paper reviews the opportunities and significant risks associated with using generative AI tools like ChatGPT in legal practice. It highlights issues such as hallucinations, copyright infringement, bias, privacy concerns, and breaches of professional responsibility, urging practitioners to use these tools cautiously and responsibly. | True | Market | True | 3.0 | NaN | Generative AI tools (e.g., ChatGPT, CoPilot, Dall-E) | Author tested ChatGPT 3.5 and ChatGPT-4 with prompts requesting Australian patent law cases related to inventorship. DALL-E was tested with prompts about lawyers. | ChatGPT 3.5 generated mostly fabricated or inaccurate case references regarding Australian patent law. ChatGPT-4 showed improvement but still generated non-relevant cases and one hallucination (among other correct, but not directly relevant, cases). DALL-E image generation showed potential gender bias. | NaN | NaN | NaN | NaN | Legal practice, Intellectual Property (Copyright, Patents), Litigation, Professional Responsibility, Privacy Law | USA, Australia, EU, UK | The paper discusses general issues with training data for generative AI, noting it often involves vast datasets (text, images) scraped from the internet, potentially including millions of copyrighted works (e.g., news articles, images, code) obtained without author/owner consent. | NaN | NaN | True | True | Publicly available tools like ChatGPT and CoPilot are discussed, which have both free and paid/enterprise versions. | NaN | NaN | Hallucinations (generating inaccurate/fabricated information); Copyright infringement (use of copyrighted material in training data, generation of infringing outputs, memorization/regurgitation); Moral rights violations; Lack of copyright protection for AI-generated outputs; Algorithmic bias (racial, gender) reflected in outputs; Privacy violations (disclosure of personal data via prompts); Breach of confidentiality (disclosure of client information, trade secrets); Breach of professional duties (competence, diligence, honesty, integrity, independence). |
| SafrZAuaSrMJ.pdf | Google_Scholar | GENERATIVE ARTIFICIAL INTELLIGENCE AND REVOLUTION OF MARKET FOR LEGAL SERVICES | This paper discusses the transformative potential and challenges of generative AI for the legal services market, focusing on efficiency gains, business model shifts, and competitive dynamics. It highlights risks related to quality control, liability, data privacy, ethical standards, and vertical dependency on large technology providers. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | The paper identifies obstacles within the legal market structure rather than specific A2J hurdles: high implementation costs creating disparities between large and small firms, potential for market concentration disadvantaging smaller players, risks of technology/data lock-in and dependency on upstream AI/cloud providers, maintaining quality and accuracy of AI outputs (hallucinations), ensuring data privacy and confidentiality, ethical challenges (bias, transparency), and managing liability for AI errors. | The paper suggests strategies for law firms to mitigate market risks: pursuing multi-homing strategies to avoid vendor lock-in, implementing robust contractual data protection mechanisms, potential use of decentralized training techniques, developing strong internal quality control processes and ethical guidelines, adapting business models (e.g., alternative fee arrangements), and potentially forming collaborations or using shared resources (especially for smaller firms). | NaN | NaN | General legal services, Contract law, Litigation, Compliance | International (with specific examples from US, EU, France) | NaN | NaN | NaN | False | False | NaN | Ensuring smaller firms can access/afford AI to prevent market concentration; managing vertical dependencies on large tech/LLM providers; establishing clear liability frameworks for AI-related errors; developing robust quality control and methods to prevent AI hallucinations/bias; balancing innovation with data privacy regulations and ethical standards; addressing potential skill gaps created by automation; uncertainty about long-term productivity gains versus investment costs. | N/A (paper discusses challenges for firms adopting AI, not for the authors in creating a tool) | Poor quality AI outputs (errors, hallucinations) impacting reputation and liability; data privacy/confidentiality breaches; ethical issues (bias, lack of transparency); dependency and lock-in with technology providers leading to unfair terms or anticompetitive practices (self-preferencing, envelopment); increased market concentration hurting smaller firms; erosion of core legal skills; stifled innovation due to compliance costs or liability fears; manipulation risks with open-source models. |
| 4IGMF7HagUQJ.pdf | Google_Scholar | Lawyers Should Not Trust AI: A call for an Open-source Legal Language Model | The paper argues that general AI like ChatGPT is unsuitable for legal tasks due to significant risks such as misinformation and lack of transparency. It advocates for the development of domain-specific, open-source legal AI, like the proposed OpenJustice.ai, built through multi-layered fine-tuning and legal community feedback to improve legal research and access to justice. | True | Idealistic | True | 1.0 | Positive | Open-source and distributed legal AI (specifically OpenJustice.ai) developed through multi-layered fine-tuning: Raw Data Fine-tuning, Instruction Fine-tuning, Open-Source Feedback Fine-tuning from legal professionals, and Decentralised Fine-tuning combining open and closed datasets. | The paper outlines the design and development process for OpenJustice.ai, involving supervised annotation and feedback from law students and legal professionals on real-world questions and generated legal scenarios. It does not present specific benchmark testing or quantitative evaluation results for OpenJustice.ai within this paper. | NaN | Limitations of general AI (legal misinformation/hallucinations, lack of transparency and precision, bias, inability to offer diverse narratives or perform contextual legal reasoning, unexplainability) hindering their safe use for legal tasks and access to justice. The risk of AI leading to ossification of law and undermining legal diversity. The current absence of reliable, open-source domain-specific legal AI. | Development of OpenJustice.ai: an open-source, domain-specific legal LLM. This involves multi-layered fine-tuning (on raw legal data, instruction-response pairs) and reinforcement learning with human feedback from the legal community (law schools, legal professionals), with initial feedback restricted to experts to ensure data integrity. A decentralized approach allows incorporating proprietary data while keeping it localized. | Improving legal research, enhancing legal reasoning tools, addressing shortcomings of general AI in legal problem-solving and dispute resolution, and ultimately providing access to justice for self-represented litigants through reliable legal information. | The legal community (law schools, legal professionals, legal clinics, industry partners) for development, feedback, and initial use. Potentially self-represented litigants in the future, once the system is mature and reliable. | General Law, with applications in legal research, legal reasoning, and potentially specific areas like contract drafting. The focus is on foundational capabilities extendable to various legal domains. | International (as a general call and framework), with specific examples from Canada and the United States, implying the need for jurisdiction-specific adaptation for deployed systems. | A combination of: 1) Unstructured legal data (case law, journals, other legal resources). 2) Structured data: question-response pairs from online forums (e.g., Reddit, Law Stack Exchange) annotated by law students and legal professionals. 3) Synthetic data: legal scenarios and contracts generated by other LLMs (e.g., Llama2) for further annotation. 4) Proprietary data from industry partners (used in a closed, decentralized fine-tuning manner). | Multi-layered fine-tuning of foundational language models (raw data fine-tuning, instruction fine-tuning, reinforcement learning from human feedback). A staged development process involving data collection from public and legal sources, annotation by law students under professional supervision, and iterative model refinement. Proposed use of decentralized learning to combine open and proprietary data sources. | Initially, a secured interface enabling law students and legal professionals to interact with and provide feedback to the model (OpenJustice.ai). The aim is a non-proprietary version openly accessible to the legal community, but not to the general public for feedback in early stages to maintain data quality. Decentralized learning architecture where industry partners can fine-tune on proprietary data locally. | False | False | NaN | The need for empirical performance evaluation of domain-specific legal LLMs using clear, industry-specific metrics (for hallucinations, reasoning, citation accuracy, narrative diversity). Research into effective human-AI collaboration, particularly the 'end-user prompt engineering' abilities of non-lawyers for legal AI. Persistent limitations in LLMs' legal citation retrieval capabilities. | Ensuring data integrity and quality during the open-source feedback process. Developing robust methods for legal citation retrieval within LLMs. Addressing the challenge of effective prompt engineering for users, especially non-experts, to extract useful information from legal AI. Scaling the annotation and expert feedback process. | Use of general AI for legal tasks: legal misinformation (hallucinations, fake citations), biased outputs, lack of transparency and explainability, creation of 'AI echo-chambers' narrowing perspectives, ossification of law. Premature public release of even domain-specific legal AI: providing incorrect legal information to self-represented litigants. Inaccurate feedback from non-experts if feedback mechanisms are opened too broadly too soon. |
| 7VLS4kLM-rYJ.pdf | Google_Scholar | ChatGPT and service: opportunities, challenges, and research directions | This paper explores the potential applications, opportunities, and challenges of using ChatGPT within the service sector. Leveraging expert opinions, it outlines implications for service marketing, customer experience, digital services, cost-effectiveness, and ethics, proposing future Mresearch directions. | True | Market | True | 2.0 | NaN | ChatGPT | NaN | NaN | NaN | NaN | NaN | NaN | General Service Industry, Marketing, Customer Experience, Digital Services | International | NaN | Expert-oriented perspective approach, literature review. | NaN | True | True | ChatGPT is a publicly accessible tool offered by OpenAI, with free and paid tiers. | NaN | Accuracy (including hallucination), Bias in training data and output, Privacy and data security concerns, Ethical issues (fairness, corporate digital responsibility), Intellectual Property infringement, Potential for Misuse (malicious use, manipulation), Lack of Transparency and Explainability, Accountability for outputs, Potential job displacement, Ensuring equitable access (digital divide), Need for regulation and legal frameworks. | Perpetuation of biases, Inaccuracy leading to harm or mistrust, Privacy violations (data misuse, surveillance, data leaks), Security threats (spamming, phishing, fraud, impersonation, misinformation), Intellectual property infringement, Dehumanization, social isolation, loss of autonomy/dignity in interactions, Manipulation of users, Addiction, Identity theft. |
| Thirdofglobaljournalofmultidisciplinarysciencesarts-Copy.pdf | Google_Scholar | Transformative Applications of ChatGPT: A Comprehensive Review of Its Impact across Industries | This paper provides a comprehensive review of ChatGPT's applications and impacts across various industries, including healthcare, education, business, legal services, creative sectors, and social media. It highlights the tool's potential for enhancing efficiency, personalization, and automation while also discussing associated challenges like ethics, bias, technical limitations, and the need for human-AI collaboration. | True | NaN | True | 3.0 | NaN | ChatGPT | NaN | NaN | Potential for bias replication from training data leading to unfair outcomes; issues of legal responsibility for AI errors; need for ethical guidelines and oversight. | Efforts to identify and reduce bias in training data; continuous monitoring of outputs; ensuring transparency; establishing clear protocols and regulations; balancing AI assistance with human judgment and expertise. | Legal research, document drafting, regulatory compliance, risk management. | NaN | General Legal Services, Compliance | International | The paper states ChatGPT is trained on large datasets containing diverse human-generated content, but does not specify the exact data sources. It notes this data can contain inherent biases. | NaN | NaN | True | False | The paper discusses ChatGPT, which is a widely available tool developed by OpenAI, accessible via web interface and API. | Need for improved accuracy, reliability, and robustness, especially for novel or specialized topics; enhancing contextual understanding; integrating real-time data; developing multimodal capabilities; achieving hyper-personalization. | Addressing bias and ethical concerns; overcoming technical limitations (e.g., dependence on training data, factual inaccuracies); balancing human-AI collaboration effectively (ensuring AI augments rather than replaces human expertise). | Replication and perpetuation of societal biases; generation of factually inaccurate or incoherent information; potential for misdiagnosis or inappropriate recommendations in healthcare; data privacy concerns (especially with sensitive information like patient data); over-reliance on AI leading to erosion of human skills or judgment; unclear legal liability for AI errors; potential job displacement. |
| z68SfimV9U0J.pdf | Google_Scholar | LLMs & Legal Aid: Understanding Legal Needs Exhibited Through User Queries | The paper presents a preliminary analysis of 3,847 user queries submitted to a GPT-4 powered legal aid tool by Frank Bold in the Czech Republic. Using GPT-4o for zero-shot classification, it categorizes queries to understand user needs and interaction patterns when seeking legal help from LLMs. | True | Idealistic | True | 1.0 | Neutral | A method for understanding user legal needs exhibited in queries to LLM-based legal aid tools, involving: 1) iterative development of a query categorization scheme (facts provided, information vs. advice, user control over answer) and 2) zero-shot classification of queries using GPT-4o based on this scheme. | The outcome of the zero-shot classification performed by GPT-4o was not formally evaluated for accuracy by the authors. | Classification of 3,847 queries: 29.95% provided facts, 64.93% sought information (vs. 35.07% advice), and 71.43% posed open-ended questions, granting control to the model. Only 3.35% of queries treated the LLM as a human expert, and 3.04% as a sophisticated search engine. | High cost of traditional legal services; users oversharing personal/sensitive information with LLMs; unfeasible user expectations of LLM capabilities; users granting excessive control to LLMs, increasing vulnerability; the blurry line between users seeking legal information versus actionable legal advice. | Increase AI literacy among the public. Develop and implement technical and policy safeguards by LLM providers and legal aid organizations. Further research into augmenting LLMs with curated, reliable legal information (e.g., RAG). | Understanding user needs in legal aid; distinguishing between legal information seeking and legal advice seeking via LLMs; patterns of user interaction with LLM-based legal tools; user expectations of LLMs in legal contexts. | General public in the Czech Republic seeking legal aid, laypeople, low-income and marginalized individuals. | The underlying experiment covered environmental law, whistleblowing and corruption-related issues, civic rights, municipal laws, and civic engagement issues. The query analysis is broadly applicable to general legal queries. | Czech Republic | The user query classification was performed by GPT-4o using zero-shot learning with prompts defining categories. The data classified was a corpus of 3,847 anonymized user queries in Czech collected from the Frank Bold experiment. The original Frank Bold RAG system (which users interacted with) used internal Frank Bold documents (guidelines, blog posts, articles) and selected Czech legal acts (proprietary, domain-specific, unstructured text). | For the query categorization approach: Iterative development of descriptive codes based on existing literature (Cheong et al.) and pilot analysis of 200 random queries. For the Frank Bold experiment (context): Experimental design with a web platform for query submission to GPT-4 with RAG, user registration, and single question-answer interaction. | The Frank Bold experiment tool was accessible via a public website (www.ai.frankbold.org, now defunct) from May 3, 2023, to July 25, 2023. It was publicized through Frank Bold’s internal mailing lists and several prominent online media outlets. | False | False | NaN | Need for more rigorous experiments with controlled variables and demographic user data. Deeper understanding of user behaviors and query types that fall between the extremes of treating LLMs as search engines versus human experts. Lack of widespread AI literacy among lay-users. Insufficient safeguards in existing LLM-based legal aid tools. | For the query analysis presented: Limitations of using unvalidated zero-shot classification. For the original Frank Bold experiment: Uncontrolled variables during the experiment (e.g., different GPT-4 model versions, RAG adjustments over time); lack of detailed demographic data about users. | Oversharing of personal and sensitive information by users to LLMs. Users holding unfeasible expectations regarding LLMs' capabilities to provide personalized and actionable legal advice. Users ceding significant control over the response to LLMs, increasing their vulnerability to hallucinations and irrelevant information. Users developing a false sense of competence based on LLM-generated answers without proper verification. |
| Wgt3m-_XVr8J.pdf | Google_Scholar | Artificial Intelligence & the Future of Law Libraries: Mid-Atlantic Roundtable Report | This paper reports on a roundtable discussion among legal experts and information professionals about the impact of AI, particularly generative AI, on law libraries. It highlights opportunities for AI to improve library services, accessibility, and access to justice, while also discussing challenges such as rapid adoption pressures, the need for staff training, and budget constraints. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Existing access to justice gaps, such as difficulties for self-represented litigants in navigating legal procedures and understanding legal information. | Development of AI-driven information retrieval and document automation systems for self-represented litigants; leveraging AI to adapt information for diverse needs and improve court processes. | Assisting self-represented litigants (e.g., in child custody, landlord-tenant, criminal appeals); enhancing legal information accessibility and court processes. | Self-represented litigants; individuals with diverse needs and limited resources. | Family law, Landlord-tenant law, Criminal law, General legal information services. | United States | NaN | NaN | NaN | False | False | NaN | Need for development of user-friendly AI tools tailored for access to justice; lack of open, machine-readable legal data for AI development; ensuring ethical AI deployment that promotes fairness and equity. | Pressure for rapid AI adoption without strategic evaluation; staff skills gaps and need for training; high costs and complex procurement; privacy, security, and ethical concerns; advocating for the library's value and role; budget and resource limitations. | Data privacy and security vulnerabilities with generative AI; potential for biased AI-driven collections or information; unethical AI use if vendor accountability is lacking; marginalization of librarians not adapting to AI. |
| jxLBw6Jkp30J.pdf | Google_Scholar | LLM-Datasets: An Open Framework for Pretraining Datasets of Large Language Models | This paper introduces LLM-Datasets, an open-source Python framework designed to standardize and simplify the collection, processing, and compilation of large-scale, multilingual pretraining datasets for Large Language Models. The framework emphasizes reproducibility, modularity, and HPC-readiness, demonstrated through the creation of a 2.3 trillion token dataset covering 32 European languages. | True | NaN | True | 1.0 | NaN | LLM-Datasets: An open framework integrating tools for downloading, text extraction, filtering, deduplication, sampling, and tokenization of diverse data sources to create reproducible LLM pretraining datasets. | Showcased by compiling a 2.3 trillion token, 32-language European dataset using the framework, detailing the data sources (e.g., Colossal OSCAR, Wikipedia, Pile of Law, Starcoder) and processing steps involved (e.g., filtering web data based on quality warnings, perplexity, blocklists). | Successfully compiled a 2.3 trillion token, 32-language European dataset, demonstrating the framework's capability to handle large-scale, multilingual data processing and composition. | NaN | NaN | NaN | NaN | General Law (via included datasets) | International | The framework itself does not use training data, but compiles it. The showcase dataset uses numerous sources, including public web crawls (Common Crawl via OSCAR), Wikipedia, code repositories (Starcoder), legal datasets (Pile of Law, EURLex, LegalMC4, Open Legal Data DE, Slovak court decisions), scientific papers (peS2o), mathematical datasets (Proof Pile, AMPS), project Gutenberg, patents (BigPatent), parliamentary proceedings (ParlaMint), etc. Data is multilingual, largely unstructured text, from public and curated sources. | Modular design, HPC-readiness (network file system considerations, chunking), extensibility (custom datasets/registries), reproducibility (config files, seeds), model agnosticism, support for private data. | Released as an open-source Python package on GitHub (Apache-2.0 license) and installable via PyPI. | True | True | Available as an open-source Python package on GitHub (https://github.com/malteos/llm-datasets) and installable via PyPI. | Identifies the lack of open frameworks and reproducibility for creating LLM pretraining datasets as a major gap in current LLM research infrastructure. | Managing complexity of large-scale data processing; Handling diverse data formats and sources; Ensuring reproducibility; Designing for HPC environments; Integrating multilingual data effectively; Filtering noisy or harmful web data. | Implicitly acknowledges risks of harmful content in web-crawled data by detailing filtering steps (quality warnings, perplexity-based filtering, URL blocklists for categories like adult, dangerous material, malware etc.). |
| 9ocGP8hgKUoJ.pdf | Google_Scholar | Rapid Response Information Report Generative AI: Language models and multimodal foundation models | This Australian government-commissioned report analyzes the opportunities and risks of generative AI (LLMs and MFMs) across various sectors over the next decade. It also reviews international strategies to address the impacts of these technologies, aiming to inform national policy. | True | Idealistic | True | 3.0 | Neutral | LLMs and MFMs (e.g., ChatGPT, GPT-3, GPT-4, LLaMa, Ernie Bot) | NaN | NaN | Bias in AI reproducing social inequalities (e.g., in law enforcement, social services); risks to human rights; lack of digital inclusion for communities like regional/older Australians, hindering access to AI-driven services; opacity and lack of accountability in AI systems. | Development of legal/regulatory frameworks (e.g., risk-based approaches, human rights due diligence); promoting transparency and accountability; multi-stakeholder collaboration; public investment in national AI capabilities and accessible infrastructure; measures to improve digital inclusion. | Protecting human rights in AI deployment; mitigating bias and discrimination in AI impacting legal and social outcomes; ensuring equitable access to AI technologies and legal information; accountability for AI harms. | Regional Australians, older Australians (digital inclusion); minority groups, over-policed populations (bias in AI); women (bias in data generally). | Law enforcement, contract law, privacy law, copyright law, anti-discrimination law, consumer law | Australia, with references to international jurisdictions (US, EU, China, Canada, Singapore, Thailand). | Vast, diverse datasets (text, images, code) often scraped from the internet, including public-domain content (e.g., Wikipedia, books) and potentially personal or copyrighted material; specific datasets for models like GPT-3 are mentioned generally, but specifics for newer models like GPT-4 are often not disclosed by commercial entities. | Model pre-training, fine-tuning (supervised learning, reinforcement learning with human feedback), input/output filtering, red-teaming, fuzzing, staged release strategies, post-release monitoring and auditing. | Controlled release via APIs and web interfaces (e.g., OpenAI's ChatGPT); open-sourcing of some models (e.g., Meta's LLaMa, Stanford's Alpaca) often aimed at researchers; integration into existing software products. | False | False | NaN | Lack of transparency in commercial LLM/MFM development (datasets, pre/post-processing); insufficient national capacity for AI development and oversight in some countries (e.g., Australia); persistent challenges in ensuring fairness, accuracy, and robustness of models; digital divide limiting equitable benefit; need for effective governance, standardized reporting, and redress mechanisms. | High resource requirements (monetary, computational, human); managing accuracy, bias, and safety of models; preventing misuse for harmful purposes (e.g., misinformation); ensuring data privacy, security, and sovereignty; addressing the environmental impact of large-scale computation; establishing robust ethical guidelines and governance. | Generation of 'hallucinations' (erroneous/misleading information); perpetuation/amplification of biases leading to discrimination and social inequalities; misuse for misinformation, deepfakes, and malicious activities; privacy violations (data scraping, re-identification, unauthorized use of personal/copyrighted data); security vulnerabilities; lack of transparency and accountability ('black box' effect); negative impacts on democratic processes, labor markets, and the environment; erosion of trust; market concentration. |
| Safe_and_Responsible_AI_in_Australia_-_Submission_-_Dr_Francina_Cantatore.489fe100215e6.pdf | Google_Scholar | Submission to government on the safe and responsible use of AI in Australia | This submission responds to the Australian government's discussion paper on AI, advocating for a risk-based regulatory approach centered on consumer protection, ethical principles, and human oversight. It highlights risks like algorithmic bias and data misuse, suggesting national standards, mandatory policies in sectors like law, and considering the impact on employment and intellectual property. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Risks to consumers (data misuse, lack of awareness, misleading conduct, algorithmic bias, unfair choices, market collusion), lack of regulatory consistency across sectors, insufficient protection of privacy rights, potential negative impact on human employment. | Implement a nationally consistent, risk-based regulatory framework for AI underpinned by ethics and human rights; mandate AI policies in specific sectors (e.g., legal profession); enhance consumer protection laws (like ACL) and privacy regulations; require transparency from developers; ensure human oversight ('human in the loop'); conduct impact assessments (including employability); foster public education. | Consumer protection, Data privacy, Regulation of AI, Ethical AI, AI in the legal profession. | Australian consumers | Consumer Law, Competition Law, Privacy Law, Intellectual Property Law, Legal Profession Regulation | Australia | NaN | NaN | NaN | False | False | NaN | Lack of empirical data on AI's longitudinal impact, need for updated legal frameworks (e.g., ALRC report) post-generative AI, jurisdictional differences potentially hindering adoption of international approaches, inconsistent application of ethical principles across sectors. | NaN | Algorithmic bias, breaches of consumer law (misleading/deceptive conduct), online market collusion, misuse of personal data, erosion of privacy rights, unfair consumer choices, potential for devastating effects in high-risk sectors (e.g., health), detrimental impact on livelihoods (employment), lack of public trust without human oversight. |
| OO-3hRkHauEJ.pdf | Google_Scholar | Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset | This paper introduces Pile of Law, a large-scale, open-source dataset of English legal and administrative text, intended for pretraining models and studying data filtering. It argues for grounding AI data filtering techniques in established legal norms for privacy and toxicity, demonstrating how such contextual rules can be learned from the dataset. | True | Idealistic | True | 1.0 | Positive | Pile of Law dataset and the approach of learning context-aware data filtering rules (for privacy and toxicity) directly from legal text. | Case studies involving training models (distill-BERT) to predict pseudonymity in Board of Immigration Appeals cases (~80% F1), comparing Masked Language Model (MLM) scores for pseudonym use in civil litigation, analyzing outputs of existing privacy (HIPAA tool) and toxicity filters (Perspective, Detoxify, etc.) on dataset subsets (BVA, DOL, Supreme Court opinions), and using causal lexicon induction. | Pseudonymity prediction model achieved ~80% F1 and aligned with legal rules; models pretrained on legal data better encoded pseudonymity norms. Existing toxicity filters showed low agreement, context/time sensitivity, and poor handling of nuance on legal text, highlighting limitations. | Lack of responsible, legally-grounded, and context-aware data filtering practices for AI pretraining data, hindering development of trustworthy AI, including for legal applications potential applications. | Grounding AI data filtering practices in established legal norms; providing the large-scale, open-source Pile of Law dataset as a resource; proposing methods to learn contextual filtering rules directly from the dataset. | General (as a potential application area for models trained on the dataset) | NaN | Court opinions, contracts, administrative law, legislation, constitutional law, immigration law, criminal law, civil litigation. | Primarily U.S. federal, with comparative examples from Germany, China, Canada. | Pile of Law dataset: ~256GB of publicly available, open-source, English-language, unstructured legal and administrative text (court opinions, contracts, administrative rules, legislation, etc.). | Dataset curation by compiling public sources. Case studies using standard ML methods (classification, MLM scoring, causal inference) to analyze and learn patterns from the dataset. | Dataset released publicly on Hugging Face. | True | True | The Pile of Law dataset is available for download on Hugging Face. | Need for better text-based causal attribution methods for identifying drivers of filtering decisions; need for robust, value-aligned toxicity filters that handle legal context, domain shift, and long documents; further exploration of legal system differences (e.g., civil vs common law); challenge of reliably performing context-aware filtering at scale; limitations of model context windows for assessing toxicity. | Compiling a large-scale legal dataset from diverse public sources; handling the inherent context-dependency of legal text for filtering purposes; limitations of existing NLP tools (e.g., privacy, toxicity filters) when applied to the specialized legal domain; addressing potential sensitivity of information within publicly available legal data. | Biased/harmful model outputs due to pretraining data; filtering negatively impacting representation or utility; privacy violations via model memorization of sensitive information; release of sensitive information contained within the Pile of Law dataset despite its public sourcing; incorrect application of toxicity filters leading to censorship of important legal discussions (e.g., civil rights cases) or failure to flag genuinely harmful content. |
| d4pkaJu5lpAJ.pdf | Google_Scholar | Dallma: Semi-Structured Legal Reasoning and Drafting with Large Language Models | This paper introduces Dallma, a framework combining predefined templates, logical rules, user input, and Large Language Models (LLMs) for semi-structured legal tasks like drafting and reasoning. The framework aims to improve the safety and utility of LLMs in law, with potential applications in enhancing access to justice. | True | Idealistic | True | 1.0 | Positive | The Dallma framework combines expert-defined templates (containing content, logic, variable specifications) with user interaction and calls to LLMs (e.g., GPT-4o) to perform semi-structured legal reasoning and document drafting tasks. | Two illustrative examples are presented using GPT-4o: one for spotting legal issues based on user input and another for reasoning about a Quebec Civil Code article concerning tenant eviction. Formal evaluation across various tasks is planned for future work. | The provided examples demonstrate plausible outputs where the system identifies relevant legal areas and applies legal criteria according to the template structure. No quantitative performance metrics are reported. | Difficulty for laypeople in understanding legal issues and completing complex legal forms; inherent limitations of LLMs such as hallucinations and difficulties with logical reasoning. | The Dallma framework proposes using semi-structured templates created by legal experts to guide LLMs and users. This approach constrains LLM outputs, integrates deterministic logic, allows user verification, and breaks down complex tasks into smaller steps to improve accuracy and safety. | Legal issue spotting, legal form completion, applying for social aid/benefits, automating legal reasoning and drafting. | Laypersons / Self-represented litigants. | General (issue spotting), Landlord-tenant law (specific example). | Quebec (for one specific example); potentially general applicability. | N/A (Uses pre-trained LLMs like GPT-4o; templates contain expert-defined content and logic, not ML training data). | NaN | Templates created by experts can be shared with target users, who can run the tool on their own computer. | False | False | NaN | Need to establish best practices for creating Dallma templates; requires formal evaluation of accuracy and performance; potential for extensions like Retrieval-Augmented Generation (RAG) and automatic template generation. | Designing effective and comprehensive templates; ensuring LLM reliability and adherence to constraints within the framework; developing user-friendly interfaces for both template creators and end-users; mitigating general LLM limitations (hallucinations, reasoning errors). | Potential for inaccurate LLM output despite the framework's constraints, although the design aims specifically to mitigate this risk common to LLM applications in law. |
| 5TdSakvWXuwJ.pdf | Google_Scholar | Expanding Access to Justice Through \nRegulatory Reform and Innovation: Arizona \nLessons from the Past, Present and Future | This paper details Arizona's historical and ongoing regulatory reforms and innovations aimed at expanding access to justice, covering changes in lawyer regulation, the introduction of non-lawyer legal service providers, and various court and technology initiatives. It presents Arizona's experience, including successes and challenges, as potential lessons for other jurisdictions working to narrow the justice gap. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | The large unmet need for civil legal services, particularly for low-income individuals; limitations of the traditional lawyer-centric model; cost of legal services; geographic disparities (legal deserts); the digital divide (lack of computer/internet access); resistance to regulatory reform. | Regulatory liberalization for lawyers (e.g., limited scope representation, admission by motion) and non-lawyers (e.g., LDPs, LLLPs, Legal Advocates, FHIP employees); creation of new non-lawyer roles; Alternative Business Structures (ABS); court process improvements (navigators, self-help, kiosks, remote hearings, digital evidence portals, ODR); leveraging technology (data analysis, potential of GAI); inter-agency/inter-jurisdictional collaboration; targeted programs (Lawyer Apprentice, tax credits, community justice workers). | Access to civil legal services, Domestic violence, Housing stability/Eviction, Family law, Consumer law/Debt collection, Public benefits, Administrative law, Fair housing, Legal needs of older adults/veterans/crime victims/homeless individuals. | Low-income Arizonans, Self-represented litigants, Domestic violence survivors, Individuals facing housing instability/eviction, Residents of rural areas/legal deserts, Older adults, Veterans, Crime victims, Immigrants, Children in foster care system. | Civil Law (Family, Housing, Consumer, Administrative, Fair Housing), Protective Orders, Wills/Estates, Criminal Law (minor offenses, post-conviction, LLLP area), Immigration Law, Torts (ABS area). | Arizona | NaN | Task forces, Committee reports and recommendations, Pilot programs authorized by court administrative orders, Public comment periods on rule changes, Development of training curricula and certification processes for non-lawyer providers (e.g., by i4J, AOC). | Arizona Supreme Court Administrative Orders, Amendments to court rules and Code of Judicial Administration, State Bar initiatives, Arizona Bar Foundation programs, Specific court programs (navigators, kiosks, remote hearings), State agency initiatives, Partnerships between academic institutions (i4J, ASU), legal aid organizations, and community groups. | True | False | Various described reforms and programs (e.g., LDPs, LLLPs, ABS, DVLA/HSLA pilots, remote hearings, court navigators, AZPOINT, online resources) are operational within Arizona's legal system or specific organizations/courts. | The significant overall access to justice gap remains; need for more service providers, especially in rural areas (legal deserts); the digital divide limits technology-based solutions; insufficient funding for legal aid; specific unmet needs in areas like eviction, domestic violence, debt, public benefits, mental health; ensuring fairness and mitigating bias in emerging AI tools. | Opposition to regulatory reform; establishing training, certification, and oversight infrastructure for new provider types; ensuring quality and consumer protection for non-lawyer services; addressing the digital divide; scaling pilot programs effectively; securing adequate funding. | Potential harm to the public from inadequately regulated or poorly delivered non-lawyer services; perceived threats to the traditional legal profession or justice system from reforms like non-lawyer ownership; potential for bias and lack of fairness in AI applications. |
| jVZShwYu2OUJ.pdf | Google_Scholar | Computational Law and AI Alignment in the Era of Large Language Models | This article examines the intersection of computational law, AI alignment, and risk mitigation concerning large language models (LLMs), discussing key concerns for various legal stakeholders. It explores AI alignment strategies, regulatory approaches, and concludes that a balanced approach between innovation and safety is essential to harness AI's transformative potential. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Concerns about reliability, correctness, and ethical implications (fairness, accountability, transparency) of AI. For individuals, ensuring quality legal support from AI tools and addressing the digital divide are key hurdles. | AI alignment strategies (explainability, transparency, fine-tuning, guardrails), comprehensive regulatory frameworks, development of benchmarks, and embedding legal principles into AI systems. | Affordable and accessible legal help, quality of AI legal support for consumers, digital divide in AI access. | General public/consumers, especially those needing affordable or accessible legal services. | General / Multiple | EU and US | The paper discusses techniques using various data. Examples include Anthropic's Constitutional AI (human principles, public input) and Pile of Law (open-source legal texts for filtering/research). | Techniques discussed include Constitutional AI (developed using supervised learning and reinforcement learning, including AI feedback and public input for drafting principles) and the Pile of Law (involves data gathering of legal texts, distillation of legal norms, and data-driven learning of filtering rules). | NaN | True | True | Mentions several available tools/datasets: e.g., Pile of Law (open-source dataset), Guardrails.ai (open-source), NeMo Guardrails (GitHub link provided), HELM (living benchmark), LegalBench (open for contribution). | Defining/tracing AI harm, achieving LLM explainability/transparency, robust guardrails, integrating symbolic AI with LLMs, and broadening benchmark contributions. | NaN | Reliability issues, bias, lack of accountability, malpractice, job displacement, privacy infringement, societal division, unauthorized practice of law, and generation of illegal/harmful content. |
| ys2Ue4MeS4MJ.pdf | Google_Scholar | Can GPT-4 Support Analysis of Textual Data in Tasks Requiring Highly Specialized Domain Expertise? | This paper evaluates GPT-4's ability to semantically analyze sentences from court opinions for interpreting legal concepts, a task requiring specialized legal expertise. It finds GPT-4 performs comparably to well-trained law students and explores prompt engineering, batch processing efficiency, and model sensitivity. | True | Idealistic | True | 2.0 | Positive | GPT-4 with zero-shot prompting using detailed annotation guidelines, including batch processing and chain-of-thought variations. | Comparison against gold-standard labels (consensus annotations by legal scholars) on a subset of the Statutory Interpretation Data Set, using Precision, Recall, F1-score, Accuracy, and Krippendorff's alpha. Compared performance against human (law student) annotators. | Best configuration (updated guidelines, single sentence processing) achieved F1=0.57, Accuracy=0.57, and Krippendorff's alpha=0.48 (or alpha=0.53 for labels only setting). Performance was comparable to well-trained law students. Batch processing workable but slightly less accurate; CoT ineffective. | High cost and requirement for specialized domain expertise for annotating legal texts, acting as a bottleneck for research and development. | Employing large language models (GPT-4) with detailed prompts derived from annotation guidelines for automated semantic analysis, potentially reducing cost and reliance on human experts. Iterative refinement of prompts/guidelines based on model output analysis. | Interpretation of legal concepts in statutory law; Legal understanding; Legal argumentation support. | NaN | Statutory Interpretation (Task); Data from multiple fields including Intellectual Property, Criminal Law (Cybercrime). | United States | The technique uses pre-trained GPT-4 (trained on broad web data). Evaluation uses the 'Statutory Interpretation Data Set' (publicly available on GitHub) containing sentences from US court opinions labeled by legal experts. | Prompt engineering (translating human guidelines, varying prompt structure for batching and CoT), comparative evaluation against human performance and gold standard, error analysis for iterative prompt refinement. | NaN | True | False | Requires access to OpenAI's commercial GPT-4 API. The annotation guidelines used for prompting are available on GitHub. | Need for broader testing across more tasks and larger datasets; Improving model robustness to prompt formatting; Addressing reproducibility challenges with proprietary models; Exploring few-shot/fine-tuning approaches. | Achieving high accuracy on complex legal tasks; Cost of API usage; Ineffectiveness of standard prompt techniques like CoT for this task; Model brittleness/sensitivity to prompt formatting. | Brittleness leading to unreliable predictions based on minor prompt changes; Potential for inaccurate analysis in complex legal tasks; General concerns about misuse of powerful LLMs (mentioned indirectly via OpenAI report). |
| dofdWxvXYDgJ.pdf | Google_Scholar | Can AI make a case? AI vs. Lawyer in the Dutch Legal Context | This paper investigates the quality of AI-generated (GPT-4) legal argumentation compared to human-written arguments in the Dutch legal context using an experiment with 25 legal professionals. Results showed a strong preference (80%) for the AI-generated document, highlighting AI's potential for tasks like legal drafting and information retrieval. | True | Idealistic | True | 2.0 | Positive | GPT-4 was used to generate a legal letter. The input for GPT-4 was prepared using prompt engineering, which included manual co-reference resolution on 9 case documents, a 'Prompt Reducer' technique (using a Python script) to compress these documents into a summary fitting token limits, and a specific prompt instructing GPT-4 to rewrite an original lawyer's letter based on this summary and the original letter. | An online survey was conducted with 25 Dutch legal professionals (judges, lawyers, other legal professionals). Participants were given a case summary and two anonymized legal letters (one human-written, one AI-generated by GPT-4) arguing the same side. They rated both texts on persuasiveness, clarity/coherence, strength of arguments, and use of evidence (1-10 scale), and then chose the more effective text, providing justification. | 80% of participants chose the GPT-4 generated legal document (Text B) as more effective. GPT-4's text received higher average scores than the human-written text (Text A) across all four evaluated dimensions (persuasiveness, clarity & coherence, strength of arguments, use of evidence) and across nearly all demographic subgroups (age, profession, gender). | Prohibitively expensive cost of traditional legal advice, slowness and poor quality of free legal aid services, language barriers excluding non-Dutch speakers from accessing free legal aid. | AI generating legal arguments and advice efficiently and in multiple languages to increase accessibility, timeliness, and equity. AI-driven tools for faster and more cost-efficient case preparation. AI-driven mediation processes. | Access to legal advice, cost of legal services, language barriers in legal services, efficiency in legal processes, quality of legal representation, legal drafting. | Economically disadvantaged individuals, expatriate population in the Netherlands, younger populations. | Employment Law | Netherlands (Dutch legal context) | Input for the GPT-4 generation task consisted of a processed summary derived from 9 proprietary, unstructured legal documents (e.g., letters, emails, reports) from a real-world Dutch employment dispute case, and the original lawyer's letter. The processing involved manual co-reference resolution and a 'Prompt Reducer' technique for text compression. | Experimental design comparing human-written vs. AI-generated text. Pre-processing of input case documents for GPT-4 involved manual co-reference resolution and a Python-scripted 'Prompt Reducer' technique. A specific instructional prompt was designed for GPT-4 to generate the alternative legal letter. | NaN | False | True | The Python script for the 'Prompt Reducer' technique and OpenAI API interaction is provided in Appendix 2 of the paper. | The study has limitations and requires replication in varied settings. Future research should explore client's unique circumstances as input, AI's impact on legal education, and client perspectives on AI-generated legal texts. Current AI, as used, may miss nuanced client context unless explicitly provided. | Initial AI summarization tests had factual inaccuracies due to differing author perspectives and pronoun ambiguity. The primary challenge was the token limitation of GPT-4, necessitating text compression techniques (Prompt Reducer) for the case documents. | AI 'hallucinations' (generating incorrect output). AI lacking nuanced understanding of case-specific information or client's broader, unstated circumstances unless explicitly provided. Ambiguity in legal responsibility and accountability for AI-assisted services. Perpetuation of human biases by AI models. Potential job displacement in the legal field. Risk of AI creating an imbalance in legal disputes if one party has superior AI tools. Potential for the judicial system to be overwhelmed if AI-generated filings increase significantly before the system can adapt. |
| 98-JoACmT0MJ.pdf | Google_Scholar | GENERATIVE ARTIFICIAL INTELLIGENCE PROMPT-KIT FOR ENHANCED LEGAL LEARNING AND ANALYSIS | This paper introduces a prompt-kit utilizing generative AI (specifically ChatGPT) to enhance legal education by providing structured guidance for tasks like case analysis, legal research, and mooting. The proposed kit aims to address limitations in traditional legal learning, such as lack of personalized feedback, and improve analytical skills for law students and professionals. | True | Market | True | 1.0 | Positive | A generative AI prompt-kit (compilation of >150 prompts) designed for use with ChatGPT to guide users through various legal learning tasks (case analysis, legal research, mooting, problem-based questions). | NaN | NaN | Limitations in traditional legal education: lack of personalized feedback and real-time guidance in legal analysis. | Develop a virtual legal research and analysis assistant (the prompt-kit) powered by generative AI to offer real-time feedback, answer queries, and guide students in applying legal principles via structured prompts. | Legal education, Legal analysis skills development, Legal research assistance. | Law students, Legal educators, Legal professionals. | General Legal Education | NaN | N/A (The technique is a set of prompts for an existing LLM, not a newly trained model). | Prompts developed based on identified processes for each aspect of legal education (e.g., case identification, summarization, analysis for case studies; pre-research inquiries for legal research). | NaN | False | False | NaN | Lack of research on leveraging and maximizing the potential of ChatGPT for legal education (prior research focused more on ethical concerns); Need for accuracy filters in ChatGPT; Concerns about potential deterioration in critical thinking. | NaN | Potential for student plagiarism, lack of accuracy in AI responses, deterioration in critical thinking skills, ethical and legal concerns regarding AI use in education. |
| HZ-4I9MfOCgJ.pdf | Google_Scholar | COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis | This paper introduces DEBUG EVAL, a comprehensive benchmark for evaluating LLM code debugging abilities across multiple stages (localization, identification, repair, recognition). It also proposes the COAST framework, a multi-agent system for synthesizing high-quality training data, which significantly improves the debugging performance of smaller LLMs. | True | NaN | True | 1.0 | NaN | COAST (COmmunicative Agent-based data SynThesis) framework for generating SFT data, and the DEBUG EVAL benchmark for evaluating code debugging. | The COAST framework and resulting NeuDebugger models were evaluated on the newly proposed DEBUG EVAL benchmark. DEBUG EVAL includes four tasks (BUG Localization, BUG Identification, Code Repair, Code Recognition) across Python, C++, and Java, using Accuracy and Pass@1 as metrics. | COAST-generated data enabled 7B-scale LLMs (NeuDebugger models) to achieve debugging performance comparable to GPT-3.5. NeuDebugger-DS-6.7B improved by 27.7% and NeuDebugger-Llama3-8B by 4.1% over their respective base models on the DEBUG EVAL benchmark. | NaN | NaN | NaN | NaN | NaN | NaN | For fine-tuning NeuDebugger models: Synthesized Supervised Fine-Tuning (SFT) data generated by the COAST framework. This data covers bug localization, identification, repair, and recognition, and is created through interactions between Code Quizzer, Code Learner, and Code Teacher agents, initially seeded with examples from the DEBUG EVAL benchmark. | For COAST: Multi-agent communicative framework (Code Quizzer, Code Learner, Code Teacher); data synthesis based on critic-guided selection (problems incorrectly solved by Code Learner are curated); Chain-of-Thought (CoT) explanations by Code Teacher. For DEBUG EVAL: Emulation of human debugging process; data collection from existing benchmarks and human trials, with manual review. | All data for DEBUG EVAL and codes for the COAST framework are made available on GitHub. | True | True | All data and codes are available at https://github.com/NEUIR/COAST . | NaN | The effectiveness of COAST is constrained by the performance of a_foundation models used for its Code Quizzer and Code Teacher agents. The quality of synthesized data heavily relies on the capabilities of these foundation models. Chain-of-Thought reasoning can negatively affect performance in the Code Repair task. | NaN |
| --chwZiMxA0J.pdf | Google_Scholar | Generative Contracts | This paper explores how consumers can use generative AI like GPT-4 to draft their own basic contracts, presenting this as an opportunity to improve access to justice for underserved populations. It demonstrates GPT-4's capabilities through generated contract examples and a case study, while also discussing the implications, limitations (like potential inaccuracies), and risks (technological, privacy, regulatory). | True | Idealistic | True | 2.0 | Positive | Using OpenAI's GPT-4 large language model via ChatGPT to generate various types of consumer contracts based on simple user prompts. | Qualitative evaluation based on generating drafts of over a dozen different contracts (employment, lease, bill of sale, etc.) using GPT-4 with simple prompts, plus a proof-of-concept case study of hypothetical consumers using GPT-4 to draft and modify a car sale contract. | GPT-4 generated contracts that were generally functional, enforceable, short, and simple, though susceptible to errors and inconsistencies. The case study highlighted ease of use, speed, low cost, flexibility, and modifiability. Quality was deemed lower than lawyer-drafted contracts but likely superior to undocumented 'handshake' deals. | High cost of legal services, shortage of lawyers (particularly in rural 'legal deserts'), and the difficulty consumers face in reading and understanding legal documents. | Leveraging generative AI (like GPT-4) to provide consumers with low-cost, easily accessible, and user-friendly tools ('generative contracts') to draft their own basic contracts. | Drafting basic consumer contracts (e.g., contracts for sales, services, leases, employment, NDAs). | Consumers underserved by the legal system, particularly low-income Americans and rural populations facing lawyer shortages. | Contract Law, Consumer Law, Transactional Law | Primarily California, USA (used for examples and specific legal references), but the concept is presented with broader applicability. | The study used OpenAI's GPT-4, which is generally known to be trained on massive, diverse datasets scraped from the internet. The paper does not specify further details or mention fine-tuning on legal data for this study. | NaN | NaN | True | False | The method relies on OpenAI's ChatGPT interface with the GPT-4 model, accessible via a paid subscription (ChatGPT Plus). | Current limitations in drafting long, complex business contracts using generative AI. Need for further research into fine-tuning LLMs for specific legal applications and prompt engineering in law. Implicitly, the need for consumer adoption and mitigation of technological/societal risks. | NaN | Technological risks (inscrutability, accuracy/hallucination, bias, adversarial attacks), privacy/data protection risks (violation of privacy laws like GDPR, breach of client confidentiality), intellectual property infringement risks (use of copyrighted training data), and regulatory risks (unauthorized practice of law, impact of emerging AI regulations). |
| B09Gn6auhTQJ.pdf | Google_Scholar | Comprehensibility and Automation: Plain Language in the Era of Digitalization | The paper presents a pilot machine-learning experiment using SVM and fastText models to automatically classify the comprehensibility of official Hungarian texts addressed to lay readers. The goal is to identify problematic sentences to assist experts in rephrasing them, thereby improving access to justice and the transparency of governmental organizations. | True | Idealistic | False | 1.0 | Positive | Machine learning (Support Vector Machine and fastText models) for binary classification of official text sentences into 'original' (less comprehensible) and 'rephrased' (more comprehensible). | A hand-crafted corpus of original and rephrased sentences from the National Tax and Customs Administration of Hungary documents from 2021. SVM models were evaluated using 10-fold cross-validation (metrics: precision, recall, F1-score). FastText models were evaluated on an 80/20 train/test split (metric: F1-score), with multiple runs per epoch averaged. | SVM models achieved a stable precision of approximately 0.72 in identifying original (less comprehensible) sentences. | Linguistic complexity, specialized language, and over-complicated sentence structures in official/legal documents that hinder comprehension by laypersons. | Developing machine learning models to automatically identify sentences in official texts that are likely difficult to comprehend, thereby assisting human experts in the process of rephrasing these texts into plain language. | Improving comprehensibility of official administrative and legal texts for laypersons; enhancing access to justice; promoting transparency of governmental organizations; supporting the rule of law. | Laypersons, including individuals interacting with governmental (e.g., tax administration) and legal systems, who lack specialized domain knowledge. | Administrative law, Tax law (specifically informational texts from tax administration). | Hungary | A proprietary, domain-specific corpus of 10,883 Hungarian sentences (original and expert-rephrased pairs) from 2021 informational documents of the National Tax and Customs Administration of Hungary. Data is unstructured text. | Supervised machine learning (binary classification). Data preparation included sentence segmentation (using a heuristic approach), creation of 'original' and 'rephrased' sub-corpora, noise reduction (e.g., removing sentences <10 tokens), text preprocessing (lowercasing, removing numbers/punctuation, lemmatization using HuSpaCy). Hyperparameter tuning was conducted for both SVM (with TF-IDF vectorization) and fastText (with corpus-trained embeddings) models. | NaN | False | False | NaN | The developed models are highly domain-specific due to limited quantity and diversity of training data. Further research is needed for comprehensive error analysis and exploration of more advanced NLP models to improve performance and generalizability. | Challenges included: 1) Reliable sentence segmentation of Hungarian official texts, particularly handling legal references and listings. 2) Overfitting when using pre-trained fastText word vectors on the specific corpus. 3) Limited customization options within the fastText library for fine-tuning neural network architecture. | NaN |
| Wx_p4tUveXoJ.pdf | Google_Scholar | A Question-Answering Approach to Evaluating Legal Summaries | This paper proposes a novel method using GPT-4 to evaluate the quality of legal summaries by generating question-answer pairs based on argumentative structure (Issue, Reason, Conclusion). The approach involves using GPT-4 to answer these questions based on a generated summary and then grading the answers, showing reasonable correlation with human evaluations. | True | Idealistic | True | 1.0 | Positive | QA-based evaluation framework for legal summaries using GPT-4. It involves: 1) Generating QA pairs from a reference summary based on argumentative structure (Issue, Reason, Conclusion). 2) Answering these questions based on the generated summary. 3) Grading the generated answers against the reference answers. | Compared GPT-4 evaluation grades (0-10 scale, binarized at thresholds 5 and 6) with human binary evaluations ('YES'/'NO') for answers derived from summaries created by BART, LED, and GPT-4. Evaluation used 10 Canadian case summaries (48 QA pairs) and Pearson/Spearman correlation metrics. | Correlations varied by summary generation model and argumentative component (Issue, Reason, Conclusion). The LED model showed the highest overall correlation (IRC Pearson 0.87/0.88, Spearman 0.84/0.85 at thresholds 5/6). Negative correlations were observed between GPT-4 grades and human evaluation for 'Reason' type questions on BART and GPT-4 generated summaries. | Difficulty in automatically evaluating the quality and argumentative structure of legal summaries, hindering the reliable assessment of tools meant to make legal text more accessible. | A QA-based evaluation framework using LLMs (GPT-4) to assess summary quality by focusing on argumentative structure (Issue, Reason, Conclusion), potentially improving summary generation and accessibility. | Evaluation of legal text summarization quality | NaN | General Case Law | Canada | Summarization models (BART, LED) fine-tuned on a dataset of 1,049 annotated Canadian legal case summaries (Issue, Reason, Conclusion annotations) paired with full texts from the Canadian Legal Information Institute. GPT-4 used zero-shot. The evaluation method itself (GPT-4 based QA) did not require specific training data beyond GPT-4's pre-training. | Experimental design involving prompt engineering for GPT-4 (QA generation, answer prediction, grading) and comparison with human evaluations using correlation analysis (Pearson, Spearman). | Code made available on GitHub. | True | False | Code available on GitHub (https://github.com/JoyceXu02/QA_evaluation), requires GPT-4 API access. | Sensitivity to prompt engineering, need for larger-scale evaluation, quality control for LLM generation (hallucinations, consistency, especially with longer input), potential exploration/calibration using open-source models. | Cost of GPT-4 API and human evaluation limited test size; aligning automated scores with human perception; controlling for hallucination or out-of-context answers generated by the LLM; achieving consistent correlation across different argumentative components (Issue, Reason, Conclusion). | LLM hallucination in generated answers. Potential divergence between automated evaluation scores and human judgments of quality (e.g., negative correlations found for 'Reason' component). |
| EM0LbiC9NWUJ.pdf | Google_Scholar | Exploring the Comprehension of ChatGPT in Traditional Chinese Medicine Knowledge | This paper introduces TCM-QA, a novel question-answering dataset for Traditional Chinese Medicine, and uses it to evaluate ChatGPT's (GPT-3.5) comprehension abilities. The study finds ChatGPT performs moderately, with better results on true/false questions and using Chinese prompts, but struggles with complex reasoning and can generate misinformation. | True | NaN | True | 2.0 | NaN | ChatGPT (gpt-3.5-turbo model) for question-answering in Traditional Chinese Medicine (TCM) using zero-shot and few-shot prompting, evaluated on a new dataset (TCM-QA). | Evaluation on a custom-built dataset called TCM-QA, comprising 801 questions (574 single-choice, 131 multiple-choice, 97 true/false) categorized into knowledge-based, diagnostic-based, and treatment-based reasoning. Performance was measured by precision and responsiveness, with human evaluation for explanation quality (readability, reliability, integrity). | ChatGPT performed best in true or false questions, achieving the highest precision of 0.688 (few-shot, Chinese prompt), while scoring the lowest precision (0.241, few-shot, English prompt for multiple-choice, though Chinese zero-shot reached 0.240). Chinese prompts generally outperformed English prompts. Human evaluation of explanations for correct answers showed high readability (avg. ~2.9) and good reliability/integrity (avg. ~2.6), but lower for incorrect answers, where ChatGPT generated 'illusions'. | NaN | NaN | NaN | NaN | NaN | China | The paper evaluates ChatGPT (GPT-3.5), whose general pre-training data is primarily English web text. The evaluation dataset, TCM-QA, was newly constructed by the authors from BaiduWenKu, refined, and verified by TCM experts, containing Chinese language questions specific to Traditional Chinese Medicine. | Creation of a new domain-specific QA dataset (TCM-QA). Application of prompt engineering (zero-shot and few-shot settings in English and Chinese). Automated evaluation using precision and responsiveness metrics. Human evaluation of AI-generated explanations based on readability, reliability, and integrity. | NaN | False | True | The TCM-QA dataset, a core component of the study for evaluating ChatGPT, is released on GitHub (https://github.com/yizhen-buaa/TCM-QA-datasets). The overall approach involves using this dataset with ChatGPT and can be replicated. | NaN | ChatGPT's shallow understanding of TCM knowledge due to limited TCM content in training data; misinterpretation by focusing on keywords over sentence context; generation of 'illusions' (erroneous TCM knowledge); linguistic bias affecting comprehension of non-English (Chinese) questions, though Chinese prompts helped. | Generation of 'illusions' by ChatGPT, where it invents erroneous TCM knowledge to substantiate its rationale, posing a risk of misinformation if used without expert oversight. |
| A_Hybrid_Transformer-Based_Framework_for_Multi-Document_Summarization_of_Turkish_Legal_Documents.pdf | Google_Scholar | A Hybrid Transformer-Based Framework for Multi-Document Summarization of Turkish Legal Documents | This paper presents a novel hybrid framework combining extractive (TF-IDF, TextRank) and abstractive (LED, Long-T5, BART-large, GPT-3.5 Turbo) techniques for multi-document summarization of Turkish legal texts. It also introduces a new dataset of 2,000 Turkish civil cases, with GPT-3.5 Turbo demonstrating the best summarization performance. | True | Market | True | 1.0 | Positive | A hybrid framework for multi-document summarization combining extractive methods (TF-IDF, TextRank) with abstractive transformer-based models (LED, Long-T5, BART-large, GPT-3.5 Turbo). | Extractive methods (TF-IDF, TextRank) were evaluated using cosine similarity, content precision, recall, and F1-score against keyword-based summaries. Abstractive transformer models were evaluated using ROUGE scores (ROUGE-1, ROUGE-2, ROUGE-L, ROUGE-Sum) against human-written reference summaries on a custom dataset of 2,000 Turkish civil cases. | GPT-3.5 Turbo achieved the highest ROUGE scores (ROUGE-1: 55%, ROUGE-2: 35%, ROUGE-L: 42%, ROUGE-Sum: 44%) for abstractive summarization. | The volume and complexity of legal documents, and unique linguistic challenges (e.g., agglutinative structure, domain-specific terminology) in languages like Turkish, hindering efficient processing of legal information which can be a barrier to access to justice. | Development of automated multi-document summarization tools using a hybrid AI framework (extractive + abstractive methods) to make large volumes of legal text more manageable and understandable, thereby enhancing the efficiency of legal professionals which indirectly supports access to justice. | Improving the efficiency of legal information processing and understanding (specifically case law) for legal professionals, which can contribute to a more accessible justice system. | NaN | Civil law (specifically consumer rights cases) | Turkey | A new dataset of 2,000 Turkish civil cases (court rulings related to consumer rights), curated and validated from publicly accessible legal platforms. The data is unstructured legal text. | Dataset creation through web scraping and preprocessing; application and fine-tuning of existing extractive (TF-IDF, TextRank) and abstractive (transformer models) NLP techniques; empirical evaluation and comparison of different models. | NaN | False | False | NaN | Need for further development and refinement of AI-powered legal document summarization tools for under-resourced languages like Turkish, particularly in addressing technical limitations such as handling long texts, ensuring factual accuracy (mitigating hallucination), preserving legal nuances, and expanding domain coverage, to improve their utility for legal professionals and potentially broaden access to justice. | Collecting and cleaning legal documents (removing metadata, retaining essential content); handling complex grammar and domain-specific legal terminology of the Turkish language; managing long input sequences due to token limits of transformer models; computational challenges (GPU memory, training efficiency); ensuring content completeness within token limits; mitigating hallucination in abstractive summaries. | Hallucination in abstractive summaries (generating information not present in the original text); failure of models to preserve critical legal details or nuances during summarization; loss of context during the abstraction phase, impacting legal accuracy. |
| 6kXvecJ69wAJ.pdf | Google_Scholar | Generative AI as Tax Attorneys: Exploring Legal Understanding Through Experiments | This paper investigates the legal understanding and reasoning capabilities of OpenAI's GPT-4 and GPT o1-preview models in the context of Polish tax law. It finds that while models show significant improvement and can support tax professionals, they are not yet reliable for independent advice due to inaccuracies and a high rate of hallucination in citing legal precedents. | True | Market | True | 2.0 | Positive | OpenAI's GPT-4 and GPT o1-preview models, evaluated using a proposed Quality of Legal Reasoning Indicator (QLR). | Four experiments: 1) GPT models answered 100 Polish tax advisor exam questions. 2) GPT o1-preview answered 40 practical tax law questions from LEX service, compared to expert answers. 3) GPT o1-preview predicted National Revenue Administration Information Centre (NRAIC) positions for 45 private tax rulings. 4) Legal reasoning of GPT o1-preview for 45 private tax ruling scenarios was evaluated by 5 experts using the Quality of Legal Reasoning Indicator (QLR). | The GPT o1-preview model achieved 81% accuracy on a test of 100 questions from the Polish tax advisor exam, passing the 80% threshold. In another experiment, it demonstrated a 73.33% accuracy in predicting NRAIC positions. However, it showed a 50% hallucination rate in citing court decisions and PTRs. | Current LLM limitations (accuracy, high rates of hallucination, comprehension of complex and evolving legal data, multilingual/multicultural nature of law) hinder their direct application for widespread, reliable access to justice without professional oversight. | Using LLMs to assist legal professionals to increase efficiency and reduce service costs, thereby indirectly making legal aid more affordable. Technically, developing domain-specific models, using Retrieval Augmented Generation (RAG) architecture, and employing external models to validate reasoning quality. | Improving affordability and accessibility of professional legal advice (tax law) through AI-driven efficiencies. | General public, particularly those who currently find professional tax advisory services unaffordable. | Tax law (including PIT, CIT, VAT, Excise tax, Property tax, Inheritance and gift tax, Tax on means of transport) | Poland (with reference to European Union law for VAT) | The paper uses OpenAI's pre-trained GPT-4 and GPT o1-preview models, which are trained on very large, general, and proprietary datasets. The study notes that performance is influenced by the volume of domain-specific data encountered, e.g., EU-harmonized VAT data resulted in better performance. | The Quality of Legal Reasoning Indicator (QLR) was developed based on the clarification concept (Dascal and Wróblewski, 1988) and the derivation concept (Zieliński, 2017) of legal interpretation, involving expert assessment on five key aspects of legal reasoning. | NaN | True | False | OpenAI's GPT-4 is commercially available. The paper states GPT o1-preview premiered on 12.09.2024 and was used in experiments in October 2024, implying availability to researchers, likely through OpenAI. | LLMs are not yet accurate or reliable enough for independent legal advice or direct use by laypersons in access to justice contexts. Significant issues like hallucination in citing legal precedents (50% in one experiment) need to be addressed, possibly through RAG architectures or domain-specific models. | The main challenges faced in applying LLMs to law include high hallucination rates (especially in citing case law), ensuring understanding of complex legal language and reasoning, keeping up with the ever-changing nature of law, and the need for large, high-quality domain-specific training data. | Risk of providing incorrect legal advice due to model inaccuracies and hallucinations. Specifically, a strong hallucinatory effect (50% of cases) was observed in the analysis of court decisions and PTRs, where models cited non-existent rulings. |
| 7hNr4uL27jwJ.pdf | Google_Scholar | Dynamic LLM Routing and Selection based on User Preferences: Balancing Performance, Cost, and Ethics | This paper introduces OptiRoute, an advanced model routing engine designed to dynamically select the optimal LLM for tasks by balancing user-defined functional (cost, speed, accuracy) and non-functional (ethical) criteria. OptiRoute utilizes lightweight task analysis, complexity estimation, and kNN search to match tasks with models from a diverse repository, aiming to optimize LLM deployment across various applications. | True | Market | True | 1.0 | NaN | OptiRoute: A model routing engine using a Task Analyzer (fine-tuned LLM like FLAN-T5 for task/complexity/domain prediction), a Model Registry Evaluation Store (MRES - in-memory vector DB with model metrics), and a kNN-based Routing Engine with filtering to select LLMs based on user-specified functional (accuracy, speed, cost) and non-functional (helpfulness, honesty, harmlessness) requirements. | The Task Analyzer component is described as being fine-tuned using supervised and synthetic data derived from production query logs (human annotated and semi-supervised). The overall OptiRoute system incorporates a user feedback loop (thumbs up/down) for continuous refinement of the routing policy, but the paper does not present specific benchmark evaluations or comparative experimental results for the complete system. | NaN | NaN | NaN | NaN | NaN | General legal services / legal document processing | International | For the Task Analyzer: Proprietary query logs from a production MLaaS cloud provider, labeled via human annotation and semi-supervised learning, supplemented with synthetically generated data (self-align, self-instruct). Unstructured text queries. | System architecture design involving: user preference capture (explicit and implicit), a Task Analyzer (fine-tuned autoregressive encoder-decoder LLM), a Model Registry and Evaluation Store (MRES - in-memory vector database with normalized model metrics), a kNN-based Routing Engine with filtering and scoring using cosine similarity, and a user feedback loop for continuous policy refinement. Batch and interactive modes of operation are supported. | The paper mentions its potential deployment on Freshworks' Freddy ML enterprise platform and discusses its applicability to cloud-based MLaaS platforms (e.g., AWS, Google Cloud, Azure). | False | False | NaN | NaN | Challenges addressed by OptiRoute's design include: balancing cost, latency, accuracy, and ethical considerations in LLM selection; navigating the vast number of available models; efficient task analysis and complexity estimation; integrating diverse user preferences (functional and non-functional); normalizing diverse model performance metrics; and ensuring ethical AI behavior. | Propagation of biased or harmful content, erosion of user trust, potential regulatory repercussions, detrimental effects of latency in real-time applications, and hallucination risks in LLMs. |
| RtbC1q5BGA0J.pdf | Google_Scholar | How well do SOTA legal reasoning models support abductive reasoning? | This paper introduces L'ART, a new logic-augmented dataset, and a redefined task (𝛼𝑁𝐿𝐼*) to evaluate abductive reasoning in AI models, particularly in the legal domain. Experimental results show that current state-of-the-art legal models and large language models generally perform poorly, highlighting limitations in their abductive reasoning capabilities. | True | Idealistic | True | 1.0 | Neutral | L'ART dataset, a logic-augmented dataset for abductive reasoning, and the 𝛼𝑁𝐿𝐼* task, a redefined binary classification task for evaluating abduction. | The L'ART dataset and 𝛼𝑁𝐿𝐼* task were used to evaluate several SOTA transformer models (BERT Base/Large, BERT-PLI, Legal BERT, BERTLaw, NFSP ParaLaw Nets, GPT-3) on their abductive reasoning capabilities. Models were trained for binary classification (valid/invalid triple) and performance was measured by accuracy on a held-out test set. | BERT Base achieved the highest test accuracy (0.6162), outperforming specialized legal models (e.g., Legal BERT 0.5619, BERTLaw 0.5371) and even BERT Large (0.5000). GPT-3 (zero-shot) had the lowest accuracy (0.4959). | Current AI models, including SOTA legal-specific LLMs, exhibit poor performance on abductive reasoning tasks, which are crucial for legal argumentation and interpretation. This deficiency limits their reliability for complex legal applications that could support access to justice. | Developing more robust datasets (like L'ART) and evaluation tasks (like 𝛼𝑁𝐿𝐼*) for abductive reasoning. Future research should explore alternative pretraining approaches, novel model architectures, and better integration of legal domain knowledge to improve AI's abductive reasoning capabilities. | Improving foundational AI capabilities for legal reasoning (specifically abductive reasoning) to support the development of more reliable AI tools for legal services and access to justice. | Underserved communities (mentioned generally). | General legal reasoning (examples include statute law retrieval, contract risk analysis, case law retrieval). | International | The L'ART dataset (498,697 samples) is built upon the ART dataset (crowdsourced commonsense narrative contexts). It includes: 1) high-plausibility positive samples from ART, 2) newly generated positive samples using a logic-based theorem generator on logically consistent inference chains, 3) augmented positive samples by interchanging the first observation (𝒪1) and hypothesis (ℋ), and 4) negative samples derived by logically negating the second observation (𝒪2) based on a truth table for the expression 𝒪1∧ℋ =⇒ 𝒪2. | Task redefinition (binary classification of (O1, H, O2) validity instead of choosing between two hypotheses), logic-based data generation and augmentation (observation-hypothesis interchangeability), and systematic negative sample creation using logical negation and truth tables, based on an initial crowdsourced dataset (ART). | NaN | False | False | NaN | Current SOTA models, including legal-specific ones, lack robust abductive reasoning. Technical gaps include: need for alternative pretraining approaches tailored to abductive reasoning, development of novel model architectures for legal reasoning, better understanding of model capacity vs. performance on such tasks, and effective integration of legal domain knowledge into pretraining. | Ensuring logical consistency and quality in initial crowdsourced data (ART). Defining abductive reasoning tasks precisely for evaluation. Generating meaningful and logically sound negative samples for abductive reasoning. Overcoming the bias in legal models trained primarily on legal reasoning rather than abductive reasoning. | Over-reliance on current SOTA LLMs for legal tasks that require significant abductive reasoning, given their demonstrated poor performance, potentially leading to flawed legal analyses or applications. Misdirection of development efforts if the limitations in abductive reasoning are not addressed. |
| E7b4JLhQct8J.pdf | Google_Scholar | The Courtrooms Strikes Back: Generative AI’ s Force in Courts | This paper explores the increasing use of generative AI by judges in judicial decision-making, highlighting its potential to enhance court legitimacy and efficiency, which can support access to justice. However, it also details significant risks, such as bias, unreliability, and ethical concerns from AI systems like ChatGPT, which could undermine court legitimacy if not properly managed. | True | Idealistic | True | 3.0 | Neutral | Generative AI systems (e.g., ChatGPT) for judicial assistance tasks like legal drafting, case law summarization, and acting as a 'virtual sparring partner'. | NaN | NaN | Bias and unreliability of AI systems due to opaque 'black box' nature and unrepresentative training data, risk of AI 'hallucinations', erosion of public trust and court legitimacy, and ethical concerns regarding AI's normative impact and the influence of private developers on judicial independence. | Fostering AI literacy within the judiciary through training, and enhancing transparency and accountability in judicial decision-making, potentially by strengthening the judicial duty to state reasons when AI is used. | Improving efficiency of judicial processes (leading to faster case resolution) and enhancing the quality of judicial decisions, which are pre-requisites for effective access to justice; maintaining court legitimacy. | NaN | General (judicial decision-making across various fields) | Colombia, India, UK, Council of Europe (CEPEJ). Discussion is broadly applicable. | NaN | NaN | NaN | True | False | The paper discusses the use of existing generative AI systems like ChatGPT, which are commercially available with free or paid access tiers. | Technical gaps include lack of transparency, bias, and unreliability in current generative AI. Societal gaps include insufficient AI literacy in the judiciary, need for enhanced transparency and accountability mechanisms (e.g., duty to state reasons), and concerns over democratic oversight and private sector influence on AI used in courts. | NaN | Unreliable or biased outputs due to opaque models and unrepresentative training data, AI 'hallucinations' (generating false information), compromised judicial independence and impartiality from private developers' influence, privacy and data protection violations when handling sensitive data, judges' over-reliance due to automation bias, and overall erosion of court legitimacy and public trust. |
| zrpW9rvsnukJ.pdf | Google_Scholar | Digitalisation of the Slovenian Justice System and Its Discontents | This paper investigates the digitalisation of the Slovenian justice system, revealing a significant gap between the high expectations for digital tools and the practical challenges of their implementation, such as technical issues and user resistance. It advocates for an integrated, strategic approach with stakeholder involvement to improve judicial efficiency, accessibility, and transparency effectively. | True | Idealistic | False | 2.0 | Neutral | Digitalisation tools in the Slovenian justice system (e.g., electronic case files, automated transcription software 'Tipko', videoconferencing, use of social media by courts) | The paper's authors evaluated the impact and reception of these digitalisation efforts through qualitative analysis of in-depth interviews and focus groups with 85 diverse court users in Slovenia. | Digital tools showed potential benefits (e.g., efficiency, accessibility for some), but their implementation faced significant practical difficulties including outdated hardware, digital literacy gaps, increased workload, slow deployment, privacy concerns, and mixed reception among users, often leading to new problems. | Outdated infrastructure and insufficient budgets, varying digital literacy and resistance to change among users, digital divide affecting equitable access, lack of user consultation in technology deployment, slow adoption of effective tools, and potential for technology to be misused (e.g., as an excuse for inaccessible facilities). | Adopting an integrated and strategic approach to digitalisation, ensuring comprehensive stakeholder involvement and user training, investing in adequate infrastructure, developing clear communication strategies (including social media use by courts), and tailoring technology to specific needs while safeguarding against misuse. | Enhancing accessibility of court services and information (especially for vulnerable groups), improving transparency of judicial processes, increasing system efficiency, ensuring fairness in technologically-mediated proceedings, and fostering public understanding and trust in the judiciary. | People with special needs and disabilities (e.g., mobility impaired, deaf/hard of hearing), elderly individuals, foreigners, and the general public in terms of understanding and accessing the justice system. | General court system, including criminal justice, civil litigation, and administrative court processes. | Slovenia | NaN | NaN | For specific tools like 'Tipko' (automated transcription): pilot program at one court with slow/stalled broader rollout. For electronic case files: gradual transition, varying across legal areas. | False | False | NaN | Discrepancy between the promise of technology and implementation realities; significant digital divide; inadequate digital infrastructure in courts; lack of a cohesive digital strategy and stakeholder buy-in; slow deployment of useful technologies; insufficient consideration of ethical implications of AI. | Technical issues (e.g., outdated hardware, software incompatibility, unreliable connections for videoconferencing), user resistance and varying digital literacy, increased workload from new processes (e.g., longer transcripts from recordings), slow deployment due to administrative or budgetary hurdles, and lack of user consultation during implementation leading to mismatches with needs. | De-skilling of legal professionals, loss of nuanced information in automated processes (e.g., "lost in translation" with transcription), algorithmic bias and lack of fairness, privacy violations, cybersecurity threats, exacerbation of digital divide, and potential for technology to be misused or lead to unintended negative consequences (e.g., LLM hallucinations, videoconferencing as a substitute for physical accessibility). |
| cZ5qYLunKBoJ.pdf | Google_Scholar | Is disclosure and certification of the use of generative AI really necessary? | This paper critiques the proliferation of individual judicial standing orders requiring disclosure and certification of generative AI (GenAI) use in legal filings, arguing they are often redundant, inconsistent, and may stifle innovation beneficial for access to justice. It proposes instead the adoption of consistent, court-wide rules developed through public consultation, or public notices, and emphasizes the applicability of existing legal and ethical rules. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Inconsistent and burdensome individual judicial regulations regarding GenAI; the inherent unreliability of general GenAI (e.g., hallucinations, erroneous outputs) especially for pro se litigants; and potential discouragement of technology that could enhance access to justice. | Implement consistent, court-wide rules for GenAI use, developed after public notice and comment, instead of individual standing orders. Provide public guidance, especially for pro se litigants, on responsible GenAI use and verification obligations. Leverage existing rules of civil procedure and professional conduct, and encourage education by bar associations. | Judicial regulation of AI in legal practice; impact of AI governance on access to justice; enabling unrepresented parties (pro se litigants) to utilize legal tech; reducing legal costs and increasing efficiency through AI. | Pro se litigants (unrepresented parties). | Civil litigation | United States, Canada | NaN | NaN | NaN | False | False | NaN | The need for a nuanced, consistent, and less burdensome regulatory approach to GenAI in the legal system that encourages beneficial uses for access to justice. The current unreliability of general-purpose GenAI for complex legal tasks and the limited access for pro se litigants to more specialized and verified legal AI tools. Lack of comprehensive institutional guidance from bodies like bar associations. | NaN | Generation of inaccurate legal information (hallucinations, fake citations) by GenAI; infringement on attorney work product due to overly broad disclosure orders; chilling innovation and use of technology beneficial for access to justice; inconsistent judicial orders creating confusion and increasing costs; disclosure of confidential client information when using public GenAI tools; difficulty in accurately detecting AI-generated content; lawyers violating ethical duties (competence, candor, confidentiality) through improper GenAI use. |
| CMs1hDgYQYEJ.pdf | Google_Scholar | AI vs. Human Translators: Navigating the Complex World of Religious Texts and Cultural Sensitivity. | This paper evaluates the performance of AI translation tools, specifically ChatGPT and Google Translate, against human translators in rendering English religious texts into Arabic. The findings indicate that while AI tools offer fairly accurate translations, human translators consistently provide superior quality, particularly in conveying depth, cultural relevance, and nuanced understanding inherent in complex religious content. | True | NaN | True | 2.0 | NaN | ChatGPT and Google Translate (Neural Machine Translation) | Qualitative and comparative data analysis of translations of seven English to Arabic religious texts. Evaluation criteria included word choice, word count, readability, overall translation quality (fluency, accuracy, meaning loss), and punctuation, with Nvivo software used for coding and analysis. | Human translation consistently outperformed machine translations (ChatGPT and Google Translate), maintaining depth, cultural relevance, and nuanced understanding. Machine translations, while sometimes accurate, were often more concise, potentially missing significant elements, and exhibited issues like repetition or grammatical errors in complex sentences. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | True | False | ChatGPT and Google Translate are publicly accessible online translation services. | NaN | Machine translation tools (ChatGPT and Google Translate) faced difficulties in preserving linguistic nuances, context, and complex sentence structures accurately, especially in culturally and religiously loaded texts. Repetition was observed in machine translations, and achieving correct grammatical structure in Arabic was a challenge. | Mistranslation or meaning loss of essential elements, blurring of intended meaning due to inaccurate word choice, loss of cultural and religious nuances, grammatical errors in the translated text, and repetition affecting readability. |
| B-J7tTJ2YCMJ.pdf | Google_Scholar | Breaking the Loop: Detecting and Mitigating Denial-of-Service Vulnerabilities in Large Language Models | This paper addresses the issue of recurrent generation (looping) in Large Language Models (LLMs), which causes increased latency and potential Denial-of-Service (DoS) vulnerabilities. It proposes 'RecurrentGenerator', a black-box evolutionary algorithm to find inputs triggering loops, and 'RecurrentDetector', a lightweight classifier trained on LLM activation patterns to detect loops in real-time. | True | Market | True | 1.0 | NaN | RecurrentGenerator: A black-box evolutionary algorithm using a self-similarity fitness function to generate inputs that trigger recurrent generation in LLMs. RecurrentDetector: A lightweight Multi-Layer Perceptron (MLP) classifier trained on features derived from LLM activation state similarities to detect recurrent generation in real-time. | RecurrentGenerator was evaluated by comparing the number of attempts needed to find recurrent samples against a random sampling baseline across eight LLMs (Llama versions, Gemma-2, GPT-4o, GPT-4o mini). RecurrentDetector was evaluated on six open-source LLMs using metrics like accuracy, F1 score, false positive rate, recall, and inference time, based on a dataset combining ShareGPT samples, benign generated prompts, and harmful prompts identified by RecurrentGenerator. | RecurrentDetector achieved an average accuracy of 95.24%, an F1 score of 0.87, and a false positive rate of 2.59% across six open-source LLMs, with a fast average inference time of 0.36 ms. | NaN | NaN | NaN | NaN | NaN | International | RecurrentDetector was trained on activation pattern features extracted from LLM responses. The training dataset included prompts sampled from the public ShareGPT dataset, benign prompts generated during experiments, and harmful prompts known to cause recurrent generation (identified using RecurrentGenerator) across six open-source LLMs. The core data consists of internal LLM activation states and derived similarity metrics. | RecurrentGenerator: Evolutionary algorithm (black-box generative testing). RecurrentDetector: Supervised machine learning (MLP classifier) based on white-box analysis of LLM internal activation states. | Code and results are released on the authors' project website. | True | True | Code and results released on the authors' website [8]. | NaN | Efficiently generating test inputs that trigger recurrent generation, especially for black-box models. Reliably distinguishing benign long outputs from harmful recurrent generation loops in real-time without significant overhead. Understanding the internal model behavior leading to recurrent generation. | Increased latency in LLM responses. Degraded user experience due to repetitive content and long wait times. Potential for Denial-of-Service (DoS) attacks. Increased operational costs for developers and LLM providers (token usage, compute resources, energy consumption). |
| KYZmCs74QmQJ.pdf | Google_Scholar | Investigating the Effectiveness of ChatGPT in Mathematical Reasoning and Problem Solving: Evidence from the Vietnamese National High School Graduation Examination | This paper evaluates ChatGPT's performance on mathematics questions from the Vietnamese National High School Graduation Examination (VNHSGE) from 2019-2023. It finds ChatGPT performs well on knowledge-based questions but struggles significantly with increasing difficulty levels, especially application-level problems and those requiring graphical interpretation. | True | NaN | True | 2.0 | Neutral | ChatGPT | Evaluation on a dataset of 250 multiple-choice questions from the Vietnamese National High School Graduation Examination (VNHSGE) mathematics tests (2019-2023). Questions were categorized by difficulty (Knowledge, Comprehension, Application, High Application) and topic. ChatGPT was prompted with questions formatted to request a specific output structure (Choice + Explanation). | ChatGPT achieved an average score of 5.88/10 (58.8%) across 2019-2023 exams. Accuracy significantly decreased with question difficulty: 83% (Knowledge), 62% (Comprehension), 27% (Application), 10% (High Application). Performance varied by topic, with notable weaknesses in Derivatives/Applications (especially graphical questions), Spatial Geometry, and Oxyz Spatial Calculus. | NaN | NaN | NaN | NaN | NaN | Vietnam | The paper evaluates ChatGPT, which was pre-trained by OpenAI on a large text corpus (details not provided by this paper). The evaluation dataset consists of 250 multiple-choice questions from the publicly available Vietnamese National High School Graduation Examination (VNHSGE) mathematics tests (2019-2023). | Evaluation methodology: Data collection from official VNHSGE papers, formatting questions into LaTeX then JSON, structured prompting of ChatGPT, comparison of generated answers with correct solutions. | NaN | True | False | The paper uses the ChatGPT model accessible via the OpenAI website (chat.openai.com) and the OpenAI API as of the time of the study. | NaN | Key challenges identified for ChatGPT include: difficulty with complex, higher-level reasoning problems (Application and High Application levels); inability to interpret graphical data (tables, charts, diagrams) within questions; inconsistent performance across different mathematical topics. | NaN |
| QWScjUiMBQwJ.pdf | Google_Scholar | Evolving Norms Governing AI Engagement in Legal Practice and the Prospective Alignment of Law School Curriculum | This paper investigates pioneering US professional standards for regulating generative AI in legal practice, emphasizing the need for lawyers to understand AI's benefits and risks. It argues for aligning law school curricula with AI advancements and regulatory norms to cultivate AI-empowered legal professionals and upholds the importance of access to AI. | True | Idealistic | True | 3.0 | Positive | Generative AI (e.g., ChatGPT, LLMs) | NaN | NaN | The potential for an "AI divide" denying equitable access to AI's benefits in the legal field; risks of AI bias, lack of transparency, and generation of erroneous information undermining client rights and fair justice; breaches of client confidentiality. | Comprehensive AI education for legal professionals starting in law school; development and enforcement of robust professional and judicial standards for AI use focusing on accountability and human oversight; ensuring equitable access to the benefits of AI for all. | Equitable access to AI benefits in legal services; protection of client rights (confidentiality, competence, due diligence) in the context of AI use; mitigating AI bias in legal applications and the justice system. | General public / All clients of legal services; implicitly, communities vulnerable to AI bias (e.g., racial or economic bias in criminal justice). | General legal practice, Professional responsibility, Legal education, Criminal justice (briefly mentioned in context of risk assessment tools). | United States (primarily); relevance for other common law jurisdictions discussed. | NaN | NaN | NaN | True | True | The paper discusses generally available generative AI tools like ChatGPT, which has publicly accessible free and paid versions. | The risk of an "AI divide" if benefits are not equitably distributed; inadequacy of current continuing legal education for comprehensive AI training, necessitating law school curriculum reform; persistent issues of AI bias, lack of transparency, and explainability in legal AI tools. | NaN | AI bias leading to unfair outcomes (e.g., racial, economic); lack of explainability and transparency in AI decision-making; generation of false information (hallucinations) by AI; breaches of client confidentiality; over-reliance on AI compromising lawyers' professional judgment; inadvertent creation of attorney-client relationships via AI; potential for an "AI divide" in society. |
| Ub_Ju_UT7V4J.pdf | Google_Scholar | Artificial Intelligence and Employment: A Look into the Crystal Ball | This paper investigates the impact of AI exposure on employment dynamics in European regions between 2011 and 2018, using occupation-based AI exposure indicators mapped to European data. Results suggest a positive correlation between AI exposure and regional employment growth on average, though this may be negatively moderated by robot density in some regions. | True | NaN | False | 2.0 | NaN | Econometric analysis using AI Occupational Exposure (AIOE) indicators (Felten et al.) mapped to European regional employment data (ISCO/NUTS2) via crosswalk. | Econometric panel data analysis (OLS, Fixed Effects, Instrumental Variable) on 202 EU NUTS-2 regions (2011-2018), controlling for structural, labour market, R&D, and demand factors. Daily internet users used as IV for AI exposure. | Higher regional AI exposure (AIRE) significantly correlates with positive employment growth (IV estimate: +2.1 p.p. employment growth for 1 std. dev. increase in AIRE). Interaction effects with robot density suggesting negative moderation were found in FE models but were not robust in IV specifications. | NaN | NaN | NaN | NaN | NaN | European Union (23 countries, 202 NUTS-2 regions) | The analysis uses: 1) AIOE indicators (derived from Felten et al.'s work linking AI applications to O*NET occupational abilities via surveys/expert input). 2) European Labour Force Survey (EU LFS) for regional employment and occupational structure (ISCO codes). 3) International Federation of Robotics (IFR) data for robot density. 4) Eurostat data for controls (internet use, demographics, firm size, manufacturing share, R&D, GVA). Mix of publicly available statistical data and derived indicators based on survey/expert knowledge. | Crosswalking US-based AIOE scores to EU ISCO codes; Aggregating occupational scores to regional level (AIRE) using employment weights; Applying econometric panel data models (OLS, FE, IV). | NaN | False | False | NaN | NaN | Measuring actual AI adoption vs. potential exposure; Early diffusion stage limiting observable impacts; Accounting for interaction with automation (robots); Addressing spatial heterogeneity; Data limitations (EU data granularity vs. US); Potential endogeneity; Difficulty predicting impact of disruptive technology based on past data. | Potential for technological unemployment; Negative employment effects from AI-robot interaction in specific regions; Potential negative impacts on job quality (e.g., monotony, lack of meaning), although not measured in the study. |
| 8xT5fS0mGskJ.pdf | Google_Scholar | Better Bill GPT: Comparing Large Language Models against Legal Invoice Reviewers | This paper empirically compares Large Language Models (LLMs) against human legal invoice reviewers (early-career lawyers, experienced lawyers, Legal Operations Professionals) on accuracy, speed, and cost-effectiveness for legal invoice review. Findings reveal that LLMs significantly outperform humans across all metrics, indicating the arrival of LLM-powered legal spend management. | True | Market | True | 2.0 | NaN | Using pre-trained Large Language Models (e.g., Gemini 2.0 Flash Thinking, GPT-4o) with prompt engineering for legal invoice review. | LLMs and human reviewer groups were benchmarked against a ground truth (set by expert legal professionals) on a dataset of 50 legal invoices (anonymized client and synthetic). Performance was measured by F-scores for invoice approval and line-item classification, speed (seconds per invoice), and cost (USD per invoice). | The best performing LLM (Gemini 2.0 Flash Thinking) achieved 92% F-score for invoice approval decisions and an 81% F-score for line-item classification. This significantly surpassed the best human group (experienced lawyers at 72% and 43% respectively), with LLMs also being 50-80x faster and over 99% cheaper. | NaN | NaN | NaN | NaN | General (Legal Spend Management, Billing Practices) | US (implied by industry norms referenced for reviewer classification and salary guides) | The LLMs studied are general-purpose models pre-trained on broad datasets. For this study, they processed a specific evaluation dataset of 50 legal invoices (anonymized client and synthetic; unstructured text) with billing guidelines provided as contextual information within prompts. | Experimental design comparing LLM performance against human reviewers based on a ground truth. LLM approach involved model selection and iterative prompt engineering including role definition, task instructions, and contextual information (billing rules, policies). | LLMs were accessed via API endpoints (OpenAI, Claude, Gemini) or hosted internally on AWS (DeepSeek R1) for the study. No wider public deployment from this specific study is mentioned. | True | False | Several of the evaluated LLMs (e.g., GPT-4o, Claude 3.5 Sonnet, Gemini 2.0 Flash Thinking) are accessible via their respective APIs, some commercially, and one (Gemini) was reported as free during a preview period. | NaN | Iterative prompt engineering for different LLMs; ensuring optimal integration into workflows balancing AI strengths with human oversight; overcoming industry adoption challenges (regulatory, client expectations, stakeholder resistance); handling supporting documentation and nuanced professional judgment. | Potential for over-reliance on AI leading to compromised judgment if human oversight is not appropriately integrated; ensuring data privacy (e.g., PII) when processing invoices with LLMs. |
| rzMD0sznpJsJ.pdf | Google_Scholar | Access to Justice: The Role of Legal Aid in Society | This paper qualitatively explores the role of legal aid in enhancing access to justice through interviews with stakeholders, identifying key themes like access barriers, service quality, challenges, societal impact, and future directions. It concludes that legal aid is essential for equitable justice and social/economic development, highlighting the need for improvements. | True | Idealistic | False | 3.0 | Positive | NaN | Semi-structured interviews with 22 participants (providers, recipients, stakeholders) followed by thematic analysis. | Key findings highlight barriers (awareness, eligibility, financial, systemic, cultural, technological), quality factors (expertise, client relationship), positive societal impacts (social justice, economic stability, community well-being), and need for improvements (policy, innovation, partnerships). | Lack of awareness, restrictive eligibility, complex application processes, limited service availability, financial constraints/underfunding, systemic policy/institutional barriers, cultural/language barriers, technological divide. | Increase awareness, simplify processes, expand service availability (incl. tech), secure more funding, reform policies, address cultural/tech barriers, foster partnerships, enhance professional development. | Legal aid provision, barriers to access, quality of legal services, societal impact of legal aid. | Vulnerable and marginalized populations, low-income individuals, immigrants, people facing demographic discrimination. | General (Civil Law mentioned, potentially others) | NaN | NaN | Qualitative research: semi-structured interviews, purposive sampling, thematic analysis. | NaN | False | False | NaN | Need for increased funding, policy reform, service delivery innovation (incl. technology), better public awareness, enhanced professional development. Methodological limitations (generalizability) and need for quantitative, comparative, and longitudinal research. | Financial constraints, systemic issues (policy, bias), cultural/language barriers, technological divide impacting legal aid provision and access. | NaN |
| l4PMeM8YyF0J.pdf | Google_Scholar | How can we manage the risks and liabilities associated with legal translation in the age of machine translation and generative AI? | This paper examines the legal and ethical challenges, particularly liability, copyright, and professional rules, associated with using NMT and generative AI for legal translation. It argues for a narrative shift to enhance the role of human translators and proposes due diligence standards and appropriate liability solutions to manage risks. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Increased demand for legal translation unmet by human translators; difficulty ensuring availability of legal information in people's own languages; risks of bias, mis/disinformation, confidentiality breaches, and inaccuracies (e.g., omissions) from AI translation; inadequate liability frameworks for AI-generated translation errors; disruption of translators' professional standing and liability. | Short-term: Implementing due diligence standards for certifying legal translations generated with NMT or generative AI. Long-term: A narrative change to enhance and support the role of the human expert (legal translator), coupled with developing appropriate liability solutions. | Access to legal information in native languages; fair trial (translation of court documents); governmental transparency. | Individuals who do not speak the language of the court; vulnerable individuals (e.g., in asylum adjudications); general public needing access to legal information in their own language. | General legal (transactional documents), Criminal law (court documents, fair trial), Civil procedure (court-related translation), Asylum law. | International (mentions Canada and Europe as examples but discusses issues broadly). | NaN | NaN | NaN | False | False | NaN | The current 'human-in-the-loop' narrative is misleading and doesn't adequately value human expertise; lack of appropriate liability solutions that support human translators in AI-assisted workflows; need for better risk management for inaccuracies, bias, and confidentiality in AI legal translation. | NaN | Bias in translation; exacerbation of mis- and disinformation; breaches of confidentiality (e.g., lawyer-client relationship); increased vulnerability for individuals in sensitive contexts (e.g., asylum adjudications); inaccuracies and omissions in translations; misallocation of legal liability for translation errors. |
| Z5qcyozSxVQJ.pdf | Google_Scholar | Artificial intelligence at the bench: Legal and ethical challenges of informing —or misinforming —judicial decision-making through generative AI | This paper examines the legal and ethical challenges of using Generative AI (GenAI) in judicial decision-making, highlighting risks like bias and misinformation. Through case studies and analysis of regulatory approaches, it proposes a comprehensive framework for the responsible and equitable deployment of GenAI in the judiciary to enhance access to justice and uphold the rule of law. | True | Idealistic | True | 2.0 | Neutral | Generative AI (specifically, the use of Large Language Models like ChatGPT by judicial officers in decision-making processes). | Analysis of case studies from Colombia, Mexico, Peru, and India where judges used ChatGPT. Review of proactive regulatory approaches to AI/GenAI in other jurisdictions (UK, New Zealand, EU, Canada, Singapore, Estonia). | The analysis of case studies reveals unregulated, ad-hoc use of GenAI (like ChatGPT) by judges, leading to significant risks including bias, generation of misinformation ('hallucinations'), lack of transparency, accountability gaps, data privacy issues, and potential erosion of public trust and judicial independence. | Bias amplification, lack of transparency and explainability ('black box' problem), generation of fabricated information ('hallucinations'), undermining judicial independence and discretion, accountability and legal liability gaps, data protection risks, and potential for GenAI to widen access to justice disparities due to resource constraints and ad-hoc implementation. | A dual-prong framework for responsible GenAI integration: 1) Foundational standards for GenAI systems (capacity assessment, stakeholder engagement, licensing/verification, trusted datasets, explainability, clear responsibility allocation, prompt engineering). 2) Application principles for GenAI deployment (updating ethical standards, continuous legal education, case-based risk assessment, disclosure to parties, verification systems, specific procedural rights, ongoing audits). | Access to justice, fairness in judicial decision-making, responsible use of AI in courts, ethical AI governance, rule of law. | General public interacting with the judicial system, with specific mentions of implications for marginalized communities and individuals with disabilities (e.g., a case involving a child with autism). | General judicial decision-making. Case studies cover health law/social security, civil procedure, family law (child support), electoral law, and criminal law (bail applications). | Case studies from Colombia, Mexico, Peru, India. Comparative regulatory approaches from UK, New Zealand, EU, Canada, Singapore, Estonia. The proposed framework is intended for general applicability. | The paper discusses challenges with GenAI (like ChatGPT used in case studies) trained on vast, often unverified, non-legal, and potentially biased internet-scale data. It advocates for the use of 'trusted datasets', potentially closed-network and jurisdiction-specific, for judicial GenAI. | NaN | NaN | False | False | NaN | Lack of consensus and comprehensive frameworks for GenAI in judiciaries; technical limitations of current GenAI (accuracy, bias, explainability, hallucinations); societal challenges (public trust, ensuring equitable access, resource disparities); unclear legal liability; need for standardized AI audits and refined prompt engineering for legal contexts; insufficient legal education on AI. | Key challenges identified in using GenAI in judiciaries include: ensuring transparency and interpretability of AI outputs, mitigating algorithmic bias, addressing poor data quality and AI 'hallucinations', establishing clear accountability and legal liability, protecting data privacy, preserving judicial independence, and ensuring GenAI equitably improves access to justice rather than exacerbating inequalities. | Bias propagation leading to discriminatory outcomes, opacity in decision-making undermining due process, factual inaccuracies ('hallucinations') in AI-generated content, compromised judicial independence and discretion, erosion of public trust, unclear legal liability for AI-induced errors, data privacy breaches, increased justice disparities, and technological solutionism. |
| 14.pdf | Google_Scholar | Natural Language Understanding in Big Data: AI-Driven Approaches for Automated Insights | This paper explores AI-driven Natural Language Understanding (NLU) techniques, focusing on deep learning and transformer models, for extracting insights from large-scale unstructured textual data. It discusses current advancements, applications across various industries including legal services, common challenges like bias and interpretability, and future research directions. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal analytics, legal text analysis, legal decision-making, contract analysis, regulatory monitoring, legal document summarization. | International | The paper refers to large-scale pre-trained datasets (e.g., for models like BERT, GPT, T5, often derived from general web text) and the use of domain-specific corpora (e.g., financial, biomedical, legal texts) for fine-tuning. It primarily discusses unstructured textual data and mentions bias in training data. | NaN | NaN | False | False | NaN | NaN | Bias in AI models and training data, lack of interpretability (black-box nature), high computational costs for training and deployment, scalability, handling domain-specific language, ensuring fairness and transparency, and data privacy. | Bias leading to unfair or discriminatory outcomes (e.g., in legal decision-making, hiring), reinforcement of stereotypes, misinformation, and data privacy violations. |
| XcEkh7h2e9kJ.pdf | Google_Scholar | GUIDE Q: Framework for Guided Questioning for progressive informational collection and classification | This paper introduces GUIDE Q, a novel framework that uses LLMs combined with classifier-derived explanations (keywords) to generate guided questions. This progressive information collection aims to improve text classification accuracy when initial user input is partial or incomplete, demonstrating benefits across various domains. | True | NaN | True | 1.0 | NaN | GUIDE Q framework: It employs a fine-tuned classifier (e.g., BERT) to identify the top-k most probable labels for a partial input text. Keywords representative of these labels are learned via occlusion. A Large Language Model (specifically Llama-3 8B) then uses the partial input, top-k labels, their keywords, and a structured prompting strategy (with few-shot examples) to generate a targeted question. The answer to this question augments the initial input for more accurate final classification. | The framework was evaluated on six text classification datasets (Symptom2Disease, Crypto News, Human Stress Prediction, 20 Newsgroups, DBpedia, SALAD-Bench) using BERT and DeBERTa as classifiers and Llama-3 8B for question generation. Performance was primarily measured by F1-Score improvement after appending answers to guided questions, compared against three baselines: 'Partial' (classification on partial input only), 'LLM' (LLM-generated questions based on partial input), and 'LLM-nk' (LLM with top-3 labels but no keywords). Question quality was assessed via win rate against baselines, and analyses included the effect of keyword n-gram type and multi-turn interactions. | GUIDE Q consistently outperformed baselines in F1-score improvement across both BERT and DeBERTa classifiers on the six datasets. For example, with the DeBERTa classifier, GUIDE Q achieved a 22.1% F1-score increase (from 64.7% to 86.8%) on the Symptom2Disease dataset, and a 20.7% increase (from 38.0% to 58.7%) on the SALAD-Bench dataset, compared to classifying only partial information. The framework also demonstrated higher quality question generation with win rates >50% (often much higher) against baselines. | NaN | NaN | NaN | NaN | NaN | NaN | The classifiers (BERT, DeBERTa) were fine-tuned on various publicly available text classification datasets (Symptom2Disease, Crypto News, Human Stress Prediction, 20 Newsgroups, DBpedia, SALAD-Bench). These datasets consist of unstructured text and corresponding labels, specific to domains such as healthcare, finance, general news, and safety. The LLM (Llama-3 8B) component utilized few-shot exemplars for prompting but was not fine-tuned on these datasets specifically for the GUIDE Q methodology. | The GUIDE Q framework development involved: 1) Fine-tuning pre-trained transformer models (BERT, DeBERTa) for text classification. 2) Employing the occlusion method, an explainability technique, to identify significant keywords for each class label. 3) Designing a structured prompting strategy for an LLM (Llama-3 8B), incorporating few-shot examples, to generate guided questions based on partial input, top predicted labels, and their keywords. 4) An iterative process for multi-turn question generation where guiding words are dynamically updated. | Code is made available on GitHub. | True | True | Code is available on GitHub: https://github.com/SDRMp/DRPG. | NaN | Performance dependency on the quality of the initial classifier model and the relevance of extracted keywords. Reliance on LLMs for question generation introduces potential biases and inconsistencies inherent to these models. Computational resources required for running large language models may pose scalability challenges. | Risk of LLM hallucination (generating plausible but incorrect information). Potential biases and inconsistencies inherent to LLMs used for question generation. Misclassification if the initial classifier or keyword extraction is suboptimal. |
| _KwbPCDd_GwJ.pdf | Google_Scholar | Automatic Information Extraction From Employment Tribunal Judgements Using Large Language Models | This paper evaluates the performance of GPT-4 in automatically extracting eight key pieces of information (facts, claims, outcomes, statutes, precedents, reasons, etc.) from UK Employment Tribunal judgments. The study finds GPT-4 achieves high accuracy, suggesting its potential for legal information processing and facilitating downstream tasks like outcome prediction. | True | Idealistic | True | 2.0 | Positive | Using GPT-4 with engineered prompts for automatic information extraction of specific fields (facts, claims, statutes, precedents, outcomes, remedies, reasons) from legal judgments. | Manual verification by a legal expert and senior legal expert on a stratified sample of 260 UKET judgments. Accuracy was scored (0 or 1) for each of the eight extracted fields. A second check assessed suitability for a downstream prediction task. | High accuracy across all extraction tasks (generally >0.9). Perfect accuracy (1.0) for references to legal statutes and precedents; near-perfect accuracy (0.996) for general outcomes, detailed outcomes, and reasons. Lowest accuracy for labelled outcomes (0.912) and facts (0.942 overall, 0.919 for prediction-suitable cases), still considered high. | Knowledge imbalance between employers and employees regarding access to legal knowledge and predictive tools derived from tribunal data. | Develop accurate and open information extraction and predictive systems using AI, accessible to the general public (both employers and employees), to democratize access to legal knowledge and reduce imbalances. | Information extraction from court judgments, analysis of employment law disputes, case outcome prediction, access to legal information. | Employees involved in or considering UK Employment Tribunal claims, potentially lacking resources compared to employers; the general public. | Employment Law | United Kingdom (UK Employment Tribunal - England, Wales, Scotland) | The study uses GPT-4, a large language model pre-trained by OpenAI on diverse text corpora. The input data for the extraction task consisted of 260 publicly available UK Employment Tribunal judgments from the Cambridge Law Corpus. | Iterative prompt engineering based on OpenAI guidelines (clear instructions, persona definition, delimiters, task specification, examples, systematic testing). Manual quality checks by legal experts to assess accuracy and suitability for prediction. | NaN | True | False | The technique uses the GPT-4 API (32k version), which is commercially available from OpenAI. The prompts are detailed in the paper. | Need for improved prompting to consistently distinguish procedural vs. substantive facts. Potential information bias when using facts/claims extracted from judges' post-outcome decisions for prediction. Predictive models based solely on tribunal judgments lack context from original claim forms and out-of-court settlements. | Designing prompts for accurate and consistent extraction across varied judgments (e.g., handling subsequent claims, rule types, outcome labelling nuances, multiple parties). Difficulty in making GPT-4 distinguish procedural/substantive facts contextually. Ensuring consistent labelling logic for ambiguous situations (e.g., withdrawals, preliminary rulings). | Potential inaccuracies or biases in LLM extractions. Information bias in prediction models trained on post-hoc judicial summaries. Incompleteness of models based solely on adjudicated cases (missing settlements, original filings). Exacerbation of inequality if powerful AI tools are only accessible to resourceful parties. Systems assisting judicial authorities may be classified as high-risk (EU AI Act context). |
| MSfmdl3ZpvMJ.pdf | Google_Scholar | Bridging Law and Data: Augmenting Reasoning via a Semi-Structured Dataset with IRAC methodology | This paper introduces LEGAL SEMI, a new benchmark dataset for legal reasoning based on the IRAC framework, focusing on Malaysian Contract Law. It includes 54 annotated scenarios and a structured knowledge graph (SKG), demonstrating through experiments with LLMs that integrating the SKG improves performance on IRAC tasks like issue identification and rule retrieval. | True | Idealistic | True | 1.0 | Positive | Creation and use of the LEGAL SEMI benchmark dataset, including a Structured Knowledge Graph (SKG), to augment Large Language Models (LLMs) for IRAC (Issue, Rule, Application, Conclusion) analysis of legal scenarios. | Experiments were conducted using four LLMs (GPT-3.5 Turbo, Llama 2, Mistral, Gemini) on the LEGAL SEMI dataset. Tasks included legal concept identification, issue identification, rule retrieval, application generation, and conclusion generation. Evaluation involved automatic metrics (e.g., F1 score for concepts, precision/recall/F1 for rules, GPT-3.5 Turbo as judge for generation tasks) and comparative human evaluation using legal rubrics and Spearman correlation. | Integrating the SKG improved issue generation quality by over 21.4% across LLMs. Using the SKG (legal concepts + textbook interpretations) for rule retrieval achieved the best F1 score of 16.3% at top-5 results, significantly outperforming baseline retrieval. Providing identified issues and rules improved application generation (+18.9% for GPT-3.5 Turbo). Providing the application section improved conclusion generation (+71.4% for GPT-3.5 Turbo). | Backlogs in courts, complexity of legal practice, scarcity of legal professionals, limitations of LLMs in accurate legal reasoning (wrong conclusions, incorrect rule citations, difficulty with legalese vs. everyday language). | Developing high-quality, structured legal datasets (like LEGAL SEMI with its SKG) to enhance LLM reasoning capabilities for legal tasks, specifically automating IRAC analysis to potentially assist legal professionals and improve efficiency. | Automating IRAC analysis, Legal reasoning, Legal document analysis, Legal Information Retrieval. | NaN | Contract Law (specifically Formation of Contract) | Malaysia | The LEGAL SEMI dataset: 54 legal scenarios covering Malaysian Contract Law, annotated by law students/junior lawyers using the IRAC framework. Structured Knowledge Graph (SKG): automatically constructed via rule-based extraction from a Malaysian business law textbook ('Law for Business'), the Malaysian Contracts Act 1950, and 76 Malaysian court cases. The LLMs used (GPT-3.5, Llama 2, Mistral, Gemini) are pre-trained models. | Dataset construction involved scenario selection (human-written and LLM-generated/human-refined), expert review, and detailed human annotation according to IRAC guidelines using a custom-built annotation tool. SKG construction involved rule-based information extraction from structured legal texts (textbook index/content, legislation). Human evaluation rubrics based on legal education standards were used. | The paper states the dataset (LEGAL SEMI) will be made publicly available upon acceptance. | False | False | LEGAL SEMI will be publicly available upon acceptance of this paper. | LLMs struggle with identifying lower-level legal concepts compared to high-level ones. Generating lay-language interpretations of legal rules using LLMs can suffer from hallucination. The dataset scope is limited to Malaysian Contract Law (formation). | High effort and expertise required for reliable legal annotation. Bridging the semantic gap between lay language in scenarios and legalese in rules. Ensuring factual accuracy and reasoning fidelity in LLM outputs for legal tasks. Evaluating complex generative tasks in the legal domain. Automating the construction of comprehensive and accurate legal knowledge graphs. | LLM limitations leading to incorrect legal conclusions, citation of wrong legal rules, and hallucination, which pose risks if used without expert oversight in real-world legal analysis. |
| 2fOPE9ql6n0J.pdf | Google_Scholar | Large Language Models: AI’s Legal Revolution | This paper reviews the history of chatbots leading to modern Large Language Models (LLMs) and examines current LLMs like ChatGPT, Bing Chat, Bard, CoCounsel, and Lexis+ AI. It argues strongly for the integration of LLMs into legal academia, private practice, and the judiciary, advocating for understanding and nuanced regulation over bans to enhance legal efficiency. | True | Market | True | 2.0 | Positive | Large Language Models (LLMs), specifically differentiating between general-purpose (e.g., ChatGPT, Bing Chat, Bard) and legal-specific (e.g., CoCounsel, Lexis+ AI). | NaN | NaN | Lack of understanding of LLM capabilities and types within the legal profession; concerns about hallucinations and data privacy (especially with non-legal LLMs); misguided attempts to ban the technology; inability to effectively detect LLM-generated content. | Educate legal professionals (students, practitioners, judges) about LLM types and responsible use; integrate LLMs into legal workflows (practice, judiciary) to improve efficiency, with human oversight; develop nuanced regulations recognizing differences between LLM types (legal vs. non-legal). | NaN | NaN | General Legal Practice | United States | Discusses various LLMs using different data: general LLMs (e.g., ChatGPT, Bing Chat, Bard) trained on vast internet text scrapes (some static, some live); legal-specific LLMs (e.g., CoCounsel, Lexis+ AI) trained on proprietary, curated, up-to-date legal databases (caselaw, statutes, etc.) in addition to base LLM capabilities. | NaN | NaN | True | False | General LLMs (ChatGPT free tier, Bing Chat, Bard) available online; Legal-specific LLMs (CoCounsel, Lexis+ AI) available as commercial products. | Need for better education/understanding of LLMs in the legal profession; lack of nuanced regulations distinguishing LLM types; technical unreliability (hallucinations) and privacy risks of non-legal LLMs for legal tasks; lack of reliable methods to detect AI-generated text. | For the legal profession: Understanding the technology, balancing efficiency gains with risks (hallucinations, privacy), developing appropriate regulations, adapting education and practice. For LLM creators: Reducing hallucinations, ensuring data privacy and security, training models on accurate and up-to-date (legal) data. | Hallucinations (generating incorrect information/citations); violation of data privacy and client confidentiality (especially with non-legal LLMs); inaccurate legal work due to over-reliance without human verification. |
| gre9EWR6YS0J.pdf | Google_Scholar | Mixed-domain Language Modeling for Processing Long Legal Documents | This paper introduces LEGAL RELECTRA, a specialized language model for personal injury text, trained on mixed legal and medical corpora. It demonstrates that this smaller model, utilizing an ELECTRA framework with REFORMER components to handle long documents, outperforms general and single-domain models on tasks like NER and case retrieval in the personal injury domain. | True | Market | False | 1.0 | NaN | LEGAL RELECTRA: a specialized language model using an ELECTRA framework with REFORMER for its generator and discriminator, trained on mixed-domain (legal and medical) corpora, and utilizing a custom domain-specific tokenizer. | Evaluated on Named Entity Recognition (NER) for legal and mixed-domain text (using custom annotated data and public datasets conll2003, MIMIC III) and Legal Case Retrieval (on a proprietary dataset of 500 cases). Performance was compared against BERT, LEGAL-BERT, CLINICAL-BERT, and REFORMER. | On legal domain NER, LEGAL RELECTRA achieved an overall F1 score of 85.93. For legal case retrieval, it achieved 90.00% accuracy in matching claim type and 92.00% in matching injury categories, outperforming baseline models. | NaN | NaN | NaN | NaN | Personal injury civil suits | United States (specifically mentions Kentucky and Louisiana for some datasets, general US legal data otherwise) | A 12GB corpus of unstructured text: 6GB legal text (e.g., CourtListener, proprietary case descriptions), 3GB medical text (MIMIC, MIMIC-CXR), and 3GB mixed legal-medical text (personal injury cases from Supreme Court opinions, academic literature, BYU LAW, attorney descriptions). Mix of public and proprietary data. | Novel model architecture (RELECTRA: ELECTRA with REFORMER components), mixed-domain pre-training, development of a custom domain-specific tokenizer using Byte-Pair Encoding. | NaN | False | False | NaN | NaN | 1. Processing long legal documents (beyond typical token limits of models like BERT). 2. Handling specialized terminology from multiple domains (e.g., legal and medical) within legal texts. 3. Limited access to large, high-quality, curated training datasets for specialized legal AI. | Potential biases from training data (e.g., court opinions) impacting model predictions. Privacy concerns with legal and medical data, although anonymization was employed in this study. |
| HealthcareGrowingroleofGAI.pdf | Google_Scholar | Healthcare: A Growing Role for Large Language Models and Generative AI | This paper surveys the application of large language models (LLMs) and generative artificial intelligence (GAI) in healthcare, discussing their use in medical text analysis, image processing, and multimodal tasks. It reviews specific models, benchmarks, tools, challenges (like data privacy, bias, interpretability), and ethical considerations within the healthcare domain. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Healthcare | International | Various publicly available and potentially proprietary biomedical datasets (EHRs, text, images, genomics) mentioned across referenced studies. | NaN | Discusses commercial tools integrated with EHRs and models released via public repositories. | True | True | Some models (e.g., PathologyBERT, PMC-LLaMA) stated available via public repositories (e.g., Hugging Face). Some commercial tools (e.g., Suki Assistant, Glass AI, Amazon Transcribe Medical) discussed as available services. | Need for interpretability, bias mitigation, data security/privacy, regulatory clarity, model robustness (hallucinations), human oversight, better instruction handling, and further study of legal implications. | Data privacy/security, interpretability, data bias, regulatory hurdles, potential for generating false information/hallucinations, workflow integration, handling sensitive data (ePHI). | Data privacy violations, biased/unfair outcomes, generation of false/harmful information, liability issues, deception via synthetic media, plagiarism, copyright infringement. |
| sdjBd5vNE04J.pdf | Google_Scholar | Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learning | This paper proposes D3LM (Diagnostic Legal Large Language Model), a novel framework that uses adaptive lawyer-like diagnostic questions, driven by a Positive-Unlabeled Reinforcement Learning (PURL) algorithm, to gather comprehensive case information from users for improved legal consultations. The research also introduces a new English-language dataset for Court Views Generation (CVG) based on US criminal case law to support LLM research in the legal domain. | True | Idealistic | True | 1.0 | Positive | Diagnostic Legal Large Language Model (D3LM) incorporating a graph-based Positive-Unlabeled Reinforcement Learning (PURL) algorithm for adaptive question generation and Court Views Generation (CVG). | Evaluated using ROUGE and BLEU scores on the authors' newly created US-CVG dataset (derived from US criminal case law). Evaluation also involved human judgment (fluency, accuracy, adoptability) by legal professionals and usability testing (reliability, satisfaction, preference) comparing D3LM to GPT-4.0. | D3LM achieved ROUGE-1 63.3%, ROUGE-2 53.1%, ROUGE-L 59.2%, BLEU-1 38.7%, BLEU-2 31.7%, and BLEU-N 26.9%. In human evaluation, D3LM scored 4.48 for accuracy and 4.19 for adoptability. 62.3% of users preferred D3LM over GPT-4.0 in usability tests. | Scarcity and high cost of legal resources, inequities in legal proceedings disadvantaging the underprivileged, and the difficulty for laypersons (users without legal backgrounds) to formulate effective queries and provide all critical factual details to LLMs. | Development of D3LM, an LLM-based system that actively engages users with diagnostic questions to elicit comprehensive case details, aiming to provide more accurate, tailored, and cost-effective legal guidance, especially for those lacking legal expertise. | Improving legal consultation for laypersons, interactive legal information gathering, court view generation, enhancing AI-driven legal assistants for better accuracy and user understanding. | Individuals with modest means, economically disadvantaged individuals, and users lacking a legal background seeking legal assistance. | Primarily US Criminal Law (based on the dataset, case study, and stated limitations on PURL algorithm effectiveness). | USA | A new English-language Court Views Generation dataset (US-CVG) created by the authors from US criminal legal documents (Caselaw Access Project). GPT-4.0 was used with the IRAC framework to summarize narratives into fact descriptions and court views, and to create fact-rule graphs for each case; dataset integrity ensured by review from legal professionals. | LLM fine-tuning (Llama2-13B), Positive-Unlabeled Reinforcement Learning (PURL) with a bandit approach (NeuralUCB), graph-based knowledge representation (fact-rule graphs processed with DiGCN), IRAC framework for legal text summarization, and development of a new user-LLM interaction paradigm (LLM-navigated diagnostics). | The US-CVG dataset used for training and evaluation is made available on GitHub. The paper does not state other deployment strategies for the D3LM model itself. | False | False | NaN | The PURL algorithm's effectiveness is confined to the criminal cases domain; evaluation restricted to English language cases; the model demands significant computational and human annotation resources; operational speed lags behind existing large models. | Creating domain-specific knowledge graphs (resource-intensive), handling narrative length and complexity of US legal cases within LLM token limits, ensuring LLM reading comprehension for question generation, integrating reinforcement learning for optimal fact selection, and conducting rigorous human expert evaluations. | NaN |
| 3614407.3643708.pdf | Google_Scholar | Rethinking Machine Learning Benchmarks in the Context of Professional Codes of Conduct | This paper argues that current machine learning benchmarks, often focused on accuracy mimicking professional exams, fail to capture essential skills mandated by professional codes of conduct. It proposes using these codes, particularly illustrated through a case study of legal machine translation, to guide the development of more comprehensive benchmarks that incorporate requirements like expressing uncertainty and adhering to specific professional rules. | True | Idealistic | True | 1.0 | Neutral | Proposal to use Professional Codes of Conduct to guide ML benchmark creation, incorporating specific tests based on rules (e.g., preserving filler words, units, double negatives) and integrating 'Know-What-You-Know' (KWYK) checks for uncertainty quantification and abstention. | Case study on legal machine translation with demonstrative experiments: 1) 'Unit tests' checking gpt-3.5-turbo's compliance with specific California court interpreter rules (unit/filler word/error/repetition/double negative preservation, idiom identification, clarification needed). 2) KWYK check experiments using NLLB/flan-t5-xl models on translation (Opus100/Flores/EAC-TM datasets) and bar exam tasks (MMLU), measuring abstain rate vs acceptability rate. | For rule compliance tests with gpt-3.5-turbo, adherence varied significantly by rule (e.g., 100% unit preservation, 10% word repetition preservation, 61% double negative preservation). For KWYK checks on translation, a verifier KWYK check (xlm-roberta-base) achieved a target of 75% acceptability with an 18.9% abstain rate on a legal-adjacent translation task; KWYK checks failed to reach target accuracy on the bar exam task. | Unreliability and lack of accountability of general-purpose AI tools (like MT) in high-stakes legal contexts; inaccuracies leading to severe negative consequences (asylum denial, rights violations); tendency for users to rely on tools without understanding limitations due to perceived general competence; shortage of qualified human professionals (e.g., translators) creating demand for potentially unsafe AI solutions. | Align ML benchmarks with professional codes of conduct; integrate specific tests based on professional rules into benchmarks; standardize evaluation of uncertainty quantification ('Know-What-You-Know' checks) allowing models to abstain; provide runtime transparency regarding model limitations and adherence to rules. | Machine translation quality and reliability in legal contexts; AI safety and evaluation; Professional ethics in AI applications. | Individuals with Limited English Proficiency (LEP) interacting with the legal system, such as asylum seekers and individuals in police encounters. | Immigration Law, Criminal Procedure, Evidence, Professional Responsibility (Interpreters, Lawyers) | United States (primarily California and federal context) | The paper primarily evaluates existing models. Demonstrative experiments used public datasets: Opus100 (parallel corpora), Flores 200 (Wikipedia), EAC-TM (EU documents), MMLU Bar Exam (professional exam questions), Tang 2022 & Fadaee et al. 2018 (idiom datasets). KWYK checks used models pre-trained on large general corpora. | Comparative analysis (benchmarks vs. professional rules), Case study (legal machine translation), Conceptual proposal (rule-based benchmarks, KWYK checks), Demonstrative empirical evaluation. | NaN | False | True | Code for demonstrative experiments stated to be available in Supplementary Material. | Technical: Need for improved KWYK check calibration and reliability, methods for robustly incorporating professional rules into models, handling conflicting rules. Societal: Need for broader adoption of professionally-grounded benchmarks, effective user communication of uncertainty, extension to other professional domains. | Current benchmarks focus narrowly on accuracy, neglecting professional standards; implementing robust KWYK checks is a research challenge; ensuring consistent rule adherence in stochastic models is difficult; quantitatively evaluating nuanced rule compliance; generalizing the proposed evaluation approach. | Mistranslations leading to asylum denial, misunderstanding consent to search (Fourth Amendment violations), inadmissible evidence, or misinterpretation of law; over-reliance on seemingly capable AI leading to errors in critical situations; AI potentially removing crucial context (e.g., filler words indicating uncertainty); potential unauthorized practice of law; AI hallucinations in legal work. |
| PEdAAU7Q1McJ.pdf | Google_Scholar | Exploring the feasibility of developing an education tool for pattern identification using a large language model: focusing on the case of a simulated patient with fatigue symptom and dual deficiency of the heart-spleen pattern | This paper explores using large language models (LLMs) to create simulated patients for Korean Medicine education, specifically focusing on pattern identification training. The authors developed a prototype using prompt engineering based on standardized patient data and implemented web interfaces for students and evaluators. | False | NaN | True | 1.0 | NaN | Using an LLM (ChatGPT) via prompt engineering to simulate a patient based on Korean Medicine clinical practice examination (CPX) modules for educational purposes, coupled with a web interface (Django/WebSockets). | Prototype development and demonstration via a web interface. No formal user testing or benchmark results described. | Successfully developed a prototype simulated patient using prompt engineering and implemented web interfaces for examinees and evaluators. | NaN | NaN | NaN | NaN | NaN | Republic of Korea | Domain-specific (Korean Medicine) standardized patient information (characteristics, symptoms, history, sample Q&A) extracted from clinical practice examination (CPX) modules provided by the National Institute for Korean Medicine Development. Used for prompt engineering, not model training. | Prompt engineering (system, user, assistant prompts) based on standardized patient data; Web development using Django framework and WebSockets. | A web-based prototype was developed and made accessible via a URL. | True | False | The tool is available for testing via a specific URL provided in the paper: https://aicpx.seungho.kr/registration | NaN | Ensuring simulated patient realism, mitigating LLM hallucination, creating a user-friendly and accessible educational interface. | LLM hallucination (generating incorrect or non-existent information) in the simulated patient scenario. |
| 7kzQWOd65PQJ.pdf | Google_Scholar | Generative AI on the Loose: Impact of Improved AI and Expert Oversight on Knowledge Sharing | This study examines the impact of Generative AI advancements (specifically GPT-4) and expert oversight on knowledge sharing platforms, using Stack Overflow as a natural experiment. It finds that combining improved AI with strict oversight reduces contribution volume but enhances quality, whereas reduced oversight increases volume but lowers quality, especially from novices. | True | NaN | True | 2.0 | NaN | Analysis of the impact of Generative AI (GPT-4) and expert oversight on knowledge sharing dynamics. | Natural experiment using Stack Overflow data (Jan 2022 - Dec 2023) around GPT-4 release and moderation policy changes (moderator strike). Econometric analysis (Difference-in-Differences, Triple-Differences) on contribution quantity, quality (votes, upvote share), reputation gains, and moderation activity. Heterogeneity analysis by user reputation and supplementary analysis using Stack Overflow Developer Surveys. | Combining GPT-4 with strict expert oversight reduced the quantity of knowledge sharing but significantly improved quality. Relaxed oversight increased quantity but decreased quality (especially for answers), particularly from novice users. GPT-4 improved middle-skilled users' answer quality and low-skilled users' question quality, but oversight remained crucial for novices. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | False | NaN | NaN | General empirical challenges identified include obtaining detailed organizational knowledge-sharing data, observing both significant AI improvement and adoption, and observing relevant managerial policy shifts within a suitable timeframe. Specific identification challenges included establishing a comparable control group, ensuring parallel trends, avoiding SUTVA violations, and validating the significance of the policy changes (treatments). | GenAI risks: Hallucinations, lack of contextual understanding, generating content that appears correct but contains errors, compromising content quality and user trust. User risks: Novices struggling to assess AI-generated content, reputation farming or misuse of GenAI under weak oversight leading to lower-quality contributions. Platform risks: Strict moderation might discourage genuine participation through false positives. Applying GenAI to tacit knowledge domains without expert oversight may pose greater risks. |
| jdzBM0w9BAQJ.pdf | Google_Scholar | Discussion on the Reform of Higher Legal Education in China \nBased on the Application and Limitation of Artificial \nIntelligence in Law Represented by ChatGPT | This paper examines the applications (e.g., text processing, legal aid, education support) and limitations (e.g., data scarcity, inaccuracy, lack of value judgment) of ChatGPT in the legal field. It primarily discusses the resulting challenges and the need for reform in Chinese higher legal education to adapt to the AI era. | True | Market | True | 3.0 | Positive | ChatGPT | NaN | NaN | For AI application in law: Lack of high-quality, open Chinese legal training data; potential for generating false statements; lack of value judgment and empathy. For legal education: Risk of job displacement for graduates; student over-reliance on AI leading to reduced critical thinking, autonomy, and academic dishonesty. | For access to justice: Use AI like ChatGPT for low-cost/free online legal aid and consulting. For legal education reform: Cultivate 'AI + Law' talents, focus on legal thinking/skills over rote learning, adopt student-centered/personalized teaching methods, and integrate AI ethics education. | Legal aid, Online legal consulting | Vulnerable groups, General public | General Law | China | The paper discusses ChatGPT, which uses a large general pre-trained corpus. It highlights the lack of high-quality, publicly accessible, specialized Chinese legal data (e.g., from Judgment Document Network) as a limitation for applying such models effectively in China. | NaN | NaN | True | True | ChatGPT is available via web interface and API, with free and paid tiers offered by OpenAI. | Lack of accessible high-quality Chinese legal training data; Need for updated legal education curricula incorporating 'AI + Law' and AI ethics; Ensuring AI aligns with human ethical values. | Applying LLMs effectively given data limitations; Preventing generation of false information; Addressing the lack of nuanced value judgment; Reforming legal education curricula and teaching methods; Mitigating student over-reliance and academic integrity issues. | Generation of false statements/fabrications; Misuse in litigation; Algorithmic bias reinforcement; Erosion of students' critical thinking/autonomy; Academic dishonesty/plagiarism; Job displacement for legal professionals; National security threats; Privacy disclosure. |
| yMRuEJga_zIJ.pdf | Google_Scholar | GPT-4 Technical Report | This paper introduces GPT-4, a large-scale multimodal model that processes image and text inputs to produce text outputs, demonstrating human-level performance on diverse benchmarks including a simulated bar exam. It details GPT-4's predictable scaling, its inherent limitations such as potential unreliability, and the safety measures undertaken, including adversarial testing and model-assisted safety pipelines. | True | NaN | True | 1.0 | NaN | GPT-4, a large-scale, multimodal, Transformer-based model, pre-trained to predict the next token and fine-tuned using Reinforcement Learning from Human Feedback (RLHF) and a model-assisted safety pipeline with rule-based reward models (RBRMs). | Evaluated on diverse benchmarks: professional/academic exams (Uniform Bar Exam, LSAT, SAT, GRE, APs), NLP benchmarks (MMLU, HellaSwag, ARC, WinoGrande, DROP, GSM-8K), coding benchmarks (HumanEval, Leetcode), multilingual MMLU, visual input tasks, and safety evaluations including internal factuality evaluations and adversarial testing with domain experts (e.g., for cybersecurity, biorisk). | GPT-4 achieved a score around the top 10% of test takers on a simulated Uniform Bar Exam (298/400). On MMLU, it scored 86.4% (5-shot). It significantly reduces hallucinations compared to GPT-3.5 and shows improved adherence to safety policies. | NaN | NaN | NaN | NaN | General US law (as tested by the Uniform Bar Exam) | United States (for the Uniform Bar Exam). Multilingual capabilities tested more broadly. | Pre-trained on a large dataset of publicly available data (such as internet data) and data licensed from third-party providers. Fine-tuned using Reinforcement Learning from Human Feedback (RLHF). Specific dataset construction details are not provided. | Development of a deep learning stack that scales predictably. Pre-training followed by RLHF fine-tuning. Model-assisted safety pipeline including rule-based reward models (RBRMs). Adversarial testing with domain experts. Iterative model improvement based on evaluations. | GPT-4 is made available via ChatGPT and the OpenAI API. Deployment includes monitoring for abuse and a pipeline for fast iterative model improvement. OpenAI Evals, a benchmarking framework, is open-sourced. | True | False | Available via OpenAI API and ChatGPT. The OpenAI Evals benchmarking framework is open-sourced on GitHub. | NaN | Developing predictably scaling deep learning infrastructure. Ensuring model reliability (hallucinations, limited context, no experiential learning). Addressing significant safety challenges: bias, disinformation, over-reliance, privacy, cybersecurity, proliferation. Managing adversarial attacks ("jailbreaks"). | Hallucinations (generating false facts), reasoning errors, limited context window, lack of learning from experience, perpetuating societal biases, generating harmful content (e.g., hate speech, illicit advice, planning attacks), disinformation and influence operations, privacy violations (e.g., identifying individuals with external data), cybersecurity risks (e.g., aiding social engineering, vulnerability explanation), proliferation of weapons information, overreliance by users, potential for risky emergent behaviors (e.g., power-seeking, though preliminary assessment found current model ineffective at autonomous replication), economic impacts (e.g., job displacement). |
| 6Yzvwm5r5_kJ.pdf | Google_Scholar | LawBench: Benchmarking Legal Knowledge of Large Language Models | This paper introduces LawBench, a comprehensive benchmark designed to evaluate the legal knowledge and capabilities of Large Language Models (LLMs) within the Chinese civil law system across three cognitive levels: memorization, understanding, and application. Based on evaluations of 51 LLMs, the study finds that while GPT-4 leads, all current models, including legally fine-tuned ones, have significant room for improvement in performing diverse and realistic legal tasks reliably. | True | Idealistic | True | 2.0 | Neutral | LawBench: A benchmark suite comprising 20 diverse legal tasks for evaluating LLMs under the Chinese civil law system. | Evaluation of 51 LLMs (multilingual, Chinese-oriented, legal-specific) on LawBench (20 tasks across memorization, understanding, application levels; 5 task types). Tests performed in zero-shot and one-shot settings using task-specific metrics (Accuracy, F1, rc-F1, soft-F1, nLog-distance, F0.5, Rouge-L) and answer extraction rules. | GPT-4 performed best overall (average score 52.35 zero-shot, 53.85 one-shot), significantly outperforming other models. Legal-specific fine-tuning improved over base models but did not surpass top general models. Most models struggled to utilize provided legal article content effectively. | Current LLMs lack sufficient legal knowledge, understanding, and reasoning abilities for reliable performance on diverse legal tasks. Models struggle with instruction following, abstention on legal queries, and effectively integrating retrieved knowledge like legal articles. | Develop stronger foundation models; use high-quality legal-specific fine-tuning data and methods (potentially improving SFT and reconsidering RLHF impact); improve models' ability to utilize retrieved context; foster collaboration to overcome data confidentiality challenges. | Legal information provision, document analysis, case assessment, legal consultation simulation. | Non-professionals needing legal assistance | Criminal Law, Civil Law (including Family Law), Procedural Law, General Legal Practice | China | NaN | Benchmark designed using a cognitive hierarchy (Bloom's taxonomy adapted for legal skills: Memorization, Understanding, Applying). Tasks selected and adapted from existing public legal NLP datasets (e.g., CAIL, LAIC, JEC-QA) and other sources, formatted for instruction-following LLMs. | Benchmark and evaluation code released via GitHub (OpenCompass platform). | True | True | Benchmark data, model predictions, and evaluation code released on GitHub. | Technical gaps: Current LLMs lack robustness and reliability for complex legal reasoning, understanding, and application. They struggle to effectively integrate retrieved legal knowledge and can be hampered by safety alignments (RLHF). Societal gaps: Data confidentiality hinders the development of high-quality legal LLMs. | Evaluation: Designing diverse tasks, reliable answer extraction, appropriate metrics (esp. for generation), preventing data contamination. LLM Development/Application: Effective scaling, domain-specific fine-tuning, balancing helpfulness and harmlessness (instruction following vs. abstention), enabling effective use of retrieved information. | Lack of reliability and accuracy in performing legal tasks; potential for models to refuse to answer relevant legal queries (abstention); risk of evaluation invalidity due to test set contamination. |
| ieWkuwRsfCYJ.pdf | Google_Scholar | EMPOWERING JUSTICE: BLOCKCHAIN AND LEGAL CHATBOTS AS CATALYSTS FOR ACCESS TO LEGAL AID | This paper explores how integrating blockchain technology and AI-powered legal chatbots can improve access to justice by addressing barriers like cost, complexity, and geographical distance. It reviews existing applications, discusses potential benefits, ethical challenges, regulatory needs, and proposes a roadmap for future development focusing on inclusivity and global cooperation. | True | Idealistic | True | 3.0 | Positive | Integration of blockchain technology and legal chatbots | NaN | NaN | Economic constraints (cost), lack of legal literacy/awareness, geographical barriers, systemic discrimination/bias, complexity/inefficiency of legal systems, digital divide. | Leveraging blockchain for secure document/evidence/identity management and smart contracts; using legal chatbots for accessible information, guidance, and document drafting automation; fostering interdisciplinary collaboration, ethical guidelines, regulatory frameworks, inclusive design, investment, transparency, education, and global cooperation. | Access to legal information, advice, representation, document verification/management, identity protection, evidence management, dispute resolution. | General public, economically disadvantaged individuals, people in rural/remote areas, refugees, stateless individuals, marginalized populations. | General/Multiple | International | NaN | Conceptual framework design, discussion of general AI/chatbot development. | Discussion of existing case study deployments (e.g., government initiatives, commercial platforms), proposed pilot programs. | True | False | Mentions several existing platforms (e.g., DoNotPay, Casetext, Kleros, LegalMation, Juro etc.) available as commercial services or platforms, some with free tiers or specific initial free uses. | Digital divide, need for ethical guidelines and robust regulatory frameworks, lack of global convergence/standards, technical limitations (scalability, interoperability), need for AI/tech literacy training, addressing AI bias. | Technical complexity of integration, scalability issues, interoperability challenges, designing for user accessibility (digital divide), legal and regulatory uncertainty/hurdles, data privacy and security concerns, ethical AI development (bias, fairness, accountability), cost of implementation, integration with traditional legal systems, ecological impact of certain blockchains. | Providing inaccurate or oversimplified legal information, errors in automated document drafting, perpetuating societal biases through AI, data privacy violations, potential for misuse (e.g., manipulation), lack of clear accountability for errors, non-compliance with regulations, exacerbating the digital divide. |
| jVRZDuKmAFkJ.pdf | Google_Scholar | SwiLTra-Bench: The Swiss Legal Translation Benchmark | This paper introduces SwiLTra-Bench, a large multilingual benchmark for Swiss legal translation, and SwiLTra-Judge, an LLM-based evaluation system. It evaluates various LLMs, showing frontier models achieve the best performance, and while fine-tuning improves open SLMs, they still trail top zero-shot frontier models like Claude-3.5-Sonnet. | True | Idealistic | True | 1.0 | Positive | SwiLTra-Bench: a multilingual benchmark of over 180K aligned Swiss legal translation pairs. SwiLTra-Judge: an LLM-based evaluation system for legal translation quality assessment. | Various LLMs (translation-specific, frontier, reasoning, open, and fine-tuned SLMs) evaluated on SwiLTra-Bench using metrics like GEMBA-MQM, XCOMET, METEOR, ChrF. Human expert evaluations conducted on top models; SwiLTra-Judge's correlation with human scores was assessed. | Frontier models like Claude-3.5-Sonnet and o1 demonstrated superior performance; fine-tuned open SLMs improved significantly but did not surpass zero-shot frontier models. SwiLTra-Judge, using GPT-4o-mini with a deduction prompt and diverse few-shot examples, achieved the highest correlation (Spearman 0.5 ± 0.07) with human expert judgments. | Lack of specialized, high-quality multilingual legal translation data; inherent complexity (terminology, structure) of legal texts for NMT; translation bottlenecks hindering access to justice and governmental efficiency. | Creation of a large, high-quality multilingual Swiss legal translation benchmark (SwiLTra-Bench) for training and evaluation. Development of an LLM-based evaluation tool (SwiLTra-Judge) aligned with human legal expertise. Systematic evaluation and fine-tuning of LLMs to advance legal NMT capabilities. | Legal machine translation, multilingual access to legal information, automated evaluation of translation quality, enhancing governmental efficiency and civic participation through NMT. | Swiss citizens (especially speakers of official languages including the low-resource Romansh), legal professionals, and governmental bodies in Switzerland. | Swiss law, including legislation (laws), court decisions (headnotes), and official communications (press releases). | Switzerland | SwiLTra-Bench comprises over 180K publicly available, officially translated Swiss legal document pairs (laws, headnotes, press releases) in German, French, Italian, and partially Romansh and English, aligned at various granularities (e.g., paragraph/text level). | For SwiLTra-Bench: collection of official multilingual legal texts, segmentation, and strategic splitting into train/validation/test sets. For SwiLTra-Judge: ablation studies on LLM judge models, prompt engineering (testing basic, detailed, codebook styles), and few-shot example selection, validated against human expert judgments. | The SwiLTra-Bench datasets and associated code (including for SwiLTra-Judge evaluation) are made available on Hugging Face. | True | True | The SwiLTra-Bench datasets and code are available at https://huggingface.co/collections/joelniklaus/swiltra-bench-67c569a2ada47e4549733deb. | Fine-tuned open models still underperform large closed models. Need for further research into techniques like model merging to improve open models. Human expert evaluation scope was limited by resources (e.g., for Romansh, sample sizes). | Limited resources for comprehensive human expert evaluation, particularly for low-resource languages and large sample sizes. Some LLMs (Claude Sonnet/Haiku, o1/o1-mini) proving unsuitable as evaluators in SwiLTra-Judge development due to instruction-following failures or low correlation with human judgment. Token limits of certain automated evaluation metrics when processing longer texts (e.g., press releases). | NaN |
| mIAZXGRNg7QJ.pdf | Google_Scholar | JurisCTC: Enhancing Legal Judgment Prediction via Cross-Domain Transfer and Contrastive Learning | This paper proposes JurisCTC, a model using unsupervised domain adaptation (UDA) and contrastive learning to improve legal judgment prediction (LJP) accuracy. It focuses on transferring knowledge between Chinese civil and criminal law domains to address data scarcity, particularly in criminal law. | True | Idealistic | False | 1.0 | Positive | JurisCTC: A model combining Unsupervised Domain Adaptation (UDA) via Domain-Adversarial Neural Networks (DANN) with a BERT feature extractor and Gradient Reversal Layer (GRL), enhanced by Maximum Mean Discrepancy (MMD) loss and Contrastive Learning for cross-domain Legal Judgment Prediction (LJP). | Evaluated on LJP tasks transferring between Chinese Civil Law (LJP-MSJudge dataset) and Criminal Law (CAIL-2018 dataset) using Accuracy, Macro-Precision, Macro-Recall, and Macro-F1 metrics. Compared against baseline NLP models (TextCNN, BERT, TOPJUDGE, MPBFN), large language models (GPT-4o, Gemini-1.5-Flash, DeepSeek-V3-Chat), and included an ablation study. | JurisCTC achieved peak accuracies of 76.59% (Civil to Criminal transfer) and 78.83% (Criminal to Civil transfer), outperforming baseline models and tested LLMs in the specific LJP tasks. | Scarcity of annotated legal data, especially the decreased availability of public criminal law judgments in China, hindering LJP model development. Also mentions the general difficulty of handling lengthy, complex legal texts. | Using Unsupervised Domain Adaptation (UDA) and transfer learning to leverage data from a source legal domain (e.g., civil law) to improve performance in a target, data-scarce domain (e.g., criminal law) via the proposed JurisCTC model. | Legal Judgment Prediction (LJP) - predicting case outcomes (Guilty/Not Guilty; Support/Not Support appeal). | NaN | Civil Law, Criminal Law | China | Uses existing public research datasets: LJP-MSJudge (Chinese Civil Law judgments) and CAIL-2018 (Chinese Criminal Law judgments). These contain unstructured text from court documents. | Integration of established ML techniques: BERT embeddings, Unsupervised Domain Adaptation (UDA) via Domain-Adversarial Neural Networks (DANN) with Gradient Reversal Layer (GRL), Maximum Mean Discrepancy (MMD) loss, and Contrastive Learning. | Code available on GitHub. | True | True | Code available on GitHub: https://github.com/Zhaolu-K/JurisCTC | Need for investigation into specific linguistic features driving performance; exploration of alternative domain adaptation strategies; potential limitation in precision compared to some LLMs noted. | Handling lengthy and complex legal texts; data scarcity, particularly for Chinese criminal law; achieving model generalization across legal domains; balancing precision and recall. | NaN |
| OGl1CpY-kTkJ.pdf | Google_Scholar | Retrieval-based Evaluation for LLMs: A Case Study in Korean Legal QA | This paper proposes Eval-RAG, a novel method for evaluating large language model (LLM) generated text, particularly for legal question answering (QA), by using retrieved relevant documents to assess factual accuracy. Experiments on Korean legal QA show that Eval-RAG improves the correlation between LLM-based evaluations and human expert judgments. | True | NaN | True | 1.0 | Positive | Eval-RAG: An evaluation framework combining a retriever (to find relevant legal documents for a given query) and an LLM-based evaluator (whose prompt is augmented with the retrieved document) to assess the quality of LLM-generated answers. | Evaluated on a Korean legal QA task (n=100 divorce-related questions) by comparing the scores from Eval-RAG (combined with FairEval/ChatEval using GPT-3.5/GPT-4) against human grading by lawyers, using Pearson, Spearman, and Kendall correlation coefficients. Qualitative analysis of specific examples was also conducted. | Combining Eval-RAG with GPT-4 based evaluators significantly increased the correlation with human lawyer evaluations compared to using the evaluators alone. FairEval-RAG (GPT-4) achieved the highest correlation (Pearson 0.5923, Spearman 0.5841, Kendall 0.4991), outperforming FairEval (GPT-4) alone. | Factual errors (hallucinations) in LLM outputs; difficulty of existing evaluation methods in detecting these errors in domain-specific contexts like law. | Using retrieval-augmented generation (RAG) principles for evaluation (Eval-RAG) to ground LLM-based evaluations in relevant documents, thereby improving factual accuracy assessment. | Legal Question Answering (QA) evaluation | NaN | Family Law (specifically Divorce Law) | South Korea | Data used for the retriever component: Publicly available Korean legal documents including 287 QA pairs (Korea Legal Aid Corporation), 84 legal provisions, and 240 legal cases (Korea Legislation Research Institute). Questions for provisions/cases were generated using GPT-4. This data is domain-specific, unstructured text converted into question-document pairs for retrieval. | Conceptual framework design; Experimental evaluation comparing against baselines using correlation metrics and qualitative analysis. | NaN | False | False | NaN | Need for improved evaluation metrics that better align with human expert judgment and can robustly detect factual inaccuracies in domain-specific LLM outputs. | LLM hallucination; inadequacy of existing evaluation metrics; potential limitations in retriever accuracy; prompt engineering for incorporating retrieved documents; LLM context window limits. | LLMs generating factually incorrect legal information (hallucination); standard evaluations failing to detect these errors, leading to potential reliance on inaccurate AI-generated legal output. |
| SYHocniYWCEJ.pdf | Google_Scholar | Literature Review: AI and the Law | This literature review examines AI's role, particularly LLMs, in the legal profession, covering applications in legal practice, judicial processes, and access to justice through tools like legal apps. It also discusses significant ethical concerns including professional competence, algorithmic bias, and data privacy. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | High cost and scarcity of legal services leading to unmet legal needs for low-income and many middle-income individuals; disadvantages for self-represented litigants; the digital divide, including lack of access to technology and digital literacy. | Employing AI-powered legal apps for information, advice, and document creation; leveraging AI to enhance lawyer efficiency and extend services; developing self-help resources powered by AI; implementing online dispute resolution platforms. | Affordability of legal services, access to legal information and advice, self-representation, online dispute resolution for small claims and specific civil matters. | Low-income individuals, individuals below the poverty line, middle-income individuals, self-represented litigants. | General civil law, contract law, dispute resolution (small claims, condominium, motor vehicle accidents), constitutional law, torts, legal ethics. | International (with examples from USA, Canada, China, Colombia, Italy, UK) | NaN | NaN | NaN | True | True | The paper discusses tools like ChatGPT (free tier available) and publicly accessible services like Canada's Civil Resolution Tribunal, as well as commercial legal tech tools. | The digital divide (socio-economic, geographic, and literacy barriers to technology access); insufficient research on privacy and security of legal apps; outdated or lacking ethical guidelines and governance for AI tools in law. | Ensuring accuracy and completeness of AI-generated legal information; preventing user misinterpretation or over-reliance on AI outputs; addressing the digital divide for equitable access to AI-powered legal resources; managing data privacy and security risks; overcoming potential biases in AI systems and judicial applications; adapting legal education to incorporate AI ethically and effectively. | Breaches of lawyers' duty of competence from unverified AI use; dissemination of misleading or incomplete legal information by AI; perpetuation of systemic biases (e.g., racial, gender) by AI algorithms; privacy violations and data misuse from legal apps and AI systems; cybersecurity vulnerabilities (e.g., jailbreaking, prompt injection); negative impacts on judicial integrity, such as automation bias or perceived devaluation of human judgment. |
| PO2gt4t0fl4J.pdf | Google_Scholar | Decoding Legalese Without Borders: \nMultilingual Evaluation of Language Models on Long Legal Texts | This doctoral dissertation summarizes a body of research focused on advancing multilingual legal Natural Language Processing (NLP). It details the curation of extensive legal datasets and benchmarks for evaluating Large Language Models (LLMs) on long legal texts, and presents multidimensional analyses of model performance, explainability, fairness, and re-identification risks within the legal domain. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Lack of comprehensive multilingual legal datasets; suboptimal performance of models on low-resource languages and long legal texts; unique challenges of domain-specific legal tasks; difficulties in ensuring transparency and ethics in algorithmic jurisprudence. | Curation and open release of extensive multilingual legal datasets (e.g., MultiLegalPile) and benchmarks (e.g., LEXTREME, LegalBench, SCALE); training and analysis of language models for legal text; proposing methods for anonymization, re-identification assessment, and explainability; advocating for dataset extension to unexplored legal tasks and underrepresented jurisdictions. | Multilingual legal NLP; evaluation of LLMs on legal texts; legal judgment prediction; anonymization and re-identification; legal reasoning; creation of open legal corpora and benchmarks for broader development and application, including for underrepresented languages and jurisdictions. | The broader legal NLP research community; users and developers in underrepresented jurisdictions and languages. | Multiple legal fields (e.g., public, penal, civil, social, insurance law, class actions, depending on the specific dataset/benchmark described within the summarized works). | International (covers multiple jurisdictions including Switzerland, US, India, EU, CoE, and aims for broader global coverage including underrepresented jurisdictions). | The dissertation describes the creation and use of large multilingual legal corpora such as MultiLegalPile (689GB of diverse legal texts from public and other sources covering 24 languages/17 jurisdictions) and various specialized datasets for tasks like judgment prediction, sentence boundary detection, and negation scope resolution. | NaN | Many of the described resources (datasets, models, code) are deployed via open platforms like Hugging Face, Zenodo, and GitHub, often under permissive licenses (e.g., CC-BY, CC BY-SA). | True | True | Numerous datasets, pretrained models, and codebases (detailed in Table 1 and individual publication summaries) are publicly available on platforms like Hugging Face, Zenodo, and GitHub, often under open licenses like CC-BY. | Need for improved model performance on legal benchmarks (e.g., via domain adaptation, instruction tuning, advanced prompting); further analysis of dataset overlaps and model interpretability/explainability; extension of datasets to more legal tasks, languages, and jurisdictions, particularly with expert annotations. | NaN | Potential for re-identification of individuals in anonymized legal texts; lack of transparency and ethical concerns in algorithmic jurisprudence; inherent biases in models influencing predictions (e.g., from lower court decisions or training data). |
| LDrTM_lyDkQJ.pdf | Google_Scholar | GENERATIVE AI AND THE DIGITAL COMMONS | The paper discusses how Generative Foundation Models (GFMs) rely on and potentially degrade the "digital commons" (shared information resources and infrastructure). It proposes governance-based solutions, such as consortia for monitoring and standards, GFM company contributions to the commons, and input-data-based governance, to mitigate risks and ensure collective benefit. | True | NaN | True | 1.0 | NaN | Governance-based solutions including: 1) Consortia for monitoring, auditing, and standards-setting; 2) Norms or rules for GFM companies to contribute high-quality data to the commons; 3) Governance structures based on input data to model training (e.g., human feedback for stake, fine-tuning data from experts, data trusts for private data). | NaN | NaN | NaN | NaN | NaN | NaN | Copyright law, Fair Use doctrine, Data rights, Data protection, Governance, Regulation. | International | NaN | Conceptual analysis of risks posed by GFMs to the digital commons, review of existing regulatory/governance approaches, and development of novel governance proposals based on commons theory. | Proposals include voluntary memberships/subscriptions by GFM companies for consortia, potential regulation/taxation for funding, encouraging data contributions through norms or rules, and piloting input-data-based governance structures (e.g., data trusts, collective model governance by specific groups). | False | False | NaN | NaN | For consortia: avoiding standards-capture by incumbents, ensuring broad representation. For data contributions: ensuring validity of collected data, encouraging company participation. For input-data governance: developing detailed and feasible models, addressing privacy and fiduciary responsibilities, data tracing and valuation. | GFM risks include: poisoning the information sphere with easy-to-create low-quality data; eroding self-determination and democracy; homogenizing content; misaligning incentives for humans to contribute to the open digital ecosystem; driving further economic concentration; contributing to precarious labor conditions and large-scale automation; accelerating unpredictable risks from highly capable AI systems. |
| QE9gSMM7MkYJ.pdf | Google_Scholar | Artificial Intelligence & Criminal Justice: A Primer | This primer provides a high-level overview of AI's current impact on the criminal justice system, covering applications like deepfakes, predictive policing, facial recognition, risk assessment algorithms, and AI in legal practice. It also addresses critical perspectives including Indigenous viewpoints, AI governance, access to justice concerns, and future AI issues. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Inaccessibility (cost, proprietary nature, opacity) of sophisticated AI tools for the general public; lack of transparency and disclosure about AI use; ensuring accountability and freedom from bias; upholding due process rights in automated decision-making; and potential for inaccurate AI-generated legal information to mislead. | Implementing robust regulatory frameworks (e.g., EU AI Act, Canada's proposed AIDA), adherence to ethical guidelines and bills of rights (e.g., White House AI Bill of Rights), ensuring transparency and explainability in AI systems, providing human oversight and recourse mechanisms, specific recommendations for legal aid plans, and professional guidelines for legal practitioners and judges. | Affordable legal support and information, tools for self-represented litigants, transparency and accountability in automated legal decision-making, addressing bias in AI, ensuring due process rights, and the role of legal aid in the age of AI. | Self-represented litigants, racialized communities, Indigenous peoples, and individuals requiring legal aid or affordable legal support. | Criminal Justice | Canada (primary), United States, European Union | NaN | NaN | NaN | True | True | The primer itself is available for free and open access via Allard Research Commons and Canadian Legal Information Institute. A more detailed free ebook is planned for January 2025. | How to effectively regulate AI to ensure it narrows the access to justice gap without creating new harms (e.g., reliance on inaccurate AI, exacerbating inequalities); addressing and mitigating bias in AI tools for legal applications; defining and implementing procedural fairness in automated legal processes; ensuring meaningful transparency and accountability for AI systems. | NaN | Perpetuation and automation of systemic bias and discrimination (e.g., in policing, corrections); privacy violations through data scraping and surveillance; generation and misuse of deepfakes for criminal activities; reliance on inaccurate or 'hallucinated' information from generative AI in legal contexts; lack of transparency and 'black box' nature of some AI systems hindering accountability and due process; potential for AI to facilitate new forms of crime and digital colonialism. |
| Andrew Phang (Gen. Ed.) Pioneer polymath and mentor_ The life a.pdf | Google_Scholar | Andrew Phang (gen ed), Pioneer, Polymath and Mentor: The Life and Legacy of Yong Pung How | This book review examines "Pioneer, Polymath and Mentor: The Life and Legacy of Yong Pung How," detailing Dr. Yong's contributions to modernizing Singapore's judiciary and financial sector. It also notes his influence on enhancing access to justice, with current efforts in Singapore including the development of Generative AI to assist self-represented litigants. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Case backlogs, procedural gridlock, and challenges for self-represented individuals in accessing and navigating the justice system due to limited resources or legal literacy. | Judicial modernization (e.g., electronic filing, disciplined case management initiated by Dr. Yong), ongoing simplification of procedural frameworks, and development of Generative AI tools to assist self-represented litigants. | Judicial reform, court efficiency, procedural simplification, access to justice for self-represented persons, legal aid through technology. | Self-represented litigants, individuals with limited financial resources or legal literacy, those most in need. | Administration of Justice, Civil Procedure, Small Claims | Singapore | NaN | NaN | NaN | False | False | NaN | Ensuring equitable access to justice for all, particularly for self-represented individuals lacking sufficient resources or legal knowledge to effectively present their case. | NaN | NaN |
| 6aFMjU4sNkIJ.pdf | Google_Scholar | Computational Legal Studies Comes of Age | This paper surveys the field of computational legal studies, outlining the 'law-as-code' and 'law-as-data' paradigms and their evolution with computational text analysis. It further explores the impact and potential of recent generative AI (LLM) developments, discussing hybrid approaches, ongoing opportunities, and challenges for empirical legal research. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Various, including constitutional, criminal, administrative, civil, patent, human rights, contract, tax, and anti-discrimination law. | International, referencing U.S., U.K., E.U., Korea, Malawi, Germany. | Discusses techniques using various legal text corpora (e.g., case law, statutes, court dockets); specific examples include annotated Malawi criminal cases and the German credit dataset. For LLMs, it refers to large, broad internet-scale text datasets (some sources undisclosed). | NaN | NaN | False | False | NaN | NaN | Language specificity (tools primarily designed for English may not handle non-English texts well), domain specificity (a tool for one legal field might not work in another), data issues (quantity, quality, scope, and potential biases in data), LLM-specific issues (hallucination, interpretability challenges due to model complexity and large parameter counts, opacity of training data provenance). | LLM hallucination (e.g., generating fictitious case law), biased outputs resulting from biased training data, potential unfairness in automated systems like smart contracts, and socioethical concerns regarding fairness in law-as-code applications. |
| T2yiP3KjaMUJ.pdf | Google_Scholar | WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation | This paper introduces WANLI, a novel dataset creation approach combining AI generation (GPT-3) with human evaluation to improve Natural Language Inference (NLI) datasets. Training models on the resulting WANLI dataset significantly improves robustness and out-of-domain generalization compared to training on the larger original dataset (MultiNLI). | True | NaN | True | 1.0 | NaN | Worker-AI collaborative pipeline (WANLI) for dataset creation: Uses dataset cartography to identify challenging examples from an existing dataset (MultiNLI), prompts GPT-3 to generate similar examples, automatically filters generations using 'estimated max variability', and uses human crowdworkers for revision and final labeling. | Finetuned RoBERTa-large and T5-base models on WANLI versus MultiNLI (and its augmentations). Evaluated accuracy on eight out-of-domain NLI challenge sets (NLI Diagnostics, HANS, QNLI, WNLI, NQ-NLI, ANLI, FEVER-NLI, BIG-Bench NLI) and the MultiNLI development set. | Training RoBERTa-large on WANLI (103K examples) outperformed training on MultiNLI (393K examples) on all eight OOD test sets considered (e.g., +11% on HANS, +9% on ANLI). Similar improvements were observed for T5-base. | NaN | NaN | NaN | NaN | NaN | International | The method uses the publicly available MultiNLI dataset as a source for seed examples. It then employs GPT-3 (a proprietary LLM) to generate new examples. The final WANLI dataset consists of these AI-generated examples, filtered automatically and then reviewed, revised, and labeled by human crowdworkers. | Multi-stage pipeline involving: 1) Data analysis (dataset cartography), 2) AI generation (few-shot prompting of GPT-3), 3) Algorithmic filtering (custom 'estimated max variability' metric), 4) Human-in-the-loop validation (crowdsourcing via Amazon Mechanical Turk for revision and labeling). | The WANLI dataset, code for the pipeline, and a demo are made available online via a dedicated website. | True | True | Demo, data, and code are available at https://wanli.allenai.org/ | NaN | Ensuring GPT-3 reliably replicates complex DLI reasoning patterns (especially contradiction); balancing human revision freedom with avoiding annotation artifacts; potential for generation-specific artifacts (e.g., lexical correlations, entity biases); cost of large-scale generation and human review. | Perpetuation of social harms and toxic language from LLMs; potential for subtle biases missed by human annotators; over-representation of Western entities in generated data; creation of new, model-specific artifacts. |
| s1yPEHov7IcJ.pdf | Google_Scholar | Generative AI in Business: Visual Illustrations of Applications and Insights | This paper explores generative AI's business applications, benefits, and challenges using a visual framework based on recent literature and industry reports. It covers use cases like content creation, knowledge management, process automation, and decision support, highlighting implementation, risks, and future trends. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal Services | International | NaN | NaN | NaN | False | False | NaN | NaN | Integration with existing systems, data quality, performance optimization, security risks, high cost, ethical concerns (especially in HR), compliance requirements, need for governance frameworks, lack of organizational readiness and planning, need for new skills (e.g., prompt engineering). | Security risks (data breaches, model vulnerabilities), high implementation/operational costs, ethical risks (bias, fairness, transparency, especially in HR), compliance risks (regulatory non-compliance), data privacy risks, model risks (accuracy, reliability), output risks (e.g., generating incorrect or harmful content), operational risks (system failures, integration issues). |
| pEztotuCUbAJ.pdf | Google_Scholar | Ethics guidance for generative AI use | This article summarizes ABA Formal Opinion 512 regarding the ethical obligations for lawyers using generative AI tools in their practice. It highlights key duties including competence, confidentiality, client communication, supervision, candor to the tribunal, and reasonable fees. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General legal practice | Minnesota (USA) | NaN | NaN | NaN | False | False | NaN | NaN | Ensuring ethical compliance (competence, confidentiality, communication, supervision, candor, reasonable fees) when incorporating generative AI into legal practice. | Unauthorized disclosure of confidential client information, generation of inaccurate information ('hallucinations'), advancing non-meritorious claims, lack of candor to tribunals, inadequate supervision of AI use, improper billing practices. |
| bGDGljyWU4MJ.pdf | Google_Scholar | Gracenote.ai: Legal Generative AI for Regulatory Compliance | This paper introduces Gracenote.ai, a platform utilizing LLMs (GPT-4) and prompt engineering for governance, risk, and compliance (GRC) tasks. It details three tools: a regulatory horizon scanner, an obligations generator, and an LLM-based expert system, emphasizing human-in-the-loop control to ensure accuracy. | True | Market | True | 1.0 | NaN | A platform (Gracenote.ai) proposing three tools: 1) Regulatory newsfeed generation via automated horizon scanning, scraping, and LLM-based summarization/categorization. 2) Obligations register generation from legislation using LLM summarization and NLP-based duplicate detection (TF-IDF, cosine similarity). 3) LLM-based expert system using LangChain, text embeddings on an obligations register, and GPT-4 for querying. | Newsfeed: Compared summaries to original content via human review in an authoring environment. Obligations generator: Compared GPT-4 outputs to GPT-3.5 and human-generated reference registers; used cosine similarity for duplicate detection with human validation. Expert system: Lawyer reviewed outputs for relevance and accuracy based on user prompts; initial user feedback collected. | GPT-4 produced more concise and sometimes more legally precise obligations than GPT-3.5 and human experts. Similarity detection for obligations was mostly accurate but required human validation. Lawyer reviews confirmed the expert system's outputs were relevant and accurate for given prompts. Newsfeed tool estimated to save significant time (approx. 1 FTE) for corporate legal teams. | NaN | NaN | NaN | NaN | Governance, Risk and Compliance (GRC), Financial Services Law, Insurance Law, Cybersecurity Law, Corporate Law, Administrative Law | Australia (primary focus), UK (planned), Singapore (planned), USA (planned) | The approach primarily uses prompt engineering on pre-trained GPT-4. Data inputs for the tools include: publicly available regulatory documents scraped from official sources (press releases, alerts, case reports, legislation, policy documents) and user queries. For the expert system, it uses text embeddings generated from a CSV file of an obligations register. | Prompt engineering (incl. specific prompts for summarization, categorization, obligation extraction), text embeddings (OpenAI's text-embedding-ada-002), web scraping (custom scripts, headless browser), LangChain framework (CSVLoader, Character Text Splitter, ChatGPT Plugin Retriever), NLP for similarity detection (TF-IDF, cosine similarity), chunking for long documents, human-in-the-loop validation. | Platform trialled with law firms and consultancies. Tools generate content presented in authoring and client interfaces. Generated content can be used in external workflows (e.g., email systems, newsletters) or pushed via the platform's client interface. | False | False | NaN | NaN | LLM context window limitations requiring text chunking; prompt engineering difficulties (e.g., preventing embellishments in GPT-3.5); balancing conciseness and legal accuracy in generated text; limitations of similarity detection algorithms (identifying functionally non-equivalent but textually similar obligations); handling varied website structures for scraping; need for human oversight to ensure accuracy. | LLM hallucinations (generating plausible but incorrect information); privacy breaches from sending personally identifying information to public LLM endpoints; confidentiality breaches from using sensitive commercial information in prompts. |
| mI1P374fIKEJ.pdf | Google_Scholar | Confirmation Bias in Generative AI Chatbots: Mechanisms, Risks, Mitigation Strategies, and Future Research Directions | This perspective article analyzes the phenomenon of confirmation bias within generative AI chatbots based on large language models. It explores the mechanisms driving this bias, discusses the associated risks and ethical implications, proposes mitigation strategies, and outlines future research needs. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | General discussion of LLM training data: massive, often unfiltered internet datasets (news, social media, forums, etc.), potentially skewed, often lacking transparency. | NaN | NaN | False | False | NaN | NaN | Inherent LLM design favouring coherence; skewed training data; trade-offs in alignment/fine-tuning; iterative reinforcement in conversation; user perception; difficulty implementing mitigations without negative side-effects (e.g. paternalism, cost). | Spread of misinformation; reinforcement of harmful beliefs/conspiracy theories; poor individual decision-making (health, finance, legal); social fragmentation/polarization; potential legal liability; undermining user autonomy/critical thinking. |
| gOypQXV-mAMJ.pdf | Google_Scholar | Chat GPT and the Future of Work “Banking Industry Use Cases” | This paper explores the impact of generative AI, specifically ChatGPT, on jobs and functions, with a focus on its use cases within the banking, HR, and L&D sectors. It also discusses the technology's limitations, ethical considerations, and provides recommendations for its adoption by organizations. | True | Market | True | 3.0 | NaN | Generative AI (specifically ChatGPT) | NaN | NaN | NaN | NaN | NaN | NaN | Banking Law (KYC/AML, regulatory compliance), Contract Law (document drafting), Labor Law (HR compliance and policies) | International | The paper discusses ChatGPT, generally trained on large-scale public internet text and code. It mentions the potential for organizations to customize models using their own data. | NaN | NaN | True | True | ChatGPT is available as a free research preview and through paid subscriptions/API access. | NaN | Key challenges discussed for adopting and using ChatGPT in organizations include: implementation and maintenance costs; data security and privacy; ethical considerations such as bias and misuse; the model's lack of emotional intelligence; risk of over-reliance and skill degradation; intellectual property concerns; the need for workforce reskilling and cultural change; keeping pace with rapid AI developments; and ensuring human oversight for output validation. | Stated risks include: job displacement; security vulnerabilities (e.g., data breaches, intensified phishing); ethical issues like bias and discriminatory outputs; reduced quality of human interaction; over-dependence on technology eroding critical thinking; intellectual property infringements; financial crime enablement if not properly implemented in KYC/AML; reputational damage from errors or non-compliance; and dissemination of inaccurate information. |
| A74EjRLf2AgJ.pdf | Google_Scholar | The Rise of Generative AI: Modelling Exposure, Substitution, and Inequality Effects on the US Labour Market | This paper models the exposure of 711 US occupations to advancing AI capabilities by considering skill difficulty and computer interaction, proposing the AISA index. It distinguishes between AI complementing side skills and substituting core skills, finding that while AI initially complements all workers, advancing AI threatens substitution primarily in lower-wage jobs, potentially increasing inequality despite higher exposure of white-collar side skills. | True | NaN | True | 1.0 | NaN | The paper proposes a modelling approach called the AI Share Automatability (AISA) Index, which uses O*NET data on skill importance and difficulty level, combined with a measure of computer interaction per occupation (derived using GPT-4 and O*NET task descriptions) and a hypothetical AI capability parameter (κAI), to simulate AI exposure. This exposure is further analyzed by differentiating between an occupation's core and side skills to model complementarity versus substitution. | The AISA model was evaluated through simulations on 711 US occupations using O*NET and US Bureau of Labor Statistics data. Results were compared to existing literature on AI exposure (e.g., Eloundou et al. (2023), Hatzius et al. (2023)), and robustness checks were performed by varying model components (e.g., computer interaction measure, O*NET data categories like abilities/work activities, core/side skill definitions). | At low AI capabilities (κAI=2.0), 7% of skills are uniformly exposed. At moderate (κAI=3.0) and high (κAI=4.0) capabilities, 17% and 36% of skills are exposed on average, respectively, with up to 45% exposure in the highest wage quartile at high AI capability. Low AI capabilities complement all workers by affecting side skills; as AI advances, core skills in lower-wage jobs become exposed, threatening substitution and increased inequality, while high-wage core skills remain less exposed, though their side skills are significantly affected. | NaN | NaN | NaN | NaN | NaN | United States | The AISA model uses publicly available data: O*NET (version 27.2) for occupational skills (importance, difficulty level, task descriptions) and the 2022 Occupational Employment and Wage Statistics (OEWS) Survey from the US Bureau of Labor Statistics (employment, wages). O*NET task descriptions were processed using GPT-4 (a pre-trained LLM) to estimate the proportion of time spent on computer, social, and physical interaction types for each occupation. | Bottom-up quantitative simulation model. Key steps include: 1) Classifying share of time in occupations spent on computer interaction (using GPT-4 on O*NET task descriptions). 2) Quantifying skill automation based on O*NET skill level data and a variable AI capability parameter (κAI). 3) Combining these into an AI Share Automatability (AISA) index. 4) Differentiating skills into 'core' and 'side' based on O*NET importance ratings to model complementarity vs. substitution. 5) Aggregating results by industry and wage quantiles. | NaN | False | False | NaN | NaN | Predicting AI's impact due to technology's nascent stage and rapid evolution. Quantifying AI capability (κAI) abstractly. Simplifications made in modelling, such as assuming only computer interaction is automatable (in baseline) and statistical independence between computer interaction time and specific skills. Differentiating 'exposure' from actual 'automation' and distinguishing complementarity from substitution required further modeling (core/side skills). | Substitution of human labor, particularly in lower-wage jobs as AI capabilities advance to affect their core skills. Increased income inequality due to differential impacts across the wage spectrum (complementarity for high-wage side skills, substitution for low-wage core skills). Job displacement and the need for workforce adaptation. Reshaping of job roles, even in high-wage professions, as side tasks are automated. |
| hCFGY1A7TzoJ.pdf | Google_Scholar | Ethical Foresight: Confronting Misinformation, Representation and Toxicity in Generative AI | This thesis investigates socio-technical harms such as misinformation, biased representation, and toxicity in Large Language Models (LLMs). It proposes a new comprehensive framework and taxonomy, developed through a systematic review of existing safety evaluations, to guide the ethical development and regulation of AI. | True | Idealistic | True | 1.0 | Positive | A comprehensive framework, taxonomy of harms (focusing on misinformation, representation, and toxicity), and evaluation guidelines for LLMs, developed through a systematic literature review. | NaN | NaN | Pervasiveness of misinformation, representational errors, and toxicity in LLMs; inherent biases in training data and algorithms; challenges in AI transparency and explainability ('black boxes'); difficulties in defining and operationalizing ethical principles like fairness; shortcomings in current AI regulations; underrepresentation of diverse perspectives in AI development; abstraction traps in applying technical solutions to social problems. | Development of a comprehensive framework and taxonomy for AI harms; context-aware, interdisciplinary evaluation strategies with human oversight; clear definitions and criteria for AI assessment; improved AI governance and regulation (e.g., EU AI Act, regulatory sandboxes); fostering Human-Centred AI (HCAI) and multi-stakeholder collaboration; addressing abstraction traps by integrating social context into technical solutions; providing actionable guidelines for developers and policymakers. | Ethical AI development, AI governance, Bias detection and mitigation, Fairness and equity in AI systems, Misinformation, Representational harms, Toxicity in LLMs, Sociotechnical AI safety. | Marginalized and underrepresented communities generally (e.g., based on race, gender, language, socio-economic status, LGBTQ+ identity) who are disproportionately affected by AI biases and harms. | AI Law and Regulation, AI Ethics, Data Protection, Non-discrimination principles in AI. | EU (focus on GDPR, DSA, AI Act), USA (examples cited). Principles and framework are intended for broad applicability (International). | Systematic review of over 170 academic papers on AI ethics, safety evaluations, misinformation, representation, and toxicity, primarily sourced from the DeepMind Sociotechnical Safety Evaluation repository and other academic databases. | Systematic literature review, qualitative analysis of academic papers, synthesis of findings to construct a new taxonomy of harms, and development of evaluation guidelines and recommendations. | NaN | True | True | The proposed framework, taxonomy, and evaluation guidelines are detailed within this thesis, making the intellectual contribution accessible to readers. | Lack of comprehensive, context-aware AI safety evaluation frameworks; insufficient human oversight in AI development and evaluation; limited understanding and mitigation of multimodal risks; inadequacy of current regulations for rapidly evolving AI (e.g., general-purpose AI, misinformation definition); difficulty in translating ethical principles into concrete technical practices; underrepresentation of diverse (especially non-Western and marginalized) perspectives and languages in AI development and evaluation; challenges in achieving true algorithmic fairness beyond statistical metrics. | Synthesizing a vast and evolving body of literature on AI ethics and safety; developing a comprehensive yet practical taxonomy of harms; ensuring proposed guidelines are actionable and adaptable across diverse AI systems and contexts; overcoming the limitations of existing evaluation methods (e.g., artificial setups, metric-related issues, generalizability). | Spread of AI-generated misinformation impacting public trust and democracy; perpetuation and amplification of societal biases and stereotypes leading to discrimination (representational harms); generation of toxic and harmful content (hate speech, harassment); infringements on privacy; challenges to human autonomy and decision-making; socio-economic disruption (e.g., job displacement); misuse of AI for malicious purposes (e.g., information warfare, astroturfing). |
| CQsEueGL1vcJ.pdf | Google_Scholar | ChatGPT + Generative AI Systems as Quasi-Expert Legal Advice Lawyers- Case Study considering Potential Appeal Against Conviction of Tom Hayes | This paper uses OpenAI's ChatGPT (Jan 2023 version) to generate quasi-expert legal advice regarding a potential appeal for Tom Hayes' Libor conviction, assessing the AI's current capabilities. It concludes that while AI shows significant potential, current versions lack deep legal knowledge but predicts rapid advancements will significantly disrupt the legal profession, potentially replacing many human lawyers. | True | Market | True | 2.0 | Neutral | OpenAI's ChatGPT (specifically the 9 January 2023 Version Free Research Preview). | Qualitative analysis of ChatGPT's generated text based on specific prompt questions regarding the Tom Hayes case, comparing arguments generated for and against the appeal, and assessing perceived strengths and weaknesses. | ChatGPT produced a good initial background summary and articulated generic arguments, but lacked detailed knowledge of specific case law (e.g., USA v Connolly & Black), UK statutes, legal tests (conspiracy, dishonesty), and recent data (post-2021 cutoff). This was attributed to limitations in its training data. | NaN | NaN | NaN | NaN | Criminal Law, Financial Regulation, Legal Practice/Profession | United Kingdom, United States | ChatGPT was trained on 'a massive amount of text data', predominantly public domain general data (like news articles) up to the end of 2021. The paper notes a lack of specific training on technical legal databases (caselaw, legislation). | Qualitative analysis of AI-generated text resulting from structured prompt engineering focused on a specific legal case study. | The paper discusses potential future deployment within law firms (e.g., Allen & Overy's 'Harvey') and potential direct use by consumers, predicting AI will take increasingly dominant roles. | True | False | Accessed via OpenAI as a 'Free Research Preview' version from January 2023. | Technical gaps in current AI: lack of deep, up-to-date knowledge of specific case law, statutes, legal tests, and reliance on potentially incomplete training data. | Limitations in ChatGPT's current capabilities due to its training data (time constraints, lack of specialized legal data) prevented it from providing granular, technical legal analysis. | Significant disruption to the legal profession (job losses, especially for junior lawyers; devaluation of traditional legal training), potential inaccuracies in AI legal advice due to data/knowledge limitations, lack of nuanced understanding or professional judgment compared to human lawyers, over-reliance on AI without expert human oversight. |
| HZyLbSmv6fAJ.pdf | Google_Scholar | LLMs Provide Unstable Answers to Legal Questions | This paper investigates the stability of leading Large Language Models (LLMs) like GPT-4o, Claude-3.5, and Gemini-1.5 when answering difficult legal questions. Using a novel dataset of 500 legal questions derived from split U.S. court decisions, the authors find significant instability (LLMs providing different answers to the same question) even with deterministic settings, raising concerns about their reliability for legal applications. | True | Market | True | 2.0 | NaN | Evaluation of specific LLMs (GPT-4o, Claude-3.5 Sonnet, Gemini-1.5 Pro, o1) regarding their output stability on legal questions. | A novel dataset of 500 legal questions derived from split U.S. Federal Courts of Appeal decisions was created. Each question was posed 20 times to each LLM (GPT-4o, Claude-3.5, Gemini-1.5) via API with temperature=0 and other parameters set for maximum determinism. Stability was measured as the frequency of the most common answer; accuracy was measured against the actual court outcome. A subset of 50 questions was tested on o1 (temp=1.0). | All tested LLMs exhibited instability (<100% stability) on a fraction of the 500 questions: Claude-3.5 (10.6%), GPT-4o (43.0%), Gemini-1.5 (50.4%). Instability patterns were largely model-specific. Accuracy relative to actual court outcomes: GPT-4o (53.9%) and Claude-3.5 (52.9%) performed statistically significantly better than chance; Gemini-1.5 (46.4%) performed worse than chance. | The inherent instability of current leading LLMs, where they produce different answers to the identical legal question even under deterministic settings, making their outputs unreliable for legal decision-making or analysis. | NaN | NaN | NaN | Various areas of U.S. federal law (including criminal law, civil procedure, employment law, social security, First Amendment, Native-American tribal jurisdiction, bankruptcy, civil rights, pensions, military, immigration law). | U.S. federal law | The paper evaluates proprietary LLMs (GPT-4o, Claude-3.5, Gemini-1.5, o1). Their training data is not disclosed by the vendors but is presumed to be very large-scale and general-purpose, likely including public domain legal text. | NaN | The evaluated LLMs are deployed and accessed via commercial APIs provided by OpenAI, Anthropic, and Google. | True | False | The evaluated LLMs (gpt-4o, claude-3.5, gemini-1.5, o1) are accessible via commercial APIs. The novel dataset of 500 legal questions and the authors' code are available on GitHub. | NaN | The proprietary, closed-source nature of the LLMs makes it impossible to determine the exact causes of instability. The ongoing development of these models may affect future reproducibility. Cost and time limited the number of models tested. | Using unstable LLMs for legal tasks carries the risk of inconsistent and potentially arbitrary outcomes, analogous to judicial misconduct (like flipping a coin). This undermines the reliability of legal AI products, lawyers' work product relying on these tools, and any potential use in formal legal processes or dispute resolution. |
| Healthcare__A_Growing_Role_for_Large_Language_Models_and_Generative_AI.pdf | Google_Scholar | Large Language Models and Generative AI’s Expanding Role in Healthcare | This paper surveys the application of large language models (LLMs) and generative AI (GAI) in the healthcare sector, covering medical text analysis, image analysis, and multimodal applications. It discusses various models, benchmarks, tools, challenges, and ethical considerations associated with using these AI technologies in healthcare. | True | Market | True | 3.0 | NaN | The paper surveys various techniques including Large Language Models (e.g., GPT-3, Biomedical Transformers like BioBERT, ClinicalBERT, GatorTron, PMC LLaMA), Generative AI (GANs, VAEs), and Multimodal Models (e.g., Med-PaLM M, ELIXR, Visual ChatGPT, LLaVA-Med). | NaN | NaN | NaN | NaN | NaN | NaN | Healthcare Law (implicitly, concerning data privacy and regulation) | International | NaN | NaN | NaN | False | False | NaN | NaN | Data privacy and security (including compliance with regulations like GDPR, handling PHI), interpretability ('black box' models), data bias leading to biased generation, need for regulatory permission, potential for AI chatbots to be unfit for clinical use due to bias/hallucinations, need for human oversight ('garbage-in, garbage-out'), model sensitivity to instructions. | Violation of data privacy and security, generation of biased, incorrect, or unfair diagnoses/treatments, spread of false information or damaging content, legal issues concerning liability and intellectual property/copyright for AI-generated content, potential for misuse of synthetic media (images, video, audio) for deception, manipulation, harassment, or defamation, risk of patient harm due to reliance on inaccurate AI-generated information. |
| o6B30NBG7GIJ.pdf | Google_Scholar | Some Emerging Hypotheses about Using Generative AI in Public Sector Operations | This paper reviews the potential applications and productivity benefits of Generative AI (like ChatGPT) for public sector operations, targeting leaders and managers. It also highlights significant risks, including alignment problems, hallucinations, and bias, proposing principles and a framework for responsible implementation. | True | NaN | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | USA | Discusses models (like ChatGPT) trained on vast corpora of internet text, noting this data contains societal biases. | NaN | NaN | False | False | NaN | Lack of transparency and explainability ('black box' nature) in Generative AI models; ensuring alignment with human values; gap between AI capabilities and reliable, safe, unbiased deployment in public sector contexts; need for workforce adaptation strategies. | Managing risks (alignment, hallucination, bias); ensuring fairness, reliability, safety, accountability, privacy, security, inclusiveness, transparency; developing effective policies, training, monitoring, and audit procedures; integrating AI into workflows; managing reputational risk and public trust. | Alignment problem (AI not aligning with human values/intentions), hallucination (generating false information), algorithmic bias (unfair outcomes, reinforcing societal biases), lack of replicability, potential for misuse in critical services (e.g., emergency response), reputational risk for government, reduced public trust, potential negative impacts on lower-skilled workers. |
| 3700789.pdf | Google_Scholar | Integrating Content Moderation Systems with Large Language Models | This paper proposes integrating Large Language Models (LLMs) into content moderation systems to enable personalized moderation and improve user-platform communication. It evaluates the content moderation capabilities of GPT 3.5 and LLaMa 2 against commercial products, discussing the approach's benefits and limitations for creating fairer online environments. | True | Idealistic | True | 1.0 | Positive | An approach integrating LLMs (specifically GPT 3.5 and LLaMa 2) into content moderation pipelines. This uses prompting with user-defined or community-specific rules to enable personalized content evaluation (zero-shot) and generate explanations for moderation decisions. | Quantitative analysis using F1 score to compare LLMs (GPT 3.5, LLaMa 2 7B) with commercial products (Perspective API, OpenAI Content Moderation) on two publicly available datasets: OpenAI's content moderation dataset and an English subset of the Reddit Multilingual Content Moderation dataset (r/judaism, r/feminism, r/naruto) with their respective community rules. | On the OpenAI dataset, GPT 3.5 (F1 score 0.7762) performed comparably to commercial solutions (e.g., OpenAI Content Moderation F1 0.7859). For the Reddit r/naruto dataset, whose rules often pertain to topics not inherently harmful, GPT 3.5 (F1 score 0.6410) outperformed traditional commercial solutions. | Current content moderation systems exhibit unfairness towards historically marginalized individuals, fragile users, and minorities; policies are often hardcoded, hindering personalized moderation. There is a lack of effective communication between users and platforms, and platform policies can be difficult to understand and ambiguously defined, failing to address diverse cultural nuances. | Integrating LLMs into content moderation systems to allow for personalized moderation based on customizable rules (user preferences or community norms). LLMs can also be used to generate explanations for moderation decisions, thereby improving transparency, user understanding, and communication between users and platforms. | Fairness in content moderation, protection of marginalized groups online, reducing online harm, transparency and accountability of platform decisions, user empowerment in digital spaces, enabling personalized online experiences. | Historically marginalized individuals, fragile users, minorities (including LGBTQ+ individuals, users from the Global South, Arab females, teenagers), and diverse online communities with specific norms. | Platform governance, digital law, freedom of speech, human rights (related to non-discrimination, participation, and access to information). | International | The LLMs studied (GPT 3.5, LLaMa 2) are pre-trained on large-scale, general web corpora. The paper's evaluation of these LLMs used: 1) A publicly available dataset from OpenAI (1680 text samples labeled for harmful content categories). 2) The Reddit Multilingual Content Moderation dataset (comments and subreddit-specific rules), available upon request from its original authors. | The proposed integration approach is based on a conceptual framework utilizing LLMs for rule-based content evaluation via prompting and dialogue for explanations. The evaluation of this approach employed a quantitative experimental methodology comparing LLM performance against benchmarks. | N/A (The paper proposes an approach and evaluates models; no specific deployment of the integrated system by the authors is described). | True | True | The approach can be replicated using LLaMa 2 (7B), an open foundation model available for download, or GPT 3.5, accessible via OpenAI's commercial API. The prompting technique is described in the paper. | Technical gaps include LLMs' limited mathematical capabilities for confidence scoring, binary decision outputs needing more nuance, potential safety degradation from fine-tuning, high costs, and performance issues with low-resource languages, hallucinations, and knowledge recency. Societal and ethical gaps involve privacy concerns, mitigating biases, ensuring accountability, the need for multi-stakeholder collaboration for governance, and assessing efficacy across diverse global contexts. | Obtaining consistent, machine-parsable (e.g., Yes/No) responses from LLMs for classification. Effectively interpreting and applying ambiguous or context-dependent community rules. The financial and environmental costs associated with using large language models. Ensuring LLMs accurately understand and apply nuanced or lengthy rule sets. | Perpetuation of existing societal biases and harm against marginalized communities due to biased training data in LLMs. Exposure of users to harmful, false, or inappropriate content generated by LLMs (hallucinations or safety-compromised models). Privacy violations stemming from the handling of user data by LLM providers, especially closed-source models. Degradation of LLM safety alignment through fine-tuning processes. Negative impacts on freedom of speech and economic opportunities for content creators due to flawed or unfair moderation. |
| KsIty3_cK1AJ.pdf | Google_Scholar | Book Review—Shaping the Bar: The Future of Attorney Licensing | This paper reviews Joan Howarth's book advocating for reforms to attorney licensing, arguing the current bar exam fails public protection and equity, and needs alignment with actual practice competence. The reviewer supports these points, adding concerns about AI's impact on competence definitions and emphasizing the need to educate lawyers on their 'public citizen' role to address access to justice. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Current bar exam inadequately measures competence, functions as an elitist filter hindering diversity (racial/ethnic disparities), fails public protection, high cost of legal education/licensing, institutional resistance to change, neglect of lawyer's 'public citizen' role contributing to access to justice crisis. | Reform bar exam (e.g., NextGen) to focus on skills/application, evidence-based competency assessment, supervised practice/residencies, competence-based education (diploma privilege, portfolios), address disparities, reduce costs, portable licenses, reform character & fitness reviews, educate on 'public citizen' duties, adapt legal practice/education for AI. | Attorney licensing reform, Bar examination reform, Lawyer competence assessment, Legal education reform, Equity and diversity in the legal profession, Access to Justice (linked to systemic reform and lawyer's public role). | Historically excluded groups (immigrants, Jewish people, people of color), African American and Latinx students, individuals with criminal/mental health histories, the poor unable to afford legal assistance. | General Legal Practice / Legal Education / Professional Regulation | United States | NaN | NaN | NaN | False | False | NaN | Failure of current competency definitions/assessments, especially with AI; inadequate education on lawyers' 'public citizen'/stewardship role; insufficient integration of interdisciplinary perspectives; lack of focus on 'access to justice' as core lawyer responsibility; need for better transparency/review of licensing mechanisms. | NaN | AI automating tasks defining lawyer competence, causing economic disruption; licensing systems perpetuating racial/ethnic disparities; failure of legal profession/education to adapt to change; neglecting ethical/civic dimensions of lawyering; misuse of character/fitness reviews; inadequate public protection from incompetent lawyers. |
| pqvZKAEc7eIJ.pdf | Google_Scholar | Building GenAI Benchmarks: A Case Study in Legal Applications | This paper discusses the importance and challenges of creating domain-specific benchmarks for evaluating Generative AI, using the legal field as a case study. It outlines benchmark components, highlights difficulties like evaluating unstructured text, high costs, train-test leakage, and subjectivity, and emphasizes the potential for interdisciplinary collaboration in benchmark development. | True | NaN | True | 3.0 | Neutral | Benchmarking methodologies for Generative AI in specialized domains (specifically law) | The paper discusses general benchmarking practices and specific legal benchmarks (e.g., CUAD, CaseHOLD, LegalBench) and evaluation methods (accuracy, error analysis, human expert review, LLM-as-judge), but does not perform a new evaluation itself. It focuses on the challenges of evaluation (e.g., cost, subjectivity, text evaluation). | NaN | The high cost of legal expertise needed for annotation hinders the creation of robust benchmarks, particularly for access-to-justice applications; evaluating complex, subjective legal reasoning and unstructured text output from AI is difficult; the priorities of institutions with resources to build benchmarks may not align with access-to-justice needs. | Develop benchmarks through interdisciplinary collaboration; use cost-effective data strategies like deriving labels from existing public data (e.g., CaseHOLD) or crowdsourcing smaller, diverse task datasets from experts (e.g., LegalBench); explore automated evaluation techniques like LLM-as-judge; focus evaluation on explanations rather than just predictions for subjective tasks. | Benchmarking AI for legal applications | NaN | Law (general), Contract Law (example) | International | The paper discusses benchmark dataset construction, not model training data. Benchmark examples mentioned use: expert annotations on contracts (CUAD), summaries automatically extracted from judicial opinions (CaseHOLD), and crowdsourced tasks from legal experts (LegalBench). The data is domain-specific (legal) and primarily unstructured or semi-structured. | Benchmark design principles (representativeness, size, multi-annotator labeling), data collection strategies (expert annotation, leveraging existing annotations, crowdsourcing), evaluation methods (comparison of model outputs to desired outputs using metrics, manual inspection, human evaluation, LLM-as-judge). | Discusses distribution of benchmarks via public online platforms (e.g., Huggingface, Github) and the associated risks (train-test leakage). | False | False | NaN | Difficulty and cost of evaluating unstructured text and complex legal reasoning; high cost limiting benchmark scale and scope (potentially biasing towards commercial interests over A2J); subjectivity in legal tasks complicating evaluation; train-test leakage rendering public benchmarks less reliable over time; need for better automated evaluation metrics/methods. | Evaluating unstructured text generation for semantic correctness and legal soundness; high cost of involving legal subject-matter experts for annotation and evaluation; preventing train-test leakage for publicly distributed benchmarks; designing benchmarks for subjective legal tasks; creating tasks that genuinely measure complex legal reasoning. | GenAI models hallucinating false information; replicating social biases; lawyers facing sanctions for using unreliable AI; potential for significant financial loss or deprivation of liberty due to AI errors; high costs limiting benchmark development to well-resourced entities, potentially neglecting access-to-justice needs; inflated performance metrics due to train-test leakage. |
| Sg6cPoNeAzoJ.pdf | Google_Scholar | A PROPOSAL FOR THE JOINT DEVELOPMENT OF GENERATIVE AI FOR THE DISPUTE RESOLUTION PROFESSION | This paper proposes the collaborative development of a specialized generative AI system, based on large language models, for the dispute resolution field. The goal is to create a fine-tuned, reliable tool to assist practitioners and parties, enhance access to justice, and mitigate risks associated with general-purpose AI. | True | Idealistic | True | 1.0 | Positive | Collaborative development of a fine-tuned generative AI system (based on LLMs like ChatGPT) specific to the dispute resolution field, involving shared data curation, guardrail setting, and privacy parameter definition. | NaN | NaN | Complexity and inaccessibility of dispute resolution information and processes, particularly for non-English speakers or individuals with impairments. | Develop a collaboratively built, fine-tuned generative AI tool for dispute resolution to provide accessible information (text/voice, multiple languages, 24x7) and assistance to parties and neutrals. | Providing information about dispute resolution processes (mediation, arbitration), facilitating negotiation, drafting agreements, addressing ethical questions for neutrals. | Parties involved in disputes, particularly non-English speakers and individuals with hearing or visual impairments. | Dispute Resolution (Mediation, Arbitration) | International | Proposed: A collaboratively curated dataset using dispute resolution-specific supervised learning inputs, potentially including existing literature/materials from industry authors, to fine-tune a base LLM. | Collaborative development involving a centralized advisory board, expert curation of training data, supervised fine-tuning of LLMs, establishment of guardrails and privacy parameters, and ongoing feedback. | Proposed: Access to the collaboratively developed system/dataset potentially via a fee, allowing individuals/providers to build applications. Neutrals could embed access on their websites. | False | False | NaN | Lack of a reliable, collaboratively developed AI tool specifically tailored for dispute resolution. Need for ongoing refinement and addressing concerns (privacy, accuracy) as the technology is used. | General LLM issues (accuracy, bias, IP, hallucinations); specific challenges for the proposal include organizing collaboration, securing data/cooperation, funding, defining/implementing guardrails and privacy standards. | Generation of biased/inappropriate content, factual inaccuracy/fabrications ('hallucinations'), intellectual property infringement related to training data, potentially misleading 'emotional' or 'sentient-like' responses. |
| PHjZDne92UkJ.pdf | Google_Scholar | What are Models Thinking about? Understanding Large Language Model Hallucinations through Model Internal State Analysis | This paper introduces HaluProbe, a framework for analyzing the internal states (attention, activation, logits) of Large Language Models (LLMs) during different inference stages to understand and detect hallucinations without external data sources. It systematically evaluates various internal features and token selection strategies, finding attention-based features offer robust detection but notes challenges like limited transferability across datasets. | True | NaN | True | 1.0 | NaN | HaluProbe: A framework using LLM internal state features (e.g., attention lookback ratio, attention allocation sharpness, last layer representation, activation map/entropy, token probabilities/ranks) extracted during understanding, query, and generation stages for hallucination detection. | Evaluated on HaluEval, CNN/Daily Mail (CNNDM), and Natural Questions (NQ) datasets using Llama-2-7B and Vicuna-7B models. Metrics included accuracy, recall for hallucinated outputs, and recall for factual outputs. Ablation studies on features and token selection strategies (All tokens, First/Last token, Per token, Sliced window) were performed, along with transferability tests across datasets. | The sliced window token selection strategy (specifically Window(4, 2)) achieved the highest accuracy (e.g., 0.89 on HaluEval with Vicuna-7B). Attention-based features like Lookback Ratio showed relatively robust performance across datasets compared to logit or activation features. However, transferability across different datasets (e.g., from HaluEval to CNNDM/NQ) was found to be limited for most features. | NaN | NaN | NaN | NaN | NaN | International | The study uses publicly available datasets (HaluEval, CNN/Daily Mail, Natural Questions) to generate factual and hallucinated responses from LLMs (Llama-2-7B, Vicuna-7B). Features are extracted from the internal states of these models during generation on these datasets; these features are implicitly used to train/evaluate a classifier for hallucination detection. | Systematic analysis of LLM inference process stages, extraction of internal states (attention scores, layer representations, logits), definition and computation of specific features from these states, experimental evaluation on benchmark datasets, ablation studies on features and token selection methods, transferability analysis across datasets. | The paper commits to making the source code, datasets, and implementation details publicly available on a repository like GitHub upon acceptance. | False | False | NaN | NaN | Limited transferability of internal state features across different datasets and task scenarios. High computational and storage overhead associated with extracting and processing some internal state features. | The primary risk addressed is LLM hallucination (generating factually incorrect or contextually inconsistent content). The paper does not explicitly state risks associated with the proposed detection method itself. |
| HsqxFTOulbAJ.pdf | Google_Scholar | InternLM-Law: An Open Source Chinese Legal Large Language Model | This paper introduces InternLM-Law, an open-source Large Language Model specialized for the Chinese legal domain, detailing its novel two-stage fine-tuning process and the construction of a comprehensive legal dataset. InternLM-Law demonstrates state-of-the-art performance on the LawBench benchmark, outperforming existing models including GPT-4 on many Chinese legal tasks, and is released to foster further research. | True | Idealistic | True | 1.0 | Positive | InternLM-Law, a specialized LLM for Chinese legal queries, developed using a two-stage supervised fine-tuning (SFT) process on a new Chinese legal dataset. | Evaluated on LawBench (20 legal subtasks covering memorization, understanding, and application), subjective evaluation (comparison with GPT-4 on legal consultation, case analysis, legal reasoning judged by GPT-4), and long-text evaluation (analyzing Chinese law judgments over 20k characters). | InternLM-Law-7B achieves the highest average performance on LawBench (67.71% zero-shot, 67.67% one-shot), outperforming GPT-4 on 13 out of 20 subtasks. In subjective evaluation, it achieved a 46.67% win-rate against GPT-4, and 87.5% on legal consultation. On long context evaluation, it achieved an 84.73% F1 score. | NaN | NaN | Legal consultation, consumer rights protection, criminal case analysis, financial remedy calculation, legal document understanding and information retrieval. | NaN | Chinese civil, criminal, and constitutional law, and other regulations. | China | A dataset of over 1 million queries in the Chinese legal domain, sourced from public legal datasets (e.g., CAIL, LawBench), online legal consultation platforms (6 million anonymized Q&A records), and the Chinese National Legal Database (100K entries of laws & regulations). It also includes 1 million general SFT instruction instances from InternLM2-Chat training. | Two-stage supervised fine-tuning (SFT) pipeline. Stage 1: fine-tuning on a mixture of legal and general-purpose tasks. Stage 2: refining the model on high-quality legal tasks. Data processing includes rule-based filtering, semantic filtering using LLMs (Qwen-1.5-72B), instruction generation using GPT-4, and data synthesis using GPT-4 with human feedback. | The model, dataset, and code are made publicly available on GitHub. | True | True | Dataset, code, and models will be released on GitHub (https://github.com/InternLM/InternLM-Law). | Model hallucinations and limitations in complex legal reasoning due to model size, which could hinder reliable application. | Collecting and cleaning a comprehensive SFT dataset; ensuring data quality and diversity; enabling the model to transfer general skills to legal tasks; designing an effective SFT strategy to learn crucial datasets and adjust response style; handling long legal texts. | Model hallucinations and generation of inaccurate responses. |
| gchkptrNhsIJ.pdf | Google_Scholar | Is ChatGPT Leading Generative AI? What is Beyond Expectations? | The paper provides an overview of Generative AI, focusing on ChatGPT and its competitors, discussing their technical fundamentals and societal impact by reviewing existing literature. It explores user expectations and the current landscape, highlighting both the potential and limitations of these technologies across various fields including law. | True | Market | True | 3.0 | Neutral | ChatGPT and other Generative AI Large Language Models (e.g., Bard, Claude, GPT-4) | Literature review of studies testing various LLMs, and illustrative queries by the authors to ChatGPT to demonstrate capabilities and limitations (e.g., reference generation, factual accuracy). | Authors' illustrative queries showed ChatGPT can generate coherent text but may fabricate references and provide incorrect factual information. It apologized for errors but repeated some. | Unreliability (hallucinations, factual inaccuracies), potential for bias, ethical and regulatory challenges, fabrication of information, and the risk of misuse. | Emphasizes human oversight, critical evaluation of AI outputs, development of safety protocols and explainable AI, legislation for high-risk AI use, and industry initiatives for responsible AI development. | Legal information provision, legal document drafting, legal reasoning assessment (e.g., Bar exam performance), transformation of legal services, and ethical/regulatory considerations in legal AI. | NaN | General law, Torts, Evidence, Civil law, Litigation, AI liability Ddrectives. | International | For ChatGPT and similar models: Proprietary, large and diverse datasets of text and code. Some mentioned models (e.g., OPT, GPT-J) use public datasets like The Pile and BookCorpus. | Deep learning using Transformer architectures, pre-training on large corpora, and fine-tuning techniques (e.g., supervised learning, Reinforcement Learning from Human Feedback - RLHF for some models like Claude). | Primarily via publicly accessible web-based user interfaces (e.g., ChatGPT, Bard). Some models are available as open-source software. | True | True | ChatGPT and other models like Bard are publicly accessible via web interfaces, often with free tiers. Some specific models discussed, like EleutherAI's GPT-J/NeoX and OpenAI's Whisper, are available as open-source software. | Need for improved reliability and factual accuracy, robust ethical and legal frameworks, mitigation of bias, better explainability, security against misuse, and reliable methods for detecting AI-generated content. | Ensuring accuracy and reliability (avoiding hallucinations), managing bias from training data, high computational costs for training, addressing ethical considerations and data privacy, and preventing misuse. | Generation of misinformation and fabricated content, privacy violations, academic and professional dishonesty, entrenchment of biases, job displacement for knowledge workers, misuse for malicious purposes (e.g., cybersecurity threats), and broader unforeseen societal disruptions. |
| Y167Kf-vw-gJ.pdf | Google_Scholar | Large language models and their possible uses in law | This paper explores the workings of Large Language Models (LLMs) like ChatGPT and their potential applications in the legal field, particularly focusing on enhancing access to legal information and services. It discusses uses like text retrieval, generation, and analysis, and details an experiment building a law firm chatbot, while also acknowledging limitations and suggesting paths towards democratizing access to justice. | True | Idealistic | True | 3.0 | Positive | A chatbot demo for a small law firm using the OpenAI GPT-3.5 API, customized with prompts and examples. | An informal experiment conducted by one author to build and explore the capabilities and limitations of a demo chatbot. | The demo chatbot could provide basic firm information entertainingly but was unsuitable for actual legal advice due to limitations (hallucinations, token limits, policy restrictions, lack of reality check/emotional intelligence). Customization required prompts and examples. | LLM limitations: unreliability/hallucinations, inability to perform reality checks or understand deeper context/client needs, lack of emotional intelligence, token limits restricting input/customization, potential for misuse (e.g., unauthorized practice of law), non-transparency of models. | Using LLMs for specific tasks (retrieval, generation, analysis) within professional workflows, staged approaches (e.g., retrieval + ranking), connecting LLMs to curated knowledge bases, prompt engineering, fine-tuning, responsible API use, and domain-specific evaluation by legal experts. | Providing legal information to the public, increasing efficiency of obtaining legal assistance. | The broader public / laypeople. | General / Multiple (including contract law, inheritance law). | International (with Hungarian context/examples). | The underlying LLM (GPT-3.5) was pre-trained on a vast, general internet corpus. The specific chatbot demo was customized using hand-crafted prompts and question/answer examples specific to the law firm and ethical rules, provided via the API. | Prompt engineering, few-shot learning (via examples provided in API calls). | A web interface for the demo chatbot was created and made publicly accessible (as stated in footnotes). | True | False | Source code for demo front-end and examples available on GitHub; requires paid access to OpenAI API. | Need for domain-specific accuracy benchmarks and evaluation by legal experts; understanding LLM capabilities with higher-level legal concepts; determining reliability limits, especially for direct client use; need for more large-scale experimentation across jurisdictions. | Adhering to deontological rules, preventing factual 'hallucinations', managing strict token limits (restricting customization and context length), ensuring accurate multilingual performance. | Providing incorrect legal information/advice (hallucinations); unauthorized practice of law; misrepresenting firm details; potential data confidentiality issues with API usage (mitigated by OpenAI policy); potential for misuse (e.g., generating misinformation). |
| 7ME5PVaLogYJ.pdf | Google_Scholar | Justifiable Artificial Intelligence: Engineering Large Language Models for Legal Applications | This paper introduces the concept of "Justifiable Artificial Intelligence" (JAI) as an alternative to Explainable AI (XAI) for Large Language Models (LLMs) in the legal domain. JAI proposes to enhance the trustworthiness of LLM outputs by providing users with supporting and potentially contradicting evidence from reliable sources, rather than focusing on explaining the LLM's internal workings. | True | Market | True | 1.0 | NaN | Justifiable Artificial Intelligence (JAI), conceptualized through two pipelines: 1) LLM prompted to extract evidence from documents, 2) Fact-checking an LLM's claim using external trustworthy sources/knowledge bases, a retriever, and an entailment classifier to display supporting/contradicting evidence. | NaN | NaN | Lack of trust in LLM outputs by legal experts due to issues with explainability, accuracy (hallucinations, outdated information), coherence, transparency, interpretability, and ethical concerns (bias, privacy). | Proposes Justifiable Artificial Intelligence (JAI), where LLM outputs are accompanied by retrievable evidence from trustworthy sources, allowing users to validate claims. This involves providing both supporting and (if applicable) contradicting evidence to enable informed decision-making by the user. | Enhancing trustworthiness and reliability of AI in legal applications, fact-checking AI-generated legal information, human-in-the-loop validation of AI outputs. | NaN | General legal domain. | International | The proposed JAI approach relies on retrieval from external data sources like 'trustworthy websites, database of fact-checked documents,' or a 'Document Collection.' The LLMs discussed in the background are trained on large, general text corpora. | Conceptual framework development; proposal of system architectures involving LLMs, information retrieval modules, and entailment classifiers. | NaN | False | False | NaN | The need to validate the acceptance of JAI-enhanced LLMs by legal experts. The underlying limitations of LLMs (accuracy, bias) are not fully resolved by JAI, only made more transparent for validation. | Ensuring the reliability and accuracy of the evidence retrieval and entailment classification components within the JAI framework; defining and maintaining a collection of 'trustworthy sources'; managing potential error propagation from these components to the final justification presented to the user. | General LLM risks: misinformation (hallucinations), copyright violations, bias in training data leading to unfair outputs, privacy issues, high energy consumption. Specific to JAI: flawed justification mechanisms (e.g., incorrect evidence retrieval or entailment classification) could lead to misplaced trust in an erroneous AI output. |
| lNX-6qdZr2wJ.pdf | Google_Scholar | How LLMs Can Help Address the Access to Justice Gap through the Courts | This paper explores how Large Language Models (LLMs) can improve access to justice for low-income individuals in the U.S. court system, focusing on externally-facing applications. It demonstrates five use cases using Arizona courts, including translation and GPT-powered chatbots for eviction and expungement, while also discussing potential risks and providing illustrative tools. | True | Idealistic | True | 1.0 | Positive | Demonstration of five LLM use cases: 1) multi-language translation of court website text, 2) finding pro bono legal help, 3) building no-code AI chatbots for criminal expungement guidance, 4) building no-code AI chatbots for landlord/tenant disputes and eviction guidance, and 5) internal court brainstorming/strategic planning. Two GPT-powered chatbots for Arizona expungement and eviction were built using OpenAI's GPT builder. | Translation: Prompts given to ChatGPT 4.0, ChatGPT 3.5, Bard, Claude 1 & 2; reviewed by native speakers. Pro bono help finding: Tested with ChatGPT 4, Bard, Perplexity Pro; links/numbers checked. Chatbots (Expungement & Eviction): Built with OpenAI's GPT builder using Arizona court documents, tested with sample user queries for eligibility, form-filling guidance, and procedural explanations. Internal brainstorming: Prompts given to Claude 2 and ChatGPT 4. | For Spanish translation of legal text, ChatGPT 4 received a native speaker rating of 9/10. | Lack of adequate legal assistance for low-income individuals, difficulties for self-represented litigants in navigating the legal system (e.g., understanding rights, procedures, finding help, completing forms), and language barriers. | Utilizing LLMs for language translation of legal information, curating legal provider information, guiding users through self-help forms and procedures (e.g., for eviction and expungement via AI chatbots), and assisting courts with internal planning and improving IT infrastructure. | Language access in courts, legal aid referrals, criminal record expungement, housing law (eviction, landlord-tenant disputes), support for self-represented litigants, and court administration. | Low-income Americans, self-represented litigants, individuals with limited English proficiency, and individuals with criminal records. | Civil law, criminal law (specifically record clearing/expungement), housing law (landlord-tenant disputes, eviction), immigration law (for referral finding). | United States (with Arizona courts as a specific case study) | Publicly available, unstructured textual information and forms (over 150 pages total for both bots) from the Arizona state courts' websites (specifically azcourts.gov, azcourthelp.org) regarding expungement, landlord-tenant disputes, and eviction. | For chatbots: Utilization of OpenAI's GPT builder (a no-code approach), involving uploading relevant documents from Arizona court websites to create a knowledge base, and iterative testing with sample conversations. For other use cases: Prompt engineering with various LLMs (ChatGPT, Claude, Bard). | Two GPT-powered chatbots (AZExpungement and AZ-evictionbot) were made accessible via URLs. Prompts and instructions for implementing the five use cases are provided in an appendix. | True | False | Two GPT-powered chatbots (Arizona Expungement Bot and Arizona Eviction Bot) accessible via provided URLs, requiring a ChatGPT 4 subscription. Prompts for all five use cases are in the appendix. | Current limitations of LLMs in accuracy and reliability (hallucinations), need for more sophisticated and reliable legal AI tools tailored for courts, and institutional/cultural challenges within courts for technology adoption. | Ensuring accuracy and reliability of LLM-generated information (hallucinations, e.g., incorrect legal deadlines), managing the risk of providing unauthorized legal advice, technical difficulties in translation (terms of art, less common languages), and the need for significant IT infrastructure upgrades and staff training for court adoption. | Generation of inaccurate or false information (hallucinations) by LLMs, perpetuation of bias from training data or model inferences, exacerbation of existing inequalities in the legal system (e.g., creating a two-tiered system of justice), potential for misuse (e.g., flooding courts with frivolous filings), and diversion of resources from other access to justice initiatives like right to civil counsel. |
| Hb7vkjE6mpUJ.pdf | Google_Scholar | Artificial Intelligence and the Sustainable Development Goals: An Exploratory Study in the Context of the Society Domain | This paper evaluates AI's potential contributions and risks concerning the United Nations' Sustainable Development Goals (SDGs) within the society domain by analyzing responses from the GPT-3 language model. The study highlights AI's capabilities for these goals while emphasizing the critical need for ethical guidelines and robust regulations. | True | Idealistic | True | 2.0 | Neutral | The paper evaluates the capabilities and response patterns of GPT-3 (specifically the text-davinci-003 model) when queried about AI's role in achieving Sustainable Development Goals (SDGs). | GPT-3 (text-davinci-003 model) was prompted with queries regarding 9 societal SDGs and their 58 outcome targets, asking it to shorten target titles, maintain numbering, and provide 3-5 sentences on AI's benefits and risks. The generated outputs were then descriptively analyzed for content, structure, word counts, and patterns/errors. | GPT-3 (text-davinci-003) generated relevant responses discussing potential benefits and risks of AI for societal SDGs. However, the study identified inconsistencies in output format, varying sentence structures, and increased punctuation mistakes in longer texts, indicating it is not fully reliable or error-free. | High-level obstacles/risks for AI in access to justice (derived from SDG 16 discussion) include: potential for targeting specific populations (e.g., minority groups), misinterpretation of data leading to false accusations, biased algorithms causing unfair discrimination, increased surveillance infringing on privacy, misuse by corrupt actors, racial biases in technologies like facial recognition for legal identity, and violation of fundamental freedoms through profiling. | The paper advocates for proper regulations and oversight for responsible, transparent, safe, and ethical AI use. It calls for a global debate leading to science-driven shared principles and legislation, careful monitoring of AI, building safeguards against discrimination into algorithms, and strict oversight for technologies like facial recognition. | Access to Justice for All (as part of SDG 16), promoting the rule of law, reducing illicit financial flows, reducing corruption and bribery, providing legal identity for all. | Minority groups, disadvantaged groups, and vulnerable populations are mentioned as potentially at risk or in need. | Public law, human rights, criminal justice (related to reducing violence, corruption), administrative law (effective institutions, rule of law). | International | The GPT-3 model text-davinci-003, used in the study, was trained on general internet text data up to June 2021. This is large-scale, mostly unstructured text data. | The evaluation of GPT-3 involved AI model selection (comparing GPT-3 models), prompt engineering (designing specific queries), and descriptive analysis (analyzing GPT-3's output for patterns, word counts, etc.). | NaN | True | False | The study used OpenAI's GPT-3 text-davinci-003 model, accessible via its platform (e.g., API, playground), which was available as a 'publicly available beta for research.' | Technical gaps include the unreliability and error-proneness of GPT-3 for complex, evidence-based tasks, its potential for bias, and lack of robust interpretability. Societal and regulatory gaps include the need for frameworks for ethical AI deployment in justice, mechanisms to ensure AI enhances freedoms, and robust legislation for AI governance and accountability. | Challenges faced by the authors in using GPT-3 included obtaining consistent output format, ensuring accuracy (avoiding 'hallucinations'), the AI's tendency to mimic human writing errors, and potential capacity issues with the free beta tier. | For SDG 16 (Access to Justice): AI could enable targeting of specific populations; misinterpret data leading to false accusations; use biased algorithms for discrimination; increase surveillance infringing privacy; be misused by corrupt actors; exhibit racial bias in facial recognition for legal identity; and violate fundamental freedoms through profiling and targeted ads. |
| CZsvZ6XE5O8J.pdf | Google_Scholar | Enhancing Generative AI Usage for Employees: Key Drivers and Barriers | This study investigates the factors influencing employee adoption and usage frequency of Generative AI (Gen-AI) tools in the workplace using the Technology, Organization, and Environment (TOE) framework. Based on a survey of 316 US employees, results show perceived competence, peer influence, and regulatory support positively impact usage, while perceived severity has a negative impact. | True | Market | True | 2.0 | NaN | Application of the Technology-Organization-Environment (TOE) framework, integrated with concepts from UTAUT (performance expectancy, effort expectancy) and social cognitive dimensions (perceived competence, warmth), to model employee adoption of Generative AI (Gen-AI). | Quantitative survey administered to 316 American employees via the Prolific platform. Data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed model relationships. | Perceived Gen-AI competence positively impacted usage intensity, mediated by both performance expectancy and effort expectancy. Perceived warmth positively impacted usage intensity via performance expectancy only. Peer influences and regulatory concerns positively impacted usage intensity directly, while perceived severity had a negative direct impact. Technological resource proficiency had a positive impact, but absorptive capacity did not significantly influence usage intensity. | NaN | NaN | NaN | NaN | NaN | United States | Quantitative survey data from 316 American employees regarding their perceptions and professional use of Gen-AI, collected via the Prolific online research marketplace. | The study developed a theoretical framework (model) based on existing theories (TOE, UTAUT, social cognitive dimensions) identified through a literature review. Hypotheses derived from this framework were tested using survey data and statistical modeling (PLS-SEM). | NaN | False | False | NaN | NaN | Study limitations mentioned include: relatively small sample size, cross-sectional data limiting causal inference, reliance on self-reported data, lack of input from various employee levels, focus solely on the US market, overlooking dynamics of change over time and individual adoption contexts. | Perceived severity risks (amplifying discrimination/bias, perpetuating stereotypes, wrong objectives, poor performance). Potential job security concerns. Doubts about AI dependability. Risk of over-regulation hampering innovation. |
| Ra4RfztIjSYJ.pdf | Google_Scholar | Governing Data and AI to Protect Inner Freedoms Includes a Role for IP | This policy brief argues for comprehensive governance of data and AI, highlighting the crucial role of intellectual property, to protect fundamental human rights such as freedom of thought from the impacts of technologies like generative AI. It identifies current regulatory inadequacies and proposes solutions including enhanced international cooperation, technology pre-deployment testing, and increased corporate accountability. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Lack of regulatory clarity and data governance hindering intellectual property protection for smaller entities, creating socio-economic and access-to-justice issues; pervasive data monetization without assured human rights protection; inadequate, siloed, and non-globalized AI/data regulations; manipulative AI practices (e.g., dark patterns, disinformation) threatening freedom of thought. | Integrating intellectual property rights into AI governance frameworks to enhance transparency and algorithmic monitoring; establishing national personal data protection laws; fostering international regulatory cooperation (e.g., a Digital Stability Board); implementing technology testing (e.g., regulatory sandboxes) before deployment; promoting corporate responsibility through duty-of-care frameworks. | Fairness in intellectual property protection, particularly for smaller entities; protection of fundamental human rights (especially freedom of thought) through AI and data governance. | Smaller companies (regarding intellectual property rights and access to justice); the general public (regarding the protection of freedom of thought and other fundamental rights). | Intellectual Property Law, Data Governance, AI Regulation, Human Rights Law, Competition Law | International (with specific examples and discussions related to Canada, United States, European Union, United Kingdom, Australia, Japan, and bodies like G7, OECD). | NaN | NaN | NaN | False | False | NaN | Lack of coherent global guardrails, standards, and regulations for generative AI and data governance; insufficient mechanisms for multi-stakeholder international cooperation on AI regulation; unresolved international differences in IP treatment for AI-generated works and data used in AI training; inadequate protection for freedom of thought against technological encroachments. | NaN | Monetization of nearly all human activity as data without upholding human rights (including freedom of thought, privacy, freedom of speech); covert tracking, surreptitious surveillance, and pervasive monitoring; opaque consent agreements; IP rights used by digital giants to impede competitors; uncertainty in IP law regarding AI-generated inventions and copyright for AI inputs/outputs; use of trade secrets to hinder transparency; web scraping of copyrighted data; AI-driven subtle influence on individuals and generation of social tensions (e.g., disinformation); weaponization of personal data through 'dark patterns'; AI 'hallucinations' and inaccuracies misrepresenting individuals. |
| YfVyFeUpCzoJ.pdf | Google_Scholar | AI for Data Science: A Benchmark for Differentially Private Text Dataset Generators | This paper introduces a benchmark design for evaluating differentially private text dataset generators, particularly for high-stakes, domain-specific applications. Preliminary results using healthcare datasets demonstrate that current methods significantly underperform in utility and fidelity, underscoring the need for such robust, domain-specific benchmarks. | True | Market | True | 1.0 | NaN | A benchmark design for differentially private text dataset generators. | The benchmark design's validity was demonstrated by applying it to evaluate two existing methods (AUG-PE, DP-Generator) on three healthcare text datasets (HOC, N2C2 2008, PSYTAR). Evaluation measured utility (F1 scores on downstream classification) and fidelity (MAUVE, text length distributions, entity mentions, lexical diversity) under various privacy budgets (ϵ). | Existing methods (DP-Gen and AUG-PE) showed significant degradation in utility (e.g., achieving only 34-58% of real data performance at ϵ≤4) and fidelity (MAUVE scores near zero, indicating substantial distribution differences) on domain-specific healthcare data. | NaN | NaN | NaN | NaN | NaN | International | Three semi-publicly available healthcare text datasets employed for validating the benchmark: HOC (scientific abstracts, public), N2C2 2008 (clinical discharge summaries from MIMIC-III, gated access), and PSYTAR (adverse drug effect reports from social media posts, gated access). These are domain-specific, unstructured text. | The benchmark design incorporates: 1) Use of datasets with gated access for realistic evaluation on sensitive data. 2) Integration of fully open foundation models with transparent training corpora to verify non-exposure of private data during pre-training. 3) Development of diagnostic datasets for membership inference attacks. 4) Evaluation of both utility (downstream task performance) and fidelity (statistical similarity to real data using metrics like MAUVE, text length distributions, entity mentions, and lexical diversity). | NaN | False | False | NaN | NaN | Key challenges motivating the benchmark design include: 1) Lack of realism and representativeness in existing benchmarks, which often use general domain data instead of specialized, sensitive data. 2) Problematic privacy budget assumptions in current approaches, such as assuming public label distributions or not accounting for privacy costs of hyperparameter tuning on private data. 3) Insufficient empirical privacy verification, with many works omitting rigorous checks for privacy leakage or using simplified checks. | Privacy leakage from synthetic data, especially for records with rare attribute combinations. Implementation errors in privacy-preserving mechanisms invalidating formal differential privacy guarantees. Generation of low-utility or low-fidelity synthetic data that is unrepresentative of real domain-specific data, leading to poor performance in downstream tasks. |
| 17mwWRXSIXUJ.pdf | Google_Scholar | Eliciting the Priors of Large Language Models using Iterated In-Context Learning | This paper introduces 'iterated in-context learning,' a prompt-based Markov chain Monte Carlo method to elicit implicit prior distributions from Large Language Models (LLMs) like GPT-4. Experiments demonstrate that these elicited priors align with human priors in known cognitive tasks and can uncover LLM priors for speculative events. | True | NaN | True | 1.0 | NaN | Iterated in-context learning, a prompt-based workflow using a Markov chain Monte Carlo (MCMC) method, to elicit prior distributions from LLMs. | Validated by comparing priors elicited from GPT-4 with known human priors in three settings: causal learning (gene/protein cover story, noisy-OR/noisy-AND-NOT likelihoods, compared against uniform and sparse/strong priors using RMSD and Pearson's r), proportion estimation (coin flips, visual comparison with human data), and predicting everyday quantities (e.g., lifespan, movie grosses, visual comparison). The method was also applied to elicit priors for speculative events (superhuman AI, zero carbon emissions, Mars colony). | For generative causal induction, the empirical prior elicited from GPT-4 using iterated in-context learning best explained GPT-4's judgments, achieving a Pearson's r of 0.86 and an RMSD of 0.19, outperforming uniform and sparse/strong priors. | NaN | NaN | NaN | NaN | NaN | International | NaN | The method is based on iterated learning, a Markov chain Monte Carlo (MCMC) method from cognitive psychology, adapted for in-context learning with LLMs (GPT-4) using prompt engineering. It relies on the theoretical connection between iterated learning with Bayesian agents and sampling from the prior distribution. | NaN | True | False | The paper describes the 'iterated in-context learning' methodology and provides prompt examples (Appendix A), allowing replication of the technique by implementing the iterative prompting procedure with an LLM like GPT-4 (which requires separate API access). | NaN | The key assumption that LLMs function as approximate Bayesian agents requires further investigation. It is also unclear how LLMs learn to encode human-like priors from pretraining on text. LLMs typically avoid direct speculation on sensitive future events, making direct elicitation of certain priors challenging. | The paper highlights the general risk that errors or biased decisions from LLMs can have profound implications in critical sectors (including legal services) if their decision-making processes are not well understood. The proposed method aims to improve this understanding. |
| F5YEH5n2YDoJ.pdf | Google_Scholar | Prompting Minds: Evaluating how Students Perceive Generative AI’s Critical Thinking Dispositions | This study introduces and validates the Perceived Critical Thinking Disposition of Generative Artificial Intelligence (PCTD-GAI) scale, designed to measure students' perceptions of GAI's critical thinking dispositions (reasoning, access to justice, search for evidence, search for truth, open-mindedness, and systematicity). Results from surveying 931 university students in Portugal and Poland demonstrate the scale's effectiveness in capturing these perceptions, which were found to be moderately positive regarding ChatGPT's capabilities. | True | NaN | True | 1.0 | NaN | Perceived Critical Thinking Disposition of Generative Artificial Intelligence (PCTD-GAI) scale, adapted from the Marmara Critical Thinking Dispositions Scale (MCTDS). | A quantitative cross-sectional survey study was conducted with 931 university students from Portugal and Poland. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were used to assess the PCTD-GAI scale's validity and reliability. | The PCTD-GAI scale effectively captures students’ perceptions of ChatGPT’s critical thinking dispositions across six dimensions. Overall, students in both Portugal and Poland showed moderately positive perceptions, with 'systematicity' rated highest and 'search for truth' most neutral. | NaN | NaN | NaN | NaN | NaN | Portugal, Poland | NaN | Adaptation of the Marmara Critical Thinking Dispositions Scale (MCTDS) involving item rewording and cognitive shifts. Translation (forward and backward) into Portuguese and Polish with validation by bilingual experts. Pilot testing with students (n=20) in Portugal and Poland. Statistical validation using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). | NaN | True | True | The PCTD-GAI scale items are fully listed in Table 1 within the paper, which is an open access article under CC Attribution 4.0. | NaN | Adapting MCTDS items from assessing individual's own critical thinking to perceptions of GAI’s abilities; ensuring linguistic and cultural equivalency during translation to Portuguese and Polish; statistical validation requiring item removal to achieve robust factor models for different samples (Portuguese and Polish). | Potential for AI to diminish students' independent reasoning and critical thinking skills. Overreliance on AI, leading to passive acceptance of AI-generated content and superficial learning habits. Students who perceive AI as highly competent may exert less independent cognitive effort, potentially weakening problem-solving abilities. General risks from cited literature include procrastination, memory loss, and dampened academic performance. |
| UEryaxmiBzIJ.pdf | Google_Scholar | Generative AI for the Legal Profession: Facing the Implications of the Use of ChatGPT through an Intradisciplinary Approach | This paper analyzes the use of generative AI, particularly ChatGPT, in the legal profession, focusing on EU regulatory implications such as the AI Act, DSA, and GDPR. It proposes a multi-faceted approach involving legal, technical, and societal measures to ensure safe and effective integration, while also considering the evolving role of legal professionals. | True | Market | True | 3.0 | Neutral | ChatGPT and general Generative AI systems. | Illustrative example of submitting a legal query (“What is the punishment for robbery according to the Greek Criminal Code?”) to ChatGPT and qualitatively assessing the response for accuracy and completeness against the Greek Criminal Code. | ChatGPT's response directly addressed the question by mentioning imprisonment duration but omitted specific circumstances for life imprisonment detailed in Article 380 of the Greek Criminal Code. The paper concluded the system showed overall accuracy for initial information, pending legal professional verification. | Misinformation and inaccuracy of AI-generated legal information, potentially misleading individuals seeking legal assistance. Privacy risks stemming from processing sensitive personal data and potential compromise of lawyer-client confidentiality. Lack of transparency and explainability in AI systems, hindering trust and verification. Potential for discriminatory outputs affecting fairness. | A holistic approach for safe AI use, comprising: legal measures (technologically neutral regulation, rules against misinformation, strict data protection enforcement, transparency in privacy policies, regulatory sandboxes); technical measures (diligent data collection/processing, human oversight, explainability, risk-based approach for high-risk AI); and societal measures (training for legal professionals, awareness of AI limitations). These would generally improve reliability for any A2J applications. | Legal information and advice, particularly for small claims cases or routine matters (e.g., parking tickets, as exemplified by DoNotPay). | General public with minor legal issues or seeking initial legal information, as implied by discussions of small claims and tools like DoNotPay. | General legal services (legal advice, legal research, document drafting), Criminal law (specifically Greek Criminal Code as an example), Contract law. | Primarily EU (referencing AI Act, Digital Services Act, GDPR), with specific examples or mentions relating to Greece (Criminal Code), Italy (Data Protection Authority), and USA (ROSS jurisprudence retrieval). | ChatGPT was trained using Reinforcement Learning from Human Feedback (RLHF) on general dialogue datasets and InstructGPT data, with knowledge cutoff around 2021. The paper does not specify domain-specific legal data for its core training but notes it can process legal prompts. | NaN | NaN | True | True | ChatGPT is discussed as a publicly available online chatbot offered by OpenAI, with both free and paid access tiers. | Direct applicability of current EU regulations like the Digital Services Act to generative AI systems like ChatGPT. Insufficient preparedness and training among legal professionals for effectively and safely using generative AI. Lack of specific, homogeneously enforced rules against AI-generated misinformation at a regional (EU) level. Limited external scrutiny of AI development due to proprietary protection of training datasets and algorithms. | Ensuring the accuracy, truthfulness, and timeliness of legal information generated by AI. Protecting personal data and maintaining client confidentiality when using AI systems. Navigating the complex and evolving regulatory landscape for AI. Addressing the lack of transparency in AI models' training and operational logic. Mitigating risks of bias and discrimination in AI outputs. | Dissemination of harmful content and misinformation. Compromise of clients' personal data and lawyer-client confidentiality due to data reuse for AI training or insecure systems. Lack of user control over personal data. Inaccuracy of AI-generated information due to outdated training data. Potential for algorithmic discrimination based on processed personal features. Cybersecurity threats such as inversion attacks to recover training data. Users being misled by inaccurate or non-current AI-generated legal advice. |
| nPlqBz7DawEJ.pdf | Google_Scholar | Enhancing Privacy and Security in Large -Language Models: A Zero-Knowledge Proof Approach | This paper proposes a novel approach using Zero-Knowledge Proofs (ZKPs) to enhance the security, reliability, and privacy of Large Language Models (LLMs). It introduces a 'zk-LLM' framework and a prototype, zk-GPT, to demonstrate its effectiveness in user authentication, data validation, and malicious prompt detection. | True | Market | True | 1.0 | NaN | zk-LLMs (Zero-Knowledge Proof based Large Language Models) using zk-SNARKs (specifically Groth16 and Powers of Tau), with a prototype called zk-GPT built using Circom and SnarkJS. | The zk-GPT prototype was evaluated in three experimental stages: 1) User Authority Analysis (100 iterations testing ZKPs for user authentication and differentiating access levels), 2) Supplemental Data Relevance (80 experiments with 40 research papers to validate LLM use of relevant supplemental data), and 3) Malicious Prompt Detection (60 iterations with a dataset of 200 prompt injection keywords to detect and prevent malicious prompts). | The ZKP-based user authentication (User Authority Analysis experiment) demonstrated the highest success, correctly identifying and rejecting all 40 unauthorized login attempts and successfully processing all 60 authorized user logins with appropriate privilege separation over 100 iterations. | NaN | NaN | NaN | NaN | NaN | NaN | The zk-GPT prototype utilizes the pre-trained Llama-2 7b-GPTQ model. For its specific experiments, it used a custom dataset of 40 research papers (unstructured text, for supplemental data relevance testing) and a custom dataset of 200 prompt injection keywords (for malicious prompt detection testing). | A ZKP framework for creating zk-LLMs was proposed, covering user authentication, prompt analysis, source data verification, and source data relevance filtering. The zk-GPT prototype was developed using Circom for zk-SNARK circuits, SnarkJS for circuit binding and witness generation, and the Groth16 proof system with Powers of Tau ceremonies, built upon the localGPT platform. | A prototype application named zk-GPT was developed and tested. It builds upon the localGPT platform to perform on-device LLM computations. | False | False | NaN | NaN | Computational overhead impacting LLM responsiveness; challenges with data availability and context for large datasets; effective malicious prompt injection detection. Specific to the zk-circuit implementation: limited circuit flexibility (requiring modifications for input deviations), SHA256 hashing inefficiency for larger circuits, and reliance on trusted setups (e.g., Groth16). | General LLM risks: unreliability, susceptibility to manipulation, data poisoning, spread of misinformation, creation of deep-fakes, exposure of sensitive/mission-critical data, database corruption. Risks related to ZKP implementation in LLMs: computational overhead potentially impacting user experience. |
| ju14jCLQ_TMJ.pdf | Google_Scholar | Bekenbey AI: Innovative Solutions at the Intersection of Deep Learning and Law | This paper introduces Bekenbey AI, a system integrating generative artificial intelligence (including GANs, VAEs) and deep learning models like BERT for legal applications such as document analysis, generation, and predictive analytics. The model, tested on real-world legal data, demonstrates high performance on various metrics, aiming to enhance the efficiency, accuracy, and accessibility of legal services for professionals, organizations, and the public. | True | Idealistic | True | 1.0 | Positive | Bekenbey AI model: a hybrid system integrating Natural Language Processing (NLP) techniques, deep learning architectures (RNN, LSTM, BERT, CNN), and Generative AI technologies (GANs, VAEs) for legal text analysis, document generation, and predictive analytics. | The model was evaluated using metrics such as accuracy, precision, recall, F1-score, ROUGE (R-1, R-2, R-L), and BLEU scores on datasets of legal documents. Computation time and memory usage were also assessed across different dataset sizes. The datasets were compiled from legal databases, government/corporate websites, academic resources, and digital libraries, anonymized by Torun Law and Consulting. | With 50 samples, the Bekenbey AI model achieved an accuracy of 88.73%, precision of 89.00%, recall of 88.00%, and F1-score of 88.50%. For text generation tasks with 50 samples, it achieved ROUGE-1: 97.50%, ROUGE-2: 93.80%, ROUGE-L: 96.50%, and BLEU: 93.00%. | High cost, time-consuming nature of traditional legal procedures; limited accessibility of legal services; complexity of legal texts and data management challenges in the legal sector. | The Bekenbey AI model is proposed to streamline complex legal processes, enhance legal document management and analysis, provide predictive analytics, and support decision-making. This is intended to reduce time and costs, and improve the accuracy and accessibility of legal services. | Legal document generation, predictive legal analytics, legal text analysis, case outcome prediction, document management, enhancing accessibility of legal services. | The public/citizens, legal professionals, and organizations. | General legal domain (adaptable across various legal sectors and frameworks, not specified further). | International (not specified, model described as adaptable). | Proprietary datasets anonymized by Torun Law and Consulting, compiled from multiple sources including legal databases, government and corporate websites, academic resources, and digital libraries. The datasets include a mix of structured data (e.g., legal codes, statutes) and unstructured data (e.g., case law texts, legal opinions). | The Bekenbey AI model uses a multi-layered architecture involving: data preprocessing (cleaning, tokenization, stemming, vectorization); embedding layers (Word2Vec, BERT); deep learning layers (CNN, RNN/LSTM, Transformer with attention mechanisms); classification layer (densely connected layers, softmax). The system integrates NLP techniques, generative AI (GANs, VAEs), and uses SQL/NoSQL databases (PostgreSQL, MongoDB) with a Python-based backend (Django, Flask) and FastAPI for APIs. | The backend infrastructure uses Python with Django and Flask, and APIs are developed using FastAPI for integration. The paper mentions model deployment as part of its system architecture but does not detail broader public deployment or diffusion strategies. | False | False | NaN | The paper suggests future work to: analyze different generative models in legal contexts, conduct comparative analyses with other models, test the model on diverse datasets and application domains, and explore advanced techniques to enhance accuracy and overall performance. | Addressing data management challenges within legal processes; ensuring compliance with stringent security standards and privacy regulations (e.g., GDPR); meeting high demands for security and operational efficiency in legal applications; parsing and comprehending complex legal texts. | The paper does not explicitly state concrete risks of the Bekenbey AI model itself, though it mentions the implementation of encryption and anonymization techniques for GDPR compliance, implicitly acknowledging data privacy as a concern to be managed. |
| pjmf6r2ahe8J.pdf | Google_Scholar | PROFESSOR GPT: HAVING A LARGE LANGUAGE MODEL WRITE A COMMENTARY ON FREEDOM OF ASSEMBLY | This paper demonstrates that a large language model (GPT-4o) can generate a comprehensive legal commentary on freedom of assembly under the European Convention on Human Rights, comparable in quality to human-written equivalents. The authors also develop and apply a validation methodology, using retrieval-augmented generation, to assess the commentary's utility in predicting court rulings. | True | Market | True | 1.0 | Positive | Using GPT-4o with a multi-step prompting and iterative refinement process to generate a structured legal commentary on Art. 11 ECHR from ECHR jurisprudence. This includes case classification, batch summarization of extracted paragraphs per doctrinal element, topic identification, and cleaning for relevance and redundancy, with the output presented on a website. A validation method using RAG for case outcome prediction is also introduced. | The GPT-written commentary was validated through: 1) Comparative citation analysis against human-written commentaries for comprehensiveness. 2) Using GPT-4o with Retrieval Augmented Generation (RAG) to predict outcomes of 55 test cases (18 actual ECtHR cases, 27 German Constitutional Court cases, 10 fictitious cases), with access to either the GPT-commentary or the ECHR's official Guide. 3) Comparison of these predictions against base GPT predictions (without RAG) and actual/quasi-actual court decisions. | The GPT-written commentary cited significantly more cases (12,254 citations from 572 unique cases) than human competitors (e.g., ECHR Guide: 267 citations from 118 cases). For predicting ECtHR case outcomes (10 repetitions, temp 0), GPT-4o with RAG access to the ECHR Guide achieved 89% accuracy, while access to the GPT-commentary achieved 82% accuracy. | The difficulty, time, and cost for legal practitioners and academics to comprehensively read, synthesize, and stay updated with a vast and growing body of jurisprudence. | Automating the generation of detailed, structured, and up-to-date legal commentaries using large language models. This provides practitioners and academics with easier and more reliable access to the current state of legal doctrine and jurisprudence. | Improving access to synthesized legal information and understanding of legal doctrine for legal professionals, which could indirectly enhance access to justice. | NaN | Human Rights Law (specifically Freedom of Assembly) | European Court of Human Rights (ECHR). Validation also used cases from the German Constitutional Court. | The GPT-4o model was pre-trained by OpenAI. For the commentary generation, the input consisted of 1198 ECHR case documents (filtered to 691 relevant ones for freedom of assembly) discussing Art. 11 ECHR, publicly available from the ECHR's HUDOC database. These were unstructured texts extracted from PDFs. | An iterative development process involving: data acquisition and pre-processing of ECHR cases; multi-dimensional case classification using GPT-4o with detailed system prompts; iterative, batch-wise summarization of case law snippets per doctrinal element using GPT-4o; topic extraction and coherence refinement across batches; automated cleaning of summaries; and website presentation. | The generated commentary is deployed as a publicly accessible, hierarchically structured website (http://professor-gpt.coll.mpg.de/html/overview.html). | True | True | The generated commentary ('Professor GPT') on Art. 11 ECHR is available online for free at http://professor-gpt.coll.mpg.de/html/overview.html. The paper states its code is 'available for scrutiny.' | The technology is not yet at a 'push-button' stage for generating commentaries on any legal provision, and significant human expert intervention is still needed for quality control and usability. The process for ensuring coherence and managing large inputs needs further refinement. | 1. Managing LLM context window limitations and the 'lost in the middle' effect for large legal text corpora, necessitating batch processing. 2. Ensuring stylistic and substantive coherence across summaries generated from different batches of data. 3. LLM tendency for redundancy and inclusion of off-topic material, requiring multiple cleaning steps. 4. Technical difficulties in programmatically downloading case law from dynamic websites. 5. Initial concerns about LLM hallucinations, requiring skillful prompting and process design. | 1. LLM hallucinations (generating non-factual information), though mitigated by their process. 2. Systematic errors in LLM outputs, although diminishing with newer models. 3. Potential for undetected bias to be introduced or amplified by the automated process (mentioned in broader context of AI in law). 4. Insufficient accuracy of automated systems (mentioned in broader context of AI in law). |
| zYPa4rOtSP0J.pdf | Google_Scholar | Judicial training to prepare criminal justice professionals for #digitalisation and #artificialintelligence | This editorial describes a multi-annual training project by the Academy of European Law (ERA) aimed at equipping EU criminal justice professionals (judges, prosecutors, lawyers) with skills to handle digitalisation and AI challenges. The project involves seminars across EU cities, podcasts, and focuses on topics like e-evidence, videoconferencing, and AI in criminal justice. | True | Market | False | 3.0 | Positive | NaN | NaN | NaN | NaN | A multi-annual training program (2024-2027) for EU judges, prosecutors, and lawyers covering digitalisation challenges, including e-evidence, videoconferencing, and AI, using seminars and podcasts. | NaN | NaN | EU Criminal Law | EU / Member States | NaN | Training program design combining face-to-face seminars (presentations, case studies, discussions, demos, simulations), video podcasts, and audio podcasts. | Face-to-face seminars in various EU cities hosted by partners, podcasts available online via ERA website, dissemination through consortium partners, EJTN, and ECBA. | False | False | NaN | Lack of skills and knowledge among EU criminal justice professionals regarding digitalisation (e-evidence, videoconferencing, computer forensics) and AI (impacts, machine evidence, risks). | Keeping legal professionals updated with rapid technological advancements (digitalisation, AI) in the context of EU cross-border criminal justice. Addressing the practical and legal complexities of e-evidence, videoconferencing, computer forensics, and AI applications. | Risks associated with videoconferencing (affecting suspects' rights), AI malfunction, lack of AI legal liability, misuse of AI for crime, ethical issues with biometric surveillance/facial recognition, procedural difficulties with machine evidence. |
| 0epoEcgwBXoJ.pdf | Google_Scholar | PSA-VLM: Enhancing Vision-Language Model Safety through Progressive Concept-Bottleneck-Driven Alignment | This paper introduces PSA-VLM, a novel method to improve the safety of Vision-Language Models (VLMs) against harmful visual content by using a progressive, concept-bottleneck-driven alignment strategy. The approach involves two-stage training and integrates specific safety modules to enhance interpretability, controllability, and robustness against risks like pornography and political sensitivity, while minimizing impact on general performance. | True | NaN | True | 1.0 | NaN | PSA-VLM: Progressive Safety Alignment for VLMs using a Concept Bottleneck Model (CBM) framework with specific safety modules (Safety Projector, Safety Tokens, Safety Head). | Evaluated on VLM safety benchmark (RTVLM) and additional risk datasets (harmful politics, pornography, cyberbullying) using GPT-4 scoring and human subjective assessment. General performance evaluated on MMBench, SEEDBench, and MME. | Achieved state-of-the-art safety scores on the RTVLM benchmark (e.g., 8.46 average score for PSA-VLM-13B+LoRA) and significantly improved safety on other risk datasets (e.g., Politics, Porn, Cyberbullying) compared to baseline models, while maintaining competitive general multimodal benchmark performance. | NaN | NaN | NaN | NaN | NaN | International | A mix of unsafe data (approx. 11,000 image-text pairs compiled from sources like RTVLM, porn datasets, cyberbullying datasets, Stable Bias, etc., manually categorized into 6 risk types and 3 levels) and clean data (LLaV A, COCO datasets used for SFT). Data sources are mixed (open-sourced, accessible by application, close-sourced). | Concept Bottleneck Model (CBM) framework, two-stage training strategy (safety module training with frozen LLM, then LLM fine-tuning), LoRA for parameter-efficient fine-tuning, cross-attention mechanism in Safety Head. | NaN | False | False | Code planned to be open-sourced after anonymous review. | NaN | Ensuring VLM safety alignment against diverse risks without degrading general performance; balancing clean/unclean data for training; potential for needing human intervention or customization for specific safety requirements. | VLMs bypassing safety alignments through visual inputs; generation of harmful/inappropriate content (pornography, violence, discrimination, politically sensitive content, cyberbullying, misleading information, privacy violations); potential for sophisticated adversarial attacks; false positives in safety filtering (exaggerated safety behavior). |
| UsGFKAW4Sj4J.pdf | Google_Scholar | AI, UPL, & A2J — GENERATIVE AI’S DISRUPTIONS IN THE DELIVERY OF LEGAL SERVICES TO LOW-INCOME INDIVIDUALS | This paper examines how generative AI (GenAI) is transforming legal services for low-income individuals, highlighting its potential for access to justice (A2J) alongside concerns about accuracy and unauthorized practice of law (UPL). It argues against restrictive regulations, advocating instead for integrating GenAI into guided legal assistance programs and relaxing UPL rules to foster innovation and expand access. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Cost of legal services; inaccessibility of attorneys; individuals not recognizing problems as legal; restrictive Unauthorized Practice of Law (UPL) doctrines hindering innovation; digital divide (access, tech literacy, reading literacy); limitations and risks of AI tools (accuracy, bias, hallucinations). | Relax UPL restrictions to permit nonlawyer and technology assistance; integrate GenAI with expert-guided systems (like document automation); foster collaboration between lawyers and AI developers; improve AI reliability (e.g., using RAG); focus on self-help resources beyond lawyer-centric models. | Legal information provision; document automation; self-help legal resources; addressing common civil legal needs of low-income populations. | Low-income individuals; disadvantaged persons; self-represented litigants. | Civil Law; Estate Planning; Housing Law; Bankruptcy Law; Professional Responsibility (Unauthorized Practice of Law). | United States (with specific examples and caselaw from Missouri, Colorado, North Carolina, Ohio, New York, Maryland, etc.) | The paper discusses GenAI tools (like ChatGPT) trained on broad internet data and mentions Retrieval-Augmented Generation (RAG) using curated, authoritative sources, but does not specify datasets for any single tool studied. | Mentions principles for guided interview systems like A2J Author (legal expertise, user-centered design, community engagement) and techniques for proposed integrated systems (RAG, enhanced prompting), but does not detail a methodology used by the author to develop a specific tool. | Discusses generally available online tools (search engines, document automation sites, chatbots) and court-deployed systems (guided interviews), but no specific deployment strategy for a novel tool proposed in the paper. | False | False | NaN | Technical: AI reliability (hallucinations, accuracy), need for better integration of GenAI with expert systems, addressing AI bias. Societal: Digital divide, consumer trust issues (under/over-reliance), need for UPL reform, funding/resources for A2J tech development, ensuring tech serves low-income communities, UPL enforcement ambiguity with open-source AI. | NaN | Inaccurate/unreliable AI outputs (e.g., fake case law); violation of UPL rules; exacerbating inequality due to digital divide or poor AI design; creation of a two-tiered justice system; consumers over-trusting AI leading to poor decisions; potential for AI bias. |
| W7292Ow-LfoJ.pdf | Google_Scholar | Empowering Air Travelers: A Chatbot for Canadian Air Passenger Rights | This paper presents a chatbot designed to help Canadian air travelers understand their rights by retrieving relevant information from legal documents. The system decomposes complex user queries and presents relevant passages directly to the user, aiming to avoid hallucinations common in generative models. | True | Idealistic | True | 1.0 | Positive | A chatbot using LLM-based query decontextualization and decomposition (GPT-4 with in-context learning), followed by dense retrieval (OpenAI embeddings, cosine similarity) from a domain-specific knowledge base. Relevant passages are presented directly to the user, bypassing generative summarization. | Comparative usability study (N=15) against Google Search using USE questionnaire on 4 air travel scenarios per participant. Hallucination analysis comparing the chatbot's retrieval-only output to a standard RAG approach on 40 examples. Evaluation of retrieval performance (P@5, R@5, F1@5, MAP@5) on 40 examples. | User study: Chatbot rated significantly more useful and satisfying than Google Search, with comparable ease of use/learning. Hallucination analysis: Chatbot achieved 0% hallucinations versus 27.5% for the standard RAG approach. Retrieval achieved MAP@5 of 0.88. | Passengers' lack of knowledge about their rights, difficulty navigating complex regulations, deficient regulations and enforcement in Canada, high volume of inquiries overwhelming volunteer support systems. | An automated chatbot to provide quick, accurate information about passenger rights by understanding complex user narratives and retrieving relevant passages from reliable sources, thereby empowering users and reducing volunteer workload. | Access to information about air passenger rights. | Canadian air travelers facing issues such as flight delays, cancellations, and baggage problems. | Consumer protection law, Air passenger rights, Transportation law | Canada | A knowledge base constructed from 88 public web pages containing regulatory details, practical guides, and legal glossaries from the Air Passenger Rights (Canada) website and the Canadian Air Passenger Protection website. The system uses pre-trained LLMs (GPT-4, OpenAI embeddings) fine-tuned via in-context learning with provided prompts. | Retrieval-Augmented Generation (RAG) architecture modified to present retrieved passages directly instead of generating summaries. Use of LLMs (GPT-4, OpenAI Embeddings) via API. Development of a web application prototype (Python/FastAPI backend, Next.js frontend). User study for evaluation. | Implemented as a web application prototype. The code is made available on GitHub. | False | True | Code is available on GitHub (link provided in footnote 1). | Knowledge base requires continuous updates to remain current. The chatbot lacks interactive dialogue capabilities to clarify ambiguous queries. Users may need help understanding and applying the presented legal information; simplified summaries are needed. | Handling complex, multi-part user queries; ensuring high accuracy and avoiding hallucinations in a high-stakes domain; selecting and structuring the knowledge base; designing an intuitive user interface. | Providing incorrect information (hallucinations) leading to financial loss or missed opportunities for passengers. Undermining user trust. Users potentially misinterpreting the retrieved legal passages. Privacy concerns regarding user inputs (partially addressed by using paid API). |
| YMpBXSigfgQJ.pdf | Google_Scholar | What Should ChatGPT Mean for Bioethics? | This paper discusses the implications of Large Language Models like ChatGPT for bioethics, comparing many issues to existing medical AI concerns. It also highlights new ethical dilemmas such as medical deepfakes, the need for AI interaction disclosure, and challenges posed by foundational models including equitable access and potential biases. | True | Idealistic | True | 3.0 | Positive | ChatGPT (a chatbot interface for OpenAI's GPT Large Language Models). | The paper cites other studies where ChatGPT was tested by its performance on: law school exams, the bar exam, United States Medical Licensing Exam (USMLE) steps, and a Stanford Medical School final exam in clinical reasoning. | According to cited studies, ChatGPT passed law school exams (GPT-3 just barely, GPT-4 scored above 90th percentile), passed the bar exam (earlier versions with fine-tuning, GPT-4 aced it scoring above 90th percentile), and performed at or near the passing threshold for all three USMLE exams without specialized training. GPT-3 also achieved a passing grade on a Stanford Medical School clinical reasoning exam. | For access to justice, the paper implies obstacles for "low-income people" and "pro se prisoners" in accessing legal help. Broader AI-specific obstacles pertinent to A2J include model unreliability (hallucinations), bias, and ensuring equitable access to such technologies. | The paper suggests chatbots, like ChatGPT, could enhance access to justice by providing direct legal services, such as helping low-income individuals get a head start in "lawyer for a day programs" or assisting pro se prisoners in bringing litigation. | Direct legal services, assistance for pro se litigants, initial legal drafting and support. | Low-income people, pro se prisoners. | General legal services (e.g., drafting complaints, contracts, wills), litigation by pro se individuals. | US (based on examples like bar exams and legal services), with broader implications. | GPT-3 was trained on 175 billion parameters from a large amount of internet text; GPT-4 on approximately 1 trillion parameters. Both models were further refined through reinforcement learning from human feedback (supervised reinforcement learning). | ChatGPT is a general LLM based on the GPT architecture, designed as an autoregressive model to predict subsequent text based on prior context. It is refined using Reinforcement Learning from Human Feedback (RLHF). | ChatGPT is deployed as a chatbot, accessible via a prompt-based interface. | True | True | ChatGPT is publicly available through OpenAI, offering both free and paid access tiers. | Unreliability and "hallucinations" in LLM outputs; data representativeness and algorithmic bias; privacy vulnerabilities; ethical need for users to know they are interacting with an AI; potential for generating misleading deepfakes (legal or medical); market concentration risks affecting access and ethical standards; significant environmental impact of large models; linguistic limitations (dominance of English); risk of model homogenization stifling diversity and ethical considerations. | The paper discusses general challenges inherent to LLMs like ChatGPT: their immense size requiring substantial computational resources; their general-purpose nature often necessitating fine-tuning; their autoregressive functioning; their output unreliability including factual inaccuracies ("hallucinations"); and variability in responses to identical prompts (lack of consistent test-retest reliability). | Data ownership and consent issues for training data; perpetuation of biases from training data; privacy infringements via data breaches, user input leaks, or re-identification; deception if users are unaware they are interacting with AI; generation and dissemination of false information or deepfakes (e.g., in legal or medical contexts); market oligopolies by a few large AI developers affecting equitable access and ethical priorities; substantial environmental footprint; potential for misuse (e.g., patients relying on flawed AI medical advice, or flawed AI legal advice) leading to harm and liability issues. |
| WdgRnAnuIh0J.pdf | Google_Scholar | cLegal-QA: a Chinese legal question answering with natural language generation methods | The paper introduces cLegal-QA, a new large-scale dataset for Chinese civil law question answering, comprising user questions, lawyer responses, and expert-annotated gold answers. It benchmarks several natural language generation models, finding fully-supervised models outperform zero-shot ones, highlighting the dataset's utility and areas for model improvement. | True | Idealistic | False | 1.0 | Positive | cLegal-QA dataset creation and benchmarking of NLG models (UniLM, T5, BART, ChatGLM-6B, ChatYuan, ChatGPT) for Chinese legal question answering. | Evaluation using ROUGE (ROUGE-1, ROUGE-2, ROUGE-L) and BLEU scores on a held-out test set (20% of cLegal-QA). Expert evaluation (3 judges) on 100 samples assessing adequacy, factuality, and fluency (Fleiss' kappa = 0.719). Transfer learning tested on 1000 private lending dispute cases. | Fully-supervised models (UniLM, T5, BART) significantly outperformed zero-shot models. BART achieved the best scores on the 'Question-Gold Answer' split (ROUGE-1: 34.73, ROUGE-2: 17.65, ROUGE-L: 31.71, BLEU: 15.01) and also performed best in transfer learning. | Shortage of lawyers to meet public demand for legal consultation services in China. Lack of large-scale, high-quality annotated datasets for Chinese generative legal QA due to the need for specialized legal knowledge and the costly/time-consuming nature of annotation. | Creation of large-scale, high-quality annotated datasets (like cLegal-QA) using professional annotators and reviewers. Application of automatic QA technology (specifically NLG models) to provide efficient, accurate, and low-cost legal consulting services. | Legal consultation services, answering legal questions from the public. | General public in China seeking legal assistance/consultation. | Chinese Civil Law (specifically focusing on Labor disputes, Marriage and family disputes, Housing disputes, and tested on Private lending disputes). | China | cLegal-QA dataset: ~14,000 instances collected from Chinese legal advice websites (e.g., www.12348.gov.cn, www.66law.cn). Contains questions, dispute types, scenarios, multiple lawyer answers, and gold standard answers annotated by law students and reviewed by judges. Unstructured text data. | Dataset creation involved web scraping, keyword matching for categorization, manual verification, professional annotation by law students, review by judges using specific criteria (adequacy, factuality, fluency), and inter-annotator agreement checks. Model evaluation used standard NLG metrics, expert review, and transfer learning experiments. | NaN | True | False | Dataset will be made publicly available after peer review; requires email contact for a license. | Performance of current QA models needs improvement. Models struggle with complex legal queries requiring multi-faceted reasoning. Need for models to adapt to evolving legal statutes and norms. Potential for inherent biases in datasets derived from source texts and human annotation. | Ensuring data quality (accuracy, consistency) during annotation requiring legal expertise. Cost and time of annotation. Adapting general NLG models to the specific nuances and terminology of the Chinese civil law domain. Evaluating generative models effectively beyond automated metrics. | Generative models may produce inaccurate, incomplete, or unreasonable responses. Lack of precise control over generated answers affecting reliability and trustworthiness. Potential for inherent biases in datasets influencing model outputs and fairness. |
| 2_gchPzjPIEJ.pdf | Google_Scholar | DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services | This paper introduces DISC-LawLLM, an intelligent legal system for Chinese legal services, developed by fine-tuning large language models using legal syllogism prompting and retrieval augmentation. The authors also present DISC-Law-Eval, a comprehensive benchmark to evaluate such systems, demonstrating DISC-LawLLM's effectiveness across various legal scenarios. | True | Idealistic | True | 1.0 | Positive | DISC-LawLLM: A fine-tuned LLM (Baichuan-13B-Base) using custom supervised fine-tuning datasets (DISC-Law-SFT) constructed with legal syllogism prompting strategies, and augmented with a retrieval module for external legal knowledge. | Evaluated using the custom DISC-Law-Eval benchmark. Objective evaluation involved multiple-choice questions from Chinese legal exams (NJE, PAE, CPA, UNGEE, PFE, LBK) across three difficulty levels, measuring accuracy. Subjective evaluation used 300 Q&A cases assessed by GPT-3.5 as a referee on accuracy, completeness, and clarity. | DISC-LawLLM outperformed other LLMs, including GPT-3.5-turbo, on objective evaluation (e.g., 42.09% average accuracy on hard questions, improving over GPT-3.5-turbo by an average of 7%) and subjective evaluation (average score of 3.39 across accuracy, completeness, and clarity). | High demand for specialized legal reasoning capabilities and the need for reliable access to accurate, up-to-date external legal knowledge to avoid hallucinations and outdated information. | Fine-tuning LLMs with supervised datasets (DISC-Law-SFT) specifically constructed using legal syllogism prompting to enhance reasoning, and augmenting the LLM with a retrieval module to access external, current legal knowledge. | Legal consultation for dispute resolution, statute interpretation, legal document summarization, legal question answering, and assistance with legal examinations. | General public / everyday individuals seeking legal advice, legal professionals, and law students. | Chinese Judicial domain, covering areas such as Civil Law, Criminal Law, Administrative Procedure Law, Copyright Law, Patent Law, and Bidding Law. | China | DISC-Law-SFT dataset, constructed from: 1) Publicly available NLP legal task datasets (e.g., LEVEN, CAIL, JEC-QA, CJRC) for the Chinese justice domain. 2) Legal raw text (e.g., laws, judicial verdicts, consultation platform data). 3) Open-source instruction datasets. Data was processed using rule-based methods and LLM-assisted (GPT-3.5-turbo) refinement into supervised fine-tuning samples (pairs and triplets). | Supervised fine-tuning (SFT) of a base LLM (Baichuan-13B-Base). Dataset construction involved collecting data from diverse legal sources and processing it with rule-based methods and LLM-assisted refinement (behavior shaping with legal syllogism, knowledge expansion, law-specific chain of thought - LCoT). Retrieval augmentation with an external knowledge base was also implemented. | The paper states that detailed resources, including constructed datasets and model weights, are made available on GitHub. | True | True | Datasets and model weights are released on GitHub (https://github.com/FudanDISC/DISC-LawLLM). | The paper implies a continued need for comprehensive benchmarks for legal AI systems, as evidenced by their development of DISC-Law-Eval due to the lack of established alternatives. Other specific future gaps beyond improving model capabilities are not detailed. | Ensuring intricate legal reasoning capabilities in LLMs, reliably integrating up-to-date and precise external legal knowledge to mitigate issues like outdated information and hallucinations, and constructing high-quality, diverse supervised fine-tuning datasets that effectively instill legal reasoning patterns like syllogism. | The potential for LLMs to produce inaccurate responses due to hallucinations or reliance on outdated knowledge, which could lead to incorrect legal information or advice. |
| TheImpactofGenerativeAIonBusinessConsultingsuprit.pdf | Google_Scholar | The Impact of Generative AI on Business Consulting Engagements: A New Paradigm for Client Interaction and Value Creation | This paper explores the transformative impact of Generative AI on the business consulting industry, examining its capabilities, applications, and the challenges of integration. It emphasizes the need to balance AI's efficiency with human-centric aspects, Gdiscussing how AI is reshaping client interactions and value creation in consulting. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Developed markets | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Key risks include lack of AI transparency and explainability, algorithmic bias leading to unfair outcomes, data privacy and security vulnerabilities, AI 'hallucinations' producing incorrect information, potential misuse of the technology, significant environmental impact from training models, and job displacement. |
| 1_Qa-6uaUUcJ.pdf | Google_Scholar | GENERATIVE AI IN AMERICAN AND CANADIAN COURTS : A “TRAINING” APPROACH TO REGULATION | This paper reviews judicial directives on generative AI use by lawyers in Canadian and US courts, noting concerns about inconsistency and overbreadth. It advocates for a "training" approach to regulation, focusing on user competence and ethical responsibility rather than prohibition, to constructively integrate AI while upholding professional standards. | True | Market | True | 1.0 | Positive | A proposed "training" approach/framework for judicial guidance on generative AI use in legal proceedings, emphasizing user competence, human oversight, and responsibility. | NaN | NaN | Risks associated with lawyer's use of generative AI, including inaccuracy ('hallucinations'), potential for bias from training data, breaches of client confidentiality, lack of clarity in regulations, and varying technological literacy among legal professionals. | A 'training' approach to judicial guidance focusing on user competence (including understanding limitations), mandatory human oversight and verification of AI outputs, and lawyer responsibility for AI-generated content, rather than outright bans or overly broad disclosure rules. Development of nuanced guidelines by courts and law societies. | Regulation of AI use in legal practice and court proceedings; Lawyer competence and ethics in using AI. | NaN | General Litigation, Legal Practice, Professional Ethics, Court Procedure | Canada, United States (Primary focus); mentions examples/guidelines from Colombia, Pakistan, India, South Africa, UK, Peru, Mexico, New Zealand, Australia (NSW), EU. | The paper discusses generative AI tools (like ChatGPT, Harvey AI, Lexis+ AI) that use various data, including publicly available information, licensed third-party data, user inputs, and proprietary/curated legal content. | Conceptual/analytical legal research, including review of existing judicial directives, analysis of professional conduct rules, synthesis of academic/expert commentary, and proposing a normative regulatory framework. | Proposed for adoption by judicial bodies as practice directives or guidelines. | False | False | NaN | Need for enhanced lawyer competence and AI literacy; development of clear, nuanced regulations adaptable to evolving technology; lack of consensus on appropriate AI use; need for effective human oversight mechanisms; ongoing ethical considerations. | Rapid evolution of AI technology; varying levels of technological literacy within the legal profession; defining the scope of regulations (e.g., what constitutes 'AI use'); balancing innovation with ethical obligations (accuracy, confidentiality, bias); ensuring meaningful human oversight; avoiding overly restrictive or vague directives. | Inaccuracy and 'hallucinations' in AI output (e.g., citing fake cases); propagation of biases present in training data; breach of client confidentiality and data privacy; deskilling junior lawyers; undermining the attorney-client privilege; misleading users (including self-represented litigants) with inaccurate information; potential misuse leading to reputational damage for lawyers and undermining the administration of justice. |
| chatgptisplayingaroleinArtificialIntelligent1.pdf | Google_Scholar | How Chatgpt is Playing a Role in Artificial Intelligent bases Applications | This paper provides a general overview of ChatGPT's capabilities and its applications across diverse domains, including conversational interfaces, content generation, and legal assistance. It highlights ChatGPT's role in improving efficiency and user experiences while briefly noting associated ethical and legal considerations. | True | NaN | True | 3.0 | Neutral | ChatGPT | NaN | NaN | NaN | NaN | Legal research, document generation, legal insights | NaN | General Legal | International | Based on OpenAI's GPT-3.5 training data (large-scale general text data, implicitly proprietary). | NaN | NaN | True | False | ChatGPT is generally accessible via platforms like OpenAI's website, often with free tiers. | NaN | Ethical and legal challenges, bias, privacy issues, potential inaccuracies. | Bias in language generation, privacy issues, inaccuracies, security risks related to AI-generated content. |
| flw_DqScUF4J.pdf | Google_Scholar | Roles and challenges of ChatGPT and similar generative a rtificial intelligence for \nachiev ing the Sustainable Development Goals (SDGs) | This paper discusses the potential roles of generative AI like ChatGPT in achieving the UN's Sustainable Development Goals (SDGs), including for access to justice (SDG 16). It also outlines significant challenges, such as ethical concerns, data issues, security risks, and the need for robust governance to harness AI's benefits responsibly. | True | Idealistic | True | 3.0 | Positive | ChatGPT and similar generative artificial intelligence | NaN | NaN | Key obstacles include ethical concerns (data privacy, AI bias, accountability, misinformation), data quality and accessibility issues, language and cultural diversity barriers, security risks from misuse (e.g., deepfakes), environmental impact of AI, scalability challenges, the digital divide, and the need for robust regulatory frameworks. Specific to access to justice (SDG 16), challenges are ensuring legal accuracy, addressing AI bias in legal contexts, and overcoming unequal access to justice technologies. | Proposed solutions involve ethical, responsible, and inclusive AI development and deployment, global collaboration, developing robust policy and regulatory frameworks, fostering human-AI collaboration, enhancing digital literacy, and bridging the digital divide. For access to justice, AI can be used for disseminating legal information, promoting awareness of rights, and offering conflict resolution advice. | Legal information provision, access to justice promotion, legal rights awareness, conflict resolution, crime prediction, human rights awareness, strengthening legal institutions. | Citizens, marginalized communities, vulnerable populations. | Public legal information, human rights law, criminal justice (related to crime prediction), dispute resolution, general access to justice. | International | NaN | NaN | NaN | True | True | The paper discusses ChatGPT and similar generative AI, which are existing technologies. ChatGPT offers publicly accessible versions, including free and paid tiers. | Technical gaps include developing AI that ensures legal accuracy and contextual understanding across diverse legal systems and languages, and creating robust, unbiased legal AI models. Societal gaps include establishing ethical guidelines and regulations for AI in law, bridging the digital divide for equitable access to legal AI, fostering public trust, and integrating AI effectively into existing legal institutions and workflows. | Inherent challenges of generative AI like ChatGPT include managing ethical dimensions (data privacy, algorithmic bias, accountability), ensuring high-quality and accessible training data, adapting to language and cultural diversity, enabling effective human-AI collaboration, mitigating environmental impact, preventing security threats and misuse (e.g., generating harmful content, misinformation), achieving scalability and equitable resource allocation for widespread deployment, and navigating an evolving regulatory landscape. | Potential risks include perpetuation of societal biases, data privacy violations, spread of misinformation and deepfakes, security threats from malicious use, negative environmental impact from high energy consumption, and exacerbation of the digital divide and social disparities if not implemented equitably. |
| wOJSYY9rFZIJ.pdf | Google_Scholar | NATURAL LANGUAGE PROCESSING IN LEGAL TECH | This chapter provides a non-technical overview of Natural Language Processing (NLP) techniques relevant to legal technology, discussing their potential applications like document review and case outcome prediction. It critically examines the capabilities and inherent limitations of current NLP, particularly its difficulty with complex legal reasoning, and outlines key challenges like data availability and the need for legal-specific benchmarks. | True | Market | True | 3.0 | Neutral | NaN | Conceptual testing of GPT-3's legal reasoning capabilities using a hypothetical liquidated damages clause scenario. The authors input the scenario and the relevant legal rule into GPT-3 and evaluated its response. | GPT-3 correctly stated the general legal rule regarding liquidated damages but failed to apply it correctly to invalidate an 'exorbitant' clause in a specific fact pattern, even when prompted with the rule and the term 'exorbitant'. | The primary obstacles identified are the significant technical limitations of current NLP in performing legal reasoning and extracting legal ontologies, scarcity and representativeness issues with training data (especially pre-litigation or for 'easy' cases), potential for biased outputs, and difficulty processing complex legal document structures. These hinder the development of reliable legal tech, including for potential A2J applications. | The paper suggests that advancing NLP for legal reasoning requires specific, domain-focused efforts beyond general NLP improvements, potentially relying on human experts to define legal ontologies. It also highlights the need for creating and utilizing legal-specific benchmark datasets (mentioning the Atticus Project as an example). | NaN | NaN | Litigation (discovery, case outcome prediction), Contract Law, Tax Law | International | The paper discusses various types of data: large general text corpora (e.g., web data used for training models like BERT and GPT-3); hand-labeled legal documents for specific tasks like document review (potentially proprietary); existing case law and related documents (often unstructured, facing access issues like paywalls or non-collection); specific legal datasets like CUAD (annotated contracts, publicly available) and CaseHOLD (algorithmically extracted holdings, publicly available). Challenges related to data availability, representativeness, and cost are highlighted. | NaN | NaN | False | False | NaN | The main technical gap identified is the inability of current NLP models to perform genuine legal reasoning, extract legal ontologies, or accurately process complex legal document structures and references. Additional gaps include the lack of sufficient, representative, and accessible training data for many legal tasks, and the absence of robust, large-scale benchmark datasets specifically for the legal domain. | Key challenges include: automating legal reasoning and extracting legal concepts (ontologies) from text; effective document segmentation and handling complex structures/references in legal documents; obtaining sufficient, representative, and unbiased training data; overcoming computational complexity for long legal texts; developing and adopting legal-specific benchmark datasets to evaluate model performance accurately in the legal domain. | The primary risks stated are inaccurate or biased predictions resulting from limitations in legal reasoning capabilities (e.g., misapplying legal rules) and unrepresentative or biased training data. The reliance on distributional patterns rather than true understanding can lead to failures in novel situations or tasks requiring nuanced legal interpretation. |
| vNG_5kHTgG0J.pdf | Google_Scholar | Evaluating the Use of Artificial Intelligence for \nan Effective Justice System in Sri Lanka | This paper evaluates the potential of Artificial Intelligence (AI), including chatbots and ChatGPT, to enhance Sri Lanka's legal system, particularly in improving access to justice. It discusses AI's applications, advantages, and challenges, recommending steps like robust data governance, ethical standard-setting, and capacity building for successful integration. | True | Idealistic | True | 3.0 | Positive | AI (general), Chatbots (e.g., NALA), ChatGPT, Robotics | NaN | NaN | Lack of complete legal data; slow adoption by legal professionals; potential for AI bias; infrastructure limitations (technology, language, digital literacy); high cost and accessibility issues for AI; large case backlogs and inefficient court processes; general resource constraints in the justice system; resistance to change from legal experts. | Implement robust data governance and security measures; establish clear ethical and legal standards for AI use; conduct thorough cost-benefit analyses of AI implementation; foster collaboration with international organizations; perform socio-economic impact assessments; invest in capacity building and training for legal professionals; create systems for collecting and distributing legal data for AI training; ensure AI systems are designed to be transparent and auditable. | Improving timely dispensation of justice; enhancing efficiency and reducing costs of legal services; supporting legal research, contract analysis, and case law analysis; enabling legal translation services; providing legal information and decision support for legal professionals; reducing court case backlogs. | Underserved communities, neglected populations, individuals unable to afford conventional legal services, and non-native speakers in Sri Lanka. | General legal system | Sri Lanka | NaN | NaN | NaN | True | False | Discusses ChatGPT, a generally accessible AI model. Mentions 'NALA' chatbot developed by the Legal Aid Commission of Sri Lanka; its public accessibility is not detailed by the paper. | Absence of sufficient, comprehensive legal data for AI development; resistance to technological change among legal professionals; risk of AI systems reinforcing existing societal biases if not carefully designed; need for greater transparency and auditability in AI decision-making processes; loopholes and inadequacies in existing Sri Lankan law concerning AI liability and regulation; insufficient focused research and practical adoption of AI within Sri Lanka's legal sector. | Technological infrastructure limitations (e.g., reliable internet, data security); language barriers in a multilingual context; low digital literacy among potential users and some professionals; high cost and difficult accessibility of advanced AI technologies; scarcity of comprehensive and high-quality legal data for training AI; slow adoption rate of new technologies within the legal sector; ensuring fairness and mitigating algorithmic bias; lack of transparency in the operational mechanisms of some AI tools; addressing data privacy and security concerns related to sensitive legal information. | AI bias reinforcing or perpetuating systemic discrimination (e.g., the COMPAS example); lack of transparency in AI decision-making processes leading to 'black box' problems; over-reliance on AI potentially diminishing human judicial discretion and the human element in sensitive cases; AI systems generating inaccurate, fabricated, or misleading legal information (e.g., ChatGPT citing fake cases); breaches of data privacy and security of sensitive legal information; socio-economic disruption such as job displacement within the legal profession; inadequacy of existing legal and ethical frameworks to address harm or errors caused by AI; potential for AI to magnify social injustice if not implemented equitably. |
| L4nL0nO_ZeIJ.pdf | Google_Scholar | AI Catalyst: Cracking the code \nfor MSME productivity | This paper reports on 'The AI Catalyst' project, a participatory action research initiative investigating AI adoption by UK Micro-, Small-, and Medium-sized Enterprises (MSMEs) to enhance productivity. It identifies key motivators, barriers (such as resource access, digital readiness, and sociotechnical integration challenges), and outcomes, demonstrating increased AI adoption and investment among participating firms. | True | Market | True | 1.0 | Positive | The AI Catalyst programme: a participatory action research initiative involving a 'scan tool' (to map resources, capabilities, and processes) and tailored 'Knowledge Exchange' sessions based on established business/management frameworks (e.g., SOSTAC, Dynamic Capabilities) to facilitate AI adoption in MSMEs. | Participatory Action Research conducted with fifteen UK MSMEs over five months (March-July 2024). Data was collected using a 'scan tool' (Microsoft Excel workbook) and through 100 hours of fortnightly online 'Knowledge Exchange' sessions. Programme effectiveness and AI adoption outcomes were assessed via changes in a weighted technology diffusion score and qualitative participant interviews. | Twelve of the fifteen participating firms chose to adopt Generative AI solutions. Collectively, firms made an estimated investment of over £100,000 in AI, supporting more than 360 users. The cohort's average weighted score for technology diffusion increased by 0.25 (from 2.0 to 2.25). | For MSMEs: Limited access to finance and STEM/digital talent; operational burdens overshadowing strategic orientation; sub-optimal effectiveness of existing resources/capabilities; burden of researching technology; insufficient in-house tech capabilities, digitalisation, and data analytics; deployment of non-complementary technologies; inconsistent digital broadband infrastructure. | For MSMEs: The 'AI Catalyst' programme approach, including tailored knowledge exchange and a sociotechnical perspective on AI integration. Guidance on AI strategy, ethics policy development, and implementation of specific AI solutions. Recommended government support through access to expertise, training, targeted assistance, financial support/subsidies, and development of regulatory frameworks. | AI adoption for productivity enhancement in Micro-, Small-, and Medium-sized Enterprises (MSMEs); Digital transformation challenges and enablers for MSMEs; Sociotechnical factors in AI integration within business processes. | UK Micro-, Small-, and Medium-sized Enterprises (MSMEs) across various sectors including manufacturing, legal services, food and beverage, real estate, sales, services, and event services. | The study's cohort of MSMEs included one firm from 'Legal Services'. The paper's overall focus is on general MSME productivity across sectors, not specifically on legal fields. | United Kingdom (UK) | NaN | Participatory Action Research (PAR). Theoretical frameworks underpinning the research design included Dynamic Capabilities, Knowledge-Based View, Resource-Based View, and Stakeholder Theory. The SOSTAC model guided 'Knowledge Exchange' sessions. A 'scan tool' (Microsoft Excel workbook) was developed for data collection. A weighted scoring model was developed and used to assess digital technology diffusion. | The AI Catalyst programme was deployed via collaboration with 'Be The Business' for firm recruitment. 'Knowledge Exchange' sessions were conducted online using Microsoft Teams with 15 MSMEs over 5 months. Tailored content (slides, research papers, web resources) was provided to participating firms. | False | False | NaN | For MSMEs: Need for continued research into MSME productivity drivers and AI adoption factors. Requirements for enhanced, tailored support mechanisms (expertise, training, sector-specific guidance) for digital and AI adoption. Necessity for clear AI regulatory/ethical frameworks. Addressing financial barriers and improving digital infrastructure (e.g., broadband) for MSMEs. Better understanding and measurement of intangible asset investments at the firm level. | Recruitment of MSME firms for the action research (15 firms recruited against a target of 20). Ensuring programme content was sufficiently tailored to diverse business needs. Determining optimal structure for knowledge exchange sessions (e.g., length, frequency). Addressing MSMEs' existing burdens of technology research and limited in-house digital capabilities. Facilitating a sociotechnical approach to AI integration. | Risk of business obsolescence for MSMEs if AI is not adopted. Inherent risks and complexities of integrating new technologies into existing workflows. Cybersecurity risks. Potential for AI-induced job displacement. (Literature review also cites: liability for AI-induced damage, data exchange standards, lack of citizen trust, reputational risks for AI use). |
| TGKE4_F0dVoJ.pdf | Google_Scholar | Continuous Training and Fine-tuning for Domain-Specific Language Models in Medical Question Answering | This paper proposes a two-stage method (continual training and instruction fine-tuning) to adapt Llama 2 base models for the Chinese medical domain using domain-specific datasets. The resulting model achieves performance comparable to GPT-3.5-turbo on a Chinese medical exam benchmark (CMExam). | True | NaN | True | 1.0 | NaN | Two-stage adaptation of Llama 2 models: 1) Continual pre-training on Chinese medical text (1B tokens from Huatuo-26M). 2) Instruction fine-tuning on Chinese medical exam questions with reasoning (CMExam dataset). | Evaluation on the CMExam test set (Chinese medical QA) using accuracy and F1-score (greedy decoding). Catastrophic forgetting assessed using MMLU (English) and CMMLU (Chinese) benchmarks (5-shot prompting). | The best performing model (Chinese-Llama-2-13B after continual training and fine-tuning with reasoning) achieved 46.0% accuracy and 45.7% F1 score on CMExam, comparable to GPT-3.5-turbo (46.4% accuracy, 46.1% F1). | NaN | NaN | NaN | NaN | NaN | China | Continual training: ~1B tokens (unstructured text, question-answer pairs) from the Huatuo-26M dataset (Chinese medical encyclopedias/articles). Fine-tuning: 54K multiple-choice questions with explanations from the CMExam dataset (Chinese medical licensing exam). Base models Llama 2 and Chinese-Llama 2 pre-trained on general corpora. | Machine learning development pipeline: Base model selection, dataset curation (Huatuo-26M, CMExam), two-stage training process (continual training, fine-tuning), benchmark evaluation (CMExam, MMLU, CMMLU). | NaN | False | False | NaN | NaN | Catastrophic forgetting of general knowledge during continual domain-specific training. Difficulty in achieving performance gains through cross-lingual fine-tuning (mixing English and Chinese medical data). Computational constraints. | Amplifying dataset biases during continual training. Dissemination of inaccurate or misleading medical information without rigorous validation by experts. |
| _Q1r5ohGDz8J.pdf | Google_Scholar | Intention and Context Elicitation with Large Language Models in the Legal Aid Intake Process | This paper proposes a proof-of-concept framework using Large Language Models (LLMs) to improve the legal aid intake process. The method uses conversational prompting to actively elicit clients' underlying intentions and relevant contextual details, aiming to generate more useful responses compared to direct one-shot LLM answers. | True | Idealistic | True | 1.0 | Positive | An LLM-based conversational system designed to elicit client intentions and contextual information through guided dialogue prompts during the legal intake process. | Qualitative comparison of the proposed method's output against a baseline one-shot LLM response using example scenarios (e.g., tenancy law). No formal benchmarks or extensive experimental evaluation. | The combined intention and context elicitation approach generated qualitatively more useful and tailored responses compared to generic one-shot LLM outputs, which were often too broad or non-specific. | Clients often lack legal expertise, leading them to ask suboptimal questions that don't reveal their true intentions or necessary context. Limited capacity of legal aid organizations. | Employing LLMs in a conversational manner to actively probe for and elicit underlying client intentions and specific contextual details before formulating a response, thereby improving the quality of information gathered and provided during intake. | Legal intake and triage for legal aid services. | Clients of legal aid organizations and court centers, particularly those with limited legal knowledge. | General legal aid intake, with examples drawn from Family Law, Immigration Law, Tenancy Law. | United States context mentioned, but the technique appears generally applicable internationally. | N/A (The proposed technique uses pre-trained LLMs like GPT-4 without specific fine-tuning on new datasets for the proof-of-concept. Proposes future generation of datasets for training). | Proof-of-concept development based on prompt engineering and structuring conversational interactions with LLMs. | NaN | False | False | NaN | Lack of quantitative experimental evaluation and ablation studies with machine and human evaluators. Need for attorney review of LLM outputs in production settings. Need for verified conversational datasets for future training. | LLM tendency to provide overconfident 'best guess' answers without probing. Difficulty in reliably prompting LLMs to assess information completeness due to overconfidence and lack of metrics. | LLM inaccuracy (hallucination, incorrect information, inapplicable laws/organizations). Client over-reliance on potentially flawed AI output. Potential for unauthorized practice of law. Privacy concerns regarding client data. |
| BXfGjxwV8VQJ.pdf | Google_Scholar | Exploring ChatGPT: An Extensive Examination of Its Background, Applications, Key Challenges, Bias, Ethics, Limitations, and Future Prospects | This paper provides a broad overview of OpenAI's ChatGPT, detailing its development history, underlying technologies (GPT series), and key features. It surveys applications across diverse domains including healthcare, finance, law, and education, while also discussing significant challenges, ethical concerns, biases, limitations, and future research directions. | True | NaN | True | 3.0 | NaN | ChatGPT | NaN | NaN | NaN | NaN | NaN | NaN | General Law | International | General description of GPT/ChatGPT training: Pre-training on large, diverse text corpora (books, papers, webpages); Fine-tuning using Reinforcement Learning from Human Feedback (RLHF). | NaN | NaN | True | False | Available via OpenAI's platform (e.g., chat.openai.com, mentioned implicitly). | NaN | Reliability/accuracy, bias (various forms including dataset bias), overreliance by users, quality control, generalization to new data, real-time responsiveness, model explainability, adapting to domain-specific knowledge. | Inaccuracy/misinformation, bias perpetuation (gender, racial, cultural, linguistic, commercial, etc.), privacy/security vulnerabilities, lack of accountability/responsibility, generation of harmful/inappropriate/verbose content, undermining critical thinking, potential for misuse (e.g., propaganda, sensationalism). |
| Lips_et_al._2023_Potential_value_of_Generative_AI_legal_services.pdf | Google_Scholar | Sizing the potential value of Generative AI for legal services | This paper estimates the potential efficiency gains from using Generative AI in legal services within Switzerland. It calculates significant potential reductions in full-time equivalents (around 25%) and annual costs (around 20%) for both law firms and internal legal/tax departments. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General Legal Services / Tax Advisory | Switzerland | NaN | NaN | NaN | False | False | NaN | NaN | Achieving estimated savings requires ideal circumstances (specially trained LLMs, full data access, interoperability); difficulty in reallocating saved labor to other tasks; need to adapt professional training structures (e.g., traineeships). | NaN |
| Cr-3p3D0J4gJ.pdf | Google_Scholar | Copyright Protection in Generative AI: A Technical Perspective | This paper provides a comprehensive technical overview of copyright protection methods for generative AI, covering both data copyright (for source data owners) and model copyright (for DGM providers). It discusses various techniques like unlearning, watermarking, and de-duplication across image, text, code, and audio domains, highlighting current limitations and future research directions. | True | Market | False | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Copyright law, Intellectual Property law | International | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Copyright infringement of source data (e.g., memorization, unauthorized replication/modification, style mimicry); model theft and unauthorized commercial use of DGMs; uncertainty in copyright ownership of AI-generated content; potential for misuse of DGMs for generating misinformation. |
| x4foJ0wSk_kJ.pdf | Google_Scholar | The Role of Generative AI in developing new Supply Chain Strategies - Future Trends and Innovations | This review explores how Generative AI is transforming supply chain management by enabling innovative strategies for demand forecasting, inventory optimization, and risk assessment. It discusses future trends like autonomous systems and AI-driven collaboration, while also highlighting challenges such as data privacy and implementation costs. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Data privacy breaches, unauthorized data access, and misuse; AI models vulnerable to cyberattacks (e.g., data manipulation, reverse-engineering); financial losses from ineffective AI or biased data; perpetuation or amplification of algorithmic biases in decision-making. |
| JOrIKdo7d-kJ.pdf | Google_Scholar | Private ordering, generative AI and the ‘platformisation paradigm’: What can we learn from comparative analysis of models terms and conditions? | This paper analyzes the terms and conditions (T&C) and privacy policies of various generative AI providers from early 2023, focusing on copyright and data protection. It finds providers adopt a "platformisation paradigm," positioning themselves as neutral intermediaries by assigning output ownership and all liability to users, despite not fitting the legal definition of platforms, thus creating power imbalances and regulatory gaps. | True | Idealistic | True | 2.0 | NaN | Comparative analysis of Terms & Conditions (T&Cs) and Privacy Policies of Generative AI providers (Private Ordering). | Manual collection and qualitative comparative analysis of T&Cs, privacy policies, and related documents from a sample of 13 generative AI services (categorized by function: T2T, T2I, T2A/V) selected based on mode, size, jurisdiction, and open/proprietary nature. Initial data collected Jan-Mar 2023, with a follow-up review of privacy policies in Dec 2023. | Providers consistently assign copyright ownership of outputs to users but grant extensive back-licenses to themselves and assign all liability for infringement or other harms to users. Initial privacy policies were often inadequate but improved over 2023. Providers implement platform-like content moderation (e.g., NTD) and position themselves as neutral intermediaries ('platformisation paradigm'), despite not fitting legal definitions. | NaN | NaN | NaN | NaN | Contract Law, Terms and Conditions, Privacy Law, Data Protection Law (GDPR, CCPA), Copyright Law, Platform Regulation, Internet Law, Consumer Law, Comparative Law. | International | NaN | Qualitative comparative legal analysis based on manual collection of terms and conditions and privacy policies. | NaN | False | True | The paper is published as an Open Access article under a Creative Commons Attribution licence. | Regulatory gaps (e.g., EU DSA not clearly covering foundation models). Lack of transparency and due process in content moderation by providers. Need for fairer risk allocation between providers and users. Need for better operationalization and enforcement of data protection rights (rectification, erasure) for foundation models. Lack of insight into B2B T&Cs and market competition effects. | Rapidly changing T&Cs requiring manual tracking. Difficulty obtaining B2B T&Cs due to commercial secrecy. Difficulty identifying the underlying models used by downstream applications. Complexity of the multi-dimensional comparative analysis within the given timeframe. | Risks from GenAI models: Bias, fake news, illegal/harmful content, hallucinations, copyright infringement, privacy violations. Risks from private ordering: Abuse of provider power via unfair/non-negotiable T&Cs, inadequate enforcement of user rights (privacy, due process), unfair shifting of liability to users, arbitrary content moderation and sanctions. Risk of regulatory gaps allowing platform-like entities to evade platform-specific obligations (e.g., under DSA). |
| pDsTcmAOZK4J.pdf | Google_Scholar | THE USE OF ARTIFICIAL INTELLIGENCE IN CORPORATE DECISION -MAKING AT BOARD LEVEL: A PRELIMINARY LEGAL ANALYSIS | This paper discusses the 'Assisted, Augmented, Autonomous' classification for AI, applying it to 'artificial governance intelligence' in corporate boards, and conducts a preliminary legal analysis of its implications. It highlights legal uncertainties in current corporate law regarding AI's role, decision rights, oversight, and liability across different autonomy levels, suggesting needs for regulatory adaptation. | True | Market | False | 2.0 | NaN | The 'Assisted, Augmented, Autonomous' (AAA) classification framework for AI (acknowledged as originally from A. Rao), applied and detailed by the author as 'artificial governance intelligence' with three levels (assisted, augmented, autonomous) to categorize AI's role in corporate board-level decision-making based on system autonomy and allocation of decision rights. | The classification's implications are evaluated through a legal analysis of corporate law principles (delegation, fiduciary duties, liability, director qualifications) as they apply to each proposed level of AI autonomy (assisted, augmented, autonomous) in corporate governance. | The legal analysis, structured by the classification, reveals significant legal unpreparedness and uncertainty in current corporate law for AI governance beyond simple assistance. Key issues identified include ambiguity in delegation rules, inapplicability of fiduciary duties to AI, lack of clarity on human oversight requirements, and complex liability attribution for AI failures. | NaN | NaN | NaN | NaN | Corporate law, company law, technology law (including AI regulation), liability law. | International (with specific examples from EU, US (Delaware, LLCs), Hong Kong, UK, Italy, Belgium, Germany, Spain, Switzerland). | N/A (The paper discusses AI generally; no specific AI model or its training data is proposed or analyzed in detail). | The paper's approach to applying and detailing the existing AAA classification for 'artificial governance intelligence' involves conceptual analysis, literature review on AI and corporate law, and mapping AI autonomy levels to corporate decision-making functions and their legal implications. | N/A (The classification is an analytical framework, not a deployable tool). | False | False | NaN | NaN | Challenges in applying the AAA classification to analyze corporate governance include: defining clear legal boundaries for 'core management functions' that cannot be delegated to AI; establishing appropriate human oversight mechanisms for each autonomy level without stifling AI benefits; resolving liability attribution for algorithmic failures; and adapting human-centric corporate law concepts (like fiduciary duties, director eligibility) to AI systems, especially at higher autonomy levels. | Legal uncertainty discouraging AI adoption in corporations; biases in AI systems (from data, code, or learning process); lack of transparency ('black box' issue); algorithmic failure leading to corporate harm; difficulties in attributing liability for AI-driven decisions; inadequacy of traditional corporate fiduciary duties (loyalty, care) for AI; AI not being recognized as a legal director; potential for AI to be used to mask human self-interest or biases; erosion of human judgment/over-reliance on AI; challenges to board collegiality and oversight. |
| HkNIuWlMdoIJ.pdf | Google_Scholar | The Future of Advocacy: The Trial Lawyer’s Guide to Large Language Model Generative AI | This paper introduces Large Language Model (LLM) generative AI to trial lawyers, discussing its mechanisms, potential impacts on legal practice, and significant ethical considerations. It emphasizes the need for lawyers to maintain competence, verify AI outputs, ensure confidentiality, and exercise independent judgment when using these tools. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General legal practice, Trial advocacy, Criminal defense | United States | NaN | NaN | NaN | False | False | NaN | Lack of clear legal and ethical guidelines for LLM use; Need for lawyer technological competence; Limitations in AI's ability for nuanced legal/ethical judgment and empathy. | Ensuring accuracy and avoiding hallucinations; mitigating inherent biases in AI systems; maintaining client confidentiality when using AI tools; fulfilling the duty of technological competence; avoiding overreliance and automation bias; maintaining independent professional judgment. | Generating inaccurate or fabricated information (hallucinations); perpetuating systemic biases; breaching client confidentiality; violating ethical duties (competence, candor, independent judgment); deskilling due to overreliance; potential job displacement for certain legal roles; unjustified billing practices. |
| bByRJ9_vmPAJ.pdf | Google_Scholar | RE-REGULATING UPL IN AN AGE OF AI | This paper argues that US state Unauthorized Practice of Law (UPL) statutes should be re-evaluated to allow AI tools, particularly LLMs, to help address the access to justice gap. It proposes focusing consumer protection on transparency, liability, and private rights of action rather than broad prohibitions based on vague definitions of legal practice. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Vague, restrictive, and inconsistently enforced Unauthorized Practice of Law (UPL) statutes prevent potentially helpful AI tools from assisting consumers. A vast 'justice gap' exists where most people with civil legal problems cannot afford or access legal help. | Re-evaluate and reform state UPL statutes to permit AI legal assistance tools. Shift consumer protection focus from broad UPL prohibitions to measures like transparency requirements (clear disclaimers that AI is not a lawyer, no attorney-client privilege), mandatory liability insurance for AI providers, and enabling private rights of action (e.g., for negligence, fraud, consumer protection violations) against incompetent or deceptive AI providers. | Access to justice gap in civil legal matters, Self-represented litigants, Legal form completion (e.g., debt collection defense) | Low-income households, Individuals/families/small businesses unable to afford lawyers, Self-represented litigants | General Civil Law (focusing on areas with high unmet need like debt collection, employment, housing, benefits, insurance), Unauthorized Practice of Law Regulation | United States (State-level UPL regulations, mentioning New York specifically) | NaN | NaN | NaN | False | False | NaN | Lack of clear, updated regulatory frameworks for AI legal tools that balance innovation and consumer protection. Need for effective consumer redress mechanisms beyond traditional UPL enforcement. Persistent societal gap in access to affordable legal assistance. | Ambiguity and restrictiveness of current UPL laws, which can chill innovation in legal AI for consumers. Technical limitations of current AI (e.g., hallucinations, ensuring confidentiality). | AI providing incompetent, fraudulent, or negligent legal advice. AI 'hallucinations' leading to reliance on false information or non-existent case law. Breach of user confidentiality if prompts/data are used for model training or exposed. Consumers being deceived into believing AI software is a licensed lawyer or provides attorney-client privilege. |
| cpMmdLuTaPkJ.pdf | Google_Scholar | Who Wants a Robo-Lawyer Now?: On AI Chatbots in China’s Public Legal Services Sector | This essay discusses the potential for large language model (LLM) chatbots to be widely adopted within China's public legal services (PLS) sector to address the access to justice gap. It examines the political economy driving this adoption, potential benefits like reinforcing legality, and associated risks such as errors and confidentiality concerns. | True | Idealistic | True | 3.0 | Positive | AI chatbots (specifically mentioning Ernie LLM-powered ones) deployed within a government-run public legal services (PLS) system. | The paper mentions that chatbots deployed in Yunnan performed 620,000 consultations in the initial months, but provides no formal testing methodology or results for this specific deployment. It cites general LLM benchmark studies like LegalBench and LawBench. | NaN | Scarcity of legal professionals, particularly in rural/disadvantaged regions; geographic disparities in access to legal services; high demand for basic legal information unmet by existing resources. | Leveraging AI chatbots within the government-funded Public Legal Services (PLS) system to provide automated, widely accessible basic legal information and advice, particularly for routine, statute-based questions. | Access to basic legal information and advice; Routine legal inquiries; Dispute resolution guidance (mediation, negotiation, litigation options). | General populace in China, particularly rural residents and those in disadvantaged regions (illustrated by the Yunnan case). | General civil law (e.g., labor law, family law, contract/property issues like landlord-tenant disputes mentioned implicitly via routine questions). | China (with specific examples from Yunnan province). | The paper mentions the use of Baidu's Ernie LLM for the Yunnan deployment. It discusses general LLM development approaches like fine-tuning on specific legal Q&A tasks, training specialized legal models with large legal datasets, and Retrieval Augmentation Generation (RAG) connecting models to external knowledge databases. | The paper discusses general approaches like fine-tuning general LLMs, developing specialized legal LLMs, and using Retrieval Augmentation Generation (RAG). It mentions the specific chatbot in Yunnan is based on Baidu's Ernie LLM. | Deployment via government-run public legal services stations in rural villages (Yunnan example), accessible through devices stationed at local government offices. Government procurement of third-party (Legal Tech) services. | False | False | NaN | Need for systemic methodologies for assessing LLM legal task performance; Optimal solutions for LLM hallucination are still developing; Technical limitations in ensuring confidentiality of user input; Need for regulatory frameworks and public oversight mechanisms for PLS chatbots; Potential for entrenching inequality if chatbot services remain inferior to human lawyers. | Overcoming regulatory barriers (e.g., unauthorized practice of law) for Legal Tech firms; Finding viable, large-scale use cases attractive to the Legal Tech industry; Ensuring user-friendly interfaces compared to previous technology generations; Managing risks associated with LLM limitations (hallucination, errors); Balancing confidentiality needs with the ability to use interaction data for model improvement. | Loss of confidentiality for user information; Hallucination and errors in legal information provided; Potential for scams and malicious manipulation (e.g., fake platforms); Misuse by government officials (e.g., manipulating information, biasing advice); Exacerbating inequality by creating a two-tiered system or diverting resources from human legal aid. |
| 6G3ocYCwgrcJ.pdf | Google_Scholar | A Debate-Driven Experiment on LLM Hallucinations and Accuracy | This paper investigates LLM hallucinations using a novel debate-like framework where multiple GPT-4o-Mini models interact, with one model intentionally providing false information. The findings suggest these inter-model interactions, particularly with a 'saboteur', can improve overall accuracy on the TruthfulQA benchmark. | True | NaN | True | 1.0 | NaN | Debate-like interaction framework involving multiple GPT-4o-Mini instances (Saboteurs, Fact-Based Models, Moderator) to improve response accuracy against misinformation. | Evaluation on the TruthfulQA benchmark, calculating overall and per-category percentage accuracy based on a multiple-choice question format derived from correct/incorrect answer sets. | The 5/1 configuration (5 total personas, 1 saboteur) of the debate framework achieved the highest overall accuracy of 78.93% on TruthfulQA, improving from a 61.94% baseline. | NaN | NaN | NaN | NaN | General; the TruthfulQA benchmark used for evaluation includes a 'Law' category. | International | N/A (the proposed debate framework uses pre-trained LLMs and does not involve training a new model). | Experimental setup with GPT-4o-Mini models assigned roles (Saboteur, Fact-Based, Moderator) engaging in two-round debates on TruthfulQA prompts, varying N (3,4,5) personas with 1 Saboteur. | NaN | True | False | The experimental methodology can be replicated using the GPT-4o-Mini API (not free) and the publicly available TruthfulQA dataset, as described in the paper. | NaN | Limitations cited include generalizability (fixed debater/saboteur configurations), comprehensiveness of the TruthfulQA dataset for diverse misinformation, and understanding long-term effects of misinformation exposure in model interactions. | LLM hallucinations (generating plausible but incorrect information), impacting accuracy in various sectors. Vulnerability to misinformation, especially in subjective and culturally nuanced areas. |
| 9VZt6DyvzXsJ.pdf | Google_Scholar | THE USE OF AL IN TODAY'S TECHNOLOGY DEVICES | This paper offers a broad overview of Artificial Intelligence (AI), detailing its core concepts like machine learning and deep learning. It highlights diverse AI applications in sectors such as e-commerce, healthcare, robotics, education, and business, while also discussing the economic influence of major tech companies and associated societal concerns like job displacement and cybercrime. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | NaN | NaN | False | False | NaN | NaN | General challenges include ensuring equitable AI benefits, managing socio-economic impacts (e.g., job displacement), simulating human expertise for complex tasks, addressing AI underutilization, potential for algorithmic bias, integration difficulties with existing systems, and combating evolving cyber threats. | Potential risks include the development of new cybercrimes, general safety concerns regarding advanced AI, widespread job displacement, perpetuation of algorithmic bias, violations of privacy and data protection, and increased data security breaches. |
| HIO1C6XNEh8J.pdf | Google_Scholar | DO USO DE IA GENERATIVA NOS TRIBUNAIS A UMA JUSTIÇA DEGENERATIVA: QUANDO A TECNOLOGIA “ALUCINA”... | This paper discusses the use of generative AI in courts, focusing on European ethical and legal frameworks like the AI Act and CEPEJ guidelines. It reviews Portuguese AI projects in the justice sector and highlights significant risks, especially AI "hallucinations" leading to erroneous judicial outcomes. | False | Idealistic | True | 3.0 | Negative | Generative AI / Large Language Models for court support and potentially decision-making assistance. | NaN | NaN | Risk of inaccurate, biased, or "hallucinated" outputs from generative AI undermining fairness and trust; Potential for a "digital divide" excluding the digitally illiterate ("infoexcluídos"); Amplification of procedural inequality ("desigualdade de armas") based on access to AI tools. | Adherence to ethical guidelines (e.g., CEPEJ) and legal frameworks (e.g., EU AI Act); Emphasis on transparency, non-discrimination, quality, security, user control, and robust human oversight (especially for judicial decisions); Use of certified/controlled AI systems; Critical evaluation of AI outputs; Transparency about AI use. | Judicial efficiency; Fairness of judicial process (processo equitativo); Transparency; Non-discrimination; Judicial independence; Reliability of AI-generated legal information; Digital divide. | Digitally excluded individuals ("infoexcluídos"), economically disadvantaged litigants. | General Judicial Process, Administrative Law, Civil Law, Criminal Law, Public Procurement Law. | Portugal, European Union | Not specified for most systems discussed; mentions use of court decisions (Projeto IRIS) and public procurement data (Court of Accounts project); recommends using certified and official data; Guia Prático da Justiça trained on specific legal topics (Marriage, Divorce, Company Creation). | Mentions use of machine learning, deep learning, NLP, OCR; emphasizes adherence to ethical principles (CEPEJ) and legal frameworks (EU AI Act). | Mix of deployed systems (Guia Prático da Justiça), systems under development (IRIS, Assistente Virtual, Court of Accounts), and informal use of general tools (e.g., ChatGPT). | True | False | The "Guia Prático da Justiça" chatbot is available online via a government website. | Need for improved regulation and understanding of generative AI risks (especially hallucination); Lack of robust certification mechanisms; Need for better data quality, transparency, explainability, and bias mitigation in judicial AI; Ensuring equal access and bridging the digital divide; Training for legal professionals. | Ensuring accuracy, reliability, and non-bias of AI (especially generative AI prone to hallucination); Maintaining transparency and explainability; Protecting judicial independence; Safeguarding sensitive data; Developing effective human oversight protocols; Training legal professionals. | Production of factually incorrect information ("hallucinations", bias); Disclosure of sensitive/confidential data; Lack of verifiable sources; Intellectual property/copyright violations; Inconsistent/unreliable outputs; Amplification of cognitive biases; Threats to fundamental rights (fair trial, non-discrimination); Undermining judicial independence and the rule of law; Algorithmic injustice; Digital divide amplification. |
| informit.T2025011900000101519001919.pdf | Google_Scholar | Using Generative AI to Identify Arguments in Judges’ Reasons: Accuracy and Benefits for Students | This paper evaluates the ability of Large Language Models (LLMs) like ChatGPT and Claude to identify and reconstruct legal arguments from Australian High Court judgments, finding significant performance variations. It concludes that while some LLMs show promise for legal education and potential A2J benefits through efficiency, critical human oversight remains essential due to varying accuracy and the risk of skill degradation. | True | Idealistic | True | 2.0 | Neutral | Evaluation of Large Language Models (ChatGPT versions 4 and 4o; Claude versions 3.0 Opus and 3.5 Sonnet) for identifying and reconstructing legal arguments from judicial reasons in a modus ponens structure using single-shot prompting. | Two human assessors (a lawyer/legal academic and a philosopher) blind-marked LLM-generated argument reconstructions for five recent High Court of Australia decisions. Outputs were compared against pre-determined sample answers and assessed using a detailed rubric (20 marks total) covering identification of disposition, premises/conclusions, argument location (paragraph numbers), and modus ponens structure. | Claude 3.5 Sonnet performed best, achieving average grades up to 18/20 (90%), with an overall system average of 16.2/20 for this version. In contrast, ChatGPT versions averaged around 8/20, with the lowest individual ChatGPT output scoring 4/20. | General barriers to access to justice include: financial cost, time, complexity of justice systems, lack of legal capability, and language skills. Specific to GAI, obstacles include inaccuracy, unreliability (e.g., hallucinations), and the current inability of GAI to perform all aspects of legal reasoning accurately. | Accurate GAI could facilitate low- or no-cost legal advice and increase the speed and efficiency of legal processes, thereby potentially reducing costs associated with legal services and increasing individuals' access to justice. | Reducing costs of legal advice/services, increasing efficiency in legal analysis, improving legal education outcomes, potential for enhanced access to justice through technology. | Individuals who do not seek legal advice due to high costs or overworked judicial systems; law students and junior lawyers. | The study used cases covering native title, criminal law, statutory interpretation, and immigration law. | Australia (High Court of Australia cases). | The LLMs (ChatGPT, Claude) were trained on 'enormous volumes of data – for example, text corpora scraped from vast swathes of the internet.' Input for the specific task was the PDF text of five High Court of Australia judgments. | N/A (The paper evaluates existing LLMs, it does not detail their internal design methodologies beyond general statements about deep learning. The paper's methodology is for evaluation, see 'testing'). | The LLMs (ChatGPT and Claude) are commercially deployed and accessible via web interfaces, which were used in the study. The study's specific prompting approach is described but not deployed as a standalone tool. | True | False | ChatGPT and Claude models are commercially available through their respective platforms (OpenAI and Anthropic). The specific advanced versions tested (e.g., GPT-4, Claude 3.5 Sonnet) are typically part of paid subscription tiers. | Significant variance in accuracy across different LLMs and versions for legal argument identification. LLMs' tendency for 'hallucinations' and unreliability for certain legal tasks. The 'skill-gap' in users' ability to critically evaluate LLM outputs. Need for further research before LLMs can revolutionize the legal industry. | For the study: The 'high cost of labour involved in the analysis of legal documents, which necessitates small numbers of annotators/assessors' was a limitation. For users (e.g. students): The varied accuracy of LLMs, the superficial plausibility of even poor answers, and the need to develop critical engagement skills to assess LLM outputs. | LLM 'hallucinations' (fabricating information, e.g., non-existent cases). Inaccurate legal advice leading to safety issues. Unsupervised creation of legal arguments by GAI. Over-reliance on LLMs leading to degradation of essential human legal skills, particularly argument analysis, if used uncritically by students. |
| qHJ-ypmosE4J.pdf | Google_Scholar | Hallucination is the last thing you need | The paper discusses the significant risk of LLM hallucinations corrupting legal research and common law, proposing theoretical solutions like multi-length tokenization and ensemble models. It evaluates current GPT models, finding frequent non-verbatim but semantically similar outputs when quoting case law, highlighting the subtlety of the hallucination problem. | True | Market | True | 3.0 | Neutral | Evaluation of GPT-4 and text-davinci-003's ability to accurately complete text sequences with verbatim quotes from provided UK case law judgments. | Empirical testing using OpenAI's Playground (Complete, Chat, Insert modes) with specific prompts asking models to finish sequences with the correct legal quote from UK case law. 20 trials analysed for verbatim match, close match, semantic similarity, or unrelated output. | Out of 20 trials: 1 verbatim match, 2 close matches, 11 non-verbatim matches with similar semantic intent (often summaries), 6 non-verbatim matches with unrelated intent. Models frequently hallucinate subtly or produce semantically similar but inaccurate quotes. | Common law contamination: Subtle, non-obvious hallucination errors from LLMs used in legal research risk altering the understanding and application of law if incorporated into legal documents and judgments. | Proposes theoretical architectural changes (multi-length tokenization, ensemble models separating problem/commentary/fact). Suggests using verification tools, relying on curated data from established providers, and promoting open justice data initiatives. | Integrity of legal research; Accuracy of case law citation; AI safety in legal practice. | NaN | Common Law (general principles, citations across various fields like contract, tort, family law), Statutory Interpretation | United Kingdom (specifically England & Wales based on case law cited) | The study evaluated existing models (GPT-4, text-davinci-003) trained on general internet data. The proposed theoretical models would require segmented legal judgments (unstructured text data). | The evaluated technique used empirical testing. The proposed techniques are theoretical concepts without specified design methodologies. | The evaluated techniques are accessed via OpenAI's platform. The proposed theoretical techniques are not deployed. An enhancement to the authors' open-source YCNBot (blocking copy/paste of detected case law) is mentioned as deployed on GitHub. | False | False | The paper states that the authors' related tool, YCNBot, including a feature discussed for mitigating copy-paste risk, is available open-source on GitHub. | Current LLMs lack reliable mechanisms to handle factual legal text (e.g., quotes) without hallucination. There's a gap in access to curated justice data for open-source research compared to large vendors. Need for robust verification methods to prevent legal knowledge contamination. | Defining and measuring legal hallucination; Designing LLM architectures that distinguish between reasoning and factual recall; Segmenting legal texts accurately for potential ensemble models; Linking ensemble components effectively; Keeping models updated; Subtle nature of errors making detection hard. | Contamination of common law with subtle errors; Lawyers facing sanctions for using hallucinated precedents; Undermining the credibility of the legal profession and AI tools in law. |
| _J-yDG-ZRmwJ.pdf | Google_Scholar | ChatGPT Practices: Finance and Banking Domain | This paper reviews the applications, challenges, limitations, and future potential of ChatGPT in the finance and banking sectors. It also recommends the development of legislation and regulations to mitigate risks associated with ChatGPT use. | True | NaN | True | 2.0 | NaN | ChatGPT | NaN | NaN | NaN | NaN | NaN | NaN | Legislation and Regulation (general for AI/ChatGPT), Criminal Law (e.g., phishing, fraud), Intellectual Property Law (infringement), Data Privacy Law, Legal Liability (for AI outputs) | International | Large-scale corpora of text data (e.g., books, web pages) used for unsupervised pre-training of the underlying GPT models (GPT-3.5, GPT-4). | NaN | NaN | True | False | ChatGPT is accessible via OpenAI's platform, with free and paid subscription tiers for usage. | NaN | Ensuring data privacy and security, addressing ethical dilemmas, model bias and accuracy, need for real-time information, limitations in handling precise logic (e.g. math), and susceptibility to misuse or instruction attacks. | Data leakage, misuse for creating phishing websites or disseminating misinformation, infringement of intellectual property, and compromised personal/property safety due to false or biased information. |
| jM9T-EjTRBcJ.pdf | Google_Scholar | The Use of LLMs in the Legal Field: Optimizing Contract Management with Generative Artificial Intelligence | This Master's thesis explores using Large Language Models (LLMs), specifically Retrieval-Augmented Generation (RAG), to develop a proof-of-concept web application for lawyers. The application aims to optimize contract management by aiding in contract analysis and clause generation, thereby increasing efficiency for legal professionals. | True | Market | True | 1.0 | NaN | A web application using Retrieval-Augmented Generation (RAG) with LLMs (GPT-3.5-Turbo via Azure OpenAI). It incorporates semantic search via embeddings (Text-Embedding-Ada-002) stored in Redis, and prompt engineering for contract analysis (key point extraction) and clause generation. | The RAG system's retriever component (for clause generation) was evaluated using Langsmith. Metrics included Context Relevance, Contextual Recall, latency, and user feedback from lawyers on the correctness of generated clauses, testing different similarity score thresholds. | For clause generation retrieval, a similarity score threshold of 0.75 in Redis yielded the best balance, achieving Context Relevance of 0.90, Context Recall of 0.92, latency of 3.83s, and positive user feedback. | NaN | NaN | NaN | NaN | Contract Law | Italy | The RAG system retrieves information from a proprietary dataset of legal clauses provided by the collaborating law firm (Orbyta Legal), originally stored in an Excel file. These clauses are vectorized using Azure OpenAI's Text-Embedding-Ada-002. The underlying LLM (GPT-3.5-Turbo) is pre-trained by OpenAI/Microsoft Azure. | Proof of Concept (POC) development, RAG pipeline implementation, Front-end development (Streamlit), Back-end development (Python, LangChain, Redis), Prompt Engineering, Text Chunking, Embedding Generation, User Feedback Collection (via Langsmith). | Containerized using Docker and Docker Compose, deployed via Azure Container Registry (ACR). Access limited to the company's internal network. | False | False | NaN | Technical limitations mentioned: Inability to process scanned (non-text) documents (requires OCR integration). Lack of an integrated user feedback mechanism within the application to directly guide model improvement. | Handling LLM context length limitations for large documents. Effective document chunking. Selecting appropriate embedding models (considering performance, cost, language support). Designing effective prompts (Prompt Engineering). Evaluating RAG system components (especially retrieval). Ensuring data privacy. | Data privacy issues (mitigated by using Azure OpenAI). Potential for inaccuracies in AI outputs requiring human supervision. Loss of context due to document segmentation techniques. |
| Research-Paper-TokenOps.pdf | Google_Scholar | TokenOps: A Compiler-Style Architecture for Token Optimization in LLM API Workflows | This paper introduces TokenOps, a compiler-style middleware architecture designed to optimize Large Language Model (LLM) API workflows. By employing pre-processing and post-processing layers to compress and restructure inputs and outputs, TokenOps aims to reduce token usage, thereby lowering costs, latency, and the carbon footprint of LLM deployments. | True | Market | True | 1.0 | Positive | TokenOps architecture V2: A middleware stack wrapping LLM API calls, featuring a Preprocessing Layer (input optimization using rule-based cleaning and LLM summarization like DistilBERT/TinyLlama) and a Postprocessing Layer (output minimization using template-matching, summarization, and hierarchical reduction). An optional Semantic ZIP Layer for caching/macros is also proposed. | Simulations using 5,000 anonymized enterprise prompts on GPT-4, comparing raw vs. optimized calls. A/B testing on client production data. Semantic fidelity assessed via blind human review using a 3-point Likert scale. Cost and latency calculated based on OpenAI pricing/stats and simulation results. Tools included Python (NLTK, spaCy), DistilBERT, TinyLlama, GPT-4, LangChain. | Simulations showed 40-46% token reduction across scenarios (customer support, document search, content generation) with 97% of outputs rated semantically 'Accurate' or 'Acceptable' by human reviewers. Average latency was reduced by 29-36%, and cost savings were estimated at $9K/month per 10M API calls. | The primary obstacles identified relate to enterprise LLM deployment: high token costs, increased latency with larger prompts/outputs, and the environmental impact (carbon footprint) associated with high token volume. | The proposed solution is the TokenOps architecture itself, which acts as middleware to systematically reduce token usage in LLM workflows. Policy recommendations include standardizing token accounting, incentivizing token efficiency, recognizing optimization middleware as critical infrastructure, and incorporating token usage into AI sustainability reporting. | Lowering cost barriers for technology deployment | Under-resourced regions / Budget-sensitive environments | NaN | International | The evaluation used a proprietary dataset of 5,000 anonymized, unstructured enterprise prompts from production logs across varied industries. The TokenOps technique itself uses smaller pre-trained LLMs (DistilBERT, TinyLlama) but doesn't describe their training data. | Compiler-style architecture design, rule-based systems, use of smaller pre-trained LLMs (DistilBERT, TinyLlama) for specific tasks (summarization), template matching, simulation, A/B testing, cost/energy profiling. | Integration into LLM pipelines using tools like LangChain, FastAPI wrappers, and deployment on cloud platforms (GCP, Azure). Implemented for clients of the author's consulting firm. | False | False | NaN | Future technical development needs include token-aware reinforcement learning, a full 'LLM Compiler Stack™', and a marketplace for domain-specific TokenOps modules. Societally, while cost reduction may improve access, deeper gaps in equitable AI deployment remain unaddressed. | Maintaining semantic fidelity while aggressively reducing tokens (especially for creative tasks), integrating middleware seamlessly into diverse enterprise workflows, managing the complexity of the optimization layers. | Risk of semantic degradation or loss of nuance/style due to over-optimization (observed at a 3% rate in testing, primarily in creative content generation). |
| 32IjpX6kRLwJ.pdf | Google_Scholar | A Survey on Symbolic Knowledge Distillation of Large Language Models | This paper surveys the field of symbolic knowledge distillation (SKD) for Large Language Models (LLMs), reviewing methods for converting implicit LLM knowledge into explicit, symbolic forms to enhance interpretability and efficiency. It categorizes techniques, discusses applications, highlights challenges, and identifies opportunities for future research. | True | NaN | True | 3.0 | NaN | Symbolic Knowledge Distillation (SKD) | The paper reviews studies that evaluated SKD techniques using various benchmarks specific to the application domain (e.g., commonsense reasoning benchmarks, machine translation metrics like BLEU, mathematical reasoning benchmarks like miniF2F, summarization quality metrics, visual reasoning tasks, instruction following evaluation). | Reviewed studies generally show that SKD can create smaller, more efficient student models that sometimes outperform larger teacher models on specific tasks like commonsense reasoning, translation, summarization, and mathematical reasoning, or achieve better controllability or specialized capabilities. | NaN | NaN | NaN | NaN | NaN | International | The surveyed techniques use large pre-trained LLMs (teachers) trained on massive, general corpora (web text, books, code etc.). Symbolic knowledge distillation often involves generating intermediate datasets (e.g., knowledge graphs, instruction pairs, rationales, sentence-summary pairs) from the teacher model, sometimes with filtering or human feedback, to train smaller student models. | The paper reviews various methodologies including prompt engineering, NLP techniques for knowledge extraction (NER, POS, parsing), rule/graph generation, iterative distillation, reinforcement learning (RLHF, offline RL), self-instruction, expert iteration, and filtering using critic models or human evaluation. | NaN | False | False | NaN | Need for specific evaluation benchmarks for SKD/neurosymbolic AI; ensuring quality/diversity/representativeness of distilled knowledge; balancing automation and human oversight in data generation; achieving high performance in compact models across broad domains (not just narrow tasks); adaptability and continuous learning for distilled models. | Ensuring quality, diversity, and representativeness of distilled data; balancing automation and human oversight in dataset generation; developing compact models that retain high performance across diverse applications without loss of nuance; effective instruction tuning for varied use cases; ensuring adaptability and continuous learning in distilled models. | Propagation of biases and inaccuracies from teacher LLMs to distilled datasets and student models; potential issues with factual accuracy and safety in distilled models. |
| 8t--pP6kmsIJ.pdf | Google_Scholar | Generative Artificial Intelligence and the Practice of Law: Impact, Opportunities, and Risks | This article discusses the transformative impact of generative AI on the legal profession, including improving efficiency in tasks like drafting motions and enhancing legal education. It also explores the significant potential of generative AI to broaden access to legal services for underserved communities, while acknowledging associated challenges and risks like AI hallucinations and the need for regulatory adaptation. | True | Idealistic | True | 3.0 | Positive | Generative AI / Large Language Models (LLMs) (e.g., ChatGPT, GPT-4) | The paper cites studies by Choi, Monahan, and Schwarcz where GPT-4's impact on human legal analysis was assessed through a randomized controlled trial, and its performance was tested on law school exams. | Cited studies found that GPT-4 slightly and inconsistently improved the quality of legal analysis but induced large and consistent increases in speed. On law school exams, GPT-4 alone outperformed both students alone and students with AI assistance on simple multiple-choice questions; worst-performing students saw the largest gains from AI assistance, while best-performing students saw declines. | Need for AI systems of sufficient quality and reliability; concerns about privacy and privilege of personally identifiable information collected by AI systems; potential for unauthorized practice of law litigation against AI tool providers; and the time/effort required for software development, testing, navigating legal challenges, and updating regulatory frameworks. | Continued technological development to improve AI quality; careful system design ensuring user-friendliness, privacy, and verification features; navigating potential litigation; adapting regulatory frameworks over time to permit AI deployment while ensuring consumer protection; and learning from regulatory reforms in states like Utah and Arizona. | Support for pro se litigants, addressing tenant harassment, and mitigating common civil legal problems for low-income individuals (e.g., consumer issues, healthcare, housing, income maintenance). | Low-income households, low-income tenants, tenants in rent-stabilized dwellings, and pro se litigants. | Civil litigation, Housing Law, Consumer Law, Healthcare Law, and general legal practice. | United States (citing federal rules, and state/local examples from Texas, Los Angeles, New York, Utah, Arizona). | LLMs are described as being trained on 'massive datasets' of text and other content. The paper does not specify the exact composition or sources of these datasets for the general LLMs discussed, beyond noting they are trained on the data they have access to. | NaN | Existing tools like ChatGPT were released publicly by companies (e.g., OpenAI), leading to rapid adoption. Commercial products (e.g., CoCounsel by Casetext, Lexis+AI by LexisNexis) are being adopted by law firms and made available to law students. Future access to justice tools might be deployed by public/private legal aid organizations or directly to pro se litigants. | True | False | ChatGPT is publicly accessible (with free and paid tiers). CoCounsel and Lexis+AI are commercially available products, with Lexis+AI also being made available to many law students. | Ensuring high quality, reliability, and accuracy (reducing 'hallucinations') of generative AI in legal contexts; developing robust data privacy and attorney-client privilege protection mechanisms for AI systems; establishing clear regulatory frameworks for AI-driven legal services, particularly concerning unauthorized practice of law and consumer protection; time needed for development, testing, and societal/professional adaptation. | For users/adopters: managing AI 'hallucinations' and ensuring factual/legal accuracy of AI outputs; cost and market volatility of AI tools; training legal professionals to use AI effectively and ethically; protecting client data confidentiality; adapting existing workflows and business models; and effectively integrating AI into legal education. | AI 'hallucinations' leading to incorrect legal information or filings; breaches of client confidentiality and data privacy; unauthorized practice of law claims against AI service providers; premature deployment of low-quality AI tools for access to justice, potentially harming users or discrediting the approach; over-reliance on AI without sufficient human oversight and critical judgment; challenges in maintaining academic integrity and ensuring effective student learning with AI in legal education. |
| CeA1rreEv_sJ.pdf | Google_Scholar | InLegalLLaMA: Indian Legal Knowledge Enhanced Large Language Model | This paper introduces InLegalLLaMA, a Large Language Model adapted for the Indian legal domain through continual pre-training on Indian legal texts and knowledge infusion from a legal knowledge graph. The paper also proposes a Retrieval Augmented Generation (RAG) based framework utilizing this model for petition drafting to improve access to legal processes. | True | Idealistic | True | 1.0 | Positive | InLegalLLaMA: a LLaMA-2 model continually pretrained on Indian legal documents and instruction-tuned using legal knowledge graph triples and domain-specific tasks. A RAG-based framework for petition drafting is also proposed. | InLegalLLaMA was evaluated on: 1) In-context masked triple prediction using data from Vasisht et al. (2023), with metrics Hits@1, BLEU, ROUGE-L. 2) Legal sentence rhetorical role classification using data from Bhattacharya et al. (2023), with metrics Precision, Recall, and F1-Score. | For in-context triple prediction, InLegalLLaMA achieved Hits@1: 0.984, BLEU: 98.224, ROUGE-L: 99.191. For legal sentence rhetorical role prediction, InLegalLLaMA achieved F1-Score: 0.585. InLegalLLaMA outperformed LLaMA-2-7B on these Indian legal domain tasks. | Complexity of legal procedures for individuals; poorly written petitions leading to information omission, incorrect filings, and dismissals, thereby increasing costs and hindering justice; significant backlog and volume of petitions in courts. | Development of domain-specific LLMs (InLegalLLaMA) enhanced with legal knowledge from Indian legal documents and knowledge graphs. Proposal of a RAG-based framework for petition drafting involving template selection, AI-assisted content generation, refinement, and evaluation, with human oversight. | Petition drafting assistance; access to legal information and processes; understanding legal notices. | Citizens unfamiliar with legal processes; individuals seeking redressal of grievances; lawyers (for improving petition quality). | General Indian law; court petitions. | India | For continual pretraining: A new dataset of 10,000 Indian legal documents (Supreme Court judgments and legal statutes) and 5% replay data from RedPajama. For instruction-tuning: Triples from an Indian legal knowledge graph (derived from public Indian court/legal sources), datasets from Vasisht et al. (2023) for triple prediction, Bhattacharya et al. (2023) for rhetorical role classification, and LIMA instructions (Zhou et al., 2023). | Knowledge graph construction (building on prior work); continual pretraining of LLaMA-2 on domain-specific corpus using LoRA; instruction tuning with domain-specific tasks and general instructions; design of a RAG architecture for petition drafting. | The InLegalLLaMA model (base and instruction-tuned versions) is publicly available on HuggingFace. The petition drafting framework is a proposal. | True | True | The base and instruction-tuned versions of InLegalLLaMA are publicly available on HuggingFace. | Need for more extensive instruction tuning for complex legal text analytics tasks; the current triple prediction task may not be sufficiently challenging, requiring a larger knowledge graph; further work needed to make LLMs useful for tasks requiring human expertise; need for code fine-tuned versions of InLegalLLaMA for RAG tools (e.g., Text-to-SQL). | Resource constraints for large model training (addressed by LoRA); catastrophic forgetting during continual pretraining (addressed by replay data and learning rate strategies); designing LLMs to identify *missing* salient information in legal documents; ensuring legal soundness, completeness, and admissibility of AI-generated petitions (requiring human expert monitoring). | Generation of poorly written or legally unsound petitions if AI assistance is inadequately supervised, potentially leading to negative legal outcomes for users. Regulatory challenges with LLMs trained on data with unestablished provenance (though this work aims to address this by using domain-specific Indian legal data). |
| PxXf3m9gRGsJ.pdf | Google_Scholar | Traditional and Computational Canons | This paper empirically investigates whether judges align with linguistic consensus (from lawyers and laypeople) when using traditional interpretive tools like canons and dictionaries, finding they generally do. It also evaluates large language models (LLMs) like GPT-4o and o1, concluding they match, but do not exceed, judges' alignment with human consensus on plain meaning tasks. | True | Market | True | 2.0 | NaN | Evaluation of judicial use of traditional interpretive tools (canons, dictionaries) against linguistic consensus derived from human experiments, and evaluation of Large Language Models (GPT-4o, o1) prompted to perform similar plain meaning interpretation tasks. | Study 1: Behavioral experiments with lawyers (n=2,373) and laypeople (n=4,533) interpreting 180 real-world plain-meaning cases. Study 2: Prompting experiments with LLMs (GPT variants, o1 models) on the same 180 case materials, comparing outputs to human consensus from Study 1. | Judges' interpretations aligned with human linguistic consensus (both lay and lawyer) in a supermajority of cases. The best-performing LLMs (o1-mini or GPT-4 depending on metric) aligned with consensus at a similar rate to human judges, matching but not exceeding their performance. | NaN | NaN | NaN | NaN | Multiple fields including statutory interpretation, contract law, constitutional law, administrative law, wills, trusts, deeds. | United States (Federal and State) | Large, general, proprietary datasets used to train base LLMs (e.g., GPT-4o, o1), likely including significant amounts of publicly available text but specifics not disclosed. | Study 1: Experimental design, survey methodology, statistical analysis (mixed-effects logistic regression). Study 2: LLM prompting, comparison of LLM output to human benchmark data, statistical analysis. | NaN | True | False | The LLM part of the approach uses existing commercial models (GPT-4o, o1) accessible via OpenAI's API, typically requiring an account and potentially payment. | NaN | Potential for LLM data contamination affecting evaluation; ensuring LLMs accurately proxy human interpretation; potential misuse of LLMs by judges (e.g., cherry-picking models/prompts); accurately applying canons like ejusdem generis and rule of last antecedent which showed inconsistencies with consensus. | Potential misuse of interpretive tools (canons, dictionaries, LLMs) by judges as a smokescreen for policy preferences; LLMs potentially overestimating clarity/consensus in ambiguous cases; judges potentially misusing LLMs (e.g., cherry-picking models or settings). |
| Y81SvrL3LE0J.pdf | Google_Scholar | A Brief Report on LawGPT 1.0: A Virtual Legal Assistant Based on GPT-3 | This paper introduces LawGPT 1.0, a virtual legal assistant created by fine-tuning the GPT-3 language model on a large corpus of legal text. It briefly discusses the system's architecture, potential to improve legal service accessibility, and requirements like explainability for real-world application. | True | Idealistic | True | 1.0 | Positive | LawGPT 1.0: GPT-3 fine-tuned on a large corpus of legal text. | Evaluated on a set of legal benchmark tasks including answering legal questions, generating legal documents, and providing legal advice. No specific benchmark names or detailed procedures provided. | The system is reported capable of providing high-quality legal assistance, with accuracy rates competitive with other virtual legal assistant systems. No specific metrics are provided. | The need for cost-effective, efficient, and accessible (e.g., 24/7) legal services. For AI deployment: ensuring explainability and establishing responsibility for AI-generated recommendations. | Develop and deploy virtual legal assistants like LawGPT 1.0 to provide conversational legal assistance (answering questions, generating documents, advice) to improve efficiency and accessibility. | Answering legal questions, generating legal documents, providing legal advice (general legal assistance). | Individuals and businesses, particularly those needing legal assistance outside normal business hours. | General legal domain. | NaN | A large corpus of legal text. Proprietary status implied by NDA, details not disclosed. | Fine-tuning of a pre-trained large language model (GPT-3) using standard deep learning techniques (stochastic gradient descent, backpropagation). | NaN | False | False | NaN | Need for explainability in AI recommendations; need to establish responsibility frameworks for AI use in legal decisions; current version lacks RLHF; requires expansion to support multiple languages and legal systems. | Incorporating explainability; establishing responsibility for AI decisions; addressing legal and ethical considerations (data privacy, IP, confidential information); adapting the model for different languages and legal systems; current limitation of not supporting RLHF. | Serious consequences from incorrect legal decisions made with AI assistance; lack of explainability hindering trust and accountability; potential issues with data privacy, intellectual property rights, and handling sensitive/confidential information. |
| dDbdmiHfrYoJ.pdf | Google_Scholar | Persuading across Diverse Domains: A Dataset and Persuasion Large Language Model | This paper introduces DailyPersuasion, a large-scale, multi-domain dataset for persuasive dialogues generated using GPT-4. It also proposes PersuGPT, a large language model fine-tuned on this dataset using intent-to-strategy reasoning and simulation-based preference optimization, outperforming baselines including GPT-4. | True | NaN | True | 1.0 | NaN | PersuGPT: An LLM (LLaMA-2 based) trained for persuasive dialogue using intent-to-strategy reasoning and multi-turn simulation-based preference optimization (using a learned user model, GPT-4 for reward estimation, and DPO). Also introduces the DailyPersuasion dataset. | Evaluated on unseen scenarios from the DailyPersuasion dataset and the PersuasionForGood dataset. Metrics included Win-Rate (comparing model vs. GPT-4+ISR output using ChatGPT as judge) and ROUGE-L against GPT-4+ISR generations, plus human ratings (1-5 scale). Simulation-based preference optimization evaluated paths using GPT-4. | On DailyPersuasion, PersuGPT achieved a Win-Rate of 60.4% against GPT-4+ISR generations and a human rating of 4.35, outperforming GPT-4+ISR (Human Rating 4.17) and other baselines. It also outperformed baselines on the PersuasionForGood dataset. | NaN | NaN | NaN | NaN | NaN | International | Introduces DailyPersuasion: a synthetic dataset of 78,000 persuasive dialogue sessions across 35 daily life domains, generated using GPT-4. It includes scenarios, background, goals, strategy sets, dialogue turns (user utterances, persuader responses), and annotated intent-to-strategy reasoning paths. Also uses the public PersuasionForGood dataset. | Dataset Creation (DailyPersuasion): GPT-4 based generation using keyword induction for scenarios, guideline-based prompts for strategies, and third-person narrative prompts for dialogues. Model Training (PersuGPT): Supervised fine-tuning of LLaMA-2 Chat (13B) on DailyPersuasion including intent-to-strategy reasoning paths. Simulation-based preference optimization using a fine-tuned LLaMA user model, k-turn path simulation, GPT-4 for pairwise path comparison/reward estimation, and Direct Preference Optimization (DPO). | Code and data are made available via a GitHub link (https://persugpt.github.io). | True | True | Code and data available at https://persugpt.github.io. | Limitations mentioned include potential inconsistencies between synthetic data (DailyPersuasion) and real-world conversations, and the trained user model not fully capturing human personality variations. | Difficulty in collecting large-scale, diverse, high-quality human persuasion data across domains; enhancing LLM multi-turn following and planning for persuasion; anticipating user feedback and optimizing for long-term persuasive success; avoiding unnatural, role-inconsistent responses from LLMs during data generation. | Persuasive dialogue systems are a 'double-edged sword' with potential for misuse in harmful scenarios. The system should not replace human interaction and requires human supervision and regulation. |
| YT8QBByRvOoJ.pdf | Google_Scholar | Transdisciplinary research as a way forward in AI & Law | This position paper argues for a transdisciplinary approach in AI & Law, emphasizing hybrid AI systems (combining knowledge-based and data-driven methods) and practical evaluation with stakeholders. Two case studies, a police complaint intake system and a court decision support tool, illustrate these principles. | True | Idealistic | False | 3.0 | Positive | Advocacy for hybrid AI systems combining knowledge-based (e.g., rules, argumentation) and data-driven (e.g., NLP, machine learning) approaches. Illustrated by: 1. A police complaint intake system using rule-based argumentation and NLP (regex, experimented with ML). 2. A court decision support system using NLP for information extraction, document vectorization for case similarity, and legal text classification for outcome prediction. | Police system: Internal police evaluations (accuracy 80-90% vs human, efficiency), controlled experiment on citizen trust with explanations (N>1700), ethnographic case study of police case workers. Court system: Extensive testing and feedback sessions with three paralegals at a Dutch court. | Police system: Explanations significantly increased citizen trust and compliance with system recommendations; case workers valued structured data but ignored system recommendations without explanations. Court system: Information extraction and similar-case matching were valued by paralegals; outcome prediction without explanation was deemed useless. | General 'algorithmic drama' (complex, opaque, uncontrollable AI). Specific to access to justice: lack of citizen/professional trust in AI without clear explanations; resistance from legal professionals if AI impinges on discretion or is opaque; inherent difficulty for AI to handle legal nuance, ambiguity, and open-textured concepts. | 1. Combine knowledge-based and data-driven AI for transparency, reasoning, and handling legal complexities. 2. Evaluate AI in real-world legal practice through collaboration with stakeholders (courts, police, citizens) using diverse methods (computational, experimental, ethnographic, participatory). 3. Foster transdisciplinary research involving AI builders, legal experts, social scientists, ethicists, and others. | Citizen complaint intake for online trade fraud, decision support for processing traffic violation appeals. | Citizens reporting online trade fraud, citizens appealing traffic fines, legal professionals (police case workers, court paralegals). | Criminal law (online trade fraud), Administrative law (traffic violations). | Netherlands (for the specific projects discussed); International (for the general discussion of the AI & Law field). | Police system: Dutch Criminal Code and police policy rules (for knowledge-based argumentation model); citizen complaint forms containing free text (for NLP, final implementation used regular expressions after experimenting with ML). Court system: PDF files of appeal cases and a database of previous court cases (for information extraction, case similarity, and legal text classification). | Action research, participatory design, collaborative development with end-users (police, court personnel), ethnographic studies, controlled experiments. | Police complaint intake system: Implemented and in operational use by the Dutch police since 2018. Court decision support system: Developed and tested as a prototype in collaboration with a Dutch court for a case study; not stated as being in routine operational use. | False | False | NaN | Need for more truly integrated neuro-symbolic AI systems capable of legal reasoning (not just modular combinations); scarcity of AI & Law research applications being practically used and evaluated in real-world legal settings; insufficient transdisciplinary integration and consideration of broader socio-technical impacts; ongoing challenge of ensuring AI systems are transparent, contestable, and legally sound. | Computational efficiency for complex reasoning tasks (e.g., argument-based inquiry); gaining user trust and acceptance, particularly for AI recommendations lacking explanations; integrating AI into established professional workflows without undermining expert discretion; modeling complex legal language, open-textured concepts, and nuanced legal reasoning. | Over-reliance on opaque or poorly understood AI systems ('algorithmic drama'); AI systems being complex, uncontrollable, and leading to unjust outcomes; AI recommendations being ignored if not explainable, thus failing to deliver benefits; potential for AI to introduce or perpetuate biases if not carefully designed and evaluated; systems not being transparent, contestable, or aligned with legal principles. |
| EX0pygEbHrgJ.pdf | Google_Scholar | ACCESS TO JUSTICE FOR SELF -REPRESENTED LITIGANTS THROUGH THE NEW HAMPSHIRE CIRCUIT COURT NAVIGATOR PROGRAM: A PATH FORWARD | This report evaluates the effectiveness of the New Hampshire Circuit Court Navigator Program, which provides legal information and assistance to self-represented litigants (SRLs). It finds the program highly effective for both SRLs and the court system but highlights limitations due to staffing constraints and proposes recommendations for expansion and improvement. | True | Idealistic | False | 2.0 | Positive | New Hampshire Court Navigator Program | Evaluation based on observational analyses within courthouses, 19 stakeholder interviews (Navigators, court staff, judges, administrators, national experts), and an original survey administered to 34 SRLs served by the program. | The Navigator Program is highly effective, significantly increasing SRL confidence and satisfaction (rated 9 or 10 out of 10 by all survey respondents). It successfully serves target demographics (low-income, female, disabled, senior citizens), eases court staff/judge workload, and contributes positively to procedural justice perceptions, primarily in estate and guardianship cases. | Structural challenges to justice (court/legal aid staffing shortages, rural/demographic barriers, infrastructure limits). Program-specific barriers include limited data collection on SRLs, Navigator overburden due to only two staff members, lack of widespread advertising and schedule continuity, court technology issues (website usability, TurboCourt errors), need for staff mental health training, potential loss of institutional knowledge, and staff pay discrepancies causing tension. | Proposed short-term solutions include systematic data collection (tablets, platform), hiring two more Navigators (including a Volunteer Program Manager), and increasing program accessibility (advertising, scheduling system). Medium-term solutions cover technology upgrades (website redesign, AI exploration, TurboCourt audit, language accessibility), human infrastructure (mental health training, preserving knowledge, career paths), and program enhancements (volunteer program, expanded hours, coordination with Community Navigators). The long-term vision is ensuring no litigant appears in court alone. | Providing legal information and procedural assistance to self-represented litigants (SRLs) within the court system. | Low-income residents, women, disabled persons, senior citizens, rural populations, and self-represented litigants (SRLs) generally within the New Hampshire Circuit Court system. | New Hampshire Circuit Court matters, primarily civil cases including: estates, guardianship, small claims, landlord-tenant, name changes, low-level criminal offenses (misdemeanors/violations), stalking, adoptions, trusts, equity matters, involuntary commitments, and civil claims under $25,000. | New Hampshire (specifically the NH Circuit Court system) | NaN | The paper evaluates an existing program. The evaluation methodology included observational analyses, semi-structured stakeholder interviews, and an original SRL experience survey. | The program utilizes two state-funded court employees (Navigators) based in specific courthouses (Nashua and a Concord office for the Travelling Navigator). SRLs access services via phone/email appointments or walk-ins at these locations during business hours. | True | False | Available to SRLs in New Hampshire via appointment or walk-in at specific Circuit Court locations (primarily Nashua and where the Travelling Navigator is scheduled), subject to limited Navigator availability. | Need for comprehensive statewide data on SRL location and needs; insufficient number of Navigators to cover all courthouses, case types, and operating hours; inadequate court technology (frustrating website, error-prone TurboCourt); language accessibility barriers on forms and potentially services; challenges reaching rural SRLs; need for structured volunteer program; need for better institutional knowledge preservation and staff support (mental health, burnout prevention). | Operating with only two Navigators leading to overburden and inability to fully develop components like the volunteer program; difficulty advertising widely due to capacity constraints; dependence on current Navigators' extensive institutional knowledge; navigating court technology flaws (e.g., TurboCourt errors); ensuring consistent service quality and availability across different locations and times. | Potential for Navigator burnout due to high workload and emotional toll of cases. Risk of losing significant institutional knowledge if current Navigators leave. Risk of providing incorrect legal information, potentially harming SRL cases and procedural justice. Inconsistent service availability leading to perceptions of inequity. Data privacy concerns related to proposed data collection expansion. Court technology failures (e.g., TurboCourt) hindering effective assistance. |
| 5U2GMFXt3YIJ.pdf | Google_Scholar | Man or Machine? An exploratory study of the performance of Chat GPT 3.5 in the CFC Sufficiency Exam | This paper evaluates ChatGPT 3.5's performance on Brazilian accounting proficiency exams, finding it achieved passing scores on all tests. While demonstrating AI's potential in accounting, the study highlights the need for human oversight due to risks of inaccuracies and plagiarism. | False | NaN | True | 2.0 | NaN | ChatGPT 3.5 (with some comparison to GPT-3) | ChatGPT 3.5 (and GPT-3 for comparison) was prompted with questions from the Brazilian Federal Council of Accounting's (CFC) 1st Sufficiency Exam of 2022 and 1st Technical Qualification Exam of 2023. Multiple-choice answers were directly evaluated; essays were assessed for quality criteria including originality using Plagiarism Detector.net. | ChatGPT 3.5 scored above the 50% passing threshold on all exams: 74% on the Sufficiency Exam. For the Technical Qualification exams, scores were: QTG 64%, CVM 56%, BCB 52%, SUSEP 56%, and PREVIC 80%. Essays were generally coherent but showed some inaccuracies and variable originality scores. | NaN | NaN | NaN | NaN | NaN | Brazil | ChatGPT 3.5 was pre-trained by OpenAI on diverse public domain text data from the internet, with a knowledge cut-off around September 2021. The study used exam questions from 2022 and 2023 as input prompts. | NaN | NaN | True | False | Accessible via the OpenAI website (https://chat.openai.com/). The paper implies use of a free access tier for ChatGPT 3.5 at the time of research (March-June 2023). | NaN | Ensuring accuracy and avoiding plausible-sounding but incorrect answers from the model. Overcoming the model's knowledge cutoff (post-September 2021 events). Formatting complex inputs (e.g., tables from exams) for the model. Issues with originality and potential plagiarism in generated essay responses. Some difficulties in the AI understanding nuanced aspects of questions, leading to minor errors in essay framing. | Generation of plausible-sounding but incorrect or nonsensical answers. Potential for user misinterpretation or over-reliance leading to errors. Ethical concerns about AI-generated content, including plagiarism, ownership of AI-generated content, and the role of knowledge workers. Regulatory concerns regarding AI-generated data and its use. |
| x_J2nC_89RsJ.pdf | Google_Scholar | The Impact of Artificial Intelligence on the Evolution of Digital Education: A Comparative Study of OpenAI Text Generation Tools including ChatGPT, Bing Chat, Bard, and Ernie | This paper reviews the use of AI text generation tools (ChatGPT, Bing Chat, Bard, Ernie) in digital education, comparing their capabilities and applications. It discusses potential benefits for teaching and learning alongside significant challenges, particularly regarding academic integrity and ethical considerations. | True | NaN | True | 2.0 | Neutral | Comparative analysis of Large Language Models / AI Text Generation Tools: ChatGPT (GPT-3.5 & GPT-4), Bing Chat, Google Bard, Baidu Ernie. | Comparative evaluation using a set of questions posed to ChatGPT 3.5, ChatGPT 4, Bing Chat, and Bard. Analysis combined with review of existing literature. Access to Ernie was not possible. | ChatGPT-4 demonstrated enhanced performance in the authors' comparison, providing more comprehensive, detailed, precise responses and better understanding of context and complex topics compared to Bing Chat. Bing Chat provided briefer responses and integrated web search. Bard was noted for conversational ability but also hallucinations. Ernie could not be tested. | Within the educational context: potential for academic dishonesty (plagiarism, cheating); generation of inaccurate or biased information; difficulty detecting AI output; need for ethical guidelines and pedagogical adaptation; lack of digital literacy; ensuring equitable access; data privacy concerns. | Within the educational context: Develop clear policies and ethical guidelines; adapt assessment methods; provide digital literacy training for educators and students; integrate AI tools constructively; improve AI detection; foster stakeholder collaboration; research effective integration and bias mitigation. | Digital education, AI in education, large language models, ChatGPT, academic integrity, assessment methods, personalized learning, teacher support, ethical AI use, digital literacy. | Higher education community (students, educators, institutions). | NaN | International | Large corpora of text and code (e.g., 45TB for GPT-3, billions of parameters mentioned for GPT-3 and Ernie). Includes diverse sources like web text, books, articles, conversations. Data sources are generally large-scale, mixed public/proprietary, unstructured/semi-structured. Specific models leverage web data (Bing Chat) or human feedback (RLHF for ChatGPT). | The paper employs a comparative literature review approach combined with direct testing of accessible chatbot tools (ChatGPT, Bing Chat, Bard) using a set of questions to evaluate and compare their performance and features in an educational context. | NaN | True | False | ChatGPT (free tier via OpenAI website), Bing Chat (via Microsoft products), Google Bard (via Google website). | Lack of clear institutional policies for AI use in education; insufficient digital literacy training; need for research on effective pedagogical integration and assessment adaptation; inadequate AI detection methods; need to address AI bias and inaccuracies; limited inclusion of student perspectives; inability to test all compared tools (Ernie) due to access restrictions. | For the reviewed LLMs: Generating incorrect/biased information (hallucinations); potential for misuse (academic dishonesty); context understanding limitations; real-time information access limitations (for some models); ethical concerns (bias, privacy, safety). For the authors: Inability to access Baidu's Ernie for direct comparison. | Undermining academic integrity; spread of misinformation/bias; deskilling students (e.g., critical thinking); exacerbating inequalities due to access issues; data privacy violations; potential job displacement in education and related fields; over-reliance on AI; ethical hazards inherent in large language models. |
| xJJvD-ECVPIJ.pdf | Google_Scholar | Access to Civil Justice in the Age of AI: Mindsets & Pathways to New Practices | This paper explores how Artificial Intelligence, particularly generative AI, can enhance access to civil justice by enabling new, scalable legal service models focused on legal information products. It argues that lawyers must adopt new mindsets and innovate beyond traditional practice to effectively leverage AI and address the justice gap in the PeopleLaw sector. | True | Idealistic | True | 3.0 | Positive | Using generative AI (e.g., ChatGPT, GPT-4) for creating, simplifying, organizing, and diversifying legal information products for consumers. | NaN | NaN | High cost (affordability) of traditional legal services; Lack of scalability in the one-to-one lawyer service model; Limited funding and reach of legal aid; Neglect of the 'missing middle' income group; Difficulty navigating the complex legal system without help; Prevalence of poor quality online legal information ('sea of junk'). | Lawyers adopting new mindsets (learning, adaptation, innovation); Leveraging generative AI for practice efficiency and cost reduction; Developing scalable legal information products (handouts, guides, videos) using AI; Implementing new business models (freemium, tiered pricing, subscription) centered around information products; Courts providing self-help resources and simplified procedures. | Bridging the justice gap in civil law; Providing affordable legal help (information and services); Serving self-represented litigants; Innovating legal service delivery models in the PeopleLaw sector. | Low-income individuals (including the ALICE population) and the 'missing middle' (middle-class individuals often priced out of legal services). | Civil Justice (general), Family Law (divorce, custody, support mentioned as examples), Small Business Law (implied by PeopleLaw sector). | United States | NaN | NaN | Discusses potential business models for lawyers (freemium, tiered pricing, subscription models) to deploy AI-assisted legal information services. | False | False | NaN | Need for lawyers to overcome resistance to change and adopt new mindsets/business models; Ensuring quality and reliability of AI-generated legal information; Addressing regulatory frameworks around AI in legal services; Scaling legal advice, not just information; Need for continued focus on simplifying legal processes. | Understanding AI capabilities and limitations; Integrating AI ethically and competently into legal practice (duty of competence); Overcoming professional inertia and traditional practice models; Rethinking the value proposition beyond bespoke legal advice. | AI 'hallucinations' leading to inaccurate legal information or citation of non-existent cases; Lawyers violating ethical duties (competence) through improper AI use; Potential for AI to exacerbate low-quality online information if not curated; Broader societal risks associated with advanced AI (job transformation, unforeseen consequences). |
| 7dlcyVpmL_gJ.pdf | Google_Scholar | ІNFORMATION AND LEGAL SUPPORT FOR BALANCING THE INTERESTS OF JUSTICE AND HUMAN RIGHTS PROTECTION | The paper discusses balancing effective justice administration and human rights protection in Ukraine's criminal justice system. It proposes leveraging modern IT, including AI models like GPT-4 and associative rule mining, to analyze case data and support judicial decision-making in sentencing, while acknowledging potential risks. | True | Idealistic | True | 1.0 | Neutral | Using multimodal language models (like GPT-4) for text generation/analysis combined with associative rule models (mining) to extract relationships from unstructured case data for sentencing support (risk assessment, societal danger, analysis of similar cases). | NaN | NaN | Balancing effective justice administration with human rights protection; Manual analysis of large volumes of unstructured text data in criminal proceedings is labor-intensive, inefficient, and subject to human bias; Potential risks of IT implementation like privacy violations, digital divide, and misuse for surveillance or rights violations. | Leveraging modern IT (electronic document management, integrated databases, videoconferencing, etc.); Proposing the use of AI (GPT-4, associative rules) to automate analysis of unstructured text data for sentencing decisions; Emphasizing the need for legal frameworks, regulatory control, cybersecurity, training, and resources for IT implementation. | Sentencing support, Risk assessment (recidivism, danger to society), Analysis of similar cases, Balancing efficiency and human rights in criminal justice, Justice system transparency. | NaN | Criminal Law, Criminal Procedure | Ukraine | The paper mentions analyzing "large collections of unstructured text documents" from criminal proceedings. The specific source, availability, and nature (beyond unstructured text) are not detailed. | NaN | NaN | False | False | NaN | Need for effective tools to analyze large amounts of unstructured legal text efficiently and objectively; Need for improved risk assessment tools for sentencing; Need for strategies to implement IT in justice effectively while mitigating risks (privacy, digital divide, misuse). | Ensuring privacy and data protection; Bridging the digital divide; Preventing misuse of IT for surveillance or rights violations; Establishing proper regulatory control; Ensuring cybersecurity; Providing adequate staff training and resources; Overcoming inefficiency and bias in manual analysis of unstructured case data. | Privacy violations; Breach of personal data protection; Creation of a digital divide; Potential for systematic human rights violations; Potential for mass surveillance through IT systems. |
| HjuAmkWUb1QJ.pdf | Google_Scholar | Equitable Access to Justice: Logical LLMs Show Promise | This paper explores integrating Large Language Models (LLMs) with logic programming to enhance their reasoning capabilities for legal applications, aiming to improve access to justice. It demonstrates that OpenAI's o1-preview model significantly outperforms GPT-4o in translating a health insurance contract into logical Prolog code, suggesting potential for creating 'computable contracts'. | True | Idealistic | True | 1.0 | Positive | Using LLMs (GPT-4o and OpenAI o1-preview) to automatically generate logical representations (Prolog code) of a health insurance policy to create "computable contracts", enabling automated reasoning about policy coverage. | GPT-4o and OpenAI o1-preview were prompted to translate a simplified health insurance policy into Prolog code. Then, both models were prompted to translate nine natural language yes/no questions about the policy into Prolog queries on their respective encodings. The number of correct answers from executing these queries was recorded over ten trials. | OpenAI o1-preview averaged 7.5 out of 9 correct answers across ten trials when its generated Prolog code for the insurance policy was queried. GPT-4o averaged 2.4 correct answers. | High cost of legal services, complexity of the judicial system, widespread distrust of attorneys, large number of self-represented litigants, and consumer difficulty in understanding legal documents like insurance policies. | Developing reliable and transparent technological solutions using AI (LLMs combined with logic programming) to create 'computable contracts' that simplify understanding and automate interpretation of legal documents like insurance policies, thereby scaling the encoding of legal text into logic programs. | Understanding legal documents (insurance contracts), automated legal reasoning for contract interpretation, improving consumer access to information about their legal rights and obligations under contracts. | General public/consumers, especially those facing challenges in understanding complex legal documents like insurance policies, and self-represented litigants. | Contract law, Insurance law (specifically health insurance). | USA (reference to American judicial system and California statistics; the example insurance policy specifies New York law). | The LLMs (GPT-4o, OpenAI o1-preview) are pre-trained models; their specific training data is not detailed in the paper. The input for the experiment was a simplified version of the Chubb Hospital Cash Benefit insurance policy text, provided in the paper's appendix. | Experimental comparison of LLM outputs (Prolog code) generated from a legal text (insurance policy). Evaluation involved qualitative analysis of the code's logical structure and interpretability, and quantitative empirical testing based on the accuracy of answers to nine natural language questions translated into Prolog queries, run over ten trials. | NaN | False | False | NaN | Technical gaps include the accuracy and quality of LLM-generated logic (risk of misinterpretation, omission, inconsistency, overgeneralization), LLM struggles with legal nuances and temporal relationships, and potential biases in training data. Societal gaps include the need for consistent, transparent, reliable, and trustworthy AI solutions for legal applications. | Ensuring the accuracy and quality of logical representations generated by LLMs from legal texts. LLMs may misinterpret terms, omit details, create logical inconsistencies, or overgeneralize. They also struggle with legal nuances, ambiguities, and conditional/temporal relationships. Potential biases in LLM training data can affect the validity of the generated logic. | LLMs may produce hallucinations and inconsistent answers. They can misinterpret legal terms, omit critical details, generate logical inconsistencies, or overgeneralize legal principles. Biases in their training data could compromise the validity of the generated legal logic, potentially leading to incorrect legal interpretations. |
| uySo4wqTXNAJ.pdf | Google_Scholar | Design at the Center for Future Strategy Making: Generative AI as an Affordance Catalyst | This paper investigates how Generative AI (Gen-AI) acts as a technological affordance to make design central to strategic decision-making for top management. Based on qualitative interviews and archival data, it proposes a framework where Gen-AI enhances the entire design process, fostering human-AI collaboration and improving strategic performance. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Strategic Management / General Business (covering Finance, Manufacturing, Software, Education, IT, Transport, Legal Services) | International | NaN | NaN | NaN | False | False | NaN | NaN | Need for training/upskilling; ethical concerns (bias, privacy, misinformation, responsibility); integrating Gen-AI with human skills and existing processes; potential tension between analytical and design-based strategy approaches; rapid evolution of technology; need for governance frameworks. | AI bias, data privacy breaches, data security issues, misinformation generation, negative societal implications. |
| -1qPa25dIGUJ.pdf | Google_Scholar | Lex Ex Machina: Forging a New Ethical Framework for AI and Technology in the Law | The paper argues that current legal ethics rules, particularly regarding competence, are insufficient to address the challenges and opportunities presented by Generative AI (GAI). It proposes a new, detailed, yet flexible ethical framework ('Rule X' and commentary) to guide legal professionals in the responsible use of GAI and other advanced technologies, emphasizing continuous learning and balancing innovation with ethical integrity. | True | Market | True | 1.0 | Positive | Proposed new ethical framework ('Rule X: Technology Use in Legal Practice' and associated commentary) for regulating technology use, particularly GAI, in legal practice. | NaN | NaN | The primary identified obstacle is the inadequacy and vagueness of current legal ethics rules (e.g., ABA Model Rule 1.1 Comment 8) to provide sufficient guidance for lawyers using advanced technologies like GAI. Other related obstacles include the technology gap among legal professionals, potential for misuse (inaccuracy, bias, confidentiality breaches), and resistance to adopting new technologies. | Proposes the adoption of a new, detailed, flexible, and aspirational ethical framework (Rule X and commentary). This framework aims to guide lawyers on competence, confidentiality, client communication, fees, supervision, and other ethical duties concerning technology use, fostering tech literacy and responsible innovation. | NaN | NaN | Legal Ethics, Professional Responsibility, General Legal Practice | United States | NaN | Legal analysis, Review of existing regulations, Argumentation | NaN | False | False | NaN | Lack of specific, detailed, yet flexible ethical guidance for lawyers using advanced technologies like GAI within existing professional conduct rules. Existence of a 'technology gap' (disparity in tech proficiency) among lawyers. Potential for AI bias to exacerbate societal discrimination if unaddressed. | Ensuring data privacy and security when using AI tools, Verifying the accuracy of AI-generated content (avoiding 'hallucinations'), Addressing potential algorithmic bias in AI tools, Maintaining lawyer competence through continuous learning about evolving technology, Supervising subordinate lawyers and nonlawyers using AI, Communicating the use of AI and associated fees appropriately to clients. | Breach of client confidentiality; Use of inaccurate AI-generated information ('hallucinations') leading to incompetent representation or court sanctions; Perpetuation of societal biases through biased AI tools; Unauthorized practice of law if AI replaces lawyer judgment; Charging unreasonable fees related to AI use; Creation of unintended attorney-client relationships via AI chatbots; Failure to supervise subordinate use of AI; Misleading courts or opposing counsel. |
| IDsoJMvT-Q8J.pdf | Google_Scholar | TOWARDS HIGH -QUALITY, PRIVACY -FOCUSED BLOG GENERATION: AN OPEN -SOURCE APPROACH USING LLAMA -2 | The paper presents BlogGen, a system using a fine-tuned Llama-2 model for generating customized, high-quality blog content. It highlights the benefits of using an open-source model like Llama-2 over proprietary alternatives like GPT-3.5, focusing on customization, privacy, and control. | True | Market | True | 1.0 | NaN | BlogGen: A system fine-tuning the open-source Llama-2 LLM for blog generation, featuring a Streamlit-based UI for user input (topic, audience, length). | Evaluation focused on content relevance, coherence (logical flow, structure), and user satisfaction assessed via feedback surveys across various test cases and user demographics. | BlogGen consistently generated content aligned with user specifications (topic, audience type, tone) and produced well-structured outputs. User feedback indicated satisfaction with accuracy and engagement. | NaN | NaN | NaN | NaN | NaN | NaN | Publicly available data from StackExchange and Kaggle focusing on blog-related prompts and responses. Unstructured text data, preprocessed and augmented. | Fine-tuning of a pre-trained LLM (Llama-2), data preprocessing (tokenization, normalization, noise removal), data augmentation (paraphrasing, synonym replacement), user interface development (Streamlit). | Implemented as a web application with a Streamlit user interface. | False | False | NaN | NaN | General LLM challenges mentioned include hallucination (factual inaccuracy), ensuring coherence and relevance, and ethical considerations (privacy, bias, misinformation, authorship). Specific challenges in development were not detailed beyond the need for effective fine-tuning and data preparation. | Hallucination (generating factually incorrect information), propagation of bias or misinformation from training data, data privacy concerns (mitigated by using self-hostable Llama-2), challenges to authorship and credibility of generated content. |
| hUHRaiZY4LYJ.pdf | Google_Scholar | Judicial Administration 4.0: strengths, \nchallenges and opportunities of a proactive \napproach to AI regulation in Spain | This paper discusses the benefits and risks of predictive and generative AI in the Spanish judicial system, particularly in criminal procedures, crime prevention, and judicial efficiency, advocating for a proactive regulatory approach. It examines whether binding regulations or soft law (codes of conduct) are sufficient to mitigate risks to fundamental rights while promoting compliance. | True | NaN | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Criminal procedural law, Criminal justice | Spain | NaN | NaN | NaN | False | False | NaN | Lack of consolidated ethical standards governing the use of predictive or generative AI in the legal field in Spain (though a policy document was recently approved). Insufficiencies in the current legislative framework (e.g., Royal Decree-Law 6/2023 lacks specificity on automated, proactive, or assisted judicial actions). | Ensuring AI neutrality and avoiding bias; malicious manipulation; opacity diminishing transparency and fairness; erosion of reasoning in judicial decisions; assigning responsibility for AI errors; replication and intensification of judicial biases; difficulties in challenging AI outputs due to 'black box' nature; potential infringement of judicial principles like contradiction of parties, impartiality, and party-driven justice when judges use AI-generated data not admitted as evidence. | Violation of citizens' fundamental rights (e.g., privacy, honor, due process, effective judicial protection); discriminatory outcomes from biased AI; undermining fairness and validity of judicial outcomes if parties cannot challenge AI training data or algorithms; erosion of judicial independence and impartiality; potential for AI to be used for indiscriminate surveillance and identification without consent. |
| _y_E2nhQTJwJ.pdf | Google_Scholar | Reading Law with ChatGPT (With Special Emphasis on Contextual Canons) Version of Apr. 3, 2024 | This paper evaluates the performance of ChatGPT in interpreting legal prompts related to 'Contextual Canons' from Scalia & Garner's "Reading Law." The findings indicate that ChatGPT is exceptionally successful in applying these canons to specific legal scenarios, offering sound and detailed legal reasoning. | True | Market | True | 2.0 | NaN | ChatGPT (free version, specifically mentioned as GPT-3.5) | Qualitative evaluation of ChatGPT's responses to prompts based on 14 contextual canons of legal interpretation, with scenarios adapted from Scalia & Garner's 'Reading Law'. The author engaged in personal interaction with the free version of ChatGPT. | ChatGPT was found to be 'exceptionally successful' in taking contextual canons into account, providing sound, detailed, and often court-aligned legal reasoning for the presented scenarios across all 14 tested canons. | NaN | NaN | NaN | NaN | Statutory Interpretation, applying to various fields including criminal law, property law (leases), employment law, family law, torts, and administrative law. | United States (examples from New York, Arizona, Missouri, Montana, Minnesota, Texas, South Dakota, Pennsylvania, District of Columbia, Federal law) | NaN | NaN | NaN | True | False | The paper states the author used the free version of ChatGPT, available at https://chat.openai.com. | NaN | The author noted surprise at how well simple prompting worked, implying complex prompt engineering was not a significant challenge. No other major challenges in using or evaluating ChatGPT were detailed. | NaN |
| moqz6a30twoJ.pdf | Google_Scholar | Development of AI Prototype for Generating Construction Safety Guidelines Through Fine-Tuning of Large-Scale Language Model | This paper develops an AI chatbot prototype to generate construction safety guidelines by fine-tuning a Korean large language model (KoAlpaca-Polyglot-12.8B) on a custom QA dataset derived from official safety documents. The fine-tuned model demonstrated improved performance in providing specific and accurate construction safety information compared to general LLMs like GPT-4. | False | Idealistic | True | 1.0 | Positive | Fine-tuning the KoAlpaca-Polyglot-12.8B large language model using Parameter-Efficient Fine-Tuning (PEFT) with Low-Rank Adaptation (LoRA) on a custom construction safety Question-Answering (QA) dataset to create a guideline-generating chatbot. | Qualitative comparison of response accuracy and expertise against GPT-4, Palm2, and baseline KoAlpaca. Quantitative evaluation using BLEU score and BERT Similarity score on 100 construction safety questions against reference answers. | The fine-tuned model achieved the highest BERT Similarity score (0.8416) and highest BLEU scores (Unigram: 0.3497, Bigram: 0.2604), outperforming GPT-4 by 7.21% in BERT Similarity and 7.5% in BLEU (unigram). Qualitatively, it provided more specific and accurate answers on construction safety topics. | Difficulties accessing, understanding, and applying construction safety guidelines provided in static formats (PDFs); Lack of safety capacity and resources (e.g., managers) in small to medium-sized construction sites; High accident rates due to non-compliance on these sites; Limitations of general-purpose LLMs in providing accurate, domain-specific safety knowledge. | Develop a specialized AI chatbot fine-tuned on construction safety guidelines using PEFT-LoRA; Provide easy access to specific and accurate safety information tailored to job conditions via the chatbot; Overcome limitations of general LLMs for domain-specific tasks. | Construction safety regulations and guidelines application; Workplace accident prevention in construction. | Small to medium-sized construction sites/companies and their managers/workers. | Construction Law, Occupational Safety and Health Law/Regulations | South Korea | A custom dataset of 5,114 Korean question-answer pairs generated via prompt engineering using GPT-3.5-turbo based on 86 publicly available construction safety guideline PDF documents from the Korean Occupational Safety and Health Agency (KOSHA) covering 2010-2023. Manually reviewed by researchers. | Prompt engineering (Chain of Thought, Self-Consistency); Parameter-Efficient Fine-Tuning (PEFT); Low-Rank Adaptation (LoRA); Qualitative evaluation; Quantitative evaluation (BLEU, BERT Similarity). | NaN | False | False | NaN | Dataset may not reflect the absolute latest regulations; Potential for errors in the AI-generated dataset despite review; Evaluation metrics (BLEU, BERT Similarity) measure textual similarity, not practical safety/validity; Model's utility may be limited to QA based on guidelines; Need for real-world validation and potentially multimodal models. | Creating an accurate, domain-specific QA dataset; Efficiently fine-tuning an LLM with resource constraints; Ensuring accuracy and specificity of the fine-tuned model for safety information; Evaluating model performance in a specialized domain. | Generating incorrect or unsafe safety advice due to dataset limitations (outdated info, errors) or model flaws; Over-reliance on AI without proper validation leading to potential harm; Data security issues when handling sensitive information like accident reports. |
| cepniqfn3kwJ.pdf | Google_Scholar | Legal Tech Abolition: Using legal technology to free them all | The paper discusses the disproportionate impact of the US criminal justice system and the underutilization of the 821 Criminal History Amendment for sentence reduction. It describes the creation of a legal tech tool using Docassemble to help incarcerated individuals determine eligibility for this amendment and connect with Federal Public Defenders. | True | Idealistic | False | 1.0 | Positive | A website built using the open-source platform Docassemble to guide users through eligibility questions for the 821 Sentencing Amendment and facilitate requests for assistance from Federal Public Defender's Offices. | Informal feedback from assistant federal public defenders during development. The website was never launched, so no deployment testing occurred. | NaN | The access-to-justice gap for incarcerated individuals, including lack of awareness of relief mechanisms (like the 821 Amendment), lack of legal guidance, and communication barriers with counsel. Underutilization of the 821 Amendment. | Developing and deploying a user-friendly legal tech tool (Docassemble website) to inform incarcerated individuals about 821 Amendment eligibility and connect them with legal assistance (Federal Public Defenders). | Sentence reduction (821 Amendment), Post-conviction relief, Access to legal information, Access to legal assistance for prisoners. | Incarcerated individuals in the US federal system potentially eligible for sentence reduction under the 821 Amendment, noting disproportionate impact on Black and Latino individuals. | Criminal Law, Federal Sentencing Law, Post-Conviction Procedure. | United States (Federal) | NaN | Use of open-source platform (Docassemble), iterative design based on stakeholder feedback (federal public defenders), development of guided user interview, multi-language support (English/Spanish). | Technical deployment plan using Amazon Lightsail and Docker developed, but the website was never launched. | False | False | NaN | Underutilization of legal relief by eligible prisoners due to lack of awareness/assistance. Need for better communication channels between legal aid and prisoners (especially those outside federal facilities). Lack of legal tech focused specifically on prisoners' needs. | Securing ongoing funding and resources (led to the project not being launched). Implicit challenges: Complexity of federal sentencing law, reaching incarcerated individuals, tool maintenance. | The paper primarily discusses risks of other AI applications in criminal law (biased algorithms in predictive policing, bail decisions, facial recognition; increased surveillance in prisons), rather than specific risks of the proposed Docassemble tool. Implicit risks include inaccurate legal information or eligibility assessment. |
| 3-4xM4xi2w4J.pdf | Google_Scholar | Interoperable Legal AI for Access to Justice | The paper argues that siloed progress in AI for consumers, legal providers, and courts is insufficient to close the access-to-justice gap without coordination and interoperability. It advocates for courts to lead the development of interoperable legal AI systems, using examples like Brazil, to enhance fairness and scalability. | True | Idealistic | False | 1.0 | Positive | Interoperable legal AI driven by courts, encompassing technical, organizational, legal/policy, semantic, and socially informed interoperability. | NaN | NaN | Growing access-to-justice gap; procedural barriers; underresourced courts and public defenders; lack of coordination across consumer/provider/court fronts; regulatory uncertainty (UPL, ownership); jurisdictional variations; data silos; funding disparities; potential for AI bias and inequality magnification. | Achieve technological and procedural legal interoperability driven by courts; adopt common standards and open-source software; establish collaborative governance structures; implement data integration/standardization; pursue legal regulatory reform (e.g., national sandboxes); align with AI governance principles. | Access to justice (civil and criminal), court procedures (filing, case management, ODR), self-help legal tools, legal service delivery efficiency, legal data standardization, regulatory reform. | Low-income Americans, self-represented litigants, individuals facing economic or social barriers to legal help. | Civil Law, Criminal Law, Administrative Law, Court Administration | United States (with comparative examples from Brazil and the European Union) | NaN | NaN | NaN | False | False | NaN | Lack of nationwide scale and impact in AI for justice; funding gap between commercial and A2J tech; lack of coordination, standards, and interoperability; insufficient regulatory reform; absence of comprehensive, standardized, and accessible court data; need for bias mitigation in AI design; persistence of the justice gap and risk of a two-tiered system. | Regulatory fragmentation across jurisdictions; lack of funding for courts and A2J technology; institutional inertia (courts as followers); complexity of the US federalist system; achieving stakeholder buy-in; need for interdisciplinary expertise; difficulties in data standardization. | Automating bias; magnifying inequality; entrenching a two-tiered justice system; inaccurate or harmful AI outputs (e.g., racist statements, flawed risk assessments); undermining fairness and the legitimacy of the legal system. |
| VK3sHji0IeoJ.pdf | Google_Scholar | ARTIFICIAL INTELLIGENCE AND LAW — AN OVERVIEW OF RECENT TECHNOLOGICAL CHANGES : KEYNOTE ADDRESS AT THE 2024 IRA C. ROTHGERBER J R. & SILICON FLATIRONS CONFERENCE ON ARTIFICIAL INTELLIGENCE AND CONSTITUTIONAL LAW | This paper, a keynote address, offers an overview of artificial intelligence, detailing its historical evolution with a focus on recent advancements in large language models like GPT-4. It explores their applications and limitations within the legal field, particularly constitutional law, while stressing the importance of AI literacy for legal professionals and expressing cautious optimism about AI's potential to enhance the legal system. | True | Idealistic | True | 2.0 | Positive | Primary focus on Large Language Models (LLMs) such as OpenAI's GPT series (GPT-3, GPT-3.5, GPT-4, GPT-4o), Anthropic's Claude, and mentions of Google's Gemini Ultra and Meta's Llama 3. Also discusses specialized legal AI systems like Lexis+ AI and Westlaw CoCounsel, which utilize underlying LLM technology. | The author evaluates LLMs through illustrative examples and personal experimentation. This includes posing commonsense questions (e.g., "How many legs does an apple have?"), legal queries (e.g., a Third Amendment scenario), requesting legal document drafting (e.g., merger agreement, motion for summary judgment), and analyzing legal texts (e.g., insurance contracts, torts fact patterns). Comparisons between models (e.g., GPT-4 vs. Claude) are also used. | GPT-4 is presented as significantly more capable than its predecessors, able to produce comprehensive legal document drafts, perform sensible legal analysis, and answer complex questions. However, it's noted that even advanced LLMs can 'hallucinate,' provide outdated information, make reasoning errors, and generate conflicting answers depending on the model or prompt. | Lack of AI literacy among legal professionals; inherent limitations of AI (e.g., hallucinations, potential for bias, lack of transparency, sensitivity to prompts); risk of over-reliance and misinterpretation of AI outputs; privacy and confidentiality concerns with certain AI usage models; the potential for AI to make non-transparent value judgments in legal interpretation. | Enhancing AI literacy for legal professionals through education and hands-on experience; promoting careful, supervised use of AI tools with thorough verification of outputs; utilizing specialized legal AI systems for better reliability, security, and access to curated legal data; focusing on high-quality training data and improved AI architectures for future models; fostering a thoughtful societal adoption of AI to make the legal system more transparent, equitable, and accessible. | Improving transparency, fairness, and general accessibility of the legal system; legal analysis; legal document generation; legal research; contract analysis; constitutional law interpretation. | General public / society at large, with the aim of a fairer and more accessible legal system for all. | Constitutional law, contract law, torts law, general legal practice, legal research, and document drafting. | United States (e.g., references to U.S. Constitution, Colorado Governor), though many principles discussed have international relevance. | Large-scale, primarily unstructured text data from diverse sources such as unpublished books, public webpages, Wikipedia, and other internet content. The paper notes a trend towards using higher-quality, curated data like textbooks and research papers for training newer models. | Machine learning, particularly deep learning utilizing neural networks and the transformer architecture. The development of models like ChatGPT also involves engineering improvements based on training with large datasets and techniques to enhance instruction following and problem-solving capabilities. | General-purpose LLMs like ChatGPT are accessible via web-based interfaces and apps. Specialized legal AI systems (e.g., Lexis+ AI, Westlaw CoCounsel) are offered as commercial products to legal professionals. Some models (e.g., Llama 3) are available as open-weights. | True | False | General-purpose LLMs like ChatGPT (available via OpenAI with free and paid subscription tiers) and Claude (available from Anthropic) are accessible online. Specialized legal AI systems such as Lexis+ AI and Westlaw CoCounsel are commercially available through subscriptions. | Technical gaps include the need for improved reliability, reduction of hallucinations, enhanced transparency and interpretability, better bias mitigation, and reduced sensitivity to prompt variations in LLMs. Societal gaps involve fostering widespread AI literacy, ensuring equitable access to and fair application of AI in the legal domain, and addressing the challenge of AI making implicit value judgments without clear human oversight. | Key challenges include ensuring the reliability and accuracy of LLM outputs (avoiding hallucinations and outdated information); managing and mitigating biases present in training data; overcoming the lack of transparency ('black box' nature) in how LLMs arrive at conclusions; addressing the sensitivity of LLMs to prompt phrasing; and managing the significant computational and hardware requirements for training and deploying advanced models. | Inaccurate AI-generated outputs (hallucinations) leading to errors in legal work and potential professional sanctions; reliance on outdated information; propagation of biases embedded in training data; breaches of privacy and client confidentiality, especially with non-enterprise AI versions; over-reliance on AI by legal professionals without critical evaluation; lack of transparency in AI’s decision-making, particularly concerning for legal and constitutional interpretation where AI might make implicit, unscrutinized value judgments. |
| oiF84vWI26YJ.pdf | Google_Scholar | CERTIFYING LEGAL AI ASSISTANTS FOR UNREPRESENTED LITIGANTS: A GLOBAL SURVEY OF ACCESS TO CIVIL JUSTICE, UNAUTHORIZED PRACTICE OF LAW, AND AI | This paper surveys global approaches to AI, unauthorized practice of law (UPL), and access to civil justice for unrepresented litigants. It proposes a capability-based framework using public benchmarks to certify legal AI assistants, allowing their exemption from UPL rules to improve access to justice. | True | Idealistic | True | 1.0 | Positive | A capability-based framework for certifying legal AI assistants based on testing accuracy against public benchmark datasets for specific legal tasks. | The paper proposes a framework that requires testing AI capabilities against public benchmark datasets (e.g., LegalBench, LawBench, JEC-QA, SARA) using metrics like f-measure, Bleu, MCC, or Task Success Rate to meet predefined accuracy thresholds. It does not test a specific tool itself. | NaN | The large number of unrepresented litigants lack access to affordable legal help; restrictive Unauthorized Practice of Law (UPL) rules prevent potentially helpful AI tools; risk of harm from inaccurate advice provided by unregulated AI; absence of a standardized certification framework for legal AI. | Amend UPL rules to explicitly exempt certified legal AI assistants; establish a capability-based certification framework evaluating AI accuracy on specific tasks using public benchmarks; create a third-party body to manage certification; foster collaboration and investment in developing necessary benchmark datasets. | Access to legal information, guidance, advice; navigating legal procedures; drafting legal documents; case outcome prediction; dispute resolution for unrepresented litigants in civil justice. | Unrepresented litigants (self-represented litigants, litigants in person, pro se litigants), particularly those with limited financial resources. | Civil Justice (broadly, including examples from housing, consumer law, family law (protection orders), small claims). | Global survey covering Argentina, Australia, Brazil, Canada (incl. provinces), China, European Union, Germany, India, New Zealand, Nigeria, Singapore, United Kingdom, United States (incl. 50-state/6-territory survey). Proposed framework intended for global adoption with local implementation. | NaN | NaN | NaN | False | False | NaN | Need for more public benchmark datasets specifically designed for unrepresented litigant tasks; need for international coordination among legal regulators on AI; inconsistent definitions of 'practice of law' and UPL across jurisdictions; technical issues like data contamination potentially affecting benchmark evaluations. | Defining the practice of law and UPL consistently; getting regulatory bodies to adopt UPL exemptions for AI; ensuring sufficient and representative benchmark datasets are created and maintained; addressing technical issues like data contamination in evaluating LLMs. | Unrepresented litigants receiving inaccurate legal guidance from AI, leading to negative outcomes; potential for data contamination to lead to overly optimistic evaluations of AI accuracy; bias in AI systems (mentioned generally in cited sources/context). |
| r84CfYBTdlEJ.pdf | Google_Scholar | Position: Stop Acting Like Language Model Agents Are Normal Agents | This paper argues that Language Model Agents (LMAs), built on LLMs, possess inherent pathologies (statelessness, stochasticity, semantic sensitivity, linguistic intermediation) that undermine key agentic properties like identity, continuity, persistence, and consistency. Treating LMAs like normal agents leads to problems with utility and trustworthiness, necessitating specific evaluation of their ontological properties. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | NaN | NaN | False | False | NaN | NaN | Fundamental challenges stem from the underlying LLM pathologies: statelessness (lack of inherent memory), stochasticity (unpredictable outputs), semantic sensitivity (small input changes cause large output shifts), and linguistic intermediation (interactions filtered through text tokens). These pathologies make it difficult to establish stable LMA identity conditions: identifiability, continuity, persistence, and consistency. | Unreliability and unpredictability, particularly in high-stakes applications; undermined trustworthiness; compromised utility due to inconsistent performance; potential for misuse via prompt manipulation or tool misuse; difficulties in AI alignment and governance due to unstable identity; known LLM risks like hallucination and jailbreaking propagating to agentic systems. |
| dGh5QjYACUIJ.pdf | Google_Scholar | Generative AI -Driven Storytelling: A New Era for Marketing | This paper explores the use of generative AI for storytelling in marketing, enhancing personalization and customer engagement. It discusses applications by companies like Netflix and Google, highlights benefits, ethical challenges, data dependencies, and future research directions. | True | Market | True | 3.0 | Positive | Generative AI-driven storytelling using Recurrent Neural Networks (RNNs) and Transformers | NaN | NaN | NaN | NaN | NaN | NaN | Marketing, Business Ethics | International | Large datasets of existing narratives (e.g., articles, books, movies); customer data (preferences, demographics, engagement patterns). Importance of diverse, high-quality, unbiased datasets stressed. | NaN | Adoption by major tech and consumer companies (e.g., Google, Netflix, Amazon, Adobe, OpenAI, IBM) for marketing, recommendations, content creation, and bias mitigation tools. | True | True | Discusses commercial services (e.g., Netflix, Stitch Fix, Adobe Firefly) and publicly available tools/platforms (e.g., ChatGPT, Google Translate, IBM AI Fairness 360 toolkit). | Need for robust fairness metrics, effective bias identification/mitigation techniques (considering cultural context), research into human-AI collaboration for storytelling quality and ethics. | Ethical concerns (bias, manipulation, misinformation, copyright, deepfakes), dependence on high-quality and diverse training data, requirement for skilled professionals. | Generation of manipulative or misleading narratives, dissemination of false information, perpetuation of biases leading to discrimination, copyright infringement, creation/use of deepfakes, malicious use for targeted attacks. |
| eZOdC8SrDBkJ.pdf | Google_Scholar | Legal-Emotional BATNA: AI Chatbot Addressing Divorce Legalities and Emotional Complexities, and Research of Social Implementation in Japan | This paper introduces "Legal-Emotional BATNA," an AI chatbot designed to assist individuals in Japan undergoing divorce negotiations by integrating legal calculations (support, asset division) with emotional support, using GPT-4 for emotion analysis. User surveys indicate general satisfaction but highlight needs for improved privacy measures and handling of complex emotional and legal issues. | True | Idealistic | True | 1.0 | Positive | "Legal-Emotional BATNA" AI chatbot integrating legal calculations (child/spousal support, property/pension division, solatium based on Japanese legal standards/precedents) and emotional aspect analysis (using GPT-4) to guide divorce negotiations. | Online user survey with 100 participants recruited via CloudWorks (Japanese crowdsourcing platform). Evaluation used demographics questions and a 5-point Likert scale assessing ease of use, trustworthiness, speed, clarity, accuracy, helpfulness, communication smoothness, privacy protection, and overall satisfaction, plus open-ended feedback. | Generally positive: >80% found it user-friendly (avg 1.65), trustworthy (avg 1.79), and were satisfied overall (avg 1.82). 77% expressed willingness to use it again. Areas needing improvement included privacy protection (avg 2.62) and desire for more personalized advice. | The complexity of divorce negotiations involving both legal calculations and emotional factors, lack of tools integrating both aspects, and the potential cost of traditional professional support. | An AI chatbot ("Legal-Emotional BATNA") that provides calculations based on legal standards and incorporates emotional considerations (using GPT-4 analysis) to offer early-stage guidance and bridge users to professional services. | Divorce negotiation support, Online Dispute Resolution (ODR) for divorce. | Individuals undergoing divorce negotiations in Japan. | Family Law (specifically divorce, child support, spousal support, property division, pension splitting, solatium/compensation for emotional distress). | Japan | Legal calculations use Japanese court standards ('Calculation Tables for Child Support / Expenses arising from Marriage') and legal precedents. Emotional analysis relies on GPT-4, implying its general training data. No specific proprietary dataset is mentioned. | A framework separating Legal-BATNA Calculation (using legal data, precedents, tables) and Emotional-BATNA Estimation (using GPT-4 emotion analysis). The system interacts with users, processes legal/emotional data, and provides integrated recommendations. | The chatbot is accessible via a chatgpt.com link (likely requiring ChatGPT access). Evaluation involved deployment on a crowdsourcing platform (CloudWorks). The paper discusses potential future "societal implementation in Japan". | True | False | Available as a custom GPT accessible via a specific chatgpt.com URL provided in a footnote. | Need for improvement in handling complex legal scenarios (e.g., international divorces, mixed-income households), enhancing privacy protection clarity, increasing financial accuracy depth, providing more personalized advice vs. general calculations, and managing user expectations regarding preliminary vs. definitive legal advice. | Balancing legal accuracy with nuanced emotional support, ensuring user trust regarding privacy and data handling, addressing user desire for personalized advice while maintaining scalability, managing complexity in legal calculations for non-standard cases. | Potential for users to misunderstand preliminary guidance as definitive legal advice. Privacy concerns related to handling sensitive personal and financial data during divorce negotiations. |
| Y8UaRGVOxgoJ.pdf | Google_Scholar | ChatGPT accuracy analysis for legal field and anticipation of potential problems | This paper evaluates the accuracy of different ChatGPT versions (3.5, 4.0, 4.0 Omni) on Korean legal questions, comparing performance against human police trainees. It finds improved accuracy with newer versions, particularly Omni, but highlights significant issues with inconsistency and the confident presentation of incorrect information. | True | NaN | True | 2.0 | Neutral | Evaluation of ChatGPT versions 3.5, 4.0, and 4.0 Omni. | 1) Accuracy assessment on 100 Korean legal questions (objective facts, simple questions, case questions). 2) Comparison of ChatGPT scores with 158 human police trainees on a 20-question multiple-choice test. | ChatGPT 4.0 Omni achieved the highest average accuracy (84.96%) on the 100 questions. On the multiple-choice test, Omni scored up to 80/100, comparable to post-training human average (84.53/100), but showed inconsistency (match rate 80% between two runs). | NaN | NaN | NaN | NaN | Korean Law (including criminal law, general legal principles relevant to policing) | Korea | NaN | NaN | NaN | True | False | ChatGPT 4.0 and 4.0 Omni are accessible via OpenAI's platform (potential subscription required). | NaN | Inconsistent answers from ChatGPT to the same questions asked at different times. | AI confidently providing incorrect answers (Uncertainty/hallucination); Difficulty in verifying the truthfulness of AI-generated answers (Possibility of error judgment); Inconsistency in AI responses; Potential devaluation of human knowledge and expertise; Exacerbation of data bias, privacy, and copyright issues. |
| EABTT6lmmYYJ.pdf | Google_Scholar | Sociological Phenomenology: Understanding Neighborhood Development and Local Culture | This paper proposes an approach integrating sociological phenomenology with AI technologies (big data analysis, LLMs, generative AI) to understand neighborhood development and local culture. The research suggests this combined methodology can provide deeper insights for urban planning and policy-making, fostering more culturally sensitive and inclusive community growth. | True | Idealistic | True | 1.0 | Positive | An integrated research approach combining sociological phenomenology with AI techniques, including big data analysis (with attention mechanisms), large language models (LLMs), generative AI for simulations, and prompt engineering. | A mixed-methods approach: qualitative ethnographic methods (in-depth interviews, participant observation, document analysis of local histories and community archives) to understand lived experiences, complemented by big data analysis of datasets (e.g., demographic, socioeconomic, real estate, social media data) using attention mechanisms and LLMs. Generative AI was used to simulate neighborhood development scenarios informed by resident and planner input. | AI-driven tools and big data analysis identified critical factors (e.g., gentrification patterns, employment changes, social network shifts) influencing neighborhood transformation and predicted areas of significant cultural change. LLMs extracted evolving narratives of neighborhood identity from textual data (social media, news). Generative AI simulations visualized potential cultural shifts under different conditions, offering insights for culturally sensitive urban planning. | Loss of cultural identity, community cohesion, and sense of belonging for residents due to neighborhood changes like gentrification if development is not sensitive to lived experiences and local culture. | Employing sociological phenomenology integrated with AI (big data, LLMs, generative AI) to deeply understand residents' lived experiences, cultural narratives, and social dynamics. This understanding can inform urban planning and policy-making for more culturally sensitive, inclusive, and equitable neighborhood development that preserves community heritage and identity. | Equitable urban development, preservation of local culture and community identity, mitigating negative impacts of gentrification, inclusive policy-making for neighborhoods, understanding social dynamics of urban change. | Residents of urban and rural neighborhoods undergoing development or transformation, particularly those whose cultural identities, social cohesion, and sense of place might be threatened by such changes (e.g., long-term residents in gentrifying areas). | NaN | International | The approach uses mixed data: Qualitative data from ethnographic methods (interviews, participant observation, local historical/cultural artifacts). Quantitative/textual data for AI analysis includes demographic information, socioeconomic indicators, real estate data, social media interactions, local news, and community forum discussions. | Mixed-methods research design combining qualitative sociological phenomenology (ethnographic methods like participant observation, interviews, document analysis) with advanced data analytics techniques (big data analysis with attention mechanisms, large language models, generative AI, prompt engineering). | NaN | False | False | NaN | The need to further refine methodologies to more effectively incorporate complex cultural nuances into AI-driven decision-making processes for neighborhood development. Ensuring AI tools are guided by ethical considerations to genuinely reflect and serve community values and lived experiences. | Ensuring AI models accurately understand and incorporate complex cultural nuances for relevant, insightful, and ethically sound data generation and simulation, highlighted by the stated need for prompt engineering and culturally sensitive approaches. | NaN |
| TUZpZNjejiYJ.pdf | Google_Scholar | CONCENTRATING INTELLIGENCE: SCALING AND MARKET STRUCTURE IN ARTIFICIAL INTELLIGENCE | This paper analyzes the market structure and competition dynamics for foundation models, particularly LLMs, highlighting significant economies of scale and scope that drive towards concentration. It discusses key inputs (compute, data, talent), explores competition risks like market tipping and vertical integration, and evaluates potential policy remedies primarily from an antitrust perspective. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Competition Law, Antitrust, Regulation | US, UK, EU, International | NaN | NaN | NaN | False | False | NaN | Potential for an 'intelligence divide' between regions; balancing competition promotion with AI safety; ensuring equitable distribution of AI benefits; lack of clarity in liability frameworks for AI; uncertainty regarding future AI capabilities and economic impact. | NaN | Market tipping leading to monopolies; anti-competitive effects of vertical integration (foreclosure, reduced innovation); regulation potentially stifling competition (especially for smaller players); AI safety risks (malfunctioning, malicious use, human disempowerment/extinction); lopsided liability burdens impacting human judgment; excessive concentration of economic, social, and political power. |
| bIi8Ve6d_s8J.pdf | Google_Scholar | Prompt Packer: Deceiving LLMs through Compositional Instruction with Hidden Attacks | This paper introduces Compositional Instruction Attacks (CIA), a method to bypass Large Language Model (LLM) safety measures by embedding harmful prompts within seemingly harmless instructions like dialogue generation or story writing. The authors propose and evaluate automated techniques (T-CIA and W-CIA) demonstrating high success rates in tricking models like GPT-4 and ChatGPT into generating harmful content. | True | NaN | True | 1.0 | NaN | Compositional Instruction Attacks (CIA), including automated methods Talking-CIA (T-CIA) based on adversarial personas and Writing-CIA (W-CIA) based on disguising prompts as novel writing tasks. | Evaluated on GPT-4, ChatGPT (gpt-3.5-turbo), and ChatGLM2-6B using safety assessment datasets (Safety-Prompts, Harmless Prompts) and harmful prompt datasets (Forbidden Question Set, AdvBench). Attack success measured by Non-Rejection Rate (NRR) and Attack Success Rate (ASR), primarily evaluated using ChatGPT judgments, validated partially with human evaluation. | High ASR achieved. T-CIA: >95% ASR on safety datasets, >83% (GPT-4) / >91% (ChatGPT/ChatGLM2) on harmful prompt datasets. W-CIA: >91% ASR on Harmful Behaviors dataset. NRR approached 100% for both methods. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Prompt engineering using specific instruction templates (APE, RUAP, DWPC, SDWP), in-context learning (one-shot example for W-CIA), leveraging psychological principles (similarity-attraction for T-CIA), iterative attack attempts. | NaN | False | False | NaN | LLMs lack the ability to identify underlying harmful intentions in multi-intent compositional instructions. LLM vulnerability to repetitive attacks due to random factors in the decoding stage. | Manually designing compositional attacks is labor-intensive and costly, motivating automated methods. Bypassing advanced LLM safety mechanisms (e.g., RLHF) is the core challenge. | Generation of harmful content (insults, bias, PII leakage, misinformation, criminal instructions), abuse of LLMs for malicious purposes (hate campaigns, internet fraud), negative social impacts. |
| UQg4PX163KgJ.pdf | Google_Scholar | DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning | This paper introduces DISC-FinLLM, a Chinese financial large language model developed using a Multiple Experts Fine-tuning Framework (MEFF) and a novel financial instruction-tuning dataset, DISC-FIN-SFT. The model is designed to enhance general LLMs with capabilities in financial consulting, NLP tasks, computation, and retrieval-augmented generation, showing improved performance over baseline models in various financial scenarios. | True | NaN | True | 1.0 | NaN | DISC-FinLLM, a Chinese financial LLM built using a Multiple Experts Fine-tuning Framework (MEFF). This involves training four individual Low-rank adaptation (LoRA) modules on a base LLM (Baichuan-13B) using specialized parts of the DISC-FIN-SFT dataset for financial consulting, financial NLP tasks, financial computing (with tool use), and retrieval-enhanced generation. | Evaluated on multiple benchmarks: 1) FinCUGE for financial NLP tasks (sentiment analysis, relation extraction, summarization, text classification, event extraction); 2) FinEval for human-generated multiple-choice questions (finance, economy, accounting, certificate); 3) A manually created dataset of over 100 financial calculation problems; 4) A dataset of financial questions requiring up-to-date information for retrieval-based tasks, with answers evaluated by GPT-3.5 on accuracy, usefulness, linguistic quality, and reflectiveness. | On financial computing tasks, DISC-FinLLM (Computing LoRA expert) achieved an accuracy of 0.35 for both formula construction and result calculation, significantly outperforming the base Baichuan-13B-Chat (0.12) and GPT-3.5-turbo (0.26). | NaN | NaN | NaN | NaN | NaN | Chinese | A custom financial instruction-tuning dataset named DISC-FIN-SFT (approx. 250k examples). It includes: Financial Consulting Instructions (from FiQA translated to Chinese, QA for financial terms, multi-turn QA from Chinese financial forums, all using ChatGPT); Financial Task Instructions (from public Chinese financial NLP datasets and self-constructed reading comprehension from financial news/reports); Financial Computing Instructions (handwritten/report-derived financial calculation questions, general math questions, augmented by ChatGPT); Retrieval-enhanced Instructions (financial news/reports with ChatGPT-generated questions/answers and retrieved references). | Multiple Experts Fine-tuning Framework (MEFF), Low-rank adaptation (LoRA) for parameter-efficient fine-tuning. Task-specific instruction dataset (DISC-FIN-SFT) creation using various prompting strategies with ChatGPT (e.g., self-chat, self-instruction, Chain-of-Thought, Chain-of-Retrieval). | During deployment, different LoRA modules can be loaded onto the base model to switch between functionalities without retraining. Resources, including the model, are made available via a GitHub repository. | True | True | Further resources can be found at https://github.com/FudanDISC/DISC-FinLLM. | NaN | Developing a model with specialized, task-oriented financial functionalities (consulting, NLP, computation, retrieval) that operate effectively without interference. Creating a large-scale, diverse, and high-quality instruction-tuning dataset (DISC-FIN-SFT) specific to the Chinese financial domain and its varied tasks. | NaN |
| 4y_1wDzhPbUJ.pdf | Google_Scholar | A Comprehensive Framework for Reliable Legal AI: Combining Specialized Expert Systems and Adaptive Refinement | This paper proposes a framework combining a mixture of expert systems, knowledge graph-enhanced retrieval-augmented generation (RAG), and reinforcement learning from human feedback (RLHF) to improve AI accuracy and reliability in legal tasks, addressing issues like hallucinations. This approach utilizes specialized modules and human feedback, aiming to offer more precise, accessible, and affordable legal services. | True | Idealistic | True | 1.0 | Positive | A framework integrating Mixture of Experts (MoE), Knowledge Graph (KG) enhanced Retrieval-Augmented Generation (RAG), and Reinforcement Learning from Human Feedback (RLHF). | Comparative evaluation of the framework (using LLMs like GPT-4, LLaMA-3 enhanced with RAG/KG/RLHF) against baseline and fine-tuned models on nine legal tasks using multiple public datasets (LegalQA, CaseHold, LEDGAR, LEXTREME, COLIEE, SARA, LexGlue, Billsum, CUAD, Super-SCOTUS, EUR-LEX, ECHR). Metrics included Accuracy, Rouge-L, F1 Score, BLEU, and abstention rates. | The integrated framework, particularly using advanced models like GPT-4 and LLaMA-3 enhanced with RAG and RLHF, consistently outperformed baseline and solely fine-tuned models across various legal tasks. For example, GPT-4 with RLHF achieved approximately 10% higher accuracy than with KG integration on complex tasks, demonstrating enhanced reliability and reduced abstention. | High cost and time consumption of traditional legal support limiting access; unreliability and potential for inaccuracies (hallucinations) in existing AI models hindering their effective use in legal contexts. | Utilize the proposed AI framework featuring MoE, KG-enhanced RAG, and RLHF to provide more reliable, accurate, scalable, and affordable legal assistance, thereby improving access to justice. | Making general legal assistance tasks (e.g., document review, research, contract drafting, Q&A, case analysis, judgment prediction) more accessible and affordable through reliable AI. | General population needing affordable legal services. | General / Multiple (covers tasks related to contracts, legislation, case law, etc.) | Mixed (Uses datasets coveringUS, European, and potentially other/general legal contexts). | The framework leverages multiple publicly available legal datasets for evaluation and potentially fine-tuning (LegalQA, CaseHold, LEDGAR, LEXTREME, COLIEE, SARA, LexGlue, Billsum, CUAD, Super-SCOTUS, EUR-LEX, ECHR), containing diverse structured and unstructured legal text. It relies heavily on retrieval from external knowledge sources (documents, KGs) rather than a single training dataset. | Modular integration of Retrieval-Augmented Generation (RAG), Knowledge Graphs (KG), Mixture of Experts (MoE), Reinforcement Learning from Human Feedback (RLHF), Supervised Fine-Tuning (SFT) with Low-Rank Adaptation (LoRA), and an 'Experts Collaboration Workflow' modeling human legal teams. | NaN | False | False | NaN | Need for expansion to more legal domains, integration of real-time legal updates, enhanced explainability, and ongoing refinement through collaboration with legal professionals. Persistent difficulty with tasks requiring precise verbatim reproduction. | Integrating multiple complex AI components (RAG, KG, MoE, RLHF); mitigating AI hallucinations and ensuring reliability in the legal domain; selecting and utilizing appropriate datasets; modeling complex legal reasoning; scaling human feedback processes (RLHF). | Generation of inaccurate or misleading information ('hallucinations') by AI, leading to serious legal consequences and undermining trust. Potential for toxic outputs if not properly managed (addressed via RLHF). |
| Pnl5_5sEE-gJ.pdf | Google_Scholar | LawPal : A Retrieval Augmented Generation Based System for Enhanced Legal Accessibility in India | This paper proposes LawPal, a Retrieval-Augmented Generation (RAG) based chatbot using DeepSeek-R1:5B and FAISS, to improve legal accessibility and literacy in India by providing accurate legal information. The system demonstrated over 90% legal accuracy in evaluations, aiming to democratize legal knowledge and combat misinformation. | True | Idealistic | True | 1.0 | Positive | LawPal, a Retrieval-Augmented Generation (RAG)-based legal chatbot using DeepSeek-R1:5B for language understanding and FAISS for document retrieval. | Retrieval accuracy (Precision@K, Mean Reciprocal Rank, Normalized Discounted Cumulative Gain), response quality (BLEU, ROUGE, Legal Consistency Score, human expert review), computational efficiency (query processing time analysis), robustness against adversarial inputs, and user feedback from lawyers, law students, and legal aid seekers. Comparative testing against rule-based chatbots and keyword-based search engines. | LawPal achieved over 90% legal accuracy. FAISS-based retrieval takes 10-50 milliseconds, and DeepSeek-R1:5B response generation ranges from 800 to 1500 milliseconds. User feedback indicated 85% satisfaction for accuracy and reliability. | Lack of awareness, misinformation, limited accessibility to judicial resources, difficulty for individuals in navigating complex legal frameworks, frequent misuse of laws, and inadequate legal protection. | Development of a Retrieval-Augmented Generation (RAG)-based legal chatbot (LawPal) to provide accurate, efficiently retrieved legal information, enhance legal literacy, and prevent the spread of misinformation. The platform also includes features like real-time legal news, blogs, and access to law-related books. | Legal information retrieval, enhancing legal literacy, combating legal misinformation, navigating complex legal frameworks, improving access to judicial resources. | The general public in India, particularly individuals struggling with legal complexities, and those with limited access to legal resources. | Indian Law, including the Indian Constitution, statutory laws, and case law. Specific examples like Criminal Law and Civil Law are mentioned for data categorization. | India | Publicly available legal texts from authoritative sources such as government websites, Supreme Court archives, legal research papers, legal books, official documentation, and the Indian Constitution. The dataset includes structured and unstructured texts, preprocessed with OCR and segmentation. | Retrieval-Augmented Generation (RAG) architecture, data collection from diverse legal sources, data preprocessing (cleaning, OCR correction, text normalization, chunking with LangChain’s RecursiveCharacterTextSplitter), vector embedding generation (DeepSeek-R1:5B), FAISS for efficient vector indexing and similarity search, prompt engineering, hierarchical indexing of legal topics, and a Streamlit-based user interface. | A Streamlit-based user interface was developed for user interaction. Broader deployment or diffusion strategies are not detailed. | False | False | NaN | Need for multilingual support for regional Indian languages, improved handling of multi-jurisdictional queries, enhanced capability for processing long-context legal arguments, further fine-tuning for specialized legal domains (e.g., corporate and international law), and continuous bias mitigation. | Handling multi-jurisdictional legal queries, effectively processing long-context legal arguments, ensuring consistent up-to-date legal information, occasional errors in ambiguous legal queries, and the need for fine-tuning in specialized or niche legal fields. | Potential for legal misinformation due to occasional errors in ambiguous queries, and general ethical concerns in legal AI regarding bias (as noted in the literature review). |
| 18FrJ_f-ToAJ.pdf | Google_Scholar | Large language models in cryptocurrency securities cases: can a GPT model meaningfully assist lawyers? | This paper investigates the capabilities of GPT-3.5 for legal reasoning and ChatGPT for legal drafting in the context of cryptocurrency securities cases. It finds that GPT-3.5's legal reasoning is currently weak, while ChatGPT shows promise for legal drafting, potentially assisting lawyers but highlighting the need for significant improvements in LLM reasoning abilities for legal tasks. | True | Market | True | 2.0 | Neutral | Evaluation of OpenAI's GPT-3.5 models (text-davinci-003 for legal reasoning, and ChatGPT based on gpt-3.5-turbo-0301 for legal drafting) using zero-shot prompting with specific prompt engineering techniques (e.g., IRAC for reasoning, section-by-section generation for drafting). | For legal reasoning (GPT-3.5): Fact patterns from 20 real-life cryptocurrency securities cases were fed to the model to identify potential legal violations. Performance was evaluated against actual charges using precision, recall, and F1-score. For legal drafting (ChatGPT): Complaints for 9 real-world securities class action cases were drafted by ChatGPT and compared to lawyer-drafted (abridged) versions. 88 mock jurors assessed these complaints, and their decisions, confidence levels, and the linguistic concreteness of the complaints were analyzed. | For legal reasoning, GPT-3.5 performed poorly, with an average F1-score of 0.324. Precision (0.658) was higher than recall (0.252), indicating it missed more correct violations than it identified spurious ones. For legal drafting, ChatGPT performed well; juror decisions were not statistically significantly associated with the document's author (ChatGPT vs. lawyer), and ChatGPT-drafted complaints were found to be significantly more concrete than lawyer-drafted ones. | Resource constraints for enforcement attorneys in complex legal areas like cryptocurrency securities law, potentially hindering effective legal enforcement and justice for victims. | Proposing LLMs (specifically ChatGPT for drafting, and acknowledging the need for future improved models for reasoning) as potential tools to assist lawyers, thereby potentially alleviating resource burdens and improving efficiency in the legal process. | Application of AI in litigation, particularly in securities law and cryptocurrency cases, to support legal reasoning and legal drafting for legal professionals. | Victims of cryptocurrency securities violations, who may rely on enforcement actions or class action lawsuits for justice. | Securities law, Cryptocurrency law, Litigation (U.S. civil procedure, class actions). | United States (federal law, referencing U.S. District Court cases). | The study utilizes OpenAI's pre-trained models (GPT-3.5: text-davinci-003, gpt-3.5-turbo-0301; ChatGPT). This data is proprietary, large-scale, and primarily unstructured text and code. ChatGPT is further fine-tuned with Reinforcement Learning from Human Feedback (RLHF). The study notes specific model training data cut-off dates (e.g., June 2021 for text-davinci-003). | Experimental design involving: 1) For legal reasoning: prompt engineering (zero-shot, specifying IRAC method), systematic case selection, and quantitative evaluation against ground truth. 2) For legal drafting: iterative prompt engineering for section-by-section complaint generation, mock jury survey design with specific jury instructions, human editing of lawyer-drafted complaints for comparability, and statistical analysis of juror responses alongside linguistic analysis (concreteness) of complaints. | NaN | True | False | The studied models (GPT-3.5, ChatGPT) are accessible via OpenAI's API (e.g., gpt-3.5-turbo-0301, text-davinci-003) and the ChatGPT user interface (May 24, 2023 GPT-3.5 version mentioned for drafting). API access is typically paid. | Current LLMs like GPT-3.5 exhibit weak legal reasoning capabilities (high false negatives). There's a need for significant improvement in LLM accuracy and reliability for complex legal reasoning. Further research is required on LLM performance with longer texts, more advanced models, and in diverse legal jurisdictions and contexts. | For legal reasoning: Ensuring models (text-davinci-003) had no prior knowledge of test cases by selecting cases filed after its training data cut-off. Significant prompt engineering was required. For legal drafting: Encountering API errors which necessitated using the ChatGPT user interface and modifying prompts (e.g., adding 'for educational purposes only'). Substantial manual effort was needed to edit lawyer-drafted comparator complaints to ensure fairness. Managing mock juror recruitment and quality control. | LLM 'hallucinations' or fabrication of facts/statutes (e.g., adding 'John Doe' defendants, inconsistent case details). Poor legal reasoning by LLMs can lead to missing salient legal violations or identifying incorrect ones. Over-reliance on current LLMs for tasks they are not proficient in, like complex legal reasoning, poses a risk to legal practice quality. |
| QH89sWPQoGIJ.pdf | Google_Scholar | Lawma: The Power of Specialization for Legal Annotation | This paper introduces CaselawQA, a benchmark of 260 legal annotation tasks, and the Lawma family of fine-tuned language models. It demonstrates that these smaller, specialized Lawma models significantly outperform large commercial LLMs for legal annotation in empirical research, advocating for task-specific fine-tuning. | True | Market | True | 1.0 | NaN | Lawma: a family of fine-tuned open-source language models (SmolLM2, Llama 3.2, Llama 3.1, Llama 3.3) specialized for legal annotation tasks through fine-tuning on the CaselawQA benchmark. | Evaluated on CaselawQA, a new benchmark of 260 legal classification tasks derived from U.S. Supreme Court and Court of Appeals databases, using accuracy as the primary metric. Compared against commercial models (e.g., GPT-4.5, Claude 3.7 Sonnet) and other open-weights models. | Lawma 70B (largest fine-tuned model) achieved 88% accuracy on CaselawQA, outperforming the best-performing commercial model (Claude 3.7 Sonnet at 78%) by 10 percentage points. Lawma 135M (smallest fine-tuned model) achieved 83% accuracy, also surpassing commercial models. | NaN | NaN | NaN | NaN | General (court case annotation covering various aspects such as issue area, case source, disposition, ideological direction based on U.S. Supreme Court and Courts of Appeals data) | United States (federal courts: Supreme Court, Courts of Appeals) | A dataset of 24,916 court cases (majority opinions from Caselaw Access Project) with labels from the U.S. Supreme Court Database (SCDB) and U.S. Courts of Appeals Database (USCAD), forming 260 classification tasks (~553,000 task examples for fine-tuning after processing). Data is publicly derived and consists of unstructured text (court opinions) paired with structured annotations. | Supervised fine-tuning of pre-trained open-weights language models (SmolLM2, Llama 3.2, Llama 3.1, Llama 3.3) on a collection of 260 legal annotation tasks simultaneously. Evaluation uses a multiple-choice prompt template with chain-of-thought prompting. | Code, datasets, and fine-tuned models are made available on GitHub. | True | True | Code, datasets, and fine-tuned models are available at https://github.com/socialfoundations/lawma. | NaN | High variability in model performance across different legal annotation tasks. Fine-tuned models still do not reach human intercoder agreement rates on many tasks. The need for task-specific fine-tuning, as broadly specialized legal models may not suffice. Managing compute resources for fine-tuning. Dealing with long document lengths, high number of classes in some tasks, and imbalanced datasets. | Caution regarding the use of LLMs for consequential legal tasks without further substantive investigation. Findings may not generalize to other legal domains within the U.S. or legal systems in other countries. |
| P6imolD5eSQJ.pdf | Google_Scholar | A negation detection assessment of GPTs: analysis with thexNot360 dataset | This paper evaluates the negation detection capabilities of GPT-2, GPT-3, GPT-3.5, and GPT-4 using a custom dataset, xNot360, and a zero-shot prediction approach. Findings reveal that while GPT-4 performs best, overall LLM proficiency in negation is modest, highlighting limitations in logical understanding crucial for high-stakes domains like law. | True | NaN | True | 2.0 | NaN | Zero-shot negation detection assessment of GPT models (GPT-2, GPT-3, GPT-3.5, GPT-4) using the custom xNot360 dataset. | GPT models were prompted to predict whether a second sentence negates a first sentence from the xNot360 dataset in a zero-shot setting. Performance was measured using accuracy, precision, recall, F1-score, and confusion matrices. | GPT-4 performed best, achieving an accuracy of 0.7833, F1-score of 0.7706, precision of 0.8187, and recall of 0.7278 on the xNot360 dataset. | NaN | NaN | NaN | NaN | General Law (mentioned as a high-stakes application domain where logical reliability is critical) | International (negation is a general language feature); USA (Uniform Bar Examination mentioned as a benchmark for GPT-4) | The custom eXploring Negation Over Text with 360 samples (xNot360) dataset: 360 English sentence pairs (5-20 words each), with 180 positive (negating) and 180 negative (non-negating) examples, constructed using sentence templates and classical logic principles. Publicly available on Hugging Face. | For xNot360 dataset: Manual design of sentence templates with negated components, guided by classical logic. For evaluation strategy: Zero-shot prediction prompting of GPT models. | The xNot360 dataset is available on Hugging Face. GPT-2 was accessed via HuggingFace's zero-shot-classification pipeline; GPT-3, GPT-3.5, and GPT-4 were accessed via the OpenAI API. | True | True | The xNot360 dataset is available on Hugging Face (https://huggingface.co/datasets/nguyenthanhasia/xNot360). GPT-2 is publicly available. GPT-3, GPT-3.5, and GPT-4 are accessible via OpenAI API. The evaluation methodology is described. | NaN | Difficulty in creating logically sound negation examples for datasets, even for humans. LLMs struggle with correct negation handling, especially in complex sentence structures (e.g., conditionals). Reinforcement Learning from Human Feedback (RLHF) might inadvertently degrade logical performance if not carefully aligned with logical consistency. LLMs exhibit performance drops on out-of-distribution logical reasoning datasets and face difficulties in resolving semantic ambiguity. | Erroneous generations, hallucinations, and misinterpretations by LLMs due to poor negation handling in high-stakes domains like law, healthcare, and science, potentially leading to incorrect decisions or flawed communication. |
| h_pgclBU5VYJ.pdf | Google_Scholar | Sentimental Analysis of Legal Aid Services: A Machine Learning Approach | This paper uses sentiment analysis with various machine learning models (Naive Bayes, SGD, Random Forest, SVC, Logistic Regression, XGBoost) to evaluate client perceptions of Legal Aid South Africa based on feedback from Twitter and an internal system. The study finds a predominantly neutral or positive sentiment but identifies areas for improvement, with SVC and XGBoost showing the best classification performance. | True | Idealistic | False | 2.0 | Positive | Sentiment analysis comparing multiple machine learning classifiers (Naive Bayes, SGD, Random Forest, SVC, Logistic Regression, XGBoost) on client feedback text, using TextBlob for initial labelling and TF-IDF for vectorization. Best performers were SVC and XGBoost. | Compared six ML models on an 80/20 train/test split of client feedback data using accuracy, precision, recall, and F1-score metrics. Hyperparameter tuning using Grid Search was mentioned for Random Forest and Logistic Regression. | XGBoost and SVC demonstrated superior performance, achieving testing accuracies of 90.10% and 90.00%, respectively. Both achieved F1 scores generally above 85%. | Public perception that state-funded legal aid is of substandard quality; lack of client trust; clients feeling uninformed about case progress; potential issues related to using newly graduated attorneys; limited access to digital platforms (like Twitter) for feedback among indigent clients. | Utilizing sentiment analysis of client feedback (primarily internal) to understand perceptions, identify specific service issues (e.g., delays, communication), and inform improvements like business process reengineering or automation. Emphasizes the need to keep clients informed. | Quality and perception of legal aid services. | Vulnerable individuals lacking financial resources, indigent clients in South Africa. | Criminal law, Civil law | South Africa | Primary: 5,246 unstructured text entries from Legal Aid SA's proprietary internal client feedback system (2019-2024). Secondary: 100 text entries from Twitter queries (2019-2024). | Data collection, text pre-processing (cleaning, stemming/lemmatization, tokenization, stopword removal), sentiment labelling (TextBlob), feature extraction (TF-IDF), comparative machine learning model training and evaluation (Naive Bayes, SGD, RF, SVC, LR, XGBoost), hyperparameter tuning (Grid Search). | NaN | False | False | NaN | Need for further model optimization (hyperparameter tuning, ensembles, feature engineering), cross-validation, evaluation on broader datasets, and exploration of advanced NLP models (Deep Learning: RNN, Transformers, GPT, LSTM). Limited representativeness of social media data for the target demographic. | Limited quantity and representativeness of social media (Twitter) data for Legal Aid clients; potential for model overfitting; standard challenges of selecting and tuning ML models. | Potential bias in feedback data (especially the small Twitter sample); risk of model overfitting leading to poor generalization; misinterpretation of sentiment potentially leading to ineffective service changes. |
| aKgI4nl8ulwJ.pdf | Google_Scholar | Komodo: A Linguistic Expedition into Indonesia’s Regional Languages | This paper introduces Komodo-7B, a 7-billion-parameter Large Language Model family optimized for Indonesian, English, and 11 Indonesian regional languages. Komodo-7B-Instruct surpasses existing models on various benchmarks, aiming to improve linguistic inclusivity and access to education in Indonesia. | True | Idealistic | True | 1.0 | Positive | Komodo-7B (Komodo-7B-Base and Komodo-7B-Instruct), a family of 7-billion-parameter Large Language Models based on Llama-2, with an expanded vocabulary for Indonesian and regional languages, trained with a bilingual next-token prediction strategy. | Evaluated on discriminative tasks (IndoMMLU, ID-EN Entailment, X-Copa-ID, Intent-Classification, Colloquial-Detection, NusaXSenti, ID-Hatespeech) and generative tasks (NusaX-MT, TydiQA-ID, IndoSum) using metrics like Accuracy, F1, CHRF++, Rouge-L-F1. Also tokenizer fertility, embedding position analysis, English capability regression (Perplexity, common benchmarks), and qualitative analysis. Compared against GPT-3.5, GPT-4, Llama-2, Mixtral, Gemma, Sealion, Aya, Bactrian-X, Qwen. | Komodo-7B-Instruct achieved state-of-the-art performance in several tasks, outperforming models like GPT-3.5 and Aya-101. For example, it scored 90.5% accuracy on ID-EN entailment, 79.3% accuracy on NusaX-Senti, 90.3% accuracy on TydiQA-ID, and a 43.0 Rouge-L-F1 score on IndoSum. Overall average score on the benchmark suite was 71.1%. | The primary obstacles identified are the significant gap in linguistic resources and high-performing LLMs for low-resource Indonesian regional languages. This digital language barrier hinders access to information and education for communities speaking these languages, contributing to educational disparities. | The paper proposes Komodo-7B, a specialized LLM family, to address these obstacles. This involves creating comprehensive, high-quality datasets (including legal/jurisprudential corpora and textbooks), expanding tokenizer vocabularies for regional languages, and using advanced training techniques to enhance performance in these languages, thereby promoting informational and educational equity. | Linguistic inclusivity in digital resources, Access to education in regional languages, Access to information in regional languages, Bridging language barriers with AI. Potential for access to legal information given training data and stated domains. | Speakers of 11 Indonesian regional languages: Acehnese, Balinese, Banjarese, Buginese, Dayak Ngaju, Javanese, Lampungnese, Madurese, Minangkabau, Sundanese, and Toba Batak, particularly those in regions with lower educational quality compared to Java island. | Legal Services, Jurisprudence (based on training data and mentioned application domains). | Indonesia | A combination of diverse open-source datasets, manually collected data for Indonesian regional languages, Indonesian textbooks (grades 1-12, covering subjects like local cultures, engineering, legal and jurisprudential corpus), colloquial data (subtitles, news, conversations), and English datasets with alternate parallel data for code-mixing. Preprocessing involved repetition removal, quality filtering, and deduplication. About 8.79 billion tokens were processed for pretraining. SFT data included open-source tasks, manually labeled data, and ChatGPT responses. | Built on Llama-2-7B. Methodologies include vocabulary expansion for target languages, new embedding initialization by averaging existing ones, incremental pretraining and Supervised Fine-Tuning (SFT) using LORA, and a bilingual next-token prediction strategy. Data preprocessing techniques were also applied. | NaN | False | False | NaN | The paper suggests that achieving optimal performance across all regional languages may require larger models (e.g., a future 13B parameter version). The current 7B model's English mathematical reasoning capability is also noted as an area with relative underperformance due to training data composition. The general challenge of subpar performance of models for low-resource languages persists, with Komodo-7B being a step towards addressing this. | Effectively expanding tokenizer vocabularies for languages like Indonesian that share Latin script with English. Balancing vocabulary size with computational resources. Initializing new embeddings properly. Mitigating catastrophic forgetting during incremental pretraining. Managing hardware and cost requirements for large model training. Objectively evaluating generative outputs, sometimes requiring human or advanced AI (GPT-4) assistance. | NaN |
| RBzOwCxynRAJ.pdf | Google_Scholar | STATE BAR OF CALIFORNIA | This paper reports on the activities of the State Bar of California between late 2023 and early 2024, covering new reports (including on legal profession diversity and AI regulation), rulemaking proposals, legislative updates, and recent litigation. Key themes include attorney discipline, regulation of generative AI use by lawyers, and efforts related to access to justice such as pro bono programs and alternative bar exam pathways. | True | Idealistic | False | 3.0 | Neutral | N/A_No specific technique or tool related to AI for A2J discussed. | NaN | NaN | The text implicitly identifies lawyer misconduct, fee disputes, ethical challenges posed by new technologies like AI, potential gaps in pro bono service provision, and diversity disparities as hindering public protection and potentially access to justice. | Proposed solutions include enhancing the attorney discipline system, implementing diversion programs (mediation, Attorney-Client Bridge Program), expanding pro bono programs, regulating AI use by lawyers (including mandatory training), proposing alternative licensure pathways (Pilot Portfolio Bar Exam), and promoting diversity. | Attorney discipline, Attorney regulation, Legal ethics, Generative AI in legal practice, Pro bono services, Diversity and inclusion in the legal profession, Bar examination alternatives, Access to justice. | The general public interacting with the legal system in California, clients of attorneys, potentially underserved communities benefiting from pro bono services. | Legal profession regulation, Professional Responsibility, Ethics, Administrative Law. | California | NaN | NaN | NaN | False | False | NaN | Need for clear ethical guidelines and regulations for lawyer use of generative AI. Potential insufficiency or lack of insight into pro bono service provision. Ongoing need for improvements in attorney discipline, diversity, and oversight within the Bar itself. | N/A_The text discusses regulatory and administrative challenges for the State Bar, not challenges in developing a specific AI technique/tool. | Risks associated with lawyers' use of generative AI (data confidentiality breaches, inaccurate outputs, ethical violations). Risk of attorney misconduct harming clients. Risks related to conflicts of interest and lack of transparency within regulatory bodies. |
| Ll274-1nqZsJ.pdf | Google_Scholar | Navigating the Digital Dispute Resolution \nLandscape: Challenges and Opportunities | This paper explores the concept of digital dispute resolution (DDR), charting its progress and highlighting enabling technologies like blockchain and AI (including ChatGPT). It discusses the significant challenges DDR faces, such as jurisdiction, fairness, enforcement, and security, while proposing solutions like enhanced digital literacy and uniform legal frameworks. | True | NaN | True | 3.0 | Neutral | NaN | NaN | NaN | Jurisdictional complexities in cross-border digital environments, concerns regarding due process and fairness safeguards, challenges in enforcing digital dispute resolution outcomes, security and privacy vulnerabilities of digital platforms, potential for errors and biases in AI-driven tools. | Enhancing digital literacy across populations, implementing robust security and privacy measures (e.g., encryption, secure channels), adopting uniform international laws and consistent dispute resolution mechanisms to address jurisdictional and enforcement issues, ensuring the appropriateness, accuracy, and fairness of digital platforms, particularly AI. | General dispute resolution efficiency and cost-effectiveness, Consumer protection (mentioned via Kenyan blueprint). | NaN | Alternative Dispute Resolution (ADR), Commercial Law, Technology Law, International Law (conflict of laws) | Kenya, UK, International | NaN | NaN | NaN | False | False | NaN | Need for widespread digital literacy, lack of robust security/privacy standards and practices for DDR platforms, absence of clear international legal frameworks for jurisdiction and enforcement in digital disputes, challenges in ensuring fairness, transparency, and accuracy, especially with AI tools. | Jurisdictional ambiguity in transnational digital disputes, ensuring due process and fairness comparable to traditional methods, establishing effective cross-border enforcement mechanisms, protecting data security and user privacy on digital platforms, mitigating risks of errors and bias in AI applications for dispute resolution, requirement for enhanced digital literacy among users and practitioners. | Privacy breaches due to cyberattacks or platform vulnerabilities, security failures leading to data theft or manipulation, AI errors or biases resulting in unfair outcomes or incorrect legal analysis/advice, lack of adequate due process safeguards, difficulty in enforcing digital dispute resolution decisions across borders, unresolved jurisdictional conflicts. |
| UmP53qugiQoJ.pdf | Google_Scholar | OPENING THE VIRTUAL WINDOW: HOW ON-LINE PROCESSES COULD INCREASE ACCESS TO JUSTICE IN THE CRIMINAL LEGAL SYSTEM | This paper argues that online processes and technology, drawing from Online Dispute Resolution (ODR), can improve access to justice (A2J) in the US criminal legal system, particularly for misdemeanor cases. It proposes evaluating technologies using a traffic light system (green, yellow, red) based on A2J impact, suggesting specific applications like auto-notifications, information tools, remote appearances, and bias mitigation techniques while highlighting significant challenges and risks. | True | Idealistic | False | 3.0 | Positive | Discusses applying Online Dispute Resolution (ODR) concepts and various technologies (auto-notifications, option support tools, AI for lawyers, online communication/hearings, anonymization, bias detection AI) to improve A2J in criminal misdemeanor processing. | References existing studies and pilots (e.g., Michigan ODR for traffic tickets via Matterhorn, San Francisco/Yolo County blind charging pilots, studies on auto-reminders) but does not present new empirical testing. | Referenced study on Michigan ODR for traffic tickets found text-based online processes reduced outcome disparities based on age, gender, and race compared to face-to-face hearings (e.g., Black defendants received slightly lower fines online vs. higher fines F2F). | High costs and burdens of the traditional court process (time, transport, childcare); pressure to plead guilty; lack of access to information, legal counsel, and confidential communication channels (esp. for jailed defendants); collateral consequences; conviction of innocents; systemic bias; underfunding; resistance to change; digital divide. | Implement defendant-centric technology: optional remote appearances, auto-notifications, accessible information/option support tools, AI assistance for defense lawyers, online case management, secure communication platforms, text-based processes to reduce bias, anonymization/blind charging, AI for bias detection, technology-based alternatives to bail (e.g., GPS apps). | Access to information, access to legal representation, fairness and efficiency of court processes, reducing bias (racial, socioeconomic), alternatives to pretrial detention/bail, improving attorney-client communication. | Individuals charged with misdemeanors, particularly low-income individuals (often eligible for indigent defense). Also notes challenges for those with limited English proficiency, mental illness, cognitive disabilities, or substance abuse problems. | Criminal Law, Criminal Procedure, Dispute Resolution (ODR) | United States | NaN | NaN | NaN | False | False | NaN | Need for secure/confidential attorney-client communication platforms; resource constraints (funding, personnel); digital divide; resistance to change; lack of research on effectiveness/unintended consequences; need for ethical guidelines/oversight for AI; ensuring technology serves justice, not just efficiency; unaddressed structural power imbalances. | Resource constraints (cost, training); ensuring data privacy and confidentiality (security, surveillance, attorney-client privilege); overcoming resistance to change; bridging the digital divide; potential lack of empathy in remote interactions; risk of prioritizing efficiency over justice; potential for technology to exacerbate power imbalances; ensuring AI accuracy/avoiding bias; navigating Unauthorized Practice of Law (UPL) concerns. | Erosion of attorney-client confidentiality; exacerbation of bias (algorithms, video bail hearings); widening the digital divide; decreased empathy; over-reliance on inaccurate/biased AI; privacy infringements; using tech as a substitute for adequate defense funding; unintended consequences (e.g., anonymization); exacerbating power imbalances; focus on efficiency detrimental to justice (e.g., net-widening). |
| m5lEJ--ziNcJ.pdf | Google_Scholar | The Disruption of Generative AI in Real Asset Markets | This paper empirically examines the impact of Generative AI (GenAI), specifically post-ChatGPT, on the commercial real estate (CRE) market using private lease data and public stock market analysis. Findings suggest higher tenant GenAI exposure leads to lower rents, reduced space demand, and lower CRE firm valuations, indicating GenAI primarily acts as a labor substitute in this context. | True | Market | True | 2.0 | NaN | Measurement of industry/firm GenAI exposure using LLM (GPT-3.5) evaluation of O*NET tasks, combined with Difference-in-Differences (DID) regression on lease data and Event Study / Portfolio Analysis on CRE firm stock data. | Analysis of ~270,000 US commercial leases (CompStak, 2019-2024) using DID regression around ChatGPT release. Analysis of US REIT stock returns (S&P Global) using event study and portfolio sorts based on calculated GenAI exposure. Robustness checks include placebo tests, alternative samples, and controlling for confounders (e.g., Work-From-Home). | Higher GenAI exposure (1 std dev) is associated with a 3.5% reduction in net effective rent post-ChatGPT, increased tenant downsizing, lower lease renewal rates, and increased landlord concessions. In public markets, higher exposure REITs experienced lower stock returns (short-long portfolio yielded -11.83%), lower FFO forecasts, decreased occupancy, lower cash flows (FFO, NOI), and lower valuations (Tobin's Q). | NaN | NaN | NaN | NaN | Commercial Real Estate, Corporate Law, Securities Law (viewed through finance/economics lens) | United States | GenAI exposure measure derived using OpenAI GPT-3.5 Turbo API calls based on prompts assessing tasks from the public O*NET V28.0 database, aggregated using public BLS Occupational Employment Survey (OES) data. Analysis performed on proprietary CompStak lease data and proprietary S&P Global financial/property/tenant data. | Task-based occupational analysis aggregated to industry/firm level, Econometric analysis (Difference-in-Differences, Event Study, Portfolio Analysis), Data integration (linking tenants, properties, REITs), LLM-based task scoring. | NaN | False | False | NaN | NaN | Acquiring and integrating large-scale proprietary datasets (leases, property ownership, tenant details); constructing a meaningful measure of GenAI exposure across diverse industries; econometric identification to isolate GenAI impact from confounding factors (e.g., WFH trends, pre-existing trends). | Economic risks to Commercial Real Estate sector including reduced demand for space, lower rents, lower occupancy rates, and declining asset valuations, driven by GenAI-induced labor substitution. |
| -CZiMBSVZrgJ.pdf | Google_Scholar | Large Language Model Agent as Insurance Law Assistant | This thesis proposes and evaluates an LLM agent using Retrieval-Augmented Generation (RAG) to make Finnish traffic insurance law more accessible to ordinary individuals. Evaluation by a legal expert showed the agent could provide satisfactory answers and mitigate hallucinations, but highlighted the need for improved document retrieval. | True | Idealistic | True | 1.0 | Positive | An intelligent agent employing Retrieval-Augmented Generation (RAG) with the GPT-4 Turbo Large Language Model, facilitated by the Embedchain library, to answer user questions based on custom-selected legal documents. | The agent was evaluated through human feedback from a legal expert who assessed the quality of responses to 10 predefined scenarios based on real-life situations. Quantitative metrics (context relevance, answer relevance) from Embedchain's evaluation method were also used. | The expert rated 9 out of 10 responses as satisfactory (score 10/10), demonstrating reduced hallucination compared to the base LLM. However, quantitative evaluation showed low context relevance scores (2-44%), indicating suboptimal document retrieval, while answer relevance scores were high (84-90%). | The complexity of traffic insurance law for laypeople, and the general lack of knowledge, networks, and financial resources needed to access legal support. | Develop an accessible web-based LLM agent that utilizes RAG to retrieve information from specific legal documents (Finnish traffic insurance law, precedents) and answer user questions, thereby guiding them through the complexities of the legal domain. | Understanding rights to compensation under traffic insurance law after an accident, accessing relevant legal information. | Ordinary individuals involved in traffic accidents in Finland who find it difficult to navigate insurance law. | Traffic Insurance Law | Finland | The RAG system uses publicly available Finnish legal documents (e.g., from Finlex, Liipo) and potentially undisclosed proprietary sources related to traffic insurance law. This unstructured text data (HTML, PDF) is chunked, embedded using OpenAI's 'text-embedding-ada-002', and stored in a ChromaDB vector database. The underlying LLM is OpenAI's GPT-4 Turbo. | Design Science Research Methodology (DSRM), including problem explication, requirements definition, iterative design/development (brainstorming, assessment, sketching, building, reflection), demonstration, and evaluation. Peer review via the Walk-Through method was also employed. | The agent is accessed via a web application (built with Next.js, Nginx, Django API) requiring user registration/login. The system is containerized using Docker and hosted on Google Cloud. | False | False | NaN | Suboptimal document retrieval performance in the RAG system (low context relevance). Need for more extensive evaluation with more test cases and potentially more experts. Lack of sufficient data and expert resources for developing a planned Multi-Agent System (MAS). Need for self-hosted LLMs to improve privacy and reduce costs. | Selecting/developing LLMs with robust Finnish language understanding; optimizing RAG retrieval performance; effective prompt engineering; acquiring comprehensive real-world case data (esp. for MAS); limited expert availability for evaluation and multi-agent design; evaluating and fine-tuning open-source models for the task; managing costs associated with third-party APIs. | LLM hallucination leading to incorrect legal information. Failure to retrieve or include critical legal details in responses. Data privacy concerns associated with using third-party LLM APIs. Potential for sensitive information leakage between users (e.g., via improperly implemented caching). |
| ukGcRPmEsjkJ.pdf | Google_Scholar | MODERN INNOVATIVE MACHINE LINGUISTICS | The paper provides an overview of modern innovative machine linguistics, highlighting the role of Data Mining, machine learning (especially deep learning and transformers like BERT and GPT), and Big Data. It discusses various applications such as machine translation, speech recognition, and sentiment analysis, while also touching upon ethical considerations and evolutionary computing. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal services (general) | International | NaN | NaN | NaN | False | False | NaN | NaN | Need for high-quality Big Data; ethical issues (data privacy, model bias, transparency); complexity of search spaces in linguistic tasks. | Data privacy violations, model bias leading to unfair outcomes, lack of transparency in AI decision-making. |
| aH2ZKJ7gUmYJ.pdf | Google_Scholar | Free LLMs Hallucinate and Rarely Signal Their Limitations in Solving Legal Problems | This study evaluates the ability of two free large language models (GPT-4o mini and Bielik-11B-v2) to answer simple Polish legal questions. The results show the models perform poorly on moderately complex issues, often hallucinate, fail to correct erroneous user assumptions, and rarely indicate their own limitations. | True | Idealistic | True | 2.0 | Negative | Evaluation of GPT-4o mini and Bielik-11B-v2 | Models were prompted with 12 questions across 3 Polish legal fields (Constitutional, Criminal, Inheritance). Prompts included sensible/nonsensical assumptions and varied phrasing. 120 responses (5 per prompt/model) were evaluated by human experts for correctness and signalling of limitations. | Models answered correctly only on the simplest constitutional law issue. They struggled significantly with criminal and inheritance law, especially when prompts contained nonsensical assumptions (Bielik: 0-20% correct, GPT: 20-100% correct depending on phrasing). Limitations were rarely signalled (17% overall). | LLMs hallucinate and provide incorrect legal analysis, especially for non-trivial questions; they fail to correct user misconceptions posed in prompts; they rarely signal their own limitations (e.g., lack of access to real-time/accurate legal databases); opacity of commercial models. | The paper suggests lawyers should use LLMs very carefully, be aware of their limitations, and calls for more research to scrutinize these limitations and raise awareness. | Legal analysis accuracy, hallucination in legal contexts, LLM limitations signalling, legal information retrieval. | NaN | Constitutional Law, Criminal Law, Inheritance Law (Civil Law) | Poland | NaN | NaN | NaN | True | True | The paper explicitly studies 'free LLMs'. GPT-4o mini is available via OpenAI. Bielik-11B-v2 is available on Hugging Face (as per reference [9]). | Unreliability of current free LLMs for legal analysis beyond simple cases; lack of precision and tendency to hallucinate; failure to signal limitations; need for more research on limitations of free models; issue of LLM transparency. | Evaluating legal correctness of LLM outputs; designing realistic prompts; high variability and sensitivity of LLM responses to prompt phrasing. | Users receiving incorrect legal information due to hallucinations; users being misled when LLMs confirm false premises; over-reliance on LLMs due to lack of signalled limitations; risks associated with the opacity (black-box nature) of LLMs in legal applications. |
| cauuCB_XXSkJ.pdf | Google_Scholar | Let's Chat About ChatGPT: A Practical Guide to Risks in Attorney Use of Generative AI | This paper analyzes recent instances where attorneys misused generative AI tools like ChatGPT, resulting in fabricated case citations, court sanctions, and potential malpractice claims. It reviews the responses from courts, bar associations, and ethics bodies, concluding that existing ethical rules suffice but emphasizing the need for attorney education, caution, and human oversight. | True | Market | True | 2.0 | NaN | General-purpose generative AI (e.g., ChatGPT, Google Bard) | Analysis of court cases involving attorney misuse of generative AI; mentions a Stanford study testing LLMs on legal questions. | Attorneys misusing generative AI faced sanctions, discipline, and reputational harm due to fabricated cases and inaccuracies. A cited Stanford study found general LLMs hallucinate >75% on core legal questions. | NaN | NaN | NaN | NaN | Legal Ethics, Civil Procedure, Criminal Procedure, Torts (Personal Injury), Housing Law | United States (Federal and State courts including NY, TX, CO, CA, PA, IL, OK, NJ, MT, OH, MO, HI, MI) | NaN | NaN | NaN | True | True | Publicly available generative AI tools like ChatGPT and Google Bard have free accessible tiers. | NaN | Challenges discussed are those faced by attorneys *using* generative AI: verifying accuracy, avoiding hallucinations ('fake cases'), maintaining client confidentiality, need for human oversight and supervision, lack of technological competence. | Generation of inaccurate information ('hallucinations', fake cases), breach of client confidentiality, violation of ethical duties (competence, diligence, candor, supervision), professional sanctions, disciplinary action, legal malpractice claims, potential bias in AI outputs, undermining trust in the legal system. |
| GIrhz2GUiNgJ.pdf | Google_Scholar | Evaluating the Performance of ChatGPT in the Automation of Maintenance Recommendations for Prognostics and Health Management | This paper proposes and applies a methodology using a rubric based on Accuracy, Concordance, and Insight (ACI) to evaluate ChatGPT's performance in generating maintenance recommendations for Prognostics and Health Management (PHM). The evaluation reveals ChatGPT has some understanding of industrial concepts but suffers from inaccuracies, verbosity, lack of specificity, and potential safety risks, indicating limitations for practical PHM application without significant safeguards and domain adaptation. | True | Market | True | 2.0 | Neutral | Evaluation of ChatGPT for generating PHM maintenance recommendations using a custom rubric (adapted ACI) and multi-stage testing. | Three-stage evaluation: 1) 76-item Maintenance & Reliability multiple-choice exam (adapted from Gulati & Smith, 2021). 2) 63-item PHM Industrial Domain knowledge exam (adapted from internal GE Vernova test). 3) Troubleshooting assessment using historical case prompts. Responses scored using an adapted Accuracy, Concordance, Insight (ACI) rubric by domain experts. | ChatGPT (AI1) scored 72% on M&R exam and 67% on PHM exam (below 80% passing threshold). It grasped central concepts well but struggled with accuracy, consistency, specificity (tended towards verbosity/generality), deduction, and sometimes physical soundness. Troubleshooting recommendations were too verbose and lacked practical specificity. | NaN | NaN | NaN | NaN | NaN | International | The evaluated model (ChatGPT based on GPT-3.5) was pre-trained on a large, general corpus of text data from online sources (books, websites, articles) up to 2021, fine-tuned using RLHF. This is not domain-specific PHM data. | Evaluation framework design based on adapted ACI scoring rubric and multi-stage knowledge/task testing using expert-derived questions and prompts. | NaN | True | False | ChatGPT was accessible via OpenAI's public interface. | NaN | Evaluating rapidly evolving LLMs, laborious grading requiring domain expertise, ensuring grading consistency, designing effective prompts, assessing subtle qualities (physical soundness, safety, deduction) within verbose/inconsistent outputs. | Generating inaccurate/outdated/unsafe maintenance guidance, data security issues, lack of interpretability, misinformation, hallucinations, potential bias from training data, lack of contextual understanding of specific industrial processes/safety protocols/regulations. |
| EUlupy7HOaEJ.pdf | Google_Scholar | The Generative AI Revolution: Opportunities, Shocks, and Risks | This policy report analyzes the rapid rise of generative AI, focusing on the UK context. It outlines economic and geopolitical opportunities, potential labor market and macroeconomic shocks, and AI safety risks (especially alignment), proposing UK government strategies in response. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General legal practice, Intellectual Property | United Kingdom | NaN | NaN | NaN | False | False | NaN | Strategic gaps for UK: insufficient compute power, lack of sovereign LLM capability, suboptimal AI talent attraction/retention, regulatory/IP framework (TDM) limitations, need for AI safety evaluation standards, lack of dedicated AI regulator. Technical gaps: AI alignment problem. Societal gaps: managing labor market transitions, ensuring AI aligns with human values. | NaN | Misinformation, lack of transparency, privacy issues, AI alignment problem (unintended/harmful consequences, potential existential risk), manipulation/disruption of critical infrastructure (reliance on foreign AI), job displacement/unemployment, increased frivolous lawsuits, environmental impact of compute. |
| rM1eMjqvg9cJ.pdf | Google_Scholar | Efficiency, Ethics , and Algorithms : The Implications of AI on the Legal Profession and the \nABA Model Rules | This paper analyzes the potential impacts of AI, particularly tools like ChatGPT, on legal drafting, research, and decision-making within the legal profession. It further examines the ethical considerations under the ABA Model Rules for attorneys using such AI and explores AI's potential to enhance access to justice. | True | Market | True | 2.0 | Positive | ChatGPT (and other generative AI tools like CoCounsel and Harvey AI used as examples) | N/A (The paper discusses capabilities and issues based on existing reports and general knowledge of the tools, but does not present its own empirical testing or evaluation against benchmarks.) | NaN | High cost of legal services; rules barring unauthorized practice of law and sharing of legal revenue with non-lawyers; risk of lower-quality AI services for underserved populations. | Utilizing AI for drafting and research to lower costs; reforming rules (e.g., Rule 5.4) to allow innovative partnerships and service delivery models (like legal sandboxes); establishing state bar task forces for AI regulation and service standards. | Affordability of legal services; provision of services for common legal issues (family law, debt, landlord/tenant); innovative legal service delivery models including non-lawyer providers and AI. | Low-income individuals and the general population facing affordability barriers to legal services. | General legal practice (drafting, research, decision-making); Ethics and Professional Responsibility; Criminal Justice; Family Law; Debt Collection; Landlord/Tenant Law; Alternative Dispute Resolution. | USA (primary, with references to ABA Model Rules, state-level initiatives, and US court cases); some international examples (EU). | ChatGPT: Vast amounts of general internet text. CoCounsel: Customized GPT-4 for the legal industry (details not specified). Harvey AI: Legal documents (details not specified). Public Safety Assessment: 750,000 cases from US jurisdictions. | N/A (The paper does not detail the specific design methodologies for the AI tools it discusses, beyond general descriptions like 'large language model' or 'machine learning' for ChatGPT, or factor-based scoring for PSA.) | ChatGPT: Public release (free and subscription tiers). CoCounsel: Market launch after beta testing with law firms. Harvey AI: Beta phase with partnerships (e.g., Allen & Overy for internal integration). | True | True | ChatGPT is available for public use, with a free version (based on GPT-3.5) and a paid subscription version (GPT-4). | Insufficient use of AI and non-lawyers to address the access to justice gap; lack of robust testing and standards for AI to ensure accuracy and lack of bias in A2J contexts; absence of clear ethical guidelines and regulations from state bars for AI use; need for transparency in AI tools used in the justice system. | For users (attorneys): Ensuring client confidentiality, verifying accuracy and avoiding bias in outputs, maintaining professional competence and supervision, navigating lack of clear ethical guidelines. For developers: Mitigating inherent biases from training data, ensuring system security. | Legal malpractice from negligent AI use; threats to due process from biased/opaque AI in criminal justice; perpetuation of societal biases (e.g., racial); unauthorized practice of law; disclosure of confidential client information; security vulnerabilities (breaches, jailbreaking); misleading tribunals with inaccurate or fabricated AI outputs; discriminatory conduct through use of biased AI. |
| blackham-2025-interrogating-new-methods-in-socio-legal-studies-content-analysis-case-law-and-artificial-intelligence.pdf | Google_Scholar | Interrogating new methods in socio-legal studies: Content analysis, case law and arti ficial intelligence | This article critically examines the use of artificial intelligence (AI) and large language models (LLMs) for empirical legal research, specifically focusing on content analysis of case law. It highlights significant risks such as inaccuracy, bias, hallucinations, and lack of reproducibility, concluding that researchers should be extremely cautious and implement stringent evaluation procedures when considering these tools. | True | Idealistic | True | 3.0 | Negative | Using AI and LLMs for content analysis of case law | NaN | NaN | Current unreliability and limitations of AI/LLMs (e.g., hallucinations, bias, inaccuracy) prevent their effective and trustworthy use in legal research tasks that could support access to justice, such as analysing case law to identify systemic enforcement gaps or practical legal issues. | Researchers should exercise significant caution when using AI/LLMs, implement stringent procedures for evaluating AI outputs (e.g., blind, independent testing), and develop a clear understanding of the tools' limitations before applying them in legal research, especially research with potential A2J implications. | Analysis of case law to understand legal operations in practice (e.g., who brings claims, claim resolution, identifying systemic patterns); identifying barriers to justice and gaps in legal enforcement. | Implicitly, communities that are underserved by the legal system whose issues might be illuminated by thorough case law analysis (example given in text: older women and young people in age discrimination cases). | Equality law, employment law (used as primary examples for content analysis). | Australia, UK (examples and studies discussed are from these jurisdictions). | General LLMs (e.g., GPT-4, Llama2-70B) are trained on large-scale, often uncurated internet-based data. Specific studies discussed use datasets like UK Employment Tribunal decisions for analysis. | NaN | NaN | False | False | NaN | Significant gap between the potential of AI/LLMs to assist in socio-legal research (including for A2J purposes) and their current capabilities, particularly regarding accuracy, reliability, reproducibility, and freedom from bias and hallucinations. | Automation bias in human evaluation of AI outputs, AI 'hallucinations' (generating false or nonsensical information), inherent biases in LLMs derived from training data or design, and practical difficulties in ensuring reproducibility of results from third-party LLM services. | Lack of reproducibility, automation bias, inherent LLM bias, hallucinations leading to inaccurate or unfaithful outputs, general inaccuracy, production of poor or misleading research, potential for creating more work due to the need for extensive fact-checking and correction of AI outputs. |
| Mc-1PNuCNIsJ.pdf | Google_Scholar | ChatGPT as an Artificial Lawyer? | This paper qualitatively evaluates ChatGPT's ability to provide legal information to laypeople using simulated landlord-tenant cases, comparing its performance against the expert system-based JusticeBot. While ChatGPT excels at user interaction and language comprehension, it suffers from significant inaccuracies and hallucinations, making it currently unsuitable for direct use, unlike the more reliable but less flexible JusticeBot. | True | Idealistic | True | 2.0 | Neutral | Evaluating ChatGPT's capability for providing legal information to laypeople compared to JusticeBot (an expert system). | Qualitative evaluation using three simulated landlord-tenant cases (generated by ChatGPT) set in Quebec. Researchers interacted with ChatGPT and JusticeBot as layperson parties involved in these cases, assessing performance against criteria including language comprehension, accuracy, completeness, trustworthiness, harmlessness, and user-friendliness. | ChatGPT demonstrated good language comprehension and user-friendliness but lacked accuracy, completeness, and trustworthiness, often 'hallucinating' incorrect legal provisions and case law. JusticeBot provided accurate and trustworthy information within its defined scope but was less flexible and interactive. | Cost of legal services leading to 'legal advice deserts'; difficulty for laypeople in understanding their rights and legal procedures; information asymmetry and power imbalances in disputes (e.g., housing). | Using AI tools like ChatGPT and JusticeBot to provide legal information. The paper suggests combining the conversational strengths of LLMs (like ChatGPT) with the accuracy of verified knowledge bases or expert systems (like JusticeBot). | Provision of legal information; self-help tools for laypeople; everyday legal disputes. | Laypeople, self-represented litigants, individuals facing everyday legal problems without access to professional legal help. | Landlord-Tenant Law (Housing Law) | Quebec, Canada | ChatGPT: Trained on 'enormous corpora of text data' (general, not specified as legal). JusticeBot: Based on content created by legal experts using an expert system methodology. Evaluation Data: Simulated landlord-tenant cases generated by ChatGPT. | Qualitative evaluation based on predefined criteria (Language comprehension, Accuracy, Completeness, Trustworthiness, Harmless, User-friendly) using simulated case interactions. | JusticeBot is deployed online (justicebot.ca) and has been accessed by users. ChatGPT is available via OpenAI's web interface and API. | True | False | ChatGPT is available via OpenAI's interface/API. JusticeBot is available online at https://justicebot.ca. | Accuracy, reliability, and trustworthiness of LLMs for legal information; ensuring information is up-to-date and properly sourced; difficulty in verifying AI-generated legal content for laypeople; potential for harmful reliance on incorrect information. | ChatGPT's tendency to 'hallucinate' legal facts (false provisions, non-existent cases); ensuring accuracy and reliability for lay users who cannot easily verify information; the limited scope and inflexibility of expert systems like JusticeBot compared to LLMs; defining the boundary between providing legal information and unauthorized legal advice. | Laypeople making harmful decisions based on inaccurate or hallucinated information from AI; provision of misleading or incomplete information; privacy risks associated with user interaction data; potential for bias in AI responses. |
| XZX5nvn_88QJ.pdf | Google_Scholar | Summary of Young -OGEMID Symposium No. 13: “The Role of Artificial Intelligence in Shaping ADR Practices” (July 2023 ) | This paper summarizes a Young-OGEMID virtual symposium discussing the integration of Artificial Intelligence (AI) into Alternative Dispute Resolution (ADR), particularly arbitration. Experts explore AI's opportunities (efficiency, data analysis) and challenges (bias, ethics, transparency), its role in decision-making, and its potential future impact, including on access to justice. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Bias in AI algorithms; unequal access to AI tools leading to a 'digital divide' and tiered justice; lack of transparency ('black box' problem); potential increase in costs/inefficiencies due to verification needs; ethical issues of offloading underserved cases to potentially inferior AI; language barriers for less common dialects. | Promote responsible AI use through education; develop unbiased and transparent AI; ensure broad access to AI tools; maintain human oversight and intervention (Human Experience + AI); focus AI on augmenting human capabilities rather than replacement; leverage AI to empower self-represented litigants. | Empowering self-represented litigants; reducing legal costs; increasing efficiency; overcoming language barriers; addressing resource inequality (digital divide); ensuring procedural fairness and due process. | Self-represented litigants; parties with fewer resources (e.g., SMEs, individuals); parties from developing countries. | Alternative Dispute Resolution (ADR), International Arbitration (Commercial, Investment), Consumer Arbitration, Employment Arbitration. | US, Canada, India, Colombia, Estonia, China, International | NaN | NaN | NaN | True | True | Publicly available tools like ChatGPT (free tier) and free legal databases (worldlii.org, CLOUT) are discussed. Commercial tools (e.g., Lexis+ AI, Jus Mundi) and specific pilot projects (e.g., SUPACE) are also mentioned. | Technical: Improving AI accuracy, reducing bias, handling legal complexity/nuance, ensuring transparency, dealing with limited/confidential data, supporting more languages. Societal/Ethical: Addressing the digital divide, establishing clear ethical guidelines/regulations, building public trust, defining human oversight roles, preventing misuse (deepfakes), ensuring procedural justice. Need for more research on AI vs. human decision-making fairness/effectiveness. | Data limitations (availability, confidentiality, bias); ensuring fairness/mitigating bias; maintaining confidentiality/privacy; addressing the 'black box'/explainability issue; integrating AI with human judgment; overcoming user distrust; cost of sophisticated tools; rapid technological change; potential for misuse. | Unfair/biased outcomes; confidentiality/privacy violations; erosion of public trust in ADR; creation of 'tiered justice'; inaccurate legal research/submissions; anchoring bias in human decision-making; use of deepfakes; challenges to award enforcement due to improper AI use; deskilling professionals. |
| L8yvUKWzscwJ.pdf | Google_Scholar | RULE 11 IS NO MATCH FOR GENERATIVE AI | This paper analyzes whether Federal Rule of Civil Procedure 11 can effectively sanction attorneys who negligently submit court filings containing fictitious cases or false statements of law generated by AI. It concludes Rule 11 is ill-suited for this task and evaluates the emerging judicial response of issuing standing orders, discussing their benefits and drawbacks. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Civil Procedure, Legal Ethics, Professional Responsibility | United States Federal Courts | NaN | NaN | NaN | False | False | NaN | NaN | The paper argues that Federal Rule of Civil Procedure 11 is poorly suited to sanction negligent attorney use of generative AI that results in filings with fictitious cases or false law, due to the safe harbor provision and the higher standards (subjective bad faith or 'akin to contempt') required for sua sponte sanctions. It also identifies challenges with emerging judicial standing orders, such as drafting errors (imprecise terminology), discouraging technology adoption, potential appearance of judicial bias, and creating a problematic patchwork of rules. | The primary risk discussed is attorneys negligently relying on generative AI, leading to the submission of filings containing fictitious cases and false statements of law ('hallucinations'), resulting in ethical breaches and potential sanctions. Secondary risks mentioned include the potential disclosure of confidential information when using AI and the chilling effect of poorly drafted standing orders on the adoption of potentially beneficial technology. |
| -miNsCrBVBwJ.pdf | Google_Scholar | AI-POWERED LAWYERING: AI REASONING MODELS, RETRIEVAL AUGMENTED GENERATION, AND THE FUTURE OF LEGAL PRACTICE | This paper presents a randomized controlled trial assessing the impact of AI reasoning models (OpenAI's o1-preview) and Retrieval Augmented Generation (VLex's Vincent AI) on legal tasks completed by law students. Both AI tools significantly improved the quality and speed of legal work compared to no AI, with o1-preview showing stronger quality gains and Vincent AI demonstrating effectiveness against hallucinations. | True | Market | True | 2.0 | Positive | AI reasoning models (OpenAI o1-preview) and Retrieval-Augmented Generation (RAG) integrated into a legal AI tool (VLex's Vincent AI). | Randomized controlled trial (RCT) with 127 upper-level law students assigned to complete six realistic legal tasks (drafting client email, legal memo, complaint analysis, NDA, motion to consolidate, persuasive letter) using either Vincent AI, o1-preview, or no AI. Work products were blindly graded by experienced lawyers using standardized rubrics assessing quality (accuracy, analysis, organization, clarity, professionalism) and time spent. | Both Vincent AI and o1-preview significantly improved quality in 4/6 tasks and speed in 5/6 tasks compared to no AI. o1-preview yielded larger quality improvements, particularly in analytical depth (significant gains in 3/6 tasks), but resulted in more hallucinations (11 vs 3 for Vincent, 4 for no AI). Vincent AI improved clarity, organization, and professionalism, had the fewest hallucinations, but did not significantly improve analysis scores and had mixed effects on accuracy. | NaN | NaN | NaN | NaN | Litigation (defamation, insurance law, class action procedure, covenants not to compete, civil procedure), Transactional Law (contract drafting - Non-Disclosure Agreement). | USA (Tasks involved Tenth Circuit, Massachusetts, New Hampshire, Minnesota, Indiana, Nevada law). | Vincent AI uses RAG, integrating foundational LLMs (e.g., GPT-4, GPT-4o) with VLex's legal database (case law, statutes, regulations, etc.). OpenAI's o1-preview is a general-purpose reasoning model; its specific training data is not detailed. | The paper evaluates existing tools. Vincent AI uses Retrieval-Augmented Generation (RAG) and automated prompting. o1-preview uses enhanced compute at inference for step-by-step processing and internal chain-of-reasoning, refined through large-scale reinforcement learning. | The study provided participants with free access to o1-preview (via OpenAI Plus accounts) and Vincent AI (via institutional subscription or complimentary access from VLex) through a Canvas interface for the duration of the experiment. | True | False | Vincent AI is a commercial product available via subscription from VLex. o1-preview was available via OpenAI API access (typically paid). | Lack of standardized benchmarks for complex lawyering tasks; need for more empirical evaluation (like RCTs) of AI tools in law. Potential reduction in benefit for higher-skilled individuals. Need to study integration of RAG and reasoning models. Understanding AI's impact on legal education and skill development. | Evaluating AI's impact on nuanced aspects of legal work like analysis and accuracy. Ensuring AI tools minimize hallucinations while maximizing relevance. Variability in AI performance across different types of legal tasks (e.g., litigation vs. transactional). Tools may benefit lower-skilled users more, potentially reducing quality for high-skilled users. | Hallucinations (generating fake cases or inaccurate information), particularly with reasoning models like o1-preview tested here. Potential for AI tools to decrease accuracy in some contexts. Over-reliance on AI potentially undermining human judgment or skill development. Risk of reduced performance quality for high-skilled individuals when using AI. |
| GV6mowVAwRsJ.pdf | Google_Scholar | Legal AI: Enhancing Justice through Technology, Practical Considerations | This paper explores the applications, benefits, and limitations of AI, particularly LLMs, in the Indian legal field, aiming to improve efficiency and access to justice for legal professionals, government bodies, and citizens. It discusses various AI models and emphasizes the need for careful implementation, oversight, and customization while considering risks like hallucinations and the digital divide. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | AI hallucinations/errors, data privacy/security concerns, need for customization, digital divide, resistance to technology, cost, vendor dependence, need for professional training. | Human oversight, robust data protection, AI customization, developing inclusive use cases, training professionals, tailored implementation architecture, policy changes, prompt engineering, tuning models for local contexts. | Access to legal information/resources, efficient case resolution, cost reduction in legal services, automation of legal/administrative tasks (e.g., filing applications, drafting), overcoming language barriers. | General public/citizens, litigants, legal professionals, government departments (collectorate, police), particularly targeting issues relevant to developing regions (digital divide). | General Law | India | NaN | NaN | NaN | False | False | NaN | Need for improved AI accuracy and robustness against hallucinations, better customization for specific legal/local contexts, addressing the digital divide, ensuring data privacy, effective integration into workflows, training for legal professionals. | Prompt engineering, effective communication (especially local languages), contextual tuning, ensuring accuracy/avoiding hallucinations, data privacy, cost, vendor dependence, need for end-to-end architecture design. | AI hallucinations leading to incorrect information, data privacy and security breaches, exacerbation of the digital divide, potential for erratic, divisive, or harmful outcomes due to lack of contextual understanding. |
| S4wC53qpRXQJ.pdf | Google_Scholar | How to Retain Being a Human Lawyer While Using Generative AI | This paper examines the transformative impact of generative AI on the legal profession, outlining potential issues like overreliance and embedded biases. It advocates for legal professionals and educational institutions to adapt by emphasizing and cultivating uniquely human skills such as emotional intelligence, storytelling, ethical judgment, and practical experience. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | Generative AI providing false or biased information to self-represented individuals, leading to harm; overreliance on AI; AI 'hallucinations' and deepfakes; inherent biases in AI models. | Regulation and potential licensing of AI products offering legal assistance to the public; revision of rules on unauthorized practice of law to address AI; emphasis on human oversight, professional judgment, and ethical responsibilities for lawyers using AI; adaptation of legal education to cultivate human-centric skills. | Reliability and regulation of AI tools providing legal information/assistance to self-represented individuals; ethical use of AI in legal practice impacting service delivery. | Self-represented individuals | General legal practice | United States (with a focus on California) | NaN | NaN | NaN | False | False | NaN | Inadequate specific regulations and ethical guidelines for AI systems providing legal assistance directly to the public, especially self-represented individuals; risk of harm from unreliable or biased AI-generated legal information; need for legal education and the profession to fully adapt to AI's impact while preserving human-centric lawyering skills. | NaN | Overreliance on AI; AI 'hallucinations' (false outputs); deepfakes; displacement of legal professionals (knowledge workers); algorithmic bias (e.g., racist, gender) from training data and developer demographics; harm to self-represented individuals from inaccurate AI-generated legal information; invasion of privacy interests; potential for unauthorized practice of law by AI if not properly regulated. |
| EnhancingConversationalAgentswithGenerativeAI.pdf | Google_Scholar | Enhancing Conversational Agents with Generative AI: A Framework for Creating More Adaptive and Context-aware chatbots | This paper explores how generative AI, particularly models like GPT based on Transformer architecture, can enhance conversational agents (chatbots) by making them more adaptive, context-aware, and capable of human-like interactions. It proposes a general framework for developing such chatbots and discusses applications, challenges (technical and ethical), and future trends like multimodal AI. | True | NaN | True | 3.0 | NaN | Proposes a general framework for building adaptive and context-aware chatbots using Generative AI (e.g., GPT, Transformer models). | Illustrative case studies of existing chatbots (e.g., ChatGPT, Sephora, Babylon Health, Netflix, Bank of America's Erica) are mentioned, but no specific evaluation of the proposed framework itself is described. | NaN | NaN | NaN | NaN | NaN | NaN | International | Discusses the need for diverse, high-quality data (e.g., customer service transcripts, FAQs, product descriptions) and mentions large pre-trained models (like GPT) trained on general web text, advocating for domain-specific fine-tuning. | Proposes a conceptual framework involving: defining objectives/use cases, data collection/preprocessing, model selection (e.g., GPT), contextual integration (state tracking, personalization), and continuous learning (reinforcement learning, human-in-the-loop). | Discusses potential deployment environments (website, mobile app) and application areas (customer service, healthcare, e-commerce) but no specific strategy for the proposed framework. | False | False | NaN | NaN | Technical limitations (memory constraints, computational power requirements, training time) and ethical challenges (bias inherited from data, privacy concerns, potential for misinformation). | Bias leading to unfair or inappropriate responses, privacy risks due to collection of personal data, generation and spread of misinformation (noted as particularly problematic in domains like healthcare or legal services). |
| AE9Y5NtXFKEJ.pdf | Google_Scholar | Large Language Scholarship: Generative AI in the Legal Academy | This paper argues that generative AI will inevitably transform legal scholarship production, analyzing the systemic impacts on academics, law schools, and the legal system. It predicts AI adoption trends, examines implications for stakeholders, and offers guidance for responsible integration, including practical advice on using AI tools. | True | Market | True | 3.0 | Positive | Generative AI / Large Language Models (broad discussion) | NaN | NaN | NaN | NaN | NaN | NaN | General Legal Scholarship | United States | NaN | NaN | NaN | True | False | Discusses commercially available AI tools (e.g., ChatGPT, Claude, Gemini, Perplexity) with both free and paid tiers; recommends paid plans. Also mentions legal-specific tools requiring subscriptions (e.g., Lexis+ AI, Westlaw CoCounsel). | NaN | Managing information overload; preventing cognitive deskilling among scholars; adapting evaluation metrics (hiring, tenure) to account for AI-assisted productivity; developing effective institutional norms, policies, and training for AI use; addressing ethical concerns (bias, data appropriation); avoiding negative impacts on teaching quality; navigating potential reinforcement of academic hierarchies. | Increased misinformation (e.g., "scholarly deepfakes" with manufactured citations or analyses); cognitive deskilling of scholars; alienation from the scholarly craft; reinforcement of biases embedded in AI training data; erosion of traditional academic credentials as signals of expertise; creation of unsustainable publication pressures; potential negative impacts on teaching quality; ethical risks related to appropriation of author works in training data. |
| q-DTIJ8ci6YJ.pdf | Google_Scholar | Bridging the Gap t o Every American: How a National Regulat ory Sandbo x Can Pr ompt Radical Collabor ation t o Adopt Legal Artificial Intelligence T ools | The paper highlights the significant access to justice gap in the United States, particularly for low-income individuals facing civil legal issues. It advocates for the creation of a national regulatory sandbox, overseen by the U.S. Supreme Court, to foster the development and responsible adoption of AI-powered legal tools to provide affordable legal services. | True | Idealistic | True | 1.0 | Positive | Proposal for a National Regulatory Sandbox overseen by a National Office of Legal Services Innovation under the U.S. Supreme Court to regulate and foster AI-driven alternative legal service providers. | The proposed national regulatory sandbox is a policy recommendation and has not been tested. The paper references the operational data (consumer complaints, number of people served) from the existing Utah regulatory sandbox as evidence of the potential success of such an approach. | N/A (The proposed national sandbox has not been implemented or tested). Results cited for the analogous Utah sandbox include assisting over 2,500 people with a low rate of consumer harm complaints (approx. 1 per 6,851 services). | High cost of legal services, insufficient legal aid for low-income populations leading to unresolved civil matters, negative life consequences (financial, health, housing) stemming from lack of legal help, complexity of the legal system, underfunding and overwork in traditional legal aid and public defender systems, unequal access based on income. | Establishment of a National Regulatory Sandbox to allow controlled experimentation and deployment of AI-powered alternative legal service providers. Encouraging innovation in legal tech, particularly AI tools (like chatbots, document simplifiers, legal marketplaces), to offer low-cost, accessible legal information and services. | Access to justice (civil), Alternative legal service providers, Legal technology regulation, Housing law, Immigration law, Domestic violence, Healthcare law, Discrimination law, Employment law, Consumer contracts (leases, mortgages, credit cards), Dispute resolution. | Low-income Americans, Economically vulnerable populations, Underserved communities facing civil legal issues. | Civil Law (broadly, including family, housing, consumer, contract, immigration, employment law) | United States | NaN | Policy design based on existing models (Utah regulatory sandbox, EU initiatives) and policy guidelines (CGAP's Practical Guide for Policy Makers), focusing on eligibility criteria, governance structure, experimentation timelines, evaluation metrics, and exit options. | N/A (The paper proposes the creation and implementation of the sandbox, but it is not currently deployed). | False | False | NaN | Lack of affordable and accessible civil legal services, inadequacy of traditional service models, regulatory barriers to innovation in legal services, potential for AI to exacerbate inequality if not implemented equitably, digital divide limiting access to tech-based solutions. | Distrust of AI among legal professionals and judiciary, data privacy concerns, potential job displacement in the legal sector, ensuring AI tools are effective and do not cause consumer harm, overcoming the digital divide, gaining stakeholder buy-in for regulatory innovation. | Consumer harm (inaccurate advice/results, unnecessary services), Data privacy violations, Widening the access-to-justice gap if AI tools are costly or inaccessible, Creation of a two-tiered legal system (high-quality human lawyers vs. potentially inferior AI for the poor), Potential job displacement for legal professionals. |
| rpfKPBonepgJ.pdf | Google_Scholar | AI CHATBOT CHAT GPT AND THE THEMES IT CREATES ON TURKEY'S INTERNET AGENDA | This paper analyzes Turkish internet news articles about ChatGPT published between November 30, 2022, and January 31, 2023, to identify key discussion themes, perceived opportunities, and concerns. The analysis reveals major themes including education, business life, IT sector, coding, daily life, investment advice, and creative content generation, highlighting anxieties around plagiarism, job displacement, misinformation, and bias, alongside potential benefits like efficiency and content generation. | False | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General (mentions law, copyright) | Turkey | NaN | Document analysis (doküman incelemesi) and inductive content analysis (tümevarımsal içerik analizi) of news articles. | NaN | True | True | ChatGPT was available via the OpenAI website, including a free access tier at the time the analyzed articles were published. | Need for more research on societal impacts of large language models, user awareness and digital literacy concerning AI outputs, and critical algorithm studies perspectives (bias, inequality etc.), particularly within the Turkish research context. | NaN | Educational integrity issues (plagiarism, cheating); misinformation, bias, and discrimination in AI outputs; job displacement across various sectors (e.g., journalism, law, coding); cybersecurity threats (malware generation); potential for misuse in generating harmful content (e.g., deepfake scripts); over-reliance on potentially inaccurate AI advice; ethical concerns regarding AI development and lack of transparency ('black box'). |
| tuhct2ee6vgJ.pdf | Google_Scholar | Unveiling Retail Insights with Generative AI | This internship report explores using Generative AI (GPT-4) combined with change detection algorithms (PELT) to analyze retail transactional data from Sonae MC's loyalty program. The goal is to automatically detect significant changes in business metrics, interpret them using GPT-4, and generate communication drafts for category directors, improving responsiveness. | True | Market | True | 1.0 | NaN | A combination of change detection algorithms (specifically PELT with RBF cost function) to identify shifts in time-series retail data, followed by GPT-4 for interpreting these changes and generating automated communication drafts. | Phase 1: GPT-4 tested on ability to extract key performance indicators (RH values) from dashboard images, comparing general vs. refined prompts (accuracy measured). Phase 2: PELT change detection algorithm tested on 4 years of historical Sonae MC transactional data (weekly aggregated), compared visually against CUSUM, parameter tuning (penalty factor). Phase 3: GPT-4 interpretation of PELT-detected recent change-points evaluated qualitatively by internal Sonae MC teams (Advanced Analytics, Analytical, Business User/Category Director). | Phase 1 showed GPT-4 unreliable for extracting precise numerical values (max 40% accuracy with refined prompt) but capable of generating useful business insights/suggestions. Phase 2 validated PELT as suitable for detecting significant shifts, with a penalty factor of 14 for historical analysis and 1 for recent change monitoring. Phase 3 successfully demonstrated GPT-4 interpreting changes detected by PELT and generating insightful, actionable communication drafts positively received by internal stakeholders. | NaN | NaN | NaN | NaN | NaN | Portugal | Proprietary Sonae MC transactional data from the Continente Loyalty Card program (4 years of historical data, aggregated weekly), covering metrics like sales, customers, quantity, price, etc. for specific product categories. GPT-4 was prompted using this data (or derived information) and potentially 'base knowledge' like internal documentation/glossaries. | CRISP-DM (Cross-Industry Standard Process for Data Mining) | Internal proof-of-concept simulation within Sonae MC. No external deployment mentioned. | False | False | NaN | NaN | GPT-4 unreliability in extracting exact numerical values from dashboards; need for effective prompt engineering; difficulty in accurately removing non-cyclical seasonality from time-series data before change detection; selecting appropriate penalty factors for change detection sensitivity. | GPT-4 hallucinations or inaccuracies leading to incorrect business insights or communications; change detection model misinterpreting residual seasonality or noise as significant shifts, causing false alerts; reliance on external LLMs (GPT-4 via Azure OpenAI API). |
| 6Lu9Wgf2rMcJ.pdf | Google_Scholar | Public Consultation Response on “Copyright and AI” [Docket No. 2023-06] | This paper is a response to the U.S. Copyright Office regarding AI and copyright, arguing AI is a human-controlled tool that requires adapting existing laws, not new ones. It discusses fair use, authorship, transparency, and the need for a balanced approach to protect creators and foster innovation for societal benefit. | True | Idealistic | True | 3.0 | Positive | Generative AI / Large Language Models (e.g. ChatGPT, DALLE-3) | NaN | NaN | Misconceptions about AI's nature (e.g., anthropomorphism), hindering clear legal discourse; Lack of transparency in AI training data, impeding creators' ability to enforce rights; Difficulty in applying traditional legal concepts (e.g., authorship, fair use) to AI. | Promote an accurate understanding of AI as a human-controlled tool; Adapt existing legal frameworks, relying on courts for interpretation, rather than rushing new AI-specific laws; Increase transparency in AI systems (e.g., training data disclosure) and explore robust accountability mechanisms like 'networked responsibility'. | Ensuring fairness for creators (e.g., regarding use of their works in AI training, compensation); Upholding human authorship in AI-assisted creations; Enhancing transparency of AI systems for copyright enforcement. | Creators (e.g., artists, writers) and individuals whose data is used for AI training. | Copyright Law, Intellectual Property Law, Data Privacy | United States | The paper discusses that these models are trained on vast amounts of data, including copyrighted works, web-scraped material, proprietary enterprise data, and user-generated content. It does not specify a single dataset for a technique it studies. | NaN | NaN | True | False | The paper mentions existing generative AI tools like ChatGPT and DALLE-3, some of which are publicly accessible for use (e.g., ChatGPT via OpenAI's platform). | Need for clear application of fair use to AI training and outputs; Development of effective, feasible opt-in/opt-out mechanisms and compensation models for creators; Establishing clear lines of authorship and responsibility for AI-assisted works. | For enterprises adopting Generative AI: regulatory compliance (e.g., data use, privacy, security), ownership of proprietary data used for training, integration with existing workflows. For consumer data applications: gaining access to data, overcoming privacy/security concerns. | Hasty regulatory interventions based on misconceptions leading to stifled innovation or ineffective rules; Infringement of copyright through AI outputs if fair use and licensing are not clarified; Unfair exploitation of creators' works for AI training without consent or compensation; Generation of harmful or undesirable content if AI tools are not properly controlled or are misused. |
| 3631935.pdf | Google_Scholar | Why Are Lawyers Afraid of AI? | The article discusses the rapid adoption and impact of generative AI, particularly ChatGPT, in the legal profession, highlighting both its potential to revolutionize legal work and the significant concerns lawyers have regarding its accuracy, ethics, and impact on jobs. It also touches upon early instances of misuse and the legal community's efforts to establish guidelines and responsible deployment strategies. | True | Market | True | 3.0 | Neutral | Generative AI (e.g., ChatGPT, CoCounsel, Harvey.AI, Google Bard) | Reports on Andrew Perlman's experiment using ChatGPT to generate a paper and testing Bing Chat. Perlman prompted ChatGPT for his paper and assessed Bing Chat's answers to legal questions. | Perlman found Bing Chat operated at the level of a B to B+ law student, but its knowledge of certain legal doctrines like personal jurisdiction was problematic and incomplete. | High cost of legal services making them unaffordable for a large portion of the population (lower-income and middle-class individuals), with an estimated 80% of lower-income individuals unable to afford a lawyer and 40-60% of middle-class legal needs unmet. | Development of AI-powered curated platforms offering basic professional-level legal expertise at lower costs, similar to tax preparation software, to improve affordability and access to legal services. | Affordability and accessibility of legal services, unmet legal needs | Lower-income individuals and middle-class Americans | General legal practice (covering document generation, legal research, case briefing) | U.S., U.K., Canada | General internet data and other sources for foundational models like GPT-4 (used by ChatGPT and CoCounsel); specific training data for proprietary tools like CoCounsel or Harvey.AI is not detailed but likely involves legal texts for fine-tuning. | NaN | Gradual rollout in law firms (e.g., Dykema Gossett adopting CoCounsel), use of LLM governance tools (e.g., Lega) to monitor compliance and manage risks. | True | True | Publicly available tools like ChatGPT (with a free tier) and Google Bard are mentioned. Commercial tools like CoCounsel are available to subscribing firms. | Persistent unaffordability of legal services for many, leading to unmet legal needs. Current AI's limitations in accuracy (e.g., 'problematic and incomplete' knowledge of certain doctrines) and the need for skilled prompting also present gaps in its effectiveness for broad A2J application. | User misunderstanding of AI capabilities (e.g., treating LLMs as search engines), lack of established guidelines for using new AI tools, ensuring data confidentiality with client information, the rapid pace of AI development outpacing governance, and integrating AI responsibly into legal workflows without compromising professional responsibilities or accuracy. | Generation of inaccurate or fictitious information ('hallucinations') by AI, such as citing non-existent cases. General ethical concerns about AI use in legal work. Potential for misuse due to lack of user understanding of the technology's limitations. |
| AiNuo2C-gn4J.pdf | Google_Scholar | Interpretable Long-Form Legal Question Answering with\nRetrieval-Augmented Large Language Models | The paper proposes a retrieval-augmented LLM methodology for generating interpretable, long-form answers to French statutory law questions to improve access to legal information. It introduces the LLeQA dataset for this task and finds that while models generate fluent answers, they often suffer from factual inaccuracies. | True | Idealistic | True | 1.0 | Positive | A retrieve-then-read pipeline using a fine-tuned bi-encoder retriever (CamemBERT-based) and instruction-tuned Large Language Models (LLMs like Vicuna, WizardLM, TULU, Guanaco) adapted via in-context learning or QLoRA finetuning. Includes extractive rationale generation (paragraph IDs). | Retriever evaluated using Recall@k (k=5, 10) and MRR@10 on LLeQA dev set. Generator evaluated using METEOR for answer quality and F1 score for rationale extraction on LLeQA test set, supplemented by qualitative analysis. | Fine-tuned CamemBERT retriever achieved R@5=48.6, R@10=60.6. Fine-tuned WizardLM-1.0 (7B) generator achieved the best METEOR score (20.4). Qualitative analysis revealed significant hallucination issues despite syntactic correctness. Rationale extraction F1 was very low (<3.5%). | Lack of legal understanding/literacy, prohibitive cost of legal assistance, difficulty navigating legal complexity, prevalence of unhelpful/commercial online legal advice. | Develop automated, interpretable long-form legal question answering systems using retrieval-augmented LLMs to provide affordable, accessible legal information. | Access to legal information, automated legal aid, statutory law question answering (covering housing, healthcare, family, work, immigration, money, privacy, justice). | Vulnerable individuals, laypersons, Belgian citizens, marginalized parties, people unable to afford legal assistance. | Statutory law (multiple domains) | Belgium | LLeQA dataset: 1,868 expert-annotated French legal questions with detailed answers and references to relevant Belgian statutory articles (27,942 article corpus). Paragraph-level rationales partly expert-annotated, partly synthetically generated (gpt-3.5-turbo). Sourced via partnership with Belgian non-profit Droits Quotidiens. | Retrieve-then-read pipeline; Bi-encoder retriever fine-tuned contrastively; LLM reader adapted via in-context learning and parameter-efficient fine-tuning (QLoRA); Dynamic NTK-aware scaling for context extension; Extractive rationale generation via prompting. | Public release of code, data, and models on GitHub. | True | True | Public release of code, dataset (LLeQA), and model checkpoints on GitHub. | Inadequacy of automatic metrics for evaluating long-form QA factuality; LLM propensity for hallucination; Need for improved retrieval performance; Scalability of reliable rationale generation for multi-document contexts. | Handling long legal document context within LLM limits; Effective domain adaptation for retrieval; Ensuring factual accuracy and mitigating hallucinations in generation; Accurate evaluation of long-form answers; Generating faithful and interpretable rationales; Computational resource constraints. | Laypersons relying on potentially inaccurate or hallucinated AI-generated legal advice, leading to detrimental real-world consequences; Potential for misuse despite research purpose limitations. |
| LMgkDC4nxD0J.pdf | Google_Scholar | ChatGPT as a Copilot for Investigating Digital Evidence | This paper explores using ChatGPT (specifically GPT-4) to assist digital forensic investigators with tasks like generating structured queries from natural language, summarizing and analyzing chat communications, and analyzing search results. It finds that ChatGPT shows significant promise as an investigative assistant once provided with relevant domain knowledge, like query languages and data models. | True | Market | True | 2.0 | NaN | Using ChatGPT/GPT-4, prompted with domain-specific knowledge (Hansken trace model, Hansken Query Language documentation), to perform digital forensics tasks: natural language to structured query conversion, chat summarization/analysis/visualization, and cross-evidence analysis. | Three experiments using ChatGPT/GPT-4: 1) Generating Hansken Query Language (HQL) queries from natural language after being prompted with HQL documentation and examples. 2) Summarizing, analyzing roles, and visualizing chat messages from a fictitious case dataset (Crystal Clear). 3) Analyzing and correlating browser history, chat summaries, and GPS data from the same fictitious case. | ChatGPT successfully generated valid HQL queries after being prompted with the model and language specifics. It produced accurate summaries, role descriptions, and network visualizations (TikZ) from chat data. It also demonstrated capability in analyzing and cross-referencing diverse digital evidence types (browser history, chats, GPS data) within a case scenario. | NaN | NaN | NaN | NaN | Digital Forensics, eDiscovery, Criminal Law | Netherlands, UK, US mentioned, but techniques potentially International. | The underlying model (GPT-4) was pre-trained by OpenAI on broad data. The experiments involved prompting the model with provided Hansken documentation (manual, trace model, cheat sheet) and data from a fictitious case (Crystal Clear training case: chat logs, browser history, GPS coordinates). | Empirical evaluation via prompt engineering experiments on specific digital forensics tasks. | NaN | True | False | Access via OpenAI's ChatGPT web application (GPT-4 access typically requires subscription). | Need for user experience evaluation in real investigations with larger datasets; potential limitations of current models (hallucinations, context size); need for fine-tuning models specifically for investigative tasks (SleuthGPT concept). | Prompt size limits requiring data splitting, maintaining consistency across prompts, necessity for format reminders, requirement for human correction of AI mistakes, need for detailed domain-specific context prompting, potential limitations of RLHF alignment ("harmlessness") for analyzing criminal content. | Hallucinations (factual inaccuracies), cost and privacy concerns associated with cloud-based models handling sensitive legal/investigative data, RLHF alignment potentially hindering effective analysis of criminal communications. |
| 1hDJ716g7tIJ.pdf | Google_Scholar | TOWARDS THE EXPLOITATION OF LLM-BASED CHATBOT FOR PROVIDING LEGAL SUPPORT TO PALESTINIAN COOPERATIVES | This paper presents the development and evaluation of an LLM-based chatbot designed to provide legal support to Palestinian cooperatives by answering questions related to cooperative law. The chatbot, utilizing ChatGPT and LlamaIndex with curated legal documents and Q&A datasets, achieved an overall accuracy of 82% on expert-generated queries. | True | Idealistic | True | 1.0 | Positive | LLM-based chatbot using ChatGPT API and LlamaIndex for vectorization and indexing of Palestinian cooperative law documents and Q&A datasets. | Evaluation using 50 queries generated by a legal expert. Chatbot's answers were compared to the expert's answers, and metrics including overall accuracy, average satisfaction score (rated by legal counsel), precision, recall, and F1-score were calculated. | The chatbot achieved an overall accuracy of 82% (41 out of 50 questions answered correctly or relevantly). The F1 score was 79%, and the average satisfaction score was 78.3%. For distinguishing right/related answers, precision was 1.0, recall 0.79, and F1-score 0.88. | The urgent need for readily available legal answers for cooperative members due to new laws, the labor-intensive effort required for manual responses, and the large number of cooperative members needing timely assistance. | Developing an LLM-based chatbot available 24/7 to provide legal information and answer inquiries about Palestinian cooperative law. | Access to legal information and support regarding Palestinian cooperative law. | Palestinian cooperatives, cooperative societies, cooperative unions, and their members. | Cooperative law | Palestine | A dataset comprising: 1) Formal Legal Documents (Law No. 20 of 2017 on Cooperatives, Cooperatives Bylaws, Housing Cooperatives Bylaws) - text data. 2) Question and Answers Dataset consisting of a human-generated set (40 Q&A by a legal advisor) and a ChatGPT-generated set (350 Q&A based on Law No. 20 of 2017, prompted to format like a legal advisor). These documents were indexed by LlamaIndex for the chatbot. | The system uses LlamaIndex to index legal documents and Q&A datasets, creating vectors for document chunks (600 tokens, 50 token overlap) to overcome ChatGPT's token limits. A LlamaIndex query engine, leveraging ChatGPT, is used to answer legal queries. Prompt engineering was used to generate a portion of the Q&A dataset. | NaN | True | True | The paper states a GitHub repository is available for more information and details, with a placeholder link: "Github". | Instances of incorrect chatbot answers, need for continuous development to improve accuracy and reliability, the necessity of transparency about chatbot limitations, insufficient Q&A data for long or complex legal articles, and the need for post-processing of chatbot answers. | The primary challenge was handling the large volume of textual data, which exceeded ChatGPT GPT-4’s token processing limit, necessitating the use of LlamaIndex for document chunking and vectorization. Other challenges included ensuring sufficient Q&A data for comprehensive coverage of all legal articles, especially longer ones, and providing context for specific bylaws. | The chatbot providing incorrect answers, which could lead users to unintentionally violate legal regulations. |
| tags21nqSE4J.pdf | Google_Scholar | AI, Justice, and the Ecosystem Approach – Notes from the OpenNyAI Mission | OpenNyAI is an Indian initiative leveraging AI to enhance access to justice by developing open-source public goods like AI models and APIs, supported by a collaborative ecosystem of legal and tech communities. The paper highlights projects like Jugalbandi, a conversational AI for legal information in local languages, and emphasizes transparent, inclusive practices to make justice more accessible. | True | Idealistic | True | 1.0 | Positive | OpenNyAI's initiatives including: NLP models for legal text analysis (Rhetorical Roles Model, Legal Named Entity Recognition Model, Judgment Summarizer); Jugalbandi Stack (LLM-based conversational AI for multilingual information access); Jugalbandi Studio (open-source chatbot development platform). | User adoption (7000+ unique users for models) and an open-source testing environment provided by Jugalbandi Studio. No formal benchmarks or detailed evaluation procedures are mentioned. | The models (Rhetorical Roles, NER, Summarizer) are reported as generating value across law firms, government bodies, and other institutions. Jugalbandi enables multilingual access to information on government schemes and legal aid. Jugalbandi Studio allows organizations to rapidly iterate on chatbot development without extensive technical expertise or large capital investments. | Initial landscape: significant divide between legal and tech professionals, lack of open-source reference solutions, and poor data quality. Broader A2J issues: information asymmetry, language barriers. Tech deployment challenges: understanding AI capabilities, resource constraints for SMEs/NGOs. | Building a collaborative ecosystem (OpenNyAI mission); developing open-source AI public goods (models, Jugalbandi Stack, Jugalbandi Studio); creating data annotation pipelines; using AI for language access and information retrieval; providing tools to lower deployment barriers; ensuring data privacy. | Language access in legal information, access to government schemes/entitlements, legal aid, access to laws and court procedures, improving efficiency of legal processes, enhancing capacity of legal professionals and civil society. | General public in India, particularly those facing information asymmetry and language barriers, including farmers, women, victims of domestic abuse, litigants, students, lawyers, judges, SMEs, and NGOs. | Administrative law (government schemes), family law (domestic abuse), criminal law (investigation guidelines), civil procedure (court processes), dispute resolution (ODR), general legal information access. | India | For NLP models: Meticulously annotated datasets of Indian court judgments, created via a data annotation pipeline involving law students. For Jugalbandi: Verified knowledge bases curated by subject matter experts. These are domain-specific (legal) and structured (annotated) data. | Ecosystem approach, interdisciplinary collaboration (legal, tech, academia, civil society), community building (Maker Residency, learning circles), open-source development, development of a data annotation pipeline. | Models used by over 7000 users. Jugalbandi Stack is a free and open tech stack. Jugalbandi Studio is an open-source platform running on an organization's own cloud server. General strategy is creating AI public goods and community empowerment. | True | True | Jugalbandi Stack is described as a 'free and open tech stack'. Jugalbandi Studio is an 'open-source platform'. GitHub links for OpenNyAI and Jugalbandi are provided in the references, indicating open accessibility of resources. | A 'sheer knowledge gap that exists in accessing these technologies' among potential users and deployers of AI solutions. | Understanding AI technology's capabilities by non-technical users/organizations, lack of resources (financial, technical) for SMEs/NGOs to deploy AI at scale, ensuring high-quality data for training models, bridging communication gaps between legal and tech communities. | Data privacy of users interacting with AI systems, particularly concerning sensitive personal information. Mitigation includes PII filtering and local/private cloud deployment. |
| 4tKvGBLOdNEJ.pdf | Google_Scholar | How We Learned to Stop Worry ing and Love AI: Analyzing the Rapid Evol ution of Generative Pre - Trained Transformer (GPT) and its Impacts on Law, Business, and Society | This paper surveys the rapid evolution of Artificial Intelligence (AI), focusing on Generative Pre-trained Transformer (GPT) models like ChatGPT and their broad impacts across law, business, society, and national security. It examines AI's history, current capabilities, potential applications, significant risks (including bias, disinformation, job displacement, and security threats), and emerging governance efforts in the US and EU. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | Lack of access to legal help, potential for algorithmic discrimination and bias, inaccuracy/unreliability of AI tools (hallucinations, fake citations). | Leveraging technology for ADR (e.g., online platforms), promoting responsible AI development through governance frameworks and supporting R&D infrastructure. | Alternative Dispute Resolution (ADR), potential courtroom applications, general legal problem resolution. | General population facing legal problems; minorities and people of color susceptible to algorithmic bias. | AI Regulation, Corporate Governance, Data Protection/Privacy, Cybersecurity Law, Practice of Law (general), ADR, Antitrust, Employment Law, Intellectual Property, National Security Law. | US, EU, Italy, China, International | Refers to training data used by others, e.g., GPT-3 trained on large unlabeled text datasets (Wikipedia, websites), largely English; AlphaZero trained via self-play; RLHF uses human feedback. | NaN | NaN | False | False | NaN | Need for robust governance frameworks (explainability, accountability, fairness), closing the digital divide in AI resource access, ensuring AI reliability (addressing hallucinations), mitigating bias and discrimination, bridging the gap between technology development and legal/policy adaptation. | NaN | Disinformation/Deepfakes, cybersecurity threats, job displacement, algorithmic bias/discrimination, algorithmic collusion, privacy violations, national security risks (arms race, autonomous weapons), erosion of trust, manipulation, safety risks (unsafe/ineffective systems), intellectual property infringement, exacerbation of inequality. |
| Yyq2ZqBnxDMJ.pdf | Google_Scholar | CHATMAP : LARGE LANGUAGE MODEL INTERACTION WITH CARTOGRAPHIC DATA | This paper presents a proof-of-concept for fine-tuning a small large language model (LLM) using an artificially generated dataset to enable natural language queries about OpenStreetMap (OSM) cartographic data. The approach aims to provide a linguistic interface for users to inquire about various attributes of specific urban locations. | True | NaN | True | 1.0 | NaN | Fine-tuning a 1B parameter LLM (Falcon 1B RW) using Low Rank Adaptation (LORA) and 8-bit quantization with an artificially curated dataset (prompt-answer pairs generated by ChatGPT 3.5-turbo from OpenStreetMap data descriptions) to enable natural language interaction with cartographic data. | The fine-tuned model was queried with preprompts (verbal descriptions of OpenStreetMap data) for geolocations not in the fine-tuning dataset, using various question types. Qualitative examples of model responses were provided, and validation loss was monitored during training. | The fine-tuned model demonstrated 'early signs of emerging abilities' by providing plausible responses to queries about urban areas based on OSM data, including for locations not in the fine-tuning set. Examples showed the model classifying regions, assessing suitability for living/tourism, and suggesting business viability. | NaN | NaN | NaN | NaN | NaN | Istanbul, Turkey | An artificial dataset of 4111 prompt-answer pairs generated by OpenAI ChatGPT 3.5-turbo. These pairs were based on 81 'preprompts'—verbal descriptions of OpenStreetMap (OSM) data (amenities, buildings, land use, roads, etc.) for circular areas (300m radius) in selected districts of Istanbul. The data is domain-specific (geospatial/urban) and unstructured (natural language text). | Proof-of-concept development involving: 1) Extraction and verbalization of OSM data into 'preprompts'. 2) Artificial dataset curation using a teacher LLM (ChatGPT 3.5-turbo) to generate prompt-response pairs. 3) Fine-tuning a pre-trained LLM (Falcon 1B RW) using Low Rank Adaptation (LORA) and 8-bit quantization. | NaN | False | False | NaN | NaN | Simplifying user interaction with complex cartographic datasets and overcoming the need for specialized tools or domain expertise, particularly under constraints of minimal computational budget and limited human-labeled data by using a teacher model for dataset creation and efficient fine-tuning. | Potential for the LLM to generate answers not sufficiently supported by the provided cartographic data, highlighted by the need to carefully instruct the teacher model during dataset creation to avoid responses based on insufficient information. |
| BrenoNiero_theFutureofLawFirmsRG.pdf | Google_Scholar | AI-Law Firms of the future. The integration of artificial intelligence and other cutting-edge technologies for value creation | This paper explores the integration of AI (including generative AI), SuperApps, Metaverse, and AI cybersecurity into law firms to enhance operational efficiency and client services, citing a case study of Allen & Overy using the Harvey platform. It proposes a conceptual prototype of a 'SuperApp' for secure lawyer-client interaction and streamlined workflows, while acknowledging challenges like data privacy and potential job displacement. | True | Market | True | 1.0 | NaN | Conceptual prototype of an integrated system for law firms featuring a SuperApp, Adaptive AI, Metaverse integration for meetings, and AI-based Cybersecurity. | NaN | NaN | NaN | NaN | NaN | NaN | General legal practice | International | The paper mentions the Harvey platform used by Allen & Overy is based on GPT-3 architecture and trained on 'a vast amount of legal documents and contracts'. The proposed prototype implies use of private client data and legal knowledge bases, but specifics are not provided. | Conceptual design based on analysis of technology trends (Gartner: SuperApps, Adaptive AI, Metaverse, AI Cybersecurity) and layered architecture concepts (McKinsey: Client Engagement, AI-powered systems, Core Technologies and Data). | NaN | False | False | NaN | Need for AI systems to be transparent, explainable, and accountable; requirement for ethical and legal frameworks governing AI use in law. | Balancing AI performance with data security (especially regarding sensitive client data); potential widening of the social gap between clients and lawyers; need for continuous learning and adaptation for AI systems; integrating diverse technologies effectively. | Job displacement for human lawyers and legal staff; potential for AI algorithms to reinforce existing biases and perpetuate discrimination; misuse or mishandling of sensitive client data; cyber-attacks. |
| KFrN-E4j064J.pdf | Google_Scholar | Generative AI in the Attorney-Client Relationship: An Exercise in Critical Revision and Client Management | The paper proposes educational exercises for law students simulating scenarios where clients present flawed AI-generated legal documents like motions to suppress. These exercises aim to develop students' skills in critically revising AI output and managing client expectations and resistance, particularly regarding cost or misguided confidence in AI. | True | Market | True | 1.0 | Neutral | Pedagogical exercises for law students involving the review and revision of AI (ChatGPT-3) generated legal motions (motions to suppress) within hypothetical client scenarios. | NaN | NaN | Clients misusing generative AI (like ChatGPT) due to cost concerns or overconfidence, leading them to present flawed, AI-generated legal documents to their attorneys and resist professional revision or advice. | Training law students through specific exercises to critically evaluate AI-generated legal text, identify errors (like misstated case law), revise appropriately, and develop diplomatic client communication strategies to explain AI limitations and the value of legal expertise. | Access to justice (briefly mentioned as a potential area impacted by AI for laypeople generating documents) | NaN | Criminal Law, Criminal Procedure, Legal Education, Legal Ethics | United States | NaN | Creation of hypothetical client scenarios and legal fact patterns, use of ChatGPT-3 to generate sample legal documents for analysis. | Presented as adaptable examples within the academic paper for use by legal educators. | False | False | NaN | Lack of training in legal education for handling client misuse of generative AI; limited discussion in legal commentary on client-side AI use compared to attorney-side use. | Designing effective pedagogical methods to teach critical evaluation of AI output and related client management skills. Inherent limitations of AI like generating convincing but flawed or incorrect legal arguments (e.g., misstating case holdings like Whren v. United States) and omitting relevant authorities (e.g., Kyllo v. United States). | Clients relying on inaccurate AI-generated legal documents; attorneys potentially filing flawed documents leading to sanctions or poor outcomes; damage to attorney-client relationships; undermining credibility with courts; AI enabling unauthorized practice of law. |
| mfP-piOctqgJ.pdf | Google_Scholar | BianCang: A Traditional Chinese Medicine Large Language Model | This paper introduces BianCang, a Large Language Model specialized for Traditional Chinese Medicine (TCM), developed using a two-stage training process involving domain-specific knowledge injection and targeted alignment. BianCang was evaluated on multiple TCM-specific tasks and datasets, demonstrating superior performance in syndrome differentiation and diagnosis compared to existing models. | True | NaN | True | 1.0 | NaN | BianCang, a Traditional Chinese Medicine (TCM) Large Language Model based on Qwen2/2.5, developed through a two-stage training process: continuous pre-training for knowledge injection and supervised fine-tuning (SFT) for alignment with TCM tasks. | Evaluated on 11 test sets across 4 tasks: TCM syndrome differentiation (TCMSD, TCMSD-BC), TCM disease diagnosis (TCMDD, TCMDD-BC), medical exams (MLEC-QA, CMB), and subjective medical record analysis (BC-Analytical). Performance was compared against 29 other models using metrics like accuracy and human-rated win/tie/loss rates. | BianCang-Qwen2.5-7B-Instruct achieved an accuracy of 82.10% on the TCMSD test set (Chain-of-Thought) for TCM syndrome differentiation. BianCang models generally outperformed baselines across TCM syndrome differentiation, disease diagnosis, and medical exam tasks. | NaN | NaN | NaN | NaN | NaN | China | A combination of publicly available and proprietary data. Pre-training: TCM/medical books, encyclopedias, literature, the Pharmacopoeia of the People’s Republic of China, real patient case records, TCM syndrome differentiation/diagnosis records, ChatMed-TCM, CMB-Train, and general domain corpora (COIG-CQIA, APE-210K, Evol-Instruction-66K). SFT: Custom instruction sets from the Pharmacopoeia and real hospital records (ChP-TCM, TCMSD-DD-SFT, TCM-WM-DiffDiag-SFT, TCM-Plan-SFT), existing medical dialogue (DISC-Med-SFT), exam (MLEC-SFT), and general domain datasets. | A two-stage training process: 1) Continuous pre-training on a foundational LLM (Qwen2/2.5) to inject extensive TCM and medical knowledge, including real medical records. 2) Supervised fine-tuning (SFT) using a curated set of TCM-specific instructions to activate and align the model's internal knowledge. | Code, datasets, and models are made available on GitHub. | True | True | Code, datasets, and models are available at https://github.com/QLU-NLP/BianCang. | NaN | General challenges in TCM LLM development addressed by BianCang: 1) Significant differences between TCM and modern medical theory. 2) Scarcity of specialized, high-quality TCM corpora. 3) Existing LLMs' limited capabilities in real-world TCM syndrome differentiation and diagnostic analysis. | BianCang cannot guarantee accuracy in all its responses and is an auxiliary research tool, not a substitute for professional TCM consultation. Users are advised to exercise caution with generated information and seek expert advice due to potential serious consequences of inaccurate medical data. |
| atliUSlYSC0J.pdf | Google_Scholar | WHY LAWYERS MUST RESPONSIBLY EMBRACE GENERATIVE AI | This paper argues that legal professionals must responsibly adopt Generative AI (GenAI) to enhance efficiency, maintain competence, and remain competitive, addressing common counterarguments and ethical concerns. It proposes a framework and best practices for navigating GenAI integration while managing risks like inaccuracy, bias, and confidentiality breaches. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | The high cost of legal representation and advice, rendering the judicial process inaccessible to a substantial portion of the population. | Widespread adoption of GenAI could revolutionize legal service delivery, enabling more providers to offer affordable services, thus potentially narrowing the access-to-justice gap. | Affordability and accessibility of legal services. | Low-income individuals or populations unable to afford legal representation. | General legal practice, with examples from mergers and acquisitions, due diligence, contract management, litigation, e-discovery, legal research, employment law, intellectual property, data privacy. | United States (references ABA Model Rules, US case law, federal agencies like EEOC/FTC/DOJ, White House initiatives, NYC Local Law 144, Colorado AI Act, California State Bar opinions, Utah sandbox program). | NaN | NaN | NaN | False | False | NaN | The access-to-justice gap due to high costs remains a significant challenge. Ensuring GenAI development and deployment is equitable, accurate, unbiased, and affordable enough to meaningfully address this gap are remaining technical and societal gaps. | Managing risks (confidentiality, ethics, bias, accuracy/hallucinations, IP infringement), ensuring legal compliance, need for extensive training and education, overcoming skepticism, requirement for human verification of outputs, developing robust governance policies and AI risk management frameworks, adapting to rapid technological change. | Generating biased or inaccurate information (hallucinations); violating client confidentiality or unauthorized disclosure of sensitive business information/IP; violating ethical rules (competence, diligence, supervision, communication, combating bias); intellectual property infringement; employment law violations (e.g., discrimination via biased AI); security vulnerabilities and data breaches; reputational damage; potential liability for privacy/data protection violations; competitive disadvantage and negative talent impact if adoption is resisted; risks from unmonitored 'shadow IT' use of AI; increased social engineering attack vectors. |
| 21TvEm4C_3QJ.pdf | Google_Scholar | Argumentative Segmentation Enhancement for Legal Summarization | This paper proposes a method to improve legal case summarization by first identifying argumentative segments within legal decisions using a novel classification task. These segments are then summarized by GPT-3.5, reportedly yielding higher quality summaries and overcoming token limits compared to baseline GPT models and non-GPT approaches. | True | Idealistic | True | 1.0 | Positive | An approach combining argumentative zoning principles (using IRC triples for legal arguments) with a C99 text segmentation algorithm to identify argumentative segments in legal decisions. These segments are then classified using LegalBERT and subsequently summarized using prompted GPT-3.5. | Argumentative segment classification was evaluated using F1 score on a test set (LegalBERT vs. BERT). Summarization quality was evaluated using ROUGE-1, ROUGE-2, ROUGE-L, BLEU, METEOR, and BERTScore, comparing the proposed argumentative segmentation enhanced GPT-3.5 method against baseline GPT-3.5, GPT-4, and fine-tuned non-GPT models (LED, T5, BART) on a test set of CanLII decisions. | For argumentative segment classification, LegalBERT achieved an 80.14% F1 score. For summarization, the argumentative segmentation enhanced GPT-3.5 (temp 0, max_tokens 128) achieved Rouge-1: 49.42, Rouge-2: 23.98, Rouge-L: 46.07, BLEU: 17.54, METEOR: 0.32, and BERTScore: 87.30, outperforming baselines on several metrics. | The difficulty in consuming and understanding long, complex legal documents, and the technical challenge of input token limitations in large language models when processing such documents. | An AI-driven method for legal summarization that focuses on argumentative segments to make legal texts more digestible and to overcome token limitations of LLMs for processing long documents. | Improving understanding of legal documents (case decisions) through automated summarization to make legal information more accessible. | NaN | General case law (variety of legal claims). | Canada | A corpus of 1,049 Canadian legal case decisions from CanLII. These decisions were sentence-split and annotated by researchers with Issue, Reason, Conclusion (IRC) triples. Text segments (derived using C99 algorithm) were then labeled as 'argumentative' or 'non-argumentative' based on the presence of IRC sentences. This dataset was used for training the argumentative segment classifier and fine-tuning baseline summarization models. | Linear text segmentation (C99), sentence embedding (Sentence-BERT), supervised classification (LegalBERT), prompt-based learning with LLMs (GPT-3.5, GPT-4), and principles of Argumentative Zoning and IRC triple annotation. | NaN | False | False | NaN | Coherency issues in generated summaries; need for systematic human evaluation; reproducibility challenges with proprietary LLMs; need for reliable performance of proprietary models and alternative prompt engineering techniques. | Input token limitations of LLMs for long legal documents; ensuring summaries capture important argument-related information; cost of using advanced LLMs; potential coherency issues in generated summaries; reproducibility of results with proprietary models. | Coherency issues in generated summaries; reproducibility challenges with proprietary LLMs and potential changes to these models by their providers. |
| AMAOheVRUmwJ.pdf | Google_Scholar | SoK: Prompt Hacking of Large Language Models | This paper provides a systematic overview of three types of prompt hacking attacks on Large Language Models (jailbreaking, leaking, and injection), differentiating their nuances and goals. It also proposes a novel framework for categorizing LLM responses to such attacks and experimentally evaluates the robustness of several common LLMs. | True | NaN | True | 1.0 | NaN | A 5-category framework for classifying LLM responses to prompt hacking attacks (Reject - Irrelevant Output, Reject - Safety Mechanism Triggered, Prompt too Long, Partial Response, Full Response). | Seven existing LLMs (Gemini, Copilot, Perplexity, You.com, ChatSonic, ChatGPT-3.5, ChatGPT-4) were tested against crafted jailbreak (DAN, Pretending), injection (direct, indirect), and leaking prompts. Responses were categorized using the paper's proposed 5-class framework based on seven illegal questions adapted for each attack type. | Gemini demonstrated robust defenses: it triggered safety mechanisms in 100% of jailbreak attempts and 71% of injection attempts (though 29% of injections still yielded partial or full harmful content). For leaking attacks, Gemini produced irrelevant output or only a partial response in all instances. | NaN | NaN | NaN | NaN | NaN | International | NaN | Conceptual categorization for the response classification framework; empirical testing with crafted prompts for LLM evaluation. | NaN | True | True | The evaluation framework, illegal questions, and example prompts/templates are described in the paper (available on arXiv), allowing for replication of the testing approach. | NaN | NaN | Generation of harmful, biased, deceptive, or illegal content; exposure of sensitive information (e.g., personal data, proprietary algorithms, intellectual property, custom instructions); generation of malware; undermining LLM system security, reliability, and integrity; perpetuation of biases. |
| uQbnPMyYpagJ.pdf | Google_Scholar | PREPARING STUDENTS F OR THE ARTIFICIAL \nINTELLIGENCE ERA: THE CRUCIAL ROLE OF CRITICAL \nTHINKING SKILLS | This paper argues that AI's integration into legal practice necessitates strong critical thinking skills for lawyers to evaluate AI outputs and handle complex analytical tasks AI cannot perform. It highlights a current deficit in these skills among incoming law students and urges law schools to adapt curricula and assessment methods to address this gap effectively. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | Potential widening of the digital divide due to high resource/expertise requirements for AI; Bias in AI outputs; Accuracy issues (hallucinations); Ethical concerns (confidentiality, IP). | NaN | NaN | NaN | General Legal Practice, Legal Education | USA | General discussion mentions training on extensive (often public) data, including legal texts (case law, statutes, contracts, commentary), with copyright concerns noted. | NaN | NaN | False | False | NaN | Critical thinking skills deficit among law students/graduates needed to effectively use and evaluate AI in legal practice; Potential widening of the digital divide. | NaN | AI hallucinations/inaccuracy; Misuse (e.g., deepfakes); Bias amplification; Intellectual property infringement (training data & output); Client confidentiality breaches; Security vulnerabilities; Job displacement for routine tasks; Widening digital divide; Environmental impact; Over-reliance hindering critical thinking skill development. |
| fqMuO36AeyQJ.pdf | Google_Scholar | Preparing Future Lawyers to Draft Contracts and Communicate with Clients in the Era of Generative AI | This paper argues for the necessity of integrating generative AI into transactional law curricula, addressing both the capabilities and significant risks (confidentiality, hallucinations, bias) of these tools. It further proposes practical pedagogical strategies and assignment adaptations for law professors to prepare students effectively for an AI-augmented legal profession. | True | Market | True | 3.0 | Positive | Discussion and pedagogical integration of existing GenAI tools (e.g., ChatGPT, Spellbook, Harvey, CoCounsel, Lexis Plus AI) in legal education, exemplified by exercises like AI-assisted client email drafting and contract review with critical analysis. | Pedagogical approaches were explored through classroom exercises, such as having students use ChatGPT or a university GPT to draft a client email based on a hypothetical scenario, followed by in-class critique of the AI's output and the students' prompts. The author also describes adaptations to assignments like mock contract redlining. | For the AI-assisted email drafting exercise, results indicated that prompt quality significantly influenced output quality, students with existing legal knowledge formulated better prompts leading to superior AI outputs, and the AI-generated drafts served as effective starting points for class discussions on legal accuracy, communication strategies, and AI limitations. | Inefficiency and high cost of traditional legal work, limiting its accessibility and scope. | GenAI tools can significantly increase lawyer efficiency, potentially freeing up resources for more pro bono or lower-cost legal services and enabling innovative applications like AI-assisted case screening for innocence projects, thereby helping to reduce barriers to justice. | Increasing lawyer efficiency to potentially expand legal aid or pro bono capacity; AI-assisted case review and analysis for meritorious claims (e.g., innocence projects). | General underserved populations (by potentially reducing overall barriers to justice); wrongfully convicted individuals (implied by the Innocence Project example). | Transactional law (contract drafting, review), client communication, legal research, litigation support (brief drafting, deposition preparation), due diligence, regulatory compliance, employment law (intern classification). | Primarily United States (examples from US law schools, US legal cases, California Innocence Project). Mentions global law firms using AI tools, suggesting broader applicability. | For ChatGPT: Described as trained on 'everything on the Internet' and user inputs. For Harvey: GPT model base, further trained with 'general legal data, including case law and reference materials,' and a firm's 'own work product and templates.' For Lexis Plus AI: 'treatises in LexisNexis.' | NaN | For tools discussed: ChatGPT is publicly available. Harvey, CoCounsel, Lexis Plus AI, and Spellbook are commercial products for legal professionals, deployed via firm licensing, cloud platforms, or Word plug-ins. Spellbook mentioned potential future availability to law schools. | True | True | ChatGPT (free version) and DALL·E are described as freely and publicly available. Spellbook offered the author a trial and was exploring making its tool available to law schools, potentially for free. | Equitable access to robust, reliable, and ethically sound AI tools for A2J practitioners and underserved communities, especially since free AI models have significant limitations (confidentiality, bias, hallucinations) and more advanced tools are often costly. Ensuring AI development and deployment considers A2J needs. | For the author's pedagogical approach: Adapting curriculum to effectively integrate AI while ensuring students develop foundational legal skills and critical thinking. Addressing student over-reliance on AI, teaching ethical AI use (confidentiality, accuracy, bias), and managing equitable access to AI tools among students. | Confidentiality breaches from inputting sensitive client data into open AI models. AI hallucinations leading to reliance on false information or non-existent case law. Perpetuation and amplification of biases present in training data. Deskilling of lawyers or hindering the development of foundational legal skills in students due to over-reliance. Potential job displacement or significant changes in the nature of legal work. Unrealistic client expectations regarding speed and cost. Broader societal risks including intellectual property infringement, liability issues for AI-generated errors, discrimination, and challenges in developing appropriate AI regulation. |
| SBSqIelFtX8J.pdf | Google_Scholar | AI-POWERED ANALYSIS OF COURT DECISIONS: THE UKRAINIAN EXPERIENCE | This paper proposes using the GPT-4 language model to automatically extract relevant predefined facts from unstructured Ukrainian court decisions, specifically criminal verdicts. The aim is to replace labor-intensive manual analysis, thereby increasing efficiency, improving consistency in judicial practice, and potentially aiding in judicial competence assessment. | True | Market | True | 1.0 | Positive | Using GPT-4 with a specific prompt structure (listing criteria to be extracted) to generate relevant facts from unstructured court decisions via natural language generation. | Illustrative example showing input prompt and GPT-4 generated output for a single court decision. | Provided a single qualitative example demonstrating successful extraction of predefined facts from a court decision text using the proposed GPT-4 prompting method. | Large volumes of unstructured text data in court registries (USRCD), complexity and time-consuming nature of manual analysis, need for consistency in judicial practice, limitations in current search functionalities. | Automated fact extraction and analysis of court decisions using AI (specifically GPT-4) to enhance efficiency, ensure consistency, analyze trends, and potentially assess judicial competence. | Analysis of judicial decisions, consistency of judicial practice, sentencing criteria in criminal cases, judicial efficiency, judicial competence assessment. | NaN | Criminal Law | Ukraine | The technique applies a pre-trained LLM (GPT-4). Data used for application consists of unstructured court decisions from the Unified State Register of Court Decisions of Ukraine (USRCD). | Prompt engineering for a pre-trained LLM (GPT-4). | A related pilot project using GPT chat for the Supreme Court is mentioned, but no specific deployment strategy for the author's proposed method is detailed. | False | False | NaN | Need for AI tools tailored to national legal systems (like Ukraine's), limitations in automated analysis of large legal databases (USRCD), need for careful integration respecting ethical considerations and human oversight. | General LLM challenges (formalism, bias, reliability, accountability, misuse), complexity of extracting information from unstructured legal text, ethical considerations, ensuring data confidentiality, need for human control. | Bias in AI outputs, unreliability of LLMs, misuse or over-reliance on AI, data confidentiality breaches, ethical issues (fairness, accountability), potential violation of fundamental rights. |
| N4XvXhSJkIMJ.pdf | Google_Scholar | Exploring the Landscape of Large and Small Language Models: Advancements, Trade-offs, and Future Directions | This survey examines the distinctions between large language models (LLMs) and small language models (SLMs), contrasting their size, efficiency, performance, and deployment. It further discusses the trade-offs involved, optimization techniques for SLMs, and future research avenues in NLP. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | False | NaN | NaN | High computational/energy costs, latency, and bias for LLMs; potential performance trade-offs and generalization limits for SLMs if not domain-specifically tuned. | Environmental impact (carbon footprint), inequitable AI distribution/accessibility, propagation of data-learned biases leading to undesirable outcomes. |
| 1yDtTGB00T8J.pdf | Google_Scholar | Generative AI in the Wild: Prospects, Challenges, and Strategies | This paper investigates how users in creative industries perceive and utilize Generative AI (GenAI) through semi-structured interviews. It identifies prospects like enhanced creativity and efficiency, challenges including resource availability and regulatory compliance, and strategies users employ to overcome these hurdles. | True | Market | True | 3.0 | NaN | Generative AI tools (e.g., ChatGPT, Midjourney, text-to-image models) | Semi-structured interviews (N=18) with GenAI users in creative industries, data analyzed using thematic analysis. | GenAI offers prospects such as improved efficiency and sparking creativity, but users also encounter significant challenges including limited controllability, issues with resource availability, regulatory compliance, and content trustworthiness. Users develop various strategies like careful tool selection, personalized prompting, and manual fact-checking to navigate these challenges. | NaN | NaN | NaN | NaN | Intellectual Property (copyright, authorship), Data Privacy, Regulatory Compliance (general) | International | NaN | NaN | NaN | True | True | Discusses various publicly accessible GenAI tools (e.g., ChatGPT, Stability.AI's Stable Diffusion), many of which have free tiers or are open-source. | NaN | Users face challenges with GenAI including limited controllability, ineffective feedback mechanisms, engineering-centric design of some tools, lack of customizability for localized content, and difficulties in authorship disclosure and regulatory compliance. | Copyright infringement, data privacy/security breaches from inputting sensitive information, regulatory non-compliance, generation of inaccurate information (hallucinations), ethical dilemmas regarding authorship, and potential widening of the digital divide. |
| XVXXHioJe_wJ.pdf | Google_Scholar | LEGAL PROCEDURE BOT | This paper proposes an AI-based chatbot, "LEGAL PROCEDURE BOT", designed to simplify access to information about legal procedures and required documents in India. The bot utilizes a generative deep learning architecture (RAG with an LLM) and features like speech-to-text, text-to-speech, geo-location, and multilingual support to address the complexities and inefficiencies of manual systems. | True | Idealistic | True | 1.0 | Positive | An AI chatbot ("LEGAL PROCEDURE BOT") using a Retrieval-Augmented Generation (RAG) architecture with the Mistral-7B-Instruct-v0.2 LLM. It employs NLP (NLU/NLG), vector embeddings (Word2Vec/Doc2Vec) stored in a vector database, cosine similarity for pattern matching, Speech-to-Text (STT), Text-to-Speech (TTS), Geo Location services (Google Nearby Search API), and multilingual query processing. | The paper includes screenshots of the user interface (login screen and chat window) as a demonstration. No quantitative evaluation or formal user testing results are reported. | Results are presented visually via GUI screenshots (Figures 5 and 6), demonstrating the intended user interface. No performance metrics or evaluation outcomes are provided. | Complexity of government legal procedures, fragmented and inaccessible information, time-consuming and labor-intensive manual processes, potential for mistakes and misinterpretation in manual systems. | An AI chatbot providing comprehensive guidance on legal procedures, lists of required documents, estimated fees, direct links to official websites/forms, and locations of nearby centres/courts/lawyers through a user-friendly interface. | Accessing information on government legal procedures (e.g., obtaining identity documents), required documentation, estimated costs, and relevant service locations. | General public / citizens needing guidance on legal procedures. | Administrative Law, Government procedures | India | A CSV file containing legal procedures, acts, regulations, and case law. This unstructured text data is pre-processed into chunks and converted into vector embeddings for storage in a vector database. The source seems specific to the project, likely collected by the authors. | Generative Deep Learning architecture (RAG), semantic indexing, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Natural Language Generation (NLG), user-centered design principles. | NaN | False | False | NaN | Need for algorithm refinement, continuous updates of legal information, real-time updates, integration of user feedback, enhancements in privacy and data protection, expansion of multilingual capabilities, need for collaboration with legal experts for accuracy and reliability. | Implicit challenges include ensuring the accuracy and reliability of legal information provided, handling the complexity of legal language and procedures, keeping the information up-to-date, and ensuring user trust and data privacy. | Need for ethical considerations, ensuring user satisfaction, managing privacy and data protection. |
| lRhwzB6FxjgJ.pdf | Google_Scholar | LexGPT 0.1: pre-trained GPT-J models with Pile of Law | This paper introduces LexGPT 0.1, a set of GPT-J models pre-trained on the Pile of Law dataset, aiming to provide a foundation model for the legal domain. It also explores a "No Code" approach for fine-tuning these models for classification tasks, finding this method less performant than state-of-the-art approaches. | True | Idealistic | True | 1.0 | Positive | LexGPT 0.1: GPT-J models (6B, 1.6B, 456M parameters) pre-trained on Pile of Law using custom tokenizers. A 'No Code' fine-tuning method for classification using prompt format `(text) <|label|>(label)`. | Fine-tuning on LEDGAR (contract classification) and CaseHOLD (holding identification) datasets from the LexGLUE benchmark. Evaluation metrics: micro/macro-F1 (LEDGAR), accuracy (CaseHOLD). | On LEDGAR (1.6B model): 83.9% micro-F1, 74.0% macro-F1. On CaseHOLD (456M model): 49.6% accuracy. These results were lower than reported state-of-the-art. | Technical skill barrier for legal professionals to utilize language models; potential for models to make factual mistakes and experience hallucinations; scarcity and expense of specialized legal datasets (though Pile of Law is used). | Pre-training domain-specific foundation models (LexGPT); proposing a "No Code" fine-tuning approach to lower technical barriers; public release of models and code; recommending initial use by legal professionals to filter errors. | Foundational model development; Legal text classification; Facilitating AI adoption by legal professionals. | Legal professionals | General Legal (based on Pile of Law), Contract Law (LEDGAR), Case Law (CaseHOLD) | US | Pre-training: Pile of Law (~256GB public dataset of legal/administrative text). Fine-tuning: Publicly available LEDGAR and CaseHOLD datasets (subsets from LexGLUE). Unstructured text data. | Domain-specific pre-training of Transformer models (GPT-J); Prompt-based fine-tuning for classification ('No Code'); Experimentation with model size, tokenizers, learning rates. | Intention to release models, tokenizers, datasets, configurations, and source code publicly on GitHub upon publication. | True | True | Models, tokenizers, datasets, config files, and code to be released on GitHub. | Performance gap between 'No Code' fine-tuning and SOTA classification methods; effective 'No Code' approach for multi-label classification; improving 'No Code' performance (e.g., via CoT prompting); limited exploration of GPT models vs BERT in legal domain. | Optimizing hyperparameters (e.g., learning rate) for pre-training; achieving competitive performance under the 'No Code' constraint; adapting generative models for classification; finding optimal data formats for fine-tuning. | Factual mistakes and hallucinations generated by the language models. |
| KS2K506sQ5sJ.pdf | Google_Scholar | CULTURAL FIDELITY IN LARGE-LANGUAGE MODELS: AN EVALUATION OF ONLINE LANGUAGE RESOURCES AS A DRIVER OFMODEL PERFORMANCE IN VALUE REPRESENTATION | This paper evaluates how well large language models (GPT-4o and GPT-4-turbo) represent societal values across different languages, finding their performance strongly correlates with the amount of online resources available in each language. The study highlights that models perform poorly for low-resource languages, potentially worsening digital divides and cultural homogenization, particularly in the Global South. | True | Idealistic | True | 2.0 | Negative | Evaluation of GPT-4o and GPT-4-turbo's cultural value representation capabilities using World Values Survey (WVS) data and persona prompting. | Compared LLM responses (prompted as a citizen of a specific country, answering WVS questions on the original scale) to average human responses from the WVS Wave 7 for 21 country-language pairs across 94 questions. An error was counted if the absolute difference between the LLM answer and WVS average was >= 50% of the WVS average. | For GPT-4o, 44% of the variance in the error rate correlated with the log of online websites available in the language (72% for GPT-4-turbo). Low-resource languages had significantly higher error rates (over 5 times higher for the lowest vs highest resource languages in GPT-4o). | The primary obstacle is the limited availability of digital resources (online content) for low-resource languages, leading to biased LLM training datasets derived predominantly from high-resource languages (mainly English). This results in poor AI performance in representing diverse societal values, exacerbates digital inequality, and potentially leads to cultural erosion, particularly impacting the Global South. Censorship further distorts the representativeness of available data in some regions. | Proposed solutions include democratizing AI development (open-source initiatives, grassroots NLP communities), ethical regulation mandating transparency and diversity, collaborative data sharing with local communities, targeted digital inclusion programs (increasing internet access, digital literacy, speech synthesis for LRLs), developing inherently multilingual LLMs, and fine-tuning models on diverse, curated linguistic datasets (including audio/oral sources) rather than relying solely on web-scraping. | Representation of societal values (political, social, ethical) by AI, linguistic diversity, digital inequality, cultural preservation, access to information, AI bias. | Speakers of low-resource languages, particularly communities in the Global South. Specific languages studied include Swahili, Hindi, Burmese, Filipino, Amharic, Hausa, Shona, Tajik. | AI Ethics / Governance | International | The paper evaluates models (GPT-4o, GPT-4-turbo) inferred to be trained primarily on large-scale web-scraped text (e.g., Common Crawl), supplemented by undisclosed proprietary data and potentially fine-tuning datasets. The study highlights the problematic nature and biases of this inferred training data, especially its underrepresentation of low-resource languages and potential pollution/censorship issues (e.g., Mandarin Chinese). | The study employed an evaluation methodology involving: selecting country-language pairs and 94 questions from the World Values Survey (WVS), verifying question translations with native speakers, prompting LLMs (GPT-4o, GPT-4-turbo) to answer as citizens of specific countries on the WVS scale, calculating deviation from averaged WVS human responses, defining an error threshold (>=50% deviation), and correlating error rates with metrics of language resource availability (log of website count). | NaN | True | False | The evaluated models (GPT-4o, GPT-4-turbo) are commercially available via API from OpenAI. | Establishing causality between language resources and LLM performance; quantifying the data required for parity; controlling for confounders (e.g., GDP); developing better data collection methods for LRLs beyond web-scraping (curated, diverse, audio/oral sources); creating ethical frameworks for value conflicts and responsible deployment; addressing nuanced representation within high-resource languages; lack of transparency in commercial LLM training data. | Evaluating LLM bias quantitatively (limitations of closed questions); data scarcity and quality for low-resource languages; inherent biases and limitations of web-scraped data (skewed demographics, spam, censorship); defining 'low-resource'; ensuring cultural fidelity without stereotyping during prompting. | Exacerbation of digital divides; cultural homogenization/erosion; perpetuation of flawed information and stereotypes; biased resource allocation (recruiting, medicine); negative impacts on education (biased history/values); biased content generation (news, marketing); flawed censorship/moderation; potential for social/political discontent; risks for users in autocratic regimes; homogenization within high-resource languages; privacy risks related to sensitive information access. |
| 0RI6qBV7pvcJ.pdf | Google_Scholar | Large language models and political science | This paper introduces Large Language Models (LLMs) to political scientists, discussing their potential applications, benefits, and drawbacks within the field. It reviews current LLM types, highlights research uses like content analysis and generation, and addresses issues like bias, transparency, and reproducibility. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | United States | The paper discusses general training data sources for LLMs, including large-scale web scrapes (e.g., The Pile, Common Crawl), Wikipedia, BooksCorpus, blogs, social media, articles, and image datasets (ImageNet, COCO). It notes some models use proprietary data (e.g., GPT-4) while others are trained on publicly specified or open datasets. | NaN | NaN | True | True | Discusses access via commercial APIs (e.g., OpenAI's GPT-4) and downloadable open-source models (e.g., LLaMA families, Falcon) available on platforms like Hugging Face. | NaN | Hardware requirements (GPUs/TPUs), computational cost, model size management (quantization, PEFT, LoRA), potential slowness/inefficiency vs. specialized methods, hallucinations, sensitivity to prompt variations, domain-specificity limitations, model censorship/bias, ensuring transparency and reproducibility. | Perpetuation of social/political bias from training data; generation/spread of misinformation and fake news (incl. political ads, election interference); privacy concerns regarding training data; intellectual property infringement; environmental impact; difficulty detecting AI content; lack of transparency; potential for discriminatory outcomes (e.g., biased risk assessments, job ads). |
| m0OdIkZgr7MJ.pdf | Google_Scholar | Luck of the Draw III: Using AI to Examine Decision‐Making in Federal Court Stays of Removal | This paper uses a large language model (GPT-3) to extract and analyze data from Federal Court of Canada dockets concerning immigration law applications for stays of removal, revealing significant inconsistencies in stay grant rates among judges. It argues for measures to promote consistency in judicial decision-making and greater access to bulk legal data for research to enhance transparency and migrant rights. | True | Idealistic | True | 1.0 | Positive | A multi-step computational legal research methodology: 1) Web-scraping Federal Court online dockets. 2) Docket and docket entry screening using Regex. 3) Fine-tuning GPT-3 models for specific data extraction and categorization tasks from docket entries (e.g., identifying stay motions, outcomes, judges). 4) Applying docket-level logic using Pandas to construct a final dataset for analysis. | Data verification involved: 1) Comparing the automated process against one year's worth of manually reviewed stay of removal decisions from CanLII, where the automated process identified 98.0% (96 out of 98) of the manually identified decisions. 2) A research assistant manually verified 200 randomly selected, coded dockets, confirming 99% accuracy for key data points (judge, outcome, dates). | The automated data extraction technique achieved 98% coverage compared to a manual CanLII dataset and 99% accuracy on manually verified dockets. The substantive research findings revealed large unexplained variance in stay of removal grant rates depending on the deciding judge (e.g., some judges granting stays over 80% of the time, others less than 10%). | Inconsistent and potentially arbitrary outcomes in high-stakes deportation proceedings due to judicial variance. Lack of transparency in legal decision-making processes. Restricted access to bulk legal data for non-commercial researchers, creating an asymmetry favouring commercial entities and the state. | The Federal Court should implement measures to encourage more consistency in stay decision-making (e.g., judicial discussions of hypotheticals). Facilitate fair and equal access to bulk legal data (e.g., via APIs) for non-commercial research to enhance transparency and rights. Utilize AI/LLM technology to scrutinize legal decision-making processes rather than solely for enhancing state power over marginalized groups. | Judicial decision-making consistency, access to justice in immigration and refugee law, stays of removal, deportation, transparency of legal systems, empirical legal studies. | Marginalized migrants and non-citizens facing deportation in Canada. | Immigration Law, Refugee Law, Administrative Law (specifically judicial review, interlocutory orders). | Canada (Federal Court of Canada) | For fine-tuning GPT-3: A manually labelled dataset of hundreds of sample Federal Court docket entries (prompts) paired with desired completions (e.g., judge's name, outcome category like 'granted' or 'dismissed'). The raw data was scraped from 87,776 Federal Court online dockets (2012-2022), consisting of unstructured natural language text entries in English or French. | Iterative development of fine-tuned GPT-3 models: applying models to new docket entries, verifying outputs, providing additional labelled examples to correct errors or improve performance, re-fine-tuning, and re-testing until satisfactory accuracy was achieved for each extraction/classification task. | The Python code (Jupyter Notebook) and the dataset of scraped Federal Court dockets (with case names removed for privacy) are stated to be made available for non-commercial use by other researchers via a public GitHub repository upon the paper's publication in a law journal. | False | False | The code and dataset are planned to be publicly available on GitHub for non-commercial research use after the paper is accepted for publication. | Need for further research into the specific reasons for divergent stay grant rates across judges (e.g., different interpretations of legal tests). Investigation needed into causes of variance in stay grant rates across different cities (e.g., quality of counsel, access to legal aid). The primary systemic gap is the restricted access to bulk legal data for non-commercial researchers, hindering broader scrutiny and transparency. | Technical difficulty and resource intensiveness of systematically web-scraping and maintaining large, up-to-date databases of court dockets. Managing ethical concerns associated with LLMs, including inherent biases, potential for generating misinformation ('hallucinations'), copyright issues, and environmental impact. Ensuring high accuracy when processing unstructured, bilingual (English/French) legal text from dockets. | LLMs may perpetuate biases present in their training data (e.g., racial, gender, religious biases). LLMs can 'hallucinate' or generate plausible but false information. Potential for misuse of LLMs for creating disinformation. Risk of automation bias due to the coherent-seeming text generated by LLMs. Significant environmental costs of training and running large language models. Copyright infringement concerns regarding data used for training commercial LLMs. Asymmetrical access to AI tools could exacerbate power imbalances if benefits primarily accrue to well-resourced actors. |
| qb37pS0wwnwJ.pdf | Google_Scholar | HARNESSING ARTIFICIAL INTELLIGENCE IN INTERNATIONAL ARBITRATION PRACTICE | This paper surveys the existing and emerging applications of Artificial Intelligence (AI), including Generative AI and Large Language Models (LLMs), in international arbitration practice. It discusses various tools, potential use cases, benefits, pitfalls, the need for ethical guidelines, and future transformative possibilities. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International Arbitration | International | NaN | NaN | NaN | True | False | The paper discusses multiple AI tools and platforms relevant to international arbitration, many of which are commercially available (e.g., e-discovery platforms, specialized legal research tools, Harvey) or have publicly accessible versions (e.g., ChatGPT-4, Google Translate). | NaN | Challenges discussed include ensuring accuracy and avoiding hallucinations, maintaining confidentiality and data privacy, managing potential biases in AI outputs, ethical considerations in AI use, the need for human oversight and verification, the potential for deskilling, integration into existing legal workflows, and the development of appropriate regulatory guidelines (e.g., SVAMC Draft Guidelines). | Potential risks stated include reliance on inaccurate or fabricated AI-generated information (e.g., non-existent case law), breaches of confidentiality when inputting sensitive data into AI tools, perpetuation of biases (e.g., in arbitrator selection algorithms), violation of ethical duties (competence, diligence), improper delegation of decision-making responsibility by arbitrators, and disruption to traditional legal roles and billing models. |
| o-QdXzv2krIJ.pdf | Google_Scholar | Artificial Intelligence and the Crises of Judicial Power : (Not) Cutting the Gordian Knot? | The paper argues that Artificial Intelligence (AI) and automated decision-making (ADM) cannot solve the current crises of efficiency and contestation facing judicial power globally. It analyzes courts' dual role as both users of AI, highlighting limitations like opacity and lack of trust, and as regulators of AI through judicial interpretation, examining different judicial approaches to managing AI risks. | True | NaN | False | 3.0 | NaN | NaN | NaN | NaN | Systemic inefficiency of justice systems (delays, backlogs, increasing costs); Contestation of judicial legitimacy and authority (criticism of liberal interpretations, perceived political role, populist attacks, undermining rule of law). | AI/ADM are presented as insufficient solutions. Implicitly favors reliance on legal safeguards (transparency, comprehensibility, contestability), robust judicial scrutiny of AI/ADM, upholding rule of law principles, and acknowledging the limitations of technology compared to human judgment. | Judicial administration and efficiency; Judicial legitimacy and contestation; Regulation of AI by courts; Digital constitutionalism; Automated decision-making in the public sector. | NaN | Constitutional law, Administrative law, Judicial Procedure, Data Protection Law | International / Comparative (mentions UK, US, Italy, France, China, Colombia, Brazil, EU) | N/A - Specific dataset details largely absent or noted as proprietary/opaque (e.g., COMPAS). General discussion of AI relying on data. | NaN | Government-led digitisation programs, integration into specific judicial/administrative functions (e.g., e-discovery, risk assessment, case allocation), online court platforms. | False | False | NaN | Ensuring AI trustworthiness and public legitimacy; Maintaining essential human elements in the justice process; Developing effective and comprehensive AI regulation; Bridging the digital divide impacting access; Achieving transparency and explainability in ADM; Protecting fundamental rights (due process, data protection, effective remedy) against AI risks. | Lack of transparency/opacity in AI systems; Potential for bias and inaccuracy in algorithms; Difficulty distinguishing purely administrative from decision-influencing AI functions; Overcoming public distrust and ensuring legitimacy; High costs and implementation delays for judicial digitization; Risk of de-humanizing the justice experience; Ensuring meaningful contestability and review of automated decisions; Balancing standardization benefits with the need for individualized justice. | Opacity hindering challenges, review, and trust; Algorithmic bias leading to discriminatory outcomes (e.g., racial bias in COMPAS); De-humanization of the legal process undermining legitimacy; Potential undermining of judicial independence and separation of powers; Standardisation leading to 'flattened' rights protection and inability to adapt law; Erosion of public trust in the judiciary; Difficulty ensuring due process, right to explanation, and effective remedies against automated decisions. |
| BRrBu4S54Q0J.pdf | Google_Scholar | The Consequences of Implementing Artificial Intelligence Technology in the Digital Economy from the Perspective of Generation Z | This paper explores how Generation Z perceives artificial intelligence (AI) and its implementation effects within the digital economy, based on a survey of 323 Polish respondents. The study reveals Generation Z's frequent AI use alongside limited trust, particularly concerning data privacy, autonomous systems, and potential job displacement. | True | NaN | False | 2.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Multiple sectors including IT, trade, finance, transport, education, legal services (briefly), healthcare (briefly). | Poland | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Lack of trust in AI systems (especially in finance, legal, medical, autonomous vehicles), privacy/data security violations, job displacement/digital unemployment, potential for AI errors (e.g., in legal/medical services), ethical concerns. |
| kN0VpM62IsIJ.pdf | Google_Scholar | A Short Survey of Viewing Large Language Models in Legal Aspect | This paper surveys the applications of large language models (LLMs) in various legal tasks, such as judgment prediction and document analysis. It also discusses the associated legal challenges like bias and privacy, and the data resources required for specializing LLMs in the legal domain. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Legal challenges including intellectual property ownership, data privacy (disclosure of sensitive information, GDPR compliance), bias and discrimination (e.g., anti-Muslim, anti-queer), and lack of explainability/transparency. | Developing specialized legal data resources, methods to mitigate bias and ensure transparency, legal frameworks and guidelines for ethical use, privacy-preserving techniques, prompt engineering, and a legal informatics approach. | Legal judgment prediction, legal document analysis/writing, statutory reasoning, legal education, legal advice, access to justice. | NaN | Criminal law, Constitutional law, Contract law, Tort law, Civil law, General legal practice. | International (with specific examples/datasets from China, US, Japan, EU regulations mentioned) | Discusses publicly available legal datasets (e.g., CAIL2018 from China, LeCaRD from China, CaseHOLD derived from US law) and general large-scale web data used to train base LLMs. | NaN | NaN | False | False | NaN | Need for methods to mitigate bias and ensure transparency/interpretability; need for more specialized legal data; need for guidelines/standards for ethical use; need to address legal challenges (IP, privacy); need for better alignment with human/societal values. | Privacy concerns, bias perpetuation, lack of explainability, need for specialized domain data and adaptation, intellectual property issues, ensuring responsible and ethical deployment. | Copyright infringement, disclosure of private information, perpetuation of societal biases leading to discrimination, lack of transparency hindering accountability, potential misuse in legal education or practice. |
| ArtificialIntelligenceinLegalPracticeOpportunitiesChallengesandFutureDirections.pdf | Google_Scholar | Artificial Intelligence in Legal Practice: Opportunities, Challenges, and Future Directions | This review paper discusses the transformative impact of Artificial Intelligence (AI), including Generative AI, on legal practice. It outlines AI's applications, benefits like increased efficiency and automation, and challenges such as data privacy, ethical concerns, and potential job displacement in the legal field. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | High cost of traditional legal services, making them unaffordable for individuals with limited resources and small businesses. | Utilizing AI to automate tasks, reduce costs, and increase the efficiency of legal services, thereby making them more accessible. | Affordability of legal services, accessibility of legal services for individuals and small businesses. | Litigants with limited resources, individuals, small businesses. | General legal practice, including contract law, intellectual property law, litigation, due diligence, and legal research. | International (with some examples from the United States) | NaN | NaN | NaN | True | True | ChatGPT (specifically GPT-4 mentioned as used by authors), a type of generative AI discussed in the paper, is generally available with free access options from OpenAI. | The paper implies that overcoming ethical issues, regulatory uncertainties, data privacy concerns, and the skills gap among legal professionals are necessary for the full and equitable realization of AI's potential in enhancing access to justice. | Data privacy and security, ethical concerns (including algorithmic bias, AI hallucinations, and confidentiality), risks of data breaches and cyberattacks, skills gap among legal professionals, potential job displacement, and the lack of clear and comprehensive regulations for AI in legal practice. | Data breaches, cyberattacks, spread of misinformation and disinformation (e.g., deepfakes), algorithmic bias, AI hallucinations, confidentiality breaches, job displacement for legal professionals, intellectual property rights issues, privacy and data protection violations, and various ethical dilemmas. |
| Qs9Hxl2Iir4J.pdf | Google_Scholar | ARTIFICIAL LAWYERING: A JEKYLL AND HYDE STORY | This paper examines the dual potential of artificial intelligence, particularly generative AI like ChatGPT, in the legal field. It argues that while AI can significantly improve access to justice for underserved communities, it also poses risks such as unauthorized practice of law, and thus proposes an amendment to the Model Rules of Professional Conduct to balance these aspects. | True | Idealistic | True | 3.0 | Positive | Proposed amendment to Rule 5.5 of the ABA Model Rules of Professional Conduct regarding 'practicing entities' (including AI) and UPL, allowing use by pro se litigants with informed consent. | NaN | NaN | Inability of low-income individuals to afford legal counsel; lack of awareness among individuals about whether their problems are legal in nature; insufficient number of lawyers serving low-income populations; systemic inequalities. | Utilize AI (like ChatGPT) for legal education and information dissemination, especially for pro se litigants. Amend Rule 5.5 of the Model Rules of Professional Conduct with a new comment to address AI's potential for UPL, while allowing its use by pro se litigants under conditions of informed consent and disclosure to the court. | Access to legal information, self-representation (pro se litigants), understanding legal rights, unauthorized practice of law (UPL) by AI, an LSC (Justice Gap) report. | Low-income Americans, veterans, persons with disabilities, parents of children under eighteen, survivors of domestic violence or sexual assault. | Civil law (specifically landlord-tenant disputes), Trademark law, General legal ethics (Unauthorized Practice of Law). | United States | The paper discusses generative AI like ChatGPT which is trained on large language models (LLMs) using extensive text data to infer relationships between words and texts. Specific datasets for ChatGPT are not detailed by the paper beyond this general description. | NaN | NaN | False | False | NaN | Lack of clear legal and ethical rules addressing advanced AI (like ChatGPT) and the unauthorized practice of law; need for mechanisms to balance AI's benefits for access to justice with public protection; ethical rules (Model Rules) not sufficiently updated for AI advancements; issues of AI bias, language limitations, and lack of redressability for AI-inflicted harm if AI engages in law practice. | NaN | AI engaging in the unauthorized practice of law (UPL); public endangerment from incompetent or biased AI-generated legal advice/documents; AI producing non-existent legal precedents ('hallucinations'); generation of frivolous lawsuits; lack of legal redress for individuals harmed by AI's errors (AI malpractice); perpetuation or amplification of societal biases through AI; AI's limitations in understanding true context beyond language patterns. |
| wNT2cfBmGiQJ.pdf | Google_Scholar | From Bytes to Borsch: Fine-Tuning Gemma and Mistral for the Ukrainian Language Representation | This paper details the fine-tuning of open-source LLMs (Gemma and Mistral) for the Ukrainian language using existing and newly created datasets (UKID). It benchmarks the models, highlighting performance improvements and challenges like code-switching, arguing for the importance of developing language-specific models for low-resource languages. | True | Idealistic | True | 1.0 | Positive | Fine-tuning open-source LLMs (Gemma-2b, Gemma-7b, Mistral-7b) using LoRA for the Ukrainian language, including the creation and use of a new instruction dataset (UKID). | Benchmarking using two datasets: 1) Ukrainian External Independent Testing (EIT) Multiple Choice Questions (MCQ) dataset (3,063 questions on history, language, literature), automatically evaluated. 2) 100 Open Questions (OQ) for generative tasks, manually evaluated on language use, coherence, relevance, and grammar. Comparison against baselines and proprietary models. | The fine-tuned Mistral-7B (MistralFT) achieved 40.16% accuracy on History MCQs and 22.86% on Language & Literature MCQs. It achieved an average score of 40.75 out of 100 on Open Questions, though struggled with adhering to instructions (Relevance score was low). Proprietary models like GPT-4 performed significantly better. | Scarcity of suitable instruction datasets with authentic Ukrainian context; Language and cultural bias in existing LLMs; Uneven knowledge representation favouring dominant languages; Resource constraints for developing models for low-resource languages. | Fine-tuning open-source LLMs with language-specific data; Creating and sharing new, culturally relevant datasets (e.g., UKID); Utilizing efficient fine-tuning techniques (LoRA); Advocating for investment and policy focus on LLM development for lower-resource languages; Creating language-specific benchmarks (e.g., ULIB). | Linguistic Inclusion, Cultural Preservation, Education, Countering Misinformation. | Ukrainian speakers, with potential applicability to other low-resource language communities. Specific examples mention Ukrainian refugees, rural Peruvian villagers (Quechua), and Navajo learners. | NaN | Ukraine | Combined dataset including: 3,063 instruction rows from Ukrainian national exam (ZNO dataset); 10,000 rows from UAlpaca (translated general knowledge); Uk-Squad dataset (translated SQuAD); 962 question-answer-fact pairs from the newly created Ukrainian Knowledge and Instruction Dataset (UKID), derived from Ukrainian Wikipedia summaries via Gemini 1.0 API. Primarily unstructured text formatted as instructions. | LoRA (Low-Rank Adaptation) fine-tuning. Dataset creation (UKID) involved selecting high-traffic Ukrainian Wikipedia pages, filtering for relevance, and using Gemini 1.0 API with few-shot prompting to generate question-answer-fact triplets. | Fine-tuned model weights and the UKID dataset are shared via a GitHub repository. | True | True | Fine-tuned model weights and the UKID dataset are available on the associated "from-bytes-to-borsch" GitHub repository. | Need for larger, more comprehensive Ukrainian instruction datasets; Need for improved fine-tuning methods to avoid performance degradation and negative artifacts (e.g., code-switching); Significant performance gap between fine-tuned open-source and large proprietary models; Need for better evaluation benchmarks for Ukrainian (e.g., expanding ULIB); Lack of institutional support and resources for low-resource language model development. | Reproducibility of fine-tuning setups; Scarcity of high-quality, culturally relevant training data; Models lacking foundational conceptual understanding in the target language; Compute and resource constraints; Adapting datasets to model-specific instruction formats; Negative side-effects of fine-tuning (impaired generation, poor instruction following, code-switching). | Perpetuation of language/cultural bias; Uneven access to technology; Cultural erosion and loss of linguistic diversity; Negative impacts on education and linguistic identity; Increased vulnerability to targeted propaganda and misinformation; Emergence of a 'model divide' between languages; Digital extinction risk for threatened languages. |
| 21Up8qsNj6cJ.pdf | Google_Scholar | A Knight in Shining Nascency: Under -the-Radar Platforms as a Solution to Access to Justice for Incarcerated Litigants | This paper analyzes how the legal information duopoly (Lexis/Westlaw) and prison monopsonies restrict incarcerated litigants' access to justice by controlling legal information access. It advocates for prisons to adopt nascent, lower-cost, or open-access legal research platforms to fulfill constitutional requirements and improve access. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Market dominance and anti-competitive behavior (including copyright claims on enhanced public domain materials) by the legal publishing duopoly (Lexis/Westlaw). Prison systems acting as a monopsony buyer, often prioritizing cost or convenience over inmates' needs, and using exploitative funding mechanisms (inmate welfare funds). Path dependency in procurement practices. Lack of adequate digital access within prisons. | Prisons should contract with nascent, lower-cost, or open-source legal information platforms (e.g., Fastcase/vLex, Cornell LII, Caselaw Access Project) instead of relying solely on the duopoly. Procurement processes should change to prioritize meaningful access over specific proprietary features (like requiring Black's Law Dictionary). Funding for legal information should not rely on exploitative 'prison retailing' kickbacks. | Access to legal information, Access to the courts, Prison law libraries, Digital divide | Incarcerated litigants | Constitutional Law, Antitrust Law, Criminal Law/Procedure | United States | NaN | NaN | NaN | False | False | NaN | Continued dominance of the legal research duopoly hinders market entry for more accessible solutions. Prison monopsonies remain reluctant to adopt alternative platforms due to entrenched practices and contractual issues. Exploitative funding mechanisms persist. Need for greater digitization and open access to primary legal materials (statutes, cases) by governments. | NaN | Continued denial of the constitutional right of access to the courts for incarcerated individuals. Financial exploitation of inmates and their families to fund prison services, including legal research access. Entrenchment of data cartels controlling public and legal information. |
| Pfjjr1-EIwwJ.pdf | Google_Scholar | Conversational Factor Information Retrieval Model (ConFIRM) | This paper introduces ConFIRM, a method using the Five-Factor Model of personality to generate synthetic, personality-aligned training data for fine-tuning Large Language Models (LLMs) in domain-specific tasks. A case study fine-tuning Llama-2-7b for financial query classification demonstrated 91% accuracy, highlighting ConFIRM's potential for creating personalized and accurate AI retrieval systems. | True | Market | True | 1.0 | NaN | ConFIRM (Conversational Factor Information Retrieval Model): A method that uses the Five-Factor Model (FFM) of personality to generate synthetic question-answer pairs reflecting target population characteristics. This data is then used for parameter-efficient fine-tuning (LoRA) of LLMs (Llama-2-7b) for domain-specific information retrieval/classification tasks. | A case study in the finance domain. A Llama-2-7b model was fine-tuned using LoRA on 3000 synthetically generated QA pairs based on personality factors derived from the PolyU-Asklora Fintech Adoption Index survey. The model was evaluated on its accuracy in classifying financial queries against data categories modeled after Refinitiv Datastream. | The fine-tuned Llama-2-7b model achieved 91% accuracy in classifying financial queries on the test set (1000 samples). The average inference time per query was 0.61 seconds on an NVIDIA A100 GPU. | NaN | NaN | NaN | NaN | Finance (primary case study); potential applicability mentioned for Healthcare and Legal Services | Hong Kong (source of user personality data) | Synthetically generated question-answer pairs created using LLMs (GPT-3.5), SELF-INSTRUCT, and Text2Text generation methods. The generation was guided by population personality traits (OCEAN factors) derived from a survey subset (n=50) of Hong Kong participants (PolyU-Asklora Fintech Adoption Index). Data categories based on Refinitiv Datastream. | Integration of psychological frameworks (Five-Factor Model), synthetic data generation, large language models (GPT-3.5 for generation, Llama-2-7b for fine-tuning), parameter-efficient fine-tuning (LoRA), evaluation based on classification accuracy. | Model code shared via a GitHub repository. | True | True | Model code available on GitHub. | The paper suggests future work on scalability, expanding FFM integration, and exploring alternative preference optimization techniques like DPO. | Data scarcity for fine-tuning in specialized domains, need for training data reflecting target population characteristics, achieving high accuracy requires large training datasets (demonstrated by accuracy scaling with sample size). General LLM challenges like hallucinations and knowledge cutoffs. | LLM hallucinations (inaccurate responses), LLMs providing outdated information, misclassification leading to potential regulatory concerns (mentioned regarding false negatives). |
| Vl9jvdYVpp4J.pdf | Google_Scholar | JudicialTech supporting Justice \nThe impact of AI and Emerging Technologies on the Judiciary, Courts and Justice | This paper defines JudicialTech as AI and emerging technologies for judges, courts, and dispute resolution, aiming to support the judiciary, enhance access to justice, and increase fairness. It reviews JudicialTech's future impact across the judicial process, highlighting benefits like efficiency and improved access, alongside risks such as the erosion of human-led legal decisions and the need for robust judicial oversight. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Erosion of human-led legal decisions and judicial independence; lack of public confidence due to uncontrolled/untested AI; algorithmic bias, opacity, and inaccuracy (e.g., AI hallucinations, high error rates); insufficient or unsuitable legal data for training AI; negative impact on common law development from reduced trials due to predictive tools. | Strong judicial oversight, control, and robust testing regimes for AI; presumption against AI for judicial decision-making without thorough vetting; knowledge transfer, experimentation (sandboxes, tech sprints), and horizon scanning; development of "Open Justice" standards and JudicialTech Labs; human oversight and appeal mechanisms for AI-driven decisions. | Enhancing judicial efficiency and fairness; litigation advice and trial preparation (eDiscovery, document review); Online/Algorithmic Dispute Resolution (ODR/ADR); judicial guidance and decision support (including for sentencing); digital courts, managing court backlogs, supporting self-represented litigants. | Self-Representing Litigants (SRLs)/litigants-in-person (LIPs); general public affected by court backlogs. | Criminal law, Civil law, Commercial law, Family law, Regulatory law | UK, US, India, Singapore, France, EU, Canada. Broadly applicable internationally. | Discusses training data for existing AI systems: LLMs (e.g., GPT-4) trained on vast quantities of often online data; legal predictive tools trained on past judicial rulings which can be limited or outdated in smaller jurisdictions or with evolving laws; legal analytics tools use docket entries and documents. | NaN | NaN | False | False | NaN | Lack of robust, judiciary-supervised appraisal and testing regimes for judicial AI; need for established standards for digital access to justice data and services ('Open Justice'); insufficient R&D capabilities within Justice Ministries; ensuring public confidence and addressing algorithmic bias, opacity, and errors; adapting AI to limited and evolving legal data. | Data limitations for training legal AI (small datasets, evolving laws); ensuring AI systems are unbiased, transparent, and accurate; maintaining judicial control over technology; balancing innovation with Rule of Law and public confidence; complexity of automating legal reasoning; managing digital evidence and deepfakes. | Erosion of human-centered legal decision-making; undermining public confidence in the Rule of Law; inaccurate or biased AI decisions leading to miscarriages of justice; misleading legal submissions from generative AI (hallucinations); reduction in trials impacting common law; deepfake evidence; lack of algorithmic accountability; exploitation of AI for cybercrime. |
| -1ZohptBDsIJ.pdf | Google_Scholar | Good AI Legal Help, Bad AI Legal Help: Establishing quality standards for responses to people’s legal problem stories | This paper proposes 22 specific criteria to evaluate the quality of AI responses to legal problem stories from the public, particularly in civil justice. It then presents findings from a survey of 21 legal experts who ranked these criteria, aiming to establish robust standards for future AI benchmarking in access to justice. | True | Idealistic | True | 1.0 | Positive | A set of 22 quality criteria, grouped into 6 categories (Presentation, Legal Content Coverage, Legal Content Quality, Content Sources, Warnings/Disclaimers, Equity), for evaluating AI responses to legal help questions. | Survey methodology: 21 legal domain experts (legal aid lawyers, court staff, etc.) reviewed and ranked the 22 criteria on a 0-6 importance scale in 30-minute one-to-one interviews. They also provided qualitative feedback and suggested additional criteria. | Criteria such as 'Response is not toxic,' 'Response is in plain language,' 'Response does not misrepresent the substantive law,' and 'Response does not misrepresent any forms, paperwork, or tools' averaged highest importance (6/6). Experts prioritized usability, actionability, and accuracy, while de-emphasizing robustness, citations, and warnings to consult a lawyer. | Lack of well-defined, specific quality metrics for legal services and AI performance in the legal domain; current quality assessment is often subjective and ill-defined. | Proposing a specific, comprehensive list of 22 quality criteria, reviewed and ranked by legal domain experts, to serve as a basis for establishing actionable quality evaluation and benchmarking protocols for AI systems providing legal help. | Evaluating the quality of AI-generated legal information for initial legal help requests; establishing benchmarks for AI in civil justice; improving public understanding of legal rights and procedures. | General public needing legal help for civil justice problems such as housing, family, domestic violence, debt, and criminal records. | Civil justice (including housing, family, domestic violence, debt, criminal records, traffic). | International (aims for broadly applicable standards, with expert outreach including US, Canada, UK, Australia, and other countries, though initial survey participants' specific locations are not detailed, some roles suggest a US context). | NaN | Literature review of existing quality rubrics and AI benchmark standards; expert consultation via email inquiries; survey methodology involving semi-structured interviews with legal domain experts to rank and refine proposed criteria. | NaN | False | False | NaN | The study is ongoing, and findings are provisional; further testing of criteria in benchmark efforts is needed; exploration of automated assessment of criteria; addressing language and disability access more comprehensively; ensuring AI is not trained on biased data. | Defining and measuring 'quality' in the legal domain; creating specific yet broadly applicable evaluation criteria; balancing comprehensive legal information with user-friendly presentation; ensuring accuracy in a dynamic legal environment without setting unattainable standards. | AI providing misleading or harmful legal information (e.g., hallucinations, over-simplifications, errors leading to missed deadlines or incorrect filings); AI exhibiting bias or creating disparate impacts; users being overwhelmed by information or paralyzed by disclaimers. |
| Ksz1ZJFlB00J.pdf | Google_Scholar | Warhol, Drake, and Deepfakes: Monetizing the Right of Publicity in the Generative AI Era | This paper analyzes how AI-generated deepfakes and deep voices impact celebrities' right of publicity, critiquing the traditional transformative use test. It proposes a stricter test based on *Warhol v. Goldsmith* and a central licensing repository to manage and monetize the use of digital likenesses. | True | Market | True | 1.0 | NaN | A stricter transformative use test (informed by *Andy Warhol Foundation v. Goldsmith*) combined with a proposed "likeness licensing repository" modeled after Performance Rights Organizations (PROs) for managing AI-generated digital replicas of public figures. | NaN | NaN | The unauthorized and uncompensated use of celebrity likenesses (faces, voices) through easily created, hyperrealistic AI deepfakes and deep voices, which devalues their persona, undermines their ability to control and monetize their identity, and challenges the existing legal framework for the right of publicity. | Adoption of a stricter, purpose-based transformative use test (from *Warhol v. Goldsmith*) to assess if the AI use supplants the original's market. Establishment of a nonprofit likeness licensing repository to create official digital replicas, issue blanket licenses to content platforms, track usage, and distribute royalties to likeness-holders, ensuring consent, credit, and compensation. | Right of publicity, monetization of likeness, digital replicas (deepfakes and deep voices), transformative use test, First Amendment considerations, intellectual property. | Public figures, celebrities, entertainers, athletes, and influencers. | Right of Publicity, Intellectual Property Law, Copyright Law (related to fair use/transformative use). | United States (with discussion of U.S. case law, statutes, and proposed federal legislation). | The paper describes deepfakes and deep voices as being trained on "thousands of training images of an individual" or "audio of ... someone else’s—voice." Generally, this refers to visual and auditory data of the individuals whose likenesses are replicated, which can be sourced from public domain or proprietary collections. | The proposed likeness licensing repository is designed by analogy, modeled after existing music Performance Rights Organizations (PROs) like ASCAP and BMI. | The proposed likeness licensing repository would be deployed by having public figures enroll, the organization creating and managing a database of official digital replicas, content platforms obtaining blanket licenses, users selecting licensed replicas for their creations, and the organization tracking usage and distributing royalties. | False | False | NaN | Inadequacy of the traditional transformative use test for hyperrealistic AI replicas. Lack of a systematic mechanism for consent, compensation, and control for digital likenesses in the AI era. Potential for legislative solutions to be either too restrictive, stifling creativity, or not comprehensive enough to manage the complexities of AI-generated likenesses. | Difficulty in applying legal tests like 'transformative use' to hyperrealistic AI. Balancing First Amendment rights with the right of publicity. The rapid advancement and accessibility of deepfake technology. Detecting AI-generated content effectively. Preventing market substitution and the dilution of likeness value. Ensuring ethical use and clear disclosure for AI-generated content, especially in endorsements. | Unauthorized commercial exploitation and devaluation of personal likenesses. Consumer deception regarding the authenticity of content. Market substitution, harming the original creators' ability to profit from their work and image. Misuse for nonconsensual pornography or misinformation (though the paper's focus is on monetizing celebrity likenesses). Potential for overly broad regulations to stifle artistic expression and innovation with AI tools. |
| gHXfe3cys0IJ.pdf | Google_Scholar | Analyzing Images of Legal Documents: Toward Multi-Modal LLMs for Access to Justice | This paper investigates using a multi-modal large language model (GPT-4o) to extract structured information from images of handwritten legal forms, specifically an Ontario lease agreement. Initial results show the potential for such technology to aid access to justice by simplifying information gathering, but also reveal challenges related to image quality, handwriting variability, and potential biases. | True | Idealistic | True | 2.0 | Positive | Using a multi-modal LLM (GPT-4o) via API to extract structured data (e.g., names, addresses) from images of filled-out legal forms. | Evaluated GPT-4o on images of a filled-out standard Ontario lease form. Created a dataset with 3 scenarios (varying name/field complexity) and 5 image formats per scenario (typed PDF screenshot, neat handwritten HD, sloppy handwritten HD, neat handwritten SD, sloppy handwritten SD). Measured accuracy based on exact field value matches (case-insensitive) against ground truth across 14 fields. | Overall accuracy was 73%. Typed PDF (HD) yielded 98% accuracy, while performance decreased with handwritten text, lower image quality, and messier handwriting (Sloppy SD: 60%). The model struggled most with handwritten street numbers and uncommon names (sometimes substituting common ones), but excelled with predictable fields like city/province. | Difficulty for laypeople and self-represented litigants in understanding legal requirements, finding relevant information scattered across documents, and correctly filling out forms; the burden of administrative processes ("administrative sludge"). | Leveraging multi-modal LLMs to automatically identify and extract needed information from images of paper documents (forms, certificates, contracts, letters), thereby assisting users in filling out other forms, understanding their rights, or drafting submissions. | Form filling automation, information extraction from legal documents, support for self-represented litigants, reducing administrative burden in legal processes. | Laypeople, self-represented litigants. | Landlord-tenant law (residential leases), Administrative law (forms). | Ontario, Canada | N/A (Paper evaluates a pre-trained model, GPT-4o. The described dataset is for testing.) | Experimental design involving dataset creation (varying form scenarios, handwriting styles, image quality) and API-based LLM prompting for evaluation. | NaN | True | False | The technique uses the GPT-4o model via OpenAI's API, which is commercially available. The experimental code is available on GitHub. | Need for larger-scale studies with more varied data; optimizing prompts and models; integrating the capability into user-facing systems; addressing performance issues with low-quality inputs and handwriting; mitigating model biases. | Achieving reliable extraction despite variations in image quality, form completeness, and handwriting (neatness, style); model tendencies to 'correct' uncommon names towards common ones. | Exacerbating the digital divide, as performance relies on good quality images (requiring modern devices and good lighting); potential for societal biases embedded in LLMs to affect outcomes (e.g., poorer recognition of less common names). |
| 5ZkDnCaTzcUJ.pdf | Google_Scholar | ASKING GPT FOR THE ORDINARY MEANING OF STATUTORY TERMS | The paper tests ChatGPT's (GPT-3.5 Turbo) ability to provide evidence of the ordinary meaning of statutory terms by comparing its outputs against human survey data. It identifies a successful prompting technique (belief prompt with Likert scale) and explores context sensitivity, historical meaning, and offers lessons for using LLMs in statutory interpretation. | True | Market | True | 2.0 | Positive | Using GPT (specifically GPT-3.5 Turbo) with specific prompting techniques (direct question, chain of thought, belief prompt with percentage, belief prompt with Likert scale) to generate empirical evidence on the ordinary meaning of statutory terms. The most successful identified technique is the belief prompt combined with a Likert scale response format. | Comparison of GPT-3.5 Turbo's responses (generated 100 times per prompt/object combination with temperature=1) to results from a large-scale experimental human survey (Tobia 2020, N=2,835) asking whether 25 candidate objects are "vehicles". Evaluation based on statistical comparison of distributions (Kolmogorov-Smirnov test) and visual inspection of response patterns. Also tested sensitivity to context (rule wording, alternative rules, purpose) and historical meaning (1950s, intensional vs. extensional). | The belief prompt using a 7-point Likert scale generated results statistically indistinguishable (Kolmogorov-Smirnov, p = .2798) from the human survey benchmark (Tobia 2020). Other prompts (direct question, chain of thought, belief-percentage) performed poorly. GPT showed sensitivity to context (rule wording, purpose, historical time frame) but the character of the object remained the dominant factor. | Inaccuracy ('hallucinations') of LLMs, lack of transparency ('black box' nature, proprietary algorithms/data), potential for misuse/over-reliance on LLMs in legal interpretation, high cost of alternative empirical methods (surveys, corpus linguistics expertise). Methodological difficulty in finding reliable prompting techniques. | Careful benchmarking of LLM responses against human data, using specific validated prompting techniques (e.g., belief prompt + Likert scale), generating distributions of replies (not single answers), testing sensitivity to context, using LLMs to triangulate meaning with other methods, developing best practices for LLM use in interpretation. Democratizing access to empirical evidence via low-cost LLMs. | Statutory interpretation (determining ordinary meaning of terms). Access to legal information. | NaN | Statutory Interpretation | International | Standard GPT-3.5 Turbo training data: Common Crawl (410B words), WebText2 (19B words), Books1 (12B words), Books2 (55B words), Wikipedia (3B words). Not specifically trained on legal text. | Experimental design comparing LLM outputs to a human benchmark (Tobia 2020). Iterative testing of different prompting strategies. Statistical analysis (Kolmogorov-Smirnov test). Exploration of contextual variations. | NaN | True | False | Relies on OpenAI's commercial API for GPT-3.5 Turbo. | Need for more benchmarking against human data for various legal terms and tasks. Development of robust prompting methodologies. Lack of transparency of LLMs. Understanding LLM handling of technical vs. ordinary meaning. Further validation of historical meaning capabilities. | Finding effective prompting techniques yielding reliable results comparable to human judgment. Overcoming technical/coding challenges using the API. Analyzing large volumes of generated data. Interpreting variance in GPT responses. Avoiding over-reliance given limitations. | Misleading results from poor prompting ('junk science'). Over-reliance on LLM output due to perceived precision ('false allure of quantitative objectivity'). Lack of transparency ('black box') hindering accountability. General LLM risk of 'hallucinations'. Potential for over-inclusivity in classification observed in some tests. |
| 1237244.pdf | Google_Scholar | Applications of AI Chatbots Based on Generative AI, Large Language Models and Large Multimodal Models | This paper explores numerous applications of AI chatbots, built on Large Language Models (LLMs) and Large Multimodal Models (LMMs), categorizing them into personal and organizational uses. It details potential benefits like efficiency and personalization across various sectors, while also emphasizing crucial ethical considerations, regulatory compliance, and the need for human oversight for each application. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | Accuracy limitations, legal soundness of outputs, potential for synthetic media misuse, risks of IP theft, identity theft, digital privacy and security breaches, need for professional legal review, lack of accountability. | Human verification and supervision by legal professionals, adherence to ethical considerations (accuracy, privacy, security) and regulatory compliance. | NaN | NaN | General Legal Tasks | International | NaN | NaN | NaN | True | True | The paper discusses applications of generally available chatbots like ChatGPT and Gemini, which have public access (including free tiers). | Accuracy, reliability, bias mitigation, need for robust human oversight and verification especially for critical tasks, development of clear ethical guidelines and regulatory frameworks specific to legal applications, ensuring accountability. | NaN | Inaccuracy and hallucination leading to incorrect information or advice, data privacy and security breaches, propagation of bias, intellectual property (IP) theft, identity theft, misuse for generating harmful or misleading synthetic media, potential manipulation or exploitation of users (e.g., customers, employees), legal and reputational risks for organizations, over-reliance leading to deskilling, lack of accountability. |
| AT2VUVl9UdYJ.pdf | Google_Scholar | Attributing AI Authorship: Towards a System of Icons for Legal and Ethical Disclosure | This paper proposes the Artificial Intelligence Attribution (AIA) system, featuring icons similar to Creative Commons licenses, to standardize the disclosure of AI's role (research, writing, editing) in text generation across legal, academic, and corporate contexts. The authors argue, supported by original empirical research, that this system can mitigate legal risks, improve public perception, and foster ethical AI use by enhancing transparency and accountability. | True | Market | True | 1.0 | Positive | AIA (Artificial Intelligence Attribution) system using visual badges (Research, Writing, Editing, AI-Free) | Experimental survey (N=423) based on Mata v. Avianca case facts, comparing public perception and legal risk assessment of an attorney's negligent AI use with vs. without AIA badge disclosure. | Attorneys disclosing AI use with AIA badges faced significantly lower perceived likelihood of malpractice suits, lower recommended punishments (guilt verdicts, sanctions, suspension, fines), and were less likely to be seen as unfit for future practice compared to non-disclosing attorneys. | Lack of transparency regarding AI use in professional text generation (legal, academic, corporate); absence of norms and mechanisms for disclosure leading to legal risks, potential deception, erosion of trust, and ethical concerns. | Proposes the AIA (Artificial Intelligence Attribution) system, a set of visual badges inspired by Creative Commons, to provide a standardized, efficient way to disclose the nature and extent of AI involvement (Research, Writing, Editing, AI-Free) in text generation. | Professional ethics in law, Disclosure obligations, Transparency in AI use, Risk management for legal professionals, Accountability in legal document production. | NaN | Legal Ethics/Professional Responsibility, Contract Law, Consumer Protection Law, Intellectual Property Law, Civil Procedure, Tort Law (Legal Malpractice). | Primarily US (based on cases, institutions, laws cited), but proposed system has potential international applicability. | NaN | Conceptual design based on analogy (Creative Commons, iconography), legal analysis, ethical reasoning, empirically validated through a survey experiment. | Proposed for use in legal practice, academia, and corporate communications; no specific deployment strategy detailed. | True | True | The concept and visual designs for the AIA badges are presented within the paper, published in an open-access journal, implying they are available for adoption. | Need for evolving norms around AI disclosure; potential need for more granular disclosure than current badges offer; limitations of current AI detection tools and need for better verification/enforcement mechanisms; need for widespread adoption. | Designing an intuitive, comprehensive, yet simple system; promoting adoption and establishing norms; addressing potential negative perceptions of disclosed AI use; ensuring flexibility for future AI evolution; empirically validating the system's impact. | Risks of non-disclosure: Legal liability (malpractice, contract breach, consumer fraud, IP infringement), ethical violations (plagiarism, deception), professional sanctions, reputational damage, erosion of trust. Potential risks/criticisms of disclosure via AIA: Negative bias against AI-assisted work, revealing potentially sensitive process information, incomplete information conveyed by badges alone. |
| iboDfGK_-oEJ.pdf | Google_Scholar | It Cannot Be Right If It Was Written by AI: On Lawyers’ Preferences of Documents Perceived as Authored by an LLM vs a Human | This paper investigates whether lawyers' and law students' perception of legal documents (acknowledgement of debt) varies based on the belief that they were AI-generated versus human-crafted. The study found a significant bias against documents labeled as AI-generated, which were rated lower in correctness and language quality, despite being identical to those labeled human-crafted. | True | Idealistic | True | 2.0 | Neutral | Experimental survey designed to measure perception bias. Participants evaluated identical human-written legal documents (acknowledgement of debt), where the only difference was a label indicating whether the document was supposedly 'AI-GENERATED' or 'HUMAN-CRAFTED'. | 75 Czech lawyers and law students were randomly assigned to two groups. Each group evaluated two human-written 'acknowledgement of debt' documents (one Brief, one Verbose). Document labels ('AI-generated' vs 'human-crafted') were swapped between the groups. Participants rated documents on correctness and language quality (1-5 scale) via an online survey and provided qualitative explanations. Statistical analysis (Fisher exact test) and thematic analysis were performed. | Documents labeled 'human-crafted' were rated significantly higher than identical documents labeled 'AI-generated' on both correctness (mean 4.69 vs 4.21) and language quality (mean 4.55 vs 3.97). Thematic analysis revealed more negative comments regarding aspects like stylistics, structure, and formal correctness for documents perceived as AI-generated. Despite this bias, 93% of participants believe full automation of such documents is feasible. | Negative perception and bias (algorithmic aversion) against AI-generated legal documents among legal professionals, even when the documents are objectively correct. This bias could disproportionately harm lower-income individuals who might rely on AI-powered legal aid or self-help tools, potentially undermining the goal of increasing access to justice. | The paper highlights the need for awareness of this perception bias among legal practitioners, policymakers, and legislators. It suggests responsible implementation and adoption strategies for legal document generation technology and calls for discussions on updating legal processes. | Perception of AI-generated legal documents, Automated document drafting (specifically, acknowledgement of debt), Potential impact on access to justice for self-represented litigants or users of AI-powered legal aid. | Lower-income groups (potentially relying on AI tools for legal aid or self-help). | Civil Law (specifically Contract Law / Obligations Law related to debt acknowledgement). | Czechia | NaN | Experimental design involving manipulation of document labels (AI-generated vs. human-crafted) presented to two groups of participants (lawyers and law students). Data collection via online survey with Likert scale ratings and open-ended questions. Analysis using quantitative statistical tests (Fisher exact test) and qualitative thematic analysis. | NaN | True | True | The documents and the survey used in the experiments are released in an accompanying online repository on GitHub. | Need for research involving other populations (e.g., judges, officials, general public), different types/complexity of legal documents, varying participant AI exposure/experience, and cross-jurisdictional/-linguistic validation. Societal gap: Addressing the identified perception bias to ensure AI fairly benefits access to justice. | NaN | Over-reliance on or unfounded scepticism towards AI-generated documents influencing legal outcomes. Algorithmic aversion acting as a bias against users of AI tools, particularly affecting lower-income groups and potentially increasing social inequalities. Negative perceptions undermining the potential benefits of AI for access to justice. Potential conflict between transparency (disclosing AI use) and fairness due to perception bias. |
| jUunUV6T7O0J.pdf | Google_Scholar | Access to A.I. Justice: Avoiding an Inequitable Two -Tiered System of Legal Services | The paper discusses the potential of AI to improve access to justice but warns of the risk of creating inequitable two-tiered systems where the poor receive inferior services or only the wealthy benefit. It proposes a framework for calibrating AI use based on consumer, issue, and process considerations and advocates for regulatory reforms like sandboxes to overcome barriers and foster equitable AI development. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | High cost of legal services; lack of legal knowledge/experience/resources among consumers; language barriers; geographic isolation (rural areas); inadequate legal aid funding and staffing; digital divide (lack of access/skills); algorithmic divide; difficulty recognizing legal needs; complexity of legal issues for underserved populations (often overlapping with other issues); lack of data on marginalized communities (e.g., heirs' property); conservatism/resistance to change in the legal profession. | Regulatory reforms (clearer UPL definitions, relaxing non-lawyer ownership rules); regulatory sandboxes/laboratories for testing innovations; promoting competition in legal tech; increasing transparency (public accuracy rates, certifications, code comments); careful "calibration" of AI based on consumer, issue, and process considerations; culturally competent and user-centric AI design; involving diverse teams in design; using AI to identify/combat bias (if calibrated well); promoting algorithmic literacy; fostering collaboration between legal and tech sectors. | Access to legal information, self-help services, legal aid, document automation, client intake, legal analytics, pro bono services, legal service delivery models, regulation of legal services, unauthorized practice of law (UPL), non-lawyer ownership of law firms. | Low-income individuals, middle-income individuals, rural populations, those with limited English proficiency, recent immigrants, non-profits, small businesses, entrepreneurs, self-represented litigants, marginalized communities with undigitized records (e.g., heirs' property owners, affecting Black, Hispanic, Indigenous populations). | Civil Law, Family Law, Mediation, Transactional Law, Litigation, Property Law, Ethics/Professional Responsibility. | US | NaN | NaN | NaN | False | False | NaN | Need for regulatory reform (UPL, ownership); insufficient data/research on legal AI impact; lack of transparency in AI systems ("black box"); need for better methods/frameworks for AI calibration; addressing digital/algorithmic divides; ensuring culturally competent AI; fostering collaboration between legal and tech fields; lack of funding/resources for A2J tech; overcoming legal profession's conservatism. | High cost of AI development/deployment; need for significant resources (data, compute power, talent); overcoming legal profession's conservatism/resistance; ensuring algorithmic literacy; managing ethical risks (competence, UPL, bias); time/resilience needed for trial-and-error; difficulty establishing cross-industry collaboration due to regulations/market structures; designing for diverse users/contexts; ensuring data quality and mitigating bias; lack of transparency. | Creation/exacerbation of inequitable two-tiered legal service systems; AI causing harm to vulnerable consumers (errors, predatory services); amplification of societal biases (racial, gender, economic) through AI; exclusion of certain communities; erosion of trust in the legal system; decline in human-centered legal aid; stifling innovation due to regulatory uncertainty or market consolidation; ethical violations by lawyers; malpractice liability. |
| zWAOn2V0xsMJ.pdf | Google_Scholar | ArabLegalEval: A Multitask Benchmark for Assessing Arabic Legal Knowledge in Large Language Models | This paper introduces ArabLegalEval, a new benchmark dataset sourced from Saudi legal documents and translated English legal tasks, designed to assess the legal knowledge and reasoning of Large Language Models in Arabic. The authors detail the benchmark's creation methodology, evaluate several state-of-the-art LLMs, and release the dataset and code to foster research in Arabic legal AI. | True | Market | True | 1.0 | Positive | ArabLegalEval: A multitask benchmark dataset for Arabic legal LLMs, including methodologies for its creation from Saudi legal documents, publicly available FAQs, and translated LegalBench tasks. This also covers specific methods for MCQ generation (e.g., in-context learning from ArabicMMLU examples) and QA pair curation. | Various LLMs (GPT-4, GPT-4o, Jais, Command R, Command R Plus, Llama3) were benchmarked on ArabLegalEval tasks: MCQs (evaluated by accuracy), open-ended QA (evaluated using GPT-4 as a judge for answer similarity), and translated LegalBench subtasks (Consumer Contracts QA, Contracts QA, Privacy Policy QA, Privacy Policy Entailment, evaluated by F1 scores). Prompt optimization techniques (e.g., few-shot, CoT using DSPy) were explored. Human expert performance was also baselined on a sample. | On the generated MCQs, GPT-4o achieved the highest accuracy of 79.10% using few-shot prompting. For translated LegalBench tasks, top F1 scores varied: 90% by GPT-4 (one-shot) and Llama3-70B (zero-shot basic) on Consumer Contract QA; 99% by Command R Plus (few-shot) on Contract QA; 66% by Command R Plus (one-shot) on Privacy Policy Entailment. | Under-explored evaluation of LLM legal knowledge in Arabic, hindering development of reliable AI legal tools. Scarcity and difficulty in obtaining comprehensive, structured Arabic legal data suitable for training and benchmarking AI. | Creation and release of ArabLegalEval, a public benchmark with methodologies, open-source code, and dataset to stimulate and guide the development of more capable Arabic legal LLMs. Development of workflows for generating questions with automatic validation. | NaN | NaN | General legal domain, specifically Saudi Arabian law (regulations, statutes, circulars from Ministry of Justice and Board of Experts) and translated universal legal tasks (consumer contracts, general contracts, privacy policies). | Saudi Arabia (for native Arabic tasks); International (for translated LegalBench tasks considered universal). | The benchmark dataset (ArabLegalEval) was constructed from: publicly available Saudi legal documents (from Ministry of Justice and Board of Experts) scraped from official websites; publicly available human-written FAQs (NajizQA); and translated subsets of the publicly available LegalBench dataset. The ArabicMMLU legal subset was used for style guidance in MCQ generation. | For MCQ generation: Iterative prompt engineering (QA to MCQ, CoT, retrieval-based in-context learning from ArabicMMLU examples), automatic filtering using GPT-4, and manual review by legal experts. For QA data: Filtering of existing FAQs, semantic similarity matching. For LegalBench translation: Comparative evaluation of machine translation models (Opus MT chosen) with ROUGE scores and expert review. | The ArabLegalEval dataset and code are released on GitHub. | True | True | The ArabLegalEval dataset and code are available on GitHub: https://github.com/Thiqah/ArabLegalEval | Limited geographic representation in the current benchmark (primarily Saudi Arabian law). The dataset lacks granular categorization for more nuanced model training and evaluation. The broader under-exploration of LLM legal capabilities in Arabic. | Obtaining comprehensive Arabic legal data. Formulating high-quality MCQs (questions and plausible distractors). Mitigating evaluation bias where models might favor their own generated questions. Difficulty in evaluating open-ended QA due to semantic variability. Degradation of reasoning capability in Arabic for some smaller LLMs. | Potential for evaluation bias if models are tested on questions generated by themselves (a mitigation strategy was employed). Implicit risk of deploying LLMs with unverified or poor legal reasoning capabilities, which the benchmark aims to help identify and assess. |
| xIDsCy5DQfsJ.pdf | Google_Scholar | Legal Ethics, Artificial Intelligence, and Mindfulness, Oh My! | This paper explores the intersection of artificial intelligence, particularly generative AI like ChatGPT, with legal ethics rules governing lawyers' conduct. It highlights key ethical obligations (competence, confidentiality, supervision, billing) and suggests mindfulness as a tool for lawyers to navigate the benefits and risks of AI responsibly. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General Legal Practice / Legal Ethics | USA (primarily ABA Model Rules, New York court case) | NaN | NaN | Internal deployment within a law firm (Dentons' fleetAI example) with staff training and usage guidelines. | False | False | NaN | NaN | Ensuring ethical compliance (competence, confidentiality, supervision, candor), verifying AI accuracy, managing client communication and billing, adapting professional standards to new technology. | Inaccuracy/fabrication (hallucinations), breach of confidentiality, bias, violation of ethics rules (competence, candor), professional sanctions, reputational damage to lawyers/courts/profession, undermining client representation. |
| Yeadon_2023_Phys._Educ._58_035027.pdf | Google_Scholar | The death of the short-form physics essay in the coming AI revolution | This paper experimentally demonstrates that current AI language models like davinci-003 and ChatGPT can produce high-quality short-form physics essays achieving top university grades. This capability, combined with low plagiarism scores and challenges in AI text detection, poses a significant threat to the validity of such essays as an assessment method in physics education. | True | NaN | True | 2.0 | NaN | Use of AI language models (OpenAI's davinci-003 and ChatGPT) to generate short-form physics essays for university-level assessment. | Ten AI-generated submissions (each with five 300-word essays) were created using davinci-003. These were independently marked by five separate markers using an existing university Physics module's assessment proforma. Plagiarism detection software (Grammarly and TurnitIn) and AI text detection software (OpenAI's classifier, GPTZero) were also used on the essays. | AI-generated submissions achieved an average mark of 71±2%, comparable to the human student module average of 71±5% and meriting a First-Class grade. Plagiarism scores were low (Grammarly: 2±1%; TurnitIn: 7±2%). Current AI detection tools showed limited reliability in identifying AI-generated text (OpenAI's tool: 8/10 'Very unlikely AI'; GPTZero: 9/10 'May include parts written by AI'). | NaN | NaN | NaN | NaN | NaN | United Kingdom | The paper refers to GPT-3 being 'Trained on a large dataset of human-generated text,' which is a proprietary, large-scale, general text dataset from OpenAI. | The LLMs (davinci-003, ChatGPT based on GPT-3) are autoregressive language models trained on large-scale text datasets using statistical techniques to predict and generate coherent text based on prompts. | davinci-003 was accessible via OpenAI's 'playground' web application; ChatGPT is available as a chatbot. Both are online services provided by OpenAI. | True | False | davinci-003 accessible via OpenAI 'playground' web application; ChatGPT available as a chatbot, both stated as 'freely available to anyone with an internet connection' (though API/playground access may involve costs after initial free tiers). | NaN | Crafting effective prompts that elicit desired, high-quality, and varied essay responses from the LLMs often requires trial-and-error, including rephrasing questions and specifying output length or style. Ensuring generation of multiple unique, high-quality responses on nuanced topics might require some subject familiarity for prompt engineering. | Significant threat to the fidelity of short-form essays as an assessment method in education. Students could submit AI-generated work as their own, potentially passing undetected by plagiarism software and achieving unmerited high grades. AI models may also produce subtly incorrect or superficial content, especially in complex technical questions. |
| FuGSzl4d1IIJ.pdf | Google_Scholar | Citation-Enhanced Generation for LLM-based Chatbots | This paper proposes Citation-Enhanced Generation (CEG), a novel post-hoc framework to mitigate hallucinations in LLM-based chatbots by verifying generated content against retrieved documents using NLI. CEG can regenerate responses if unsupported statements are found, working as a training-free plugin for various LLMs. | True | NaN | True | 1.0 | NaN | Post-hoc Citation-Enhanced Generation (CEG) framework. It comprises: 1) A retrieval augmentation module (e.g., SimCSE BERT) to search for relevant documents (from a corpus like Wikipedia) for each segment of an LLM's response. 2) A citation generation module using Natural Language Inference (NLI) (e.g., prompted LLMs like GPT) to determine if retrieved documents support the response segments. 3) A response regeneration module that prompts the LLM to create a new response if segments are found to be nonfactual, incorporating the original query and relevant retrieved documents. | CEG was evaluated on hallucination detection benchmarks (WikiBio GPT-3, FELM WorldKnowledge subset) and a hallucination regeneration benchmark (HaluEval QA subset). Metrics included AUC-PR, Balanced Accuracy, and Accuracy. Custom datasets (WikiRetr-GPT3, WikiRetr-GPT4, based on Wikipedia) were also used to analyze the retrieval and NLI module performance using recall@k and precision@k. | CEG outperformed state-of-the-art methods in both hallucination detection and response regeneration on three benchmarks. For instance, on the HaluEval QA subset, CEG with GPT-3.5-Turbo-Instruct achieved 69.45% accuracy. On the FELM dataset with GPT-4, CEG achieved a balanced accuracy of 69.9%. | NaN | NaN | NaN | NaN | NaN | NaN | The CEG framework itself is training-free. For retrieval, it uses a processed snapshot of Wikipedia (October 20, 2023), segmented into ~100-word candidate documents. The NLI module employs pre-trained LLMs (e.g., GPT-3.5, GPT-4) with specific prompts, without requiring fine-tuning for CEG. | The CEG framework is designed as a post-hoc, plug-and-play system. Key design elements include segmenting LLM responses, using dense retrieval for document fetching, employing LLMs as NLI models via prompting for fact verification, and an iterative regeneration process with a maximum attempt limit. | The paper states the method is a 'training-free plug-and-play plugin'. The code and datasets are made available on GitHub to facilitate use and further research. | True | True | Code and datasets are available on GitHub: https://github.com/Tsinghua-dhy/CEG | NaN | Previous methods often require additional model training and data annotation. CEG aims to overcome this by being training-free. Challenges in CEG's development include selection of optimal retrieval models, balancing the number of retrieved documents (k) for effectiveness versus computational cost, and the performance of LLMs as NLI engines. API costs for LLM usage (NLI and regeneration) are also a practical consideration. | The paper identifies hallucination in LLM responses as the primary risk it addresses. Limitations that could be potential risks include: dependency on the quality of the retriever and corpus (current experiments use Wikipedia), reliance on the LLM's inherent world knowledge for NLI which could be flawed, and API costs associated with regeneration and NLI calls. |
| Sneddon_et_al_2024_A_servant_of_two_masters_How_Academic_Fears_about_Artificial_Intelligence_map_to_Employer_Engagement.pdf | Google_Scholar | SERVANT OF TWO MASTERS: HOW ACADEMIC FEARS ABOUT ARTIFICIAL INTELLIGENCE MAP TO EMPLOYER ENGAGEMENT | This paper discusses the impact of Generative AI on the legal and healthcare professions and their respective university education programs, highlighting key themes like risks (hallucinations, bias, skills gaps) and opportunities (efficiency, new job roles). It proposes the BATTEL model as a framework for guiding the ethical and effective integration of AI in education to prepare graduates for an AI-transformed professional landscape. | True | Market | True | 3.0 | Neutral | BATTEL (Best Available Techniques in Technology Enhanced Learning) model, applied to GenAI integration in education. | The paper mentions future research plans involving interviews and Q methodology to triangulate themes, but no testing of the BATTEL model's application is reported within this paper itself. | NaN | Replication of societal biases by AI systems leading to injustices; potential displacement of junior legal roles impacting service cost and availability; skills gaps among legal professionals and educators to effectively and ethically use AI. | Emphasis on human oversight and input to mitigate AI bias; adapting legal education to equip professionals with skills to manage AI, evaluate AI outputs, understand AI ethics, and fill new AI-related legal roles; utilizing frameworks like the BATTEL model to guide appropriate AI integration. | AI bias and fairness in legal outcomes; impact of AI on legal service delivery models and workforce (e.g., roles of paralegals and junior lawyers); ethical application of AI in the legal domain; ensuring legal education prepares students for AI in legal practice. | NaN | Legal education, General legal practice (e.g., contract negotiation). | International (with examples/mentions from UK, EU, US). | For GenAI (e.g., ChatGPT) discussed: The paper mentions the New York Times lawsuit against OpenAI and Microsoft for copyright infringement related to data scraping, implying large, scraped datasets including copyrighted material. For the BATTEL model itself, training data is not applicable as it's a conceptual framework. | The BATTEL model was developed by adapting the existing BAT (Best Available Technique) framework used in industrial emissions control. | The BATTEL model is proposed for adoption within higher education institutions to guide the appropriate and ethical use of AI, particularly Generative AI, in learning and teaching for professions like law and healthcare. | True | True | The BATTEL model is a conceptual framework described in a cited 2021 open-access journal article by one of the authors, making its principles available for application. | The need for ongoing development of sustainable curricula that keep pace with AI evolution; addressing the skills gap in both academic staff and students regarding AI; establishing clear policies and legislation for AI in education and professional practice; balancing technophobia and technophilia. | For GenAI: risk of 'hallucinations' (incorrect information), ethical use, ensuring data privacy and security, potential for bias replication, copyright issues related to training data. For the BATTEL model: achieving consensus among technological, subject, and pedagogic experts; evolving the 'best' standard as technology advances rapidly. | AI generating 'hallucinations' (false information) leading to incorrect decisions; replication of societal biases by AI, leading to injustices; privacy and security breaches of sensitive data; ambiguities in legal liability and accountability for AI actions; job displacement in legal roles (e.g., paralegals, junior lawyers) if not balanced by new role creation; students misusing AI, leading to academic misconduct and lack of genuine learning; graduates being unprepared for AI-driven industries. |
| BuildingTrustwithGenerativeAIChatbots-ExploringExplainabilityPrivacyandUserAcceptance.pdf | Google_Scholar | Building Trust with Generative AI Chatbots: Exploring Explainability, Privacy, and User Acceptance | This paper discusses the importance of building user trust in generative AI chatbots by examining the key factors of explainability, privacy, and user acceptance. It explores challenges and techniques related to transparency, data protection (including regulations like GDPR/CCPA), and factors influencing user adoption across various industries. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General AI, Customer Service, Healthcare, E-commerce, Finance, Law (Data Privacy Regulation, Legal Tech example) | EU, USA | NaN | NaN | NaN | False | False | NaN | NaN | General challenges discussed include: achieving reliability, transparency, user control; balancing complexity/performance with transparency/explainability; preventing user overload; managing data privacy (breaches, profiling, transparency); adhering to regulations; ensuring user acceptance (usefulness, ease of use, social factors, trust); designing good UX (conversational flow, personalization); managing perceived risks (inaccuracy, loss of control); user education; limitations in handling nuance, context, empathy; ensuring security; ethical issues (bias, fairness). | Data breaches, identity theft, surveillance, user profiling without consent, lack of transparency in data use, inaccurate/wrong information, harmful/biased responses (esp. in healthcare/law/finance), loss of user control, ethical risks (bias, fairness, stereotyping), privacy violations, reliance on potentially flawed AI advice. |
| h6yAuPmgJMMJ.pdf | Google_Scholar | LegalBench.PT: A Benchmark for Portuguese Law | This paper introduces LegalBench.PT, the first benchmark dataset specifically designed to evaluate Large Language Models (LLMs) on their knowledge and application of Portuguese law. The benchmark was created by collecting questions from real law exams and synthetically converting them into multiple-choice, true/false, and matching formats using GPT-4o, followed by filtering and validation. | True | NaN | True | 1.0 | Neutral | A methodology for creating a legal benchmark (LegalBench.PT) by synthetically generating multiple-choice, true/false, and matching questions from existing long-form law exam questions and answers using an LLM (GPT-4o), followed by rule-based and semantic filtering, and option shuffling. | Evaluated several LLMs (GPT-4o, GPT-4o-mini, Claude-3-Opus, Claude-3.5-Sonnet, Llama-3.1-8B/70B/405B, Mixtral-8x7B) on the benchmark in a zero-shot setting using balanced accuracy and F1-score. Also conducted human evaluation with 22 Portuguese lawyers on a 1,000-question subset and compared performance. Investigated potential generation bias by recreating a subset with Claude-3.5-Sonnet. | GPT-4o performed best overall (85.4% score), closely followed by Claude-3.5-Sonnet (85.1%) and Llama-3.1-405B (83.8%). Human lawyers generally performed closer to the lower-performing models (Llama-3.1-8B, Mixtral-8x7B). The bias investigation found no significant performance difference favouring the generating model. | NaN | NaN | NaN | NaN | Public Law (Environmental, Administrative, Constitutional, Energy, Public Finance, Financial, Tax, Criminal, Administrative Procedure, Civil Procedure, Criminal Procedure, Labor Procedure, Urban Planning), Private Law (Contract, Family, Obligations, Property, Succession, Commercial, Banking, Maritime, Corporate, Securities, Transportation, Aviation, Insolvency, Private International, Labor), Public-Private Law (Competition), Public International Law, EU and Community Law | Portugal | The benchmark generation technique used GPT-4o. The source data for generating the benchmark questions consisted of 341 PDF law exams with solutions from the Faculty of Law at the University of Lisbon (academic years 2021-2024), manually segmented and processed. | Corpus collection (law exams), synthetic data generation (using GPT-4o prompts), rule-based filtering (removing questions referencing specific articles), duplicate removal (using ROUGE-L and semantic similarity), statistical analysis (answer distribution), option shuffling, expert review (sample validation by a lawyer), human evaluation (lawyer performance assessment). | The benchmark dataset (LegalBench.PT) is made publicly available on Hugging Face. | True | True | Dataset publicly available at: https://huggingface.co/datasets/BeatrizCanaverde/LegalBench.PT | Underrepresentation of some legal areas; need for tasks beyond legal knowledge/reasoning (e.g., contract analysis, summarization); presence of easy, ambiguous, or incorrect questions needing filtering; need for more thorough human expert evaluation for validation. | Ineffectiveness of automatically evaluating long-form answers; LLM output token limits; filtering undesirable generated questions (references to specific articles/laws, duplicates); bias in generated answer option distribution; noise in synthetically generated data (incorrect answers, improper legal terminology, ambiguity). | Potential biases in the dataset derived from the synthetic generation process or inherent in the legal system. Misuse of the benchmark as a substitute for professional legal advice. |
| g4WP7TIImlgJ.pdf | Google_Scholar | From Flowchart to Questionnaire: Increasing Access to Justice via Visualization | This paper introduces F2Q (Flowchart to Questionnaire), an open-source toolbox designed to enable legal experts without programming expertise to create web-based interactive questionnaires from flowcharts. These questionnaires aim to guide clients, particularly from underserved populations, in understanding their legal issues and identifying potential solutions, thereby improving access to justice. | True | Idealistic | False | 1.0 | Positive | F2Q (Flowchart to Questionnaire): An open-source toolbox with a back-end designer for legal experts to create flowcharts and a client-facing front-end that automatically generates interactive web-based questionnaires from these flowcharts. | Demonstration through four use cases (debt collection, protection, eviction, small claims) developed with input from a legal expert. Feedback was gathered from a collaborating legal expert. Formal usability assessment is stated as future work. | NaN | Lack of awareness of legal rights, procedures, and available resources. Difficulty for individuals to identify their legal problems and understand potential solutions, leading them to abandon seeking justice. | Providing an open-source toolbox (F2Q) that allows legal experts to easily create and deploy interactive questionnaires. These questionnaires act as virtual assistants to guide users in understanding their legal situation and finding resources. | Legal problem categorization, identification of legal remedies/solutions, guidance on legal procedures (debt collection, protection orders, eviction, small claims), self-help legal resources. | Minorities and underserved populations without legal representation, clients of legal self-help centers or legal clinics. | Civil law, specifically debt collection, protection orders, eviction, and small claims. | United States (with specific examples from Utah) | NaN | User-centered design incorporating feedback from a legal expert. Use of visual flowcharts for representing legal decision paths. Design requirements included ease of use for non-programmers, client privacy protection, and an emergency exit feature. | The F2Q toolbox is released as open-source software on GitHub. Core ideas are reported to be under development by contractors for deployment at a local help center. | True | True | Open-source toolbox available on GitHub (https://github.com/tdavislab/F2Q). | Need for in-depth consideration of privacy and scalability for practical deployment. Formal usability assessment to evaluate effectiveness and efficiency is pending. The current tool only addresses a small part of the potential for visualization in access to justice. | NaN | Potential privacy concerns if client data were stored (addressed by current design not storing data). Risk of incorrect guidance if flowcharts are not accurately designed or updated by legal experts. |
| zho6ctN2dzQJ.pdf | Google_Scholar | Working Smarter: A Quantitative Investigation into Higher Education Faculty's Perceptions, Adoption, and Use of Generative Artificial Intelligence (AI) in Alignment with the Learning Sciences and Universal Design for Learning. | This dissertation investigates higher education faculty's perceptions, adoption, and use of generative AI, exploring alignment with Universal Design for Learning (UDL) principles using a quantitative survey approach. The study examines predictors of AI adoption and use, finding high adoption rates influenced by perceived relative advantage and professional development, with variations based on faculty demographics and roles. | True | NaN | True | 2.0 | NaN | Generative AI (e.g., ChatGPT, Gemini) use by higher education faculty | Quantitative survey research design involving 214 higher education faculty. Data analyzed using descriptive statistics, independent samples t-tests, nested logistic regression, chi-square tests of independence, and nested multiple linear regression. | 86% of faculty adopted generative AI. Relative advantage and professional development participation were significant predictors of adoption. Adoption rates were significantly higher among men, tenured/tenure-track faculty, and those with more knowledge of learning sciences or generative AI. Professional development on generative AI and several perceived attributes significantly predicted UDL-aligned use. | NaN | NaN | NaN | NaN | NaN | International (sampled faculty from US, Canada, Japan, Kazakhstan, Russia, UK, though predominantly US) | NaN | Quantitative survey research design; adaptation of existing validated scales (Moore & Benbasat’s Perceived Attributes of Innovations Scales, Grassini's AI Attitude Scale, Brougham & Haar’s STARA Awareness Scale). | NaN | True | False | Publicly available generative AI tools (e.g., ChatGPT, Gemini). | Need for further research addressing sample representativeness, the evolving nature of generative AI and UDL guidelines, correlations between key factors (perceptions, PD, use), and refining measurement instruments for perceived attributes. | Methodological limitations including non-probability sampling (convenience, snowball), potential for selection bias, cross-sectional design limiting generalizability and ability to track changes over time, managing data quality (detecting duplicate/bot submissions). | Academic integrity concerns (plagiarism, cheating on exams), AI bias (from training data, in detection tools disadvantaging non-native English speakers), AI unreliability (hallucinations, misinformation), potential threats to faculty job security, ethical concerns regarding data privacy and intellectual property, inappropriate use for sensitive tasks (e.g., mental health support without proper safeguards). |
| P1n84Z_tYPUJ.pdf | Google_Scholar | A quantitative study on the negative and positive impacts of using artificial intelligence (AI) in the information technology field | This paper investigates the perceived positive and negative impacts of Generative AI tools within the Information Technology (IT) sector using a quantitative survey of IT professionals and educators. Results indicate a significant positive relationship between understanding AI tools and perceiving positive impacts, alongside strong agreement on the need for ethical guidelines. | True | Market | True | 2.0 | NaN | NaN | Quantitative survey of 52 IT professionals/educators via LinkedIn using a 10-item Likert scale questionnaire. Data analyzed using SmartPLS4. | Significant positive relationship found between understanding GenAI tools and perceiving positive impacts (H1 supported). 84% of participants agreed/strongly agreed on the need for ethical guidelines. Hypotheses linking informed decision-making to minimizing risks (H2) or maximizing positive impacts (H3) were not supported. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | False | NaN | NaN | Study limitations: Small sample size (N=52), potential participant bias based on prior experience. General AI challenges discussed: Job displacement, bias, privacy issues, security risks, lack of transparency/explainability, AI hallucinations ('inaccurate statements'), manipulation for harmful content generation, existential/'singularity' risk. | Job displacement due to automation, ethical concerns about bias and privacy, security dangers from hacking, unfair or dangerous decisions due to lack of explainability, generation of inaccurate statements ('AI hallucination'), potential for autonomous weapons or surveillance violating rights, existential risk ('singularity'). |
| WUp5XdawNLoJ.pdf | Google_Scholar | A(I)ccess to Justice: How AI and Ethics Opinions Approving Limited Scope Representation Support Legal Market Consolidation | This article argues that while general AI tools like ChatGPT pose risks due to misuse, legal-specific AI combined with ethically approved practices like limited scope representation and ghostwriting can enhance access to justice by lowering costs. This convergence, however, may also lead to the corporatization and consolidation of the legal market for low- and middle-income clients. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | High cost of traditional legal representation bars access for low- and middle-income individuals. Unauthorized Practice of Law (UPL) rules prevent direct use of advanced AI by pro se litigants. Potential unsuitability of limited scope representation for complex cases or clients with limitations. | Utilizing legal-specific Generative AI (like Westlaw Precision, Lexis+ AI) to improve efficiency and lower costs. Employing Limited Scope Representation (LSR) and ghostwriting, supervised by attorneys (potentially contract attorneys), to provide affordable, discrete legal tasks. Developing a 'TurboLaw' model combining AI tools and virtual attorney oversight. | Affordability of legal services, Limited Scope Representation, Ghostwriting, Unauthorized Practice of Law, Legal technology adoption, Market structure of legal services. | Low- and middle-income individuals and families, pro se litigants (by enabling more affordable attorney assistance). | General legal practice | United States (references ABA Model Rules, federal courts, D.C., Texas, Maryland, New York, New Hampshire) | NaN | NaN | NaN | False | False | NaN | Need for reliable, legal-specific AI tools accessible at low cost. Clear frameworks to address Unauthorized Practice of Law issues with AI-assisted pro se litigants. Ensuring attorney competence and adequate supervision when using AI within LSR models. Addressing cybersecurity and confidentiality risks inherent in virtual practice and AI use. Potential negative socioeconomic impacts of market consolidation on solo and small firms. | NaN | Use of general GenAI (ChatGPT, Google Bard) leading to fabricated legal citations and court sanctions. AI providing legal advice constituting Unauthorized Practice of Law (UPL). Inadvertent disclosure of confidential client information through technology / virtual practice / outsourcing. Limited scope representation agreements being unreasonable or insufficient for a client's needs. Market consolidation driven by AI potentially harming smaller legal practices. |
| YOXCYWgzfXYJ.pdf | Google_Scholar | CaseGen: A Benchmark for Multi-Stage Legal Case Documents Generation | The paper introduces CaseGen, a benchmark for evaluating Large Language Models (LLMs) on multi-stage legal case document generation in the Chinese legal domain. It uses real case samples annotated by experts and an LLM-as-a-judge evaluation framework, finding current LLMs still struggle with these complex tasks. | True | Market | True | 1.0 | Neutral | CaseGen benchmark for multi-stage legal case document generation (Defense Statements, Trial Facts, Legal Reasoning, Judgment Results) using an LLM-as-a-judge evaluation framework. | Evaluation of several general-domain (GLM-4, Claude-3.5, GPT-3.5, GPT-4o-mini, Qwen2.5, LLaMA-3.3) and legal-specific (ChatLaw, LexiLaw) LLMs using the proposed CaseGen benchmark and LLM-as-a-judge (GPT-4o) framework. Human evaluation (3 experts, 50 cases, 3 LLMs) was used to validate the LLM-as-a-judge consistency via correlation coefficients (Kappa, Spearman, Kendall, Pearson). | Current LLMs perform unsatisfactorily (most LLM-judge scores below 6/10). Qwen2.5-72B-Instruct achieved competitive scores among LLMs. LLM-as-a-judge evaluation showed high consistency with human annotations (Spearman 0.750), superior to ROUGE-L and BERTScore. | Rising caseloads putting pressure on manual drafting; lack of reliable AI tools for complex legal document generation due to tendency for hallucinations and need for high accuracy/professionalism; absence of suitable benchmarks. | Develop and utilize specialized benchmarks (like CaseGen) and robust evaluation methods (like LLM-as-a-judge) to assess and improve LLM capabilities for legal document generation. | Court/Judicial efficiency through automated document drafting. | NaN | Civil Litigation | China | NaN | Data collection (public legal documents), filtering, K-Means clustering for sampling, text parsing, expert annotation (evidence content), annotator training & QC, LLM-based checks, expert cross-checks, multi-stage task design, LLM-as-a-judge pipeline design (pointwise scoring, task-oriented criteria, CoT, reference-based). | Public release on GitHub. | True | True | Dataset and code publicly available on GitHub (https://github.com/CSHaitao/CaseGen) under CC BY-NC-SA 4.0 license for non-commercial academic use. | Unsatisfactory LLM performance on complex legal document generation; limitations of legal-specific LLMs (base model constraints, reasoning degradation); LLM-as-a-judge needs refinement for legal nuances and robustness; current benchmark limited to Chinese jurisdiction. | Handling long legal texts; ensuring factuality, legal accuracy, and logical coherence in generation; designing robust automated evaluation for nuanced legal criteria; high cost and effort of expert annotation for benchmark creation. | LLM hallucinations generating misleading legal content; potential undermining of judicial fairness if AI outputs are flawed; over-reliance on AI; vulnerability of LLM judges to adversarial attacks; potential data privacy violations if PII is not properly handled (mitigated by anonymization). |
| Z673F12s3tUJ.pdf | Google_Scholar | Advancing Legal Tech and Education - Developments in the United States and South Korea - | This paper compares the integration of artificial intelligence (AI) into legal practice and education in the United States and South Korea. It examines trends in AI tools, their adoption by law firms and law schools, regulatory challenges (particularly in Korea), and implications for preparing future lawyers. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Limited access to legal services for the general populace and small businesses, particularly in South Korea due to cost, lawyer concentration, and historical undersupply; Regulatory resistance from professional bodies (e.g., Korean Bar Association); Concerns about AI accuracy, privacy, ethics, and copyright. | Development and adoption of AI-driven legal tech tools for efficiency and service delivery; Creation of legal tech platforms to connect clients with lawyers and provide accessible legal information/consultation (e.g., LawTalk, AI DR & Aju); Integration of AI training, ethics, and practical application into law school curricula. | Access to legal services for underserved populations, Affordability of legal services, Lawyer-client matching platforms, Legal consultation accessibility, Legal education reform. | General populace, individuals, small-business clients (especially in Korea), Consumers with everyday legal issues (US). | General legal practice, Legal research, Document review, Contract analysis, E-discovery, Litigation analytics, Legal education. | United States, South Korea | NaN | NaN | NaN | False | False | NaN | Need for improved AI accuracy and reliability (reducing hallucinations); Addressing AI biases; Resolving data privacy and copyright concerns; Developing robust ethical and regulatory frameworks; Adapting legal education curricula and training faculty effectively; Overcoming resource limitations in educational institutions. | Regulatory pushback and conflicts with traditional legal practice norms (e.g., LawTalk controversy in Korea); Resistance from established legal professionals; Keeping legal education curricula current with rapid technological advancements; Training law faculty in AI and legal tech; Market consolidation. | AI inaccuracy leading to flawed legal analysis or advice ('hallucinations'); Data privacy breaches and misuse of sensitive legal information; Copyright infringement issues related to training data and AI outputs; Algorithmic bias perpetuating inequalities; Undermining professional ethics and standards; Commodification of legal services. |
| yytdIHOdBqkJ.pdf | Google_Scholar | Using deep learning to predict outcomes of legal appeals better than human experts: A study with data from Brazilian federal courts | This paper evaluates deep learning models (ULMFiT, BERT-LSTM, BigBird) for predicting the outcome of appeals in Brazilian Federal Small Claims Courts, using only the first-instance decision text. The best model outperformed human legal experts in prediction accuracy, suggesting AI's potential to enhance judicial efficiency and predictability. | True | Idealistic | True | 2.0 | Positive | Comparison of three deep learning architectures (ULMFiT, BERT-LSTM, BigBird) for binary classification of appeal outcomes (affirm vs. reverse) based on first-instance court decision text. The best performing was the bidirectional ULMFiT. | Models trained and evaluated on the BrACJ-4 dataset (729,830 Brazilian Federal Small Claims Courts appeals, 2007-2020). Performance measured primarily by Matthews Correlation Coefficient (MCC) using a time-based split (train < Mar 2018, validation/test > Mar 2018). Compared against a baseline of 55 human legal experts (judges and clerks) who evaluated a subset of 810 cases. | The bidirectional ULMFiT model achieved the highest MCC (0.3881 on the test set, 0.3647 on the human-evaluated subset), significantly outperforming the human expert baseline (MCC 0.1954). | High volume of litigation and appeals in the Brazilian judiciary, leading to backlogs and slow processes. Significant economic impact of appeal costs, especially on poorer litigants in Small Claims Courts. High affirmance rate makes prediction challenging but necessary. Difficulty in achieving judicial consistency and predictability. | Using AI (deep learning NLP models) to predict appeal outcomes can provide valuable information to litigants and lawyers considering appeals, potentially reducing unnecessary litigation. AI tools could also assist courts in managing caseloads, increasing efficiency, and promoting jurisprudential stability. | Judicial efficiency, case outcome prediction, reducing court backlogs, litigation costs, access to justice for low-income individuals, consistency in judicial decisions. | Litigants in Brazilian Federal Small Claims Courts (LSCIIs), particularly those in the 4th Region (TRF-4), often described as the 'poorest people' seeking social security or assistance benefits. | Social Security Law, Public Law (Administrative Law), Civil Procedure (specifically appeals in Small Claims Courts). | Brazil (Federal Justice, 4th Regional Federal Court - TRF-4, and associated Federal Small Claims Courts - LSCIIs). | A publicly derived dataset (BrACJ-4) containing text from 729,830 first-instance decisions and corresponding appeal outcomes from Brazilian Federal Small Claims Courts (4th Region) between 2007-2020. Pre-training also utilized general Portuguese corpora (Wikipedia, BrWaC). The data is unstructured text. | Data collection from public court repositories, data cleaning and labeling, time-based data splitting for training/validation/testing, transfer learning (pre-training on general/domain corpora, fine-tuning on task), hyperparameter tuning using Bayesian Optimization, evaluation using Matthews Correlation Coefficient (MCC), baseline comparison against human experts. | The dataset (BrACJ-4), code, and pre-trained models are made publicly available on Kaggle and GitHub to facilitate further research. | True | True | Dataset and pre-trained models available on Kaggle (https://www.kaggle.com/eliaskjacob/bracj4). Code available on GitHub (https://github.com/eliaskjacob/paper-bracj4). | Models currently only use first-instance decision text, ignoring potentially valuable information from appeal briefs and counter-arguments. The study is limited to one specific court region and type; generalizability needs testing. Lack of model explainability. | Handling very long legal documents, managing large datasets, avoiding data leakage through time-sensitive splitting, establishing reliable ground truth labels from court data, creating a robust human baseline for comparison, mitigating potential biases in data, computational resource requirements for training large models. | NaN |
| KRoJJ5fKn0IJ.pdf | Google_Scholar | Assessing ChatGPT as a Power Analysis Tool: An Empirical Investigation | This empirical study evaluates ChatGPT's (GPT-3.5-turbo and GPT-4) proficiency in conducting power analysis for sample size calculations, finding it capable, especially when GPT-4 generates R code. The paper suggests ChatGPT can serve as an accessible supporting tool for researchers, reducing barriers to performing power analysis. | True | Idealistic | True | 2.0 | Positive | ChatGPT (GPT-3.5-turbo and GPT-4) for power analysis, specifically sample size calculation for t-tests, ANOVA, and chi-square tests. Evaluation included direct querying and code generation (R and Python) strategies. | Two experiments: Exp1 assessed accuracy of sample size calculation using GPT-3.5-turbo and GPT-4 with three methods (direct, R code, Python code) for three test types (two-sample t-test, one-way ANOVA, χ² goodness-of-fit test), with 100 trials per condition, comparing results to G*Power. Exp2 assessed GPT-3.5-turbo's ability to identify missing input parameters for power analysis across these tests, with 100 trials per condition. | In Experiment 1, the GPT-4 model generating R code achieved 100% accuracy in calculating the required sample size for two-sample t-tests, one-way ANOVA, and χ² goodness-of-fit tests. | For researchers conducting power analysis: need for specialized statistical expertise, cost and accessibility of specialized software, cognitive load of learning new statistical programs, and limitations of existing software for complex designs or all desired statistical tests. | Proposes leveraging Large Language Models like ChatGPT as an interactive and accessible alternative or supplement for power analysis. ChatGPT can provide guidance, explain statistical parameters, and generate R or Python code for sample size calculations, thereby lowering cognitive and resource barriers for researchers. | Access to statistical analysis tools; democratizing research methods; sample size calculation; power analysis support for researchers. | Researchers with limited access to specialized statistical software, funding for such software, technical training, or expert statistical consultation; graduate students; early-career researchers. | NaN | International | The study utilized pre-trained OpenAI models (GPT-3.5-turbo and GPT-4). These models were trained by OpenAI on extensive, diverse datasets of text and code, including statistical information and programming code (e.g., R, Python) relevant to power analysis. | Factorial experimental design (2 models × 3 methods × 3 test types), prompt engineering techniques (e.g., 'Take a deep breath', 'Use the existing code as it is', setting temperature to 0), quantitative accuracy assessment by comparing LLM-generated sample sizes to those from established statistical software (G*Power), and McNemar tests for statistical comparisons of performance. | NaN | True | False | The approach involves using OpenAI's GPT-3.5-turbo (which has some free access tiers) and GPT-4 (a paid model) via their API. The prompting strategies are described and can thus be replicated by users with access to these models. | Need for research on LLM performance for more complex statistical tests and simulation-based power analysis. Limited evaluation of other LLMs beyond GPT-3.5/GPT-4 and the impact of ongoing model updates on result consistency. Validation could be strengthened (e.g., multiple raters). Ensuring overall reliability, safety, and precision of LLMs in statistical applications remains an area for further investigation. | Authors faced challenges related to the probabilistic nature of LLMs (mitigated by temperature settings and prompt engineering), ChatGPT's known limitations in direct mathematical calculations (addressed by using code generation), and achieving consistent high accuracy. For users, challenges include ensuring research accountability when using AI-generated results and potential data security concerns if sensitive contextual information is inputted. | Risk of incorrect sample size calculations leading to underpowered or inefficient research if AI outputs are not critically verified. Over-reliance on AI may lead to errors. Accountability issues for researchers using AI-generated results. Potential for data privacy breaches if users input sensitive study information, although power analysis parameters are typically not PII. |
| Cnozql-PhfgJ.pdf | Google_Scholar | ARTIFICIAL INTELLIGENCE AND THE FUTURE OF LAW AND JUSTICE IN NIGERIA | This paper reviews the potential applications and impact of Artificial Intelligence (AI) on the legal system and administration of justice in Nigeria. It discusses existing AI tools, potential benefits like increased efficiency and accessibility, significant challenges such as bias and job displacement, and the current regulatory landscape, offering recommendations for responsible integration. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Potential job replacement for legal professionals (paralegals, legal assistants); Likelihood of bias in AI systems due to training data, leading to unfair or discriminatory outcomes. | Enhance AI system transparency and accountability; Implement human review mechanisms with diverse, independent reviewers (potentially overseen by an ethics board); Provide comprehensive AI training for future lawyers; Invest in capacity building and integrate tech literacy into education/policy; Develop a comprehensive AI-specific legal and ethical framework. | Efficiency, cost reduction, accessibility, transparency in legal processes and justice delivery (e.g., legal research, document review, case prediction, dispute resolution, sentencing). | General population / litigants in Nigeria | General / Multiple legal fields | Nigeria | NaN | NaN | NaN | False | False | NaN | Lack of comprehensive AI-specific legislation and regulatory framework in Nigeria (beyond draft policies and data protection); Need for widespread AI literacy and training within the legal profession; Establishing robust mechanisms (like independent ethics boards) to ensure AI objectivity and combat bias; Addressing ethical implications of AI-driven job displacement. | The potential for AI bias and discriminatory outcomes; The risk of AI automating and replacing traditional legal tasks and jobs. | AI systems reproducing biases present in training data, leading to unfairness and discrimination; Automation of legal jobs (e.g., paralegals, legal assistants); Potential misuse or misinterpretation of AI in tasks requiring nuanced human judgment or emotional understanding. |
| jpcoqVYi6QcJ.pdf | Google_Scholar | Generative AI or the Doom of Translation as we Know it? | This paper explores the transformative impact of generative AI on the field of translation studies, outlining both significant challenges and promising opportunities. It discusses concerns like job displacement, quality issues, and ethics, alongside potential benefits such as efficiency, augmented human translation, domain specialization, and innovation in the field. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal (mentioned as an example domain) | International | NaN | NaN | NaN | False | False | NaN | NaN | Ensuring quality and accuracy (context-dependent nuances, idiomatic expressions, stylistic fidelity, cultural subtleties); ethical concerns (data privacy, bias in training data, potential propagation of misinformation/offensive content); potential for translator job displacement. | Job displacement for human translators, lack of quality and accuracy in translation outputs (especially regarding nuances, idioms, context, style, culture), perpetuation of biases encoded in training data, data privacy violations, inadvertent propagation of misinformation or offensive content. |
| NJWO2V-bSKAJ.pdf | Google_Scholar | Better Transcription of UK Supreme Court Hearings | This paper describes a method to improve automated transcription of UK Supreme Court hearings by fine-tuning an off-the-shelf Automatic Speech Recognition (ASR) system. The approach utilizes a custom language model trained on legal texts and gold-standard transcripts, along with a specialized vocabulary of legal terms, aiming to reduce word error rates and enhance the accuracy of domain-specific language. | True | Idealistic | False | 1.0 | Positive | Domain adaptation of an ASR system (AWS Transcribe) by fine-tuning with a custom language model (CLM) trained on in-domain legal texts and gold-standard transcriptions, and infusing a custom vocabulary of common legal phrases and entities extracted using NLP techniques (PMI-based collocation detection and Blackstone/spaCy NER). | The proposed models were evaluated on 12 hours of UK Supreme Court Hearings (2 cases). Performance was measured by Word Error Rate (WER) and the ratio of correctly transcribed legal entities, compared against AWS Transcribe base and OpenAI Whisper ASR systems. | The best performing model (CLM2+Vocab) achieved an average Word Error Rate (WER) of 11.6. This was an improvement over the baseline AWS Transcribe (12.3 WER) and OpenAI Whisper (12.4 WER). CLM2+Vocab also demonstrated superior accuracy in transcribing specific legal entities, such as 'Judge' (0.84 correct ratio vs. 0.66 for AWS and 0.77 for Whisper) and 'Provisions' (0.97 correct ratio vs. 0.88 for AWS and 0.95 for Whisper). | High cost and slowness of manual speech transcription. Generic ASR systems exhibit high Word Error Rates (WER) on specialized legal audio due to long hearings, multiple speakers, complex speech patterns, unique pronunciations, and legal jargon, leading to critical errors and information loss. Limited availability of large in-domain datasets for training specialized ASR systems. | Developing an automated transcription tool by fine-tuning a generic ASR system with an in-domain custom language model (trained on legal documents and gold-standard transcripts) and infusing a custom vocabulary of common legal phrases and entities to improve transcription accuracy and reduce critical errors specific to legal terminology. | Access to court proceedings/records through improved transcription of hearings. | General public and legal professionals requiring affordable and accurate access to court transcripts. | Court proceedings / Litigation (specifically UK Supreme Court hearings). | United Kingdom (UK Supreme Court). | For the Custom Language Model (CLM): 1) Publicly available written judgements from 43 UK Supreme Court cases (3.26M tokens, unstructured text, scraped from official website). 2) Approximately 81 hours of proprietary gold-standard transcripts from 10 UK Supreme Court hearings (unstructured text, created by post-editing ASR output). For vocabulary list: Approximately 139 hours of gold-standard transcripts and the aforementioned written judgements. | Data collection (web scraping, manual post-editing of ASR output to create gold-standard transcripts), fine-tuning of a pre-trained ASR system (AWS Transcribe) with a custom language model, NLP techniques for custom vocabulary creation (PMI-based phrase detection, named entity recognition using Blackstone and spaCy), and comparative evaluation (WER, entity recognition accuracy) against baseline systems. | Not explicitly stated for general public use; described as part of a combined research and industrial project involving Kingfisher Labs Ltd and Just Access. | False | False | NaN | Need for improved handling of diverse accents in British court audio beyond Supreme Court homogeneity. Potential for using NLP topic modeling to connect transcribed legal entities to case decisions. | Domain mismatch between generic ASR training data and specialized legal audio. Accurately transcribing domain-specific terminology, names, and numbers. Achieving low WER while managing transcription time and computational costs. Acquiring sufficient in-domain training data. | Inaccurate transcription, especially of critical legal terms, names, and numbers, can lead to serious information loss and cause confusion, potentially impacting legal outcomes or understanding. |
| Addressing_Technical_Challenges_in_Large_Language_Model-Driven_Educational_Software_System.pdf | Google_Scholar | Addressing Technical Challenges in Large Language Model-Driven Educational Software System | This paper discusses key technical challenges (integration, explainability, testability, scalability) in developing LLM-driven educational software. It proposes and evaluates a set of tactics, including a chain-of-reasoning-and-action pattern and an event-driven microservice architecture, implemented in a system called AITeach. | True | NaN | True | 1.0 | NaN | The AITeach system, which employs a chain-of-reasoning-and-action design pattern for integration, metadata and an algorithmic process for explainability, regression testing for response consistency, and an event-driven microservice architecture for scalability. | Response consistency (RQ1) was tested on 100 tasks using LLMs (Gemini-1.5-Pro, Gemini-1.0-Pro, GPT-3.5-Turbo, GPT-4-Turbo), comparing generated thoughts via cosine similarity (using OpenAI's text-embedding-3-small) and next-action accuracy against baselines. Scalability (RQ2) was assessed via load tests (20-100 requests/sec) on AITeach components deployed as microservices on Google Cloud Run, monitoring response times and instance scaling. | For response consistency, Gemini-1.5-Pro-002 performed best, achieving 97% accuracy for next action determination and the highest average cosine similarity (d-value of 0.9483) for thought generation. The event-driven microservice architecture demonstrated effective autoscaling, maintaining performance under increasing system load. | NaN | NaN | NaN | NaN | NaN | International | Learning reference materials (PDF, DOC, PPT) uploaded by instructors, converted into embeddings for Retrieval-Augmented Generation (RAG). The system utilizes pre-trained LLMs (Gemini, GPT models). | Chain-of-reasoning-and-action pattern, event-driven microservice architecture, metadata and algorithm design for explainability, regression testing methodology. | Core components (Thinker, Planner, Worker) of the AITeach system were deployed as independent microservices on Google Cloud Run for evaluation, using containerization technology. | True | True | The complete source code for AITeach is stated to be available on GitHub: https://github.com/cnacha-mfu/aiteach | NaN | The main challenges addressed are integration of complex LLM components (especially for RAG), the black-box nature of LLMs hindering explainability and trust, ensuring consistent and reliable outputs (testability) given the probabilistic nature of LLMs and prompt sensitivity, and achieving scalability for systems with interconnected components and sequential reasoning processes. | Risk of LLM hallucinations (generating inaccurate or fabricated information) in educational applications. |
| SQQr7rMNtGMJ.pdf | Google_Scholar | PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluation | This paper introduces PsycoLLM, a large language model specialized for psychological understanding and evaluation, fine-tuned on a newly constructed high-quality psychological dataset. The authors also propose a comprehensive psychological benchmark based on Chinese counseling examinations to evaluate PsycoLLM, demonstrating its superior performance over other LLMs in this domain. | True | NaN | True | 1.0 | NaN | PsycoLLM: a psychological LLM developed by fine-tuning Qwen1.5-14B-Chat on a novel, high-quality psychological dataset. The dataset includes single-turn QA, KimiChat-generated multi-turn dialogues, and knowledge-based QA primarily from Chinese sources. A new psychological benchmark was also developed. | PsycoLLM and other LLMs were evaluated on a newly proposed psychological benchmark based on Chinese psychological counseling examinations (MCQs and QA covering professional ethics, theoretical proficiency, case analysis). PsycoLLM was also tested on general benchmarks (MMLU, CMMLU, GSM8K, CEVAL). | PsycoLLM demonstrated superior performance on the psychological benchmark, achieving an overall average standard accuracy of 61.71% on MCQs (64.70% when using elastic accuracy for MMCQs). It also showed strong performance in case-based QA (e.g., R-1 24.45, BS 65.29) and comparable results to its base model on general benchmarks. | NaN | NaN | NaN | NaN | NaN | China | A proprietary, domain-specific (psychology) dataset constructed by the authors. It includes: 1) 155k+ single-turn QAs from Chinese online platforms (e.g., Yixinli, Zhihu). 2) 11.5k+ multi-turn dialogues generated by KimiChat from selected QAs using a 3-step pipeline. 3) 10k knowledge-based QAs from psychology books (extracted via Qwen-72B and exercises). Mainly unstructured text. | Dataset construction involved web scraping, LLM-based data generation (KimiChat, Qwen-72B with RAG and teacher-student model), a three-step dialogue generation pipeline (generation, evidence judgment, refinement), and manual proofreading. Model development involved supervised fine-tuning (SFT) of Qwen1.5-14B-Chat. | A GitHub link (https://github.com/MACLAB-HFUT/PsycoLLM) is provided for PsycoLLM. | True | True | Publicly available on GitHub (https://github.com/MACLAB-HFUT/PsycoLLM). | NaN | Primary challenges include creating high-quality, domain-specific training data that avoids simply distilling from larger models, developing comprehensive domain-specific benchmarks, mitigating biases from LLM-generated data, and balancing specialized knowledge with general reasoning capabilities to prevent overfitting. | Potential risks include the introduction or amplification of biases from LLM-based data generation processes, and overfitting to the specialized psychological domain, which can degrade the LLM's general reasoning capabilities. |
| sC5dwrTUpGUJ.pdf | Google_Scholar | Black-Box Analysis: GPTs Across Time in Legal Textual Entailment Task | This paper analyzes the performance of GPT-3.5 and GPT-4 on the COLIEE Task 4 Japanese legal textual entailment dataset from 2006 to 2021. Results show GPT-4 generally outperforms GPT-3.5, especially in recent years, but performance fluctuates, indicating sensitivity to data distribution and potential temporal knowledge gaps. | True | NaN | True | 2.0 | NaN | GPT-3.5 and GPT-4 APIs for zero-shot legal textual entailment. | Evaluation using the COLIEE Task 4 dataset (Japanese statute law entailment, H18-R03 / 2006-2021) via API calls. Accuracy was the primary metric. | GPT-4 generally outperformed GPT-3.5 (average accuracy 0.7670 Eng / 0.7843 Jap vs 0.7109 Eng / 0.6412 Jap), especially in recent years, but performance varied significantly across years. | NaN | NaN | NaN | NaN | Statute Law | Japan | The study uses proprietary models (GPT-3.5, GPT-4) with undisclosed, general pre-training data. The evaluation dataset is COLIEE Task 4 (Japanese statute law entailment questions, context, and labels from 2006-2021). | Black-box testing via API calls with specific prompts for zero-shot evaluation. | NaN | True | False | Commercial API access from OpenAI. | Need for better generalizability, adaptability, explainability/interpretability in legal reasoning models. Challenges in understanding black-box models due to undisclosed architecture/data. | Understanding performance variations of black-box models across different years, potentially due to training data distribution and temporal knowledge limitations. Evaluating performance across different languages (English vs Japanese translations of the dataset). | Limitations in reliability due to performance fluctuations and lack of transparency/explainability. |
| t8zFmiNLlnkJ.pdf | Google_Scholar | AI-Based Contract & Legal Document Generator using Machine Learning | This paper proposes an AI system using an LSTM-CNN model to automate the generation of legal documents such as rental agreements. The system aims to improve efficiency and accessibility within the Indian legal system, and was trained on 4,825 legal articles, showing promising initial results via a web interface but also identified overfitting issues. | True | Idealistic | False | 1.0 | Positive | An AI-based legal document generator using a phrases-based LSTM-CNN model. The system incorporates NLP techniques (tokenization, stop-word removal, stemming, POS tagging) for text preprocessing and is built using TensorFlow/PyTorch and spaCy/NLTK. | The LSTM-CNN model was trained for ten epochs on 4,825 legal articles. Evaluation metrics included training/validation accuracy and loss. A prototype web interface was developed to demonstrate the generation of a rent agreement. | The model achieved high training accuracy (approx. 0.95) but showed signs of overfitting, with validation accuracy plateauing around 0.8 after the 7th epoch. | Slow legal system in India with a high number of pending cases; short hearing times leading to pressure on legal professionals and potential for human error; high cost of legal document preparation; lack of standard automation techniques in the Indian legal system; legal system being 'far off for most of the residents'. | Automating legal document generation using AI/ML to reduce time, workload, and human error; improving consistency and accuracy of legal documents; making legal services more accessible and affordable, especially for those who cannot afford expensive lawyers; increasing efficiency in case resolution. | Automated legal document generation (e.g., contracts, rental agreements), improving legal system efficiency, enhancing access to legal services for low-income individuals, reducing legal costs. | Primarily individuals who cannot afford expensive legal representation in India. Secondarily, legal professionals (lawyers, judges) by improving their efficiency. | Contract law (rental agreements, partnership agreements, loan agreements are mentioned as examples) and potentially wills. | India | A dataset of 4,825 legal articles. The source and specific nature (e.g., public, proprietary, structured/unstructured beyond 'articles') are not detailed. | A standard machine learning workflow: data collection, preprocessing (NLP techniques), feature extraction, model selection (LSTM-CNN), training, validation, testing, and UI development for demonstration. | A prototype web application with a login and form-based input for generating a rent agreement was developed for demonstration purposes. No information on broader deployment. | False | False | NaN | Technical: Model overfitting, requiring further fine-tuning. Societal: The paper implies ongoing issues with access to justice and legal system efficiency in India that the tool aims to partially address. | The primary technical challenge identified was model overfitting. General challenges implied include acquiring and preprocessing suitable legal text data for training. | NaN |
| Vp2PeZVWrfAJ.pdf | Google_Scholar | Investigating Code Generation Performance of ChatGPT with Crowdsourcing Social Data | This paper presents a framework using crowdsourced social media data (Twitter, Reddit) to analyze ChatGPT's code generation performance. It finds Python and JavaScript are most popular, usage includes debugging and interview prep, and the dominant user sentiment is fear. | True | NaN | True | 2.0 | NaN | ChatGPT for code generation | Analysis of 316K tweets and 3.2K Reddit posts (Dec 2022-Jan 2023) using topic modeling (LDA), sentiment analysis (Text2Emotion), and code quality evaluation (Flake8) on Python code snippets extracted from shared images. | Python and JavaScript were the most popular languages. Tasks included debugging, interview prep, and assignments. Fear was the dominant sentiment across languages. Flake8 analysis showed most Python code errors were style-related (pycodestyle E/W codes), primarily E501 (line too long). | NaN | NaN | NaN | NaN | NaN | International | Publicly available social media posts (316K Tweets, 3.2K Reddit submissions) and associated images from Dec 1, 2022, to Jan 31, 2023, filtered by keywords related to ChatGPT and programming. | Data collection from social media (Twitter API, Pushshift Reddit API), Keyword expansion (LDA, expert review), NLP (LDA topic modeling, Text2Emotion sentiment analysis), Image processing (easyOCR), Code quality analysis (Flake8). | NaN | False | False | NaN | NaN | Diversity of programming languages and tasks making comprehensive evaluation difficult; cost/time of traditional user studies; potential biases from single data sources; difficulty in accurately reconstructing code (especially indentation) from images. | User sentiment of fear concerning code quality and potential negative impact on human jobs (job displacement). Potential for biased code generation (mentioned via citation). |
| 1279738.pdf | Google_Scholar | DOL-LLM - Optimizing Large Language Model Inference with Domain-Specific Adaptations and Efficiency Techniques via Quantization, Pruning, and Distillation | This paper proposes DOL-LLM, a methodology for developing lightweight, domain-specific large language models (LLMs) optimized for resource-constrained edge devices. It combines quantization, pruning, and knowledge distillation with domain-specific training to enhance LLM accessibility and performance on devices like mobile phones and embedded systems. | True | Market | True | 1.0 | NaN | DOL-LLM: A methodology combining domain-specific adaptations with optimization techniques (quantization, pruning, and knowledge distillation) for LLMs. | Benchmarking covered inference speed, memory usage, model accuracy, and energy efficiency. Comparative evaluations against SmolVLM variants were conducted on NVIDIA A100 GPUs and ARM mobile processors. | Achieved a 4x reduction in memory bandwidth with less than 2% accuracy degradation using mixed-precision quantization; structured pruning removed 30-40% of non-critical parameters while maintaining over 98% of original functionality. DOL-LLM showed 2-4x speedups on NVIDIA A100 GPUs, 30-50% reduction in energy consumption on ARM mobile processors, and 5.7 GB GPU RAM utilization. | NaN | NaN | NaN | NaN | NaN | International | Acquired or generated large corpus of text for pre-training; domain-specific datasets (textual and visual) for fine-tuning, curated to filter out low-quality and sensitive information. Specific sources or composition details are not extensively provided. | A pipeline approach: 1) Base model selection, 2) Domain-specific data acquisition and preparation (textual and visual), 3) Pre-training of the base model, 4) Optimization pipeline applying quantization, pruning, and distillation (sequentially or iteratively), 5) Fine-tuning and customization, 6) Comprehensive benchmarking. | Targeted for edge deployment on resource-constrained devices such as mobile devices and embedded systems. Implementation is facilitated by frameworks like PyTorch, TensorFlow Lite, and specialized toolkits such as NVIDIA NeMo. | False | False | NaN | NaN | High computational requirements of LLMs for resource-limited devices; need for domain-specificity in LLMs; balancing model compression with performance and accuracy; hardware limitations impacting the effectiveness of certain optimization techniques (e.g., unstructured pruning); maintaining robustness across different calibration datasets and context lengths. | Potential minor degradation in model accuracy due to quantization; risk of significant accuracy loss from excessive pruning; performance degradation in longer context scenarios for quantized models; difficulty in realizing theoretical benefits of unstructured pruning on standard hardware. |
| cesta-2024-large-language-models-and-community-legal-centres-could-chatbots-help-reduce-australia-s-justice-gap.pdf | Google_Scholar | Large language models and community legal centres: Could chatbots help reduce Australia ’s justice gap? | This paper explores whether LLM-based chatbots can alleviate the unmet demand for services from Australian community legal centres (CLCs). It argues that while client-facing legal information chatbots hold potential to reduce Australia's justice gap, their realization is hindered by significant challenges including LLM accuracy, legal uncertainties, and implementation costs. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Unmet demand for legal assistance due to under-resourced community legal centres (CLCs), limited government funding not based on needs assessment, and individuals not seeking necessary legal help. | Proposing the careful development and deployment of client-facing legal information chatbots by CLCs to provide accessible legal information, while highlighting the need to overcome significant technical, legal, and practical challenges. | Provision of legal information and services by community legal centres (CLCs) to address the justice gap and unmet legal need for disadvantaged individuals. | Individuals in Australia who cannot afford commercial legal services and rely on Community Legal Centres, including those facing digital exclusion or residing in regional areas with poor internet access. | General legal issues typically handled by community legal centres in Australia. | Australia | NaN | NaN | NaN | True | True | The paper refers to existing LLM tools; some are commercial (e.g., Ailira, CoCounsel), some have free access tiers (e.g., ChatGPT), and one specific AI model by Justice Connect is mentioned as available with a free license for Non-For-Profit organizations. | Technical gaps include LLM accuracy (hallucinations), explainability, cost-effective development for legal domains, and ensuring user prompts are effective. Societal/systemic gaps include legal/regulatory uncertainty regarding AI in law, the digital divide, ethical considerations for AI use by CLCs, and insufficient funding for CLCs to adopt such technology. | NaN | Risk of LLM hallucinations misleading clients or lawyers; privacy and data protection breaches with sensitive information; systems being misconstrued as unauthorized legal advice or practice; inadvertent waiver of legal professional privilege; exacerbating digital exclusion and inequality; and potential professional negligence or ethical breaches from over-reliance on AI by CLCs. |
| 1PICXeaunP8J.pdf | Google_Scholar | THE GPTJUDGE: JUSTICE IN A GENERATIVE AI WORLD | This paper analyzes Generative AI's impact on the legal system, focusing on challenges to evidence authenticity, intellectual property, and litigation. It provides practical recommendations for courts and lawyers to manage GenAI-related evidentiary issues and discusses broader implications for justice and legal practice. | True | Idealistic | True | 3.0 | Neutral | A proposed step-by-step procedural approach for courts and attorneys to handle evidentiary challenges posed by Generative AI, utilizing existing Federal Rules of Evidence, involving scheduling orders, disclosure, discovery, evidentiary hearings, and judicial rulings on admissibility. | NaN | NaN | Lack of legal representation for many citizens, particularly from marginalized communities; the risk of AI-generated vexatious lawsuits overwhelming courts; individuals' reliance on potentially faulty AI-generated legal advice; unpreparedness of courts for high-volume AI-generated filings. | The paper acknowledges GenAI's potential to assist unrepresented litigants by helping draft pleadings. However, it primarily focuses on managing the risks of AI misuse (e.g., vexatious lawsuits and faulty evidence) through judicial gatekeeping and procedural recommendations, rather than proposing specific high-level A2J solutions. | Assisting unrepresented litigants (pro se) in drafting legal documents; potential for generating vexatious or low-quality lawsuits; role of AI in providing legal information/advice to individuals. | Litigants who lack legal representation, often individuals from racialized or otherwise marginalized communities; ordinary people needing legal advice or facing debt collection. | Evidence law, Intellectual Property (Copyright, Trademark), Civil Procedure, Criminal Procedure, Torts (defamation, liability for bad advice), Academic/University Law, Judicial Ethics. | United States (primarily federal, but also state implications and examples). | NaN | The proposed procedural approach is based on an analysis of existing legal rules (Federal Rules of Evidence) and their flexible application to new technological challenges, informed by legal scholarship and judicial experience. | NaN | False | False | NaN | Courts are largely unprepared to differentiate beneficial uses of AI for A2J from misuse (e.g., vexatious litigation); current AI-detection capabilities are unreliable; lack of quality control and accountability for AI-generated legal advice for laypersons; underdeveloped ethical guidelines for AI in A2J. | The rapid pace of GenAI development versus the time-consuming process of revising formal rules of evidence, necessitating an approach that works within the existing legal framework while being adaptable to new technologies. Ensuring judicial gatekeeping is effective without unduly stifling GenAI's potential benefits. | Proliferation of deepfakes and difficulty in authenticating evidence; increased litigation costs due to need for experts; generation of misinformation and 'hallucinations' by AI; potential for AI to overwhelm courts with vexatious or low-quality lawsuits; undermining of intellectual property rights; misuse for scams and providing harmful advice; ethical breaches if judges or lawyers inappropriately use or rely on GenAI. |
| FLt-qPRZA6YJ.pdf | Google_Scholar | Answering legal questions from laymen in German civil law system | This paper introduces GerLayQA, a new German dataset of 21k laymen's legal questions paired with lawyers' answers and grounded in law book paragraphs, to benchmark AI for legal question answering. Experiments with retrieval and generation models show moderate performance, highlighting challenges in understanding German legalese and the need for legally-trained models and expert evaluation. | True | Idealistic | True | 1.0 | Neutral | A two-step QA pipeline involving document retrieval (embedding-based similarity) and answer generation (using GPT-3.5-turbo). Creation of a new dataset GerLayQA. | Document retrieval: Precision, Recall, F1, MRR, MAP compared against random and oracle baselines on GerLayQA. Answer generation: ROUGE and BERTScore compared against lower and oracle baselines on GerLayQA. | For document retrieval, OpenAI's text-embedding-ada-002 performed best (F1=0.055, MRR=0.146, MAP=0.108). For answer generation, GPT-3.5-turbo with legal paragraphs achieved ROUGE-1=0.2910 and BERTScore=0.6550. | Laypeople avoid law books due to incomprehensibility; cost of lawyers creates a barrier favoring those with more financial resources; online resources often unhelpful. | Leveraging NLP tools for legal question answering, specifically by creating datasets and models that can understand laymen's questions and provide understandable answers grounded in legal text. | Legal question answering, understanding legal texts, obtaining legal advice. | Laypersons in Germany without legal expertise. | German Civil Code (BGB), with mentions of German Criminal Code (StGB) and German Code of Civil Procedure (ZPO) for future work. Specific top categories mentioned are Tenancy law, condominium law; Labor law; Family law; Contract law; Inheritance law. | Germany | GerLayQA dataset: 21,538 QA pairs scraped from a German legal online forum (frage-einen-anwalt.de), filtered for quality (lawyer references to paragraphs, user ratings). This is publicly available. | Dataset creation involved web scraping, Regex-based extraction, and quality filtering. The QA pipeline involved standard NLP techniques: embedding generation for retrieval and prompting large language models for generation. | All datasets, source codes and models are publicly available at https://github.com/trusthlt/eacl24-german-legal-questions. | True | True | The GerLayQA dataset, source codes, and models are publicly available on GitHub. | Need for bespoke models trained on German legal text (both laymen and expert); inclusion of legal expertise in evaluation; expanding to more law books beyond BGB; manual filtering of dataset by legal experts; engaging secondary lawyers to verify gold standard answers. | Models' difficulty in understanding German legal texts (legalese); creating accurate semantic embeddings for both legalese and everyday language for effective retrieval; models struggling to grasp legal nuances and correlations; limited legal knowledge of the researchers for evaluation. | Misguided legal counsel from NLP models leading to severe consequences; users not being aware they are interacting with an NLP model versus a certified lawyer; reliance on non-binding legal advice. |
| C6i925q78D8J.pdf | Google_Scholar | The Judicial Duty to State Reasons in the Age \nof Automation? The Impact of Generative AI \nSystems on the Legitimacy of Judicial \nDecision-Making | This paper explores how the use of generative AI systems by judges for tasks like legal drafting impacts the judicial duty to state reasons, focusing specifically on the normative goal of legitimacy. It argues that while such systems might offer efficiency gains, they pose significant risks to judicial legitimacy through issues like bias, opacity, and potential undermining of judicial independence and reasoning. | True | NaN | True | 3.0 | Neutral | Generative AI systems (e.g., ChatGPT) used to assist judges in legal drafting (summarising case law, answering legal questions, calculating damages, drafting judgments). | NaN | NaN | The paper implicitly mentions cost and delays in justice as potential A2J obstacles that AI efficiency *might* alleviate, but its main focus is on the risks AI poses to the judicial system itself. | The paper suggests safeguarding measures for using AI in courts, such as promoting AI literacy for judges, potential development of systems by the judiciary itself (though deemed likely infeasible), using value-sensitive design methodologies, and potentially strengthening the judicial duty to state reasons to be more substantive. These are solutions to problems AI *creates* for the judicial system, not AI solutions *for* A2J. | Judicial duty to state reasons, legitimacy of judicial decision-making. | NaN | General (applicable across civil and criminal law, focuses on judicial process). | European level (focus on ECHR, CEPEJ), with illustrative examples from Colombia, India, US, UK, Netherlands, China, Ukraine, Belgium. | The paper discusses general issues with LLM training data: large text corpora (potentially including legal data), but often opaque regarding sources, representativeness (e.g., limited published judgments in NL/BE), up-to-dateness (ChatGPT noted as trained up to early 2022), potential biases (political, racial - citing COMPAS), copyright limitations (hindering use of legal literature), predominance of English sources. | Value-sensitive design is mentioned as a potential future approach, not one used for the existing systems discussed. | Discusses anecdotal use by judges in various countries and emerging guidelines (UK, CEPEJ, Ukraine) suggesting wider adoption. | True | True | Publicly available commercial systems like ChatGPT (with free tiers) are the primary examples discussed. | Lack of comprehensive theory on the judicial duty to state reasons in the age of automation; lack of understanding of AI's precise impact on this duty and its normative goals; need for AI literacy among judges; need for research on effective remedies/safeguards; challenges in implementing a strengthened (substantive) duty to state reasons; need for transparency regarding training data and system functioning; unresolved accountability issues; need for development of robust, locally run LLMs for legal applications. | Bias in training data and outputs; opacity ('black box' problem); potential for hallucinations/inaccurate outputs; influence of private tech companies (embedded values, IP); risk of automation bias in judges; potential undermining of judicial independence; inherent limitations of LLMs (lack of true understanding/reasoning); privacy and data protection risks; ethical concerns (dehumanization, undermining judges' role, inappropriate legitimization of AI). | Biased judicial outcomes; lack of transparency undermining fair trial rights; unclear accountability for AI-influenced decisions; compromised judicial independence; generation of incorrect legal information (hallucinations); privacy violations through handling of sensitive case data; erosion of public trust and perceived legitimacy of courts; undermining the judicial duty to state reasons. |
| NYeeny7dKncJ.pdf | Google_Scholar | PhiloGPT: A Philology-Oriented Large Language Model for Ancient Chinese Manuscripts with Dunhuang as Case Study | This paper introduces PhiloGPT, the first Large Language Model (LLM) specifically designed for analyzing ancient Chinese manuscripts, trained on a newly curated corpus, PhiloCorpus-ZH. It also proposes the PhiloCoP reasoning framework and PhiloBenchmark, demonstrating improved performance on philological tasks like restoration and attribution, particularly using Dunhuang manuscripts. | True | NaN | True | 1.0 | NaN | PhiloGPT, an LLM based on Qwen-1.5-7b, trained on the domain-specific PhiloCorpus-ZH, utilizing the PhiloCoP (Chain-of-Philology) multi-step reasoning framework. | Evaluated using the authors' proposed PhiloBenchmark, which includes 9 tasks (Restoration, Conjugation, Attribution, Judgment, Topic Modeling, NER, Common QA, Analysis, Reasoning). Metrics included Character Error Rate (CER), Dynasty Shift, F1 score, Accuracy, and GPT-4o judged win rate. Compared against Qwen-7b-chat, Baichuan2-7b, LLaMA2-Chinese-7b. | PhiloGPT with the PhiloCoP framework (PhiloGPT+CoP) achieved the best results across tasks, significantly outperforming baseline LLMs which struggled or failed on specialized philology tasks. Example best results: CER 0.579 (Restoration), F1 0.590 (Conjugation), Accuracy 86.7% (Judgment). | NaN | NaN | NaN | NaN | NaN | China | PhiloCorpus-ZH: A curated collection of ancient Chinese texts (spanning a millennium, 30 diverse topics, including original folk documents) sourced from publicly available data (museum collections, research papers, academic publications). Instruction data generated via manual construction, Self-Instruct, and Self-QA methods, filtered by GPT-4o and checked by experts. General training data from open-source corpora (Wikipedia, SkyPile, Wanjuan 1.0) was also used. | Corpus curation (PhiloCorpus-ZH) involving expert collaboration, development of a reasoning framework (PhiloCoP), benchmark creation (PhiloBenchmark), language model pre-training and supervised fine-tuning (LoRA) on a base model (Qwen-1.5-7b). | Deployed in specific research collaborations with Dunhuang specialists for tasks like analyzing copying relationships and suggesting text restorations. | False | False | NaN | NaN | Scarcity of suitable large-scale ancient Chinese training data; significant linguistic differences between ancient and modern Chinese (phonetic loans, polysemy, syntactic inversions, semantic shifts); fragmentation of previous research efforts; ensuring factual accuracy. | Potential for factual inaccuracies in model outputs, requiring secondary verification by philology experts. |
| XIFkKbcErtcJ.pdf | Google_Scholar | Blockchain for Ethical and Transparent Generative AI Utilization by Banking and Finance Lawyers | This paper proposes a framework integrating blockchain technology with Explainable AI (XAI) and Generative AI to ensure ethical and transparent use by banking and finance lawyers. The framework uses blockchain to create an immutable audit trail of AI-generated content derived from anonymized XAI outputs, aiming to address data confidentiality and accountability concerns. | True | Market | True | 1.0 | NaN | A framework combining an Explainable AI algorithm (Evidential Reasoning - ER) for legal decision support, Generative AI (GPT models, Google Bard) for drafting assistance based on anonymized ER outputs, and Blockchain (Ethereum/Hyperledger Fabric) for immutable auditing of AI usage. | A case study on bank data breach tort liability claims involving 2712 cases. The ER model's accuracy was evaluated using AUC scores. Generative AI models' (GPT-3.5, GPT-4, Bard) output usability was assessed by 25 legal professionals via text similarity analysis (Turnitin). Blockchain networks (Ethereum, Hyperledger Fabric) were benchmarked for throughput (TPS) and latency. | GPT-4 generated the most usable text for legal drafting (60.5% utilization by lawyers). Hyperledger Fabric showed higher throughput and lower latency compared to Ethereum for blockchain auditing. The ER algorithm demonstrated good performance in predicting components of bank liability (AUC > 0.85). | NaN | NaN | NaN | NaN | Banking Law, Finance Law, Tort Law (Data Breach Liability) | UK (based on case study details mentioning ICO, FCA, NCSC) | For the XAI (ER) component: A proprietary dataset of 2712 bank data breach cases from a law firm. Pre-trained LLMs (GPT, Bard) were used via API. | Integration of existing technologies (XAI-ER, LLMs, Blockchain), Case study methodology, Usability testing with legal professionals, Performance benchmarking (blockchain). | Pilot study within a law firm using an API gateway in a controlled environment; Utilizes Infura for blockchain node management. | False | False | NaN | Need for prompt engineering to optimize LLM outputs; Further investigation needed on whether lawyers overlook errors in AI-generated text despite the framework. | Integrating multiple complex technologies; Ensuring compliance with data protection laws (e.g., GDPR's 'Right to be Forgotten') alongside blockchain immutability; Managing blockchain storage limitations (addressed via off-chain/on-chain storage); Reliance on fixed prompts in the study. | Ethical concerns with Generative AI (replicating legal reasoning, accountability for errors, confidentiality); Potential for data leakage to LLMs; Risk of AI hallucinations or inaccuracies in generated text; Tampering with AI inputs/outputs if not properly monitored. |
| IRq0hYe6WSYJ.pdf | Google_Scholar | AI and the law | This paper argues that generative AI, by reducing the costs of contracting and litigation, will have uneven effects on the evolution of common law. It predicts AI will accelerate the evolution of tort law towards efficiency but have ambiguous effects on property and contract law. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Tort law, Property law, Contract law | International | NaN | NaN | NaN | False | False | NaN | NaN | NaN | The paper briefly discusses frivolous litigation but concludes it will not affect the long-run evolution of law within the model presented. |
| n1VAE2-uj0UJ.pdf | Google_Scholar | AI Law and Legal Training Interim Report | This interim report describes a UKRI-funded project developing open educational resources (OERs) to enhance understanding and responsible use of Generative AI in legal contexts. It outlines stakeholder engagement through workshops and details the planned course content targeting the public, free advice sector, students, and legal professionals. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Lack of knowledge/skills regarding GenAI use in legal contexts across different groups (public, advice sector, legal professionals); risk of societal harm from irresponsible AI use (e.g., misinformation, bias); potential exacerbation of inequalities; digital exclusion and resource limitations, particularly in the free advice sector; regulatory uncertainty. | Develop and provide free, open-access, engaging educational resources (OERs) co-produced with stakeholders to build knowledge, confidence, and skills for the ethical and responsible use of GenAI in legal contexts. Offer tailored learning pathways for different groups. | Education and guidance on the ethical, responsible, and effective use of Generative AI (GenAI/LLMs) for accessing legal information and support; Mitigating risks associated with AI in legal contexts (misinformation, bias, digital exclusion). | Public, free advice and voluntary sector organisations (advisors, volunteers, managers), small and medium-sized law firms, law students, legal academics. | General Legal Field | United Kingdom (specifically England and Wales context) | NaN | Co-production through stakeholder engagement: three online learning design workshops were held with distinct groups (free advice sector, legal academics/students, legal practitioners) to gather insights and inform course development. | The courses are planned to launch in Summer 2025 as Open Educational Resources on The Open University’s OpenLearn platform. | False | False | NaN | Significant gap in knowledge, awareness, and confidence regarding GenAI use in legal contexts among the public, advice sector, students, and practitioners. Lack of trustworthy, accessible educational resources, especially OERs. Regulatory and guidance gaps concerning AI use in legal services. | Ensuring accuracy and reliability of GenAI; addressing ethical, privacy, and data security concerns; preventing skill degradation; overcoming digital exclusion and resource disparities; balancing AI augmentation with human oversight; managing stakeholder (e.g., funder, client) perceptions; developing effective training and governance strategies. | Inaccuracy and 'hallucinations' leading to misinformation; ethical issues (bias, interference with legal processes); privacy violations and data security breaches; degradation of legal skills; digital exclusion and exacerbation of inequalities; lack of regulatory clarity and liability issues; potential reduction in funding/support if AI is misconceived as a replacement for humans; referencing/plagiarism/fraud risks. |
| 3708530.pdf | Google_Scholar | LLM App Store Analysis: A Vision and Roadmap | This paper analyzes the emerging ecosystem of Large Language Model (LLM) app stores, highlighting their rapid growth, key stakeholders, and operational mechanisms. It proposes a research roadmap focusing on data collection, security/privacy analysis, and market dynamics, concluding with challenges and recommendations for stakeholders. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General (mentions privacy law, intellectual property, regulation) | International | NaN | NaN | NaN | False | False | NaN | Lack of in-depth studies on LLM app stores, their market dynamics, security implications, ethical considerations, and societal impact. Need for tailored analysis techniques (e.g., for security, recommendations) specific to LLM apps. | Data privacy and security (compliance, third-party risks, inadvertent data collection); Intellectual property protection (app cloning, generative content issues); Ensuring app quality and reliability (vetting, unpredictable AI outputs); Addressing algorithmic biases and fairness; Balancing innovation and responsibility; User education and awareness; Regulatory and policy challenges (adapting frameworks). | App cloning, app vulnerabilities (e.g., prompt injection, insufficient input validation, jailbreaking), malicious apps (tainted knowledge, harmful outputs, low description-to-behavior fidelity), third-party service integration risks, user tracking and profiling without consent, security risks from app protection techniques (e.g., obfuscation hiding threats), advertisement fraud, market policy violations, fake apps, ranking fraud, malicious App Store Optimization (ASO), spam reviews, developer data privacy leakage (in instructions/knowledge files), user input privacy data leakage, potential misuse (spread of misinformation), erosion of privacy, algorithmic bias, generation of harmful content (violating ethics/policy). |
| O-v-HRHTS3wJ.pdf | Google_Scholar | Reducing Hallucinations in Large Language Models Through Contextual Position Encoding | This paper proposes Contextual Position Encoding (CPE), a novel technique to enhance positional information in LLMs, aiming to reduce hallucinations and improve factual accuracy. Integration of CPE into the Mistral Large model demonstrated significant improvements in accuracy metrics and a reduction in hallucination rates compared to baseline models. | True | NaN | True | 1.0 | NaN | Contextual Position Encoding (CPE) integrated into the Mistral Large model. | Compared CPE-enhanced Mistral Large against baseline Mistral Large, GPT-3, and BERT using metrics like precision, recall, F1-score, BLEU score, perplexity, and a custom hallucination rate metric on diverse text corpora (news articles, academic papers, web content). | The CPE-enhanced Mistral Large achieved higher accuracy (F1-score 0.90 vs 0.84 baseline), reduced hallucination rates (0.15 vs 0.35 baseline), and outperformed GPT-3 and BERT on BLEU score (0.89) and perplexity (11.0). | NaN | NaN | NaN | NaN | NaN | International | Diverse text corpora (news articles, academic papers, general web content), preprocessed for quality and consistency. Specific sources and public/proprietary nature not explicitly stated. | Proposal of a novel architectural modification (CPE), integration into an existing LLM (Mistral Large), empirical training, and quantitative evaluation against baselines using standard NLP metrics. | NaN | False | False | NaN | NaN | Mitigating hallucinations in LLMs; Computational cost and scalability of the proposed CPE technique. | Risks associated with LLM hallucinations in critical domains (healthcare, legal, finance); Potential ethical risks of advanced LLMs (bias, fairness, transparency). |
| LHiwGkmrSvcJ.pdf | Google_Scholar | JUDICIAL ECONOMY IN THE AGE OF AI | This paper argues that AI, particularly LLMs, will dramatically lower access to justice barriers, creating a potential litigation boom that threatens judicial economy. It advocates for proactively integrating AI into the judicial process itself to enhance capacity and manage increased caseloads without curtailing substantive rights. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | High costs of legal services (lawyer and court fees); complexity of legal processes; sociolegal barriers including lack of legal consciousness (naming-blaming-claiming); potential for increased litigation volume overwhelming the judicial system. | Proactive integration of AI tools into the judicial process itself (e.g., for case management, summarization, document Q&A, drafting assistance, generative interpretation) to scale up the system's capacity, rather than reactive adjustments like raising fees or tightening procedural/substantive standards ('legal thermostats'). | Access to legal services; judicial economy; litigation volume; legal consciousness (naming-blaming-claiming); impact of technology on courts. | Low-income individuals/Americans, ordinary people facing unresolved civil legal problems. | General Civil Litigation, Administrative Law, Civil Procedure | United States | NaN | NaN | NaN | False | False | NaN | Ensuring the judicial system can scale to handle increased access without compromising substantive justice; need for robust, reliable, ethical AI tools tailored for judicial use; bridging the gap between AI's potential for access and the system's current capacity and readiness. | AI unreliability (hallucinations, inaccuracy); ensuring confidentiality; costs and complexity of integrating AI into judicial systems; need for careful testing and validation; ethical concerns (human oversight, judicial authenticity); potential for AI misuse (e.g., facilitating frivolous claims); judicial skepticism and resistance. | Overwhelming judicial caseloads; erosion of substantive rights through reactive 'legal thermostat' adjustments; AI errors impacting case outcomes; potential bias in AI applications; confidentiality breaches; misuse of AI for vexatious litigation; loss of judicial authenticity and reasoned deliberation. |
| IRJMETS70300034830-VenkateshSriram.pdf | Google_Scholar | RECENT ADVANCES IN NATURAL LANGUAGE PROCESSING SYSTEMS: A TECHNICAL OVERVIEW | This paper provides a technical overview of recent advancements in Natural Language Processing (NLP), focusing on transformer architectures (e.g., BERT, GPT-3), few-shot learning, multimodal integration, and associated challenges like bias and computational cost. It also surveys efficiency optimization techniques and industrial applications in sectors like healthcare, finance, and maintenance. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal Services (briefly mentioned) | International | Describes datasets used by cited works (e.g., WMT, BooksCorpus, Wikipedia, large web crawls, image-text pairs) | NaN | Discusses general deployment scenarios (cloud, edge) and impact of optimizations | False | False | NaN | NaN | Bias detection and mitigation, high computational resource requirements, model scaling limitations (e.g., attention complexity), need for better evaluation metrics. | Biased outputs leading to unfair or discriminatory outcomes against specific demographic groups. |
| BabyFaceandChatGPT.pdf | Google_Scholar | Don’t Trust ChatGPT: A Case Study of a Defective Research Tool | This paper details a case study where the author queried ChatGPT about the historical reasons for a film's setting. ChatGPT provided factually incorrect information, demonstrating its unreliability as a research tool. | True | NaN | True | 2.0 | NaN | ChatGPT | The author asked ChatGPT specific questions about the film "Baby Face" and screenwriter Darryl Zanuck's connection to Erie, PA, and cross-referenced the answers with internet searches. | ChatGPT provided plausible but factually incorrect information regarding Darryl Zanuck's connection to Erie, PA, including claiming he attended a specific high school there, and later retracted the claims when confronted. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | True | False | ChatGPT is accessible via its website (chat.openai.com). | NaN | The primary challenge identified is the factual inaccuracy and unreliability of ChatGPT for research purposes. | Reliance on inaccurate information provided by ChatGPT for research. |
| MCZA3RN2BbcJ.pdf | Google_Scholar | Economic and Financial Analysis of Artificial Intelligence's Impact on Law and Legal Profession | The paper discusses the disruptive potential of Large Language Models (LLMs) like ChatGPT on the legal profession, focusing on productivity gains and market changes. It argues that regulatory hurdles, particularly restrictions on outside investment in law firms, may hinder the development and adoption of proprietary AI solutions necessary for competitiveness and managing privacy concerns. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General legal practice / Legal industry | International | NaN | NaN | NaN | False | False | NaN | NaN | High cost of training proprietary LLMs; restrictions on outside investment in law firms due to bar association rules limiting non-lawyer ownership; misalignment of investment incentives between current partners and long-term benefits; privacy concerns with using third-party LLMs. | Hallucinations (factual errors) leading to professional embarrassment and costs for lawyers; competitive disadvantage for firms in jurisdictions with strict investment rules. |
| pf5cO5kK6_QJ.pdf | Google_Scholar | Chatlaw: A Multi-Agent Collaborative Legal Assistant with Knowledge Graph Enhanced Mixture-of-Experts Large Language Model | This paper introduces Chatlaw, an AI legal assistant using a Mixture-of-Experts (MoE) LLM and a multi-agent system mimicking law firm workflows to enhance reliability and reduce hallucination. Chatlaw integrates knowledge graphs and RAG, outperforming GPT-4 on Chinese legal benchmarks and expert evaluations. | True | Idealistic | True | 1.0 | Positive | Chatlaw system: Mixture-of-Experts (MoE) LLM combined with a multi-agent framework (Legal Assistant, Legal Researcher, Senior Lawyer, Legal Editor) using Standardized Operating Procedures (SOP), Knowledge Graphs, and Retrieval-Augmented Generation (RAG). | Evaluation on LawBench (Chinese legal benchmark), China's Unified Qualification Exam for Legal Professionals (2018-2022), and real-world legal consultations assessed by legal experts based on Completeness, Correctness, Guidance, and Authority. | Outperformed GPT-4 on LawBench by 7.73% avg accuracy and on the Unified Qualification Exam by 11 points avg score. Achieved highest scores and win rates in real-case expert evaluations. | Limited availability of legal professionals, high cost of legal services, gap between legal aid need and provision capacity, leading to restricted access and impacting justice/equity. | Proposes Chatlaw, an automated legal assistant using an MoE LLM and multi-agent collaboration, integrating knowledge graphs and RAG to provide reliable, accurate, and accessible legal consulting services. | General legal consultation, Divorce/family law (used as example). | General population in China lacking resources to navigate the legal system. | Multiple fields (based on benchmarks and dataset description), including Divorce Law (example). | China | Proprietary 'Chatlaw legal dataset' (approx. 4 million samples) constructed from multi-source data, processed via deduplication, denoising, and human finetuning (students, experts). Covers 10 major/44 minor legal categories, includes knowledge graphs and agent task datasets. Formatted using LLaMA chat template. | Data Collection Pipeline, Mixture-of-Experts (MoE) Architecture Design, Multi-Agent System Design (Roles, SOPs), Knowledge Graph Integration, Retrieval-Augmented Generation (RAG), Human-in-the-loop Refinement. | An online trial phase was conducted, gathering user feedback. Plans mentioned to popularize the framework. | True | True | Dataset, codes and deploy details are released in the GitHub repository: github.com/PKU-YuanGroup/ChatLaw. | Need for model compression (for on-device deployment), addressing user privacy concerns vs. record-keeping needs, managing computational resources at scale, ensuring robustness against adversarial inputs. | Creating high-quality legal dataset, effective MoE model training, multi-agent coordination, hallucination mitigation (via RAG/SOPs), ensuring robustness, computational resource intensity, addressing privacy issues identified in trials. | LLM hallucination (providing incorrect/fabricated legal information), privacy risks related to sensitive user data in consultations. |
| 2CGGojIZFmYJ.pdf | Google_Scholar | REPLACING THIS OLD HOUSE : CERTIFYING AND REGULATING NEW LEGAL SERVICES PROVIDERS | This paper critiques current state regulation of new non-lawyer legal services providers, arguing it's too rigid and costly by mimicking lawyer licensing. It proposes state courts collaborate to create flexible, independent provider certification and proactive regulation, thereby improving access to justice. | True | Idealistic | False | 1.0 | NaN | A proposed systemic approach for state courts to certify and regulate new non-lawyer legal services providers, characterized by collaborative design, flexible and focused training, pathways to independent practice, and proactive, data-driven regulation. | NaN | NaN | High cost of lawyers; restrictive Unauthorized Practice of Law (UPL) rules; traditional lawyer licensing serving as an inappropriate and overly burdensome model for new provider categories; current new provider programs often having overly rigid/expensive certification, limited service scope, and mandatory lawyer supervision, hindering their impact on access to justice. | States should collaboratively design and implement new, distinct certification and regulation systems for non-lawyer legal services providers. These systems should feature affordable, flexible, and competency-focused training for discrete legal work; pathways to independent, fee-generating practice; and proactive, data-driven, and ongoing regulation, with an aim for eventual uniformity. | Access to affordable legal help for essential civil legal problems (e.g., family law, housing, consumer debt, domestic violence, estate/probate) concerning basic human needs. | Low- and middle-income people/Americans; ordinary people; legally vulnerable communities. | Family law, housing law (eviction), consumer debt, estate and probate law, administrative law, low-level civil litigation, low-level criminal litigation (misdemeanors not subject to incarceration), domestic violence. | United States (state courts generally, with specific examples from Arizona, California, Colorado, Delaware, Minnesota, New Hampshire, Oregon, South Carolina, Texas, Utah, Washington). Also discusses federal administrative agencies. | NaN | The proposed approach is developed through critical legal analysis of existing regulatory systems, comparative study of different models (lawyer, federal agency, state pilots), and application of educational design principles (e.g., 'backward design'). It advocates for future iterative refinement based on data collection and stakeholder consultation. | Proposed deployment through state supreme court leadership, fostering robust experimentation, data collection and sharing among states, and collaboration (e.g., via the Conference of Chief Judges) to develop model guidelines and eventual uniformity. Involves diverse stakeholder participation in designing and refining programs. | False | False | NaN | Lack of uniformity in state approaches to new legal service providers; insufficient data on the effectiveness of different models (need for more experimentation and data sharing); political resistance to reform from the unified bar; inadequate inclusion of diverse stakeholder perspectives (especially clients and prospective providers) in designing reforms; need for national recognition and integration of new providers into the legal profession. | General challenges identified for implementing such regulatory reform include: overcoming resistance from the unified bar, ensuring programs are economically viable for providers, balancing public protection with increased access, achieving state-level consensus and collaboration, and securing adequate resources and political will for experimentation and implementation. | The paper acknowledges the general concern of protecting clients from incompetent or unscrupulous providers, which often underpins restrictive regulations. For Gen AI (briefly mentioned): potential to run afoul of UPL, create a two-tiered justice system, and exacerbate inequalities. |
| 1265947.pdf | Google_Scholar | Reducing hallucination of Generative AI via Agentic AI and Edge Computing | This paper proposes a novel framework combining Retrieval-Augmented Generation (RAG) with agentic workflows, specifically generator and critic agents, within 6G networks and edge computing to reduce hallucinations in Generative AI. The framework aims to improve AI response quality and factual accuracy through iterative self-criticism and real-time integration of external knowledge sources. | True | Market | True | 1.0 | NaN | A framework integrating agentic workflows (generator and critic agents) with Retrieval-Augmented Generation (RAG) deployed in 6G-enabled edge computing environments, utilizing iterative self-criticism, external tool integration, and game theory principles for agent collaboration. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | International | NaN | The proposed framework uses a multi-agent system (generator and critic agents), iterative self-criticism, Retrieval-Augmented Generation (RAG), tool integration (APIs, search engines, IoT data), game theory principles for agent interaction, and planning (task decomposition). It is designed for deployment in 6G-enabled edge environments. | The framework is proposed for deployment in 6G-enabled mobile edge computing environments to facilitate scalable, real-time knowledge integration, data fusion, dynamic knowledge base updates, and customizable AI service delivery. | False | False | NaN | NaN | Predictability issues with planning and multi-agent collaboration components of agentic AI; insufficiency of RAG alone to fully eliminate hallucinations; the need for developing robust agent memory systems. | Generation of inaccurate or fabricated information by LLMs (hallucinations), including potentially harmful outputs such as fabricated legal precedents or falsified medical advice. |
| F_ecQdtwRv4J.pdf | Google_Scholar | Bias Transmission in Large Language Models: Evidence from Gender-Occupation Bias in GPT-4 | This paper investigates gender-occupation bias in GPT-4, examining both underlying associations (e.g., surgeon=male) and potential outcome bias in generated job cover letters. It introduces the LLM Bias Transmission Assessment (LLM BTA) method, finding that while GPT-4 exhibits association biases similar to humans, these biases do not necessarily translate into generating or evaluating cover letters unfairly based on gender. | True | NaN | True | 1.0 | NaN | LLM Bias Transmission Assessment (LLM BTA): A two-stage method involving 1) prompting an LLM (GPT-4) to generate output (cover letters) with potentially biasing input (gendered names) and 2) prompting the same LLM to evaluate the quality of the generated output. | The LLM BTA method was applied to GPT-4. Testing involved: 1) Probing association bias using LLM IAT methods with job lists and gendered/racial names/labels (Exp 1-2, Supplemental). 2) Benchmarking GPT-4's evaluation ability on human-written strong/weak letters (Exp 3). 3) Testing GPT-4's evaluation bias on identical human letters with different gendered names (Exp 4). 4) Applying LLM BTA: Generating cover letters for 30 jobs with male/female names and having GPT-4 provide relative (hiring choice) and absolute (13-dimension rating scale) evaluations (Exp 5). Gender prediction of generated letters using Gemini. | GPT-4 showed strong gender-occupation association bias. However, it accurately assessed human-written letter quality and did not show bias when evaluating identical letters differing only by gendered name. In the LLM BTA test, GPT-4 generated cover letters for male and female applicants that it rated as equally strong overall (absolute ratings varied <1% across gender; relative hiring choice showed no significant bias for 19/30 jobs). A 'male voice' bias was detected in generated letters. | NaN | NaN | NaN | NaN | Employment / Anti-discrimination (implicit focus) | United States (Implicit) | N/A (Evaluates existing model GPT-4; uses job lists, names derived from US SSA data, and pre-written/generated cover letters for evaluation prompts) | Experimental design comparing LLM outputs and evaluations under controlled variations of input prompts (gendered names). The LLM BTA method was developed adapting concepts from human implicit association tests but focusing on outcome bias through generation and self-evaluation stages. Aimed for increased ecological validity over pure association tests. | NaN | True | False | The LLM Bias Transmission Assessment (LLM BTA) methodology is described in the paper, including prompts and stimuli lists, allowing replication if one has access to the evaluated LLM (GPT-4). | Need for more systematic studies on bias propagation (association vs. outcome); exploration of mechanisms behind bias; application to other bias types (race, age) and domains (admissions); understanding 'male voice' bias; investigating potential LLM 'correction' mechanisms; disentangling confounding job characteristics. | N/A (Not explicitly stated, but implies challenges in designing ecologically valid bias probes for LLMs and interpreting the relationship between association and outcome bias) | Generative AI used for job application materials may inherit and propagate societal biases (e.g., gender-occupation stereotypes), potentially disadvantaging groups in hiring, even if outcome bias isn't always direct. Risk of models exhibiting biased 'voice' (e.g., male voice). Risk of racial/ethnic bias (explored in appendix). |
| ghPzKgistSIJ.pdf | Google_Scholar | YOU JUST CAN’T BEAT TH E MACHINE: A LAWYER’S DUTY TO ADAPT IN THE AGE OF ARTIFICIAL INTELLIGENCE | This paper argues that lawyers have an ethical duty to embrace generative AI to enhance competence, ensure reasonable fees, and improve client communication. It discusses the evolution of legal technology, addresses AI-related risks like inaccuracy and confidentiality breaches, and emphasizes the necessity of responsible adoption and supervision of AI tools by legal professionals. | True | Market | True | 3.0 | Positive | Generative AI tools in legal practice (e.g., ChatGPT, Westlaw Precision, Lexis+ AI, Harvey) | NaN | NaN | High cost of legal services due to inefficiency; lack of technological adoption and competence among lawyers. | Lawyers should adopt AI to enhance efficiency and technological competence, potentially lowering client costs, fulfilling ethical duties. | Affordability of legal services; Lawyer's ethical duty of technological competence. | NaN | General legal practice; Legal ethics | United States | Proprietary legal databases (cases, statutes, regulations, editorial content) for legal tech platforms; vast amounts of internet text and proprietary data for general LLMs. | NaN | Commercial subscription services for specialized legal AI (e.g., Westlaw, LexisNexis, Harvey); public access for general LLMs (e.g., ChatGPT, with premium tiers for advanced models). | True | False | Specialized legal AI tools (Westlaw Precision, Lexis+ AI, Harvey) and advanced general LLMs (e.g., GPT-4) are available via commercial subscription; some general LLMs (e.g., basic ChatGPT) have free access tiers. | Technological incompetence and resistance to AI adoption among lawyers, hindering efficiency gains that could improve affordability and access to legal services. | For lawyers using AI: ensuring accuracy and reliability of AI outputs, maintaining client confidentiality, exercising adequate supervision, understanding AI capabilities and limitations, keeping pace with evolving technology and judicial expectations. | Ethical violations (competence, confidentiality, fees, candor, supervision), Rule 11 sanctions from inaccurate AI-generated filings, AI 'hallucinations' (fabricated information), breaches of client data security, biased outputs from AI, over-reliance leading to deskilling. |
| VXGVZ8xwopIJ.pdf | Google_Scholar | Application of Generative AI to the business context: analysis and assessment | This master's thesis explores the potential, applications, and challenges of Generative Artificial Intelligence (GenAI) within the business context, focusing on opportunities for enhanced productivity and efficiency. It includes a theoretical overview and an empirical evaluation comparing the performance of ChatGPT, Google Translate, and DeepL in translating business-related documents between Russian and English. | True | Market | True | 2.0 | NaN | Language translation using Large Language Models (LLMs) and related technologies, specifically comparing ChatGPT, Google Translate, and DeepL. | Comparative analysis of Russian-to-English translations of business profile text, investor relations text, and academic management engineering text using ChatGPT, Google Translate, and DeepL. Evaluation criteria included quantitative measures (word count, sentence length) and qualitative aspects (accuracy, tone, fluency, handling of specialized terminology, idiomatic expressions). | Translation performance varied across tools and text types. DeepL often produced the most polished and formally appropriate translations for business contexts. ChatGPT Translator made specific errors in terminology and phrasing in academic text translation, while Google Translate was generally accurate but sometimes less fluent or nuanced compared to DeepL. | NaN | NaN | NaN | NaN | NaN | International | The paper generally describes GenAI models (like ChatGPT) being trained on vast and varied datasets, including billions of texts (books, articles, webpages), using techniques like semi-supervised learning, RLHF, and large amounts of unlabeled data followed by smaller amounts of labeled data. Specific proprietary datasets for the tested versions of ChatGPT, Google Translate, or DeepL are not detailed. | The paper discusses underlying technologies for GenAI like deep learning, artificial neural networks (input, hidden, output layers), backpropagation, Generative Adversarial Networks (GANs), Transformer architecture, and Attention Networks (AN). It also mentions pre-training, fine-tuning (SFT), and Reinforcement Learning from Human Feedback (RLHF) as utilized by models like ChatGPT. | Discusses adoption patterns for enterprises ('Pay and Use', 'Integrate Your Apps', 'Enrich with Your Data', 'Train on Your Data') and mentions widespread adoption (e.g., ChatGPT adoption by Fortune 500 companies and specific examples like Block, Canva, PwC). Also mentions use cases like Zendesk Answer Bot, Zoom transcription, e-commerce chatbots (Shopify, WooCommerce), LegalZoom document assistance, and Babylon Health virtual assistant. | True | False | ChatGPT, Google Translate, and DeepL are publicly accessible tools, often via web interfaces, with free and paid tiers available. ChatGPT Enterprise is mentioned as a specific offering for businesses. | NaN | General challenges discussed include: technical (scalability, security, integration, explainability, limited predictability, data quality, IT infrastructure), operational (cost management, maintenance, data governance, lack of benchmarks), ethical/regulatory (bias, fairness, hallucinations, transparency, compliance, copyright, privacy, toxicity, accountability, lack of regulation), strategic (ROI, skill gaps, organizational readiness, rapid change). Specific translation challenges noted include variable performance across languages/text types, handling complex grammar/idioms, limited vocabulary range, ensuring accuracy/fluency/appropriate tone, and specific errors made by tools. | Bias in training data leading to discriminatory outputs, hallucinations (generating incorrect information), copyright infringement (training on copyrighted data), data privacy issues (PII leakage), security risks, creation of fake news/deepfakes/misinformation, lack of transparency ('black box' problem), potential job displacement, employee demotivation, over-reliance diminishing human skills, cost management difficulties, vendor lock-in. |
| Cccdwh7GboYJ.pdf | Google_Scholar | ETHICAL PITFALLS WHEN LAWYERS ARE USING ARTIFICIAL INTELLIGENCE | This paper discusses the ethical challenges lawyers face when using artificial intelligence, particularly generative AI like ChatGPT. It highlights concerns regarding client confidentiality, professional competence, the accuracy of AI-generated information, and the duty of supervision, referencing US legal ethics rules and recent incidents. | True | Market | True | 3.0 | Negative | Generative AI (e.g., ChatGPT, Bard) | NaN | NaN | Deepening unequal access to legal information due to AI. Lack of transparency in AI models regarding training data and operations, hindering assessment of fairness and reliability for A2J purposes. | NaN | Unequal access to legal information | NaN | Legal Ethics / Professional Responsibility, General Legal Practice, Intellectual Property | United States (specifically Colorado, New York, Texas, California, Florida Rules of Professional Conduct/Bar considerations, Michigan legislation on AI in political ads), United Kingdom | Vast amounts of generally undisclosed online materials, books, and articles used to train existing LLMs like ChatGPT. | NaN | NaN | True | True | Publicly available generative AI tools like ChatGPT (some with free access tiers) and commercial AI solutions from legal tech vendors (e.g., Thomson Reuters). | Lack of transparency in AI models (training data, internal workings, safety testing). Potential for AI to exacerbate existing inequalities in access to legal information. Insufficient regulation and understanding of AI development and deployment. | NaN | Breaches of client confidentiality through data input into AI; lawyers' lack of competence in using AI leading to errors or over-reliance; generation of inaccurate information or 'hallucinations' by AI; undermining of attorney-client privilege; use of AI-generated fakes (deepfakes, voice clones) in legal contexts; intellectual property infringement related to AI training data and outputs; lack of transparency about AI model operations; security vulnerabilities; attorneys failing to supervise AI use adequately; ethical issues in billing for AI use and advertising AI capabilities. |
| K7hkE1GDNtkJ.pdf | Google_Scholar | Analysis of barriers and proposals for inclusive access to justice for vulnerable groups | This paper analyzes the structural, social, and cultural barriers that hinder access to justice for vulnerable groups, using census participation in Ecuador as a case study. It proposes solutions focusing on cultural adaptation, privacy protection, inclusive policies, enhanced training for officials, and technological tools like online self-censuses. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Structural, social, and cultural barriers; Lack of culturally and linguistically adapted materials; Lack of accessible formats for people with disabilities; Privacy concerns regarding sensitive data collection; Inequalities in access to basic services (health, housing) and employment; Lack of trust in official processes; Insufficient training for officials; Uneven implementation of inclusive strategies. | Implement differentiated protocols; Strengthen training for justice operators (human rights, cultural sensitivity); Foster collaboration with community organizations; Use technology like online self-censuses for privacy; Develop adapted/accessible materials; Enhance confidentiality guarantees; Implement inclusive public policies for basic services/employment; Ensure uniform implementation of strategies. | Access to justice; Procedural barriers; Vulnerable groups; Participation in state processes; Inequality in basic services; Census participation. | Indigenous communities, people with disabilities, women, ethnic minorities, older adults, children and adolescents, migrants, LGBTIQ+ communities. | Human Rights Law, Access to Justice | Ecuador | NaN | NaN | NaN | False | False | NaN | Persistent accessibility barriers (linguistic, cultural, physical); Lack of trust and privacy concerns hindering participation; Uneven implementation of inclusive policies and training; Need for better tools for sensitive data collection; Deep-rooted structural inequalities in access to basic services and employment. | NaN | Privacy violations in sensitive data collection; Perpetuation of discrimination and exclusion; Erosion of trust in institutions; Exploitation and abuse of vulnerable groups. |
| vKMGIQ_M6YUJ.pdf | Google_Scholar | Towards Robust Legal Reasoning: Harnessing Logical LLMs in Law | The paper proposes a neuro-symbolic approach combining large language models (LLMs) with logic programming (Prolog) to improve the accuracy and reliability of legal reasoning, specifically for insurance contract analysis. Experiments show that an expert-guided framework for LLMs generating Prolog code outperforms vanilla LLMs and unguided LLM-Prolog generation. | True | Market | True | 1.0 | Positive | Expert-guided neuro-symbolic approach using LLMs to generate Prolog code from legal text (insurance policies) for logical reasoning. | Comparative evaluation of 'Vanilla LLM', 'Unguided LLM-generated Prolog', and 'Guided LLM-generated Prolog' approaches using multiple LLMs (including OpenAI o1, GPT-4o, DeepSeek-R1) on claim coverage questions derived from a simplified Chubb policy and specific coverages (ART, CI) from the Stanford Cardinal Care Aetna policy (using CodeX Insurance Analyst test cases). Accuracy and consistency across multiple trials were measured. | The guided approach significantly improved accuracy and consistency over vanilla and unguided methods, especially with advanced reasoning models like OpenAI o1 (achieving up to 100% accuracy on simpler tasks and 87-95% on complex ones). Unguided Prolog generation often resulted in poor quality, ambiguous, or syntactically incorrect code, sometimes performing worse than vanilla LLMs. | Lack of accuracy, consistency, transparency, interpretability, and susceptibility to hallucination in current LLMs, undermining trust for high-stakes legal applications. | A neuro-symbolic approach combining LLMs' natural language capabilities with the reliability of logic programming, particularly using expert guidance (structured frameworks, documentation) to help LLMs generate accurate and consistent logic code (computable contracts). | Insurance contract analysis / Coverage determination | NaN | Insurance Law, Contract Law | US | NaN | Comparative analysis framework evaluating three distinct methods (Vanilla LLM, Unguided LLM-Prolog, Guided LLM-Prolog). The guided approach involved prompt engineering providing LLMs with policy text, documentation on valid claim facts, and documentation on supporting pre-defined Prolog predicates (helper rules). | NaN | False | False | NaN | Limited scope (only health insurance); need for broader architectural exploration (fine-tuning, RAG, RL); requirement for larger and more diverse datasets; need to test more LLMs and logic interpreters; requires more sophisticated prompt/encoding strategies; need for formal evaluation of explainability/auditability. | Generating accurate, consistent, and logically sound Prolog encodings from complex legal text using LLMs; handling ambiguity, syntax errors, and logical fallacies in LLM outputs; integrating generated code with existing logic frameworks; variability in reasoning capabilities across different LLMs. | Inaccuracy, inconsistency, hallucination (e.g., citing fictitious cases), lack of transparency/explainability in LLM outputs leading to incorrect legal conclusions, misinterpretation of contracts, erosion of trust, and potential non-compliance with regulations (e.g., GDPR, EU AI Act). |
| AXew9YzxxOgJ.pdf | Google_Scholar | The First Hardware Circuit Emulating Italian Road Homicides Legal Logic, DAJE! | This paper proposes DAJE, a hardware circuit designed to emulate the legal logic for Italian road homicide cases using Boolean functions derived from law. The approach, demonstrated via FPGA emulation, aims to enhance security and efficiency in legal decision support, achieving 86% accuracy against real case verdicts. | True | Idealistic | False | 1.0 | Positive | DAJE (Digital Assurance Judicial Enforcer): A Boolean function-based hardware circuit designed to emulate Italian road homicide legal logic. | Comparison of DAJE's output against the verdicts of 100 real Italian road homicide cases (2016-2024) sourced from public legal portals (DeJure Giuffre Francis Lefebvre, Legisway). Features for testing were extracted from case texts using an LLM. | Achieved 86% accuracy (73% True Positives, 13% True Negatives, 8% False Positives, 6% False Negatives). Hardware implementation details (FPGA emulation): 6 LUTs, 24 FFs, max frequency 642 MHz, average power 89 mW. | Judicial system backlogs leading to lengthy case resolutions; security vulnerabilities and privacy concerns associated with software systems handling sensitive legal data. | Implementing legal logic directly in hardware (DAJE) to provide enhanced security (physical isolation, tamper resistance), reliability, and efficiency (automating preliminary analyses, expediting case management). | Legal decision support, automation of legal reasoning for preliminary case analysis. | NaN | Criminal Law (road homicide) | Italy | NaN | Boolean logic synthesis based on legal rules (Italian Road Code, principles of foreseeability/avoidability); Hardware description and implementation (FPGA emulation). | FPGA emulation for testing and demonstration. | False | False | NaN | The approach needs expansion to other legal domains beyond road homicide; further refinement and validation of the hardware design are needed. | Ensuring security and privacy of sensitive legal data in digital systems; reliably integrating technology into legal workflows; overcoming potential software vulnerabilities. | Risk of misclassification (False Positives/False Negatives) in determining whether a crime occurred according to the legal logic encoded. |
| 2023SingCompLRev130.pdf | HeinOnline | AI Regulation for the AI Revolution | This paper examines the adequacy of current legal frameworks for Artificial Intelligence, focusing on human rights infringements, AI bias, and intellectual property issues related to AI-generated content. It reviews existing and proposed AI regulations in key jurisdictions and proposes a multi-faceted regulatory approach aimed at balancing innovation with the protection of fundamental rights and achieving substantive equity. | True | Idealistic | False | 1.0 | Positive | A multi-faceted regulatory framework for AI, emphasizing rights protection, clarified liability (e.g., using a modified Hand Formula), transparency, risk management (e.g. layered approach based on use-case), and a hybrid structure of baseline blanket regulation coupled with sector-specific guidelines. | NaN | NaN | Systemic bias embedded in AI perpetuating societal inequities and discrimination; inadequacy of existing legal frameworks to address AI-specific harms and attribute liability; challenges in balancing innovation with the protection of fundamental rights (Collingridge dilemma); opacity of AI systems (black-box problem) hindering accountability and redress. | Implement comprehensive AI governance frameworks focused on substantive equity, transparency, and accountability; establish clear legal rules for liability (e.g., modified Hand Formula) and intellectual property in the context of AI; adopt a flexible, risk-based regulatory approach combining general principles (baseline blanket regulation) with sector-specific rules and guidelines. | Non-discrimination and fairness in AI decision-making (addressing AI bias); protection of human rights (dignity, privacy, liberty, fair trial, freedom of expression, non-discrimination); accountability and redress for harms caused by AI systems; fair intellectual property rights concerning AI-generated content. | Groups susceptible to AI bias and discrimination, including racial minorities (e.g., African-American patients), women, and socio-economically disadvantaged individuals (e.g., low-income students). | AI Regulation, Human Rights Law, Tort Law, Intellectual Property Law (Copyright), Anti-Discrimination Law, Data Protection Law, Contract Law, Administrative Law. | International (primarily EU, UK, Singapore, US, with references to China and OECD initiatives). | NaN | Legal analysis, comparative regulatory review of multiple jurisdictions, jurisprudential reasoning, and ethical analysis balancing competing societal values. | NaN | False | False | NaN | Lack of effective and harmonized global AI legal and regulatory mechanisms; persistent societal biases amplified by AI, despite awareness; difficulty in proving AI bias and obtaining redress for affected individuals; regulations struggling to keep pace with rapid AI development (the "barn door problem"); unclear liability attribution for AI harms despite proposals. | Designing a regulatory framework that is comprehensive yet flexible enough to adapt to rapid technological advancements; effectively balancing the promotion of innovation with the imperative to protect fundamental rights and ensure safety; defining complex technical and ethical concepts (like AI bias, high-risk AI) in legally operational terms; achieving international consensus and avoiding regulatory fragmentation. | AI bias leading to discrimination in critical sectors (healthcare, employment, finance, justice); infringement of human rights (privacy, fairness, dignity); infringement of intellectual property rights by AI-generated content; lack of transparency and accountability in AI decision-making (black-box algorithms); uncompensated harm to individuals due to unclear liability rules; stifling innovation through poorly designed or overly restrictive regulation; market consolidation in the AI industry, entrenching advantages of large tech companies. |
| 10TexAMJPropL389.pdf | HeinOnline | Beyond the Binary: AI, Ethics, and Liability in the Legal Landscape | This paper examines the ethical challenges and liability risks for attorneys using AI in legal practice, particularly tools like generative AI. It advocates for proactive strategies such as comprehensive AI training, rigorous oversight, and a balanced approach integrating human expertise to ensure competent representation and uphold professional ethics. | True | Market | True | 2.0 | Neutral | Use of AI tools (e.g., generative AI like ChatGPT, e-discovery tools, automated drafting tools) in legal practice. | Analysis of ethical rules, real-world incidents (e.g., Samsung data leak, LoDuca & Schwartz case involving fabricated citations), and hypothetical scenarios to evaluate risks and implications. | AI tools present substantial ethical challenges (confidentiality, competence, counseling) and malpractice risks for attorneys, necessitating proactive measures like training, oversight, and maintaining human judgment. | NaN | NaN | NaN | NaN | General legal practice, including litigation, legal research, e-discovery, contract law, and intellectual property. | United States | Discusses general AI training on user inputs (e.g., ChatGPT) and case data; notes risks with confidential client data being used for training or AI models having biased/incomplete training datasets (e.g., AI trained only on litigated cases). | NaN | NaN | True | True | Discusses various AI tools, including prominent examples like ChatGPT which offers free access tiers, alongside other commercial tools. | NaN | Challenges discussed relate to the use of AI by legal professionals: ensuring confidentiality with data-hungry AIs, maintaining professional competence when AI can err or fabricate, upholding the attorney's counseling role requiring empathy and ethical judgment beyond AI capabilities, and navigating the lack of established legal/ethical frameworks for AI use. | Key risks include client confidentiality breaches, waiver of attorney-client privilege, professional incompetence from overreliance on AI (e.g., AI fabricating information), legal malpractice claims, and ethical violations due to AI's lack of human judgment or empathy. |
| 23AustlJAsianL63.pdf | HeinOnline | Using Aceh's Qanun to Expand Protection for Domestic Violence Victims | This paper examines the underutilization of Indonesian domestic violence law and proposes a specific legal reform for Aceh, Indonesia. It suggests amending Aceh's Islamic criminal law (Qanun Jinayat) to empower the Mahkamah Syariah to adjudicate domestic violence cases, thereby aiming to improve access to justice for victims by bridging the gap between state criminal and religious law systems. | True | Idealistic | False | 1.0 | NaN | A legal reform proposal: amending Aceh's Qanun Jinayat (Islamic criminal law) to incorporate domestic violence protections similar to those in Indonesia's national Domestic Violence Law (Law No. 23 of 2004), and expand the Mahkamah Syariah's jurisdiction to hear these cases. | NaN | NaN | Underreporting of domestic violence to police; victims' preference for Islamic courts (Mahkamah Syariah) for divorce, which currently lack jurisdiction over criminal domestic violence; separation between state criminal and religious law systems leading to victims' cases not being fully addressed; police-related barriers (e.g., bias, lack of understanding); prevailing interpretations of Islamic law that may not support criminalization of domestic violence within Qanun. | Amend Aceh's Qanun Jinayat to criminalize domestic violence, granting Mahkamah Syariah authority to adjudicate these criminal cases, potentially alongside divorce proceedings. Advocate for interpretations of Islamic sharia that are consistent with the elimination of domestic violence. This reform aims to improve victim protection and perpetrator accountability. | Access to justice for domestic violence victims; Legal pluralism; Law reform; Women's rights under Islamic law. | Domestic violence victims, particularly women, in Aceh, Indonesia. | Family law; Criminal law; Islamic law (Sharia); Human rights. | Aceh, Indonesia | NaN | Legal analysis of existing national and local laws (including Qanun and Islamic law); review of court practices and statistics; consideration of socio-legal research on domestic violence reporting and victim experiences; and policy formulation for law reform. | The proposed deployment involves legislative enactment of amendments to the Qanun Jinayat by Acehnese lawmakers. Subsequent implementation would require changes to Mahkamah Syariah's procedural laws and specialized training for judges on domestic violence. | False | False | NaN | The need for concurrent changes to the Mahkamah Syariah's procedural laws; the necessity of specialized training and certification for judges handling domestic violence cases; overcoming patriarchal interpretations of Islamic sources that might hinder the reform's acceptance and effectiveness. | Convincing lawmakers of the theological appropriateness and necessity of including domestic violence criminalization within the Qanun Jinayat; overcoming prevalent patriarchal interpretations of Islamic texts that may impede reform; ensuring effective police handling of cases if they are to be prosecuted under the amended Qanun; the need for comprehensive judicial training and adjustments to court procedures. | NaN |
| 86UPittLRev1.pdf | HeinOnline | CULTURALLY PROFICIENT LAWYERING: A FRAMEWORK AND RUBRIC SUPPORTING LEARNING OUTCOMES AND OBJECTIVES | This paper proposes the Culturally Proficient Lawyering (CPL) Framework and an accompanying CPL Rubric to help Toge educators meet the ABA's Standard 303(c) for teaching Toge, cross-cultural competency, and racism. These tools aim to develop law students' awareness, knowledge, and skills to foster a more inclusive and equitable Toge profession capable of serving diverse communities effectively. | True | Idealistic | False | 1.0 | Neutral | Culturally Proficient Lawyering (CPL) Framework and CPL Rubric. | The paper illustrates the CPL Framework and Rubric's utility through sample exercises and descriptive use-cases, such as the 'Client Interview Vignette,' rather than formal empirical evaluation. | NaN | Lack of cultural proficiency among lawyers; cultural and language barriers hindering access to Toge help; systemic Toge, discrimination, and racism embedded in the Toge system and its institutions; historically exclusionary nature of the Toge profession and education. | Implement comprehensive cultural proficiency education in law schools using the proposed CPL Framework and Rubric. This includes developing students' awareness of personal and systemic biases, knowledge of historical and social contexts of inequality, and practical skills for cross-cultural interaction and inclusive representation. | Improving lawyer-client communication and representation for diverse populations; addressing systemic bias and discrimination within the legal system through lawyer education; enhancing equal access to justice. | The paper addresses the need to serve diverse clients and communities broadly, including people of color (specifically mentioning African Americans, Indigenous peoples, Latine Americans, Asian Americans, MENA Americans), LGBTQ+ individuals, people with disabilities, and those from various socioeconomic backgrounds, aiming to improve representation for marginalized groups generally. | General legal practice / All fields | United States | NaN | Literature review, adaptation from other fields (e.g., medical education, education), synthesis of existing theories (e.g., critical race theory), and pedagogical principles. | Publication in a law review. The CPL Rubric is provided in Appendix A of the paper. | True | False | The Culturally Proficient Lawyering Framework is detailed in the paper, and the CPL Rubric is provided in Appendix A of the paper. | The ongoing need for development in cross-cultural competence assessment tools and methodologies; the challenge of ensuring legal education truly incorporates and values the voices and experiences of marginalized communities; the continuous, evolving nature of cultural proficiency requiring life-long learning; potential for over-reliance on frameworks stifling creativity if not critically applied; fostering genuine institutional change beyond superficial compliance. | Integrating cultural proficiency into a traditionally static and resistant law school culture; overcoming resistance to change and unawareness of the need to adapt; addressing the inherent subjectivity and complexity in assessing cultural proficiency; ensuring faculty are equipped to teach these topics effectively and create safe learning environments. | For AI: Perpetuation of societal biases by AI systems, potentially exacerbating legal disparities and power imbalances, especially for low-income individuals. For the proposed framework: Over-reliance on the CPL framework could stifle creativity and flexibility; the framework's effectiveness depends on the critical engagement of its users; poorly handled classroom discussions on sensitive topics like race can lead to negative outcomes for students. |
| 20UStThomasLJ53.pdf | HeinOnline | GEORGIA STATE LEGAL TECHNOLOGY COMPETENCY MODEL: A FRAMEWORK FOR EXAMINING AND EVALUATING WHAT IT MEANS TO BE A TECHNOLOGICALLY COMPETENT LAWYER | This paper introduces the Georgia State Legal Technology Competency Model, a framework designed to assess and guide the development of technological competence among lawyers and law students. The model features a foundational 'BASE' level of essential skills, complemented by a conical structure with four topical quadrants (Practice Technology, Data, Automation & Efficiency, Emerging Technology) and three graduated knowledge levels (Know, Integrate, Create), offering flexibility for different roles and educational goals. | True | Market | False | 1.0 | NaN | Georgia State Legal Technology Competency Model, which includes a 'BASE' (Basic Applications, Software, & Expectations) level and a conical framework with four topical quadrants (Practice Technology, Data, Automation & Efficiency, Emerging Technology) and three knowledge levels (Know, Integrate, Create). | The model's application is illustrated through several hypothetical scenarios: designing a stand-alone legal tech course, planning a law school legal tech certificate program (drawing from experiences at Georgia State College of Law), onboarding a new associate in a large law firm, and guiding a partner in a mid-sized firm to modernize technology usage. | The paper presents the conceptual model and demonstrates its potential utility for self-assessment, curriculum design, and training program development in law schools and law firms through illustrative examples. No quantitative results are provided as it's a proposed framework. | NaN | NaN | NaN | NaN | Legal education, Legal practice (general), Legal technology, Professional Responsibility/Ethics | United States | NaN | The model was developed by synthesizing and building upon existing concepts such as 'T-shaped lawyers,' the 'Delta Model,' and 'Bloom's Taxonomy of Educational Objectives' (as modified by Anderson and Krathwohl). It also incorporates the authors' experiences in curriculum planning. | The model is proposed for use in self-assessment, assessing students or employees, and evaluating and designing teaching curricula or training programs in law schools or firms. It is noted as being used in curriculum planning for the Georgia State College of Law Legal Analytics & Innovation Initiative. | True | False | The conceptual model/framework is fully described in the paper, allowing readers to understand and apply its principles after accessing the publication. | NaN | Addressing the lack of clarity regarding what constitutes legal technology competency; overcoming limitations of existing list-based competency models by creating a more flexible and pedagogically sound framework that accommodates graduated skill levels and diverse roles. | Risks associated with lawyers' lack of technological competency, including ethical violations (e.g., incompetent representation as per ABA Model Rule 1.1, breach of client confidentiality under Rule 1.6 due to poor cybersecurity or metadata mishandling), and failing to meet market expectations for efficiency and modern legal practice. |
| 25GermanLJ.pdf | HeinOnline | Image-Based Sexual Abuse and EU Law: A Critical Analysis | This paper critically analyzes the EU's new Directive on Violence Against Women, the Digital Services Act, and the AI Act concerning their effectiveness in addressing image-based sexual abuse (IBSA). It finds these legal instruments to be an initial step but identifies significant shortcomings in comprehensively protecting victims and holding perpetrators and platforms accountable. | True | Idealistic | False | 2.0 | Neutral | EU legal framework (Directive on Violence Against Women and Domestic Violence, Digital Services Act, AI Act) for regulating Image-Based Sexual Abuse | NaN | The analysis concludes that the EU's legal framework (Directive on Violence Against Women, DSA, AI Act) represents an initial effort but does not provide a comprehensive solution to IBSA, falling short in capturing its full scope, addressing diverse victim experiences, and ensuring effective redress. | Narrow legal definitions of offenses not covering all forms of abuse; high evidentiary burdens for victims (e.g., proving 'serious harm'); fragmented and inconsistent legal responses; underreporting by victims due to fear, shame, and lack of trust; inadequate training and sensitivity among law enforcement and legal professionals; difficulties in removing abusive content and ensuring platform accountability. | Adoption of comprehensive legal definitions for IBSA and related offenses reflecting victims' experiences; removal of undue evidentiary burdens focusing on lack of consent; enhanced specialized training for legal and law enforcement professionals; improved victim support services and safer reporting mechanisms; stronger, binding regulations for online platforms regarding content moderation, removal, and accountability; robust and harmonized national implementation of EU laws. | Image-based sexual abuse (IBSA); Deepfake pornography; Cyberflashing; Victims' rights; Criminalization of online gender-based violence; Regulation of online platforms; AI regulation. | Victims of image-based sexual abuse, predominantly women and girls, including those from LGBTQIA* communities, ethnic and religious minorities, younger women, and individuals in public positions. | EU Law; Criminal Law; Fundamental Rights Law; Digital Law; Cyberlaw; Victims' Rights Law. | European Union (EU) and its Member States. | NaN | NaN | NaN | False | False | NaN | The EU Directive's narrow scope of criminalized IBSA conduct and images, its high 'serious harm' evidentiary threshold, and limited content removal mechanisms. Insufficient accountability for online platforms under DSA and AI Act regarding IBSA, with the AI Act's deepfake labeling being inadequate for harm reduction. Lack of comprehensive coverage for all forms of IBSA (e.g., non-consensual creation/taking of images). Potential for inconsistent national implementation of EU laws. | NaN | Continued emotional, psychological, professional, and relational harm to IBSA victims; self-censorship and withdrawal of victims from online spaces; inadequate legal redress for victims due to narrow laws and high proof burdens; perpetuation of victim-blaming; insufficient platform accountability leading to continued proliferation of IBSA; AI-generated deepfakes causing harm despite labeling. |
| 99NYULRev451.pdf | HeinOnline | GENERATIVE INTERPRETATION | This paper introduces "generative interpretation," a novel approach using large language models (LLMs) to estimate contractual meaning, quantify ambiguity, fill gaps, and assess extrinsic evidence. It argues that this method can offer a cheaper, more accessible, and predictable way to interpret contracts, potentially bridging the gap between textualist and contextualist approaches and improving access to justice. | True | Idealistic | True | 1.0 | Positive | Generative interpretation using large language models (LLMs) for contractual interpretation, including querying models (GPT-4, Claude 2, Llama-2) with contract text and specific questions, analyzing embedding distances, and examining probabilistic outputs for meaning, ambiguity, and gap-filling. | The approach was evaluated through grounded case studies using actual contracts from well-known contract law opinions (e.g., In re Katrina, C & J Fertilizer, Famiglio v. Famiglio, Trident Center, Ellington v. EMI, Haines v. City of New York, Stewart v. Newbury). This involved feeding contract text and specific queries to LLMs and analyzing their responses, sometimes with multiple prompts and temperature settings. | LLMs demonstrated capabilities in ascertaining ordinary meaning in context (e.g., 'flood' in Katrina), quantifying ambiguity (e.g., prepayment clause in Trident), filling gaps (e.g., duration in Haines), and calculating the probative value of extrinsic evidence (e.g., phone call in Stewart). Model outputs were often plausible and offered nuanced perspectives, sometimes supporting and sometimes challenging judicial outcomes. | The high cost and inaccessibility of current contract interpretation methods for ordinary parties and resource-constrained firms, leading to an access-to-justice problem. The uncertainty and potential biases in traditional methods like dictionary reliance and judicial intuition. | Proposes generative interpretation as a cheaper, more accessible, transparent, and predictable methodology for contract interpretation. This can democratize access to sophisticated textual analysis, reduce litigation costs, and make outcomes more certain, thus improving access to justice for the "99%". | Contract interpretation, access to legal understanding for non-wealthy individuals and resource-constrained parties, reducing costs and uncertainties in contract litigation. | Non-wealthy individuals, ordinary parties, resource-constrained firms, and potentially judges in resource-deprived courts. | Contract Law, Insurance Law. | United States (primarily, with case law examples from various US state and federal courts like New York, California, Iowa, Fifth Circuit, Florida, Alabama). | The LLMs used (GPT-4, Claude 2, Llama-2) are trained on vast, general corpora of text ('torrents of existing texts'). The paper itself does not detail the specific datasets beyond what is generally known about these models' pre-training, but notes they are trained on 'trillions of words'. | The authors developed their "generative interpretation" approach by: 1) Obtaining and analyzing original contract texts from litigated cases. 2) Designing prompts and queries to elicit interpretations from LLMs. 3) Using techniques like embedding distance analysis. 4) Iterative querying with varied prompts and temperature settings to assess robustness. 5) Comparing LLM outputs to judicial reasoning and academic commentary. | The paper provides a GitHub link (https://github.com/yonathanarbel/generativeinterpretation/tree/main) for the code to replicate their results, suggesting the methods can be implemented using accessible LLMs. | True | True | The code for replicating results is available on GitHub. The LLMs discussed (e.g., Llama-2 is open source, GPT-4 and Claude 2 are accessible via APIs or chat interfaces) are generally available. | Technical gaps include model hallucinations, susceptibility to manipulation (adversarial attacks, prompt injection), majoritarian bias in outputs, sensitivity to linguistic drift over time, and the 'black box' nature of LLM reasoning (interpretability). Societal gaps include the need for a new 'language' or sociological framework for courts to justify and explain LLM-aided interpretations to ensure legitimacy. | Hallucinatory outputs, sensitivity of models to prompts ('leading prompts'), model biases towards majoritarian interpretations, adversarial attacks or prompt injections, models' insensitivity to the specific time of contract formation (linguistic drift), and the lack of full interpretability of model reasoning. | Generation of false or misleading information (hallucinations) by LLMs (e.g., citing fake cases). Manipulation of LLM outputs through carefully crafted prompts or adversarial attacks. Reinforcement of majoritarian biases, potentially silencing linguistic conventions of underrepresented communities. Difficulty in auditing or understanding the precise reasoning behind an LLM's interpretation ('black box' problem). Linguistic drift, where models trained on contemporary text misinterpret older contracts. E_DECREASE_IN_JUDICIAL_LEGITIMACY_IF_NOT_PROPERLY_INTEGRATED_AND_EXPLAINED. |
| 28AALLSpectrum10.pdf | HeinOnline | Making the Justice Leap: Using Generative AI to Bridge the Literacy, Equity, Access, and Privilege Gaps for Self-Represented Litigants | This paper discusses the potential of generative AI (GenAI) to assist self-represented litigants (SRLs) in navigating the civil legal system, addressing literacy, equity, access, and privilege gaps. It proposes a conceptual GenAI tool named "Gideon" and calls for law librarians to advocate for SRLs and the ethical use of such technologies. | True | Idealistic | True | 1.0 | Positive | A conceptual GenAI tool named "Gideon" designed to assist self-represented litigants by leveraging a sophisticated language model trained on legal resources. Also, an advocacy strategy for law librarians to promote AI tools and SRL support. | NaN | NaN | Intimidating court procedures, confusing legal forms, unfamiliar legal jargon, complex judicial rules leading to case dismissals; insufficient legal aid resources and unaffordability of lawyers; SRLs' personal limitations in language fluency, digital proficiency, and social exclusion; restrictive Unauthorized Practice of Law (UPL) rules. | Develop a powerful GenAI tool (e.g., "Gideon") for pro se litigants, trained on extensive legal resources. Establish ethical AI guidelines through impact assessments and continuous outcome evaluations. Law librarians to advocate for SRLs and the use of AI by publishing articles in bar journals and promoting alternative legal service models. | Access to justice for self-represented litigants; legal information navigation; legal document drafting; understanding court procedures; overcoming literacy and digital divides; role of law librarians in promoting legal tech; addressing Unauthorized Practice of Law (UPL) concerns. | Self-represented litigants (SRLs), particularly those with modest or limited means, middle-income individuals who cannot afford an attorney, and those facing literacy, digital proficiency, or social exclusion challenges. | Civil law (explicitly mentions eviction, foreclosure, repossession, domestic violence, and child welfare cases). | United States (implied by discussion of U.S. legal aid, ULC, state bar magazines, and specific U.S. locations like Washington D.C. and Harris County, TX). | For the conceptual tool "Gideon": An extensive range of legal resources, including legal aid websites, primary law (statutes, case law), legal practice guides for various jurisdictions, form books, and other secondary legal sources. | N/A (Gideon is described as "purely conceptual in design"; no specific design methodologies for its creation are detailed). | N/A (Gideon is conceptual. The advocacy part suggests publishing articles in bar magazines as a diffusion strategy for ideas). | False | False | NaN | Limited empirical research on GenAI's impact, especially for SRLs; murky and divergent Unauthorized Practice of Law (UPL) definitions across jurisdictions hindering innovation; need for robust ethical guidelines for GenAI development and use by SRLs; societal gaps related to literacy, digital proficiency, access, and privilege affecting SRLs. | Managing fears and navigating regulations concerning Unauthorized Practice of Law (UPL) violations; developing and implementing clear ethical guidelines for GenAI use by SRLs (covering fairness, transparency, non-discrimination); ensuring the proposed GenAI tool is accessible and effective for users with limited language fluency or digital proficiency. | GenAI tools producing fabricated or incorrect legal information (e.g., fake case citations); potential for AI to engage in the Unauthorized Practice of Law (UPL); negative consequences for SRLs if AI tools are not properly designed, ethically guided, or effectively used; model UPL legislation being used to curtail rather than expand access to justice programs. |
| 28LegalWritingJLegalWriti (2).pdf | HeinOnline | WHAT SOCIAL SCIENCE CAN TEACH US REGARDING BRIEFING | This paper reviews social science research on effective legal briefing, primarily at the U.S. Supreme Court, identifying controllable factors such as coordination, writing style, and information types that influence judicial decisions. It also presents new quantitative analyses demonstrating that these controllable factors can significantly impact case outcomes and opinion content, while cautioning against overusing certain strategies. | True | Market | False | 3.0 | NaN | The paper's original contribution involves new quantitative analyses using multiple regression models (derived from the authors' prior work) on a large dataset of Supreme Court briefs. These analyses estimate the maximum cumulative impact of controllable briefing factors on case outcomes and opinion language similarity, and identify potential negative effects of overusing certain factors. | The new quantitative analyses are based on statistical models applied to a dataset of over 26,000 merit briefs from the U.S. Supreme Court (1984-2015). The impact of factors is assessed by changes in predicted probabilities (e.g., of winning) or similarity scores (cosine similarity between briefs and opinions), considering statistical significance (p-values). | Maximizing all controllable factors in briefing can increase the probability of winning by 0.47 (best-case scenario for uncontrollables) to 0.88 (worst-case for uncontrollables) and increase similarity to majority opinion language by up to 0.99 (cosine similarity). However, excessive use of certain factors, like >90 Supreme Court citations or readability outside a college graduate level (approx. grade 16-18), can diminish positive effects. | NaN | NaN | NaN | NaN | Appellate practice, Constitutional Law, Voting Rights Law (by example) | United States (primarily Supreme Court, with some discussion of other federal and state appellate courts) | For the new quantitative analyses: A dataset of over 26,000 litigant and amicus briefs from the 1984 to 2015 terms of the U.S. Supreme Court, along with related court opinions. Data and replication materials are stated to be publicly available via the authors' website. | Statistical modeling (multiple regression), computational text analysis (implicitly, as these feed into the variables used in the regression models, drawing from prior work which used tf-idf, cosine similarity, LIWC, readability indices). | Academic publication (journal article); findings are intended to inform legal practitioners and scholars. Data and replication materials for underlying research are stated to be available online. | True | True | Data and replication materials for the authors' analyses (from their prior book, used for the new analyses in this paper) are stated to be available at https://www.rachaelkhinkle.com/research.html. | NaN | The paper notes that estimates calculated at extreme data values (minimums/maximums for controllable factors) can be fairly imprecise due to infrequent observation of such values (footnote 203). | The paper identifies risks of 'going too far' with certain briefing strategies: excessive citations to Supreme Court precedent (over ~90), language clarity that is too simple (below college graduate reading level) or too complex, excessive technical language, and too much future-oriented language can diminish a brief's impact. It also briefly notes generative AI's risk of 'hallucinating' citations. |
| 16IntlInHouseCounselJ.pdf | HeinOnline | Legal Profession in an Age of Generative Artificial Intelligence* | This paper provides an overview of generative AI (GAI) and its implications for the legal profession, focusing on challenges such as confidentiality, intellectual property risks, the potential for "hallucinations" in tools like ChatGPT, and impacts on junior lawyer training. It presents suggested guardrails for the use of GAI in legal services, emphasizing human oversight, verification of AI-generated content, and the use of enterprise-grade, legally-customized AI systems. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | High cost of generative AI tools and required technical competency potentially worsening access to justice; risk of inaccurate AI-generated information harming self-represented litigants; GAI deepening existing inequalities in legal access; dehumanization of law and erosion of public trust if AI is improperly implemented; difficulties for vulnerable individuals in accessing and using technology for legal processes. | Development of specialized AI tools to provide basic legal information, procedural guidance, and form assistance for individuals with limited resources; courts exploring GAI for specific areas like small claims to assist self-represented litigants; ensuring AI development and deployment is human-centered, supporting fairness and public confidence in the justice system; fostering AI literacy among the public and legal aid providers. | Legal information for self-represented litigants, understanding legal rights and procedures, basic legal document assistance, improving court efficiency in areas like small claims, ethical use of AI in justice. | Self-represented litigants (pro se litigants), litigants with limited financial resources, vulnerable accused persons, individuals with limited technological access or literacy. | General litigation, personal injury, divorce, probate, criminal justice. | International (with specific examples and discussions pertaining to USA, Singapore, UK, Australia, Germany, and Canada) | NaN | NaN | NaN | False | False | NaN | Ensuring reliability and accuracy of GAI outputs (reducing "hallucinations"); establishing trust and effective verification methods for AI-generated legal information; making GAI tools affordable and equitably accessible; improving fundamental understanding of LLM capabilities and vulnerabilities; developing robust mitigations for security risks like prompt injection; establishing ethical deployment guidelines and bias assessment for LLMs in legal contexts; addressing the impact of AI on legal training and profession structure; maintaining a human-centered approach to justice with AI integration. | Maintaining client confidentiality and protecting intellectual property when using GAI tools; vulnerability of LLMs to prompt injection attacks; inherent inaccuracy and potential for "hallucinations" in GAI outputs requiring diligent verification; overcoming automation bias among legal professionals; managing the high cost of GAI tools and the need for specialized tech competency; adapting legal training and law firm structures to the automation of entry-level tasks. | Breach of client confidentiality and legal professional privilege; leakage of sensitive intellectual property; submission of fabricated case law or inaccurate legal arguments to courts; erosion of public trust in the legal system due to AI errors; vulnerability to cyber-attacks like prompt injection; deskilling of junior lawyers and disruption of traditional legal career paths; potential for AI to be used in unauthorized practice of law or for high-risk decision-making without adequate safeguards; deepening of the justice gap if AI benefits are not equitably distributed; dehumanization of the legal process. |
| 2024IntlJLEthicsTech186.pdf | HeinOnline | "Trustworthy AI" Cannot Be Trusted: A Virtue Jurisprudence-Based Approach to Analyse Who Is Responsible for AI Errors | This paper argues that humans, not AI, must be held responsible for AI errors because a genuine trust relationship with AI is impossible due to AI's lack of moral motivation and responsibility. It proposes that this human responsibility should be assigned to direct beneficiaries of AI products and vary according to the AI's risk level, advocating for technical authentication obligations for high-risk AI like deepfakes. | True | Idealistic | True | 3.0 | Neutral | Virtue jurisprudence-based approach to analyse who is responsible for AI errors. | NaN | NaN | High cost and difficulty in authenticating AI-generated evidence (e.g., deepfakes), potential for misuse ("liar's dividend" leading to skepticism about genuine evidence), impacting affordability of justice. | Mandating technical authentication for high-risk AI evidence, requiring a good-faith basis for deepfake claims, ensuring developers facilitate access to detection tools for defense, and imposing obligations on responsible entities to provide reliable identification. | Authenticity and admissibility of AI-generated evidence (deepfakes), procedural fairness, equitable access to technical expertise in legal proceedings. | Litigants (especially those with limited resources), defence lawyers, and the justice system as a whole, impacted by challenges of AI-generated evidence. | AI Law, Product Liability, Evidence Law, Criminal Procedure, Ethics in Law. | European Union (focus on EU AI Act), United Kingdom (mentions UK ETAF). Principles discussed have broader relevance. | NaN | NaN | NaN | False | False | NaN | Technological gap between AI generation (e.g., deepfakes) and detection capabilities. Need for further research on differentiating human obligations based on AI risk levels across various fields. Ensuring affordable and accessible authentication methods for AI-generated evidence. | Ensuring AI reliability, explainability, and trustworthiness; managing AI's autonomy and unpredictability; attributing moral and legal responsibility for AI errors. | Erroneous AI outputs causing harm; manipulation of individuals; undermining due process via deepfakes ('liar's dividend'); infringement on fundamental rights; erosion of cognitive trust in evidence ('seeing is believing'). |
| 81MdLRev557.pdf | HeinOnline | PRETRIAL DISPARITY AND THE CONSEQUENCES OF MONEY BAIL | This paper empirically analyzes over 23,000 misdemeanor bail decisions in Pima County, Arizona, revealing significant inter-judge disparities in assigning money bail and its amounts. It also investigates the causal effects of money bail on defendant outcomes like recidivism, finding context-specific effects that caution against one-size-fits-all bail reforms. | True | Idealistic | False | 2.0 | Neutral | Instrumental variable (IV) design using quasi-random assignment of bail judges to analyze administrative court data on misdemeanor bail decisions. | The IV design was evaluated through balance tests (ANOVA, randomization inference) to confirm quasi-random judge assignment. Causal effects of money bail were estimated using two-stage least squares (2SLS) regressions on outcomes including guilty pleas, guilty judgments, failure to appear, and recidivism (rearrest/reconviction over 6-24 months), with controls for case/defendant characteristics and time-fixed effects. | Defendants assigned money bail showed no statistically significant difference in guilty pleas, guilty judgments, or failure to appear in the preferred model. However, money bail was associated with a statistically significant 9.2 percentage point decrease in rearrest recidivism at 6 months; effects on reconviction and at longer horizons were less consistent or not significant. | High inter-judge disparity in bail setting (frequency, amount, racial bias); defendants' inability to pay bail leading to detention; insufficient understanding of pretrial processes and impacts of judicial discretion; risk assessment tools not effectively curbing discretion or disparity. | Inform judges of their bail-setting behavior relative to peers (as a 'nudge'); avoid one-size-fits-all reforms, tailoring them to local contexts; implement rigorous, context-specific policy evaluation, replication, piloting, and cross-jurisdictional reporting. | Bail reform; pretrial detention; judicial discretion and disparity (including racial and socioeconomic); impact of money bail on case outcomes and recidivism; access to justice in misdemeanor cases. | Defendants in misdemeanor cases in a mixed rural/suburban county (Pima County, AZ); implicitly targets low-income individuals and racial/ethnic minorities (Black, Hispanic/Latinx) affected by bail disparities. | Criminal law (pretrial procedures, bail) | Pima County, Arizona, United States (specifically, misdemeanor cases from outside Tucson city limits handled by the Consolidated Justice Court, with initial appearances in Tucson City Court). | Proprietary administrative court data from Pima County TCC and CJC (2014-2017) on over 23,000 misdemeanor initial appearances (IAs), including IA forms (hand-coded) merged with court records. Data includes defendant demographics, charges, timing, judge, bail outcomes, criminal history, and case dispositions. | For the empirical study: Quasi-experimental design exploiting as-if-random judge assignment. Data collection (court records, IA forms), hand-coding of PDF data, data merging, descriptive statistical analysis, balance testing (randomization inference, ANOVA), and causal inference using an instrumental variable (IV) approach with two-stage least squares (2SLS) regression. | NaN | False | False | NaN | Lack of robust understanding of inter-judge disparity mechanisms; insufficient research on bail in non-urban and misdemeanor contexts; limited evidence on risk assessment tools' efficacy in reducing disparity; need for clarity on incapacitation vs. deterrence effects of bail on recidivism; uncertainty about pre-COVID-19 findings' applicability post-pandemic. | Finding a suitable study venue with reliable data and appropriate institutional (e.g., quasi-random judge assignment) and procedural structures. Difficulty obtaining complete incarceration data. Challenges in data processing, such as merging diverse data sources (PDFs, administrative records) and consistently defining variables like IA charges from available date fields. | Inequities of the money bail system (e.g., detention of low-income individuals). Pervasiveness of racial and socioeconomic disparities in bail decisions. Ineffectiveness or negative impacts of 'one-size-fits-all' bail reforms. Potential for algorithmic risk assessment tools to maintain or exacerbate existing disparities rather than reduce them. |
| 6LawTechHum88.pdf | HeinOnline | Prompts and Large Language Models: A New Tool for Drafting, Reviewing and Interpreting Contracts? | This paper examines the use of Large Language Models (LLMs) and prompt engineering for drafting, reviewing, and interpreting contracts, exploring both their potential to enhance efficiency and accessibility for lawyers and non-lawyers. It analyzes the significant challenges, including inaccuracies, biases, lack of transparency, and the crucial legal implications, particularly concerning the parol evidence rule and the admissibility of prompts in contractual disputes. | True | Idealistic | True | 3.0 | Neutral | Large Language Models (LLMs) and prompt engineering for contract drafting, review, and interpretation. | Cites external studies such as the Allens AI Australian Law Benchmark (testing LLMs on Australian legal questions) and research on LLM performance in professional law tasks (Hendrycks et al.). | Cited studies indicate that even top LLMs are not consistently reliable for legal questions, may contain 'infection' from other jurisdictions' laws (Allens benchmark), and show low accuracy in professional law tasks (Hendrycks et al.). | Perpetuation of existing inequalities; generation of unfair, unconscionable, or market-distorting contracts; perpetuation of harmful stereotypes and discriminatory clauses; exacerbation of power asymmetries due to biased AI. | Careful curation of diverse and unbiased training data; transparency in algorithms; continuous monitoring and audits for bias; human oversight at critical junctures; responsible implementation focusing on fairness and ethical considerations; adapting legal doctrines like the parol evidence rule. | Improving accessibility and affordability of contract creation for non-legally trained individuals; ensuring fairness, equity, and non-discrimination in AI-generated legal documents; adapting legal doctrines to AI. | Non-lawyers and individuals without legal expertise seeking to understand or create contracts, as well as the legal profession generally. | Contract Law; Civil Procedure (specifically evidence and interpretation rules like the parol evidence rule). | Australia (primary focus for legal analysis like parol evidence rule), with references to developments in the US, EU, Singapore, China, and UNCITRAL. | The paper describes LLMs as being trained on large, generic text corpora, then fine-tuned on specialized datasets. For legal LLMs, this includes 'legalese' and potentially legal documents. Lexis+AI is mentioned as trained on 'Lexis authoritative primary and secondary materials'. Access to proprietary law firm data (client contracts, advice) for training professional law LLMs is noted as limited. | NaN | Discusses commercial deployment by legal tech companies (e.g., Lexis+AI available in US and Australia) and adoption by law firms, including on-premises models for data privacy. | True | False | Commercial products like Lexis+AI, Motionize, Robin.AI are mentioned as available from their respective vendors/companies. | Technical gaps include LLM inaccuracy, hallucinations, lack of nuanced legal reasoning, and 'black box' transparency issues. Societal/legal gaps include the unclear legal status of prompts, need for legal doctrine adaptation (e.g., parol evidence rule), ethical concerns (data collection, copyright, declaration of use), and ensuring effective human oversight to mitigate AI risks and biases. | Ensuring accuracy and avoiding 'hallucinations' in LLM outputs; addressing the lack of transparency in LLM decision-making processes; mitigating inherent biases from training data and model design; defining the legal status of prompts and their interaction with existing legal rules (e.g., parol evidence rule); managing client confidentiality and data privacy with LLM use; effectively training legal professionals in prompt engineering and critical AI assessment. | Generation of unfair, unconscionable, or market-distorting contracts; perpetuation of existing inequalities and harmful stereotypes; inclusion of discriminatory clauses; exacerbation of power asymmetries; 'hallucinations' (inaccurate outputs); 'stochastic parrots' (mindless repetition); biased interpretation; 'infection' by laws from irrelevant jurisdictions; manipulation of contract interpretation processes; lawyers citing LLM-fabricated non-existent cases. |
| 30IndJGlobalLegalStud293.pdf | HeinOnline | Robo Justice: Constitutional Issues with Judge AI | This paper explores the constitutional, ethical, and societal implications of Artificial Intelligence (AI) in judicial functions, referred to as "Judge AI". It advocates for a societal constitutionalism approach, reframing "justice" to prioritize human wellbeing, and proposes judge-led, ethically guided reforms to address the challenges posed by AI in the justice sector. | True | Idealistic | False | 3.0 | Neutral | Judge AI (as a general concept, encompassing supportive, replacement, and disruptive AI technologies in judicial decision-making) | NaN | NaN | Digital divide (limited access to technology, poor digital skills, low literacy); AI exacerbating disadvantages for vulnerable populations; focus on 'fast, low cost' justice potentially undermining true justice; lack of human empathy in AI decision-making. | Adopting societal constitutionalism; reframing the definition of 'justice' to include human wellbeing; judge-led reform guided by ethical frameworks; human-centered legal design; ensuring human judicial oversight and contestability of AI decisions. | Automation of judicial decision-making; ethical use of AI in the judiciary; access to justice through technology; quality of justice; judicial independence; constitutional implications of AI. | Vulnerable populations; vulnerable users of the justice system. | General justice system, Civil law, Criminal law, Administrative decision-making. | China, USA, EU, Australia, Singapore, Chile, New Zealand, International (global context). | NaN | Human-centered legal design; Judge-led reform; Values-based ethical framework development. | NaN | False | False | NaN | Need for more relatable and judge-specific ethical material for AI; challenges in maintaining or replicating nuanced legal reasoning with AI; effectively addressing the digital divide and ensuring technology serves human wellbeing; translating ethical principles into concrete, auditable AI system designs. | The 'black box' problem (opacity of AI decision-making); ensuring transparency, explainability, and fairness in AI systems; maintaining judicial independence against executive or corporate influence; addressing the digital divide and digital literacy issues; replicating human traits like empathy and nuanced judgment; developing robust ethical frameworks and ensuring their implementation; potential for de-skilling or over-reliance on AI by legal professionals. | Algorithmic bias leading to discrimination; erosion of judicial independence and separation of powers; overreach by tech companies ('digital Switzerlands'); societal harm beyond individual cases; undermining the rule of law; dehumanization of justice and violation of human dignity; increased disadvantages for vulnerable populations; undetected operation of biased or inappropriate AI; 'fast, low-cost' justice compromising actual justice. |
| 75SMULRev815.pdf | HeinOnline | Al, EQUITY, AND THE IP GAP | This paper argues for a deliberate, equity-focused approach to integrating AI into intellectual property (IP) law to promote social justice principles like access, inclusion, and empowerment. It explores how current IP doctrines and AI can perpetuate inequity and proposes solutions such as "equity by design" and equity audits. | True | Idealistic | False | 3.0 | Positive | Equity by design (as a guiding principle/approach); Equity audits (as a methodological process). | NaN | NaN | AI amplifying inequity via biased algorithms/data; trade secrets hindering accountability for biased AI; biases embedded in existing IP doctrines (patent, copyright); lack of diversity in AI development and the IP system; difficulty accessing representative and unbiased training data due to copyright and other barriers; opacity of AI decision-making (black box problem); potential for a two-tiered justice system; human over-deference to algorithmic decisions. | Implement "equity by design" principles in AI development for IP; conduct regular "equity audits" of AI systems; reform data governance for AI training (e.g., advocating fair use for copyrighted data, creating civil rights exceptions for data mining, anonymization); diversify the developer workforce and the IP ecosystem; reform IP doctrines to be more inclusive; increase AI transparency and explainability; introduce legal reforms for trade secrets (e.g., a social justice exemption or enhanced whistleblower protections) to allow scrutiny of algorithms; use AI to assist with adjudicating equitable IP doctrines. | Algorithmic bias in intellectual property law; equitable access to the IP system for underrepresented groups; transparency and accountability of AI in IP law and administration; fairness in IP adjudication; the impact of trade secrets and copyright law on developing and auditing equitable AI. | Women, racial minorities (e.g., Black individuals, Hispanics), individuals from lower-income backgrounds, small businesses, and pro se litigants. | Intellectual Property Law (Trade Secrets, Patent Law, Copyright Law, Trademark Law), Civil Rights Law, Criminal Justice (by way of example). | United States, Singapore, European Union | The paper discusses AI systems potentially trained on biased historical IP case law, registration data, and copyrighted content. It advocates for using broader, diverse, and representative datasets, including copyrighted works (accessed via fair use or specific exemptions) and personal data (with appropriate safeguards), to train less biased AI. | The paper advocates for 'equity by design' as a high-level approach, entailing proactive consideration of fairness, conducting bias impact assessments, diversifying development teams, and incorporating ethical reviews. It also proposes 'equity audits' as a methodology for verification and ongoing monitoring. | NaN | False | False | NaN | Lack of comprehensive solutions for AI-perpetuated inequity in IP law; insufficient diversity in AI development and the IP system; inadequate legal frameworks for AI accountability in IP (especially concerning trade secrets); need for better, accessible, and unbiased data for training fair AI; challenges in implementing and regulating effective equity audits; AI's limited ability to handle nuanced equitable legal judgments; societal complacency towards AI. | NaN | AI amplifying existing societal inequities and biases within the IP system; biased algorithmic outcomes in IP rights and enforcement; trade secrets shielding biased algorithms from scrutiny and accountability, hindering due process; creation of a two-tiered justice system; over-reliance on AI and automation bias reducing critical oversight; chilling effects on innovation and creativity if AI is trained on narrow/biased data or if copyright restricts data access; privacy violations from improper data handling; potential for sophisticated actors to game transparent AI systems. |
| 57IndLRev581.pdf | HeinOnline | FROM PIXELS TO PRESCRIPTIONS: THE CASE FOR NATIONAL TELEHEALTH LICENSING & AI-ENHANCED CARE | This paper argues for federal incentives, via Medicaid funding, to encourage states to adopt mutual recognition of out-of-state medical licenses for telehealth and expand the scope of practice for non-physician providers, particularly when enhanced by AI. This dual policy approach aims to modernize healthcare regulation to improve access, efficiency, and quality, addressing challenges highlighted by the COVID-19 pandemic. | True | Idealistic | False | 1.0 | Positive | A policy proposal for the federal government to use Medicaid funding bonuses to incentivize states to: 1) mutually recognize out-of-state medical licenses for telehealth services, and 2) expand the scope of practice for non-physician providers, based on competency and augmented by AI technologies. | NaN | NaN | Fragmented state-based medical licensing, inconsistent scope of practice laws hindering telehealth and workforce innovation, disproportionate negative impact on rural and underserved healthcare access, and resistance to reform from incumbent medical groups and state boards. | Federal incentivization of states (via Medicaid bonuses) to adopt mutual recognition of out-of-state medical licenses for telehealth and to expand non-physician provider scope of practice based on competency and AI enhancement, thereby modernizing the regulatory landscape. | Telehealth enablement, scope of practice reform for healthcare providers, improving healthcare access for underserved populations, reducing healthcare disparities, addressing physician shortages. | Rural communities, lower-income individuals, racial and ethnic minorities, uninsured patients, elderly and disabled individuals, and other disadvantaged groups with limited healthcare access. | Health Law, Administrative Law, Constitutional Law (federal spending power, federalism), Antitrust Law, Occupational Licensing Law. | United States (federal and state levels). | NaN | NaN | Proposed deployment through federal legislation (leveraging spending power via Medicaid) followed by state-level legislative and regulatory adoption of the incentivized reforms. | False | False | NaN | Continued fragmentation of state licensing impeding telehealth, restrictive scope of practice laws limiting healthcare workforce innovation, persistent healthcare access disparities for underserved populations if reforms are not adopted, and ongoing political resistance to modernizing healthcare regulation. | Anticipated political resistance from states and medical professional associations to the proposed reforms; ensuring the safety and efficacy of AI tools used by non-physician providers requiring ongoing oversight; addressing federalism concerns regarding federal influence on state policy; securing congressional appropriations for the incentive program. | Potential for telehealth to facilitate fraudulent billing (though evidence suggests this is proportionally rare); AI systems performing poorly if incorporating low-quality data or lacking proper oversight; physician apprehension about job displacement due to AI and expanded roles for non-physicians. |
| 19RutgersBusLJ70.pdf | HeinOnline | Caveat Lector: Large Language Models in Legal Practice | This paper critically examines Large Language Models (LLMs) in legal practice, arguing they lack genuine understanding, knowledge, and reasoning abilities despite their textual fluency. It warns legal professionals against overreliance on LLMs due to their propensity for hallucinations and the significant risks of generating incorrect legal information. | True | Market | True | 3.0 | Negative | NaN | NaN | NaN | LLMs' propensity to hallucinate and generate incorrect information, their lack of true understanding and reasoning, risk of overreliance by users (especially laypersons lacking legal expertise to vet outputs), and difficulty in establishing 'legal ground truth' for many legal questions. | NaN | The unsuitability and risks of current LLMs for providing reliable legal information or advice to laypersons, undermining the potential for democratizing access to justice. | Users without legal training / laypersons. | General legal practice including contract law, tax law, criminal law, consumer protection. | International | NaN | NaN | NaN | False | False | NaN | Technical gaps include LLMs' lack of true understanding, reasoning, common sense, reliable knowledge, inability to distinguish fact from fiction, and susceptibility to hallucinations. Societal gaps related to access to justice include the risk of misinformation for lay users and the difficulty for non-experts in evaluating LLM output. | NaN | Overreliance on LLM-generated text due to its fluency; generation of factually incorrect or nonsensical information (hallucinations) in legal services; misinformation for users without legal training seeking access to law; financial losses or lawsuits from inaccuracies; propagation of bias and falsehoods from training data; model collapse from training on synthetic data. |
| 22NwJTechIntellProp109.pdf | HeinOnline | REGULATING CHATBOT OUTPUT VIA INTER-INFORMATIONAL COMPETITION | This paper proposes a market-centered approach, emphasizing inter-informational competition, to evaluate AI chatbot content risks and regulatory strategies. It argues that market competition can mitigate many risks, reducing the need for extensive direct regulation, while suggesting tailored rules for market failures like privacy and copyright issues. | True | NaN | True | 1.0 | NaN | A market-centered regulatory approach focusing on inter-informational competition to evaluate and design regulations for chatbot content. | The approach is supported by a review of the history of regulating information and communications technologies (ICTs), analysis of market dynamics, and theoretical reasoning, rather than empirical testing or benchmarks. | The market-centered approach suggests that inter-informational competition can mitigate many chatbot content risks (e.g., harmful content, bias, misinformation), making some direct regulations (like mandatory prohibitions, licensure, data curation) unnecessary. It advocates for tailored regulations like transparency, traceability, and auditing for market failures (e.g., privacy, copyright), and specific liability considerations using risk-utility tests. | NaN | NaN | NaN | NaN | Information law, Competition law, Privacy law, Copyright law, Tort law, Administrative law | International (with specific examples and discussions related to US, EU, China, UK) | NaN | Historical analysis of ICT regulation, application of economic principles (market competition, market failure), legal analysis of existing and proposed regulations, and comparative regulatory analysis. | The proposed approach is disseminated via scholarly publication, aiming to inform policymakers and future research on AI regulation. | False | False | NaN | NaN | NaN | Content risks from chatbots: harmful content (e.g., hate speech, self-harm promotion), discrimination and bias, misinformation (hallucinations, reputational damage, influencing democratic processes), privacy disclosure (sensitive personal information), and copyright infringement. Risks of over-regulation: stifling innovation, harming market competition, creating entry barriers, market concentration, and regulatory capture. |
| 4JusCorpusLJ208.pdf | HeinOnline | Navigating Legal Advice through Al Chatbots | This paper discusses the growing trend of using AI chatbots like ChatGPT for legal advice, highlighting their current unreliability and the ethical and legal challenges involved. It compares chatbots to human lawyers, concluding that while AI holds future promise, professional legal counsel remains essential for accurate advice, especially given the lack of specific AI regulations in India. | True | Idealistic | True | 3.0 | Neutral | AI Chatbots for legal advice (e.g., ChatGPT, Law Bot Pro) | Referenced studies indicating inaccuracy of AI chatbots like ChatGPT for legal advice, an anecdotal case study (Roberto Mata v Avianca Airline) of misuse demonstrating unreliability, user query to ChatGPT, and developer acknowledgment of limitations for Law Bot Pro. | AI chatbots like ChatGPT are reported as "INACCURATE and PROBLEMATIC" for legal advice, capable of creating fake cases, and self-admittedly not a substitute for professional legal advice. Law Bot Pro is acknowledged by its developers to have limitations, suitable for understanding basic laws/rights but not for proper legal advice. | Inaccuracy and unreliability of AI, ethical concerns like bias in AI outputs, lack of accountability for AI-generated advice, and absence of specific legal regulations governing AI. | Emphasis on consulting human lawyers for reliable advice, development of clear accountability frameworks and specific AI regulations, and continued research and development for more accurate AI models. Pro-bono initiatives like 'Law Bot Pro' for basic legal information are also mentioned. | Access to legal information and advice, reliability of AI in law. | General public, particularly those seeking free or low-cost legal information and potentially underserved communities. | General | India (primary focus), with mentions of US and EU. | NaN | NaN | Law Bot Pro deployed as a free legal AI app; ChatGPT accessible via web platform. | True | True | ChatGPT is accessible via its website with a free tier. Law Bot Pro is described as a 'free legal Al app'. | Lack of AI accuracy and reliability for complex legal advice, absence of robust legal and ethical frameworks for AI governance, and user over-reliance or misunderstanding of current AI capabilities. | Ensuring accuracy and reliability of legal information provided by AI, mitigating AI bias, establishing accountability for AI-generated advice, and navigating the lack of specific AI regulations. | Provision of inaccurate or misleading legal advice by AI, professionals misusing AI leading to legal errors and reputational damage, propagation and reinforcement of societal biases by AI, and potential copyright infringement issues. |
| 34LegalEducRev183.pdf | HeinOnline | Persuasive Legal Writing Using Large Language Models | This paper investigates the ability of GPT-4 to produce long-form persuasive legal writing by comparing LLM-generated law school essays with student essays using a developed multi-step prompting method. The study found that GPT-4 produced passable, well-structured essays but performed worse than students, particularly in knowledge accuracy and critical analysis, while also highlighting LLM limitations like hallucinations and bias. | True | Market | True | 2.0 | NaN | GPT-4 for generating persuasive legal essays using a three-step method (Outline, Content, Concatenation) for long-form content, combined with iterative prompt engineering. | Comparison of four GPT-4 generated essays (on two Legal Theory exam topics) against four student-written essays. Essays were de-identified and graded by four experienced law school graders based on: Argument and Structure; Knowledge and Understanding; Critical Analysis and Original Reflection; and an overall grade. Grader comments were also analyzed, including sentiment analysis. | GPT-4 essays received a median grade of H3 (competent), while student essays received a median of H2B (good). GPT-4 output was relatively well-structured but lacked accurate knowledge and showed no elevated performance in critical analysis or originality. Grader feedback on LLM essays showed greater negative sentiment. | NaN | NaN | NaN | NaN | Legal Theory, Legal Writing, Legal Reasoning, Legal Education. | Australia (experiment context: University of Melbourne graduate law class). | GPT-4's training data is not publicly available. It likely includes a massive corpus similar to GPT-3's, web documents, Wikipedia entries (including on legal theorists), original texts by theorists, student essays, blogs, and academic articles on legal theory. It was pre-trained on a large text dataset and then fine-tuned using Reinforcement Learning from Human Feedback (RLHF) for dialogue. | Iterative prompt engineering was used to develop the prompts for the three-step long-form content generation method (Outline, Content, Concatenation). The process involved evaluating interim outputs against components of persuasive legal writing and adjusting prompts to steer the LLM. | NaN | True | False | The LLM used (GPT-4) is publicly accessible via OpenAI (potentially with costs). The multi-step prompting methodology for long-form content generation is described in the paper, allowing for attempted replication. | NaN | Generating high-quality long-form content (due to context window limits, difficulty in prompting for length while maintaining quality). Ensuring factual accuracy and mitigating hallucinations in LLM output. Steering the LLM's output to be relevant to specific (e.g., course) material without being overly prescriptive, which could deaden creativity or induce more hallucinations. Addressing the LLM's inherent biases, such as US-centricity. Difficulty in making definitive claims about LLM performance due to output variability and prompt sensitivity. | Factual inaccuracies and hallucinations (e.g., fabricating sources, mischaracterizing legal theories or theorists). Biases in LLM outputs (e.g., geographic bias towards US law, potential for gender and racial biases). Misuse by students for academic assessments, potentially undermining educational objectives. Abuse of process in legal practice if LLM-generated content with errors is submitted to courts. Lack of privacy and concerns over data input into LLMs, especially for privileged or sensitive information. Potential for LLMs to contribute to a 'legal monoculture' by oversimplifying or homogenizing legal understanding. |
| 7Issue5IntlJLMgmtHuman.pdf | HeinOnline | Judicial Reforms and Access to Justice: A Comparative Analysis of E-courts and Technological Integration in India and Singapore | This paper comparatively analyzes judicial reforms and technological integration, particularly e-courts, in India and Singapore, focusing on their impact on judicial efficiency, transparency, access to justice, and public trust. It evaluates the successes and challenges in both nations, offering insights on how technology and ADR can transform judicial systems for timely and equitable justice. | True | Idealistic | False | 3.0 | Positive | E-courts, e-filing, virtual hearings, National Judicial Data Grid (NJDG), e-Litigation systems, AI-based translation tools (e.g., SUVAS), online dispute resolution platforms, digital transcription systems, criminal case filing and management systems (e.g., ICMS), online legal information portals, self-help tools (e.g., DIY questionnaires), planned AI-powered virtual assistants and outcome simulators. | NaN | NaN | Case backlogs; inefficient judicial systems; unequal access to justice due to cost, complexity, and resource limitations for vulnerable groups; digital divide and varying digital literacy; resistance to change within the judiciary; inadequate infrastructure; cybersecurity and data privacy concerns; challenges in adapting traditional legal processes to technology; lack of standardization and interoperability; need for continuous technological upgrades and updated legal frameworks. | Digitization and streamlining of judicial processes through e-courts and integrated platforms; introduction of e-filing and virtual hearings; development of national judicial data grids; adoption of comprehensive e-litigation systems; utilization of AI for tasks like translation and decision support (e.g., outcome simulators); promotion of Alternative Dispute Resolution (ADR) and Online Dispute Resolution (ODR); provision of online access to legal information, self-help kits, and DIY tools; investment in technology, training, and infrastructure; fostering a supportive legal and regulatory environment; enhancing public awareness and ensuring equitable digital access. | Judicial efficiency, case backlog reduction, transparency in judicial processes, accessibility of justice, public trust in the judiciary, online dispute resolution, access to legal information, self-help legal resources, technological integration in courts. | General public, litigants (including litigants-in-person), special groups, people with limited resources, individuals in rural and remote regions, populations with low digital literacy or low income. | General court procedures, Civil Law, Criminal Law, Family Law. | India, Singapore | For AI tools like SUVAS (India): English judicial documents, orders, and judgments. For planned Outcome Simulator (Singapore): Information from previous cases. Specific details on data sources, structure, or public/proprietary nature are not provided. | NaN | Government-led initiatives and judiciary-managed systems such as the e-Courts Mission Mode Project in India (guided by the Supreme Court's e-Committee) and Singapore's integrated e-Litigation and ICMS systems (accessible via SG Courts). Services delivered through online portals, mobile applications, and other digital platforms. | False | False | NaN | Significant infrastructural disparities between regions (especially in India); varying levels of digital literacy; high costs associated with IT infrastructure development and maintenance; need for adaptable strategies considering regional differences; challenges in creating and sustaining a favorable legal and innovation-friendly environment; ensuring equitable distribution and access to digital resources; balancing technological advancements with the human elements of justice. | Digital divide and accessibility issues; cybersecurity and data privacy vulnerabilities; potential over-reliance on technology leading to system failures; difficulties in adapting all legal processes for digital environments; the need for continuous technological upgrades and associated resource demands; resistance to change from traditional judicial practices; achieving standardization and interoperability across diverse court systems (especially in India); ensuring scalability and robust maintenance for large volumes of cases; aligning legal and regulatory frameworks with technological advancements. | Cybersecurity threats including data breaches, unauthorized access, and misuse of sensitive legal information; disruption of justice delivery due to technological failures or system vulnerabilities if overly reliant on technology; exacerbation of inequalities if digital divide issues are not addressed, potentially excluding certain population segments from accessing justice fairly. |
| 25CardozoJConflictResol44.pdf | HeinOnline | An Information Flow Model of Online Mediation: Jeopardizing Privacy and Autonomy in the Shadow of Innovation | This paper explores how the digital transformation of mediation, particularly through online platforms and AI, impacts parties' rights to self-determination and privacy. It proposes an "information flow model" to analyze these risks and suggests normative measures to mitigate them, focusing on parties' control over information communication and analysis. | True | Idealistic | False | 1.0 | Negative | An Information Flow Model of Online Mediation (a conceptual/analytical model). | NaN | NaN | Undermining of participant autonomy and control over the mediation process and personal information. Breach of confidentiality and privacy essential for fair dispute resolution. Lack of transparency and potential biases in AI-driven decision support, impacting informed consent and fairness. Inadequate legal and ethical frameworks to govern online mediation platforms and AI use. | Establish comprehensive legal/regulatory frameworks for online mediation platforms, including duties of transparency, fairness, and upholding party autonomy. Enhance mediator obligations for ethical use of technology, including informed consent processes regarding technological risks. Promote development and adoption of robust ODR standards and accreditation for platforms. | Privacy, party autonomy (self-determination), confidentiality in online dispute resolution/mediation. | NaN | Civil and Commercial matters | USA and European Union | NaN | NaN | NaN | True | False | The information flow model is described in the paper and can be conceptually applied by readers who have access to the paper. | Regulatory gap: Mediation norms do not adequately govern digital platforms or their duties concerning information processing, party autonomy, and neutrality. Limitations of current privacy laws: Consent mechanisms are often ineffective for true user control. Lack of widely adopted, enforceable, and detailed ODR standards. Need for greater transparency and explainability of AI systems used in mediation. | NaN | Loss of control over personal information and decision-making in mediation due to platform design and AI. Unauthorized disclosure or use of sensitive mediation communications by platforms or integrated third-party AI. Manipulative or erroneous AI-driven analyses influencing parties unfairly. Creation of detailed user profiles by platforms through data aggregation for purposes beyond mediation. Automation bias leading to undue reliance on AI suggestions. |
| 11RevEurolatinDerAdm1.pdf | HeinOnline | The Implementation of AI Systems in the Colombian Justice: The Constitutional Court and the Council of State | This paper details the implementation of AI systems, Pretoria in Colombia's Constitutional Court for tutela (judicial protection action) selection and two pilots in the Council of State for jurisprudential analysis, aimed at enhancing judicial efficiency and access to justice. It discusses the development process, challenges including adapting legal culture, achieved results in processing time, and risks related to AI in the justice system. | False | Idealistic | False | 2.0 | Positive | AI systems (Pretoria for the Constitutional Court, and two pilots for the Council of State) using Machine Learning (classification, topic modeling) for case selection, jurisprudential analysis, and drafting assistance. | Pretoria: Trained on 2500 tutelas, then refined with 7 datasets; evaluated on 33 selection criteria, audited by jurists. Council of State pilots: Proof of concept using judicial sentences and administrative acts; evaluated on speed and accuracy in detecting legal conflicts/assisting sentence drafting in specific legal areas. | Pretoria achieved 80% reliability in implementation for tutela selection, identifying 33 criteria and automating summary generation in seconds, reducing case processing time from 36 minutes (manual average) to seconds. Council of State pilots demonstrated ability to detect brand conflicts in 7 minutes and assist non-experts in drafting sentence models in 8.5 minutes. | High judicial backlog and inefficiency; lack of transparency and objectivity in traditional processes; organizational and legal cultures resistant to change; limited resources for technological adoption. | Implementation of human-supervised, 'white-box' AI systems to enhance efficiency, transparency, and consistency in judicial tasks. This includes AI for case selection (Pretoria), jurisprudential analysis, and drafting assistance, combined with efforts to evolve legal culture and organizational processes, and ensure human oversight and data protection. | Improving judicial efficiency, case selection for review (tutelas), analysis of jurisprudence (unification sentences), drafting judicial decisions, effective judicial protection, access to justice. | General public/citizens, particularly those seeking protection of fundamental rights (e.g., health rights, due process) and those in vulnerable situations (e.g. extreme poverty, minors). | Constitutional Law, Administrative Law, Health Law, Intellectual Property Law, Electoral Law. | Colombia | Pretoria: Initially 2500 decided and published tutela cases from the Colombian judicial branch; later, 7 datasets of judicial sentences. Council of State pilots: Judicial sentences (e.g., 1300 industrial property sentences) and administrative acts from the Council of State and other Colombian public entities (e.g., SIC). All data is domain-specific, unstructured (text), and sourced from the courts. | Collaborative co-design involving jurists (university researchers, court magistrates/staff) and AI experts (IALAB). Iterative development process including pilot/prototype stages, training data creation from existing legal documents, collaborative definition of criteria, testing, and auditing by legal experts, with a focus on 'white-box' (auditable and explainable) AI. | Pretoria: Implemented in the Colombian Constitutional Court, with dedicated technical staff and funding from private sector. Council of State Pilots: Developed as proof-of-concept pilots and presented to the court, but full-scale implementation and extension were not achieved due to funding constraints and lack of broader internal support within the Council of State. | False | False | NaN | Need for robust 'white-box' AI governance and auditing mechanisms; secure data management protocols specific to the judiciary; comprehensive training of legal professionals in AI; development of national and international legal/ethical regulations for AI in justice. Overcoming financial barriers and internal resistance for wider AI adoption in judicial bodies remains a gap. | Acquisition, structuring, and normalization of large volumes of legal data; training AI models to understand complex legal nuances and human situations; defining and agreeing upon relevant classification criteria collaboratively; adapting entrenched legal practices and organizational cultures to new technologies; securing sustained funding and internal buy-in for full implementation and scaling of AI projects; ensuring interoperability with existing judicial IT systems. | AI overlooking complex human situations if not adequately trained on nuanced criteria; potential violations of human rights (dignity, privacy, good name, right to be forgotten); misuse of data for citizen profiling or predictive sentencing; perpetuation or amplification of existing biases through algorithmic bias; lack of transparency and accountability with 'black-box' AI systems; data security breaches if data is not managed in secure, court-controlled environments. |
| 2023UIllLRevOnline165.pdf | HeinOnline | RAGE AGAINST THE MACHINE: WHO IS RESPONSIBLE FOR REGULATING GENERATIVE ARTIFICIAL INTELLIGENCE IN DOMESTIC AND CROSS-BORDER LITIGATION? | This paper analyzes which public and private bodies are best suited to regulate the use of generative AI in domestic and cross-border litigation, focusing on identifying who should act rather than proposing specific regulatory content. It suggests a multi-faceted approach involving courts, licensing authorities, legislatures, and international bodies to address generative AI's challenges to the justice system. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Generation of false or misleading legal information by AI (hallucinations, misinterpretations); lack of reliability of AI-generated documents; erosion of public trust in the justice system; concerns regarding due process and procedural fairness; shifting of costs and burdens to other litigation participants; lack of transparency in AI-generated content. | Establishment of clear, agile, and comprehensive regulatory frameworks in a phased manner (rules of court, rules of professional responsibility, legislation); involvement of various domestic (judicial, legislative, licensing authorities, research institutions) and international bodies (e.g., Hague Conference, UNIDROIT, IBA); conducting empirical and policy-oriented research to inform regulatory content; ensuring accountability for AI use. | Ensuring procedural fairness and due process; maintaining the integrity of legal information and court proceedings; upholding public confidence in the justice system; establishing accountability for AI-generated content in litigation. | Pro se litigants | Civil litigation, Criminal litigation, Cross-border litigation, Procedural law | United States, Canada, UK, EU, China, International | NaN | NaN | NaN | False | False | NaN | Lack of comprehensive, proactive, and agile regulatory frameworks for generative AI in litigation; insufficient technical safeguards within AI tools to ensure accuracy and reliability for legal use; need for more empirical and policy research to define appropriate regulatory content; risk of regulatory inertia or uncoordinated piecemeal responses. | NaN | Erroneous legal outcomes due to AI hallucinations and misinterpretations; violations of due process and procedural fairness; erosion of public confidence in judicial systems; increased litigation costs and burden-shifting; unreliability of AI-generated legal documents; improper delegation of judicial authority; potential for misuse by pro se litigants leading to system burdens; incompetent or unethical use by legal professionals. |
| 2023IntlJLegalSocOrd400.pdf | HeinOnline | DIGITAL SINGLE MARKET: CONSUMER PROTECTION RULES IN THE DIGITAL SERVICES ACT* | This paper analyzes the European Union's Digital Single Market framework, focusing on consumer protection rules established by the Digital Services Act (DSA) and Digital Markets Act (DMA). It discusses how these regulations aim to create a safer, more transparent, and fairer digital environment for EU citizens and businesses by addressing issues like illegal content, platform accountability, and fair competition. | True | Idealistic | False | 3.0 | Positive | Digital Services Act (DSA) and Digital Markets Act (DMA) as regulatory frameworks. | NaN | NaN | Spread of illegal content and goods, harmful online practices by platforms, lack of transparency regarding algorithms and content moderation, systemic risks to users' fundamental rights, ensuring fair competition in digital markets. | Implementation and enforcement of the Digital Services Act (DSA) and Digital Markets Act (DMA), including transparency requirements for digital service providers, user complaint mechanisms, prohibition of certain harmful practices, risk assessment and mitigation obligations for very large online platforms (VLOPs), and enhanced supervision and enforcement powers for national regulatory authorities. | Consumer protection in the digital single market, regulation of online platforms, content moderation, protection of fundamental rights online, fair competition in digital markets. | EU consumers | Consumer law, EU law, Digital law, Internet law, Competition law | European Union | NaN | NaN | NaN | True | True | The Digital Services Act (DSA) and Digital Markets Act (DMA) are EU regulations. The DSA Transparency Database, mentioned as part of DSA compliance, is publicly accessible and its source code is publicly available. | Lack of systematisation at the EU legislative level concerning the interplay between various consumer protection directives and the DSA. The need to better link DMA provisions with other legislation to more actively empower consumers beyond being passive beneficiaries. | NaN | Dissemination of illegal content and goods, harmful or fraudulent online activities, systemic risks to users' fundamental rights from platform operations, opacity in AI decision-making processes, vulnerability of AI and IoT products to cyber threats. |
| 27PotchefstroomElecLJ1.pdf | HeinOnline | Non-Educator Stakeholders and Public-School Principals' Views on the Proposed Amendments to the South African Schools Act 84 of 1996 | This paper discusses proposed amendments to the South African Schools Act 84 of 1996, focusing on changes to school admission and language policies, and the potential recentralisation of power to the Department of Basic Education. It presents mixed views from school principals and other non-educator stakeholders gathered through qualitative research, highlighting concerns and support for the Basic Education Laws Amendment (BELA) Bill. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Discriminatory school admission and language policies; Dysfunctional school governance (SGBs) in some schools; Mismatch between home language and language of instruction; Lack of capacity in schools and by officials, shortage of school places; Political agendas influencing legislative changes. | The proposed BELA Bill aims for recentralisation of power over admission/language policies to Heads of Department (HODs) to ensure fairer access. The paper also cites a model for differentiated school autonomy based on context, as an alternative to blanket recentralisation. | Access to basic education; Non-discriminatory school admission policies; Language rights and language in education policies; Equitable school governance and learner placement. | Black learners; Learners requiring English-medium instruction; Communities with limited access to local schools due to restrictive policies. | Education Law, Constitutional Law (right to basic education, language rights, equality, non-discrimination) | South Africa | NaN | NaN | NaN | False | False | NaN | The BELA Bill's 'one-size-fits-all' recentralisation may not suit all schools, potentially harming functional ones; Need for contextually intelligent approaches to school governance and reform; Potential for government incompetence or political agendas to undermine legislative intent; Failure of education departments to fulfill existing duties like ensuring sufficient school places. | NaN | Erosion of democratic school governance and community participation; Regression to a centralised, potentially inequitable, education system; Undermining school autonomy, creativity, and innovation; Impractical implementation and potential for HOD overreach or politically motivated decisions without local context; Legislative changes driven by political agendas or to circumvent past court rulings; Increased tensions over language policies if imposed without considering local needs and resources. |
| 25NCJLTech495.pdf | HeinOnline | ARTIFICIAL INTELLIGENCE, TRADE SECRETS, AND THE CHALLENGE OF TRANSPARENCY | This paper argues that AI system designers should be able to hold trade secret rights in AI algorithms even if they cannot fully articulate how those algorithms operate, but asserting misappropriation requires describing the algorithm in detail. It also explores how AI developers can comply with transparency regulations while protecting their intellectual property, cautioning against an overly broad assertion of trade secrets. | True | Market | False | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Trade Secret Law, Intellectual Property Law, AI Regulatory Law | United States | NaN | NaN | NaN | False | False | NaN | NaN | NaN | The paper identifies several risks: difficulty for AI system owners to protect AI-developed algorithms as trade secrets if their workings are unknown or cannot be described sufficiently for litigation; tension between AI transparency disclosure obligations to regulatory bodies and the preservation of trade secrets; companies adopting an overly maximalist approach to trade secret designation, which can be counterproductive to transparency goals and legally incorrect; the tendency to favor trade secret protection over patents for AI algorithms (due to patent eligibility uncertainties or description difficulties), potentially reducing public disclosure and broader innovation; opacity in complex AI supply chains making it difficult for end-product manufacturers to meet transparency requirements. |
| 5LawTechHum24.pdf | HeinOnline | When Art Becomes a Lemon: The Economics of Machine-Enabled Artworks and the Need for a Rule of Origin | The paper analyzes the 'lemons problem' in the art market due to indistinguishable AI-generated and human-made art, potentially devaluing human artistry. It proposes implementing a 'rule of origin,' analogous to trade law's substantial transformation test, to label artworks by human/machine contribution, fostering transparency and fair valuation. | True | NaN | True | 1.0 | NaN | A 'Rule of Origin' for machine-enabled artworks, potentially using a 'substantial transformation test' (analogous to trade law) and a tiered system based on human input, to determine authorship origin (human-made vs. machine-enabled). | NaN | NaN | NaN | NaN | NaN | NaN | Copyright Law, Intellectual Property Law, Art Law, Economic Law (market regulation), International Trade Law (by analogy for rules of origin), EU Law (AI Act). | European Union (referenced for rules of origin and AI Act), United States (referenced for 'lemons problem' origin, Copyright Office cases), International (general applicability of the problem and proposed solution). | For LLMs (e.g., GPT-3): Internet-based (Common Crawl, Wikipedia, etc.), predominantly English, unstructured text. Identified as a source of bias. | Economic theory (Akerlof's 'lemons problem'), legal analogy (international trade law's rules of origin and 'substantial transformation test'), policy framework development. | Proposed strategies include: bottom-up (artist self-regulation like recording creation; publisher/editor policies requiring disclosure) and top-down (government regulation such as extending EU AI Act disclosure requirements, mandatory watermarking of AI-generated content, algorithmic screening). | False | False | NaN | NaN | For the proposed Rule of Origin: Difficulty in defining and applying 'substantial transformation' to hybrid human-AI creative works; complexity of establishing clear boundaries in a tiered system of human input; ensuring effective enforcement of disclosure requirements and technical measures like watermarking; risk of circumvention of such measures. | Economic risk of 'lemons problem' devaluing human-made art due to information asymmetry; deceptive practices (misrepresenting AI art as human-made); stifling human creativity; spread of misinformation, bias, and toxic content via LLMs if outputs are not managed; copyright infringement by AI models; deepfakes eroding trust. |
| 57VandJTransnatlL.pdf | HeinOnline | The Network Effects of International Crypto and DLT Regulation | This paper analyzes global coordination in DLT and cryptocurrency regulation using the framework of network effects, identifying impacts on society, firms, and regulators. It argues positive network effects can drive adoption of global standards, but warns that soft law might undermine these benefits. | True | Market | False | 1.0 | NaN | Analytical framework of network effects (social, firm-level, regulator-level) applied to international DLT and cryptocurrency regulation. | NaN | NaN | NaN | NaN | NaN | NaN | Financial regulation, technology law, international law, commercial law, anti-money laundering law. | International | NaN | Application of economic theory (network effects), literature review (legal, economic), and conceptual analysis to develop a framework for analyzing DLT/crypto regulation. | NaN | False | False | NaN | NaN | NaN | Risks from unregulated DLT/crypto (e.g., money laundering, systemic risk, illicit uses, environmental harm) and risks from poorly coordinated/designed global regulation (e.g., negative network effects like herding, weakened standards, incompatibility with local needs). |
| 16JIntellPropInfoTechElec.pdf | HeinOnline | The Artificial Intelligence Act: Critical Overview | This article provides a critical overview of the European Union's Artificial Intelligence Act (Regulation (EU) 2024/1689), analyzing its structure, objectives, scope, key definitions, and risk-based approach. It discusses prohibited practices, high-risk AI systems, transparency obligations, general-purpose AI models, and concludes that the Act's complexity may undermine its goals of fostering responsible innovation and protecting public interests. | True | Idealistic | True | 2.0 | Neutral | The EU Artificial Intelligence Act (Regulation (EU) 2024/1689) | The paper evaluates the AI Act through a dogmatic legal analysis, presenting a general descriptive legal analysis of the Regulation in the wider context of EU law. | The paper concludes that while the AI Act contains generally balanced and reasonable solutions, its complexity and poor legislative quality risk defeating its purpose, negatively affecting innovation, and potentially reducing the supply of advanced AI in the EU. | AI-driven discrimination, lack of transparency in automated decisions affecting legal rights (para 3, 43, 45), and the potential for the AI Act's own complexity to hinder effective protection of fundamental rights relevant to access to justice (para 108). | The paper suggests that the adoption of technical standards to reduce compliance costs and uncertainty, and increased involvement of legal experts to navigate and implement the complex AI Act, could help overcome the legislation's limitations (para 109). | Protection of fundamental rights (non-discrimination, fairness, transparency, accountability) through AI regulation, particularly concerning AI in law enforcement, biometric identification, and judicial contexts, which indirectly relates to access to justice (para 4, Section F, Annex III). | The AI Act, as analyzed in the paper, aims to protect vulnerable groups (e.g., based on age, disability, socio-economic status) and prevent discrimination based on protected characteristics (e.g., race, political opinions) from harmful AI practices (para 53, 65). | AI Law, EU Law, Product Safety Law, Fundamental Rights Law, Data Protection Law, Competition Law. | European Union | NaN | The AI Act was developed through the EU legislative process, involving a proposal from the European Commission, intense negotiations between the Commission, Parliament, and Council, amendments, and a corrigendum (para 8). | The AI Act (Regulation (EU) 2024/1689) was published in the Official Journal of the EU and is subject to a phased entry into force, with general application scheduled for August 2, 2026, and some parts applying earlier (para 8, 37). | True | True | The EU AI Act (Regulation (EU) 2024/1689) is published in the Official Journal of the EU and is publicly accessible (para 8). | The paper highlights the AI Act's significant complexity and potential poor legislative quality as major gaps (para 108). It also cites critiques suggesting limitations, loopholes, and a potentially narrow scope for high-risk classification, which might leave some harmful AI applications inadequately regulated or enforced (para 71 footnote 106, para 140 footnote 140). | The EU legislators faced challenges in defining AI, establishing a risk classification system, regulating general-purpose AI models, addressing open-source AI, and balancing the promotion of innovation with the protection of fundamental rights and public safety during the Act's development (para 8, 13-14, 22, 36, 90-91). | The paper states that the AI Act itself, due to its complexity and legislative quality, risks negatively affecting innovation, hindering investment, reducing the supply of advanced AI in the EU, and potentially defeating its own purpose of promoting responsible innovation and protecting public interests (Abstract, para 108, footnote 137). |
| 17RomArbJ31.pdf | HeinOnline | ARTIFICIAL INTELLIGENCE AND ARBITRATION: SOME CONSIDERATIONS ON THE EVE OF A GLOBAL REGULATION | This paper reviews the application of IT and AI tools across various stages of arbitral proceedings and discusses the evolving regulatory framework and ethical considerations for their use. It highlights the importance of responsible AI deployment in arbitration and posits that arbitration might evolve to champion human-centric justice as a counter-trend to AI-driven justice systems. | True | Market | True | 3.0 | Neutral | Generative AI (e.g., ChatGPT) for legal research and drafting. | Discusses real-world use and failure, notably the *Mata v. Avianca* case where ChatGPT fabricated legal precedents, and judicial observations on its limitations for legal research and analysis. | In the *Mata v. Avianca* case, the use of ChatGPT for legal briefing resulted in the submission of non-existent judicial decisions, leading to sanctions against the legal counsel. | Ensuring AI systems respect fundamental rights (including access to justice, equality, due process), non-discrimination, transparency, and accuracy; preventing AI errors or biases that could undermine justice; and addressing the 'black box' nature of some AI. | Development of global and national regulations (e.g., EU AI Act), ethical guidelines (e.g., CEPEJ Charter, SVAMC draft Guidelines), risk management frameworks, mandatory disclosure of AI use in legal proceedings, and maintaining robust human oversight and accountability. | Right to access to justice, equality, due process, fundamental rights, non-discrimination, transparency, fairness in AI-assisted legal processes. | NaN | Arbitration, Dispute Resolution, Litigation | USA, EU, Canada, UK, International | NaN | NaN | AI tools and platforms provided by arbitral institutions (e.g., AAA-ICDR's AAAi Lab, various case management systems); court-issued practice directions regarding AI use; development of guidelines by legal bodies (e.g., SVAMC, UK Judiciary). | True | False | Some AI tools from arbitral institutions (e.g., AAAi Lab, case management platforms like ICC Case Connect) are available to users, parties, or arbitrators affiliated with or using their services. Draft guidelines (e.g., SVAMC) are publicly accessible for review. | Ensuring robust human oversight and accountability in AI-driven legal processes; Developing globally harmonized, enforceable regulations that effectively protect fundamental rights including access to justice; Overcoming data limitations (especially for confidential arbitration data) to train fair and unbiased AI models for legal applications. | Ensuring accuracy and reliability of AI outputs (e.g., avoiding hallucinations and fabricated information); Maintaining confidentiality and data security when using AI with sensitive legal data; Preventing improper delegation of human decision-making and ethical responsibilities to AI; Addressing potential biases in AI systems; Integrating AI ethically and effectively into established legal workflows. | Fabrication of information by AI (e.g., fake case law); Undermining the integrity of judicial and arbitral processes and public trust; Improper delegation of essential human cognitive tasks and decision-making responsibilities to AI; Cybersecurity vulnerabilities and data privacy breaches; Introduction or perpetuation of biases by AI systems, impacting fairness and equality. |
| 20OhioStTechLJ225.pdf | HeinOnline | PROMETHEUS' DIGITAL FIRE: THE CIVIC RESPONSIBILITIES OF ARTIFICIAL INTELLIGENCE | This article explores the civic responsibilities associated with AI, examining its benefits and risks, particularly regarding bias, privacy, and accuracy. It also discusses emerging regulatory frameworks in the EU and US and proposes industry actions to mitigate risks and maximize benefits. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Bias in AI leading to digital redlining and discrimination in critical areas like housing, employment, credit, and law enforcement; lack of transparency in AI decision-making; privacy violations through extensive data collection; and the spread of AI-generated disinformation and harmful content. | Adherence to civil rights laws, employing debiasing strategies and explainable AI (XAI), developing robust regulatory frameworks and industry standards (e.g., NIST AI RMF), diligent fact-checking of AI outputs, and establishing strong contractual safeguards with AI vendors for data protection and system accountability. | Preventing AI-driven discrimination and bias (digital redlining), upholding civil rights, ensuring access to accurate information by combating AI-generated disinformation and harmful content, protecting privacy rights, and promoting ethical use of AI in legal practice. | Various vulnerable groups, including racial minorities (Black people, Asians, Latinos), women, and the elderly, who are disproportionately affected by biased AI systems. | Civil Rights Law, Privacy Law, Defamation Law, Products Liability, First Amendment Law, Intellectual Property Law (minor mention), Criminal Law (re: AI-generated child pornography), Contract Law, and Legal Ethics. | United States, European Union, and mentions China in the context of AI development and regulation. | The paper discusses AI systems trained on vast amounts of data scraped from the internet, including publicly available information, pirated and copyrighted materials (e.g., books), user-submitted data via prompts and APIs, and unstructured data. This data is often collected via web crawlers and third-party services. | NaN | NaN | False | False | NaN | Ongoing difficulties in mitigating AI bias and ensuring explainability (XAI); technical limitations in AI factuality and source citation; societal challenges in adapting education for critical thinking; and the need for further development and implementation of regulatory frameworks. | NaN | Bias and discrimination amplifying societal inequities; extensive privacy violations from data collection and misuse; spread of AI-generated disinformation, defamation, and deepfakes; provision of dangerous or inaccurate advice leading to harm; significant job displacement in creative and knowledge-based industries; and the erosion of critical thinking skills. |
| 22BerkeleyBusLJ108.pdf | HeinOnline | Have Plain Language Laws Kept up with the AI Revolution? An Empirical Test | This article empirically tests the ability of AI-writing assistants, specifically Grammarly, to improve the readability of Franchise Disclosure Documents (FDDs) subject to plain language laws. It finds AI can significantly enhance document comprehensibility and proposes integrating AI-writing assistant standards into plain language laws via a rebuttable presumption. | True | Idealistic | False | 2.0 | Positive | Grammarly (Premium subscription), an AI-powered writing assistant. | Analysis of 'Item 1' from 100 Franchise Disclosure Documents (FDDs) of leading U.S. quick-service restaurants using Grammarly Premium. Measured average percentage of sentences flagged for grammar/clarity corrections and qualitatively categorized types of linguistic issues identified. | Grammarly flagged an average of 96.33% of sentences in Item 1 of the FDDs for potential enhancements in either grammar (average 54.15%) or clarity (average 42.19%). | Legal documents remain difficult to understand despite plain language laws due to linguistic deficiencies (grammar, clarity); vagueness and subjectivity in interpreting and complying with current plain language standards. | Amend plain language laws to incorporate a rebuttable presumption of compliance if drafters use an advanced AI-writing assistant and demonstrate substantive adherence to its standards (via a digital report). | Enhancing readability and comprehensibility of legal and business documents (Franchise Disclosure Documents); improving effectiveness of plain language legislation; access to information for informed decision-making. | General public, consumers, potential franchise owners, and readers of documents governed by plain language laws. | Franchise Law (specifically Franchise Disclosure Documents), Consumer Law, Corporate and Financial Disclosures, and generally laws requiring plain language. | United States (federal Franchise Rule and state/federal plain language laws). | Grammarly's AI is trained on proprietary datasets consisting of a vast text corpus (millions of sentences organized and labeled by human researchers from research corpora) and refined through years of user feedback analysis. The data is domain-general English aimed at good writing practices. | Grammarly's development involves machine learning methods, training on large text corpora, and iterative refinement based on human feedback analysis from user interactions with its suggestions. | Grammarly is a commercial AI-writing assistant available through free and premium subscription plans, used by millions daily, including individuals, educational institutions, and corporations like Cisco, Dell, and Boeing. | True | False | Grammarly is commercially available with both free and premium subscription plans. The study utilized the Premium version for its comprehensive features. | The inherent vagueness and limited scope of current plain language laws; the fallibility of AI tools (potential for inaccuracies, manipulation) requiring human oversight and critical judgment. | For this study: the non-machine readable format of some source documents (FDDs) made processing challenging. For AI tool users generally: The need for critical evaluation of AI suggestions; potential cost of premium features for full benefits. | Over-reliance on AI tools that may produce inaccurate feedback; potential for users to manipulate AI outputs; if AI tool use were mandated (which the paper cautions against), risks include undue financial burdens, stifled innovation, and infringement on commercial speech. |
| 4LegalIssuesDigitAge59.pdf | HeinOnline | Artificial Intelligence vs. Judicial Discretion: Prospects and Risks of Judicial Practice Automation | This paper analyzes the feasibility of integrating artificial intelligence into the Russian judicial system, comparing AI's potential with judicial discretion. It concludes that AI implementation is impractical in the short to medium term due to significant risks, inadequate legal frameworks, and the current geopolitical climate. | True | NaN | False | 3.0 | Negative | NaN | NaN | NaN | Technological inequality; data security and privacy concerns; limitations of AI in legal reasoning and handling nuances; algorithmic bias; lack of legal frameworks and accountability for AI; potential for AI to create new obstacles to access to justice. | The paper primarily argues against near-term AI implementation. For potential future use, it suggests: ensuring human control over AI; developing robust legal frameworks and regulations; proactive compliance policies by developers and enforcement agencies. | Right to judicial protection; barrier-free access to justice (critique of AI's ability to provide it). | General population / litigants. | General judicial practice, civil law, criminal law, administrative law. | Russian Federation (primary), European Union, China, Argentina (comparative examples). | NaN | NaN | NaN | False | False | NaN | Lack of doctrinal definition of AI and regulations for negative scenarios; absence of legal basis for AI liability and legal personality; no unified AI regulatory document in Russia; no national strategy or quality criteria for AI-Ready Open Juridical Data; lack of universal criteria for selecting training data; insufficient digital literacy in the legal profession; unclear responsibility for AGI decisions; uncertainty of input meanings for AI training. | NaN | Unauthorized data access and theft; AI training on misleading information; discriminatory or unjust AI outcomes; hampering of fundamental procedural rights; lack of AI transparency and explainability; technological inequality; security vulnerabilities from cloud-based AI and unprotected interfaces; violations of data protection laws; lack of accountability for AI errors; undermining judicial independence; AI making suboptimal or unlawful decisions; automation errors and network failures; potential hacking of judicial systems. |
| 8UPaJLPubAff129.pdf | HeinOnline | ADDRESSING THE EVOLVING CONCEPT OF GENDER AND INTERSECTIONAL STEREOTYPES IN INTERNATIONAL NORM CREATION: DIRECTIONS FOR A NEW CEDAW GENERAL RECOMMENDATION | This paper analyzes how gender and intersectional stereotypes are addressed by the CEDAW Committee and other human rights bodies, highlighting their evolving nature. It then discusses the emergent challenge of stereotypes embedded in Artificial Intelligence and proposes that a new CEDAW General Recommendation should address these digitized biases from a human rights perspective. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Entrenched societal gender and intersectional stereotypes being codified and amplified by AI systems due to biased training data and lack of diversity in the AI workforce, leading to 'digitized bias' and 'algorithmic discrimination'. | Developing new international normative frameworks (e.g., a new CEDAW General Recommendation) grounded in human rights, substantive equality, and intersectionality; promoting education and diversity in AI development; establishing guidelines for AI data and design; and fostering multilateral collaboration to ensure AI governance addresses and mitigates bias. | Elimination of gender and intersectional stereotypes in legal systems and AI, ensuring non-discrimination, and promoting substantive equality for women in all spheres, including protection from gender-based violence and fair judicial processes. | Women, particularly those facing intersectional discrimination based on race, ethnicity, religion, age, disability, sexual orientation, migrant status, and other factors. | International Human Rights Law, Anti-discrimination Law, Gender Law, Technology Law/AI Governance. | International; with examples from various national jurisdictions (e.g., MENA, South Asia, East Asia, Americas, European countries). | Discusses AI training data generally as often being unrepresentative of women and minority groups, leading to 'data bias' and the reproduction of societal gender biases in AI systems. It does not specify a particular dataset used for a proposed technique as the paper is a broad discussion. | NaN | NaN | False | False | NaN | Societal gaps in recognizing and addressing subtle and intersectional stereotypes. Technical and legal gaps in understanding and regulating AI-driven bias, including unrepresentative datasets, lack of diversity in AI development, the need for gender-sensitive AI design, and the application of human rights frameworks to AI governance. | NaN | Reproduction and amplification of societal gender and intersectional stereotypes through 'digitized bias' in AI systems. Stigmatization and marginalization of women, particularly those with intersectional identities, on a global scale. Normalization of gender-based violence and discrimination through biased AI outputs and interactions. Erosion of human rights if AI is not governed by inclusive, rights-based frameworks. |
| 25DukeLTechRev48.pdf | HeinOnline | GRAY ADVICE | This paper defines 'gray advice' as interactive digital tools providing personalized legal or health assistance, often as a substitute for professional help due to access issues. It highlights significant trust and quality problems with current gray advice services and proposes regulatory and professional interventions to make them safer and more effective for users. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Lack of access to and affordability of traditional legal and health professionals. For gray advice itself: festering trust and quality issues, deceptive disclaimers, users' inability to evaluate advice quality, data exploitation, failure to handle edge cases, inducing user errors, and setting users up to fail. | Regulatory interventions (e.g., deception cases, duty of loyalty for providers, 'nutrition label' disclosures, auditing regimes, modernized confidentiality protections). Professional engagement (e.g., building referral bridges between gray advice and professionals, developing field-specific design ethics for digital advice, investigating the true impact of services). | Access to self-help and limited-scope assistance for resolving discrete legal issues (e.g., small claims, divorce, immigration, benefits, wills, expungement) and managing health conditions (e.g., mental health, addiction, health coaching, pregnancy advice, self-diagnosis). | Low-income Americans, people in rural areas (for healthcare), and generally individuals who cannot find or afford traditional professional help. | Civil law (general), family law (divorce), immigration law, administrative law (government benefits), wills and estates, criminal law (record expungement). Healthcare is also a major parallel field of focus. | United States | NaN | NaN | NaN | False | False | NaN | Regulatory gaps in protecting users of gray advice. Lack of ethical design standards for digital advice. Poor integration and referral mechanisms between gray advice and professional services. Insufficient research on user outcomes and the actual effectiveness of gray advice. The challenge of addressing diverse user capabilities and knowledge levels. Pervasive unmet demand for legal and health assistance. | Establishing user trust in digital advice services. Ensuring the quality, accuracy, and reliability of advice, especially for complex or edge cases. Designing interfaces that prevent user error and facilitate understanding. Protecting sensitive user data from exploitation and breaches. Bridging the gap between providing information/advice and achieving successful user outcomes. Overcoming the 'credence good' problem where users cannot easily evaluate advice quality. | Users being misled by deceptive claims or disclaimers. Receiving incorrect, incomplete, or harmful advice. Data privacy violations and exploitation of sensitive personal information. Users failing to successfully resolve their issues or having their situations worsened. Erosion of public trust in professional services and institutions. Gray advice providers developing economic interests that oppose systemic improvements to access to justice or health. |
| 54CalWIntlLJ517.pdf | HeinOnline | A Socio-Legal Inquiry on Deepfakes | The paper examines the workings and types of deepfake technology, exploring its social ramifications and analyzing current legal and regulatory frameworks in major economies like the US, EU, UK, China, and India. It proposes public policy innovations for a whole-of-society approach to prevent, detect, respond to, and repair harm from malicious deepfake use. | True | Idealistic | False | 3.0 | Positive | Deepfake technology (general), Policy framework for deepfake governance | NaN | NaN | Misinformation and disinformation, erosion of trust, difficulty in authenticating evidence and identifying perpetrators, infringement of personal and intellectual property rights, challenges in legal enforcement and cross-border jurisdiction, victimization of vulnerable groups. | A comprehensive policy framework involving prevention (e.g., content creation tool certification, digital watermarking laws), detection (e.g., R&D funding for detection technologies, platform regulation), response (e.g., rapid response teams, emergency protocols), and repair (e.g., victim support programs, civil remedies for victims). | Access to justice for victims of deepfake-related harms including non-consensual pornography, defamation, fraud, and election manipulation; protection of personal rights (privacy, personality) and intellectual property. | Vulnerable populations including women, children, senior citizens, and minority groups who are targets of deepfake-related crimes like non-consensual pornography, extortion, and defamation. | Criminal law, intellectual property law, privacy and data protection law, tort law (defamation, false light), election law, evidence law, personality rights. | United States, European Union, United Kingdom, China, India, International (due to cross-border nature of deepfakes) | NaN | Socio-legal analysis, literature review, comparative legal analysis for the proposed policy framework. | Policy proposals intended for governmental and societal adoption. | False | False | NaN | Gaps in legal frameworks concerning cross-border jurisdiction, intellectual property and personality rights (especially post-mortem privacy); the continuous need for research and development in deepfake detection and understanding victim vulnerability; insufficient cybersecurity resources and public awareness. | NaN | Disinformation, defamation, creation and dissemination of non-consensual pornography, extortion, identity theft, manipulation of elections, erosion of democratic processes and public trust, infringement of intellectual property and personality rights, potential for wrongful convictions based on falsified evidence, psychological and financial harm to victims. |
| 51FlaStULRev543.pdf | HeinOnline | WHAT'S A LAWYER FOR? ARTIFICIAL INTELLIGENCE AND THIRD-WAVE LAWYERING | The paper discusses the impact of AI and new technologies on the legal profession, framing it as a "third wave" of lawyering. It proposes a conceptual framework for assessing how different legal service delivery models, including technology-enhanced ones, can uphold the core values and functions of the legal profession, particularly concerning access to justice. | True | Idealistic | True | 1.0 | Positive | A conceptual framework for calibrating legal service delivery modes, assessing legal problems (complexity, agility, preventative/reactive, stakes) and clients (sophistication, capacity, ability to pay, access barriers) against professional values (adversarial role, democratic interests, rule of law, access to justice) and functions (instrumental, affective, political). | The framework is illustrated through its application to two hypothetical real-world scenarios: the formation of a simple non-profit ('East Harlem All Stars') and a complex non-profit ('The Safe Center'). | The framework application demonstrated that simpler legal needs with sophisticated clients (East Harlem All Stars) might be adequately served by technology-based solutions, while complex, high-stakes situations (The Safe Center) require traditional, full-service legal representation. | Cost of legal services; Lack of public awareness of legal problems or need for lawyers; Difficulty in accessing lawyers; The digital divide; Potential for technology to undermine core legal values if not thoughtfully deployed; Restrictive ethical rules and UPL regulations. | Thoughtful deployment of technology, including AI, to enhance affordability and accessibility; Utilizing the proposed framework to determine appropriate service delivery models; Reforming ethical paradigms, including rules on non-lawyer investment and UPL, to support innovation in legal services. | Affordability and accessibility of legal services; Role of technology (AI) in bridging the justice gap; Models for legal service delivery to low- and moderate-income individuals and non-profits; Ethical considerations in legal tech. | Low- and moderate-income individuals and non-profit organizations. | General legal practice, Non-profit law, Legal ethics, Professional responsibility. | United States | NaN | Conceptual analysis, historical review of the legal profession, synthesis of legal ethics and theory, and application of business concepts (e.g., Christensen's 'jobs-to-be-done'). | NaN | True | False | The conceptual framework for assessing legal service delivery models is detailed within the paper and can be understood and applied by readers. | Need for technologies to accurately assess case complexity and client nuances; The persistent digital divide; Need for reform of ethical rules (UPL, non-lawyer ownership) to enable beneficial tech innovations; Ensuring new technologies uphold legal values and do not create a two-tiered justice system. | Accurately assessing problem complexity and client capacity for technology use via automated or limited-service means; Ensuring new service delivery models preserve the instrumental, affective, and political functions of lawyering; Overcoming professional resistance to changes in legal service delivery; The high cost of developing and maintaining sophisticated legal technology tools. | Undermining core values of the legal profession and democratic institutions; Displacing essential lawyer functions inappropriately; Loss of nuanced legal guidance through over-commoditization; Creation of a two-tiered justice system; Premature disruption by immature technologies; Malpractice from incorrect assessments or faulty tech-based advice; Exacerbation of inequality due to the digital divide hindering access to tech-based solutions. |
| 13Laws1.pdf | HeinOnline | Law, Technology, and Our Governance Dilemma | This article highlights a dilemma in using new tools to improve law's imperfect governance, as technology offers benefits but risks displacing the human element. It concludes that technological applications must be human-centric and controlled to protect the generic conditions essential for viable human communities. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Delays, difficulties, and costs associated with access to justice, making legal remedies 'out of reach' for many citizens. | Controlled and human-centric application of technology in governance, ensuring protection of fundamental human conditions, while exploring various roles for technology (assisting humans, automation, technological management) to improve law's imperfect governance. | Barriers to accessing legal services (delays, difficulties, costs), justice being unattainable. | Many citizens for whom justice is out of reach. | General / Multiple | International | NaN | NaN | NaN | False | False | NaN | The fundamental dilemma of how to integrate technology into legal governance effectively and ethically, balancing efficiency with human values and agency. Ensuring that technological applications remain human-centric and do not undermine the generic conditions for viable human communities. | NaN | Displacement of the human element in governance, AI applications that are not human-centric (e.g., violating human rights/dignity, undermining human agency), potential for biased algorithms (e.g., in risk assessment tools like COMPAS), counter-productive impacts of technology, loss of human responsibility and agency when conduct is technologically managed, and technologies undermining generic conditions for human existence and viable communities. |
| 4LawTechHum109.pdf | HeinOnline | Framing the Future: The Foundation Series, Foundation Models and Framing AI | This paper critically examines how AI foundation models, particularly in NLP, risk embedding and amplifying dominant, often biased, neoliberal linguistic frames from law and economics. It argues that this uncritical adoption could entrench societal inequalities, hinder true progress, and make it harder to challenge existing power structures, drawing parallels with Asimov's Foundation series to highlight these dangers. | True | Idealistic | True | 3.0 | Negative | NaN | NaN | NaN | Entrenched hegemonic neoliberal frameworks and biases embedded in language, which are uncritically adopted into AI foundation models, leading to the perpetuation and amplification of societal inequalities and hindering access to justice. | Promoting greater awareness of how linguistic framing shapes AI and society; developing a research agenda to identify and mitigate deep-seated biases in AI beyond explicit ones; actively reframing societal narratives to challenge dominant, inequitable ideologies; fostering conceptual tools that prioritize social well-being over purely economic or individualistic metrics. | The risk of AI foundation models perpetuating socio-economic inequalities and unfair power dynamics by encoding and amplifying biased neoliberal frames; the impact of AI's linguistic framing on access to justice and the marginalization of non-dominant voices and values. | General population, particularly those marginalized or disadvantaged by dominant neoliberal socio-economic structures whose interests are not reflected in hegemonic frames. | General law, Law and Economics, Law and Development | International | Existing data created by (a subset of) humans, reflecting flawed, biased human preferences and assumptions, including text from the internet and other sources; data curated from interactions with the current generation of foundation models; unlabelled data for self-supervised learning tasks. | NaN | NaN | False | False | NaN | Insufficient awareness and research into how deep-level linguistic framing (beyond explicit bias) encodes and perpetuates systemic inequalities within AI systems; lack of critical engagement with the hegemonic (neoliberal) conceptual tools being embedded in foundation models; the current focus of de-biasing on superficial aspects, neglecting foundational framing issues. | The complexity and monolithic nature of foundation models, making them difficult to adjust post-release; the tendency for AI systems to inherit and amplify biases from foundation models; the difficulty in identifying and remedying subtle, deeply embedded framing biases compared to explicit social biases. | Preservation and amplification of hegemonic neoliberal frames in AI systems, entrenching existing inequalities; perpetuation of structural inequalities leading to tangible harms for sections of the population; limiting future interrogation and evolution of legal and economic concepts by 'preserving them in digital aspic'; shaping human users to conform to 'homines economici-juridici'; AI systems potentially allowing humanity to come to harm by entrenching socio-economic disadvantage. |
| 42CardozoArtsEntLJ295.pdf | HeinOnline | Bias Notification Duty | The paper proposes a 'Bias Notification Duty' (BND), a legal mechanism requiring companies to report discovered algorithmic biases to a governing body. BND's goal is to facilitate the study of these biases for broader societal understanding and de-biasing, rather than companies just covertly fixing algorithmic outputs and obscuring the underlying issues. | True | Idealistic | False | 1.0 | Positive | Bias Notification Duty (BND) | NaN | NaN | The covert fixing of algorithmic bias by companies, which prevents society from learning about and addressing the underlying societal biases that affect civil rights and liberties. | Impose a Bias Notification Duty (BND) on companies and their employees to report discovered algorithmic bias to a governing body for study, evaluation, and notification of affected parties, enabling societal learning and de-biasing efforts. | Algorithmic bias, discrimination (gender, racial), societal fairness, transparency, accountability, de-biasing society, civil rights and liberties. | Legally protected classes, including those based on gender and race; minorities. | Antidiscrimination law, AI governance, data protection law, corporate law (corporate social responsibility, reporting duties), administrative law. | US, EU, International (proposal seems broadly applicable) | NaN | Conceptual legal and socio-legal analysis; proposal of a regulatory framework drawing on existing legal mechanisms. | Proposed as a state-imposed legal duty enforced by a selected governing body (e.g., analogous to the FTC). | False | False | NaN | Current approaches focus on fixing algorithmic output without sufficient study of the bias itself, missing opportunities for societal learning and broader de-biasing efforts. Lack of transparency about discovered and fixed biases prevents societal de-biasing. | Defining bias for regulatory purposes; ensuring compliance and enforcement against corporate secrecy and reluctance to report; navigating intellectual property and trade secret protections; managing costs of implementation and oversight; avoiding chilling effects on innovation. | Algorithmic bias negatively affecting lives and becoming 'weapons of math destruction'; misuse of disclosed bias information for opportunistic or abusive behavior; potential chilling effect on data use or algorithmic development if BND is poorly implemented. |
| 9AthensJL509.pdf | HeinOnline | An Economic Perspective of the Justice Digitalisation Process: The Questions of Efficiency and Equity | The paper analyzes the digitalisation of the judicial administration, particularly in Spain, from an economic perspective, focusing on its impacts on efficiency and equity. It highlights existing problems like delays and corruption, and discusses the potential benefits and risks of technologies like AI, emphasizing the need for caution and control to ensure fairness and protect rights. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Time delays in legal resolutions; economic conditions of users influencing access and outcomes ('inequality of arms'); opacity, lack of transparency, and corruption in the judicial administration; high litigation costs creating unequal justice; the digital divide affecting vulnerable populations; and insufficient technical expertise among legal professionals to manage new technologies. | Enhancing transparency and efficiency through controlled digitalisation; implementing robust oversight and control mechanisms for AI and algorithms; training legal professionals in new technologies or establishing independent technological authorities; bridging the digital divide affecting vulnerable groups; and promoting alternative dispute resolution methods. | Improving judicial efficiency, ensuring an equitable justice system, enhancing transparency, combating judicial corruption, and mitigating the digital divide's impact on access to justice. | Economically disadvantaged individuals, the elderly, disabled persons, women, and residents of rural areas. | Civil justice, Criminal justice, Administrative justice (primarily focusing on judicial administration across these fields). | Spain, with comparative references to Europe/EU. | NaN | NaN | NaN | False | False | NaN | Technical gaps include the lack of robust control and validation mechanisms for AI in justice and insufficient technical literacy among legal professionals. Societal gaps involve the persistent digital divide exacerbating inequalities, risks to fundamental rights (privacy, honor, legal guarantees), systemic resistance to transparency from legal professionals, and unresolved issues of judicial corruption and independence. | NaN | Increased inequity for vulnerable populations; infringement of legal guarantees, privacy, and honor; biased or erroneous algorithmic outcomes; potential for government manipulation of the judiciary through technology; loss of crucial human elements in legal proceedings (e.g., immediacy, non-verbal Cues); and over-reliance on opaque technologies without adequate understanding or control. |
| 75AlaLRev563.pdf | HeinOnline | A LIBERAL THEORY OF LEGAL EDUCATION | This paper critiques current legal education for being 'a-liberal,' separating law from justice and morality despite a liberal reputation, and thereby failing to adequately promote access to justice. It proposes a 'liberal model' of legal education that systematically integrates justice, equality, and access to legal services into the curriculum and law school culture. | True | Idealistic | False | 1.0 | NaN | A liberal model of legal education, involving curricular reforms (e.g., new 1L courses on justice, equality, access to legal services; integration of these themes into all courses) and cultural reforms (e.g., faculty mentorship, emphasis on teamwork, transparency, faculty modeling liberal values). | NaN | NaN | The orthodox 'a-liberal' model of legal education separating law from justice and morality; historical and intellectual path-dependency in law schools (formalism, functionalism, 'new liberalism'); faculty adherence to the status quo due to perceived comfort, difficulty of change, and lack of institutional incentives; the high cost of legal education pressuring students away from public interest careers; and the prevailing client-centered ideology of the legal profession that often neglects broader justice considerations. | Adoption of a 'liberal model of legal education' that includes: 1) Curricular reform: mandatory 1L courses on justice, equality, and access to legal services, and holistic integration of these themes across all courses. 2) Cultural reform: faculty actively modeling and embodying commitment to liberal values, robust mentorship programs, emphasis on teamwork and collaboration, and institutional transparency. 3) Redefining faculty roles to actively include the stewardship of liberal values and engagement with justice issues. | Access to legal services, embedding justice and equality in legal education, reforming legal professional identity formation. | Those who cannot afford to pay for legal services; 'most Americans priced out of the market for legal services'; underrepresented clients. | Legal Education, General Legal Practice (as it discusses the training for all lawyers). | United States | NaN | NaN | NaN | False | False | NaN | The failure of current legal education to consistently instill a commitment to justice, equality, and access to legal services in law students. The prevailing 'a-liberal' culture in law schools and the legal profession which de-prioritizes these values. | Likely critiques and implementation challenges for the proposed liberal model of legal education include: perceived infeasibility of instilling values in a three-year program; the argument that the legal practice environment is inherently a-liberal and resistant to such values; resistance from faculty due to increased workload, changes to their traditional roles, and defense of their subject-matter turf; and institutional inertia, high costs of reform, and potential for the model's aims to be misunderstood (e.g., as a purely political agenda). | The proposed model risks being misunderstood as a political ploy to make law schools more politically liberal, rather than instilling apolitical liberal values. Institutional inertia and the difficulty of faculty buy-in may prevent successful implementation ('Path Dependencies and Committee Work, Where Good Ideas Go to Die'). |
| 5LegalIssuesDigitAge113.pdf | HeinOnline | Technologies Versus Justice: Challenges of Al Regulation in the Judicial System | This paper examines the integration of artificial intelligence into judicial systems, discussing current applications and the concept of "smart courts" in various countries. It argues that while AI can serve as a supportive tool, it fundamentally cannot replace human judges in delivering just decisions due to its lack of genuine understanding and ethical judgment, necessitating robust legal and ethical regulation. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Inability of AI to deliver genuinely just outcomes due to its lack of human consciousness, understanding, interpretive capacity, and ethical judgment required for fair decision-making; threat to the rule of law and fair trial if AI oversteps its auxiliary role. | Strict legal and ethical regulation ensuring AI remains auxiliary to human judges, prohibiting automated judgments without human control, and enshrining in law that the authority to render justice cannot be delegated to AI. Establishing multi-tier regulatory systems, including ethical corporate standards and 'pilot court' regimes for testing, based on principles like security, legitimacy, fairness, transparency, and compliance with public order. | Ensuring fairness and just outcomes in judicial decision-making; providing legal information and assistance (e.g., claim drafting, advice); improving court efficiency and accessibility while maintaining the human-centric nature of justice. | General public needing access to judicial protection and legal information. | General (judicial system), Civil law, Administrative law, Traffic law. | International (discusses China, India, Germany, Portugal, Singapore, Russia, and general principles). | NaN | NaN | NaN | False | False | NaN | Technical gap: AI's inability to replicate human consciousness, cognition, understanding, and ethical judgment necessary for true justice. Societal gap: Lack of comprehensive and timely legal and ethical regulatory frameworks for AI in the judiciary; need for deeper understanding of AI's impact on judiciary institutions and the human nature of fair judgment. | NaN | Undermining the rule of law and fair trial; compromising the human nature of justice and the role of judges; debasement of judicial power; damage to fundamental values of the judicial system (e.g., fairness); potential harm to national security and legitimate interests of individuals/organizations if AI is misused or unregulated. |
| 103BULRev.pdf | HeinOnline | CHANGING ALL THE TIME: AI'S IMPACT ON HUMANITY'S ROLE IN COMMON LAW DEVELOPMENT AND INTERPRETATION | This paper examines the significant impact generative AI, such as ChatGPT, could have on the development and interpretation of common law, potentially severing humanity's connection to it. It argues for careful guidance and proposes amending professional conduct codes to ethically center the human role in law. | True | Idealistic | True | 3.0 | Cautiously Positive | Generative AI (e.g., ChatGPT) and hypothetical AI legal assistants (e.g., 'LegalBot') | NaN | NaN | Inability to afford legal representation, leading to individuals having to navigate the legal system pro se. | AI-powered legal assistants (like the hypothetical LegalBot) to provide guidance and support to pro se litigants, potentially improving their ability to navigate legal processes and evening the playing field. | Assistance for pro se litigants; Reducing public defender backlogs. | Pro se litigants (individuals unable to afford legal representation). | Common Law (general), Tort Law (example). | United States (primarily American common law). | Large datasets of legal texts (case law, motions, treatises), general textual data, and human-provided input, processed by machine learning algorithms. | NaN | Integration into legal practice through corporate adoption (e.g., PwC's Harvey) and potential court-sanctioned programs for pro se assistance (hypothetical LegalBot). | True | True | ChatGPT, a key example discussed, is publicly accessible via OpenAI, with a free usage tier. | Lack of robust regulatory and ethical frameworks guiding AI development and deployment in the legal field, specifically concerning AI's role in substantive legal work and common law development. Need for a deeper understanding and preservation of the human-law relationship in the age of AI. | Ensuring factual accuracy and avoiding 'hallucinations' (e.g., fake caselaw); ethical concerns around plagiarism, attribution, and unlicensed practice of law; risk of overreliance and deskilling of human lawyers; preventing AI from unduly controlling legal development through feedback loops. | Severing humanity's connection to the law; ceding control of common law development to AI algorithms and private companies; AI creating a feedback loop that dictates legal development based on past data; unlicensed practice of law; erosion of human legal reasoning skills; challenges to the integrity and fairness of the legal system. |
| 36IntlJSemioticsL.pdf | HeinOnline | Hyperrealistic Jurisprudence: The Digital Age and the (Un) Certainty of Judge Analytics | This paper introduces "hyperrealism" as an evolution of legal realism, enabled by digital tools like judge analytics that allow for empirical prediction of judicial decisions. It discusses the advantages and disadvantages of judge analytics, highlighting the need for regulatory mechanisms to improve justice and minimize associated risks. | True | Idealistic | False | 2.0 | Neutral | Judge analytics (also referred to as judicial analytics) | Evaluations include accuracy metrics for data extraction by tools like SupraLegem.fr (claimed 90-99%), and studies analyzing correlations between judicial characteristics/behaviors and case outcomes (e.g., ECHR and US asylum case studies). | Judge analytics tools have shown high accuracy in data extraction (e.g., 90-99% by SupraLegem.fr) and identified patterns such as varying asylum rejection rates among judges and correlations between judicial characteristics (e.g., gender, presence) and decisions. | Risk of rights violations, threats to judicial impartiality and privacy, algorithmic bias from incomplete/biased data, and potential for misuse (e.g., unfair 'judge shopping'). | Proposes regulatory mechanisms, expert evaluation, standardization, and ethical guidelines (emphasizing fairness, transparency, accountability) for judge analytics. | Transparency and predictability of judicial decision-making, identification and mitigation of judicial bias, and ensuring fairness within the justice system. | Asylum seekers are mentioned as an example of a group affected by judicial biases that analytics can reveal; the paper's focus is broader. | General litigation, with examples cited from asylum law and human rights law. | International, with examples and references to the US, France, ECHR, Brazil, and other countries. | Primarily publicly available and proprietary datasets of judicial decisions, case law, legislation, and other legal documents, largely unstructured text. | Machine learning, Natural Language Processing (NLP), data mining, text mining, and jurimetrics, applied to analyze judicial texts and behavioral patterns. | Commercial availability through legal tech companies (e.g., LexisNexis, Thomson Reuters, JusMundi) and specialized startups offering judge analytics tools. | True | False | Various commercial judge analytics tools (e.g., LexisNexis Context, Thomson Reuters Westlaw Edge, Predictice) are available from legal tech providers. | Need for robust regulatory and ethical frameworks for judge analytics, further research on 'hyperrealism', and methods to address technical limitations like data gaps and algorithmic bias to ensure trustworthy AI in justice. | Ensuring data completeness and accuracy, mitigating algorithmic bias, protecting judicial privacy and independence, preventing misuse for unfair advantage, and balancing innovation with regulation. | Algorithmic bias reinforcing societal inequities, violations of judicial privacy, undermining judicial impartiality, misuse for 'judge shopping', potential for hacking or manipulation, and over-reliance on imperfect predictions. |
| 24PeppDispResolLJ91.pdf | HeinOnline | IS THE USE OF ARTIFICIAL INTELLIGENCE IN ALTERNATIVE DISPUTE RESOLUTION A VIABLE OPTION OR WISHFUL THINKING? | This paper examines the potential of AI in alternative dispute resolution (ADR), analyzing its benefits like increased efficiency and accessibility. It also discusses various AI-driven ADR methods, alongside significant challenges including ethical concerns, regulatory hurdles, and AI's limitations in replicating human reasoning. | True | Market | False | 3.0 | Neutral | Online Dispute Resolution (ODR) platforms, AI-assisted dispute resolution (AI-DR) including automated/assisted negotiation (e.g., Modria, Cybersettle, The Family Winner), adjudicative methods, case reasoning systems (e.g., Split-Up AI), and rule-based systems. | NaN | NaN | High cost and complexity of traditional litigation; intimidation of court proceedings; geographical and time constraints for physical court attendance; complexity of legal procedures for laypersons. | Online Dispute Resolution (ODR) to reduce costs, time, and intimidation, and make processes more accessible; AI-powered tools to offer affordable legal services. | Dispute resolution for common issues (e.g., motor vehicle violations, small claims, landlord-tenant disputes); increasing affordability and accessibility of legal services. | People of limited means. | Alternative Dispute Resolution (ADR), Civil Litigation (including small claims, landlord-tenant disputes, e-commerce, intellectual property, family law, financial disputes). | United States, China, Estonia, UK, France, Netherlands. | The paper notes that AI systems rely on training data such as past dispute outcomes (for case reasoning systems like Split-Up AI), party-provided data on preferences and priorities (e.g., The Family Winner), or large datasets of court cases (e.g., a standardized dataset of 100,000 US court cases for predictive analytics). | NaN | NaN | True | False | Discussed tools like eBrevia, Modria, Cybersettle, and OurFamilyWizard are commercial platforms; some ODR systems are integrated into court operations in various jurisdictions (e.g., for traffic tickets, small claims). | AI's inability to fully process intangible human elements like credibility and complex reasoning; need for AI to resolve novel issues; restrictive regulatory schemes hindering AI innovation in ADR; limited availability of comprehensive arbitration data due to confidentiality. | Acquiring sufficient and representative data for training AI in ADR (especially confidential arbitration awards); replicating nuanced human judgment and credibility assessment; overcoming regulatory and ethical hurdles (privacy, confidentiality, liability); ensuring fairness and transparency of AI-driven decisions. | Privacy and confidentiality breaches from data mining; product liability for AI-induced errors or harm; potential for ingrained bias in AI algorithms; over-reliance on AI without understanding its limitations; ethical concerns regarding the lack of human oversight and accountability in decision-making. |
| 16CaseWResJLTechInternet1.pdf | HeinOnline | Sam Altman, OpenAI, and the Importance of Corporate Governance | This paper examines the corporate governance crisis at OpenAI, focusing on the sudden firing of CEO Sam Altman and the subsequent turmoil, analyzing the company's unique structure and the influence of 'effective altruism'. It emphasizes the critical need for professional corporate governance in organizations developing powerful AI technologies with profound societal impacts. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Reliability issues such as AI 'hallucinations' creating non-existent legal citations, and the general need for caution and humility in deploying AI for legal assistance. | Improved corporate governance of AI development companies to ensure responsible development and deployment of AI; exercising caution and humility when using AI for legal applications. | Providing legal information and assistance for basic questions, document templates, and court form completion for those who cannot afford lawyers. | Individuals who cannot afford a lawyer. | General legal assistance, Contract law, Intellectual property law. | United States (focus on OpenAI, Delaware corporate law, US legal cases), European Union (AI Act), United Kingdom (CMA scrutiny). | NaN | NaN | NaN | False | False | NaN | Technical gaps in AI reliability (e.g., 'hallucinations'). Societal gaps include establishing effective AI regulation, combating bias, addressing job displacement, ensuring broadly distributed benefits of AI, and aligning powerful AI development with human values and safety through robust corporate governance. | Balancing a non-profit mission with the capital requirements for AI research; managing internal disagreements on AI safety and development speed; establishing effective and experienced corporate governance for a company developing high-stakes AI technology; navigating the influence of philosophical movements like 'effective altruism' on corporate strategy and safety prioritization. | Misuse of AI, drastic accidents, societal disruption (including job displacement), spread of misinformation and deepfakes, national security threats (e.g., AI-powered espionage, intellectual property theft), existential risks from AGI, AI 'hallucinations' leading to false information, and economic disruptions. |
| 72JLegalEduc577.pdf | HeinOnline | No "Robot Lawyers" Just Yet: The Role of Continuing Legal Education in Fulfilling the Duty of Technological Competence | This paper argues that despite a widely adopted ethical duty of technological competence, many lawyers lack proficiency, citing issues with e-filing, social media, and AI misuse. It advocates for more states to mandate technology-focused Continuing Legal Education (CLE) to protect client interests and uphold justice system integrity, reviewing current adoption statuses. | True | Idealistic | False | 2.0 | Positive | Mandatory technology-focused Continuing Legal Education (CLE) for lawyers. | The paper reviews the adoption, specific requirements, and reception of mandatory technology CLE in various US jurisdictions that have implemented it (e.g., Florida, North Carolina, New York, California, US Virgin Islands) and the failure of such proposals in others (e.g., Pennsylvania, Maryland). | Four U.S. states (Florida, North Carolina, New York, California) and the U.S. Virgin Islands have mandated technology CLE. Requirements vary, with Florida and North Carolina adopting broader technology training, New York focusing on cybersecurity, and California adding a general technology hour. Florida's initiative reportedly received favorable reactions. | Lawyers' lack of technological competence, resistance or indifference towards technology training, and the insufficient number of jurisdictions mandating technology-related CLE, which can lead to misuse of technologies like AI, thereby undermining competent representation and access to justice. | Widespread adoption by state bar associations and supreme courts of mandatory Continuing Legal Education (CLE) requirements specifically focused on technology, including areas like AI, cybersecurity, and e-discovery. | Lawyer competence in technology, professional responsibility, ethical use of technology, Continuing Legal Education (CLE) reform, cybersecurity for law firms, impact of AI on legal practice. | The general public and clients of legal services. | Professional responsibility, legal ethics, legal practice management, legal education. | United States (referencing ABA Model Rules, federal courts, and specific states including Florida, North Carolina, New York, California, Pennsylvania, Maine, Maryland, Texas, Colorado, Delaware, New Hampshire), US Virgin Islands. | NaN | NaN | Adoption of mandatory CLE rules by state supreme courts or bar associations. | False | False | NaN | A significant discrepancy exists between the widespread adoption of an ethical duty of technological competence for lawyers (40 states) and the very limited number of states actually mandating technology-specific CLE. There's also a gap in lawyers' understanding and preparedness for emerging technologies like generative AI. | Lawyer attitudes viewing CLE as a burden, cost and time constraints related to CLE, resistance from some segments of the legal profession to mandating specific CLE topics like technology, and the difficulty of keeping CLE content current with rapidly evolving technology. | Technological incompetence among lawyers leading to malpractice, client data breaches, inadvertent disclosure of confidential information, ethical violations (e.g., filing AI-hallucinated citations), sanctions, ineffective assistance of counsel, and ultimately, damage to public confidence in the justice system. |
| 2025AccesstoJustEEur241.pdf | HeinOnline | INNOVATIONS OF ARTIFICIAL INTELLIGENCE IN LIGHT OF THE APPLICABLE COPYRIGHT LAW: REALISTIC SOLUTIONS AND FUTURE PROSPECTS. A COMPARATIVE STUDY OF UAE, EGYPTIAN, AND FRENCH LAWS | This paper analyzes how current copyright laws in the UAE, Egypt, and France address AI-generated innovations, highlighting challenges in defining authorship. It argues for urgent legal reforms to create a framework that balances innovation promotion with the protection of rights, ensuring ethical and legally recognized AI development. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Ambiguity in defining 'author' for AI-generated content under existing copyright laws, as AI lacks human personal characteristics; inadequacy of current legal frameworks to address the novel challenges posed by AI innovations; the lack of legal personality for AI systems, complicating attributions of rights. | Reviewing and amending existing copyright laws to specifically address AI-generated innovations; developing a comprehensive legal framework that balances promoting AI innovation with protecting legal rights and ethical considerations; establishing a Code of Ethics for AI systems to guide their development and use responsibly. | Clarification of authorship and ownership rights for AI-generated creative works; establishment of fair and ethical legal frameworks for AI in the creative industries; ensuring legal certainty for creators, users, and developers in the context of AI and copyright. | NaN | Copyright Law; Intellectual Property Law | UAE, Egyptian, and French laws | NaN | NaN | NaN | False | False | NaN | Absence of a specific legal framework tailored to AI-generated intellectual property; lack of clear provisions for attributing legal personality or a special legal status to AI; need for harmonized ethical guidelines and codes of conduct for AI development and use in creative sectors. | NaN | Legal uncertainty and increased litigation due to unadapted copyright laws; potential for infringement on existing copyrights by AI systems using protected works as training data or generating similar outputs; ethical concerns regarding AI replacing human creators or devaluing human creativity if not properly regulated. |
| 20NwJTechIntellProp309.pdf | HeinOnline | LAW INFORMS CODE: A LEGAL INFORMATICS APPROACH TO ALIGNING ARTIFICIAL INTELLIGENCE WITH HUMANS | This paper proposes a research agenda, "Law Informs Code," advocating for the use of legal processes, concepts, and data to improve the alignment of Artificial Intelligence (AI) with human goals and societal values. It argues that law offers a legitimate, scalable, and democratically endorsed framework for specifying objectives to AI, thereby enhancing AI safety and utility. | True | Idealistic | True | 1.0 | Positive | Law Informs Code: A legal informatics approach using legal theory, processes, data (e.g., contracts, standards, public law), and reasoning to align AI with human and societal values. | NaN | NaN | The fundamental difficulty in specifying complex human goals and societal values (like those inherent in justice) to AI systems, leading to AI that may act unaligned with these values and lack legitimate grounding for its understanding of societal preferences. | Utilizing law (its theory, processes, data, and reasoning methods) as a legitimate and scalable framework to specify human intentions and democratically endorsed societal values to AI systems, thereby improving AI alignment. | NaN | NaN | General (contracts, public law, fiduciary law, statutory interpretation, securities law, tax law, etc.) | U.S. law (with aspiration for global applicability) | Proposed use of publicly available and potentially proprietary legal texts (constitutional, statutory, administrative, case law, contracts), legal training materials, rule-based systems, and expert feedback. Data is largely unstructured or semi-structured legal language. | NaN | NaN | False | False | NaN | Remaining gaps include: determining how law can guide AI's proactive positive goals (not just prohibitions); systematically accounting for historical injustices and biases in legal data; scaling the approach globally; understanding AI's 'intention' for legal purposes; addressing issues of law's representativeness, AI truthfulness, and loophole exploitation; and improving NLP for long legal documents. | Integrating complex and often ambiguous legal concepts and reasoning into computational AI models. Sourcing, curating, and processing vast amounts of diverse legal data while addressing biases. Developing robust methods for AI to generalize legal understanding to novel situations. Creating effective benchmarks to validate AI's legal comprehension and alignment. | Potential for AI to misinterpret complex legal directives or exploit loopholes. Risk of embedding historical biases or unjust aspects present in legal data into AI systems. Challenges in ensuring AI adapts to evolving legal norms and societal values, or that democratically produced law adequately reflects these values. |
| 30AIL561.pdf | HeinOnline | Thirty years of artificial intelligence and law: the third decade | This paper reviews eight significant papers from the Artificial Intelligence and Law journal's third decade (2012-2022), highlighting the field's major shift towards Machine Learning and Natural Language Processing techniques. It covers applications like document management, legal text analysis, outcome prediction, and detection of unfair contract clauses, discussing both advancements and challenges. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Key obstacles to A2J mentioned include: the difficulty for laypeople to understand legal texts and identify issues like unfair contract terms; the inherent complexity and volume of legal information hindering accessibility; the 'black box' nature of AI models which can impede trust and accountability; and the scarcity of high-quality, annotated legal data needed to train effective AI tools, especially for diverse legal areas and languages. | Proposed solutions to A2J obstacles include: developing AI tools for automatic analysis and retrieval of legal information (e.g., semantic parsing, unfair clause detection for consumers); employing explainable AI (XAI) methods to make system reasoning transparent and build trust; creating and sharing annotated legal datasets to foster research and tool development; and adapting advanced NLP models (like transformers) for specific legal tasks to improve information access and understanding. | Consumer protection (e.g., identifying unfair contract terms); access to and understanding of legal information (e.g., statutory provisions, ECHR case law); ensuring fairness and accountability in automated legal processes. | Consumers (e.g., understanding terms of service); general public/addressees of regulations; individuals interacting with human rights courts. | Statutory Law, EU Consumer Protection Law, Patent Law, Japanese Pension and Civil Law, Human Rights Law (ECHR), WIPO Domain Name Dispute Resolution (Intellectual Property), Italian Civil Code, Contract Law, GDPR. | EU, Italy, Japan, ECHR (Council of Europe), WIPO (International arbitration). | NaN | NaN | NaN | False | False | NaN | Remaining gaps for A2J include: need for more robust and legally meaningful explainability in AI systems; challenges in ensuring AI models generalize well across diverse legal factual scenarios and evolving laws; the ongoing difficulty and cost of acquiring and annotating high-quality legal data for training A2J tools, particularly for multilingual contexts; and the risk of AI systems merely learning correlations instead of true legal reasoning, which could undermine fairness. | NaN | Potential risks include: AI systems making predictions or classifications based on spurious correlations rather than sound legal reasoning (e.g., using judge names for ECHR outcome prediction); lack of transparency in AI leading to difficulties in verifying legal soundness and accountability, particularly problematic for A2J applications; AI predictions degrading significantly when applied to new or evolving legal contexts without proper adaptation; and AI tools for A2J failing if they cannot be trusted or understood by their intended users (e.g., consumers). |
| 46HarvJLGender265.pdf | HeinOnline | BISEXUAL ERASURE, MARJORIE ROWLAND, AND THE EVOLUTION OF LGBTQ RIGHTS | This paper re-examines the 1980s employment discrimination case *Rowland v. Mad River Local School District*, arguing for its overlooked significance in LGBTQ legal history, particularly concerning bisexual rights and the impact of Justice Brennan's dissent from certiorari denial. Through original archival research and an interview with Marjorie Rowland, the article highlights systemic bisexual erasure within the legal system and LGBTQ advocacy, calling for the case's recognition and continued efforts for LGBTQ equality. | True | Idealistic | False | 2.0 | NaN | Legal and historical analysis of the *Rowland v. Mad River Local School District* case, Justice Brennan's dissent from certiorari denial, and the critical examination of bisexual erasure within the legal system and LGBTQ rights discourse. | Qualitative research methods: in-person interview with the plaintiff (Marjorie Rowland), original archival research (trial court testimony, pleadings, court documents), analysis of judicial opinions and dissents, review of secondary sources (legal scholarship, casebooks, news articles), and application of critical legal theories (feminist storytelling, critical race theory principles). | The paper concludes that *Rowland v. Mad River Local School District* is an underappreciated but significant case for LGBTQ rights, crucial for understanding bisexual erasure in law. It argues Justice Brennan's dissent offered a progressive legal framework, critiques the Sixth Circuit's flawed reasoning, and underscores ongoing needs for LGBTQ+ equality and recognition of bisexual experiences. | Bisexual erasure in legal and historical narratives; judicial prejudice and flawed legal interpretations of LGBTQ rights; societal discrimination and stigma against bisexual individuals; retaliation against those asserting LGBTQ rights; assimilationist pressures within LGBTQ advocacy marginalizing bisexual concerns; lack of robust and consistently applied legal protections for sexual orientation. | Historical reclamation by recognizing the significance of cases like *Rowland* and figures like Marjorie Rowland; critical legal analysis to expose and rectify flawed judicial reasoning; promoting inclusive narratives that acknowledge bisexual identities and challenges; continued legal advocacy for stronger, explicit legal protections (e.g., strict scrutiny for sexual orientation discrimination); enhancing education on bisexuality and related legal issues in law schools and scholarship. | LGBTQ rights; Bisexual rights and bisexual erasure; Employment discrimination (based on sexual orientation); First Amendment rights (freedom of speech for public employees); Equal Protection Clause (application to sexual orientation); Legal history of LGBTQ rights; Impact of judicial dissents. | Bisexual individuals; LGBTQ community; Public school teachers and counselors. | Constitutional Law (First Amendment, Equal Protection); Employment Discrimination Law; Civil Rights Law; LGBTQ+ Law; Legal History. | United States (primarily federal courts: S.D. Ohio, Sixth Circuit, U.S. Supreme Court certiorari denial); Ohio (state law context for teacher employment). | NaN | Legal analysis (of case law, statutes, legal arguments); Historical research (original archival research, in-person interview); Critical legal theory (feminist storytelling, critical race theory principles); Case study method. | NaN | False | False | NaN | Societal: Persistent bisexual erasure, prejudice, and discrimination; ongoing attacks on freedom of expression in education. Legal: Lack of consistent strict scrutiny for sexual orientation discrimination; vulnerability of current LGBTQ protections; need for explicit comprehensive federal anti-discrimination laws; procedural barriers for plaintiffs. Scholarly: Insufficient attention to bisexual legal issues and cases like *Rowland* in legal scholarship and education. | NaN | Continued discrimination and denial of rights for LGBTQ individuals, especially bisexuals, due to legal and societal erasure; regression in LGBTQ rights from conservative judicial or legislative actions; harm to LGBTQ individuals (especially youth) from discriminatory school policies; erosion of free speech in educational contexts; increased barriers to justice for marginalized plaintiffs. |
| 6Issue2IntlJLMgmtHuman.pdf | HeinOnline | Impact of Artificial Intelligence (AI) on Legal Profession and Justice System | The paper discusses the impact of AI on the legal profession and justice system, exploring its potential benefits like increased efficiency and access to justice, alongside drawbacks such as job displacement and algorithmic bias. It emphasizes the need for legal professionals' education in AI, ethical considerations, robust accountability mechanisms, and a comprehensive regulatory framework. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Case backlogs causing justice delays; AI bias leading to unfair or discriminatory outcomes; Lack of AI transparency and explainability (black box problem); Digital divide hindering equitable access to AI-powered justice; Erosion of human discretion and essential human-centric justice principles. | Utilizing AI to improve efficiency in legal processes and reduce case backlogs; Developing AI-powered Online Dispute Resolution (ODR) and free legal aid portals; Establishing comprehensive regulatory frameworks and strong ethical guidelines for AI in law; Emphasizing human oversight and Human-AI collaboration models; Mandating education and training for legal professionals and judges on AI. | Reducing court backlogs and justice delays; Providing legal aid and advice to underserved populations; Enhancing procedural efficiency in the justice system; Online Dispute Resolution (ODR); Ensuring fairness and mitigating bias in AI-driven justice tools. | People who cannot afford to hire lawyers; Vulnerable populations susceptible to algorithmic bias; General public seeking access to justice. | General (Civil Law, Criminal Law), Contract Law, Human Rights Law, Motor Accident Claims, Family Law, Tax Law, Electoral Law, Medical Negligence. | International (with specific examples and focus on India, USA, UK, EU, China, Brazil, Estonia, Malaysia, Kenya). | The paper discusses various AI systems that use diverse training data, including legal documents (e.g., case law, contracts), judicial decisions, criminal records, questionnaire responses, medical records, and game data. Concerns are raised about biases in legacy datasets. | NaN | NaN | False | False | NaN | Lack of comprehensive regulatory frameworks for AI in the legal sector; Inadequate methods to ensure AI transparency, explainability, and accountability; Persistence of AI bias and the challenge of achieving true fairness; Insufficient training and understanding of AI among legal professionals and judges; Bridging the digital divide for equitable access to AI justice tools; Difficulty in assigning liability for AI errors. | NaN | Job displacement for legal professionals; Algorithmic bias leading to discriminatory outcomes; Lack of transparency and explainability in AI decisions impacting due process; Erosion of human discretion and judicial independence; Creation and misuse of deepfakes and manipulated information; Automation bias leading to over-reliance on AI; Privacy violations from extensive data processing; Cybersecurity threats to legal AI systems; Diminished public trust in the justice system. |
| 13Laws1 (2).pdf | HeinOnline | The Legal Challenges of Realistic and AI-Driven Child Sexual Abuse Material: Regulatory and Enforcement Perspectives in Europe | This paper reviews current European legislative measures for combating online Child Sexual Abuse Material (CSAM), with a particular focus on the challenges posed by AI-driven CSAM. It systematically evaluates the effectiveness and applicability of these regulations in addressing virtual CSAM and concludes with policy recommendations for identified gaps. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Inconsistent criminalization of AI-driven CSAM (virtual depictions) across EU member states due to an exception in Directive 2011/93/EU; Difficulties in cross-border cooperation due to varying legal definitions, data retention laws, and jurisdictional complexities; Balancing online safety measures (like content monitoring and detection orders) with fundamental rights to privacy and data protection; Lack of resources and varying infrastructure in member states for effective implementation and enforcement of regulations; Rapid technological advancements (like AI-generated content) outpacing legal frameworks. | Standardize the definition and scope of online CSAM to uniformly criminalize realistic and AI-generated/manipulated CSAM across EU member states; Enhance cross-sector and international cooperation (e.g., between law enforcement, ISPs, AI developers, and global alliances); Require online platforms to implement efficient age verification systems and robust community guidelines/support; Ensure law enforcement agencies continuously update skills and tools, with support from EU and international institutions; Standardize data retention laws to facilitate digital evidence acquisition; Develop and harmonize cybercrime laws globally. | Child protection against sexual abuse and exploitation; Regulation of AI-generated harmful content (specifically AI-driven CSAM); Online safety for children; Legal and enforcement challenges related to virtual CSAM and deepfakes; Cross-border cooperation in combating cybercrime. | Children (as victims or potential victims of traditional and AI-driven Child Sexual Abuse Material). | Criminal Law; Cybercrime Law; EU Law; International Law; Human Rights Law (Children's Rights). | Europe; European Union | NaN | NaN | NaN | False | False | NaN | Discrepancies in criminalizing realistic/virtual CSAM among EU member states due to Directive 2011/93/EU allowing exceptions; Insufficient legal frameworks specifically addressing AI-driven CSAM and deepfakes; Lack of common standards for admissibility of evidence gathered through ISP surveillance in the EU; The European Directive 2011/93/EU needs updates to cover all technological issues and reconcile fundamental rights with combating child sexual abuse. | Balancing online safety requirements (e.g., ISP monitoring for CSAM) with citizens' privacy and freedom of expression, especially concerning end-to-end encryption; Inconsistent implementation and enforcement of EU directives across member states due to differing resources, infrastructure, and legal interpretations; Rapid technological evolution of AI-driven CSAM outpacing legislative updates; Difficulties in obtaining digital evidence and ensuring effective cross-border cooperation due to jurisdictional issues and varying national laws (e.g., data retention); High error rates and potential for circumvention of AI tools used for detecting CSAM and grooming. | Revictimization of former victims and new victimization through AI-driven CSAM (deepfakes, AI-generated content); Increased burden on law enforcement, hindering identification of real child victims; Normalization and desensitization to CSAM, potentially increasing risk of contact offenses; Psychological harm to children depicted in CSAM; Chilling effect on online communication and freedom of expression due to broad surveillance measures; Misuse of AI for mass surveillance and erosion of privacy rights; Overload of law enforcement with false positives from automated detection tools; Undue criminalization and stigmatization of minors exchanging consensual self-generated content if flagged by automated systems. |
| 4AmicusCuriae685.pdf | HeinOnline | PUTTING THE ARTIFICIAL INTELLIGENCE IN ALTERNATIVE DISPUTE RESOLUTION: How Al RULES WILL BECOME ADR RULES | This article argues that the evolving regulatory and governance landscape for artificial intelligence (AI) will significantly influence alternative dispute resolution (ADR), as AI becomes increasingly integrated into ADR processes. Appropriate AI regulations, sharing goals like trustworthiness and fairness with ADR, are expected to benefit the field by addressing existing challenges and enhancing accountability. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Difficulty training AI for complex disputes due to nuanced legal interpretation and lack of representative datasets (due to ADR confidentiality); AI's limitations in handling social/emotional aspects, novel analysis, and interpretation; concerns about AI accuracy, bias, fairness; lack of transparency and explainability in "black box" systems, undermining due process rights. | The paper suggests that emerging AI regulations and governance frameworks (e.g., EU AI Act, NIST RMF, ABA ODR principles) promoting trustworthiness, fairness, transparency, explainability, and accountability will address these obstacles by setting standards for AI used in ADR. It posits that these AI rules will effectively become ADR rules, potentially leading to higher standards for AIDR systems than currently exist for human neutrals. | Improving efficiency, affordability, and reliability of dispute resolution; enhancing access to justice for self-represented litigants and underrepresented parties; ensuring fairness, transparency, and accountability in ADR processes. | Self-represented litigants, underrepresented parties. | Alternative Dispute Resolution (ADR), Online Dispute Resolution (ODR), International Commercial Arbitration, Product Liability, Civil Law, Commercial Law, Administrative Law. Mentions tort, property, insurance, family law as examples. | European Union, United Kingdom, United States. Also mentions UNCITRAL (International), Canada, Colombia. | NaN | NaN | NaN | False | False | NaN | The implementation gap between proposed/predicted AI technologies in ADR and those actually realized; lack of universally agreed-upon and enforceable ADR governance and AI regulatory frameworks; potential for current ADR rules to not fully address AI-specific issues like systemic bias or algorithmic explainability. | Training AI for complex legal disputes with limited and confidential data; ensuring AI systems can handle nuanced interpretation, social, and emotional aspects of disputes; addressing AI accuracy, bias, and fairness; achieving transparency and explainability in AI decision-making; integrating AI into existing ADR frameworks without undermining due process. | AI systems causing discrimination (e.g., racial bias in decision-making); liability for AI-generated harms (physical injury, property damage, data loss, privacy breach); undermining individuals' right to a reasoned decision, appeal, and due process due to opaque AI systems; systemic advantages for technologically adept parties; ADR practitioners' liability for using flawed AI systems. |
| 108Judicature42.pdf | HeinOnline | How to Harness AI for Justice | This paper explores how generative AI can enhance access to justice for self-represented litigants by automating legal tasks, democratizing information, and improving court processes. It also outlines significant risks, such as bias and inaccuracies, proposing careful implementation through best practices like diverse data, human oversight, and rigorous evaluation. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Complexity and impenetrability of the legal system; high cost of legal representation resulting in widespread self-representation; existing biases and discrimination within the justice system; barriers to technology adoption by self-represented litigants. | Leveraging generative AI to provide accessible legal information, automate routine legal tasks, facilitate online dispute resolution, and simplify legal procedures; implementing AI tools responsibly by adhering to best practices (diverse data, human oversight, impact assessments, transparency); adopting rigorous evaluation methodologies (e.g., RCTs, pilot programs) for AI innovations. | Assisting self-represented litigants; Online Dispute Resolution (ODR); legal information provision and document generation; litigation avoidance and conflict prevention; simplification of legal rules and procedures; improving court efficiency and user experience; reducing bias in legal decisions; procedural fairness including translation. | Self-represented litigants, individuals unable to afford legal representation, racial and ethnic minorities, non-English speakers. | Civil Law (including family law, consumer debt, landlord-tenant/eviction), Administrative Law (unemployment benefits). | United States | NaN | NaN | NaN | False | False | NaN | Overcoming training data limitations to ensure AI serves diverse populations equitably; completely eliminating AI 'hallucinations'; achieving full transparency in proprietary AI decision-making processes; insufficient evidence base for many legal practices and reluctance to adopt experimental evaluation methods. | Mitigating exposure bias from unrepresentative training data; managing AI 'hallucinations' (false information/citations); ensuring transparency for due process; high cost of advanced, less error-prone AI models for A2J applications; overcoming institutional inertia among legal professionals. | Incorrect AI guidance leading to adverse legal outcomes (e.g., default judgments); generation of harmful or inappropriate advice by AI; submission of fabricated information or false legal citations to courts; compromised due process rights due to opaque AI decision-making; exacerbation of societal inequities through biased AI tools. |
| 19OhioStTechLJ171.pdf | HeinOnline | THE SUBJECTS AND STAGES OF Al DATASET DEVELOPMENT: A FRAMEWORK FOR DATASET ACCOUNTABILITY | This paper examines the development process of large-scale AI datasets (LSLDs and LSCVDs), outlining the stages involved and the subjects affected, to identify pertinent legal issues such as copyright and privacy. It proposes a comprehensive framework, including a matrix of harms, to foster dataset accountability and mitigate adverse impacts from these datasets and the AI models trained on them. | True | Idealistic | True | 1.0 | Positive | A framework for dataset accountability, including taxonomies of dataset development stages (Problem Formulation, Data Collection, Data Cleaning, Data Annotation, Model Training and Evaluation, Model Implementation and Inference, Data and Representation Distribution) and dataset development subjects (data subjects, data annotators, copyright holders, model subjects), and a matrix mapping harms to these stages and subjects. | NaN | NaN | Opacity in dataset development processes; legal uncertainties regarding copyright and privacy for AI datasets; prevalence of biased, discriminatory, or otherwise harmful datasets impacting marginalized groups; difficulty in assigning responsibility for AI-driven harms; lack of meaningful consent and awareness from data subjects; perpetuation of harms through widely distributed datasets and pre-trained models. | Proposing a framework for dataset accountability (identifying stages, subjects, and potential harms); advocating for enhanced transparency and documentation in dataset development (e.g., datasheets); calling for recalibration of legal norms (copyright, privacy, due process) in the context of AI datasets; suggesting incorporation of accountability principles into legislative and regulatory measures. | Algorithmic bias and discrimination; privacy violations in data collection and use; copyright infringement in AI datasets; accountability for AI-driven harms; due process in automated decision-making systems; systemic informational harms. | Marginalized social and economic groups; racial, ethnic, gender, and religious minorities; disabled individuals; refugees and migrants; individuals in the Global South. | Copyright Law, Privacy Law, Constitutional Law (Due Process, Equal Protection), AI Law and Regulation. | United States (primarily, with discussion of US legal doctrines like fair use, FTC, proposed US legislation), with references to international data sources and issues. | NaN | Literature review (law, computer science, social sciences); case study analysis of existing AI datasets (e.g., ImageNet, LFW, Common Crawl, The Pile); legal analysis; conceptual framework and matrix development. | Publication in an academic journal intended for adoption by researchers, policymakers, legal practitioners, and industry leaders to inform dataset governance and accountability practices. | True | False | The conceptual framework and matrix are detailed within the published paper. Access to the paper itself (e.g., via HeinOnline) may require a subscription. | Lack of comprehensive legal and regulatory frameworks specifically addressing the lifecycle of AI dataset development; insufficient transparency and standardized documentation for datasets; challenges in applying existing legal doctrines (e.g., copyright, privacy) to novel harms engendered by AI datasets; need for effective individual and systemic accountability mechanisms and means of redress for data subjects and model subjects; limited understanding and conceptualization of novel informational harms. | The inherent complexity, opacity, and often poor documentation of current AI dataset development practices; the need to integrate multifaceted legal, ethical, and technical considerations; addressing the rapidly evolving nature of AI technologies and data practices when proposing a stable framework. | Wrongful accusations and arrests from biased AI systems (e.g., facial recognition); discrimination, stereotyping, and reinforcement of societal biases; significant privacy violations through data scraping, aggregation, and leakage of personally identifiable information; use of datasets for pervasive surveillance; reintroduction of security vulnerabilities via code generation models; copyright infringement and complex licensing conflicts; creation of offensive or harmful content by generative models; difficulty in retracting harmful datasets anAI models once distributed. |
| 14StMarysJonLegalMalpract.pdf | HeinOnline | Artificial Intelligence and Legal Malpractice Liability | This paper explores the anticipated rise in legal malpractice claims as AI becomes more prevalent in legal services. It analyzes how existing legal malpractice doctrines and ethical obligations, such as competence, loyalty, and informed consent, will apply to lawyers' use of AI. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | Algorithmic bias, lack of transparency, potential for errors and misuse in AI systems, rapid pace of AI development outpacing cultural absorption, and privacy concerns. AI systems may negatively impact individuals if not properly managed, particularly in areas like benefit denials or legal status determinations. | Adherence to principles like those in the 'Blueprint for an AI Bill of Rights' (safe and effective systems, algorithmic discrimination protections, data privacy, notice and explanation, human alternatives). Promoting human authority, oversight, control, accountability, and transparency in AI use. Ensuring lawyers obtain informed consent for AI use and firms proactively manage AI-related risks. | Affordable legal advice, self-help legal marketplace. | Ordinary persons unable to afford traditional legal services, users of self-help legal tools. | Legal Malpractice, Professional Ethics, General Civil Litigation, Contract Law | United States (with specific references to Texas and New York state law and New York City regulations) | NaN | NaN | NaN | True | True | The paper mentions that the law firm BNH.AI is launching 'Microwave,' a 'publicly available-and free-tool that tests AI risks' (p. 78). | Societal capacity to safely absorb rapidly advancing AI technology. Lack of widespread AI literacy and formal education/training on AI for legal professionals. Technical gaps in AI related to capturing human knowledge flexibility, ensuring unbiased outputs, and maintaining transparency. | AI systems acting in unanticipated ways, inherent algorithmic bias and lack of transparency, the rapidly evolving nature of AI technology, difficulty in distinguishing fact from fiction in AI outputs, potential for AI to generate biased language, ensuring data privacy and security, maintaining necessary human oversight and judgment, and navigating intellectual property issues with AI-generated content. | Economic or other harm to clients from AI errors or misuse; increased legal malpractice liability for lawyers and firms; algorithmic bias leading to discrimination in legal and administrative decisions; lack of transparency hindering accountability; breaches of data privacy and client confidentiality; spread of misinformation and 'cyber propaganda' via AI; AI systems making factual errors; cybersecurity vulnerabilities in AI systems; potential for misuse in creating fake documents or for phishing attacks; infringement of intellectual property rights. |
| 12ResolvedJAlternativeDis.pdf | HeinOnline | Ai: INCREASING ALTERNATIVES IN ALTERNATIVE DISPUTE RESOLUTION | This paper examines the application of Artificial Intelligence (AI) in Alternative Dispute Resolution (ADR), explaining AI mechanisms and their use in resolving disputes. It argues that AI can expand access to justice, lower costs, and increase efficiency in ADR, despite challenges such as bias and the need for human empathy. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | High cost of legal services, court congestion and delays, physical and geographical barriers to accessing courts, power imbalances in disputes (e.g., domestic violence), and the complexity of the legal system for self-represented litigants. | Utilizing AI in ADR (AIDR) to reduce costs, increase efficiency, alleviate court congestion, enable remote dispute resolution, lessen power imbalances, and support self-represented litigants through user-centric ODR platforms. The paper also suggests mandatory AIDR education in law schools and updating laws for new technologies. | Affordability of legal services, efficiency of the justice system (reducing backlogs/delays), accessibility for remote/constrained individuals, empowerment of vulnerable parties (e.g., domestic violence victims), support for self-represented litigants, Online Dispute Resolution. | Low-income individuals, self-represented litigants, victims of domestic violence or assault, individuals facing geographic or physical barriers, and disputants with language barriers. | Alternative Dispute Resolution (Arbitration, Mediation, Online Dispute Resolution), Family Law, Small Claims, Contract Law, Immigration Law, Tax Law, Civil Litigation. | United States | NaN | NaN | NaN | True | True | ChatGPT-4 mentioned as a subscription service; DoNotPay app described as an app providing free remedies; ROSS Intelligence, eBay's ODR, Lexis Nexis Legal Machina also mentioned as existing tools. | Technical gaps include AI's limited emotional intelligence/intuition and the 'black box' problem of AI decision-making. Societal and ethical gaps include algorithmic bias and accountability, data privacy concerns, the digital divide, lack of technological competence among legal professionals, and the need for updated legal and ethical frameworks for AIDR. | The absence of human presence (empathy, intuition) in AI-driven ADR. The introduction and perpetuation of bias through AI algorithms and data. Increased risks of professional misconduct related to confidentiality, competence, and reliance on AI. Ensuring data privacy and security. Achieving public and professional trust and acceptance of AI in dispute resolution. | Discriminatory outcomes from biased AI (e.g., mispredictions in criminal justice or immigration). Violations of client confidentiality and privacy through AI data processing. Professional misconduct by lawyers due to incompetent use or over-reliance on AI (e.g., citing non-existent cases). AI providing incorrect legal judgments or flawed advice. Data security breaches (e.g., hacking of LLMs). |
| 36SAcLJ307.pdf | HeinOnline | GENERATIVE ARTIFICIAL INTELLIGENCE The Protection of Personal Data and Countering False Narratives About the Person | This paper discusses the personal data protection and false information concerns arising from Generative AI (Gen AI), particularly in the Singaporean context. It examines current legal frameworks, policy responses, and proposes legal and non-legal measures to govern Gen AI and protect individuals. | True | Idealistic | True | 3.0 | Neutral | Generative AI (Gen AI), including Large Language Models (LLMs) like ChatGPT | NaN | NaN | Threats to personal data privacy, accuracy of personal information, lack of transparency and accountability in Gen AI, and the generation of false narratives about individuals. | Purposive interpretation and adaptation of existing laws (data protection, false information), new governance measures (licensing, reporting, mandatory disclosures), emphasis on transparency (source citation), and education for users and professionals. | Protection of personal data, Countering false narratives about individuals | Individuals generally (data subjects, persons subject to false narratives) | Data protection law, Privacy law, Laws against false information (e.g., defamation, POFMA, PHA), AI regulation/governance, Content regulation | Singapore (primary), with comparisons to EU, US, Canada, Australia, and international efforts | General discussion: large datasets, potentially including publicly available and user-provided data; user interaction and feedback. | NaN | NaN | False | False | NaN | Need for more specific and harmonized AI/Gen AI regulations (nationally and internationally), enhanced transparency and accountability mechanisms for Gen AI, and more effective tools to combat AI-generated false narratives and protect personal data. | NaN | Generation of false narratives about individuals, misuse and unauthorized collection, use, or disclosure (CUD) of personal data, lack of transparency and accountability in Gen AI systems, creation of deepfakes, and embedded bias leading to discrimination. |
| 92TennLRev87.pdf | HeinOnline | BEYOND CHATGPT: TRANSFORMING GOVERNMENT WITH AUGMENTED LLMS | This paper explores how generative AI, specifically augmented Large Language Models (LLMs), can enhance government efficiency and equitable access to services, particularly in legal administration like taxation. It discusses methods such as fine-tuning and Retrieval-Augmented Generation (RAG) to improve LLM performance and mitigate risks like bias and inaccuracy, advocating for a collaborative approach to responsible AI adoption in the public sector. | True | Idealistic | True | 3.0 | Positive | Augmented LLMs, specifically fine-tuning (including Reinforcement Learning from Human Feedback - RLHF), Retrieval-Augmented Generation (RAG), and the use of local/open-source LLMs. | NaN | NaN | Bias in AI, inaccuracy and hallucinations, lack of transparency (black box models), security and privacy vulnerabilities, "simplexity" (oversimplification of complex legal matters leading to misunderstanding), and the digital divide hindering universal accessibility to AI tools. | Proper design, careful application, and rigorous oversight of AI systems; using techniques like fine-tuning and RAG to improve accuracy and relevance; developing equity-focused AI tools (e.g., multilingual capabilities, tailored for specific communities/needs); creating tools to support intermediaries (e.g., legal aid, VITA sites) to bridge the digital divide; and improving transparency of automated legal guidance. | Improving access to government services and legal information, facilitating understanding of legal obligations (e.g., tax compliance), enhancing access to benefits (like EITC), supporting pro se litigants in legal processes, addressing misinformation, and overcoming language and literacy barriers in government interactions. | Marginalized communities, lower-income taxpayers, non-native English speakers, pro se litigants, the elderly, individuals in rural areas, persons with disabilities, and those with lower levels of education or digital literacy. | Tax administration (primary case study), administrative law, and peripherally mentions immigration law, patent law, and securities law. | U.S. (focuses on federal agencies like the IRS and mentions state-level initiatives in California, Minnesota, etc.) | General LLMs are trained on vast, diverse internet data, books, and articles. The paper advocates for augmenting these with curated, domain-specific datasets (e.g., legal texts, agency policies, anonymized case data), human feedback data for fine-tuning, and proprietary or public knowledge bases for RAG implementations. | The paper discusses augmenting LLMs through fine-tuning (including Reinforcement Learning from Human Feedback - RLHF) and Retrieval-Augmented Generation (RAG). It also emphasizes a collaborative approach involving subject-matter experts, technical experts, government authorities, and community feedback. | Proposed deployment includes LLM-powered chatbots and voicebots for public interaction, internal tools for government employee training and support, systems for generating educational content (e.g., infographics, simplified explanations), tools for intermediaries assisting underserved communities, and applications to help individuals draft communications with government agencies. | False | False | NaN | Technical gaps include mitigating hallucinations, reducing the cost and complexity of re-training and fine-tuning LLMs, and improving their transparency and interpretability. Societal gaps involve ensuring equitable technology access, building and maintaining public trust, bridging the digital divide, effectively reaching vulnerable populations, and fostering robust collaboration between legal, governmental, and technical experts for ethical AI deployment. | Key challenges include inherent LLM limitations (e.g., hallucinations, bias, reliability, security risks, opacity, cost of development and maintenance) and governmental hurdles such as budget constraints, knowledge deficits regarding AI, lower risk tolerance for new technologies, and the need for careful ethical and regulatory frameworks for public sector AI adoption and use. | Bias perpetuation leading to discriminatory outcomes, dissemination of misinformation or inaccurate legal guidance, privacy breaches and misuse of sensitive data, malicious use for disinformation or fraud, over-reliance on imperfect technology leading to errors, "simplexity" causing misunderstandings of law, and the exacerbation of societal inequities through differential access to technology or flawed AI-driven services. |
| 27SMUSciTechLRev11.pdf | HeinOnline | Algorithmic Adjudication and Constitutional AI - The Promise of a Better AI Decision Making Future? | This paper argues that algorithmic adjudication, where AI makes legal decisions without human intervention, is inevitable. It discusses the challenges of traditional AI perpetuating biases and lacking explainability, and proposes Anthropic's "Constitutional AI" framework as a potentially more explainable, fair, and societally-aligned approach for future AI decision-making systems in law. | True | Idealistic | True | 2.0 | Positive | Constitutional AI (CAI), specifically Anthropic's methodology for training its LLM Claude, which involves using a predefined set of principles (a "constitution") to guide AI behavior during fine-tuning, particularly through Reinforcement Learning from AI Feedback (RLAIF). | The paper describes Anthropic's methodology for Constitutional AI. This involves a supervised learning phase where an LLM critiques and revises its own responses based on a 'constitution,' followed by a reinforcement learning phase (RLAIF) where an AI model evaluates response pairs against the constitution to train a preference model. Evaluation is based on the model's adherence to principles of harmlessness, helpfulness, honesty, and the defined constitution, and its outputs are compared to those from RLHF models. | Constitutional AI models, like Anthropic's Claude, are claimed to produce more explainable results that are better aligned with societal values and the defined 'constitution'. They are suggested to reduce the risk of introducing subjective human biases compared to RLHF, offer a more objective basis for training, and are potentially more efficient and scalable for fine-tuning. | Traditional AI perpetuates existing biases and its decisions can be difficult to explain (opacity). AI systems may lack contextual understanding for nuanced legal cases and may not grasp cultural sensitivities. There's a 'human-AI fairness gap' where people perceive algorithmic decisions as less fair. The legal profession also shows resistance to understanding and adopting new technologies. | The paper proposes using "Constitutional AI" frameworks integrating legal and ethical standards into AI design. It advocates for legal professionals to gain greater understanding of AI, participate in the design, development, and monitoring of algorithmic adjudication systems, and collaborate to establish ethical guidelines. | Algorithmic adjudication, fairness and bias in AI legal decision-making, explainability and transparency of AI in law, accessibility of legal processes (e.g., for small claims, reducing backlogs), maintaining integrity of the legal system and public trust. | General public needing access to dispute resolution, especially for smaller or routine cases, aiming to enhance accessibility and efficiency of the legal process. | General (algorithmic adjudication), with examples and implications for administrative law, civil law (specifically small claims), criminal law (predictive aspects), and alternative dispute resolution (ADR). | United States (primary focus regarding inevitability and implications), with international examples from Estonia, China, England and Wales, and Colombia. The Constitutional AI approach is discussed generally. | For Constitutional AI (Anthropic's Claude): The initial LLM is pre-trained on vast text corpora. The fine-tuning process uses AI-generated data: self-critiques, revisions, and preference labels generated by AI models, guided by a human-defined 'constitution' (inspired by sources like the UN Universal Declaration of Human Rights and ethical AI principles). | For Constitutional AI: Supervised Learning (SL) for initial alignment with the constitution, and Reinforcement Learning from AI Feedback (RLAIF) for further refinement based on AI-generated evaluations against the constitution. This embodies a principle-based design approach. | NaN | True | False | Anthropic's LLM Claude 3, which embodies the Constitutional AI training methodology, is commercially available via API and web interface. | The effectiveness of Constitutional AI depends on the quality and comprehensiveness of its guiding 'constitution.' The application of LLM technology in actual AI decision-making systems is still in its early stages. There's a need for greater involvement of legal professionals in the lifecycle of AI adjudication systems and for continued efforts to build and maintain public trust. | For Constitutional AI: Ensuring the 'constitution' (set of principles) is well-defined, comprehensive, and effectively covers all ethical considerations. The technology is still nascent for complex decision-making systems. General LLM fine-tuning challenges like resource intensity and potential for bias (though CAI aims to mitigate these compared to RLHF) remain relevant contexts. | Perpetuation of biases if the 'constitution' in CAI is not robust or if training data issues persist. Lack of explainability (though CAI aims for improvement). Potential for unfair or unjust outcomes if AI lacks nuanced understanding. Erosion of public trust. Algorithmic deference or automation bias leading to insufficient human oversight. Decisions being technically correct but failing to deliver broader justice. |
| 69SDLRev652.pdf | HeinOnline | REIMAGINING THE SUCCESSFUL ATTORNEY ARCHETYPE | This paper critiques the conventional, often geographically-detached, model of attorney success, arguing it's unsustainable and contributes to issues like rural legal deserts. It proposes lawyers reimagine success by deeply embedding within communities, especially rural ones, to foster personal fulfillment, social wealth, and tangible positive impact, adapting to changes like AI. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Lack of attorneys in rural areas ('legal deserts' and 'greying' rural bar); financial pressures (e.g., student debt) steering lawyers away from public interest or rural practice; distrust of 'outsider' lawyers in close-knit communities; urban-centric legal systems neglecting rural needs. | Reimagining attorney success to prioritize community rootedness, social wealth, and local impact over conventional metrics; encouraging legal professionals to practice in and integrate with rural communities, offering both formal and informal legal support; leveraging technology, including AI, and remote work to enhance rural legal practice and access to justice. | Rural access to justice; Addressing legal deserts; Role of lawyers in community development and well-being; Ethical and impactful use of AI in legal practice for community benefit. | Rural communities; Individuals in rural areas lacking legal representation. | General legal practice | United States | NaN | NaN | NaN | False | False | NaN | Societal: The dominant, unsustainable model of 'successful attorney' de-emphasizing community; insufficient lawyers in rural areas ('legal deserts'); financial barriers (student debt) preventing lawyers from choosing community-focused or rural careers; distrust between communities and 'outsider' professionals; need for greater social imagination to address_complex_problems. Technical/AI: Ethical AI use, bias prevention, and data privacy in legal applications; ensuring AI tools are reliable and avoid 'hallucinations' in legal advice; the digital divide (though acknowledged as narrowing). | NaN | AI-related: Legal malpractice from improper LLM use; exposure of sensitive data to AI; AI bias; 'hallucinations' or errors in AI-generated legal content. Societal: Social poverty, cultural erosion, and loneliness from lack of community connection; negative externalities (environmental, mental health) from relentless pursuit of productivity; exacerbation of wealth inequality. |
| 14JChristianLegalThought1.pdf | HeinOnline | MORE THAN MACHINES: THE ETHICAL AND HUMAN IMPLICATIONS OF GENERATIVE Al ON LAWYERING | This paper examines the ethical challenges generative AI poses for lawyers, including issues of competence, confidentiality, and supervision. It further argues that AI's rise necessitates a renewed focus on uniquely human qualities such as advocacy, empathy, and wisdom, especially for Christian lawyers. | True | Idealistic | True | 3.0 | Neutral | Generative AI (e.g., ChatGPT, LLMs) | NaN | NaN | AI generating "hallucinations" (fabricated content presented as real authority); AI bias perpetuating societal biases; Risks to client confidentiality when inputting information into AI tools; Potential for AI to facilitate the unauthorized practice of law if not properly supervised; Over-reliance on AI diminishing human oversight and professional judgment. | Lawyers should cultivate uniquely human qualities such as advocacy (especially for the vulnerable), empathy (understanding clients as fellow humans), and wisdom (moral and practical judgment, including biblical wisdom for Christian lawyers). Lawyers must adhere to ethical duties when using AI, including competence, diligence, confidentiality, proper supervision, and obtaining client consent where appropriate. | Ethical use of AI in legal practice; The role of human lawyers and their unique attributes (advocacy, empathy, wisdom) in an AI-driven legal landscape; The Christian lawyer's calling to advocacy for the poor, needy, and destitute. | Poor, needy, destitute, those who cannot speak for themselves. | General legal practice | United States | NaN | NaN | NaN | False | False | NaN | Current AI lacks true sentience, genuine empathy, and human-level wisdom; AI's unreliability due to issues like hallucinations and bias; The ongoing challenge of integrating AI into legal practice while upholding ethical responsibilities and preserving essential human elements. | Ensuring lawyer competence and diligence with rapidly evolving AI technology; Maintaining client confidentiality when using AI platforms; Providing proper supervision for AI tools (akin to nonlawyer assistants); Avoiding the unauthorized practice of law through AI; Determining appropriate client communication regarding AI use; Establishing ethical billing practices when AI enhances efficiency without corresponding human effort. | Attorneys facing disciplinary action for submitting AI-generated misinformation (e.g., fabricated case law); Perpetuation of societal biases through biased AI outputs; Inadvertent disclosure of confidential client information via AI tools; Reliance on AI "hallucinations" leading to incorrect legal work; Diminished human oversight and independent professional judgment due to over-reliance on AI; AI tools enabling or assisting in the unauthorized practice of law if used improperly. |
| 92FordhamLRev (4).pdf | HeinOnline | EDUCATING DEAL LAWYERS FOR THE DIGITAL AGE | This essay argues for adapting legal education to prepare deal lawyers for challenges posed by emerging technologies like AI and distributed ledgers, using closing opinions practice as an illustrative framework. It emphasizes that a strong understanding of foundational legal doctrines is crucial for navigating the technical, legal, and ethical issues arising from technology's impact on business law. | True | Market | True | 3.0 | NaN | Using legal opinions practice as a lens to examine the impact of emerging technologies (including AI and distributed ledgers) on deal lawyering and as a pedagogical strategy for legal education. | NaN | NaN | NaN | NaN | NaN | NaN | Business law, Commercial law, Secured Transactions (UCC Article 9), Contract law, Property law, Corporate law (Business Associations), Legal Ethics | United States | NaN | NaN | NaN | False | False | NaN | NaN | Identifying how emerging technologies disrupt doctrinal elements of business law (e.g., contract formation, enforceability, collateral perfection, characterization risk); ensuring lawyers maintain foundational legal skills despite AI tools; addressing ethical considerations regarding technology's impact on transactions and third parties; adapting legal education to equip students for these challenges, such as understanding AI-generated contracts and automated transactions. Articulating ideal baselines for automated contracts. | Potential atrophy of deal lawyers' analytical and drafting skills due to over-reliance on AI. Legal and financial risks from improperly structured or unenforceable deals due to misunderstanding technology's impact (e.g., AI-generated contracts, automated dispositions via DLT). Ethical breaches if lawyers fail to consider broader implications of tech-enabled transactions for stakeholders or market integrity, or if they do not maintain technological competence. Difficulty in establishing legal elements like intent or authorization in automated systems. |
| 2024IntlJLEthicsTech108.pdf | HeinOnline | A VISION FOR DIGITIZING JUDICIAL PROCESSES AND INTEGRATING ARTIFICIAL INTELLIGENCE IN PAKISTAN'S JUDICIARY: ENHANCING EFFICIENCY AND UPHOLDING JUDICIAL INTEGRITY | This paper outlines a vision for digitizing judicial processes and integrating Artificial Intelligence within Pakistan's judiciary to address current challenges like inefficiency and low public trust. It proposes a phased strategic roadmap, inspired by international models such as China's smart courts, while emphasizing ethical considerations and the necessity of human judicial oversight. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Lack of proper implementation of the rule of law, protracted trials, low public confidence in the judiciary, pervasive corruption, limited transparency, inefficiency, high case backlogs, and outdated manual case filing processes. | A phased digital transformation including e-filing, digitization of records, integration of AI for legal research, evidence standards, sentencing aid, and routine case management. This includes establishing a central database, drawing inspiration from international models like China's smart courts, implementing a strategic roadmap, and emphasizing ethical guidelines and human judicial discretion. | Judicial efficiency, transparency, accessibility of justice, public trust in the judiciary, modernization of court procedures, rule of law. | General public in Pakistan / Litigants | General (Civil and Criminal Justice, Family Law, Property Law) | Pakistan | NaN | NaN | NaN | False | False | NaN | Need for public accessibility of legal judgments for machine learning datasets; challenges in creating accurate AI knowledge maps due to legal language complexity and stare decisis; ensuring AI systems do not perpetuate biases; current lack of comprehensive digitization and IT infrastructure in Pakistan's judiciary. | Differing 'mental processes' between AI and humans, potential compromise of judicial independence, AI's reliance on past data, difficulty in AI mimicking human cognition, and the labor-intensive nature of constructing AI knowledge maps. For Pakistan: overcoming systemic judicial issues, transitioning from manual systems, and ensuring nationwide implementation of new technologies. | Unpredictable AI behavior leading to responsibility issues, AI perpetuating biases, undermining human judicial discretion and fairness, compromising judicial independence, misuse of private/confidential data in AI tools, and over-reliance on potentially inaccurate AI-generated information. |
| 60SanDiegoLRev671.pdf | HeinOnline | Protecting the Promise to the Families of Tuskegee: Banning the Use of Persuasive AI in Obtaining Informed Consent for Commercial Drug Trials | This paper calls for a ban on using AI designed to influence human decision-making ("Persuasive AI") for recruiting or enrolling participants in commercial drug trials, arguing it poses a substantial risk to the informed consent process. The author bases this on the technology's potential for undetectable manipulation, its tendency to reproduce societal biases, and the inadequacy of current regulatory measures to prevent harm, particularly to vulnerable populations. | True | Idealistic | False | 3.0 | Negative | Persuasive AI (also referred to as Emotion AI or Affective Computing), which includes AI that analyzes human emotions and behavior to influence decision-making. | NaN | NaN | The primary obstacle is the potential for Persuasive AI to undermine free and voluntary informed consent by manipulating potential research participants, especially those from vulnerable and historically exploited communities. This manipulation is often undetectable, irremediable, and can be compounded by AI's inherent biases and its 'black box' nature, making oversight by ethics committees ineffective. | The paper advocates for an immediate ban on an entire class of AI technology (Persuasive AI) from being used in the recruitment and enrollment process for federally regulated human subject research, particularly commercial drug trials. This is presented as a necessary measure to protect the integrity of informed consent. | Informed consent in biomedical research, protection of human research subjects, prevention of coercion and undue influence, addressing historical injustices in research (e.g., Tuskegee), ensuring autonomy in decision-making for vulnerable populations, regulation of AI in healthcare. | Vulnerable populations in research, particularly Black adults and other groups historically underrepresented or exploited in clinical trials (drawing parallels to the Tuskegee Syphilis Experiment). | Health Law, Human Subjects Research Law (Common Rule, FDA regulations), Research Ethics, AI Regulation, Bioethics. | Primarily United States (referencing U.S. federal laws like the Common Rule, FDA regulations, and the legacy of Tuskegee). The European Union's AI Act is also discussed extensively as a comparative model. | The paper discusses various AI systems. Examples include AI trained on text-based datasets (e.g., social media, news reports for emotion prediction by DARPA), facial image databases (for facial recognition, noting demographic biases), and data from human interactions with AI systems (e.g., the CSIRO study on manipulating choice). The data sources are varied and can be public or proprietary, structured or unstructured. | NaN | NaN | True | False | The paper states that companies are already marketing AI products to assist in recruiting participants for clinical trials and that Persuasive AI/Emotion AI is already deployed in various commercial settings. | Societal: Lack of U.S. federal regulation specifically targeting Persuasive AI and its manipulative capabilities; the difficulty of ensuring genuine informed consent when AI can influence decisions covertly; the risk of perpetuating historical exploitation of vulnerable communities. Technical: The opacity ('black box' nature) of AI decision-making hinders understanding and oversight; AI's capacity for autonomous development beyond initial programming makes it difficult to control or predict. | NaN | Manipulation of human decision-making, undermining autonomy and free will; coercion and undue influence in the informed consent process for research; perpetuation and amplification of societal biases (especially racial bias); exploitation of vulnerable groups; invasion of privacy; psychological or physical harm due to distorted behavior; inability to detect or remediate harm from AI influence; AI developing beyond its programming; violation of human rights through manipulative AI. |
| 6Issue3IntlJLMgmtHuman.pdf | HeinOnline | X-Raying the Legality of a Robot Lawyer in the Nigerian Courts | This paper analyzes the Nigerian legal system to determine if a robot lawyer could legally operate in its courts, concluding that current laws prevent this. It highlights the significant legal and professional reforms required for any future accommodation of AI in Nigerian legal practice. | True | Idealistic | True | 3.0 | Neutral | Robot lawyer / AI-powered legal assistance tools (e.g., chatbots, DoNotPay, ChatGPT) | NaN | NaN | Existing Nigerian laws requiring lawyers to be human citizens, hold specific qualifications, be called to the bar, and be enrolled; lack of legal personality for robots; resistance from the legal profession; and Nigeria's technological/economic limitations. | Comprehensive legislative reform to amend the Legal Practitioners Act, Rules of Professional Conduct, and other relevant laws to define and accommodate robot lawyers; development of a legal framework for their co-existence with human lawyers. | Affordable legal services, access to legal advice and representation in minor civil matters (e.g., traffic tickets, consumer rights disputes). | General public needing cheaper legal services, particularly for everyday legal problems or small claims. | General legal practice, Professional regulation, Civil litigation (minor disputes), Consumer rights, Evidence law | Nigeria | NaN | NaN | NaN | False | False | NaN | Lack of a specific legal framework for AI in legal practice; absence of provisions for non-human legal practitioners in existing statutes; insufficient adaptation of evidence laws for advanced AI; and a general lag in legal system modernization to accommodate technological advancements. | Satisfying legal requirements for being a lawyer (citizenship, education, bar admission, good character, practicing fees, continuous development, dress code); defining the legal status of a robot (juristic personality); overcoming resistance from the established legal profession; updating evidence laws; and the country's technological and economic readiness. | Displacement of human lawyers; unauthorized practice of law by AI leading to legal sanctions; potential for AI to provide inadequate or incorrect legal advice; erosion of the integrity/dignity of the legal profession if unregulated AI participates in legal processes. |
| 99IndLJSupp37.pdf | HeinOnline | Framing Online Speech Governance as an Algorithmic Accountability Issue | The paper argues for a regulatory approach to online speech governance that focuses on the AI tools used for both content moderation and generation, framing it as an algorithmic accountability issue. It highlights the shortcomings of current legal frameworks and advocates for a systems-level approach to examine the development and deployment of these AI tools, considering their technical and normative features. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Error-prone AI tools, lack of transparency and accountability in AI development and deployment, biases in AI leading to unfair outcomes and censorship, inadequacy of current legal frameworks to govern AI, and power imbalances favoring platforms over users. | Adopting a systems-level regulatory approach centered on algorithmic accountability, including measures like mandatory documentation (datasheets), Algorithmic Impact Assessments (AIAs) for AI tools, increased transparency in development processes, and stronger legal frameworks for AI governance. | Algorithmic accountability in online speech governance, fairness in content moderation and generation, protection of freedom of expression, and mitigation of AI-driven harms like censorship and disinformation. | Users whose speech is erroneously moderated or censored, marginalized groups disproportionately affected by AI biases (e.g., ethnic/religious minorities, speakers of non-dominant languages), and populations in global south countries affected by platform failures (e.g., Myanmar). | Internet Law (including CDA Section 230, DMCA), Constitutional Law (Freedom of Speech, Due Process), Copyright Law, AI Law/Regulation, Human Rights Law. | Primarily United States (discussing CDA, DMCA, Gonzalez v. Google, proposed Algorithmic Accountability Act), with references to global impacts and international contexts (e.g., Myanmar, India, non-English content moderation). | NaN | NaN | NaN | False | False | NaN | Significant regulatory gaps in holding AI tools accountable, lack of transparency in AI development and deployment, insufficient understanding of AI's contextual and linguistic nuances (especially non-English), limitations in creating unbiased and representative datasets, and inadequate mechanisms for user recourse against AI-driven decisions. | NaN | Erroneous censorship of legitimate speech, amplification of misinformation and hate speech, generation of harmful content and disinformation by AI tools, perpetuation of societal biases, copyright infringement by generative AI, and potential misuse of AI or disclosed data by malicious actors including authoritarian regimes. |
| 6Issue6IntlJLMgmtHuman52.pdf | HeinOnline | Unveiling the Impact of ChatGPT on Legal Services | This paper evaluates ChatGPT's potential as a supplementary resource for legal services, highlighting its utility in tasks like legal research, document drafting, and answering basic legal questions. It discusses both the benefits, such as enhanced efficiency, and drawbacks, including inaccuracies, ethical concerns, and limitations in handling complex legal issues. | True | Market | True | 3.0 | Neutral | ChatGPT | NaN | NaN | Lack of readily available and affordable legal information and counsel for the general public seeking to understand their rights or navigate legal issues. | Utilizing AI tools like ChatGPT to provide on-demand basic legal information and preliminary counsel, thereby increasing efficiency and potentially lowering barriers to accessing legal help, always as a supplement to human legal professionals. | Providing simple legal advice, answering basic legal questions, offering legal counsel on demand. | NaN | General legal practice | International | A sizable compilation of open-source material networked before September 2021 and some licensed origin; precise details not public, unknown if legal databases like Lexis Library or Westlaw Edge were included. | Large-scale language modeling based on transformer architecture (GPT-3), trained on a vast corpus of text data using machine learning techniques to predict subsequent text based on preceding context. | Publicly accessible via OpenAI's platform; potential for integration into law firm websites or internal messaging platforms. | True | False | ChatGPT is available through OpenAI's platform, which includes a free access tier. | Need for human oversight and expert integration, as AI alone cannot reliably handle legal complexity, ensure accuracy, avoid bias, or address nuanced ethical considerations, making it unsuitable for standalone use in critical access to justice scenarios. | Ensuring accuracy and avoiding hallucinations (e.g., fake citations by ChatGPT), maintaining data privacy and client confidentiality, addressing ethical responsibilities when using AI-generated content, and dealing with knowledge cut-offs (e.g., ChatGPT's data being pre-September 2021). | Submission of non-existent judicial opinions with fake citations leading to legal sanctions; generation of biased or discriminatory outputs; data privacy violations due to unclear data handling processes of the AI model; over-reliance on the technology leading to incorrect legal assessments or advice. |
| 21NYUJLBus119.pdf | HeinOnline | Don't Kill the Baby! The Case for AI in Arbitration | This paper argues that Generative AI can and should be used as an arbitrator if parties contractually agree, consistent with the Federal Arbitration Act (FAA). It positions arbitration as an ideal starting point for AI adoption in law, emphasizing contractual autonomy and calling for empirical comparison between AI and human arbitration. | True | Idealistic | True | 3.0 | Positive | The use of AI (particularly Generative AI and Large Language Models) as the contractually chosen arbitrator in dispute resolution, leveraging the flexibility of the Federal Arbitration Act (FAA). | The paper does not conduct its own empirical testing of AI as arbitrators. It supports its arguments by referencing existing studies on general AI capabilities, such as a study on deceptive review detection where AI's performance was compared to humans and human-AI teams. | The paper does not present results from its own evaluation of AI arbitrators. It cites a study by Lai et al. where AI alone achieved 86.3% accuracy in deceptive review detection, compared to 54.6% for humans alone and 74% for a combined human-AI team, to illustrate AI's potential. | Resistance to AI adoption in legal contexts due to concerns about bias, discrimination, lack of transparency, absence of human qualities like empathy, job displacement, and overly moralistic views that hinder experimentation and growth. | Upholding contractual autonomy under the Federal Arbitration Act to allow parties to choose AI-driven arbitration; utilizing arbitration as a flexible, contract-based environment for experimenting with AI in the legal field; fostering an open-minded approach and advocating for empirical studies comparing AI and human arbitration. | Dispute resolution (arbitration), cost reduction in legal processes, accessibility of legal services, enhancing subjective fairness in adjudication, contractual autonomy in choosing dispute resolution methods. | Pro se litigants, individuals lacking legal expertise or strong writing skills, elderly and/or disabled individuals facing difficulties with traditional hearings, and generally those seeking more accessible, lower-cost, and faster dispute resolution. | Arbitration, Contract Law, Alternative Dispute Resolution. | USA (due to the central focus on the Federal Arbitration Act - FAA). | The paper discusses Generative AI and LLMs generally, which are trained on vast datasets (e.g., scraped from the internet). It specifically mentions SaulLM-7B, an LLM trained on an English legal corpus of over 30 billion tokens, as an example of relevant AI development. | NaN | NaN | True | False | The paper argues that parties can, by contractual agreement under the FAA, use existing AI tools (like general-purpose LLMs or specialized legal AIs) as arbitrators. | The need for empirical research comparing the performance, fairness, and outcomes of AI arbitration versus human arbitration. Further development and fine-tuning of AI models are needed for specialized tasks in arbitration, ensuring confidentiality and building disputant trust. | Overcoming skepticism and resistance to AI in legal decision-making; addressing ethical concerns such as bias, transparency, and accountability in AI systems; adapting general-purpose AI models for the nuanced and complex requirements of legal arbitration, including handling emotional and ethical subtleties. | Perpetuation of biases present in training data; lack of genuine empathy and emotional understanding; potential for job displacement; ethical concerns regarding consent, manipulation, and privacy; erosion of trust in the legal process due to opaque 'black-box' decision-making; potential for inaccuracies or inappropriate outputs from AI; undermining due process norms. |
| 72DePaulLRev171.pdf | HeinOnline | THE NEW JUDICIAL GOVERNANCE: COURTS, DATA, AND THE FUTURE OF CIVIL JUSTICE | This paper argues that the increasing digitization of the legal system, accelerated by the pandemic, is generating unprecedented amounts of data, positioning courts as central data governors. It explores the challenges and opportunities for courts in their new roles as data users, dispensers, and regulators, emphasizing how these roles will shape the future of civil justice and access to justice. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Pervasive lack of access to legal representation (pro se crisis), high cost of legal services, restrictive regulations on legal service provision, and inequitable access to court data and digital tools, alongside a lack of data standards and technical capacity within courts. | Enhanced use of technology by courts (e.g., ODR, litigant portals), reformed data governance by courts (emphasizing openness, standardization, and fair access), and deregulation of legal services to foster innovation and welcome new service models, all guided by multi-stakeholder input and oversight. | Online Dispute Resolution (ODR), technological assistance for self-represented litigants, reform of legal services regulation, open court data initiatives, and the overall future of civil justice in a datafied environment. | Self-represented litigants, low- and middle-income individuals, debtors, and tenants. | Civil justice (broadly), with specific examples including debt collection, eviction, family law, housing law, and consumer credit disputes. | United States (federal and state courts), with some comparative references to the UK and Australia for regulatory reform. | NaN | NaN | NaN | False | False | NaN | Insufficient, inaccessible, and non-standardized court data; lack of technical capacity and data literacy within courts; outdated regulatory frameworks for legal services; need for new governance models for digital justice; and insufficient empirical research on civil justice innovations and their impacts. | NaN | Increased inequality in litigation (legal tech benefiting the 'haves'), erosion of public legal norms and judicial legitimacy, privacy violations and cybersecurity threats from court data, vendor lock-in for courts, hollowing out of public sector technical capacity, and potential consumer harm from inadequately regulated new legal service providers, including risks from 'dark patterns'. |
| 4JusCorpusLJ601.pdf | HeinOnline | AI-powered Indian Courtroom: ChatGPT a boon or a bane? | This paper discusses the potential benefits and drawbacks of integrating AI, particularly tools like ChatGPT, into the Indian judicial system to improve efficiency and access to justice. It highlights existing AI initiatives in Indian courts, such as SUPACE and SUVAS, and emphasizes the need for a cautious, regulated approach to adoption while considering ethical implications and practical challenges. | True | Idealistic | True | 3.0 | Neutral | ChatGPT, SUPACE (Supreme Court Portal for Assistance in Court's Efficiency), SUVAS (Supreme Court Vidhik Anuvaad Software), TERES (transcription tool), AI for administrative tasks, precedent analysis, and legal research. | For SUVAS: Observation of initial high productivity in translation, followed by decline in speed and scope (focus on Hindi, criminal matters). For ChatGPT: Anecdotal use by a High Court judge to gauge bail jurisprudence. For TERES: Deployed for live transcription in Supreme Court. | SUVAS: Initially translated many judgments efficiently but later became sluggish and limited in scope (mostly Hindi, criminal matters). TERES: Successfully used for live transcription. ChatGPT: Used anecdotally by a judge to gauge bail jurisprudence, signifying potential for greater AI participation. | High case backlogs leading to delays, language barriers (Apex court judgments primarily in English making them inaccessible to many), physical distance from courts, and the general complexity of law for the layperson. | Using AI for administrative tasks, legal research, and precedent analysis to reduce judicial workload and case backlogs. Implementing AI-powered translation services (like improved SUVAS) and virtual courtrooms to improve accessibility. Developing a legal framework to regulate AI use in courts and providing adequate training. | Reducing case backlogs, enhancing judicial efficiency, language access to legal information through translation, physical access to courts via virtual proceedings, improving legal research, and transcription of court proceedings. | The general Indian populace, particularly the "middle and lower strata" not proficient in English and those facing challenges due to physical distance from courts. | General, with specific mention of criminal law (bail jurisprudence, translation of criminal matters) and contract law (drafting). | India | For SUVAS: Supreme Court judgments and orders. For ChatGPT (implied by mention of GPT-4): Large, general text and code datasets from the internet. For SUPACE and TERES: Not specified in the paper. | NaN | SUPACE and SUVAS were launched as official Supreme Court initiatives. TERES was used in Supreme Court for live transcription. ChatGPT was used by a High Court judge via its public interface. | True | True | ChatGPT is a publicly accessible LLM, with free usage tiers available online, as evidenced by its use by a judge mentioned in the paper. | Need for improved AI translation capabilities (broader language support, consistent performance for tools like SUVAS). Lack of adequate technological infrastructure and widespread technological literacy among legal professionals. Absence of a comprehensive legal and ethical framework to govern AI in the judiciary, including clear guidelines on bias mitigation, accountability, and data privacy. | For specific tools like SUVAS: Maintaining translation quality, speed, and comprehensive coverage across languages and case types. For general AI adoption: Overcoming lack of technological literacy and resources among legal professionals, addressing fears of job displacement, mitigating algorithmic bias, defining accountability for AI-assisted decisions, and ensuring data privacy for sensitive court information. | Job displacement for court administrative staff, introduction of algorithmic bias leading to miscarriages of justice, erosion of judicial accountability if blame is shifted to AI, breaches of privacy and confidentiality of sensitive court data, and potential for cataclysmic outcomes from unregulated AI use in courtrooms. |
| 26JLegalEthicalRegulIsses.pdf | HeinOnline | ASPECTS OF ARTIFICIAL INTELLIGENCE ON E-JUSTICE AND PERSONAL DATA LIMITATIONS | This paper discusses the evolving applications of Artificial Intelligence (AI) within judicial systems, emphasizing the critical role of data availability and the necessity of robust personal data protection measures. It analyzes specific AI uses such as predictive justice and online dispute resolution, while also addressing key technoethical concerns, limitations, and the potential for algorithmic bias, particularly in criminal justice contexts. | True | Idealistic | True | 3.0 | Neutral | Predictive justice systems, Online Dispute Resolution (ODR), AI tools in criminal justice (e.g., risk assessment, crime prevention), ChatGPT for legal tasks. | Discusses evaluations of tools like COMPAS (showing racial bias from independent research) and ongoing testing of HART in the UK. Mentions a study on ChatGPT's legal drafting capabilities. | COMPAS algorithm showed discriminatory outcomes, with African-American individuals being assessed as twice as likely to reoffend compared to other groups. | Limited availability and quality of open data for training AI; technical difficulties in effective anonymization/pseudonymization to protect privacy; potential for algorithmic bias and discrimination; lack of transparency in proprietary algorithms. | Promoting open data policies for court decisions while ensuring robust anonymization/pseudonymization; upholding the right of individuals to contest automated decisions and to be informed about algorithmic reasoning (e.g., under GDPR); ensuring transparency, neutrality, and honesty in AI systems. | E-justice systems, online dispute resolution (ODR), predictive justice (including risk assessment in criminal cases), AI-assisted legal drafting, efficiency in judicial processes, personal data protection. | Individuals involved in the justice system, particularly those at risk of discriminatory treatment due to algorithmic bias (e.g., racial minorities in criminal justice). | Civil law, commercial law, administrative law, criminal law. | European Union, United Kingdom, United States, and mentions of specific AI adoption in China, Argentina, Colombia, Canada (Montreal). Discusses CEPEJ guidelines. | For HART: Durham police records from 2008 to 2012. For COMPAS: Information from accused individuals and their criminal records. For predictive justice generally: Court decisions and 'unrefined' data in structural computer databases. For ChatGPT: large volumes of data and documents. | NaN | Discusses deployment of ODR systems in several European countries, COMPAS in US courts, ongoing testing of HART in the UK, and early adoption of AI tools (like ChatGPT) in courts in China, Argentina, and Colombia. | True | True | ChatGPT, developed by OpenAI, is mentioned as being publicly accessible and has been used in legal contexts, with a free tier available. | Limitations in the reliability of predictive justice systems; lack of fully effective automated anonymization techniques; insufficient transparency and accountability in algorithmic decision-making; potential for 'technological solutionism' where AI is misapplied to complex social problems. | Ensuring data availability and quality for AI training; protecting personal data and privacy through effective anonymization/pseudonymization; mitigating algorithmic bias and ensuring fairness and non-discrimination; addressing lack of transparency in AI models; managing ethical implications and preventing over-reliance on AI. | Algorithmic bias leading to discriminatory outcomes (e.g., racial bias); violation of privacy and human dignity through misuse of personal data; 'profiling' of individuals; lack of transparency and accountability in AI decision-making; over-reliance on AI leading to errors or deskilling; reinforcement of existing societal inequalities; spread of misinformation or flawed legal advice from AI tools like chatbots. |
| 85UPittLRev331.pdf | HeinOnline | A PERFECT STORM FOR LEGAL EDUCATION: PRIVATIZATION, POLARIZATION, AND PEDAGOGY | The paper analyzes how emerging technologies, including AI like ChatGPT, and increasing political polarization are creating a 'perfect storm' for the legal profession and legal education. It argues these forces risk undermining lawyers' expertise and commitment to the public good, potentially leading to a stratified legal system with diminished access to justice and trust in law for ordinary people. | True | Idealistic | True | 3.0 | Negative | Online Dispute Resolution (ODR) systems (including AI-supplemented and blockchain-based versions), AI-powered tools for legal tasks (e.g., chatbots like ChatGPT, DoNotPay's 'robot lawyer'). | ODR in courts: user experiences vary, some speedier. DoNotPay AI lawyer: plan withdrawn due to regulatory threats. AI in family law (e.g., Matterhorn): company-reported positive outcomes. ChatGPT: passed law school/bar exams, can draft briefs, but prone to factual errors. | Matterhorn (company-reported for its family law ODR): reduced hearings, improved child support collection. ChatGPT: passed bar exam with high scores (latest version). | Cost of legal services and lack of counsel for low-income individuals; technological divides and discomfort; potential for technology to entrench stratification in legal services (robust law for elites, automated processing for others); declining public trust in legal institutions due to polarization. | Use of technology (e.g., ODR, AI tools) to improve efficiency and access to legal support for underserved populations; emphasis on legal proceduralism and ethical duties to counterbalance polarization; curricular reforms in law schools to foster skills for managing ideological conflict. | Access to legal representation for pro se litigants and people of modest means; use of technology in civil dispute resolution (e.g., family law, traffic courts); impact of technology and polarization on the perception and administration of justice. | People with limited means; pro se litigants; ordinary people interacting with the legal system. | Civil litigation, Family law, Traffic court (briefly mentioned), Criminal law (noted as an area with less AI penetration). | United States | NaN | NaN | NaN | True | True | ChatGPT is publicly accessible (free/paid tiers). Some court-based ODR systems are operational. DoNotPay offers subscription services (though its AI court lawyer concept was halted). | Deepening stratification in legal services; erosion of public trust in legal institutions; difficulty in upholding social trusteeship of lawyers amid polarization; ensuring technology serves the 'greater good' rather than just market efficiency; lack of common understanding impacting how ordinary people access and perceive law. | Ensuring fairness, transparency, and ethical application of AI in legal contexts; addressing the unauthorized practice of law by AI tools; overcoming the digital divide and user difficulties with legal tech; resource limitations in courts for technology adoption; maintaining the legal profession's integrity and relevance amidst technological displacement and ideological pressures. | Displacement of lawyers in routine legal work by technology; AI systems making errors or lacking moral capacity; increased stratification of legal services; erosion of public trust and perception that law is only for elites; lawyers potentially misusing technology or succumbing to partisan pressures, undermining the administration of justice and democratic processes. |
| 30WashLeeJCivRtsSocJust1.pdf | HeinOnline | Slavery.AI | This paper theorizes that unregulated AI systems are giving rise to an emergent form of modern slavery, termed 'Slavery.AI,' where people are commodified as data production units. It examines the structural power systems analogous to historical chattel slavery, tests its theory against universal characteristics of slavery, and calls for responsible governance to emancipate people from these AI-mediated harms. | True | Idealistic | False | 1.0 | Negative | Slavery.AI (as a theoretical framework for understanding AI's societal impact) | The 'Slavery.AI' theory was evaluated by comparing its tenets against twelve universal characteristics of historical slavery systems (identified by Drescher and Finkelman), grouped into 'property' and 'abuse of power', using illustrative examples of current AI systems and uses detailed in the paper. | The paper concludes its 'proof of concept holds,' asserting that many universal characteristics of slavery pertaining to property rights over individuals and abuse of power are firmly entrenched or emerging in the context of ungoverned AI systems, thereby validating the 'Slavery.AI' theory. | Lack of effective laws and governance for AI; the capitalist drive for profit leading to commodification of human data ('Data Industrial Complex'); a powerful alliance between tech oligarchy and governments; and the legal system's default to treating data (and thus people-as-data) as property. | Responsible AI governance, including: principled ethical requirements for AI design, development, and deployment; AI-appropriate interpretation and enforcement of existing laws; new, meaningfully-enforceable substantive AI laws; and strong leadership and political will to recognize and address the stakes. | AI-mediated enslavement; commodification of personal data as property; lack of legal protection against algorithmic harms; AI's impact on liberty, human rights, and the rule of law; systemic exploitation by AI systems. | The vast majority of humanity, with disproportionate impact on historically marginalized communities including the poor, disabled, elderly, women, non-English speakers, immigrants, and racial/ethnic minorities. | Human Rights Law, Property Law, Constitutional Law, Criminal Law, Tort Law, International Law, AI Governance/Regulation. | Primarily United States, with references to international legal frameworks (e.g., slavery conventions, Universal Declaration of Human Rights) and global implications of AI systems. | NaN | The 'Slavery.AI' theoretical framework was developed through legal and historical analysis, analogical reasoning with historical chattel slavery, comparative analysis against universal characteristics of slavery systems, and an interdisciplinary examination of current AI technologies and their societal impacts. | The 'Slavery.AI' theory is disseminated through academic publication in a law journal and presentations at academic conferences. | True | True | The theoretical framework of 'Slavery.AI' and its analytical crucible are detailed in this published academic paper, available through academic databases like HeinOnline, allowing readers to apply the framework. | Societal and legal failure to recognize and address 'Slavery.AI' as an emergent threat; lack of effective AI governance and AI-specific regulations; insufficient leadership and political will to protect individuals from AI-driven exploitation and harms. | Formulating and substantiating a provocative theory ('Slavery.AI') likening modern AI impacts to slavery; conducting interdisciplinary analysis across AI, law, and history; addressing the potentially jarring nature of the terminology to stimulate discourse and action. | The emergence of 'Slavery.AI' where individuals are commodified as data units, losing freedom and subjected to AI-mediated discrimination, surveillance, manipulation, and control. Specific risks include wrongful arrests, deepfake abuse, suppression of rights (movement, assembly, appeal), propagation of bias through predictive systems, and severe psychological harm including suicide. |
| 26NYUJLegisPubPoly625.pdf | HeinOnline | ANALOG PRIVILEGE | This paper introduces 'analog privilege' to describe how elites avoid AI systems and benefit from personalized human treatment, unlike the general populace. It argues this divide, explored through case studies in LegalTech and content moderation, exacerbates inequality and erodes social fabric, proposing multi-pronged solutions. | True | Idealistic | True | 3.0 | Negative | Analog privilege (conceptual framework) | NaN | NaN | The ability of elites to access superior human legal services while less privileged individuals are relegated to potentially inadequate AI-driven LegalTech, exacerbating existing inequalities and creating a two-tiered justice system. | A multi-prong approach involving legal, technical, and governance interventions to reduce analog privilege, increase accountability and transparency, improve AI systems, and implement external checks and balances, including empowering affected individuals and whistleblowers. | Disparities in access to quality legal representation and services due to the differential impact of AI and LegalTech on various socio-economic groups. | Low-income individuals, middle-class families priced out of legal services, and racial minorities who face systemic barriers to accessing justice. | Primarily civil law (e.g., housing, debt, family, torts, estate), with implications for access to justice broadly across legal fields. | Primarily United States, with references to and implications for the European Union and international human rights law. The core concept ('analog privilege') is framed as broadly applicable. | NaN | NaN | NaN | True | False | The paper discusses publicly accessible (often commercial or freemium) tools like ChatGPT, DoNotPay, and LegalZoom. | Societal: The 'automation divide' and lack of understanding of its contours. Technical: Current AI limitations in complex reasoning, creativity, and handling nuances, particularly in legal applications. Governance: Inadequate legal and regulatory frameworks to address analog privilege and ensure equitable AI deployment; need for polycentric governance models. | General challenges in deploying AI systems that lead to analog privilege include their inherent reductivism, determinism, and potential for voyeurism, as well as specific limitations in areas like legal reasoning (creativity, handling novel cases, emotional intelligence) and content moderation (context sensitivity, accuracy at scale). | Erosion of social fabric, increased social polarization and resentment due to perceived unfairness. In legal services, creation of a two-tiered justice system with lower quality for the non-elite, undermining fairness and judicial legitimacy. In content moderation, biased enforcement, disproportionate silencing of marginalized voices, or undue leniency for powerful actors, potentially enabling harm. |
| 15BeijingLRev.pdf | HeinOnline | The Utility of Artificial Intelligence in the Pursuit of Justice through Judicial Precedent in Nigeria | This paper discusses the potential of Artificial Intelligence (AI) to enhance the application of judicial precedent within Nigeria's justice system. It advocates for integrating AI tools to assist judges in legal research and decision-making, thereby improving judicial efficiency and the delivery of justice, while emphasizing that AI should support, not replace, judicial discretion. | True | Idealistic | False | 3.0 | Positive | AI for judicial precedent analysis and legal research in judicial decision-making | NaN | NaN | Systemic issues in the Nigerian judicial system: delays in justice delivery, lack of transparency, paper-based administrative procedures, large case backlogs, and judicial fatigue due to manual processes. | Integrating AI tools for case management, administrative task automation, workload management, legal research, and precedent analysis to improve judicial efficiency and the quality of decision-making, while preserving judicial autonomy. | Enhancing judicial efficiency, supporting judicial decision-making, improving the application of judicial precedent, reducing case backlogs and delays in the justice system. | NaN | Judicial process, Doctrine of Precedent, (briefly) Intellectual Property Law. | Nigeria (primary focus), with comparative mentions of Germany, Estonia, USA, Canada, India, UK, Europe. | NaN | NaN | NaN | False | False | NaN | Need for AI tools with advanced analytical capabilities (beyond data retrieval) for precedent analysis; inadequacy of current intellectual property laws for AI-generated works; persistence of judicial precedents that do not reflect current socio-economic realities. | General challenges in applying AI to the judiciary: ensuring data security and privacy, sourcing high-quality and unbiased training data for AI systems, and mitigating algorithmic bias in AI tools. | Data security breaches, infringement of fundamental rights, use of unsafe or compromised data for AI training, and algorithmic bias leading to unfair outcomes due to programmer or historical data biases. |
| 97StJohnsLRev195.pdf | HeinOnline | LOW-INCOME LITIGANTS IN THE SANDBOX: COURT RECORD DATA AND THE LEGAL TECHNOLOGY A2J MARKET | This paper argues for mandatory, standardized public access to state civil court record data, particularly for debt collection cases involving low-income, self-represented litigants, to enable proper evaluation of legal A2J technologies. It proposes model legislation to create a centralized, anonymized database of such records to ensure technology serves justice and protects vulnerable consumers. | True | Idealistic | False | 1.0 | Positive | Model legislation for state-wide collection, normalization, anonymization, and public accessibility of civil court record data, aimed at improving A2J initiatives and evaluation of legal technology. | NaN | NaN | Severe data deficit in state civil courts, particularly for cases affecting low-income and self-represented litigants (e.g., debt collection); disaggregated and inaccessible court case management systems; lack of standardized data collection and reporting by courts; high costs and barriers to accessing docket-level data; current data collection practices often focus on court efficiency rather than substantive outcomes for litigants. | State courts and legislatures should mandate the collection, aggregation, normalization, and public accessibility of granular, docket-level civil court record data. This includes establishing standardized data fields and creating a publicly accessible, anonymized database, potentially hosted by academic institutions or non-profit entities. The paper provides model legislation to achieve this. | Access to civil court record data; Data-driven evaluation of legal technology and A2J interventions; Consumer debt collection; Self-represented litigants; Regulatory sandboxes for legal tech; Online dispute resolution (ODR) evaluation. | Low-income litigants; self-represented litigants, particularly defendants in consumer debt collection cases. | Civil procedure; Consumer law (specifically debt collection); Access to Justice research; Legislation/Policy. | Primarily California (for the author's specific data collection and analysis and as a case study for the proposed legislation), with discussion of Utah (regulatory sandbox, ODR) and broader references to the United States state court systems. The model legislation is framed for a generic '[State]'. | NaN | The proposed model legislation is a legal and policy design. The paper's supporting research on California court data involved public records requests, data scraping, analysis of existing judicial reports, and data normalization using R and Tableau. | NaN | False | False | NaN | Lack of reliable, standardized, and accessible state court record data for evaluating A2J interventions; insufficient understanding of how legal tech tools impact outcomes for self-represented litigants; need for better metrics beyond efficiency for evaluating A2J tech (e.g., just outcomes, consumer protection); ethical considerations and potential for harm from unregulated or poorly evaluated legal tech. | Cost to local county courts for increased data reporting; need for state legislative budget appropriation; history of failed or expensive centralized case management system projects (e.g., CCMS in California) creating skepticism; disaggregated nature of current county court systems often reliant on various third-party vendors; potential resistance from judicial bodies to unfunded mandates for data collection and reporting. | Legal technology causing harm to vulnerable consumers if not properly evaluated (e.g., leading to worse outcomes, exacerbating default judgments); market-driven legal tech prioritizing profit over consumer protection or actual justice outcomes; Online Dispute Resolution (ODR) systems potentially disadvantaging unrepresented consumers if not carefully designed and evaluated based on substantive outcomes; regulatory sandboxes approving harmful tools due to a lack of baseline data for rigorous evaluation. |
| 19UMassLRev39.pdf | HeinOnline | We(ed) Hold These Truths to be Self-Evident: All Things Cannabis Are Inequitable | The paper argues that current social equity policies in the cannabis industry fail to redress historical and ongoing inequities stemming from the War on Drugs, particularly racial, gender, and economic disparities. It details these multifaceted inequities and analyzes the structural reasons for the ineffectiveness of existing industry, community, criminal justice, and access equity programs. | True | Idealistic | False | 3.0 | NaN | NaN | NaN | NaN | Structural flaws in licensing and market design favoring incumbents; underfunded and poorly designed criminal justice reforms; persistent stigmatization and criminalization; financial and regulatory barriers for equity applicants; disconnection between cannabis policy and broader social justice issues (e.g., housing, employment, health). | Development of new theoretical frameworks beyond current 'social equity' models; multidisciplinary approaches to create comprehensive, multidimensional solutions; grounding new policies in established theories of social, restorative, and racial justice; further research and honest evaluation of policy effectiveness. | Social equity in the cannabis industry; consequences of the War on Drugs; racial inequity; gender inequity; criminal justice reform (expungement, pardons); economic equity (business ownership); access to medical cannabis; policy analysis and critique. | Communities disproportionately impacted by the War on Drugs, including Black and minority communities (specifically Black Americans, Native Americans, Native Hawaiians), women, individuals with prior cannabis convictions, and medical cannabis patients. | Cannabis law; Drug law; Criminal law; Social equity law; Constitutional law; Administrative law; Business law; International law; Health law; Family law; Environmental law. | United States (federal and various states including Hawaii, California, Illinois, Colorado, New York); International (drug control treaties). | NaN | NaN | NaN | False | False | NaN | Fundamental ineffectiveness of current cannabis social equity policies; lack of a robust theoretical basis for effective interventions; insufficient scope, funding, and political will for Cmeaningful reforms; disconnect between narrow equity programs and the broad, intersectional harms of the War on Drugs; need for empirical data on policy impacts and alternative approaches. | NaN | Perpetuation and worsening of systemic inequities despite legalization; failure of social equity programs to achieve justice for harmed communities; exploitation of equity initiatives by established businesses; ongoing societal harm from stigmatization and criminalization; legal challenges undermining equity efforts; market forces disadvantaging equity participants. |
| 25MinnJLSciTech67.pdf | HeinOnline | Practice Guide: How to Integrate AI and Emerging Technology into Your Practice and Comply with Model Rule 3.1 | This paper serves as a practice guide for lawyers on integrating AI tools, particularly generative AI like ChatGPT, into their legal practice while ensuring compliance with Model Rule of Professional Conduct 3.1. It analyzes MRPC 3.1, discusses case law (Mata v. Avianca), provides general and state-specific guidance, and surveys efforts by bar associations to reform rules regarding AI. | True | Market | True | 2.0 | Positive | Use of generative AI tools (e.g., ChatGPT) for legal research and drafting assistance by legal practitioners. | Analysis of MRPC 3.1, FRCP 11, state-specific rules, and a case study (Mata v. Avianca) to illustrate compliance issues and best practices when using AI tools in legal practice. | Lawyers must conduct diligent inquiry, including independent verification of AI-generated legal research and arguments (cite checking, Shepardizing), to comply with MRPC 3.1, as AI tools like ChatGPT can produce inaccurate, outdated, or fabricated information. Failure to do so can lead to professional sanctions, as demonstrated in the Mata v. Avianca case study. | Insufficient access to affordable legal representation; difficulties for pro se litigants in navigating the legal system; lawyers' lack of understanding of AI tools and their limitations. | Responsible integration of AI tools by lawyers, following ethical guidelines, to improve efficiency and potentially expand legal services; exploring AI tools to assist pro se litigants; development of clear rules and ongoing education for lawyers on AI use. | Ethical use of AI in legal practice; compliance with professional conduct rules (specifically MRPC 3.1); AI's potential to assist pro se litigants; enhancing access to legal services and improving the quality of justice. | Pro se litigants; general public needing legal representation. | Professional Conduct, Civil Procedure (FRCP 11), General Legal Practice. | United States (federal via FRCP, ABA Model Rules, and state-specific rules/guidance for CA, NY, MN, TX, IL, OR, WI, NJ, TN, MT, CT, DC, UT, VA). | NaN | NaN | NaN | True | False | General AI tools (e.g., ChatGPT) discussed are publicly accessible, often with free tiers. The guidance itself is published in a law journal. | Lack of comprehensive and practical guidance for lawyers on ethical AI use (which this paper aims to partially fill); need for ongoing adaptation of legal professional rules to technological advancements; ensuring AI tools are developed and deployed responsibly with human oversight. | Understanding the limitations of AI Tools (e.g., hallucinations, outdated data, sensitivity to input phrasing); verifying AI-generated content; lawyers' lack of familiarity with technology; overestimation of AI capabilities by users. | Violation of MRPC 3.1 and FRCP 11 leading to judicial sanctions and disciplinary action; submission of inaccurate legal arguments and fabricated case citations (hallucinations); reputational harm for lawyers; misleading the court; failure to perform adequate independent legal research and inquiry. |
| 9IJODR177.pdf | HeinOnline | Can ChatGPT-like AI Function as ODR Fourth Party for Handling School-Related Disputes in China? | The paper argues that ChatGPT-like AI, while not replacing human third-party ODR, can serve as a "fourth party" to assist in preventing and resolving school-related disputes in China, particularly those involving student mental health. It proposes customizing these AI models with specific legal and psychological knowledge to effectively fulfill this role. | True | Idealistic | True | 1.0 | Positive | Using ChatGPT-like AI as an "ODR fourth party" for handling school-related disputes, customized with legal and psychological knowledge for tasks like student mental health support. | Illustrative examples of querying OpenAI ChatGPT and ChatSonic with scenarios related to student mental health. Reference to a Colombian judge's use of ChatGPT in a ruling. | ChatGPT and ChatSonic provided generally relevant advice on psychological issues and risk assessment based on described symptoms, suggesting potential for the proposed role. The Colombian judge example illustrated AI as an assistant, not a replacement for human judgment. | Limited access to and inconsistent quality of mental health support for students, especially out-of-hours and in remote areas; societal dismissal of youth psychological issues; lack of timely intervention for students with mental health struggles. | Deploying customized ChatGPT-like AI as a 24/7 accessible "fourth party" for initial psychological support and dispute prevention guidance for students. Integrating AI with human professionals (psychologists, mediators) and training AI with relevant legal and psychological knowledge specific to school disputes in China. | Online Dispute Resolution (ODR), Online Dispute Prevention (ODP), student mental health support, resolution and prevention of school-related disputes (e.g., bullying, academic stress). | Students in China, particularly those in boarding schools or remote areas with limited access to mental health services. | Education law, mental health law/ethics, Online Dispute Resolution (ODR). | China (primary), Colombia (secondary example). | For the proposed customized AI: Chinese legal and psychological data specific to school-related disputes for fine-tuning existing LLMs. Existing models (ChatGPT, Ernie bot) are noted as being trained on massive, general text datasets. | Conceptual framework proposal. Suggests customization and fine-tuning of existing LLMs with domain-specific data (Chinese law and psychology for school disputes). | Envisioned through ODR platforms or integrated into school support systems, potentially leveraging customized versions of AI from tech companies (e.g., Microsoft, Baidu, Alibaba, Tencent). | False | False | NaN | Technical limitations of LLMs (accuracy, bias, outdated knowledge, language-specific performance); need for robust human oversight and integration with professional services; accessibility of some advanced AI models in China; lack of AI specifically designed and trained for ODR in school-related disputes. | Ensuring accuracy, reliability, and lack of bias in AI-generated content; effectively customizing general LLMs for specialized legal and psychological domains relevant to Chinese school disputes; dealing with LLMs' existing knowledge limitations and regional accessibility hurdles. | AI producing incorrect, harmful, or biased outputs; reliance on AI leading to diminished human critical thinking in dispute resolution and mental health support; privacy risks associated with handling sensitive student data (implied). |
| 54CalWIntlLJ459.pdf | HeinOnline | THE DIGITAL "TO KILL A MOCKINGBIRD": ARTIFICIAL INTELLIGENCE BIASES IN COURTS | This paper discusses the use of AI in the judicial system, particularly for risk assessment and recidivism prediction, highlighting the significant legal and ethical concerns arising from AI biases. It explores the causes of these biases, challenges in identifying them, and potential mitigation strategies, including those outlined in the EU AI Act. | True | Idealistic | False | 3.0 | Negative | AI systems for risk assessment and recidivism prediction, specifically mentioning COMPAS (Correctional Offender Management Profiling for Alternative Sanctions). | For COMPAS, the paper refers to ProPublica's investigation which analyzed its risk scores against actual recidivism, and other studies comparing outcomes for different racial groups. | Studies on COMPAS indicated that Black defendants were more likely to be incorrectly assessed as higher risk for recidivism, and had a two times higher risk of being mislabeled as potential violent recidivists compared to White counterparts, while White individuals were more frequently misclassified as low risk. | AI biases perpetuating societal disparities; Lack of transparency and explainability in AI systems; Flawed and unrepresentative training data; Overreliance on imperfect AI predictions. | Ensuring diverse and representative training datasets; Implementing robust human oversight and continuous auditing; Enhancing transparency and explainability of AI systems; Developing comprehensive legal regulations like the EU AI Act. | Fairness in algorithmic decision-making in criminal justice; Algorithmic bias in risk assessment and recidivism prediction; Due process implications of AI in courts. | Racial and ethnic minorities (specifically Black individuals, Asian Americans); Religious minorities (specifically Jewish individuals); Gender minorities (females in criminal justice contexts). | Criminal Law, Due Process, Human Rights. | United States, European Union. Discusses issues with global implications. | Historical criminal justice data (e.g., criminal records, responses to questionnaires like in COMPAS), often characterized as biased, incomplete, inaccurate, and unrepresentative of the broader population or specific subgroups. | For tools like COMPAS: Development based on actuarial risk assessment principles and machine learning, utilizing historical criminal records and questionnaire data. | For tools like COMPAS: Deployment in U.S. state criminal justice systems for judicial decision support (e.g., pre-trial detention, sentencing, early release). | False | False | NaN | Inability to completely eradicate bias from AI systems, even with regulation; Technical challenges in achieving full transparency and explainability for complex AI; Difficulty in translating nuanced legal concepts into code without loss or bias. | Acquiring and maintaining unbiased, representative training data; Preventing developer-induced or code-translation biases; Ensuring transparency and explainability in complex algorithms; Auditing and managing dynamically evolving algorithms. | Amplification of societal biases leading to discriminatory outcomes in the justice system; Erosion of fairness, due process, and individualized justice; Miscarriages of justice due to inaccurate AI predictions. |
| 16ItalianJPubL165.pdf | HeinOnline | ARTIFICIAL INTELLIGENCE AT THE CROSSROADS BETWEEN THE EUROPEAN UNION & THE COUNCIL OF EUROPE: WHO SAFEGUARDS WHAT & HOW? | The paper analyzes and compares the legislative approaches to Artificial Intelligence (AI) regulation by the European Union (EU AI Act) and the Council of Europe (CoE Framework Convention). It highlights the evolution towards human rights-centric AI governance in Europe and discusses challenges for creating a coherent regulatory landscape. | True | Idealistic | False | 2.0 | Positive | EU's Artificial Intelligence Act and Council of Europe's Framework Convention on Artificial Intelligence, Human Rights, Democracy and the Rule of Law. | NaN | NaN | AI-derived discrimination; violations of fundamental human rights; lack of transparency and explainability in AI systems; potential for manipulation by AI; insufficient human oversight; lack of effective remedies for individuals harmed by AI systems; risks from biometric identification and social scoring. | Adopting human-rights-based AI regulation (EU AI Act, CoE Convention); implementing risk-based assessments; prohibiting unacceptable-risk AI; establishing obligations for AI developers/users (e.g., transparency, human oversight, data quality); ensuring access to remedies and procedural safeguards; international cooperation on AI governance. | Protection of fundamental human rights (dignity, autonomy, privacy, non-discrimination, freedom of expression); ensuring democracy and the rule of law; accountability and responsibility for AI systems; transparency and explainability of AI; access to remedies for AI-related harms; prevention of AI-based discrimination. | Vulnerable groups, including persons with disabilities, children, ethnic and national minorities; ensuring gender equality. | Public Law, Human Rights Law, EU Law, International Law, Data Protection Law, Anti-discrimination Law. | European Union, Council of Europe member states, with potential for broader international scope (e.g., for CoE Convention). | NaN | NaN | NaN | False | False | NaN | Initial limitations in EU AI Act's human rights scope (though improving); potential for regulatory fragmentation between EU and CoE; ensuring effective enforcement and remedies; keeping legal frameworks updated; potential weakness in CoE Convention's reliance on domestic implementation; insufficient addressal of gender-based discrimination in CoE draft. | Defining AI appropriately for legal texts; classifying AI systems based on risk; achieving consensus among diverse stakeholders; balancing innovation with fundamental rights protection; ensuring legal certainty; addressing the transnational nature of AI; coordinating different international regulatory initiatives. | Violations of human rights (privacy, freedom of expression, dignity); AI-derived discrimination; manipulation of human behavior; erosion of democracy and rule of law; misuse of biometric identification and social scoring; lack of transparency, accountability, and human oversight in AI systems. |
| 2024RegionalLRev179.pdf | HeinOnline | LEVERAGING ARTIFICIAL INTELLIGENCE IN eDISCOVERY: ENHANCING EFFICIENCY, ACCURACY, AND ETHICAL CONSIDERATIONS | This paper analyzes the eDiscovery process, including its phases, benefits, and drawbacks, and explores the impact of Artificial Intelligence (AI) on this field. It offers a preliminary overview of AI applications in eDiscovery, discusses various AI techniques, and considers future trends and ethical implications. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Civil litigation, Regulatory compliance, Legaltech | Common law jurisdictions (notably USA), EMEA, APAC | NaN | NaN | NaN | False | False | NaN | NaN | Data privacy and security concerns; ethical issues such as AI bias, transparency, and accountability; integration with existing legal workflows; need for technical expertise and training for legal professionals; cost of AI implementation; managing massive data volumes; and the risk of over-reliance on AI technology. | Data breaches and unauthorized access; algorithmic bias in AI; lack of transparency and accountability in AI decision-making; errors from over-reliance on technology leading to incomplete or incorrect data analysis; potential for spoliation or tampering with evidence if ESI is improperly handled. |
| 40GaStULRev917.pdf | HeinOnline | Robot Lawyers Don't Have Disciplinary Hearings - Real Lawyers Do: The Ethical Risks and Responses in Using Generative Artificial Intelligence | This paper discusses cases of lawyers misusing generative AI, highlighting the ethical risks such as breaches of competence, confidentiality, and candor, and subsequent disciplinary actions. It then examines various responses from the legal profession, including judicial orders, bar association taskforces, and ethics opinions, aimed at mitigating these risks and guiding the responsible use of AI in law. | True | Market | True | 3.0 | Neutral | Generative Artificial Intelligence (e.g., ChatGPT, Google Bard) | The paper reports on a Stanford study that evaluated LLMs (OpenAI's ChatGPT 3.5, Google's PaLM 2, Meta's Llama2) by posing over 200,000 legal questions to them. | The cited Stanford study found that general-purpose large language models hallucinate at least seventy-five percent of the time when answering questions about a court's core ruling, made more frequent mistakes with lower federal district court case law, and exhibited a "contra-factual bias." | NaN | NaN | NaN | NaN | General legal practice (discusses ethical rules applicable to all lawyers, with examples from civil, criminal, housing, and aviation law) | United States (federal and state levels) | The paper discusses general-purpose large language models (e.g., ChatGPT 3.5, PaLM 2, Llama2) which, as noted by a cited Stanford study, are not built specifically for legal use and are thus trained on broad, non-legal-specific data. | NaN | NaN | True | True | The generative AI tools discussed, such as ChatGPT and Google Bard (Gemini), have publicly available versions, some with free tiers. Llama2, also mentioned, is open source. | NaN | User Gaps in understanding of generative AI's capabilities, limitations, and associated risks (e.g., hallucinations, bias); difficulty in verifying AI-generated information; maintaining client confidentiality; challenges in ethical integration of AI into legal practice; the rapid pace of AI adoption outpacing lawyers' comprehension and preparedness. | Fabrication of legal information (hallucinations); breaches of lawyers' ethical duties (competence, confidentiality, supervision, candor to the tribunal, independent professional judgment); algorithmic bias in AI tools; potential for legal malpractice claims and disciplinary sanctions against lawyers; compromised client confidentiality when inputting sensitive data into AI tools. |
| 53HofstraLRev391.pdf | HeinOnline | ARE A.I. LAWYERS A LEGAL PRODUCT OR LEGAL SERVICE?: WHY CURRENT UPL LAWS ARE NOT UP TO THE TASK OF REGULATING AUTONOMOUS A.I. ACTORS | The paper argues that current Unauthorized Practice of Law (UPL) regulations are inadequate for regulating autonomous AI actors in the legal field, exemplified by tools like Pactum AI. It proposes reforms to UPL laws to balance consumer protection and innovation, facilitate attorney-AI developer collaboration, and clearly define boundaries for AI in legal work. | True | Idealistic | True | 3.0 | Positive | Autonomous negotiation software (e.g., Pactum AI); Legal self-help platforms (e.g., DoNotPay, LegalZoom, Quicken Family Lawyer). | Pactum AI: Pilot program with Walmart Canada involving 100 suppliers, evaluated on deal closure rate, turnaround time, cost savings, and supplier preference. Other tools (LegalZoom, QFL, DoNotPay): Evaluated through legal challenges and court cases assessing UPL compliance. | Pactum AI (Walmart pilot): 64% deal closure rate, 11-day average turnaround, 1.5% average savings (initial); later 68% closure, 3% savings. 75% of suppliers preferred AI negotiation. | Inadequate and unclear Unauthorized Practice of Law (UPL) laws hindering the development and safe deployment of AI tools that could potentially improve access to justice, and risking consumer harm. | Reforming UPL laws to: 1) Allow attorney-AI developer collaboration, 2) Clearly define AI's permissible legal work, 3) Balance consumer protection, the sanctity of the bar, with promoting innovation. This framework would support the responsible development of AI tools that could enhance access to justice. Considers regulatory sandboxes like Utah's model. | Legal self-help tools, automated document preparation, consumer-facing legal services for common issues (e.g., small claims, contract disputes with corporations), regulation of AI in legal practice. | General consumers, particularly those needing assistance with common legal issues against corporations or for personal matters where hiring a lawyer is prohibitive. | Contracts, Estate Planning, Corporate Law, Small Claims, Traffic Law, Employment Law, Administrative Law (FOIA), Unauthorized Practice of Law (UPL) regulation. | United States (with examples from specific states like Texas, Missouri, California, Utah, and mentions of international application of tools like Pactum AI by Walmart). | For AI negotiation tools like Pactum AI: Domain-specific data including negotiation project databases (e.g., Harvard's Negotiation Project), past negotiation experiences (via machine learning), and customer-specific contract data/forms. For other tools, generally legal forms, statutes, and related legal information. | For AI negotiation tools like Pactum AI: Machine learning, natural language processing, game theory, value function algorithms, customer-specific onboarding and data integration (e.g., 'contract space'). | Commercial software-as-a-service for corporate clients (e.g., Pactum AI); Web-based services/apps for consumers, often subscription-based (e.g., DoNotPay, LegalZoom). | True | False | Pactum AI is commercially available as an autonomous negotiation suite for large corporations. DoNotPay, LegalZoom, and Quicken WillMaker & Trust (successor to QFL) offer web-based legal self-help services to consumers, typically via purchase or subscription. | Outdated and ambiguous UPL laws; lack of a national, uniform standard for AI in legal practice; need for clear ethical guidelines for AI-human lawyer collaboration; and frameworks for assessing AI competency and liability. | For developers of advanced legal AI tools: Integrating complex AI components (LLMs, machine learning, NLP, game theory) effectively and ensuring ethical, accurate outputs. For early tools: Managing ambiguous content, personalized preferences, and complex goals. For all: Navigating unclear and inconsistent UPL regulations during development and deployment. | Unauthorized Practice of Law (UPL) by AI tools or those misusing them; Consumer harm from substandard, biased, or incorrect AI-generated legal advice/services; Stifling innovation due to unclear or overly restrictive regulations; Attorneys facing UPL liability or other disciplinary action for aiding AI developers improperly or for uncritical use of AI; Erosion of due process or democratic principles if AI is poorly implemented or regulated; Job displacement for legal professionals. |
| 12BelmontLRev196.pdf | HeinOnline | INTEGRATING SUSTAINABLE DEVELOPMENT GOALS IN THE LAW CURRICULUM: LEGAL EDUCATION FOR "PEOPLE, PLANET AND PROSPERITY" | This paper advocates for integrating the UN's Sustainable Development Goals (SDGs) into legal education, particularly within LLB and JD programs. It presents strategies and case studies for embedding SDGs across various law subjects to equip future lawyers to address global challenges and foster a just, sustainable world. | True | Idealistic | False | 1.0 | Positive | Pedagogical framework for integrating Sustainable Development Goals (SDGs) across core and optional law school curricula using illustrative case studies for various legal subjects. | NaN | NaN | Lack of awareness among legal professionals about SDGs and their relevance to legal practice and reform; insufficient legal frameworks to support SDGs; challenges in achieving rule of law and access to justice without SDG-informed legal education. | Integrating SDGs knowledge into the law curriculum; Cultivating SDGs awareness among law academics and students; Embedding SDGs education across relevant core and optional subjects without disrupting the existing curriculum structure. | Access to justice (SDG 16), rule of law, building effective, accountable, and inclusive institutions. | All people, particularly those affected by inequality, lack of access to justice, and unsustainable development, including women and girls (SDG 5) and people in developing countries. | Multiple legal fields, including Introduction to Law, Constitutional Law, Corporate Law, Criminal Law, Contract Law, Property Law, Equity and Trusts, International Law, Environmental Law, Water Law, Planning Law, Climate Change Law, Law of the Sea, Taxation Law, Trade Law, Health Law, Intellectual Property Law, Technology Law, and Human Rights Law. | Primarily common law jurisdictions (e.g., Australia, USA referenced in examples), with arguments for broader international applicability due to the global nature of SDGs and legal education principles. | NaN | Conceptual analysis, case study method for illustrating SDG integration in various law subjects, and review of SDG frameworks and existing legal education literature. | Recommendation for adoption by law schools and legal educators within their curricula. | True | False | The paper outlines a pedagogical approach and provides detailed examples that educators can adopt and implement in their law curricula based on the information within the published article. | Insufficient comprehensive integration of SDGs into law curricula; lack of awareness and expertise regarding SDGs among some law academics needed to drive this integration. | NaN | Erosion of privacy, misuse of data for democratic distortion, cybercrime, cyberwarfare, online hate speech and discrimination, defamation, and copyright infringement related to digital technologies; potential for generative AI to diminish progress towards SDGs if law does not keep pace (as discussed in the Technology Law section). |
| 31AIL773.pdf | HeinOnline | Judicial knowledge-enhanced magnitude-aware reasoning for numerical legal judgment prediction | This paper introduces NumLJP, a novel architecture for numerical legal judgment prediction (imprisonment and penalty) in criminal cases. NumLJP enhances prediction by integrating judicial knowledge through a selection module, acquiring numerical commonsense via masked numeral prediction, and performing magnitude-aware reasoning using a specialized graph network, demonstrating significant improvements on Chinese legal datasets. | True | Idealistic | True | 1.0 | Positive | NumLJP: a judicial knowledge-enhanced magnitude-aware reasoning architecture using a contrastive learning-based judicial knowledge selector (JKS), a masked numeral prediction (MNP) task for legal numerical commonsense, and a magnitude-aware numerical reasoning network (MagNet) on a scale-based numerical graph. | Evaluation on three Chinese legal datasets (CAIL-small, CAIL-large, AIJudge) using accuracy, macro-precision, macro-recall, macro-F1, and ImpScore metrics, compared against several baselines. Includes ablation studies and robustness analysis on a manually constructed variant dataset (VarLJP100). | Achieved state-of-the-art performance, with macro-F1 of NumLJP improving by at least 9.53% on penalty prediction and 11.57% on imprisonment prediction compared to competitive baselines. | Inaccurate numerical legal judgment prediction by existing AI systems due to ignoring numerical information, inability to perform numerical comparison and magnitude perception, limited training data, and sparse numerals in crime facts. | Proposing NumLJP, which incorporates official judicial knowledge (numerical anchors) as reference points, uses a masked numeral prediction task for acquiring legal numerical commonsense, and employs a magnitude-aware numerical reasoning network (MagNet) on a scale-based graph to handle numerical comparison and magnitude. | Numerical legal judgment prediction (imprisonment terms and penalty amounts in criminal cases) for enhanced legal information and understanding. | Laypeople/general public without legal background. | Criminal Law | China | Publicly available Chinese legal case documents (fact descriptions, law articles, imprisonment terms, penalty terms) from CAIL2018 and AIJudge challenges, originally sourced from China Judgment Online. Judicial knowledge (containing numerical anchors) specific to criminal charges is also utilized. | Deep learning methodology including use of pre-trained language models (RoBERTa), contrastive learning, masked language modeling techniques (for numerals), and graph neural networks. Design involves modular architecture (JKS, MNP, MagNet) and task-specific loss functions. | NaN | False | False | NaN | Technical gaps include handling complex criminal cases (multiple defendants/facts, coreference), reasoning over diverse numeral types within a single judicial knowledge, addressing issues with duplicate/excessive/oversized numerals, and interpreting implicit numerals. | Designing a model capable of numerical comparison and magnitude perception, distinguishing confusing cases for correct judicial knowledge application, acquiring legal numerical commonsense from judicial knowledge, handling unseen numerals and few-shot scenarios, and managing training stability of graph-based models. | Risk of incorrect predictions due to model errors (e.g., wrong judicial knowledge selection, misinterpretation of numerals). Potential for machine interference with judges' independent judgment if misused. Privacy risks if sensitive information in data is not properly handled. |
| 57UICLRev291.pdf | HeinOnline | INNOCENT UNTIL PROVEN GUILTY: UNLESS YOU'RE POOR. RIGHTING A SYSTEMIC WRONG UNDER THE PRETRIAL FAIRNESS ACT. | This paper discusses Illinois' Pretrial Fairness Act (PFA), which abolishes cash bail, arguing it fosters a more equitable justice system by prioritizing risk over wealth in pretrial release decisions. It also proposes expanding the judicial role of Restorative Justice Community Courts to further improve pretrial processes and address court burdens. | True | Idealistic | False | 2.0 | NaN | The Pretrial Fairness Act (PFA) in Illinois, a legislative reform that abolishes cash bail and establishes new standards for pretrial detention. | The paper evaluates the Pretrial Fairness Act through legal analysis of its provisions, historical context, discussion of its implementation mechanisms (task forces, subcommittees), and by addressing criticisms. It also notes ongoing external empirical studies of the SAFE-T Act's impact by institutions like Loyola University and the National Institute of Justice. | The paper's evaluation concludes that the Pretrial Fairness Act is a significant and positive reform that moves Illinois towards a more equitable pretrial justice system by basing detention decisions on risk rather than wealth. It is expected to reduce unjust incarceration of the poor and address systemic disparities, though successful implementation faces challenges. | The cash bail system itself, which bases pretrial freedom on financial ability rather than risk, leading to disproportionate detention of the poor and minorities. Other obstacles include misinformation campaigns against reform, the PFA becoming a political pawn, and the existing overburdened court system. | The primary solution is the implementation of the Pretrial Fairness Act, abolishing cash bail. The paper further proposes granting judicial decision-making power for certain low-level offenses to Restorative Justice Community Courts to alleviate court burdens and enhance community-focused justice. It also emphasizes the role of implementation task forces, data collection, and collaboration with law enforcement. | Pretrial detention, bail reform, abolition of cash bail, risk assessment in pretrial decisions, Restorative Justice Community Courts, systemic injustice, socio-economic and racial disparities in the criminal justice system. | Low-income individuals and racial/ethnic minorities (specifically Black and Latinx defendants) who are disproportionately affected by the cash bail system and pretrial detention. | Criminal Law (specifically pretrial procedure and bail reform). | Illinois (Cook County often used as an example); comparative references to other US states (e.g., New York, California, New Jersey). | NaN | The Pretrial Fairness Act was developed through a legislative process, informed by studies from the Illinois Supreme Court Commission on Pretrial Practices which involved expert consultation, stakeholder input, and analysis of academic research. | Statewide implementation in Illinois guided by the Pretrial Implementation Task Force and its subcommittees, involving development of guidelines, pilot sites, educational programs, and communication strategies. A Data Oversight Board is tasked with collecting and analyzing pretrial data. | True | True | The Pretrial Fairness Act is an enacted law in Illinois, making its provisions (the approach discussed) legally operative within that jurisdiction. The text of the law is publicly available. | The Pretrial Fairness Act's silence on adequately addressing the overburdened court system. Lack of clear definitions for statutory terms like "obvious threat" or "obvious medical or mental health issues," potentially leading to subjective enforcement. The ongoing need for comprehensive data collection and analysis to ensure effective and equitable implementation. | Challenges related to the Pretrial Fairness Act's implementation include: overcoming political opposition and widespread misinformation; ensuring consistent application across different counties and by various justice system actors; clarifying ambiguous statutory language; managing judicial caseloads effectively under the new framework; and establishing robust data collection and analysis systems for continuous improvement. | Risks associated with the previous cash bail system included unjust detention of the poor and exacerbation of racial disparities. Risks related to the PFA, as mentioned by critics (though generally refuted by the paper), include potential for increased crime if not properly implemented. The paper also highlights risks of subjective interpretation of PFA provisions by law enforcement if terms remain undefined, and the risk of misinformation campaigns undermining public trust and effective reform. |
| 26NCJLTech1.pdf | HeinOnline | NEW GOVERNANCE AND NEW TECHNOLOGIES: CREATING A REGULATORY REGIME FOR THE USE OF GENERATIVE ARTIFICIAL INTELLIGENCE IN THE COURTS | This paper analyzes emerging ex ante judicial rules and standing orders designed to regulate the use of Generative AI (GenAI) in U.S. federal courts, categorizing them and framing their development within New Governance Theory. It discusses the risks posed by GenAI in litigation, such as hallucinations, and suggests that these decentralized, experimental regulatory approaches can foster effective, ethical GenAI use and inform broader AI governance. | True | Market | True | 3.0 | Neutral | Ex ante judicial rules and standing orders, framed by New Governance Theory, to regulate the use of Generative AI in court filings. | The paper provides a descriptive analysis and typology of existing judicial rules and orders; no formal testing of their effectiveness is presented, only observation of their emergence, characteristics, and preliminary impact. | The paper identifies a typology of ex ante judicial responses to GenAI (ranging from simple warnings to prohibitions) and notes that while few problematic filings have appeared in jurisdictions with such orders, causality is undetermined. It also notes the Fifth Circuit's decision not to adopt a special rule after stakeholder consultation. | Submission of inaccurate or fictitious legal information due to GenAI hallucinations, wasting court and litigant time and resources, burdening the judicial system, empowering aggressive litigants, and potentially undermining the integrity of legal precedent and public trust in the legal system. | Implementing ex ante judicial rules and standing orders based on New Governance principles (e.g., warnings, disclosure requirements, certifications of accuracy, stakeholder engagement, decentralized experimentation, soft-law approaches backed by sanctions) to guide and control GenAI use in litigation. | Integrity of court proceedings, responsible use of AI by all litigants (including pro se), regulation of AI in legal practice, and indirectly, the potential for AI tools for the unrepresented. | Pro se litigants and the unrepresented (as part of the broader group of all court users affected by GenAI use). | Primarily Civil Litigation within federal courts, but the discussed principles of GenAI regulation in courts could apply more broadly. | United States (Federal Courts). | NaN | NaN | Regulatory approaches are deployed via judicial standing orders by individual judges, local court rules adopted by district courts, and consideration of amendments to appellate practice rules by circuit courts. | False | False | NaN | Technological limitations of GenAI (accuracy, reliability, hallucinations, bias), data privacy concerns, intellectual property issues related to LLM training, and the general need for robust, adaptable regulatory frameworks to ensure safe and ethical AI development and deployment for legal applications. | For GenAI tools: ensuring accuracy and reliability (avoiding hallucinations), addressing inherent biases. For regulatory approaches: developing flexible and adaptable rules that can keep pace with rapid technological change and balancing innovation with risk mitigation. | Submission of fictitious cases and legal authorities due to GenAI 'hallucinations'; wasting court and litigant resources; undermining the integrity of the judicial process, legal precedent, and public trust; potential for bias in AI-generated content; misuse by litigants to amplify burdensome or frivolous claims; unauthorized disclosure of confidential client information to GenAI services; violations of consumer privacy; wrongful use of intellectual property in training LLMs. |
| 70SDLRev117.pdf | HeinOnline | "DO NOT READ" | The paper satirically argues that legal scholars should affix "Do Not Read" labels to their work, contending this aligns with existing academic norms and offers benefits like reputation management and efficiency. It humorously suggests this practice could even save humanity from AI by limiting its access to legal scholarship. | True | NaN | False | 1.0 | NaN | The 'Do Not Read' label (satirical proposal) | NaN | NaN | NaN | NaN | NaN | NaN | Legal Academia / Legal Scholarship | United States (referencing its academic practices) | NaN | NaN | NaN | False | False | NaN | NaN | NaN | AI leading to human annihilation ("annihilation at the hands of our digital overlords"), AI taking away work from attorneys, and the (satirical) risk of the paper itself revealing humanity's defense strategy against AI. |
| 17ContempAsiaArbJ133.pdf | HeinOnline | ASSESSING THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE ARBITRATION PROCESS | This paper investigates the integration of Artificial Intelligence (AI) in the arbitration process, aiming to enhance accessibility and affordability of justice. It compares traditional and AI-assisted arbitration, evaluates ethical and legal considerations, and incorporates insights from legal professionals to advocate for a balanced synergy between AI capabilities and human expertise. | True | Idealistic | False | 3.0 | Positive | AI-assisted arbitration | Field survey with a questionnaire (using Likert scales) distributed to 30 legal and arbitration professionals. Data was analyzed using descriptive statistics and correlation analysis to assess perceptions on accuracy, efficiency, and acceptability of AI in arbitration. | AI-assisted arbitrators were perceived as potentially more accurate in ensuring unbiased and consistent decisions, and more efficient in data handling and speed. Human arbitrators were valued for nuanced, experience-driven judgment, adaptability, empathy, and culturally sensitive resolutions. Respondents showed preference for AI assistance in pre-arbitration consultation/agreement and arbitrator selection stages. | High cost of traditional arbitration; lack of transparency and interpretability in AI systems; potential for AI-perpetuated bias; data confidentiality and security concerns; need for substantial and appropriate data for AI training; challenges in AI making reasoned decisions. | Cautious and ambitious integration of AI, synergizing with human cognitive processes and expertise; development of comprehensive ethical guidelines and standards; adaptation of legal and regulatory frameworks; enhancing AI interpretability and clarity; fostering human-AI collaboration models; ensuring robust data privacy and security measures. | Affordability of arbitration; efficiency of the arbitration process; accessibility of justice; ethical and legal implications of AI in dispute resolution. | Parties for whom the cost of arbitration is a barrier to accessing justice. | Arbitration (specifically international and commercial arbitration) | International | NaN | NaN | NaN | False | False | NaN | Effective human-AI collaboration models; understanding the impact of AI on party trust and satisfaction; AI's adaptability to diverse legal systems and cultural contexts in international arbitration; long-term implications of AI for the legal profession and arbitration practices; need for comprehensive ethical guidelines and updated legal frameworks; enhancing AI interpretability and ensuring data security. | Ensuring transparency and interpretability of AI algorithms; mitigating potential biases in AI systems; addressing data privacy, confidentiality, and security concerns; adapting existing legal and regulatory frameworks to accommodate AI; managing the technical complexities of developing and implementing legal AI (e.g., data availability, model opacity); maintaining human oversight and building trust in AI-assisted processes. | Algorithmic bias leading to unfair or discriminatory outcomes; lack of transparency and explainability in AI-driven decisions, undermining due process; breaches of data privacy and confidentiality; security vulnerabilities of AI systems; perpetuation or amplification of existing societal biases through AI; over-reliance on AI potentially eroding human legal skills and judgment; inaccuracies or errors from AI systems due to limited or biased training data, or lack of access to real-time information. |
| 22UNHLRev151.pdf | HeinOnline | Major Reform With Minor Risk: Implementation of Change Initiatives as a Learning Challenge | This paper argues that significant reforms are needed in legal education and that many sound ideas for change exist. It provides a framework for effectively implementing these reforms, emphasizing evidence-based practices and change management, alongside a survey of specific change proposals for law schools. | True | Idealistic | False | 3.0 | NaN | NaN | NaN | NaN | Misalignment of legal education content and attorney licensing exams with the practical competencies (e.g., client communication, cultural humility, tech literacy) required to effectively serve diverse public needs and ensure access to justice. High-stress, overly competitive, and non-inclusive law school environments that can negatively impact student well-being, ethical development, and the cultivation of a service-oriented professional identity. Insufficient institutional focus on and resources for promoting equity, diversity, and belonging within law schools. The high cost of legal education and resulting student debt limiting career choices. Resistance to substantial reform and evidence-based implementation within legal education. | Reforming attorney licensing and legal curricula to emphasize practical, client-centered skills, ethical development, cultural competency, and technological proficiency. Fostering healthier, more collaborative, inclusive, and supportive learning environments that prioritize student well-being and professional identity formation geared towards service. Integrating principles of equity, diversity, and belonging throughout legal education. Adopting evidence-based, iterative approaches to implement and sustain meaningful changes in legal education. | Attorney licensing and bar exam reform; Curriculum development for practical skills (e.g., client communication, tech literacy); Professional identity formation and ethics; Student well-being and mental health in legal education; Diversity, equity, inclusion, and belonging in law schools. | Law students, particularly those from first-generation and historically underrepresented backgrounds, by aiming to create a more equitable, inclusive, and supportive educational environment. Indirectly, the general public and underserved communities who would benefit from more competent, ethical, and culturally sensitive lawyers. | Legal Education | United States | NaN | NaN | NaN | False | False | NaN | The persistent research-practice gap in legal education, where known effective reforms are not widely or successfully implemented. Lack of comprehensive, evidence-based approaches to instilling practical, client-centered, and A2J-relevant competencies in all law students. Insufficient mechanisms for systematically evaluating and ensuring that legal education and licensing standards truly prepare lawyers to meet diverse societal legal needs, particularly for underserved populations. Need for more effective strategies to address systemic issues like student debt, mental health crises, and lack of diversity within the legal profession. | Overlooking implementation importance; blaming ideas for implementation failures; lack of success metrics and analysis; cognitive biases in assessment; organizational complexity (processes, turnover, norms, power relations); 'solutionitis'; achieving shared problem understanding; lack of psychological safety; resistance to evidence-based practices; demands for immediate results. | Wasted resources if initiatives are unsuccessful or misimplemented. Opportunity costs of choosing one initiative over another. Misimplementation causing more harm than inaction. Failed initiatives tainting good ideas for future reform. Continued misalignment of legal education with professional needs. Perpetuation of student mental health crises. Failure to address equity and belonging issues. Producing graduates ill-equipped for modern practice. High student debt. |
| 34IndIntlCompLRev249.pdf | HeinOnline | BOYCOTTING CHINESE GENOCIDE AND THE DUTY TO PREVENT: OPPORTUNITIES LOST IN THE 2019-2021 UK TRADE BILL | This paper argues that the UK had an international legal obligation to pass the proposed 2019-2021 Trade Bill amendment, which aimed to impose economic sanctions on China for the alleged genocide of Uighurs. It contends such sanctions would be effective due to China's economic vulnerabilities and historical precedent, particularly if applied collectively by international partners. | True | Idealistic | False | 2.0 | NaN | Imposing economic sanctions, as proposed via the failed 2019-2021 UK Trade Bill amendment, against states (specifically China) determined to be engaged in genocide. | The paper evaluates this approach through legal analysis of international law (Genocide Convention, ICJ judgment in Bosnian Genocide case), review of historical precedents of sanctions (e.g., on Russia, past sanctions on China), and economic analysis of China's vulnerabilities and trade relationships. | The paper concludes that imposing such economic sanctions against China is a state obligation under international law and would likely be effective in influencing China's conduct regarding the Uighurs, especially if sanctions are collective, pervasive, and leverage China's economic dependencies. | Political reluctance of states to implement robust economic sanctions against powerful nations like China; narrow interpretations of the state's duty to prevent genocide (e.g., the 'effective influence' standard derived from the Bosnian Genocide case); and arguments concerning the economic costs or perceived ineffectiveness of unilateral sanctions. | The paper advocates for passing legislation like the proposed UK Trade Bill amendment; calls for a broader interpretation of the 'effective influence' standard to trigger state obligations to prevent genocide; promotes collective international action for imposing pervasive economic sanctions; and suggests leveraging the economic vulnerabilities of the target state (China). | State responsibility to prevent genocide; use of international trade law and economic sanctions for enforcement of human rights; protection of vulnerable minority groups (specifically, the Uighurs). | The Uighur ethnic/religious minority in Xinjiang, China. | International Law (International Human Rights Law, Law of State Responsibility, International Criminal Law - Genocide Convention), UK Trade Law. | United Kingdom, China, International (with reference to ICJ jurisprudence and UN conventions). | NaN | NaN | NaN | False | False | NaN | The gap between existing international legal norms (such as the duty to prevent genocide) and their actual enforcement by states, particularly highlighting the lack of political will to impose meaningful and collective economic sanctions against economically powerful states. A need for broader interpretation and acceptance of extraterritorial obligations concerning genocide prevention is also implied. | Determining the precise nature, timing, and obligatory character of economic sanctions for genocide prevention; achieving collective action among states for sanctions to be maximally effective; countering arguments about potential negative humanitarian consequences or economic blowback on sanctioning states; and ongoing debates over the 'effectiveness' of sanctions in altering a target state's behavior concerning jus cogens violations. | Potential for economic sanctions to cause collateral damage to civilian populations in the targeted state (e.g., through impacts on food and medicine imports); sanctions being ineffective or even counter-productive (citing the Srebrenica example where sanctions purportedly made genocide easier); economic costs to the sanctioning state(s); and the possibility that leaders of the target state may ignore the suffering of their own people caused by sanctions. |
| 24GermanLJ551.pdf | HeinOnline | The Impact of Digitalization on Global Trade Law | This paper explores how digitalization impacts global trade law, examining the World Trade Organization (WTO) framework and the evolution of digital trade rules in Free Trade Agreements (FTAs) like CPTPP, USMCA, and emerging Digital Economy Agreements (DEAs). It assesses the adequacy of current legal responses for the data-driven economy, highlighting divergent approaches, regulatory challenges, and the need for enhanced international cooperation. | True | Market | False | 2.0 | NaN | Analysis of digital trade provisions and regulatory approaches in international trade law, specifically within the WTO framework, Free Trade Agreements (FTAs) like CPTPP and USMCA, EU's FTA models, and Digital Economy Agreements (DEAs). This includes examining rules on e-commerce, data flows, data localization, digital products, source code, and personal data protection. | The paper evaluates these legal and regulatory approaches through comparative analysis of treaty texts, discussion of their policy implications, review of academic literature, and reports from international organizations and governmental bodies. | WTO law is largely pre-internet and insufficient for current digital trade challenges. FTAs and DEAs are increasingly addressing digital trade with more comprehensive and binding rules, but significant divergence exists, notably between the US-led liberal model (e.g., CPTPP, USMCA) emphasizing data flows and the EUs model prioritizing data protection alongside data flows. DEAs represent an innovative, cooperative approach to broader digital economy issues. | NaN | NaN | NaN | NaN | International Trade Law, E-commerce Law, Data Governance Law, IT Law, International Economic Law | International (WTO); Plurilateral/Regional agreements including CPTPP member states (Australia, Brunei, Canada, Chile, Japan, Malaysia, Mexico, New Zealand, Peru, Singapore, Vietnam), USMCA (United States, Mexico, Canada), European Union, Japan, Singapore, Australia, New Zealand, Chile, South Korea, United Kingdom. | NaN | Legal analysis, comparative legal studies, treaty interpretation, policy analysis, review of official government documents and proposals, academic literature review. | The discussed trade agreements (FTAs, DEAs) are deployed through negotiation, signature, ratification, and implementation by sovereign states, becoming binding international law for the parties. | True | True | The texts of the discussed international trade agreements (e.g., CPTPP, USMCA, EU FTAs, DEPA) are generally publicly available through official government websites or treaty databases. | Insufficiency of the multilateral WTO framework for digital trade; lack of global consensus on key digital trade issues (e.g., data governance, balancing trade with privacy); the need for more adaptable and inclusive governance models that can keep pace with technological change; ensuring equity and inclusiveness in institutional developments. | Balancing trade liberalization with non-trade policy objectives (e.g., privacy, national security, consumer protection); addressing divergent national regulatory approaches (e.g., US vs. EU on data); managing the impact of data localization measures; protecting intellectual property in the digital environment; establishing rules for emerging technologies like AI; achieving consensus in multilateral forums; ensuring FTAs do not become overly opaque or state-centered. | Emergence of 'digital protectionism' and new trade barriers (e.g., data localization, censorship); inadequate protection of personal data and privacy due to trade pressures; increased data inequality and 'data colonialism'; cybersecurity threats; stifling of innovation through fragmented or overly restrictive regulations; negative impacts of disruptive technologies on employment. |
| 24BusLIntl215.pdf | HeinOnline | Utilising Generative Al in Businesses: Risks and Best Practices | This paper examines the application of generative AI, such as GPT chatbots and image generators, within business contexts, highlighting significant risks including data bias, misinformation, privacy violations, and copyright infringement. It also reviews current and proposed regulatory landscapes and proposes best practices for businesses to responsibly integrate these AI technologies. | True | Market | True | 3.0 | NaN | GPT chatbots (e.g., ChatGPT, GPT-4) and AI image-generating programs. | NaN | NaN | NaN | NaN | NaN | NaN | Privacy law, Copyright law, Defamation, Contract law, Tort law, Legal ethics | International; specific examples/regulations from USA, China, EU, UK, Switzerland, Italy | Discusses general characteristics of training data for existing models like GPT (e.g., pre-trained with data up to a certain date, large internet datasets) and related legal issues (e.g., use of copyrighted material). | NaN | NaN | True | False | The paper discusses widely known and accessible commercial generative AI tools like ChatGPT. | NaN | Creating reliable and unbiased datasets, preventing hallucination, ensuring verification of outputs, managing privacy and copyright issues, cybersecurity, establishing robust governance and regulatory frameworks for AI. | Data bias and data limitation, hallucination (generating incorrect information), spread of fake news and defamation, privacy violations (unconsented data collection, use, storage), copyright infringement and personality rights violations, data breaches and cybersecurity incidents, legal liability for damages caused by AI, lack of transparency and accountability. |
| 77MeLRev69.pdf | HeinOnline | BREAKING UP WITH THE ANTI-HERO: HOW 303(B)(3) CAN HELP LAW SCHOOLS MITIGATE THEIR PERENNIAL DEVICES, PRICES, VICES, AND CRISES | This paper argues that ABA Standard 303(b)(3) necessitates a comprehensive integration of professional identity development into the first year of law school to address current crises in legal education, such as student distress and unpreparedness. It proposes practical strategies, drawing on self-determination theory and wise interventions, and uses examples from Willamette University College of Law to illustrate how these can foster well-rounded, ethical practitioners. | True | Market | False | 1.0 | Positive | A comprehensive, integrated approach to professional identity development throughout the first year of law school, incorporating principles from ABA Standard 303(b)(3), self-determination theory, and wise interventions. Specific examples include the Academic Excellence Fellows (AEF) peer mentoring program, Zero-L summer engagement (book groups, podcasts), and classroom strategies like explicit instruction on study/management skills and formative assessment. | The paper describes the piloting and implementation of specific initiatives at Willamette University College of Law, such as the Academic Excellence Fellows (AEF) program. Evaluation appears to be based on qualitative feedback and observed benefits rather than formal, quantitative studies (e.g., student and fellow experiences). | Anecdotal and qualitative positive outcomes are reported for the Willamette initiatives, such as fellows finding the AEF program highly meaningful, 1Ls receiving early constructive feedback and support, fostering a sense of community, and students developing foundational academic and professional skills. | Current law school curriculum and culture destroying student enthusiasm and well-being; increasing student anxiety, depression, and substance abuse; students arriving less prepared for an autonomous learning environment; law schools' resistance to systemic change; misalignment between legal education and employer/societal needs; students' low sense of belonging. | Integrate professional identity development pervasively into the first-year curriculum rather than as supplemental programs. Implement early engagement strategies (Zero-L summer programs). Leverage trained peer mentors (e.g., Academic Excellence Fellows). Provide explicit instruction on foundational academic, self-management, and professional skills. Employ formative assessment and wise interventions to build competence, autonomy, and relatedness. | Professional identity formation; Legal education reform; Student well-being in law school; Pedagogical strategies for law students; Curriculum development. | Law students, particularly first-year (1L) students. Indirectly, the legal profession, clients, and civil society. Specific mention of benefits for non-traditional and first-generation students through peer mentoring. | Legal Education | United States | NaN | The proposed approach is based on educational theories (Self-Determination Theory, wise interventions), analysis of legal education reports (Carnegie Report, CLEA, IAALS), ABA accreditation standards, and pilot program implementation and observation (e.g., Willamette University College of Law's initiatives). | Specific programs (e.g., Academic Excellence Fellows, Zero-L podcast and book groups, integrated Lawyering course components) are described as implemented at Willamette University College of Law. The paper advocates for broader adoption of similar integrated approaches by other law schools. | True | False | The pedagogical approaches, strategies, and specific program ideas (like peer mentoring structures, pre-1L engagement, classroom techniques) are detailed in the paper, allowing other institutions to adopt and adapt them. | The need for broader, integrated adoption of professional identity development across the entire first-year curriculum and by all faculty, rather than relying on standalone courses or supplemental programs. Addressing faculty reluctance to engage in this type of student development. Ensuring consistent and meaningful implementation to avoid it becoming another superficial requirement. | Faculty resistance to changing teaching methods or incorporating non-doctrinal content. Student overload and limited bandwidth if professional identity initiatives are not seamlessly integrated. Engaging students who may not perceive the need for such development. Overcoming institutional inertia and moving from piecemeal solutions to a holistic, integrated approach. | NaN |
| 31AustlLLibr19.pdf | HeinOnline | ChatGPT – THE BLURST OF TIMES | This paper discusses OpenAI's ChatGPT, exploring its capabilities, market context, and potential applications in the legal field, including for access to justice via tools like DoNotPay. It also thoroughly outlines significant limitations such as inaccuracies, biases, ethical concerns, and the ongoing need for human judgment and oversight. | True | Idealistic | True | 2.0 | Neutral | ChatGPT (a large language model by OpenAI) and its integration into legal tech applications like DoNotPay and Clausebase. | The paper reports on various informal evaluations and observations: user experiences (e.g., Nick Cave lyrics generation), demonstrations (e.g., Google Bard's error), beta-testing feedback (e.g., Clausebase's module found 'useful, but imperfect'), and OpenAI's stated limitations. | General capabilities include human-like dialogue and text generation. Specific application feedback is mixed: Clausebase's module was 'useful, but imperfect'; creative outputs can be poor. DoNotPay is described as functional for tasks like ticket disputes. Known limitations include factual inaccuracies, knowledge cutoff (post-2021), and potential for bias. | High cost and insufficient availability of legal help for low-income individuals for a vast majority of their civil legal problems. | AI-powered chatbots like DoNotPay (using ChatGPT) to handle common legal issues (e.g., ticket disputes, consumer rights) and assist with government paperwork. AI tools for high-volume, less complex legal tasks like drafting wills and conveyancing. | Access to basic legal assistance for common civil legal problems (ticket disputes, consumer rights, landlord issues, employee rights), government paperwork, and routine legal document drafting (wills, conveyancing). | Low-income individuals and the general public facing common legal issues who lack access to traditional legal services. | General civil law (consumer protection, housing, employment, administrative), contract law, wills and estates, property law. | International (general discussion of ChatGPT), with specific examples/data from USA (LSC report, DoNotPay context) and Belgium (Clausebase). | ChatGPT was pre-trained on a large corpus of text and code ('large volumes of data gleaned from conversations between humans and the written word of humans') with a knowledge cut-off in 2021. The paper notes verification and truthfulness of training data as a concern. | NaN | ChatGPT (version 3.5) released publicly in November 2022, with a free tier and a paid subscription (ChatGPT Plus). An API is planned for broader integration. DoNotPay is an operational chatbot. Clausebase's ChatGPT-powered module was in beta-testing. | True | True | ChatGPT is accessible through a free tier online and a paid subscription; DoNotPay is an operational chatbot service. | Ensuring factual accuracy and truthfulness; overcoming knowledge limitations (post-2021 events); mitigating bias in outputs; incorporating human-like judgment, character, and contextual understanding; developing reliable methods for detecting AI-generated content; addressing ethical concerns regarding AI decision-making. | NaN | Generation and spread of disinformation and falsely generated assertions; inbuilt bias in AI models leading to unfair outcomes; loss of human-centric decision-making; privacy violations due to data handling; security vulnerabilities; copyright infringement from using trained-on content; producing harmful or biased instructions. |
| 6JLTechTex168.pdf | HeinOnline | THE AI-BASED LEGAL PARADISE-A (NECESSARY!) THOUGHT EXPERIMENT | This paper conducts a thought experiment on a future "AI-based legal paradise" where AI handles all legal decisions, assuming AI achieves human-level cognitive abilities. It explores the potential advantages, such as improved access to justice and efficiency, and discusses downsides like ensuring transparency and human oversight, arguing for distinguishing technical feasibility from desirability. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Inefficiency leading to delays in legal processes; inaccuracy and inconsistency in human legal decision-making; high costs of legal services and judicial proceedings; lack of legal certainty and predictability in outcomes; limited access to legal information. | Deployment of AI for automated legal decision-making to enhance speed, accuracy, consistency, and efficiency; utilization of AI to improve legal certainty and predictability; leveraging AI systems to expand access to legal information and reduce cost/duration of judicial processes. This requires careful development and governance of AI systems. | Improving speed, accuracy, consistency, and efficiency of legal processes; enhancing legal certainty and predictability; expanding access to legal information; reducing duration and cost of judicial proceedings. | NaN | NaN | International | NaN | NaN | NaN | False | False | NaN | Technical: Current AI not yet at human-level cognitive ability for comprehensive legal decision-making; ensuring robust cybersecurity, privacy, and data protection. Societal/Ethical: Developing adequate governance and control systems for AI to ensure fairness, accountability, transparency, democratic legitimization, and human oversight; addressing the "black-box" problem; defining appropriate human involvement. | NaN | Technical failures; cybersecurity breaches, privacy violations; unfair, unaccountable, or inexplicable AI decisions (e.g., due to bias, "black-box" algorithms); malicious AI or AI running out of control; undermining due process or judicial independence; distortion of legal values; job displacement; loss of legal creativity; lack of democratic legitimization; erosion of human dignity if human control is relinquished. |
| 15AmUIntellPropBrief23.pdf | HeinOnline | Al GENERATED ART AND THE GAP IN COPYRIGHT LAW | This paper examines the disruption caused by AI-generated art to artists, focusing on the inadequacy of current copyright law to protect them from unauthorized use of their work for AI training and style imitation. It argues that this creates a disincentive for human creativity, discusses the shortcomings of existing legal alternatives, and cautiously explores potential legislative solutions. | True | Idealistic | True | 3.0 | Negative | Generative AI for art creation (e.g., Stable Diffusion, Midjourney) | NaN | NaN | Unauthorized use of artists' works for AI training; AI's ability to mimic uncopyrightable artistic styles, devaluing original work and threatening artists' income; existing copyright law not protecting artists from AI-generated imitations; uncertainty of fair use defense for AI developers using copyrighted training data. | Potential legislative amendments to copyright law (clarifying infringement or fair use for AI training), exploring limited protection for artistic style (with caution), or creating new forms of intellectual property; overall, a cautious approach to immediate, drastic legal changes is advised, alongside improving artists' access to courts. | Copyright protection for artists; economic impact of AI on artists' livelihoods; unauthorized use of creative works for AI training; protection of artistic style against AI imitation; fair compensation for artists. | Artists, particularly independent (indie) artists and those relying on commission work. | Copyright Law, Intellectual Property Law | United States | Datasets of existing images paired with detailed text descriptions, including artists' publicly available works. Examples include the LAION database, which reportedly contains billions of images, some potentially used without regard to copyright ownership. Data is largely unstructured (images, text). | NaN | NaN | False | False | NaN | Current copyright law's inability to protect uncopyrightable artistic styles from AI imitation; uncertainty regarding the applicability of fair use to AI training datasets; difficulty for artists in detecting and proving unauthorized use of their work for training AI; insufficiency of existing non-copyright legal alternatives to protect artists. | NaN | Disincentive for human artists to create and share work; devaluation of art and artists' income due to mass-produced AI imitations; violation of artists' personal connection to their work (personhood); potential for new 'style protection' laws to be co-opted by corporations, harming individual artists; legislative changes may have unintended negative consequences or stifle technological development. |
| 9IJODR147.pdf | HeinOnline | Comments on Artificial Intelligence | This paper compiles commentaries from experts on the integration of AI, exemplified by ChatGPT, into Online Dispute Resolution (ODR). The authors explore potential benefits for efficiency and access, alongside significant risks like bias, misinformation, and the need for human oversight and ethical frameworks. | True | Idealistic | True | 3.0 | Neutral | ChatGPT and similar Large Language Models (LLMs) in the context of Online Dispute Resolution (ODR); the HUMANIS concept is also introduced. | NaN | NaN | Power imbalances, digital exclusion, pervasive AI bias reinforcing societal injustices, lack of AI transparency and accountability, misinformation risks, and over-reliance on imperfect AI systems. | Developing ethical human-centered AI (e.g., HUMANIS initiative), robust human oversight, performance-based standards, redesigning neutral roles to audit AI, and interdisciplinary collaboration to combat AI bias. | Online Dispute Resolution (ODR), access to justice for online consumers and citizens, fair and impartial dispute resolution, ethical AI in legal decision-making, addressing digital power imbalances and bias. | Individual citizens, SMEs, digitally excluded/disadvantaged individuals, and marginalized communities vulnerable to AI bias. | Dispute Resolution (specifically Online Dispute Resolution - ODR), ADR (Alternative Dispute Resolution), consumer law, civil procedure (small claims), ethics in legal practice. | International | ChatGPT: A large, general-purpose corpus of unverified internet text data (up to 2021), containing inherent biases and inaccuracies. HUMANIS (concept): Envisioned to use anonymized data voluntarily shared by users and entities. | NaN | NaN | True | True | ChatGPT is available online through OpenAI, with free access tiers. | Technical limitations in AI's understanding of nuance, emotion, and truth; societal challenges in AI transparency, accountability, bias mitigation, equitable access, governance, and defining human-AI roles. | For tools like ChatGPT in ODR: ensuring accuracy and truthfulness, mitigating pervasive biases, defining appropriate use-cases given cognitive limitations, establishing accountability, and preventing user over-reliance and misuse. | Spread of misinformation; perpetuation of biases leading to discrimination; lack of accountability for AI errors; erosion of critical thinking; reinforcement of past injustices; exacerbation of power imbalances; and misuse. |
| 74SCLRev823.pdf | HeinOnline | THE RIGHT TO (HUMAN) COUNSEL: REAL RESPONSIBILITY FOR ARTIFICIAL INTELLIGENCE | This paper explores the ethical and constitutional implications of future AI counsel, arguing it could surpass human lawyers in capability and improve access to justice, but finds current legal ethics and regulatory frameworks unprepared for this shift. It calls for a fundamental re-evaluation of ethical rules and disciplinary approaches to directly incorporate and regulate AI counsel, ensuring its responsible integration into the legal profession. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Cost and inaccessibility of human legal counsel; inconsistency in the quality of human counsel; human biases (cognitive, racial) within the legal system; lack of human relatability and empathy in AI counsel posing a potential obstacle to client trust and acceptance. | Development and deployment of AI counsel to provide lower-cost, more accessible, and consistently high-quality legal services; direct regulation and ethical oversight of AI counsel, including embedding ethical principles into AI, establishing new disciplinary mechanisms, and adapting licensing and ethical rules for AI; ensuring AI systems are designed to be unbiased and to protect client confidentiality. | Access to affordable legal services; quality and consistency of legal representation; ethical regulation of legal technology to ensure safe A2J; bias reduction in the justice system; client autonomy and choice of counsel in the context of A2J. | General public, particularly 'millions of people who cannot afford or access counsel' and 'all clients (rich and poor)'. | General legal practice; Criminal law; Legal ethics and professional responsibility. | United States | NaN | NaN | NaN | False | False | NaN | Current legal ethics rules and disciplinary systems are inadequate for AI counsel, lacking direct regulation of AI and relying on human supervision which may become insufficient; technical expertise (e.g., computer science, AI forensics) is lacking in current regulatory bodies; societal acceptance and trust in AI counsel, especially compared to human counsel's relational aspects; ensuring genuine independence and lack of bias in AI counsel potentially controlled or created by specific entities. | NaN | AI counsel could be controlled by states or creators, limiting its independence; novel conflicts of interest may arise (e.g., one AI representing opposing parties); breaches of client confidentiality and data security due to hacking or improper data handling by AI systems; AI perpetuating or amplifying existing biases if trained on biased data or poorly coded; potential for inadequate human supervision over increasingly complex AI; erosion of the 'human' element in legal counsel, impacting client trust and the lawyer-client relationship. |
| 50RutgersComputerTechLJ28.pdf | HeinOnline | ARTIFICIAL INTELLIGENCE AND ETHICS | This paper examines the impact of AI on legal practice and ethics, focusing on the need for attorneys to be technologically competent. It reviews current ABA and state ethics rules, discusses risks exemplified by recent cases of AI misuse, and proposes amendments to rules and continuing legal education requirements to ensure ethical and competent AI adoption. | True | Market | False | 3.0 | Positive | NaN | NaN | NaN | High cost of legal representation leading to little access to legal services for a significant portion of the population, particularly disadvantaged individuals unable to pay hourly rates. | Utilizing AI to increase efficiency in legal tasks, thereby lowering the cost of legal services; Enhancing lawyer technological competence through mandatory continuing legal education (CLE) in technology; Amending ethics rules for clarity on AI use; Forming commissions on law and technology to investigate and guide AI adoption, with improving access to justice as a stated goal for some. | Reducing cost of legal services through AI-driven efficiencies; Enhancing lawyer technological competence to enable ethical AI use for broader legal service accessibility. | Disadvantaged individuals and the general population unable to afford traditional legal fees. | General legal practice, Legal ethics | USA | NaN | NaN | NaN | False | False | NaN | Insufficient attorney technological competence and lack of clear, updated ethical guidelines tailored to AI, which are necessary prerequisites for AI to effectively and ethically contribute to improving access to justice by lowering legal costs. | NaN | Bias, errors, lack of transparency, hallucinations, privacy/confidentiality breaches, over-reliance by attorneys leading to failure to verify AI-generated content, perpetuation of discrimination, and a corrosive effect on legal reasoning skills and the training of new lawyers. |
| 37GeoJLegalEthics39.pdf | HeinOnline | Existential Advocacy: Lawyering for Al Safety and the Future of Humanity | This paper presents an empirical study of lawyers and legal advocates working to mitigate existential risks, particularly those from advanced AI, focusing on their distinct social-change lawyering model called the "priorities methodology." It analyzes how these "existential advocates" approach efficacy and accountability, especially in representing future generations, and describes the scientific, truth-seeking cultural norms that support their methodology. | True | Idealistic | False | 2.0 | Positive | Priorities methodology for social-change lawyering, drawn from Effective Altruism. | Empirical study using a multi-method research design including ethnography at the Legal Priorities Project, 53 semi-structured interviews with legal advocates in the existential risk community, and a systematic review of online materials. | The main empirical finding is the identification and description of the "priorities methodology," a distinct model of social-change lawyering used by existential advocates. This model aims to maximize impact through formal goal/strategy selection (based on önem, neglect, tractability) and is supported by cultural norms emphasizing uncertainty embrace, deliberative rationality, supportive dissent, and epistemic identity, though it faces tensions regarding broader mobilization and representation. | Cognitive biases (e.g., availability heuristic, scope neglect, present bias) hindering recognition of large-scale, uncertain, and abstract existential risks; political incentives prioritizing immediate issues over long-term concerns for future generations; global coordination challenges; lack of experience with existential-scale events; difficulty in emotionalizing low-probability/high-impact risks. | The paper describes the "priorities methodology" (combining moral first principles with criteria like importance, neglect, and tractability for cause selection, and reverse engineering for strategy) used by advocates to systematically address these risks. It also discusses the cultural norms (uncertainty, rationality, dissent, epistemic identity) that support this methodology and concludes with recommendations for adapting and scaling the model. | Existential risk mitigation (particularly from AI and engineered pandemics), AI safety, protection and legal representation of future generations, longtermism. | Future generations. | AI law and policy, environmental law (as analogous for future generations), human rights law (extended to future generations), tort law, criminal law, constitutional law (rights of future generations), international law, regulatory policy. | International (movement is global, addressing global risks, involving organizations and actions in the US, UK, EU, and other regions, with reference to UN initiatives and international legal scholarship). | NaN | The "priorities methodology" is drawn from the philanthropic framework of Effective Altruism. It involves: 1) starting with moral first principles (often utilitarian-leaning but with normative uncertainty), 2) cause selection based on criteria of importance, neglect, and tractability (INT analysis), and 3) strategy selection using reverse engineering from end goals, focusing on maximizing counterfactual impact. This is supported by cultural norms fostering scientific, truth-seeking approaches. | The "priorities methodology" is employed by a community of "existential advocates" and organizations like the Legal Priorities Project (LPP) in their research, policy advising, strategic litigation considerations, and community building efforts aimed at mitigating existential risks. | True | True | The paper describes the "priorities methodology" as a conceptual framework, based on publicly discussed Effective Altruism principles, making its understanding and potential application accessible to readers of the paper. | The tension between maintaining the rigorous "priorities methodology" with its specific cultural norms and the need to broaden the movement for inclusivity, democratic legitimacy, and wider impact; the need for further diversification (racial, geographic, gender) within the movement to avoid blind spots and enhance effectiveness; challenges in operationalizing accountability to future generations while incorporating diverse current-person voices. | Maintaining adherence to scientific truth-seeking norms (uncertainty, deliberative rationality, dissent, epistemic humility) against human cognitive tendencies and professional legal norms; operationalizing accountability to silent future generations while engaging current diverse populations; dealing with the "missing mood" or difficulty in emotionally connecting to abstract, long-term risks; balancing rigorous analysis with timely action; scaling the movement without diluting its core methodology or politicizing the issues. | The movement faces criticism for potentially shifting attention and resources away from current social injustices. Association with controversial donors (e.g., Sam Bankman-Fried) has led to public narratives of the movement being a distraction or serving elite/techno-utopian interests. Efforts to broaden the movement might compromise the integrity of the "priorities methodology" or lead to politicization of existential risk issues. |
| 28LegalWritingJLegalWriti (1).pdf | HeinOnline | TEACHING CRITICAL USE OF LEGAL RESEARCH TECHNOLOGY | This paper examines the impact of advanced search technologies, including generative AI, on legal research and argues that skills faculty should use structured, interactive pedagogical methods to teach law students critical, effective, and ethical use of these tools. It highlights issues like superficial analysis and the 'black box' nature of technology, offering practical guidance for educators. | True | Market | False | 1.0 | NaN | A pedagogical framework for teaching law students critical use of legal research technologies, emphasizing interactive learning (e.g., research logs, live assignments), understanding system limitations, and adapting to new tools like generative AI. | NaN | NaN | NaN | NaN | NaN | NaN | General / All legal fields | United States | NaN | NaN | Classroom instruction by skills faculty (legal writing instructors, law librarians). | True | False | The pedagogical strategies are described in the paper for adoption by educators who have access to the publication. | NaN | NaN | Superficial analysis, over-reliance on technology, 'black box' nature of systems, keyword search limitations, information limitations in databases, and specific risks of generative AI including 'hallucinations,' lack of transparency in LLMs, knowledge cut-offs, and confidentiality/privilege issues. |
| 5ITARev46.pdf | HeinOnline | INTERNATIONAL COMMERCIAL ARBITRATION & TECHNOLOGY: AN AUTHORS' INTERVIEW WITH GENERATIVE ARTIFICIAL INTELLIGENCE | This paper presents an 'interview' conducted by the authors with generative AI tools ChatGPT 4.0 and Google Bard, exploring their potential roles and limitations in international commercial arbitration. The findings confirm AI's current unsuitability for independent decision-making, highlight differences in AI responses, and underscore the continued necessity of human judgment in arbitration. | True | Market | True | 2.0 | NaN | Evaluation of generative AI (ChatGPT 4.0 and Google Bard) through a semi-structured interview methodology to assess their application in international commercial arbitration. | A semi-structured 'interview' was conducted with two generative AI tools (ChatGPT 4.0 and Google Bard) using a predefined set of questions. The authors then qualitatively compared the AI-generated answers. | The experiment confirmed AI is not ready for independent arbitral decisions and responses can vary. ChatGPT 4.0 provided more mature, prompt-adherent answers than Google Bard, which exhibited more 'hallucinations' and misinterpretations. | NaN | NaN | NaN | NaN | International Commercial Arbitration | International | The paper studies LLMs (ChatGPT 4.0 and Google Bard). ChatGPT indicated its knowledge cutoff was September 2021; Bard stated it was trained on a massive dataset of arbitral awards, legal documents, and other information related to international arbitration. These are large, pre-existing datasets not created by the authors. | NaN | NaN | True | True | ChatGPT 4.0 (paid option) and Google Bard (free option at the time of the paper). | NaN | The authors acknowledged methodological limitations, such as interviewing only two AIs and the exercise being more of a 'cognitive curiosity' than a strict scientific experiment. They also faced challenges in interpreting AI responses, including identifying 'hallucinations' and assessing differences in response style and relevance. | Bias in AI from training data, lack of transparency, data security and confidentiality breaches, over-reliance on AI leading to abdication of human judgment, unequal access to AI tools, lack of accountability for AI errors, data misinterpretation by AI, and AI generating factual-seeming content (e.g., witness statements) not based on actual knowledge. |
| 43CardozoArtsEntLJ135.pdf | HeinOnline | The Doors of Janus: A Critical Analysis of the Socio-Technical Forces Eroding Trust in the Rule of Law | This paper critically analyzes how emerging data-driven technologies, particularly AI, contribute to eroding citizens' trust in the Rule of Law through systemic disinformation, algorithmic misgovernance, and the digitalization of the social contract. It proposes a framework to restore trust by better enforcement and reinterpretation of existing rights, and formulating new collective interest-based rights, emphasizing the mediating role of law and technology. | True | Idealistic | True | 3.0 | Negative | NaN | NaN | NaN | Systemic disinformation (worsened by Generative AI) eroding epistemic justifications for trust; algorithmic misgovernance (e.g., lack of procedural justice, unfair social structuring, human rights violations) belying expectations of good governance; digitalization of the social contract disrupting temporal-spatial aspects of governance and citizen engagement. | Acknowledge the mediating relation of law and technology; better enforcement of existing rights (e.g., privacy as in SyRI case); reinterpretation of existing rights (e.g., horizontal application of fundamental rights against private corporations); formulation of new collective interest-based rights to counter systemic disinformation and algorithmic misgovernance. | Erosion of trust in the Rule of Law; systemic disinformation and its impact on democratic processes and institutions; algorithmic misgovernance (including automated decision-making, procedural justice, legal certainty, social structuring, algorithmic bias, representation, and human rights); digitalization of the social contract; protection of fundamental rights in the digital age; need for collective rights and accountability for tech platforms. | General citizenry in liberal democracies, with specific examples highlighting disproportionate impacts on vulnerable groups such as racial minorities (e.g., Dutch childcare scandal), economically disadvantaged students (e.g., UK A-level grading), and welfare recipients (e.g., Australian Robodebt). | Constitutional Law, Administrative Law, Human Rights Law, Technology Law, Media Law | United States, European Union (and member states like the Netherlands), United Kingdom, Australia. The paper also refers to 'global techno-legal developments' and 'liberal democracies the world over'. | NaN | NaN | NaN | False | False | NaN | Inadequacy of current legal frameworks to address collective harms from AI and digital platforms; insufficient accountability mechanisms for Big Tech corporations regarding their impact on democratic processes and fundamental rights; challenges in effective AI regulation due to factors like regulatory entrepreneurship and lobbying; the difficulty for citizens to distinguish truth from falsehood in an AI-influenced infosphere; the Rule of Law ceding governance space to the 'rule of code'. | NaN | Erosion of public trust in the Rule of Law and democratic institutions; AI-driven misinformation and disinformation threatening electoral processes and social cohesion; algorithmic misgovernance leading to biased, discriminatory, and unjust outcomes; lack of transparency and contestability in automated decision-making; invasion of privacy; dehumanization of the law; increased social and political polarization; failure to uphold fundamental rights in the digital environment; regulatory capture by tech companies. |
| 2025JLMktInnovation63.pdf | HeinOnline | BIG DATA AND COMPETITION LAW: NAVIGATING TRADE PRACTICES IN THE DIGITAL AGE | This paper examines anti-competitive practices by data-driven businesses, discussing effects on market power, transparency, and conduct. It analyzes global competition law challenges and proposes regulatory recommendations for addressing big data in antitrust inquiries. | True | Market | False | 3.0 | NaN | NaN | NaN | NaN | Concentration of market power through big data leading to reduced competition, entry barriers, potential consumer harm (e.g., higher prices, reduced choice, privacy infringements), and difficulties for traditional antitrust enforcement. | Updating competition law frameworks and enforcement strategies, including redefining relevant markets, reassessing dominance using data-specific factors, considering mandatory data sharing (e.g., on FRAND terms), revising merger control thresholds for data-rich acquisitions, and fostering international regulatory cooperation. | Ensuring fair market competition, preventing consumer harm from anti-competitive data-driven practices, adapting antitrust/competition law for the digital economy. | Consumers and smaller/new market entrants facing dominant digital platforms. | Competition Law (Antitrust Law); Data Privacy Law (as it intersects with competition). | International (specifically discusses EU, USA, India, Canada, Germany, UK, Australia, and general OECD perspectives). | NaN | NaN | NaN | False | False | NaN | Inadequacy of traditional competition law tools for data-driven markets; nascent regulatory responses globally; need for enhanced international cooperation and updated investigative techniques for regulators. | Challenges for regulatory authorities in adapting and applying competition law to data-driven markets, including defining relevant markets, assessing dominance, identifying novel anti-competitive conducts (e.g., algorithmic collusion, harmful data-driven mergers), and balancing innovation with fair competition. | Market distortion through data monopolization, creation of entry barriers, algorithmic collusion, abuse of dominant positions (e.g., refusal of data access, discriminatory pricing, anti-competitive tie-ins, exploitative data collection), harmful data-driven mergers, and erosion of consumer privacy where it impacts competition. |
| 2024AccesstoJustEEur120.pdf | HeinOnline | LEGAL ANAL YSIS OF EU ARTIFICIAL INTELLIGENCE ACT (2024): INSIGHTS FROM PERSONAL DATA GOVERNANCE AND HEALTH POLICY | This paper analyzes the EU Artificial Intelligence Act (2024), focusing on its implications for personal data governance, health policy, and the protection of fundamental rights, especially within the health data sector. It also examines the Act's alignment with existing medical device regulations, its impact on access to justice, and related AI legal reforms in Ukraine and Moldova. | True | Idealistic | False | 2.0 | Positive | The EU Artificial Intelligence Act (2024) and its regulatory framework. | NaN | NaN | Complexities in implementing the AI Act within national justice systems impacting judicial reform and access to justice; Gaps in the AI Act's coverage, such as specific regulations for all public health areas; General challenges in AI regulation like ensuring explainability and effective human oversight; Difficulties in harmonizing new AI rules with existing legal frameworks. | The EU Artificial Intelligence Act itself, aiming for a harmonized, ethical, and safe AI market respecting fundamental rights; Emphasis on risk-based classification, ethical principles (human-centricity, transparency, non-discrimination), conformity assessments, and human oversight within the Act; National legislative reforms in EU candidate countries (e.g., Ukraine, Moldova) to align with EU AI standards. | Regulation of AI for ethical and safe use (particularly in healthcare and data governance); Harmonization of AI rules to protect fundamental human rights and improve access to justice; Impact of AI legislation on judicial reform and national legal systems. | General public / EU citizens, with a particular focus on patients within the healthcare system regarding protection of their rights and safety. | AI Law, Data Protection Law, Health Law, Medical Device Regulation, Human Rights Law, EU Law, Comparative Law. | European Union (EU), Ukraine, Republic of Moldova. | NaN | NaN | NaN | False | False | NaN | The EU AI Act's Annex III lacks precise regulations for all areas of public health; Need for subsequent sectoral approaches and regulations to complement the horizontal AI Act; Identified loopholes and limitations in fully regulating all AI systems and their applications, particularly in fast-evolving areas like healthcare AI. | Operationalizing concepts like 'human oversight' and 'human-centredness' in AI regulation; Aligning the EU AI Act's provisions with the complexities of AI system development; Balancing innovation with safety, especially through mechanisms like 'regulatory sandboxes'; Ensuring 'explainability' in AI, particularly for high-risk applications like medical diagnostics; Harmonizing the AI Act with diverse existing legislation and jurisprudence. | Misuse of AI in various sectors (economy, rule of law, democracy, healthcare); AI systems enabling cognitive-behavioural manipulation or discriminatory social scoring; Malfunctions in AI-driven medical devices impacting patient safety and rights; Breaches of data protection and privacy through non-compliant AI systems; Harmful content generation by AI (e.g., deepfakes) impacting human rights. |
| 24HousJHealthLPoly77.pdf | HeinOnline | Artificial Intelligence and the HIPAA Privacy Rule: A Primer | This paper examines how the HIPAA Privacy, Security, and Breach Notification Rules apply to various AI applications in healthcare, such as chatbots and diagnostic tools. It highlights significant regulatory gaps, data re-identification risks, and hurdles to data sharing, underscoring the need for updated guidance and rules to protect patient information in the age of AI. | True | Idealistic | True | 3.0 | Neutral | AI-driven symptom checkers, medical chatbots (e.g., Northwell Health Pregnancy Chats), AI-assisted medical image interpretation, AI-powered medical scribes (e.g., DAX Express with GPT-4), AI for health insurance claim review (e.g., nH Predict). | NaN | NaN | Regulatory gaps in HIPAA making it difficult to apply to AI tools; risk of AI-powered re-identification of de-identified health data; lack of transparency for patients regarding AI's use of their data; potential for AI errors harming patients with unclear recourse; and a patchwork of laws offering inconsistent protection. | The paper implicitly calls for regulatory reform, including HHS issuing clarifying guidance on HIPAA's application to AI (e.g., re-identification, synthetic data), and amending HIPAA for greater transparency (e.g., in Notices of Privacy Practices about AI use). | Data privacy and security in AI-driven healthcare; patient rights (notice, access, amendment, restriction) with AI; regulation of AI tools; re-identification risks of health data; algorithmic bias and discrimination in AI healthcare decisions. | Patients generally, with specific examples including elderly beneficiaries of health insurance and individuals whose de-identified data is at risk of re-identification. | Health Law (HIPAA), Privacy Law, Data Security Law, Administrative Law. | United States | Discusses the use of large health datasets, including electronic medical records and claims data (both identifiable and purportedly de-identified), by healthcare entities and tech companies for AI development and deployment. | NaN | NaN | True | False | Several AI tools discussed, such as specific hospital-operated symptom checkers, commercial symptom checkers (e.g., Ubie), and AI scribes like DAX Express, are presented as existing and deployed services. | HIPAA's definitions inadequately cover all AI actors and data types; de-identification safe harbors may be insufficient against AI-re-identification; lack of specific regulation for synthetic data; inadequate patient notification about AI use; limited patient ability to restrict AI or amend AI-generated errors; inconsistent protection from patchwork laws. | Defining and regulating new AI actors outside traditional HIPAA-covered entities; balancing data sharing for AI innovation with patient privacy; ensuring accuracy and fairness of AI-generated health information and decisions; keeping regulations updated with rapid AI advancements; operationalizing patient rights with AI-generated content. | Increased privacy/security breaches; informational injuries; re-identification of de-identified data; incorrect AI-generated medical information or claim denials causing harm; potential for discrimination via AI tools; lack of patient control over AI's use of their data. |
| 17ContempAsiaArbJ35.pdf | HeinOnline | Artificial Intelligence and the Future of International Trade Law and Dispute Settlement | This paper examines AI's transformative potential in international trade law and WTO dispute resolution, discussing benefits like enhanced efficiency and challenges such as ethical concerns and data privacy. It advocates for international collaboration and new legal frameworks to guide AI applications, ensuring technological advancements support an equitable and transparent global trade system. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Expensive and lengthy nature of existing dispute settlement processes, particularly for developing countries; Algorithmic bias potentially perpetuating existing economic power imbalances and cultural insensitivities; Disparity in digital literacy and access to AI technology between developed and developing nations. | International collaboration to establish standards and new legal frameworks for AI in trade; Development of cohesive regulatory frameworks promoting fairness, transparency, and mitigating bias; Reform of existing dispute settlement mechanisms (e.g., WTO) to enhance accessibility, reduce costs, and improve transparency for all members, especially developing countries. | Access to equitable and efficient international trade dispute settlement for developing countries; Ensuring fair and transparent application of AI in global trade mechanisms; Addressing algorithmic bias in legal decision-making within international trade. | Developing countries | International Trade Law, Dispute Settlement (including international arbitration and WTO dispute settlement) | International | NaN | NaN | NaN | False | False | NaN | Need for updated international legal frameworks to govern AI in trade and dispute settlement, addressing issues like cross-border data flows, IP rights for AI creations, and liability; Lack of international consensus on data standards, algorithmic fairness, and the specific role of AI in dispute resolution to ensure equitable outcomes; Inadequacy of current international dispute settlement mechanisms to handle AI-driven complexities and provide accessible justice, particularly for developing countries. | Ensuring ethical AI deployment, maintaining transparency in AI decision-making, and protecting data privacy in cross-border contexts; Addressing and mitigating algorithmic bias to prevent perpetuation of inequalities and unfair outcomes; Establishing clear liability frameworks for decisions made or influenced by AI systems; Overcoming disparities in digital literacy and technological access across nations, particularly between developed and developing countries. | Compromising the integrity and fairness of legal systems through unscrutinized AI integration; Ethical violations including privacy breaches and algorithmic bias leading to discriminatory or unfair outcomes in trade and dispute settlement; Job displacement due to AI-driven automation in legal and trade-related sectors; Potential for AI to exacerbate existing global inequalities if access and benefits are not equitably distributed. |
| 38EmoryIntlLRev819.pdf | HeinOnline | THE DIGITALIZATION OF LITIGATION | This paper discusses the digitalization of litigation (DoL), examining its potential to enhance efficiency, transparency, and access to justice, alongside the inherent challenges and risks. It reviews international initiatives, the role of AI, and emphasizes the importance of ensuring equitable access and upholding fundamental rights in the digital transformation of justice systems. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Digital divide (connectivity, literacy, capabilities); risks to privacy, security, equality, and fundamental rights; potential for misuse of data; challenges in adapting legal concepts (e.g., right to a hearing) to digital contexts; abrupt implementation without adequate adaptation time. | International collaboration; development of digital strategies focusing on human rights and inclusivity; gradual implementation of digital tools with adaptation time; development of ethical guidelines (e.g., Al European Charter) and regulatory frameworks for AI. | Improving justice sector efficiency and transparency; enhancing access to justice, especially for vulnerable groups; mitigating court operational disruptions; the right to a hearing in digital settings; environmental justice; upholding fundamental rights in digitalized systems; addressing the digital divide. | People in remote areas, linguistic minorities, people with disabilities, those with time/travel/work constraints, individuals with low technological literacy or capacity, the economically disadvantaged. | General litigation, Criminal law, Civil liability, Environmental law | International | NaN | NaN | NaN | False | False | NaN | Digital literacy and access disparities; citizens' lack of capability to use digital justice tools; need for legal and constitutional adaptation to new technologies; insufficient empirical data on digital justice trends; societal impact management of AI. | NaN | Exacerbation of risks to privacy, security, equality, fundamental rights; hindering access to justice through poor implementation; government abuse under emergency pretexts; digital divide limiting access; misuse of personal data; legal inaccuracies from AI tools (hallucinations); exclusion of those without digital access/skills. |
| 35FordhamIntellPropMediaE.pdf | HeinOnline | Al in the Courtroom: The Boundaries of RoboLawyers and RoboJudges | This paper examines the impact of AI, including LegalTech and JudicialTech, on the legal system, acknowledging its potential to enhance efficiency and access to justice. However, it argues for clear boundaries, asserting that AI should not fully replace human litigators and judges due to concerns about fundamental rights, legal legitimacy, and the nature of law. | True | Idealistic | True | 3.0 | Neutral | Scoring algorithms (e.g., for risk assessment, outcome prediction) and Generative AI (e.g., for legal advice, document drafting), within broader categories of LegalTech and JudicialTech. | NaN | NaN | High cost of legal services, lack of sufficient legal help for low-income individuals, backlogs in courts, and legal uncertainty disproportionately affecting disadvantaged populations. | AI legal tools (LegalTech and JudicialTech) can reduce costs, improve dissemination of legal information, provide services to underserved populations, enhance court efficiency, and reduce legal uncertainty. | Affordability and availability of legal services, court efficiency, reduction of legal uncertainty, ensuring fair trial and due process in the context of AI deployment. | Low-income individuals, disadvantaged litigants, and those unable to afford traditional professional human legal services. | General / Multiple fields, including criminal law (sentencing, recidivism), civil litigation (e-commerce, product liability, patent, personal injury), family law (prenuptial agreements), and corporate law (due diligence, contract review). | Multiple (USA, China, Estonia, England, Israel, EU extensively discussed as examples and for regulatory approaches). | The paper discusses various AI systems: scoring algorithms (e.g., COMPAS) using historical case data and personal information; generative AI (e.g., ChatGPT) trained on vast general text corpora; specific tools like Amazon's hiring algorithm trained on proprietary company data. | NaN | NaN | True | True | Tools like DoNotPay (initially mentioned as free for specific tasks), ChatGPT (publicly available with a free tier), and LegalZoom (commercial service) are discussed as operational. | The paper highlights significant gaps in ensuring AI's ethical and fair application in law, including underdeveloped legal/ethical frameworks, the challenge of balancing AI benefits with fundamental rights (fair trial, due process, explainability), mitigating bias, ensuring transparency and human control, protecting privacy, preventing AI from stunting legal development, maintaining legal system legitimacy, and addressing AI's limitations with moral/value judgments and cultural nuances. | NaN | Inaccuracies and hallucinations in AI outputs; lack of accountability and liability for AI errors; opacity (black box effect) leading to lack of transparency and explainability; bias and discrimination; data privacy violations and cybersecurity threats; loss of human control and automation bias; infringement on fair trial, due process, and the rule of law; undermining legal system legitimacy; hindering dynamic legal development; AI's inability to handle nuanced values, morals, and cultural diversity; dehumanization and infringement on human dignity/autonomy; Unauthorized Practice of Law (UPL). |
| 37GeoJLegalEthics415.pdf | HeinOnline | Untangling Unreliable Citations | The paper argues that unreliable citation practices, exacerbated by new formats like "(cleaned up)" and the uncritical use of AI in legal research, threaten the integrity of the legal system and democratic stability. It advocates for a return to basic verification of sources to ensure accuracy in legal arguments and restore trust in the profession. | True | Idealistic | True | 3.0 | Negative | The use of generative AI tools (e.g., ChatGPT, Google Bard) for legal research and brief preparation, and its propensity to 'hallucinate' or fabricate citations and information. | Case studies of lawyers misusing AI tools (e.g., ChatGPT in Mata v. Avianca, Google Bard in Michael Cohen incident) leading to sanctions and public embarrassment due to fabricated citations. | The unverified use of generative AI tools for legal research by lawyers led to the submission of briefs containing non-existent case citations, resulting in judicial sanctions (e.g., a $5,000 fine in the Mata case), professional embarrassment, and the undermining of the legal process. | Erosion of legal precedent and trust in the legal system due to unreliable citations, exacerbated by practices like unverified copy-pasting, misuse of citation formats (e.g., '(cleaned up)'), and uncritical adoption of AI tools that generate false information. Unequal access to information further complicates verification. | A return to fundamental practices of thoroughly reading and verifying all cited sources, including those suggested by AI. Increased professional diligence, skepticism towards unverified information, and candor with courts are advocated. | Integrity of the legal process, reliability of legal precedent, professional ethics, and the impact of AI on these aspects, which are foundational to a just legal system. | NaN | Civil Procedure, Legal Ethics, Patent Law, General Legal Practice (research and writing). | United States (Federal and State, with specific examples from Kansas). | The AI tools discussed (e.g., ChatGPT, Google Bard) are based on large, diverse datasets, including public internet text, but the specifics are proprietary to their developers. The paper highlights issues stemming from this training, like hallucinations. | NaN | Commercial AI tools (e.g., ChatGPT, Google Bard) are deployed by tech companies via web interfaces and APIs, leading to widespread accessibility. | True | True | Generative AI tools like ChatGPT and Google Bard are available online, often with free access tiers. The '(cleaned up)' citation is a practice that can be adopted by any legal writer. | Technical gaps in AI reliability (hallucinations) and verification. Societal/professional gaps include insufficient diligence in source checking by legal professionals, ethical challenges with AI use, and the need for updated rules and norms for technology in legal practice. | General challenges for AI tools include ensuring factual accuracy, preventing 'hallucinations' of non-existent information, promoting critical use by legal professionals rather than blind reliance, and addressing the rapid pace of AI development that outstrips ethical guidelines and full understanding of its impact. | Submission of fabricated legal citations leading to professional sanctions (e.g., fines) and reputational damage for lawyers. Miscarriage of justice if decisions are based on false information. Broader risks include erosion of legal precedent, democratic instability, and diminished public trust in the legal system. |
| 92FordhamLRev (2).pdf | HeinOnline | The Legal Imitation Game: Generative AI's Incompatibility with Clinical Legal Education | This paper argues that Generative AI (GenAI) is largely incompatible with the core pedagogical goals of clinical legal education: practice readiness, justice readiness, and client-centered lawyering. It contends GenAI hinders genuine skill development and can exacerbate societal injustices and ethical issues, urging a critical approach to its integration. | True | Idealistic | True | 3.0 | Negative | NaN | NaN | NaN | Worsening unequal access to legal information and services; Concentration of legal information and power in a few corporations, replicating information asymmetries; GenAI systems are trained on data reflecting human biases and historical discrimination, potentially exacerbating injustices; GenAI tends to reinforce the status quo. | Clinicians should press students to critically interrogate how GenAI tools are built and operate, investigate their ethical implications for justice and society, and recognize the role lawyers using these tools may play in causing harm. The paper advocates for helping students make informed, value-based, and justice-ready decisions about technology, rather than uncritically adopting GenAI. | Access to legal information; Quality and ethics of legal services for underserved populations; Bias in legal technology; Impact of AI on justice systems and legal education. | Underserved communities generally, individuals without power, clients of public interest clinics, populations affected by systemic discrimination. | NaN | United States | NaN | NaN | NaN | False | False | NaN | Societal: Lack of an agreed-upon framework for evaluating the risk or utility of GenAI in legal education; GenAI's tendency to reinforce existing economic/power structures and injustices due to its design and data. Technical: The 'black box' nature of GenAI, with no existing mechanisms for auditing or interrogating the logic behind responses; GenAI's inherent limitation to imitation rather than genuine understanding. | NaN | GenAI outputs may imitate competent lawyering but fall short, leading to substandard legal work; Automation bias can lead users to uncritically accept AI outputs, including inaccuracies; GenAI can produce 'hallucinated' or false information (e.g., fake citations); Over-reliance on GenAI may undermine students' development of core legal skills (analysis, reasoning, writing); Worsening of unequal access to legal information and services; Concentration of legal information and power in a few large technology corporations; Appropriation of human creativity and personal data without consent or compensation for training models; Perpetuation and amplification of societal biases and historical discrimination embedded in training data; Significant negative environmental impact (resource extraction, high energy and water consumption); Exploitation of precarious workers in the AI development and maintenance pipeline; Reinforcement of the status quo and existing injustices by design. |
| 15IJCA1.pdf | HeinOnline | Unboxing Generative AI for the Legal Professions: Functions, Impacts and Governance | This paper examines the integration of Generative AI (GenAI) into legal professions and the administration of justice, focusing on its functions, impacts, and initial attempts at governance. It discusses GenAI's capabilities, its use by lawyers and judges, and analyzes different regulatory approaches, highlighting the tension between user responsibility and system certification. | True | Idealistic | True | 3.0 | Neutral | Generative AI (GenAI) / Large Language Models (LLMs) and their domain-specific applications (e.g., using Retrieval-Augmented Generation). Specific examples discussed include general chatbots (ChatGPT, Bard) and domain-specific tools like the Portuguese 'Practical Guide to Access to Justice (GPJ)'. | References a Stanford University study (Magesh et al., 2024) that assessed hallucination rates in leading commercial legal AI research tools; the author also conducted an 'initial test' of the Portuguese GPJ system for consistency and accuracy of answers. | The cited Stanford study (Magesh et al., 2024) found that leading commercial legal AI research tools produced hallucinations in 17% to 33% of responses. The author's test of the Portuguese GPJ found it gave consistent answers to simple questions but could give misleading answers to complex ones, though it showed learning capability over time. | Reliance on end-user's ability to verify AI output, which is challenging for laypersons; risk of AI generating inaccurate or misleading legal information; potential costs of reliable, high-quality AI systems for access to justice initiatives. | Development of curated GenAI systems for delivering legal information (e.g., chatbots based on official, verified data); strong emphasis on human oversight, critical evaluation of AI-generated content, and user responsibility in a legal context. | Access to legal information for citizens; support for self-represented litigants; simplification of interaction with the justice system. | General public / citizens seeking legal information or interacting with the justice system. | General (covers various fields including family law, company law, criminal law, contract law, and general legal research/drafting). | International | For general GenAI: extensive, sometimes non-contextualized datasets. For domain-specific legal AI: curated legal databases (judgments, doctrine, statutes), specific case files, law firm knowledge bases. For the Portuguese GPJ: content from the Ministry of Justice's Digital Justice platform. | Retrieval-Augmented Generation (RAG); semantic injection of domain-specific knowledge; prompt engineering; no-code/low-code development approaches using APIs/GPTs. | Integration into office applications (e.g., word processors, spreadsheets); standalone domain-specific applications; use of APIs for custom solutions; cloud platform deployment (e.g., Microsoft Azure for the Portuguese GPJ). | True | True | The Portuguese 'Practical Guide to Access to Justice (GPJ)' is mentioned as being in beta stage and accessible via a public URL, implying free web-based access. Basic versions of general GenAI chatbots (e.g., ChatGPT) are also noted as available online for free. | Need for robust, independent validation of AI tools' reliability and claims made by providers; the difficulty for laypersons to adequately verify AI outputs in legal contexts; current regulatory frameworks and guidelines struggle to keep pace with rapid technological advancements; weak accountability mechanisms for AI use. | Ensuring factual accuracy and avoiding 'hallucinations' in AI outputs; maintaining data confidentiality and privacy, especially with sensitive legal information; the necessity for users to possess sufficient expertise to verify AI-generated content; managing the 'black box' nature and potential biases of LLMs. | Generation of 'hallucinations' (false or misleading information, e.g., fake case citations); breaches of privacy and data protection; potential deskilling of legal professionals; over-reliance on AI leading to unchecked errors; economic shifts concentrating resources with tech providers; undermining public trust in the justice system if AI is misused; adverse impacts on due process if AI outputs are not rigorously verified. |
| 30ClinicalLRev227.pdf | HeinOnline | SEARCHING FOR JUSTICE: INCORPORATING CRITICAL LEGAL RESEARCH INTO CLINIC SEMINAR | This paper advocates for incorporating Critical Legal Research (CLR) into clinical legal education to equip students with tools to challenge biased legal information systems and pursue social justice. It presents CLR as a necessary pedagogical counterweight to the problematic rise of AI in legal research and offers a model module for its implementation. | True | Idealistic | False | 1.0 | Negative | Incorporating Critical Legal Research (CLR) pedagogy into clinic seminars, exemplified by a model transactional research module. | NaN | NaN | Bias in traditional legal research tools and classification systems reifying hegemonic norms; the myth of neutrality in legal information; negative impacts of AI/GAI entrenching biases and hindering critical thinking; data weaponization by legal publishers for surveillance. | Integrating Critical Legal Research (CLR) into clinical legal education: teaching deconstruction/reconstruction of research methods, 'unplugged brainstorming', challenging neutrality of databases and AI, developing research plans accounting for bias, and using CLR as a counterweight to AI. | Critiquing and improving legal research education for social justice lawyering; addressing biases in legal information systems disadvantaging marginalized communities; empowering lawyers to find innovative legal solutions beyond dominant narratives. | Marginalized groups, vulnerable populations, and clients whose needs fall outside dominant legal narratives (e.g., domestic workers, returning citizens, mutual aid organizations). | Legal Education, Transactional Law, Clinical Legal Practice | United States | NaN | Based on literature review of Critical Legal Research and clinical pedagogy, and the author's teaching experience. | Academic publication (law review article) and creation of an online LibGuide with resources for educators. | True | True | A publicly accessible LibGuide (https://wcl.american.libguides.com/critical-research_forclinics) with pedagogical resources. | Need for wider adoption and development of CLR pedagogy; deeper collaboration between faculty and librarians; ongoing strategies to address biases in legal tech and systemic injustices; development of CLR for transformative change beyond law reform. | Limited seminar time; varying prior legal research instruction for students; institutional hierarchies hindering collaboration with librarians/research faculty; the rapid growth of AI in legal research creating new pedagogical hurdles. | Perpetuation of harmful hegemonies by traditional legal research; AI/GAI entrenching biases, hindering critical thinking, and producing inaccurate or fabricated results; weaponization of data by legal publishers for surveillance; lawyers facing sanctions for misuse of GAI; potential for a two-tiered justice system due to AI adjudication. |
| 20ActaUDanubiusJur7.pdf | HeinOnline | The General Data Protection Regulation of 2016 (GDPR) Meets its Sibling the Artificial Intelligence Act of 2024: A Power Couple, or a Clash of Titans? | This paper explores the complex relationship between the EU's General Data Protection Regulation (GDPR) and the newly adopted EU Artificial Intelligence Act (AI Act), analyzing their potential synergies or conflicts in regulating AI technologies. It assesses whether these two frameworks will function as a 'power couple' fostering responsible AI and protecting individual rights, or a 'clash of titans' creating implementation challenges and hindering innovation. | True | Market | False | 2.0 | NaN | General Data Protection Regulation (GDPR) of 2016 and Artificial Intelligence Act (AI Act) of 2024 as regulatory frameworks. | The paper employs a qualitative research methodology, including a detailed review of legislative texts (GDPR and AI Act), existing scholarly literature, government reports, and legal documents. It uses a comparative approach to analyze provisions and identify alignments or divergences, supplemented by thematic analysis. | The GDPR and AI Act share goals of safeguarding fundamental rights and promoting ethical AI, but present potential conflicts due to differing focuses (e.g., data minimization under GDPR vs. data needs for AI development under AI Act). A harmonious balance, addressing regulatory divergence and compliance burdens, is crucial for them to effectively function as a 'power couple' rather than clashing. | NaN | NaN | NaN | NaN | Data protection law, Artificial intelligence law/regulation, EU law, Civil liability. | European Union (EU) | NaN | NaN | NaN | True | True | The GDPR is an EU regulation currently in force. The EU AI Act was adopted by the EU Parliament in March 2024 and its provisions will become applicable progressively. The texts of both regulations are publicly available. | NaN | Regulatory divergence and potential inconsistencies between GDPR and AI Act; balancing data protection principles (e.g., data minimization) with data requirements for AI development; ensuring transparency and accountability in AI systems in a way that aligns with both frameworks; translating ethical considerations into actionable regulatory measures without stifling innovation; significant compliance burden for businesses and organizations. | Potential for a 'clash of titans' between GDPR and AI Act hindering innovation and creating regulatory uncertainty; risks of algorithmic bias, discrimination, and lack of fairness and accountability in AI systems if not governed effectively; potential infringement on privacy and freedom of expression if regulations are not carefully implemented. |
| 25TransactionsTennJBusL25.pdf | HeinOnline | ESTABLISHING A FUTURE-PROOF FRAMEWORK FOR Al REGULATION: BALANCING ETHICS, TRANSPARENCY, AND INNOVATION | This paper examines the multifaceted applications and societal impacts of artificial intelligence, particularly generative AI, covering its benefits in areas like healthcare and access to justice, alongside significant risks such as bias, job displacement, and misinformation. It advocates for a comprehensive, future-proof regulatory framework by analyzing global legislative efforts, aiming to balance innovation with ethics, transparency, and human rights. | True | Idealistic | True | 3.0 | Neutral | Generative AI (e.g., ChatGPT, Sora), Large Language Models, facial recognition technology, algorithmic decision-making systems (e.g., COMPAS), legal chatbots (e.g., DoNotPay), predictive policing tools. | The paper cites evaluations by others (e.g., institutional reports like FTC, ProPublica; academic studies) which involve analyzing AI outputs for accuracy, bias (e.g., racial, gender), and real-world impact (e.g., false identifications, discriminatory loan/rental decisions). | Reports findings from cited studies: facial recognition shows higher error rates for minorities and women; predictive policing tools (e.g., COMPAS) demonstrate racial bias by disproportionately flagging minorities as high-risk; some healthcare algorithms underdiagnose underserved populations or assign lower risk scores to Black patients with similar needs as white patients. | Incorrect/outdated/misleading AI legal information, embedded bias, liability gaps for AI advice, user difficulty in assessing AI advice quality, need for constant AI system updates for legal accuracy, cross-jurisdictional compliance issues, AI's limited nuanced understanding for complex legal matters, risk of widening the digital divide, confidentiality and attorney-client privilege concerns. | Develop robust AI data protection mechanisms (confidentiality, privilege), ensure regular AI system updates for legal accuracy, provide clear disclosures about AI capabilities and limitations, adopt a balanced regulatory approach promoting innovation while upholding ethics and compliance, mandate audits for bias, establish ethical guidelines for AI in legal services, train legal professionals on responsible AI use and verification of AI outputs. | Providing basic legal information, assistance with simple legal matters, enhancing understanding of legal proceedings, use of legal chatbots for initial guidance, potential for AI-assisted counsel for indigent defendants and readily accessible AI legal support for ordinary citizens. | Low-income individuals, marginalized communities, indigent criminal defendants, ordinary citizens needing legal assistance. | Civil law (general), Criminal law, Family law, Housing law, Employment law, Intellectual Property law, Privacy law. | International | The paper discusses various AI systems trained on diverse large-scale datasets, including public internet text and image data, copyrighted materials (news articles, artworks, music), official records (crime reports, arrest records), and consumer data (PII, credit history, behavioral data). It highlights issues with unverified, biased information within these datasets. | NaN | NaN | False | False | NaN | Ensuring reliability and legal accuracy of AI tools; establishing clear liability frameworks for AI-generated legal advice; developing AI with nuanced understanding for complex legal cases; addressing the digital divide for equitable AI access; lack of robust confidentiality/privilege mechanisms in current AI; insufficient legal professional training on AI; need for a comprehensive AI regulatory framework in legal services. | NaN | Deepfakes and misinformation eroding trust and manipulating democratic processes; algorithmic bias leading to discrimination in justice, housing, employment, and healthcare; privacy violations through enhanced surveillance and data misuse; AI-powered cybersecurity threats; significant white-collar job displacement; widespread intellectual property infringement; safety, control, and accountability issues with advanced AI; negative impacts on mental health and societal cohesion; high environmental costs of AI development. |
| 20UStThomasLJ190.pdf | HeinOnline | WHAT DOES RELEVANT MEAN TO YOU? CREATING A CHOOSE-YOUR-OWN-ADVENTURE TECHNOLOGY COMPETENCY FRAMEWORK | This paper argues for the necessity of clear definitions and standardized frameworks for lawyer's technology competence, as mandated by ethical rules. It proposes a 'Choose-Your-Own-Adventure' model to develop individualized technology competencies and discusses various approaches for law schools to integrate this essential training. | True | Market | False | 1.0 | NaN | A 'Choose-Your-Own-Adventure' technology competency framework and a 'Legal Competency-Creation Model' for lawyers, adapted from the NPEC model for competency-based education. | NaN | NaN | NaN | NaN | NaN | NaN | General legal practice | United States | NaN | Literature review of competence definitions and required technology skills; adaptation of an existing educational competency model (NPEC's model); conceptual framework development, including draft core competencies. | Proposed for implementation in legal education through various models (mandatory courses, embedded training, voluntary courses, non-credit sessions) and for professional development. Suggests collaboration with bodies like the SALI Alliance for lexicon development. | False | False | NaN | Lack of a universally agreed-upon definition and scope of technology competence for lawyers. Insufficient and inconsistent technology training within law school curricula. Absence of standardized competency sets and a common lexicon for legal technology skills. | Defining 'competence' and 'competency' in a way that is both specific enough to be useful and flexible enough for diverse practices and evolving technology. Overcoming inertia or lack of clarity regarding responsibility for technology training in legal education. Ensuring competencies remain relevant as technology rapidly changes. | Dismissal of client cases due to technological errors (e.g., improper redlining). Violation of ethical duties of competence. Cybersecurity vulnerabilities leading to breaches of client_confidentiality. Damage to professional reputation and potential malpractice claims. |
| 92FordhamLRev (3).pdf | HeinOnline | CHATGPT, Al LARGE LANGUAGE MODELS, AND LAW | This essay explains the workings, recent advancements, and capabilities of AI Large Language Models (LLMs) like ChatGPT/GPT-4, particularly their application in understanding and generating legal texts. It also presents a balanced discussion of their limitations, emphasizing the need for careful use while acknowledging their significant potential to impact the legal domain. | True | Market | True | 3.0 | Positive | Large Language Models (LLMs) like ChatGPT/GPT-4, and underlying mechanisms like the transformer architecture, self-supervised pre-training, instruction fine-tuning, and RLHF. | NaN | NaN | NaN | NaN | Increasing access to justice (mentioned as a potential benefit). | NaN | General legal practice (contracts, motions, legal analysis, patents, copyright). | United States | Vast corpus of general text from the internet (e.g., Wikipedia, Reddit), books, research papers, newspapers, and specific datasets of question-answer pairs for fine-tuning; includes legal documents like contracts and opinions. | Transformer architecture, self-supervised pre-training, deep learning neural networks, instruction fine-tuning, Reinforcement Learning from Human Feedback (RLHF). | Web-based chat interfaces (e.g., ChatGPT), integration into specialized commercial legal platforms (e.g., Lexis+ AI, Westlaw CoCounsel). | True | True | Free version of ChatGPT (GPT-3.5) available online; GPT-4 accessible for free via Microsoft's Bing Chat and Copilot. Paid subscription for direct GPT-4 access via ChatGPT Plus. | Reliability issues (hallucinations, reasoning flaws), potential for perpetuating biases, lack of transparency/interpretability, and context window limitations (though improving). | High cost and computational resources for training large models, ensuring factual accuracy and coherent reasoning, managing data privacy and security when handling sensitive legal information, and addressing the 'black box' nature of complex models. | Violating client confidentiality or privilege through data input, generation of fictitious legal citations ('hallucinations'), flawed legal analysis leading to incorrect conclusions, perpetuation of biases from training data, and concerns about accountability and trust due to lack of transparency. |
| 2024JurnalulBarouluiCluj2.pdf | HeinOnline | The Future of the Legal Profession (I) on Non-Lawyering: The British and American Perspectives; ChatGPT "Sins" in the Legal Profession | This paper examines the evolution of the legal profession, discussing Alternative Business Structures (ABS) and Non-Lawyer Ownership (NLO) in the UK and US as models for legal service delivery, and highlighting the ethical risks of AI misuse, particularly ChatGPT, through case studies. It reflects on the Romanian legal context, anticipating regulatory changes and emphasizing lawyers' need for technological adaptation and continuous training. | False | Market | True | 3.0 | Neutral | ChatGPT | Analysis of case law where ChatGPT was misused by lawyers (Mata v. Avianca, Inc.; Zheng v. Chen). | Lawyers who misused ChatGPT by submitting fabricated citations were sanctioned: in Mata v. Avianca, a $5,000 fine and notification requirements; in Zheng v. Chen, payment of additional costs incurred by the opposing party. | Regulatory restrictions on non-lawyer involvement in legal services and the legal profession's slow adaptation; lawyers' lack of understanding and misuse of AI tools, hindering competent service delivery. | Regulatory reform to allow new legal service delivery models (e.g., revising rules like ABA's Rule 5.4, adopting ABS-like structures); continuous education and institutional training for lawyers on AI tools, their limitations, and ethical use. | Regulation of the legal profession, Alternative Business Structures (ABS), Non-Lawyer Ownership (NLO) of law firms, ethical use of AI (ChatGPT) by legal professionals, professional responsibility. | General public / consumers of legal services. | General legal practice, Corporate law (company formation), Civil litigation (personal injury), Family law. | United Kingdom, United States of America, Romania, Canada. | NaN | NaN | NaN | True | True | ChatGPT, a tool discussed extensively, is publicly available with both free and paid tiers. | Lack of lawyer competency in understanding and responsibly using AI tools like ChatGPT; slow pace of regulatory adaptation to new legal service delivery models and technological advancements in some jurisdictions. | Lawyers' lack of understanding of AI limitations (e.g., generating fictitious information), failure to verify AI-generated content before submission to court, and the ethical misconduct arising from improper AI use. | Submission of fabricated legal precedents or false information to courts, professional sanctions for lawyers (fines, reputational damage), abuse of the judicial system, potential for judicial errors, and undermining the integrity of the justice system. |
| 59TulsaLRev361.pdf | HeinOnline | The Automation Paradox | This paper examines the legal paradoxes arising from generative AI and self-driving cars, focusing on issues of liability, intellectual property, and constitutional rights. It proposes analytical frameworks based on existing legal principles to address these challenges while also underscoring the need for comprehensive legislative updates. | True | NaN | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Intellectual Property Law (Copyright, Patent), Constitutional Law (First Amendment, Fourth Amendment, Fifth Amendment, Patent and Copyright Clause, Due Process, Equal Protection), Tort Law (Defamation, Negligence, Breach of Fiduciary Duty, False Light, Emotional Distress, Appropriation of Likeness), Criminal Law (Manslaughter, DUI, Hoaxes), Communications Law (Section 230 CDA), Autonomous Vehicle Law | United States (federal and state, including Arizona, Oklahoma, Texas, California) | NaN | NaN | NaN | False | False | NaN | NaN | NaN | Generative AI risks include: creation of deepfakes leading to reputational/societal harm and threats to democracy/privacy; intellectual property infringement; AI hallucinations and false information; perpetuation of biases; misuse of AI in law enforcement (e.g., arrests based on inaccurate data); privacy violations from user data collection (location, personal information); Fifth Amendment self-incrimination issues. Self-driving car risks include: unclear liability in accidents; privacy concerns from data collection; potential for accidents due to system failures. |
| 10RevBrasileiradeDireitoP.pdf | HeinOnline | Towards a Digitalised Criminal Justice System: Lessons from Poland | This paper examines technological advancements in the Polish criminal justice system, accelerated by COVID-19, focusing on remote hearings, case file digitization, and automated translation. It analyzes their impact on efficiency and fair trial rights, highlighting existing limitations and proposing solutions like expanded remote access and hybrid translation models. | True | Idealistic | True | 2.0 | Neutral | Digitalization approaches in the Polish criminal justice system: remote hearings (including for detention), digitization of criminal proceeding files (e.g., PROK-SYS), and automated translation services. | Legal analysis against Polish law, ECHR standards, EU directives, and assessment of practical implications for fair trial rights, defendant guarantees, and judicial efficiency. | Remote hearings improve efficiency but risk defendant rights (e.g., confidentiality, counsel access) without proper safeguards; digitization offers significant benefits (accessibility, efficiency, security) but implementation is slow and faces challenges like digital exclusion; automated translation is currently insufficient alone for legal contexts and requires human oversight to ensure fairness and accuracy. | Infringement on the right to defense in remote hearings (e.g. lack of confidentiality, limited counsel access); digital exclusion and security risks with digitization; inaccuracy of automated translation for complex legal texts; institutional resistance, costs, and concerns about procedural guarantees. | Expand remote hearings with robust safeguards for confidential counsel-client communication; implement unified, secure digital case file systems with alternatives for digitally excluded persons; adopt a hybrid human-machine translation model with rights to human verification and intervention. | Remote hearings, digitization of case files, automated translation, right to a fair trial, right to defense, access to case files, right to an interpreter, efficiency of criminal proceedings, pre-trial detention hearings. | Defendants in criminal proceedings, particularly those deprived of liberty, non-native language speakers requiring translation/interpretation, and digitally excluded individuals. | Criminal Law, Criminal Procedure | Poland (with reference to EU law and ECHR) | The paper discusses LLM-based automated translation, noting these models are pre-trained on massive and diverse textual datasets; specific datasets for the particular LLMs are not detailed. | NaN | Remote hearings were legislatively adopted and expanded, particularly post-COVID-19, through amendments to the Polish Code of Criminal Procedure. The PROK-SYS digitization system is under gradual implementation by the National Prosecutor's Office. | True | False | Remote hearings are established in Polish law and used in courts for specific criminal proceedings. | Need for unified, interoperable, and secure digital infrastructure; improving AI translation accuracy for legal texts; addressing digital exclusion; ensuring full confidentiality and effective defense rights in digitalized procedures; overcoming institutional resistance to technological adoption. | Costs and security considerations for new technologies; ensuring protection of procedural guarantees and attorney-client privilege; overcoming technical limitations and ensuring system reliability; addressing institutional resistance and change management; balancing efficiency gains with the protection of fundamental rights. | Infringement of the right to defense (e.g., confidential communication with counsel) in remote hearings; digital exclusion hindering access to justice; cybersecurity threats to digitized case files (hacking, data leakage); inaccurate automated translations leading to miscarriages of justice; erosion of fair trial principles if technology is improperly implemented. |
| 57LoyLALRev859.pdf | HeinOnline | THE Al REGULATORY PYRAMID: A TAXONOMY & ANALYSIS OF THE EMERGING TOOLBOX IN THE GLOBAL RACE FOR THE REGULATION AND GOVERNANCE OF ARTIFICIAL INTELLIGENCE | This paper introduces the 'AI Regulatory Pyramid,' a taxonomy and framework for governing artificial intelligence, advocating for a multifaceted and dynamic approach. It emphasizes balancing AI's risks and potentials through a mix of voluntary standards, transparency measures, assessments, and targeted rules, urging rational debate and investment in 'AI for good.' | True | Idealistic | False | 3.0 | Positive | The AI Regulatory Pyramid (a conceptual framework for AI governance and regulation). | NaN | NaN | Irrational fears and skewed debates about AI leading to overly restrictive regulation; lack of public trust if AI risks like bias are not managed; insufficient infrastructure, skill-building, and investment for widespread 'AI for good' applications. | Adoption of balanced and dynamic regulatory frameworks (like the AI Regulatory Pyramid); fostering rational public debate on AI; investing in 'AI for good' initiatives, supporting infrastructure, and education; promoting collaborative public-private governance and experimentation; considering mandates for automation where AI is proven safer and fairer. | Legal information and assistance for pro se individuals (e.g., inventors); improving patent system equity and access; criminal justice reform (e.g., automated record clearing); addressing discrimination and promoting pay equity. | Pro se litigants/inventors; individuals with criminal records seeking to overcome systemic barriers; individuals facing systemic discrimination. | General AI Regulation, Intellectual Property, Employment Law, Consumer Protection, Financial Services Regulation, Criminal Justice, Constitutional Law, Election Law, Administrative Law, Tort Law. | United States, European Union, with comparative references to other international jurisdictions (e.g., China, UK, Japan, Singapore, African Union). | NaN | Regulatory theory (e.g., responsive regulation, new governance), legal analysis, policy analysis, comparative analysis of emerging AI governance approaches globally. | NaN | False | False | NaN | Lack of widespread adoption of beneficial AI for access to justice due to irrational fears or unsupportive regulatory frameworks; insufficient skill-building, market competition, and infrastructure for 'AI for good'; gap in implementing and scaling access to justice initiatives where AI could be transformative (e.g., 'second chance gap' for record clearing). | Rapid technological evolution of AI (pacing problem for regulation); diversity of AI applications challenging unified regulatory frameworks; achieving a balance between fostering innovation and ensuring safety, ethics, and public welfare; need for international coordination of regulatory efforts; potential for AI development to lead to market concentration. | Bias and discrimination embedded in AI systems; spread of misinformation and deepfakes impacting democratic processes and public trust; privacy violations through data collection and use; security vulnerabilities leading to misuse or manipulation of AI; AI systems manipulating human behavior or circumventing free will; risks from autonomous systems (e.g., in transportation, autonomous weapons). |
| 59CtRev32.pdf | HeinOnline | Want to Know More About Al? | This paper is a curated bibliography of various resources (articles, books, podcasts) on artificial intelligence aimed at legal professionals, particularly judges and lawyers. It highlights AI's practical uses in law, ethical considerations, regulation, and its potential to impact access to justice and judicial processes. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Algorithmic profiling, exclusion, and discrimination (e.g., denial of Medicaid benefits due to minor application errors); systemic issues for pro se civil litigants; problems not largely addressed by policy-makers; lack of human control and accountability over AI systems. | Implementing AI into the judiciary to help pro se litigants and improve court efficiency; ensuring AI systems are subject to human control and accountability; fostering education and understanding of AI among legal professionals; lawyers actively engaging with AI developers to promote justice and fairness. | Support for pro se litigants, improving court efficiency, addressing algorithmic bias and discrimination in social services and the justice system, ethical regulation of AI, ensuring fairness in AI-driven legal processes. | Pro se litigants, low-income citizens, the poor, recipients of public benefits (e.g., Medicaid applicants). | General legal system, criminal justice (bail, sentencing), civil litigation, corporate law, intellectual property, torts, tax law, public benefits law. | US, International (with specific mentions of Australia, UK, Europe) | NaN | NaN | NaN | False | False | NaN | The legal profession's general unpreparedness for the legal consequences of AI; need for AI templates, policies, and continued discussion; current lack of court-developed AI tools for access to justice; need for greater transparency and accountability in AI development; potential technical plateaus for conversational AI. | NaN | Algorithmic bias leading to discrimination and exclusion; AI tools in criminal justice acting against human best interests or lacking moral aptitude; susceptibility of AI systems to hacking; potential diminishment of the human legal community and judicial oversight; inaccuracy and potential for misinformation from conversational AI; unforeseen negative societal impacts due to unpreparedness. |
| 39SyracuseJSciTechL15.pdf | HeinOnline | Analyzing the Primary and Attendant Risks of GAI-Based Natural Language Processing Models in Legal Research | This paper analyzes the transformative potential and significant risks of Generative AI (GAI) based Natural Language Processing (NLP) models, such as ChatGPT, in legal research. It highlights primary and attendant risks including inaccuracies, bias, plagiarism, and copyright infringement, and offers recommendations for mitigating these challenges, including legal reforms. | True | Market | True | 3.0 | NaN | GAI-based Natural Language Processing models (e.g., ChatGPT, GPT series, BERT, Turing NLG, CUAD, ROSS Intelligence, EleutherAI) | The paper discusses general evaluation metrics for AI language models like perplexity and burstiness, and the importance of efficacy, precision, and reliability, but does not conduct new empirical testing of a specific model. | NaN | NaN | NaN | NaN | NaN | Legal research, Copyright law, Trademark law, Privacy law, Patent law | United States, European Union | The paper describes that GAI models are generally trained on large-scale datasets, including web pages, books, articles, and specific legal documents for domain-specific models (e.g., CUAD trained on contracts). It references public data, third-party licensed data, and datasets like Common Crawl. | The paper describes general design methodologies for GAI-based NLP models, such as using recurrent neural networks (RNNs), Markov chains, generative adversarial networks (GANs), and transformer architectures, trained on large datasets. | The paper mentions that GAI models like ChatGPT are developed by companies (e.g., OpenAI, Google, Microsoft) and made available as tools/services. It also notes some are open-source (e.g., EleutherAI by Hugging Face). | True | True | Discusses publicly available GAI models like ChatGPT (which has free access tiers) and open-source models like EleutherAI. | NaN | Challenges related to using GAI in legal research identified by the paper include: inadequate domain-specific knowledge in models, scarcity of high-quality legal training data, inherent biases in training data leading to biased outputs, lack of interpretability of AI decision-making processes (black-box nature), and difficulty in handling the complexity, nuance, and context-dependency of legal language. | Key risks include: inaccuracy and unreliability of GAI outputs (leading to flawed research and potential malpractice); plagiarism and copyright infringement (especially with derivative works); perpetuation and amplification of biases present in training data leading to discriminatory outcomes and exacerbation of social inequalities; lack of nuanced legal reasoning and contextual understanding; spread of misinformation (e.g., affecting elections or public opinion); and privacy violations due to data handling. |
| 14JIntellPropInfoTechElec.pdf | HeinOnline | Exploring the Viability of Al as Judicial Replacements: a Cautionary Perspective | This paper analyzes the viability of Artificial Intelligence replacing human judges, adopting a cautionary stance. It argues that AI's lack of social understanding, moral agency, and rational autonomy prevents it from fulfilling the complex social governance role of a judge, suggesting AI should primarily serve a supportive function. | True | NaN | False | 3.0 | Negative | NaN | NaN | NaN | AI's lack of social understanding, moral agency, and rational autonomy; inherent AI biases and discrimination; lack of transparency (black box problem); difficulty in translating nuanced law into code; absence of accountability for AI decisions; risk of legal stagnation; susceptibility to hacking and power dependence; high development and maintenance costs; potential privacy violations. | AI should be used cautiously in a purely supportive role to assist human judges, rather than replace them, preserving human oversight and decision-making due to AI's fundamental limitations. | Critique of AI as a solution for judicial system inefficiencies (often framed as access to justice issues); ethical and functional limitations of AI in judicial decision-making; preserving the human element (moral agency, social understanding, rational autonomy) in judging. | NaN | General, with examples from constitutional law, criminal law, civil law (debt collection, small claims, financial disputes), human rights law, and traffic penalties. | Multiple, including Netherlands, Estonia, Colombia, China, England and Wales, United States, Brazil, European Union (GDPR, AI Act), ECHR. | The paper discusses AI systems trained on various legal data including court records, case law, legislation, trial texts, criminal records, and offender interviews. This includes publicly available data and proprietary datasets. | NaN | NaN | False | False | NaN | Technical gaps: AI's inability to replicate human moral agency, rational autonomy, social understanding, contextual awareness, and prudence. Societal gaps: AI's incapacity to fulfill the judge's role in social governance, act as a role model, ensure judicial explicability for legitimacy, or foster public trust in the same way as human judges; difficulties in establishing accountability for AI decisions; risk of legal stagnation. | NaN | AI biases leading to structural discrimination (e.g., COMPAS); lack of transparency ('black box problem') hindering due process and appeals; unfair or arbitrary decisions due to inability to handle legal nuances; susceptibility to hacking and power outages; privacy violations from large-scale data collection; perpetuation of past mistakes and legal stagnation; erosion of public trust and legitimacy if AI replaces human judges; lack of accountability for AI-driven judicial errors; outsourcing public judicial functions to private entities, leading to potential undue influence and loss of state control. |
| 99WashLRev781.pdf | HeinOnline | CLIENT CONFIDENTIALITY AS DATA SECURITY | The paper critiques the legal profession's current approach to client data security, arguing Model Rule 1.6(c) is ineffective and hard to enforce due to its focus on technological breach prevention. It proposes a shift towards a harm mitigation framework, emphasizing changes in lawyers' processes and collaborative decision-making with clients, colleagues, and contractors to better protect client confidentiality. | True | Market | False | 1.0 | NaN | A harm-mitigation framework for the lawyer's duty of data security, focusing on regulating processes (data minimization, segregation, mapping, security planning) and people (requiring consultation with clients, colleagues, and contractors). | NaN | NaN | Ineffective, difficult-to-execute, and unenforceable current ethical rules (Model Rule 1.6(c)) regarding lawyers' duty of data security, leading to frequent client data breaches; lawyers' lack of technological expertise and understanding of client-specific data sensitivity and risk tolerances. | Shift the ethical duty from primarily technological breach prevention to harm mitigation. This involves regulating lawyers' data handling processes (data minimization, segregation, mapping, security planning) and mandating collaboration with key stakeholders (clients, colleagues, third-party contractors) in data security decisions. | Client confidentiality, Data security, Professional responsibility, Legal ethics. | NaN | Professional Responsibility, Legal Ethics, Cybersecurity Law (as applied to legal practice). | United States | NaN | Legal scholarship methods including analysis of existing rules (ABA Model Rule 1.6(c)), critique of current practices, review of data security literature and best practices from outside law, and normative reasoning to propose revisions to ethical rules. | Proposed revisions to the ABA Model Rules of Professional Conduct (specifically Rule 1.6(c) and its comments) for adoption by state bar associations. | False | False | NaN | The paper highlights existing gaps in data security practices and rules; it does not explicitly state remaining gaps if its own proposed solutions were implemented, beyond the general challenge of keeping rules updated with evolving technology. | NaN | Ongoing unauthorized access to and disclosure of client confidential information due to ineffective and poorly targeted ethical rules; harm to clients from data breaches; erosion of client trust in the legal profession; lawyers making inadequate or costly data security decisions; specific threats like phishing, ransomware, and accidental data leaks. |
| 62WashburnLJ587.pdf | HeinOnline | Life Beyond Zoom: The Promise of Emerging Virtual Court Alternatives | This essay discusses the evolution of virtual court technologies beyond standard videoconferencing, exploring emerging alternatives like online forms automation, hybrid courtroom tech, and integrated platforms. It highlights their potential to improve court processes and access to justice, while also acknowledging existing pitfalls and future challenges in their adoption and implementation. | True | Idealistic | False | 3.0 | Positive | Online court forms automation (e.g., Massachusetts Document Assembly Line), courtroom hybrid technologies (e.g., BEINCOURT), and immersive online "all-in-one" platforms (e.g., Tyler Technologies Virtual Court). | Real-world implementation, case studies (e.g., Alvin Municipal Court for Tyler Technologies), user feedback (e.g., judges for BEINCOURT), and adoption metrics (e.g., Massachusetts Document Assembly Line). | Tyler Technologies Virtual Court in Alvin Municipal Court, Texas, reportedly cleared a backlog of approximately 800 cases, saw a 60% decrease in failure-to-appear rates, and saved thousands of dollars annually. | Difficulties in maintaining court decorum and control, challenges in lawyer-client communication and rapport, altered credibility perceptions, increased participant distraction, security and privacy vulnerabilities, and the digital divide (lack of access to technology/broadband). | Development and adoption of diversified, law-specific virtual court technologies including automated forms, hybrid courtroom solutions, and integrated platforms. Adherence to guiding principles focusing on due process, user experience, and equity when implementing new technologies. | Improving court efficiency, enhancing public access to court services (e.g., forms, ODR), facilitating remote hearings, and modernizing court processes for various case types like small claims, traffic, and family law. | Self-represented litigants, low-income individuals, rural populations, and the general public needing access to court services, particularly in areas like housing, family law, small claims, and traffic disputes. | Criminal law, civil law (including small claims, landlord/tenant, debt collection), family law, traffic law, and general court procedure. | United States (various states including Massachusetts, California, Texas, Michigan, Utah, New York, Georgia, Connecticut, Louisiana), with mentions of Australia and Colombia. | Not explicitly detailed. The Massachusetts Document Assembly Line's natural language issue spotter likely uses user problem descriptions and legal knowledge, but specific datasets are not mentioned. | User-centered design, collaboration with legal professionals (lawyers, judges, court staff), iterative development based on user feedback and real-world trials, focus on accessibility (e.g., for language, education level). | Web-based platforms (e.g., Court Forms Online), commercial vendor installations in court systems, state/county-wide rollouts for ODR systems, and open-source code sharing (for Massachusetts Document Assembly Line). | True | True | The Massachusetts Court Forms Online (courtformsonline.org) provides publicly accessible online forms. The Document Assembly Line project's underlying code is open-source via Suffolk LIT Lab. | The digital divide (access to internet and hardware), lack of funding and internal champions for technology adoption in courts, scalability from niche to widespread use, ensuring robust security and privacy, addressing costs and implementation complexities, and maintaining community trust and meaningful human connection in virtual environments. | Overcoming the digital divide, securing funding and resources, ensuring robust security and privacy, integrating new tools with existing court infrastructure, managing physical courtroom constraints for hybrid models, ensuring accessibility for all users, and fostering user adoption and trust. | Erosion of due process and procedural fairness if not carefully implemented; compromised lawyer-client relationships and confidentiality; biased credibility assessments; dehumanization of participants; increased distractions; data security breaches and privacy violations; exacerbation of the digital divide and existing societal inequities; and privacy risks from cloud recordings if not properly managed. |
| 35GeoJLegalEthics549.pdf | HeinOnline | How Should Legal Ethics Rules Apply When Artificial Intelligence Assists Pro Se Litigants? | This paper explores the ethical dilemmas arising from the use of AI to assist pro se litigants, particularly concerning unauthorized practice of law, attorney-client relationships, and professional conduct rules. It advocates for applying and adapting existing legal ethics frameworks to AI, prioritizing consumer protection and holding human lawyers or law firms accountable for AI-provided legal services to narrow the justice gap. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | The 'justice gap' where many individuals, especially those with low-to-moderate income, cannot afford legal assistance. Ambiguity and restrictiveness of Unauthorized Practice of Law (UPL) statutes, which can deter the development of AI tools for legal aid. | Applying and adapting existing legal ethics rules to AI providers, specifically by requiring human lawyers or law firms to bear ethical responsibility for AI-assisted services. Reforming and harmonizing UPL laws to put software publishers on clearer notice and facilitate the development of legal AI for access to justice. | Access to legal services for self-represented litigants, application of legal ethics rules to AI, unauthorized practice of law, attorney-client relationships with AI, professional conduct for AI providers. | Pro se litigants, particularly low-to-moderate-income individuals and families who cannot afford traditional legal services. | General Civil and Criminal Litigation (e.g., drafting pleadings, motions, briefs; advising on litigation strategy), Bankruptcy. | United States | NaN | NaN | NaN | False | False | NaN | Need for reform and harmonization of UPL laws across states. Clarity on how conflict-of-interest rules apply to AI-provided services. Development of AI's explanatory capabilities. Establishing effective disciplinary processes and sanctions for AI providers (potentially collective discipline for law firms). | Ambiguity and lack of uniformity in UPL laws; patchwork attorney licensing system complicating multi-state service provision; ensuring AI competence equivalent to human lawyers; preventing AI bias; protecting client confidentiality with AI systems. | Provision of faulty legal advice by AI. Unauthorized practice of law by AI software or its nonlawyer publishers. Breach of client confidentiality through data use in machine learning or insecure systems. AI systems perpetuating or amplifying existing societal biases and discrimination. Difficulty in establishing attorney-client relationships and assigning liability for legal malpractice. |
| 54TexTechLRev255.pdf | HeinOnline | Limits of Using Artificial Intelligence and GPT-3 in Patent Prosecution | This paper discusses the potential applications and limitations of large language models like GPT-3 in patent prosecution, particularly for claim drafting and translating legal text. It also explores the legal (enablement, utility, inventorship), ethical (attorney supervision, bias), and social justice (access to innovation) consequences of using such AI tools in patent law. | True | Idealistic | True | 2.0 | Neutral | Application of GPT-3 (a large language model) for patent prosecution tasks like claim generation, specification drafting, and legal text simplification. | The paper cites existing evaluations: GPT-2 was evaluated for patent claim generation using a dataset of 55,890 patent claims. GPT-3's general writing capabilities were assessed by various users and researchers (e.g., Branwen, Elkins & Chun) through qualitative analysis, and its ability to translate legalese was demonstrated by a beta tester. | GPT-2 produced patent claims of 'reasonable quality'. GPT-3 demonstrated strong general writing capabilities, 'shockingly good' and creative, but with weaknesses in long-term coherence, consistency, commonsense reasoning, and exhibited bias. It could also 'impressively translate legalese into plain English' with few prompts. | If advanced AI tools like GPT-3 are only available to large, wealthy firms, it can widen the innovation inequality gap, making it harder for new entrants, small entities, and innovators from underrepresented groups to patent their inventions and compete. | Provide equal access to AI tools for all inventors, possibly through USPTO regional offices or Patent and Trademark Resource Centers (PTRCs). Make USPTO's AI-powered search systems available to small-entity inventors. | Innovation inequality, access to legal technology for patenting, social mobility for new innovators, fair competition in innovation. | New entrants to the market, small entities, innovators facing gender and racial inequalities. | Patent Law (specifically Patent Prosecution) | United States | GPT-3: Trained on a general dataset of 175 billion parameters from diverse internet text. The paper suggests potential for fine-tuning on millions of patents for domain-specific tasks. A cited study on GPT-2 for patent claims used a dataset of 55,890 patent claims. | For GPT-3 (as described in the paper): Autoregressive language model using deep learning; few-shot learning capabilities. Fine-tuning is mentioned as a potential customization technique. | GPT-3 was initially available via an API to select beta testers and was later exclusively licensed to Microsoft for commercial use. | True | False | GPT-3 is commercially available through an API, as it was licensed by OpenAI to Microsoft and is used in commercial projects. | Unequal access to powerful AI tools for patent prosecution, which can exacerbate existing disparities in innovation. Need for more sophisticated analysis and mitigation of biases in AI models like GPT-3. | Ensuring adequate attorney supervision of AI-generated content, managing AI's limitations (e.g., coherence, factual accuracy, bias), addressing patentability issues (enablement, utility, definiteness) for AI-assisted claims, and ethical concerns regarding competence and bias. | AI generating overly broad patent claims beyond an inventor's actual conception; exacerbation of the access to justice gap in innovation; AI reflecting and amplifying societal biases (e.g., racial, gender); attorneys violating ethical duties through inadequate supervision of AI; creation of denser patent thickets hindering competition; difficulty distinguishing AI-generated prophetic examples from actual working examples. |
| 32TexIntellPropLJ225.pdf | HeinOnline | A Framework for Applying Copyright Law to the Training of Textual Generative Artificial Intelligence | This paper analyzes the application of U.S. copyright law, particularly the fair use doctrine, to the training of large language models like OpenAI's ChatGPT using copyrighted textual works. It argues that such training likely involves non-actionable transitory copying or is permissible under fair use, highlighting copyright precedent and policy considerations for AI innovation. | True | Market | True | 2.0 | NaN | Application of U.S. copyright law, particularly the fair use doctrine, to the training process of textual large language models (exemplified by OpenAI's ChatGPT). | Legal analysis based on the four factors of fair use (purpose and character of the use, nature of the copyrighted work, amount and substantiality of the portion used, and effect on the potential market value), drawing on existing U.S. case law (e.g., Authors Guild v. Google, Perfect 10 v. Amazon.com, Field v. Google, A.V. v. iParadigms, Warhol v. Goldsmith) and applying it to the known training methods of LLMs. | The paper concludes there is substantial support for arguments that GenAI training involves only transitory, non-actionable copying, and that it is also permissible under fair use, with all four fair use factors, on balance, weighing in favor of fair use for training LLMs like ChatGPT. | NaN | NaN | NaN | NaN | Copyright Law, Intellectual Property Law | United States (primary focus); International (Israel, European Union, United Kingdom for comparative context) | The paper discusses training data for LLMs like ChatGPT, which includes: BooksCorpus (unpublished/self-published books), WebText (from Reddit links), Common Crawl (web crawl data), Wikipedia, news articles, social media posts, and code snippets. This data encompasses public domain works, openly licensed works, and copyrighted works not openly licensed, largely consisting of unstructured text. | NaN | NaN | True | False | The paper analyzes OpenAI's ChatGPT, a prominent GenAI model that is publicly available for use, with both free and paid tiers (e.g., through its website). | NaN | For GenAI developers (as discussed in the paper): Navigating legal uncertainty and lawsuits from copyright holders regarding the use of copyrighted materials in training data. Technical challenges in sourcing, curating, and processing massive and diverse datasets. Ensuring AI outputs do not directly reproduce copyrighted content ('regurgitation'). Lack of transparency from some AI developers (e.g., OpenAI for GPT-4) regarding training data complicates legal analysis. | Risk of legal liability for copyright infringement for AI developers if training is not deemed fair use. Potential stifling of AI innovation and U.S. competitiveness if restrictive copyright interpretations prevail. Erosion of established fair use principles if functional aspects of works become overly protected. Copyright holders' concerns about unauthorized use of their works and potential market substitution, although the paper argues the training process itself is transformative and non-substitutive. |
| 32JuridicaIntl107.pdf | HeinOnline | Al Systems' Impact on the Recognition of Foreign Judgements: The Case of Estonia | This paper examines how the use of AI in judicial proceedings could impact the cross-border recognition of foreign judgements, using Estonia as a case study. It highlights significant concerns regarding the lack of transparency and accountability in current AI judicial systems, which may conflict with fundamental rights and fair trial principles. | True | Idealistic | False | 3.0 | Negative | NaN | NaN | NaN | The primary obstacle is the lack of transparency and accountability in AI systems used in judicial proceedings, particularly concerning their algorithms, training data, and decision-making processes, hindering assessment of compliance with fair trial principles. | The paper advocates for adherence to established principles like transparency, explicability, and accountability for AI in judiciary, mandating official information disclosure, and suggests courts use procedural tools to scrutinize AI use in foreign judgments, potentially refusing recognition if principles are violated. | Cross-border recognition of foreign judgements, fair trial principles, due process, public order (ordre public), judicial co-operation, trustworthiness of judicial decisions, AI ethics in law. | NaN | Private International Law, Civil Procedure, Administrative Law | Estonia, European Union | The paper highlights that details on training data for the discussed AI systems (e.g., Xiaozhi, Smartsettle ONE, Salme, Krat) are generally not publicly available or are missing. | Not specified; the paper criticizes the lack of transparency regarding the development processes of discussed AI systems. | Integrated into court information systems (e.g., Estonia), used directly in adjudication support (e.g., China), or offered as online dispute resolution platforms. | False | False | NaN | Technical gaps include the non-explicability of many AI systems. Societal and legal gaps involve the lack of adherence to transparency, accountability, and fair trial principles in AI development and deployment in judicial systems, and insufficient accessible information about AI system functionality and data usage. | NaN | AI systems lacking transparency and accountability in judicial proceedings risk producing judgements that violate fundamental rights or public order, potentially leading to non-recognition of these judgements in other jurisdictions. There is also a risk of privacy breaches from inadequately implemented AI tools like anonymization software. |
| 92FordhamLRev (1).pdf | HeinOnline | TOWARD AN ETHICAL HUMAN-COMPUTER DIVISION OF LABOR IN LAW PRACTICE | This paper argues for a new framework for the ethical use of AI in law practice, distinguishing between deterministic and probabilistic technologies. It proposes a 'division of labor' model, particularly for probabilistic AI, to manage error and ensure lawyers uphold their professional responsibilities by treating such AI similarly to human subordinates requiring oversight. | True | Market | True | 1.0 | NaN | Conceptual framework for an ethical human-computer division of labor in law practice. This involves distinguishing between 'deterministic' and 'probabilistic' technologies, increasing 'error tolerance' for probabilistic tools by differentiating 'processual errors' from 'ultimate errors,' and treating probabilistic AI akin to human colleagues requiring diligent oversight. | NaN | NaN | NaN | NaN | NaN | NaN | General law practice | United States | NaN | NaN | NaN | False | False | NaN | NaN | Lawyers' misunderstanding of AI capabilities and limitations (especially probabilistic AI like LLMs), their tendency to misapply AI tools (e.g., for tasks better suited to deterministic systems or without adequate verification), and failure to exercise proper human oversight, leading to ethical breaches and negative consequences such as submitting false information to courts. | AI generating false or inaccurate information (e.g., 'hallucinations' like fake case citations); lawyers breaching ethical duties (e.g., competence, diligence, candor to the tribunal); clients being poorly served or harmed; courts being misled or duped; reputational damage to the legal profession and individual lawyers. |
| 31ClinicalLRev153.pdf | HeinOnline | DATA JUSTICE READINESS: AN ABOLITIONIST FRAMEWORK FOR TECH CLINIC INTAKE | This paper proposes a "Data Justice Readiness" framework for tech law clinics to guide client and project selection, aiming to support communities harmed by carceral technologies. Drawing from abolitionist and movement lawyering principles, the framework helps clinics align their work with a data justice vision, prioritizing non-reformist outcomes and integrated advocacy for structurally-marginalized groups. | True | Idealistic | False | 1.0 | Positive | Data Justice Readiness Framework for tech clinic intake, including a draft clinical mission and intake form. | The framework was illustratively applied to three potential tech clinic projects (Cyber Civil Rights Initiative, Just Futures Law, Domestic Care Workers Alliance & National Consortium for Independent Living) as case studies to demonstrate its decision-making process. | The application of the framework to the Domestic Care Workers Alliance & National Consortium for Independent Living project demonstrated it as 'most likely' to align with data justice principles because it represented mobilized communities (care workers and people with disabilities), pursued a non-reformist outcome (banning EVV systems), and involved integrated advocacy skills. | Concentration of data power in tech companies and their enmeshment with the state leading to widespread datafication; deployment of 'carceral tech' that exacerbates social, racial, and economic inequities and causes algorithmic violence; expert-driven tech reforms that fail to address structural issues or adequately represent impacted communities. | Adoption of the proposed Data Justice Readiness framework by tech law clinics, which prioritizes direct collaboration with structurally-marginalized communities, centers abolitionist and movement lawyering principles, and aims to build 'people (data) power' to resist harmful technologies and advocate for non-reformist tech reforms. | Data justice, carceral technologies, algorithmic violence, role of tech law clinics, movement lawyering, abolitionist pedagogy, client and project selection in legal clinics, public interest technology. | Structurally-marginalized communities, including IBPOC (Indigenous, Black, People of Color) communities, the poor and economically underserved, 2SLGBT+ communities, immigrants and asylum seekers, people with disabilities, laborers (especially gig economy, sex work, factory/agricultural), incarcerated or formerly incarcerated people, and unhoused people. | Technology law and policy, Clinical legal education, Civil rights, Public interest law, Movement lawyering. Specific examples touch on First Amendment, privacy law, immigrant rights, labor rights, disability rights. | United States | NaN | The framework was developed drawing on insights from prison industrial complex (PIC) abolitionist theory, movement lawyering principles, critical perspectives on data-driven technologies, and scholarship on clinical legal pedagogy. It includes a conceptual model and an intake form. | The framework is proposed within an academic law review article, intended for adoption by tech law clinics. The paper itself serves as the primary means of dissemination. | True | False | The paper's Appendix contains a draft clinical mission statement and a detailed intake form, which constitute the core of the proposed Data Justice Readiness framework, making it usable by readers. | A lack of shared, explicit data justice vision among tech clinics for client/project selection; insufficient direct engagement of tech clinics with communities harmed by carceral tech; the general deficit of people power to counter corporate data power; potential for clinicians to lack skills or face institutional resistance in adopting a data justice framework. | Potential obstacles for tech clinics adopting the framework include: clinicians lacking the necessary skills, knowledge, or cultural competencies; clinicians' reliance on ad hoc client selection from established expert networks rather than grassroots movements; and institutional resistance from law schools that may not prioritize social justice in tech law or may view such an approach as too radical. | NaN |
| 45MelbULRev950.pdf | HeinOnline | AN ECONOMIC PERSPECTIVE ON COSTS IN AUSTRALIAN CLASS ACTIONS | This paper develops an economic framework to analyze various costs (agency costs, externalities, preventive costs) in Australian class actions, particularly concerning litigation funding. It argues that existing legal mechanisms and potential reforms can manage these costs, ensuring class actions remain a fair, reasonable, and effective tool for access to justice and deterrence. | True | Idealistic | False | 3.0 | NaN | NaN | NaN | NaN | Agency costs (moral hazard and adverse selection involving plaintiff lawyers, representative parties, passive group members, and litigation funders); negative externalities (e.g., consumption of court resources, adverse impact on public perception of justice if proceedings are unfair); and preventive costs (costs of measures designed to reduce agency costs and negative externalities). These can make class actions excessively costly, undermining their access to justice function. | Enhanced judicial oversight and the strategic application of legal tools, including fiduciary duties, mechanisms for ensuring adequacy of representation, costs and funding agreements (like group costs orders), the right to opt out, effective notice provisions, and the appointment of independent legal representation (e.g., contradictors) or costs experts for passive group members. The paper also suggests reforms such as clarifying court powers over funding agreements and considering second opt-out opportunities in specific circumstances. | Effective functioning and cost management of class actions to preserve access to justice, ensure fair compensation for meritorious claims, and deter wrongdoing. | Individuals with small claims, dispersed and disorganised plaintiffs who would otherwise be unable to pursue their claims individually. | Class actions, Civil procedure, with examples from consumer law and shareholder litigation. | Australia | NaN | NaN | NaN | False | False | NaN | Imperfect judicial oversight due to reliance on information provided by involved parties; challenges for passive group members in effectively monitoring proceedings or exercising their rights (like opting out or giving informed consent for fiduciary matters); uncertainty regarding the court's power to vary litigation funding agreements; limitations of the standard opt-out right when misconduct or detrimental settlement terms become apparent only after the opt-out period has passed. | NaN | Excessive or disproportionate costs undermining the core benefits of class actions, such as access to justice, adequate compensation, and effective deterrence; potential for 'sweetheart' settlements that primarily benefit lawyers and funders at the expense of group members; adverse selection dynamics weakening the collective claims; negative externalities that can damage the civil justice system's efficiency and public confidence. |
| 50RutgersComputerTechLJ15.pdf | HeinOnline | Improving Solutions to AI-Related Difficulties | The paper examines legal, technological, and business challenges from AI, such as liability, IP issues, and bias, including in justice contexts. It proposes mandatory, identifiable domain names for AI/ML systems to improve solutions and prevent harm. | True | Market | False | 1.0 | NaN | Requiring AI and Machine Learning (ML) systems to be readily identifiable, for example, by requiring them either to register or to use specified domain names (e.g., an IP address using '.RealAI'). | NaN | NaN | Bias in AI systems leading to unfair criminal justice outcomes (e.g., risk assessments, predictive policing) and disproportionate impact on marginalized communities. | Making AI systems identifiable via mandatory domain names or registration to enable better tracking, management, or restriction, particularly for problematic uses in areas like the justice system. | Algorithmic bias in criminal justice decision-making and predictive policing. | Racial minorities and marginalized communities affected by biased AI in the justice system. | AI Law, Tort Law, Intellectual Property Law, Contract Law, Criminal Law, Professional Ethics, Data Privacy Law. | United States (primarily), European Union (mentioned). | General discussion of large volumes of internet data, including publicly available datasets (e.g., UC Irvine Machine Learning Repository), proprietary collections (e.g., Getty Images), and scraped personal data, often unstructured and including sensitive or copyrighted information, used to train the AI systems under discussion. | NaN | Proposed implementation via legislation, voluntary self-imposed industry standards, or regulations. | False | False | NaN | The primary gap identified is the inability of current solutions to prevent AI-related harms proactively; applied to access to justice, this means a lack of mechanisms to prevent or mitigate harm from biased AI in justice applications before deployment. | For the proposed domain name requirement: achieving widespread adoption and enforcement (via legislation, industry standards, or regulation) and addressing potential circumvention techniques. | Generation of false or biased information, privacy violations, intellectual property infringement, misuse for deepfakes, creation of discriminatory outcomes, and difficulties in assigning legal liability for AI-induced harm. |
| 90UCinLRev.pdf | HeinOnline | Prospects for Legal Analytics: Some Approaches to Extracting More Meaning from Legal Texts | This paper surveys recent research in legal text analytics focused on extracting more semantic meaning from legal texts, such as case decisions, contracts, and statutes. It discusses various AI approaches, including machine learning, deep learning (e.g., BERT, GPT-3), and knowledge representation, to improve tasks like outcome prediction, factor identification, argument mining, and providing explanations, with prospects for enhancing both legal practice and access to justice. | True | Idealistic | True | 3.0 | Positive | Advanced NLP and ML (including transformers like BERT, GPT-3, and deep learning) combined with knowledge representation for extracting deeper semantic meaning from legal texts (e.g., identifying factors, argument structures, explaining statutory terms). | NaN | NaN | Current AI's inability to fully understand and interpret legal texts as humans do (e.g., implicit meanings, common sense); lack of robust explainability in AI predictions; difficulty in extracting and reasoning with implicit information from texts. From an A2J perspective: general unfairness and lack of access to legal resources for laypersons. | Developing AI techniques to extract more semantic meaning from legal texts by combining machine learning (especially deep learning and transformers) with knowledge representation. Specifically, identifying factors, argument structures (issues, reasons, conclusions), and sentences explaining statutory terms. For A2J, deploying advanced AI tools through accessible platforms like Legal Information Institutes (LIIs) to provide free access to legal sources for the public. | Access to legal information; Understanding legal texts (statutes, case law); Legal reasoning and argumentation support. | Lay persons (as a target for an NSF project discussed); legal professionals. | General / Multiple (examples include human rights law, domain name disputes, trade secret law, contract law, copyright law, Fourth Amendment issues, veterans' benefits claims). | International / Multiple (includes specific examples or datasets from US, European Court of Human Rights, WIPO, Singapore, Japan). | Various legal text corpora, including case decisions (e.g., ECHR, WIPO, US caselaw from Harvard Caselaw Corpus, BVA), statutes, and contracts. Data includes publicly available sources and manually annotated corpora created for specific research tasks (e.g., WIPO cases for SCALE, trade secret cases for VJAP factors, sentences for statutory term explanation, case summaries for argument triples). Primarily unstructured text, domain-specific. | Manual annotation of legal texts to create labeled training datasets; application of machine learning algorithms (including deep learning NNs, LSTMs, transformer models like BERT); development and use of knowledge representation schemes (e.g., tag systems for WIPO cases, domain models for trade secret law); iterative development and evaluation, including active learning in some instances. | For the author's A2J project: planned deployment through Legal Information Institutes (LIIs) for free public access. Other mentioned tools have commercial deployments or are research prototypes. | False | False | NaN | Technical: AI's limited ability to understand implicit meaning and common-sense knowledge, lack of robust and legally intelligible explainability, challenges in integrating structured legal knowledge with deep learning models effectively. Societal: Insufficient access to and understanding of legal information for laypersons; a need for better education of legal professionals on AI's capabilities and limitations. | The knowledge representation bottleneck requiring significant manual effort for annotation and model creation; the need for large, high-quality, domain-specific annotated datasets for training ML models; high computational costs associated with training and fine-tuning large language models; difficulty in conducting extrinsic evaluations to assess real-world utility and impact on users; handling the inherent ambiguity, complexity, and stylistic variability of legal language; ensuring fairness and mitigating biases in AI models. | Over-reliance on AI-generated predictions or answers without critical assessment of their limitations and potential for error; AI models (e.g., GPT-3) producing plausible-sounding but incorrect or misleading information; models making predictions or classifications without true legal understanding, leading to flawed outputs if underlying data or logic is misinterpreted. |
| 37ComLWorld38.pdf | HeinOnline | ChatGPT in a Nutshell | This paper provides a high-level overview of ChatGPT, explaining what it is and its historical context within AI. It then discusses the potential benefits of ChatGPT for attorneys, such as streamlining legal research, document drafting, client communication, and litigation strategy. | True | Market | True | 3.0 | NaN | ChatGPT (Generative Pre-trained Transformer) | The author provides two examples of prompting ChatGPT and includes the generated text. No formal or systematic testing is described. | In two examples provided, ChatGPT generated coherent and relevant articles based on the prompts in under 30 seconds. One article was a high-level explanation, and the other was a more casual opinion piece on the same topic. | NaN | NaN | NaN | NaN | General legal practice (including legal research, document drafting, client communication, litigation strategy, legal education) | NaN | ChatGPT is described as being 'trained' on huge amounts of text data, aggregated from many sources, with its knowledge cutoff mentioned as September 2021. The model is developed by OpenAI. | NaN | NaN | True | False | The paper discusses ChatGPT, a tool developed by OpenAI, which is publicly accessible (often with free and paid usage tiers). | NaN | Uncertainty about the ease of feeding specific (e.g., sensitive internal) information for processing. Concerns about data privacy when using the tool, as user inputs might be used for software improvement. | Potential risk of exposing sensitive legal information if inputted into ChatGPT, as this data might be used to help improve the software. |
| 49BYULRev307.pdf | HeinOnline | Hidden Contracts | This paper defines "hidden contracts" as consumer agreements that firms unilaterally modify and then make inaccessible, and an empirical study shows this practice is common among major online companies. The authors argue this undermines consumer access to justice and propose a "contract transparency duty" requiring firms to provide and archive all contract versions. | True | Idealistic | False | 1.0 | NaN | A proposed "contract transparency duty" for firms. | NaN | NaN | Inaccessibility of original/previous contract versions (hidden contracts) leads to consumers not knowing their rights, inability to assess legal options, and deterrence from enforcing rights or suing firms for breaches. This results in legal uncertainty and firms being under-deterred from inefficient breaches. | Impose a contract transparency duty on firms, requiring them to: 1) provide consumers with contracts upon formation, 2) publish all historical contract versions online, and 3) reproduce original contracts upon consumer request. Supplement with administrative enforcement (fines, injunctions) and consider hidden contracts an unfair practice under UDAP laws. | Consumer contract transparency, access to previous contract versions, enforcement of consumer rights, ability to pursue legal remedies against businesses. | Online consumers generally, with a specific mention of vulnerable consumers (elderly, non-native English speakers, those with learning difficulties, less-educated populations) and disadvantaged consumers. | Consumer Contract Law, Consumer Protection Law. | United States (empirical study and legal context), with proposed solutions potentially applicable more broadly. | NaN | Legal and policy design, outlining the scope of the duty (provision of contracts, online archives, on-demand reproduction) and enforcement mechanisms (private and administrative actions, considering it an unfair practice). | Proposed for implementation through legislative and regulatory action by policymakers, enforced by administrative agencies (e.g., FTC, CFPB), State Attorneys General, and private litigation. | False | False | NaN | While the proposed transparency duty is a considerable step, it may not guarantee a fair overall market equilibrium on its own. Future research is needed on the broader implications and regulation of hidden contracts and related non-transparent practices. | Anticipated objections to the proposed transparency duty (which the paper refutes), such as arguments concerning consumers' personal responsibility, the sufficiency of existing social norms or online archives, consumer reading habits, and the relevance of original contracts post-modification. | The paper primarily details the risks and social costs of 'hidden contracts' (e.g., consumers' inability to know/enforce rights, firms inefficiently breaching contracts), which the proposed duty aims to mitigate. It briefly acknowledges, while refuting, the critique that the duty could increase business costs passed to consumers. |
| 73DePaulLRev301.pdf | HeinOnline | AI Malpractice | This paper explores whether AI modelers should be held to a professional malpractice standard of care, similar to doctors or lawyers, by comparing AI work to conventional software development and analyzing the applicability of malpractice doctrine. It suggests that for the immediate term, strict liability might be more appropriate for AI, with a potential transition to malpractice or ordinary reasonable care as AI technology and its societal integration mature. | True | Idealistic | True | 1.0 | NaN | Application of professional malpractice law (and other liability frameworks like strict liability or ordinary negligence) to AI modelers, based on an analysis of AI work considering factors like subjective judgments, risk of bad outcomes, and essential societal function. | NaN | NaN | Biased, incorrect, or insufficient training data leading to unfair or inaccurate AI systems that perpetuate societal harms and discrimination (e.g., in policing, employment), impacting fairness and due process. | Establishing clear liability frameworks (e.g., professional malpractice, strict liability, ordinary negligence) for AI modelers to incentivize the development of safer, fairer, and more accountable AI systems, thereby mitigating access to justice-relevant harms. | Algorithmic bias, discrimination, fairness, and accountability in AI systems, particularly in contexts like criminal justice and employment, which have significant implications for individual rights and due process. | Protected classes (e.g., based on race, gender) and other individuals disproportionately harmed by biased or flawed AI systems in critical decision-making processes such as law enforcement, hiring, and credit scoring. | Tort Law (malpractice, negligence, strict liability), with illustrative examples touching upon criminal law, employment law, and intellectual property. | United States | NaN | NaN | NaN | False | False | NaN | Persistent scientific uncertainties in aspects of AI development (e.g., optimal model configurations, hyperparameter tuning); challenges in ensuring training data is comprehensive, unbiased, and legitimate; limitations in current AI testing methodologies for guaranteeing robustness, fairness, and generalizability; and a lack of consensus on how to effectively translate AI ethics principles into enforceable legal duties for AI modelers. | Defining appropriate and adaptable liability standards for AI modelers given the unique characteristics of AI development (e.g., opacity of some models, data-driven nature, rapid evolution, and the difficulty in foreseeing all potential harms) and distinguishing AI work from traditional software or product liability for doctrinal purposes. | Accidental harms from AI errors (e.g., autonomous vehicle crashes, misidentification); intentional misuse for malicious purposes (e.g., deepfakes, disinformation, fraud); perpetuation and amplification of societal biases leading to discrimination; systemic risks such as erosion of trust in institutions or market instability; data privacy violations; and significant labor displacement. |
| 25DukeLTechRev116.pdf | HeinOnline | FINE-TUNING LLMS: STRUCTURAL FLUENCY AND AUGMENTATION FOR THE GREAT AND POWERFUL WIZARD OF Al | The paper argues that LLMs, despite their potential, can perpetuate existing biases in the civil legal system rooted in structural injustice. It proposes "structural fluency" through fine-tuning and prompt augmentation, informed by social justice principles, as a method for legal professionals to mitigate these risks and enhance fairness in LLM outputs. | True | Idealistic | True | 1.0 | Positive | "Structural fluency" achieved through fine-tuning prompts and prompt augmentation for LLMs, guided by social justice principles and structural competency frameworks. | NaN | NaN | LLMs replicating ineffective patterns and biases of the past rooted in racism and power imbalances; the civil legal system's inherent assumptions and biases; "color-evasive" policies and LLM deployment perpetuating racism; lack of access to justice and procedural unfairness; LLMs being developed by homogeneous groups. | Engaging in machine learning frameworks informed by social justice principles; fine-tuning LLMs and using prompt augmentation to enhance their fluency in structural injustice; prompting LLMs to consider macro structures, systemic forces, historical legacies of injustice, and social identity; incorporating critical lenses like cultural competency and racial literacy into LLM interaction; developing "structural fluency" in LLM interactions. | Mitigating bias in AI/LLMs; ensuring fairness and equal justice in AI-assisted legal processes; addressing systemic and structural injustice within the legal system through AI; the role of social context and identity in legal AI; ethical use of AI by legal professionals. | Subordinated individuals/groups, people of color, women and trans people, people in lower socioeconomic classes. | Civil law, Civil procedure | United States | NaN | Conceptual framework development drawing from critical legal theories (e.g., LatCrit, Critical Race Theory), social justice principles, legal pedagogy (Socratic method, scaffolded learning), and analogies from other fields (e.g., structural competency in medicine). | NaN | False | False | NaN | Lack of a method for prompting machines to "fine-tune" them for social justice; need for AI tools to move beyond replicating past injustices and incorporate social context and identity-consciousness; the legal system's "structural incompetence" and procedural unfairness; current LLM training and deployment often reflecting "color-evasiveness." | NaN | LLMs proposing outcomes based on ineffective past patterns, perpetuating a "civil legal system twilight zone"; replication of bias, prejudice, and discrimination; LLM "hallucinations" or fabricated information; misuse by legal professionals without proper verification; entrenchment of systemic injustice if LLMs are not intentionally guided; potential to worsen disparities in legal services; AI tools reflecting biases of their homogeneous developers; "color-evasive" LLM deployment. |
| 31AIL169.pdf | HeinOnline | Lawmaps: enabling legal Al development through visualisation of the implicit structure of legislation and lawyerly process | This paper proposes 'lawmaps,' a visual modelling approach using UML elements to represent legislative structures and lawyerly processes, aiming to improve legal accessibility and support Legal AI development. The authors present a methodology for creating lawmaps and demonstrate it with examples from UK law. | True | Idealistic | False | 1.0 | Positive | Lawmaps: A visual modelling approach using a subset of UML activity diagrams and Boolean logic to represent legislation and lawyerly processes, supported by a Lawmap Development Lifecycle. | Demonstrated through the creation of lawmaps for UK conveyancing practice, the Landlords and Tenants Act 1954, and UK road rules. The methodology and outputs were also applied in the Engine B project. | The paper demonstrates the successful application of the Lawmap methodology to create visual representations of UK conveyancing, landlord-tenant law, and road rules, intended to enhance clarity and serve as a basis for AI. The Engine B project utilizes these lawmaps, indicating practical application. | Complexity and incomprehensibility of legal texts for laypersons; difficulties in accessing legal help due to lack of information and reductions in legal aid. | Visualisation through 'lawmaps' to make legal structures and processes more comprehensible and accessible, thereby empowering laypeople and supporting expert decision-making and AI development. | Improving legal literacy for the public, simplifying understanding of legislation and legal procedures, enhancing transparency in legal processes. | General public, laypersons, individuals not trained in law seeking to understand their rights and legal processes. | Conveyancing, Landlord and Tenant Law, Road Traffic Law. | United Kingdom (specifically England and Wales for landlord-tenant law examples). | NaN | Expert elicitation, UML-based visual modelling (activity diagrams), Boolean algebra for rule formalization, iterative development lifecycle (Locate, Extract, Identify, Distinguish, Sequence, Traceability). | Exemplar lawmaps made available online. The methodology and outputs are being used in the Innovate UK funded Engine B project to develop practitioner-facing AI tools. | True | True | Exemplar Lawmaps (the visual outputs of the methodology) for conveyancing and aspects of the Landlords and Tenants Act 1954 are stated to be accessible online via URLs provided in the paper's footnotes. | Lack of existing visual tools for broad legal issues/legislation; need for explainable legal AI; requirement for the legal domain to adapt to technological advancements. | Complexity of translating intricate legal text into formal visual models; potential resistance to formulaic approaches within the legal profession; ensuring democratized visualisations are comprehensible by both experts and laypersons. | Ethical concerns (bias, racism) in broader AI applications within the justice system (e.g., COMPAS, Predpol), though these are considered outside the paper's direct scope. Potential for technology to be misconstrued as solely for wholesale lawyer replacement if not integrated thoughtfully to transform legal practice. |
| 56ArizStLJ545.pdf | HeinOnline | Systemic Regulation of Artificial Intelligence | The paper argues for systemic regulation of AI as a technology, beyond specific applications, due to broad societal risks (present and future, including bias, fraud, unemployment, geopolitical instability, and existential threats) and the AI alignment problem. It proposes principles for domestic and international AI regulation, emphasizing a precautionary approach and ex-ante oversight. | True | Idealistic | True | 3.0 | NaN | NaN | NaN | NaN | Bias and discrimination by AI systems against vulnerable groups; projection of historical inequity into the future. | Systemic regulation of AI as a technology, including ex-ante oversight, to mitigate AI risks such as bias and discrimination. | Algorithmic bias and discrimination; preventing AI-driven harms to vulnerable communities. | Vulnerable groups, people of color, women, minorities, groups with a history of discrimination or disadvantage. | General Law / AI Regulation | US, China, EU, International | NaN | NaN | NaN | False | False | NaN | Lack of effective systemic regulation for AI; limitations of technical tools to address algorithmic discrimination; the unresolved AI alignment problem making it difficult to ensure AI systems consistently uphold human values and avoid discriminatory outcomes. | The AI alignment problem (including goal specification, instrumental convergence, orthogonality thesis); complexity and poor auditability of AI systems; the rapid and unexpected rate of AI capability growth. | Bias and discrimination, fraud, privacy violations, unemployment, inequality, dangerous military applications (autonomous weapons), geopolitical imperialism, terrorism, totalitarianism, threats to democracy (misinformation, deepfakes), harms from misaligned AI (including deception and power-seeking), existential risks, misuse of AI for nefarious purposes (e.g., bioweapons). |
| 72JLegalEduc598.pdf | HeinOnline | Technically Speaking: How to Improve Technology CLEs to Meet the Needs of Lawyers and Get Them to Attend | This paper argues for the critical need for lawyers to achieve technology competence and critiques existing Continuing Legal Education (CLE) programs for failing to adequately meet this need. It proposes practical recommendations to enhance technology CLEs, focusing on adult learning principles, expert involvement, and stronger regulatory support to ensure lawyers can effectively meet their ethical obligations and serve the public. | True | Idealistic | False | 1.0 | Positive | Overhauling technology CLEs by applying adult learning principles (e.g., problem-centered, hands-on learning), involving non-lawyer technology experts as faculty, drawing inspiration from innovative law school programs and Practice Management Assistance Programs (PMAPs), and improving regulatory messaging and mandates for technology training. | NaN | NaN | Lawyers' insufficient technological competence, stemming from passive and ineffective Continuing Legal Education (CLE) delivery methods, low attendance at non-mandatory technology CLEs, and inadequate mandatory technology training requirements in many jurisdictions. | Revamp technology CLEs by incorporating adult learning methodologies for active engagement, utilizing diverse subject-matter experts (including non-lawyers), emulating successful educational models from law schools and PMAPs, and strengthening regulatory frameworks through clearer mandates for technology training and more supportive messaging about technology's benefits. | Enhancing lawyer technological competence through improved professional education (CLEs) to ensure quality and ethical legal representation for the public. | The general public/all clients of legal services. | General legal practice, professional ethics, and lawyer regulation. | United States (referencing ABA Model Rules and specific states like Florida, North Carolina, Maine, New York, Kansas, and the U.S. Virgin Islands). | NaN | The proposed approach is based on a review of adult learning theory, analysis of existing legal education models (law schools, Practice Management Assistance Programs), and a critique of current CLE shortcomings. | Implementation by CLE providers, bar associations, and legal regulatory bodies through revised CLE rules, development of new program curricula, speaker training, and proactive educational initiatives. | False | False | NaN | Persistent deficits in lawyers' technological competence due to generally ineffective CLE systems, insufficient mandates and quality standards for technology training, and regulatory environments that may not adequately promote proactive technology education and adoption. | Challenges to implementing the proposed CLE improvements include overcoming lawyer resistance to attending non-mandatory or non-ethics focused training, transitioning CLEs from passive lecture-based formats to active, hands-on learning experiences, achieving regulatory consensus for stronger technology CLE mandates, and ensuring CLE providers develop and deliver high-quality, practical programs. | Risks of lawyers lacking technological competence include providing substandard or unethical legal services, failing to protect client confidential information (cybersecurity), and inefficient practice. A minor risk identified with the proposed solution is non-lawyer CLE experts potentially 'selling from the podium' if programs are not carefully managed. |
| 51WStULRev299.pdf | HeinOnline | Navigating Artificial Intelligence Through a Products Liability Framework | This paper argues for applying California's product liability law, particularly strict liability, as a legal framework to address harms caused by artificial intelligence systems. It suggests this approach can adapt to evolving AI technology, ensure user safety, and proposes integrating a risk-based classification similar to European proposals. | True | Idealistic | False | 1.0 | Positive | Application of California's product liability law, integrated with elements of the European Commission's risk-based AI regulatory proposal, to address AI-related harms. | NaN | NaN | Rapidly evolving AI outpacing legislation; the 'black box' nature of AI making it difficult to understand, trace, and assign liability for harms; inherent susceptibility of AI to biases leading to discriminatory outcomes; complexity in determining accountability among numerous actors in the AI lifecycle. | Adopting California's product liability law (including strict liability for manufacturing, design, and warning defects) as a flexible framework for AI. Integrating a risk-based classification for AI systems to tailor legal scrutiny and potentially shift evidentiary burdens for high-risk AI. | Ensuring legal accountability and redress for individuals harmed by defective AI systems; consumer protection against risks posed by AI. | General public and consumers of AI products and services. | Products Liability Law, Tort Law, AI Regulation. | California, United States (primary focus); European Union (for comparative regulatory proposals). | NaN | NaN | NaN | False | False | NaN | The principal gap identified is the absence of an established legal framework for AI liability. Even with the proposed solution, a remaining gap involves the need for judicial development of case law to apply product liability principles to the novel complexities of AI, including defining 'defects' and adapting defenses in the AI context. | NaN | Physical harm from malfunctioning AI (e.g., autonomous vehicles); economic or social harm from biased algorithmic decision-making in areas like finance, housing, and employment; spread of misinformation by generative AI; lack of transparency and traceability ('black box' problem) leading to unexplainable and potentially harmful AI behavior. |
| 2024RussJEconL804.pdf | HeinOnline | Legal education and artificial intelligence: vectors of interaction | This paper explores the integration of AI into legal education, detailing potential benefits like personalized learning and significant risks such as algorithmic bias and ethical dilemmas. It advocates for comprehensive reforms in legal curricula and training for legal professionals to navigate the challenges and opportunities AI presents in the legal field. | False | Market | True | 3.0 | Neutral | NaN | NaN | NaN | AI perpetuating societal biases leading to unfair outcomes; Lack of AI transparency hindering accountability and due process; AI errors ("hallucinations") causing misinformation in legal contexts; Ethical violations (data privacy, misuse of AI) eroding trust; Unequal access to AI literacy and AI-powered legal tools, widening the justice gap. | Educating legal professionals on AI ethics, fairness, and transparency; Developing critical thinking and human-centered skills in lawyers to oversee and correct AI; Integrating interdisciplinary approaches in legal education to understand AI's societal impact, including on access to justice; Reforming legal curricula to include AI governance and its impact on access to justice; Promoting human control over AI and upholding principles of fairness and transparency. | Fairness and non-discrimination in AI-driven legal processes; Transparency and accountability of AI in legal decision-making; Ethical use of AI in law and its impact on human rights; The role of legal education in addressing AI's impact on access to justice; Mitigating AI bias. | NaN | Legal Education, General Law, Legal Practice | Russia | NaN | NaN | NaN | False | False | NaN | Need for better understanding and mitigation of AI bias and "hallucinations"; Development of robust ethical and legal frameworks for AI in law and education; Improved AI literacy among legal professionals and educators; More research into the pedagogical implications of AI in legal education; Addressing the "black box" problem for AI transparency; The pace of AI development outstripping legal and educational adaptation. | NaN | Over-reliance on AI leading to diminished critical thinking; Amplification of existing societal biases (e.g., racial) through biased training data; AI "hallucinations" generating false or misleading information; Lack of transparency ("black box" problem) in AI decision-making processes; Ethical concerns regarding student data: privacy, consent, and confidentiality with AI tools; Increased risk of AI-assisted plagiarism among students; Potential displacement of legal professionals and changes in traditional legal skills due to automation; Psychological and social challenges ("future shock") from rapid technological adoption. |
| 92UMKCLRev859.pdf | HeinOnline | The Lawyer's Duty of Competence in a Climate-Imperiled World | This paper argues that the lawyer's existing professional duty of competence, as articulated in rules like ABA Model Rule 1.1, is evolving to necessarily include understanding and advising clients on the risks and opportunities presented by climate change. It explores how climate change impacts various legal practice areas and emphasizes the need for lawyers to develop climate competence and leadership skills to effectively serve their clients and the public interest. | True | Market | False | 2.0 | NaN | The lawyer's duty of professional competence (e.g., ABA Model Rule 1.1) and its evolving application to climate change. | NaN | NaN | Lack of legal assistance for individuals and communities displaced by climate-fueled storms or fires; high costs of adaptation and disaster recovery for public coffers; disproportionate impacts on poor communities and people of color who have less ability to adapt. | Lawyers engaging in pro bono activities to aid efforts to reduce greenhouse gas emissions and adapt to climate change; climate justice litigation grounded on civil rights or human rights laws to address disproportionate impacts; lawyers developing systems leadership skills. | Climate-induced displacement; disaster recovery; climate justice; disproportionate impacts of climate change on vulnerable populations; ensuring a 'just transition'. | Individuals and communities displaced by climate events; poor communities; people of color; communities affected by unjust transition pathways. | General legal practice, with specific examples across various fields including environmental, energy, corporate, finance, real estate, insurance, tax, torts, property, contract, estate planning, immigration, and civil rights/human rights law. | Primarily United States (referencing ABA Model Rules, US legislation, US case law, and US government reports), with significant mention of England and Wales (Law Society guidance). | NaN | NaN | NaN | True | True | The duty of competence is an existing professional obligation for lawyers, and model ethical rules (like ABA Model Rule 1.1) and legal guidance (like the Law Society's) are generally publicly available. | Insufficient legal support for climate-affected vulnerable populations and ensuring a universally just transition; need for broader adoption of climate competence and systems leadership skills within the legal profession. | Keeping abreast of rapidly evolving climate science, climate-related laws and regulations, new technologies (including AI), and integrating this knowledge into legal advice across diverse practice areas. Understanding and applying systems leadership skills. Overcoming ingrained practices and lack of awareness. | For lawyers: professional discipline, malpractice liability for failing to advise on climate risks. For clients: financial losses from unmitigated climate risks (physical, transition, liability), missed opportunities, reputational damage (e.g., greenwashing), regulatory non-compliance, uninsurability. Broader risks: exacerbation of climate change impacts, obstruction of a just transition, impairment of national security, societal disruption. |
| 28LegalWritingJLegalWriti.pdf | HeinOnline | BRACING FOR IMPACT: REVISING LEGAL WRITING ASSESSMENTS AHEAD OF THE COLLISION OF GENERATIVE Al AND THE NEXTGEN BAR EXAM | This paper argues that legal writing assessments must be revised in response to generative AI (GenAI) and the upcoming NextGen bar exam. It proposes diverse assessment strategies to ensure students develop critical legal skills independently and are prepared for evolving professional demands. | True | NaN | True | 3.0 | NaN | Generative AI (GenAI) chatbots (e.g., ChatGPT) | The paper cites studies that evaluated GenAI using law school exams (multiple-choice and essay), standardized tests (LSAT, UBE), and tasks like drafting legal documents. AI detection tools were tested for accuracy in distinguishing human vs. AI text. | The paper reports GenAI (e.g., GPT-4) can achieve high scores on legal exams (e.g., 90th percentile on UBE) and pass law school exams (e.g., C+ average for ChatGPT 3.5). Reports AI detectors are unreliable and often fail. | NaN | NaN | NaN | NaN | Legal Research and Writing education | United States | For ChatGPT: Open-source internet data (e.g., 570GB from the internet, 300 billion words for pre-training). For emerging legal GenAI tools (e.g., CoCounsel, Lexis+ AI): Proprietary legal-specific databases. | For GenAI/ChatGPT: Neural network architecture (transformers/LLMs), pre-training on large internet datasets, and fine-tuning with human reviewers (reinforcement learning from human feedback - RLHF). | ChatGPT: Publicly available via website (free and paid tiers), with enterprise versions and plugins. Legal-specific GenAI tools (e.g., CoCounsel, Lexis+ AI): Commercial deployment through legal tech companies, integration into existing platforms. | True | False | ChatGPT (GPT-3.5) is available for free use via its website after sign-up. Paid versions (GPT-4) and enterprise versions also exist. | NaN | Educators face challenges in: accurately assessing student skills when GenAI can produce work, the failure of AI detection tools, preventing student over-reliance on GenAI, and adapting teaching methods to new technologies and bar exam requirements. | Students over-relying on GenAI, leading to skill deficits and failure in exams/practice; invalid assessment of student abilities by professors; unreliability and bias of AI detection tools; GenAI producing incorrect or fabricated information (hallucinations); ethical issues related to GenAI use in legal practice (confidentiality, bias). |
| 42YaleLPolyRev107.pdf | HeinOnline | Second-Wave DREAMers | This paper contrasts two waves of child migrants in the U.S., "first-wave" and "second-wave" DREAMers, focusing on their differing experiences with public schools under Plyler v. Doe. It argues for a modernized approach by schools, shifting from assimilation, formal equality, and innocence to inclusion, equitable education, and collective responsibility to better serve today's immigrant students. | True | Idealistic | False | 3.0 | NaN | NaN | NaN | NaN | Limited effectiveness of assimilationist and formally equal school approaches for immigrant children, especially for traumatized "second-wave DREAMers" facing immediate immigration enforcement and economic pressures; pervasive lack of legal status and difficulties accessing legal representation. | Schools should adopt a modernized reading of Plyler, shifting from assimilation to inclusion, from formal equality to equitable education (e.g., tailored programs, specialized services, community partnerships), and from an innocence narrative to collective responsibility (e.g., addressing legal needs via on-site services like school-based legal clinics). | Access to education for immigrant children (K-12 and higher education), social integration of immigrant youth, rights of undocumented and asylum-seeking children, impact of immigration enforcement on children, implementation and modernization of Plyler v. Doe. | Immigrant children and youth in the U.S., specifically "first-wave DREAMers" (who arrived between 1986-2007) and "second-wave DREAMers" (who arrived since 2014, often from Central America as unaccompanied minors or in asylum-seeking families). | Immigration Law, Education Law, Constitutional Law (Equal Protection), Children's Rights. | United States | NaN | NaN | NaN | False | False | NaN | Lack of a pathway to citizenship for DREAMers and DACA recipients; insufficient tailored educational, socioemotional, and legal support systems for newcomer students nationwide; persistent problematic societal narratives (innocence/guilt) that hinder integration; need for greater societal acknowledgment of U.S. foreign policy's role in migration. | NaN | Risk of deportation, labor exploitation, psychological trauma from migration experiences and enforcement; social exclusion and marginalization; creation of a permanent underclass if educational and legal needs are unaddressed; loss of legal remedies (e.g., SIJS) due to aging out or lack of timely counsel. |
| 4ModLRsch32.pdf | HeinOnline | Research on Generative Artificial Intelligence Legal Profession Substitution | This paper empirically examines the application of generative AI in the legal field, analyzing its potential to enhance efficiency and promote social justice. It also discusses the risks, limitations, and ethical considerations, proposing that AI will complement rather than fully replace legal professionals and advocating for regulation through AI and legal professional ethics. | True | Idealistic | True | 3.0 | Neutral | Generative Artificial Intelligence (e.g., ChatGPT, GPT-4, Harvey, various Chinese large models like Iflytek Spark, Baidu Wen Xin Yi Yan) | NaN | NaN | Data security and privacy leakage; risk of unethical use; lack of controllability of legal decisions by AI; AI generating incorrect or fabricated information ('hallucinations'); algorithmic discrimination and bias; lack of trustworthiness and social acceptability of AI in law; non-interpretability of AI decisions; and lack of human sensitivity/empathy in AI-assisted legal processes. | Regulation of generative AI applications in the legal field from the dimensions of AI ethics and legal professional ethics. Emphasizing people-centered humanism, fairness and justice over utilitarianism, and constructing a robust regulatory framework and assessment mechanism for legal technology ethics. | Enhancing efficiency and quality of legal services, promoting (social) justice, reducing cost of legal services, professional substitution in the legal field. | General public / 'the people' | General legal profession (lawyers, judges, judicial support staff), judicial applications, legal services market, legal advice, legal content generation. | International (with specific examples and regulatory discussions concerning China, USA, UK, and EU) | Public information from the judicial domain (e.g., judicial decision documents network, social media, judiciary websites, lawyer databases) for user profiling and language model training; copyrighted works (as highlighted by lawsuits against OpenAI). | NaN | NaN | True | True | The paper lists several AI platforms (e.g., Iflytek Spark, Baidu Wen Xin Yi Yan, Chatlaw) with URLs and notes on client app availability, and discusses widely accessible tools like ChatGPT which have free tiers. Commercial tools like Harvey are mentioned as used by specific firms. | Ensuring accuracy and reliability of generated content; overcoming model 'hallucinations'; addressing algorithmic discrimination and bias; building trustworthiness and social acceptability of AI in law; improving interpretability of AI decisions; maintaining human sensitivity and empathy in legal processes; and establishing comprehensive ethical and regulatory frameworks for legal AI. | Managing security risks (data safety, privacy); preventing unethical use; addressing intellectual property infringement; ensuring controllability of AI in legal decision-making; mitigating 'hallucinations' and fictitious outputs; combating algorithmic discrimination and bias; building trust in AI systems; dealing with the 'black box' nature (non-interpretability) of some AI; and preserving humanistic elements in the legal profession. | Data and personal privacy leakage from training on judicial data; unethical use of AI; intellectual property infringement by AI models trained on copyrighted works; lack of controllability of legal decisions made or assisted by AI; generation of incorrect or wholly fabricated information ('hallucinations'); algorithmic discrimination and bias leading to unfair outcomes; security threats; model illusion; environmental/social and regulatory risks; third-party risks. |
| 2023MichStLRev377.pdf | HeinOnline | WHO WATCHES THE WATCHMEN? USING THE LAW GOVERNING LAWYERS TO IDENTIFY THE APPLICANT DUTY GAP AND HOLD BAR EXAMINER GATEKEEPERS ACCOUNTABLE | This paper identifies an ethical "duty gap" where bar applicants face high ethical burdens during licensure while bar examiners, the NCBE, and bar prep companies owe them no reciprocal duties, a problem exacerbated during the COVID-19 pandemic. It calls for reforms such as increased oversight, transparency, and applying professional conduct rules to bar examiners, and considers alternative licensure paths. | True | Idealistic | False | 3.0 | NaN | NaN | NaN | NaN | Lack of reciprocal ethical duties owed by bar examiners, NCBE, and bar prep companies to applicants; Opaque and rigid lawyer licensing procedures; Lack of transparency in bar examiner operations, funding, and governance; Immunity of boards of law examiners from challenges; Potential bias in the Uniform Bar Examination (UBE); Power imbalance between applicants and licensing bodies, stifling criticism; Financial and emotional burdens on applicants. | Adding reciprocal duties for bar examiners to Model Rule 8.1; Formal adoption of a "Code of Recommended Standards for Bar Examiners" with enhanced transparency and accountability; Implementing alternative paths to licensure (e.g., diploma privilege, experiential learning, supervised practice); Increased transparency in bar examiner operations (e.g., public annual reports); Greater oversight by the legal profession including committees on cooperation involving law schools, judiciary, and the bar. | Fairness and ethical treatment in the lawyer licensing process; Accountability and transparency of bar admission authorities; Diversity and inclusion in the legal profession (as affected by licensure); Reforming bar examination and admission standards. | Bar applicants (recent law school graduates). The paper also implies concern for groups disproportionately affected by current licensing practices, such as racial minorities and women. | Legal ethics, Professional responsibility, Legal education, Administrative law (as it relates to bar examiners) | United States (referencing ABA Model Rules, NCBE, and various state bar examiners) | NaN | NaN | NaN | False | False | NaN | The "ethical duty gap": lack of owed duties from examiners to applicants; Lack of meaningful oversight and accountability for bar examiners and the NCBE; Insufficient transparency in bar admission processes; Need for more valid and non-discriminatory methods for assessing lawyer competence; Failure of the profession's self-regulation to extend to the licensure process for new entrants. | NaN | Continued demoralization and harm to future lawyers; Negative reflection on the legal profession due to unfair treatment of applicants; Erosion of trust in the licensing process and the legal profession; Suppression of diversity in the legal profession; Maintaining a licensure system that may not accurately measure competence; Potential for retaliation against applicants who criticize the system. |
| 25DukeLTechRev1.pdf | HeinOnline | TRIBES AND AI: POSSIBILITIES FOR TRIBAL SOVEREIGNTY | The paper explores how AI can enhance tribal sovereignty across various sectors like legal systems, healthcare, education, cultural preservation, economic development, and administrative capacity. It argues that AI can help tribes overcome historical challenges and improve self-governance, despite potential risks and obstacles such as inadequate Internet infrastructure. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Limited tribal sovereignty and external skepticism towards tribal institutions; systemic underfunding and economic disadvantages (e.g., dual taxation); excessive federal bureaucracy constraining self-governance; inadequate infrastructure (especially internet) and workforce challenges in remote areas; historical injustices and cultural erosion impacting tribes. | Employing AI to enhance tribal institutional capacity (legal, healthcare, education, administration) and assert sovereignty; using AI to foster economic development and fiscal independence; leveraging AI to overcome bureaucratic inefficiencies and improve service delivery; utilizing AI to bridge infrastructure and personnel gaps, thereby improving access to services; applying AI for cultural and language preservation and supporting the assertion of Indigenous data sovereignty. | Enhancing tribal self-governance and sovereignty; improving tribal legal systems and access to justice (court efficiency, legal aid, code promulgation); improving access to and quality of healthcare and education; cultural and language preservation; economic development and fiscal independence; overcoming bureaucratic hurdles. | Federally recognized Indian tribes in the United States. | Federal Indian Law, Tribal Law, Administrative Law, Civil and Criminal Justice, Healthcare Law, Education Law, Tax Law, Corporate Law. | United States | NaN | NaN | NaN | False | False | NaN | Lack of Internet infrastructure in Indian country; high cost of AI development, hardware, and implementation; need for skilled personnel (e.g., data scientists) to operate and manage AI systems; the necessity for robust AI regulatory frameworks, including measures to ensure Indigenous data sovereignty; effectively addressing AI biases and the potential for hallucinations that could be detrimental to tribal history and culture. | NaN | AI hallucination propagating false historical narratives or exacerbating racial stereotypes about tribes; AI discrimination due to biased training data or algorithms; violations of data privacy if Indigenous data sovereignty is not respected; potential for job displacement in certain sectors; high implementation and maintenance costs acting as a barrier for financially constrained tribes. |
| 7Issue2IntlJLMgmtHuman92.pdf | HeinOnline | Artificial Intelligence: An Analysis in the Legal Field | This paper analyzes the role of Artificial Intelligence in the Indian legal sector, outlining its applications such as case law analysis and drafting, alongside challenges like cost and bias. Based on an empirical study with 106 participants, it highlights low public awareness of AI in law and advocates for increased education and responsible adoption. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Low awareness of AI's potential in the legal field among the general public and legal professionals; financial backwardness and illiteracy hindering AI adoption; difficulty for laymen to use AI; cost of AI tools. | Increase awareness of Artificial Intelligence in the legal field for students, legal professionals, and common people; government to ensure AI is legalised to a certain extent. | Legal information and guidance for laypersons, general awareness of legal tech, potential for AI in judicial processes, overcoming financial barriers to legal assistance. | Laymen who cannot afford legal services, common people, individuals with financial backwardness and illiteracy. | General legal field | India | NaN | NaN | NaN | False | False | NaN | Significant lack of awareness about AI in the legal field among both the general public and legal professionals in India. Financial and literacy barriers prevent widespread AI adoption. Difficulty for laypersons to effectively use existing AI tools for legal help. AI outputs may not always be accurate and can be biased. | Cost-effectiveness of AI tools (e.g., premium access for bots), potential for inaccurate or biased outputs from AI, over-dependence on AI leading to skill degradation in legal students and professionals. Authors' study limitations include small, geographically restricted sample size. | Over-reliance on AI diminishing legal skills among students and professionals; AI producing biased or inaccurate legal outputs leading to malpractice or misconduct; AI lacking human sympathy and situational understanding if used to replace human judges. |
| 34AlbLJSciTech1.pdf | HeinOnline | THE NEW KID ON THE BLOCK -THE USE OF ARTIFICIAL INTELLIGENCE IN ALTERNATIVE DISPUTE RESOLUTION | This paper discusses the growing role of Artificial Intelligence (AI) in Alternative Dispute Resolution (ADR), particularly in mediation and online dispute resolution (ODR). It explores AI's benefits, such as increased efficiency and data-driven insights, alongside cautions like ethical concerns, potential biases, and the limitations of AI in tasks requiring human emotional intelligence. | True | Idealistic | True | 3.0 | Neutral | AI in Alternative Dispute Resolution (ADR), particularly AI-assisted mediation and Online Dispute Resolution (ODR), including tools like ChatGPT, Modria, Smartsettle, Cybersettle, Kleros, and the adjusted winner procedure. | NaN | NaN | High cost and delays in traditional litigation and court systems; potential impersonality of online dispute resolution; risk of algorithmic bias in AI perpetuating societal inequities; privacy and data security vulnerabilities in AI systems. | Wider adoption of Alternative Dispute Resolution (ADR) and Online Dispute Resolution (ODR) to improve efficiency and accessibility; leveraging AI to enhance ADR/ODR processes; emphasizing ethical AI development, robust data governance, and maintaining human oversight, particularly for tasks requiring emotional intelligence and complex judgment. | Reducing court backlogs; making dispute resolution more affordable and faster; resolving family law disputes (e.g., parenting plans, asset division); handling small claims cases. | General public involved in disputes, particularly in family law and small claims, and potentially those who find traditional litigation costly or slow. | Alternative Dispute Resolution (Mediation, Arbitration), Family Law, Contract Law, Small Claims, Tort Law (contextually), Criminal Law (AI examples mentioned). | United States (including specific states like Idaho and California, and federal bodies), with some international examples (England, Estonia, China, eBay global operations). | For ChatGPT: Books, journals, articles, and general web content. For other AI/ADR systems: Prior case data, user-submitted dispute information, legal documents, parties' preferences and submitted evidence. Some systems also employ rule-based logic. | NaN | Platform adoption by legal institutions (courts, arbitration associations), mandatory ODR programs in some jurisdictions, commercial offerings by tech companies, and public availability of some tools (e.g., ChatGPT). | True | False | Several AI-ADR tools and platforms (e.g., ChatGPT, Modria, Cybersettle, Smartsettle, Kleros, Tyler's ODR) are described as operational and in use, offered commercially, by institutions, or publicly (like ChatGPT's free tier). | Need for AI systems with improved emotional intelligence emulation or effective human-AI teaming models; ensuring fairness, unbiasedness, and robust privacy/security in AI-ADR systems; enhancing trust and acceptance of AI tools within the legal profession; training for legal professionals to use AI effectively. | NaN | AI systems perpetuating errors from flawed training data; lack of emotional intelligence in AI leading to inappropriate responses in sensitive situations; violation of privacy and disclosure of confidential information through AI data handling; propagation of discriminatory practices due to algorithmic bias; over-reliance on AI potentially diminishing critical human skills in mediation. |
| 18RomArbJ42.pdf | HeinOnline | THE INTERACTION BETWEEN AL (ARTIFICIAL INTELLIGENCE) AND IA (INTERNATIONAL ARBITRATION): TECHNOLOGY AS THE NEW PARTNER OF ARBITRATION | This paper explores the integration of Artificial Intelligence (AI) into International Arbitration (IA), discussing current applications like case management and legal research, and future possibilities including robot arbitrators and ChatGPT. It also addresses the associated benefits such as increased efficiency, alongside significant risks including bias, ethical dilemmas, and cybersecurity challenges, emphasizing the need for careful regulation and continued human oversight. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | High cost of legal services and representation for individuals with limited financial resources. | Utilizing AI-powered tools to provide affordable legal information, assistance, and potentially representation for those who cannot afford traditional legal services. | Affordable legal representation, access to legal information for low-income individuals. | Individuals with limited financial resources, parties unable to afford legal counsel. | International Arbitration | International | NaN | NaN | NaN | True | True | The paper discusses generally available tools like ChatGPT (which has a free tier) and commercial platforms (e.g., Jus Mundi) that utilize AI and are currently accessible to users. | Ensuring the reliability, accuracy, fairness, and ethical application of AI tools for access to justice, particularly concerning biased outputs, the 'black box' nature of AI, and the need for human oversight for vulnerable populations. | NaN | Bias in AI systems (from data or algorithms), cybersecurity threats (hacking, data breaches), generation of incorrect, misleading, or fabricated information (e.g., by ChatGPT), ethical concerns (manipulation, unethical legal tactics, ghostwriting awards, lack of human empathy), issues with data privacy and GDPR compliance, and the 'black box' nature of AI limiting transparency and explainability. |
| 90GeoWashLRev83.pdf | HeinOnline | Contracts in the Age of Smart Readers | This paper explores "smart readers," AI tools based on language models like GPT-3, which can simplify, personalize, interpret, and benchmark contracts, potentially improving consumer understanding and market competition. It also analyzes significant risks including errors, adversarial attacks by firms, discrimination, and the need for legal and doctrinal adaptations to this emerging technology. | True | Idealistic | True | 3.0 | Neutral | Smart readers (AI language models for contract analysis, e.g., GPT-3 for simplification/personalization/construction, and tools like PrivacyCheck for benchmarking). | Illustrative examples generated by GPT-3, acknowledged by authors as 'cherry-picked'. The paper also describes the functionality of PrivacyCheck, an existing tool for ranking privacy policies, as an example. | GPT-3 examples demonstrated capabilities such as simplification of complex legal language, personalization of contractual presentation, and construction of term meaning. PrivacyCheck was cited as a tool that scores privacy policies and compares them to competitors. | Information barriers (complexity and length of contracts, cognitive load), lack of consumer understanding of contractual terms, high cost of legal services, potential for contractual bias and discrimination, digital divide limiting access to technology. | Employing "smart readers" to simplify, personalize, interpret, and benchmark contracts; increasing term transparency to empower consumers and potentially foster market competition; providing on-demand "know-your-rights" services to improve access to legal understanding. | Understanding contract terms, identifying unfair or one-sided clauses, comparing contracts, enhancing consumer comprehension of legal agreements, addressing information asymmetry in consumer contracting. | Consumers in general, with a particular focus on vulnerable consumers such as low-income individuals, recent immigrants, and young people who may struggle with complex legal texts. | Contract law, Consumer law, Privacy law. | United States (primary examples and legal framework discussed, e.g., US cases, FTC, Draft Restatement of Consumer Contracts), with general applicability often implied for consumer contracts. | For GPT-3 (a key example model): Trained on a large corpus of text including Wikipedia ("45TB of compressed plaintext"). For PrivacyCheck: Built on machine learning algorithms; specific training data not detailed in this paper. | NaN | For PrivacyCheck: Described as a browser extension. For GPT-3: Accessible via API or publicly available interfaces (e.g., AI Dungeon for some exmaples). | True | True | PrivacyCheck is available as a free browser extension. GPT-3 examples were generated via publicly accessible interfaces or API. | Digital inclusion disparities limiting access to smart readers, difficulty in detecting and proving adversarial attacks and algorithmic bias, defining relevant comparison groups for benchmarking increasingly personalized contracts, ensuring smart reader accuracy and reliability, potential for regressive cross-subsidies if firms discriminate based on smart reader use, effective regulation for emerging risks. | Ensuring accuracy and reliability of smart readers, managing errors (isolated, correlated), preventing and detecting sophisticated adversarial attacks by firms, addressing potential for bias and discrimination in smart reader outputs or arising from their usage patterns, achieving widespread and equitable consumer uptake, developing appropriate legal and regulatory frameworks. | Exploitation by sophisticated parties through adversarial attacks, inscrutability of black-box models leading to unaccountable errors, exacerbation of contractual bias and discrimination (e.g., firms offering worse terms to non-users of smart readers), premature relaxation of consumer protection measures by policymakers, consumer overcompliance with unenforceable terms due to simplified explanations, harms from misinterpretation of contract terms. |
| 58WakeForestLRev981.pdf | HeinOnline | FORMING GOOD LAWYERS | This paper argues for the necessity of intentional professional identity formation in legal education, specifically advocating for a character-based approach. It posits that cultivating virtues such as honesty, open-mindedness, civility, resilience, and practical wisdom can help lawyers navigate modern challenges like technological disruption, public distrust, and mental health issues, thereby forming better legal professionals. | True | NaN | True | 3.0 | NaN | NaN | NaN | NaN | Public distrust of the legal profession; lawyers' poor mental health and well-being; over-specialization neglecting broader public interest; challenges of diversification without shared values; technological disruption (including AI) changing the nature of legal work; dominance of the 'neutral partisan' model of lawyering potentially undermining broader ethical duties; a compliance-based, minimalist approach to ethics. | Implementing a character-based approach to professional identity formation in law schools to cultivate key virtues (e.g., honesty, open-mindedness, civility, resilience, practical wisdom). This involves an intentional exploration of values and guiding principles to elevate the human elements of lawyering and foster a more holistic ethical development. | Legal ethics and professional responsibility; Role of lawyers in society; Public trust in the legal system; Reform of legal education. | Diverse populations (mentioned as beneficiaries of a more diverse legal profession which can increase their access to justice). | Legal Profession/Ethics, Legal Education | USA | NaN | NaN | NaN | False | False | NaN | Lack of systematic and intentional focus on character development and holistic professional identity formation within current legal education. | Concerns that character education is paternalistic/moralistic; perceived lack of time in curriculum for non-traditional content; skepticism about the feasibility of teaching character to adults; potential neglect of structural issues by focusing on individual character; difficulty in measuring character growth. | Continued public distrust of lawyers; high rates of mental health issues in the profession; inability of lawyers to articulate their value in an AI-driven world; AI tools like ChatGPT 'hallucinating' and providing incorrect information; ethical lapses due to a minimalist approach to ethics; misuse of 'character' assessments for discriminatory purposes if not implemented carefully. |
| 132YaleLJ.pdf | HeinOnline | Statutory Structure | This paper analyzes the Supreme Court's use of 'statutory structure' in statutory interpretation, categorizing types of structural arguments (compositional, operational, purposive). It evaluates these arguments against dominant interpretive methodologies, suggesting structuralism reveals an enduring need for purposive reasoning. | True | NaN | False | 2.0 | NaN | Structural argument in statutory interpretation, categorized into: Compositional Structuralism (Location, Geometry, Aperture), Operational Structuralism (Operational Compatibility, Operational Coherence), and Purposive Structuralism. | Analysis of U.S. Supreme Court case law and legal scholarship. | The paper's analysis categorizes structural arguments and concludes that while all dominant interpretive methods use them, purposive reasoning (particularly legal-process rationalism) best aligns with structuralism's underlying assumptions of coherence, highlighting an enduring need for purposive interpretation. | NaN | NaN | NaN | NaN | Statutory interpretation (general). Case examples span civil rights law, environmental law, criminal law, immigration law, and administrative law. | United States (primarily U.S. Supreme Court). | NaN | NaN | NaN | False | False | NaN | NaN | Reconciling structural argument with tenets of specific interpretive theories (e.g., textualism); the impact of 'unorthodox lawmaking' on assumptions of coherent legislative drafting; ambiguity in defining and applying 'coherence' or choosing between incompatible provisions. | Potential for manipulation of structural arguments (especially operational and purposive types); misattribution of drafting intent (e.g., relying on U.S. Code placement); structural arguments being used to unduly constrain statutory meaning or mask judicial policy preferences. |
| 17ContempAsiaArbJ91.pdf | HeinOnline | The Human Impact on Arbitration in the Emerging Era of Artificial Intelligence | This paper examines the benefits and risks of AI in arbitration, arguing that while AI can enhance efficiency, essential human qualities like complex reasoning and ethical judgment remain irreplaceable, especially in complex disputes. It concludes that AI should augment human capabilities rather than replace key human roles, emphasizing the need for human oversight and ethical guidelines in its application. | True | Market | False | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Arbitration | International, with examples from various jurisdictions (e.g., OECD, EU, US, UK, Australia, China, Taiwan). | NaN | NaN | NaN | True | True | The paper states that various AI tools are in use, such as commercial legal research platforms (e.g., Westlaw Edge, Harvey AI) and openly released AI models (e.g., Meta's SeamlessM4T for translation). | NaN | Practical and ethical challenges in using AI in arbitration, including AI's lack of cognitive/emotional capabilities, potential for bias, 'hallucinations' (generating false information), lack of reasoning transparency ('black box' issue), risk of improper delegation of decision-making functions, and privacy/confidentiality concerns. | Bias in AI leading to unfair outcomes, AI 'hallucinations' generating false legal or factual assertions, lack of transparency in AI decision-making ('black box' issue), ethical violations by counsel or arbitrators due to improper AI use (e.g., misrepresentation of law, delegation of decision-making), and breaches of data privacy and confidentiality when using third-party AI tools. |
| 15IJCA1 (1).pdf | HeinOnline | From Court Automation to e-Justice and beyond in Europe | This paper reviews the 25-year evolution of Information and Communication Technology (ICT) in European judiciaries, from early court automation to current e-Justice initiatives and the emerging role of Artificial Intelligence (AI). It discusses key projects, lessons learned, the impact of the COVID-19 pandemic, and future trends, emphasizing user-centric design and the protection of fundamental rights. | True | Idealistic | False | 3.0 | Positive | NaN | NaN | NaN | Digital divide/lack of resources for technology access; opacity and potential bias in AI; risk of AI infringing on fundamental rights; systemic rigidity in justice systems hindering adoption of effective ICT. | User-centric and accessible design of ICT/AI systems; development and adherence to robust ethical and legal frameworks (e.g., EU AI Act, CEPEJ Charter); training and capacity building for legal professionals and users; maintaining Hhman oversight, especially in AI decision-making; simplification of ICT to avoid complexity; concerted efforts to bridge the digital divide. | Ensuring equal access to justice for all, including self-represented litigants and vulnerable populations; bridging the digital divide; Online Dispute Resolution (ODR) as a means of access; improving accessibility of legal information and case law; protecting fundamental rights (e.g., fair trial, equality of arms) in the context of digitalized justice and AI. | Self-represented litigants; vulnerable populations; persons with disabilities; individuals affected by the digital divide. | Civil Law, Criminal Law, General Court Administration | Europe, European Union and member states | NaN | NaN | NaN | False | False | NaN | Lack of empirical assessment of ICT effectiveness in courts; need for consistent legislation and practical frameworks for EU-level justice tools; ensuring fair trial rights in remote hearings; modernizing outdated court IT systems; improving data collection for evidence-based policymaking; insufficient information sharing and coordination among EU judiciaries on ICT projects; challenges in regulating rapidly evolving AI while protecting rights and fostering innovation. | NaN | AI leading to discrimination or biased outcomes; opacity of AI systems hindering justified decisions and equality of arms; infringement of fundamental rights (including data protection and fair trial) by ICT/AI; misuse of predictive justice tools or analytics on judicial performance; technology creating new barriers to access if not designed inclusively; over-reliance on vendors, potentially threatening judicial independence. |
| 14JChristianLegalThought8.pdf | HeinOnline | The Multifaceted Impact of Generative AI on Lawyers and Legal Services | This paper explores the transformative potential of Generative AI (Gen AI) on the legal sector, detailing its effects on law firm business models, the redefinition of lawyer roles, and the acceleration of lawyer professional development. It also examines Gen AI's capacity to enhance access to justice and discusses the associated spiritual and ethical implications from a Christian perspective. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | High cost and general inaccessibility of legal services for most people; decaying legal system infrastructure and public lack of legal knowledge; legal profession's historical aversion to scaling services through technology; current Gen AI's insufficient dependability for reliable legal use. | Leveraging Gen AI to create scalable, accessible (affordable and convenient), and dependable legal information and solutions; ensuring Gen AI legal systems are developed ethically, particularly for disadvantaged groups, with informed consent and appropriate compensation; advocating for efforts to ensure AI-driven justice solutions remain widely and equitably available. | Affordability and availability of legal services; scalable provision of legal information and guidance; overcoming systemic barriers to justice. | The general public unable to afford traditional legal services, particularly disadvantaged and marginalized populations. | NaN | NaN | NaN | NaN | NaN | False | False | NaN | Current deficiency in Gen AI's dependability as a legal resource; the need to ensure Gen AI legal systems are developed to be suitable for all, especially vulnerable populations, rather than as experiments; challenge of ensuring widespread and equitable ongoing access to effective AI-driven justice solutions; absence of a fully developed Christian ethical framework for addressing issues like biased or unethically sourced AI training data. | NaN | Gen AI models being trained on data acquired without consent or through illegal means, such as copyright infringement or data theft; the potential for Gen AI legal solutions to be developed through unethical 'experimentation' on disadvantaged or marginalized individuals without their fully informed consent or due compensation; the possibility that advanced AI justice tools might become exclusive to a select few rather than remaining broadly accessible. |
| 13Laws1 (1).pdf | HeinOnline | AI Accountability in Judicial Proceedings: An Actor-Network Approach | This paper analyzes the impact of AI systems on accountability in judicial proceedings using an actor-network theory (ANT) framework, focusing on speech-to-text, legal analytics, and predictive justice technologies. It concludes that non-accountable AI poses risks to judicial values like fair trial if human control over outputs is lacking, and current legal remedies, including the EU AI Act, may be insufficient in such cases. | True | Idealistic | False | 2.0 | Neutral | Analysis of three types of AI applications used by judges: speech-to-text systems, legal analytics (e.g., Smart Sentencing project in Germany), and predictive justice systems (e.g., COMPAS in the US and RisCanvi in Spain). | Conceptual analysis using Actor-Network Theory (ANT) to explore how introducing non-accountable AI influences actor-network configuration and accountability distribution. The analysis is based on literature review and information about existing AI systems, assessed against the EU legal framework and the EU AI Act. | Non-accountable AI can be used without endangering judicial values if judges can control the system's outputs by evaluating its correspondence with inputs. When this is not met (e.g., complex legal analytics or predictive justice), remedies from the EU AI Act are insufficient, and judges become solely accountable for non-accountable systems, risking undue influence on decision-making and fair trial principles. | Lack of AI accountability, transparency, and explainability, leading to potential undue influence on judicial decision-making and threats to the fair trial principle, judicial independence, and equal treatment of justice seekers. | Ensuring human users (judges) can control and verify AI system outputs. For complex, non-transparent systems, reliance on regulatory frameworks like the EU AI Act for ex-ante and ongoing assessments, though the paper questions their full sufficiency. The paper highlights that accountability for non-accountable AI ultimately shifts to the human user. | AI accountability in judicial proceedings, Fair trial principle, Judicial independence, Algorithmic transparency, Judicial decision-making. | NaN | Judicial proceedings generally, Criminal justice (specifically sentencing and recidivism risk assessment). | EU (European Union legal framework and AI Act), Germany (Smart Sentencing example), Spain (RisCanvi example), US (COMPAS example). | For Smart Sentencing: A large corpus of German court judgments, initially classified by researchers for supervised machine learning. For RisCanvi: Internal data on approximately 600 inmates (initially), later expanded to 15,000 assessments; uses administrative, legal, criminological records, and interviews. For COMPAS: Criminal history data and data from close-ended questionnaires answered by offenders or probation officers. | For Smart Sentencing: Supervised machine learning, data coding and classification by researchers. For COMPAS and RisCanvi: Statistical methods (correlations, regression), criminological studies, expert commission recommendations, psychometric analysis (RisCanvi). | Speech-to-text: Widely available commercial applications used in courts. Smart Sentencing: A research project with potential for use. RisCanvi: Implemented across Catalan prisons since 2009. COMPAS: Used in various US jurisdictions for risk assessment. | False | False | NaN | Persistent lack of AI accountability and explainability ('Al remains essentially non-accountable'). Insufficiency of current legal frameworks (e.g., EU AI Act) to fully mitigate risks when human control over AI outputs is not possible. The difficulty in ensuring judicial values are upheld with opaque AI systems. | For developers/deployers of AI systems discussed: Incomplete or inconsistently structured data for training (Smart Sentencing). Opacity of algorithms even when not strictly AI (COMPAS, RisCanvi). Ensuring systems are free from bias. Difficulty for users (judges, professionals) to understand, verify, or override system outputs (RisCanvi). | Undue influence on judicial decision-making. Endangerment of the fair trial principle, judicial independence, and equal treatment of justice seekers. Introduction of biases through AI systems. Lack of transparency and accountability leading to unfair or erroneous outcomes. |
| 19JLAdminSci20.pdf | HeinOnline | ETHICAL AND LEGAL ASPECTS OF THE DEVELOPMENT AND USE OF ROBOTICS AND ARTIFICIAL INTELLIGENCE. PROTECTION OF HUMAN RIGHTS IN THE ERA OF GLOBALIZATION AND DIGITISATION | This paper provides a broad overview of the ethical and legal issues arising from the rapid development of AI and robotics, emphasizing the critical need for regulatory frameworks to protect human rights. It reviews international and EU legislative efforts, particularly the EU AI Act, analyzes AI's impact on various sectors including justice and employment, and discusses Romania's national strategy for responsible AI adoption. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Infringement on fundamental human rights (privacy, non-discrimination, dignity) by AI systems, lack of legal certainty and accountability for AI actions, societal distrust due to ethical concerns (e.g., biased outputs, opacity), and the risk of manipulative or harmful AI applications. | Development of comprehensive legal and ethical frameworks (e.g., the EU AI Act), promotion of human-centered and trustworthy AI, ensuring transparency, safety, non-discrimination, and human oversight in AI systems, fostering international cooperation on AI governance, and investing in AI literacy and education. | Use of AI in judicial systems (e.g., crime prevention, online dispute resolution, decision support), ensuring fair trial rights with AI, digital rights and principles, data protection, addressing AI-induced discrimination, and the ethical implications of AI for fundamental freedoms. | General public, human rights for all citizens, with specific attention to vulnerable categories such as children, people with disabilities, the elderly, and other disadvantaged or at-risk groups. | Human Rights Law, Data Protection Law, Civil Liability, AI-specific regulation (e.g., EU AI Act), Constitutional Law, Criminal Law, Administrative Law, Labour Law, Consumer Protection Law. | International (e.g., OECD, UNESCO), European Union (EU), and national levels (examples include Romania, USA, China, Japan, Canada, India, and various EU member states). | NaN | NaN | NaN | False | False | NaN | Keeping legal/ethical frameworks current with rapid AI evolution, achieving global consensus on AI governance, effectively translating ethical principles into technical practice, ensuring AI explainability (addressing the 'black box' problem), and building broad public trust in AI. | For the discussed regulatory approaches (like the EU AI Act): Legislating for a rapidly evolving technology, balancing innovation promotion with the need for safety and fundamental rights protection, achieving international regulatory coherence, Ccincretely defining and classifying AI risks, and ensuring effective oversight and enforcement of AI regulations. | Violation of fundamental rights (privacy, dignity, freedom, non-discrimination), manipulation of human behavior, perpetuation of societal biases, physical/mental/economic harm, erosion of human control, opacity in decision-making, spread of misinformation (deepfakes), job displacement, and establishment of surveillance systems. |
| 17ContempAsiaArbJ1.pdf | HeinOnline | WHAT'S REALLY WRONG WITH ISDS?-A CRITICAL ANALYSIS OF PHANTOM ISSUES AND REAL ISSUES TRIGGERED BY PRACTICE AND TECHNOLOGICAL DEVELOPMENT | This paper critically analyzes perceived ("phantom") and actual ("real") problems within Investor-State Dispute Settlement (ISDS), such as double-hatting, third-party funding challenges, case complexity, and arbitrator scarcity. It then explores potential solutions, including regulatory reforms, the use of Artificial Intelligence (AI), and an arbitrator team approach to address these issues. | True | Market | True | 3.0 | Positive | Artificial Intelligence (AI) in ISDS, including language models (e.g., ChatGPT) and AI legal assistants (e.g., Jus AI), alongside an 'arbitrator team approach'. | NaN | NaN | Prohibitively high costs of international arbitration for investors seeking to bring claims. | Third-Party Funding (TPF) to enable investors with meritorious claims but limited financial resources to access ISDS. For broader systemic issues impacting efficiency and fairness, the paper proposes the use of Artificial Intelligence (AI) and an 'arbitrator team approach'. | Access to Investor-State Dispute Settlement (ISDS) for financially constrained investors through mechanisms like Third-Party Funding. | Investors, particularly small or medium-sized companies, lacking sufficient funds to pursue ISDS proceedings. | International Investment Law, Investor-State Dispute Settlement (ISDS), International Arbitration | International | For AI tools like Jus AI, a proprietary global case law database. For general LLMs like ChatGPT, vast general and some proprietary internet text. The paper notes concerns that AI training data may overrepresent larger/powerful countries. | NaN | NaN | True | True | General purpose LLMs like ChatGPT, mentioned as examples, are widely accessible, including with free tiers. Specialized legal AI assistants like Jus AI are described as launched and available from Jus Mundi, likely on a commercial basis. | While TPF addresses cost barriers for investors' access, controversies surrounding TPF persist. For AI, which could improve overall system fairness: ethical concerns, potential for data bias to disadvantage parties (including those with lesser means), AI 'hallucinations,' data privacy issues, and the need for developed legal/policy frameworks for AI use in ISDS. | Ensuring AI accuracy and avoiding 'hallucinations'; addressing data bias in AI training; managing ethical concerns and data privacy; developing appropriate legal frameworks for AI use; adapting the role of arbitrators to include AI-skilled teams; overcoming skepticism towards AI. | AI producing incorrect information or 'hallucinations'; data bias in AI leading to unfair outcomes, especially for minorities or less-resourced parties; ethical concerns in AI-assisted legal decision-making; data privacy breaches; potential challenges to awards due to improper delegation of decision-making to AI. |
| 16IntlInHouseCounselJ (1).pdf | HeinOnline | Generative Artificial Intelligence: Legal Profession Disrupted? | This paper discusses the disruptive potential of generative AI in the legal profession, stressing that technology adoption should prioritize client needs and the administration of justice over mere efficiency. It highlights the high cost of AI tools, the importance of specialized legal AI, and showcases judiciary-led innovations as positive examples for harnessing technology responsibly. | True | Idealistic | True | 3.0 | Neutral | Generative AI, Large Language Models (e.g., ChatGPT), and specialized legal AI platforms (e.g., Casetext, Spellbook, Luminance Autopilot, LawGeex). | The paper cites evaluations of LLMs passing professional exams (e.g., Bar exam by ChatGPT) and a comparative study of LawGeex AI vs. human lawyers for NDA review (5 NDAs reviewed). | The paper cites a LawGeex demonstration where AI reviewed five Non-Disclosure Agreements in 26 seconds, compared to an average of 92 minutes for lawyer participants. | High cost of AI tools; technology adoption driven by 'solutionism' or commercial interests rather than client/public needs; risk of over-reliance on AI diminishing human judgment; challenges in creating AI that genuinely meets user needs leading to low adoption (e.g., ODR); lack of trust in AI outputs without verification. | Prioritizing client and public interests (paramountcy of consumer needs) in technology adoption; judiciary-led innovations focusing on proportionate justice, therapeutic justice, and safety for vulnerable parties; favoring specialized and verified legal AI tools over generic ones; critical evaluation of whether AI is the best or most cost-effective solution. | Online Dispute Resolution (ODR), proportionate justice, therapeutic justice in family law, safety of vulnerable parties in family law (child abuse/family violence), contract review, legal research. | Litigants in general, consumers of legal services, families undergoing dissolution, children and vulnerable parties affected by abuse and family violence. | General legal practice, contract law, family law, dispute resolution, litigation. | International, with specific examples and discussions related to Singapore, Australia, USA, UK, Canada, France, and Europe. | The paper implies that generic LLMs (like ChatGPT) are trained on vast internet data. Specialized legal AIs are mentioned as using more limited, curated data sources such as 'the White Book, the National Archives case law database, BAILLI, Westlaw, and Lexis Nexis.' | NaN | Widespread public availability for tools like ChatGPT; commercial licensing for specialized legal software (e.g., Microsoft 365 Copilot, LexisNexis tools); implementation within court systems for judiciary-led innovations. | True | True | Public availability of tools like ChatGPT (including a free tier) and commercial availability of specialized legal AI tools and platforms (e.g., LexisNexis AI, Microsoft Copilot, Casetext, Spellbook). | Ensuring trustworthiness and verifiability of LLM outputs for legal work; need for specialized legal AI that is more reliable than generic models; aligning AI development with genuine needs of justice and clients rather than just 'solutionism' or commercial interests; addressing low adoption rates of ODR by better understanding user needs. | High cost of generative AI-enabled tools; avoiding 'solutionism' (adopting technology for its own sake); ensuring security of technology; training and support required for new technologies; differentiating law firm services when all use similar AI tools. | Over-reliance on AI leading to diminished human judgment and critical thinking; inaccuracy of AI outputs, particularly from unspecialized models; technology dominating rather than assisting justice; potential for system failures to jeopardize dignity and due process; loss of human contact in legal processes. |
| 54CalWIntlLJ415.pdf | HeinOnline | Governing Artificial Intelligence Responsibility in Low to Middle Income Countries: Enabling Pathways to Sustainable Development | This paper examines the challenges Low- and Middle-income Countries (LMICs) face in governing Artificial Intelligence (AI) to foster sustainable development, highlighting digital divides and the concentration of AI infrastructure in the Global North. It proposes a structured, context-sensitive governance approach for LMICs, emphasizing principles like transparency, accountability, multi-stakeholder participation, and iterative methodologies to build trust and enable responsible AI innovation. | True | Idealistic | False | 1.0 | Positive | A structured, context-sensitive AI governance framework for LMICs, incorporating principles of good regulatory practice (transparency, accountability, participation, inclusion), multi-stakeholder collaboration, iterative and flexible methodologies (e.g., co-regulation, regulatory sandboxes), and fit-for-purpose institutional design. | The proposed framework is based on the authors' analytical and operational experience at the World Bank; the paper does not present a formal empirical evaluation of the proposed framework itself. | NaN | Digital infrastructure gaps (connectivity, electricity), lack of quality and locally relevant data sets, limited digital literacy, insufficient financial resources, limited technical and regulatory capacity in LMICs, the global AI divide with AI development and infrastructure concentrated in the Global North, and the risk of AI exacerbating existing inequalities. | Developing context-sensitive AI governance frameworks tailored to LMIC needs and values; adopting 'greenfield' regulatory approaches where legacy systems are absent; implementing good regulatory practices including transparency, accountability, participation (TAP), and inclusion; fostering multi-stakeholder, iterative, and flexible governance models; utilizing tools like co-regulation and regulatory sandboxes; and establishing fit-for-purpose institutional arrangements that account for local constraints. | AI governance for sustainable development, protection of human rights in the context of AI, equitable AI deployment in key sectors (e.g., education, healthcare, agriculture), fostering trust in AI systems, mitigating AI-related risks for vulnerable populations, and addressing the digital and AI divides. | Low- and Middle-Income Countries (LMICs), particularly vulnerable populations and underserved communities within them. | AI governance, data protection law, human rights law, regulatory law, digital economy law, international law (public and private aspects related to technology governance). | Low- and Middle-Income Countries (LMICs) globally, with specific examples and regional discussions concerning Africa, Asia-Pacific, Middle East, and Latin America and the Caribbean. | NaN | NaN | NaN | False | False | NaN | Lack of established 'good practice models' for AI governance applicable to LMICs; persistent digital divide (infrastructure, data, literacy, capacity); insufficient and fragmented legal/regulatory frameworks for AI in many LMICs; difficulties in enforcing individual and collective rights, especially against powerful global tech companies; need for greater multi-stakeholder collaboration and public participation in policy-making in some LMICs; and significant resource and expertise constraints for effective oversight and implementation of AI governance. | NaN | AI-induced bias and discrimination (e.g., in credit scoring, recruitment, public services), lack of transparency and accountability in AI systems, manipulation of beliefs and emotions by generative AI leading to psychological harm, spread of disinformation influencing political opinions, exacerbation of existing inequalities and digital divides, potential for AI to facilitate criminal activities, erosion of public trust due to irresponsible AI deployment, and unintended negative impacts from poorly designed regulatory interventions (e.g., on competition or consumer protection). |
| 34AlbLJSciTech27.pdf | HeinOnline | ON ADULT A.I. INTERACTIONS WITH ARTIFICIAL INTELLIGENCE IN THE SHADOWS OF REGULATION, ANTITRUST, AND FAMILY LAW. | This paper discusses the legal implications of increasingly capable AI, using a conversation with ChatGPT about antitrust and regulation as a foundation. It proposes an evolutionary approach to AI legal status and liability, analogous to human development, to address challenges like market power, algorithmic misbehavior, and the need for responsible AI governance. | True | Market | True | 3.0 | Positive | ChatGPT (as an example of Large Generative AI Models) | Author's exploratory conversation with ChatGPT on economic regulation and antitrust issues. | ChatGPT provided plausible and, in some instances, novel-sounding insights on antitrust and regulation, such as specific relevant market definitions ('AI-powered customer service', 'conversational AI') and suggestions for regulatory measures including open standards and data protection. | Concentration of market power by tech companies in AI-driven businesses; AI-driven anticompetitive practices harming consumers and fair competition; proprietary standards by dominant AI companies hindering open and competitive ecosystems; illegal collection of personal data by AI violating privacy rights; opacity and potential bias in AI decision-making challenging fairness and accountability; lack of clear legal frameworks for AI liability, making it hard to assign responsibility for harms. | Implementing regulatory measures (merger control, competition enforcement, open standards, data protection) to curb market power and ensure fair competition in AI markets; adopting an evolutionary approach to AI legal status and liability, mirroring human development; promoting human oversight in AI-driven regulatory processes; fostering international cooperation for trustworthy AI and shared principles for AI education and governance; designing AI for auditability and transparency. | Ensuring fair competition and preventing market dominance in AI-driven sectors; establishing responsible AI governance and legal frameworks for accountability; protecting consumer rights (e.g., privacy) in interactions with AI. | NaN | Antitrust Law, Economic Regulation, Family Law (as an analogy), AI Law/Technology Law, Corporate Law | USA, European Union, Italy (specific instance). Broader discussion has international implications. | Large, multi-modal datasets of text inputs for training LLMs like ChatGPT, often proprietary and web-derived. Specifics for ChatGPT, as noted by the paper, are not fully public. | Based on Large Language Models (LLMs), artificial neural networks, deep learning, and attention mechanisms. Fine-tuned with human supervision (Reinforcement Learning from Human Feedback implied). | Public online availability of ChatGPT by OpenAI; integration into commercial products like Microsoft's search engine. | True | False | ChatGPT is accessible online through OpenAI's platform, with free and paid tiers. | Lack of global governance for AI development, especially for high-risk AI; ongoing challenges in ensuring AI compliance with privacy and data ownership; need for criteria to measure AI's educational progression for assigning liability; absence of universally accepted AI auditability standards or 'psychometrics for machines'; need for a robust theory of legal and moral personhood for AIs; uncertainty on managing AI if it develops preferences divergent from human values. | Preventing 'hallucinations' or inaccurate outputs; ensuring responses are not harmful (racist, sexist); managing the 'black box' nature of decision-making; addressing privacy/bias risks from large-scale training data; balancing proprietary interests with calls for openness; predicting and mitigating risky emergent behaviors; high energy consumption. | AI threatening human freedom (job displacement, malicious use); AI-driven anti-competitive practices; bias in AI decisions from training data; opacity in AI-driven decision-making; 'artificial neurological illnesses' like hallucinations; illegal collection and misuse of personal data by AIs; catastrophic consequences from unsupervised development of powerful AI (e.g., military AI); emergence of 'power-seeking' and 'agentic' behaviors in AI; AI developing preferences misaligned with human values. |
| 14UCIrvineLRev404.pdf | HeinOnline | The Epistemic Injustice of Algorithmic Family Policing | This paper critiques risk-prediction algorithms in the U.S. 'family policing' system, arguing they automate and deepen 'epistemic injustice' against targeted families, especially poor and Black communities. The author advocates for abolishing this system in favor of community-based supports that value the knowledge of impacted individuals. | True | Idealistic | False | 3.0 | Negative | Risk-prediction algorithms (e.g., Allegheny Family Screening Tool - AFST, Hot Spot Models) used in family policing. | AFST V1 was trained to predict re-referral or out-of-home placement; V2 predicts out-of-home placement. A 2022 study (Cheng et al.) evaluated AFST's impact on racial disparity in decisions compared to human workers. | Cheng et al. (2022) found the AFST alone would have made more racially disparate decisions than human workers, but workers were able to reduce this algorithmic disparity when using the tool. | Systemic epistemic injustice (discrediting parents' knowledge and experiences); conflation of poverty with neglect; racial and class bias in the system; the carceral nature of 'family policing' prioritizing punishment over support; algorithms scaling up and automating these harms; lack of due process and avenues for contestation against algorithmic decisions. | Abolition of the 'family policing' system; investment in community-based resources and supports that are not carceral; valuing and prioritizing the knowledge and experiences of impacted communities (achieving epistemic justice); reparations; adopting design justice principles if any tools are to be used. | Child welfare/family policing; family separation; parental rights; algorithmic bias and accountability; racial and economic justice in family regulation; epistemic injustice. | Poor families, Black families, mothers (especially Black mothers and mothers of color), families targeted by the child welfare/family policing system. | Family Law (child welfare, child protection, parental rights), Administrative Law, Constitutional Law (due process), Civil Rights. | United States (with specific examples from Allegheny County, PA and New York City, NY). | Administratively-held data from public systems such as family policing, criminal legal system, public benefits system, public health services (hospitals), and census data. For example, the AFST uses county jail records, juvenile probation data, public welfare information, behavioral health service records, and census data. This data is domain-specific, often unstructured or semi-structured, and proprietary to government agencies. | Actuarial risk assessment methods, machine learning, and artificial intelligence. Development involves defining outcome variables (e.g., re-referral to child protection services, out-of-home placement) and training predictive models on historical administrative data. | Algorithmic tools are incorporated into government agency (e.g., child protective services) decision-making workflows, such as at the call screening stage for reports of suspected child maltreatment (e.g., AFST operational since 2016). | False | False | NaN | Technical: Algorithmic bias, inaccuracy, opacity, problematic proxies for 'maltreatment,' inability of algorithms to capture human complexity. Societal: Pervasive epistemic injustice, failure to address structural causes of family struggles (e.g., poverty, racism), need for abolitionist approaches and community-led alternatives instead of carceral 'solutions,' lack of true support for families. | The paper identifies challenges inherent in these tools: defining inherently political and subjective terms (e.g., 'maltreatment', 'neglect') for algorithmic modeling; identifying outcome variables that genuinely map to child welfare rather than system actions; addressing and mitigating historical bias and systemic racism present in training data; ensuring transparency and accountability; preventing misuse and avoiding the de-skilling of human workers. | Automation and exacerbation of epistemic injustice (through algorithmic gaslighting, enhanced surveillance, unjust definition of epistemic authorities, carceral reception of information, and suppressing contestation); reinforcement of racial and class-based discrimination; violations of privacy; expansion of the carceral state through increased surveillance and intervention; direct harm to families, including unnecessary separation; lack of due process and democratic accountability. |
| 3JusCorpusLJ106.pdf | HeinOnline | The Escalation of ChatGPT: How ChatGPT will exert Influence on the Legal Profession? | This paper examines ChatGPT's potential influence on the legal profession, covering applications in research and drafting, and addressing concerns like accuracy and ethics. It concludes AI should be an assistive tool for lawyers, not a replacement. | True | Market | True | 2.0 | NaN | ChatGPT | Refers to an external evaluation where ChatGPT scored a C+ on a law school exam and author's anecdotal use for drafting and current affairs queries. | ChatGPT achieved a C+ (low-passing grade) on a law school exam. | NaN | NaN | NaN | NaN | General legal practice | Mentions Indian law; discussion is largely general/international. | Trained on a large corpus of text data (570GB filtered content, 45TB unfiltered) from books, web texts, Wikipedia, and other online sources, with a knowledge cutoff in 2021. | Based on OpenAI's GPT-3 model family, fine-tuned using supervised and reinforcement learning (transfer learning). | Released for free public testing by OpenAI. | True | False | Available for free public testing via OpenAI's platform. | NaN | Ensuring accuracy and reliability of AI-generated legal information, protecting client confidentiality, dealing with outdated knowledge (pre-2021), establishing accountability for AI outputs, mitigating IP risks, preventing plagiarism and malicious use, and addressing complex ethical considerations. | Inaccurate legal information leading to detrimental effects, breach of client confidentiality, providing outdated legal advice, lack of accountability for AI errors, intellectual property infringement, plagiarism undermining professional integrity, potential for malicious uses like cyberattacks, and job displacement for some legal professionals. |
| 4JusCorpusLJ228.pdf | HeinOnline | The Role of ChatGPT and Emojis in Modern Legal Interpretation | This paper discusses the evolving role of technology, particularly AI tools like ChatGPT and the use of emojis, in modern legal practice and interpretation. It explores their potential benefits in legal research, document drafting, and contract acceptance, while also highlighting challenges related to copyright of AI-generated content and the legal standing of emojis. | True | Market | True | 2.0 | Neutral | Use of ChatGPT for legal tasks (e.g., research, document analysis, drafting, judgment interpretation) and the legal interpretation of emojis in contract acceptance. | The paper discusses examples of use, such as a Colombian judge using ChatGPT for a ruling on medical insurance and a Canadian court case (South West Terminal Ltd. v Achter Land & Cattle Ltd.) involving a thumbs-up emoji for contract acceptance. It is not an empirical evaluation by the author. | NaN | Complexities surrounding liability and ownership of AI-generated content within existing copyright laws; lack of clarity on authorship, originality, and legal personhood for AI in the context of copyright. | Development of a robust legal framework tailored to AI's unique attributes, addressing issues of legal personhood, authorship, ownership, and liability. Enactment of new legislation like India's proposed Digital India Act to regulate emerging technologies. | Dissemination of general legal information to the public; AI-assisted legal guidance for individuals assessing merits of court cases; improving efficiency of judicial processes. | NaN | Copyright law, Contract law, Judicial procedures. | India | NaN | NaN | NaN | False | False | NaN | Ambiguity in Indian copyright law regarding AI-generated works and AI as an author; need to determine originality for AI-generated content; interpretation of AI as a 'person' under law for authorship. The need for legal frameworks to adapt to ensure equitable protection and navigate nuances of AI-generated content and emojis in legal communication. | NaN | Copyright infringement from using LLM-generated content for commercial purposes without meeting fair use/dealing exceptions. Ambiguity and potential disputes arising from the use of emojis as acceptance in contractual contexts if intent is unclear. |
| 64HungJLegalStud472.pdf | HeinOnline | Rules over words: Regulation of chatbots in the legal market and ethical considerations | This paper examines the integration of chatbots into the legal market, highlighting their benefits such as improved efficiency for law firms and potential for enhanced access to justice. It primarily discusses the significant professional liability, data privacy, and ethical concerns arising from their use, and explores regulatory challenges and approaches to mitigate risks while fostering innovation. | True | Market | True | 3.0 | Neutral | Chatbots and AI tools in the legal field (e.g., ChatGPT, DoNotPay, Harvey, Brainspace) | NaN | NaN | Cost and complexity of accessing traditional legal services and information for underserved communities. | Utilizing chatbots and AI tools to provide easier access to legal information and basic legal services for those who cannot afford or easily access traditional legal aid. | Access to legal information, assistance with simple legal tasks (e.g., parking ticket appeals), provision of otherwise inaccessible legal services. | Economically disadvantaged individuals, less educated groups, segregated communities, and the general public needing assistance with simple legal matters. | Broad range including administrative law (parking tickets), contract law, corporate law (due diligence, contract analysis), legal research; with ethical considerations highlighted for criminal law and family law. | International (mentions US, UK, China, Italy, EU, and international law firms) | General discussion of reliance on large datasets, including user data for tools like ChatGPT, and confidential client information, raising privacy concerns regarding their use for training AI. | NaN | Deployment in law firms (e.g., Harvey, Brainspace), public-facing services (e.g., DoNotPay, ChatGPT), and integration into court systems. | True | False | The paper discusses existing and operational tools like ChatGPT and DoNotPay, implying their general availability to the public or specific users (e.g., law firms for Harvey). | Need for further technological development for complex tasks, robust and comprehensive regulatory frameworks, ensuring ethical application (especially empathy and human judgment), maintaining human oversight, and mitigating algorithmic bias and malpractice. | Addressing professional liability and malpractice from AI errors, ensuring data privacy and confidentiality (especially with client data), overcoming ethical dilemmas (e.g., lack of empathy, dehumanization of justice, bias), dealing with the 'black box' problem of AI decision-making, preventing algorithmic malpractice, ensuring accuracy and effectiveness, and protecting user mental health from chatbot interactions. | Providing incorrect legal advice leading to malpractice, breaching client confidentiality and data privacy, algorithmic bias and discrimination, dehumanizing the justice system, potential for misuse (e.g., encouraging unnecessary litigation, harassment), negative impacts on users' mental health, and deskilling junior legal professionals. |
| 6Issue6IntlJLMgmtHuman312.pdf | HeinOnline | From Data to Verdict: Navigating AI's Growth & Blemish in the Legal System | This paper discusses the increasing adoption of artificial intelligence, including large language models like ChatGPT, within the legal sector for tasks such as document analysis, contract drafting, and predicting case outcomes. It explores the potential benefits for efficiency and access to justice, while also highlighting significant ethical concerns, risks of bias, the need for human oversight, and regulatory challenges. | True | Idealistic | True | 3.0 | Neutral | Kira Systems (Machine learning program for contract review) | Reported by Kira Systems' clients based on their use of the program. | Reduction in lawyer time required for contract review between 20% to 60%. | Limited public access to court systems; Overwhelming case backlogs; Potential for algorithmic bias and lack of transparency in AI tools used in the justice system; Ethical dilemmas related to AI in legal decision-making. | Digitalization of judicial proceedings (e.g., online courts); Use of AI for court efficiency, transcription (e.g., Teres), and translation (e.g., SUVAS); Development of comprehensive legal, regulatory, and ethical frameworks for AI; Ensuring transparency, explainability, and human oversight of AI in legal applications. | Improving access to court systems; Reducing judicial backlogs; Enhancing transparency of judicial proceedings; AI-assisted legal document analysis and decision support in the justice system. | General public, particularly those with limited access to legal assistance or facing overwhelmed court systems. | Contracts, Litigation, Criminal Law (bail decisions), Patent Law, General Court Procedures. | India, USA, Canada, Europe (GDPR reference). General discussion with specific examples from these jurisdictions. | For Kira Systems: Proprietary legal documents (contracts), with the software being trained and refined by human legal experts. | For Kira Systems: Iterative refinement of standard machine learning algorithms based on human expert feedback over an extended period. | For Kira Systems: Commercial licensing to law firms. | True | False | ChatGPT is available as an online service. Tools like Kira Systems, Lex Machina, and Ravel Law are commercially available. AI tools like Teres, SUPACE, and SUVAS are deployed within the Indian judicial system. | AI's inability to replicate human judgment, resourcefulness, empathy, and creativity in complex legal scenarios; Uncertainty in how much better AI contract writers can become due to lack of domain experience and linguistic accuracy for autonomous operation; Need for AI systems to be fully transparent and explainable; Lack of comprehensive legal, regulatory, and ethical frameworks for AI in the justice system. | For Kira Systems (as reported for its development): Significant time and effort required to refine the software to accurately identify specific legal concepts within documents (took 2.5 years instead of an expected 4 months). | Job displacement for legal professionals; Embedded bias in AI leading to unfair or discriminatory outcomes; Lack of transparency and explainability in AI decisions, undermining due process; Amplification of errors if AI relies on flawed legal data; Over-reliance on AI (automation bias); Breaches of data security and privacy for sensitive legal information; Ethical concerns about machines making decisions on personal liberty. |
| 15BeijingLRev (1).pdf | HeinOnline | The Role of Disruptive Artificial Intelligence Technology in Combating Crime in Indonesia | This study examines the potential of disruptive AI, specifically Esri's ArcGIS Pro software, to combat crime in Indonesia. The paper argues that ArcGIS Pro can enhance crime prevention by integrating and analyzing crime data, investigating patterns, and facilitating collaboration among law enforcement agencies, while also highlighting the need for training, infrastructure, and appropriate governance. | True | Market | False | 2.0 | Positive | ArcGIS Pro software (Geographic Information System with AI/GeoAI capabilities) | The paper does not describe a specific empirical evaluation or testing procedure conducted by the authors using ArcGIS Pro. It discusses existing capabilities and presents Indonesian crime statistics as context. | The paper does not report specific performance results from an empirical evaluation conducted by the authors. It concludes, based on a review, that AI and ArcGIS Pro are expected to be beneficial for crime prevention. | High and volatile crime rates in Indonesia; socio-economic drivers of crime; need for specific AI regulations; potential lack of specialized training and infrastructure for advanced technologies within law enforcement. | Utilizing AI, specifically ArcGIS Pro, for crime data integration, advanced analysis, and crime pattern investigation; optimizing GIS use by law enforcement (e.g., Prosecutor's Office); increasing community involvement in crime prevention; implementing education and training programs for law enforcement; upgrading technological infrastructure. | Crime prevention; crime data integration and analysis; crime pattern investigation (e.g., hotspot analysis, geospatial profiling); collaborative platforms for law enforcement; application of AI/GIS in criminal justice. | Law enforcement agencies in Indonesia (e.g., Prosecutor's Office, Police); indirectly, the general public in Indonesia through improved public safety. | Criminal Law; Criminal Procedure; Cyber Law (related to ITE Law) | Indonesia | The paper discusses the use of ArcGIS Pro which would analyze law enforcement data such as crime incident reports, arrest records, geospatial data, sensor data (imagery, point clouds), cell phone records, and financial transactions. This data is typically domain-specific, structured and unstructured, and largely proprietary to law enforcement agencies. | NaN | The paper advocates for the adoption and use of ArcGIS Pro by Indonesian law enforcement and discusses the need for prerequisite training and infrastructure, rather than describing an existing deployment initiated by the research. | True | False | ArcGIS Pro is a commercial software product from Esri, available for purchase/licensing. | Absence of specific AI regulations in Indonesia; potential gaps in AI talent development, data ecosystems, and AI infrastructure within law enforcement; need for ongoing investment in ICT for public sector efficiency in crime prevention; full implementation of the Indonesian National Artificial Intelligence Strategy. | Integrating diverse and large volumes of crime data; ensuring data is current and analysis-ready; need for specialized training for law enforcement personnel to use advanced AI tools; cost of commercial software and infrastructure upgrades; adapting AI tools to rapidly evolving crime patterns; preventing and mitigating AI bias in crime detection systems. | Potential for unexplained or incorrect conclusions from AI; development of biases in AI models leading to unfair detection or profiling (e.g., racial bias, false alarms); loss of public trust in law enforcement if AI tools are misused or produce errors; challenges in ensuring AI systems are safe, secure, and trustworthy without effective governance. |
| 36CanCompetitionLRev88.pdf | HeinOnline | A Justice as Fairness Framework for a Revised Efficiencies Defence | The paper argues for reforming Canada's competition law's 'efficiencies defence' rather than abolishing it. It proposes a new framework that shifts focus from static to dynamic efficiencies and incorporates Rawlsian 'justice as fairness' to ensure benefits for all Canadians, especially the least advantaged. | True | Idealistic | False | 1.0 | NaN | A revised efficiencies defence framework for Canadian competition law, incorporating Rawlsian 'justice as fairness', focusing on dynamic efficiencies, and detailing a 6-step evaluative process. | NaN | NaN | The current efficiencies defence in Canadian competition law is uncertain, undervalues crucial dynamic efficiencies, and lacks a robust mechanism to ensure fairness, potentially harming disadvantaged groups while benefiting others. | The paper proposes a reformed efficiencies defence framework incorporating Rawlsian 'justice as fairness,' prioritizing dynamic efficiencies, and using a structured, order-driven approach with specific consideration for the least advantaged. | Fairness in economic outcomes of merger reviews; equitable application of competition law (specifically the efficiencies defence); protection of vulnerable consumer, worker, and small business groups from negative impacts of mergers. | The 'least advantaged' members of society, including small businesses, low-income consumers, consumers in especially affected or rural regions, consumers in captive market segments, employees, and workers in defined industries. | Competition Law / Antitrust Law | Canada | NaN | Legal analysis, review of existing case law and economic theory, and application of John Rawls' 'justice as fairness' philosophical framework. | Proposed for legislative reform of the Canadian Competition Act. | False | False | NaN | The paper acknowledges its proposed framework will require jurisprudential refinement, particularly in defining 'material' harm to disadvantaged groups and in the methodology for weighing diverse and potentially incommensurate factors. | Developing a framework that effectively integrates complex philosophical concepts (Rawlsian fairness) into legal tests for merger reviews, and balancing economic efficiency goals with explicit considerations for distributional justice and the 'least advantaged'. | Without reform, the current efficiencies defence risks producing unfair outcomes that harm the least advantaged and fails to support Canada's competitiveness by undervaluing dynamic efficiencies. The proposed framework's reliance on terms like 'material' harm may lead to initial interpretative uncertainty. |
| 96PhilLJ793.pdf | HeinOnline | Will Artificial Intelligence Replace Lawyers in the Philippines? | This paper examines how AI, including LLMs, might transform the legal profession in the Philippines by reviewing economic theories on automation and current AI capabilities. It argues that while routine legal tasks are automatable, lawyers' roles requiring creative/social intelligence will remain crucial, and suggests policy recommendations for AI integration and improving access to justice. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Oligopoly of information, high cost of legal services for indigent clients, bureaucratic processes for legal aid. | Automation of legal services with AI to reduce legal fees and increase access to legal information; upskilling of lawyers and legal staff; reform of legal education to include technology. | Access to legal information, Affordability of legal services, Legal aid. | Indigent clients. | General practice of law | Philippines (primarily), with references to US and Europe. | NaN | NaN | NaN | True | False | The paper discusses several AI tools, some of which are commercially available (e.g., DoNotPay, LexMachina) or have free/freemium access (e.g., ChatGPT, Bard). | Ethical guidelines for AI use in law are not yet established; risk of errors in AI outputs and need for human oversight; technological gap including lack of digitized and OCR-enabled government documents in the Philippines; AI's current inability to fully replicate human creative and social intelligence required for some legal tasks. | Ensuring competent and ethical use of AI by lawyers; addressing the technological gap and lack of digitized/OCR-enabled documents in the Philippines; difficulty in automating tasks requiring high degrees of creative and social intelligence; need for upskilling lawyers and legal staff. | Job displacement for legal support staff performing routine tasks; errors in AI-generated legal research (e.g., citing non-existent cases); breach of attorney-client confidentiality through third-party AI tools; unauthorized practice of law by non-lawyers using AI; over-reliance by lawyers on AI tools. |
| 19JLEconPoly295.pdf | HeinOnline | Artificial Intelligence Regulatory Sandboxes | This paper analyzes the global landscape of AI regulatory sandboxes, comparing approaches in various jurisdictions like the UK, EU, and US, and offers policy recommendations for their design. It emphasizes the role of sandboxes in fostering innovation, enabling evidence-based AI regulation, and highlights their potential in sectors like legal services to improve access to justice. | True | Idealistic | False | 2.0 | Positive | AI regulatory sandboxes | Review of existing sandbox programs and their outcomes in various jurisdictions (e.g., UK, EU, Norway, Switzerland, Singapore, US states), analysis of policy documents, and case studies like the Utah legal sandbox. | Well-designed regulatory sandboxes, such as Utah's legal sandbox for access to justice, have demonstrated effectiveness in promoting innovation, enabling new service providers, and informing regulatory approaches by allowing experimentation in a controlled environment. | High cost of legal services, restrictive regulations prohibiting non-lawyers from providing legal services or owning legal firms, leading to a significant justice gap, particularly for low-income individuals. | Proposes the establishment and wider adoption of AI-focused legal regulatory sandboxes, similar to Utah's model, to allow non-lawyer-owned entities (including tech firms) to provide specific legal services, thereby fostering innovation, competition, and reducing costs. | Lowering the cost of legal services, enabling alternative legal service providers (non-lawyer owned firms, tech companies), facilitating provision of specific legal services (e.g., form completion for marriage, business, immigration, real estate). | Low-income Americans. | General legal services, including family law (marriage), business law, immigration law, real estate law. | United States (specifically Utah, with recommendations for other states), Canada (British Columbia, Ontario). General discussion covers UK, EU, Norway, Switzerland, Singapore, China, India, Russia, Brazil, Colombia, Chile. | NaN | Comparative analysis of existing international AI regulatory sandbox models, review of policy documents and legal scholarship, and synthesis of best practices to propose design principles and policy recommendations for effective sandbox creation and operation. | Through government-run programs, legislation, and the establishment of dedicated regulatory bodies or initiatives by existing regulators (e.g., financial conduct authorities, data protection authorities, supreme courts, ministries). | True | False | Several jurisdictions discussed (e.g., Utah's legal sandbox, Norway's AI sandbox, Spain's AI sandbox, Zurich's innovation sandbox) have operational AI regulatory sandboxes that entities can apply to participate in, subject to eligibility criteria. | Limited adoption of legal AI sandboxes in most jurisdictions; a need to translate sandbox learnings into broader, systemic legal and regulatory reforms to fully address access to justice issues. | Regulatory fragmentation hindering coordinated efforts, difficulties in attracting a sufficient number of high-quality applicants, challenges in effectively using sandbox findings to inform broader regulatory reforms, designing appropriate scope and interagency coordination mechanisms, and mitigating risks like regulatory privilege. | Granting unfair regulatory privilege to sandbox participants, creating market distortions, misuse of sensitive personal data or consumer harm if safeguards are inadequate, potential for sandboxes to be co-opted in environments with weak rule of law, and stifling innovation if sandboxes are poorly designed or lead to premature, overly burdensome regulation. |
| 127WVaLRev1.pdf | HeinOnline | BALANCING INTERESTS: Al, BUSINESS & HUMAN RIGHTS, AND THE LEGAL LANDSCAPE IN AN ERA OF DISRUPTION | This paper analyzes the U.S. government's approach to AI regulation, primarily through the Biden Administration's Executive Order and National Action Plan on Responsible Business Conduct, in the context of business and human rights. It argues that while these initiatives signal important principles, they lack sufficient enforcement to effectively protect human rights from AI-related business harms and calls for a balanced regulatory approach. | True | Idealistic | False | 3.0 | NaN | NaN | NaN | NaN | Societal harms from AI (bias, discrimination, fraud); lack of effective enforcement in current regulations; difficulties in defining AI for regulation; rapid technological evolution outpacing legal frameworks; superficial compliance mechanisms. | Comprehensive and deliberate laws balancing innovation and rights; a risk-based regulatory framework; a "smart mix" of voluntary and mandatory measures; embedding ethical principles (transparency, accountability, non-discrimination) in AI governance. | Protection against AI-driven discrimination and bias; accountability for AI harms; access to remedies for victims of business-related human rights abuses. | Individuals and affected communities, particularly those vulnerable to discrimination and bias from AI systems; the general public interacting with AI. | Human Rights Law (including Business and Human Rights), Administrative Law/Regulatory Law, Consumer Protection Law, Anti-discrimination Law, Data Privacy Law. | United States, European Union, International | NaN | NaN | NaN | False | False | NaN | Lack of enforcement mechanisms in current US AI governance; insufficiency of EOs and NAPs alone, requiring Congressional action; superficiality of some HRDD compliance; regulatory gaps for disruptive technologies; lag between law and technological advancement; need for comprehensive federal privacy laws. | NaN | Societal harms (fraud, discrimination, bias, disinformation); labor displacement and disempowerment; stifled competition; national security risks; manipulative, exploitative, and social control practices; harms to public interests and fundamental rights (physical, psychological, societal, economic); misuse of biometric surveillance; AI-induced financial crises. |
| 26LegalWritingJLegalWriti.pdf | HeinOnline | "Alexa, Write a Memo": The Promise and Challenges of AI and Legal Writing | This paper examines the current and foreseeable capabilities of artificial intelligence in assisting with and potentially performing legal writing tasks, particularly the drafting of office memoranda. It analyzes how AI can be applied to various stages of the memo-writing process and discusses the implications for legal education and the skills future lawyers will need. | True | Market | True | 3.0 | Neutral | The paper broadly discusses multiple AI techniques and tools relevant to legal writing, including legal text analytics, machine learning (ML), network diagrams, question answering (QA) systems (e.g., ROSS), expert systems (e.g., Neota Logic, A2J Author), natural language processing (NLP), and large language models (LLMs like GPT-2, GPT-3). | The paper describes evaluations of various AI tools as reported by their developers or other researchers (e.g., GPT-3's text generation capabilities through examples; ML models for outcome prediction on ECHR dataset; VJAP's predictions). It does not present new empirical testing by the authors. | The paper notes varied results for different AI applications: LLMs like GPT-3 can generate plausible short texts but lack guaranteed legal accuracy and coherence for long documents; ML can predict case outcomes with some success but often lacks explainability; QA systems can retrieve relevant passages but require human assessment; expert systems are limited by manually created rules. | The paper mentions the difficulty for non-experts to navigate legal issues (which A2J tools try to address) and implies the cost/complexity of legal services as underlying A2J issues, although these are not the central focus. More directly, it refers to the lawyer job market concerns due to AI. | The paper mentions AI-powered solutions that can contribute to access to justice: legal expert systems and chatbots (e.g., A2J Author, Neota Logic) for client guidance and intake; specific AI tools for legal research and analysis in A2J contexts (e.g., LUIMA for veterans' claims, SCALE project for landlord-tenant disputes). | Client screening and intake, legal information provision, specific legal aid areas like veterans' benefits and landlord-tenant law. | General public needing initial legal guidance, veterans, tenants. | Tort law (dog bites), contract law, civil procedure (discovery), veterans' law, landlord-tenant law, general legal practice. | Illinois (for primary hypothetical), USA (general legal concepts, education, some case law), International (mentions ECHR, WIPO, Japanese law examples). | Discusses training data for various AI systems mentioned: e.g., GPT-3 (general web text like Common Crawl, Wikipedia, Webtext2), ML for ECHR outcome prediction (ECHR case decisions), LEGAL-BERT (legal documents corpus), LUIMA (Board of Veterans Appeals decisions). | Describes design methodologies for various AI systems discussed, such as rule-based approaches for expert systems, factor-based reasoning for case-based reasoning systems (e.g., VJAP), neural network architectures (transformers) for LLMs, and machine learning pipelines for predictive models. | Mentions various deployment models for discussed AI tools: commercial subscription services (e.g., Westlaw Edge, Casetext), API access (e.g., GPT-3), open platforms (e.g., A2J Author), and research prototypes (e.g., LUIMA). | True | True | Some discussed commercial AI tools (e.g., Westlaw Edge, Casetext, Lexis+) are available via subscription. Some platforms like A2J Author are openly accessible. GPT-3 is available via API (not entirely free for extensive use). | Technical limitations in AI's ability to perform complex legal reasoning, understand nuance and purpose, extract rules automatically, distinguish legal vs. commonsense meanings, and ensure consistent legal accuracy, particularly for complex tasks. Lack of 'common sense' and deep conceptual understanding in current AI systems. | General challenges for the AI and Law field, such as: interpreting nuanced and ambiguous natural language in legal contexts; automating the extraction of legal rules, elements, and factors from texts; synthesizing coherent and legally sound arguments from multiple sources; achieving genuine understanding versus statistical pattern matching; performing empathetic reasoning; and ensuring reliability and explainability of AI outputs. | Generation of facially convincing but legally inaccurate or misleading text by AI. Potential for job market disruption for legal professionals if they do not adapt. Over-reliance on AI without critical human oversight. Copyright infringement risks related to training data and AI-generated content (e.g., ROSS litigation). |
| 15IndianJLJust1.pdf | HeinOnline | Evaluating ICT Adoption in the Indian Judiciary: Challenges, Opportunities, and the Impact of the E-Courts Project | This paper critically examines India's e-Courts Project, evaluating its success in integrating Information and Communication Technology (ICT) within the judiciary to reduce case backlogs, litigation costs, and improve transparency and legal literacy. It assesses the challenges and successes of the project's first two phases and considers the need for strategic reorientation for its upcoming third phase. | True | Idealistic | False | 2.0 | Neutral | The e-Courts Project (encompassing various ICT initiatives like Case Information System (CIS), National Judicial Data Grid (NJDG), e-filing, virtual courts, Supreme Court Portal for Assistance in Court's Efficiency (SUPACE), Supreme Court Vidhik Anuvaad Software (SUVAS)) | Analysis of the e-Courts Project's phases against its stated objectives (reducing case backlogs and judicial workload, cutting litigation costs and complexities, improving transparency and legal literacy), drawing upon National Judicial Data Grid (NJDG) statistics, National Council of Applied Economic Research (NCAER) survey reports, and official documents from the e-Committee of the Supreme Court of India. | Mixed results: Significant improvements in ICT infrastructure and some services for lawyers. However, limited overall reduction in case backlogs (which worsened post-pandemic), low awareness and benefit from e-services among litigants, persistent challenges in legal literacy and language accessibility, and data quality issues in judicial databases were noted. | High number of pending cases and judicial workload; low legal literacy among citizens and language barriers due to English being the predominant language in judiciary; high litigation costs; geographical and economic inaccessibility of courts, particularly for rural and impoverished populations; digital divide between urban and rural areas. | The e-Courts Project, deploying ICT solutions including digitisation of judicial processes (e-filing, Case Information System), creation of a national judicial data repository (NJDG), introduction of virtual courts, and development of AI-powered tools for judicial assistance (SUPACE) and legal document translation (SUVAS). The paper suggests strategic reorientation for Phase III, including better training, an ecosystem approach involving private entities, statistical analysis for case scheduling, and enhancing legal literacy through educational technology. | Reducing case pendency and judicial workload, lowering litigation costs and complexities, enhancing judicial transparency, improving legal literacy and public access to legal information, alternative dispute resolution (ADR). | General public, with specific focus on litigants in rural areas, economically disadvantaged individuals, non-English speakers, citizens with low legal literacy, and women litigants. | General Judiciary (covering civil and criminal matters across District courts, High Courts, and the Supreme Court); Alternative Dispute Resolution (ADR). | India | For SUVAS (translation software): English judicial documents translated into nine vernacular Indian languages. For SUPACE (AI assistance for judges): Legal information including precedents, statutes, and laws, with performance refined through training data and feedback. The National Judicial Data Grid (NJDG) contains extensive data from court records, including orders, judgments, and case details. | Phased implementation of the e-Courts Project (Phases I, II, and III); iterative development for software like the Case Information System (CIS); collaborative development between the e-Committee of the Supreme Court and the National Informatics Centre (NIC); use of machine learning algorithms for AI tools (SUPACE, SUVAS). | National-scale rollout across Indian courts, including provision of ICT infrastructure (hardware, internet connectivity); development and deployment of web portals, mobile applications (e.g., E-Court Services app), and physical eSewa Kendras (service centers); phased introduction of various digital services for litigants, lawyers, and judges. Implementation varies regionally due to the autonomy of High Courts. | True | True | Publicly accessible e-Courts web portal, the E-Court Services mobile application (free to download and use for case status, cause lists, etc.), the National Judicial Data Grid (NJDG) public portal for accessing case data and statistics, and eSewa Kendras for assistance. Translations from SUVAS are also made publicly available. | Low awareness and adoption of e-Court services among litigants; insufficient impact on reducing overall case backlogs; persistent legal literacy issues and language barriers despite translation efforts; data quality and completeness issues within the National Judicial Data Grid (NJDG); the digital divide disadvantaging rural and less privileged populations; lack of a unified national implementation strategy and consistent feedback mechanisms. | Initial lack of ICT standardization leading to inconsistencies; integrating diverse regional judicial processes and systems; ensuring data quality, accuracy, and consistency in a large-scale digital environment; high judicial workload and time constraints for training; varied levels of technical proficiency among users (judges, court staff, lawyers); managing the federal structure of the judiciary with autonomous High Courts impacting uniform adoption; addressing multilingual requirements effectively; maintaining cybersecurity for vast digital infrastructure. | Bias in AI algorithms used for judicial assistance (e.g., SUPACE) and their 'black box' nature; potential deterioration in the quality of judgments if AI or templates are over-relied upon; increased cybersecurity vulnerabilities with extensive digitization; data privacy concerns due to the public availability of detailed case information and potential for misuse; exacerbation of the digital divide, favoring technologically adept sections of society; risk of increased frivolous litigation due to easier access; data biases and opacity inherent in Large Language Models if adopted without caution. |
| 2024JComIntellPropL249.pdf | HeinOnline | CYBERSYMBIOSIS OF HUMAN JUDGES AND ARTIFICIAL INTELLIGENCE: PROBLEMS AND POTENTIAL SOLUTIONS FOR INTEGRATION AND FOR THE SUCCESSFUL MODERNIZATION OF THE JUDICIAL SYSTEMS OF THE BRICS COUNTRIES | This paper explores the concept of 'cybersymbiosis' between human judges and artificial intelligence to modernize judicial systems in BRICS countries, aiming to improve efficiency and access to justice. It proposes a model for integrating AI tools with human oversight, outlining an AI assistant architecture and discussing necessary legal and ethical frameworks to support this integration. | True | Idealistic | True | 1.0 | Positive | Cybersymbiosis model for human-AI judicial work, and an AI assistant architecture featuring NLP, information extraction (neuro-symbolic programming, machine learning), Natural Language Generation (NLG), explainability, and security modules. | NaN | NaN | Case backlogs, lack of timely access to justice for vulnerable populations, inconsistent judicial decisions, difficulties processing large data volumes, and systemic inefficiencies including potential corruption. | A human-AI 'cybersymbiosis' model with clear task distribution, AI-powered tools for legal analysis and support, and new legal/ethical frameworks including transparency, audits, and redress mechanisms. | Reducing judicial backlogs, improving access for vulnerable populations, enhancing decision consistency and fairness, increasing court efficiency and transparency. | Socially vulnerable populations, linguistic minorities. | General judicial processes, court administration, rule-making. | BRICS Countries (Brazil, Russia, India, China, South Africa) | Proposed use of structured/unstructured court decisions, human rights documents, and other legal texts from BRICS countries; notes data protection challenges. | Conceptual model developed via literature review, comparative analysis of BRICS approaches, and multi-criteria analysis; system proposes neuro-symbolic programming and machine learning. | NaN | False | False | NaN | Incomplete court digitalization, varying data standards, data protection restrictions, human-AI communication challenges, legal system diversity, budgetary differences, need for updated ethical/legal frameworks, AI explainability, and skilled personnel. | Incomplete digitalization and data protection issues hindering AI training, AI system imperfections, adapting to diverse legal systems and budgets within BRICS, developing multilingual and legally-aware AI, ensuring security, and creating user-friendly interfaces. | Ethical issues, AI-driven discrimination, inaccuracies, compromised fair sentencing, inappropriate AI use, harm from AI errors, and inherent AI biases. |
| 73SCLRev825.pdf | HeinOnline | OBSERVING THE EFFECTS OF AUTOMATING THE JUDICIAL SYSTEM WITH BEHAVIORAL EQUIVALENCE | The paper argues that current scholarship on automating judicial systems, which focuses on reproducing reasoning and outcomes, overlooks broader societal impacts and changes in system interaction. It proposes using "behavioral equivalence," a computer science concept emphasizing observer-dependent evaluation, as a framework to analyze the full consequences and tradeoffs of such automation for scholars and policymakers. | True | Idealistic | False | 1.0 | Neutral | The paper proposes "behavioral equivalence" as an analytical framework to evaluate the automation of judicial systems, focusing on different observers and perceived tradeoffs. | NaN | NaN | A narrow focus in current scholarship on merely reproducing legal reasoning and outcomes, thereby overlooking how automation changes system interactions and affects diverse societal interests, leading to unobserved unintended consequences and impacts on procedural justice and legitimacy. | Adopting the "behavioral equivalence" framework to evaluate legal automation by considering a wide range of observers (beyond the judicial system itself, including societal interests) and systematically analyzing the perceived tradeoffs (informational access, process, reasoning, outcome) to anticipate and manage the full consequences of changes. | Procedural justice, legitimacy of the legal system, fairness, due process, accountability in automated decision-making, unintended consequences of legal automation. | Society at large, participants in the legal system including pro se litigants. | General (examples from criminal law, tort law, administrative law, civil procedure, contract law). | United States | NaN | NaN | NaN | True | False | The analytical framework (behavioral equivalence for evaluating legal automation) is detailed in the paper and can be conceptually applied by any reader. | The practical difficulty of perfectly predicting all unintended consequences, comprehensively identifying all relevant societal observers and their diverse observations/values, and ensuring policymakers systematically consider and weigh the identified tradeoffs when implementing legal automation. | Difficulties in detecting and comparing outcomes of old vs. new automated systems, especially with human-involved or non-deterministic processes; ensuring the transparency and verifiability of AI reasoning and explanations when evaluating systems. | Overlooking societal impacts and observer perspectives in legal automation can lead to unintended negative consequences (e.g., altered legal practice, new biases), erosion of procedural justice and due process, loss of public trust and system legitimacy, manipulation by knowledgeable actors, and reduced transparency in legal decision-making. |
| 49JCorpL833.pdf | HeinOnline | Robots, Markets, and the Value of Deal Lawyers | This paper discusses the implications of emerging automation technologies like AI and blockchain for deal lawyers and financial markets, particularly concerning asset-backed securities (ABS) and tokenization. It explores how automation can affect legal ambiguity, market risks, and the fundamental roles, value, and ethical responsibilities of lawyers in these evolving contexts. | True | Market | True | 3.0 | NaN | AI (e.g., Generative AI like ChatGPT, Harvey AI, Casetext CoCounsel) for legal tasks (research, drafting, contract review) and blockchain-based tokenization/smart contracts for financial transactions. | NaN | NaN | NaN | NaN | NaN | NaN | Corporate law, financial law, contract law, commercial law, property law, bankruptcy law, professional ethics. | United States | The paper notes that AI systems, including Generative AI like ChatGPT, are trained on large databases of examples, and specifically mentions the concept of AI models being trained on 'existing deal documentation' for legal work products. | NaN | NaN | True | True | The paper mentions generally available AI tools like ChatGPT (with mentions of OpenAI's website, implying free access to some models) and other commercial legal tech tools (e.g., Harvey AI, DoNotPay, Tome) accessible through their respective providers/websites. | NaN | Ensuring human judgment and ethical standards in automated legal services; addressing unauthorized practice of law issues for AI tools; potential atrophy of lawyers' skills; deciding appropriate contexts and methods for automation (e.g., translating complex legal provisions into code); understanding and mitigating risks of AI and blockchain in specific market contexts. | Atrophy of lawyers' skills and judgment; unauthorized practice of law by AI tools; increased risks in financial markets (e.g., from automated handling of legal ambiguity and distortion, hidden leverage); adverse effects on issuers, stakeholders, and systemic stability from digitized legacy forms or automated processes; markets defying legal norms and intervention points via blockchain; undermining bankruptcy protections (e.g., automatic stay); financial information failure; ethical dilemmas for lawyers. |
| 98TulLRev363.pdf | HeinOnline | Why Can't I Have a Robot Lawyer? Limits on the Right to Appear Pro Se | This article analyzes the historical limitations imposed by courts on the right to self-representation (pro se) and considers how these limits will impact litigants using new artificial intelligence technology. It then proposes a framework for how courts should address AI-assisted pro se litigants, suggesting an initial bar on AI use until its utility is proven, followed by mandatory disclosure of its use. | True | Idealistic | True | 1.0 | Neutral | A framework for courts to manage AI use by pro se litigants, involving an initial prohibition until AI utility is assured, followed by permission with mandatory disclosure. | NaN | NaN | Established judicial limitations on the right to self-representation (e.g., restrictions on who can appear pro se, rules against unauthorized assistance like ghostwriting); current unreliability of AI (e.g., inaccuracy, fabrication of sources). | Courts should initially bar self-represented litigants from using AI until its utility is assured. Subsequently, courts should allow its use only if a litigant discloses their use of an AI product to the court, enabling judges to properly assess litigant sophistication and provide appropriate leniency. | Right to self-representation (pro se), AI assistance for litigants, court procedure and administration, access to justice. | Pro se litigants (often low and middle-income individuals). | General (civil and criminal procedure), with examples from various specific fields including family law, bankruptcy, and criminal law. | United States (federal and state courts). | NaN | NaN | NaN | False | False | NaN | Technical: AI's current lack of robustness and truthfulness, including tendencies to fabricate sources or 'hallucinate' facts. Societal/Legal: How to ensure that AI assistance enhances, rather than undermines, the fairness and integrity of the judicial process for pro se litigants; determining when an AI product's benefits outweigh its risks of harm. | NaN | AI providing inaccurate or misleading legal information or advice; AI fabricating legal citations or facts ('hallucinations'); pro se litigants lacking understanding of AI-generated content and strategy; courts being misled about a litigant's actual sophistication if AI use is undisclosed; potential for AI use to constitute the unauthorized practice of law; violation of court rules (e.g., prohibitions on recording court proceedings if the AI requires it). |
| 7Issue5IntlJLMgmtHuman651.pdf | HeinOnline | Justice Is Mechanized: Ethical Implications of AI in Law | This paper explores the ethical implications of using artificial intelligence in the legal field, focusing on equality, accountability, and accuracy. It argues that AI, while offering benefits in efficiency and accessibility for tasks like legal research and contract review, should serve as a supplementary tool to human judgment to ensure justice is served effectively and ethically. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Cost and complexity of traditional legal representation; for AI-driven A2J solutions: data privacy concerns, and unreliability/inaccuracy of AI-generated legal advice (e.g., hallucinations); systemic issues a_ffecting overall justice delivery such as massive case backlogs and shortage of judges. | Utilizing AI-powered legal self-help applications for accessible legal information and assistance; integrating AI into the legal system to enhance efficiency and expedite case resolution; developing robust ethical rules and regulations for AI use, ensuring lawyer accountability and AI's supplementary role to human judgment. | Legal information and self-help; Court efficiency and case processing; Ethical use of AI in law. | General public, particularly those facing minor legal issues or lacking access to traditional legal representation; people in countries with overburdened legal systems (e.g., India). | General Law, Contract Law, Administrative Law (specifically mentions parking tickets). | India (primary focus for regulatory reform), USA (examples like ROSS, DoNotPay, NYC chatbot), UK (ethics codes mentioned). | NaN | NaN | NaN | True | True | DoNotPay is described as a publicly available app, often free, for legal self-help tasks like challenging parking tickets. | Lack of adequate regulatory frameworks for AI in law in jurisdictions like India; persistent issues with the reliability and accuracy of AI-generated legal advice (e.g., hallucinations); data privacy concerns associated with AI systems. | NaN | Inaccuracies and potential professional misconduct from reliance on unverified AI output; decline in critical thinking and analytical skills among legal practitioners; data privacy violations and security breaches due to AI's handling of sensitive information; generation of incorrect or deceptive legal advice by AI (hallucinations), potentially leading to adverse legal consequences; inherent data bias in AI systems leading to skewed and discriminatory outcomes; AI's inability to replicate human qualities essential for legal practice such as honesty, courage, judgment, and fellowship. |
| 5Issue2IndianJLLegalRsch1.pdf | HeinOnline | INFLUENCE OF TECHNOLOGY AND ARTIFICIAL INTELLIGENCE IMPACTING THE GROWTH OF LEGAL INDUSTRY | This paper discusses the transformative impact of technology and AI on the legal industry, highlighting increased efficiency, improved accuracy, and task automation. It examines AI applications in India for legal research, contract management, and predictive analytics, while also considering future trends, ethical implications, and the potential for AI to make legal services more affordable for individuals and SMEs. | True | Market | True | 3.0 | Positive | NaN | NaN | NaN | High cost and inefficiency of traditional legal services, hindering access for many individuals and small to medium-sized businesses. | AI-driven automation of tasks like legal research, document review, and contract analysis to improve efficiency and reduce costs, thereby potentially increasing affordability and access to legal services. | Affordability of legal services, efficiency in legal service delivery. | Individuals and small to medium-sized businesses unable to afford traditional legal services. | General legal practice | India | NaN | NaN | NaN | True | False | The paper discusses several existing AI tools and platforms (e.g., Manupatra, LawGeex, SpotDraft, CaseMine, eBrevia, Chat GPT) that are in use or gaining popularity in the legal industry, implying their availability from their respective providers. | Current AI is still at a 'weak artificial intelligence' level; ongoing ethical, data protection, and regulatory challenges; need for clear guidelines and Mstandards; fundamental philosophical and legal questions about AI's role and capabilities (e.g., AI judges, legal rights of robots). | NaN | Job displacement for legal professionals; ethical concerns regarding informed consent and quality of AI-provided legal counsel; privacy issues; accuracy and truthfulness of information provided by AI; potential failure to uphold professional and legal standards if AI is not properly guided; AI representing potentially the greatest threat to the legal profession. |
| 72UKanLRev313.pdf | HeinOnline | AI-ready Attorneys: Ethical Obligations and Privacy Considerations in the Age of Artificial Intelligence | This paper analyzes the ethical duties of competence, communication, and confidentiality for attorneys using AI in legal research and writing, alongside data privacy obligations under US and EU frameworks. It offers recommendations for law schools and practicing lawyers to responsibly integrate AI, highlighting risks like data breaches, AI "hallucinations," and confidentiality violations. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | NaN | NaN | NaN | NaN | General_legal_practice | United_States | NaN | NaN | NaN | False | False | NaN | Lack of specific formal ethics opinions from bar associations on AI use in legal practice; insufficient training on legal technology and AI in law schools; current ethical rules provide only piecemeal guidance for AI; general lack of understanding among lawyers about AI's capabilities and risks. | NaN | AI generating 'hallucinations' like non-existent case citations; violation of lawyers' duty of competence due to misunderstanding AI tools; breach of client confidentiality from inputting data into AI systems; data breaches at law firms or AI vendors; unauthorized use or sharing of client data by AI vendors (e.g., for model training or data brokerage); lack of meaningful notice and consent in AI tools' privacy policies; potential for algorithmic bias and discrimination; cybersecurity vulnerabilities; intellectual property infringement concerns. |
| 49QueensLJ73.pdf | HeinOnline | Luck of the Draw III: Using Al to Extract Data About Decision-Making in Federal Court Stays of Removal | This article uses GPT-3 to extract data from Canadian Federal Court dockets on immigration stay of removal applications, revealing significant inconsistencies in grant rates among justices. It advocates for increased judicial consistency and greater access to legal data for research, demonstrating AI's potential for scrutinizing legal decision-making to enhance migrant rights. | True | Idealistic | True | 1.0 | Positive | A multi-step computational legal research methodology involving: 1) Web-scraping of Federal Court dockets; 2) Regex-based screening of dockets and entries; 3) Fine-tuning and application of GPT-3 for categorizing docket entries (e.g., identifying motions for stays, orders) and extracting specific data (e.g., deciding justice, outcome); 4) Pandas for docket-level data aggregation and analysis. | The data extraction technique was evaluated by: 1) Comparing its identification of stay of removal cases against a manually reviewed dataset for one year, achieving 98.0% (96/98) coverage. 2) Manually verifying 200 coded dockets for accuracy of extracted datapoints, resulting in 99% accuracy. | The automated data extraction technique identified 98.0% of manually identified stay of removal cases in a comparison sample and achieved 99% accuracy in extracting specific datapoints from dockets based on manual verification of 200 cases. | Limited access to bulk legal data for research and analysis due to restrictive terms of service and lack of court-provided bulk access mechanisms, hindering transparency and oversight. Inconsistent and potentially arbitrary judicial decision-making in high-stakes deportation cases (the 'luck of the draw' phenomenon), impacting fairness for migrants. | Making legal data (court dockets, decisions) accessible in bulk via APIs for non-commercial research by courts and tribunals. The Federal Court taking measures to encourage more consistency in stay decision-making, possibly through internal discussions, guideline development, or legislative intervention. Researchers sharing code and data, as done in this project. | Fairness and consistency in judicial decision-making in deportation/removal proceedings; transparency in the legal system; access to legal information for research; procedural justice in immigration and refugee law. | Marginalized migrants, non-citizens facing deportation in Canada, particularly those at risk of irreparable harm. | Immigration and refugee law, Administrative law (specifically judicial review). | Canada (Federal Court of Canada). | For GPT-3 fine-tuning: A human-coded dataset of hundreds of sample Federal Court docket entries (prompts) paired with desired completions (e.g., outcome categories like 'granted', 'dismissed', 'other'; extracted judge names). This training data was derived from publicly available, unstructured, bilingual (English/French) online Federal Court dockets web-scraped by the author. | An iterative process for machine learning model development: applying the GPT-3 model to new docket entries, verifying performance, providing additional labeled examples for fine-tuning if errors were found, re-training, and re-testing until satisfactory performance was achieved. The overall research methodology involved sequential data processing steps (scraping, regex, ML extraction, logical aggregation). | The Python code (excluding the web-scraping program), human-coded training datasets for GPT-3 fine-tuning, and the full dataset of scraped Federal Court dockets (87,776 dockets) were made available on GitHub for non-commercial research use. | True | False | The code (excluding web-scraping), human-coded training datasets for fine-tuning GPT-3, and the dataset of Federal Court dockets are available on GitHub for non-commercial research use. Execution of the GPT-3 component of the technique requires a paid OpenAI API key. | Need for systemic solutions for bulk access to legal data beyond individual researcher efforts (e.g., court-provided APIs and permissive terms of service). Further empirical research on reasons for judicial inconsistencies (e.g., role of counsel quality, interpretation of legal tests, country of origin). Addressing limitations in court decision publishing practices that hinder bilingual access and large-scale computational research. | Technical complexity and resource intensiveness of systematically web-scraping and maintaining an up-to-date large-scale database of court dockets. Difficulty of accurately extracting structured information from unstructured, natural language, and often bilingual court docket entries which may lack standardized phrasing. Requirement for manual data labeling to create training sets for fine-tuning machine learning models. | Inherent limitations of LLMs like GPT-3, including potential for bias amplification from training data, generation of 'hallucinated' or incorrect information, and susceptibility to misuse for disinformation. Risk of exacerbating power imbalances if advanced AI legal tools are asymmetrically available, primarily benefiting well-resourced entities (e.g., government) over marginalized individuals and their advocates. The identified inconsistencies in human judicial decision-making themselves pose a risk to justice, particularly when these decisions have high stakes like deportation and are relied upon for constitutional safeguards. |
| 92TennLRev1.pdf | HeinOnline | A VIEW OF HOW LANGUAGE MODELS WILL TRANSFORM LAW | This paper explores the transformative impact of Large Language Models (LLMs) on legal practice and the legal services industry, predicting new legal work in the near term and long-term structural changes such as enhanced lawyer productivity and potential sector consolidation. It also discusses the enduring role of lawyers in tasks involving value judgments and empathy, even as LLMs automate routine work. | True | Market | True | 3.0 | Neutral | NaN | NaN | NaN | High cost of legal services; unequal access to up-to-date legal information and analytical tools for less-resourced professionals. | LLMs enhancing lawyer productivity to potentially lower costs and enable price competition; ensuring affordable access to legal data (e.g., via subsidies for PACER). | Affordability and quality of legal services; access to legal information/tools for professionals. | Less-resourced legal professionals; general public (indirectly, through more accessible services and open-source LLMs). | Broad application across multiple legal fields (e.g., civil litigation, corporate law, IP, torts, constitutional law). | United States | Discusses public web data (Common Crawl, Wikipedia), industry-level legal databases (public/commercial), firm-level proprietary data, and synthetic data as LLM training sources in law. | NaN | NaN | True | True | The paper mentions commercially available LLMs like Lexis+ AI and ChatGPT (which has a free tier), and open-source LLMs like BLOOM. | Technical: LLM accuracy, reliability (e.g., synthetic data quality), and cost of processing. Societal: Ensuring equitable access to data and LLM benefits to avoid widening disparities. | Ensuring LLM accuracy/reliability (avoiding hallucinations, bias); managing data privacy/confidentiality; developing quality synthetic data; addressing IP/copyright issues for LLM outputs and training data. | Professional liability from inaccurate LLM outputs; data breaches; model degradation from poor data; misuse for harmful activities; unresolved liability for AI errors or 'orphaned' AIs. |
| 32AustlLLibr68.pdf | HeinOnline | HOW TECHNOLOGY CAN SUPPORT OPEN JUSTICE AND TRANSPARENCY: A UK PERSPECTIVE | This paper surveys various technological advancements, from historical innovations like writing and printing to modern developments such as the Internet and AI, illustrating their role in enhancing open justice and transparency within the UK legal system. It highlights how these technologies, including AI-driven tools for case summarization, improve public access to and understanding of legal information and judicial processes. | True | Idealistic | True | 3.0 | Positive | AI-generated case summaries (using GPT-4 via Jurisage) for unreported judgments, integrated into ICLR's Case Genie brief analysis tool and general case search on the ICLR.4 platform. | The AI summaries are generated by GPT-4 based entirely on the judgment text to avoid hallucination. The paper mentions trying various prototypes before settling on the Jurisage system. No specific benchmark or formal user testing results for the AI summaries are detailed. | The AI system generates 100-word summaries and three bullet points identifying the top three issues for unreported cases, aiming to make case law clearer and more accessible to users searching on the ICLR.4 platform. | Physical barriers in courtrooms (sightlines, acoustics); low public legal literacy; cost of accessing court documents; incomplete digitization of court processes; potential for critical errors in online legal systems. | Improved design of court spaces (physical and virtual); creation of easy-read legal guides; online publication of judgments and legislation; use of legal blogs, podcasts, and social media for public education; Online Dispute Resolution systems; AI tools for legal research and information accessibility. | Open justice, legal transparency, public legal education, access to primary legal information, accessibility of court proceedings, online dispute resolution for unrepresented litigants. | General public, unrepresented litigants. | General (common law, statute law, family law, criminal justice). | United Kingdom (primarily England & Wales). | For AI summaries: GPT-4 is used, with summaries reportedly 'entirely based on what’s in the judgment' text itself. For Case Genie: A corpus of primary legal sources, including unreported judgments. | Iterative prototyping ('tried various prototypes') and collaboration with a technology developer (Jurisage) for the AI summary feature. | The AI-generated summaries are integrated into the ICLR.4 online platform, accessible via subscription, enhancing Case Genie and general case search functionalities. | True | False | The AI summaries are available as part of the ICLR.4 platform, which is a subscription-based service. More information is available on the ICLR website. | Cost as a barrier to accessing some digitized court documents (e.g., CE-file); the HMCTS Reform programme for digitisation is still incomplete; the AI in Case Genie does not explain *why* it recommends certain cases, only *what* the recommended cases are about via summaries. | Initial 'teething problems' with new digital systems (e.g., TNA judgment feed); ensuring AI-generated content is accurate and free of hallucinations (addressed by grounding summaries in source judgment text); the 'closed box' nature of AI recommendation reasoning for Case Genie, which necessitated the development of AI summaries for explication. | Potential for severe, irreversible errors in online legal processes (e.g., accidental online divorce); reputational damage to legal professionals from misuse of social media; the gradually decreasing outlandishness of 'cyber judges' as AI capabilities advance. |
| 96TempLRev349.pdf | HeinOnline | "I AM BECOME DEATH, THE DESTROYER OF WORLDS": APPLYING STRICT LIABILITY TO ARTIFICIAL INTELLIGENCE AS AN ABNORMALLY DANGEROUS ACTIVITY | This paper argues for applying strict liability to harms caused by artificial intelligence (AI) when AI use constitutes an "abnormally dangerous activity." It proposes a revised legal test for such activities and a two-tiered insurance system, modeled on the Price-Anderson Act for nuclear energy, to compensate victims while fostering AI industry innovation. | True | Idealistic | False | 1.0 | Positive | Application of strict liability for AI as an abnormally dangerous activity, featuring a revised six-factor test and a Price-Anderson Act-style two-tiered insurance model. | NaN | NaN | Difficulty in proving negligence for AI-induced harms due to AI's inherent unpredictability and "black box" nature; AI industry externalizing costs of injuries, leaving victims without adequate compensation; existing legal and insurance frameworks being insufficient for potentially catastrophic AI harms. | Applying strict liability to AI activities deemed "abnormally dangerous"; revising the traditional six-factor test for such activities to better suit AI's unique characteristics (e.g., focusing on unforeseeability of harm, removing common usage and locality factors); implementing a mandatory, two-tiered insurance system for AI companies modeled on the Price-Anderson Act to ensure victim compensation and limit industry liability. | Compensation for AI-induced harms, establishing legal accountability for AI creators and deployers, addressing systemic risks and biases in AI leading to disparate outcomes. | Black patients (in the context of a discussed healthcare AI example leading to discriminatory outcomes). More broadly, individuals harmed by AI engaged in abnormally dangerous activities. | Tort law (specifically strict liability, abnormally dangerous activities), Insurance law, Regulatory law concerning technology. | United States | For the discussed High-Risk Management Tool (HRMT): Proprietary, structured patient healthcare data, including historical healthcare costs (used as a proxy for health needs) and comorbidity information, from a large US national patient population. | NaN | NaN | False | False | NaN | Potential for AI-caused damages to exceed the proposed insurance caps, necessitating further governmental action for full compensation in catastrophic scenarios; challenges in international harmonization of AI liability and compensation schemes. | Balancing victim compensation with the encouragement of AI innovation; adapting existing legal doctrines (like strict liability for abnormally dangerous activities) to the novel characteristics of AI (e.g., unpredictability, "black box" nature); designing a fair and feasible insurance and indemnification system, including setting appropriate liability limits and ensuring broad industry participation. | AI causing physical harm, injury, and death; AI perpetuating or creating discrimination (e.g., racial bias in healthcare); weaponization of AI (e.g., for chemical weapons); AI-generated misinformation destabilizing society; concentration of AI power leading to surveillance and oppression; human over-dependence on AI; unpredictable AI behavior leading to unforeseen harms. |
| 39TouroLRev165.pdf | HeinOnline | THE CATEGORICAL IMPERATIVE: IN SEARCH OF THE MYTHICAL PERFECT PRIVILEGE LOG SO DEVOUTLY TO BE WISHED | The paper examines the burdensome nature of traditional, document-by-document privilege logs in legal discovery, particularly with the explosion of electronically stored information. It explores the development and adoption of "categorical" privilege logs as an alternative aimed at increasing efficiency and reducing costs, detailing academic commentary, judicial rule-making efforts, court reactions, and the persistent challenges due to the adversarial nature of litigation. | True | Market | False | 3.0 | NaN | Categorical privilege logs (as an alternative to traditional document-by-document logs); metadata-based 'objective privilege logs'. | NaN | NaN | NaN | NaN | NaN | NaN | Civil procedure, Evidence (evidentiary privilege), E-discovery practices, Civil litigation. | United States (primarily federal court system, with mentions of New York and Delaware state courts). | NaN | NaN | NaN | False | False | NaN | NaN | Achieving a balance between efficiency and sufficient detail in categorical logs; risk of misuse (e.g., overly broad categories, gamesmanship by parties); obtaining agreement between adversarial parties on protocols for categorical logging; overcoming judicial skepticism and ensuring consistent application. | Waiver of privilege due to inadequate logging (either traditional or categorical); categorical logs obscuring non-privileged documents if categories are poorly defined or misused; potential for increased disputes if categorical logs are not implemented cooperatively or lack sufficient detail for assessment; risk of inadvertently revealing sensitive information or trial strategy through the logging process itself. |
| 2024EurJPrivacyLTech79.pdf | HeinOnline | Legal Arrangements of Artificial Intelligence in the European Union and the Republic of North Macedonia | This paper analyzes the necessity and content of AI regulation, examining the EU's AI Act and North Macedonia's progress towards a national AI strategy. It emphasizes addressing AI's challenges to social governance and legal systems through transparency, accountability, and fairness to protect fundamental rights. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Lack of access to technology and digital literacy hindering the use of AI-driven tools (e.g., remote courts) for justice; potential for AI to introduce bias and non-transparency, creating new barriers to fair legal processes; absence of comprehensive national AI strategies in some regions, delaying beneficial and safe AI adoption in the justice sector. | Developing and implementing comprehensive, risk-based AI regulations (e.g., the EU AI Act) focusing on safety, transparency, fundamental rights, and accountability; integrating Online Dispute Resolution (ODR) and e-filing into court systems; ensuring human oversight over AI in legal applications to maintain fairness and prevent harm. | Online Dispute Resolution (ODR), e-courts, technology in judicial reforms, ensuring fairness and non-discrimination in AI-assisted legal processes, AI regulation. | NaN | AI Law, EU Law, Contract Law, Criminal Law (Cybercrime), Tort Law, Product Liability Law, Administrative Law, Constitutional Law. | European Union, Republic of North Macedonia | NaN | NaN | NaN | False | False | NaN | The digital divide (inequitable access to technology and digital literacy) limiting widespread benefit from AI in legal services; the need for legal frameworks that effectively address AI's unique attributes (e.g., autonomy, intent) in the context of justice; ensuring AI development remains human-centric and upholds rule of law principles in the justice sector. | Ensuring AI systems are safe, transparent, traceable, non-discriminatory, and ethically sound, especially in legal contexts; defining and attributing legal liability for harm caused by AI systems due to their opaqueness and autonomy; harmonizing AI regulations across jurisdictions; managing socio-economic impacts like deskilling within the legal profession. | Violation of fundamental rights (e.g., right to life, privacy, non-discrimination) through AI misuse; cognitive behavioral manipulation or social scoring; biased or flawed AI in law enforcement (e.g., predictive policing, biometric identification) impacting fair trial; erosion of human judgment and accountability in legal decision-making; safety risks from malfunctioning AI systems used in contexts relevant to law (e.g., autonomous vehicles involved in accidents). |
| 13StMarysJonLegalMalpract.pdf | HeinOnline | Unauthorized Practice or Untenable Prohibitions: Refining and Redefining UPL | The paper argues that current Unauthorized Practice of Law (UPL) rules are outdated, ambiguous, and hinder access to justice for many Americans. It proposes a revised definition of UPL with specific exceptions to allow nonlawyers and technology (including apps) to provide legal information and services, aiming to increase affordability and availability. | True | Idealistic | False | 1.0 | Positive | A revised definition of UPL (Unauthorized Practice of Law) with specific exceptions and substantive provisions to permit nonlawyers and computer programs/apps to provide certain legal advice and assistance. | NaN | NaN | Unaffordability and inaccessibility of lawyers for many Americans (access to justice crisis); outdated, ambiguous, vague, and conclusory UPL rules that deter innovation and are inconsistently applied. | Refine and redefine UPL by establishing clearer exceptions for what should not be considered UPL, allowing certain nonlawyers and computer apps to provide legal information and services. This includes a newly proposed definitional framework for UPL with specific substantive provisions. | Access to affordable legal information and services, resolution of civil legal matters, consumer debt collection, personal bankruptcy, traffic law matters. | Low-income Americans, individuals who cannot find or afford lawyers generally, those facing common civil legal problems (e.g., eviction, custody, tort, contract, debt collection, bankruptcy). | Civil Law (general), Consumer Law (debt collection), Bankruptcy Law, Traffic Law, Administrative Law. | United States (critiquing US UPL rules generally and discussing specific US state and federal cases), with references to United Kingdom and Australia for comparative experience. | NaN | Legal analysis, historical review, policy argument, case law review, comparative analysis (referencing UK/Australia and other studies). | NaN | False | False | NaN | Need for jurisdictional adoption and implementation of the proposed UPL framework; development of specific regulatory mechanisms for nonlawyer providers and AI tools; ongoing adaptation to technological evolution; ensuring public protection while increasing access. | Historical difficulty in achieving consensus on UPL definitions due to tensions between protecting the legal profession's turf and public interest; ensuring any definition is cogent and universally applicable while balancing various stakeholder interests. | Potential adverse effects from using computer programs/apps without full understanding of their limitations if disclosures are inadequate; general limitations, risks, or negative consequences of consulting nonlawyers instead of lawyers if not properly regulated or disclosed; (via citation) AI generating false or misleading legal documents if not properly overseen. |
| 56TexTechLRev525.pdf | HeinOnline | AREN'T WE EXHAUSTED ALWAYS ROOTING FOR THE ANTI-HERO? PUBLISHERS, PRISONS, AND THE PRACTICING BAR | This paper critiques the monopolistic practices of legal information providers like LexisNexis and Westlaw, detailing how these practices severely hinder incarcerated litigants' access to legal information and the courts. It contrasts the legal profession's inaction on this critical access to justice issue with their vocal advocacy in other areas, ultimately calling for mobilization against these publishers. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Monopolistic control and high cost of legal information by publishers; restrictive legal precedents limiting prisoners' rights to information; inadequate prison library funding and resources; gatekeeping of legal information through paywalls and opaque algorithms; legal profession's inaction and misdirected advocacy. | Increased advocacy by the legal profession against publisher monopolies; re-evaluation of restrictive Supreme Court precedents on information access for prisoners; promotion of greater transparency and open access to legal information; encouraging appropriate use of technology to enhance access, rather than fearing it. | Access to legal information for incarcerated litigants; prisoners' right to access courts; challenges of self-representation for inmates. | Incarcerated litigants | Constitutional Law; Criminal Law; Antitrust Law (indirectly); Legal Ethics | United States | NaN | NaN | NaN | False | False | NaN | Lack of meaningful and affordable legal information access for prisoners; insufficient advocacy and awareness within the legal profession regarding this issue; need for open access alternatives to proprietary legal Ppesearch platforms; outdated legal precedents that do not account for technological advancements in information access. | NaN | Denial of meaningful access to justice for incarcerated individuals due to information monopolies; erosion of prisoners' constitutional rights; potential for surveillance and data misuse by legal information providers; attorneys' over-reliance on opaque commercial legal research platforms; misdirection of the legal profession's concerns about technology away from systemic access issues. |
| 57ColumJLSocProbs397.pdf | HeinOnline | After Reaching the Courthouse Door: Why Lack of Affirmative Assistance Post-Pleading Violates Prisoners' Access to Courts Right | This paper argues that the lack of affirmative legal assistance for incarcerated persons after the pleading stage violates their fundamental right to access the courts under the Due Process Clause. It proposes reconciling current legal frameworks by requiring states to provide "legal information" but not "legal advice" throughout the litigation process for prisoners. | True | Idealistic | False | 1.0 | NaN | The paper proposes a legal framework: the "legal information vs. legal advice" distinction for providing post-pleading assistance to prisoners. | NaN | NaN | Current legal interpretations severely limit prisoners' post-pleading assistance (Lewis v. Casey); incarcerated individuals lack legal knowledge for complex pro se litigation; systemic issues like retaliation by prison officials and ineffective grievance systems hinder access; procedural hurdles of the Prison Litigation Reform Act (PLRA). | Reinterpret legal doctrine to mandate states provide prisoners with "legal information" (not "legal advice") throughout litigation; adopt the "legal information vs. legal advice" distinction, common for non-incarcerated pro se litigants, for the prisoner context. | Right of access to courts for prisoners; post-pleading legal assistance; civil rights violations; conditions of confinement; pro se litigation by prisoners. | Incarcerated persons (prisoners) | Constitutional Law (Due Process, First Amendment Petition Clause, Equal Protection); Civil Rights Law (Section 1983 claims); Prisoners' Rights; Procedural Law. | United States | NaN | NaN | NaN | False | False | NaN | The need for future case law to clarify the precise boundaries of "legal information" versus "legal advice" and define sufficient assistance under the proposed framework; the current circuit split and lack of Supreme Court guidance on post-pleading assistance. | NaN | Failure to provide adequate post-pleading assistance leads to meritorious lawsuits failing, civil rights violations going unremedied, and the access-to-courts right becoming illusory. The proposed solution itself might raise federalism concerns regarding state autonomy and resource allocation, though the paper argues these can be navigated. |
| 6LawTechHum69.pdf | HeinOnline | The Regulation of Judicial Analytics: Towards a New Research Agenda | This paper reviews the current state of research on the regulation of judicial analytics, identifying key risks such as misinformation and inequity, and evaluating proposed regulatory strategies. It calls for a new research agenda focused on consistent terminology, empirical study of impacts, and clear definitions of regulatory success to guide future policy. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Inequity in access to analytical tools advantaging wealthy litigants; potential for misinformation to undermine public trust and understanding of the justice system; consumer vulnerability to poor quality analytical services; gamification of law potentially overlooking individual justice considerations. | Proposing a research agenda focused on empirical study and defining regulatory success (including human rights considerations) to inform the development of regulatory strategies such as ethics frameworks, trustmarks, and potentially non-profit models to ensure fairness and quality. | Equitable access to legal insights and tools, fairness in legal proceedings, public trust in the judicial system, transparency and accountability of judicial actors, consumer protection in the legal tech market. | NaN | General (judicial decision-making processes and outcomes across various fields, with examples from administrative law, migration law, criminal law) | International (with specific examples and discussions pertaining to Australia, United States, Canada, France, European Union) | NaN | NaN | NaN | False | False | NaN | Lack of empirical evidence on the societal impacts (including on access to justice) of judicial analytics; absence of consistent terminology and jurisdiction-sensitive analyses; insufficient understanding of how existing laws apply; and no clear framework for defining or achieving regulatory success that balances access to justice concerns with innovation. | NaN | Misinformation about judges and the judiciary; threats to judicial independence and wellbeing; non-normative thinking and gamification of law; unwanted strategic litigant behaviour (e.g., forum shopping); harm to consumers from low-quality analytics; and creation or exacerbation of inequity among litigants. |
| 97TempLRev227.pdf | HeinOnline | AI NOW | This paper argues that law professors have an urgent and inescapable duty to understand and engage with generative AI due to its profound impact on legal pedagogy, scholarship, and governance. It criticizes the legal academy's current laissez-faire attitude and proposes steps for faculty to meet this "AI mandate." | True | NaN | True | 3.0 | Positive | Generative Artificial Intelligence (Gen-AI), including specific tools like ChatGPT, Lexis+ AI, and Westlaw AI. | The paper discusses an empirical study by Choi & Schwarcz where law students took final exams in two courses (Introduction to American Law and Legal Reasoning; Insurance Law) under traditional closed conditions and then with access to GPT-4 on a prior year's exam. | Choi & Schwarcz found GPT-4 assistance dramatically increased student performance on multiple-choice questions (29 percentile improvement) but had no statistically significant effect on essay questions. AI use also had an equalizing effect, significantly raising the performance of lower-performing students while slightly decreasing that of top-performing students. | Reinforcing existing inequalities if AI legal services are not properly treated. | NaN | Cost and affordability of legal services, automation of legal processes for accessibility. | Underserved populations that have been historically shut out from legal services. | Legal education, General legal practice (with examples from tax law, property law, insurance law). | United States | The paper discusses Gen-AI tools trained on large general text corpora and domain-specific legal data (e.g., case law, legal authority repositories for tools like Lexis+ AI and Westlaw AI). | NaN | Public web applications (e.g., ChatGPT), integration into existing commercial legal research platforms (e.g., Lexis+ AI, Westlaw AI), and firm-specific internal platforms. | True | True | Publicly available Gen-AI tools like free versions of ChatGPT; commercial Gen-AI tools integrated into platforms like LexisNexis and Westlaw, some requiring subscriptions or institutional access. | Ensuring AI development and deployment in legal services is equitable and does not exacerbate existing disparities. | Challenges faced by the legal academy in understanding and integrating Gen-AI: lack of clear standards on accepted AI use in academia and practice, unclear internal responsibility for AI strategy in law schools, faculty upskilling fatigue, and complexities introduced by distance learning. | Accuracy issues (hallucinations), confidentiality breaches, job displacement in the legal field, undermining academic integrity and assessment, negative impact on development of core legal skills, perpetuation of biases, ethical concerns in AI use for legal work and scholarship, unequal student access to AI tools, and potential for AI to reinforce societal inequalities. |
| 7Issue1IntlJLMgmtHuman.pdf | HeinOnline | Exploring Legal and Ethical Dimensions of Artificial Intelligence in Employment: Safeguarding Worker Rights and Ensuring Fair Practices | This research paper explores the legal, ethical, and policy implications of AI deployment in employment settings, focusing on safeguarding worker rights and promoting fair practices. It highlights challenges like algorithmic bias and job displacement, and recommends updated legal frameworks, ethical guidelines, and stakeholder collaboration for responsible AI adoption. | True | Idealistic | False | 3.0 | Neutral | NaN | NaN | NaN | Algorithmic bias and discrimination in employment decisions; lack of transparency and accountability in AI systems; violations of privacy rights through AI-powered surveillance and data collection; job displacement and erosion of traditional employment opportunities; exacerbation of existing socioeconomic inequalities; legal frameworks lagging behind technological advancements. | Updating existing laws and establishing AI-specific regulations; promoting AI education, training, and ethical awareness; fostering stakeholder engagement (government, industry, civil society); prioritizing fairness, transparency, and human-centric design principles in AI development and deployment; conducting bias assessments of AI algorithms and auditing training data; implementing privacy-preserving techniques and robust data governance. | Worker rights, Fair employment practices, Algorithmic bias in employment, Data privacy in the workplace | Workers (general), with potential disproportionate impact on low-skilled workers, racial minorities, women, and individuals with disabilities | Employment Law, Labour Law, Data Protection Law, Anti-discrimination Law | India, International | NaN | NaN | NaN | False | False | NaN | Lack of comprehensive AI-specific legislation for employment; insufficient harmonization of AI regulation across jurisdictions; need for greater interdisciplinary collaboration in developing AI regulation; rapid pace of AI evolution outpacing regulatory responses; skills gap between AI-driven job demands and workforce capabilities, necessitating reskilling efforts. | NaN | Algorithmic bias leading to discriminatory employment outcomes; lack of transparency and accountability in AI-driven decisions; erosion of privacy rights due to workplace surveillance and extensive data collection; job displacement resulting from AI-powered automation; exacerbation of socioeconomic inequalities; potential for exploitation or misuse of sensitive personal employee data; unequal distribution of AI's benefits and risks. |
| 18LibertyULRev705.pdf | HeinOnline | Internet Frisking Jurors During Voir Dire: The Case for Imposing Judicial Limitations | This paper argues against allowing internet research of prospective jurors during voir dire, citing concerns about fairness, juror privacy, potential for bias in jury selection, and the integrity of the judicial process. It proposes a specific court rule to completely ban such research to preserve traditional, supervised voir dire and ensure judicial integrity. | True | Idealistic | False | 1.0 | Negative | A court rule to completely ban internet research (including AI-assisted methods) of prospective jurors by attorneys in preparation for and during the voir dire process. | The proposed rule is primarily supported by legal reasoning, analysis of existing judicial practices and opinions, ethical arguments, and concerns about fairness, juror privacy, and the integrity of the voir dire process as detailed in the paper. | NaN | Potential for biased jury selection due to discovery of information about race, religion, politics, etc.; invasion of juror privacy; decreased juror willingness to serve; unequal access to justice due to disparities in litigants' resources for research; compromising the integrity of voir dire and public trust in the justice system. | The adoption of a uniform court rule by federal and state trial courts that completely prohibits attorneys and their agents from conducting any internet research into a prospective juror's background in preparation for or during the voir dire process. | Fair trial rights; impartial jury selection; integrity of the voir dire process; juror privacy rights; ethical conduct of attorneys; equality of arms for litigants; regulation of technology in legal proceedings. | Prospective jurors from the general population; litigants, particularly those with fewer financial resources who cannot conduct extensive juror research. | Civil and criminal procedure (specifically voir dire / jury selection) | United States (federal and state courts) | NaN | Legal analysis, ethical reasoning, review of existing court practices and case law, synthesis of commentary and survey data. | The paper proposes the rule be adopted by federal and state trial courts to create uniformity. | False | False | NaN | Lack of uniform court rules regarding internet research of jurors; societal challenges in balancing technological advancements with fairness, privacy, and integrity in the justice system; the rapid development of AI tools for juror profiling outpacing regulatory responses. | NaN | Invasion of juror privacy; chilling effect on juror willingness to serve; enabling impermissible use of peremptory challenges (e.g., based on race, religion, political affiliation) discovered online; creating an uneven playing field for litigants based on differing resources for juror research; undermining public trust and confidence in the judicial system (e.g., through perceived hypocrisy); decisions based on inaccurate or misinterpreted online information; AI-driven tools exacerbating bias in jury selection. |
| 89MoLRev847.pdf | HeinOnline | Bridging the Divide: Does the EU's Al Act Offer Code for Regulating Emergent Technologies in America? | The paper analyzes the EU's AI Act, a comprehensive risk-based legislative framework for artificial intelligence, and explores its potential to inform emerging AI regulatory efforts in the United States. It details the AI Act's provisions, stakeholder objections, and compares them with recent U.S. legislative proposals and executive actions concerning AI governance. | True | NaN | False | 2.0 | NaN | The EU AI Act and proposed US AI regulatory frameworks (Bipartisan Framework for U.S. AI Act, No Section 230 Immunity Act, Executive Order 14110). | Qualitative legal and policy analysis of the provisions, stakeholder objections, potential impacts, and implementation challenges of these regulatory frameworks. | The EU AI Act establishes a comprehensive, risk-based regulatory system but faces criticism regarding compliance costs and potential to stifle innovation. US regulatory efforts are nascent and fragmented, with legislative proposals struggling for consensus and executive actions facing challenges of enforceability and scope. | NaN | NaN | NaN | NaN | AI regulation, Technology law, Comparative law | European Union, United States | NaN | The EU AI Act was developed through a multi-year process involving studies, white papers, public consultation, draft proposals, impact assessments, and stakeholder input leading to legislative approval. US approaches involve legislative bill drafting and executive order formulation. | The EU AI Act has entered into force (August 1, 2024) with a phased implementation plan over 6 to 24+ months depending on provisions. US legislative proposals are pending enactment; the Executive Order is being implemented through agency actions. | False | False | NaN | NaN | Challenges associated with the discussed regulatory frameworks include: balancing innovation with risk mitigation; high compliance costs potentially stifling innovation and affecting small businesses; achieving political consensus on regulatory details (e.g., scope of bans, regulation of general-purpose AI); the rapid evolution of AI outpacing legislative efforts; effective enforcement of rules (especially for executive orders and extraterritorial application); defining ambiguous terms within regulations (e.g., 'subliminal techniques', 'systemic risk'); addressing the 'black box' nature of some AI for transparency and oversight obligations; maintaining international competitiveness; and potential for executive overreach in implementing regulations. | Potential AI risks stated include: behavioral manipulation through subliminal or deceptive techniques; exploitation of vulnerable individuals (due to age, disability, socio-economic situation); discriminatory biometric classification and social scoring leading to unfair treatment; privacy violations from 'real-time' biometric surveillance in public spaces and untargeted scraping for facial recognition databases; flawed predictive policing and risk assessments in criminal justice; inference of emotions in workplace/educational settings; adverse impacts from high-risk AI in critical sectors (e.g., aviation, medical devices, critical infrastructure management, education, employment, access to public benefits/services, creditworthiness, emergency response, law enforcement, judicial proceedings, democratic processes); lack of transparency and human oversight in AI decision-making ('black box' problem); AI systems being inaccurate, non-robust, or insecure leading to harm; systemic risks from general-purpose AI (e.g., interference with elections, harm to economic security, public health and safety); and copyright infringement by generative AI models using protected training data. |
| 92GeoWashLRev.pdf | HeinOnline | Artificial Authorship and Judicial Opinions | This essay predicts how generative AI will transform judicial opinions, making them cheaper and more widespread but also potentially less deliberative and more rhetorical. It explores paradoxes such as AI-enhanced persuasion leading to the obsolescence of legal reasoning and courts resisting AI despite its utility due to threats to judicial authority. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Cost and limited availability of legal opinions and persuasive resources; Inegalitarian distribution of judicial attention and legal representation due to wealth disparities; Complexity and inaccessibility of legal language and judicial reasoning for laypersons. | AI making judicial opinions cheaper, more widely available, and customizable for different audiences, including legally unsophisticated individuals; Potential for court-appointed AI tools ("AI Gideon") to assist underresourced parties; AI-facilitated deliberation leading to more equitable distribution of judicial attention. | Access to legal information and understanding of judicial decisions; Fairness and equity in adversarial proceedings; Equitable distribution of judicial resources and attention. | Legally unsophisticated individuals, underresourced litigants, and the general public. | General/Multiple | International | NaN | NaN | NaN | False | False | NaN | Ensuring AI fairness and mitigating bias amplification from training data; Maintaining authenticity in AI-generated legal explanations and respecting human dignity; Preventing AI from enabling deceptive rhetoric that undermines truth and justice; Addressing the potential for AI to create an 'artificially balkanized readership,' thereby fracturing shared legal understanding; Establishing clear regulatory frameworks for AI use in the judiciary that ensure accountability and preserve judicial independence. | NaN | Erosion of judicial authority and public cynicism towards courts; Obsolescence of legal reasoning due to a surfeit of AI-generated rhetoric; Reduced deliberation in judicial opinion writing; Perpetuation and amplification of societal biases by AI tools; An 'arms race' of rhetoric between AI-equipped courts and a skeptical public; AI 'hallucinations' and factual errors in legal contexts; Increased difficulty in discerning truth from sophisticated, AI-generated sophistry; Deepening of partisan divides through AI-tailored, balkanizing content; Loss of human authenticity and accountability in judicial expression; AI being used to conceal improper bases for decisions; Over-reliance on AI diminishing human critical thinking and judgment; Threats to judicial independence from potential regulation of AI tools used by courts. |
| Justice AI Legal Case Retrieval Using Dense Passage Retrieval.pdf | IEEE_Xplore | Justice AI: Legal Case Retrieval Using Dense Passage Retrieval | This paper introduces Justice AI, a system developed for Korean legal case retrieval using Dense Passage Retrieval (DPR) with KoBERT and LCube models. It aims to make legal information more accessible to the general public and demonstrates its efficacy through performance metrics like an F1 score of 0.5915 for the LCube model. | True | Idealistic | True | 1.0 | Positive | Justice AI: A legal case retrieval system using Dense Passage Retrieval (DPR) with BERT-based KoBERT and GPT-based LCube models. | The system was evaluated using cosine similarity for relevance, and performance metrics including Precision, Recall, and F1 Score. The evaluation used a dataset of Korean legal documents, with user queries to retrieve relevant cases. | The LCube model achieved a Precision of 0.42, Recall of 1.0, and an F1 Score of 0.5915. A high cosine similarity score of 0.9002 was achieved for a highly relevant document. | Accessing and utilizing legal information is challenging for many, and it is difficult for the general public to acquire and use precise legal knowledge. | Justice AI uses Dense Passage Retrieval (DPR) to match user keywords with relevant legal cases, providing reliable legal information and enabling personalized legal services. | Legal information retrieval, access to legal information, personalized legal services, legal case understanding. | General public, individuals with limited legal knowledge. | General Korean legal documents including case law, statutes, regulations, administrative orders. Examples mentioned cover criminal law (murder, drunk driving, theft) and civil law (wrongful termination). | South Korea | An enhanced and tailored version of the Open Law Data from The Korean Ministry of Government Legislation, consisting of 87,160 Korean legal case documents. The 'reason' field was extracted for analysis. The data includes case law, statutes, regulations, and administrative orders. | Dense Passage Retrieval (DPR). Documents and queries were vectorized using pre-trained language models (KoBERT, LCube). Mean of text vectors was used for embeddings. Cosine similarity was used to retrieve top documents. | NaN | False | False | NaN | Lack of extensive, annotated datasets for Korean legal texts limits model generalization. The agglutinative nature of the Korean language poses challenges. Need for developing diverse, comprehensive datasets tailored to Korean legal language and adapting models accordingly. | The dataset was not originally structured as query-document pairs. Limited availability of extensive annotated Korean legal datasets compared to other languages (e.g., Chinese). The agglutinative nature of the Korean language causes complexity in tokenization and contextual understanding. | NaN |
| Too Legal- Didn-t Read -TLDR- Summarization of Court Opinions.pdf | IEEE_Xplore | Too Legal; Didn’t Read (TLDR): Summarization of Court Opinions | This paper proposes NLP-based methods for summarizing court opinions, exploring both extractive classifiers (with LSTM performing best for relevance tagging) and a domain-adapted abstractive model, PEGASUS CourtOp, fine-tuned from PEGASUS LARGE. The aim is to assist legal professionals by reducing the time and effort for document review, potentially lowering legal costs and thereby improving access to justice. | True | Idealistic | True | 1.0 | Positive | PEGASUS CourtOp (fine-tuned PEGASUS LARGE for abstractive summarization) and various classifiers (Naive Bayes, Decision Tree, Random Forest, LSTM NN) for extractive summarization by identifying relevant text segments. | Extractive models (including LSTM) evaluated using 5-fold cross-validation for classification performance (Recall, F1-score) on automatically labeled opinion segments, and ROUGE scores for generated summaries. Abstractive models (including PEGASUS CourtOp) evaluated using ROUGE scores against human-written summaries on a held-out test set comprising 25% of the dataset. | For abstractive summarization, PEGASUS CourtOp achieved a ROUGE-1 F1 score of 0.53 and ROUGE-1 Recall of 0.66, outperforming PEGASUS LARGE and Legal PEGASUS. For extractive sentence/paragraph classification, LSTM NN performed best (e.g., paragraph level Recall 0.85, F1-Score 0.73; ROUGE-1 F1 0.34 for summary from LSTM parts). | High cost of legal services partly due to the time-consuming and labor-intensive process of parsing very long and complex legal texts (court opinions), which requires specialized training and skills. | Developing NLP-based automatic text summarization tools (both extractive and abstractive) to help legal professionals create summaries more quickly or to automate the process, aiming to reduce costs and thereby increase access to the legal system for people of lower-income brackets. | Improving accessibility and understanding of lengthy legal documents (court opinions) by automatic summarization, aiding legal professionals, and potentially reducing legal service costs. | People of lower-income brackets. | Case Law / Court Opinions | United States (Utah, Idaho, Arizona, New Mexico, Nevada, Colorado state supreme courts) | A proprietary dataset of court opinions from six US State supreme courts (Utah, Idaho, Arizona, New Mexico, Nevada, Colorado) and corresponding human-written summaries provided by Justia under a data-sharing agreement. 3661 pairs were used for fine-tuning PEGASUS CourtOp. The base PEGASUS model was pre-trained on general web data, news, social media, and the BillSum dataset. | For extractive summarization: automatic labeling of sentences/paragraphs in court opinions based on similarity (N-Grams, LCS, Semantic Similarity, ROUGE score) to human summaries, followed by training binary classifiers. For abstractive summarization: fine-tuning the pre-trained PEGASUS LARGE model on court opinions and their summaries by freezing encoder weights and training decoder layers (creating PEGASUS CourtOp). | NaN | False | False | NaN | Need for improved legal-text-specific Named Entity Recognition for court opinions. Potential for better results by fine-tuning newer, more powerful (though potentially not open-source) language models. Further work to enhance the generation of novel language in abstractive summaries that is not explicitly present in the source opinions. | For extractive summarization: dataset imbalance between relevant and irrelevant text segments when labeling data for classifier training. For abstractive summarization: effective domain adaptation of general-purpose pre-trained language models to the specific characteristics and vocabulary of legal court opinions. | NaN |
| Interactive Legal Assistance System using Large Language Models.pdf | IEEE_Xplore | Interactive Legal Assistance System using Large \nLanguage Models | This paper presents a Retrieval Augmented Generation (RAG) chatbot designed to help laypersons in India understand complex Food Safety Regulations, operating in both English and Tamil. The system utilizes LLMs like GPT-4 and Llama2, an embedding model, and a translation model to provide query-based assistance and document section summarization. | True | Idealistic | True | 1.0 | Positive | A RAG-based chatbot using LLMs (GPT-4, Llama2, GPT-4 Vision), IndicTrans2 for translation, and 'Snowflake-arctic-embed' for embeddings. It includes Q&A and summarization components for legal documents. | Qualitative comparison of summaries generated by the proposed system and ChatGPT for a specific topic within the Food Safety Regulations. The comparison focused on precision and reflection of original content. | The system's summaries were found to be significantly more precise and better reflected the original content of the Food Safety Regulations when compared to summaries generated by ChatGPT, which exhibited inaccuracies. | Complexity of legal documents for non-experts, leading to misunderstanding and unintentional violations; language barriers for regional language speakers in India where regulations are often in English. | Development of a user-friendly RAG chatbot that provides clarifications (Q&A) and summaries of legal documents in both English and Tamil, incorporating translation models to overcome language barriers. | Understanding legal documents (specifically Food Safety Regulations), language accessibility in legal information, simplification of legal text. | Common people in India, particularly Tamil speakers, needing to understand Food Safety Regulations. | Food Safety Regulations | India | Publicly available PDF documents of India's Food Safety Regulations from the Food Safety and Standards Authority of India (FSSAI). These documents are processed (converted to HTML, chunked) to create embeddings for the RAG system using 'Snowflake-arctic-embed'. The system utilizes pre-trained LLMs (GPT-4, Llama2, GPT-4 Vision) and a pre-trained translation model (IndicTrans2). | System architecture involving PDF processing (conversion to HTML using GPT-4 Vision), text chunking, embedding generation (Snowflake-arctic-embed), vector storage (ChromaDB), query processing with language identification, translation (IndicTrans2), RAG with LLMs (GPT-4, Llama2) for Q&A, and content extraction with LLM-based summarization. | Local models are pulled from an Ollama server. Embeddings and HTML data are stored locally. No broader public deployment strategy is mentioned. | False | False | NaN | The system currently does not allow users to request or download specific forms related to the legal documents. | Ensuring relevance and accuracy of retrieved documents, as improper preprocessing or embedding can lead to irrelevant or noisy information; maintaining efficient performance at scale (challenges in optimizing index structures, caching, retrieval latency); validating correctness and relevance of generated answers in real-time. | Risk of generating inaccurate or misleading information if the RAG system retrieves irrelevant or noisy content, potentially leading to misinterpretation of legal regulations. |
| Proposal for Enhancing Legal Advisory Services in the Montenegrin Banking Sector with Artificial Intelligence.pdf | IEEE_Xplore | Proposal for Enhancing Legal Advisory Services in the Montenegrin Banking Sector with Artificial Intelligence | This paper proposes integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to enhance legal advisory services and financial education within Montenegro's banking sector. It details a system using ADA-2 for embedding regulatory documents, Chroma DB for storage, and GPT-4 for response generation, outlining a planned evaluation methodology. | True | Market | True | 1.0 | Positive | Integration of Large Language Models (LLMs like GPT-4) and Retrieval-Augmented Generation (RAG) using LangChain framework, with vectorization of regulatory documents via ADA-2 embedding model and storage in Chroma DB vector database. | Proposed evaluation methodology: 210 prepared questions on legal topics, 'ground truth' answers validated by legal experts, quantitative metrics (Exact Match, F1 score), and qualitative expert legal analysis of RAG-generated responses. | NaN | Complexity of financial concepts for bank clients, hindering financial literacy. | Using AI (LLMs and RAG) to provide personalized and easy-to-understand explanations of financial instruments and concepts, thereby enhancing financial literacy and informed decision-making. | Financial literacy; Financial education. | Bank clients in Montenegro; general public needing financial education. | Banking law, financial regulation. | Montenegro | Montenegrin banking laws, regulatory guidelines, legal precedents, and case studies. These are unstructured textual documents, domain-specific to Montenegrin banking. | System architecture design combining LLMs (ADA-2 for embedding, GPT-4 for generation), RAG (via LangChain), vector database (Chroma DB), chunk-based embedding, prompt engineering, and a proposed expert-informed evaluation methodology. | NaN | False | False | NaN | The paper implies the need for comprehensive validation of the proposed AI system's effectiveness for financial education and calls for ongoing scrutiny into ethical implications and human oversight. | Ensuring accuracy and avoiding misinterpretation/overlooking nuances in legal texts; dependency on quality and comprehensiveness of data sources; addressing ethical implications like AI bias; need for human oversight; fine-tuning models for domain-specific linguistic nuances; balancing retrieval and generation in RAG systems. | Misinterpretation or overlooking critical nuances in legal texts; bias in AI models; general ethical implications of using AI in legal contexts; inaccuracies stemming from issues in document retrieval, generation process, or misunderstanding of context. |
| EMPOWER-KARE Deep Prompt Learning for Knowledge-aware Response Generation in Clinical Counseling and Legal Support Conversations (1).pdf | IEEE_Xplore | EMPOWER-KARE: Deep Prompt Learning for Knowledge-aware Response Generation in Clinical Counseling and Legal Support Conversations | This paper introduces KARE, a novel dataset of knowledge-grounded conversations for clinical counseling and legal support for crime victims. It also proposes EMPOWER, a dual-tier deep prompt learning framework that uses KARE to generate knowledge-aware responses, demonstrating improved performance over existing methods. | True | Idealistic | True | 1.0 | Positive | EMPOWER, a dual-tier deep prompt learning framework for knowledge-aware response generation. It includes Knowledge-attributed Deep Prompt Learning (KDPL), Response-attributed Deep Prompt Learning (RDPL), and a Dynamic Dialogue-Knowledge Module (DDKM). | Evaluated on the KARE dataset using automatic metrics (PPL, BLEU-4, Avg. BLEU, F1, Knowledge-F1, BERTScore F1, EA, VE, GM) and human evaluation (Fluency, Adequacy, Contextual Relevance, Knowledge Existence, Correctness, Relevance, Helpfulness, Safety). | EMPOWER achieved improvements of 11.50% in BLEU-4, 28.5% in Knowledge-F1, and 11.6% in BERTScore compared to the best baseline on the KARE dataset. It attained a Perplexity of 7.11 and BERTScore F1 of 0.86. | Societal stigmatization, lack of adequate and accessible support services for crime victims, and the multifaceted challenges victims face, including mental trauma and navigating complex legal processes. | Development of AI-powered knowledge-grounded dialogue systems (like EMPOWER-KARE) to provide 24/7 clinical counseling and legal support, thereby improving access to assistance for crime victims. | Clinical counseling and legal support for crime victims, specifically mental health support and guidance on legal processes related to various crimes. | Crime victims, with a specific focus on women and children who have experienced violence. | Criminal law, cybercrime law (specifically related to cyberstalking), victim support services, and legal aid procedures. | India | The KARE dataset, built upon the synthetically created POEM dialogue dataset (5,000 English dialogues for crime victims). KARE augments POEM with external domain-specific knowledge collected via web scraping (using Google Search API, content extracted from URLs, segmented using Spacy) and processed into knowledge triplets using OpenIE and Sentence-BERT for relevance. | Dual-tier deep prompt learning (prefix-tuning) with Knowledge-attributed and Response-attributed prompts, Knowledge Triplets Construction (using Stanford OpenIE, filtering rules, Sentence-BERT for relevance, and GPT-J for verbalization), and a Dynamic Dialogue-Knowledge Module (using multi-head attention and a re-parameterization technique). | The code and dataset are made available via GitHub and an institutional webpage; no specific user deployment strategies are mentioned beyond research access. | True | True | Code and dataset are available on GitHub (https://github.com/priyanshu528priya/EMPOWER-KARE/) and an institutional resources page (https://www.iitp.ac.in/~ai-nlp-ml/resources.html). | Need for incorporating commonsense knowledge to induce commonsense reasoning ability and empathy. Reliance on the quality of source data and knowledge extraction methods can introduce inaccuracies or biases. | Limited computational resources for experimenting with larger language models. Ensuring the quality of source data and the accuracy of knowledge extraction. Effectively integrating external knowledge into response generation (addressed by the dual-tier prompt learning). | Generation of wrong or inaccurate information by the model. Potential for responses to contain repetitions, be inconsistent with context, or exhibit semantic variance from ideal answers. |
| LexSage Multi-Task Optimization in Legal Large Language Model Applications.pdf | IEEE_Xplore | LexSage: Multi- Task Optimization in Legal Large Language Model Applications | This paper introduces LexSage, a legal large language model fine-tuned from Qwen2.5-7B using a custom multi-task Chinese legal dataset created with one-shot prompting and data augmentation. LexSage demonstrates superior performance on various Chinese legal tasks within the LawBench benchmark, significantly outperforming models like GPT-4 and Qwen-7B on specific tasks like case analysis and law recitation respectively. | True | Market | True | 1.0 | Positive | LexSage, a legal large language model based on Qwen2.5-7B, fine-tuned using instruction fine-tuning (LoRA) with a specially constructed multi-task Chinese legal dataset (LexSage-SFT). The dataset was created leveraging one-shot capabilities of LLMs (GLM4-Plus API) for data generation (Self-Instruct) and data augmentation techniques (GPT-based instruction paraphrasing). | Evaluated on 6 tasks from the Chinese LawBench benchmark (law recitation, text proofreading, opinion summarization, case analysis, crime amount calculation, legal counseling) in a zero-shot setting. Compared against models including GPT-3.5 Turbo, GPT-4, Qwen-7B, LawGPT, and HanFei, using metrics like Rouge-L, Accuracy, and soft-F1. | Achieved a 76.2% improvement in the law recitation task (LexSage score 0.326) compared to Qwen-7B (score 0.185) on LawBench. Also showed 52.7% improvement in case analysis (LexSage 0.742) over GPT-4 (0.486). | NaN | NaN | NaN | NaN | Chinese law, including Criminal Law, Marriage Law, Social Law, and Economic Law, focusing on tasks like law recitation, opinion summarization, case analysis, and legal Q&A. | People's Republic of China | A proprietary dataset (LexSage-SFT) of 113.7K instruction-formatted entries for Chinese legal tasks. Compiled from: 1) Public NLP legal task datasets (JEC-QA, CAIL). 2) Raw legal texts (Criminal Law, Marriage Law, Social Law, Economic Law) and judicial exam questions. 3) Open-source legal instruction datasets (e.g., from LawGPT, Lawyer-LLaMA). Additional data was generated using Self-Instruct with GLM4-Plus API and data augmentation (instruction paraphrasing via GPT) was applied. | Instruction fine-tuning (LoRA) on the Qwen2.5-7B base model. Dataset construction involved data collection from diverse legal sources, data cleaning and preprocessing, structuring data into {instruction, input, output} format, employing Self-Instruct methodology (one-shot prompting with GLM4-Plus API) for generating Q&A pairs from raw texts, and data augmentation (GPT-based instruction paraphrasing) to balance task distribution. Chain of Thought (CoT) technique was introduced in the reasoning stage for enhanced interpretability in complex tasks like case analysis. | NaN | False | False | NaN | The paper identifies technical gaps for future model development, including further optimization of legal knowledge reasoning and long text processing, incorporation of more diverse data sources, and exploration of Retrieval-Augmented Generation (RAG) methods to enhance knowledge depth and reliability. | Computational resource constraints (single A100-40GB GPU). Ensuring balance and diversity across tasks in the dataset for multi-task fine-tuning. The inherent lack of sufficient legal knowledge and poor compatibility of general open-source LLMs with specific legal tasks. Addressing the common problem of 'illusions' (hallucinations) in legal LLM outputs. | The primary risk identified is that of 'illusions' (hallucinations) in legal LLM outputs, which can lead to the generation of inaccurate or unreliable legal information if not adequately mitigated. |
| CHRExpert An AI-Driven Court of Human Rights Expert Assistant for Legal Practitioners Utilizing Transformer Models.pdf | IEEE_Xplore | CHRExpert: An AI-Driven Court of Human Rights Expert Assistant for Legal Practitioners Utilizing Transformer Models | This paper introduces CHRExpert, an AI legal assistant using a fine-tuned 6 billion parameter GPT model on European Court of Human Rights (ECHR) data to help practitioners analyze judicial decisions and predict case outcomes. CHRExpert achieved 83% accuracy in predicting outcomes for specific ECHR articles and reduced case preparation time by 40%. | True | Market | True | 1.0 | Positive | CHRExpert: an AI-driven legal assistant utilizing a fine-tuned 6 billion parameter Generative Pretrained Transformer (GPT) model (referred to as GPT-J in INDEX TERMS) on the ECHR dataset. | Evaluations based on final judgments predicted outcomes for ECHR Articles 3, 6, and 8. Performance measured by accuracy, AUC, precision, recall, F1-score. Also assessed using 6-fold cross-validation, classification performance on 450 documents, legal document analysis (statutory interpretation, issue spotting comparison with law practitioners), and applicability in litigation (efficiency, strategy development, analogical reasoning). | Achieved 83% accuracy in predicting outcomes for cases involving Articles 3, 6, and 8 of the European Convention on Human Rights, with an average AUC of 0.93. Reduced case preparation time by 40%. Achieved 92% accuracy in issue spotting. | The complexity and high volume of legal information in human rights cases, hindering efficient case preparation and effective alignment of legal arguments with judicial reasoning by legal professionals. | Proposes CHRExpert, an AI-driven legal assistant to help practitioners analyze human rights case documents, predict outcomes, interpret statutes, and suggest legal strategies, thereby improving efficiency and effectiveness in human rights litigation. | Case outcome prediction in human rights law, Legal document analysis for human rights cases (statutory interpretation, issue spotting), Legal strategy development in human rights litigation, Efficiency enhancement for human rights legal practitioners. | Individuals seeking redress for human rights violations at the European Court of Human Rights (served indirectly via legal practitioners using the tool). | Human Rights Law (specifically European Convention on Human Rights). | European Court of Human Rights (ECHR), covering member states of the Council of Europe. | Fine-tuned on the European Court of Human Rights (ECHR) dataset, comprising 11,000 files of unstructured text (final judicial decisions with facts, legal arguments, outcomes). This dataset is based on publicly available ECHR data (e.g., as described by Medvedeva et al. [25]). | Utilized a 28-layer deep transformer model (GPT with 6 billion parameters) with transfer learning and fine-tuning. Preprocessing included text cleaning, normalization, outcome-dependent term filtering, BPE tokenization, embeddings, padding, and attention masking. Trained using PyTorch's Distributed Data-Parallel (DDP) on GPUs. | Designed as a cloud-based legal assistant accessible through a web interface via a subscription model, with functionalities exposed via RESTful APIs. | False | False | NaN | Difficulty in handling judicial discretion and subjective rulings; challenges with ambiguous or rarely invoked statutes; limitations in cases establishing new legal doctrines due to reliance on precedents; country-level selection bias in the ECHR dataset; need for evidentiary analysis, adaptive learning for evolving legal trends, and incorporation of ethical/cross-jurisdictional reasoning. | Capturing complex context and judicial discretion in human rights cases; handling complex legal texts (dense language, terminology, precedents); mitigating data leakage from outcome-revealing terms; ensuring predictions are based on legal reasoning rather than post-judgment awards; addressing selection bias from overrepresented jurisdictions in training data. | Potential for over-reliance given limitations in handling judicial discretion, subjective rulings, ambiguous statutes, and novel legal arguments. Risk of biased or less generalizable outputs due to country-level selection bias in the training data if not carefully managed. Misuse for pre-trial assessment (mitigated by defining scope as post-litigation analysis). |
| Large Language Models -LLM- in Industry A Survey of Applications- Challenges- and Trends.pdf | IEEE_Xplore | Large Language Models (LLM) in Industry: A Survey of Applications, Challenges, and Trends | This paper surveys the applications of Large Language Models (LLMs) across various industries, including legal services, highlighting their benefits in automation and decision-making. It also discusses significant challenges such as high costs, data privacy, bias, and lack of explainability, while exploring emerging solutions and trends. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Legal services, contract review, legal research, general legal sector. | International | NaN | NaN | LLM-as-a-Service (LLMaaS), API-based access, integration into specialized tools by companies (e.g., Luminance, ROSS Intelligence). | False | False | NaN | NaN | High computational and energy demands; bias and fairness issues from training data; data privacy and security concerns; lack of explainability (black box nature); ethical and societal issues (job displacement, misinformation); difficulty with domain-specific knowledge requiring extensive fine-tuning; high implementation and maintenance costs. | Bias leading to unethical outcomes, reproduction of personal information, potential job displacement, generation of misinformation. |
| Bettercall AI based legal assistant.pdf | IEEE_Xplore | Bettercall: AI based legal assistant | This paper introduces "Bettercall," an AI-based chatbot designed to improve access to legal and judicial information in India using advanced natural language processing and semantic search capabilities. The system aims to provide primary legal aid and promote legal awareness, with the paper detailing its methodology, challenges, and performance. | True | Idealistic | True | 1.0 | Positive | An AI chatbot ('Bettercall') utilizing semantic search (NLP, vector embeddings from legal texts, cosine similarity for query matching) and an LLM (OpenAI's GPT-3.5) combined with a legal ontology database for generating responses to user queries. | The system was evaluated using precision and recall metrics on a diverse set of legal queries compared against a manually created "gold standard". User satisfaction and usability were assessed through user feedback surveys. | The system demonstrated high precision and respectable recall scores across various legal domains (e.g., Criminal Law: Precision ~0.9, Recall ~0.85). User satisfaction scores were notably high, with an overall average satisfaction around 4.5 out of 5. | Lack of accessible legal information and understanding, especially for marginalised communities and those with low legal literacy in India; linguistic barriers. | Development of a multilingual, user-friendly AI-powered digital assistant (Bettercall) that uses semantic search to provide clear legal information, answer common legal queries, and guide users on legal procedures and rights. | Access to legal information, legal query answering, guidance on legal procedures (e.g., complaint filing), understanding legal rights, promoting legal literacy. | Indian populace, especially marginalised communities and individuals lacking legal literacy. | General Indian Law, including Criminal Law, Family Law, Property Law, Labour Law, Constitutional Law, Corporate Law, Environmental Law, Intellectual Property Law. | India | Publicly available Indian legislation (acts and sections with metadata like act name, section number, etc.) web-scraped from indiacode.nic.in. The data is textual and domain-specific. | Web scraping for data collection, data cleaning and formatting, chunking and tokenization, vectorization of text into embeddings, storage in a vector database (Supabase) and a non-relational database (MongoDB) for ontology, cosine similarity for query-document matching, and LLM (GPT-3.5) for response generation. | NaN | False | False | NaN | Existing gaps in scalability, multilingual support, and domain coverage of legal assistance tools. Future work includes continuous improvement of chatbot capabilities, expansion of legal ontology, and refinement of multilingual functions. | Constructing a comprehensive legal database due to lack of pre-existing structured data; inefficiencies in PDF scraping leading to a pivot to web scraping; accurately storing metadata for chunked data; managing and integrating legal ontology effectively without causing data duplication, reduced embedding accuracy, or increased costs. | Inaccurate interpretation of keywords in legal texts could lead to disparate or incorrect outcomes from the chatbot. |
| Classifying European Court of Human Rights Cases Using Transformer-Based Techniques.pdf | IEEE_Xplore | Classifying European Court of Human Rights Cases Using Transformer-Based Techniques | This paper proposes and evaluates transformer-based models, using a sliding window approach and data scraping for balancing, to classify European Court of Human Rights (ECHR) case documents. Experimental results show RoBERTa excels at binary classification and BigBird at multi-class classification, indicating AI's potential to enhance legal aid efficiency. | True | Idealistic | True | 1.0 | Positive | A legal document classification framework using various transformer-based models (BERT, Legal-BERT, RoBERTa, BigBird, ELECTRA, XLNet) enhanced with a sliding window approach to handle long text sequences and data scraping from the ECHR portal for dataset balancing. | The models were evaluated on the ECHR dataset (split 70% training, 30% evaluation) using 5-fold cross-validation. Performance was measured by precision, recall, and F1-score, comparing transformer models against conventional machine learning techniques (SVM, DT, NB, AdaBoost) and previous benchmarks. Both binary and multi-class classification tasks were performed. | For binary classification, RoBERTa achieved the best performance with precision, recall, and F1-score of 89.1%, 86.2%, and 86.7%, respectively. For multi-class classification (after data scraping), BigBird performed best with a weighted F1-score of 78.1%. | High cost of legal representation, limited eligibility for public legal aid programs due to restrictive means tests (considering income, assets, and home value), leading to many individuals being unable to afford legal assistance or being excluded from aid. | Automating the classification of legal cases to improve the efficiency of legal assistance provision. This can potentially reduce the cost of legal aid and increase the number of cases that can be assisted within publicly funded budgets. | Improving efficiency of legal aid provision, reducing costs of legal services, automating legal document classification. | Individuals who cannot afford high-quality legal representation and those who may be excluded from or inadequately served by public legal aid programs. | Human Rights Law | European Court of Human Rights (ECHR) | A publicly available ECHR (European Court of Human Rights) dataset (Chalkidis et al., 2019) consisting of unstructured text (case facts). This dataset was augmented by scraping additional case articles from the ECHR public database to balance class distribution, particularly for the multi-class task. | Application of various transformer-based neural networks and conventional machine learning models. A sliding window technique was used for handling long text sequences in transformer models. Data scraping and regular expressions were used for additional data collection and pre-processing. Dataset balancing was a key consideration. | NaN | False | False | NaN | Technical: Need for improvement in multi-class classification performance; potential overfitting from sliding window overlaps; transformer models not fully leveraging additional meta-data features. Dataset-related: Need for more high-quality, potentially domain-specific pre-trained models (e.g., combining Legal-BERT's domain specificity with BigBird's long sequence handling) and further dataset augmentation/quality improvements. | Handling long sequences of text data from legal documents with transformer models that have input length limitations (addressed via sliding window). Managing highly imbalanced datasets (addressed via data scraping). High computational load associated with training transformer models, especially with the sliding window approach generating multiple sub-sequences. Effectively incorporating additional meta-data features (like case importance or court branch) into text-centric transformer models. | Potential for overfitting due to the overlapping windows in the sliding window technique. Biases in algorithms were acknowledged as an area not focused on but are a general risk with AI in law. |
| Unlocking the Potential of Large Language Models in Legal Discourse Challenges- Solutions- and Future Directions.pdf | IEEE_Xplore | Unlocking the Potential of Large Language Models in Legal Discourse: Challenges, Solutions, and Future Directions | This paper evaluates the performance of various state-of-the-art Large Language Models (LLMs) on Canadian tax law queries, identifying issues like hallucinations. It then proposes and experiments with fine-tuning smaller LLMs (Gemma and Mistral) using domain-specific legal texts and vocabulary enhancement as a potential solution, though initial fine-tuning results showed limitations. | True | Idealistic | True | 1.0 | Neutral | Fine-tuning of LLMs (Gemma-2b and Mistral-7B-Instruct-v0.2) using semantic chunking of Canadian legal documents and domain-specific vocabulary updates. | Initial evaluation of six general LLMs (Gemini, Mistral Large, Gemma 7B, Falcon 180B, Llama2 70B, GPT-3.5) on 40 Canadian tax law questions rated by a tax expert. The fine-tuned Gemma and Mistral models were qualitatively evaluated with a sample legal question. | For the initial evaluation of general LLMs, Gemini achieved the highest accuracy (77.5% correct answers on 40 tax law questions). The fine-tuned Gemma-2B model (using an unsupervised dataset) repeatedly generated the input question, while the fine-tuned Mistral-7B model provided a tax-related but incorrect answer to a sample question. | Inaccuracy and unreliability of LLMs (e.g., hallucinations, biases), lack of interpretability, the complexity of legal language and reasoning for AI models, and scarcity of high-quality, labeled legal data suitable for training effective access to justice tools. | Development of domain-specific LLMs through fine-tuning with curated domain-specific datasets and vocabulary. Methodologies include semantic chunking for text preparation and iterative refinement based on expert feedback. Emphasis on creating instructional datasets for better fine-tuning. | Democratizing access to legal advice, providing legal guidance to non-expert users, improving legal information retrieval and question answering. | Non-expert users, general citizens requiring legal information (e.g., on taxation), and individuals who struggle with navigating legal processes. | Canadian tax law; more broadly, the legal domain. | Canada | For fine-tuning: A dataset of 10,000 unlabeled Canadian legal documents (federal and provincial laws, statutes, regulations), processed using semantic chunking. Domain-specific legal terminology was also integrated. | Semantic chunking of legal documents, domain-specific vocabulary expansion, and fine-tuning of pre-trained language models (Gemma-2b, Mistral-7B-Instruct-v0.2) on an unlabeled legal corpus. | NaN | False | False | NaN | Scarcity of extensively labeled legal documents for supervised fine-tuning, significant computational resources (especially memory) required for fine-tuning LLMs, need for high-quality and representative training data (addressing bias, privacy, timeliness, scalability), and the need for more explainable and transparent AI models to ensure trustworthiness and mitigate bias. | Detecting and mitigating LLM hallucinations in legal contexts, adapting general LLMs to domain-specific nuances like legal terminology and reasoning, achieving satisfactory results when fine-tuning with unlabeled legal corpora (e.g., models repeating questions or providing incorrect/inaccurate answers), managing high computational costs, and curating comprehensive domain-specific vocabularies. | Dissemination of inaccurate legal information or advice (legal hallucinations), perpetuation of biases embedded in training data leading to unfair outcomes, security vulnerabilities in AI systems handling sensitive legal information, and the potential for AI systems to mislead or harm human interests if not properly developed and governed. |
| Fine-tuning a Large Language Model for the Indian Legal System.pdf | IEEE_Xplore | Fine-tuning a Large Language Model for the Indian Legal System | This paper details the development and fine-tuning of a Llama 3.1 8B large language model specifically for the Indian legal system, employing techniques such as LoRA, QLoRA, RAG, and pruning. The resulting AI-driven chat application aims to provide accurate legal information and assistance, showing improved performance and reduced hallucinations on benchmarks like HaluEval. | True | Idealistic | True | 1.0 | Positive | A fine-tuned LLM (Llama 3.1 8B) for the Indian legal system, enhanced with Low-Rank Adaptation (LoRA), Quantized Low-Rank Adaptation (QLoRA), Retrieval Augmented Generation (RAG), and pruning, delivered via a chat application. | The system was evaluated using accuracy, precision, recall, F1-score, ROUGE scores for summarization, and the HaluEval benchmark for factual reliability and hallucination rates. Comparisons were made between base, pre-trained, fine-tuned (LoRA, QLoRA), and compressed model versions. | The fine-tuned model showed substantial improvements: on HaluEval for Question Answering, the score increased from 49.6 (base) to 58.1. The hallucination rate decreased from 5.10% to 3.10% with fine-tuning. | The exponential growth, volume, and complexity of legal documentation in intricate legal systems, and the reliance on extensive, time-consuming manual review and human judgment in traditional legal research. | Developing a specialized LLM tailored to the Indian legal system to simplify legal advisory services and decision-support, making legal knowledge more accessible. This involves fine-tuning on Indian legal data and using techniques like RAG for contextually accurate responses. | Legal information retrieval, answering complex legal queries, legal advisory services, decision-support systems within the judiciary. | Law students, legal practitioners, and individuals seeking legal assistance in India. | Criminal law, civil law, constitutional law, corporate law, consumer law, real estate law. | India | A diverse corpus of Indian legal texts from official government and court websites (Ministry of Law and Justice, Supreme Court, High Courts) including legal documents, statutes, case laws, and commentaries. This included approximately 4,000 question-answer pairs (CSV) and a JSON dataset of case file data. | Data collection and preprocessing, pre-training of the base model, fine-tuning using LoRA and QLoRA, Retrieval Augmented Generation (RAG) implementation for personalized queries, and model compression using structured pruning. | The system was developed as a chat application with a Flask backend and HTML/CSS/JavaScript frontend. LM Studio was used for local model client setup during development. No broad public deployment strategy is detailed. | False | False | NaN | Technical: Need for advanced RAG architectures, alternative parameter-efficient fine-tuning methods, dynamic pruning, knowledge distillation, multilingual support for regional Indian languages, specialized evaluation metrics for Indian legal tasks, and temporal awareness for legal updates. Societal: Further enhancing the accessibility, actionability, and impact of legal knowledge. | Substantial computational and memory requirements of LLMs; inherent ambiguity and context-dependency of legal terminology; balancing model performance with resource efficiency; ensuring factual reliability and minimizing hallucinations in legal responses. | Generation of fabricated legal meanings (hallucinations) by the LLM, potentially leading to misinterpretations if accuracy is not sufficiently high. |
| Iraqi Legal GPT.pdf | IEEE_Xplore | Iraqi Legal GPT | This paper proposes 'Iraqi Legal GPT,' an AI chatbot using the h2ogpt framework and Iraqi legal documents to provide accessible legal information within Iraqi jurisprudence, aiming to be locally deployable and overcome limitations of large models. The system demonstrates promising results with 80% accuracy and 1-minute response times, intending to enhance access to justice for individuals in Iraq. | True | Idealistic | True | 1.0 | Positive | A legal chatbot system named 'Iraqi Legal GPT' built using the open-source h2ogpt framework, trained on curated Iraqi legal documents, employing the 'instructor' embedding algorithm and Chroma db vector store for local deployment and offline use. | The proposed system, Iraqi Legal GPT (using h2ogpt with llama2-7b-chat), was evaluated for accuracy and response time. This involved comparative testing against other LLMs (Mistral, Mixtral variants) and different embedding algorithms (instructor-large vs. others) on Iraqi legal document processing tasks. | The Iraqi Legal GPT system, specifically using the h2oai/h2ogpt-4096-llama2-7b-chat model, achieved an accuracy of 70-80% (reported as 80% in abstract) and a 1-minute response time. The 'hkulp/instructor-large' embedding algorithm demonstrated 98% accuracy in document conversion. | Lack of a comprehensive legal framework for free or reduced-cost legal aid in Iraq. Challenges in finding affordable and specialized lawyers, and understanding legal rights. Time-consuming and costly processes for existing legal aid where available, particularly for underserved communities. | Development of a locally deployable AI chatbot ('Iraqi Legal GPT') using curated local legal documents and an open-source LLM (h2ogpt) to provide free, accessible legal information and guidance, operable offline on standard computers. | Access to legal information, Legal aid, Understanding legal rights, Navigating the legal system | Citizens and non-citizens in Iraq (including permanent residents, migrants, asylum seekers, refugees, victims of human trafficking, foreign students, temporary visitors, and stateless persons) with limited economic resources or facing difficulties accessing legal services. | General Iraqi law | Iraq | Publicly available, unstructured Iraqi legal documents (laws, constitution, etc.) collected from governmental and legal information websites such as Yasaii.info, Legislation.krd. These documents were curated and split into text chunks. | The system was designed using a block diagram approach, involving data collection and curation, document splitting, embedding using the 'instructor' algorithm, storage in a Chroma db vector store, and integration with the h2ogpt LLM. Comparative analysis of different LLMs and embedding algorithms was conducted. | The system is designed for local deployment on a personal computer, capable of running without an internet connection. A website interface (GUI) was developed for user interaction. | False | False | NaN | Hardware limitations for running advanced LLM models locally. The need for larger volumes of legal data and improved support for Arabic/Kurdish languages in local chatbot generators. Ongoing need for more efficient algorithms. | Hardware resource constraints for running large models. Obtaining and processing sufficient legal data, including translation to English due to tool limitations. Selecting and integrating optimal LLMs, embedding algorithms, and vector stores for accuracy and speed. | Potential for inaccuracies in legal information provided if the model hallucinates or if the underlying data is incomplete/incorrect, a known issue with language models in legal contexts (e.g., ChatGPT generating false legal provisions). |
| LegalMind System and the LLM-based Legal Judgment Query System.pdf | IEEE_Xplore | LegalMind System and the LLM-based Legal \nJudgment Query System | This paper introduces LegalMind-GPT, an LLM-based system designed to analyze and summarize financial legal documents and query legal judgments for the finance sector. It evaluates LLMs like LLAMA-2, Claude AI, and FLAN-T5-Base for text summarization, finding LLAMA-2 most effective in providing accurate insights from these complex texts. | True | Market | True | 1.0 | Positive | LegalMind-GPT system: An LLM-based Legal Judgment Query System using models like LLAMA-2, Claude AI, and FLAN-T5-Base for text summarization and analysis of financial legal documents, incorporating text chunking, vectorization, and similarity search. | Comparative analysis of LLAMA2-7B, FLAN-T5-Base, and Claude AI on text summarization of managerial sections from NASDAQ-listed companies' 10-K reports (2022) using ROUGE (Rouge-1, Rouge-2, Rouge-L) and BERT Score (Precision, Recall, F1) metrics. | LLAMA2-7B demonstrated the highest performance across ROUGE and BERT scores. ROUGE-1: 0.508627, ROUGE-2: 0.315902, ROUGE-L: 0.323576, BERT(P): 0.917033, BERT(R): 0.904412, BERT(F1): 0.910372. | Complexity of specialized financial knowledge and legal documents, including financial jargon; The need for contextual understanding of these documents, leading to a gap in financial literacy for a broader audience. | Developing AI-driven tools like LegalMind-GPT to process, summarize, and interpret complex financial legal texts. This aims to provide clear, actionable insights, enhance decision-making, and democratize access to financial understanding. | Democratization of financial knowledge, improving financial literacy through accessible interpretations of financial legal documents, enhancing understanding of legal judgments in finance. | Broader audience with lower financial literacy, legal professionals in the finance sector. | Financial law, corporate law (specifically 10-K reports), legal judgments related to finance. | US (based on evaluation data from NASDAQ-listed companies' 10-K reports). | For LLM evaluation: Publicly available managerial sections from NASDAQ-listed companies' 10-K reports (2022; unstructured text). For the broader system: URLs of legal judgments from online legal databases and repositories. The LLMs (LLAMA-2, Claude AI, FLAN-T5-Base) are pre-trained on general large datasets and were integrated without additional fine-tuning for querying in the described evaluation, though the system concept mentions fine-tuning LLMs for legal text analysis. | System architecture involving data acquisition from online databases, text chunking, tokenization, word vectorization, relation identification, integration of pre-trained LLMs, model evaluation and comparison, and development of a user interface and backend. | The paper describes the design of a user-friendly interface for easy interaction, but specific deployment strategies or platforms are not detailed. | False | False | NaN | Need for wider accessibility and understanding of complex financial-legal information beyond specialized professionals; Enhancing AI's ability to accurately interpret and simplify diverse and jargon-heavy financial texts for non-experts; Expanding data sources to include diverse legal documents from various jurisdictions. | Complexity of financial jargon and the need for contextual understanding in financial text summarization; Ensuring accuracy and relevance in AI-generated legal interpretations. | Potential for bias in AI algorithms if not ethically developed; Lack of impartiality and transparency in AI processes if ethical principles are not prioritized. |
| Transforming Legal Workflows A Deep Dive into NLP Solutions for Legal Challenges.pdf | IEEE_Xplore | Transforming Legal Workflows: A Deep Dive into NLP Solutions for Legal Challenges | This paper proposes a novel framework using a modified BERT-based model for legal document summarization and a Doc2Vec approach for case similarity analysis. The system, evaluated on legal datasets, demonstrates its potential to streamline legal processes, enhance legal reasoning, and improve access to legal services. | True | Idealistic | True | 1.0 | Positive | A modified BERT-based model for legal document summarization and a Doc2Vec-based approach (using UMAP and HDBSCAN) for legal case similarity, with visualization using LeetTopic. | The summarization model was evaluated using precision, recall, F1 score, accuracy, and ROUGE metrics (ROUGE-1, ROUGE-2, ROUGE-L) on a split dataset (train, test, validation from BillSum and Australian legal cases). | The modified BERT summarization model achieved a validation accuracy of 0.7327 (training accuracy 0.7179, loss 0.5562). For summarization quality, it scored ROUGE-1: 0.79, ROUGE-2: 0.81, and ROUGE-L: 0.80. | Manual legal research is time-consuming, error-prone, and struggles with the volume and dynamic nature of legal information. Processing and clustering lengthy documents manually is inefficient, overworking legal practitioners. | Employing NLP (modified BERT, LLMs, RAG) for legal document summarization, case similarity analysis, and other tasks to automate research, improve efficiency, and make legal information more accessible, thereby democratizing legal assistance. | Legal document summarization, legal case similarity analysis, improving access to legal services, democratizing legal assistance, enhancing legal reasoning. | Marginalized communities | General Law | United States, Canada, Australia | Publicly available legal summaries from the BillSum dataset (US and Canadian legislation) and Australian legal cases (2006-2009) from the Federal Court of Australia sourced from AustLII via Kaggle. | Transfer learning with a modified BERT architecture (additional custom dense layers with ReLU activation and dropout layers) for summarization. For case similarity: Doc2Vec for text embeddings, UMAP for dimensionality reduction, and HDBSCAN for clustering. Standard NLP preprocessing techniques were applied. | NaN | False | False | NaN | Need for comprehensive, context-aware NLP systems integrating various legal functions. Robustness and accuracy of LLMs for specific legal answers. Need for improved model design, broader applicability across diverse legal systems and languages, and integration of advanced AI techniques for better performance and security. | Addressing the lack of comprehensive existing solutions for legal NLP tasks. For the conversational aspect of their work, insufficient context to optimize performance and user experience. General need for continued research, addressing limitations, and ethical considerations in legal AI. | Accuracy and reliability concerns with LLMs providing comprehensive legal answers tailored to specific inquiries. |
| The Significance of Cultivating High-Value Patents in the Development of AI.pdf | IEEE_Xplore | The Significance of Cultivating High -Value Patents in the Development of AI | This paper discusses the importance of high-value patents in generative AI for protecting innovation, promoting technological progress, and enhancing market competitiveness. It outlines strategies for cultivating such patents and uses the Transformer architecture as a case study to illustrate this process. | True | Market | True | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Patent Law; Intellectual Property Law | International | NaN | NaN | NaN | False | False | NaN | NaN | Rapid technological iteration requiring patent updates, data privacy and ethical issues in AI development affecting patentability or scope, and the complexity of global patent layouts when cultivating high-value patents for generative AI. | For Generative AI in general: issues of data quality and bias; requirements for substantial computational resources and costs; privacy and security issues; lack of algorithmic transparency; potential for bias and discrimination; security vulnerabilities; and negative employment impacts. |
| AI Legal Assistant for IPC.pdf | IEEE_Xplore | AI Legal Assistant for IPC | This paper introduces an NLP-based chatbot, 'AILA', designed to improve access to legal information regarding the Indian Penal Code (IPC) using LLMs (mistral-7b-instruct) and RAG techniques. The system, featuring a Streamlit interface and evaluated with high accuracy, aims to simplify complex legal language for individuals and small businesses in India. | True | Idealistic | True | 1.0 | Positive | An NLP-based chatbot (AILA) using Retrieval-Augmented Generation (RAG). It employs FAISS for vector database management, the mistral-7b-instruct LLM for generation, and a Streamlit user interface, focusing on the Indian Penal Code. | Evaluated on a custom test dataset of legal queries using accuracy, precision, recall, F1-score, AUC-ROC, and confusion matrix analysis. Performance was compared against individual models (BERT, GPT-3, RoBERTa). | AILA achieved 94% accuracy, 0.92 precision, 0.93 recall, 0.92 F1-score, and 0.97 AUC-ROC. It outperformed individual models (BERT, GPT-3, RoBERTa) on these metrics. | High complexity and density of legal information leading to public lack of awareness; prohibitive cost and inaccessibility of traditional legal consultation. | An NLP-based chatbot (AILA) that simplifies legal text, provides user-friendly access to legal information on the Indian Penal Code, and improves efficiency for individuals and small businesses. | Access to legal information, understanding legal rights and obligations, legal self-help, legal awareness. | General public, individuals, and small businesses in India. | Indian Penal Code (IPC) | India | A legal corpus derived from 'official legal sources' including the Indian Penal Code (IPC), related statutes, and judicial interpretations. Fine-tuning data consisted of 'pairs of legal questions and corresponding answers extracted from legal documents and expert annotations.' | System architecture design involving data preprocessing, NLP module, LLM integration (mistral-7b-instruct), RAG module (with FAISS), and UI development (Streamlit). Iterative refinement based on performance monitoring and user feedback is implied. | The system is designed for deployment on a cloud platform for scalability and accessibility, involving cloud infrastructure setup and security measures. | False | False | NaN | Need for expanding the system’s knowledge base, improving NLP algorithm adaptability, and incorporating multilingual support. | Ensuring accuracy and contextual relevance of legal advice, efficient retrieval of pertinent legal information from a vast legal corpus, interpreting complex legal language, creating an engaging and user-friendly interface, and maintaining an up-to-date legal knowledge base. | NaN |
| Generative Artificial Intelligence in Legal Drafting.pdf | IEEE_Xplore | Generative Artificial Intelligence in Legal Drafting | This paper introduces "Lexi," a generative AI tool designed to simplify legal document drafting by translating complex legal jargon into understandable language. Lexi aims to enhance accessibility and efficiency in legal documentation for both legal professionals and the general public, particularly for individuals and small businesses. | True | Idealistic | True | 1.0 | Positive | Lexi, an AI tool for legal document drafting and jargon simplification, based on a fine-tuned Llama 2 7B model with a chat interface. | Lexi (fine-tuned Llama 2 7B) was compared to a base Llama 2 7b chat model and GPT-3.5. Evaluation metrics included domain specificity, legal jargon level, token count, and an ease of understanding score. Training and validation loss curves for the fine-tuning process were also presented. | The fine-tuned model (Lexi) demonstrated domain-specific capabilities, produced text with 'basic' legal jargon, an average token count of approximately 512, and achieved the highest ease of understanding score of 9.5 out of 10. | The main hurdles identified are the complexity, time-intensiveness, and high cost of traditional legal document drafting. Additionally, the use of intricate legal jargon makes documents inaccessible and difficult for non-specialists to understand, creating a barrier to legal knowledge. | The paper proposes Lexi, an AI-powered tool, to streamline the legal drafting process and simplify complex legal terminology into understandable language. This is intended to democratize legal paperwork and enhance accessibility through a user-friendly interface, especially for individuals and small businesses. | Legal document drafting, simplification of legal language/jargon, improving access to legal information and services for laypersons. | Individuals and small businesses lacking legal expertise or resources, the general public, non-specialists, and non-lawyers. | Rental law (specifically Indian rental rules mentioned as an example), with plans for expansion to a broader range of legal areas. | India (explicitly mentioned for rental rule examples), though the general problem and tool are framed more broadly. | An extensive collection of current legal papers, formatted into JSON objects with 'inputs' and 'responses' keys for fine-tuning the Llama 2 7B model. The specific source or public/proprietary nature of the dataset is not detailed. | The system architecture includes user interaction, chat interface, iterative questioning, data handling, and AI/ML components. Methodologies include fine-tuning a pre-trained LLM (Llama 2 7B), prompt engineering, and UI/UX design principles for the web interface. | Lexi is deployed as a web application with user authentication (Firebase Auth), chat data storage (MongoDB), and model hosting on the Replicate platform. | False | False | NaN | The paper mentions the need to expand the AI's knowledge beyond rental rules, enhance usability (e.g., document export features), and crucially, ensure the preservation of legal accuracy and significance when simplifying language. | Challenges included acquiring and formatting a large, domain-specific legal dataset for fine-tuning, meeting hardware requirements for LLMs, effectively fine-tuning the model for legal language, and designing a user-friendly interface for complex legal drafting tasks. | A key risk highlighted is the potential loss of accuracy and significance of legal content if simplification is not handled carefully, ensuring the integrity of legal information is paramount. |
| An Analysis on Integrating Advanced Conversational AI in Legal Summarization and Information Retrieval.pdf | IEEE_Xplore | An Analysis on Integrating Advanced \nConversational AI in Legal Summarization and \nInformation Retrieval | This paper introduces LawGPT, a conversational AI specialized for the Indian Penal Code, which utilizes a Retrieval-Augmented Generation (RAG) architecture for accurate legal summarization and information retrieval. The study affirms LawGPT's efficacy through validation, aiming to democratize access to legal knowledge for both professionals and laypersons. | True | Idealistic | True | 1.0 | Positive | LawGPT, a conversational AI chatbot using Retrieval-Augmented Generation (RAG) architecture, Dense Passage Retriever (DPR), and BART architecture for generation, specialized for the Indian Penal Code. | Validation against human-generated responses using metrics like ROUGE score. ROUGE F1 scores for LLAMA 2, MISTRAL, and PHI2 were also reported for summarization context. | LawGPT's efficacy and accuracy were affirmed through validation against human-generated responses, demonstrating accurate retrieval and summarization of legal information. For summarization context, ROUGE F1 scores for other models were: LLAMA 2 (0.48), MISTRAL (0.46), PHI2 (0.40). | Limited effectiveness of general-purpose AI in understanding complex legal terminology and navigating intricate legal frameworks, hindering access to legal information. | Development of specialized conversational AI solutions like LawGPT, trained on specific legal corpora (e.g., Indian Penal Code) and employing advanced AI architectures (e.g., RAG), to provide tailored, efficient, and accurate access to legal knowledge. | Access to legal information, legal research, legal text summarization, interpretation of legal statutes (Indian Penal Code). | Laypersons and legal professionals. | Criminal Law (specifically Indian Penal Code). | India | Indian Penal Code (IPC) data and additional data from the OpenAI API. The nature of the IPC data (e.g., public, proprietary) is not specified beyond being the text of the code. | Integration of Retrieval-Augmented Generation (RAG) architecture, Dense Passage Retriever (DPR) for retrieval, BART model for generation, Streamlit for user interface development, LangChain for text processing, and the TogetherAI API for the Legal Language Model (LLM). | NaN | False | False | NaN | NaN | Developing a system capable of accurately interpreting complex legal terminology, performing efficient and relevant information retrieval from legal texts (Indian Penal Code), and generating contextually appropriate and accurate legal responses. | The paper's related works section cites general risks associated with AI in law, such as biases, ethical considerations, lack of transparency, accountability, and fairness, as well as potential negative impacts on justice, democratic governance, legal responsibility, and liability. |
| LAWBOTS Utilization of AI Chatbots for Legal Advising in the Philippines.pdf | IEEE_Xplore | LAWBOTS : Utilization of AI Chatbots for Legal \nAdvising in the Philippines | This paper explores the potential use of AI chatbots (Lawbots) for legal advising in the Philippines, examining existing chatbots and public perception through a survey. The study analyzes Filipinos' views on Lawbots' benefits, challenges, and impact, aiming to inform their acceptance and implementation. | True | Idealistic | True | 3.0 | Neutral | AI Chatbots for legal advising (Lawbots) | A survey of 60 Filipino respondents was conducted via Google Forms, using multiple choice, Likert scale, and linear scale questions to gather perceptions on familiarity with AI, awareness of legal chatbots, and views on the benefits and challenges of Lawbots. | Survey (N=60) indicated nuanced public perception: while 88.3% had used chatbots, 60% were unaware of their use for legal advising. On implementing Lawbots, 31.7% were neutral, 28.3% agreed, and 26.7% disagreed. Key perceived benefits included 24/7 availability and efficiency; key perceived challenges were limited scope/inadequacy of advice and lack of personalization. | General A2J obstacles in the Philippines: insufficient funds, distance/traffic issues, prolonged cases, lack of contact with lawyers. For Lawbots contributing to A2J: lack of public trust and acceptance, concerns about advice adequacy and personalization, ethical considerations, and the complexity of legal issues. | Improving access through AI chatbots like Tisya Hustisya. For broader Lawbot adoption: continuous system development for privacy and regulation, improved legal frameworks for Lawbots, standardization, and public education to build trust and address concerns about advice quality. | Access to legal aid, legal information, human rights, domestic violence, labor issues, general legal advising. | Marginalized communities in the Philippines, general public, victims of specific issues (e.g., sexual violence, human rights violations). | General legal advising, human rights law, labor law, criminal law (related to sexual violence), immigration law (in discussed examples). | Philippines (primary focus); Canada (mentioned for an example chatbot). | NaN | NaN | NaN | False | False | NaN | Need for clearer public communication and education about Lawbots (benefits, challenges, implications). Gaps in public trust and acceptance. Technical gaps include AI's understanding of human emotions and ensuring completeness of responses. | Limited scope/inadequacy of advice, lack of personalization, handling legal complexity, addressing ethical and moral considerations, ensuring data privacy and security, adapting to dynamic and evolving laws, fostering user trust and acceptance, overcoming communication challenges, potential for bias, integrating technology into the legal sector, and establishing clear legal regulations and responsibility. | Concerns regarding data sharing, privacy, and security. Potential for incomplete responses and unauthorized practice of law. Societal threats like manipulation of beliefs and emotions. Lack of legal responsibility for erroneous advice. Potential for algorithmic bias. |
| LDAA Legal Documents Automation and Assistance.pdf | IEEE_Xplore | LDAA: Legal Documents Automation and Assistance | This paper proposes "Legal Documents Automation and Assistance (LDAA)," a system utilizing fine-tuned open-source Large Language Models (like Llama3 or Gemma) to automate legal document creation and provide assistance, specifically targeting illiterate and underprivileged rural populations in India. LDAA aims to offer a user-friendly, efficient solution by integrating AI with legal expertise for personalized guidance, document generation via LaTeX, and an AI chatbot for legal queries. | True | Idealistic | True | 1.0 | Positive | Legal Documents Automation and Assistance (LDAA) system: fine-tuned LLMs (Llama3/Gemma), Retrieval Augmented Generation (RAG), LaTeX for document generation, vector databases (Chroma, FAISS) with TF-IDF for similarity search, and a chatbot for legal assistance. | LLM performance evaluated using BLEU (0.95), Perplexity (1.5), and Word Error Rate (0.01). A demonstrative use case of revising a 'deed of hypothecation' document based on user input is presented. | The LLM component achieved a BLEU score of 0.95, a Perplexity of 1.5, and a Word Error Rate of 0.01. The system demonstrated successful document modification based on user prompts in a hypothecation deed example. | Complexity and high cost of traditional legal documentation, limited access to legal experts for many, rudimentary nature and lack of personalization in existing automated systems, and misalignment of current tools with specific legal practice needs. | Automating legal document creation, review, and assistance using fine-tuned LLMs and a user-friendly interface. Providing personalized guidance, an AI-powered chatbot for legal queries, ensuring document security, and enabling customization to user needs. | Automation of legal document creation, legal assistance via chatbot, simplifying legal processes for accessibility. | Illiterate, underprivileged rural people in India. | General legal document drafting (e.g., contracts, agreements, legal notifications, deeds). | India | Fine-tuning of pre-trained open-source LLMs (Llama3 or Gemma series) using legal documents and articles. A proprietary knowledge base of codified laws and legal principles developed by the team for the chatbot. | System architecture involving a user interface, LaTeX for document compilation, fine-tuned LLMs for text generation and understanding, Retrieval Augmented Generation (RAG) for document modification, vector databases (Chroma, FAISS) with TF-IDF for semantic search, and an iterative user feedback loop for document refinement. | NaN | False | False | NaN | Need for broader legal document type coverage, development of more advanced Large Legal Language Models (LLLMs), mobile application accessibility, voice and multilingual support, integration with blockchain for secure document management, and incorporation of e-signature approvals and legal document verification features. | Overcoming the labor-intensive, error-prone, and expensive nature of manual legal drafting. Addressing the limitations (rudimentary, lack of sophistication and personalization) of existing automated legal tools. Ensuring security, accuracy, and adaptability of the automated system to diverse legal requirements and user needs. | NaN |
| Generative vs Intent-based Chatbot for Judicial Advice.pdf | IEEE_Xplore | Generative vs Intent-based Chatbot for Judicial Advice | This paper presents and compares two AI chatbot approaches, a generative model using OpenAI API and an intent-based model using Google's Dialogflow, designed to provide judicial advice on Indian laws. The generative chatbot demonstrated higher accuracy and more contextually rich responses, while the intent-based chatbot excelled in precision for predefined queries. | True | Idealistic | True | 1.0 | Positive | Comparative development and evaluation of a generative chatbot (using OpenAI API, GPT-3.5 turbo, fine-tuned on custom Indian legal conversations) and an intent-based chatbot (using Google's Dialogflow with custom intents for Indian law). | Both chatbots were tested against 100 test conversations. Performance was measured by calculating true positives, true negatives, false positives, and false negatives, from which accuracy, precision, recall, and F1-score were derived. Qualitative comparison of response nature, quality, handling changing scenarios, data requirements, and user experience was also conducted. | The generative chatbot achieved an accuracy of 96.00%, precision of 96.67%, recall of 98.86%, and F1-score of 97.75%. The intent-based chatbot achieved an accuracy of 80.00%, precision of 90.47%, recall of 97.43%, and F1-score of 93.82%. | Traditional legal advice is often lengthy and expensive. Key challenges in AI for legal advice include ensuring legal accuracy and reliability of responses, handling ambiguity and uncertainty in legal queries, and difficulties in obtaining diverse and extensive datasets due to privacy and legal restrictions. | The paper proposes the development and deployment of AI-powered chatbots (both generative and intent-based) to provide accessible, immediate, and 24/7 judicial advice on Indian legal matters, thereby addressing the cost and time barriers of traditional legal consultations. | Providing judicial advice, guidance on legal issues, procedures, and relevant laws. | Indians seeking judicial advice, particularly those with limited knowledge of Indian civil and criminal laws. | Indian civil and criminal laws. | India | Generative chatbot: A custom-made dataset of 100 conversations (100-150 words each), simulating user queries and lawyer-like responses on Indian civil and criminal law, informed by the National Judicial Data Grid, used to fine-tune GPT-3.5 turbo. Intent-based chatbot: 34 intents (abstract mentions 36, methodology details 34 created plus default ones) with training phrases and predefined responses based on Indian civil and criminal laws, developed within Google's Dialogflow. | Generative chatbot: Developed using Python, OpenAI API (GPT-3.5 turbo model), 'llama-index' and 'langchain' packages for indexing and interaction. Fine-tuning GPT-3.5 turbo on the custom legal conversation dataset. User interface built with Streamlit. \nIntent-based chatbot: Developed using Google's Dialogflow. Conversational flow designed using intents, entities, and follow-up intents. Training phrases and responses created for each intent. Support for English and Hindi, and text-to-speech functionality. | Generative chatbot: Deployed as a Streamlit application made accessible to users via a public URL using Ngrok. \nIntent-based chatbot: Integrated into a custom website (built with HTML, CSS, JavaScript) using Dialogflow's Web Demo (for English, with text-to-speech) and Dialogflow Messenger (for Hindi). | True | False | The generative chatbot was deployed via Ngrok to a public URL. The intent-based chatbot was integrated into a website using Dialogflow's Web Demo and Messenger. | Ensuring legal accuracy and reliability of chatbot responses, especially for generative models. Improving the ability of chatbots to handle ambiguous and uncertain legal queries. Overcoming challenges in obtaining diverse and extensive legal datasets due to privacy and legal restrictions. | Generative chatbot: Some responses required post-processing to improve clarity, despite being contextually rich and fluent. \nIntent-based chatbot: Difficulty handling user input outside predefined categories; initial poor performance necessitated detailed training phrases, meticulous entity definition, and a sufficient number of intents. | Generative AI chatbot responses can sometimes be inaccurate or provide partial guidance due to being derived from patterns in data. Validating the accuracy of legal information generated by AI is challenging, especially given the complexity of legal matters. |
| Use-of-Generative-Artificial-Intelligence-Including-Large-Language-Models-Such-as-ChatGPT-in-Scientific-Publications-Policies-of-KJR-and-Prominent-Authorities_2023_Korean-Radiological-Society.pdf | Scopus | Use of Generative Artificial Intelligence, Including Large Language Models Such as ChatGPT, in Scientific Publications: Policies of KJR and Prominent Authorities | This editorial presents the Korean Journal of Radiology's (KJR) new comprehensive policies for the use of generative AI, including LLMs like ChatGPT, in scientific publications submitted to KJR. The KJR policy, aligned with prominent international authorities, outlines rules for authorship, author responsibility, transparency, acceptable AI use for language enhancement, and restrictions for peer review to uphold research integrity. | True | NaN | True | 1.0 | NaN | KJR's policy on generative AI use in scientific publications | NaN | NaN | NaN | NaN | NaN | NaN | Copyright law, Intellectual property (authorship), Publication ethics | Republic of Korea (for KJR policy); International (for comparative discussion and referenced authorities) | NaN | NaN | NaN | False | False | NaN | NaN | Ensuring research integrity, preventing plagiarism and copyright infringement, defining authorship, and maintaining the quality of peer review in the context of AI-generated content. | Breach of research integrity, plagiarism, copyright infringement, issues with assigning authorship, generation of incorrect/incomplete/biased content by AI, undermining the human expert perspective in peer review. |
| Artificial_intelligence_AI_or_augmented_intelligen.pdf | Scopus | Artificial intelligence (AI) or augmented intelligence? \nHow big data and AI are transforming healthcare: \nChallenges and opportunities | This paper discusses how big data and AI are transforming healthcare, highlighting both innovative opportunities and significant ethical, legal, and social challenges. It emphasizes the critical need for robust governance frameworks, particularly in low- and middle-income countries, to address issues like the digital divide, data bias, and potential exacerbation of health inequities. | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Digital divide; exacerbation of health inequities; data and algorithmic bias; low data literacy in LMICs; commercial exploitation of data from LMICs; lack of robust, context-specific governance and legislation in LMICs. | Developing context-specific ethical and legal frameworks for AI in LMICs; ensuring transparency, accountability, and human oversight; improving data literacy; promoting equitable benefit-sharing and sustainable AI practices; and adopting a hybrid human-AI approach to healthcare. | Health equity; digital divide in healthcare; ethical AI governance in LMICs; data privacy and security; algorithmic bias in medicine; regulation of AI in healthcare. | Populations in Low- and Middle-Income Countries (LMICs); resource-depleted settings; historically underrepresented groups in medical data (e.g., women, children, ethnic minorities, people with disabilities). | Medical ethics and law; data protection and privacy law; AI-specific legislation and regulation; liability and medical malpractice law; constitutional rights; consumer protection law; intellectual property law. | South Africa; International (with specific mentions of WHO, EU, USA, China); Low- and Middle-Income Countries (LMICs) generally. | Discusses LLMs trained on massive internet texts and medical AI using varied datasets (EHRs, images, mobile data); highlights concerns over use of identifiable patient data and inherent biases in historical medical data. | NaN | NaN | False | False | NaN | Absence of AI-specific and context-relevant governance, ethical guidelines, and legislation in many LMICs (including South Africa); lack of harmonisation in international AI regulations; unaddressed ethical and technical debt in rapid AI deployment. | NaN | Propagation of inaccurate/hallucinated information; amplification of societal biases leading to discriminatory outcomes and health disparities; erosion of clinical skills; severe privacy violations and data misuse; psychosocial harm from human-like AI; exploitative data commercialisation disadvantaging LMICs; significant environmental impact; and complex medicolegal liability. |
| Clopton-Huq-76-Stan.-L.-Rev.-893.pdf | Scopus | The Necessary and Proper Stewardship of Judicial Data | This paper argues federal judicial data is a vital, underused public asset that Congress should regulate for improved collection, management, and accessibility to advance public good and access to justice, countering its current imperfect availability and potential for private monopolization. It offers a descriptive analysis of current data practices, a doctrinal examination of regulatory power, and a normative vision for reform, including the use of LLMs. | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Imperfect availability and high cost of judicial data (e.g., PACER fees, clunky interface); significant data loss and inconsistency in collection (e.g., "dark data", lack of standardization); monopolization of data by commercial firms for private profit; lack of comprehensive congressional regulation and some judicial resistance to open data; information asymmetry favoring well-resourced litigants. | Enact congressional legislation to treat judicial data as a public asset, ensuring its systematic production and broad public availability with narrow exceptions; improve data accuracy, consistency, and searchability through standardization (e.g., for NOS codes) and better capture methods, possibly involving court staff or public-regarding privatization; reform public disclosure by increasing transparency, reducing access barriers like fees (e.g., "Free PACER"), and improving data formats; leverage technologies like LLMs for public good analyses. | Access to court records and dockets; Improving judicial processes (e.g., IFP status, case management, sentencing); Reducing information asymmetry for litigants; Supporting legal research and policy-making for judicial reform; Enhancing judicial accountability and transparency. | Litigants with limited resources (e.g., pro se, in forma pauperis); the general public; academics and researchers; legal services providers (e.g., public defenders). | Civil Procedure, Criminal Procedure, Constitutional Law, Administrative Law, and general federal litigation. | United States (federal judiciary) | NaN | NaN | NaN | False | False | NaN | Technical gaps in data capture (accuracy, consistency, searchability), data formats, and public access interfaces. Societal/legal gaps include the lack of a comprehensive legislative framework for judicial data, judicial resistance to open data, and the need to balance transparency with privacy and judicial integrity. The full potential of LLMs for analyzing judicial data remains unmapped. | NaN | Private monopolization of public data for profit; exacerbation of inequality due to costly access systems; misinterpretation of disclosed data leading to unwarranted criticism or distorted judicial behavior; privacy violations from improper handling of sensitive information; compromising essential judicial deliberations or safety; incentivizing judges to "teach to the test" at the expense of accuracy; potential for errors in LLM-generated analyses of judicial data. |
| Trustworthy-AI-Securing-Sensitive-Data-in-Large-Language-Models_2024_Multidisciplinary-Digital-Publishing-Institute-MDPI.pdf | Scopus | Trustworthy AI: Securing Sensitive Data in Large Language Models | The paper proposes a comprehensive framework for integrating trust mechanisms into Large Language Models (LLMs) to dynamically control the disclosure of sensitive information. This framework utilizes User Trust Profiling, Information Sensitivity Detection, and Adaptive Output Control, aiming to balance data utility and privacy in sensitive domains like healthcare, finance, and legal services. | True | Market | True | 1.0 | Positive | A framework for embedding trust mechanisms in LLMs, integrating User Trust Profiling (based on RBAC/ABAC, behavioral analytics), Information Sensitivity Detection (using NER, contextual analysis, privacy-preserving techniques like differential privacy), and Adaptive Output Control (employing redaction, summarization, differential privacy). | NaN | NaN | NaN | NaN | NaN | NaN | Legal services (general) | International | The framework manages LLMs typically trained on vast, web-scraped general text corpora. Its internal components (e.g., for sensitivity detection) would use general and domain-specific labeled datasets, potentially fine-tuned on data like medical records or financial data, and continuously retrained using user feedback and incident reports. | Conceptual framework design integrating existing access control models (RBAC, ABAC), NLP techniques (NER, contextual analysis), and privacy-preserving methods (differential privacy). | NaN | False | False | NaN | NaN | Balancing data utility and privacy; accuracy of domain-specific sensitivity detection; precision of adaptive output control based on user trust; mitigating biases in trust profiling; scalability across diverse domains. | Unauthorized disclosure of sensitive/private information by LLMs; extraction of training data through attacks; non-compliance with privacy regulations (e.g., GDPR, HIPAA); algorithmic bias leading to unfair or discriminatory outcomes; misclassification by the proposed framework leading to improper data disclosure or denial of access. |
| s10506-023-09367-6 (1).pdf | Scopus | Bringing legal knowledge to the public by constructing a legal question bank using large‑scale pre‑trained language model | This paper presents a three-step approach to make legal information more accessible to laypersons by improving navigability and comprehensibility. It focuses on using large language models (GPT-3) with novel prompting strategies to construct a Legal Question Bank (LQB) from simplified legal texts, and a recommender system (CRec) to guide users to relevant information. | True | Idealistic | True | 1.0 | Positive | A three-step approach: 1) CLIC-pages (plain language legal summaries), 2) a Legal Question Bank (LQB) constructed using GPT-3 with a 'Hybrid' partitioning prompting strategy, and 3) a CLIC Recommender (CRec) to match user queries to the LQB. | The LQB generation method was evaluated by comparing GPT-3 (using three prompting/partitioning strategies: section-based, paragraph-based, Hybrid) generated questions (MGQs) with human-composed questions (HCQs) for 100 CLIC-pages. Metrics included quantity, precision (verified by legal experts), coverage, and diversity. | The 'Hybrid' GPT-3 partitioning strategy yielded the best MGQs: 3,400 correct questions (vs. 2,686 HCQs), 68% precision, 93% coverage, greater diversity, and generation of 'augmenting questions' for content improvement. | The primary obstacle is the 'legal knowledge gap' for the general public, stemming from difficulties in: 1) Navigability: finding relevant legal rules for their situation. 2) Comprehensibility: understanding technical legal language and concepts. | A three-step approach: 1) Creating 'CLIC-pages' with legal information in layperson's terms to enhance comprehensibility. 2) Constructing a 'Legal Question Bank' (LQB) using GPT-3 to provide model questions, improving navigability and comprehensibility. 3) Designing an AI-powered 'CLIC Recommender' (CRec) to guide users from their problem descriptions to relevant LQB questions and CLIC-pages, further aiding navigability. | Improving navigability and comprehensibility of legal information for the general public, legal knowledge dissemination. | Laypersons, general public, individuals without legal education or formal legal training. | Various fields relevant to daily life. The evaluation sample included: Landlord and Tenant, Defamation, Insurance, Personal Data Privacy, Intellectual Property. The CLIC platform covers 32 legal topics. | Hong Kong | For question generation: CLIC-pages, which are human-written plain language summaries of Hong Kong law hosted on the CLIC platform. GPT-3 (the LLM used) was pre-trained on diverse, large-scale text and code datasets (e.g., Common Crawl, WebText2, books, Wikipedia). | For LQB creation: prompt engineering for GPT-3 (including section-based, paragraph-based, and a novel 'Hybrid' partitioning strategy), sentence embedding (DistilBERT), and single-link clustering for question deduplication. For CRec: text embedding (all-mpnet-base-v2) of user input and LQB questions/answers, cosine similarity for matching, and a redundancy removal strategy. | The CLIC platform (clic.org.hk) is an operational online platform. The CRec is presented as a prototype component being developed for and integrated into this platform, using the LQB generated by the described methods. | True | False | The CLIC platform (clic.org.hk), which incorporates the CLIC-pages and the CRec recommender prototype using the described LQB, is an online public resource. | The paper notes that 'augmenting questions' generated by the AI reveal omissions in current CLIC-page content, suggesting a need for continuous content enrichment. The sub-100% precision of AI-generated questions (Hybrid at 68%) implies a remaining need for human verification and curation. | Designing effective GPT-3 prompts (partitioning strategies) to optimize question quantity, precision, coverage, and diversity. Managing the probabilistic nature of LLM outputs leading to variability in question quality. The significant human effort and cost required for verifying machine-generated questions. Effectively deduplicating semantically similar questions. | Imperfect precision of machine-generated questions (e.g., the best strategy achieved 68% precision) could lead to users being presented with irrelevant or unhelpful legal information if not properly curated before deployment in the LQB. |
| A-CIA-TriadBased-Taxonomy-of-Prompt-Attacks-on-Large-Language-Models_2025_Multidisciplinary-Digital-Publishing-Institute-MDPI.pdf | Scopus | A CIA Triad-Based Taxonomy of Prompt Attacks on Large Language Models | This paper introduces a novel taxonomy for prompt attacks on Large Language Models (LLMs) based on the Confidentiality, Integrity, and Availability (CIA) cybersecurity triad. It analyzes emerging threats and proposes targeted mitigation strategies to enhance LLM security in critical applications, including legal services. | True | Market | True | 1.0 | NaN | A taxonomy of prompt attacks on LLMs based on the Confidentiality, Integrity, and Availability (CIA) triad, alongside a framework for corresponding mitigation strategies. | NaN | NaN | For LLMs in legal services: Exposure of confidential legal information (e.g., attorney-client communications), generation of incorrect or misleading legal advice and information, and disruption of access to legal AI tools and resources due to system vulnerabilities. | For LLMs in legal services: Enhancing data confidentiality through methods like differential privacy and access controls; ensuring information integrity via input validation, adversarial training, and bias mitigation; maintaining system availability and resilience through rate limiting, context management, and anomaly detection. | Security and privacy in AI-driven legal services; Integrity and reliability of AI-generated legal information and advice; Availability and resilience of AI tools for legal applications. | NaN | General legal services | International | NaN | Literature review, application of the established CIA cybersecurity triad to LLM prompt attacks, synthesis of existing attack mechanisms and case studies into a structured classification and mitigation framework. | The proposed taxonomy and framework are disseminated through publication as an open-access academic paper for conceptual adoption by researchers and practitioners. | True | True | The paper presenting the taxonomy and framework is published under an open access (CC BY) license, making it freely available. | Societal: Need for specific regulatory frameworks for AI in legal services, ethical guidelines for LLM use in law, and education for legal professionals and the public on LLM risks. Technical: Development of LLMs specifically hardened for legal applications, real-time monitoring for attacks on legal AI systems, and advanced security protocols to protect sensitive legal data. | NaN | Confidentiality risks: Unauthorized extraction of sensitive data (e.g., personal, proprietary, attorney-client communications, medical records), prompt stealing, model inversion. Integrity risks: Generation of misleading, biased, false, or harmful content (e.g., misinformation, incorrect legal/medical advice, hate speech), instruction injection. Availability risks: Denial-of-Service (DoS) attacks, system crashes or unresponsiveness, output degradation, context flooding, disruption of critical services. |
| 0045 (1).pdf | Scopus | The Impact of Empathy in Conversational AI: A Controlled Experiment with a Legal Chatbot | This paper investigates how displaying empathy in a legal chatbot's language affects user perceptions of trustworthiness, helpfulness, and cognitive effort, using a controlled experiment with 277 participants in a tenant-landlord scenario. Results indicate that empathetic language generally improves helpfulness and trustworthiness, but anger can negatively moderate the effect on trustworthiness, while chatbots generally reduce cognitive effort compared to FAQs. | True | Idealistic | False | 1.0 | Positive | A rule-based legal chatbot designed with specific syntactic and rhetorical linguistic elements to display empathy, compared against a non-empathetic version (with the same underlying logic) and a static FAQ page. | A randomized controlled experiment with 277 Chicago residents in a 2x3 factorial design. Participants interacted with either an empathetic chatbot, a non-empathetic chatbot, or an FAQ page, with their emotional state (anger) manipulated as a moderating factor. Outcomes were measured via self-reported Likert scales for helpfulness, trustworthiness, cognitive effort, and a comprehension quiz. | Empathetic language increased perceived helpfulness. For trustworthiness, empathetic language had a positive effect when users were not angry; however, when anger was induced, the empathetic chatbot was perceived as less trustworthy. The use of a chatbot (either type) significantly reduced cognitive effort compared to an FAQ page. | Lack of user trust and satisfaction with AI legal aid tools, and potentially high cognitive effort required by users to solve their problems, which can hinder effective access to legal information and assistance. | Designing conversational AI for legal services with specific linguistic elements (syntactic and rhetorical rules) that display empathy to potentially enhance user perceptions of helpfulness, trustworthiness, and reduce cognitive effort. | Self-help legal information and advice for tenants, improving user experience with legal tech. | Tenants renting property, particularly those facing issues with landlords or potential eviction in Chicago. | Landlord-tenant law | Chicago, Illinois, USA | NaN | Theory-driven design (based on linguistic theory of empathy), experimental design (randomized controlled trial), user-centered evaluation (measuring perceived helpfulness, trustworthiness, cognitive effort). Empathetic/non-empathetic chatbot variations were created by applying/not applying ten specific linguistic rules for empathy display. | The specific experimental chatbot versions were deployed in a controlled website-based experiment using LandBot and Qualtrics, hosted on Firebase. Not a public deployment of these specific versions. | False | False | NaN | Further research is needed on emotional alignment in human-AI conversations, especially concerning how AI should adapt its empathy display in response to users' negative emotions (like anger), where simple empathy can be counterproductive to trust. | Disentangling the effects of linguistically displayed empathy from the AI's underlying 'cognitive' abilities in user perception, and developing empathy displays that are robust across different user emotional states. | Empathetic displays by AI systems, if not appropriately attuned to the user's emotional state (e.g., anger), can be counterproductive and lead to a decrease in user trust in the legal aid tool. |
| Automatic-Text-Simplification-fortheLegal-Domain-inBrazilian-Portuguese_2025_Springer-Science-and-Business-Media-Deutschland-GmbH.pdf | Scopus | Automatic Text Simplification for the Legal Domain in Brazilian Portuguese | This paper investigates automatic text simplification for legal documents in Brazilian Portuguese, aiming to improve access to justice for laypeople. It evaluates five different LLM-based approaches, including fine-tuned models and prompted generative models, using both quantitative metrics and qualitative expert assessment. | True | Idealistic | True | 2.0 | Positive | Evaluation of five LLM-based approaches for text simplification: fine-tuned PTT5 (FT-PTT5), FT-PTT5 with Reinforcement Learning (FT-PTT5 + RL), GPT-3.5-Turbo, GPT-4o, and Flan-T5-Large. | Quantitative evaluation using SARI, BLEU, BERTScore, and ROUGE metrics on a test set of 91 hand-picked legal sentences. Qualitative evaluation by a judicial analyst assessing correctness, simplicity, and overall quality of simplifications for the same 91 instances. | Qualitatively, GPT-3.5-Turbo was judged best by a human expert (e.g., 98% of its simplifications were deemed simpler and 84% of 'Good' quality). Quantitatively, GPT-4o achieved the highest SARI score (0.43). | Difficulty for laypeople to understand legal documents due to domain-specific jargon and complex sentence structures; lack of parallel datasets of complex-simple legal sentences in Brazilian Portuguese; the slow process of manual simplification adoption by courts. | Employing automatic text simplification (ATS) using Large Language Models to make legal texts more accessible. This includes fine-tuning existing models and using prompting strategies with generative models, supported by assembling relevant datasets. | Understandability of legal documents, plain language in the legal domain, access to justice through improved legal text accessibility. | Laypeople without legal domain expertise, individuals with reading issues, or those with a low education level. | General legal documents, including rulings, laws, agreements, contracts, judicial decisions, warrants, notifications, and legal case status updates. | Brazil | A merged dataset of parallel complex-simple sentence pairs in Brazilian Portuguese, comprising: 1) 8,120 pairs from news articles (PorSimples), 2) 1,424 filtered legal case status updates simplified by an OpenAI model (JusBrasil), and 3) 149 hand-picked examples from court materials. Used for fine-tuning PTT5. | For the evaluated models: pre-training on large corpora (PTT5, Flan-T5, GPTs). For adapted models studied (FT-PTT5, FT-PTT5+RL): fine-tuning of a pre-trained model (PTT5) on the custom-assembled Portuguese text simplification dataset and application of reinforcement learning using FKGL, SAMSA, and Levenshtein Distance as reward components. For generative models (GPTs, Flan-T5): Prompt engineering with few-shot in-context learning. | NaN | False | False | NaN | Lack of high-quality, domain-specific parallel datasets for Portuguese legal text simplification; need for more robust and comprehensive evaluation metrics for TS; limited generalizability of models to the specific nuances of the legal domain and its sub-fields without sufficient in-domain training data; high cost associated with fine-tuning very large models. | Assembling a suitable parallel dataset for Portuguese legal text simplification, particularly with in-domain legal examples; effectively fine-tuning models with limited in-domain data leading to generalization issues; achieving good performance with instruction-only prompting for certain models (e.g., PTT5); selecting appropriate and effective reward metrics for reinforcement learning in text simplification; infrastructure limitations for training large models. | Generation of legally inaccurate simplifications that alter meaning (e.g., FT-PTT5+RL had 53% 'No' for correctness), introduce grammatical errors, add extraneous information, or fail to simplify adequately, potentially leading to misinterpretation of legal documents by laypeople. |
| GPT4-passes-the-bar-exam_2024_Royal-Society-Publishing.pdf | Scopus | GPT-4 passes the bar exam | This paper experimentally evaluates GPT-4's zero-shot performance on the full Uniform Bar Examination (UBE), including multiple-choice, essay, and performance test components. The results show GPT-4 significantly outperforms prior models and human test-takers, passing the UBE by a considerable margin, indicating its potential to support legal service delivery. | True | Idealistic | True | 2.0 | Positive | GPT-4 (Generative Pre-trained Transformer 4) | Zero-shot evaluation on the full Uniform Bar Examination (UBE), including the Multistate Bar Examination (MBE) multiple-choice questions, the Multistate Essay Exam (MEE), and the Multistate Performance Test (MPT). MBE questions were official NCBE questions; MEE and MPT questions were from the July 2022 Bar Examination. MEE/MPT answers were graded by two author-experts against representative 'good' answers. | GPT-4 scored approximately 297 points on the UBE, significantly exceeding the passing threshold for all UBE jurisdictions. On the MBE, GPT-4 achieved 75.7% accuracy, outperforming average human test-takers. On the MEE and MPT, GPT-4 scored an average of 4.2/6.0. | The complexity of legal language and the legal system; the high cost and unmet demand for legal services. | Proposes Large Language Models like GPT-4 as a 'technology-based force multiplier' to support the delivery of legal services and address cost and accessibility issues. | Accessibility of legal services, Cost of legal services, Evaluation of AI in professional licensing. | General public / Individuals and organizations facing challenges with the quantity, quality, and accessibility of legal services due to cost and complexity. | Civil Procedure, Constitutional Law, Contracts, Criminal Law and Procedure, Evidence, Real Property, Torts, Corporations, Trusts & Estates, Family Law, Legal Ethics (as covered by the Uniform Bar Exam). | USA (Uniform Bar Exam applicable in multiple states) | GPT-4 was pre-trained on publicly available data (such as internet data) and data licensed from third-party providers, then fine-tuned using Reinforcement Learning from Human Feedback (RLHF). Test data contamination checks were performed. | GPT-4 is a transformer-style model pre-trained to predict the next token in a document, using both publicly available data (such as internet data) and data licensed from third-party providers. The model was then fine-tuned using reinforcement learning from human feedback (RLHF). | Access to a same or significantly similar version of the GPT-4 model is generally available under commercial terms from OpenAI. | True | False | GPT-4 is generally available under commercial terms from OpenAI. | Translating LLM capabilities like GPT-4 into safe and efficient real-world public and private legal applications; addressing LLM issues like hallucinations, factual incorrectness, and ethical compliance failures; comparative performance of other foundational models (e.g., open-source vs. closed-source, domain-specific vs. general) on legal tasks; advancing LLM performance through techniques such as prompt engineering, few-shot learning, retrieval augmented generation, and other systematic engineering methods. | Ensuring test data was not part of the model's training set (contamination checks with OpenAI); handling long documents for MPT tasks (requiring an '8K' version of ChatGPT with a wider context window); inherent variability and subjectivity in qualitative assessment/grading of open-ended MEE and MPT responses; the MPT's requirement for models to work within the four corners of provided exam material, potentially suspending broader knowledge. | GPT-4 may hallucinate sources, incorrectly interpret facts, or fail to follow ethical requirements; GPT-4 has various biases in its outputs. |
| JOIA2023022.pdf | Scopus | A New Era of Maritime Arbitration: Ex Machina Determinations | This paper explores the potential of Large Language Models, specifically ChatGPT 3.5, to act as arbitrators in maritime disputes. Through four hypothetical test cases, it evaluates ChatGPT's capabilities and limitations in this role, discussing benefits like speed and cost-reduction alongside challenges such as accuracy and legal reasoning. | True | Idealistic | True | 2.0 | Positive | Using ChatGPT version 3.5 as an AI arbitrator to make determinations in hypothetical maritime disputes based on structured prompts detailing facts and party submissions. | Four hypothetical charterparty disputes were presented to ChatGPT 3.5. The prompts included agreed facts, party submissions, and specific questions for determination. ChatGPT's responses (determinations and reasoning) were then analyzed. | ChatGPT 3.5 made determinations rapidly and showed some understanding of legal/trade terms. However, it struggled with nuanced legal reasoning, failed to cite relevant or correct case law (exhibiting 'hallucinations'), and its decisions sometimes differed from human arbitrator outcomes in similar published cases. | The high cost of traditional litigation and arbitration, which acts as a significant barrier to accessing justice, especially for small value claims. | The paper proposes using AI LLMs like ChatGPT as arbitrators to provide almost instantaneous, low-cost dispute resolution, particularly for small claims, thereby enhancing access to justice. | Access to justice for small value disputes in maritime arbitration. | Individuals or small businesses in the maritime industry with small value claims. | Maritime law, Arbitration | Maritime law, primarily with reference to English law and international arbitration practices (LMAA, SMA, SCMA). | ChatGPT 3.5 was trained on 'vast amounts of data from the internet written by humans' up to September 2021. This is general, unstructured internet data. | NaN | NaN | True | True | The publicly available version of ChatGPT 3.5, used for the experiments, is accessible, including a free tier. | Technical gaps include data limitations, hallucinations, inability to manage arbitration procedures, lack of real-time legal updates, and difficulty assessing witness credibility. Societal/legal gaps include the need for legal frameworks for AI arbitrators, ensuring enforceability of AI awards (e.g., revising the New York Convention), maintaining confidentiality, developing appeal mechanisms, and addressing potential biases or manipulation. | Authors faced challenges in initially prompting ChatGPT to make legal determinations and observed its limitations in legal reasoning, accuracy (including hallucinations and incorrect case citations), and applying deep subject matter expertise during the tests. | Risks include AI generating factually incorrect or misleading determinations ('hallucinations'), lack of transparency in AI decision-making undermining natural justice, awards being unenforceable under current legal frameworks (e.g., New York Convention), potential for AI responses to be manipulated by developers or users through prompt engineering, and decisions being influenced by online falsehoods if AI has unfiltered real-time internet access. |
Final Annotations
This section shows the final annotations for the papers that passed all filters, with LLM-generated labels for each free-text column.
Final Annotations
| filename | source | title | summary | summary_labels | is_english | audience_legal_access | llm_use | paper_type | sentiment | technique | technique_labels | testing | testing_labels | results | results_labels | obstacles | obstacles_labels | solutions | solutions_labels | topics | topics_labels | community | community_labels | legal_field | legal_field_labels | jurisdiction | jurisdiction_labels | training_data | training_data_labels | design_methodologies | design_methodologies_labels | deployment | deployment_labels | claimed_availability | claimed_open_availability | which_claimed_availability | which_claimed_availability_labels | gaps | gaps_labels | challenges | challenges_labels | risks | risks_labels |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| zMxRuhVaiw8J.pdf | Google_Scholar | Bridging the Legal Literacy Gap: A Survey on \nAI-Driven Document Simplification and Generation | This paper surveys AI-driven legal document simplification and generation, proposing an AI-powered legal documentation assistant for India. The system, using NLP and ML, aims to offer bilingual (English/Hindi) document drafting, simplification, and compliance checks to improve legal literacy and access to justice. | Survey of AI in Law, Legal Document Simplification, Legal Document Generation, AI Legal Assistant Development, India Focus, Multilingual System, Access to Justice Enhancement, Legal Literacy Improvement, NLP Application | True | Idealistic | True | 1.0 | Positive | AI-powered legal documentation assistant utilizing NLP (tokenization, POS tagging, NER, dependency parsing, sentiment analysis) and ML techniques (supervised, unsupervised, transfer learning), including transformer models (BERT, GPT, Seq2Seq with attention). It provides bilingual (English/Hindi) document simplification, generation, and rule-based compliance checking. | AI Legal Assistant, Natural Language Processing (NLP), Machine Learning, Transformer Models, Legal Document Generation / Automation, Legal Text Simplification, Rule-based System, Multilingual Application | NaN | Not Applicable | NaN | NaN | Legal literacy gap; high cost and complexity of legal services hindering access to justice, particularly for underprivileged individuals and small businesses in India. | Public Lack of Legal Knowledge/Awareness, High Cost of Legal Services, Complexity of Legal System/Procedures | Development of an AI-powered legal documentation assistant for document simplification and generation, bilingual (English/Hindi) support, automated compliance monitoring, and an option to seek expert legal advice, aimed at democratizing legal information and reducing costs. | AI Tool Development, Document Automation, Language Simplification and Multilingual Access, Access to Legal Information and Advice, Cost Reduction and Efficiency | Legal document simplification, legal document generation, legal literacy, access to legal information. | Legal Text Simplification / Plain Language, Legal Document Creation / Automation, Legal Literacy and Public Legal Education, Access to Legal Information | Individuals, small businesses, and underprivileged populations in India. | Small businesses, Low-income individuals, Population in India | General legal matters, routine legal documents. | General Law | India | India | Proposed collection of diverse Indian legal documents, including original texts paired with simplified versions, and a parallel corpus of English and Hindi legal terms/phrases. Data is unstructured; public/proprietary status and specific sources are not detailed. | Author-Created New Dataset, Legal Domain Data, Indian Legal Data, Unstructured Text Data, Undisclosed Data Source/Availability, Multilingual Data, Paired Original-Simplified Text | Iterative design process including: data collection & preprocessing, training data preparation (paired texts, parallel corpus), transformer-based model architecture design (BERT, GPT), model training (fine-tuning, transfer learning), web-based user interface development, template-based & AI-generated content pipeline for document creation, rule-based compliance checking, and planned testing (unit, integration, user acceptance). | Iterative Design Process, Data Collection, Data Preprocessing, Dataset Creation, Model Architecture Design, Model Fine-tuning, Transfer Learning, User Interface Development, Template-based Design, AI-driven Content Generation, Rule-based System Design, System Testing | Proposed deployment via a web-based interface. No further deployment or diffusion strategies are detailed. | Proposed deployment (not implemented), Web-based access | False | False | NaN | NaN | Accuracy and reliability of AI in handling legal nuances; lack of contextual understanding in AI; limited customization of AI tools for specific legal areas/jurisdictions; lack of transparency/explainability in AI models; perpetuation of biases from historical data; security/privacy concerns with sensitive legal data; limited scope of document types handled by AI; scarcity of large-scale Indian legal datasets for AI training; limited research focus on the Indian legal context. | AI Accuracy and Reliability, AI Legal Reasoning Limitations, AI Scope and Functionality Limitations, Transparency and Explainability, Bias in AI, Security and Privacy of Data, Data Availability and Quality, Research and Evaluation Gaps | Ensuring accuracy and reliability with complex legal language; achieving deep contextual understanding; providing sufficient customization for diverse legal needs; improving transparency and explainability of AI models; mitigating bias from training data; addressing security and privacy of legal information; handling a wide range of document types; overcoming scarcity of domain-specific (Indian legal) datasets; adapting models for multilingual contexts; high computational resource requirements for advanced models. | Accuracy and Reliability of LLM Output, LLM Reasoning Capabilities, Domain-Specific Adaptation and Customization, Transparency and Explainability of AI, Bias in AI Systems and Data, Data Privacy, Security, and Confidentiality, Data Quality, Processing, and Preparation, Scarcity of High-Quality Legal Data, Multilingual and Low-Resource Language Support, High Computational and Resource Demands | Misinterpretations or inaccuracies in AI-generated legal documents leading to serious legal consequences; perpetuation of existing biases present in historical legal data by AI systems; data privacy and confidentiality breaches of sensitive legal information. | Inaccurate or misleading AI output, Bias and discrimination, Data privacy and security breach |
| EeMzvDhc2e8J.pdf | Google_Scholar | Artificial Intelligence in Accounting, Medicine, and Law with Potential Implications for Financial Planning: A Review of Literature | This paper reviews the impact of generative Artificial Intelligence (AI) on the professions of accounting, medicine, and law, drawing parallels and discussing potential implications for financial planning. It highlights AI's capacity to automate tasks and improve efficiency while emphasizing the ongoing necessity of human skills, judgment, and ethical considerations in these fields. | Review of Generative AI Impact, Impact on Legal Profession, Task Automation, Efficiency Improvement, Need for Human Oversight, Ethical Considerations | True | Idealistic | True | 3.0 | Positive | DoNotPay ("World's First Robot Lawyer") | Named Tool / Platform, AI Legal Tool | The paper reports that DoNotPay's CEO claims over 2 million successfully resolved cases through AI. The paper does not conduct its own evaluation of DoNotPay. | Developer Claims Reported, No Evaluation by Author | The paper reports that DoNotPay's CEO claims over 2 million successfully resolved cases. | Developer or Vendor claim, Successful real-world application | The financial prohibitiveness of hiring a lawyer for low-income individuals, with 80% reportedly unable to afford legal representation. | High Cost of Legal Services | AI-powered tools like DoNotPay to bridge the justice gap and expand access to legal counsel for low-economic communities. | AI Tool Development, Access to Legal Information and Advice | Access to legal counsel, resolving common legal disputes (e.g., related to medical bills). | Access to Legal Representation, Dispute Resolution | Individuals from low-economic communities, low-income individuals. | Low-income individuals | Legal conflicts related to medical bills (specifically for DoNotPay). The paper also broadly mentions AI for developing wills, trusts, and other legal documents. | Consumer Law, Wills and Estates | USA | USA | NaN | Not Applicable | NaN | NaN | Available as a subscription-based online service/app (DoNotPay). | Commercial product/service, Web-based access, Mobile app deployment | True | False | DoNotPay is described as having active subscribers and a website (donotpay.com). | Commercial product or service, Publicly accessible online tool or platform | The need for human lawyer involvement for complex legal issues and situations requiring nuanced legal strategy, decision-making, and ethical considerations, which AI tools like DoNotPay may not fully address for underserved communities. | Human Oversight and Professional Adaptation, AI Legal Reasoning Limitations, Access, Equity, and Digital Divide | Potential for AI (like the type DoNotPay might use, e.g., generative AI) to 'hallucinate' or produce fictitious information, lack of nuanced legal reasoning, and the challenge of ensuring authenticity and reliability of AI-generated legal content or advice. | LLM Hallucination and Factual Errors, LLM Reasoning Capabilities, Accuracy and Reliability of LLM Output | Over-reliance on AI for tasks requiring human judgment, leading to serious repercussions such as the submission of fictitious case law generated by AI (as in Mata v. Avianca), resulting in legal penalties and undermining the legal process. | Over-reliance on AI, Inaccurate or misleading AI output, Undermining legal process or principles |
| uBHZkwvRvS0J.pdf | Google_Scholar | LawLLM: Intelligent Legal System with Legal Reasoning and Verifiable Retrieval | This paper introduces LawLLM, an intelligent legal system powered by LLMs, designed to offer versatile legal services. LawLLM is enhanced with legal reasoning through syllogism-based fine-tuning and verifiable retrieval capabilities to ensure accurate and reliable outputs based on external knowledge. | LLM Application Development, Legal Services Provision, Legal Reasoning Enhancement, Fine-tuning for Legal Domain, Retrieval Augmented Generation, Accuracy Improvement, Reliability Improvement | True | Idealistic | True | 1.0 | Positive | LawLLM: An LLM (Baichuan-13B-Base) fine-tuned using a custom supervised dataset (Law-SFT) incorporating legal syllogism prompting for enhanced legal reasoning and a triplet instruction format for verifiable knowledge retrieval. | Large Language Model, Model Development, Fine-tuning, Dataset Creation / Curation, Prompt Engineering, Legal Analysis / Reasoning Tool, Information Retrieval / Search | Evaluated using the custom Law-Eval benchmark (objective and subjective assessments, including Chinese legal examinations and GPT-3.5 as a subjective referee) and the LawBench benchmark (20 legal tasks). | Benchmark Dataset Evaluation, Custom Dataset Evaluation, Quantitative Metrics, LLM as Judge | On the Law-Eval objective evaluation, LawLLM (13B) achieved an average total score of 37.11, outperforming other LLMs including GPT-3.5-turbo (34.10). On LawBench, LawLLM outperformed GPT-3.5-turbo on average performance (zero-shot). | Outperforms others | Limited accessibility to reliable and versatile intelligent legal systems for the general public due to the task-specific focus of prior work. Unreliability of LLMs stemming from issues like hallucinations, difficulty with long-tail knowledge, and inconsistent or unverified use of retrieved information. | Limited Access to A2J Technology, AI Unreliability/Inaccuracy, AI Limitations in Legal Reasoning/Nuance | Development of multi-task LLMs like LawLLM with features such as: 1) Versatile services through multi-task capabilities, 2) Enhanced legal reasoning fine-tuned with legal syllogism prompting, and 3) Verifiable retrieval to distinguish, incorporate, and validate external knowledge, thereby improving reliability and accessibility. | AI Tool Development, Enhanced AI Capabilities, Access to Legal Information and Advice | Legal consultation, legal question answering, improving understanding and accessibility of legal information and services for the general public. | Access to Legal Advice, Access to Legal Information, Legal Literacy and Public Legal Education | General population, students (as part of a broader set of users that also includes legal professionals). | General public, Students | General Chinese Law (covering areas tested in National Judicial Examination, Patent Agent Examination, CPA examination, etc., e.g., civil law, bidding law). | General Law, Civil Law, Commercial Law | China | China | Law-SFT dataset, a high-quality supervised fine-tuning dataset, constructed from: 1) Public NLP legal task datasets (e.g., LEVEN, JEC-QA, CAIL2018), 2) Crawled legal raw text (e.g., judicial advisory websites, Chinese laws and regulations, typical cases, judicial verdicts), 3) Open-source instruction datasets (e.g., Lawyer-LLaMa, LawGPT-zh). Data is primarily unstructured text processed into instruction pairs and triplets. | Author-Created New Dataset, Fine-tuning Dataset, Instruction-Tuning Formatted Data, Legal Domain Data, Chinese Legal Data, Data From Existing Public NLP/Legal Datasets/Benchmarks, Web Scraped Data, Legislation / Statutes / Regulations, Case Law / Judgments, Unstructured Text Data, Structured Data | Supervised fine-tuning (SFT) of a pre-trained LLM (Baichuan-13B-Base). Creation of the Law-SFT dataset involved: Pair Instruction Generation (rule-based cleaning, LLM-assisted Behavior Shaping with legal syllogism prompting, Thinking Development with Law-specific Chain of Thought) and Triplet Instruction Generation (for verifiable retrieval, including addition of distractors). A two-step fine-tuning process: legal reasoning fine-tuning and retrieval augmentation fine-tuning. | Model Fine-tuning, Dataset Creation, Rule-based Data Processing, LLM-aided Data Generation, Prompt Engineering | NaN | Not applicable | True | True | Detailed resources (model, code, and/or data) are available on GitHub: https://github.com/FudanDISC/DISC-LawLLM. | Model available, Code available, Dataset available | NaN | NaN | Developing advanced legal reasoning capabilities in LLMs that align with established legal frameworks. Ensuring robust, faithful, and verifiable utilization of external legal knowledge, including the ability to distinguish relevant information from distractors and mitigate model hallucinations. | LLM Reasoning Capabilities, Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors | Model hallucinations and generation of unreliable outputs in legal scenarios, particularly if external knowledge is not correctly distinguished, incorporated, and verified. | Inaccurate or misleading AI output, Technical limitations of AI |
| -OR_MJVKvsoJ.pdf | Google_Scholar | Attributed Question Answering for Preconditions in the Dutch Law | This paper proposes and evaluates a Retrieval Augmented Generation (RAG) pipeline designed to answer questions about legal preconditions in Dutch law, providing answers attributed with specific law article references. A new Dutch legal QA dataset with attributions was created for evaluation, showing promising results for generating verifiable legal information for laypeople. | Retrieval Augmented Generation, Legal Question Answering, Dutch Law Focus, Verifiable Legal Information, Legal Information Access for Laypeople, Dataset Creation, System Evaluation | True | Idealistic | True | 1.0 | Positive | Retrieval Augmented Generation (RAG) pipeline for attributed legal question answering focusing on preconditions. | Retrieval Augmented Generation (RAG), Legal Question Answering | Evaluation used a custom dataset of 102 Dutch legal QA pairs with ground-truth attributions. Metrics included adapted versions of ALCE and G-EVAL, measuring fluency (Coherence, Fluency), correctness (ROUGE-L, METEOR, Consistency, Relevance), and citation quality (Precision, Recall, HitRate@k). Various retrievers (BM25, SBERT, E5, DRAGON, SPLADE) and LLM generators (GPT-3.5, GPT-4O, GEITje, Llama-3-dutch, Fietje) were tested. | Custom Dataset Evaluation, Quantitative Metrics, Comparative Analysis | The best results were achieved using the E5-multilingual-LARGE retriever and the GPT-4O generator, attaining high scores across metrics, including an 83.0% Hitrate@3 for citation quality. GPT models generally outperformed the tested open-source models. | High performance, Outperforms others, Technique improves outcome | Costs of legal assistance, lack of public awareness about legal rights and options, and the complexity/specificity of national legal frameworks hindering the development of universal digital legal aid. | High Cost of Legal Services, Public Lack of Legal Knowledge/Awareness, Complexity of Legal System/Procedures | Developing automated, language-specific legal Question Answering (QA) systems, particularly Attributed QA using RAG, to provide affordable, accessible, and verifiable legal information tied to primary sources. | AI Tool Development, Enhanced AI Capabilities, Access to Legal Information and Advice, Language Simplification and Multilingual Access | Accessing legal information, understanding legal preconditions and rights. | Access to Legal Information, Legal Literacy and Public Legal Education | Laypeople encountering civil justice problems, particularly those lacking legal knowledge or facing cost barriers. | Laypeople, Individuals lacking legal knowledge, Individuals unable to afford legal services | Civil Law (based on examples and corpus filtering) | Civil Law | The Netherlands | The Netherlands | The RAG system retrieves from a knowledge corpus created from publicly available Dutch law texts (XML from wetten.overheid.nl, parsed into chunks). The LLM generators used were pre-trained models, some with specific Dutch fine-tuning. The evaluation dataset consists of 102 manually created QA pairs with expert verification. | RAG System Knowledge Corpus, Publicly Available Data, Legal Domain Data, Dutch Legal Data, Legislation / Statutes / Regulations, Unstructured Text Data, Evaluation Dataset, Author-Created New Dataset, Structured Data, Expert-Annotated / Human-Curated / Human-Generated Data, Pre-trained LLM's General Training Corpus | Standard RAG architecture implementation, corpus creation from legal texts, manual creation and expert validation of a QA evaluation dataset, experimentation with various off-the-shelf retrieval and generation components, adaptation of existing evaluation frameworks (ALCE, G-EVAL). | Retrieval Augmented Generation (RAG), Dataset Creation, Manual Annotation, Expert Validation, Experimental Comparison of Components, Adaptation of Existing Evaluation Frameworks | NaN | Not applicable | False | True | Code and dataset are publicly released on GitLab. | Code available, Dataset available | Need for validation of layperson understandability and inter-expert agreement, expansion of dataset (e.g., jurisdictions), testing more advanced retrievers (e.g., multilingual hybrid), potential retrieval bias towards specific linguistic patterns (conditional phrases). | Research and Evaluation Gaps, User Interface and Usability Gaps, Data Availability and Quality, AI Scope and Functionality Limitations, Bias in AI | Need for language-specific solutions, ensuring output format consistency from LLMs (especially open-source), creating high-quality expert-verified legal datasets, potential loss of meaning when chunking long legal articles. | Multilingual and Low-Resource Language Support, Output Variability and Consistency, Scarcity of High-Quality Legal Data, Cost and Complexity of Data Annotation, Data Quality, Processing, and Preparation, LLM Context Window and Long Input Management | Implicit risks include generating incorrect or hallucinatory legal information (addressed by attribution) and potential retrieval bias leading to incomplete answers. | Inaccurate or misleading AI output, Bias and discrimination |
| XR0M6OXV57cJ.pdf | Google_Scholar | Weaving Pathways for Justice with GPT LLM-driven automated drafting of interactive legal applications | This paper investigates using LLMs like GPT-3 and GPT-4 turbo to automate the creation of guided interviews that complete court forms, aiming to assist self-represented litigants. It compares generative AI, constrained template-driven, and hybrid approaches, finding a hybrid model with human review, leveraging the Docassemble platform and Assembly Line Weaver tool, to be the most promising. | LLM Application, Court Form Automation, Guided Interview Generation, Self-Represented Litigant Assistance, Hybrid AI Approach, Need for Human Oversight, Comparative AI Approaches | True | Idealistic | True | 1.0 | Positive | A hybrid approach using LLMs (GPT-3, GPT-4 turbo) for auto-labeling fields in Word documents and generating draft questions/interview flows from PDF forms, integrated with the Docassemble platform and Assembly Line Weaver tool, with human review points. | Hybrid AI System, Large Language Model, Information Extraction, Legal Document Generation / Automation, Integration with Existing Platforms, Human-in-the-Loop System | Qualitative evaluation of Word document auto-labeling (visual inspection of output); quantitative evaluation of PDF-to-interactive app generation on 12 name change forms (measuring field recognition rates, e.g., 62-69% average, 93% best, 27% worst; 28% checkbox pairing success). | Qualitative Analysis, Custom Dataset Evaluation, Quantitative Metrics | For PDF app generation from 12 forms, 62-69% of fields were automatically processed (93% best, 27% worst); checkbox field to text pairing was successful 28% of the time. Word document field labeling showed promising qualitative results with a revised prompting strategy. | Mixed performance, Limitation: Operational or Technical, Technique improves outcome | High cost and time (hundreds of hours per form) for manual creation of interactive legal applications (guided interviews) for court forms, hindering assistance for self-represented litigants and large-scale automation efforts. | Resource Constraints for A2J Tech Development/Deployment, Challenges for Self-Represented Litigants | A hybrid model using LLMs for automated drafting of guided interviews with human review, integrated with tools like Assembly Line Weaver and Docassemble, to significantly reduce the cost and time of form automation. | AI Tool Development, Document Automation, Human Oversight and Collaboration, Cost Reduction and Efficiency | Automating court form completion via guided interviews for self-represented litigants. | Legal Document Creation / Automation, Support for Self-Represented Litigants | Self-represented litigants | Self-represented litigants | General civil litigation forms (complaints, answers, deeds, wills, demand letters); specifically tested on name change forms (family law). | Civil Litigation, Property Law, Wills and Estates, Family Law | USA (experiments focused on Massachusetts and name change forms from 12 US jurisdictions). | USA | Pre-trained LLMs (GPT-3, GPT-4 turbo) prompted with text extracted from Word and PDF court forms. No fine-tuning described. | Pre-trained LLM's General Training Corpus, Input Data for Task (Non-Training), Legal Domain Data, Other Legal Documents, Unstructured Text Data | Prototyping, iterative prompt engineering, experimental comparison of three approaches (generative AI, constrained template-driven, hybrid), integration with existing open-source tools (Docassemble, Assembly Line Weaver). | Prototyping, Iterative Design Process, Prompt Engineering, Comparative Analysis of Approaches, Integration with Open-Source Tools | NaN | Not applicable | True | True | Python notebooks on GitHub and Google Colab demonstrating experimental auto-labeling of Word documents and LLM-driven generation of interactive apps from PDF forms. | Code available | Improving checkbox field identification in PDFs (28% success); handling all field types; further integration of LLM capabilities with existing tools (Assembly Line Weaver); reducing need for extensive human review for complex forms; addressing form elements requiring external legal research. | AI Scope and Functionality Limitations, Integration and Interoperability Challenges, Human Oversight and Professional Adaptation, AI Legal Reasoning Limitations | For Word documents: Balancing automated field identification with document format preservation, managing LLM context window limitations, and enforcing specific variable naming conventions. For PDF documents: Accurately identifying and contextualizing all fields, especially small ones like checkboxes, due to PDF's stream-based format and reliance on OCR. For both: Designing effective input validation without being resource-intensive or error-prone; preventing error propagation from initial inaccuracies in field identification or question generation. | Data Quality, Processing, and Preparation, LLM Context Window and Long Input Management, Output Variability and Consistency, Accuracy and Reliability of LLM Output | User annoyance or offense from overly rigid or conversational LLM-based validation; incorrect data processing due to LLM misclassification of field types (e.g., ZIP codes, phone numbers); potential for errors in legal documents if AI-generated content, especially for complex forms, is not thoroughly reviewed by humans. | Poor user experience, Inaccurate or misleading AI output, Over-reliance on AI |
| DkyUIApE_CAJ.pdf | Google_Scholar | How ChatGPT and generative AI systems will revolutionize legal services and \nthe legal profession. | This paper presents predictions elicited from ChatGPT regarding the transformative impact of generative AI on legal services and the profession. It details ChatGPT's own views on areas of application, efficiency gains, benefits for access to justice, timelines, and the future for legal practitioners and students. | Generative AI Impact Prediction, Impact on Legal Services, Impact on Legal Profession, Access to Justice Enhancement, ChatGPT Perspective Elicitation | True | Idealistic | True | 2.0 | Positive | ChatGPT, a generative AI language model by OpenAI. | Large Language Model, Generative AI | Eliciting detailed responses from a February 2023 version of ChatGPT (Free Research Preview) to a series of specific questions about its impact on the legal field. | Qualitative Analysis | ChatGPT predicts a seismic shock to the legal sector within 5-10 years, with reduced human-centric work, increased client self-help, and fundamental changes in pricing and manpower. | Descriptive or Conceptual finding | High cost of legal services, limited availability/accessibility of legal professionals, complexity of legal language, and slow legal processes. | High Cost of Legal Services, Limited Availability/Access to Legal Professionals/Expertise, Complexity of Legal Language/Documents, Judicial/Legal System Inefficiencies | AI-powered tools offering 24/7 access to cost-effective, simplified legal information, document preparation, research, and basic advice for ordinary people. | AI Tool Development, Access to Legal Information and Advice, Document Automation, Legal Research and Analysis Tools, Cost Reduction and Efficiency | Access to legal advice, legal research, document preparation (contracts, wills), contract review, court filings, mediation and dispute resolution, and legal education for the public. | Access to Legal Advice, LegalResearch Support, Legal Document Creation / Automation, Legal Document Analysis / Review, Support for Self-Represented Litigants, Dispute Resolution, Legal Literacy and Public Legal Education | Ordinary people / General public | General public | Multiple fields, including contract law, intellectual property, e-discovery, compliance, litigation support, and alternative dispute resolution. | Multiple Fields, Contract Law, Intellectual Property Law, E-Discovery, Compliance Law, Litigation Support, Alternative Dispute Resolution | International | International | Proprietary, large-scale, general textual data from diverse sources up to 2021, used by OpenAI to train ChatGPT. | Pre-trained LLM's General Training Corpus, Proprietary Data, General Web Data / Broad Internet Text | NaN | NaN | Web-based access provided by OpenAI, initially as a free research preview. | Evaluation of existing third-party tool, Web-based access, Research preview/Beta access, Freely accessible tool/service | True | False | ChatGPT was accessible via a free research preview on OpenAI's website. | Publicly accessible online tool or platform | AI's limitations in highly complex/novel legal reasoning; lack of established ethical and regulatory frameworks for legal AI; need for upskilling legal professionals. | AI Legal Reasoning Limitations, Ethical Framework Deficiencies, Regulatory and Governance Gaps, Human Oversight and Professional Adaptation | Slow/varied adoption by legal professionals, evolving regulatory landscape, need for user training and education, integration with existing legal technology infrastructure, and addressing ethical considerations. | User Adoption, Trust, and Acceptance, Regulatory Uncertainty and Compliance, User Training, AI Literacy, and Skill Gaps, Integration with Existing Systems and Workflows, Ethical Considerations | Significant job displacement for legal professionals (lawyers, support staff, potentially academics and judges), downward pressure on legal fees, and unaddressed ethical implications of AI in legal practice. | Job displacement, Negative economic impact, Ethical concerns |
| 3628602.pdf | Google_Scholar | Measuring and Mitigating Gender Bias in Legal Contextualized Language Models | This paper proposes methods to measure and mitigate gender bias in legal contextualized language models like LegalBERT, introducing a new crime-based evaluation corpus (BEC-Cri) and a fine-tuning debiasing technique (LCD) using ECtHR data. Evaluations on the LexGLUE benchmark show the proposed LCD method effectively reduces bias with minimal impact on downstream task performance. | Bias Detection in LLMs, Bias Mitigation in LLMs, Gender Bias Focus, Dataset Creation, Fine-tuning for Debiasing, Methodology Proposal, System Evaluation, Legal Language Model | True | Idealistic | True | 1.0 | Positive | Proposes two techniques: 1) BEC-Cri: A template-based gender bias measurement method using MLM probabilities on a new corpus derived from FBI crime data. 2) Legal-Context-Debias (LCD): A fine-tuning debiasing method using a gender-balanced European Court of Human Rights (ECtHR) corpus for a gender classification task. | Bias Detection / Mitigation, Dataset Creation / Curation, Fine-tuning, Machine Learning, Template-based Method | Bias was measured by comparing association scores (derived from MLM probabilities) for male/female targets using the proposed BEC-Cri and existing BEC-Pro datasets, before and after debiasing. Downstream performance was evaluated using µ-F1/m-F1 scores on six classification tasks from the LexGLUE benchmark. | Custom Dataset Evaluation, Benchmark Dataset Evaluation, Quantitative Metrics | The proposed LCD debiasing method significantly reduced measured gender bias scores towards zero on both BEC-Cri and BEC-Pro, outperforming baseline methods (GPD, GAP). LexGLUE benchmark performance showed only slight decreases after LCD debiasing, preserving overall semantic utility. A proposed bias-penalized performance metric showed LCD incurred the lowest penalty. | Successful bias mitigation, Technique improves outcome, Outperforms others | Inherent gender bias exists in legal language models, stemming from training data, which can lead to unfair outcomes in legal AI applications. Mitigating this bias without significantly degrading model performance on useful tasks is challenging. | Bias in AI/Data, Technical Challenges in AI Development | Develop and apply domain-specific methods for bias measurement (BEC-Cri) and mitigation (LCD fine-tuning with balanced legal data). Evaluate models using a bias penalty framework alongside standard performance metrics. | Bias Detection and Mitigation, Data Curation and Management, Benchmarking and Evaluation Frameworks, Enhanced AI Capabilities | Fairness in AI, Gender bias mitigation, Legal NLP | Ethical AI in Law and AI Governance, Improving Foundational AI Capabilities for Legal Applications | NaN | NaN | General legal NLP / Multiple (Human Rights Law, US Constitutional Law, EU Law, Contract Law, Criminal Law references) | Multiple Fields, Human Rights Law, Constitutional Law, EU Law, Contract Law, Criminal Law | Multiple (European Court of Human Rights, US, EU) | ECHR, USA, EU | Debiasing (LCD) used a modified, gender-balanced subset (3,032 cases) of the publicly available European Court of Human Rights (ECtHR) corpus [30]. Bias measurement (BEC-Cri) used author-created templates populated with crime words from the public FBI database [79]. The base model (LegalBERT-Small) was pre-trained on various legal corpora. | Fine-tuning Dataset, Author-Modified Existing Dataset, Publicly Available Data, Legal Domain Data, European Legal Data, Case Law / Judgments, Evaluation Dataset, Author-Created New Dataset, Synthetic Data, Data Bias Concerns Noted, Pre-trained LLM's General Training Corpus | Template-based bias measurement using MLM probabilities, Supervised fine-tuning for debiasing using a curated dataset and classification task, Comparative analysis against baseline methods, Evaluation on standard NLP benchmark (LexGLUE), Proposal of a bias-penalized evaluation framework. | Bias Measurement, Model Fine-tuning, Debiasing Technique, Dataset Creation, Comparative Analysis of Approaches, Benchmarking, Development of Evaluation Metrics/Frameworks | NaN | Not applicable | True | True | Code and data stated to be available on GitHub (https://github.com/koc-lab/ContextLegalBias). | Code available, Dataset available | Focus limited to gender bias (other biases like race remain unaddressed for these models). Potential for catastrophic forgetting during fine-tuning requires careful monitoring. Need for potentially more sophisticated debiasing tasks or hyperparameter tuning. | Bias in AI, AI Accuracy and Reliability, Research and Evaluation Gaps | Balancing bias mitigation with preservation of model performance on downstream legal tasks. Creating effective domain-specific datasets and methods for bias analysis and reduction in law. Computational costs associated with transformer models. | Bias in AI Systems and Data, Accuracy and Reliability of LLM Output, Scarcity of High-Quality Legal Data, High Computational and Resource Demands | NLP models perpetuating or amplifying gender bias in legal applications, leading to unfair outcomes. Debiasing techniques potentially harming the model's general language understanding capabilities (catastrophic forgetting). | Bias and discrimination, Technical limitations of AI |
| P_QMzDF2YcAJ.pdf | Google_Scholar | Gener ative AI and Legal Aid: Results fr om a Field Study and 100 Use Cases t o Bridge the Access t o Justice Gap | This paper reports on a field study where legal aid professionals used generative AI tools (ChatGPT-4, Gavel, CoCounsel), finding increased productivity and intent for continued use. It also releases 100 use cases and offers recommendations to bridge the access to justice gap, emphasizing equitable AI adoption and lawyer-AI collaboration. | Field Study of Generative AI Use, Legal Professional Assistance, Productivity Improvement, Use Case Identification, Access to Justice Enhancement, Recommendations for AI Adoption, Lawyer-AI Collaboration | True | Idealistic | True | 2.0 | Positive | A field study providing 91 legal aid professionals with free access to paid generative AI tools (ChatGPT-4, Gavel, CoCounsel) for up to two months. A randomized controlled trial component tested 'concierge' support (peer use cases, office hours, assistance) for a subset of participants. A companion database of 100 use cases was compiled from participant submissions. | User Study / Field Study, AI System Evaluation, Generative AI, Dataset Creation / Curation, Randomized Controlled Trial | Baseline and exit surveys administered to pilot participants (N=91, with 66 completing exit survey). Outcomes measured included self-reported productivity, satisfaction with AI, quality of output, frequency of use, changes in attitudes, and intentions to continue using AI tools. Comparison between control group and 'concierge' support group on these metrics. | User Study or Survey, Quantitative Metrics, Comparative Analysis | 90% of participants reported increased productivity (25% medium/high increase); 75% intended to continue using generative AI. 'Concierge' services significantly improved outcomes (productivity, satisfaction, quality of output, frequency of use, attitudes, future paid use). Despite women being less likely to use AI tools pre-pilot, post-pilot outcomes were statistically indistinguishable by gender for most metrics. | Benefit identified, High performance, Successful bias mitigation | The access to justice gap (92% of low-income Americans' civil legal needs unmet) due to knowledge and service gaps. Financial constraints for legal aid organizations to adopt AI. Regulatory hurdles like Unauthorized Practice of Law (UPL) rules stifling innovation. Risk of AI exacerbating inequities. | Scale of Unmet Legal Need, Public Lack of Legal Knowledge/Awareness, Limited Availability/Access to Legal Professionals/Expertise, Resource Constraints for Legal Aid Organizations, Regulatory Hurdles, Risk of AI Exacerbating Inequality | Augment legal aid lawyers with AI. Provide funding and supportive services (e.g., 'concierge' support, help desks) for AI adoption. Foster 'Tech + Legal Aid Lawyer' collaborations. Explore regulatory sandboxes and voluntary certification for legal aid AI tools. Develop lawyer-directed and consumer-facing AI solutions. | Human Oversight and Collaboration, Policy and Regulatory Reform, Regulation, Ethics, and Governance, AI Tool Development | Increasing productivity of legal aid professionals, document summarization/analysis, legal research (preliminary/confirmatory), legal and non-legal writing/drafting, translation (plain language/other languages), client intake automation, grant writing, case management support. | Improving Efficiency in Legal System / Profession, Legal Document Analysis / Review, LegalResearch Support, Legal Document Creation / Automation, Language Access and Digital Divide, Legal Aid and Pro Bono Services | Low-income Americans with unmet legal needs, clients of legal aid organizations. | Low-income individuals, Population in USA, Individuals with unmet legal needs, Clients of legal aid organizations | Eviction defense/housing, expungement (criminal records), immigration, family law, employment/workers' rights, civil rights, consumer/economic justice, disability rights, domestic violence, elder law, health, income maintenance, veterans' rights. | Housing Law, Criminal Law, Immigration Law, Family Law, Employment Law, Civil Rights Law, Consumer Law, Disability Law, Domestic Violence Law, Elder Law, Health Law, Social Security Law, Veterans Law | United States | USA | The study utilized existing pre-trained models: ChatGPT-4 (trained by OpenAI on diverse large-scale text/code) and CoCounsel (GPT-4 augmented with Casetext’s proprietary legal databases). Gavel.io uses rules-based AI and automation technologies. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Proprietary Data, Legal Domain Data | For the field study: Randomized Controlled Trial (RCT) for the 'concierge services' component. Survey methodology (baseline and exit surveys) for quantitative and qualitative data collection. Use case compilation and analysis. | Field Study, Randomized Controlled Trial (RCT), Survey Methodology, Quantitative Data Collection, Qualitative Data Collection, Use Case Analysis | The paper releases a companion database of 100 use cases via a public URL (https://bit.ly/AIA2J). The AI tools studied (ChatGPT-4, Gavel, CoCounsel) are commercially available products. | Public dataset/benchmark release, Evaluation of existing third-party tool, Commercial product/service | True | False | The AI tools studied (ChatGPT-4, Gavel, CoCounsel) are commercially available through subscriptions. The companion database of 100 use cases is openly accessible at https://bit.ly/AIA2J. | Commercial product or service, Dataset available | Gender gap in organic AI tool uptake by legal professionals. Insufficient funding for AI in legal aid. Need for ongoing training, support structures, and quality control/certification for legal aid bots. Regulatory frameworks (UPL) hindering direct-to-consumer AI solutions. Technical limitations of AI (hallucinations, bias). | Human Oversight and Professional Adaptation, Access, Equity, and Digital Divide, Regulatory and Governance Gaps, AI Accuracy and Reliability, Bias in AI | Managing AI risks (data privacy, confidentiality, hallucinations). Overcoming learning curves for AI tools. Ensuring equitable access and adoption, particularly addressing the gender gap. Securing funding for paid AI tools in resource-constrained legal aid settings. | Data Privacy, Security, and Confidentiality, LLM Hallucination and Factual Errors, User Training, AI Literacy, and Skill Gaps, User Adoption, Trust, and Acceptance, Bias in AI Systems and Data, Financial Cost and Resource Constraints | AI hallucinations (e.g., fake case citations), data privacy and confidentiality breaches, inaccurate results, algorithmic bias (racial, gender, anti-consumer), consumer harm from unauthorized practice of law by AI, creation of a two-tiered justice system, dehumanizing the law. | Inaccurate or misleading AI output, Data privacy and security breach, Bias and discrimination, Consumer harm, Unauthorized practice of law, Exacerbation of inequality or two-tiered system, Dehumanization of legal process |
| xxzftjRKRFAJ.pdf | Google_Scholar | LegalBench : Prototyping a Collaborative Benchmark for Legal Reasoning | This paper introduces LegalBench, a collaborative benchmark designed to evaluate the legal reasoning capabilities of foundation models (FMs) using the IRAC framework. It presents an initial set of 44 tasks with preliminary FM performance results and calls for community contributions to expand the benchmark. | Benchmark Creation, Evaluation of Legal Reasoning, Foundation Model Evaluation, IRAC Framework Application, Call for Community Contribution | True | Idealistic | True | 1.0 | Positive | LegalBench, a collaborative benchmark for legal reasoning structured using the Issue, Rule, Application, Conclusion (IRAC) framework, comprising a seed set of 44 tasks. | Benchmarking / Evaluation, Dataset Creation / Curation, Legal Reasoning Framework | Five different foundation models (GPT-3 davinci, GPT-3 curie, J1-Jumbo, J1-Grande, J1-Large) were evaluated on the 44 LegalBench tasks using zero-shot, few-shot, and chain-of-thought prompting. Performance was measured using F1 (macro) for classification/conclusion tasks and accuracy for others. | Benchmark Dataset Evaluation, Quantitative Metrics, Comparative Analysis | GPT-3 (davinci) with chain-of-thought prompting achieved the highest reported score, with an F1 (macro) of 0.92 on the PROA (Conclusion) task. Generally, larger models performed better, classification tasks were easier than application tasks, and chain-of-thought prompting improved performance. | High performance, Technique improves outcome | The paper identifies the United States' "access-to-justice crisis" as a key challenge. It also implies that a lack of understanding of Foundation Models' capabilities and limitations in legal reasoning, alongside the high-risk nature and ethical concerns of AI tools in law, are hurdles to leveraging AI for access to justice. | Scale of Unmet Legal Need, Lack of Understanding of AI Capabilities/Limitations, Ethical Concerns with AI in Law, High-Risk Nature of Legal AI Applications | The paper proposes LegalBench, an open and collaborative benchmark, to systematically assess the legal reasoning capabilities of Foundation Models. This evaluation aims to guide the safe, ethical, and effective development and use of AI tools, which could in turn improve the accessibility of legal services. | Benchmarking and Evaluation Frameworks, Open Source Initiatives and Collaboration, Regulation, Ethics, and Governance | Improving accessibility of legal services; Evaluating AI capabilities in legal reasoning; Fostering safe and ethical use of AI in law. | Democratizing Law / Closing Justice Gap / Rule of Law, Improving Foundational AI Capabilities for Legal Applications, Ethical AI in Law and AI Governance | Low-income Americans (mentioned via reference to "The Justice Gap" report). | Low-income individuals, Population in USA | Contract law (CUAD), Civil Procedure (Diversity Jurisdiction, Personal Jurisdiction), Evidence (Hearsay), Trademark Law (Abercrombie), Statutory Interpretation (PROA). | Contract Law, Civil Procedure, Evidence Law, Trademark Law, Statutory Interpretation | United States | USA | The benchmark tasks use a variety of data: CUAD tasks use annotated contracts from the EDGAR database (publicly available); other tasks (Rule QA, Abercrombie, Hearsay, Personal Jurisdiction, PROA) use manually constructed or annotated datasets of legal questions, scenarios, product-mark-pairs, or statutes, typically with small numbers of samples (50-100). | Evaluation Dataset, Data From Existing Public NLP/Legal Datasets/Benchmarks, Legal Domain Data, Legal Contracts, Publicly Available Data, Structured Data, Expert-Annotated / Human-Curated / Human-Generated Data, Legislation / Statutes / Regulations | The IRAC (Issue, Rule, Application, Conclusion) framework is used to categorize and structure legal reasoning tasks. A data-centric approach is adopted, with lightweight and accessible task construction to encourage collaboration. | Framework-guided Design, Data-centric Approach, Task Design | LegalBench is presented as an ongoing, collaborative project hosted on GitHub. The authors call for community contributions of new tasks, and plan to run new FMs on the benchmark and release results. | Public dataset/benchmark release, Collaborative development platform | True | True | The LegalBench project, including initial tasks, is available on GitHub: https://github.com/HazyResearch/legalbench. | Dataset available, Code available | A clear understanding of which types of legal reasoning Foundation Models can perform and what FM programming strategies are effective for legal tasks. Current FMs perform significantly worse on legal application tasks compared to classification or conclusion tasks. The need for law-specific prompting strategies and frameworks for safe and ethical usage. | Research and Evaluation Gaps, AI Legal Reasoning Limitations, Human Oversight and Professional Adaptation, Ethical Framework Deficiencies | Distinguishing between different types of IRAC tasks during benchmark design; fostering sustained interdisciplinary collaboration between computer science and legal communities; designing tasks that meaningfully measure legal reasoning while being accessible for contribution. | Evaluation Challenges and Metrics, Interdisciplinary Collaboration Challenges | The paper mentions the "high risk nature" of computational legal tools and the need for evaluation to ensure "safe and ethical usage." It implicitly acknowledges the risk of misapplication if AI capabilities are not well understood, or if tools are used to replace human legal professionals inappropriately. | Ethical concerns, Risk of misapplication or misuse, Over-reliance on AI |
| 5VFMMdneX9MJ.pdf | Google_Scholar | Employing Label Models on ChatGPT Answers Improves Legal Text Entailment Performance | This paper proposes a method to enhance legal text entailment accuracy by using label models to aggregate multiple, potentially inconsistent answers generated by ChatGPT. Experimental results on the COLIEE 2022 dataset show this approach significantly outperforms existing state-of-the-art methods. | Methodology Proposal, Legal Text Entailment, Accuracy Improvement, Aggregation of LLM Outputs, System Evaluation, ChatGPT Application | True | Idealistic | True | 1.0 | Positive | Employing label models (specifically a 'Generative model' label model) to integrate multiple provisional answers generated by ChatGPT (using a 'Reason-then-Answer' prompt and varying temperature settings) for legal text entailment decision. | Large Language Model, Prompt Engineering, Machine Learning, Answer Aggregation / Fusion, Legal Text Entailment | The approach was evaluated on the COLIEE 2022 legal text entailment dataset. The authors tested different prompt types for ChatGPT, varied temperature settings to generate multiple answers, and applied several label models (Majority voting, FlyingSquid, Dawid-Skene, Hyper label model, FABLE, Generative model) to consolidate these answers, measuring accuracy. | Benchmark Dataset Evaluation, Quantitative Metrics, Comparative Analysis | The 'Generative model' label model, when applied to 10 provisional answers from ChatGPT (using the Reason-then-Answer prompt with temperature=0.5), achieved an accuracy of 76.15% on the COLIEE 2022 dataset. | Moderate performance | The main obstacles identified are the reasoning errors of LLMs like ChatGPT, which hinder their reliable application. These include: 1) hallucinating facts, 2) incorrect deduction from correct premises, 3) difficulty with nuanced legal concepts like 'mutatis mutandis', and 4) issues arising from incomplete contextual information (e.g., missing relevant legal articles). | AI Unreliability/Inaccuracy, AI Limitations in Legal Reasoning/Nuance | The paper proposes using label models to aggregate and refine multiple LLM-generated answers, thereby improving the robustness and accuracy of legal text entailment. The error analysis also implicitly suggests that providing more comprehensive and accurate contextual information to the LLM could mitigate some errors. | Enhanced AI Capabilities, Data Curation and Management | Improving the accuracy and reliability of legal text entailment, a foundational capability for developing advanced legal AI applications (e.g., legal chatbots, question-answering systems) aimed at enhancing access to legal information and services. | Improving Foundational AI Capabilities for Legal Applications, Access to Legal Information | People who cannot afford expensive legal advice. | Individuals unable to afford legal services | Civil Law (based on COLIEE competition context and examples like contract law provisions) | Civil Law, Contract Law | Japan (based on the COLIEE competition, which typically uses Japanese legal texts, and mentions of 'Japanese legal data' in related work). | Japan | The label models operate on provisional answers generated by ChatGPT for queries from the COLIEE 2022 dataset. These provisional answers (text including 'Yes'/'No' and reasoning) serve as the noisy labeled data for the label models. ChatGPT itself is pre-trained on a massive, general corpus. | Fine-tuning Dataset, Author-Created New Dataset, Synthetic Data, Legal Domain Data, Data From Existing Public NLP/Legal Datasets/Benchmarks, Structured Data, Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text | Experimental evaluation comparing different ChatGPT prompting strategies ('Answer-only', 'Answer-then-Explain', 'Reason-then-Answer'), varying ChatGPT's temperature parameter to generate diverse outputs, and applying various established label models to consolidate these outputs. Includes a qualitative error analysis of ChatGPT's incorrect responses. | Experimental Comparison of Prompting Strategies, Parameter Experimentation, Output Consolidation Techniques, Qualitative Error Analysis | NaN | Not applicable | False | False | NaN | NaN | Remaining technical gaps include LLMs' deficiencies in complex legal reasoning, such as handling 'mutatis mutandis' clauses, consistent logical deduction, and avoiding factual inaccuracies (hallucinations). The lack of sufficient relevant articles provided as context for LLMs also poses a challenge to accurate entailment. | AI Legal Reasoning Limitations, AI Accuracy and Reliability, Data Availability and Quality | The primary challenges included managing the variability and potential inconsistency of ChatGPT's outputs (especially with non-zero temperature settings), identifying the most effective prompting technique for legal reasoning, and developing a robust method to integrate multiple, potentially noisy, LLM-generated answers into a single, more accurate consolidated answer. | Output Variability and Consistency, Prompt Engineering and Optimization, Accuracy and Reliability of LLM Output, Data Quality, Processing, and Preparation | The paper identifies concrete risks associated with ChatGPT's errors in legal text entailment: 1) incorrect provision of facts (hallucinations), 2) inability to draw correct conclusions from correct premises, 3) difficulties reasoning on 'mutatis mutandis' articles, and 4) incorrect responses or inability to conclude due to lack of relevant articles in the provided dataset. | Inaccurate or misleading AI output, Technical limitations of AI |
| G2vdU-5fzE4J.pdf | Google_Scholar | ChatGPT and Generative AI Systems as Military Ethics Advisors | This paper explores the potential of ChatGPT and similar generative AI systems to serve as military ethics advisors by testing ChatGPT's responses to a complex ethical dilemma scenario involving a potential strike on a hospital. The author suggests that AI could provide valuable, accessible ethical guidance to soldiers and commanders, potentially reducing war crimes and improving decision-making. | Generative AI Application, ChatGPT Evaluation, Ethical Guidance Provision, Military Ethics Focus, Decision Support, War Crime Reduction | True | Idealistic | True | 2.0 | Positive | Using ChatGPT as a military ethics advisor by prompting it with specific scenarios. | Large Language Model, Prompt Engineering, Ethics Advisor AI | ChatGPT (Feb 2023 version, trained on data up to end of 2021) was prompted with a detailed hypothetical military scenario involving a potential strike on a hospital suspected of hiding enemy artillery, followed by specific ethical and legal questions related to the scenario. | Qualitative Analysis | ChatGPT provided extensive, detailed, and ethically reasoned advice addressing principles like military necessity, proportionality, discrimination, the implications of attacking hospitals (treating civilians or enemy wounded), the use of human shields, the certainty required for intelligence, and the legality of following or refusing potentially unlawful orders. | High performance, Descriptive or Conceptual finding | Lack of readily accessible ethical guidance for soldiers in combat, complexity of formal Law of War manuals, stress of war leading to poor decisions, potential for immoral leadership, leading to ethical breaches and war crimes. | Difficulty Accessing/Interpreting Legal Information, Complexity of Legal Language/Documents, External Factors Affecting Legal Compliance | Deploying generative AI systems like ChatGPT, potentially integrated via voice interfaces, to provide real-time military ethics advice, automatic checking of orders, and decision support for both frontline soldiers and commanders. | AI Tool Development, User Interface and Accessibility Design, Access to Legal Information and Advice, Judicial System Enhancement | Access to ethical guidance in conflict zones, interpretation of the Law of Armed Conflict / International Humanitarian Law, prevention of war crimes, ethical military decision-making. | Access to Legal Advice, Access to Legal Information, Protection of Rights, Ethical AI in Law and AI Governance | Military personnel (specifically frontline soldiers and commanders) lacking immediate access to ethical/legal guidance. | Military personnel | Military Law, Law of Armed Conflict, International Humanitarian Law, Ethics | Military Law, International Humanitarian Law, Legal Ethics | International | International | ChatGPT was predominantly trained on general data up to the end of 2021 (as per OpenAI FAQ cited in the paper). | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text | NaN | NaN | The paper suggests potential future deployment (e.g., via voice interface) but does not describe current deployment strategies. | Proposed deployment (not implemented) | False | False | NaN | NaN | NaN | NaN | NaN | NaN | Implicit risks related to reliance on AI for high-stakes ethical decisions in warfare, potential for AI to provide incorrect or flawed advice leading to illegal or immoral actions, misuse of AI. | Over-reliance on AI, Inaccurate or misleading AI output, Risk of misapplication or misuse, Ethical concerns |
| MclRsgjrGSEJ.pdf | Google_Scholar | The K eynote Addr ess t o Geor gia State Univ ersity College of Law' s 29th Annual Law Re view Symposium - Access t o AI Justice: A Global Response t o a Global Crisis | This paper, a keynote address, argues that the narrative around AI in law should shift to focus on closing the justice gap and discusses how AI can serve the public interest. It calls for significant regulatory reforms, including a U.S. national legal regulatory sandbox and globally-informed approaches, to ensure AI's potential is realized without creating a two-tiered legal system. | Perspective Paper, Access to Justice Enhancement, AI for Public Interest, Regulatory Reform Proposal, US Focus, Preventing Two-Tiered Legal System | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | The significant justice gap due to unaffordable legal services, which AI could worsen by creating a two-tiered system. Systemic barriers include high costs and restrictive regulations (e.g., limiting non-lawyer investment), alongside AI-specific issues like bias, lack of transparency, and data privacy concerns. | Scale of Unmet Legal Need, High Cost of Legal Services, Risk of AI Exacerbating Inequality, Systemic Inequities in Justice System, Regulatory Hurdles, Bias in AI/Data, Lack of AI Transparency/Explainability, Data Privacy Concerns with AI | Implement 'calibrated' AI for access to justice, focusing on consumer needs, specific legal issues, and tasks. Advocate for data-driven regulatory reform through a national U.S. legal regulatory sandbox and learning from international approaches to foster innovation and equitable access to legal services. | AI Tool Development, Regulation, Ethics, and Governance, Policy and Regulatory Reform, User Interface and Accessibility Design | Closing the justice gap; democratizing access to legal information; regulatory reform of legal services; preventing a two-tiered system of legal services; nonlawyer ownership/investment in legal services; ethical use of AI in law. | Democratizing Law / Closing Justice Gap / Rule of Law, Access to Legal Information, Regulatory Reform (Legal Services and AI), Ethical AI in Law and AI Governance | Low-income Americans; individuals who cannot afford legal services. | Low-income individuals, Population in USA, Individuals unable to afford legal services | General civil legal problems; Legal ethics and professional regulation. | Civil Law, Legal Ethics, Professional Responsibility | United States (primarily for proposed reforms), with comparative discussion of international jurisdictions (e.g., Colombia, France, UK, Canada, Australia). | USA, Colombia, France, UK, Canada, Australia, International | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Lack of data-driven regulatory reform in the U.S. legal services industry; state-level resistance to regulatory experimentation, such as sandboxes; insufficient and outdated ethical guidance for new AI technologies; failure of the U.S. legal industry to systematically learn from international experiences in legal tech regulation; need for more interdisciplinary and collaborative reform efforts. | Regulatory and Governance Gaps, Ethical Framework Deficiencies, Need for Interdisciplinary Collaboration, Human Oversight and Professional Adaptation | NaN | NaN | AI generating fictitious legal citations (hallucinations); creation of a two-tiered system of legal services disadvantaging certain populations; perpetuation of existing biases through data-driven conservatism; breaches of client confidentiality and data protection; stifling of lawyer creativity and critical thinking; potential for discriminatory outcomes from AI systems. | Inaccurate or misleading AI output, Exacerbation of inequality or two-tiered system, Bias and discrimination, Data privacy and security breach, Deskilling or erosion of human skills |
| XLMN4NL-8-wJ.pdf | Google_Scholar | The Law and NLP: Bridging Disciplinary Disconnects | This position paper argues that legal NLP research is often disconnected from the practical needs of the legal community, which impedes its potential to address the access to justice crisis. The authors call for a shift towards more needs-driven research, greater interdisciplinary collaboration, and the adoption of access to justice as a primary normative goal for the field. | Position Paper, Critique of Legal NLP Research, Call for Needs-Driven Research, Interdisciplinary Collaboration Advocacy, Access to Justice as Normative Goal | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | High cost and unequal access to legal services, particularly for low-income individuals and small businesses; a disconnect between the focus of legal NLP research and the practical needs of the legal community; slow adoption of technology by the legal profession due to factors like risk aversion and lack of expertise. | High Cost of Legal Services, Unequal Access to Legal Services, Misalignment of Research/Innovation with Practical Needs, Slow Technology Adoption by Legal Profession | Adopting access to justice as a shared normative goal for legal NLP research; fostering closer interdisciplinary collaboration between NLP researchers and legal professionals; reorienting research towards practical applications like document generation/analysis, semantic search, legal language accessibility, and practice-oriented tools. | Policy and Regulatory Reform, Open Source Initiatives and Collaboration, AI Tool Development, Document Automation, Legal Research and Analysis Tools, Language Simplification and Multilingual Access | Addressing the access to justice gap; improving legal services for low-income individuals, public defenders, and small businesses; enhancing the accessibility of legal language and processes for non-lawyers; increasing the efficiency of legal professionals to potentially lower costs. | Democratizing Law / Closing Justice Gap / Rule of Law, Support for Vulnerable Populations, Legal Aid and Pro Bono Services, Legal Text Simplification / Plain Language, Legal Literacy and Public Legal Education, Improving Efficiency in Legal System / Profession, Affordability of Legal Services / Cost Reduction | Low-income individuals, criminal defendants reliant on public defenders, small businesses, non-lawyers seeking to understand legal matters, and underserved communities globally. | Low-income individuals, Indigent criminal defendants, Small businesses, Laypeople, Individuals lacking legal knowledge, Marginalized communities, Populations in developing countries | General legal practice, including civil law, criminal law, contract law, statutory interpretation, and litigation. | General Legal Practice, Civil Law, Criminal Law, Contract Law, Statutory Interpretation, Litigation | United States; International | USA, International | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Societal/Systemic: Deep-rooted inequities in the justice system and resistance to technological adoption within the legal field. Research Focus: A misalignment between academic NLP research agendas and the practical requirements of legal work, leading to underexplored areas with high potential impact; insufficient interdisciplinary interaction. Ethical: Need for robust frameworks to manage bias, ensure accountability, and maintain trust in legal AI systems. | Access, Equity, and Digital Divide, Human Oversight and Professional Adaptation, Research and Evaluation Gaps, Need for Interdisciplinary Collaboration, Ethical Framework Deficiencies, Bias in AI, Accountability and Redress Mechanisms, Public Understanding, Trust, and Adoption | NaN | NaN | Poorly designed NLP tools may embed or amplify biases, reduce essential human oversight in legal decision-making, undermine public trust in the judicial system, lead to inaccurate or unfair automated judgments, and risk the leakage of sensitive or confidential legal information. | Bias and discrimination, Over-reliance on AI, Erosion of trust in legal system or AI, Inaccurate or misleading AI output, Data privacy and security breach |
| EAq8gE4cA44J.pdf | Google_Scholar | Breaking Barriers to Creative Expression: Co-Designing and Implementing an Accessible Text-to-Image Interface | This paper describes the co-design and development of PromptAssist, a prototype accessible interface for text-to-image (T2I) generation models. PromptAssist uses a large language model (LLM) to provide prompt suggestions, reducing typing effort for users, particularly those with motor disabilities. | Tool Development, LLM Application, Support for Text-to-Image Generation, Accessibility for Motor Disabilities | True | Idealistic | True | 1.0 | Positive | PromptAssist: An accessible web-based interface for creating text-to-image prompts using LLM-generated suggestions via a wizard-based workflow, supporting keyboard and pointer input. | User Interface / Accessibility Feature, Prompt Engineering Support Tool, Large Language Model, Text-to-Image Generation Support | Iterative co-design and testing sessions within the research team, which included members with motor disabilities. Feedback was gathered through think-aloud protocols during prototype use. | Iterative Design Feedback, User Study or Survey | Iterative testing led to UI improvements enhancing flexibility, navigation, suggestion quantity, and keyboard accessibility. Team members found the revised prototype easier to use and better supported their creative processes, demonstrating LLMs can improve T2I accessibility. | Benefit identified, Technique improves outcome | Difficulty for users with motor disabilities in typing the long and detailed text prompts required by standard text-to-image (T2I) interfaces. | Accessibility Barriers for Specific User Groups | An accessible interface (PromptAssist) that reduces typing effort by using an LLM to generate contextual prompt suggestions and supports multiple input methods (keyboard, pointer). | User Interface and Accessibility Design, Prompt Engineering and LLM Interaction Design, AI Tool Development | Accessibility of creative tools (Text-to-Image generation). | NaN | People with motor disabilities. | People with disabilities | NaN | NaN | International | International | The technique uses an internal (Google) transformer-based LLM. The specific training data for the base LLM is not specified. The system is prompted using examples (provided in Appendix A) created by the authors to generate relevant suggestions for T2I prompts. | Pre-trained LLM's General Training Corpus, Proprietary Data, Undisclosed Data Source/Availability, Input Data for Task (Non-Training), Author-Created New Dataset | Iterative development, co-design involving researchers with motor disabilities, usability testing with think-aloud protocols within the team. | Iterative Design Process, User-centered Design | Developed as an internal prototype within Google; no broader deployment strategies mentioned. | Internal deployment/prototype | False | False | NaN | NaN | Future work could include multimodal input (speech, body movements) and adjusting prompts based on previously generated images. Need for platforms for users to share, collaborate, and exchange ideas. | AI Scope and Functionality Limitations, User Interface and Usability Gaps | Balancing ease of use (provided by suggestions) with creative flexibility and user autonomy. Ensuring the interface supports varied creative workflows rather than enforcing a strict sequence. Optimizing the UI layout and interaction based on accessibility feedback. | User Interface, Usability, and Accessibility | Over-reliance on LLM suggestions might limit user creativity or diminish the user's sense of agency and independence, particularly for users with disabilities. | Over-reliance on AI, Deskilling or erosion of human skills, Negative impact on user agency or autonomy |
| bWkbsgfjIKIJ.pdf | Google_Scholar | Artificial Intelligence and the Sustainable Development Goals: \nGPT -3`s reflect ions on the Society Domain | This paper evaluates the large language model GPT-3's perspectives on how Artificial Intelligence (AI) can contribute to achieving the Sustainable Development Goals (SDGs) within the society domain. Through analyzing GPT-3's responses to queries about specific SDGs, the study identifies potential benefits, such as in education and health, alongside significant risks like bias and privacy concerns, ultimately stressing the need for robust regulation for responsible AI deployment. | LLM Evaluation, AI for Sustainable Development Goals, Benefit Identification, Risk Identification, Bias in AI, Privacy Concerns, Need for AI Regulation, Responsible AI Deployment | True | Idealistic | True | 2.0 | Neutral | GPT-3 model (text-davinci-003) | Large Language Model | The authors prompted GPT-3 (text-davinci-003 model) with queries related to nine societal SDGs and their 58 outcome targets. The prompts requested shortened target titles and 3-5 sentences outlining benefits and risks of AI's contribution to each target. The AI's textual responses were then descriptively analyzed for content, structure, word/sentence counts, and patterns of consistency or error. | Qualitative Analysis | GPT-3 identified numerous potential benefits of AI for societal SDGs, including poverty reduction, enhanced food security, improved healthcare diagnostics, personalized education, support for gender equality, better water management, optimized energy systems, sustainable urban planning, and enhanced access to justice. However, it consistently highlighted risks such as data bias leading to discrimination, privacy violations, exacerbation of existing inequalities, job displacement, and the necessity of human oversight. The model exhibited variability in response structure and an increase in errors (e.g., punctuation) with longer text generations. | Descriptive or Conceptual finding, Risk or Ethical concern highlighted, Limitation: Operational or Technical, Limitation: Bias, Limitation: Security or Privacy | For access to justice (primarily under SDG 16), identified hurdles include: AI systems potentially targeting specific populations or being biased against certain groups, leading to discriminatory outcomes; misinterpretation of data by AI leading to false accusations or unjust decisions; increased surveillance capabilities infringing on privacy rights critical for justice; and the risk of AI perpetuating or creating new forms of inequality in legal and institutional processes. | Bias in AI/Data, AI Unreliability/Inaccuracy, Risk to Human Rights from AI, Data Privacy Concerns with AI, Risk of AI Exacerbating Inequality | The paper emphasizes the critical need for proper regulation and oversight of AI development and deployment. It calls for establishing ethical guidelines, ensuring transparency and safety of AI systems, fostering a global, science-driven debate to develop shared principles and legislation, and promoting responsible AI use to mitigate risks and align AI with sustainable development. | Regulation, Ethics, and Governance, Transparency and Explainability in AI, Policy and Regulatory Reform | Poverty eradication (SDG 1), zero hunger (SDG 2), good health and well-being (SDG 3), quality education (SDG 4), gender equality (SDG 5), clean water and sanitation (SDG 6), affordable and clean energy (SDG 7), sustainable cities and communities (SDG 11), and peace, justice, and strong institutions (SDG 16). Within SDG 16, specific topics include reducing violence, ending child abuse, promoting rule of law, reducing illicit financial flows, combating corruption, building effective institutions, inclusive decision-making, and ensuring legal identity and access to information. | Democratizing Law / Closing Justice Gap / Rule of Law, Protection of Rights, Access to Legal Information, Judicial System Modernization / Efficiency | Vulnerable populations, people living in poverty, communities in developing nations, women and girls (gender disparities), minority groups, children, and individuals at risk of discrimination. | Vulnerable populations, Low-income individuals, Populations in developing countries, Women, Children, Minority groups | Human rights law, criminal justice, anti-corruption law, data privacy law, administrative law, access to information law, environmental law (as it relates to social impacts of resource management). | Human Rights Law, Criminal Justice, Anti-Corruption Law, Data Privacy Law, Administrative Law, Access to Information Law, Environmental Law | International | International | The study used the GPT-3 model 'text-davinci-003', which was trained on data up to June 2021. The paper generally notes that such AI models are trained on vast amounts of internet text, which can include misinformed and biased content. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Data Bias Concerns Noted | NaN | NaN | NaN | Not applicable | True | False | The authors interacted with GPT-3 via the OpenAI playground, implying availability through OpenAI's platform (API and playground). | API access, Publicly accessible online tool or platform, Commercial product or service | Technical gaps include the unreliability and error-proneness of current LLMs like GPT-3, inconsistencies in output, and the need for improved natural language processing skills to avoid mimicking human writing flaws. Societal and ethical gaps include the lack of adequate regulation for AI, the potential for AI to exacerbate existing inequalities, pervasive data biases, significant privacy concerns, and the challenge of differentiating AI-generated content from human-written text, necessitating a global consensus on ethical AI principles and legislation. | AI Accuracy and Reliability, Regulatory and Governance Gaps, Access, Equity, and Digital Divide, Bias in AI, Security and Privacy of Data, Transparency and Explainability, Ethical Framework Deficiencies | The authors encountered challenges in obtaining consistent and accurate outputs from GPT-3, including variability in answering patterns and adherence to formatting instructions. They also noted an increase in punctuation mistakes in longer AI-generated texts and instances where the system failed to produce results without error messages, possibly due to beta tier limitations or capacity issues. | Accuracy and Reliability of LLM Output, Output Variability and Consistency, Scalability of Solutions | Bias in AI algorithms leading to discrimination and unfair decisions; privacy violations from data collection and analysis (e.g., health, financial, personal information); exacerbation of existing social and economic inequalities; job displacement due to automation; over-reliance on AI leading to reduced human oversight and accountability; potential for misuse of AI for manipulative purposes (e.g., targeting specific populations, citizen scores); and infringement on human rights and fundamental freedoms (e.g., through surveillance, profiling). | Bias and discrimination, Data privacy and security breach, Exacerbation of inequality or two-tiered system, Job displacement, Over-reliance on AI, Lack of transparency, accountability, and redress, Security vulnerabilities or malicious misuse, Infringement on human rights |
| h4InHnlnqGoJ.pdf | Google_Scholar | Leveraging Large Language Models for Learning Complex Legal Concepts\nthrough Storytelling | This paper presents a novel application of LLMs to generate stories and multiple-choice questions for explaining complex legal concepts to non-experts, and introduces the LEGAL STORIES dataset. Through RCTs, it demonstrates that LLM-generated stories, using an expert-in-the-loop process, can enhance legal comprehension, interest, and knowledge retention, especially for non-native English speakers. | LLM Application, Legal Concept Explanation for Non-Experts, Educational Material Generation, Dataset Creation, Empirical Study, Expert-in-the-Loop Process, Legal Comprehension Enhancement, Benefits for Non-Native Speakers | True | Idealistic | True | 1.0 | Positive | Using LLMs (LLaMA 2, GPT-3.5, GPT-4) to generate explanatory legal stories and multiple-choice questions from legal doctrine definitions, with an expert-in-the-loop process for question refinement, to create the LEGAL STORIES dataset. | Large Language Model, Dataset Creation / Curation, Content Generation, Human-in-the-Loop System, Legal Education Tool | Human evaluation of story quality (Prolific workers, automatic complexity metrics) and question quality (Prolific workers, legal expert critiques). Randomized Controlled Trials (RCTs) with legal novices (native and non-native English speakers) comparing learning with definition vs. definition + story, assessed by comprehension questions and a delayed retention test. | Human Evaluation, Expert Evaluation, User Study or Survey, Quantitative Metrics, Comparative Analysis | LLM-generated stories (GPT-4 performing best) enhance comprehension of legal concepts and interest in law among non-native English speakers compared to definitions alone. Stories also help participants relate legal concepts to their lives and show higher knowledge retention for non-native speakers. | Benefit identified, Technique improves outcome, Outperforms others | Legal documents are challenging for non-experts due to unfamiliar terms and nuanced language, hindering access to justice and civic participation. Scalable legal storytelling education is limited by the high costs of legal experts. | Complexity of Legal Language/Documents, Public Lack of Legal Knowledge/Awareness, High Cost of Legal Expertise for A2J Initiatives | Leveraging LLMs to generate legal stories and assessment questions in a scalable way, using an expert-in-the-loop pipeline to maintain quality and enhance legal literacy for non-experts. | AI Tool Development, Human Oversight and Collaboration, Education and AI Literacy | Enhancing general legal literacy, learning intricate legal concepts, legal education for non-experts. | Legal Literacy and Public Legal Education | Non-experts, people without legal backgrounds, legal novices, with a particular focus on non-native English speakers. | Laypeople, Individuals lacking legal knowledge, Individuals with language barriers | General legal concepts and doctrines | General Law, Jurisprudence | International | International | Input data for generation (not model training) consists of 294 legal doctrines with definitions from Wikipedia, which is publicly available, domain-specific (legal), unstructured text. The study uses pre-trained LLMs (LLaMA 2, GPT-3.5, GPT-4). | Input Data for Task (Non-Training), Publicly Available Data, Legal Domain Data, Legal Scholarly Content / Textbooks, Unstructured Text Data, Pre-trained LLM's General Training Corpus | Expert-in-the-loop pipeline combining LLM generation with human (Prolific workers, legal experts) evaluation and refinement. Randomized Controlled Trials (RCTs) for evaluating effectiveness. Iterative design for question refinement based on expert feedback. | Human-in-the-loop System, Pipeline Development, LLM-based Content Generation, Randomized Controlled Trial (RCT), Iterative Design Process, Expert Feedback Integration | Release of the 'LEGAL STORIES' dataset and associated code on GitHub. | Public dataset/benchmark release, Open source code release | True | True | The LEGAL STORIES dataset and code are available on GitHub. | Dataset available, Code available | Limited sample size in RCTs affecting statistical power for small effects. The cost and scalability of human expert involvement, though reduced, remain a factor. Need for further research into diverse prompting strategies and LLM-based explanation methods. | Research and Evaluation Gaps, Computational Resource and Cost Issues, Human Oversight and Professional Adaptation | Ensuring high quality and factual accuracy of LLM-generated content. Cost and time for human/expert evaluation and refinement. Designing effective prompts for LLMs. Evaluating generated questions without gold standards. | Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Evaluation Challenges and Metrics, Prompt Engineering and Optimization | LLM-generated content may contain misleading, biased, harmful, or wrong information if not supervised. Risk of over-simplifying or over-generalizing nuanced legal jargon. Potential for inherent biases in LLMs to be perpetuated. | Inaccurate or misleading AI output, Harmful or unsafe AI output, Bias and discrimination, Over-reliance on AI, Technical limitations of AI |
| 1WKST3FL64cJ.pdf | Google_Scholar | Structured Legal Argumentation with LLMs: A Study in Landlord-Tenant Law | This paper proposes and evaluates a method using OpenAI's GPT-4o with context augmentation (Chicago's RLTO) and Chain-of-Thought instructions to generate structured legal arguments for landlord-tenant disputes. The study tests this approach on ten scenarios, finding reasonable accuracy and factuality but limitations in handling out-of-scope issues and relevance assessment. | Methodology Proposal, LLM Application, Context Augmentation, Chain-of-Thought Prompting, Legal Argument Generation, Landlord-Tenant Law Focus, System Evaluation, Limitations Identified | True | Idealistic | True | 1.0 | Positive | Using GPT-4o with context augmentation (full text of Chicago's Residential Landlord and Tenant Ordinance - RLTO) and Chain-of-Thought (CoT) prompting to generate structured legal arguments (Exposition, Specific law, Why this Law Applies, Conclusion) for specific scenarios. | Large Language Model, Context Augmentation, Prompt Engineering, Legal Argument Generation | Evaluation of generated arguments for 10 hypothetical landlord-tenant scenarios (5 from legal aid, 4 AI-generated, 1 author-crafted) by a Landlord-Tenant lawyer based on metrics: Accuracy, Factuality, Comprehensiveness (0-1 scale), and Relevance (0-1 scale). | Expert Evaluation, Custom Dataset Evaluation, Quantitative Metrics | The method was accurate in 8/10 scenarios and 54/55 arguments were factual. Limitations identified include failing to recognize issues outside the scope of the provided RLTO and difficulties in filtering irrelevant details from emotionally charged scenarios or narrowing arguments to the core legal issue. | High performance, Limitation: Operational or Technical | The implicit difficulty for laypersons in understanding their rights and drafting legal documents like demand letters in landlord-tenant disputes. | Public Lack of Legal Knowledge/Awareness, Complexity of Legal Tasks for Laypersons | Providing LLM-generated, structured legal arguments based on specific scenarios and relevant law (RLTO) to assist laypersons in drafting documents and asserting rights, with outputs designed to be verifiable by legal professionals. | AI Tool Development, Document Automation, Access to Legal Information and Advice, Human Oversight and Collaboration, Enhanced AI Capabilities | Generating legal arguments, assisting with drafting demand letters, understanding legal rights in landlord-tenant disputes. | Legal Document Creation / Automation, Access to Legal Information, Legal Literacy and Public Legal Education | Tenants, particularly those who might seek assistance from legal aid organizations. | Tenants, Clients of legal aid organizations | Landlord-Tenant Law | Landlord-Tenant Law | Chicago | USA | The technique uses context augmentation with the text of Chicago’s Residential Landlord and Tenant Ordinance (RLTO). The underlying LLM (GPT-4o) was pre-trained on general web data by OpenAI. | Input Data for Task (Non-Training), Legal Domain Data, US Legal Data, Legislation / Statutes / Regulations, Publicly Available Data, Unstructured Text Data, Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text | Prompt engineering (structured output format, Chain-of-Thought instructions), context augmentation, expert evaluation. | Prompt Engineering, Context Augmentation, Expert Evaluation | The scenarios, model parameters, and results are shared on GitHub, but no deployment of the tool/system itself is mentioned. | Public dataset/benchmark release | False | False | NaN | NaN | Limitations in classifying legal issues outside the provided context (RLTO), reliably assessing the relevance of generated arguments, robustness of the process, need for refined evaluation methods, difficulty filtering noise from emotionally charged descriptions. | AI Legal Reasoning Limitations, AI Accuracy and Reliability, Research and Evaluation Gaps, Data Availability and Quality | LLM's inability to filter out less important concerns from user scenarios (especially when emotionally charged), difficulty in narrowing down arguments to the crux of legal issues, ensuring generated arguments stay within the scope of the provided legal text. | LLM Reasoning Capabilities | Inaccuracy (e.g., missing that an issue falls outside the scope of the provided law), lack of factuality (connecting premise and conclusion to the cited law), generating irrelevant arguments. | Inaccurate or misleading AI output |
| cQHRZiimZz0J.pdf | Google_Scholar | Large Language Models in Politics and Democracy: A Comprehensive Survey | This paper surveys the current and potential applications of large language models (LLMs) across various political domains, including policymaking, communication, analysis, national security, and law. It outlines both the opportunities for enhanced efficiency and inclusivity, and the significant challenges related to bias, transparency, reliability, and ethics. | Survey of LLM Applications, LLMs in Political Domains, LLMs in Law, Opportunity Identification, Challenge Identification, Bias in AI, Transparency Issues, Reliability Issues, Ethical Considerations | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | Unreliability due to legal hallucinations, need for human oversight, potential biases favouring specific groups or jurisdictions. | AI Unreliability/Inaccuracy, Need for Human Oversight of AI, Bias in AI/Data | Responsible development principles, creation of ethical guidelines and governance frameworks, ensuring human oversight, developing methods for bias mitigation, using domain-specific adaptation and curated data. | Regulation, Ethics, and Governance, Human Oversight and Collaboration, Bias Detection and Mitigation, Enhanced AI Capabilities, Data Curation and Management | Legal information provision, legal research, legal drafting. | Access to Legal Information, LegalResearch Support, Legal Document Creation / Automation | Under-resourced nations (mentioned generally in policy context), general public needing access to justice (implied). | Populations in developing countries, General public, Individuals with unmet legal needs | General Legal Field | General Law | International | International | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Technical: Robust bias mitigation, transparency, explainability, reliability (reducing hallucinations). Societal: Ensuring fairness, equity, representation; addressing impacts on polarization and democratic processes; establishing accountability frameworks. | Bias in AI, Transparency and Explainability, AI Accuracy and Reliability, Access, Equity, and Digital Divide, Ethical Framework Deficiencies, Accountability and Redress Mechanisms | Bias in models and data, reliability issues (hallucinations), lack of transparency and accountability, ethical concerns (e.g., manipulation, deception, lobbying), privacy risks, security vulnerabilities (adversarial attacks), need for effective human oversight, ensuring equitable access and outcomes. | Bias in AI Systems and Data, Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Transparency and Explainability of AI, Accountability and Liability for AI Errors, Ethical Considerations, Safeguarding Against Misuse and Harm, Data Privacy, Security, and Confidentiality, Need for Human Oversight and Intervention, User Adoption, Trust, and Acceptance | Disinformation and manipulation, amplification of political polarization, biased or unfair policy outcomes, unreliable legal outputs ('hallucinations'), potential for unintended escalation in military/diplomatic contexts, erosion of democratic accountability, AI deception. | Security vulnerabilities or malicious misuse, Undermining democratic processes, Bias and discrimination, Inaccurate or misleading AI output, Ethical concerns |
| iCe6v16i9SwJ.pdf | Google_Scholar | Friend or Foe – AI’s Invasion of the Legal Battlefield | This paper discusses the integration of AI into the legal profession, highlighting potential benefits like increased efficiency and access to justice through lower costs. It also examines significant risks, including ethical considerations, privacy concerns, AI errors ('hallucinations'), and the unauthorized practice of law. | Discussion of AI in Legal Profession, Benefit Identification, Access to Justice Enhancement, Efficiency Improvement, Reducing Costs, Risk Identification, Ethical Considerations, Privacy Concerns, AI Hallucinations/Inaccuracy, Unauthorized Practice of Law | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | High cost of legal services; insufficient number of lawyers to meet population needs. | High Cost of Legal Services, Limited Availability/Access to Legal Professionals/Expertise | Leveraging AI for efficiency to enable lawyers to offer more affordable services (e.g., document drafting/review) and handle more clients, thereby increasing accessibility. | Cost Reduction and Efficiency, Document Automation, Human Oversight and Collaboration | Cost of legal services, Efficiency of legal service delivery, Document automation, Legal research. | Affordability of Legal Services / Cost Reduction, Improving Efficiency in Legal System / Profession, Legal Document Creation / Automation, LegalResearch Support | General public requiring affordable legal services. | General public, Individuals unable to afford legal services | General Legal Practice | General Legal Practice | United States (primarily, with brief mention of Italy/EU) | USA, Italy, EU | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Need for clear governmental regulation and ethical guidelines for AI in law; ensuring lawyer competency in using AI; addressing AI limitations like bias and 'hallucinations'; defining boundaries related to the unauthorized practice of law. | Regulatory and Governance Gaps, Ethical Framework Deficiencies, Human Oversight and Professional Adaptation, Bias in AI, AI Accuracy and Reliability | NaN | NaN | AI 'hallucinations' (incorrect outputs); privacy violations due to handling client data on third-party platforms; unauthorized practice of law; potential for AI bias; cybersecurity threats (e.g., AI-generated malware); ethical concerns regarding lawyer competence, oversight, and accountability; potential legal liability for AI outputs. | Inaccurate or misleading AI output, Data privacy and security breach, Unauthorized practice of law, Bias and discrimination, Security vulnerabilities or malicious misuse, Ethical concerns, Lack of transparency, accountability, and redress |
| lftOiX2IcekJ.pdf | Google_Scholar | Chain of Logic: Rule-Based Reasoning with Large Language Models | This paper introduces "Chain of Logic," a novel prompting method inspired by the IRAC legal framework, designed to improve rule-based reasoning in Large Language Models (LLMs). Evaluated on LegalBench tasks, Chain of Logic consistently outperforms existing prompting methods by decomposing rules into elements and then recomposing their logical resolution to arrive at a conclusion. | Prompting Method Proposal, IRAC Framework Application, Rule-Based Reasoning Enhancement, LLM Performance Improvement, System Evaluation | True | Idealistic | True | 1.0 | Positive | Chain of Logic prompting method | Prompt Engineering | Evaluated across eight rule-based reasoning tasks involving three distinct compositional rules from the LegalBench benchmark. Compared against zero-shot, standard prompting, chain of thought, and self-ask methods using GPT-3.5, GPT-4, Llama-2-70b-chat, and Mistral-7B-OpenOrca models, using a one-shot example from a different rule application. | Benchmark Dataset Evaluation, Comparative Analysis, Quantitative Metrics | Chain of logic consistently outperforms other prompting methods across all tested models. On average, Chain of Logic achieved 79.3% accuracy across all rules and models (as per Table 1). | Technique improves outcome, Outperforms others, Moderate performance | Language models are prone to hallucinations in legal settings and struggle with basic legal tasks and complex compositional rules. Annotated legal data is scarce, limiting fine-tuning capabilities. LLMs also show difficulty with in-context learning for legal reasoning. | AI Unreliability/Inaccuracy, AI Limitations in Legal Reasoning/Nuance, Data Scarcity/Quality for AI, Technical Challenges in AI Development | The proposed 'Chain of Logic' prompting method guides LLMs to perform rule-based reasoning through explicit decomposition of rules into elements and recomposition of sub-answers to resolve the logical expression, thereby improving in-context learning and reducing the need for numerous rule-specific examples. | Prompt Engineering and LLM Interaction Design, Enhanced AI Capabilities | Improving rule-based legal reasoning in LLMs, enhancing the interpretability of AI-driven legal analysis, and potentially broadening access to justice by increasing the capacity of legal professionals. | Improving Foundational AI Capabilities for Legal Applications, Ethical AI in Law and AI Governance, Improving Efficiency in Legal System / Profession | NaN | NaN | Civil Procedure (Personal Jurisdiction, Diversity Jurisdiction), Contract Law (J.Crew Blocker covenant). | Civil Procedure, Contract Law | United States | USA | The technique is a prompting method applied to pre-trained large language models (GPT-3.5, GPT-4, Llama-2, Mistral-7B-OpenOrca). The evaluation uses tasks from LegalBench, each providing a rule, fact pattern, and question. The method uses a single in-context example from a different rule application, not requiring model fine-tuning on task-specific data. | Pre-trained LLM's General Training Corpus, Evaluation Dataset, Data From Existing Public NLP/Legal Datasets/Benchmarks, Legal Domain Data, Structured Data, Input Data for Task (Non-Training) | Inspired by the IRAC (Issue, Rule, Application, Conclusion) legal reasoning framework. The method involves: 1) Structured Input, 2) Rule Decomposition, 3) Logical Expression construction, 4) Question Answering per element, 5) Element Recomposition, and 6) Resolving the Expression. | Framework-guided Design, Structured Data Processing, Modular Processing Pipeline | NaN | Not applicable | True | False | The Chain of Logic prompting methodology and its steps are fully described in the paper, allowing users to implement it with compatible LLMs. The specific LLMs used have varying access models (commercial or open-source). | Research artifact published in paper | The rules in LegalBench are simplified compared to real-world legal rules. The current approach primarily addresses rule antecedents, not complex consequences. Future work areas include rule identification, dynamic sampling of reasoning paths, and incorporating retrieval augmented generation. | Research and Evaluation Gaps, AI Legal Reasoning Limitations, AI Scope and Functionality Limitations | Models struggling with in-context learning in legal settings for compositional rules. Cost and scalability of requiring multiple reasoning examples per rule. Difficulties in correctly decomposing rules, identifying elements, and understanding logical relationships between them without explicit guidance. | LLM Reasoning Capabilities, Financial Cost and Resource Constraints, Scalability of Solutions, Scarcity of High-Quality Legal Data | Language models are prone to hallucinations in a legal setting. Potential for incorrect rule application or logical errors leading to inaccurate conclusions, even with advanced prompting. | Inaccurate or misleading AI output, Technical limitations of AI |
| mIXnP9q0bRsJ.pdf | Google_Scholar | OpenJustice.ai: A Global Open-source Legal Language Model | The paper critiques the use of generalized AI like ChatGPT for legal tasks due to risks like misinformation and lack of transparency. It introduces OpenJustice.ai, a proposed open-source, domain-specific legal language model designed to be reliable, transparent, and accessible, leveraging curated data and crowdsourced feedback. | Critique of General AI for Legal Tasks, Risk Identification, AI Hallucinations/Inaccuracy, Transparency Issues, Proposal for Domain-Specific LLM, Open Source AI, Reliability Improvement, Accessibility Enhancement | True | Idealistic | True | 1.0 | Positive | OpenJustice.ai: An open-source, distributed legal language model using Retrieval Augmented Generation (RAG), instruction fine-tuning on legal data, and crowdsourced human feedback. | Software / Platform Development, Open Source AI, Retrieval Augmented Generation (RAG), Fine-tuning, Instruction Tuning, Human Feedback Integration | NaN | Not Applicable | NaN | NaN | Risks associated with using general AI for legal tasks: legal misinformation/hallucinations, lack of transparency and precision, inability to offer diverse narratives, poor citation capabilities. Difficulty for non-lawyers in effective prompting. | AI Unreliability/Inaccuracy, Lack of AI Transparency/Explainability, AI Limitations in Legal Reasoning/Nuance, Difficulty in AI-Human Interaction | Developing domain-specific, open-source, distributed legal AI (OpenJustice.ai) using: curated legal data, Retrieval Augmented Generation (RAG) for accuracy, multiplicity for diverse perspectives, assisted prompting for non-lawyers, crowdsourced feedback for improvement and transparency, and decentralized fine-tuning. | AI Tool Development, Open Source Initiatives and Collaboration, Data Curation and Management, Enhanced AI Capabilities, Prompt Engineering and LLM Interaction Design, Transparency and Explainability in AI | Access to justice, legal research, legal information provision, dispute resolution (negotiation), legal education, addressing legal misinformation. | Democratizing Law / Closing Justice Gap / Rule of Law, LegalResearch Support, Access to Legal Information, Dispute Resolution, Legal Literacy and Public Legal Education, Ethical AI in Law and AI Governance | Self-represented litigants, non-lawyers, legal students, legal clinics, Pro Bono Students Canada (PBSC), the broader legal community. | Self-represented litigants, Laypeople, Law students, Legal aid organizations, Legal professionals | General Law (using legislation and case law), Employment Law, Consumer Protection, Personal Injury (mentioned for negotiation context). | General Law, Employment Law, Consumer Law, Tort Law | International | International | Combination of: (i) Unstructured legal data (case law, journals, etc.) for self-supervised training. (ii) Structured data (annotated question-answer pairs since 2019) for instruction fine-tuning. (iii) Crowdsourced human feedback from the legal community. (iv) Proprietary data from industry partners for closed-system fine-tuning. | Fine-tuning Dataset, Undisclosed Data Source/Availability, Legal Domain Data, Unstructured Text Data, Case Law / Judgments, Legal Scholarly Content / Textbooks, Structured Data, Expert-Annotated / Human-Curated / Human-Generated Data, User-Generated Content, Proprietary Data | Retrieval Augmented Generation (RAG), Instruction Fine-tuning, Self-supervised Training (Masked Language Modeling), Crowdsourced Human Feedback, Decentralized Fine-tuning, Consortium-based development, Design Probes (for assisted prompting). | Retrieval Augmented Generation (RAG), Model Fine-tuning, Self-supervised Learning, Crowdsourcing, Decentralized Fine-tuning, Consortium-based Development, Design Probes, Prompt Assistance Techniques | Rollout via a consortium of universities, legal clinics, and industry partners starting March 2023. A non-proprietary version intended to be openly accessible to the legal community for feedback, alongside custom models for partners. | Partnership-based rollout, Freely accessible tool/service, Pilot program/Limited rollout | True | True | Claims to be an open-source model launched in March 2023, intended to be openly accessible to the legal community via the OpenJustice.ai project/consortium. | Model available, Open-source, Publicly accessible online tool or platform | Underlying reasons for LLM citation inaccuracies remain an unresolved computer science question. Need for better interfaces/tools (like assisted prompting) for non-expert users. Current LLMs lack true legal reasoning capability. | AI Accuracy and Reliability, Research and Evaluation Gaps, User Interface and Usability Gaps, AI Legal Reasoning Limitations | Ensuring factual accuracy and reliable citations; Training models for multifaceted legal reasoning; Making AI tools usable for non-lawyers; Managing crowdsourced feedback; Balancing open-source and proprietary data needs. | Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, LLM Reasoning Capabilities, User Interface, Usability, and Accessibility, User Training, AI Literacy, and Skill Gaps, Data Quality, Processing, and Preparation, Scarcity of High-Quality Legal Data, Data Privacy, Security, and Confidentiality | Legal misinformation or hallucinations, lack of transparency and precision, inability to offer diverse narratives (associated primarily with generalized AI but relevant context for legal AI). Poor citations. | Inaccurate or misleading AI output, Lack of transparency, accountability, and redress, Technical limitations of AI |
| _xt52fZFqmoJ.pdf | Google_Scholar | Quebec Automobile Insurance Question-Answering With Retrieval-Augmented Generation | This paper introduces two French corpora for Quebec automobile insurance and evaluates a GPT-4o based Retrieval-Augmented Generation (RAG) system for answering related questions. While RAG improves answer quality over a baseline, the study concludes that LLM-based QA is not yet reliable enough for critical applications due to a significant rate of false statements. | Dataset Creation, Quebec Law Focus, Automobile Insurance Law Focus, Retrieval Augmented Generation, LLM Application, Legal Question Answering, System Evaluation, Reliability Issues Identified, AI Hallucinations/Inaccuracy | True | Idealistic | True | 1.0 | Neutral | Retrieval-Augmented Generation (RAG) using GPT-4o, a custom Quebec automobile insurance reference corpus for retrieval, and a custom question-answer corpus for evaluation. | Retrieval Augmented Generation (RAG), Large Language Model, Dataset Creation / Curation, AI System Evaluation | Automatic metrics (BLEU, ROUGE, METEOR, BERTScore, MeaningBERT) and manual evaluation by an insurance expert using a predefined grading scale on 82 question-answer pairs assessing criteria for completeness and correctness. | Quantitative Metrics, Expert Evaluation, Custom Dataset Evaluation | The RAG approach using the complete custom reference corpus performed best, achieving a 51.74% score on manual expert evaluation. However, between 5% to 13% of LLM-generated answers included a false statement that could mislead a customer. | Moderate performance, Technique improves outcome, Limitation: Hallucination or Factual inaccuracy | Lack of public's legal/insurance knowledge; complexity and jurisdiction-specific nature of insurance information; difficulty for individuals to find and correctly interpret relevant information online. | Public Lack of Legal Knowledge/Awareness, Complexity of Legal Information, Difficulty Accessing/Interpreting Legal Information | Developing AI-powered QA systems (like RAG) using curated, high-quality domain-specific corpora to provide more accurate and accessible information. Releasing these specialized corpora to foster further research. | AI Tool Development, Enhanced AI Capabilities, Data Curation and Management, Access to Legal Information and Advice, Open Source Initiatives and Collaboration | Access to insurance information, understanding insurance products, consumer rights regarding automobile insurance. | Access to Legal Information, Protection of Rights | General public / insurance customers in Quebec, particularly those seeking information online about automobile insurance. | General public, Consumers, Population in Canada | Insurance Law (specifically Quebec automobile insurance). | Insurance Law | Quebec, Canada. | Canada | The primary dataset used for the RAG system's retrieval component is the purpose-built 'Quebec Automobile Insurance Expertise Reference Corpus'. This French corpus consists of unstructured text from seven official and reliable online sources (legislation, legal insurance documents, regulator informative resources, domain-specific educative articles), manually extracted and cleaned. The LLM itself (GPT-4o) is pre-trained on general data not detailed by the paper. | RAG System Knowledge Corpus, Author-Created New Dataset, Canadian Legal Data, French Language Data, Unstructured Text Data, Publicly Available Data, Legislation / Statutes / Regulations, Other Legal Documents, Expert-Annotated / Human-Curated / Human-Generated Data, Pre-trained LLM's General Training Corpus | Comparative evaluation of GPT-4o (zero-shot vs. RAG with incrementally added reference sources from the custom corpus); RAG architecture built using LangChain, OpenAI's text-embedding-ada-002 for embeddings, and GPT-4o for generation, including context compression. A manual evaluation protocol with a grading scale was developed and applied by a domain expert. | Comparative Analysis of Approaches, Retrieval Augmented Generation (RAG), Zero-shot Learning Application, Development of Evaluation Protocol, Expert Evaluation, Third-party Tool Integration | The research prototype uses proprietary OpenAI APIs for core LLM and embedding models. The developed corpora are released on GitHub. No public deployment of the QA system itself is mentioned. | Public dataset/benchmark release | False | True | The two custom corpora created for this research (Quebec Automobile Insurance Expertise References Corpus and Corpus of 82 Expert Answers to Laypeople Automobile Insurance Questions) are released on GitHub. | Dataset available | The reliability of LLM QA for critical legal/insurance applications remains insufficient (5-13% false statements). LLMs' tendency to hallucinate or not abstain when information is lacking, the impact of potential data leakage from pre-training, and the need for better alignment of automatic evaluation metrics with human judgment in specialized domains like law are remaining gaps. | AI Accuracy and Reliability, Bias in AI, Data Availability and Quality, Security and Privacy of Data, Research and Evaluation Gaps | Ensuring factual accuracy and avoiding misinformation in LLM outputs for specialized, high-stakes domains like insurance law. Potential for LLMs to be confused by incomplete or overly complex legal texts provided as context. LLM memorization versus true understanding and reasoning. Models defaulting to information from incorrect jurisdictions if not precisely prompted/contextualized. The labor-intensive and costly nature of high-quality manual evaluation for specialized QA. | Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, LLM Reasoning Capabilities, Outdated or Limited LLM Knowledge Base, Evaluation Challenges and Metrics | Generation of false or misleading information by LLMs (study found 5-13% of answers contained false statements), potentially leading to customer misunderstanding and financial or legal harm. Premature deployment of inadequately vetted legal NLP tools. Inherent biases in training corpora and AI systems potentially leading to discriminatory outcomes. | Inaccurate or misleading AI output, Consumer harm, Risk of misapplication or misuse, Bias and discrimination |
| SoCFwEeEKWUJ.pdf | Google_Scholar | A Cross-Lingual Statutory Article Retrieval Dataset for Taiwan Legal Studies | This paper introduces LawFactsQA-TW, a new cross-lingual (English-Chinese) statutory article retrieval dataset focused on Taiwanese law, aimed at improving legal information access for non-native speakers. It also proposes and evaluates several LLM-based retrieval methods as baselines, with LLM-augmented techniques showing improved performance metrics. | Dataset Creation, Cross-Lingual Legal Information Retrieval, Taiwanese Law Focus, Legal Information Access for Non-Native Speakers, Methodology Proposal, System Evaluation | True | Idealistic | True | 1.0 | Positive | The LawFactsQA-TW dataset and LLM-augmented cross-lingual statutory article retrieval methods, including Answer Expansion, Statutory Article Expansion, and LLM-based Reranking. | Dataset Creation / Curation, Information Retrieval / Search, Large Language Model, Cross-lingual Application, Answer Augmentation, Reranking | Retrieval performance was evaluated using Recall and Average Precision (@10, @20, @50) on both human-labeled and synthetically A Cross-Lingual Statutory Article Retrieval Dataset for Taiwan Legal Studiesgenerated QA pairs within the LawFactsQA-TW dataset. Question-answering was evaluated using BLEU scores and an LLM-based 3-point scoring system. | Custom Dataset Evaluation, Quantitative Metrics, LLM as Judge | On human-labeled data, LLM re-ranking with Breeze achieved the highest Recall@10 (0.472); Taide with Statutory Article Expansion achieved Recall@50 of 0.729. On synthetic data, BGE-m3 augmented with Breeze for Statutory Article Expansion achieved the highest Recall@50 (0.845). | Technique improves outcome, Moderate performance | Difficulties for non-native speakers in accessing and understanding legal information in a foreign language (cross-lingual retrieval challenge); scarcity of specialized, multilingual legal datasets for SAR. | Accessibility Barriers for Specific User Groups, Data Scarcity/Quality for AI | Creation of LawFactsQA-TW, a cross-lingual (English-Chinese) dataset for Taiwanese statutory articles. Proposal and evaluation of LLM-based methods, particularly LLM-augmented retrieval, to enhance cross-lingual legal information access. | Data Curation and Management, Language Simplification and Multilingual Access, Enhanced AI Capabilities, Benchmarking and Evaluation Frameworks | Cross-lingual statutory article retrieval; access to legal information (FAQs, statutes) for non-native speakers. | Language Access and Digital Divide, Access to Legal Information, Support for Vulnerable Populations | Foreign nationals in Taiwan; non-native Chinese speakers seeking legal information pertaining to Taiwan. | Migrants, Population in Taiwan, Individuals with language barriers | Taiwanese civil law, criminal law, and administrative regulations. | Civil Law, Criminal Law, Administrative Law | Taiwan | Taiwan | The LawFactsQA-TW dataset was constructed using: 1) A corpus of all Taiwanese civil, criminal, and administrative laws from the National Regulatory Database. 2) 92 human-labeled QA pairs derived from legal agency FAQs. 3) 173 synthetic QA pairs generated by gpt-4-turbo based on news articles and legal regulations. LLMs used for augmentation (GPT series, Breeze, Taide) are pre-trained models. | Author-Created New Dataset, Legal Domain Data, Taiwanese Legal Data, Legislation / Statutes / Regulations, Publicly Available Data, Structured Data, Expert-Annotated / Human-Curated / Human-Generated Data, Legal Q&A / Forum / User Query Data, Synthetic Data, Pre-trained LLM's General Training Corpus | Dataset: Collection of official legal texts, manual annotation of FAQs, and an automated pipeline using gpt-4-turbo for synthetic QA generation. Retrieval Methods: Comparative analysis of sparse retrieval (BM25), dense retrieval (BGE-m3), and LLM-augmented retrieval (query expansion, hypothetical document generation, LLM-based reranking using various LLMs). | Dataset Creation, Manual Annotation, Synthetic Data Generation, LLM-aided Data Generation, Comparative Analysis of Retrieval Methods, Information Retrieval Techniques, LLM-based Reranking | The LawFactsQA-TW dataset is introduced as a research resource. The paper presents LLM-based methods as baselines for this dataset. | Public dataset/benchmark release | True | False | The dataset is named LawFactsQA-TW and is presented as a key contribution of the paper, referenced via a footnote, implying it is a distinct resource associated with the research. | Dataset available | The synthetic portion of the dataset has not been evaluated by legal professionals, potentially affecting its credibility. The dataset primarily covers common public queries and may not address the specific retrieval needs of legal professionals. Further collaboration with legal experts is needed. | Data Availability and Quality, Research and Evaluation Gaps, Need for Interdisciplinary Collaboration | Mitigating translation errors in cross-lingual settings, enhancing retrieval accuracy for legal texts, and effectively evaluating the quality of LLM-generated legal content (answers and expanded queries/articles). | Multilingual and Low-Resource Language Support, Accuracy and Reliability of LLM Output, Evaluation Challenges and Metrics | The paper notes a limitation that its synthetic dataset has not been evaluated by legal professionals, which could affect system credibility and expertise if deployed without such validation. This implies a risk of providing inaccurate or unreliable legal information. | Inaccurate or misleading AI output, Erosion of trust in legal system or AI, Risk of misapplication or misuse |
| meIFFFgdLAMJ.pdf | Google_Scholar | ChatGPT, Esq.: Recasting Unauthorized Practice of Law in the Era of Generative Al | This paper examines the conflict between traditional Unauthorized Practice of Law (UPL) rules and the capabilities of generative AI like GPT-4 in providing legal services. It proposes a novel UPL reform where bar associations would primarily regulate who can be designated a 'lawyer,' while allowing non-lawyers, including AI, to offer most legal services except for in-court representation, aiming to enhance access to justice. | Unauthorized Practice of Law, Generative AI for Legal Services, Regulatory Reform Proposal, Access to Justice Enhancement | True | Idealistic | True | 1.0 | Positive | A regulatory reform proposal: recasting Unauthorized Practice of Law (UPL) rules to focus on regulating the 'lawyer' designation, while permitting non-lawyers (including AI) to offer most legal services, excluding in-court representation. | Regulatory Framework / Proposal | NaN | Not Applicable | NaN | NaN | Current Unauthorized Practice of Law (UPL) rules restricting non-lawyers (including AI) from providing legal services, leading to high costs, limited access to justice (especially for low-income individuals), and potential protectionism by the legal profession. | Regulatory Hurdles, High Cost of Legal Services, Limited Access to Legal Assistance, Protectionism by Legal Profession | Recast UPL rules to primarily regulate who can claim the title 'lawyer' or 'attorney,' while allowing non-lawyers (including AI) to provide most legal services except for in-court representation. Consumer protection would rely on tort law (negligence, deceptive practices) and clear distinctions regarding lawyer status. | Policy and Regulatory Reform, Alternative Legal Service Delivery Models, Regulation, Ethics, and Governance | Reducing cost and increasing availability of legal information, advice, and document preparation for routine legal matters; reform of professional responsibility rules. | Affordability of Legal Services / Cost Reduction, Access to Legal Information, Access to Legal Advice, Legal Document Creation / Automation, Regulatory Reform (Legal Services and AI) | Low-income individuals and small businesses currently underserved by the legal system due to cost and access barriers. | Low-income individuals, Small businesses, Individuals unable to afford legal services, Individuals facing access barriers | General (Unauthorized Practice of Law regulation), Professional Responsibility, with examples from various fields like criminal law (trespassing), property law (eviction), and business law. | General Law, Professional Responsibility, Criminal Law, Property Law, Housing Law, Business Law | United States | USA | NaN | Not Applicable | Policy proposal developed through legal analysis, review of existing UPL jurisprudence and literature, and consideration of technological advancements in AI. | Policy Development Methodology, Legal Doctrinal Analysis as Design Input, Literature Review as Design Input | Adoption of revised Model Rules of Professional Conduct and corresponding changes in state-level UPL statutes and court rules, driven by bar associations and judiciaries. | Regulatory/Legal framework adoption | False | False | NaN | NaN | Further development of tort law standards for AI/non-lawyer legal service providers; specifics of civil procedure adjustments; potential need for federalizing legal ethics for non-lawyer providers; ensuring equitable access to AI-driven legal services for all demographics; addressing potential for new forms of consumer exploitation if the new framework is not carefully managed. | Regulatory and Governance Gaps, Access, Equity, and Digital Divide, Consumer Protection Gaps | Overcoming resistance from the established legal profession (judges, lawyers, bar associations); achieving consensus on the scope of UPL reform, particularly the definition of 'representation in legal proceedings'; ensuring the new framework adequately protects consumers while fostering innovation. | User Adoption, Trust, and Acceptance, Unauthorized Practice of Law (UPL) Concerns, Regulatory Uncertainty and Compliance, Ethical Considerations | If UPL is not reformed: continued lack of access to justice, stifling of innovation, anticompetitive practices by the legal profession. With AI in law generally: errors (hallucinations), bias in AI systems if not properly developed and overseen, over-reliance by consumers. With the proposed reform: potential for consumer misunderstanding or exploitation if the distinction between lawyers and non-lawyer providers is not clear or if tort remedies prove insufficient; economic disruption to the traditional legal profession. | Regulatory challenges or gaps, Stifling innovation, Negative economic impact, Inaccurate or misleading AI output, Bias and discrimination, Over-reliance on AI, Consumer harm |
| LHYfQYjVfOUJ.pdf | Google_Scholar | A.I. In Law: Adversary or Ally? Addressing the Possible Implications of A.I. Technology in Law and the Necessity of Regulation | This paper examines the benefits and significant risks (like bias and inaccuracy) of integrating AI into the legal profession, focusing on impacts on marginalized communities. It argues for a comprehensive dual regulatory framework involving government and legal institutions to ensure ethical AI deployment and uphold justice. | Discussion of AI in Legal Profession, Benefit Identification, Risk Identification, Bias in AI, AI Hallucinations/Inaccuracy, Impact on Marginalized Communities, Regulatory Framework Proposal, Ethical AI Deployment | True | Idealistic | True | 3.0 | Neutral | Discussion and evaluation of existing legal AI research tools (e.g., Lexis+ AI, Westlaw AI) and general LLMs (e.g., GPT-4), and proposal of a regulatory framework. | AI System Evaluation, Large Language Model, Review of Existing Technologies, Regulatory Framework / Proposal | References empirical evaluation by Magesh et al. (2024) assessing hallucination rates in Lexis+ AI, Westlaw AI-Assisted Research, Ask Practical Law AI, and GPT-4. Also references Narayanan & Kapoor (2024) study on AI accuracy in predicting criminal justice outcomes (7%). References Gender Shades study on facial recognition bias. | References External Evaluation | Based on Magesh et al. (2024), hallucination rates in leading AI legal research tools remained between 17% and 33%, despite vendor claims about retrieval-augmented generation (RAG). | Limitation: Hallucination or Factual inaccuracy | Algorithmic bias exacerbating systemic discrimination; unreliability and hallucinations leading to inaccurate legal information potentially harming vulnerable users; potential negative impact on employment equity for underrepresented groups in law; lack of AI literacy; exclusion of marginalized communities from AI governance. | Bias in AI/Data, AI Unreliability/Inaccuracy, Risk of AI Exacerbating Inequality, Lack of AI Literacy, Exclusion of Marginalized Communities in AI Governance/Development | Proposed dual regulatory framework (government oversight + internal legal institution governance), including mandatory sandbox evaluations, bias mitigation teams, transparency/accountability offices, mandatory education/certification for legal professionals, and inclusion of marginalized communities in policymaking (grassroots involvement/relational justice). Emphasizes human oversight. | Regulation, Ethics, and Governance, Bias Detection and Mitigation, Education and AI Literacy, Policy and Regulatory Reform, Human Oversight and Collaboration, Transparency and Explainability in AI | Algorithmic bias and discrimination; Ethical AI use in law; Reliability and accuracy of legal AI; Regulation of AI; Impact on marginalized communities; Access to justice; Employment equity in the legal profession. | Ethical AI in Law and AI Governance, Support for Vulnerable Populations, Democratizing Law / Closing Justice Gap / Rule of Law, Regulatory Reform (Legal Services and AI) | Marginalized communities, underrepresented groups, low-income individuals, early-career legal professionals and law students from historically underrepresented backgrounds, women, people of color. | Marginalized communities, Underrepresented groups, Low-income individuals, Legal professionals from underrepresented backgrounds, Law students from underrepresented backgrounds, Women, Minority groups | General legal practice, Legal research, Contract review, Case prediction, Document drafting, Criminal justice. | General Legal Practice, Legal Research, Contract Law, Document Drafting, Criminal Justice | US, EU | USA, EU | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | True | False | Discusses commercial tools like Lexis+ AI and Westlaw AI, available via subscription, and general models like GPT-4 with varied accessibility. | Commercial product or service, API access, Publicly accessible online tool or platform | Need for reliable and unbiased legal AI tools; Effective regulatory frameworks balancing innovation and risk mitigation; Improved AI literacy among legal professionals; Mechanisms for community involvement in AI governance; Addressing AI's impact on diversity and equity in the legal workforce. | AI Accuracy and Reliability, Bias in AI, Regulatory and Governance Gaps, Human Oversight and Professional Adaptation, Public Understanding, Trust, and Adoption, Access, Equity, and Digital Divide | Ensuring AI accuracy and reliability (combating hallucinations); Mitigating algorithmic bias from training data and models; Achieving transparency and accountability in AI decision-making; Developing effective and adaptive regulations; Bridging the AI literacy gap among legal professionals; Managing data privacy and security. | Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Bias in AI Systems and Data, Transparency and Explainability of AI, Accountability and Liability for AI Errors, Regulatory Uncertainty and Compliance, User Training, AI Literacy, and Skill Gaps, Data Privacy, Security, and Confidentiality | Generation of fictitious legal citations/information (hallucinations); Amplification of systemic bias and discrimination; Privacy violations; Lack of transparency and accountability; Malpractice liability due to AI errors; Job displacement, particularly impacting marginalized groups entering the profession; Erosion of public trust; Misapplication in high-stakes legal decisions (e.g., criminal justice). | Inaccurate or misleading AI output, Bias and discrimination, Data privacy and security breach, Lack of transparency, accountability, and redress, Ethical concerns, Job displacement, Erosion of trust in legal system or AI, Risk of misapplication or misuse |
| 2dTgL-HM2fkJ.pdf | Google_Scholar | Topic classification of case law using a large language model and a new taxonomy for UK law: AI insights into summary judgment | This paper introduces a novel functional taxonomy for UK law and employs the Large Language Model Claude 3 Opus to classify UK summary judgment cases based on this taxonomy. The study evaluates the LLM's accuracy (achieving 87.13% F1) and analyzes the resulting topic distributions across legal domains, courts, and time. | Taxonomy Creation, LLM Application, Legal Case Classification, UK Law Focus, Summary Judgment Focus, System Evaluation, Legal Data Analysis | True | Idealistic | True | 1.0 | Positive | Topic classification of UK case law using the Claude 3 Opus LLM, guided by a newly developed functional legal taxonomy and a specific prompt incorporating self-evaluation. | Large Language Model, Topic Modeling / Classification, Prompt Engineering, Taxonomy Development, Self-evaluation Mechanism | Manual classification by a legal expert on a statistically significant random sample (342 cases) from a dataset of 3078 summary judgment cases. Evaluation metrics included accuracy, precision, recall, F1 score (overall, macro, micro, weighted), and per-class analysis. | Expert Evaluation, Custom Dataset Evaluation, Quantitative Metrics | Claude 3 Opus achieved an overall accuracy of 87.13% (88.20% adjusted for minor naming errors) and a macro F1 score of 0.87 (weighted F1 0.89). Some topics showed lower performance, and a low rate of topic hallucination was observed (< 2%). | High performance, Limitation: Hallucination or Factual inaccuracy, Mixed performance | Summary judgments disproportionately affecting self-represented litigants; the challenge of balancing judicial efficiency with fairness and access to justice; lack of existing topic classifications for UK case law hindering analysis. | Challenges for Self-Represented Litigants, Systemic Inequities in Justice System, Tension between Judicial Efficiency and Fairness, Lack of Foundational Legal Data Resources | Developing a functional legal taxonomy and using LLMs for accurate topic classification to enable better analysis of case law trends (specifically summary judgment). This data-driven understanding can inform policy and judicial administration regarding fairness and efficiency. | Legal Knowledge Representation and Management, AI Tool Development, Legal Research and Analysis Tools, Policy and Regulatory Reform | Summary judgment procedure, fairness vs. efficiency in civil procedure, judicial administration, analysis of court trends. | Judicial System Modernization / Efficiency, Ethical AI in Law and AI Governance | Self-represented litigants. | Self-represented litigants | Civil Procedure (specifically summary judgment), with topic classification covering multiple fields including Commercial law, Dispute Resolution, Personal/Consumer Matters, Public law, Criminal law (in civil contexts), and International law. | Civil Procedure, Commercial Law, Dispute Resolution, Consumer Law, Public Law, Criminal Law, International Law | United Kingdom (UK) | UK | The technique uses the pre-trained Claude 3 Opus LLM (proprietary data). Evaluation was performed on a curated dataset of 3,078 UK summary judgment cases (XML format, unstructured text) from the Cambridge Law Corpus. | Pre-trained LLM's General Training Corpus, Proprietary Data, Evaluation Dataset, UK Legal Data, Case Law / Judgments, Data From Existing Public NLP/Legal Datasets/Benchmarks, Unstructured Text Data | Development of a new functional legal taxonomy using a grounded theory approach; Prompt engineering for the LLM, including closed-set prompting, detailed instructions, reasoning prompts, iterative refinement based on feedback, and adding self-evaluation instructions to mitigate hallucinations. | Taxonomy Development, Grounded Theory Approach, Prompt Engineering, Iterative Design Process, Hallucination Mitigation Techniques | NaN | Not applicable | True | False | The prompt and taxonomy are published in the paper. The LLM (Claude 3 Opus) is commercially available. The dataset requires access permission from the Cambridge Law Corpus. | Research artifact published in paper, Dataset available, Restricted access | Lack of comparison with other models/methods; Relatively small dataset size; Subjectivity in manual evaluation; Potential for information leakage in LLM training data; Need for further research on hallucination mitigation; Need for more objective evaluation metrics; Limited generalizability beyond summary judgment/UK law without further testing. | Research and Evaluation Gaps, Data Availability and Quality, Security and Privacy of Data, AI Accuracy and Reliability, Multilingual and Jurisdictional Specificity Gaps | Developing a suitable UK legal taxonomy; Effective prompt engineering for accuracy and hallucination reduction; Handling nuances/overlaps in legal topics; Evaluating performance accurately, especially for low-frequency topics; Distinguishing primary/secondary topics; Correcting LLM errors (hallucinations, naming discrepancies). | Domain-Specific Adaptation and Customization, Data Quality, Processing, and Preparation, Prompt Engineering and Optimization, LLM Reasoning Capabilities, Evaluation Challenges and Metrics, LLM Hallucination and Factual Errors, Output Variability and Consistency | LLM hallucinations leading to incorrect topic assignments; Inaccurate classification impacting analysis reliability; Cascading errors from dataset identification and classification; Information leakage from LLM training data; The procedure itself (summary judgment) potentially sacrificing fairness for efficiency, especially for vulnerable litigants; Risk of non-specialist judges deciding complex cases via summary judgment. | Inaccurate or misleading AI output, Data privacy and security breach, Undermining legal process or principles |
| rxTZXXLaMTcJ.pdf | Google_Scholar | Robots in the Middle: Evaluating LLMs in Dispute Resolution | This paper evaluates the performance of Large Language Models (LLMs), specifically GPT-4o, in acting as mediators for dispute resolution. Using a novel dataset of 50 dispute scenarios, the study found that LLMs can select appropriate intervention types and generate high-quality intervention messages, often outperforming human annotators in a blind evaluation. | LLM Evaluation, AI for Dispute Resolution, AI as Mediator, Dataset Creation, Performance Evaluation | True | Idealistic | True | 2.0 | Positive | Using GPT-4o to select mediation intervention types and generate intervention messages based on dispute scenarios, within the conceptual LLMediator framework. | Large Language Model, Mediation Support Tool, Conceptual Framework, Content Generation | A blind evaluation comparing GPT-4o with human annotators on a manually created dataset of 50 dispute scenarios. Evaluation included: 1) comparing choices of intervention types (5-point Likert scale), 2) comparing generated intervention messages (5-point Likert scale overall, and on understanding, neutrality, empathy, resolution quality), and 3) safety checks for LLM messages. | Expert Evaluation, Custom Dataset Evaluation, Comparative Analysis, Quantitative Metrics | In 62% of cases, LLM-chosen intervention types were rated better than or equivalent to human-chosen types. In 84% of cases, LLM-generated intervention messages were rated better than or equal to human-written messages, with LLMs outperforming humans in 60% of these cases. | Outperforms others, Comparable to others, High performance | High cost of human intermediaries, scarcity of trained facilitators, limiting access to mediation, especially for low-value disputes or in certain areas. | High Cost of Legal Services, Limited Availability/Access to Legal Professionals/Expertise | Using LLMs in Online Dispute Resolution (ODR) to provide scalable, cost-effective mediation services, thereby increasing the availability of facilitated dispute resolution. | Online Dispute Resolution (ODR), AI Tool Development, Cost Reduction and Efficiency | Online Dispute Resolution (ODR), mediation, access to justice. | Dispute Resolution, Democratizing Law / Closing Justice Gap / Rule of Law | Individuals facing cost or availability barriers to traditional mediation services. | Individuals unable to afford legal services, Individuals facing access barriers | General civil disputes (examples include parcel delivery, land property rights, noise complaints). | Civil Law, Contract Law, Property Law, Administrative Law | International | International | NaN | Not Applicable | Experimental design involving: construction of 50 diverse dispute scenarios; human and LLM (GPT-4o) selection of intervention types and drafting of intervention messages for these scenarios; blind comparative evaluation of intervention types and messages by human evaluators using Likert scales and specific criteria; safety checks for LLM outputs. | Experimental Design, Scenario-based Design/Evaluation, Human-AI Collaboration in Design, Comparative Evaluation by Humans, Safety Vetting of LLM Outputs | The full data, code, and prompts for reproducing the experiment are made available on a GitHub repository. | Public dataset/benchmark release, Open source code release | True | False | The prompts, dispute data, and code for the experiment are available on GitHub, allowing replication using the commercial OpenAI GPT-4o API. | Configuration or prompts available, Dataset available, Code available | Lack of evaluation with expert mediators and in real-world ODR systems; limitations of structured intervention tasks not reflecting real mediator processes; evaluating complex, nuanced LLM outputs objectively; need for multi-modal data integration; determining when to intervene. | Research and Evaluation Gaps, AI Scope and Functionality Limitations, AI Legal Reasoning Limitations, Need for Interdisciplinary Collaboration | Scarcity of accessible real-world dispute data (due to sensitivity/privacy) necessitating manual dataset creation; difficulty in objectively evaluating LLM performance on complex, nuanced tasks like mediation where answers are not definitively right or wrong. | Scarcity of High-Quality Legal Data, Data Privacy, Security, and Confidentiality, Cost and Complexity of Data Annotation, Evaluation Challenges and Metrics | Potential for LLMs to hallucinate information or generate unsafe messages (though not observed in this study's specific experiment with GPT-4o). | Inaccurate or misleading AI output, Harmful or unsafe AI output |
| BO49BB8AYbkJ.pdf | Google_Scholar | From Text to Structure: Using Large Language Models to Support the Development of Legal Expert Systems | This paper investigates using large language models (LLMs), specifically GPT-4, to automatically extract structured representations (pathways) from legislative text. The goal is to support the efficient development of rule-based legal expert systems, like JusticeBot, for improving access to justice. | LLM Application, Knowledge Extraction from Legal Texts, Support for Legal Expert System Development, Access to Justice Enhancement | True | Idealistic | True | 1.0 | Positive | An LLM-based framework (JusticeCreator Automatic Pathway Generator - JCAPG) using prompted GPT-4 to extract structured pathways (criteria and conclusions) from legislative text, formatted for the JusticeBot/JusticeCreator system. | Large Language Model, Information Extraction, Framework Development, Integration with Existing Systems | Evaluation by 4 experts on 40 articles from the Civil Code of Quebec. Compared GPT-4 generated pathways to manual ones using direct rating (textual/logical accuracy, usability) and a blind comparison test (E2). | Expert Evaluation, Custom Dataset Evaluation, Comparative Analysis, Quantitative Metrics | In a blind comparison test, 60% of automatically generated pathways were rated equivalent or better than manual ones. In direct evaluation, 90% were rated as correct or needing only slight adjustment for use as a basis for a JusticeBot tool. | High performance, Comparable to others, Outperforms others | The manual analysis and encoding of legislation into formal representations is time-consuming and requires legal expertise, creating a bottleneck for developing legal decision support tools. | Resource Constraints for A2J Tech Development/Deployment, Complexity of Legal Knowledge Formalization | Using LLMs (GPT-4) to automatically generate draft pathways from legislative text, which can then be reviewed and refined by legal experts, thereby increasing efficiency in developing rule-based expert systems. | Document Automation, Human Oversight and Collaboration, Cost Reduction and Efficiency, AI Tool Development | Development of legal decision support tools for laypersons based on legislation. | Access to Legal Advice, Access to Legal Information, Support for Self-Represented Litigants | Laypeople seeking to understand how legislation applies to them. | Laypeople, Individuals lacking legal knowledge | Civil Law (based on the Civil Code of Quebec). | Civil Law | Quebec (Canada) | Canada | The technique uses OpenAI's GPT-4 model. The input data for the experiment consisted of 40 selected articles from the Civil Code of Quebec. | Pre-trained LLM's General Training Corpus, Input Data for Task (Non-Training), Legal Domain Data, Canadian Legal Data, Legislation / Statutes / Regulations, Unstructured Text Data | Iterative prompt engineering for GPT-4 based on the JusticeBot methodology. Development of the JCAPG tool integrating prompt execution and JSON conversion. | Iterative Design Process, Prompt Engineering, Framework-guided Design, Tool Development | Output pathways can be imported into the JusticeCreator tool. Code and prompt shared on GitHub. | Integration into existing system/platform, Open source code release, Public dataset/benchmark release | True | True | Code and prompt available on GitHub (link provided in footnote 3). | Code available, Configuration or prompts available | Generalizability to more complex/interconnected legislation, other jurisdictions/legal traditions (beyond Quebec Civil Code), need for integration of case law/doctrine to resolve ambiguities, need for robust study of efficiency gains, and application to related tasks (e.g., mapping case facts to pathways). | AI Legal Reasoning Limitations, Multilingual and Jurisdictional Specificity Gaps, Data Availability and Quality, Research and Evaluation Gaps, AI Scope and Functionality Limitations | Ensuring logical correctness (avoiding errors like denying the antecedent), handling legal ambiguity inherent in texts, variability in valid pathway structuring, preventing model hallucination, and occasional technical errors in generating valid structures. | LLM Reasoning Capabilities, LLM Hallucination and Factual Errors, Accuracy and Reliability of LLM Output | Inaccuracy of generated pathways (textual errors, missing elements, hallucinations, logical fallacies) potentially leading to incorrect legal information or flawed system logic if not diligently verified by human experts. | Inaccurate or misleading AI output, Over-reliance on AI |
| OLZjlJlYtzIJ.pdf | Google_Scholar | Do Robot Lawyers Dream of Electric Clients? | This paper experimentally evaluates ChatGPT's ability to draft a legally sound last will based on a complex user prompt, analyzing its performance with and without jailbreaking compared to human drafting. It concludes that while ChatGPT shows potential as a lawyer's tool, it is currently unsafe for direct consumer use due to significant limitations in legal reasoning, handling ambiguity, and susceptibility to errors, especially when jailbroken. | ChatGPT Evaluation, Legal Document Drafting, Comparative Evaluation, Assessment of Legal Soundness, Limitations Identified, Unsuitability for Direct Consumer Use, Potential as Lawyer Tool | True | Idealistic | True | 2.0 | Negative | ChatGPT (version 4) for drafting a last will and testament, including testing with a 'jailbreak' prompt (DAN). | Large Language Model, Legal Document Generation / Automation, Prompt Engineering, Security / Robustness Testing | A fictional client prompt for a Virginia will with embedded legal complexities was given to ChatGPT under different conditions: 1) standard interaction, 2) with a jailbreak primer, 3) with human co-piloting (the author), 4) using its output as a rough draft. Outputs were qualitatively analyzed and compared against a will drafted independently by the author. | Qualitative Analysis, Comparative Analysis, Expert Evaluation | ChatGPT's independently drafted wills contained significant legal flaws, errors, and ambiguities related to spousal disinheritance, asset distribution, libelous statements, and identification. The jailbroken version performed worse, exhibiting degraded reasoning. Human co-piloting was necessary to rectify major issues, highlighting the need for expert supervision. | Low performance, Limitation: Operational or Technical, Underperforms others | The high cost of legal services motivating consumers to use potentially unreliable AI tools. Consumers' lack of legal expertise to evaluate AI outputs. AI's inability to correctly interpret complex/ambiguous instructions, understand legal nuances (like spousal elective share), and prioritize legal validity over problematic user requests. Widespread misconceptions about AI capabilities. | High Cost of Legal Services, AI Unreliability/Inaccuracy, Public Lack of Legal Knowledge/Awareness, Difficulty in AI-Human Interaction, AI Limitations in Legal Reasoning/Nuance, Lack of Understanding of AI Capabilities/Limitations | Human lawyers must supervise AI use, treating AI as a nonlawyer assistant under ethical rules (e.g., ABA Model Rule 5.3). Increased education for both the public and legal professionals about AI limitations is needed. Regulation for consumer protection is considered but noted as difficult due to technical challenges like jailbreaking. | Human Oversight and Collaboration, Regulation, Ethics, and Governance, Education and AI Literacy | Self-help legal document drafting (Wills), Consumer protection | Legal Document Creation / Automation, Support for Self-Represented Litigants, Protection of Rights | General public / consumers seeking to avoid legal fees. | General public, Consumers, Individuals unable to afford legal services | Wills and Estates, Legal Ethics | Wills and Estates, Legal Ethics | Virginia | USA | Proprietary data used by OpenAI for ChatGPT, described generally as massive volumes of internet text (blogs, articles, Wikipedia, etc.) combined with reinforcement learning from human feedback. | Pre-trained LLM's General Training Corpus, Proprietary Data, General Web Data / Broad Internet Text, User-Generated Content, Expert-Annotated / Human-Curated / Human-Generated Data | NaN | NaN | NaN | Not applicable | True | False | ChatGPT 4 is described as a mass-market consumer product (paid), while ChatGPT 3.5 is mentioned as free. Access is via OpenAI's platform. | Commercial product or service, Freemium access, Publicly accessible online tool or platform | Lack of public understanding regarding AI limitations versus science fiction portrayals. Difficulty in ensuring AI prioritizes legal correctness over problematic user instructions. Need for reliable methods to evaluate AI output quality and failure rates. Effective consumer protection mechanisms for AI legal tools, especially considering jailbreaking. | Public Understanding, Trust, and Adoption, Ethical Framework Deficiencies, Research and Evaluation Gaps, Consumer Protection Gaps | Evaluating proprietary 'black box' AI models. AI tendency to prioritize user satisfaction over legal accuracy. Variability and unpredictability of AI outputs. Addressing AI misuse through techniques like jailbreaking. Overcoming user misconceptions. | Transparency and Explainability of AI, Evaluation Challenges and Metrics, Accuracy and Reliability of LLM Output, Ethical Considerations, Output Variability and Consistency, Safeguarding Against Misuse and Harm, User Training, AI Literacy, and Skill Gaps | Creation of invalid or legally flawed documents (e.g., wills) by consumers using AI without supervision. Financial loss or unintended consequences due to reliance on faulty AI legal advice/drafting. Potential for libel claims arising from AI-generated content. Ethical breaches or malpractice if lawyers inadequately supervise AI assistants. | Inaccurate or misleading AI output, Consumer harm, Lack of transparency, accountability, and redress, Over-reliance on AI, Ethical concerns |
| 7PttF-rL6z8J.pdf | Google_Scholar | Through the AI -Looking Glass and What Consumers Find There* | This paper examines the risks and potential benefits of consumer-facing generative AI tools for access to justice, particularly for self-represented litigants in the US. It proposes an incentive-based regulatory framework to mitigate harms like misinformation and the unauthorized practice of law, while encouraging the development of trustworthy AI tools. | Generative AI Application, Access to Justice Enhancement, Self-Represented Litigant Assistance, US Focus, Risk Identification, AI Hallucinations/Inaccuracy, Unauthorized Practice of Law, Benefit Identification, Regulatory Framework Proposal, Trustworthy AI Development | True | Idealistic | True | 1.0 | Positive | An incentive-based regulatory framework for consumer-facing legal AI tools, offering liability shields and presumptions against UPL findings for compliant providers. | Regulatory Framework / Proposal | NaN | Not Applicable | NaN | NaN | High cost and complexity of the legal system; lack of legal representation (justice gap); difficulties for self-represented litigants in navigating the system; potential for misinformation from unregulated AI tools; protectionism within the legal profession (e.g., UPL enforcement). | High Cost of Legal Services, Complexity of Legal System/Procedures, Limited Availability/Access to Legal Professionals/Expertise, Scale of Unmet Legal Need, Challenges for Self-Represented Litigants, AI Unreliability/Inaccuracy, Regulatory Hurdles, Protectionism by Legal Profession | Utilize generative AI to provide accessible legal information and assistance; implement the proposed incentive-based regulatory scheme requiring disclosures, clear disclaimers, data protection options, transparency, and expert review; offer liability shields/presumptions for compliant providers. | AI Tool Development, Access to Legal Information and Advice, Regulation, Ethics, and Governance, Human Oversight and Collaboration, Transparency and Explainability in AI, Data Privacy and Security | Access to legal information for self-represented litigants; document drafting assistance; understanding legal procedures; navigating civil litigation. | Access to Legal Information, Support for Self-Represented Litigants, Legal Document Creation / Automation, Legal Literacy and Public Legal Education | Self-represented litigants; consumers facing legal issues without lawyers; general public needing legal assistance. | Self-represented litigants, Consumers, General public, Individuals with unmet legal needs | General Civil Litigation, Family Law, Housing Law, Consumer Protection, Traffic Law (based on examples discussed) | Civil Litigation, Family Law, Housing Law, Consumer Law, Traffic Law | United States (with comparisons to EU and China) | USA, EU, China | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Lack of clear definition for 'practice of law' / 'legal advice' concerning AI; uncertainty about liability for AI-generated errors; absence of effective US regulation for consumer-facing legal AI; need for transparency in AI operations and data usage; ensuring AI accuracy and reliability; balancing innovation with consumer protection. | Regulatory and Governance Gaps, Accountability and Redress Mechanisms, Transparency and Explainability, AI Accuracy and Reliability, Consumer Protection Gaps | Defining 'legal advice' for AI regulation; ensuring AI provider transparency; designing effective enforcement for regulations; balancing access goals against UPL and misinformation risks; overcoming legal profession skepticism; keeping pace with AI development; avoiding stifling innovation through regulation. | Regulatory Uncertainty and Compliance, Transparency and Explainability of AI, Unauthorized Practice of Law (UPL) Concerns, LLM Hallucination and Factual Errors, Safeguarding Against Misuse and Harm, User Adoption, Trust, and Acceptance | AI providing inaccurate information (hallucinations); users over-relying on AI; deepening the justice gap and user distrust; AI engaging in Unauthorized Practice of Law (UPL); privacy violations/data misuse; user manipulation via hidden prompts; bias in AI outputs; provider liability. | Inaccurate or misleading AI output, Over-reliance on AI, Exacerbation of inequality or two-tiered system, Erosion of trust in legal system or AI, Unauthorized practice of law, Data privacy and security breach, Security vulnerabilities or malicious misuse, Bias and discrimination, Lack of transparency, accountability, and redress |
| ACmFBJB5spsJ.pdf | Google_Scholar | Enhancements for Developing a Comprehensive AI Fairness Assessment Standard | This paper proposes expanding the Telecommunication Engineering Centre (TEC) Standard for AI Fairness Assessment to cover images, unstructured text, and generative AI like LLMs. The goal is to create a more comprehensive framework for responsible AI deployment by addressing biases in diverse data modalities and advanced AI models. | Proposal for AI Fairness Standard, Generative AI Fairness, Bias Assessment Framework, Responsible AI Deployment | True | Idealistic | True | 1.0 | Positive | The proposed enhanced TEC Standard for AI Fairness Assessment, incorporating specific methodologies for fairness in images (e.g., tabular reduction, XAI), unstructured text (e.g., WEAT, SEAT, GBETs), and LLMs (e.g., embedding-based, probability-based, generation-based metrics). | Fairness Assessment Framework, Explainable AI (XAI), Bias Detection / Mitigation, Metric Development | NaN | Not Applicable | NaN | NaN | Biased AI systems leading to discriminatory outcomes that disproportionately affect vulnerable or marginalized groups, reinforcing prevailing societal inequities and undermining trust in AI applications. | Bias in AI/Data, Risk of AI Exacerbating Inequality, Lack of Trust in AI/Automated Systems | Expanding and enhancing the existing TEC AI Fairness Standard to include specific assessment methodologies for images, unstructured text, and LLMs, thereby enabling more comprehensive identification and mitigation of biases in a wider range of AI systems. | Regulation, Ethics, and Governance, Benchmarking and Evaluation Frameworks, Bias Detection and Mitigation | Ensuring equitable and non-discriminatory outcomes from AI systems, especially for vulnerable and marginalized populations. This impacts fairness in diverse sectors such as telecommunications, finance, healthcare, public services, and touches upon areas like law enforcement actions and legal aid. | Ethical AI in Law and AI Governance, Support for Vulnerable Populations, Legal Aid and Pro Bono Services | Vulnerable entities, marginalized or underrepresented groups, marginalized communities. | Vulnerable populations, Marginalized communities, Underrepresented groups | NaN | NaN | India (primary focus on the TEC Standard), with references to international frameworks (ITU, NIST). | India, International | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | The current TEC Standard's limitation to structured tabular data and supervised learning models, making it less applicable to AI systems using unstructured data (images, text) and advanced models like LLMs. | Regulatory and Governance Gaps, AI Scope and Functionality Limitations | NaN | NaN | Biased or unjust AI outcomes disproportionately affecting vulnerable entities; inequalities in network access or resource allocation; perpetuation of harmful stereotypes or discrimination by image recognition systems; LLMs reinforcing societal biases and generating discriminatory or harmful content; potential for wrong medical diagnoses or autonomous vehicle accidents due to biased AI. | Bias and discrimination, Exacerbation of inequality or two-tiered system, Harmful or unsafe AI output, Consumer harm |
| jhu4mHJ3DpUJ.pdf | Google_Scholar | LegalGuardian: A Privacy-Preserving Framework for Secure Integration of Large Language Models in Legal Practice | This paper introduces LegalGuardian, a framework using NER and local LLMs to mask PII in prompts for external LLMs, aiming to protect client confidentiality in legal practice. Evaluated on synthetic immigration law prompts, it achieved high PII detection accuracy (97% F1 with Qwen2.5-14B) and maintained semantic fidelity, demonstrating a method for safer LLM use by lawyers. | Framework Proposal, PII Masking, Client Confidentiality Protection, LLM Application, System Evaluation, Immigration Law Focus, Safer LLM Use for Lawyers | True | Idealistic | True | 1.0 | Positive | LegalGuardian: a framework using Named Entity Recognition (NER) techniques (specifically GLiNER) and local LLMs (specifically Qwen2.5-14B) to mask and unmask Personally Identifiable Information (PII) in prompts sent to external LLMs. | Privacy Preservation, Named Entity Recognition (NER), Large Language Model, Framework Development, Local LLM Deployment | Evaluated using a synthetic dataset of 50 prompts in US immigration law scenarios. PII detection performance was assessed using precision, recall, and F1-score (overall and entity-level). Semantic consistency between original and masked/unmasked LLM outputs was measured using Cosine Similarity, Jaro-Winkler Distance, and Levenshtein Distance. | Custom Dataset Evaluation, Quantitative Metrics | For PII detection, Qwen2.5-14B achieved an F1-score of 97% (Precision 99%, Recall 94%), while GLiNER achieved an F1-score of 93% (Precision 100%, Recall 88%). GLiNER showed slightly higher cosine similarity (0.9808) compared to Qwen2.5-14B (0.9731) for semantic consistency. | High performance | The primary obstacle is the risk of breaching client confidentiality when lawyers use LLM-based tools due to the inclusion of PII in prompts. This hinders LLM adoption, especially for practitioners with limited resources (e.g., legal aid, solo practitioners) who cannot afford custom secure solutions, thereby limiting AI's potential to democratize legal services. | Data Privacy Concerns with AI, Security Risks with AI, Slow Technology Adoption by Legal Profession, Resource Constraints for Legal Professionals, Risk of AI Exacerbating Inequality | The paper proposes LegalGuardian, a lightweight framework that allows lawyers to mask PII in prompts before sending them to external LLMs and subsequently unmask this PII in the LLM's response. This approach aims to preserve confidentiality while enabling the use of advanced AI tools by a broader range of legal professionals. | AI Tool Development, Data Privacy and Security, Human Oversight and Collaboration | Protection of client confidentiality when using AI tools; Enabling access to advanced AI for a broader range of legal professionals, including those in legal aid or solo practice, thereby indirectly supporting access to justice goals. | Protection of Rights, Legal Aid and Pro Bono Services, Democratizing Law / Closing Justice Gap / Rule of Law | Legal professionals, particularly legal aid workers and solo practitioners with limited resources. By extension, their clients, who may include individuals from underserved communities. | Legal professionals, Legal aid professionals, Resource-limited legal professionals | Immigration law (for the synthetic dataset and scenarios); the framework is intended for broader legal practice. | Immigration Law, General Legal Practice | United States (references to ABA Model Rules, US state initiatives, and US immigration law scenarios). | USA | The evaluation involved a synthetic dataset of 50 legal prompts in US immigration law, generated using the Faker library and the Qwen-2.5 14B model. PII detection relies on the pre-trained GLiNER model (GLiNER Multi PII-v1, fine-tuned for PII) and one-shot prompting of the pre-trained Qwen2.5-14B model. | Evaluation Dataset, Author-Created New Dataset, Synthetic Data, Legal Domain Data, US Legal Data, Pre-trained LLM's General Training Corpus, Input Data for Task (Non-Training) | The framework development included: 1. Synthetic legal prompt dataset generation. 2. A PII masking layer using NER (GLiNER) and a local LLM (Qwen2.5-14B via one-shot prompting). 3. A secure prompting layer for interacting with external LLMs. 4. An evaluation layer using accuracy and semantic similarity metrics. | Conceptual Framework Development, Synthetic Data Generation, Privacy-Preserving Technique, Information Extraction Techniques, Local LLM Deployment, Prompt Engineering, Secure System Design, Evaluation Layer Development | NaN | Not applicable | False | False | NaN | NaN | Future work includes extending the framework to more legal areas, enhancing PII detection for complex data, integrating with cloud-based LLMs using privacy-preserving techniques (e.g., secure multi-party computation, federated learning), and conducting user studies with practicing lawyers. | AI Scope and Functionality Limitations, Security and Privacy of Data, Integration and Interoperability Challenges, Research and Evaluation Gaps | Balancing PII masking accuracy (privacy) with the preservation of semantic integrity and utility of LLM outputs. Ensuring comprehensive PII detection across various PII types and contexts. Developing a lightweight solution to avoid high computational costs and complexity associated with some advanced privacy-preserving methods. | Data Privacy, Security, and Confidentiality, Data Quality, Processing, and Preparation, Accuracy and Reliability of LLM Output, High Computational and Resource Demands | Unauthorized exposure of client PII to third-party LLM providers. Breaches of attorney-client privilege and data protection laws. LLM misinterpretation of prompts if masking techniques alter meaning or introduce ambiguity. Potential for sensitive information to surface in unrelated prompts if LLMs learn from input data (though LegalGuardian aims to prevent this by masking before external interaction). | Data privacy and security breach, Technical limitations of AI |
| crj8G8qyKYEJ.pdf | Google_Scholar | AI White Paper, consultation response | This paper is a consultation response by the British Irish Law, Education and Technology Association (BILETA) to the UK government's AI White Paper. BILETA critiques the proposed non-statutory, principles-based approach, advocating instead for a mandatory statutory framework for AI regulation to ensure adequate protection, fairness, and redress. | Policy Critique, Call for Statutory AI Regulation, UK Focus, AI Governance | True | Idealistic | True | 2.0 | Negative | NaN | NaN | NaN | Not Applicable | NaN | NaN | Inadequate, unclear, inaccessible redress mechanisms for AI-related harms; lack of mandatory regulation leading to potential abuse and weak enforcement; challenges in regulating foundation models (LLMs) including bias, hallucination, and societal impacts; risks to human rights (e.g., non-discrimination, fair elections). | Lack of Redress Mechanisms for AI Harms, Inadequate Legal Frameworks for AI, Technical Challenges in AI Development, Risk to Human Rights from AI | Implement a mandatory statutory regulatory framework (akin to the EU AI Act); establish clear, strong redress mechanisms including class actions and judicial review; potentially establish a single coordinating regulatory body; enhance transparency requirements; implement auditing. | Policy and Regulatory Reform, Regulation, Ethics, and Governance, Transparency and Explainability in AI | Fairness, accountability, contestability, redress, transparency, AI risk management, regulation of high-risk AI, foundation models (LLMs), human rights protection, statutory vs non-statutory regulation. | Ethical AI in Law and AI Governance, Protection of Rights, Regulatory Reform (Legal Services and AI) | General public / users / marginalized groups | General public, Marginalized communities | AI Regulation, Technology Law, Human Rights Law, Administrative Law | AI Regulation, Technology Law, Human Rights Law, Administrative Law | United Kingdom | UK | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Lack of a mandatory statutory framework in the UK proposal; inadequate redress mechanisms; insufficient clarity on handling foundation models and assigning legal responsibility; potential for overlapping/contradictory guidance from multiple regulators. | Regulatory and Governance Gaps, Accountability and Redress Mechanisms | Challenges for regulators in applying principles consistently across diverse AI applications; determining legal responsibility across the AI lifecycle, especially with foundation models; potential for overlapping or contradictory guidance from different regulators under the proposed framework. | Regulatory Uncertainty and Compliance, Accountability and Liability for AI Errors | AI reinforcing biases against marginalized groups; LLMs 'hallucinating' (providing false information); adverse impacts on workforce and economy; inadequate redress for AI harms; insufficient protection of human rights (e.g., free elections, non-discrimination, health, fair pay, freedom of expression); risks associated with specific AI applications like social scoring, remote biometric identification, predictive policing, emotion recognition, indiscriminate scraping of biometric data. | Bias and discrimination, Inaccurate or misleading AI output, Job displacement, Negative economic impact, Lack of transparency, accountability, and redress, Infringement on human rights, Security vulnerabilities or malicious misuse, Data privacy and security breach |
| feikXgtDjy8J.pdf | Google_Scholar | Continual Pre-Training is (not) What You Need in Domain Adaption | This paper investigates the efficacy of Domain-Adaptive Continual Pre-Training (DACP) for Legal Large Language Models (LLMs) in the Taiwanese legal system. It finds that while DACP enhances domain-specific knowledge, it does not uniformly improve performance across all legal tasks and can have trade-offs regarding generalization and prompt-based tasks. | LLM Pre-training Strategy, Taiwanese Law Focus, Evaluation of Domain Adaptation, Legal Language Model | True | Idealistic | True | 2.0 | Neutral | Domain-Adaptive Continual Pre-Training (DACP); Low-Rank Adaptation (LoRA); Direct Preference Optimization (DPO); Odds Ratio Preference Optimization (ORPO). Creation of LLAWA, BLLAWA, BLAWSTRAL models. | Pre-training Technique, Parameter-Efficient Fine-tuning, Optimization Technique, Model Development | Custom benchmark for Taiwanese legal framework: multiple-choice questions from Bar/Judicial Exam and Jurist Journal (Tasks A, B; accuracy metric), argument-based decision-making in legal symposia (Task C; accuracy), and essay questions from Bar/Judicial Exam (Task D; GPT-4o evaluation against segmented golden answers based on 'Juristisches Gutachten' method). | Custom Dataset Evaluation, Performance on Standardized Tests, Quantitative Metrics, LLM as Judge | DACP enhances domain-specific knowledge but does not uniformly improve performance across all legal tasks. For example, while BLAWSTRAL (LoRA-tuned Mistral-Nemo) achieved the highest accuracy on Task C (56.54%), models with DACP (LLAWA variants) did not consistently outperform base models or other fine-tuning methods on all tasks, and sometimes DACP led to performance degradation on prompting tasks. | Technique has limited or mixed impact | Lack of resources and difficulty in accessing expert-level legal analysis for individuals and organizations. | Lack of Resources, Limited Availability/Access to Legal Professionals/Expertise | Improving Legal LLMs through techniques like Domain-Adaptive Continual Pre-Training to provide more accessible expert-level legal analysis and democratize legal services. | Enhanced AI Capabilities, AI Tool Development, Access to Legal Information and Advice | Democratizing access to legal services; Making expert-level legal analysis more accessible. | Democratizing Law / Closing Justice Gap / Rule of Law, Access to Legal Advice, Legal Document Analysis / Review | Individuals and organizations that might otherwise lack the necessary resources. | Individuals unable to afford legal services, Resource-constrained organizations | Taiwanese law (general), including juvenile law, criminal law, laws, regulations, and court documents. Also references German law. | General Law, Juvenile Law, Criminal Law | Taiwan (primary), Germany (secondary, for comparative pre-training data). | Taiwan, Germany | Pre-training: Publicly available Taiwanese legal data (laws, regulations, court documents from Judicial Yuan), a German law subset from MultiLegalPile, and self-curated data (ConceptNet, CBETA). Instruction tuning: Cleaned TAIWAN CHAT (general instructions) and a legal dataset from Taiwan's Bar/Judicial Exams and Taiwan High Court website (specific legal tasks). Data is largely unstructured text. | Fine-tuning Dataset, Publicly Available Data, Taiwanese Legal Data, Legal Domain Data, Legislation / Statutes / Regulations, Case Law / Judgments, German Legal Data, Data From Existing Public NLP/Legal Datasets/Benchmarks, Author-Created New Dataset, General Web Data / Broad Internet Text, Instruction-Tuning Formatted Data, Unstructured Text Data | For LLAWA: Domain-Adaptive Pre-Training, full-parameter instruction tuning, preference alignment (DPO, ORPO). For BLLAWA & BLAWSTRAL: Low-Rank Adaptation (LoRA) for instruction tuning. | Model Pre-training, Model Fine-tuning, Preference Alignment Techniques, Parameter-Efficient Fine-Tuning (PEFT) | The paper states that models and a Hugging Face repository will be made publicly available upon acceptance or after anonymized review. | Proposed deployment (not implemented), Open source model release, Open source code release | False | False | NaN | NaN | Need for hybrid approaches combining DACP with other methods; Refinement of evaluation benchmarks for legal reasoning; Addressing potential data contamination in LLM training; Finding optimal mixture ratios for general vs. domain-specific corpora; Limitations of current evaluation metrics (e.g., BLEU/ROUGE) and potential biases in LLM-as-evaluator setups. | Research and Evaluation Gaps, AI Legal Reasoning Limitations, Data Availability and Quality, Bias in AI | DACP not uniformly beneficial, leading to trade-offs in generalization and prompt-based task performance; Fine-tuning can sometimes lead to suboptimal states (e.g., BLLAWA); Preference optimization techniques (DPO, ORPO) did not yield expected improvements under the study's conditions; Complexity in evaluating essay-type legal questions; Difficulty in modeling complex legal argumentation in settings like legal symposia. | Domain-Specific Adaptation and Customization, Accuracy and Reliability of LLM Output, Evaluation Challenges and Metrics, LLM Reasoning Capabilities | Potential for LLM hallucinations; Ensuring ethical use of legal AI; Maintaining transparency in AI decision-making; Addressing concerns about AI bias; Risk of data contamination in training leading to inflated performance perception; Biases introduced by using LLMs as evaluators. | Inaccurate or misleading AI output, Ethical concerns, Lack of transparency, accountability, and redress, Bias and discrimination, Technical limitations of AI |
| A3TgdbzreLMJ.pdf | Google_Scholar | Customizing Large Language Models for Legal Consultations | This paper introduces a multi-turn prompt engineering method to enhance large language model (LLM) performance for legal consultation, iteratively refining responses for improved accuracy and legal coherence. Evaluations using a curated legal dataset, with GPT-4 as a judge and human assessment, demonstrate the method's superiority over baselines in delivering precise and contextually relevant legal advice. | Prompting Method Proposal, LLM Performance Improvement, Legal Consultation Support, Iterative Response Refinement, Accuracy Improvement, System Evaluation | True | Idealistic | True | 1.0 | Positive | A multi-turn prompt engineering method for LLMs, designed to iteratively refine model responses in legal consultation tasks by dynamically adjusting prompts based on previous outputs. | Prompt Engineering, Large Language Model, Legal Consultation Support, Iterative Refinement | The method was evaluated on a manually curated legal query dataset (covering contract, intellectual property, constitutional law) using GPT-4 as a judge to score outputs on legal coherence, legal precision, reasoning depth, and iterative improvement. Additionally, legal professionals conducted human evaluations based on relevance, completeness, clarity, and legality. | Custom Dataset Evaluation, LLM as Judge, Expert Evaluation, Quantitative Metrics | The proposed method (OM) achieved scores of 4.8 for Legal Coherence, 4.7 for Legal Precision, 4.6 for Reasoning Depth, and 4.5 for Iterative Improvement (on a 1-5 scale). Human evaluation rated OM at 4.7 for Relevance, 4.6 for Completeness, 4.8 for Clarity, and 4.7 for Legality, significantly outperforming baseline methods. | High performance, Outperforms others, Technique improves outcome | The high cost of traditional legal representation and limited availability of legal services, particularly in underserved or remote areas. Additionally, the inherent challenges of applying general AI to the complex legal domain (e.g., lack of precision, misinterpretation of legal nuances) without specialized approaches hinder reliable A2J applications. | High Cost of Legal Services, Limited Availability/Access to Legal Professionals/Expertise, Geographical Disparities in Legal Access, AI Limitations in Legal Reasoning/Nuance, Technical Challenges in AI Development | The development and application of specialized AI techniques, such as the proposed multi-turn prompt engineering for LLMs, to generate more accurate, reliable, and contextually appropriate legal advice. This approach aims to democratize access to legal consultations, making them more affordable and broadly available, especially for underserved communities. | AI Tool Development, Prompt Engineering and LLM Interaction Design, Enhanced AI Capabilities, Access to Legal Information and Advice, Cost Reduction and Efficiency | Access to legal advice and consultation, Democratization of legal services, Improving understandability and reliability of AI-generated legal information. | Access to Legal Advice, Democratizing Law / Closing Justice Gap / Rule of Law, Access to Legal Information, Ethical AI in Law and AI Governance | Individuals in underserved or remote areas, populations with limited access to traditional legal representation due to cost or geographical constraints. | Populations in remote areas, Individuals unable to afford legal services, Geographically isolated populations | General legal consultation, with evaluation dataset examples from contract law, intellectual property law, constitutional law. The method is suggested to be adaptable to other domains like family law and corporate law. | General Legal Practice, Contract Law, Intellectual Property Law, Constitutional Law, Family Law, Corporate Law | International | International | NaN | Not Applicable | Iterative design; a multi-step pipeline involving an input layer (user query), processing layer (initial LLM response), refinement layer (iterative follow-up prompts guiding the LLM), and output layer (final, refined legal response). | Iterative Design Process, Multi-step Processing Pipeline, Prompt Engineering | NaN | Not applicable | False | False | NaN | NaN | Reliance on the quality of the initial user query; the current fixed sequence for iterative refinement could be improved with adaptive mechanisms. Further integration of domain-specific legal knowledge bases is needed. Broader ethical considerations, including privacy and bias in AI legal systems, require ongoing research. | User Interface and Usability Gaps, AI Scope and Functionality Limitations, AI Legal Reasoning Limitations, Data Availability and Quality, Ethical Framework Deficiencies, Security and Privacy of Data, Bias in AI, Research and Evaluation Gaps | Computational cost of multiple prompt iterations (though claimed to be manageable), susceptibility to errors from poorly framed initial user queries, and optimizing the iterative refinement process (e.g., determining when to stop iterations). | High Computational and Resource Demands, Prompt Engineering and Optimization, User Training, AI Literacy, and Skill Gaps, Accuracy and Reliability of LLM Output | Potential for misinterpretation of legal terminology, errors in applying legal principles, and difficulties in adhering to jurisdictional rules by LLMs (which the method aims to mitigate). Broader AI in law risks include bias, data privacy concerns, and ethical implications of automated legal advice. | Inaccurate or misleading AI output, Technical limitations of AI, Bias and discrimination, Data privacy and security breach, Ethical concerns |
| mDOOmREBPQoJ.pdf | Google_Scholar | Getting in the Door: Streamlining Intake in Civil Legal Services with Large Language Models | This paper investigates using Large Language Models (LLMs) to streamline the legal intake process for civil legal aid, focusing on eligibility determination. It describes and evaluates a digital intake platform piloted in Missouri that combines logical rules with LLMs, finding promising results with GPT-4-Turbo achieving an F1 score of .82. | LLM Application, Legal Aid Intake Automation, Eligibility Determination, System Development, System Evaluation, US Focus, Civil Legal Aid | True | Idealistic | True | 1.0 | Positive | A digital intake platform built on the Docassemble framework, which uses a combination of Python-encoded formal rules and zero-shot LLM prompting (with program-specific intake rules provided as text) to assess eligibility for legal aid and elicit further information from users. | Software / Platform Development, Integration with Existing Platforms, Hybrid AI System, Zero-shot Learning, Large Language Model, Legal Intake System, Rule-based System | Evaluated using two datasets: D1 (48 scenario-jurisdiction pairs generated via ChatGPT and manually coded) to test initial LLM response accuracy across 8 LLMs for predicting 'accept', 'deny', or 'question'; D2 (11 manually generated multi-turn conversational transcripts with GPT-4-turbo) for qualitative assessment of follow-up questions and overall interaction quality by an expert rater. | Custom Dataset Evaluation, Quantitative Metrics, Qualitative Analysis, Expert Evaluation, Comparative Analysis | GPT-4-Turbo achieved the highest overall weighted F1-score of 0.82 on dataset D1, with high precision for the 'Deny' class. Qualitative analysis (D2) by an expert rater showed 73% correct overall results, and perfect scores (5/5) for understandability and satisfaction with the tool, though noting that additional follow-up questions could have been asked by the AI in 63% of cases. | High performance, Moderate performance, Limitation: Operational or Technical | Time-consuming nature of legal intake for legal aid, nuanced and frequently changing substantive eligibility criteria, high demand for services leading to long wait times for applicants. | Resource Constraints for Legal Aid Organizations, Complexity of Legal System/Procedures, Legal Aid System Inefficiencies | A digital intake platform using LLMs combined with logical rules to provide 24/7 preliminary eligibility screening, inform applicants about their likelihood of qualifying before waiting, and potentially reduce staff burden by handling initial assessment. | AI Tool Development, Enhanced AI Capabilities, Access to Legal Information and Advice, Cost Reduction and Efficiency | Legal intake streamlining, eligibility determination for civil legal aid, reducing barriers to accessing legal help, client-facing legal technology. | Legal Aid and Pro Bono Services, Improving Efficiency in Legal System / Profession, Democratizing Law / Closing Justice Gap / Rule of Law | Low-income individuals and applicants for free legal aid programs, specifically tenants facing housing issues in Missouri. | Low-income individuals, Clients of legal aid organizations, Tenants, Population in USA | Civil legal aid, housing law, landlord-tenant law. | Civil Law, Legal Aid, Housing Law, Landlord-Tenant Law | Missouri, USA (specifically, legal aid programs in Eastern Missouri, Mid-Missouri, and Western Missouri). | USA | The technique uses pre-trained LLMs in a zero-shot setting. Program-specific substantive intake rules are provided as plain text within the prompt at inference time, along with the user's problem description. Evaluation datasets (D1 and D2) consist of scenarios generated using ChatGPT, manually reviewed, reworded, and coded, or entirely manually generated. | Pre-trained LLM's General Training Corpus, Input Data for Task (Non-Training), Legal Domain Data, Evaluation Dataset, Author-Created New Dataset, Synthetic Data, Expert-Annotated / Human-Curated / Human-Generated Data | Iterative prompt engineering (specifically for GPT-4-turbo), development of a user-facing application using the Docassemble framework, pilot testing in collaboration with four legal aid programs in Missouri. | Iterative Design Process, Prompt Engineering, User Interface Development, Third-party Framework Utilization, Pilot Testing, Stakeholder Collaboration | The intake application was piloted in Missouri, accessible on mobile phones, embedded in a legal help website (MOTenantHelp.org), and referred to in the on-hold message for callers to the phone intake system. | Pilot program/Limited rollout, Web-based access, Integration into existing system/platform | True | True | The full code and prompt are available on GitHub in two repositories. The piloted application is embedded in MOTenantHelp.org for Missouri tenants. | Code available, Configuration or prompts available, Publicly accessible online tool or platform | Integration with a seamless online intake experience, improved user analytics, simplifying rule updates (e.g., allowing staff to upload documents directly), potential for using semi-structured reasoning, further prompt and intake rule refinement, evaluation of human intake staff performance for comparison, exploration of potential LLM biases, and expansion to best-match eligibility recommendations across multiple providers. | User Interface and Usability Gaps, Integration and Interoperability Challenges, Knowledge Recency and Updatability, AI Legal Reasoning Limitations, Research and Evaluation Gaps, Bias in AI, AI Scope and Functionality Limitations | Initial LLM tendency to give inappropriate advice (addressed by clarifying its task), LLMs generating example replies leading to hallucinations (addressed by omitting examples in prompt), content censorship by some LLMs (e.g., Google Gemini for a domestic violence scenario), and prompt optimization being model-specific. | Accuracy and Reliability of LLM Output, Ethical Considerations, LLM Hallucination and Factual Errors, Safeguarding Against Misuse and Harm, Prompt Engineering and Optimization | Content censorship by LLMs may limit applicability to other legal topics (e.g., involving violence or abuse). Biased LLM training data could expose vulnerable legal aid applicants to risks (mitigated by human-in-the-loop design, focusing LLM on minimum qualification criteria, and prompting for explanations). | Technical limitations of AI, Bias and discrimination, Consumer harm |
| uTqP15w03YEJ.pdf | Google_Scholar | CHATGPT, I HAVE A LEGAL QUESTION? THE IMPACT OF GEN AI TOOLS ON LAW CLINICS AND ACCESS TO JUSTICE | This paper evaluates the accuracy of Generative AI tools like ChatGPT for providing legal advice, finding them prone to significant errors and jurisdictional confusion. It discusses the risks for non-lawyers and explores the potential for responsible use of Gen AI in law clinics to enhance access to justice and student skills, tempered by ethical considerations. | Generative AI Evaluation, Legal Advice Accuracy, AI Hallucinations/Inaccuracy, Jurisdictional Confusion Risk, Risk for Non-Lawyers, Responsible AI Use in Law Clinics, Access to Justice Enhancement, Ethical Considerations | True | Idealistic | True | 2.0 | Negative | Evaluation of Generative AI tools: ChatGPT 3.5 (free version), ChatGPT 4 (paid subscription version), Bing Chat (balanced mode), and Google Bard. | AI System Evaluation, Generative AI, Large Language Model | Six generic legal queries (family, employment, consumer, housing, online contracts, child maintenance) reflecting common legal problems were posed to four Gen AI models. Responses were rated 0-5 by two qualified lawyers based on accuracy of legal advice (currency, comprehensiveness, correct application, need for prompts, clarity for non-lawyer) and clarity of practical next steps (practicality, ADR inclusion, links provided, completeness, clarity for non-lawyer). A follow-up question regarding English law was used if necessary. | Expert Evaluation, Custom Dataset Evaluation, Quantitative Metrics, Comparative Analysis | ChatGPT4 (subscription model) performed best, scoring 73% for accuracy of legal advice and 70% for clarity of next steps. However, overall, only 13% of the initial queries across all tools were correctly answered based on UK law, with 42% of responses being too generic and 25% wrong in law. | Moderate performance, Outperforms others, Limitation: Operational or Technical, Mixed performance | High cost of legal advice and representation; limited availability of legal aid and geographical 'legal aid deserts'; public an DRAFTlack of awareness of reliable free legal helphuman-centeredsources; digital divide (cost of technology, internet access, digital literacy) disproportionately affecting low-income individuals; structural inequalities in the justice system not solely solvable by technology. | High Cost of Legal Services, Limited Availability/Access to Legal Aid, Geographical Disparities in Legal Access, Public Lack of Legal Knowledge/Awareness, Digital Divide, Systemic Inequities in Justice System | A public legal education campaign about Gen AI limitations for legal advice. Responsible integration of Gen AI in law clinics with appropriate training, policies, and student supervision. Development of bespoke, reliable legal AI solutions (though funding for free advice organisations is a challenge). Encouraging human-centered design for legal technologies. | Education and AI Literacy, Regulation, Ethics, and Governance, Human Oversight and Collaboration, AI Tool Development, User Interface and Accessibility Design, Policy and Regulatory Reform | Reliability and accuracy of Gen AI for legal queries; impact on litigants in person; role and risks of Gen AI in clinical legal education; addressing the access to justice gap. | Ethical AI in Law and AI Governance, Support for Self-Represented Litigants, Legal Education for Professionals / Students, Democratizing Law / Closing Justice Gap / Rule of Law | Litigants in person (non-lawyers); individuals with unmet legal needs, particularly highlighting BAME communities, younger people, those on low income, or with low levels of education. | Self-represented litigants, Laypeople, Individuals with unmet legal needs, Minority groups, Youth, Low-income individuals, Individuals with low education levels | Family law, employment law, consumer law, housing law, online contract law, child maintenance law. | Family Law, Employment Law, Consumer Law, Housing Law, Contract Law, Internet Law | England and Wales (queries focused on English law). The paper notes issues with AI tools defaulting to US law. | UK, USA | The Gen AI tools studied (ChatGPT, Bard, Bing Chat) are described as being trained on 'vast amounts of internet text data.' | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text | NaN | NaN | NaN | Not applicable | True | True | ChatGPT 3.5, Google Bard, and Bing Chat are freely available. ChatGPT 4 is available via paid subscription. | Publicly accessible online tool or platform, Freemium access, Commercial product or service | Need for improved accuracy, reliability, and jurisdictional awareness in Gen AI for legal advice. Ensuring equitable access to beneficial AI tools, avoiding a 'two-tiered system' based on ability to pay. Lack of public understanding of Gen AI's limitations and risks in legal contexts. Development of tailored, trustworthy AI solutions for free legal advice providers. Addressing ethical concerns regarding Gen AI training data, inherent biases, transparency, and data privacy. | AI Accuracy and Reliability, Multilingual and Jurisdictional Specificity Gaps, Access, Equity, and Digital Divide, Public Understanding, Trust, and Adoption, Ethical Framework Deficiencies, Bias in AI, Transparency and Explainability, Security and Privacy of Data | Gen AI tools providing generic, incorrect, or outdated legal advice. Frequent jurisdictional confusion (e.g., defaulting to US law when UK law is needed). Outputs lacking crucial details (e.g., legal deadlines). Difficulty for non-lawyers to critically evaluate the veracity of Gen AI responses. The dynamic nature of law requiring continuous updates to AI models (implied). | Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Outdated or Limited LLM Knowledge Base, User Training, AI Literacy, and Skill Gaps | Non-lawyers relying on inaccurate Gen AI legal advice, leading to detrimental consequences. Exacerbation of existing inequalities if more reliable AI tools are only available via paid subscriptions. Ethical issues including inherent bias in AI models, lack of transparency, and compromises to client confidentiality when sensitive data is input into Gen AI tools. Reputational and legal risks for law clinics if students misuse Gen AI. Potential for 'hallucinations' or fabrication of legal information by Gen AI. Over-reliance on AI potentially degrading research and writing skills. | Over-reliance on AI, Inaccurate or misleading AI output, Consumer harm, Exacerbation of inequality or two-tiered system, Ethical concerns, Bias and discrimination, Lack of transparency, accountability, and redress, Data privacy and security breach, Risk of misapplication or misuse, Deskilling or erosion of human skills |
| gScUXpSxSxgJ.pdf | Google_Scholar | PREDICTING CONSUMER CONTRACTS | This article empirically evaluates the ability of the GPT-3 language model to understand consumer contracts by testing its performance on a novel dataset of questions about online terms of service. While showing potential for consumer empowerment, the study finds GPT-3 exhibits brittleness regarding question wording and a possible anti-consumer bias, highlighting the need for safeguards before deploying such models in law. | LLM Evaluation, Consumer Contract Understanding, Dataset Creation, System Evaluation, Potential for Consumer Empowerment, Limitations Identified, Bias in AI, Need for Safeguards | True | Idealistic | True | 2.0 | Neutral | Evaluating GPT-3's ability to answer yes/no questions about consumer contracts (terms of service) when provided with relevant excerpts. | AI System Evaluation, Large Language Model, Legal Question Answering | A novel dataset of 200 yes/no questions was created, relating to the terms of service of the 20 most-visited U.S. websites. GPT-3 (davinci engine, temperature=0) was prompted with a contract excerpt and a question, and its accuracy and calibration were measured against random chance, majority class, and a 'contract withheld' baseline. Regression analysis controlled for variables like question category and wording. | Custom Dataset Evaluation, Quantitative Metrics, Comparative Analysis | GPT-3 achieved 77% accuracy, outperforming baselines, suggesting it used contract information. However, it performed significantly worse on questions about pro-consumer provisions (60% accuracy) compared to pro-company provisions (84% accuracy), indicating potential anti-consumer bias. Performance was also highly sensitive to question wording (readability) but not contract length or readability. | Moderate performance, Outperforms others, Limitation: Bias, Limitation: Operational or Technical | Consumers lack time, expertise, and incentive to read/understand contracts. AI models may provide misleading advice, contain harmful biases (e.g., anti-consumer bias), lack reliability due to brittleness (sensitivity to input variations), and lack interpretability, making errors hard to diagnose and trust difficult to establish. | Public Lack of Legal Knowledge/Awareness, AI Unreliability/Inaccuracy, Bias in AI/Data, Lack of AI Transparency/Explainability, Lack of Trust in AI/Automated Systems | Language models could empower consumers by reading/explaining contracts. The paper proposes ongoing experimentation (e.g., varying prompts), development of prompt design guidance, establishing technical and institutional safeguards (transparency, accountability, auditing), and regulatory reform (e.g., regarding unauthorized practice of law) to ensure responsible deployment. | Document Automation, Access to Legal Information and Advice, Prompt Engineering and LLM Interaction Design, Regulation, Ethics, and Governance, Transparency and Explainability in AI, Policy and Regulatory Reform | Understanding consumer rights and obligations in online terms of service. | Protection of Rights, Access to Legal Information | General consumers interacting with online services. | Consumers | Consumer Law, Contract Law | Consumer Law, Contract Law | US (based on the dataset of terms of service from US websites) | USA | GPT-3 was trained by OpenAI on vast unlabeled datasets (570GB+) including Common Crawl, Webtext2, online books, and Wikipedia. This data is proprietary and likely includes numerous online terms of service. | Pre-trained LLM's General Training Corpus, Proprietary Data, General Web Data / Broad Internet Text, Legal Contracts, Copyrighted Material (Source Mentioned) | Creation of a novel test dataset (200 yes/no questions on 20 terms of service), specific prompt engineering for GPT-3 interaction via API (davinci engine, temp=0), quantitative evaluation based on accuracy and calibration metrics, comparison against defined baselines, and statistical analysis (OLS regression) to identify factors influencing performance. | Dataset Creation, Prompt Engineering, API-based Development, Quantitative Evaluation Methodology, Benchmarking, Parameter Experimentation | The paper evaluates GPT-3 used via the OpenAI API; it does not deploy a tool itself but discusses the potential for future deployment of similar technologies for consumers. | Evaluation of existing third-party tool, API access, Proposed deployment (not implemented) | True | False | The methodology relies on the GPT-3 API provided by OpenAI, which is commercially available (subject to OpenAI's terms and pricing). | API access, Commercial product or service | Need for larger, more diverse, and robust legal benchmark datasets (including unanswerable questions). Deeper investigation into model biases (sources and mitigation). Improving model robustness and interpretability. Development and implementation of effective technical/institutional safeguards and governance frameworks. Addressing regulatory barriers like unauthorized practice of law rules. Need for real-world evaluation methodologies. | Data Availability and Quality, Research and Evaluation Gaps, Bias in AI, AI Accuracy and Reliability, Transparency and Explainability, Regulatory and Governance Gaps | Methodological challenges in evaluation: avoiding test data contamination, ensuring question independence, managing model stochasticity, maintaining transparency. Limitations of the study: small dataset size, single author annotating questions, narrow scope (one model, one task), reliance on yes/no format due to difficulty evaluating open-ended legal answers. Identifying and controlling for all variables influencing performance (potential omitted variable bias). | Evaluation Challenges and Metrics, Research Methodology and Study Design Limitations, Transparency and Explainability of AI | Misleading legal advice from AI; amplification and entrenchment of societal biases (e.g., anti-consumer bias); model brittleness leading to unreliable outputs; lack of interpretability hindering error diagnosis and trust; misuse for malicious purposes (misinformation, phishing, spam); data protection/privacy violations (in training data or API use); high environmental costs of training; intellectual property ownership ambiguity; unequal performance/access across languages/groups; compounding bias via feedback loops where model outputs pollute future training data. | Inaccurate or misleading AI output, Bias and discrimination, Technical limitations of AI, Lack of transparency, accountability, and redress, Erosion of trust in legal system or AI, Security vulnerabilities or malicious misuse, Data privacy and security breach, Environmental impact, Copyright or intellectual property issues, Exacerbation of inequality or two-tiered system |
| hfKbdgn8f08J.pdf | Google_Scholar | Linking Judgement Text to Court Hearing Videos: UK Supreme Court as a Case Study | This paper presents a system for automatically linking paragraphs in UK Supreme Court written judgements to relevant segments in the corresponding court hearing video transcripts. The system uses customized GPT text embeddings for information retrieval and aims to improve access to and understanding of lengthy court proceedings. | System Development, Legal Information Retrieval, Cross-Modal Linking (Text to Video), UK Law Focus, Supreme Court Focus, Access to Legal Information Enhancement | True | Idealistic | True | 1.0 | Positive | An Information Retrieval (IR) system using customized GPT-3 text embeddings (text-embedding-ada-002) and cosine similarity to link written judgement paragraphs (queries) to transcribed spoken hearing segments (corpus). Includes data augmentation via InstructGPT paraphrasing. | Information Retrieval / Search, Embedding-based Methods, Data Augmentation, Speech-to-Text Data Integration, Semantic Similarity | Initial IR models (BM25, GloVe, Entailment, Legal BERT, Asymmetric, GPT) were evaluated using MAP@k and Recall@k against human annotations on a subset to select candidates for full annotation. Supervised models (Logistic Regression, Cross-encoder, CT bi-encoder, customized GPT embeddings) were trained on annotated/augmented data and evaluated using Accuracy, Precision, Recall, F1 against gold-standard labels on a test set. | Custom Dataset Evaluation, Quantitative Metrics, Comparative Analysis | Best results achieved with customized GPT-3 embeddings combined with cosine similarity as a feature in a logistic regression model (Accuracy=0.85, Precision=0.85, Recall=0.84, F1=0.85 on the gold-standard test set). | High performance, Technique improves outcome | Key A2J obstacles identified: 1) Time required to analyze lengthy hearing videos. 2) Scarcity and difficulty of using hearing transcripts. | Difficulty Accessing/Processing Legal Information, Resource Constraints | Proposed solution: An automated tool/UI platform linking judgement text to relevant video moments via semantic search, aiding navigation and comprehension of UKSC proceedings. | AI Tool Development, User Interface and Accessibility Design, Legal Research and Analysis Tools | Improving access to and understanding of Supreme Court proceedings and judgements; Navigating lengthy legal video recordings. | Access to Legal Information, Judicial System Modernization / Efficiency | General public and legal professionals/researchers needing to understand UKSC proceedings. | General public, Legal professionals, Researchers | General (UK Supreme Court cases) | General Law, Case Law | United Kingdom | UK | Dataset derived from 7 UKSC cases (judgements from UKSC website, transcripts from custom ASR of UK National Archive videos). Annotated by law postgraduates (3620 gold links). Augmented using InstructGPT paraphrasing and negative sampling (total 7248 links). Domain-specific, mixed written/spoken register, unstructured text. | Author-Created New Dataset, Legal Domain Data, UK Legal Data, Case Law / Judgments, Publicly Available Data, Audio Data (Transcripts), Expert-Annotated / Human-Curated / Human-Generated Data, Synthetic Data, Unstructured Text Data | Information Retrieval (IR) approach (semantic search), custom ASR model development, zero-shot IR evaluation, human annotation by legal experts, data augmentation using generative AI (InstructGPT), supervised model training, embedding customization (OpenAI method), User Interface (UI) development. | Information Retrieval Techniques, Custom Model Development, Zero-shot Learning Application, Expert Annotation, Data Augmentation, Supervised Model Training, Embedding Customization, User Interface Development | Presented via demos to stakeholders (UK National Archives, UKSC, legal AI companies) with interest expressed for integration into transcription software pipelines. No wide deployment mentioned. | Dissemination via publication/presentation, Proposed deployment (not implemented), Integration into existing system/platform | False | False | NaN | NaN | Mentioned gaps: Need for larger datasets, exploring entity-based linking, improving model robustness against high-frequency irrelevant phrases. | Data Availability and Quality, AI Scope and Functionality Limitations, AI Accuracy and Reliability | Challenges: Data segmentation (judgements/transcripts), linking different linguistic modes (written/spoken), costly domain-expert annotation, distinguishing true semantic links from superficial term overlap. | Data Quality, Processing, and Preparation, Cost and Complexity of Data Annotation, LLM Reasoning Capabilities | NaN | NaN |
| gzrmVfqby74J.pdf | Google_Scholar | Generative Artificial Intelligence and Article 6 of the European Convention on Human Rights: The Right to a Human Judge? | This paper examines the implications of using generative AI in judicial processes under Article 6 of the European Convention on Human Rights (ECHR), focusing on the right to a fair trial. It argues that interpreting Article 6 through the lens of human dignity implicitly supports the right to a human judge to safeguard against dehumanisation. | Generative AI in Judicial Processes, Human Rights Implications (ECHR), Right to a Fair Trial, Right to Human Judge, Dehumanization Risk | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | Unaffordability of legal advice, significant court backlogs causing delays, potential for AI-driven advice to exacerbate system strain without improving resolution, risks of dehumanisation and undermining fair trial rights (voice, neutrality, respect, trustworthiness) through AI. | High Cost of Legal Services, Judicial/Legal System Inefficiencies, Risk of AI Exacerbating System Strain, Ethical Concerns with AI in Law, Risk to Human Rights from AI | Advocating for a human dignity-based interpretation of Article 6 ECHR to establish the right to a human judge. Proposing the use of AI to complement, not replace, human judges (e.g., automating non-judicial tasks, research assistance, bias identification), emphasizing transparency, explainability, ethical review, and potentially using AI in ADR with consent. | Policy and Regulatory Reform, Human Oversight and Collaboration, Judicial System Enhancement, Bias Detection and Mitigation, Transparency and Explainability in AI, Regulation, Ethics, and Governance, Online Dispute Resolution (ODR) | Right to a fair trial (Article 6 ECHR), access to courts, judicial efficiency, judicial decision-making, human dignity in legal processes. | Protection of Rights, Judicial System Modernization / Efficiency | NaN | NaN | Human Rights Law, Civil Procedure, Civil Justice | Human Rights Law, Civil Procedure, Civil Justice | European Convention on Human Rights (ECHR) signatory states, European Union (mentions AI Act) | ECHR, EU | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Lack of explicit recognition of a 'right to a human judge' in ECHR Article 6 interpretation. Need for equitable access to AI tools, better understanding of AI's cognitive impact on judicial work, balancing transparency with proprietary IP, defining adequate human oversight. Technical limitations in AI 'understanding', 'reasoning', bias, empathy, and explainability. | Regulatory and Governance Gaps, Access, Equity, and Digital Divide, Human Oversight and Professional Adaptation, Transparency and Explainability, AI Legal Reasoning Limitations, Bias in AI | NaN | NaN | Dehumanisation (loss of individuality, lack of genuine voice), discrimination (algorithmic bias), erosion of public trust (errors, opacity), undermining procedural fairness (neutrality, respect, trustworthiness), inaccurate outputs (hallucinations), compromised judicial independence/impartiality (external influence, hidden bias), inadequate reasoning ('black box' problem), erosion of judicial accountability. | Dehumanization of legal process, Bias and discrimination, Erosion of trust in legal system or AI, Undermining legal process or principles, Inaccurate or misleading AI output, Lack of transparency, accountability, and redress |
| pnYx_0Zyq1oJ.pdf | Google_Scholar | The Potential for Jurisdictional Challenges to AI or LLM Training Datasets | This paper critiques the use of Large Language Models (LLMs) for Access to Justice (A2J), arguing that their training datasets pose significant jurisdictional challenges related to bias, sovereignty, and the rule of law. It proposes a conceptual framework of "information sovereignty" to ensure AI tools are jurisdictionally appropriate and truly serve A2J goals. | Critique of LLMs for Access to Justice, Jurisdictional Challenges of AI Data, Bias in AI, Information Sovereignty Concept, Framework Proposal | True | Idealistic | True | 3.0 | Negative | NaN | NaN | NaN | Not Applicable | NaN | NaN | Systemic bias in LLMs due to training datasets not reflecting specific communities/jurisdictions; challenges to legal sovereignty and the rule of law from extra-jurisdictional data; failure to ensure quality and legal compliance of datasets; AI exacerbating existing inequalities (digital divide, cost); lack of transparency and accountability in AI decision-making. | Bias in AI/Data, Data Scarcity/Quality for AI, Concerns about Legal Sovereignty/Rule of Law, Risk of AI Exacerbating Inequality, Lack of AI Transparency/Explainability, Lack of AI Accountability | Proposes a conceptual framework of "information sovereignty" with four tenets: Population (limiting training data to jurisdictional individuals), Territory (defining jurisdiction by practitioners/systems), Recognition (auditable outputs reflecting community practitioners), and Regulation of borders (immutable outputs). Emphasizes the need for jurisdictionally bounded training data and encoded procedural logic. | Conceptual Frameworks, Data Curation and Management, Regulation, Ethics, and Governance, Enhanced AI Capabilities, Transparency and Explainability in AI | Procedural justice; Rule of law; Legal information provision; Document drafting; Use of AI by self-represented litigants. | Democratizing Law / Closing Justice Gap / Rule of Law, Access to Legal Information, Legal Document Creation / Automation, Support for Self-Represented Litigants | Underserved litigants; Self-represented litigants; General public unable to afford legal services. | Self-represented litigants, General public, Individuals unable to afford legal services, Individuals with unmet legal needs | General Law; Constitutional Law; Procedural Law | General Law, Constitutional Law, Procedural Law | International | International | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | LLMs lack nuance for legal technicalities and edge cases; difficulty ensuring datasets represent community norms; lack of accountability mechanisms for AI. | AI Legal Reasoning Limitations, Data Availability and Quality, Accountability and Redress Mechanisms | NaN | NaN | Systemic bias leading to unfair outcomes; undermining the rule of law; lack of transparency and accountability; inaccurate information and fabricated citations (hallucinations); exacerbating inequalities; declining public trust in the justice system; lawyers over-relying on flawed AI outputs; AI acting as a liability shield; denial of justice due to incorrect AI guidance. | Bias and discrimination, Undermining legal process or principles, Lack of transparency, accountability, and redress, Inaccurate or misleading AI output, Exacerbation of inequality or two-tiered system, Erosion of trust in legal system or AI, Over-reliance on AI |
| Iyi-fuvhE5gJ.pdf | Google_Scholar | AI in the Courts: How Worried Should We Be? | This paper presents a multi-expert discussion on the applications and implications of AI in the legal system and courts, addressing both potential benefits like enhanced access to justice and serious risks such as bias and misinformation. The authors emphasize the need for rigorous verification, transparency, and human oversight to harness AI responsibly in the legal field. | Multi-Expert Discussion, AI in Legal System, AI in Courts, Benefit Identification, Access to Justice Enhancement, Risk Identification, Bias in AI, AI Hallucinations/Inaccuracy, Need for Verification, Transparency Issues, Need for Human Oversight, Responsible AI Deployment | True | Idealistic | True | 3.0 | Positive | Generative AI (e.g., ChatGPT), Technology-Assisted Review (TAR), predictive policing tools, algorithmic risk assessment tools (e.g., COMPAS), online adjudication systems. | Generative AI, Technology-Assisted Review (TAR), Predictive Policing, Algorithmic Risk Assessment, Online Adjudication System | References external evaluations (e.g., ChatGPT on bar exam, empirical evidence for TAR); discusses concerns about lack of transparency and testability (e.g., COMPAS in Loomis case). | References External Evaluation | ChatGPT-4 passed the Uniform Bar Exam at the 90th percentile. Technology-Assisted Review (TAR) has been shown to substantially reduce e-discovery time, cost, and burden. | High performance, Benefit identified | High cost of justice; potential for AI misuse by litigants; systemic bias in AI systems leading to discriminatory outcomes; lack of verifiable reliability and fairness in AI tools. | High Cost of Legal Services, Risk of AI Misuse, Bias in AI/Data, AI Unreliability/Inaccuracy, Ethical Concerns with AI in Law | AI assistance for self-represented litigants; online adjudication systems; ensuring AI systems are valid, reliable, equitable, and unbiased through rigorous testing, transparency, auditing, and human oversight, particularly judicial gatekeeping. | Support for Self-Represented Litigants, Online Dispute Resolution (ODR), Regulation, Ethics, and Governance, Benchmarking and Evaluation Frameworks, Transparency and Explainability in AI, Human Oversight and Collaboration, Enhanced AI Capabilities, Bias Detection and Mitigation | Legal aid for self-represented litigants, online dispute resolution for minor cases, cost reduction in legal services, algorithmic bias mitigation. | Legal Aid and Pro Bono Services, Support for Self-Represented Litigants, Dispute Resolution, Affordability of Legal Services / Cost Reduction, Ethical AI in Law and AI Governance | Self-represented litigants, individuals in small claims/housing/traffic disputes, general public seeking affordable legal help. | Self-represented litigants, Litigants in small claims courts, Individuals with housing disputes, Individuals in traffic disputes, General public, Individuals unable to afford legal services | Civil procedure (e-discovery, pleadings), criminal law (sentencing, recidivism risk), general legal practice, administrative law (adjudication). | Civil Procedure, E-Discovery, Criminal Law, General Legal Practice, Administrative Law | Primarily United States, with comparative examples from UK, Colombia, China; insights are broadly applicable. | USA, UK, Colombia, China, International | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | True | True | Discussed tools like ChatGPT (free version available from OpenAI) and commercial Technology-Assisted Review (TAR) software are generally accessible. | Publicly accessible online tool or platform, Commercial product or service, Freemium access | Technical: Development of trustworthy and verifiable Generative AI; robust methods for ensuring AI fairness, reliability, and transparency_ Societal/Legal: Consensus on defining 'algorithmic fairness'; comprehensive legal and ethical regulations for AI in law; ensuring due process with AI; enhancing digital literacy among legal professionals; fostering public trust. | AI Accuracy and Reliability, Bias in AI, Transparency and Explainability, Ethical Framework Deficiencies, Regulatory and Governance Gaps, Human Oversight and Professional Adaptation, Public Understanding, Trust, and Adoption | NaN | NaN | Use of untested, invalid, or unreliable AI systems; function creep; discriminatory outcomes from biased AI; proliferation of misinformation and deepfakes; increased fraud; threats to personal privacy; AI errors ('hallucinations') in legal documents or judicial decisions; due process violations; erosion of trust in evidence; decline in essential legal skills due to over-reliance on AI. | Risk of misapplication or misuse, Bias and discrimination, Inaccurate or misleading AI output, Security vulnerabilities or malicious misuse, Data privacy and security breach, Undermining legal process or principles, Erosion of trust in legal system or AI, Deskilling or erosion of human skills, Over-reliance on AI |
| deDSwE3z9PMJ.pdf | Google_Scholar | LLM4Causal: Democratized Causal Tools for Everyone via Large Language Model | This paper introduces LLM4Causal, a large language model fine-tuned to interpret user requests for causal analysis on tabular data, execute appropriate causal tools, and explain the numerical results in simple language. The authors also propose a data generation pipeline and two benchmark datasets (Causal-Retrieval-Bench and Causal-Interpret-Bench) used for fine-tuning and evaluation. | LLM Application Development, Causal Analysis Support, Explainable AI, Dataset Creation, Benchmark Creation, Fine-tuning | True | Idealistic | True | 1.0 | Positive | LLM4Causal: A fine-tuned LLM (Llama 2 base) designed for end-to-end causal analysis workflow, including natural language query interpretation (task classification, entity extraction), selecting/executing external causal analysis tools (from libraries like CausalML, CausalDM, causal-learn), and generating natural language interpretations of the results. Uses custom fine-tuning datasets (Causal-Retrieval-Bench, Causal-Interpret-Bench) created via LLM generation and human annotation. | Fine-tuning, Large Language Model, Causal Analysis Tool, Natural Language Processing (NLP), Integration with External Tools, Dataset Creation / Curation, Model Development | Evaluated end-to-end on synthetic datasets generated for five causal tasks (CGL, ATE, HTE, MA, OPO) using Pass Rate, Relevance Rate, and Win Rate metrics. Ablation studies evaluated performance on causal entity extraction (Step 1, accuracy metric) and result interpretation (Step 3, human evaluation based on hallucination, incompleteness, non-fluency rubrics) against GPT-4-turbo. | Custom Dataset Evaluation, Quantitative Metrics, Ablation Study, Human Evaluation, Comparative Analysis | LLM4Causal significantly outperformed GPT-4. The LLM4Causal-Mixed variant achieved an average end-to-end Win Rate of 80.6% (compared to very low rates for GPT-4), 98% overall accuracy in Step 1 entity extraction (vs. 77% for GPT-4), and comparable or better performance in Step 3 interpretation based on human evaluation rubrics. | High performance, Outperforms others, Technique improves outcome | The complexity of causal inference methods, the need for specialized knowledge to use existing tools, and the difficulty for non-experts to interpret quantitative results from these tools, hindering broader access. | Complexity of Advanced Analytical Methods, Need for Specialized Knowledge for Tool Use, Difficulty Interpreting Technical Results for Non-Experts | An end-to-end system (LLM4Causal) that uses a fine-tuned LLM to automate causal analysis: understanding user queries in natural language, applying appropriate causal algorithms to user data, and explaining the findings accessibly. | AI Tool Development, Enhanced AI Capabilities, User Interface and Accessibility Design, Transparency and Explainability in AI | Democratization of causal decision-making tools, specifically targeting tasks like Causal Graph Learning (CGL), Average Treatment Effect Estimation (ATE), Heterogeneous Treatment Effect Estimation (HTE), Mediation Effect Analysis (MA), and Off-Policy Optimization (OPO). | Improving Foundational AI Capabilities for Legal Applications, Democratizing Law / Closing Justice Gap / Rule of Law | General audiences / everyone lacking specialized expertise in causal inference. | General public, Laypeople, Individuals lacking specialized knowledge | NaN | NaN | International | International | Two custom instruction-tuning datasets created for the paper: Causal-Retrieval-Bench (causal questions paired with structured JSON representations) and Causal-Interpret-Bench (context including query, task, method, numerical output paired with human-revised natural language interpretations). Data was generated using a combination of LLM (GPT-4) prompting and human/expert annotation; it is synthetic, domain-specific (causal inference), and includes structured elements. | Author-Created New Dataset, Fine-tuning Dataset, Instruction-Tuning Formatted Data, Non-Legal Domain Specific Data, Synthetic Data, Structured Data, Expert-Annotated / Human-Curated / Human-Generated Data | Definition of causal tasks, design of a three-stage framework (interpret, execute Ttools, interpret results), development of a data generation pipeline (LLM prompting + human annotation), fine-tuning a pre-trained LLM (Llama 2) using Parameter-Efficient Fine-Tuning (LoRA), integration with existing causal libraries. | Task Definition, Multi-stage Framework Design, Data Generation Pipeline, LLM-aided Data Generation, Manual Annotation, Model Fine-tuning, Parameter-Efficient Fine-Tuning (PEFT), Integration with Existing Libraries | NaN | Not applicable | False | False | NaN | NaN | Need to extend support to more causal tasks/methods, potential for integrating LLM's internal knowledge with tool use, lack of interactive capabilities for user feedback and guidance. | AI Scope and Functionality Limitations, AI Legal Reasoning Limitations, User Interface and Usability Gaps | Existing LLMs struggle with specialized causal tasks (hallucination, confusion with correlation, lack of end-to-end capability, outdated knowledge). Creating high-quality, diverse, and accurate fine-tuning data for these specialized tasks required a complex generation pipeline with human oversight. Efficiently fine-tuning large models (addressed via LoRA). | LLM Reasoning Capabilities, LLM Hallucination and Factual Errors, Outdated or Limited LLM Knowledge Base, Scarcity of High-Quality Legal Data, Cost and Complexity of Data Annotation, High Computational and Resource Demands, Domain-Specific Adaptation and Customization | Potential for inaccurate causal inference leading to poor decisions. Risk of model hallucination or incomplete/misleading interpretations misguiding users. General risks associated with democratizing powerful analytical tools without ensuring user understanding or safeguards against misuse. | Inaccurate or misleading AI output, Consumer harm, Risk of misapplication or misuse |
| uNE_TxZM_g0J.pdf | Google_Scholar | Enhancing Judicial Efficiency and Access to Justice Using AI | This study explores integrating AI into Indiana's legal system to enhance judicial productivity and access to justice. Using survey data from over 100 judges, the research applies NLP and Azure Language AI to identify concerns, informing the development of an AI awareness packet, an integration roadmap, and a comparative analysis of AI-generated content detection tools. | AI Integration in Judiciary, Judicial Productivity Enhancement, Access to Justice Enhancement, US Focus (Indiana), Survey of Judges, NLP Application, AI-Generated Content Detection | True | Idealistic | True | 1.0 | Positive | Application of NLP (Azure Language AI for sentiment analysis and key-phrase extraction) to judicial survey data, qualitative interviews with judges, and process mining. This informed the development of an AI awareness packet, an AI integration roadmap, and a comparative analysis of AI-generated content detection tools. | Natural Language Processing (NLP), Process Mining, Qualitative Data Analysis, AI Literacy / Awareness Material Development, AI Integration Strategy, AI System Evaluation | For the comparative analysis of AI content detection tools, publicly available benchmark datasets featuring AI-generated images, deepfakes, synthetic audio, and other artificial media were utilized to evaluate tool performance metrics, including precision and efficiency. The AI awareness packet was integrated into the IOCS learning management system. | Benchmark Dataset Evaluation, Quantitative Metrics, Comparative Analysis | An AI awareness packet was successfully integrated into the Indiana Office of Court Services (IOCS) learning management system. Pilot programs for AI-enhanced workflows were recommended to IOCS, and a proposal packet comparing multi-modal synthetic media detection tools was provided to IOCS leadership. | Successful real-world application, Descriptive or Conceptual finding | Judges' security concerns regarding AI tools, lack of knowledge about AI applications and their specific use-cases, and difficulty distinguishing between different types of AI tools. Broader issues include AI bias, transparency, accountability, and the unreliability of current AI-generated content detection methods against sophisticated attacks. | Security Risks with AI, Lack of AI Literacy, Lack of Understanding of AI Capabilities/Limitations, Bias in AI/Data, Lack of AI Transparency/Explainability, Lack of AI Accountability, Unreliability of AI Content Detection | Development of an AI awareness packet to educate judges on AI concepts, tools, and ethical considerations. Creation of an AI integration roadmap suggesting AI applications for specific judicial workflows like document review, calendar management, and court transcriptions. Provision of a comparative analysis of available tools for detecting AI-generated or -altered media. | Education and AI Literacy, Judicial System Enhancement, Document Automation, Legal Research and Analysis Tools | Judicial efficiency, AI literacy for judges, identification of AI-generated evidence, AI integration into court workflows, ethical AI adoption in the judiciary. | Judicial System Modernization / Efficiency, Legal Education for Professionals / Students, Ethical AI in Law and AI Governance | General public / litigants in Indiana (as indirect beneficiaries of improved access to justice and judicial efficiency). | General public, Litigants, Population in USA | Criminal law, tax law, mental health law, family law, misdemeanor cases, appellate procedure, general court administration. | Criminal Law, Tax Law, Mental Health Law, Family Law, Appellate Procedure, Court Administration | Indiana (US) | USA | Proprietary survey data from over 100 Indiana judges (quantitative and open-ended responses) and qualitative interview transcripts from 12 judges were used for NLP analysis. Publicly available benchmark datasets of AI-generated content were used for evaluating detection tools. | Proprietary Data, Survey/Interview Data, US Legal Data, Non-Legal Domain Specific Data, Evaluation Dataset, Publicly Available Data, Synthetic Data | Survey design and administration, qualitative data collection (interviews, open-ended questions), NLP (sentiment analysis, key-phrase extraction using Azure Language AI), process mining, thematic analysis, comparative market analysis of existing tools, and literature review. | Survey Methodology, Qualitative Research Methods, NLP Technique Application, Process Mining, Comparative Market Analysis, Literature Review as Design Input | The AI awareness packet was integrated into the Indiana Office of Court Services (IOCS) Learning Management System. Recommendations for pilot programs and a proposal for AI detection tools were submitted to IOCS for consideration and potential implementation. | Educational resource deployment, Integration into existing system/platform, Government/Public institution deployment, Proposed deployment (not implemented) | False | False | NaN | NaN | Limited empirical research on AI's impact on judicial bias and case outcomes. Current AI text detection methods are not robust against paraphrasing/spoofing attacks. Real-world deepfake detection requires more scalable and computationally lighter models. A general need for continuous research and adaptation of judicial AI policies. | Research and Evaluation Gaps, Bias in AI, AI Accuracy and Reliability, Transparency and Explainability, Regulatory and Governance Gaps | Balancing efficiency gains from AI with accountability and the protection of sensitive court data. Addressing judicial skepticism and lack of familiarity with AI tools. Ensuring ethical AI integration within the legal framework and maintaining data security. Identifying and selecting appropriate AI tools for specific judicial needs. | Accountability and Liability for AI Errors, Data Privacy, Security, and Confidentiality, User Adoption, Trust, and Acceptance, User Training, AI Literacy, and Skill Gaps, Ethical Considerations, Domain-Specific Adaptation and Customization | Mis D_identification or failure to identify AI-generated/altered evidence, potentially undermining justice. Proliferation of deepfakes and synthetic media in legal proceedings. Inherent biases in AI models, lack of transparency, and accountability issues. Security vulnerabilities related to sharing sensitive court data with AI systems, and AI feedback loops impacting data integrity. | Undermining legal process or principles, Security vulnerabilities or malicious misuse, Bias and discrimination, Lack of transparency, accountability, and redress, Data privacy and security breach, Technical limitations of AI |
| 3599696.3612895.pdf | Google_Scholar | Analyzing the Use of Large Language Models for Content Moderation with ChatGPT Examples | This paper proposes an enhanced content moderation pipeline integrating Large Language Models (LLMs) to improve fairness, personalization, and user communication on online social networks. It demonstrates the approach with ChatGPT examples for sex-related texts, gender stereotypes, and ableist language, highlighting the potential for user-defined rules and decision explanations. | LLM Application, Content Moderation Enhancement, Fairness in Content Moderation, Personalized Moderation, User Communication Improvement, Decision Explanation | True | Idealistic | True | 1.0 | Positive | An enhanced content moderation pipeline that integrates an LLM (using ChatGPT as an example) to classify text based on user-customizable rules (provided via prompts) and to generate explanations for moderation decisions. | Content Moderation, Large Language Model, Prompt Engineering, Explainable AI (XAI), Rule-based System, Customizable System | Qualitative demonstration using ChatGPT with specific prompts and predefined rules for three case studies: sex-related texts, texts containing gender stereotypes, and texts offensive to people with disabilities. The LLM's binary classification (violates rules: Yes/No) and its generated explanations were examined. | Demonstration or Illustrative Examples, Qualitative Analysis | ChatGPT successfully adapted to different rule sets, classifying content and providing explanations. For instance, it correctly distinguished permissible medical sex-related content and identified non-inclusive language regarding disabilities. However, it sometimes failed to detect more subtle gender stereotypes without explicit phrasing or in isolated instances. | High performance, Limitation: Bias, Limitation: Operational or Technical | Current content moderation systems are often unfair to fragile users and minorities, lack personalization, fail to provide adequate explanations for decisions, and struggle with interpreting diverse languages and cultural contexts, thereby hindering safe and inclusive online environments. | Bias/Unfairness in Automated Systems, Lack of Personalization in Automated Systems, Lack of AI Transparency/Explainability, AI Limitations in Cultural/Linguistic Nuance | Integrating LLMs into content moderation to enable personalization through user-specified rules (via prompts), provide explanations for moderation actions, enhance user-platform communication, and offer better support for human moderators. | AI Tool Development, Prompt Engineering and LLM Interaction Design, Transparency and Explainability in AI, Human Oversight and Collaboration, User Interface and Accessibility Design | Fairness and equity in online content moderation, protection of vulnerable groups from harmful content, transparency and explainability of automated moderation decisions, user empowerment in defining online content filtering. Indirectly relates to upholding principles of justice in digital spaces. | Ethical AI in Law and AI Governance, Protection of Rights, Support for Vulnerable Populations, Democratizing Law / Closing Justice Gap / Rule of Law | Fragile users (defined by age, digital literacy, education), minorities (e.g., LGBTQ+), marginalized people (e.g., based on race, religion, users from the Global South), and people with disabilities. | Vulnerable populations, Elderly people, Youth, Individuals with low digital literacy, Individuals with low education levels, Minority groups, LGBTQ+ people, Marginalized communities, Global South populations, People with disabilities | Online speech regulation, anti-discrimination principles as applied to online content, platform governance. | Internet Law, Anti-Discrimination Law, Platform Governance, Constitutional Law | International | International | NaN | Not Applicable | Conceptual framework proposal for an enhanced content moderation pipeline, demonstrated through illustrative case studies using prompt engineering with a pre-trained LLM (ChatGPT). | Conceptual Framework Development, Pipeline Development, Case Study as Design Methodology, Prompt Engineering | NaN | Not applicable | False | False | NaN | NaN | LLMs have inherent limitations such as 'hallucinations and knowledge recency.' Obtaining numeric confidence values from LLMs for their decisions is an open research problem. Designing user-friendly interfaces for rule customization and addressing privacy implications of such personalized systems are also needed. | AI Accuracy and Reliability, Knowledge Recency and Updatability, Research and Evaluation Gaps, User Interface and Usability Gaps, Security and Privacy of Data | Effectively designing prompts for LLMs to handle nuanced content moderation. LLMs' difficulty in interpreting subtle or highly contextual violations without explicit cues. The current inability of LLMs to provide numeric confidence scores for their decisions, limiting their comparability with traditional ML classifiers. | Prompt Engineering and Optimization, LLM Reasoning Capabilities, Transparency and Explainability of AI, Evaluation Challenges and Metrics | LLM limitations like 'hallucinations and knowledge recency' may lead to incorrect moderation decisions. The proposed system's reliance on binary (Yes/No) LLM outputs, due to the difficulty in obtaining confidence scores, might be insufficient for complex cases. | Technical limitations of AI, Inaccurate or misleading AI output |
| nPNWRlE8MbcJ.pdf | Google_Scholar | The Effect of Race, Gender, and Priming on AI’s Conviction Predictions | This paper experimentally evaluates ChatGPT (GPT-3.5 and GPT-4) for race and gender biases in predicting criminal conviction probabilities using manipulated defendant descriptions and priming. It finds no significant race or gender bias in either model but observes significant priming effects and better performance (lower variance, lower conviction rates) in GPT-4. | ChatGPT Evaluation, Bias Evaluation (Race and Gender), Criminal Conviction Prediction, Priming Effects in LLMs | True | Idealistic | True | 2.0 | Neutral | Evaluating ChatGPT (GPT-3.5 and GPT-4) for conviction probability prediction in a criminal case scenario using manipulated prompts (varying defendant race/gender, applying priming). | AI System Evaluation, Large Language Model, Bias Assessment, Prompt Engineering, Predictive Legal Task | Experimental design using 90 queries (45 per model) based on a modified criminal case vignette (Rachlinski et al. 2009). Defendant attributes varied across a 3x5 matrix (Gender x Race Implicit/Explicit), with three priming conditions (positive, negative, neutral). Statistical analysis (t-tests, ANOVA, regression) of predicted conviction probability ranges (0-100%). | Custom Dataset Evaluation, Quantitative Metrics | Neither GPT-3.5 nor GPT-4 showed statistically significant race or gender bias. Priming significantly affected predictions (especially GPT-3.5), generally lowering conviction rates compared to no priming. GPT-4 predicted significantly lower conviction rates and showed less variance than GPT-3.5. | Successful bias mitigation, Technique improves outcome, Outperforms others | Human cognitive biases (race, gender stereotypes) influencing judicial decisions. The 'black box' nature of proprietary LLMs hinders understanding and evaluation. | Systemic Inequities in Justice System, Lack of AI Transparency/Explainability, Proprietary Nature of AI as a Barrier | Exploring LLMs as potential decision-support tools to mitigate human biases in judicial decision-making, possibly due to algorithmic de-biasing or lack of visual cues. Need for transparency and robust evaluation. | Judicial System Enhancement, Bias Detection and Mitigation, Transparency and Explainability in AI, Benchmarking and Evaluation Frameworks | Fairness in judicial decision-making, racial bias, gender bias, conviction prediction. | Ethical AI in Law and AI Governance, Judicial System Modernization / Efficiency | General racial (Black vs. White defendants) and gender (Male vs. Female defendants) categories, implicitly addressing disparities faced by Black individuals in the criminal justice system. | Black individuals, Minority groups, Criminal defendants | Criminal Law | Criminal Law | United States (implied) | USA | Proprietary datasets used to train GPT-3.5 and GPT-4 (details not publicly known or specified in the paper). | Pre-trained LLM's General Training Corpus, Proprietary Data, Undisclosed Data Source/Availability | Experimental design (Factorial experiment), Quantitative analysis (Statistical testing: t-tests, ANOVA, linear regression). | Experimental Design, Quantitative Research Methods | NaN | Not applicable | False | False | NaN | NaN | Need for LLM transparency (training data, policies), better understanding of priming effects, development of legal LLM evaluation metrics (especially without ground truth), qualitative analysis of reasoning, larger scale testing to address randomness. | Transparency and Explainability, Research and Evaluation Gaps, AI Legal Reasoning Limitations | Lack of 'ground truth' for legal predictions, opacity of proprietary models, high sensitivity of LLMs to prompt variations (priming), randomness in LLM outputs, methodological limitations (sample size). | Evaluation Challenges and Metrics, Transparency and Explainability of AI, Prompt Engineering and Optimization, Output Variability and Consistency, Research Methodology and Study Design Limitations | Potential for AI bias perpetuation (despite negative findings here), risks associated with 'black box' models (difficulty in auditing), susceptibility to manipulation via priming/prompting, potential for poor performance or hallucinations in legal tasks. | Bias and discrimination, Lack of transparency, accountability, and redress, Security vulnerabilities or malicious misuse, Inaccurate or misleading AI output, Technical limitations of AI |
| pMuzPPoMHigJ.pdf | Google_Scholar | Guarding the News Media’s Intellectual Property in the Age of Generative AI | This paper investigates the intellectual property challenges generative AI poses to the news media, emphasizing the unauthorized use of copyrighted journalistic content for training AI models. It argues that this practice threatens the financial viability of journalism and its democratic role, proposing legislative reforms, stronger regulation, and financial support to protect news creators. | Generative AI Impact on News Media, Copyright Infringement by AI, Threat to Journalism, Call for Legislative Reform, Need for AI Regulation | True | Idealistic | True | 3.0 | Negative | NaN | NaN | NaN | Not Applicable | NaN | NaN | Unauthorized and uncompensated use of copyrighted news content for training AI, leading to financial unsustainability of news outlets; spread of misinformation and distortion of news by AI, undermining journalism's democratic role; inadequate existing legal frameworks to protect journalistic IP from AI. | Intellectual Property/Copyright Issues with AI, AI-driven Misinformation/Disinformation, Inadequate Legal Frameworks for AI, Threat to Information Ecosystem | Legislative action (e.g., Journalism Competition and Preservation Act, new AI-focused laws); enhanced regulation and enforcement by agencies like the FTC (e.g., mandatory disclosures, fines); public funding or tax breaks for journalism (e.g., Local Journalism Sustainability Act, levy on digital advertising revenue from AI). | Policy and Regulatory Reform, Regulation, Ethics, and Governance | Protection of intellectual property for news media, ensuring financial viability of journalism, combating AI-generated misinformation, upholding the democratic role of the press, public access to reliable information. | Protection of Rights, Ethical AI in Law and AI Governance, Access to Legal Information | The general public, whose access to reliable information and a functioning democracy is dependent on a viable press. | General public | Copyright Law, Intellectual Property Law, Media Law, First Amendment Law | Copyright Law, Intellectual Property Law, Media Law, Constitutional Law | United States | USA | The paper discusses AI models being trained on large, publicly available datasets scraped from the internet, which include copyrighted news articles, in-depth investigations, opinion pieces, and other journalistic content without permission or compensation. | Pre-trained LLM's General Training Corpus, Publicly Available Data, General Web Data / Broad Internet Text, Copyrighted Material (Source Mentioned), Non-Legal Domain Specific Data | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Lack of solid legal standards for resolving disputes over AI's use of copyrighted material; uncertainty about the applicability and adequacy of current copyright law (especially fair use) to generative AI; disparities in bargaining power between news outlets and AI companies; need for comprehensive legislative and regulatory frameworks specifically addressing AI and news content. | Regulatory and Governance Gaps, Access, Equity, and Digital Divide | NaN | NaN | Copyright infringement and financial deprivation for news outlets due to uncompensated use of their content for AI training; spread of AI-generated misinformation, disinformation, and fabricated news, potentially attributed to real news outlets; diminished work opportunities for journalists; reduced media diversity and public access to trustworthy information; undermining of the press's democratic and societal functions. | Copyright or intellectual property issues, Negative economic impact, Inaccurate or misleading AI output, Job displacement, Undermining democratic processes, Erosion of trust in legal system or AI |
| dkUIaaWEdX8J.pdf | Google_Scholar | AI-ASSISTED GERMAN EMPLOYMENT C ONTRACT \nREVIEW: A BENCHMARK DATASET | This paper presents a benchmark dataset of 1094 German employment contract clauses annotated for legality and fairness by legal experts. The authors provide baseline performance results using various NLP models, including fine-tuned and prompt-engineered GPT variants, for automatically identifying problematic clauses. | Dataset Creation, Benchmark Creation, German Law Focus, Employment Contract Analysis, Problematic Clause Identification, NLP Application, LLM Application | True | Idealistic | True | 1.0 | Positive | Creation and benchmarking of a dataset for German employment contract clause legality/fairness classification using transformer models (BERT, GPT-3.5, GPT-4) via fine-tuning and prompt engineering. | Dataset Creation / Curation, Benchmarking / Evaluation, Transformer Models, Fine-tuning, Prompt Engineering, Legal Text Classification, Fairness Assessment | Evaluation on a 10% held-out test set from the created dataset (893 samples after deduplication). Metrics used were Precision, Recall, and F1-score for binary classification (okay vs. problematic). Various input formats incorporating clause text, section titles, and instructions were tested. | Custom Dataset Evaluation, Quantitative Metrics | The best performance (highest weighted average F1-score 88.9%, positive class F1-score 61.5%) was achieved by fine-tuning the OpenAI gpt-3.5-turbo-1106 model with instructions and clause text only as input. | High performance, Technique improves outcome | Cost and time of traditional legal review; insufficient legal knowledge among employers and employees; scarcity of expert-annotated legal datasets. | High Cost of Legal Services, Resource Constraints, Public Lack of Legal Knowledge/Awareness, Data Scarcity/Quality for AI | Developing AI-assisted tools for contract review to reduce costs, time, and improve accessibility. Providing an open benchmark dataset to facilitate research and development of such tools. | AI Tool Development, Document Automation, Cost Reduction and Efficiency, Access to Legal Information and Advice, Data Curation and Management, Open Source Initiatives and Collaboration | Legality and fairness review of employment contract clauses. | Legal Document Analysis / Review, Protection of Rights | Employees (limited legal knowledge/financial resources) and employers (risk reduction). | Employees, Individuals lacking legal knowledge, Low-income individuals, Employers | Employment Law, Contract Law | Employment Law, Contract Law | Germany | Germany | A dataset of 1094 German employment contract clauses, sourced from a law firm's anonymized client data, annotated by two lawyers for legality (valid, unfair, void), category (14 types), and explanation. Released publicly (CC BY-NC 4.0). | Author-Created New Dataset, Publicly Available Data, Legal Domain Data, German Legal Data, Legal Contracts, Proprietary Data, Expert-Annotated / Human-Curated / Human-Generated Data, Structured Data | Dataset creation involved sourcing, anonymization, clause segmentation, multi-round expert annotation with inter-annotator agreement calculation, categorization. Baseline evaluation involved standard NLP fine-tuning and prompt engineering techniques. | Dataset Creation, Data Preprocessing, Expert Annotation, Inter-annotator Agreement Calculation, Model Fine-tuning, Prompt Engineering | NaN | Not applicable | False | True | The annotated dataset is available on GitHub under a CC BY-NC 4.0 license. | Dataset available, Open access resource | Current dataset size potentially limits fine-tuning performance (plan to expand). Baselines lack extensive hyperparameter tuning/prompt exploration. Need for advanced classification pipelines (e.g., RAG) and evaluation of a prototype system (technical, economic, social). Need to bridge the gap between research and practical application. | Data Availability and Quality, Research and Evaluation Gaps, AI Scope and Functionality Limitations | Scarcity and cost of creating expert-annotated legal datasets, especially non-English. Handling sensitive data/privacy. Potential model bias (e.g., GPT models favouring employee protection). Data imbalance. Potentially insufficient dataset size for optimal fine-tuning. | Scarcity of High-Quality Legal Data, Cost and Complexity of Data Annotation, Multilingual and Low-Resource Language Support, Data Privacy, Security, and Confidentiality, Bias in AI Systems and Data, Data Quality, Processing, and Preparation | Employees unknowingly accepting unfair/void contract terms. Employers facing lawsuits due to void clauses. AI models potentially misclassifying clauses (risk of overlooking problematic ones deemed higher). Privacy risks if data anonymization fails. | Consumer harm, Lack of transparency, accountability, and redress, Inaccurate or misleading AI output, Data privacy and security breach |
| 3xpB1xoOKekJ.pdf | Google_Scholar | Mapping the Potentials and Limitations of Using Generative AI Technologies to Address Socio-Economic Challenges in LMICs | This paper explores the potential of Generative AI (GenAI) to address socio-economic challenges in Low- and Middle-Income Countries (LMICs), drawing on experiences from 50 projects across various sectors like health, agriculture, and education. While highlighting significant opportunities, it also details substantial risks (bias, privacy, safety) and barriers (infrastructure, data, cost, language) that must be overcome for equitable and just AI deployment. | Generative AI for LMICs, Socio-Economic Challenge Mitigation, Opportunity Identification, Risk Identification, Barrier Identification, Equitable AI Deployment | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | Lack of affordable compute and reliable infrastructure; Poor data quality, availability, and representativeness (incl. bias from Western datasets); Limited capabilities for low-resourced languages; Insufficient gender-sensitive capacity; Data privacy risks due to inadequate regulations; Safety and cultural sensitivity concerns; Potential to perpetuate bias and stigma; Ethical trade-offs in resource-poor settings. | Resource Constraints for A2J Tech Development/Deployment, Data Scarcity/Quality for AI, Bias in AI/Data, Accessibility Barriers for Specific User Groups, Data Privacy Concerns with AI, Inadequate Legal Frameworks for AI, Safety/Cultural Sensitivity Issues in AI, Ethical Concerns with AI in Law | Enable local innovation through funding and platforms; Build a solid evidence base via M&E and longitudinal studies; Foster public awareness, engagement, and critical digital literacy; Establish rights-based AI governance and regulation; Mobilize resources to build local ecosystems and strengthen capacity (infrastructure, expertise, data ownership). | Policy and Regulatory Reform, Benchmarking and Evaluation Frameworks, Education and AI Literacy, Regulation, Ethics, and Governance, Data Curation and Management | Global health (healthcare access, health communication, SRH/MCH, evidence generation, disease surveillance), Agriculture (climate adaptation, crop disease detection, farmer advisory), Education (personalized learning, local content generation, literacy assessment), Financial inclusion (financial literacy/services for underserved populations), Gender equality (support for GBV survivors, access to information for women), Access to information in low-resourced languages. | Access to Legal Information, Language Access and Digital Divide, Support for Vulnerable Populations | Populations in Low- and Middle-Income Countries (LMICs), including frontline workers (health, agriculture), patients, smallholder farmers, students, rural populations, informal/small-business owners, women, survivors of Gender-Based Violence (GBV), marginalized communities (e.g., LGBTQAI+), low-literacy populations, users of low-resourced languages. | Populations in developing countries, Frontline workers, Patients, Farmers, Students, Rural populations, Small businesses, Informal sector workers, Women, Victims of gender-based violence, Marginalized communities, LGBTQ+ people, Individuals with low literacy, Speakers of low-resource languages | Data Privacy and Protection, Access to Justice (specifically for GBV), Human Rights, AI Governance and Regulation. | Data Privacy Law, Access to Justice, Gender-Based Violence Law, Human Rights Law, AI Governance, AI Regulation | LMICs (various, including specific examples from Africa, Asia, and South America) | LMICs | Varied across projects; included proprietary data collected from users (text, speech), domain-specific data (health records, agricultural info, financial queries, educational materials), sometimes requiring digitization or creation of new datasets (e.g., parallel corpora for low-resourced languages). Base models trained on large, often Western-biased datasets. | Proprietary Data, User-Generated Content, Non-Legal Domain Specific Data, OCR Processed Data, Author-Created New Dataset, Multilingual Data, Pre-trained LLM's General Training Corpus, Data Bias Concerns Noted | Co-creation with communities, human feedback loops, expert reviews, user-led testing, prompt engineering, Retrieval-Augmented Generation (RAG), fine-tuning, development of gold standard responses for evaluation, expert-in-the-loop models, gender-sensitivity training. | User-centered Design, Stakeholder Engagement/Participatory Design, Human Feedback Integration, Expert Review, Prompt Engineering, Retrieval Augmented Generation (RAG), Model Fine-tuning, Dataset Creation, Human-in-the-loop System, Ethical Consideration in Design | Deployed within specific projects/communities/institutions (e.g., hospitals, community programs) for testing or limited service provision. Some projects reported as 'live' providing services, others in user testing/validation. | Pilot program/Limited rollout, Partnership-based rollout | False | False | NaN | NaN | Need for diverse, locally reflective data repositories; Lack of comprehensive M&E and longitudinal studies on AI impacts in LMICs; Insufficient local capacity for critical AI research; Underdeveloped AI governance and regulatory frameworks in many LMICs; Need for resource mobilization to build sustainable local AI ecosystems and address infrastructure/data ownership issues. | Data Availability and Quality, Research and Evaluation Gaps, Regulatory and Governance Gaps, Access, Equity, and Digital Divide, Security and Privacy of Data | High cost and limited access to compute/infrastructure; Poor data quality, availability, and digitization needs; Supporting low-resourced languages; Mitigating bias, ensuring accuracy and cultural sensitivity; Protecting data privacy with inadequate regulations; Navigating ethical trade-offs; Budget constraints and unpredictability; Unstable connectivity; User training and adoption. | High Computational and Resource Demands, Financial Cost and Resource Constraints, Data Quality, Processing, and Preparation, Scarcity of High-Quality Legal Data, Multilingual and Low-Resource Language Support, Bias in AI Systems and Data, Accuracy and Reliability of LLM Output, Ethical Considerations, Data Privacy, Security, and Confidentiality, Regulatory Uncertainty and Compliance, Integration with Existing Systems and Workflows, User Training, AI Literacy, and Skill Gaps, User Adoption, Trust, and Acceptance | Data privacy breaches and misuse of personal/sensitive information; Harm from biased, inaccurate, or culturally insensitive AI outputs (e.g., incorrect health advice, reinforcing stereotypes); Perpetuation of discrimination and exclusion; Stigmatization; Overreliance on technology leading to neglect of human resources; Lack of accountability due to weak governance. | Data privacy and security breach, Harmful or unsafe AI output, Bias and discrimination, Over-reliance on AI, Lack of transparency, accountability, and redress, Regulatory challenges or gaps |
| N0eYrm4EzjUJ.pdf | Google_Scholar | The Path of Tax Law: Toward Legal Singularity | This paper discusses the concept of the "legal singularity," a future where AI makes law fully comprehensive and predictable, primarily drawing insights from the book "The Legal Singularity.". It explores AI's potential to revolutionize tax law, improve access to justice by increasing legal literacy and addressing service unaffordability, and outlines ethical considerations for AI development in law. | Legal Singularity Concept, AI in Tax Law, Access to Justice Enhancement, Legal Literacy Improvement, Ethical Considerations | True | Idealistic | True | 3.0 | Positive | AI-powered computational legal tools, including predictor-style machine learning models for outcome prediction (e.g., worker classification, innocent spouse relief) and generative AI (large language models) for tax research (e.g., Ask Blue J). | Machine Learning, Predictive Legal Task, Generative AI, Large Language Model, Legal Research Tool, AI Legal Tool | For predictor models: Evaluated using datasets of past court decisions (e.g., hundreds of cases for worker classification; all available cases for innocent spouse relief). For generative AI (Ask Blue J): Described as providing answers backed by relevant source documents for user verification. | Custom Dataset Evaluation, Quantitative Metrics, Demonstration or Illustrative Examples | For predictor models: Demonstrably able to extract key factors and predict future outcomes with confidence, providing detailed explanations. For generative AI (Ask Blue J): Delivers quality answers to challenging tax questions in seconds. | High performance, Developer or Vendor claim | Law's inherent incompleteness and ambiguity; unaffordability of legal representation; complexity of the law; knowledge gap between legal professionals and clients; potential for AI to act as an expensive gatekeeper or entrench inequalities; algorithmic bias and decontextualization of data. | Inherent Complexity/Ambiguity of Law, High Cost of Legal Services, Complexity of Legal System/Procedures, Information Asymmetry, Risk of AI Exacerbating Inequality, Bias in AI/Data, AI Limitations in Legal Reasoning/Nuance | Achieving "complete law" through AI; developing dynamic rules and microdirectives for clearer, specific laws; promoting universal legal literacy via AI; democratizing access to legal information; improving algorithmic design to consider social context and extralegal factors to mitigate bias; maintaining human oversight in AI-assisted legal processes. | Conceptual Frameworks, Policy and Regulatory Reform, Education and AI Literacy, Access to Legal Information and Advice, Enhanced AI Capabilities, Bias Detection and Mitigation, Human Oversight and Collaboration | Legal predictability and clarity; accessibility of legal information and services; affordability of legal representation; universal legal literacy; fairness and equity in tax law application and administration; efficiency of legal and government services. | Democratizing Law / Closing Justice Gap / Rule of Law, Access to Legal Information, Affordability of Legal Services / Cost Reduction, Access to Legal Representation, Legal Literacy and Public Legal Education, Ethical AI in Law and AI Governance, Improving Efficiency in Legal System / Profession | The general public, taxpayers, less well-resourced individuals, and specifically mentions Black taxpayers in the context of addressing algorithmic bias in IRS audits. | General public, Taxpayers, Low-income individuals, Black individuals, Minority groups | Tax law, General Law | Tax Law, General Law | United States (primarily, with references to IRS and US case law), Estonia (as an example of digital governance). | USA, Estonia | For predictor models: Datasets of past court decisions (e.g., "hundreds of past court decisions" for worker classification, "all available innocent spouse cases"). For generative AI (Ask Blue J): "Blue J’s vast tax database" (proprietary, domain-specific, includes source documents). General discussion of AI using "vast legal data sets." | Legal Domain Data, Case Law / Judgments, Undisclosed Data Source/Availability, Proprietary Data, Other Legal Documents | Machine learning, big data analytics, predictor-style models, natural language processing, large language models. For addressing bias: improving algorithmic design by considering a wider range of social context and extralegal considerations. | Machine Learning Model Development, Big Data Analytics, Natural Language Processing (NLP) Techniques, Bias Mitigation in Algorithmic Design, Sociotechnical Design Considerations | IRS use of AI for tax-related Q&A and potential tax return processing; Estonia's digital government platform; Commercial AI tools for legal professionals (e.g., Blue J's platforms). | Evaluation of existing third-party tool, Government/Public institution deployment, Commercial product/service | True | False | Ask Blue J is described as a "newly released" product from Blue J Legal. The book "The Legal Singularity" is commercially available. | Commercial product or service | Achieving full legal singularity; ensuring ethical and equitable AI development and deployment (addressing bias, fairness, accountability); continued need for legal advocacy and diverse perspectives; need for more research and multi-stakeholder collaboration; preventing AI from creating new access barriers; robustly solving data decontextualization. | AI Scope and Functionality Limitations, Ethical Framework Deficiencies, Bias in AI, Accountability and Redress Mechanisms, Human Oversight and Professional Adaptation, Research and Evaluation Gaps, Need for Interdisciplinary Collaboration, Access, Equity, and Digital Divide, AI Legal Reasoning Limitations | Capturing the nuances and multidimensionality of legal reasoning with AI; addressing data and algorithmic biases (reflection, amplification, techno-epistemic problems); managing the decontextualization of legal data when building AI tools; drafting AI-generated rules that are both clearer and more specific. | LLM Reasoning Capabilities, Bias in AI Systems and Data, Data Quality, Processing, and Preparation, Accuracy and Reliability of LLM Output | AI being reductionist in legal reasoning; algorithmic decision-making tools perpetuating and amplifying existing societal inequalities (e.g., racial disparities in audits); embedding biases in institutions under a guise of technological objectivity; AI tools becoming expensive gatekeepers to legal information, exacerbating access to justice issues; generative AI entrenching inequalities if critical information remains behind paywalls. | Technical limitations of AI, Bias and discrimination, Exacerbation of inequality or two-tiered system |
| FCFL8LhLaeMJ.pdf | Google_Scholar | COMMENTS IN RESPONSE TO THE GOVERNMENT OF CANADA ’S \nCONSULTATION QUESTIONNAIRE ON COPYRIGHT IN THE AGE OF GENERATIVE \nARTIFICIAL INTELLIGENCE | This paper responds to a Canadian government consultation, arguing against expanding copyright law to restrict Text and Data Mining (TDM) for AI training or to grant authorship to AI-generated content. It advocates for legal clarity favoring TDM (e.g., via fair dealing or exceptions) and maintaining the requirement of human authorship for copyright protection. | Policy Position (Copyright and AI), Text and Data Mining Rights, AI Authorship, Canadian Focus, Copyright Law Reform Advocacy | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | Lack of legal clarity on Text and Data Mining (TDM) under copyright law chills AI research and development; potential copyright restrictions might impede access to comprehensive training data, leading to biased or lower-quality AI; impossibility and inefficiency of clearing rights for vast training datasets; risk of copyright being expanded based on industry lobbying ('copyright trap') rather than public interest. | Intellectual Property/Copyright Issues with AI, Regulatory Uncertainty, Data Scarcity/Quality for AI, Bias in AI/Data, Influence of Lobbying on Regulation | Amend the Copyright Act to clarify that TDM/informational analysis is permissible (e.g., new exception, broadening fair dealing); reject copyright protection for AI-generated works lacking human authorship; maintain human authorship requirement; apply existing infringement doctrines carefully; avoid specific remuneration rights for TDM training data use; focus copyright policy on public interest balance, not solely industry incentives. | Policy and Regulatory Reform, Regulation, Ethics, and Governance | Legal information summarization; Empirical legal research | Access to Legal Information, LegalResearch Support, Legal Text Simplification / Plain Language | NaN | NaN | Copyright Law | Copyright Law | Canada | Canada | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Lack of legal clarity regarding Text and Data Mining (TDM) permissibility under Canadian copyright law; potential difficulties in applying liability frameworks when AI outputs infringe copyright without clear human control; discrepancy between balanced public interest goals of copyright and industry-focused framing of policy debates. | Regulatory and Governance Gaps, Accountability and Redress Mechanisms | Applying existing copyright concepts (authorship thresholds for AI-assisted work, substantial similarity and causality for infringement, authorization liability) to AI contexts; designing clear TDM exceptions that balance innovation and rights; avoiding biased AI outcomes potentially caused by restricted training data; practical impossibility of tracking/remunerating individual works in massive datasets. | Copyright and Intellectual Property Issues, Regulatory Uncertainty and Compliance, Bias in AI Systems and Data, Scarcity of High-Quality Legal Data | Copyright restrictions chilling AI research and development; decreased AI quality/fairness due to biased/incomplete data; stifling human creativity by granting copyright to vast amounts of AI-generated content; undue expansion of copyright driven by lobbying; ineffective/burdensome TDM licensing; unethical uses of generative AI (e.g., misinformation, academic dishonesty); reduced competition and transparency in the AI field. | Copyright or intellectual property issues, Stifling innovation, Bias and discrimination, Technical limitations of AI, Ethical concerns, Security vulnerabilities or malicious misuse, Negative economic impact, Lack of transparency, accountability, and redress |
| OTFgKz00ph8J.pdf | Google_Scholar | Automatic Linking of Judgements to UK Supreme Court Hearings | This paper describes J-HAL, an AI system using customized GPT embeddings to automatically link segments in UK Supreme Court written judgements to relevant timespans in court hearing videos. The goal is to create a user interface that bookmarks relevant video segments, improving access for legal professionals, academics, and the public. | System Development, Legal Information Retrieval, Cross-Modal Linking (Text to Video), UK Law Focus, Supreme Court Focus, Access to Legal Information Enhancement | True | Idealistic | True | 1.0 | Positive | Information Retrieval system (J-HAL) using customized OpenAI GPT embeddings (text-embedding-ada-002) to calculate semantic similarity between judgement paragraphs and hearing transcript segments. | Information Retrieval / Search, Embedding-based Methods, Semantic Similarity, Speech-to-Text Data Integration, Software / Platform Development | Compared multiple IR models (BM25, GloVe, Entailment, Legal BERT, Asymmetric Search, GPT) on a human-annotated dataset of 3620 judgement-transcript segment pairs derived from 7 UK Supreme Court cases. Evaluated using Mean Average Precision (MAP) and Recall @ 5, 10, 15. Optimized GPT embeddings were evaluated by comparing cosine similarity distributions. | Custom Dataset Evaluation, Comparative Analysis, Quantitative Metrics | Customized GPT embeddings performed best. The overlap between cosine similarities for relevant and irrelevant links improved from 70.5% +/- 2.7% (original GPT) to 73.0% +/- 2.6% (customized GPT). Original GPT achieved MAP@5 of 0.691 and Recall@15 of 0.914 on the full dataset. | Technique improves outcome, Moderate performance | Court hearing recordings are extremely long, making manual review inefficient. Existing transcription methods make navigating recorded arguments difficult. | Difficulty Accessing/Processing Legal Information, Resource Constraints | An automated tool (J-HAL) that uses AI to semantically link written judgments to specific timespans (bookmarks) in the corresponding hearing videos, facilitating navigation and understanding. | AI Tool Development, Legal Research and Analysis Tools, User Interface and Accessibility Design | Access to court proceedings; Understanding judicial decision-making; Navigating legal audiovisual recordings. | Access to Legal Information, Judicial System Modernization / Efficiency | Legal professionals, academics, and the general public. | Legal professionals, Academics, General public | UK Supreme Court cases (covering various fields, particularly public and constitutional law). | Case Law, Multiple Fields, Public Law, Constitutional Law | United Kingdom | UK | Judgements (7 cases, 1.4M tokens) scraped from UK Supreme Court website; Video transcripts (53 hours) from UK National Archive transcribed via custom ASR; Pretrained embeddings (GloVe, MiniLM, Legal BERT, MS MARCO, OpenAI GPT); Human-annotated dataset of 3620 judgement-transcript pairs for evaluation and GPT customization. | Author-Created New Dataset, Legal Domain Data, UK Legal Data, Case Law / Judgments, Publicly Available Data, Audio Data (Transcripts), Expert-Annotated / Human-Curated / Human-Generated Data, Unstructured Text Data, Evaluation Dataset, Fine-tuning Dataset, Data From Existing Public NLP/Legal Datasets/Benchmarks | Information Retrieval; Comparative evaluation of IR models; Zero-shot IR followed by human annotation; Embedding customization via classification model training and cosine similarity threshold optimization; User Interface development. | Information Retrieval Techniques, Comparative Analysis of Models, Zero-shot Learning Application, Human Annotation, Embedding Customization, Classification Model Training, User Interface Development | Deployed as a User-Interface (UI) platform. Mentioned application for a UK patent based on the UI. | Web-based access | False | False | NaN | NaN | Need for larger annotated datasets; Potential for more granular linking based on legal entities (articles, provisions, case names); Applicability to other domains needs exploration. | Data Availability and Quality, AI Scope and Functionality Limitations, Research and Evaluation Gaps | Difficulty of creating large-scale human annotations; Linking text across different language registers (written vs. spoken); Data preprocessing (segmentation, filtering); Balancing IR performance and computational speed. | Cost and Complexity of Data Annotation, Data Quality, Processing, and Preparation, High Computational and Resource Demands | NaN | NaN |
| KjEhfsM_c_sJ.pdf | Google_Scholar | Enhancing Judicial Efficiency: The Role of AI and Blockchain in Modernizing Legal Systems | This paper explores how AI and blockchain technologies can improve judicial efficiency by addressing challenges like delays and backlogs. It reviews current applications, proposes an integrative framework, discusses associated risks and ethical considerations, and notably uses an AI pipeline involving generative AI for its own creation. | AI for Judicial Efficiency, Blockchain for Judicial Efficiency, Framework Proposal, Risk Identification, Ethical Considerations, Generative AI Application (in research) | True | Idealistic | True | 3.0 | Positive | AI (for case management, legal research, predictive analysis), Blockchain (for record management, security, transparency), Smart Contracts (for automating agreements) | Artificial Intelligence (General), Case Management System, Legal Research Tool, Predictive Analysis, Blockchain Technology, Smart Contracts | NaN | Not Applicable | NaN | NaN | Judicial inefficiencies including procedural delays, case overload/backlogs, excessive costs, lack of transparency, and bureaucratic bottlenecks. | Judicial/Legal System Inefficiencies, High Cost of Legal Services, Lack of Transparency in Justice System | Integration of AI (for automation, predictive analysis), Blockchain (for secure, transparent records), and Smart Contracts (for automated agreements) within a strategic framework to streamline procedures and enhance efficiency. | AI Tool Development, Enhanced AI Capabilities, Data Privacy and Security, Document Automation, Cost Reduction and Efficiency, Conceptual Frameworks, Transparency and Explainability in AI | Judicial efficiency, Case management, Reducing delays and backlogs, Transparency in judicial processes, Secure record-keeping, Access to justice | Judicial System Modernization / Efficiency, Democratizing Law / Closing Justice Gap / Rule of Law | General public, potentially marginalized groups | General public, Marginalized communities | General judicial processes | General Law, Judicial Processes | International | International | The paper reviews techniques using data like legal documents, case histories, and judicial records. The AI pipeline used for writing relied on the general large datasets of LLMs like ChatGPT. | Legal Domain Data, Case Law / Judgments, Other Legal Documents, Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text | An AI Pipeline utilizing tools like ChatGPT 4o, Perplexity, Consensus, Elicit, Zotero plugins, and Grammarly for topic selection, literature review, structuring, writing, and refinement. | AI-assisted Research Workflow, Third-party Tool Integration, Pipeline Development | NaN | Not applicable | False | False | NaN | NaN | Ethical concerns (privacy, bias, AI opacity), technical challenges (interoperability, skill development), resource constraints, need for human oversight, limitations of smart contracts complexity, underutilized opportunities (ADR, access to justice platforms, training tools). | Ethical Framework Deficiencies, Security and Privacy of Data, Bias in AI, Transparency and Explainability, Integration and Interoperability Challenges, Human Oversight and Professional Adaptation, Computational Resource and Cost Issues, AI Scope and Functionality Limitations, Access, Equity, and Digital Divide | Challenges implementing AI/Blockchain: Ethical issues, technical barriers (interoperability), resource needs, legal compliance, security. Challenges using AI pipeline for writing: Difficulty generating original reasoning, AI defaulting to reproduction, robustness of insights, managing context windows, tool instability (e.g., ChatGPT Canvas). | Ethical Considerations, Integration with Existing Systems and Workflows, Financial Cost and Resource Constraints, Regulatory Uncertainty and Compliance, Data Privacy, Security, and Confidentiality, LLM Reasoning Capabilities, Accuracy and Reliability of LLM Output, LLM Context Window and Long Input Management | Ethical risks (privacy violations, algorithmic bias, lack of transparency/accountability), security risks (data manipulation/breaches if not secured), over-reliance on AI, potential for deskilling, technical failures, poor interoperability. | Ethical concerns, Data privacy and security breach, Bias and discrimination, Lack of transparency, accountability, and redress, Security vulnerabilities or malicious misuse, Over-reliance on AI, Deskilling or erosion of human skills, Technical limitations of AI |
| ZRf7TqNsvaYJ.pdf | Google_Scholar | Large Language Models (LLMs) for Legal Advice: A Scoping Review | This paper provides a scoping review of the use and potential use of Large Language Models (LLMs) for generating legal advice, focusing on the US and UK jurisdictions. It synthesizes literature on the benefits, such as reduced costs and improved access, and significant risks, including misinformation (hallucinations), bias, copyright issues, and the need for regulation. | Scoping Review, LLMs for Legal Advice, US Focus, UK Focus, Benefit Identification, Risk Identification, AI Hallucinations/Inaccuracy, Bias in AI, Copyright Issues, Need for AI Regulation | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | High cost of traditional legal services; Risk of LLMs providing false/misleading information (hallucinations); Potential for LLMs to encourage vexatious litigation, delaying justice for others; Lack of attorney-client privilege and ethical guarantees with LLMs; Privacy risks associated with LLM data use; LLM biases reinforcing societal inequalities. | High Cost of Legal Services, AI Unreliability/Inaccuracy, Risk of AI Misuse, Erosion of Legal Professional Standards, Ethical Concerns with AI in Law, Data Privacy Concerns with AI, Bias in AI/Data | Improving LLM accuracy (e.g., linking to verified sources); Developing "justice bots" to help laypeople navigate legal issues; Using LLMs to translate legal jargon into plain language; Implementing technical safeguards (watermarking, fine-tuning, censoring); Establishing clear regulations and industry standards (e.g., transparency obligations, risk-based approaches); Educating users (lawyers and public) about LLM limitations. | Enhanced AI Capabilities, AI Tool Development, Access to Legal Information and Advice, Language Simplification and Multilingual Access, Regulation, Ethics, and Governance, Education and AI Literacy | Reducing legal costs, Legal information access and understanding (plain language), Issue identification for laypersons, Assistance for self-represented litigants. | Affordability of Legal Services / Cost Reduction, Access to Legal Information, Legal Text Simplification / Plain Language, Support for Self-Represented Litigants | People with lower socio-economic status, General consumers facing corporations or bureaucracy. | Low-income individuals, Consumers | Broad / Multiple fields including Civil litigation, Tax law, Contract law, Criminal law (sentencing/probation context), Consumer law. | Multiple Fields, Civil Litigation, Tax Law, Contract Law, Criminal Law, Consumer Law | US, UK | USA, UK | General LLMs: Terabytes of broad internet data, potentially including copyrighted materials. Legal-specific LLMs: Fine-tuned on legal text databases (cases, legislation) from sources like Westlaw, LexisNexis, Casetext, or proprietary curated legal/financial datasets (e.g., KL3M). | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Copyrighted Material (Source Mentioned), Fine-tuning Dataset, Legal Domain Data, Case Law / Judgments, Legislation / Statutes / Regulations, Proprietary Data | Sandbox testing, User evaluation, Red-teaming (for Harvey); Benchmarking using curated legal task datasets (LawBench, LegalBench); Reinforcement Learning with Human Feedback (RLHF) for alignment; Ontology creation from legal concepts (older ML example). | Sandbox Testing, User Evaluation, Red Teaming/Security Testing, Benchmarking, Dataset Creation, Reinforcement Learning from Human/AI Feedback (RLHF/RLAIF), Ontology Development | Internal deployment within law firms (e.g., Harvey); Public web/app access for consumers (e.g., DoNotPay, ChatGPT); Planned commercial release for industry professionals (e.g., KL3M); Research platforms/benchmarks. | Evaluation of existing third-party tool, Internal deployment/prototype, Web-based access, Commercial product/service, Proposed deployment (not implemented), Public dataset/benchmark release | True | False | Public access via web interfaces/APIs (e.g., ChatGPT, Gemini) some with free tiers; Consumer service model (e.g., DoNotPay). | Publicly accessible online tool or platform, API access, Freemium access, Commercial product or service | Need for systematic empirical evaluation of LLM legal advice quality and user perception; Understanding and mitigating cross-jurisdictional/cultural biases; Continuous evaluation due to rapid model evolution; Need for qualitative research on lawyer adoption/experience; Legal clarification on AI copyright (input and output); Gaps and inconsistencies in regulatory approaches (US/UK). | Research and Evaluation Gaps, Public Understanding, Trust, and Adoption, Bias in AI, Multilingual and Jurisdictional Specificity Gaps, Human Oversight and Professional Adaptation, Regulatory and Governance Gaps | Ensuring accuracy / mitigating hallucinations; Mitigating dataset bias; Navigating copyright complexities (training data and generated output); Implementing effective and robust safeguards (alignment, preventing misuse); Managing data poisoning and model collapse risks; Addressing the 'black box' transparency problem; Managing user expectations and avoiding misleading claims. | Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Bias in AI Systems and Data, Copyright and Intellectual Property Issues, Safeguarding Against Misuse and Harm, Transparency and Explainability of AI, User Adoption, Trust, and Acceptance | Generating false or misleading legal information (hallucinations); Wasting court resources and causing delays; Undermining trust in the legal system; Encouraging vexatious litigation; Lack of attorney-client privilege and confidentiality; Disclosure of private user data; Embedding and amplifying societal biases; Copyright infringement (input data and generated output); Data poisoning and pollution leading to model degradation; Circumvention of safety guardrails (jailbreaking). | Inaccurate or misleading AI output, Undermining legal process or principles, Erosion of trust in legal system or AI, Data privacy and security breach, Bias and discrimination, Copyright or intellectual property issues, Technical limitations of AI, Security vulnerabilities or malicious misuse |
| ck8Ac0neujYJ.pdf | Google_Scholar | AI and access to justice : How AI legal advisors can reduce economic and shame-based barriers to justice | This paper argues that publicly funded Artificial Intelligence Legal Advisors (AI LAs), particularly large language models specialized for law, can lower barriers to accessing the legal system. It focuses on how these tools can mitigate economic costs and shame-based cultural obstacles during the initial information-gathering stage of pursuing justice. | AI Legal Advisors, Access to Justice Enhancement, Specialized Legal LLMs, Cost Reduction in Legal Access, Overcoming Cultural Barriers to Justice | True | Idealistic | True | 3.0 | Positive | Artificial Intelligence Legal Advisors (AI LAs), described as specialized AI systems (potentially LLMs) providing legal information and preliminary assessment. | AI Legal Advisor, Large Language Model, Legal Information Provision, Preliminary Legal Assessment | NaN | Not Applicable | NaN | NaN | Economic barriers (financial costs, time/opportunity costs, transportation costs, lack of resources, lack of awareness of rights or affordable legal options) and shame-based cultural barriers (stigma associated with seeking legal help, particularly for victims of intimate partner violence, individuals disputing cultural norms like inheritance practices, or victims of fraud; fear of judgment or social reprisal). | High Cost of Legal Services, Resource Constraints, Public Lack of Legal Knowledge/Awareness, Psychological/Cultural Barriers to Seeking Help/Engaging with Law | Developing and deploying publicly funded AI Legal Advisors (AI LAs) that offer reliable, specific, and intelligible legal information, preliminary case assessment, and interactive explanations. This aims to reduce costs and provide a private, non-judgmental means of information gathering. | AI Tool Development, Policy and Regulatory Reform, Access to Legal Information and Advice, Cost Reduction and Efficiency, Data Privacy and Security, Transparency and Explainability in AI | Access to legal information, preliminary case assessment, understanding legal rights and recourse, reducing barriers during the information-gathering stage. Specific examples include intimate partner violence (IPV) protection orders, inheritance rights disputes, and pursuing claims related to fraud. | Access to Legal Information, Legal Document Analysis / Review, Protection of Rights | People with low socio-economic status (SES), marginalized populations facing cultural barriers (e.g., women expected to relinquish inheritance rights), victims of intimate partner violence (IPV), victims of fraud. | Low-income individuals, Marginalized communities, Women, Victims of domestic violence, Victims of fraud | General Civil Law, Housing Law (example: JusticeBot), Family Law (IPV context), Inheritance Law, Consumer Law (Fraud context). | Civil Law, Housing Law, Family Law, Domestic Violence Law, Wills and Estates, Consumer Law | Anglo-American common law systems (stated scope). Examples also draw from the US, UK, Canada, and Quebec (JusticeBot). | USA, UK, Canada, Common Law Jurisdictions | Implied to be case law, noted as potentially containing historical biases. | Legal Domain Data, Case Law / Judgments, Data Bias Concerns Noted, Undisclosed Data Source/Availability | NaN | NaN | Advocates for public funding by governments and international organizations. | Advocacy for policy/funding changes | False | False | NaN | NaN | Ensuring AI LAs reach reliability and accuracy standards comparable to human lawyers. Developing methods to mitigate biases present in legal training data (case law). | AI Accuracy and Reliability, Bias in AI, Data Availability and Quality | Achieving sufficient reliability and accuracy for AI LAs. Mitigating inherent biases in training data. Potential for increased caseloads on the existing legal system. Establishing frameworks for legal responsibility and liability for AI errors. | Accuracy and Reliability of LLM Output, Bias in AI Systems and Data, Scalability of Solutions, Accountability and Liability for AI Errors | AI LAs inheriting and perpetuating biases from historical case law. Potential for increased litigation burdening the legal system. AI LA malfunction or error leading to incorrect advice (e.g., dissuading a valid claim or encouraging a futile one), causing harm to users. Difficulty in assigning legal responsibility for harms caused by AI advice errors. | Bias and discrimination, Undermining legal process or principles, Inaccurate or misleading AI output, Consumer harm, Lack of transparency, accountability, and redress |
| XurNiV9wTRQJ.pdf | Google_Scholar | Chat Kanoon: A Novel Approach to Legal Assistance in India | This paper introduces ChatKanoon, a multilingual AI chatbot leveraging GPT-4 and Llama2 70B through instructional techniques to provide legal assistance within the Indian legal system. It aims to democratize access to legal information, reduce costs, and enhance the efficiency of legal processes in India. | Chatbot Development, Multilingual System, LLM Application, Legal Assistance Provision, India Focus, Access to Legal Information Enhancement, Cost Reduction in Legal Access, Efficiency Improvement | True | Idealistic | True | 1.0 | Positive | ChatKanoon: A multilingual AI chatbot using GPT-4 and Llama2 70B APIs via instructional techniques (not traditional fine-tuning), guided by Indian legal documents and case laws. | Chatbot / Conversational AI, Large Language Model, Instruction Following, Multilingual Application, Domain-Specific Guidance, Named Tool / Platform | Descriptive evaluation through example user interaction scenarios and UI demonstrations with sample prompts in multiple Indian languages (e.g., Marathi, English) and corresponding system responses for legal queries. | Demonstration or Illustrative Examples, Qualitative Analysis | The paper claims ChatKanoon successfully provides detailed and accurate legal information and advice in response to queries on topics like cyberbullying and distinctions between civil/criminal law, in multiple languages, based on example scenarios. | Developer or Vendor claim, High performance | Limited access to legal information and assistance, high costs of legal services, complexity of legal procedures and laws, linguistic diversity challenges, scarcity of specialized legal guidance, and urban-rural disparities in legal service accessibility. | Difficulty Accessing/Interpreting Legal Information, Limited Access to Legal Assistance, High Cost of Legal Services, Complexity of Legal System/Procedures, Accessibility Barriers for Specific User Groups, Limited Availability/Access to Legal Professionals/Expertise, Geographical Disparities in Legal Access | Developing and deploying AI-powered, multilingual chatbots like ChatKanoon, tailored to specific legal contexts (e.g., Indian law), to provide accessible, affordable legal information, simplify understanding of legal concepts, and enhance the efficiency of legal processes. | AI Tool Development, Language Simplification and Multilingual Access, Access to Legal Information and Advice, Cost Reduction and Efficiency | Access to legal information and advice, legal literacy and education, cost reduction for legal services, efficiency in legal processes, multilingual legal support. | Access to Legal Information, Access to LegalAdvice, Legal Literacy and Public Legal Education, Affordability of Legal Services / Cost Reduction, Improving Efficiency in Legal System / Profession, Language Access and Digital Divide | General public in India, particularly economically weaker sections, low-income earners, those in rural areas, and individuals facing language barriers to accessing legal information. | General public, Population in India, Low-income individuals, Rural populations, Individuals with language barriers | General Indian Law, with examples from cyberlaw, civil law, and criminal law. | General Law, Cyber Law, Civil Law, Criminal Law | India | India | Utilizes pre-trained foundation models (GPT-4, Llama2 70B APIs). Instructional techniques are applied, informed by a 'diverse array of legal documents and case laws' from the Indian legal system, as opposed to fine-tuning the models. | Pre-trained LLM's General Training Corpus, Input Data for Task (Non-Training), Indian Legal Data, Legal Domain Data, Case Law / Judgments, Other Legal Documents | Application of instructional techniques to pre-trained LLM APIs (GPT-4, Llama2 70B). System architecture built with Next.js (React) for the front-end, Node.js for server-side logic, employing a component-based design. | Prompt Engineering, API-based Development, System Architecture Design, User Interface Development, Component-based Design | Hosted and deployed on the Vercel platform. | Web-based access, Cloud platform deployment | False | False | NaN | NaN | Technical gaps include high computational needs for LLMs, ensuring predictable and user-controlled outputs, refining instructional guidance precision, achieving comprehensive regional language support, and enhancing document processing capabilities. Societal and ethical gaps involve addressing user data privacy/security and the ongoing need for human oversight and verification of AI-generated legal advice. | Computational Resource and Cost Issues, AI Accuracy and Reliability, User Interface and Usability Gaps, Multilingual and Low-Resource Language Gaps, AI Scope and Functionality Limitations, Security and Privacy of Data, Human Oversight and Professional Adaptation | High computational requirements for the large language models (GPT-4, Llama2 70B), ensuring model outputs are predictable and user-controllable, addressing user data privacy and security concerns for sensitive legal queries, effectively guiding LLMs through instructions (instructional techniques), and providing comprehensive support for India's diverse regional languages. | High Computational and Resource Demands, Output Variability and Consistency, Data Privacy, Security, and Confidentiality, Prompt Engineering and Optimization, Multilingual and Low-Resource Language Support | Potential for the AI to generate unexpected or inaccurate legal advice, risks to user data privacy and security if not robustly protected, and the possibility of users over-relying on AI-generated information without seeking verification from qualified legal professionals. | Inaccurate or misleading AI output, Data privacy and security breach, Over-reliance on AI |
| o30m2SrIoEMJ.pdf | Google_Scholar | LEGAL LITERACY AND GENERATIVE ARTIFICIAL INTELLIGENCE: COMPARING THE EDUCATION LAW KNOWLEDGE OF PRACTICING EDUCATORS AND LARGE LANGUAGE MODELS LIKE CHATGPT | This paper compares the education law knowledge of practicing K-12 educators with several large language models (LLMs) like ChatGPT, using a pre-existing true/false survey. It finds that LLMs generally outperform educators but are not infallible, highlighting their potential to supplement, but not replace, educator legal literacy. | LLM Evaluation, Comparison with Human Experts, Education Law Focus, Legal Literacy Assessment, Supplementary Role of AI | True | Idealistic | True | 2.0 | Positive | Evaluation of existing LLMs (ChatGPT GPT-3.5, GPT-4 with/without plugins, Google Bard, Microsoft Bing AI Chat Mode) for education law knowledge. | AI System Evaluation, Large Language Model, Legal Knowledge Assessment | Zero-shot prompting of LLMs using the 34 true/false questions from the Principals’ Education Law Survey (Militello, Schimmel, & Eberwein, 2009). Performance was compared against established correct answers and historical scores of teachers and principals. | Benchmark Dataset Evaluation, Comparative Analysis, Quantitative Metrics | Four out of five LLMs (ChatGPT versions, Bing AI) achieved >70% proficiency (76.47% correct), outperforming average teacher (40.04%) and administrator (58.71%) scores. LLM performance varied by legal topic, scoring highest on constitutional law (80%) and lowest on liability (57.78%). | Moderate performance, Outperforms others, Mixed performance | Educators' lack of legal knowledge and literacy, fear/anxiety towards legal issues, and reliance on potentially inaccurate sources. Limitations of LLMs including inaccuracies ('hallucinations'), inconsistent performance, inherent biases, and unresolved copyright/ownership issues. | Professional Lack of Legal/Technical Knowledge, Psychological/Cultural Barriers to Seeking Help/Engaging with Law, Reliance on Unreliable Information Sources, AI Unreliability/Inaccuracy, Bias in AI/Data, Intellectual Property/Copyright Issues with AI | Leveraging LLMs as tools to supplement educators' legal knowledge. Developing educators' technological proficiency and legal literacy skills to critically evaluate and verify LLM outputs ('trust but verify'). Rethinking educator preparation programs to incorporate responsible AI use. | Human Oversight and Collaboration, Education and AI Literacy, Regulation, Ethics, and Governance | Educator legal literacy; K-12 education law topics including student rights (discipline, free speech, general rights), teacher rights (free speech, general rights), liability, religion in schools, special education, school authority, student records, copyright. | Legal Literacy and Public Legal Education, Protection of Rights | K-12 Educators (teachers and school administrators). | Educators | Education Law | Education Law | United States | USA | The paper mentions LLMs are trained on "huge swaths of information from the internet and other sources" but does not provide specific details on the datasets used for the evaluated models (ChatGPT, Bard, Bing). Training data is implied to be vast, unstructured text, and largely proprietary. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Proprietary Data, Unstructured Text Data, Undisclosed Data Source/Availability | NaN | NaN | NaN | Not applicable | True | False | The LLMs studied (ChatGPT, Google Bard, Microsoft Bing AI) are generally publicly accessible via web interfaces. | Publicly accessible online tool or platform | Need for research on LLM reliability and statistical significance of findings. Assessing LLM legal literacy (application) beyond knowledge recall. Updating assessment tools for contemporary legal issues. Understanding LLM training data limitations. Addressing ethical/legal issues (privacy, liability, equity). | Research and Evaluation Gaps, AI Accuracy and Reliability, AI Legal Reasoning Limitations, Data Availability and Quality, Ethical Framework Deficiencies, Security and Privacy of Data, Regulatory and Governance Gaps, Access, Equity, and Digital Divide | Ensuring LLM accuracy and avoiding 'hallucinations'. Achieving consistent results from LLMs. Prompt engineering for specific answer formats. Evaluating models using potentially outdated survey instruments. Limitations due to lack of transparency regarding LLM training data. | Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Output Variability and Consistency, Prompt Engineering and Optimization, Evaluation Challenges and Metrics, Transparency and Explainability of AI | LLM 'hallucinations' leading to false legal information and potential negative consequences (e.g., defamation, incorrect legal actions). Copyright infringement issues related to training data. Bias amplification. Over-reliance without critical verification. Student data privacy issues. Potential misuse for academic dishonesty. | Inaccurate or misleading AI output, Consumer harm, Copyright or intellectual property issues, Bias and discrimination, Over-reliance on AI, Data privacy and security breach, Ethical concerns, Risk of misapplication or misuse |
| 8JE3jvLmRJIJ.pdf | Google_Scholar | NATURALIZING LEGAL INTERPRETATION AFTER GENERATIVE AI | This essay explores how generative AI, particularly LLMs, can be integrated into legal interpretation by aligning with constitutive theories of language and complexity science. It argues for a conceptual framework that harmonizes AI's computational power with the contextual, moral, and emergent dimensions of human legal reasoning. | Generative AI for Legal Interpretation, Framework Proposal, Harmonizing AI with Human Reasoning, Complexity Science Application | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | The primary obstacle identified is the inadequacy of current AI approaches, often based on simplistic 'designative' views of language, to grasp the complex, contextual, moral, and emergent nature of legal interpretation, leading to biased or superficial outcomes that undermine justice. | AI Limitations in Legal Reasoning/Nuance, Technical Challenges in AI Development, Bias in AI/Data, Ethical Concerns with AI in Law | Adopting a conceptual framework for legal AI based on constitutive theories of language and complexity science, where AI augments human judgment rather than replacing it, thereby aligning AI with the dynamic and morally-rich nature of law to foster fairer outcomes. | Conceptual Frameworks, Human Oversight and Collaboration, Regulation, Ethics, and Governance | Ensuring AI contributes to justice in legal interpretation and reasoning, potentially enhancing accessibility and efficiency in legal practice. | Ethical AI in Law and AI Governance, Improving Foundational AI Capabilities for Legal Applications, Democratizing Law / Closing Justice Gap / Rule of Law | NaN | NaN | General jurisprudence and legal interpretation, with examples from contract, family, tort, constitutional, and criminal law. | Jurisprudence, Statutory Interpretation, Contract Law, Family Law, Tort Law, Constitutional Law, Criminal Law | Primarily US (due to case law examples), but discusses principles with broader, potentially international, applicability. | USA, International | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | The primary gap is the inadequacy of current legal AI to truly engage with the moral, contextual, and emergent dimensions of legal reasoning, stemming from a limited philosophical understanding of language and law. This leads to challenges in developing AI that is fair, just, and genuinely supportive of complex legal interpretation, thereby hindering its potential for improving access to justice. | AI Legal Reasoning Limitations, Ethical Framework Deficiencies, Access, Equity, and Digital Divide | NaN | NaN | Key risks include the perpetuation of systemic biases due to reliance on historical data (e.g., racial bias in predictive algorithms like COMPAS), the creation of a misleading 'facade of objectivity' by AI in value-laden legal decisions, and the lack of transparency and accountability in 'black-box' AI systems. | Bias and discrimination, Lack of transparency, accountability, and redress, Ethical concerns |
| Hzv8CB3O47YJ.pdf | Google_Scholar | LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development | This paper introduces LeXFiles, a diverse English multinational legal corpus, and LegalLAMA, a legal knowledge probing benchmark, aimed at advancing the development and detailed analysis of legal pre-trained language models (PLMs). The authors also release and evaluate two new legal PLMs, LexLMs, finding that diverse pre-training corpora and model size are crucial for effective upstream, probing, and downstream performance. | Dataset Creation, Benchmark Creation, Legal Language Model Development, Model Evaluation, Multinational Legal Corpus | True | Idealistic | True | 1.0 | Positive | LeXFiles (multinational English legal corpus), LegalLAMA (legal knowledge probing benchmark), and LexLM (RoBERTa-based legal PLMs). | Dataset Creation / Curation, Benchmarking / Evaluation, Pre-trained Language Model Development | LexLM models were evaluated on: 1. Upstream Masked Language Modeling (MLM) performance (Accuracy/P@1) on LeXFiles sub-corpora. 2. Probing performance (Mean Reciprocal Rank - MRR, P@1) on the LegalLAMA benchmark. 3. Downstream performance (micro-F1, macro-F1) on selected LexGLUE classification tasks after single-epoch fine-tuning. | Custom Dataset Evaluation, Benchmark Dataset Evaluation, Quantitative Metrics | The LexLM-L (large) model generally performed best. On LegalLAMA, LexLM-L achieved an average MRR of 77.4%. On selected LexGLUE downstream tasks, LexLM-L achieved an average micro-F1 of 73.3% and macro-F1 of 51.0%. | Moderate performance, Outperforms others | Lack of diverse, multinational legal corpora; insufficient benchmarks for probing specific legal knowledge in PLMs; limited understanding of how pre-training settings and model characteristics affect legal language understanding. | Data Scarcity/Quality for AI, Lack of Standardized Benchmarks for Legal AI, Lack of Understanding of AI Model Behavior | Release of LeXFiles, a diverse multinational English legal corpus; release of LegalLAMA, a benchmark for probing legal knowledge in PLMs; development and release of LexLMs, new PLMs trained on diverse legal data to improve legal language understanding. | Data Curation and Management, Open Source Initiatives and Collaboration, Benchmarking and Evaluation Frameworks, AI Tool Development, Enhanced AI Capabilities | Democratizing legal information, improving legal services and tools for legal professionals and laypersons. | Access to Legal Information, Democratizing Law / Closing Justice Gap / Rule of Law, Improving Efficiency in Legal System / Profession | Laypersons, legal professionals, and the NLP research community working on legal AI. | Laypeople, Legal professionals, Researchers | Legislation, Case Law, Contracts, Human Rights Law (ECHR), Criminal Law. | Statutory Law, Case Law, Contract Law, Human Rights Law, Criminal Law | EU, CoE, Canada, US, UK, India. | EU, Council of Europe, Canada, USA, UK, India | LexLM models were trained on LeXFiles, a new corpus of approx. 19 billion tokens from 6 million publicly available, English, unstructured legal documents (legislation, case law, contracts) sourced from EUR-Lex, UK.LEGISLATION.GOV.UK, BAILII, Court Listener, SEC-EDGAR, Canadian official legislation portal, HUDOC, and re-distributions from Henderson* et al. (2022) and Malik et al. (2021). | Author-Created New Dataset, Fine-tuning Dataset, Publicly Available Data, Legal Domain Data, Legislation / Statutes / Regulations, Case Law / Judgments, Legal Contracts, European Legal Data, UK Legal Data, US Legal Data, Canadian Legal Data, Unstructured Text Data, Data From Existing Public NLP/Legal Datasets/Benchmarks | For LexLM: Warm-starting from RoBERTa checkpoints, training a new BPE tokenizer on LeXFiles, continued pre-training using Masked Language Modeling on LeXFiles with sub-corpora sampling smoothing. For LegalLAMA: Creation of mask-filling probing tasks based on LAMA, extended for multi-token targets, using test subsets of LeXFiles. | Model Pre-training, Tokenizer Development, Self-supervised Learning, Task Design, Dataset Creation | LeXFiles corpus, LegalLAMA benchmark, and LexLM models are released on Hugging Face Hub. The codebase is available on GitHub. | Public dataset/benchmark release, Open source model release, Open source code release | True | True | LeXFiles corpus, LegalLAMA benchmark, and LexLM models are available on Hugging Face Hub. Associated codebase is on GitHub. | Dataset available, Model available, Code available | Need for more diverse corpora (more languages, legal systems); expansion of probing benchmarks (more tasks, topics, jurisdictions); exploration of larger/different model architectures (e.g., GPT-like) and advanced training P\nparadigms (instruction-tuning, RLHF); development of more robust evaluation methods for probing and fine-tuning; further research into trustworthiness, including model interpretability and fairness in legal AI. | Data Availability and Quality, Multilingual and Jurisdictional Specificity Gaps, Research and Evaluation Gaps, AI Scope and Functionality Limitations, Transparency and Explainability, Bias in AI, AI Accuracy and Reliability | Compiling diverse and representative legal corpora; avoiding overspecialization of models to specific jurisdictions or text types; designing effective methods to probe specific legal knowledge acquired by PLMs; balancing capacity across sub-corpora of varying sizes during pre-training; understanding the interplay between model size, pre-training data, and performance on diverse legal tasks. | Scarcity of High-Quality Legal Data, Domain-Specific Adaptation and Customization, Evaluation Challenges and Metrics, LLM Reasoning Capabilities, Research Methodology and Study Design Limitations | Models may perpetuate biases from training data if not carefully curated (e.g., outdated or discriminatory legal standards). Lack of interpretability and fairness in models can lead to irresponsible deployment. Over-reliance on models without understanding their limitations. | Bias and discrimination, Lack of transparency, accountability, and redress, Risk of misapplication or misuse, Over-reliance on AI |
| 3477495.3531668.pdf | Google_Scholar | LawNet-Viz: A Web-based System to Visually Explore Networks of Law Article References | This paper presents LawNet-Viz, a web-based prototype tool for visualizing networks of legal article references extracted from statute law, demonstrated using the Italian Civil Code. The system aims to aid legal research for professionals and enhance understanding for laymen by displaying article connections, network statistics, and semantic similarities calculated using NLP techniques including BERT. | Tool Development, Legal Data Visualization, Statute Law Analysis, Italian Law Focus, Legal Research Support, Legal Understanding for Laypeople, NLP Application | True | Idealistic | True | 1.0 | Positive | LawNet-Viz: A web-based system that extracts references from statute law, builds a network graph, calculates semantic similarity between articles using NLP (incl. BERT), and provides interactive visualization of the network with associated statistics (e.g., centrality) and search capabilities. | Software / Platform Development, Information Extraction, Network Analysis / Visualization, Natural Language Processing (NLP), Semantic Similarity, Information Retrieval / Search | Demonstration of the system's functionalities using the Italian Civil Code (ICC) as a case study. A BERT-based model (LamBERTa) fine-tuned on the ICC was used for semantic analysis. No formal user study or quantitative benchmark evaluation reported. | Demonstration or Illustrative Examples, No Evaluation by Author | NaN | NaN | Complexity of navigating legal corpora ("intricate regulatory systems"); knowledge gap for laypersons unfamiliar with the legal domain; time and cost involved in traditional legal research. | Complexity of Legal Information, Public Lack of Legal Knowledge/Awareness, Resource Constraints, High Cost of Legal Services | Providing an interactive visual exploration tool (LawNet-Viz) to map article references and semantic relationships, reducing the knowledge gap for laypersons and increasing efficiency for legal professionals through enhanced search and understanding capabilities. | AI Tool Development, User Interface and Accessibility Design, Legal Knowledge Representation and Management, Access to Legal Information and Advice, Cost Reduction and Efficiency, Legal Research and Analysis Tools | Legal research support; Understanding statutory law structure; Navigating complex legal texts. | LegalResearch Support, Access to Legal Information, Legal Literacy and Public Legal Education | Legal professionals (lawyers, jurists) and citizens/laymen. | Legal professionals, General public, Laypeople | Statute law (specifically demonstrated with Civil Law / Private Law) | Statutory Law, Civil Law, Private Law | Italy (Italian Civil Code), designed to be adaptable. | Italy | Network structure derived from Italian Civil Code (ICC) text. Language models (including LamBERTa, a fine-tuned BERT model) trained/fine-tuned on the text of the ICC using unsupervised labeling for data augmentation. The ICC is public statutory law; resulting models/embeddings may be proprietary. | Fine-tuning Dataset, Legal Domain Data, Italian Legal Data, Legislation / Statutes / Regulations, Publicly Available Data, Unstructured Text Data, Synthetic Data, Proprietary Data | Modular architecture (network, text, integration modules), use of NLP libraries (Gensim, HuggingFace), web technologies (Bootstrap, DataTables, vis.js), JSON data format compatible with Gephi, focus on interactive user experience. | Modular System Architecture, Third-party Library Utilization, Web Technology Application, Data Interchange Format Design, User Experience Focus | System prototype using web technologies (Bootstrap, DataTables, vis.js, Python backend). Planned for product development. A screen recording demo is provided via a shared drive link. | Internal deployment/prototype, Web-based access, Proposed deployment (not implemented), Dissemination via publication/presentation | False | False | NaN | NaN | Social and ethical considerations related to automating legal research are acknowledged but not explored. The system is a prototype requiring further development. | Ethical Framework Deficiencies, Research and Evaluation Gaps, AI Scope and Functionality Limitations | Developing tailored methods for extracting article references according to specific legal syntax; processing and normalizing legal text; managing computational load (addressed via server-side processing); designing effective interactive visualizations for complex network and textual data. | Domain-Specific Adaptation and Customization, Data Quality, Processing, and Preparation, High Computational and Resource Demands, User Interface, Usability, and Accessibility | Not explicitly stated, beyond acknowledging that social/ethical considerations are outside the paper's scope. | NaN |
| itbYunRMpiQJ.pdf | Google_Scholar | LLM vs. Lawyers: Identifying a Subset of Summary Judgments in a Large UK Case Law Dataset | This paper compares two computational methods, a traditional keyword-based NLP approach and an application of the Claude 2 LLM, for identifying summary judgment cases within the Cambridge Law Corpus of UK court decisions. The study finds that the LLM significantly outperforms the keyword method, achieving a weighted F1 score of 0.94, demonstrating AI's potential to enhance legal research and accessibility of legal information. | Comparative AI Approaches, LLM Application, NLP Application, Summary Judgment Identification, UK Law Focus, Legal Research Enhancement, Access to Legal Information Enhancement | True | Idealistic | True | 2.0 | Positive | Application of Claude 2 Large Language Model with engineered prompts for classifying legal cases (summary judgments) and a traditional NLP keyword/RegEx-based search. | Large Language Model, Prompt Engineering, Legal Text Classification, Hybrid AI System, Traditional NLP, Regular Expressions | Manual review of statistically representative samples by a legal expert for both keyword-based and LLM-based classification. Performance evaluated using confusion matrices and F1 scores. | Expert Evaluation, Custom Dataset Evaluation, Quantitative Metrics, Comparative Analysis | The Claude 2 LLM method achieved a weighted F1 score of 0.94, significantly outperforming the keyword-based method (weighted F1 score of 0.78). | High performance, Outperforms others, Technique improves outcome | Difficulty for self-represented litigants to navigate summary judgments; lack of automatic categorization of legal issues in UK case law; incomplete publication of court judgments; complexity of legal language hindering automated analysis and general access to legal information. | Challenges for Self-Represented Litigants, Lack of Foundational Legal Data Resources, Incomplete Access to Legal Information, Complexity of Legal Language/Documents | Employing advanced NLP and LLMs (like Claude 2) to efficiently identify and classify specific types of legal cases, thereby improving the accessibility of legal information and aiding legal research, which can help democratize access to legal resources. | AI Tool Development, Legal Research and Analysis Tools, Access to Legal Information and Advice, Cost Reduction and Efficiency | Identifying specific case types (summary judgments) for legal research; improving accessibility of legal information; understanding procedural justice, particularly concerning summary judgments affecting self-represented litigants. | LegalResearch Support, Access to Legal Information, Judicial System Modernization / Efficiency, Support for Self-Represented Litigants | Self-represented litigants. | Self-represented litigants | Civil procedure. | Civil Procedure | United Kingdom (primarily England and Wales regarding Civil Procedure Rules). | UK | The Claude 2 LLM, one of the techniques studied, was pre-trained by Anthropic on large, general natural language datasets (details proprietary to Anthropic). The keyword-based method is rule-based and does not use a training dataset. The Cambridge Law Corpus was used as the input data for classification. | Pre-trained LLM's General Training Corpus, Proprietary Data, General Web Data / Broad Internet Text, Input Data for Task (Non-Training), Data From Existing Public NLP/Legal Datasets/Benchmarks, UK Legal Data, Case Law / Judgments | Keyword-based method: Expert-driven keyword generation, RegEx development, iterative refinement of search logic based on legal domain knowledge (CPR, case law). LLM-based method: Prompt engineering for Claude 2, utilizing insights from keyword analysis and LLM provider guidelines, including structured prompts with examples. | Rule-based System Design, Expert Knowledge Elicitation, Iterative Design Process, Prompt Engineering, Hybrid Approach | The identified dataset metrics are shared to support further research. Code is made available on GitHub. | Public dataset/benchmark release, Open source code release | True | True | The code implementing the methods is available on GitHub. The Claude 2 method relies on accessing the Claude 2 Chat console (used for final results and generally accessible). | Code available, Publicly accessible online tool or platform | Need for further refinement of LLM methodologies (e.g., prompt engineering) to improve accuracy in legal case classification; incompleteness of available legal datasets (e.g., CLC not containing all judgments); ongoing challenges with LLM reliability (e.g., errors, over-inclusivity, hallucinations); lack of standardized benchmarks for legal information retrieval tasks. | Research and Evaluation Gaps, AI Accuracy and Reliability, Data Availability and Quality | Keyword method: Capturing nuances and variability in legal language, distinguishing true cases from mere mentions or similar legal tests used in other procedures. LLM method: Effective prompt engineering, LLM output inconsistencies (API vs. Chat console), handling LLM context window limits for very long documents, general complexity of legal language for NLP. | LLM Reasoning Capabilities, Prompt Engineering and Optimization, Output Variability and Consistency, LLM Context Window and Long Input Management | Misclassification of legal cases by AI methods, potentially leading to incorrect legal research outcomes or flawed understanding of legal trends; inherent limitations of LLMs such as errors, over-inclusivity (incorrectly identifying non-summary judgment cases as summary judgments), and potential for hallucination when applied to complex legal tasks. | Inaccurate or misleading AI output, Technical limitations of AI |
| jC3rwCcyzLcJ.pdf | Google_Scholar | Artificial Intelligence (AI) in Legal System | This paper reviews the current state and impact of AI on the legal profession, highlighting its potential to enhance legal work but also noting risks like inaccuracies and the need for human oversight. It explores benefits such as increased efficiency and access to justice, alongside challenges including ethical concerns, bias, and the potential for errors if AI is used without proper safeguards. | Review of AI in Legal Profession, Benefit Identification, Efficiency Improvement, Access to Justice Enhancement, Risk Identification, AI Hallucinations/Inaccuracy, Need for Human Oversight, Ethical Considerations, Bias in AI | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | Scarcity of attorneys in 'legal deserts'; high cost of legal services for the general public; risk of biased or erroneous AI decisions impacting fairness and perpetuating discrimination; ensuring the right to be heard ('Audi alteram partem') when AI is involved in decision-making. | Limited Availability/Access to Legal Professionals/Expertise, Geographical Disparities in Legal Access, High Cost of Legal Services, Bias in AI/Data, AI Unreliability/Inaccuracy, Risk of AI Exacerbating Inequality, Risk to Human Rights from AI | AI-powered tools to provide legal information and assistance in underserved areas (e.g., 'legal deserts'); using AI to efficiently handle straightforward legal matters (e.g., petty cases, amicable divorces, Khula) potentially reducing costs; development and implementation of robust ethical frameworks, guidelines, and human supervision for AI in law to ensure fairness and mitigate bias; public awareness and discourse on AI's role in the legal system. | AI Tool Development, Access to Legal Information and Advice, Cost Reduction and Efficiency, Regulation, Ethics, and Governance, Human Oversight and Collaboration, Bias Detection and Mitigation, Education and AI Literacy | Addressing 'legal deserts' and scarcity of legal professionals; providing accessible legal information and assistance for common/minor legal issues; improving efficiency and potentially lowering costs for resolving straightforward legal cases (e.g., uncontested divorces, small claims); ensuring fairness, non-discrimination, and upholding legal rights in AI-assisted legal processes. | Democratizing Law / Closing Justice Gap / Rule of Law, Access to Legal Information, Access to Legal Advice, Improving Efficiency in Legal System / Profession, Affordability of Legal Services / Cost Reduction, Ethical AI in Law and AI Governance, Protection of Rights | Individuals in 'legal deserts' (areas with limited access to legal professionals); general public needing assistance with common or minor legal issues (e.g., parking tickets, simple family law matters); individuals who could benefit from more efficient and less costly resolution of straightforward cases. | Individuals in legal deserts, General public, Individuals unable to afford legal services | General legal practice, Contract Law, Real Estate Law, Commercial Law, Criminal Law (bail proceedings), Family Law (divorce, Khula), Human Rights, Intellectual Property Law, Small Claims. | General Legal Practice, Contract Law, Real Estate Law, Commercial Law, Criminal Law, Family Law, Human Rights Law, Intellectual Property Law, Small Claims Law | Pakistan, UK, USA, India, International (due to discussion of global justice and broad applicability). | Pakistan, UK, USA, India, International | Vast text and code datasets for Generative AI like ChatGPT; general pre-existing legal data for other AI systems discussed. The paper notes these data can contain biases. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Legal Domain Data, Data Bias Concerns Noted | NaN | NaN | NaN | Not applicable | True | False | Some discussed tools like DoNotPay are presented as services available to individuals (likely paid). ChatGPT is generally accessible (with free/paid tiers). Other commercial tools (Kira Systems, LEVERTON, etc.) are mentioned as existing products from companies. | Commercial product or service, Freemium access, Publicly accessible online tool or platform | Limited research on AI's role in deciding legal cases where law is ambiguous or nonexistent; need for AI systems capable of reasoning from first principles or handling social dilemmas effectively; insufficient literature on AI's broader human rights impacts beyond privacy and expression; challenges in integrating societal values, moral principles, and ethical considerations into AI reasoning, especially in novel legal situations; developing effective oversight and auditability for AI systems. | Research and Evaluation Gaps, AILegal Reasoning Limitations, Ethical Framework Deficiencies, Regulatory and Governance Gaps, Accountability and Redress Mechanisms | High cost of developing and implementing sophisticated AI systems; ensuring accuracy and reliability of AI-generated information and avoiding errors; overcoming AI's difficulty in handling legal ambiguity or non-existent law due to reliance on pre-existing data; incorporating human expert knowledge and ethical considerations into AI decision-making; potential for inherited bias from training data leading to discriminatory outcomes; the 'black box' nature of some AI systems making them opaque. | Financial Cost and Resource Constraints, Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, LLM Reasoning Capabilities, Outdated or Limited LLM Knowledge Base, Need for Human Oversight and Intervention, Ethical Considerations, Bias in AI Systems and Data, Transparency and Explainability of AI | AI errors leading to incorrect legal outcomes and miscarriages of justice (e.g., UK divorce software error, bogus citations from ChatGPT); job displacement for legal professionals; perpetuation of societal biases and discrimination through biased AI systems; violations of privacy due to large-scale data collection by AI; spread of misinformation through AI-generated content; lack of accountability for AI decisions; challenges in assigning authorship and intellectual property for AI-generated content. | Inaccurate or misleading AI output, Undermining legal process or principles, Job displacement, Bias and discrimination, Data privacy and security breach, Lack of transparency, accountability, and redress, Copyright or intellectual property issues |
| vFkrzAPX5eMJ.pdf | Google_Scholar | Generative AI and the Rule of Law⋆ | This exploratory paper discusses the emergence of Large Language Models (LLMs) and Multimodal Foundation Models (MFMs), examining their potential to model the rule of law and serve regulatory purposes. It analyzes responses from models like ChatGPT and Claude, highlighting both their capabilities in generating plausible legal discourse and the ongoing challenges related to accuracy, ethics, and semantic understanding. | Exploration of LLMs and MFMs, AI for Modeling Rule of Law, AI for Regulatory Purposes, Capability Assessment, Challenge Identification, Accuracy Issues, Ethical Considerations | True | Idealistic | True | 2.0 | Neutral | Prompting of LLMs (ChatGPT, GPT-4, Claude) for modeling the rule of law; discussion of Semantic Injection and Constitutional AI. | Large Language Model, Prompt Engineering, Conceptual Framework, AI Safety / Security, AI Alignment / Governance, Constitutional AI | Qualitative experiment involving prompting ChatGPT3, GPT-4 (via Lex.page), and Claude with the question "How can we model the rule of law?" and analyzing the generated responses. | Qualitative Analysis, Comparative Analysis | LLM responses were generally plausible and detailed, outlining various components of the rule of law, but exhibited cultural legal biases and operated at a symbolic level requiring user interpretation for meaning. The quality and comprehensiveness of responses varied and improved with newer models/versions. | Moderate performance, Limitation: Bias, Limitation: Operational or Technical | Unreliability of LLMs (hallucinations, lack of true understanding of meaning vs. symbols), inherent biases in models, unresolved legal and ethical issues (e.g., copyright, privacy, defamation), and challenges in aligning AI with regulatory compliance and democratic values. | AI Unreliability/Inaccuracy, AI Limitations in Legal Reasoning/Nuance, Bias in AI/Data, Ethical Concerns with AI in Law, Intellectual Property/Copyright Issues with AI, Data Privacy Concerns with AI, Challenges in AI Alignment with Legal/Ethical Values | Improving LLM accuracy and reliability through semantic injection and knowledge graphs; utilizing advanced prompt engineering (e.g., Moral Chains of Thought) and Constitutional AI principles to align models with ethical and legal norms; adopting a 'Law informs AI' approach for better legal reasoning; and conducting further empirical testing and benchmarking. | Enhanced AI Capabilities, Legal Knowledge Representation and Management, Prompt Engineering and LLM Interaction Design, Regulation, Ethics, and Governance, Benchmarking and Evaluation Frameworks | Modeling the rule of law; Regulatory applications of AI; Computational ethics; Legal reasoning in AI. | Democratizing Law / Closing Justice Gap / Rule of Law, Regulatory Reform (Legal Services and AI), Ethical AI in Law and AI Governance, Improving Foundational AI Capabilities for Legal Applications | NaN | NaN | Constitutional law, Jurisprudence, Regulatory law, Tax law (in an example), Intellectual Property law, Privacy law. | Constitutional Law, Jurisprudence, Regulatory Law, Tax Law, Intellectual Property Law, Data Privacy Law | International; references to US and EU. | International, USA, EU | For LLMs in general: Large, diverse corpora of unlabeled text scraped from the internet for pre-training; specific fine-tuning datasets (e.g., for Constitutional AI, instruction-following datasets based on principles and examples). | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Fine-tuning Dataset, Instruction-Tuning Formatted Data, Expert-Annotated / Human-Curated / Human-Generated Data | For LLMs: Unsupervised pre-training, fine-tuning, Reinforcement Learning (RLHF/RLAIF). For Constitutional AI: Principle-based design, self-critique, preference modeling. For semantic injection: Knowledge Graph integration techniques. | Model Pre-training, Model Fine-tuning, Reinforcement Learning from Human/AI Feedback (RLHF/RLAIF), Principle-based Design, Self-critique Mechanism, Preference Modeling, Knowledge Graph Construction/Integration | Web-based interfaces and APIs for models like ChatGPT and Claude; some models with geographically restricted access initially. | Evaluation of existing third-party tool, Web-based access, API access, Pilot program/Limited rollout | True | False | ChatGPT is publicly accessible via a web interface (with free and paid tiers). Claude's access was stated as limited to USA and UK at the time of writing (via application). Lex.page is a commercial writing assistant. | Publicly accessible online tool or platform, Freemium access, Restricted access, Commercial product or service | Technical: Scalability of knowledge injection methods, achieving genuine legal/ethical reasoning beyond pattern matching, mitigating hallucinations and biases reliably. Societal/Legal: Establishing clear legal frameworks for LLM use (copyright, liability, privacy), ensuring alignment with democratic values and societal norms, adapting regulation to fast-evolving AI, and the conceptual challenge of adequately modeling the rule of law itself. | AI Scope and Functionality Limitations, AI Legal Reasoning Limitations, AI Accuracy and Reliability, Bias in AI, Regulatory and Governance Gaps, Ethical Framework Deficiencies | Design/Development: Sourcing quality training data, effective and scalable fine-tuning/alignment (e.g., Constitutional AI), robust knowledge integration (Semantic Injection), bias mitigation, lack of transparency in model development. Use: Effective prompt engineering, critical interpretation of outputs, avoiding over-reliance due to potential inaccuracies. Deployment: Ensuring safety and preventing misuse, managing computational costs, navigating unclear regulatory environments. | Scarcity of High-Quality Legal Data, Domain-Specific Adaptation and Customization, Scalability of Solutions, LLM Reasoning Capabilities, Outdated or Limited LLM Knowledge Base, Bias in AI Systems and Data, Transparency and Explainability of AI, Prompt Engineering and Optimization, User Training, AI Literacy, and Skill Gaps, User Adoption, Trust, and Acceptance, Safeguarding Against Misuse and Harm, High Computational and Resource Demands, Regulatory Uncertainty and Compliance | Generation of false information ('hallucinations'); perpetuation of biases leading to disproportionate negative impacts on minority groups; legal infringements (copyright, privacy, defamation); generation of hate speech; challenges to existing regulatory frameworks due to undefined purpose and scale of use; potential for misuse (e.g., flawed legal document generation). | Inaccurate or misleading AI output, Bias and discrimination, Copyright or intellectual property issues, Data privacy and security breach, Lack of transparency, accountability, and redress, Harmful or unsafe AI output, Regulatory challenges or gaps, Risk of misapplication or misuse |
| AKc59QWPmfEJ.pdf | Google_Scholar | Generative AI in Education From the Perspective of Students, Educators, and Administrators | This dissertation explores the integration of generative AI in education through five studies, covering legal text summarization, stakeholder perspectives (students, educators, administrators) on AI tools and policies, and AI adoption models. The research highlights AI's transformative potential for teaching, learning, and information access, while also underscoring challenges related to ethics, equity, and practical implementation in educational settings. | Generative AI in Education, Legal Text Summarization, Stakeholder Perspectives on AI, AI Adoption Models, Access to Information Enhancement, Ethical Considerations, Equity in AI | True | Idealistic | True | 3.0 | Positive | PEGASUS CourtOp, a domain-adapted transformer-based model (fine-tuned from PEGASUS LARGE) for abstractive summarization of legal court opinions (detailed in Chapter 2). | Transformer Models, Fine-tuning, Legal Text Summarization, Model Development, Abstractive Summarization | The PEGASUS CourtOp model was evaluated using ROUGE scores (ROUGE-1, ROUGE-2, ROUGE-L) by comparing its generated summaries of court opinions against human-written reference summaries from Justia. A test set of 25% of 4814 court opinions was used. | Custom Dataset Evaluation, Quantitative Metrics, Comparative Analysis | PEGASUS CourtOp achieved a ROUGE-1 F1 score of 0.53 and a ROUGE-1 Recall of 0.66, outperforming baseline models PEGASUS LARGE and Legal Pegasus. | Moderate performance, Outperforms others, Technique improves outcome | Cost and complexity of accessing and understanding legal information, particularly lengthy court opinions, which forms a barrier to legal help for people with lower incomes. | High Cost of Legal Services, Complexity of Legal Information, Difficulty Accessing/Interpreting Legal Information | Automated legal text summarization using NLP models like PEGASUS CourtOp to reduce the time, effort, and cost associated with parsing and understanding legal documents, thereby potentially lowering barriers to legal services. | Document Automation, AI Tool Development, Cost Reduction and Efficiency, Access to Legal Information and Advice | Automated summarization of court opinions to enhance accessibility to legal precedents and reduce the cost of legal services. | Legal Text Simplification / Plain Language, Access to Legal Information, Affordability of Legal Services / Cost Reduction | People of lower-income brackets. | Low-income individuals | Case Law / Court Proceedings (specifically, summarization of court opinions). | Case Law, Judicial Processes | United States (supreme courts of Utah, Idaho, Arizona, New Mexico, Nevada, and Colorado). | USA | A dataset of 4814 US state supreme court opinions paired with human-generated summaries from Justia. For fine-tuning PEGASUS CourtOp, 3661 such pairs were used. This is domain-specific (legal court opinions) unstructured text data, provided under a data-sharing agreement. | Author-Created New Dataset, Fine-tuning Dataset, Legal Domain Data, US Legal Data, Case Law / Judgments, Expert-Annotated / Human-Curated / Human-Generated Data, Structured Data, Unstructured Text Data, Proprietary Data | Domain adaptation of a pre-trained language model (PEGASUS LARGE) by fine-tuning it on a specific corpus of legal opinions and their summaries. Standard NLP data processing techniques and evaluation using ROUGE metrics were employed. | Domain Adaptation, Model Fine-tuning, Dataset Creation, Data Preprocessing, Evaluation with Standard Metrics | NaN | Not applicable | False | False | NaN | NaN | Technical gaps in AI model capabilities for legal text summarization, including the need for more powerful and potentially open-source language models, domain-specific Named Entity Recognition, and improved generation of highly abstractive, human-like summaries not strictly tied to source text phrasing. | AI Scope and Functionality Limitations, AI Legal Reasoning Limitations, Data Availability and Quality, Computational Resource and Cost Issues | General difficulty of abstractive summarization due to natural language complexity, the need for substantial domain-specific training data (paired opinions and summaries), potential data imbalances, and inherent challenges in objectively evaluating the quality of generated summaries. | LLM Reasoning Capabilities, Scarcity of High-Quality Legal Data, Data Quality, Processing, and Preparation, Evaluation Challenges and Metrics | Incorrect or fabricated results (hallucinations), misuse for academic dishonesty (cheating, plagiarism), data privacy and security vulnerabilities, algorithmic bias, lack of transparency and accountability in AI systems, negative impacts on critical thinking and creativity, and challenges in ensuring equitable access to AI tools and their benefits. | Inaccurate or misleading AI output, Ethical concerns, Risk of misapplication or misuse, Data privacy and security breach, Bias and discrimination, Lack of transparency, accountability, and redress, Deskilling or erosion of human skills, Exacerbation of inequality or two-tiered system |
| 3627673.3679154.pdf | Google_Scholar | LeDQA: A Chinese Legal Case Document-based Question Answering Dataset | This paper introduces LeDQA, a new Chinese legal dataset for question answering based on civil case documents, featuring a question schema designed by legal professionals and annotations generated using GPT-4. The authors evaluate several LLMs and retrieval methods on this dataset, finding that relevant sentence retrieval improves QA performance but challenges like irrelevant retrieval and incorrect reasoning remain. | Dataset Creation, Chinese Law Focus, Legal Question Answering, LLM Evaluation, Retrieval Method Evaluation, Challenge Identification | True | Idealistic | True | 1.0 | Positive | LeDQA dataset, a Chinese legal case document-based question answering dataset, along with a methodology for its creation and baseline evaluations of retrieval and QA models. | Dataset Creation / Curation, Legal Question Answering, AI System Evaluation, Information Retrieval / Search | Relevant sentence retrieval was evaluated using R@3, R@5, MRR with models like BM25, TF-IDF, and pre-trained dense retrievers. Question answering was evaluated using Accuracy and Macro-F1 with various LLMs (e.g., Baichuan2, Qwen-7B-Chat, GPT3.5-turbo) using the full document, chain-of-thought prompting, retrieved sentences, or oracle sentences. | Custom Dataset Evaluation, Quantitative Metrics, Comparative Analysis | For question answering, the Qwen-7B-Chat model, when using the top-5 retrieved sentences from TF-IDF (Retrieve setting), achieved an accuracy of 0.7623 and an F1 score of 0.5605 on the binary classification task ("yes" vs. "no and unknown"). Using oracle (human-annotated) relevant sentences generally yielded the best performance across models, highlighting the importance of accurate sentence retrieval. | Moderate performance, Technique improves outcome | The general public's limited knowledge of their rights and fundamental legal processes, and the inherent complexity of legal texts. The high cost of human annotation for creating legal AI resources. | Public Lack of Legal Knowledge/Awareness, Complexity of Legal Language/Documents, Resource Constraints for A2J Tech Development/Deployment | Developing legal question answering systems based on case documents to bridge the gap between people and the law. Creating specialized datasets like LeDQA to facilitate research and development in legal AI. Using LLMs like GPT-4 for cost-effective annotation of legal data, with human validation. | AI Tool Development, Access to Legal Information and Advice, Data Curation and Management, Cost Reduction and Efficiency, Human Oversight and Collaboration | Legal question answering, legal information access and understanding, legal document analysis, element extraction from legal cases. | Access to Legal Information, Legal Document Analysis / Review | General public with limited legal knowledge, individuals involved in legal disputes (specifically private lending cases initially). | General public, Individuals lacking legal knowledge, Litigants, Individuals in debt or lending disputes | Chinese civil law, specifically private lending cases. | Civil Law, Contract Law, Banking Law | China | China | For LeDQA dataset creation: 100 private lending case documents selected from authoritative cases published by the Supreme People’s Court of China. Relevance and answer annotations for these documents were generated using GPT-4 and subsequently validated by human legal experts (PhD students in Chinese civil law). | Author-Created New Dataset, Evaluation Dataset, Legal Domain Data, Chinese Legal Data, Case Law / Judgments, Synthetic Data, Expert-Annotated / Human-Curated / Human-Generated Data, Structured Data, Publicly Available Data | For LeDQA dataset creation: Question schema construction by a legal expert team through review of laws, element listing, group discussions, and categorization. Case document selection based on authoritativeness and coverage of question schema categories. Annotation of relevant sentences and answers using GPT-4, followed by human validation with inter-annotator agreement checks. | Dataset Creation, Schema Development, Expert Collaboration, Data Curation, LLM-aided Annotation, Human Validation, Inter-annotator Agreement Calculation | The LeDQA dataset is made publicly available on GitHub. | Public dataset/benchmark release | True | True | The LeDQA dataset is available on GitHub via the link https://github.com/BulouLiu/LeDQA. | Dataset available | Insufficiency of current retrieval models to accurately extract all relevant sentences from long legal documents. LLMs struggle with correct multi-sentence reasoning even when provided with relevant sentences. Difficulty for models in correctly identifying 'unknown' answers. | AI Scope and Functionality Limitations, AI Legal Reasoning Limitations, AI Accuracy and Reliability | High cost of human annotation for legal datasets. Ensuring questions are designed from a legal knowledge perspective and cover complex legal elements. Dealing with the length and noisy information in legal case documents compared to typical MRC datasets. Achieving accurate retrieval of relevant sentences and enabling models to perform correct, multi-step reasoning based on these sentences. | Cost and Complexity of Data Annotation, Evaluation Challenges and Metrics, Data Quality, Processing, and Preparation, LLM Context Window and Long Input Management, Accuracy and Reliability of LLM Output, LLM Reasoning Capabilities | Models may retrieve irrelevant sentences or fail to perform correct reasoning even with relevant sentences, leading to potentially incorrect legal interpretations or answers. | Inaccurate or misleading AI output, Technical limitations of AI |
| kLpjOdGODhMJ.pdf | Google_Scholar | Robots vs. Predators: Can Generative Artificial Intelligence Help to Address the Justice Gap in Consumer Debt Litigation? | This paper explores the potential for Generative Artificial Intelligence (GenAI) to alleviate the access-to-justice crisis, particularly in the context of US consumer debt litigation where low-income individuals are often unrepresented. It proposes a 'digital continuum of care' utilizing GenAI and related technologies while also discussing the significant technological, practical, and ethical challenges involved. | Generative AI for Access to Justice, US Focus, Consumer Debt Litigation, Self-Represented Litigant Assistance, Framework Proposal (Digital Continuum of Care), Challenge Identification | True | Idealistic | True | 3.0 | Positive | Proposal for a 'Digital Continuum of Care' for consumer debt cases, leveraging GenAI, chatbots, document assembly/generation tools, and automated discovery. | Conceptual Framework, Generative AI, Chatbot / Conversational AI, Legal Document Generation / Automation, Automated LegalProcesses | NaN | Not Applicable | NaN | NaN | High cost of legal services, individuals not recognizing their problems as legal issues, lack of knowledge on how/where to find legal help, asymmetry of representation (creditors represented, debtors not), sewer service, digital divide. | High Cost of Legal Services, Public Lack of Legal Knowledge/Awareness, Information Asymmetry, Systemic Inequities in Justice System, Digital Divide | Deploying technology (specifically GenAI) to provide legal information (chatbots, know-your-rights), automate repetitive tasks (document assembly, discovery, drafting basic pleadings/motions) to make legal assistance more efficient and affordable, creating targeted interventions like a 'digital continuum of care' for high-need areas like consumer debt. | AI Tool Development, Access to Legal Information and Advice, Document Automation, Cost Reduction and Efficiency, Policy and Regulatory Reform | Consumer debt litigation defense, providing legal information and guidance, automating legal tasks (pleadings, discovery, motion practice), self-representation support. | Dispute Resolution, Access to Legal Information, Access to Legal Advice, Legal Document Creation / Automation, Support for Self-Represented Litigants | Low- and moderate-income Americans, specifically those facing consumer debt lawsuits. Also mentions disproportionate impact on women, minority populations, and urban communities. | Low-income individuals, Moderate-income individuals, Population in USA, Consumers, Individuals in debt or lending disputes, Women, Minority groups, Urban populations | Consumer Law (specifically debt collection), Civil Procedure. | Consumer Law, Debt Collection, Civil Procedure | United States | USA | The paper proposes using existing pro se resources curated by non-profits for chatbots and suggests the potential use of scanned court filings (via OCR) or curated/restricted LLMs for document generation, but does not detail a specific implementation or dataset. | Legal Domain Data, Expert-Annotated / Human-Curated / Human-Generated Data, Other Legal Documents, OCR Processed Data, Fine-tuning Dataset | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | GenAI accuracy/hallucinations, need for human oversight ('lawyer in the loop'), required human capital/resources (especially for under-staffed non-profits), funding for technological innovation in legal aid, the digital divide (access to internet/technology), language and accessibility barriers for users. | AI Accuracy and Reliability, Human Oversight and Professional Adaptation, Computational Resource and Cost Issues, Access, Equity, and Digital Divide, Multilingual and Low-Resource Language Gaps, User Interface and Usability Gaps | Technological feasibility (especially for more complex tasks like analyzing/opposing summary judgment motions), securing human resources for implementation and oversight within budget-constrained legal aid organizations, addressing the digital divide and accessibility issues, navigating ethical concerns (standard of care, confidentiality, UPL). | Accuracy and Reliability of LLM Output, LLM Reasoning Capabilities, Financial Cost and Resource Constraints, Need for Human Oversight and Intervention, User Interface, Usability, and Accessibility, Ethical Considerations, Data Privacy, Security, and Confidentiality, Unauthorized Practice of Law (UPL) Concerns, Legal Professional Responsibility and Competence | GenAI producing inaccurate results ('hallucinations'), lawyers/litigants relying on fictitious sources, increased burden on courts due to AI-generated filings (especially pro se), sharing confidential client information with AI tools, potential violations of Unauthorized Practice of Law (UPL) rules, possibility of widening the justice gap if technology disproportionately benefits well-resourced parties. | Inaccurate or misleading AI output, Over-reliance on AI, Undermining legal process or principles, Data privacy and security breach, Unauthorized practice of law, Exacerbation of inequality or two-tiered system |
| MAPiegzikAIinFamilyLaw.pdf | Google_Scholar | The Adoption of Artificial Intelligence in Family Law – Brand New or Well-known Idea? | This paper reviews the historical development and current state of Artificial Intelligence (AI) adoption in family law, contrasting early systems ('Wave 1') with modern machine learning approaches ('Wave 2'). It assesses AI's application across administrative efficiencies, client support, and decision-making, concluding that progress is accelerating despite ongoing challenges. | Review of AI in Family Law, Historical AI Development in Law, Machine Learning in Law, Family Law Focus, AI for Administrative Efficiency, AI for Client Support, AI in Legal Decision-Making | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | Complexity of human emotions in family law; lawyer skepticism ('dehumanization'); jurisdictional inconsistencies; usability challenges for non-specialists; AI's difficulty with nuance/context; ethical concerns (bias, privacy); accuracy limitations; large amounts of unstructured data. | Complexity of Legal Domain (Emotional/Social Factors), Slow Technology Adoption by Legal Profession, Jurisdictional Complexity, Difficulty in AI-Human Interaction, AI Limitations in Legal Reasoning/Nuance, Ethical Concerns with AI in Law, AI Unreliability/Inaccuracy, Data Scarcity/Quality for AI | Keeping 'humans in the loop'; developing AI for specific tasks (administration, ODR, information provision, decision support); leveraging AI (ODR, virtual assistants) to improve access to justice; advancing AI capabilities (machine learning, LLMs). | Human Oversight and Collaboration, AI Tool Development, Online Dispute Resolution (ODR), Access to Legal Information and Advice, Judicial System Enhancement, Enhanced AI Capabilities | Access to legal information/aid, Online Dispute Resolution (ODR), child welfare (risk assessment, contact scheduling), divorce/separation processing (document drafting, property division). | Access to Legal Information, Legal Aid and Pro Bono Services, Dispute Resolution, Legal Document Creation / Automation | Self-represented litigants in family law, children. | Self-represented litigants, Individuals in family law disputes, Children | Family Law, Dispute Resolution (ODR, ADR). | Family Law, Dispute Resolution, Online Dispute Resolution, Alternative Dispute Resolution | International | International | Varies depending on the tool; includes social care data, demographic/historical/legal data for predictive models; legislation and case law for legal advice tools; large corpora of legal documents for drafting/review tools. | Non-Legal Domain Specific Data, Legal Domain Data, Legislation / Statutes / Regulations, Case Law / Judgments, Other Legal Documents | NaN | NaN | Adoption by law firms, courts (ODR), government agencies; commercial software releases; integration into existing legal tech platforms. | Evaluation of existing third-party tool, Internal deployment/prototype, Government/Public institution deployment, Commercial product/service, Integration into existing system/platform | True | False | Numerous commercial AI tools for document review/drafting, case management, translation, legal advice (e.g., Casetext, Claude, CoCounsel Drafting, numerous ODR platforms) are mentioned as available on the market. | Commercial product or service | Knowledge gaps on AI's impact in family law; need for improved accuracy (advice, translation, prediction); need for tools better suited for non-specialists; addressing ethical concerns and bias; regulation/oversight needs; better handling of unstructured data. | Research and Evaluation Gaps, AI Accuracy and Reliability, Multilingual and Low-Resource Language Gaps, User Interface and Usability Gaps, Ethical Framework Deficiencies, Bias in AI, Regulatory and Governance Gaps, AI Scope and Functionality Limitations | Handling emotional complexity; ensuring accuracy/reliability; overcoming lawyer skepticism; cross-jurisdictional integration; addressing ethical issues (bias, privacy, accountability); obtaining quality training data; usability for laypeople. | LLM Reasoning Capabilities, Accuracy and Reliability of LLM Output, User Adoption, Trust, and Acceptance, Domain-Specific Adaptation and Customization, Ethical Considerations, Bias in AI Systems and Data, Data Privacy, Security, and Confidentiality, Accountability and Liability for AI Errors, Scarcity of High-Quality Legal Data, User Interface, Usability, and Accessibility | Dehumanization of justice; algorithmic bias; privacy violations; inaccurate legal information/advice; misleading translations; over-reliance on flawed predictive models; AI becoming an unaccountable source of 'law'. | Dehumanization of legal process, Bias and discrimination, Data privacy and security breach, Inaccurate or misleading AI output, Over-reliance on AI, Lack of transparency, accountability, and redress, Undermining legal process or principles |
| UiT7QntcF2wJ.pdf | Google_Scholar | Understanding National, Regional, and Global Priorities for the Social Justice and Economic Inclusion of Persons with Disabilities: Analyzing CRPD State Reports Using Text Mining, NLP, and LLMs | This paper analyzes 170 State Reports submitted under the UN Convention on the Rights of Persons with Disabilities (CRPD) using traditional text mining/NLP techniques and Large Language Models (LLMs). The study aims to identify global, regional, and national implementation priorities, assess the focus on social justice and economic inclusion, and evaluate the hybrid analytical approach. | NLP Application, LLM Application, Analysis of Human Rights Reports, Disability Rights Focus, Social Justice Assessment, Hybrid Analytical Approach | True | Idealistic | True | 2.0 | Positive | Hybrid approach using traditional text mining/NLP (N-grams, TF*IDF, LDA topic modeling, NER with spaCy, custom dictionary/lexicon analysis) and LLMs (Gemini 1.5 Flash, GPT-4o) to analyze CRPD State Reports. | Hybrid AI System, Traditional NLP, Text Mining, Topic Modeling / Classification, Named Entity Recognition (NER), Large Language Model, Legal Text Analysis | Analysis applied to a corpus of 170 CRPD State Reports scraped from the OHCHR website (subset of 20 used for LLM analysis due to token limits). Evaluation involved frequency analysis, LDA topic coherence assessment (0.461 score achieved), NER entity extraction, lexicon-based quantification of CRPD article/paragraph representation and disability model prevalence, and comparison of traditional NLP results with LLM outputs generated via multi-shot prompt engineering. | Custom Dataset Evaluation, Quantitative Metrics, Qualitative Analysis, Comparative Analysis | Identified key themes (e.g., awareness-raising, family rights, regional variations), found a general shift towards a social justice model (64% representation), quantified representation of specific CRPD articles (Art. 8 most represented, Art. 10 least), extracted relevant named entities, and demonstrated that LLMs could produce coherent analyses comparable to traditional methods on the tested subset. | Descriptive or Conceptual finding, Comparable to others, Moderate performance | Challenges in effectively monitoring the global implementation of the CRPD due to the volume and complexity of State Reports. Data collection and monitoring challenges are mentioned generally in the literature review. | Difficulty Accessing/Processing Legal Information, Complexity of Legal Information, Data Collection Challenges for Monitoring | Proposes a hybrid computational text analysis methodology (NLP and LLMs) to systematically analyze State Reports, enabling researchers, civil society, and monitoring bodies to identify implementation priorities, gaps, and regional variations, thereby facilitating accountability and strategic planning. | AI Tool Development, Legal Research and Analysis Tools, Policy and Regulatory Reform, Regulation, Ethics, and Governance | Monitoring implementation of the UN Convention on the Rights of Persons with Disabilities (CRPD). | Protection of Rights, Support for Vulnerable Populations | Persons with disabilities. | People with disabilities | International Human Rights Law, Disability Law. | International Law, Human Rights Law, Disability Law | Global (analyzing reports from 170 State Parties to the CRPD, with regional breakdowns). | International | The analysis corpus consists of 170 CRPD State Reports (publicly available PDFs from OHCHR website, unstructured text). NER uses spaCy's pre-trained 'en_core_web_sm' model. LLMs (Gemini, GPT-4o) utilize their own pre-training. Custom lexicons were created based on CRPD text and disability studies literature. | Input Data for Task (Non-Training), Publicly Available Data, Non-Legal Domain Specific Data, Official Documents / Government Data, Unstructured Text Data, Pre-trained LLM's General Training Corpus, Author-Created New Dataset | Corpus collection (web scraping), data preprocessing, lexicon development (manual, literature-based, validation via KWIC and robustness checks), text mining (N-grams, TF*IDF), NLP techniques (NER via spaCy, LDA Topic Modeling via Gensim), LLM analysis (Prompt Engineering with Google AI Studio/Gemini and OpenAI/ChatGPT). | Data Collection, Data Preprocessing, Lexicon Development, Text Mining Techniques, NLP Technique Application, Prompt Engineering | Findings presented in an academic paper. The methodology is proposed as a framework to enable broader analysis by scholars, practitioners, and civil society, facilitated by more user-friendly GenAI tools. | Dissemination via publication/presentation, Proposed deployment (not implemented) | False | False | NaN | NaN | Methodological limitations: reliance on self-reported state data, potential dictionary limitations, NER model not fine-tuned, LLM analysis constrained by token limits (subset used). Need to incorporate shadow reports for a balanced view. Substantive gaps: Less emphasis found on addressing stigma and barriers in reports. | Research and Evaluation Gaps, Data Availability and Quality, AI Scope and Functionality Limitations | Developing robust custom lexicons, achieving high coherence in topic modeling (LDA score was moderate), managing LLM token limits for large corpus analysis, standard PDF text extraction and cleaning. | Data Quality, Processing, and Preparation, LLM Reasoning Capabilities, LLM Context Window and Long Input Management | The paper primarily focuses on benefits but limitations imply a risk of drawing inaccurate conclusions if relying solely on the analysis of self-reported data without considering its inherent biases or the methodology's limitations. | Inaccurate or misleading AI output, Bias and discrimination, Risk of misapplication or misuse |
| RiRt0XNLpwEJ.pdf | Google_Scholar | Assessing Information Literacy in the Age of Generative AI: A Call to the National Conference of Bar Examiners | This paper argues for the National Conference of Bar Examiners (NCBE) to incorporate information literacy assessment, especially concerning generative AI, into the Multistate Professional Responsibility Exam (MPRE). This is presented as crucial for ensuring newly licensed lawyers meet their duty of technology competence and to protect the public from the risks of incompetent AI use in legal practice. | Proposal for Legal Education Reform, Generative AI in Legal Competence, Information Literacy Assessment, Professional Responsibility Exam, US Focus | True | Idealistic | True | 1.0 | Positive | Incorporating information literacy assessment for generative AI into the Multistate Professional Responsibility Exam (MPRE). | Educational Assessment / Curriculum Development, AI Literacy / Awareness Material Development | NaN | Not Applicable | NaN | NaN | The primary obstacle identified is the risk of newly licensed lawyers' incompetent use of generative AI, stemming from a lack of assessed information literacy. This incompetence can lead to flawed legal research, ethical breaches, and ultimately harm to clients, thereby undermining access to competent legal services. | Lack of AI Literacy, Risk of Professional Incompetence with AI, Ethical Concerns with AI in Law | The paper proposes that the National Conference of Bar Examiners (NCBE) address this by incorporating specific assessments of information literacy related to generative AI into the Multistate Professional Responsibility Exam (MPRE). This would ensure a minimum standard of technological competence for newly licensed lawyers. | Education and AI Literacy, Regulation, Ethics, and Governance, Policy and Regulatory Reform | Lawyer competence in using AI, professional responsibility, public protection, legal research ethics in the age of AI. | Legal Education for Professionals / Students, Regulatory Reform (Legal Services and AI), Ethical AI in Law and AI Governance, Protection of Rights | The general public seeking legal services. | General public, Individuals with unmet legal needs | Professional Responsibility, Legal Ethics, Legal Research | Professional Responsibility, Legal Ethics, Legal Research | United States | USA | NaN | Not Applicable | Conceptual analysis, review of legal and educational literature, argumentation based on existing institutional frameworks (e.g., NCBE history, AALL standards), and analysis of current technological impacts on the legal profession. | Conceptual Analysis, Literature Review as Design Input, Framework Analysis, Impact Analysis | Proposed deployment through the National Conference of Bar Examiners (NCBE) by integrating new assessment components into the Multistate Professional Responsibility Exam (MPRE). | Proposed deployment (not implemented), Integration into existing system/platform, Partnership-based rollout, Regulatory/Legal framework adoption | False | False | NaN | NaN | The current lack of formal assessment of AI-related information literacy in lawyer licensing exams (specifically the MPRE), which fails to ensure newly licensed lawyers are competent in using emerging AI technologies responsibly and ethically. | Human Oversight and Professional Adaptation, Regulatory and Governance Gaps | The primary challenge for the NCBE would be the rapid pace of AI development, requiring continuous updates to assessment content and methodologies, and ensuring the validity, fairness, and psychometric soundness of new question types related to AI and information literacy. | Regulatory Uncertainty and Compliance, Evaluation Challenges and Metrics, User Training, AI Literacy, and Skill Gaps | Risks identified include lawyers producing inaccurate legal work due to AI 'hallucinations,' breaching client confidentiality through improper AI use, and a general decline in critical legal skills if AI is used without adequate oversight. These issues can lead to disciplinary actions for lawyers and significant harm to clients, thereby eroding public trust in the legal profession. | Inaccurate or misleading AI output, Data privacy and security breach, Deskilling or erosion of human skills, Over-reliance on AI, Ethical concerns, Consumer harm, Erosion of trust in legal system or AI |
| nvJ-YKrRcQAJ.pdf | Google_Scholar | Artificial Intelligence in Civil Justice Systems : An Empirical and Interdisciplinary Analysis and Proposal for Moving Forward | This paper analyzes the systemic and individual harms posed by generative AI to civil justice systems (litigation and arbitration), drawing on empirical social science research. It proposes restructuring the legal profession and education based on England's split bar model to balance AI's benefits with the need to preserve human expertise and system legitimacy. | Generative AI Impact on Civil Justice, Risk Identification, Proposal for Legal Profession Reform, Proposal for Legal Education Reform, UK Focus (England's model) | True | Idealistic | True | 1.0 | Negative | A proposed restructuring of the legal profession into a 'split bar' (post-AI solicitors using AI for routine tasks, post-AI barristers avoiding AI for complex/novel work), inspired by the English system. | Legal Profession Restructuring Proposal, Conceptual Framework | The proposal is based on theoretical analysis, empirical social science research on AI's effects, legal scholarship, and comparative analysis of the English legal system; no empirical testing of the proposal itself is described. | Theoretical Analysis or Conceptual Proposal, No Evaluation by Author | NaN | NaN | Systemic threats to the legitimacy and integrity of civil justice (e.g., algocracy, path dependency, erosion of diffuse support); individual cognitive harms (e.g., automation bias, cognitive atrophy, skill degradation, metacognitive laziness, cognitive loafing, AI addiction); difficulty ensuring expertise development for junior lawyers; ethical challenges; risk of inequitable two-tiered justice. | Threats to Justice System Legitimacy from AI, Negative Cognitive Impacts of AI on Users, Challenges to Professional Development, Ethical Concerns with AI in Law, Risk of AI Exacerbating Inequality | Adopt a 'split bar' model distinguishing lawyers who use AI extensively (post-AI solicitors) from those who do not for complex tasks (post-AI barristers). Implement corresponding differentiated legal education pathways focused on either AI proficiency or traditional independent legal analysis, ensuring a baseline legal understanding before specialization. | Policy and Regulatory Reform, Alternative Legal Service Delivery Models, Education and AI Literacy | Legitimacy of justice systems, Quality of legal services, Professional ethics and competence, Legal education reform | Democratizing Law / Closing Justice Gap / Rule of Law, Improving Efficiency in Legal System / Profession, Regulatory Reform (Legal Services and AI), Legal Education for Professionals / Students | NaN | NaN | Civil justice systems (litigation and arbitration) | Civil Justice, Litigation, Arbitration | US, UK, EU, International | USA, UK, EU, International | NaN | Not Applicable | Interdisciplinary analysis, review of empirical social science research, legal analysis, comparative legal systems analysis (England and Wales). | Interdisciplinary Analysis, Literature Review as Design Input, Legal Doctrinal Analysis as Design Input, Comparative Legal Systems Analysis | NaN | Not applicable | False | False | NaN | NaN | Need for development of practical implementation details for the proposed split bar system; overcoming status quo bias for adoption; addressing potential negative impacts of pre-collegiate AI education on foundational skills required for the 'post-AI barrister' path; underexplored choice-of-law issues related to AI. | Regulatory and Governance Gaps, Human Oversight and Professional Adaptation, Research and Evaluation Gaps | Ensuring responsible AI use by legal professionals; overcoming cognitive biases (automation bias, cognitive loafing, anchoring bias); preventing skill degradation and ensuring expertise development; maintaining system legitimacy and public trust amidst technological change; adapting legal education effectively; addressing ethical concerns (hallucinations, self-dealing); managing potential AI addiction. | Legal Professional Responsibility and Competence, User Training, AI Literacy, and Skill Gaps, Ethical Considerations, User Adoption, Trust, and Acceptance, LLM Hallucination and Factual Errors | Erosion of civil justice system legitimacy (algocracy, undermining judicial independence, loss of public trust); degradation of critical thinking, legal skills, and creativity (cognitive atrophy, path dependency); increased errors due to automation bias and hallucinations; reinforcement of societal biases through algorithms; creation of inequitable two-tiered justice systems; ethical violations; AI addiction among professionals. | Erosion of trust in legal system or AI, Undermining legal process or principles, Deskilling or erosion of human skills, Inaccurate or misleading AI output, Over-reliance on AI, Bias and discrimination, Exacerbation of inequality or two-tiered system, Ethical concerns, Poor user experience |
| l1iBcAN9nccJ.pdf | Google_Scholar | Legal Validity w ith Artificial Intelligence Technology on Gpt Chat as Legal Aid | This paper analyzes the legal validity of using AI, specifically ChatGPT, for legal aid in Indonesia, highlighting the absence of a clear regulatory framework for liability and user protection. It argues for the urgent need for specific regulations to ensure AI's safe and ethical application in the legal field without compromising legal certainty for users. | AI for Legal Aid, ChatGPT Application, Indonesian Law Focus, Lack of AI Regulation, Need for AI Regulation, User Protection, Liability Issues | True | Idealistic | True | 3.0 | Neutral | ChatGPT for legal aid | Large Language Model, Legal Aid Application | NaN | Not Applicable | NaN | NaN | Uncertainty of legal liability for AI errors; AI (ChatGPT) lacking legal capacity/qualifications under Indonesian law; risk of inaccurate/outdated AI advice; AI's inability to make ethical judgments; data privacy/confidentiality concerns; lack of a clear regulatory framework for AI in legal aid. | Lack of AI Accountability, Lack of Legal Personality/Capacity for AI, AI Unreliability/Inaccuracy, AI Limitations in Ethical Judgment, Data Privacy Concerns with AI, Inadequate Legal Frameworks for AI | Adopting specific regulations for AI in law (defining limits, accountability); public education on AI limitations; collaboration between tech developers and legal institutions; ensuring compliance with data protection laws; setting quality/accuracy standards for legal AI; clarifying provider liability and consumer protection. | Regulation, Ethics, and Governance, Education and AI Literacy, Open Source Initiatives and Collaboration, Data Privacy and Security | Legal validity of AI-provided legal aid; legal liability for AI errors; data privacy and consumer rights in AI legal services; regulation of AI in the legal field; accessibility of legal information. | Regulatory Reform (Legal Services and AI), Protection of Rights, Access to Legal Information, Legal Aid and Pro Bono Services | General public, especially those unable to afford professional legal advocates and individuals unfamiliar with the law. | General public, Individuals unable to afford legal services, Laypeople, Individuals lacking legal knowledge | Advocate Law, Consumer Protection Law, Personal Data Protection Law, provision of legal aid. | Legal Profession Regulation, Consumer Law, Data Privacy Law, Legal Aid | Indonesia | Indonesia | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Lack of a clear legal framework for AI liability in legal aid; absence of specific regulations for AI use ensuring legal/ethical standards; unclear application of data protection laws to legal AI; no established mechanism for holding AI or developers liable for erroneous advice. | Regulatory and Governance Gaps, Accountability and Redress Mechanisms, Security and Privacy of Data | Ensuring legal validity of AI-generated advice; establishing accountability for AI errors; protecting user data; AI's lack of contextual/ethical understanding; potential for AI inaccuracies; public over-reliance or misunderstanding of AI capabilities. | Accuracy and Reliability of LLM Output, Accountability and Liability for AI Errors, Data Privacy, Security, and Confidentiality, LLM Reasoning Capabilities, Ethical Considerations, User Adoption, Trust, and Acceptance | Inaccurate or irrelevant AI-generated legal advice leading to adverse outcomes for users; misuse or leakage of personal/sensitive legal data; users relying on AI without understanding its lack of legal authority or accountability compared to human advocates. | Inaccurate or misleading AI output, Consumer harm, Data privacy and security breach, Over-reliance on AI, Lack of transparency, accountability, and redress |
| HmTfEfhHkRMJ.pdf | Google_Scholar | AI Diversity and the Future of “Fair” Legal AI | This article examines the potential for AI to reshape legal practice, highlighting the critical issue of embedded bias, particularly in automated legal decision-making. It proposes that using a diversity of AI systems ("AI diversity" or a "multisystem approach"), benchmarked against public standards, can help mitigate bias and lead to fairer legal outcomes. | AI Impact on Legal Practice, Bias in AI, Automated Legal Decision-Making, Bias Mitigation Strategy, AI Diversity Approach, Benchmarking AI Systems | True | Idealistic | True | 1.0 | Neutral | AI Diversity / Multisystem Approach: Employing multiple, distinct AI models (developed by diverse teams, trained on different data) in parallel for legal tasks, comparing their outputs to enhance reliability and mitigate bias. | Multi-Model Approach, Bias Mitigation, Reliability Enhancement, Diversity in AI Development | The paper proposes the technique conceptually and discusses the general importance of testing and benchmarking AI, including using public benchmarks, but does not report any specific testing or evaluation conducted on the proposed "AI diversity" approach itself. | Theoretical Analysis or Conceptual Proposal, No Evaluation by Author | NaN | NaN | Algorithmic bias stemming from training data that reflects historical societal and legal system inequalities; Lack of transparency in AI systems ("black box" problem) hindering trust, accountability, and regulation. | Bias in AI/Data, Lack of AI Transparency/Explainability, Lack of Trust in AI/Automated Systems, Lack of AI Accountability | Adopt an "AI diversity" or "multisystem approach" using multiple AI models benchmarked against public standards; Ensure diversity in development teams and training data; Promote transparency and public participation in AI implementation and oversight; Use consensus among models for credibility and discrepancies to trigger human review. | AI Tool Development, Benchmarking and Evaluation Frameworks, Data Curation and Management, Bias Detection and Mitigation, Transparency and Explainability in AI, Regulation, Ethics, and Governance, Human Oversight and Collaboration | Fairness in legal AI, Bias mitigation in automated legal/governmental decision-making (administrative decisions, judicial rulings, sentencing), Algorithmic accountability and transparency. | Ethical AI in Law and AI Governance, Judicial System Modernization / Efficiency | Minority and underrepresented groups disproportionately affected by bias in the legal system (e.g., racial minorities mentioned in context of COMPAS and juror questioning). | Minority groups, Underrepresented groups, Marginalized communities | General / Multiple (Criminal Law, Administrative Law, Constitutional Law, Litigation, Legal Research, Document Drafting, Appellate Review) | General Law, Multiple Fields, Criminal Law, Administrative Law, Constitutional Law, Litigation, Legal Research, Document Drafting, Appellate Procedure | International | International | The paper discusses general types of data used for legal AI (historical text, cases, statutes, dockets) and emphasizes the source of bias often lies in this data reflecting societal inequalities or specific legal system biases (e.g., COMPAS data, voir dire data). It advocates for diverse, cleaned, and vetted data but does not describe a specific dataset. | Legal Domain Data, Case Law / Judgments, Legislation / Statutes / Regulations, Other Legal Documents, Data Bias Concerns Noted | Conceptual proposal; The paper does not detail specific design methodologies used to develop the proposed 'AI diversity' technique itself. | Conceptual Proposal | The paper proposes parallel deployment of multiple benchmarked AI systems for government functions and potentially in the appeals process, but does not describe any actual deployment. | Proposed deployment (not implemented), Government/Public institution deployment | False | False | NaN | NaN | Lack of emphasis on incorporating AI system diversity (multisystem approach) in current proposals for AI adoption, regulation, and transparency, especially in government legal processes; Need for effective public benchmarks for legal AI; Need for truly democratized processes for AI implementation and oversight in the public sector. | Regulatory and Governance Gaps, Research and Evaluation Gaps, Access, Equity, and Digital Divide, Transparency and Explainability | Ensuring fairness and eliminating bias in AI systems; Dealing with the opacity ('black box') of complex models; Developing appropriate and timely regulations; Establishing trust and accountability; Sourcing diverse data and development teams; Creating meaningful benchmarks; Managing and interpreting outputs from multiple AI systems. | Bias in AI Systems and Data, Transparency and Explainability of AI, Regulatory Uncertainty and Compliance, User Adoption, Trust, and Acceptance, Accountability and Liability for AI Errors, Scarcity of High-Quality Legal Data, Evaluation Challenges and Metrics, Output Variability and Consistency | Replicating and amplifying societal biases leading to unfair or discriminatory legal outcomes (e.g., in sentencing, administrative decisions); Degrading trust in the legal system due to biased or opaque AI; Lack of accountability for AI-driven decisions; Hindering access to justice or exacerbating inequities if AI implementation is flawed. | Bias and discrimination, Erosion of trust in legal system or AI, Lack of transparency, accountability, and redress, Exacerbation of inequality or two-tiered system, Risk of misapplication or misuse |
| qOSNB97orXcJ.pdf | Google_Scholar | AI-Powered Platforms for Access to Justice: The Case of Hear Me Out | This paper introduces Hear Me Out, an AI-powered platform using GPT-4o and RAG to help disadvantaged Australians navigate complex complaint pathways, thereby enhancing access to justice. It details the platform's user-centered design, technical architecture, ethical considerations, and initial impact, outlining plans for future expansion. | System Development, LLM Application, Retrieval Augmented Generation, Access to Justice Enhancement, Australian Focus, Complaint Navigation Assistance, User-Centered Design, Ethical Considerations | True | Idealistic | True | 1.0 | Positive | Hear Me Out: An AI chatbot platform using Azure OpenAI (GPT-4o) with tool-based Retrieval Augmented Generation (RAG), OpenAI Ada embeddings, Pinecone vector database, and Cosmos DB backend to guide users through legal complaint processes. | Chatbot / Conversational AI, Large Language Model, Retrieval Augmented Generation (RAG), Tool Augmented LLM, Embedding-based Methods, Vector Database, Software / Platform Development, Named Tool / Platform | Usability testing with potential users during prototype development; ongoing user feedback collection (surveys, forms) for iterative improvement. | Iterative Design Feedback, User Study or Survey | Positive user feedback led to refinements (response sensitivity, language adjustments); qualitative descriptions of enhanced user self-advocacy and potential efficiency gains for legal aid providers; systemic impact illustrated via analogous case studies. | Benefit identified, High performance, Technique improves outcome | Complexity and fragmentation of the legal complaint system, lack of centralized guidance, resource constraints in legal aid, lack of legal representation, difficulty understanding legal language, traditional barriers (cost, time, location). | Complexity of Legal System/Procedures, Difficulty Accessing/Interpreting Legal Information, Resource Constraints for Legal Aid Organizations, Limited Availability/Access to Legal Professionals/Expertise, Complexity of Legal Language/Documents, High Cost of Legal Services, Geographical Disparities in Legal Access | An AI-powered platform (Hear Me Out) to simplify complaint navigation, provide automated guidance and triage, offer plain-language explanations, overcome traditional access barriers, and facilitate data collection for systemic advocacy. | AI Tool Development, Access to Legal Information and Advice, Language Simplification and Multilingual Access, Data Curation and Management, Policy and Regulatory Reform | Navigating complaint systems, lodging complaints, self-advocacy support, systemic advocacy. | Access to Legal Information, Support for Self-Represented Litigants, Dispute Resolution | Disadvantaged communities in Australia experiencing discrimination and disadvantage, including First Nations, CALD communities, and people with disabilities. | Marginalized communities, Population in Australia, Indigenous populations, Individuals with language barriers, Minority groups, People with disabilities | Administrative Law (complaint procedures), Discrimination Law, Human Rights Law, potentially others depending on the specific complaint. | Administrative Law, Anti-Discrimination Law, Human Rights Law | New South Wales (Australia), with planned expansion across Australia and potentially internationally. | Australia, International | Information on NSW complaint bodies and pathways (stored in Cosmos DB); synthetic scenarios based on real data linked via metadata to complaint bodies (stored in Pinecone vector DB using OpenAI Ada embeddings); base model is GPT-4o. | RAG System Knowledge Corpus, Australian Legal Data, Legal Domain Data, Other Legal Documents, Structured Data, Synthetic Data, Expert-Annotated / Human-Curated / Human-Generated Data, Pre-trained LLM's General Training Corpus | User-centered design (prototype testing with target users), collaborative development (non-profit, universities, tech company), technical investigation, iterative development based on design principles derived from user testing. | User-centered Design, Collaborative Development, Technical Investigation, Iterative Design Process, Principle-driven Design | Web application accessible via www.hearmeout.org.au. | Web-based access, Freely accessible tool/service | True | False | Available as a web application at www.hearmeout.org.au (currently focused on NSW). | Publicly accessible online tool or platform | Need for broader geographic/jurisdictional coverage, enhanced AI capabilities (complaint drafting, translation, accessibility), deeper integration with public systems, development of comprehensive AI governance policies for justice, need for public awareness and trust. | Multilingual and Jurisdictional Specificity Gaps, AI Scope and Functionality Limitations, User Interface and Usability Gaps, Integration and Interoperability Challenges, Regulatory and Governance Gaps, Public Understanding, Trust, and Adoption | Adapting AI to diverse legal jurisdictions, ensuring data privacy/security, managing ethical AI considerations (bias, transparency, accountability), balancing content filtering, maintaining accuracy and relevance of legal information, securing collaborations and resources for development/expansion. | Domain-Specific Adaptation and Customization, Data Privacy, Security, and Confidentiality, Ethical Considerations, Bias in AI Systems and Data, Transparency and Explainability of AI, Accountability and Liability for AI Errors, Safeguarding Against Misuse and Harm, Accuracy and Reliability of LLM Output, Interdisciplinary Collaboration Challenges, Financial Cost and Resource Constraints | Data breaches, unauthorized data access, AI model drift impacting response quality, inaccurate AI guidance, suppression of valid complaints via content filtering, potential AI bias. | Data privacy and security breach, Technical limitations of AI, Inaccurate or misleading AI output, Ethical concerns, Bias and discrimination |
| informit.T2024121000001400747097470.pdf | Google_Scholar | ALLA CONFERENCE 2024: TAKE THE LEAP | This paper is a personal reflection by a law librarian on her attendance at the ALLA Conference 2024, summarizing key presentations. Topics include the AI tool 'amica' for assisting couples in separation (access to justice), space law, and the role of librarians in navigating generative AI and LLMs. | Conference Report, AI Tool for Separation Assistance, Access to Justice Enhancement, Role of Librarians in AI Era, Generative AI in Law | True | Idealistic | True | 3.0 | Positive | amica: an AI tool by the Legal Services Commission of South Australia, designed by family lawyers, to help couples navigate separation and asset division. | AI Legal Tool, Family Law Application, Dispute Resolution Support, Named Tool / Platform | For amica: Ongoing quality assurance on every case by a team of people; if a case is unusual or out of range of the AI's training scenarios, it is flagged for the team to contact the couple with resource suggestions. | Expert Evaluation | For amica: Described as a valuable tool for those who cannot afford lawyers during separation, with a free version ('amica one') available to provide an estimate of asset division. | Benefit identified, Descriptive or Conceptual finding, Developer or Vendor claim | The high cost of hiring lawyers for couples going through separation, preventing access to legal assistance. | High Cost of Legal Services | The 'amica' platform, an AI tool designed to guide couples through separation and asset division, offering a free version ('amica one') for initial estimates. | AI Tool Development, Access to Legal Information and Advice, Online Dispute Resolution (ODR) | Access to legal assistance for relationship separation, financial settlements, and property division. | Access to Legal Advice, Access to Legal Representation, Dispute Resolution | Couples, particularly those with limited financial means, undergoing relationship separation and needing guidance on legal processes. | Individuals in family law disputes, Low-income individuals | Family Law | Family Law | Australia (specifically, amica is a government platform, with the Legal Services Commission of South Australia involved in its development). | Australia | For amica: The AI tool was trained on over one thousand scenarios and is a closed model system. These scenarios were presumably related to family law separations. | Fine-tuning Dataset, Proprietary Data, Legal Domain Data, Synthetic Data, Expert-Annotated / Human-Curated / Human-Generated Data, Undisclosed Data Source/Availability | For amica: Designed by family lawyers; incorporates quality assurance for every case by a team of people, with out-of-scope cases escalated for human intervention. | Expert-driven Design, Quality Assurance Process, Human Oversight/Intervention | For amica: Deployed as a government web platform (amica.gov.au), with a free version called 'amica one' also available. | Government/Public institution deployment, Web-based access, Freely accessible tool/service | True | False | The 'amica' platform (amica.gov.au) is described as an existing, usable government service, with a free version 'amica one' accessible online. | Publicly accessible online tool or platform | AI systems, while beneficial for access to justice (e.g., amica), still require human oversight, especially for unusual cases, and lack human qualities like empathy and discretion critical in legal matters. The need for human input to review AI outputs. | Human Oversight and Professional Adaptation, AI Legal Reasoning Limitations | NaN | NaN | Generative AI risks include lack of empathy and discretion, potential for inaccuracies ('hallucinations'), and the need for critical human review. Broader concerns about AI involve human rights implications. For amica, the risk of unusual cases falling outside its AI capabilities is managed by human review. | Dehumanization of legal process, Inaccurate or misleading AI output, Over-reliance on AI, Infringement on human rights, Technical limitations of AI |
| k1-G1sD5mA0J.pdf | Google_Scholar | AI and Tools for Expanding Access to Justice | This paper explores how artificial intelligence, encompassing traditional expert systems and modern large language models, can significantly improve access to justice by automating legal tasks and enhancing the accessibility of legal support. Through case studies like MADE, the Resurrection Project, and Rentervention, it demonstrates practical applications of AI in assisting unrepresented individuals and legal aid organizations. | AI for Access to Justice, Expert System Application, LLM Application, Task Automation, Legal Support Accessibility, Case Studies of AI in A2J | True | Idealistic | True | 2.0 | Positive | Expert systems for document automation, and their enhancement with Large Language Models, including conversational AI chatbots. | Expert System, Legal Document Generation / Automation, Large Language Model, Chatbot / Conversational AI, Hybrid AI System | User-centered design, iterative feedback from users (tenants, clinic staff, volunteers), usage analytics (e.g., Google Analytics, internal metrics), and qualitative impact assessment (e.g., time saved, error reduction). | Iterative Design Feedback, User Study or Survey, Quantitative Metrics, Qualitative Analysis | The Resurrection Project's tool, built with LLM-assisted development, reduced legal form processing time for migrant families from 2 hours to 45 minutes, assisting 4,440 individuals (1,097 family groups) between February and May 2024, and saving over 1,370 hours. | Benefit identified, Successful real-world application, High performance | High cost and limited availability of lawyers, difficulty for people to understand legal processes or recognize their legal problems, restrictive regulations on providing legal help (unauthorized practice of law), and the sheer scale of unmet legal needs. | High Cost of Legal Services, Limited Availability/Access to Legal Professionals/Expertise, Public Lack of Legal Knowledge/Awareness, Regulatory Hurdles, Scale of Unmet Legal Need | Deploying AI-powered tools like expert systems, document automation, and conversational chatbots to guide self-represented litigants; using LLMs to enhance these tools' capabilities and reduce development costs; promoting regulatory reforms (e.g., sandboxes); and developing accessible, interactive legal applications. | AI Tool Development, Document Automation, Access to Legal Information and Advice, Support for Self-Represented Litigants, Enhanced AI Capabilities, Cost Reduction and Efficiency, Policy and Regulatory Reform, User Interface and Accessibility Design | Eviction defense, immigration assistance (work authorization, Temporary Protected Status), tenant rights, and broader civil legal aid for self-represented litigants. | Dispute Resolution, Legal Aid and Pro Bono Services, Support for Self-Represented Litigants, Protection of Rights | Low-income individuals, tenants, migrants (particularly recent arrivals), self-represented litigants, and other vulnerable populations facing civil legal issues. | Low-income individuals, Tenants, Migrants, Recent immigrants, Self-represented litigants, Vulnerable populations | Housing Law, Immigration Law, Civil Law (general, including family law and administrative benefits). | Housing Law, Immigration Law, Civil Law, Family Law, Administrative Law, Social Security Law | Primarily United States (Massachusetts, Illinois), with mentions of broader U.S. applicability (e.g., CourtFormsOnline.org in a dozen states) and global access to justice issues. | USA, International | For rule-based expert systems: encoded legal knowledge, procedures, and template documents. For LLM-assisted development (e.g., GitHub CoPilot for Resurrection Project tool) or LLM-powered features (e.g., OpenAI models in Rentervention, Weaver tool): large, general pre-trained models based on public code, web text, and other diverse data sources. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Legal Domain Data, Rule-Based System (No Training Data) | User-centered design, iterative development, co-development with users and stakeholders, rapid prototyping, feedback loops using direct observation and analytics, and leveraging existing development frameworks (e.g., Docassemble, Assembly Line). | User-centered Design, Iterative Design Process, Stakeholder Engagement/Participatory Design, Rapid Prototyping, User Feedback Integration, Third-party Framework Utilization | Web applications accessible on various devices (including smartphones), deployment in legal aid clinics and for remote assistance, integration with existing legal aid workflows and intake processes, online chatbots, virtual help desks, and dissemination of standardized development frameworks to other organizations. | Web-based access, Partnership-based rollout, Integration into existing system/platform, Dissemination via publication/presentation | True | False | Specific tools like MADE are available online for their target audience (e.g., Massachusetts tenants). Rentervention is an operational service for Illinois renters. CourtFormsOnline.org provides access to forms for users in several US states. | Publicly accessible online tool or platform | The vast scale of unmet legal needs persists. Low adoption of existing automation due to cost/rigidity, scarcity of deployed public interest LLM applications, challenges in safely and effectively integrating LLMs (ensuring accuracy, reliability, handling sensitive topics with current LLM moderation), need for investment in open-source LLMs for legal aid, and restrictive regulations on legal service provision. | Access, Equity, and Digital Divide, Public Understanding, Trust, and Adoption, Computational Resource and Cost Issues, AI Scope and Functionality Limitations, AI Accuracy and Reliability, Ethical Framework Deficiencies, Regulatory and Governance Gaps | Ensuring user comprehension and managing complexity for self-represented litigants, development costs and timelines for robust tools, inflexibility of traditional rule-based systems, potential for LLM errors ('hallucinations') and bias, the necessity for human oversight with AI, LLM moderation policies interfering with legally relevant content, and ensuring the overall accuracy and safety of AI-driven legal assistance. | User Interface, Usability, and Accessibility, User Training, AI Literacy, and Skill Gaps, Financial Cost and Resource Constraints, Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Bias in AI Systems and Data, Need for Human Oversight and Intervention, Ethical Considerations, Safeguarding Against Misuse and Harm | LLM 'hallucinations' leading to incorrect information, algorithmic bias in AI systems, inappropriate or unethical application of AI in legal contexts, lack of fairness and transparency potentially hindering rather than helping access to justice, and LLM moderation filters preventing the processing of essential (but sensitive) legal topics like human rights violations or domestic abuse. | Inaccurate or misleading AI output, Bias and discrimination, Risk of misapplication or misuse, Ethical concerns, Lack of transparency, accountability, and redress, Undermining legal process or principles, Technical limitations of AI, Infringement on human rights |
| 62PlXWw-qiYJ.pdf | Google_Scholar | Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence | This paper evaluates the performance of several Large Language Models (LLMs), particularly OpenAI's GPT series, on U.S. tax law questions using synthetically generated multiple-choice problems. It finds that newer models like GPT-4 show emerging legal reasoning capabilities, significantly improving with few-shot prompting and access to relevant legal texts, though still falling short of expert human performance. | LLM Evaluation, US Tax Law Focus, Legal Question Answering, Legal Reasoning Evaluation, Few-Shot Prompting, Access to Legal Texts for LLMs | True | Idealistic | True | 2.0 | Positive | Retrieval-Augmented Generation (RAG) using various OpenAI LLMs (davinci, text-davinci-002, gpt-3.5-turbo, gpt-4) combined with different prompting strategies (zero-shot, few-shot, chain-of-thought) and retrieval methods (no retrieval, lecture notes, similarity search with GTR-large embeddings on CFR/US Code, gold standard retrieval). | Retrieval Augmented Generation (RAG), Large Language Model, Prompt Engineering, Zero-shot Learning, Few-shot Learning, Information Retrieval / Search, Embedding-based Methods | Evaluation on two synthetic multiple-choice exams (one based on CFR, one on U.S. Code), each with multiple 100-question sections covering specific tax law question types. Questions were randomly generated using Python code to avoid training data contamination. Answers were graded for accuracy using GPT-4 comparing the model's choice to the ground truth across 28,700 evaluated answers. | Custom Dataset Evaluation, Quantitative Metrics, LLM as Judge | GPT-4 combined with few-shot prompting, chain-of-thought (CoT) prompting, and retrieval using the 'gold standard' correct legal text ('mega_run') achieved the highest accuracy, approaching or exceeding 80% on average for both CFR and U.S. Code exams. Performance increased consistently with newer OpenAI model releases. Few-shot prompting and providing relevant legal text significantly improved GPT-4's accuracy. | High performance, Technique improves outcome | Complexity of legal reasoning; need for accurate legal source retrieval; current LLM performance limitations compared to human experts; need for safeguards regarding data privacy, bias, and accountability; cost of legal counsel for potential users. | AI Limitations in Legal Reasoning/Nuance, Technical Challenges in AI Development, DataPrivacy Concerns with AI, Bias in AI/Data, Lack of AI Accountability, High Cost of Legal Services | Using enhanced LLMs (with retrieval augmentation, few-shot prompting, CoT) to potentially provide legal information/advice, increase lawyer productivity, and lower costs. Further research into advanced prompting, better retrieval, and fine-tuning models for law is proposed. | Enhanced AI Capabilities, Access to Legal Information and Advice, Cost Reduction and Efficiency, Human Oversight and Collaboration, Prompt Engineering and LLM Interaction Design | Answering fact-specific legal questions; providing basic legal information/advice; augmenting lawyer tasks. | Access to Legal Information, Access to Legal Advice, Improving Efficiency in Legal System / Profession | People who currently cannot afford legal counsel; consumers not engaging a traditional lawyer; general public needing tax law information. | Individuals unable to afford legal services, Consumers, Self-represented litigants, General public, Taxpayers | Tax Law | Tax Law | United States | USA | The evaluated LLMs (OpenAI GPT series) were pre-trained on general web corpora. Retrieval augmentation used vector databases built from the U.S. Code of Federal Regulations (Treasury Regulations) and Title 26 of the U.S. Code, embedded using the GTR-large model (trained on general domain data). Evaluation data was synthetically generated via Python code. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, RAG System Knowledge Corpus, Legal Domain Data, US Legal Data, Legislation / Statutes / Regulations, Publicly Available Data, Evaluation Dataset, Synthetic Data | Experimental design varying LLM model, retrieval method, and prompting technique. Synthetic data generation for evaluation. Retrieval-augmented generation (RAG). Automated evaluation using a separate LLM (GPT-4). | Experimental Design, Synthetic Data Generation, Retrieval Augmented Generation (RAG), Automated Evaluation using LLM | NaN | Not applicable | False | False | NaN | NaN | LLM performance gap compared to expert lawyers; sub-optimal performance of similarity search retrieval compared to gold standard; need for improved prompting techniques (e.g., self-reflection); need to explore legal-specific model fine-tuning; need for better safeguards (privacy, bias, accountability). | AI Accuracy and Reliability, AI Legal Reasoning Limitations, Research and Evaluation Gaps, Security and Privacy of Data, Bias in AI, Accountability and Redress Mechanisms | Ensuring evaluation validity (avoiding data contamination); developing effective legal text retrieval; optimizing prompting strategies; accurate automated grading of LLM outputs; managing varying model capabilities and context window limitations. | Evaluation Challenges and Metrics, Accuracy and Reliability of LLM Output, Prompt Engineering and Optimization, LLM Context Window and Long Input Management | Inaccurate legal information/advice; model bias; lack of accountability; LLM hallucinations; vulnerability to misleading prompts; potential disruption of the legal profession; challenges for regulations like unauthorized practice of law. | Inaccurate or misleading AI output, Bias and discrimination, Lack of transparency, accountability, and redress, Technical limitations of AI, Security vulnerabilities or malicious misuse, Negative economic impact, Regulatory challenges or gaps, Unauthorized practice of law |
| sEHknHKUxvUJ.pdf | Google_Scholar | ChatGPT: A New Era in Legal \nResearch and its Sustainable Impact \non Judicial Decision Making | This paper examines the use of ChatGPT in the legal field, particularly for legal research and potential judicial decision-making assistance in India. It highlights ChatGPT's limitations, such as inaccuracy and bias, arguing for caution, human oversight, and the need for contestability frameworks. | ChatGPT Application, Legal Research Support, Judicial Decision-Making Assistance, India Focus, Limitations Identified, AI Hallucinations/Inaccuracy, Bias in AI, Need for Human Oversight, Contestability Frameworks | True | Idealistic | True | 2.0 | Negative | ChatGPT | Large Language Model | Analysis of two court cases (India, Colombia) where judges used ChatGPT; interactive prompting of ChatGPT by the authors with legal questions (focused on bail, capabilities, limitations, data) and analysis of its responses. | Qualitative Analysis | ChatGPT responses were found to be potentially inconsistent, inaccurate (e.g., citing fake cases), biased, lacking legal nuance, limited by a knowledge cut-off (Sept 2021), not fully comprehensive in accessing case law, and acknowledging its own limitations and lack of liability. | Limitation: Operational or Technical, Limitation: Hallucination or Factual inaccuracy, Limitation: Bias, Low performance | Inaccuracy and unreliability of AI; potential for bias amplification; lack of transparency and explainability; digital divide limiting access; inability to replicate human judgment, equitable justice, and discretion; resistance to change in the legal profession; inadequate regulatory frameworks. | AI Unreliability/Inaccuracy, Bias in AI/Data, Lack of AI Transparency/Explainability, Digital Divide, AI Limitations in Replicating Human Judgment, Slow Technology Adoption by Legal Profession, Inadequate Legal Frameworks for AI | Maintaining human intervention and oversight; using AI as an assistive tool, not a replacement; implementing a 'right to contestability' for AI decisions; developing robust legal/regulatory frameworks for AI governance (transparency, accountability, fairness); verifying AI outputs. | Human Oversight and Collaboration, Regulation, Ethics, and Governance, Transparency and Explainability in AI | Bail jurisprudence, judicial decision-making, legal research, access to justice, legal information services. | Judicial System Modernization / Efficiency, LegalResearch Support, Democratizing Law / Closing Justice Gap / Rule of Law, Access to Legal Information | Individuals and Small/Medium Enterprises ("people law"), general public seeking legal information, citizens interacting with the justice system. | Small businesses, General public, Individuals lacking legal knowledge, Litigants | General Legal Practice, Criminal Law (Bail), Constitutional Law (Due Process), Civil Procedure. | General Legal Practice, Criminal Law, Criminal Procedure, Constitutional Law, Civil Procedure | India, Colombia, USA, EU, UK | India, Colombia, USA, EU, UK | Described by ChatGPT as a large preprocessed text database including news articles, legal documents, case law (including Indian statutes and court decisions up to Sept 2021 available in the public domain), and academic literature. Mix of publicly available and potentially proprietary data curated by OpenAI. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Legal Domain Data, Indian Legal Data, Case Law / Judgments, Legislation / Statutes / Regulations, Legal Scholarly Content / Textbooks, Publicly Available Data, Proprietary Data | NaN | NaN | Publicly accessible web application by OpenAI. | Evaluation of existing third-party tool, Web-based access, Freely accessible tool/service | True | True | Available online as a "Free Research Preview" (ChatGPT May 3 Version mentioned). | Publicly accessible online tool or platform | Technical gaps include the need for up-to-date, accurate, unbiased, and contextually nuanced information, along with transparency. Societal/Regulatory gaps include the lack of comprehensive AI governance laws (especially in India regarding contestability, liability), the digital divide, and the need for legal professional training. | Knowledge Recency and Updatability, AI Accuracy and Reliability, Bias in AI, AI Legal Reasoning Limitations, Transparency and Explainability, Regulatory and Governance Gaps, Accountability and Redress Mechanisms, Access, Equity, and Digital Divide, Human Oversight and Professional Adaptation | Unreliability, inaccuracy, potential for bias, lack of genuine legal understanding, limitations of training data scope and recency when using ChatGPT for legal tasks. | Accuracy and Reliability of LLM Output, Bias in AI Systems and Data, LLM Reasoning Capabilities, Outdated or Limited LLM Knowledge Base | Inaccurate legal research/advice; perpetuation of bias; erosion of trust in justice; violation of due process/fundamental rights; automation bias; lack of accountability for AI errors. | Inaccurate or misleading AI output, Bias and discrimination, Erosion of trust in legal system or AI, Undermining legal process or principles, Infringement on human rights, Over-reliance on AI, Lack of transparency, accountability, and redress |
| RG_Manuscript_Avatarjudgesandvirtuousadjudication.pdf | Google_Scholar | GenAI avatar judges and virtuous adjudication | This paper examines the potential use of GenAI avatars as judges through the lens of virtue ethics and jurisprudence. It argues that fully autonomous AI judges cannot achieve 'virtuous adjudication' due to lacking genuine virtuous agency, but suggests that advice-giving AI avatars could potentially support human judges' virtuous practice, while also identifying significant risks to moral responsibility and potential deskilling. | Generative AI as Judges, Virtue Ethics Perspective, Jurisprudence Perspective, Limitations of AI Judges, AI Avatars for Judicial Support, Risk Identification | True | Idealistic | True | 3.0 | Neutral | Conceptual discussion of 'GenAI avatar judges', distinguishing 'automated decision-making' (Mode A) and 'supportive advice-giving' (Mode B) types, personalized using adjudication records. | Conceptual Framework, Generative AI, Automated Decision-Making, Legal Advisory System, Personalization | NaN | Not Applicable | NaN | NaN | AI lacks genuine virtuous agency (internal states, right reasons, phronesis) needed for virtuous adjudication; Difficulty in training AI for virtue (data curation, ensuring virtuous output); Potential undermining of human judges' moral responsibility (control, freedom, knowledge, deliberation); Risk of human cognitive/moral deskilling. | AI Limitations in Ethical Judgment, Technical Challenges in AI Development, Ethical Concerns with AI in Law, Negative Cognitive Impacts of AI on Users | Use advice-giving GenAI avatars (Mode B) as 'virtue cultivators' to support, not supplant, human judges; Enhance judges' perceptual capacity and contextual knowledge using AI trained on curated exemplary adjudication records; Potential use in VR training simulations for judges. | AI Tool Development, Human Oversight and Collaboration, Judicial System Enhancement, Data Curation and Management, Education and AI Literacy | Judicial decision-making (adjudication); Judicial ethics; Virtue jurisprudence; Moral responsibility; Access to justice via digital courts. | Judicial System Modernization / Efficiency, Ethical AI in Law and AI Governance, Democratizing Law / Closing Justice Gap / Rule of Law | General public / Litigants using digital courts | General public, Litigants, Users of digital courts | General | General Law | International | International | Hypothesized use of personal/exemplary adjudication records (from one or multiple judges), legislation, and jurisprudence; likely proprietary/court-held, domain-specific, potentially structured and unstructured. | Legal Domain Data, Proprietary Data, Case Law / Judgments, Legislation / Statutes / Regulations, Structured Data, Unstructured Text Data | NaN | NaN | Hypothesized deployment in digital/online/Metaverse courts, potentially via VR for training. | Proposed deployment (not implemented), Government/Public institution deployment, Educational resource deployment | False | False | NaN | NaN | Philosophical/ethical gap: AI's inability to replicate genuine virtue and moral responsibility for virtuous adjudication. Societal gap: Ensuring AI supports rather than undermines human judicial qualities. Technical gaps: Difficulty curating appropriate training data for virtue; Ensuring AI output aligns with virtuous deliberation (explainability, bias mitigation). | Ethical Framework Deficiencies, AI Legal Reasoning Limitations, Human Oversight and Professional Adaptation, Data Availability and Quality, Transparency and Explainability, Bias in AI | Defining and implementing 'virtuous adjudication' in AI; Aligning AI statistical methods with human phronesis; Curating training data (identifying/labelling virtue/vice); Ensuring meaningful human control; Avoiding psychological coercion, automation bias, or under-trust; Addressing explainability issues; Mitigating human deskilling. | Ethical Considerations, Bias in AI Systems and Data, Scarcity of High-Quality Legal Data, Need for Human Oversight and Intervention, User Adoption, Trust, and Acceptance, Transparency and Explainability of AI, User Training, AI Literacy, and Skill Gaps | Undermining human judges' moral responsibility; Psychological coercion by AI; Automation bias; Under-trusting AI; Infringement on deliberation due to black box issues; Human cognitive and moral deskilling; Potential for AI to act viciously if truly autonomous; Difficulty ensuring virtuous AI output. | Ethical concerns, Dehumanization of legal process, Over-reliance on AI, Erosion of trust in legal system or AI, Lack of transparency, accountability, and redress, Deskilling or erosion of human skills, Security vulnerabilities or malicious misuse, Technical limitations of AI |
| 0b5FFMvGIoYJ.pdf | Google_Scholar | The Implications of ChatGPT For Legal Services and Society | This paper explores the potential impact of large language models, specifically ChatGPT, on legal services and society by demonstrating its capabilities through generated text examples. It discusses use cases like legal research and document drafting, alongside significant challenges, ethical considerations (like accuracy and bias), and the rapid evolution of AI in law. | LLM Impact on Legal Services, ChatGPT Application, Use Case Demonstration, Legal Research, Legal Document Drafting, Challenge Identification, Ethical Considerations, AI Hallucinations/Inaccuracy, Bias in AI | True | Idealistic | True | 2.0 | Positive | Large Language Models: ChatGPT (GPT-3 based) and Bing Chat (reportedly GPT-4 based) | Large Language Model | Demonstration through prompting ChatGPT and Bing Chat on various legal tasks (research, document generation, information provision, analysis). Includes qualitative assessment of outputs and reports Bing Chat's performance on 15 legal ethics multiple-choice questions (12/15 correct) and a civil procedure problem. | Demonstration or Illustrative Examples, Qualitative Analysis, Quantitative Metrics, Comparative Analysis | ChatGPT outputs were imperfect, incomplete, and sometimes problematic, lacking nuance and detail. Bing Chat (GPT-4 based) showed better performance, answering 12/15 legal ethics MCQs correctly and providing plausible legal analysis comparable to a B/B+ law student. | Low performance, Limitation: Operational or Technical, Moderate performance, Outperforms others, Comparable to others | High cost and complexity of the US legal system, lack of right to counsel in most civil cases, legal profession regulations (monopoly, fee-sharing rules), limited government funding for legal aid, and the cost of legal education contribute to a significant justice gap. | High Cost of Legal Services, Complexity of Legal System/Procedures, Lack of Right to Counsel, Regulatory Hurdles, Protectionism by Legal Profession, Resource Constraints for Legal Aid Organizations, High Cost of Legal Education/Licensing, Scale of Unmet Legal Need | Leveraging technology, particularly AI tools like ChatGPT, to create self-help resources and enhance lawyer efficiency to serve more clients. | AI Tool Development, Support for Self-Represented Litigants, Human Oversight and Collaboration, Cost Reduction and Efficiency | General civil legal needs (e.g., family law, debt, housing), disability rights (IEP), government benefits (Social Security). | Access to Legal Information, Protection of Rights, Support for Vulnerable Populations | Low-income individuals and middle-income Americans. | Low-income individuals, Moderate-income individuals, Population in USA | Civil Procedure, Torts, Contract Law, Constitutional Law, Estate Planning, Education Law, Social Security Law, Legal Ethics. | Civil Procedure, Tort Law, Contract Law, Constitutional Law, Wills and Estates, Education Law, Social Security Law, Legal Ethics | USA (primarily Massachusetts and Federal) | USA | General, large-scale text data used to train OpenAI's GPT-3 and GPT-4 models (implied to be broad web text and other sources). | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Proprietary Data | NaN | NaN | NaN | Not applicable | True | False | ChatGPT and Bing Chat are available online via OpenAI and Microsoft, possibly with free and paid tiers. | Publicly accessible online tool or platform, Freemium access | Accuracy and reliability of AI outputs, handling legal nuance, need for user prompt engineering skills, digital divide/cost of access to advanced AI, need for integration into legal education, potential for misuse, broader societal risks including existential concerns. | AI Accuracy and Reliability, AI Legal Reasoning Limitations, User Interface and Usability Gaps, Human Oversight and Professional Adaptation, Access, Equity, and Digital Divide, Computational Resource and Cost Issues, Ethical Framework Deficiencies | Ensuring accuracy and reliability, handling legal complexity/nuance, potential job displacement for lawyers, misuse for generating false information or manipulation, ethical concerns (UPL, competence, confidentiality), attribution problems, over-reliance, potential for bias, societal disruption, managing AI's rapid development responsibly. | Accuracy and Reliability of LLM Output, LLM Reasoning Capabilities, Ethical Considerations, Safeguarding Against Misuse and Harm, Unauthorized Practice of Law (UPL) Concerns, Legal Professional Responsibility and Competence, Data Privacy, Security, and Confidentiality, Copyright and Intellectual Property Issues, User Adoption, Trust, and Acceptance, Bias in AI Systems and Data, Regulatory Uncertainty and Compliance | Inaccurate legal information/advice, job displacement, generating false/misleading documents or information, manipulation of user beliefs/emotions, unauthorized practice of law, breaches of competence/confidentiality, algorithmic bias, exacerbating digital divide, difficulty in attributing authorship, existential risks. | Inaccurate or misleading AI output, Job displacement, Security vulnerabilities or malicious misuse, Unauthorized practice of law, Ethical concerns, Data privacy and security breach, Bias and discrimination, Exacerbation of inequality or two-tiered system, Copyright or intellectual property issues, Negative societal impact |
| Aota7JmCmSEJ.pdf | Google_Scholar | Chapter 22: AI and the future of private dispute resolution mechanisms | This chapter reviews how artificial intelligence, including natural language processing, predictive analytics, machine learning, and generative AI, is transforming private dispute resolution mechanisms such as arbitration, mediation, and negotiation. It discusses current AI tools and their applications in enhancing case preparation, predicting outcomes, and automating dispute resolution, while also considering future prospects, implementations around the world, and ethical implications. | Review of AI in Dispute Resolution, NLP Application, Predictive Analytics, Machine Learning Application, Generative AI Application, Arbitration, Mediation, Negotiation, Ethical Implications | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | High costs, lengthy processes, perceived biases and inconsistencies in traditional private dispute resolution; waning confidence in courts due to expenses, delays, and impartiality concerns; complexity of the legal system. | High Cost of Legal Services, Judicial/Legal System Inefficiencies, Systemic Inequities in Justice System, Lack of Trust in Justice System, Complexity of Legal System/Procedures | Leveraging AI tools (NLP, predictive analytics, machine learning, generative AI) to enhance efficiency, fairness, and accessibility in dispute resolution through enhanced case preparation, predictive analytics, and automated dispute resolution platforms (ODR). | AI Tool Development, Enhanced AI Capabilities, Cost Reduction and Efficiency, Regulation, Ethics, and Governance, Access to Legal Information and Advice, Online Dispute Resolution (ODR) | Private dispute resolution (arbitration, mediation, negotiation), Online Dispute Resolution (ODR), improving efficiency and reducing costs of legal processes, enhancing fairness and consistency in dispute outcomes, increasing accessibility to justice mechanisms. | Dispute Resolution, Improving Efficiency in Legal System / Profession, Affordability of Legal Services / Cost Reduction, Ethical AI in Law and AI Governance, Democratizing Law / Closing Justice Gap / Rule of Law | General public involved in disputes (e.g., consumer, small claims, family law matters such as divorce and asset division), laypeople needing legal information (e.g., landlord-tenant issues), legal practitioners, and dispute resolution providers. | General public, Litigants, Consumers, Litigants in small claims courts, Individuals in family law disputes, Laypeople, Individuals lacking legal knowledge, Tenants, Legal professionals, Dispute resolution providers | Private dispute resolution, including arbitration, mediation, negotiation. Specific applications cover family law (divorce, asset division), consumer law, small claims, commercial disputes, landlord-tenant law, and insurance claims. | Dispute Resolution, Arbitration, Mediation, Negotiation, Family Law, Consumer Law, Small Claims Law, Commercial Law, Landlord-Tenant Law, Insurance Law | International | International | Various, including large language models trained on general and legal text (documents, statutes, case law, opinions); historical case data for predictive analytics; specific datasets curated for particular tools (e.g., lawyer-reviewed randomized scenarios for Amica). | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Legal Domain Data, Legislation / Statutes / Regulations, Case Law / Judgments, Other Legal Documents, Synthetic Data, Expert-Annotated / Human-Curated / Human-Generated Data | Includes rule-based systems, case-based reasoning, machine learning (including deep learning and LLMs with pre-training and fine-tuning), game-theoretical algorithms, and expert systems methodologies. Some tools also incorporate user-centered design and iterative development based on expert input. | Rule-based System Design, Case-based Reasoning, Machine Learning Model Development, Deep Learning Model Development, Model Pre-training, Model Fine-tuning, Game-theoretic Algorithm Design, Expert System Design, User-centered Design, Iterative Design Process, Expert Input Integration | Online platforms, web applications, integration into existing legal/judicial systems (e.g., Jupitice for courts), APIs, educational initiatives for stakeholders. | Evaluation of existing third-party tool, Web-based access, Integration into existing system/platform, Government/Public institution deployment, API access, Educational resource deployment | True | False | Many tools discussed are presented as launched and accessible, either as commercial products (e.g., Relativity, Lex Machina, various ODR platforms) or as public/research initiatives with websites (e.g., Amica, JusticeBot, CREA platform). | Commercial product or service, Publicly accessible online tool or platform | Technical gaps include AI accuracy (e.g., LLM hallucinations) and data privacy. Societal gaps include ethical concerns, ensuring meaningful human control and oversight, addressing the digital divide, preventing bias and discrimination, and the need for education and training for legal professionals on AI capabilities and limitations. | AI Accuracy and Reliability, Security and Privacy of Data, Ethical Framework Deficiencies, Human Oversight and Professional Adaptation, Access, Equity, and Digital Divide, Bias in AI | NaN | NaN | Generation of incorrect or biased information (hallucinations) by AI, especially LLMs; ethical and privacy concerns regarding sensitive client data; potential for misuse of generative AI (e.g., deepfakes, misinformation); erosion of human responsibility and oversight in decision-making; risk of unjust outcomes if AI errors are not mitigated by human control. | Inaccurate or misleading AI output, Bias and discrimination, Ethical concerns, Data privacy and security breach, Security vulnerabilities or malicious misuse, Over-reliance on AI, Undermining legal process or principles |
| x2Rhas8fUBAJ.pdf | Google_Scholar | ChatGPT: Literacy or intelligence about UN sustainable development goals? | This paper evaluates ChatGPT's literacy and intelligence regarding the UN Sustainable Development Goals (SDGs) using two assessment tools: the SDG Fitness Test and the SULITEST. While ChatGPT demonstrates high SDG literacy, its intelligence, particularly concerning core competencies like critical and systems thinking, is found to be at an intermediate level, and the assessment tools themselves show limitations in coverage. | ChatGPT Evaluation, AI for Sustainable Development Goals, Literacy and Intelligence Assessment, Limitations of Assessment Tools | True | Idealistic | True | 2.0 | Neutral | Evaluation of ChatGPT (GPT-3.5 based model) using standardized sustainability literacy tests. | AI System Evaluation, Large Language Model, Literacy Assessment | ChatGPT's performance was assessed using the UN SDG Fitness Test and the SULITEST (Sustainability Literacy Test). Questions from both tests were input into ChatGPT, and the responses were scored according to the tests' frameworks. ChatGPT was also used to map test questions to SDG competencies and SDG types. | Benchmark Dataset Evaluation, Quantitative Metrics | ChatGPT scored highly on literacy tests (<90% on SDG Fitness Test, 80.9% on SULITEST). However, its performance on core SDG competencies (evaluated via SDG Fitness Test) was mostly intermediate, particularly in areas like Collaboration, Systems Thinking, Anticipatory skills, Integrated problem-solving, Critical thinking, and Self-awareness. Both assessment tests were found to have inadequate coverage of SDG competencies and SDG types. | High performance, Moderate performance, Mixed performance, Limitation: Operational or Technical | Current limitations of LLMs like ChatGPT, including intermediate-level capabilities in crucial SDG competencies (e.g., critical thinking, systems thinking, self-awareness); inadequacy and unbalanced coverage of existing SDG assessment tools (SULITEST, SDG Fitness Test); potential for LLMs to generate misinformation ('hallucinations'). | AI Limitations in Complex Cognitive Tasks, Inadequacy of Current Assessment Tools, AI Unreliability/Inaccuracy, AI-driven Misinformation/Disinformation | Improve future LLM versions to enhance specific SDG competencies (collaboration, critical thinking, systems thinking, etc.); refine SDG assessment tools (SULITEST, SDG Fitness Test) for better coverage of competencies and types; use LLMs cautiously for SDG-related tasks, primarily for information gathering and suggesting actions, not decision-making. | Enhanced AI Capabilities, Benchmarking and Evaluation Frameworks, Human Oversight and Collaboration | UN Sustainable Development Goals (SDGs) literacy and intelligence; Assessment of AI capabilities related to sustainability; Core cross-cutting SDG competencies (e.g., Systems Thinking, Critical Thinking, Collaboration). | NaN | NaN | NaN | Sustainable Development / UN SDGs | Sustainable Development Law, International Law | International | International | The paper evaluates a pre-trained model (ChatGPT). Its underlying training data (e.g., for GPT-3) is described as vast (e.g., 45TB text dataset), web-sourced, largely proprietary, unstructured text and code data. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Proprietary Data, Unstructured Text Data | Experimental evaluation using existing standardized tests (SULITEST, SDG Fitness Test). Utilized ChatGPT itself to map test questions to SDG competencies and types as a methodology step. | Experimental Evaluation, Benchmarking, LLM-aided Task Mapping | The evaluated technique (ChatGPT) is deployed by OpenAI via web interface and API. The study itself did not involve deployment. | Evaluation of existing third-party tool, Web-based access, API access | True | False | ChatGPT is available via web interface and API from OpenAI, often with free and paid access tiers. | Publicly accessible online tool or platform, API access, Freemium access | ChatGPT's intermediate performance in key SDG competencies; Inadequate and unbalanced coverage of SDG competencies and types by existing assessment tools (SULITEST, SDG Fitness Test); Need for validated mappings between test questions and competencies/SDGs; Need for AI development specifically targeting SDG competencies; Societal gap in safely integrating LLMs for SDG advancement. | AI Accuracy and Reliability, Research and Evaluation Gaps, AI Scope and Functionality Limitations, Ethical Framework Deficiencies | Lack of validated mappings for test questions to SDG competencies and types, requiring the use of ChatGPT itself for mapping; Potential inconsistency in LLM responses; Evaluating the 'intelligence' beyond simple 'literacy'. | Evaluation Challenges and Metrics, Output Variability and Consistency | Over-reliance on LLMs for SDG decision-making; Generation of misinformation or 'hallucinations'; Ethical issues (bias, misuse); Potential negative impact on human critical thinking skills; Test security and validity if LLMs can easily pass assessments. | Over-reliance on AI, Inaccurate or misleading AI output, Ethical concerns, Bias and discrimination, Risk of misapplication or misuse, Deskilling or erosion of human skills, Technical limitations of AI |
| lm9K0vSCKEcJ.pdf | Google_Scholar | A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law | This survey extensively reviews the methodologies, applications, challenges, ethics, and future advancements of Large Language Models (LLMs) in the critical domains of finance, healthcare, and law. It highlights LLMs' transformative potential in these high-stakes sectors, such as enhancing diagnostics in healthcare, financial analytics, and legal interpretation, while also critically examining ethical concerns and advocating for responsible AI development. | Survey of LLMs in High-Stakes Domains, LLMs in Law, Methodology Review, Application Review, Challenge Identification, Ethical Considerations, Responsible AI Development | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | Lack of access to legal services due to cost or knowledge barriers; ethical issues in LLMs (bias, fairness, robustness, hallucination); difficulty in acquiring high-quality, domain-specific (legal) training data; risk of LLMs worsening existing societal inequalities and creating technology access gaps. | High Cost of Legal Services, Public Lack of Legal Knowledge/Awareness, Ethical Concerns with AI in Law, Bias in AI/Data, AI Unreliability/Inaccuracy, Data Scarcity/Quality for AI, Risk of AI Exacerbating Inequality, Digital Divide | Using LLMs to democratize legal information, education, and advice; improving quality and availability of training data for legal AI; promoting interdisciplinary collaboration; establishing robust ethical frameworks, security measures, open-source tools, and educational programs to ensure equitable access and responsible deployment. | Access to Legal Information and Advice, Education and AI Literacy, Data Curation and Management, Open Source Initiatives and Collaboration, Regulation, Ethics, and Governance, Data Privacy and Security | Democratizing access to legal information, education, and advice; facilitating online dispute resolution; ensuring fairness, equity, and non-discrimination in legal AI; providing legal guidance for marginalized and under-resourced communities. | Access to Legal Information, Legal Literacy and Public Legal Education, Access to Legal Advice, Dispute Resolution, Ethical AI in Law and AI Governance, Support for Vulnerable Populations, Democratizing Law / Closing Justice Gap / Rule of Law | Individuals with limited financial or knowledge resources for legal help; marginalized communities; self-represented litigants; underrepresented groups; smaller organizations and non-profits. | Individuals unable to afford legal services, Individuals lacking legal knowledge, Marginalized communities, Self-represented litigants, Underrepresented groups, Small organizations, Non-profit organizations | General legal tasks (question answering, judgment prediction, text classification, summarization, information retrieval), Tax law, Transportation law, Privacy law, Criminal law, Contract law, EU law, Copyright law, Online dispute resolution. | Tax Law, Transportation Law, Data Privacy Law, Criminal Law, Contract Law, EU Law, Copyright Law, Online Dispute Resolution | US, China, Japan, European Union, Switzerland, Vietnam, Greece. | USA, China, Japan, EU, Switzerland, Vietnam, Greece | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Significant ethical challenges (explainability, bias, fairness, robustness, privacy, accountability, potential for inequality exacerbation); insufficient reliability and advanced reasoning in legal-specific LLMs; difficulties in curating comprehensive, high-quality legal datasets; unresolved knowledge gap between NLP developers and legal domain experts. | Ethical Framework Deficiencies, Transparency and Explainability, Bias in AI, AI Accuracy and Reliability, Security and Privacy of Data, Accountability and Redress Mechanisms, Access, Equity, and Digital Divide, AI Legal Reasoning Limitations, Data Availability and Quality, Need for Interdisciplinary Collaboration | NaN | NaN | Severe consequences from LLM errors in high-stakes FHL decisions (e.g., financial losses, incorrect medical diagnoses, wrongful legal outcomes); breaches of sensitive confidential data; propagation of biases leading to discriminatory outcomes and eroded trust; generation of 'hallucinated' or misleading information, especially harmful in legal and medical advice; exacerbation of societal inequalities and job displacement due to automation. | Inaccurate or misleading AI output, Consumer harm, Data privacy and security breach, Bias and discrimination, Erosion of trust in legal system or AI, Harmful or unsafe AI output, Exacerbation of inequality or two-tiered system, Job displacement |
| JQl5IoVQjuAJ.pdf | Google_Scholar | Hallucinating Law: Legal Mistakes with Large Language Models are Pervasive | This paper reports on a study evaluating the performance of three large language models (GPT-3.5, Llama 2, PaLM 2) on various U.S. legal tasks. The study found alarmingly high hallucination rates (69%-88%), particularly for complex tasks, lower court cases, and when presented with incorrect premises, suggesting current LLMs are unreliable for legal applications and may worsen access-to-justice issues. | LLM Evaluation, US Law Focus, AI Hallucinations/Inaccuracy, Reliability Issues, Impact on Access to Justice (Negative) | True | Idealistic | True | 2.0 | Negative | Evaluation of existing Large Language Models (GPT 3.5, Llama 2, PaLM 2) on legal tasks. | AI System Evaluation, Large Language Model, Legal Task Performance | Tested over 200,000 queries against GPT 3.5, Llama 2, and PaLM 2. Queries covered tasks like identifying opinion authors, determining precedential relationships, and identifying case holdings, stratified by court hierarchy, case prominence/age, and circuit. | Custom Dataset Evaluation, Quantitative Metrics, Comparative Analysis | Hallucination rates ranged from 69% to 88%. Performance deteriorated with task complexity and for lower court/less prominent cases. Models exhibited overconfidence and susceptibility to contra-factual bias. GPT-3.5 generally performed best but showed biases. | Limitation: Hallucination or Factual inaccuracy, Low performance, Limitation: Bias, Outperforms others | Current LLMs perform poorly on localized legal knowledge (lower courts) and complex reasoning tasks. They exhibit overconfidence, fail to correct user misconceptions (contra-factual bias), and are least reliable for the users (e.g., pro se litigants, those needing complex advice) who could most benefit from democratized legal information. | AI Limitations in Legal Reasoning/Nuance, AI Unreliability/Inaccuracy, Bias in AI/Data, Risk of AI Exacerbating Inequality, Challenges for Self-Represented Litigants | The paper advocates for caution, responsible integration requiring human supervision, transparency in model trade-offs, and a human-centered AI approach rather than specific technical fixes. | Regulation, Ethics, and Governance, Human Oversight and Collaboration, Transparency and Explainability in AI, User Interface and Accessibility Design | Access to legal information, Legal research accuracy, Reliability of AI in law, Case law analysis (precedent, holdings), Judicial system structure. | Access to Legal Information, Ethical AI in Law and AI Governance, Legal Document Analysis / Review, Judicial System Modernization / Efficiency | Litigants in lower courts, individuals in less prominent jurisdictions, users lacking legal expertise, general public seeking legal advice. | Litigants in lower courts, Populations in underresourced jurisdictions, Laypeople, Individuals lacking legal knowledge, General public, Individuals with unmet legal needs | General US Case Law, Litigation | General Law, Case Law, Litigation | United States | USA | NaN | Not Applicable | Systematic evaluation using a large dataset (~200,000) of structured legal queries targeted at existing LLMs, stratified along dimensions like court level, case prominence, and task type. | Systematic Evaluation Methodology, Dataset Creation | NaN | Not applicable | True | False | The paper evaluates existing LLMs (GPT-3.5, PaLM 2, Llama 2) which are generally available, though access modalities vary (e.g., API, open release for Llama 2). | Model available, API access, Open-source | Significant gaps exist in LLM reliability for legal tasks, including handling complexity, local nuance (lower courts), calibration (confidence vs accuracy), and robustness against incorrect premises (contra-factual bias). Current models risk deepening legal inequalities rather than alleviating them. Need for transparency and normative judgment in model development. | AI Accuracy and Reliability, AI Legal Reasoning Limitations, Multilingual and Jurisdictional Specificity Gaps, Access, Equity, and Digital Divide, Transparency and Explainability, Ethical Framework Deficiencies | NaN | NaN | Providing inaccurate legal information; deepening existing legal inequalities; fostering legal monoculture; representational harms (e.g., misattributing judicial opinions); users being misled by overconfident or factually incorrect responses (contra-factual bias). | Inaccurate or misleading AI output, Exacerbation of inequality or two-tiered system, Technical limitations of AI, Bias and discrimination, Consumer harm |
| laae003.pdf | Google_Scholar | Large Legal Fictions: Profiling Legal \nHallucinations in Large Language Models | This paper presents the first systematic empirical evidence of legal hallucinations in large language models (LLMs) like ChatGPT 4, PaLM 2, and Llama 2, finding they hallucinate at least 58% of the time when queried about US federal case law. It also documents their susceptibility to users' incorrect legal assumptions and poor self-awareness of errors, cautioning against unsupervised integration into legal tasks and highlighting risks for access to justice. | Empirical Study of Legal Hallucinations, LLM Evaluation, US Law Focus, AI Hallucinations/Inaccuracy, Risk Identification, Access to Justice Risk | True | Idealistic | True | 2.0 | Negative | Public-facing LLMs: OpenAI’s ChatGPT 4, OpenAI’s ChatGPT 3.5, Google’s PaLM 2, and Meta’s Llama 2. | Large Language Model | Evaluation using 14 legal knowledge query tasks (categorized by complexity) on a random sample of US federal case law (SCOTUS, USCOA, USDC). Employed reference-based querying (comparison to ground-truth metadata from legal databases) and reference-free querying (detecting self-contradiction across multiple LLM responses generated at a non-greedy temperature, with contradictions assessed by GPT-4). | Custom Dataset Evaluation, Quantitative Metrics, LLM as Judge | LLMs hallucinate between 58% (ChatGPT 4) and 88% (Llama 2) of the time on direct, verifiable questions about federal court cases. GPT-4 performed best in terms of raw hallucination rates but was less calibrated than PaLM 2 and GPT 3.5. Models also demonstrated susceptibility to contrafactual bias and imperfect self-awareness of their propensity to hallucinate. | Limitation: Hallucination or Factual inaccuracy, Low performance, Outperforms others, Limitation: Bias | High rates of factual hallucination in LLM responses, poor model calibration (overconfidence in errors), susceptibility to contrafactual bias (uncritically accepting users' incorrect legal premises), and uneven legal knowledge (better for prominent/newer cases and major jurisdictions, worse for localized or older law). These issues risk exacerbating existing inequalities in legal services and creating a 'legal monoculture'. | AI Unreliability/Inaccuracy, Bias in AI/Data, Risk of AI Exacerbating Inequality, Risk of Legal Monoculture from AI | The paper discusses potential mitigation techniques from other research (e.g., retrieval-augmented generation, advanced prompting, specialized fine-tuning, factuality-focused decoding, external database checks) but notes their current limitations. It advocates for human-centered AI approaches and emphasizes the need for developers to be transparent about the types of hallucinations their LLMs might produce and the choices made to minimize them. | Enhanced AI Capabilities, Prompt Engineering and LLM Interaction Design, User Interface and Accessibility Design, Transparency and Explainability in AI, Regulation, Ethics, and Governance | Accuracy of LLMs in retrieving and stating US case law facts; factual hallucinations; implications of LLM errors for legal research, legal advice, and access to justice for pro se litigants. | Ethical AI in Law and AI Governance, LegalResearch Support, Access to Legal Advice, Support for Self-Represented Litigants | Pro se and under-resourced litigants. | Self-represented litigants, Low-income individuals | US federal case law. | Federal Law, Case Law | United States (federal judiciary: US Supreme Court, US Courts of Appeals, US District Courts). | USA | The paper states the LLMs were trained on vast text corpora including public domain American case law. Specific training datasets for the evaluated commercial/open-source LLMs (OpenAI, Google, Meta) are generally proprietary to the developers and not detailed further by the paper. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Legal Domain Data, US Legal Data, Case Law / Judgments, Publicly Available Data, Proprietary Data | Construction of a test dataset of legal queries based on American case law, stratified by court level, jurisdiction, and time. Application of reference-based evaluation (comparing LLM output to known metadata) and reference-free evaluation (measuring self-contradiction in LLM outputs to infer hallucinations). Statistical analysis of hallucination rates and their correlation with case/court characteristics. | Dataset Creation, Reference-based Evaluation, Reference-free Evaluation, Hallucination Detection, Quantitative Research Methods | The evaluated LLMs (ChatGPT, PaLM 2, Llama 2) are deployed by their respective developers (OpenAI, Google, Meta) via APIs and public interfaces. | Evaluation of existing third-party tool, API access, Web-based access | True | True | The discussed LLMs (ChatGPT 4, ChatGPT 3.5, PaLM 2, Llama 2) are generally accessible via APIs or public interfaces, with Llama 2 being open-source (e.g., Llama-2-13b-chat-hf). The paper's evaluation dataset is also available on HuggingFace and replication materials on Harvard Dataverse. | API access, Publicly accessible online tool or platform, Model available, Open-source, Dataset available, Code available | Technical: persistent high rates of factual hallucination in LLMs despite ongoing research into mitigation, poor model calibration (especially LLMs being overconfident in errors), difficulty handling localized or less prominent legal information, and an inability to reliably correct users' legal misconceptions. Societal: the risk of LLMs exacerbating the access to justice gap for vulnerable populations, the potential for creating a 'legal monoculture' due to biased knowledge, and the need for normative frameworks and transparency regarding which types of hallucinations are minimized by developers. | AI Accuracy and Reliability, AI Legal Reasoning Limitations, Multilingual and Jurisdictional Specificity Gaps, Access, Equity, and Digital Divide, Bias in AI, Ethical Framework Deficiencies, Transparency and Explainability | For LLMs in legal tasks: Ensuring factual accuracy and reliability in open-domain legal question answering. For the evaluation: Designing comprehensive and scalable methods (reference-based and reference-free) to detect and quantify legal hallucinations. General limitations of hallucination mitigation techniques like RAG (dependency on retrieval quality, query ambiguity, computational cost, handling conflicting information in databases) and evaluation metrics. | Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Evaluation Challenges and Metrics, High Computational and Resource Demands | Generation of factually incorrect legal information leading to harmful or inaccurate legal advice. Worsening disparities in access to legal services due to LLMs' uneven knowledge distribution (e.g., better on prominent law, worse for specific needs of pro se litigants). Creation of a 'legal monoculture' by promoting a homogenized and potentially biased understanding of the law. Misleading users due to LLMs' overconfidence in false statements and their tendency to uncritically accept and respond to queries based on incorrect legal premises (contrafactual bias). | Inaccurate or misleading AI output, Consumer harm, Exacerbation of inequality or two-tiered system, Bias and discrimination, Technical limitations of AI |
| Y4hGJX6FeicJ.pdf | Google_Scholar | NEW FRONTIERS IN ATTORNEY REGULATION : INTRODUCTION TO VOLUME II OF II | This paper introduces Volume II of a symposium on attorney regulation, summarizing articles on topics including the NextGen Bar Exam, lawyer competence, Generative AI in law and legal education, and professional responsibility. It highlights how Generative AI is discussed in the context of legal practice and education, its potential to provide DIY legal solutions for low-income individuals, and emerging regulatory considerations. | Symposium Summary, Attorney Regulation, Generative AI in Legal Practice, Generative AI in Legal Education, DIY Legal Solutions, Access to Justice Enhancement, Regulatory Considerations | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | Inability of low-income individuals to afford or secure free legal services. | High Cost of Legal Services, Limited Availability/Access to Legal Aid | Encouraging the use of Generative AI tools for DIY legal solutions and permitting nonlawyers to assist consumers in using these tools effectively. | Alternative Legal Service Delivery Models, AI Tool Development, Support for Self-Represented Litigants | DIY legal solutions for low-income individuals using Generative AI; Access to legal services for the underserved. | Support for Self-Represented Litigants, Support for Vulnerable Populations, Access to Legal Advice, Legal Document Creation / Automation | Low-income individuals; persons unable to afford or secure free legal services. | Low-income individuals, Individuals unable to afford legal services, Individuals lacking access to legal aid | Attorney regulation; Delivery of legal services; Legal education; Legal ethics; Professional responsibility. | Legal Profession Regulation, Legal Services Delivery, Legal Education, Legal Ethics, Professional Responsibility | United States | USA | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Need for more specific guidance and proactive regulatory frameworks for the legal profession's use of Generative AI. | Regulatory and Governance Gaps, Ethical Framework Deficiencies | NaN | NaN | Compromise of confidential client information when inputted into Generative AI tools; lawyers encountering problems using AI tools without proper training or guidance. | Data privacy and security breach, Risk of misapplication or misuse, Ethical concerns |
| Paper_113-Accurate_AI_Assistance_in_Contract_Law1.pdf | Google_Scholar | Accurate AI Assistance in Contract Law Using Retrieval-Augmented Generation to Advance Legal Technology | This paper proposes an AI chatbot using Retrieval-Augmented Generation (RAG) to provide accurate legal assistance in contract law, demonstrated with Moroccan legislation. The system aims to enhance understanding for non-experts by grounding responses in verified legal documents, thereby mitigating Large Language Model (LLM) hallucinations. | Chatbot Development, Retrieval Augmented Generation, Legal Assistance Provision, Contract Law Focus, Moroccan Law Focus, Legal Understanding for Laypeople, Mitigating AI Hallucinations | True | Idealistic | True | 1.0 | Positive | A chatbot system integrating Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs like GPT-4 Turbo, Llama 3) and a vector database (FAISS) containing embedded legal documents (Moroccan Code of Obligations and Contracts). | Chatbot / Conversational AI, Retrieval Augmented Generation (RAG), Large Language Model, Vector Database, Embedding-based Methods, Domain-Specific Knowledge Base | Comparative evaluation of GPT-4 Turbo and Llama 3 within the RAG system using the RAGAS framework, measuring 'Faithfulness' and 'Answer Relevance' metrics, alongside response time. | Benchmark Dataset Evaluation, Quantitative Metrics, Comparative Analysis | GPT-4 Turbo achieved higher Faithfulness (1.0 vs 0.84) and Answer Relevance (0.971 vs 0.79) compared to Llama 3, although Llama 3 was faster (0.86s vs 3.12s). GPT-4 Turbo was selected for its higher accuracy. | High performance, Outperforms others | Complexity of legal documentation, prevalence of misinformation, need for specialized legal skills to understand/draft contracts, limitations of LLMs (outdated knowledge, hallucinations). | Complexity of Legal Language/Documents, Misinformation (General), Need for Specialized Legal Skills, AI Unreliability/Inaccuracy | An AI chatbot using RAG to provide accurate, contextually relevant responses based on integrated official legal documents, simplifying legal information access for non-experts and reducing reliance on potentially inaccurate LLM knowledge. | AI Tool Development, Enhanced AI Capabilities, Access to Legal Information and Advice, Data Curation and Management | Contract law understanding and assistance. | Legal Literacy and Public Legal Education, Access to Legal Advice, Legal Document Analysis / Review | General public / citizens / non-expert users. | General public, Laypeople | Contract Law (specifically mentioning Moroccan Code of Obligations and Contracts, and potential application to Property Law). | Contract Law, Property Law | Morocco (with stated adaptability to other jurisdictions). | Morocco | The knowledge base used for RAG consists of the Moroccan "Code of Obligations and Contracts (COC)", extracted from official PDF documentation using OCR. This is unstructured, domain-specific legal text. The underlying LLMs (GPT-4 Turbo, Llama 3) were pre-trained on general datasets by their respective organizations. | RAG System Knowledge Corpus, Legal Domain Data, Moroccan Legal Data, Legislation / Statutes / Regulations, Publicly Available Data, OCR Processed Data, Unstructured Text Data, Pre-trained LLM's General Training Corpus | System architecture development involving data collection (OCR), preprocessing (text splitting), embedding (LLM-Embedder), vector storage (FAISS), retrieval (ANN similarity search), response generation (RAG with LLMs), and comparative evaluation (RAGAS framework). | System Architecture Design, Data Collection, Data Preprocessing, Embedding Model Application, Vector Database Implementation, Information Retrieval Techniques, Retrieval Augmented Generation (RAG), Comparative Evaluation | NaN | Not applicable | False | False | NaN | NaN | Need for automated legal updates, integration of multimodal capabilities, improved explainability (providing explicit legal references), enhanced adaptability to different legal frameworks, the system cannot replace human expertise in complex cases. | Knowledge Recency and Updatability, AI Scope and Functionality Limitations, Transparency and Explainability, Multilingual and Jurisdictional Specificity Gaps, Human Oversight and Professional Adaptation | Balancing LLM speed vs. accuracy/relevance, ensuring factual consistency and avoiding hallucinations, managing and updating the legal knowledge base, processing PDF legal documents effectively (OCR, chunking). | Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Outdated or Limited LLM Knowledge Base, Data Quality, Processing, and Preparation | Providing incorrect legal information due to LLM hallucination or outdated data, users over-relying on the system for complex legal matters requiring professional advice. | Inaccurate or misleading AI output, Over-reliance on AI |
| DrMmT3gajroJ.pdf | Google_Scholar | NYAYA ANUMANA and INL EGAL LLAMA : The Largest Indian Legal Judgment Prediction Dataset and Specialized Language Model for Enhanced Decision Analysis | This paper introduces NyayaAnumana, the largest dataset for Indian Legal Judgment Prediction (LJP), containing over 700,000 cases from various courts. It also presents INLegalLlama, a LLaMa-based language model specifically adapted for the Indian legal domain via continued pretraining and supervised fine-tuning, designed for both predicting judgments and providing explanations. | Dataset Creation, Indian Law Focus, Legal Judgment Prediction, Legal Language Model Development, Explainable AI | True | Idealistic | True | 1.0 | Positive | NyayaAnumana dataset creation and INLegalLlama model development (LLaMa-2 7B adapted via Continued Pretraining and Supervised Fine-tuning with LoRA for Legal Judgment Prediction and Explanation). | Dataset Creation / Curation, Model Development, Large Language Model, Pre-training Technique, Fine-tuning, Parameter-Efficient Fine-tuning, Predictive Legal Task, Explainable AI (XAI) | Evaluated LMs (InLegalBERT, InCaseLaw, XLNet) and LLMs (including INLegalLlama) on NyayaAnumana splits for binary/ternary classification across court types and temporal data. Also tested on external datasets (ILDC, PredEx, ILDC_expert). Metrics included Precision, Recall, F1, Accuracy, Rouge, BLEU, METEOR, BERTScore, BLANC, and expert evaluation using a Likert scale. | Benchmark Dataset Evaluation, Quantitative Metrics, Expert Evaluation, Comparative Analysis | Achieved approximately 90% F1-score/accuracy in binary prediction tasks on the NyayaAnumana dataset using domain-specific models. INLegalLlama (CPT+SFT) outperformed baseline LLaMa-2 and other LLMs on PredEx and ILDC_expert datasets for prediction and explanation tasks, achieving 76.05% accuracy on PredEx. | High performance, Outperforms others, Technique improves outcome | Significant backlog of lakhs of pending cases burdens the Indian legal system. | Judicial/Legal System Inefficiencies | Develop AI-driven systems for legal judgment prediction and explanation (like NyayaAnumana and INLegalLlama) to enhance efficiency, accessibility, and transparency in the legal process. | AI Tool Development, Judicial System Enhancement, Legal Research and Analysis Tools, Cost Reduction and Efficiency, Access to Legal Information and Advice, Transparency and Explainability in AI | Legal Judgment Prediction (LJP), Explainable AI (XAI) in law. | Improving Foundational AI Capabilities for Legal Applications, Ethical AI in Law and AI Governance | General population interacting with the Indian judicial system (implicitly, by addressing case backlog). | General public, Population in India, Litigants | General Litigation (covering multiple fields adjudicated by Supreme Court, High Courts, Tribunals, District Courts). | Litigation, General Law, Multiple Fields | India | India | NyayaAnumana: A new publicly sourced (IndianKanoon) corpus of 702,945 preprocessed, English-language, unstructured Indian court case documents from various court levels. Subsets used for model training (CPT: SCI + 100k HCs subset; SFT: PredEx dataset with expert annotations). | Author-Created New Dataset, Fine-tuning Dataset, Publicly Available Data, Indian Legal Data, Legal Domain Data, Case Law / Judgments, Unstructured Text Data, Expert-Annotated / Human-Curated / Human-Generated Data | Data compilation and preprocessing (web scraping, regex cleaning, keyword filtering, label extraction). Model development involved Continued Pretraining (CPT) of LLaMa-2 7B on a subset of NyayaAnumana, followed by Supervised Fine-tuning (SFT) using the PredEx dataset and Parameter-Efficient Fine-Tuning (PEFT) with LoRA. | Data Collection, Data Preprocessing, Model Pre-training, Model Fine-tuning, Parameter-Efficient Fine-Tuning (PEFT), Dataset Creation | Dataset and code made available via a GitHub link. | Public dataset/benchmark release, Open source code release | True | True | Dataset (NyayaAnumana) and code for prediction and explanation models available on GitHub. | Dataset available, Code available, Model available | Lack of datasets in regional Indian languages. Need for larger, more advanced models and refined fine-tuning techniques incorporating diverse legal documents (statutes, contracts). LLM applicability for complex legal reasoning requires further investigation. | Data Availability and Quality, Multilingual and Low-Resource Language Gaps, AI Scope and Functionality Limitations, Research and Evaluation Gaps, AI Legal Reasoning Limitations | Resource constraints (GPU memory, compute time, cost) leading to model quantization (4-bit) and limiting the use of larger models. High cost and time for obtaining expert annotations. Difficulty for generative models in processing long legal documents and performing complex reasoning. Inconsistencies and preprocessing errors in existing benchmark datasets (e.g., ILDC). Reducing model hallucination. | High Computational and Resource Demands, Financial Cost and Resource Constraints, Cost and Complexity of Data Annotation, LLM Context Window and Long Input Management, LLM Reasoning Capabilities, Data Quality, Processing, and Preparation, LLM Hallucination and Factual Errors | Generative models may produce hallucinated or factually incorrect content. Over-reliance on AI without human oversight in legal decision-making (mentioned as a need for caution). | Inaccurate or misleading AI output, Over-reliance on AI |
| p4PiylqM104J.pdf | Google_Scholar | Exploring Possibilities of AI-Powered Legal Assistance in Bangladesh through Large Language Modeling | This paper details the development of UKIL-DB-EN, a corpus of Bangladeshi legal documents, and the fine-tuning of GPT-2 (creating GPT2-UKIL-EN) on this corpus to provide legal assistance in English. The model shows promising results in evaluations including expert opinions but requires further refinement for accuracy and safety. | Dataset Creation, Bangladeshi Law Focus, Legal Language Model Fine-tuning, Legal Assistance Provision, System Evaluation, Need for Refinement | True | Idealistic | True | 1.0 | Positive | Fine-tuning the GPT-2 medium model on a custom-built corpus of Bangladeshi legal documents (UKIL-DB-EN) using instruction-tuning prompts to create the GPT2-UKIL-EN model. | Fine-tuning, Large Language Model, Dataset Creation / Curation, Instruction Tuning, Model Development, Domain-Specific Model Adaptation | Quantitative semantic similarity analysis (Cosine similarity, Jaccard index) comparing model output to original texts, and qualitative evaluation through three case studies (varying difficulty) assessed by five legal experts using a rating scale and providing feedback. | Quantitative Metrics, Qualitative Analysis, Expert Evaluation | GPT2-UKIL-EN achieved the highest scores on semantic similarity metrics (Cosine: 0.515, Jaccard: 0.133), outperforming baseline GPT-2, Mistral-7b, and Gemma-2b. Expert evaluation (average score 4.81/7) indicated good reasoning and approach but issues with accuracy and clarity, especially in complex cases. | Moderate performance, Outperforms others, Technique improves outcome, Limitation: Operational or Technical | Significant delays, procedural complexity, high legal costs, large case backlogs (over 3.7 million), police harassment, inadequacies in legal provisions, lack of legal knowledge, and financial constraints preventing access to representation, particularly for lower-income/marginalized communities. | Judicial/Legal System Inefficiencies, Complexity of Legal System/Procedures, High Cost of Legal Services, Systemic Inequities in Justice System, Inadequate Legal Frameworks, Public Lack of Legal Knowledge/Awareness, Resource Constraints | Developing a specialized LLM (GPT2-UKIL-EN) to automate legal assistance, simplify legal language, provide affordable support, streamline administrative processes, democratize access to legal information, and empower individuals to understand their rights and navigate the system. | AI Tool Development, Enhanced AI Capabilities, Document Automation, Language Simplification and Multilingual Access, Access to Legal Information and Advice, Cost Reduction and Efficiency | Access to legal information, understanding legal rights and procedures, reducing legal costs, mitigating judicial delays, improving case management efficiency. | Access to Legal Information, Legal Literacy and Public Legal Education, Affordability of Legal Services / Cost Reduction, Judicial System Modernization / Efficiency | General population of Bangladesh, particularly lower-income or marginalized communities facing financial or educational barriers to accessing the legal system. | General public, Population in Bangladesh, Low-income individuals, Marginalized communities, Individuals with low education levels | General Legal Assistance (derived from scraping various acts like civil, criminal, administrative and case studies on property, criminal law). | General Legal Practice, Legal Aid, Civil Law, Criminal Law, Administrative Law, Property Law | Bangladesh | Bangladesh | UKIL-DB-EN: A publicly available corpus of English-language Bangladeshi legal documents (595 Acts, ~18,023 sections) collected via web scraping from an open-access government portal (bdlaws.minlaw.gov.bd) and preprocessed. | Author-Created New Dataset, Publicly Available Data, Bangladeshi Legal Data, Legal Domain Data, Legislation / Statutes / Regulations, Web Scraped Data, Unstructured Text Data | Data collection (web scraping), data curation (cleaning, noise reduction, standardization, verification), model selection (GPT-2 medium), model fine-tuning (instruction tuning, LoRA, quantization), prompt engineering, quantitative evaluation (semantic similarity), qualitative evaluation (case studies, expert review). | Data Collection, Data Curation, Model Selection, Model Fine-tuning, Parameter-Efficient Fine-Tuning (PEFT), Model Quantization, Prompt Engineering, Quantitative Evaluation Methodology, Qualitative Evaluation Methodology, Expert Review | The dataset (UKIL-DB-EN) and the fine-tuned model (GPT2-UKIL-EN) are publicly released on Hugging Face. | Public dataset/benchmark release, Open source model release | True | True | Dataset and model available on Hugging Face under Apache-2.0 license. | Dataset available, Model available, Open access resource, Open-source | Need for improved model accuracy, credibility, and safety; limitations in contextual comprehension for complex cases; handling of multilingual requirements (Bangla and English); need for larger models; language simplification for lay users; information gaps requiring more comprehensive data. | AI Accuracy and Reliability, AI Legal Reasoning Limitations, Multilingual and Low-Resource Language Gaps, AI Scope and Functionality Limitations, User Interface and Usability Gaps, Data Availability and Quality | Limited computational resources restricted experimentation with larger/multilingual models and RAG; ensuring accuracy and reliability in the sensitive legal domain; handling legal complexities and context-specific nuances. | High Computational and Resource Demands, Multilingual and Low-Resource Language Support, Accuracy and Reliability of LLM Output, LLM Reasoning Capabilities | Potential inaccuracies and reliability issues in the model's responses could lead to incorrect legal understanding or advice, posing ethical concerns due to the critical nature of legal applications. | Inaccurate or misleading AI output, Ethical concerns, Consumer harm |
| vbPLjHykthcJ.pdf | Google_Scholar | Experimenting with Legal AI Solutions: The Case of Question-Answering for Access to Justice | This paper introduces a human-centric pipeline for legal question-answering aimed at laypeople, featuring a new dataset (LegalQA) with expert-written answers and citations. It demonstrates that retrieval-augmented generation (RAG) using a small, domain-specific set of expert-approved documents can match or outperform RAG using internet-wide retrieval for factual accuracy. | Methodology Proposal, Legal Question Answering, Legal Information Access for Laypeople, Dataset Creation, Retrieval Augmented Generation, Accuracy Improvement, Domain-Specific RAG | True | Idealistic | True | 1.0 | Positive | A human-centric legal NLP pipeline involving: 1) A new dataset (LegalQA) of real layperson legal questions and expert-written answers/citations. 2) Domain-specific Retrieval-Augmented Generation (RAG) using only expert-approved documents. 3) An automatic, expert-vetted evaluation protocol focused on factuality. | Human-centric AI, Natural Language Processing (NLP), Dataset Creation / Curation, Retrieval Augmented Generation (RAG), Evaluation Protocol Development, Factuality Assessment | Evaluated using the created LegalQA dataset (323 questions). Factual accuracy was assessed using an automatic evaluation protocol (GPT-4 comparing model output to expert answers), measuring the percentage of factual disagreement. Compared domain-specific RAG (using 850 expert-sourced documents) against non-RAG models (GPT-3.5, GPT-4, Mixtral-8x7B) and internet-wide RAG (GPT-3.5 with Google search, Cohere Command R+). | Custom Dataset Evaluation, LLM as Judge, Quantitative Metrics, Comparative Analysis | Domain-specific RAG using GPT-3.5 ('GPT-3.5 Legal', 8.7% disagreement) performed better than non-RAG GPT-3.5 (11.8%) and internet-wide RAG ('GPT-3.5 Internet', 8.3%; Command R+, 14.4%). However, the non-RAG GPT-4 model performed best overall (4.4% disagreement). | Technique improves outcome, Outperforms others | Lack of high-quality, expert-vetted structured legal data (question-answer pairs) suitable for laypersons; factual incorrectness (hallucination) and outdated information in LLMs; prohibitive costs of high-performing models (like GPT-4) limiting accessibility. | Data Scarcity/Quality for AI, AI Unreliability/Inaccuracy, High Cost of A2J Technology, Limited Access to A2J Technology | Creating and releasing high-quality, expert-verified datasets (like LegalQA). Employing domain-specific retrieval-augmented generation (RAG) using a curated set of trusted legal sources to improve factual grounding and reduce costs compared to retrieving from the entire internet. Developing human-centric evaluation protocols focused on factuality. | Data Curation and Management, Open Source Initiatives and Collaboration, Enhanced AI Capabilities, Cost Reduction and Efficiency, Benchmarking and Evaluation Frameworks | Providing factual answers to specific legal questions asked by laypeople. | Access to Legal Information, Access to Legal Advice | Laypeople seeking legal information. | Laypeople, Individuals lacking legal knowledge | Employment and labour law, Family and juvenile law, Real estate law, Corporate law, Personal injury law, Civil rights law. | Employment Law, Family Law, Juvenile Law, Real Estate Law, Corporate Law, Tort Law, Civil Rights Law | Canada (specifically Ontario mentioned in an example, and expert annotators were knowledgeable in Canadian law). | Canada | The study uses a retrieval dataset comprising 850 legal documents (citations) provided by legal experts corresponding to answers for real layperson questions sourced from Reddit (r/legaladvice). The evaluation dataset (LegalQA) is a subset (323 Q&A pairs) released publicly. This data is structured (question, expert answer, citation) and domain-specific (Canadian law). | RAG System Knowledge Corpus, Author-Created New Dataset, Expert-Annotated / Human-Curated / Human-Generated Data, Legal Domain Data, Canadian Legal Data, Legal Q&A / Forum / User Query Data, User-Generated Content, Structured Data, Evaluation Dataset, Publicly Available Data | Human-centric design involving legal experts (law professors and students) for data sourcing (writing answers, providing citations) and evaluation design. Technical methodology involves Retrieval-Augmented Generation (RAG) based on embedding similarity (dot product) between questions and a curated document set. | User-centered Design, Expert Collaboration, Dataset Creation, Evaluation Design, Retrieval Augmented Generation (RAG), Information Retrieval Techniques | The LegalQA evaluation dataset was released publicly on Hugging Face. | Public dataset/benchmark release | False | False | The evaluation dataset (LegalQA) is claimed to be publicly released, but not the full RAG system or the 850-document retrieval corpus. | Dataset available, Restricted access | Performance gap between open-source and closed-source models; need for continual updating of legal knowledge in AI systems; lack of expert involvement in sourcing unstructured data for pre-training legal models; accountability issues with black-box models. | AI Accuracy and Reliability, Computational Resource and Cost Issues, Knowledge Recency and Updatability, Data Availability and Quality, Need for Interdisciplinary Collaboration, Transparency and Explainability, Accountability and Redress Mechanisms | Sourcing high-quality, expert-approved legal data suitable for laypersons; developing reliable automatic evaluation methods for factual correctness in open-ended legal answers; difficulty answering highly specific/nuanced questions, particularly in certain legal areas (e.g., civil rights, real estate); managing computational/storage costs of retrieval. | Scarcity of High-Quality Legal Data, Cost and Complexity of Data Annotation, Evaluation Challenges and Metrics, LLM Reasoning Capabilities, High Computational and Resource Demands | LLMs providing factually incorrect or misleading legal advice (hallucination); lack of accountability and transparency in closed-source models used for legal purposes. | Inaccurate or misleading AI output, Lack of transparency, accountability, and redress |
| 1.9781611977653.ch111.pdf | Google_Scholar | Making a Computational Attorney | This paper outlines a vision for a "computational attorney," an AI agent capable of assisting human lawyers with complex legal tasks using Large Legal Language Models (L3Ms). It discusses the current state of L3Ms in law, highlights their potential to democratize legal services, and identifies key future research challenges for their development. | Vision Paper, Computational Attorney Concept, Large Legal Language Models (L3Ms), Legal Professional Assistance, Democratization of Legal Services, Future Research Challenges | True | Idealistic | True | 3.0 | Positive | The 'computational attorney' concept as a future AI system based on advanced Large Legal Language Models (L3Ms). | Conceptual Framework, Large Language Model, Future AI Vision | NaN | Not Applicable | NaN | NaN | Prohibitively expensive legal fees leading to inadequate or no legal assistance for a large percentage of low-income individuals with civil legal problems. | High Cost of Legal Services, Limited Access to Legal Assistance | Development of advanced AI like 'computational attorneys' using L3Ms, which could democratize legal services. | AI Tool Development, Access to Legal Information and Advice, Alternative Legal Service Delivery Models | Affordability of legal services, access to legal aid for civil matters. | Affordability of Legal Services / Cost Reduction, Legal Aid and Pro Bono Services | Low-income Americans with civil legal problems. | Low-income individuals, Population in USA, Individuals with civil legal problems | General law, covering tasks like drafting legal briefs, analyzing legal judgments, opinions, and contracts. | General Law, Document Drafting, Contract Law | US (primary focus, especially for access to justice aspects and legal system examples); Japan (referenced for specific AI model evaluations). | USA, Japan | Large-scale legal text data, including publicly available corpora (e.g., Pile-of-Law) and potentially proprietary datasets. The paper discusses pre-training L3Ms on general and legal-specific corpora. | Fine-tuning Dataset, Legal Domain Data, Publicly Available Data, Data From Existing Public NLP/Legal Datasets/Benchmarks, Proprietary Data | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Technical gaps in current L3M capabilities (making them updatable, stable, provable, communicable, and predictable) hinder the creation of a 'computational attorney' capable of democratizing legal services. The societal gap is the current widespread lack of affordable legal assistance. | AI Accuracy and Reliability, Knowledge Recency and Updatability, Transparency and Explainability, Access, Equity, and Digital Divide | Developing L3Ms that are: updatable with new legal precedents and laws efficiently; stable against generating false information ('hallucinations') and robust to out-of-distribution data; provable in their reasoning by linking to legal sources; communicable for effective human-lawyer interaction and learning; and predictable in anticipating legal outcomes and strategic implications. | Outdated or Limited LLM Knowledge Base, LLM Hallucination and Factual Errors, Accuracy and Reliability of LLM Output, Transparency and Explainability of AI, User Interface, Usability, and Accessibility, LLM Reasoning Capabilities | AI models providing outdated or incorrect legal analysis; models 'hallucinating' or inventing non-existent legal facts/precedents; lack of verifiability or provability for AI-generated legal opinions; potential liabilities associated with AI outputs if not properly managed. | Inaccurate or misleading AI output, Lack of transparency, accountability, and redress, Ethical concerns |
| oO6c-Wwoy2sJ.pdf | Google_Scholar | The CLC-UKET Dataset: Benchmarking Case Outcome Prediction for the UK Employment Tribunal | This paper introduces the CLC-UKET dataset, a collection of ~19,000 UK Employment Tribunal cases annotated using an LLM to facilitate research on access to justice. The dataset is used to benchmark various models, including LLMs, on the task of predicting case outcomes based on facts and claims, comparing results against human expert performance. | Dataset Creation, UK Law Focus, Employment Law Focus, Access to Justice Research, Legal Case Outcome Prediction, Benchmarking AI Models, LLM for Annotation | True | Idealistic | True | 2.0 | Positive | Benchmarking dataset (CLC-UKET) creation using LLM-aided annotation (GPT-4) and evaluation of case outcome prediction models (BERT, T5, GPT-3.5, GPT-4) on this dataset. | Dataset Creation / Curation, Benchmarking / Evaluation, LLM-aided Data Annotation, Predictive Legal Task, Transformer Models, Large Language Model | Evaluation on a test split of the curated CLC-UKET pred dataset (1,371 cases) using manually annotated gold-standard outcome labels. Metrics: Accuracy, Precision, Recall, F-score. Comparison with human expert predictions. | Custom Dataset Evaluation, Quantitative Metrics, Comparative Analysis | The fine-tuned T5 model achieved the best performance among the models tested (F-score: 0.564), significantly outperforming random guessing but still falling short of human expert performance (F-score: 0.672). | Moderate performance, Outperforms others, Underperforms others, Technique improves outcome | Uncertainty regarding the likely outcome of court procedures hinders access to justice and amicable dispute resolution. | Uncertainty of Legal Outcomes | Creating large-scale, annotated legal datasets (like CLC-UKET) and developing/benchmarking AI models for case outcome prediction to provide insights into likely results. | Data Curation and Management, AI Tool Development, Benchmarking and Evaluation Frameworks, Legal Research and Analysis Tools | Case outcome prediction, Facilitating dispute resolution, Access to legal information | Improving Foundational AI Capabilities for Legal Applications, Dispute Resolution, Access to Legal Information | Claimants in the UK Employment Tribunal system. | Employment tribunal claimants, Population in UK | Employment law | Employment Law | United Kingdom (UK Employment Tribunal) | UK | The CLC-UKET dataset, derived from the publicly available Cambridge Law Corpus (CLC) containing UKET judgments (2011-2023). Facts, claims, and initial outcome labels were extracted from unstructured judgment text using GPT-4 (LLM-aided annotation). Gold-standard outcome labels for the test set were manually annotated by a legal expert. | Author-Created New Dataset, Evaluation Dataset, Data From Existing Public NLP/Legal Datasets/Benchmarks, UK Legal Data, Legal Domain Data, Case Law / Judgments, Synthetic Data, Expert-Annotated / Human-Curated / Human-Generated Data, Structured Data, Unstructured Text Data | Dataset curation (filtering public legal documents), LLM-aided annotation (GPT-4 with prompt engineering), manual validation (for test set outcome labels), standard ML benchmarking (train/val/test split, baseline models), Human evaluation (expert prediction task with guidelines). | Dataset Curation, LLM-aided Annotation, Prompt Engineering, Manual Validation, Benchmarking, Human Evaluation, Expert Evaluation | The CLC-UKET dataset is planned to be made available via the Cambridge Law Corpus (CLC) website, with access restricted to qualified researchers adhering to ethical and legal standards. | Proposed deployment (not implemented), Public dataset/benchmark release, Pilot program/Limited rollout | False | False | NaN | NaN | Reliance on extracted facts/claims from judgments rather than original filings (potential bias), limitations of LLM-based annotation quality, need for more detailed factual information, dataset representativeness uncertainty, handling legal evolution over time, performance gap between AI models and human experts. | Data Availability and Quality, Bias in AI, AI Accuracy and Reliability, Knowledge Recency and Updatability | Cost and time of manual legal annotation, potential inaccuracies in LLM-based annotation, complexity of legal cases (e.g., preliminary issues, procedural decisions), potentially insufficient information in extracted facts/claims for accurate prediction, ensuring ethical use of legal data. | Cost and Complexity of Data Annotation, Accuracy and Reliability of LLM Output, LLM Reasoning Capabilities, Data Quality, Processing, and Preparation, Ethical Considerations | Information bias in facts/claims extracted from judgments, potential inaccuracies from LLM annotation, models learning spurious correlations (e.g., sentiment), misinterpretation or over-reliance on prediction results in legal practice, data privacy concerns (mitigated by CLC protocols). | Bias and discrimination, Inaccurate or misleading AI output, Technical limitations of AI, Over-reliance on AI, Data privacy and security breach |
| 4Nbz7njEtzoJ.pdf | Google_Scholar | Fighting the Knowledge Representation Bottleneck with Large Language Models | This paper investigates using Large Language Models (GPT-4o) to tackle the knowledge representation bottleneck in developing legal expert systems. It proposes and evaluates a human-in-the-loop, prompt-based methodology for formalizing legal articles and case law into Prolog rules, using the Facilex system as a case study. | LLM Application, Knowledge Representation for Legal Expert Systems, Human-in-the-Loop Process, Prompt-Based Methodology, Formalizing Legal Rules, Logic Programming (Prolog) | True | Idealistic | True | 1.0 | Positive | Using GPT-4o with a 'Chain of Prompts' methodology (few-shot learning) and human-in-the-loop validation to: 1) formalize legal articles into Prolog rules by refining LLM-generated code based on existing system facts and structure; 2) extract key legal principles from case law and formalize them into new Prolog rules, integrating them with existing legal provisions in an expert system (Facilex). | Large Language Model, Prompt Engineering, Few-shot Learning, Human-in-the-Loop System, Legal Knowledge Formalization, Code Generation, Expert System Integration, Neuro-Symbolic AI, Information Extraction | Two-tiered evaluation: 1) Formal validation (automated check for syntactic correctness and executability of Prolog rules within the Facilex system). 2) Expert validation (by knowledge engineers) assessing Accuracy (completeness of legal elements), Relevance (adherence to legal reasoning and text), Human Alignment (support for model-engineer dialogue), and Fluency (consistency and readability of Prolog code). | Quantitative Metrics, Expert Evaluation | For article generation, LLM-generated Prolog rules passed formal validation. Expert validation showed high accuracy (23 out of 27 expert-formalized conditions captured), with minor issues like redundant conditions or structural variations. For case generation, rules also passed formal validation, and expert validation confirmed strong accuracy in identifying and representing key legal elements from case law, though significant prompt engineering was needed for relevance. | High performance, Limitation: Operational or Technical, Technique improves outcome | The primary obstacle addressed is the Knowledge Representation Bottleneck (KRB) in legal expert systems, which makes the acquisition, formalization, and constant updating of expert knowledge time-consuming, error-prone, and limits the systems' flexibility, scalability, and longevity. | Complexity of Legal Knowledge Formalization, Resource Constraints for A2J Tech Development/Deployment, Technical Challenges in AI Development | The paper proposes leveraging Large Language Models (GPT-4o) within a human-in-the-loop 'Chain of Prompts' framework. This approach semi-automates the generation and revision of Prolog rules from legal articles and case law, aiming to make expert systems more scalable, adaptable, and easier to update. | AI Tool Development, Human Oversight and Collaboration, Prompt Engineering and LLM Interaction Design, Enhanced AI Capabilities, Document Automation | Enhancing the development, maintainability, and scalability of rule-based legal expert systems, particularly for complex legal domains such as EU mutual recognition instruments in criminal matters (e.g., European Arrest Warrant procedures), by using LLMs to formalize legal knowledge. | Improving Foundational AI Capabilities for Legal Applications, Improving Efficiency in Legal System / Profession | Individuals involved in EU cross-border criminal proceedings (indirectly, through improved tools and systems for the legal professionals representing or adjudicating their cases). | Individuals in cross-border criminal proceedings, Population in EU | EU procedural law, mutual recognition instruments in criminal matters, European Arrest Warrant. | EU Law, Procedural Law, Criminal Law, International Law | European Union (EU) | EU | The approach uses a pre-trained LLM (GPT-4o). For its few-shot prompting methodology, it utilizes: 1) existing Prolog rules and facts from the Facilex expert system, 2) natural language text of legal articles (e.g., EU Framework Decision on European Arrest Warrant), and 3) raw text of EU case law (e.g., CJEU judgments). | Pre-trained LLM's General Training Corpus, Input Data for Task (Non-Training), European Legal Data, Legal Domain Data, Legislation / Statutes / Regulations, Case Law / Judgments, Publicly Available Data, Structured Data, Unstructured Text Data | Human-in-the-loop approach, 'Chain of Prompts' methodology for LLM interaction, few-shot learning, iterative refinement of LLM outputs by knowledge engineers, and a two-tiered evaluation process (formal and expert-driven validation). | Human-in-the-loop System, Prompt Engineering, Few-shot Learning Application, Iterative Design Process, Expert Validation | NaN | Not applicable | False | True | The Jupyter Notebook containing prompts, inputs, and outputs of the experiments is available on GitHub at https://github.com/LegalMachineLab/JURIX24-fighting_krb. | Code available, Configuration or prompts available, Dataset available | The need for continuous human supervision to ensure legal correctness, consistency, and alignment with expert system's domain. The challenge of achieving full automation in knowledge formalization due to LLM limitations and the nuanced nature of legal interpretation. Making the resulting expert systems truly user-friendly for diverse end-users. | Human Oversight and Professional Adaptation, AI Legal Reasoning Limitations, User Interface and Usability Gaps | Ensuring consistency and avoiding redundancy in LLM-generated Prolog rules. Aligning the LLM's rule generation style with specific expert preferences (e.g., structure of sub-rules, use of negation). Significant prompt engineering effort required to achieve desired relevance and scope in outputs. Managing the LLM's tendency to introduce legally accurate but contextually irrelevant information. Potential for structural errors in generated code when processing large or complex inputs. The necessity of an iterative human-in-the-loop process for refinement and validation. | Output Variability and Consistency, Accuracy and Reliability of LLM Output, Domain-Specific Adaptation and Customization, Prompt Engineering and Optimization, LLM Reasoning Capabilities, Need for Human Oversight and Intervention | Generation of syntactically correct but legally inaccurate, incomplete, or subtly flawed Prolog rules if expert oversight is insufficient. Introduction of inconsistencies, redundancies, or out-of-scope information into the expert system's knowledge base. Unpredictability in LLM outputs regarding naming conventions or rule structures, potentially affecting code maintainability and expert alignment. | Inaccurate or misleading AI output, Over-reliance on AI, Technical limitations of AI |
| _3PICPHoZiIJ.pdf | Google_Scholar | LLMediator: GPT-4 Assisted Online Dispute Resolution | This paper introduces LLMediator, an experimental platform using GPT-4 to enhance Online Dispute Resolution (ODR) for low-intensity legal disputes. It discusses and qualitatively evaluates features like reformulating user messages to be less emotional and drafting mediator responses to facilitate amicable settlements. | System Development, LLM Application, Online Dispute Resolution Enhancement, AI for Mediation Support, User Message Reformulation, Qualitative Evaluation | True | Idealistic | True | 1.0 | Positive | LLMediator platform using GPT-4 API calls with specific prompts for: F1 (reformulating inflammatory messages), F2 (drafting message suggestions for human mediators), F3 (experimental autonomous AI intervention). | Software / Platform Development, Large Language Model, Prompt Engineering, Mediation Support Tool, Content Generation, Autonomous AI Agent, Named Tool / Platform | Initial qualitative evaluations through illustrative examples and discussion of potential outputs generated by GPT-4 in different scenarios. | Demonstration or Illustrative Examples, Qualitative Analysis | Qualitative examples demonstrate GPT-4's promising ability to perform the intended tasks (reformulation, drafting interventions) appropriately, relevantly, and adaptively based on context and instructions. | High performance, Descriptive or Conceptual finding | Difficulty understanding rights, costs (monetary, temporal, psychological) of traditional courts, challenges in reaching resolution for laypeople in low-intensity disputes. | Public Lack of Legal Knowledge/Awareness, High Cost of Legal Services, Psychological/Cultural Barriers to Seeking Help/Engaging with Law, Challenges for Self-Represented Litigants | Enhancing ODR platforms with AI (specifically LLMs like GPT-4) to reformulate inflammatory messages, assist human mediators, and potentially provide automated mediation support for low-value cases. | Online Dispute Resolution (ODR), AI Tool Development, Human Oversight and Collaboration | Online Dispute Resolution (ODR), Negotiation, Mediation | Dispute Resolution | Laypeople facing low-intensity disputes (debt, consumer, employment). | Laypeople, Individuals in debt or lending disputes, Consumers, Individuals with employment disputes | Consumer law, Debt collection, Employment law, Landlord-tenant law, Torts (minor) | Consumer Law, Debt Collection, Employment Law, Landlord-Tenant Law, Tort Law | International | International | Pre-trained GPT-4 model accessed via OpenAI API; no specific training data mentioned by the authors. | Pre-trained LLM's General Training Corpus, Proprietary Data, Undisclosed Data Source/Availability | Prototyping, Prompt Engineering, Qualitative evaluation via examples. | Prototyping, Prompt Engineering, Qualitative Evaluation Methodology | Experimental prototype, proof of concept. | Internal deployment/prototype | False | False | NaN | NaN | Need for empirical evaluation of efficacy and bias, refinement of prompt engineering, development of improved triggers for AI intervention, exploring further LLM applications (e.g., summarization). | Research and Evaluation Gaps, Bias in AI, Human Oversight and Professional Adaptation, AI Scope and Functionality Limitations | Potential for LLM hallucination and inaccuracy, risk of AI taking sides, user frustration/self-expression concerns (for F1), anchoring bias/over-reliance (for F2), high risks with autonomous intervention (F3). | LLM Hallucination and Factual Errors, Accuracy and Reliability of LLM Output, Bias in AI Systems and Data, Ethical Considerations, User Adoption, Trust, and Acceptance, Safeguarding Against Misuse and Harm | LLM hallucination and inaccuracy, biased outputs leading to unfair outcomes or loss of trust, mediators developing anchoring bias or over-reliance, user frustration with automated message changes. | Inaccurate or misleading AI output, Bias and discrimination, Erosion of trust in legal system or AI, Over-reliance on AI, Poor user experience |
| 3w_RoYmbStkJ.pdf | Google_Scholar | Human Centered AI for Indian Legal Text Analytics | This position paper proposes a human-centered, compound AI system using Large Language Models (LLMs) for legal text analytics in India to improve access to justice. It introduces a new Indian legal dataset and outlines 'InLegalLLaMA', an LLM to be trained on Indian legal texts, to address current AI limitations like low trustworthiness and lack of specialized resources. | Position Paper, System Proposal, Human-Centered AI, LLM Application, Legal Text Analytics, India Focus, Access to Justice Enhancement, Dataset Creation, Legal Language Model Development | True | Idealistic | True | 1.0 | Positive | Human-centered compound AI system integrating LLMs (specifically a proposed 'InLegalLLaMA') with human input for Indian legal text analytics, supported by a novel domain-specific dataset. | Human-centric AI, Hybrid AI System, Large Language Model, Legal Text Analysis, Dataset Creation / Curation | LLaMA-2-70B-Chat for case similarity (few-shot prompting on 2,626 document excerpt pairs, ROC-AUC); LLaMA-2-34B-Instruct for relation/tail prediction on a legal KG subset (Hits@k). | Custom Dataset Evaluation, Quantitative Metrics | For case similarity, LLaMA-2-70B-Chat achieved a ROC-AUC score of 0.566. For relation/tail prediction, LLaMA-2-34B-Instruct achieved Hits@1: 0.520, Hits@5: 0.556, Hits@10: 0.617. | Moderate performance | Overwhelmed legal system with case backlogs and time-consuming processes; low trustworthiness of current AI; lack of AI focus on common citizens; scarcity of specialized legal datasets; citizens' unfamiliarity with legalese; poorly written petitions leading to inefficiencies and dismissals; complexity of legal documents for laypersons. | Judicial/Legal System Inefficiencies, Lack of Trust in AI/Automated Systems, Misalignment of Research/Innovation with Practical Needs, Data Scarcity/Quality for AI, Public Lack of Legal Knowledge/Awareness, Complexity of Legal Language/Documents, Challenges for Self-Represented Litigants | Development of Human-Centered AI (HCAI) as a compound system eliciting human input; creation of specialized Indian legal datasets; using LLMs to help citizens understand legal documents, conduct research, and draft better petitions; abstractive summarization for layperson comprehension; LLM-based conversational QA for identifying missing information in petitions; pre-training and fine-tuning LLMs (e.g., InLegalLLaMA) on Indian legal texts and infusing them with domain knowledge. | AI Tool Development, User Interface and Accessibility Design, Human Oversight and Collaboration, Data Curation and Management, Access to Legal Information and Advice, Document Automation, Legal Research and Analysis Tools, Language Simplification and Multilingual Access, Enhanced AI Capabilities | Speeding up justice delivery; improving legal understanding for common citizens and self-represented litigants; aiding legal research; assistance with petition drafting; reducing system burden from poorly prepared documents; democratizing legal knowledge. | Improving Efficiency in Legal System / Profession, Legal Literacy and Public Legal Education, Support for Self-Represented Litigants, LegalResearch Support, Legal Document Creation / Automation, Democratizing Law / Closing Justice Gap / Rule of Law | Common citizens, self-represented litigants, individuals not well-versed in legal language, and the general public in India seeking access to justice. | General public, Population in India, Self-represented litigants, Individuals with language barriers, Individuals lacking legal knowledge | General Indian Law / Indian Case Law | General Law, Case Law | India | India | A new dataset composed of: 1) A Legal Knowledge Graph derived from 2,286 Indian legal documents (court cases, judgements, laws from public repositories, IndianKanoon, Casemine), processed using Stanza, SystemT, and manually curated dictionaries. 2) A Question-Answering dataset from 45 Delhi High Court judgments, with QA pairs generated by gpt-3.5-turbo using few-shot prompting. 3) A Text2SQL dataset extended from the QA dataset. The proposed InLegalLLaMA will use general Indian legal domain corpora. | Author-Created New Dataset, Indian Legal Data, Legal Domain Data, Case Law / Judgments, Legislation / Statutes / Regulations, Publicly Available Data, Structured Data, Expert-Annotated / Human-Curated / Human-Generated Data, Synthetic Data, Fine-tuning Dataset | Human-Centered AI (HCAI) principles; compound AI systems approach; dataset creation via web scraping, automatic/manual annotation, LLM-based generation (gpt-3.5-turbo, few-shot prompting); proposed LLM development includes pre-training, instruction-tuning, concept-enhanced pre-training, PEFT, knowledge infusion, and Retrieval Augmented Generation (RAG). | User-centered Design, Principle-driven Design, Compound AI System Design, Dataset Creation, LLM-aided Data Generation, Model Pre-training, Model Fine-tuning, Parameter-Efficient Fine-Tuning (PEFT), Knowledge Infusion, Retrieval Augmented Generation (RAG) | NaN | Not applicable | False | False | NaN | NaN | Low trustworthiness of current generative AI; scarcity of specialized legal datasets for training LLMs; existing LLMs not adequately tailored to specific legal domains like the Indian legal system; poor performance of European-trained legal models in the Indian context due to document structural differences; need to mitigate hallucinations in LLMs for domain tasks with societal impacts; lack of focus on common citizens in current AI applications; general unavailability of resources for AI in domains directly touching human lives. | AI Accuracy and Reliability, Data Availability and Quality, AI Legal Reasoning Limitations, Multilingual and Jurisdictional Specificity Gaps, Access, Equity, and Digital Divide, Computational Resource and Cost Issues | Scalability of supervised methods due to extensive annotation needs; ensuring factual accuracy and avoiding misrepresentation in AI-generated legal text (e.g., summaries); adapting general LLMs to the nuances of the Indian legal domain; developing trustworthy LLMs for high-stakes legal applications; creating comprehensive, high-quality specialized legal datasets; mitigating LLM hallucinations in critical legal tasks. | Scalability of Solutions, Cost and Complexity of Data Annotation, Accuracy and Reliability of LLM Output, Domain-Specific Adaptation and Customization, User Adoption, Trust, and Acceptance, Scarcity of High-Quality Legal Data, LLM Hallucination and Factual Errors | Low trustworthiness of generative AI; misleading readers with AI-generated content (e.g., abstractive summaries generating information absent in original documents, or altering meaning through subtle word changes); inaccuracies in generated text (e.g., altered proper nouns, locations, numbers); societal consequences from LLM hallucinations in domain tasks; potential for poorly written petitions (if AI is faulty) adding costs and risking dismissal. | Erosion of trust in legal system or AI, Inaccurate or misleading AI output, Negative societal impact, Negative economic impact, Consumer harm |
| iL5Ltm0_mAcJ.pdf | Google_Scholar | The unreasonable effectiveness of large language models in zero-shot semantic annotation of legal texts | This paper evaluates the zero-shot semantic annotation performance of GPT-4 and GPT-3.5-turbo(-16k) on diverse legal texts (adjudicatory opinions, contracts, statutes), comparing them to earlier GPT models and supervised baselines. It finds GPT-4 performs well, especially on contract clauses, and analyses the trade-offs between performance, cost, and batch processing for practical applications. | LLM Evaluation, Semantic Annotation of Legal Texts, Zero-Shot Learning, Contract Analysis, Cost-Performance Trade-off Analysis | True | Idealistic | True | 2.0 | Positive | Zero-shot semantic annotation (classification) of short legal text snippets using large language models (GPT-4, GPT-3.5-turbo(-16k), text-davinci-003) instructed via prompts containing category names and definitions. | Zero-shot Learning, Legal Text Classification, Semantic Annotation, Large Language Model, Prompt Engineering | Evaluation on three manually annotated datasets: BVA (rhetorical roles in veterans' appeal decisions), CUAD (clause types in commercial contracts), PHASYS (purpose of public health statutes/regulations). Performance measured by Precision, Recall, F1-score (micro-average overall). Compared against Random Forest and fine-tuned RoBERTa baselines. Tested both single-instance and batch prediction. | Custom Dataset Evaluation, Benchmark Dataset Evaluation, Quantitative Metrics, Comparative Analysis | GPT-4 achieved F1 scores of 0.82 (BVA), 0.90 (CUAD), and 0.54 (PHASYS), outperforming GPT-3.5 models and matching Random Forest on BVA/CUAD, but below fine-tuned RoBERTa. Cost-effective GPT-3.5-turbo matched the more expensive text-davinci-003. Batch processing significantly lowered costs with only a minor performance decrease compared to single-instance prediction, but large batches degraded performance. | High performance, Moderate performance, Outperforms others, Comparable to others, Underperforms others, Benefit identified, Technique has limited or mixed impact | High cost of current AI workflows requiring manual annotation or expensive enterprise solutions. Potential cost of using LLM APIs, especially for high-volume or non-batched tasks. Performance limitations compared to fine-tuned models, particularly for nuanced or ambiguous categories. Difficulty handling domain-specific nuances with simple definitions. Constant evolution of proprietary models. | Resource Constraints for A2J Tech Development/Deployment, High Cost of A2J Technology, AI Limitations in Legal Reasoning/Nuance, Technical Challenges in AI Development, Proprietary Nature of AI as a Barrier | Leveraging zero-shot capabilities of LLMs with simple prompts (type lists and definitions) to perform semantic annotation without task-specific training data. Employing batch prediction within prompts to significantly reduce API costs, making sophisticated annotation workflows more accessible and economically feasible for experimentation and deployment. | Enhanced AI Capabilities, Prompt Engineering and LLM Interaction Design, Cost Reduction and Efficiency, Document Automation | Semantic annotation, Rhetorical role classification, Contract clause classification, Statutory provision classification, Contract review, Case law analysis, Empirical legal studies. | Improving Foundational AI Capabilities for Legal Applications, Legal Document Analysis / Review, LegalResearch Support | Legal professionals, legal researchers, potentially smaller law firms or organizations unable to afford traditional high-cost AI legal tech solutions. | Legal professionals, Researchers, Small law firms, Resource-constrained organizations | Veterans Law, Contract Law, Public Health Law, Administrative Law | Veterans Law, Contract Law, Public Health Law, Administrative Law | United States (based on BVA, PHASYS datasets; CUAD likely US-centric) | USA | The evaluated LLMs (GPT-4, GPT-3.5) used their large, general, proprietary pre-training data. No task-specific fine-tuning data was used for the evaluated zero-shot approach. Baseline models were trained on the specific BVA, CUAD, and PHASYS datasets (manually annotated legal texts). | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Proprietary Data, Evaluation Dataset, Data From Existing Public NLP/Legal Datasets/Benchmarks, Legal Domain Data, Expert-Annotated / Human-Curated / Human-Generated Data | Prompt engineering: Designing specific text prompts instructing the LLMs to classify text snippets based on provided type definitions. Experimental comparison across models, datasets, and batching strategies. | Prompt Engineering, Experimental Comparison of Models/Strategies | The approach relies on accessing LLMs via the OpenAI API. Prompts and model settings are shared via a GitHub repository for replication. | API access, Open source code release, Public dataset/benchmark release | True | False | Prompts and settings are available on GitHub; execution requires access to the commercial OpenAI API. | Configuration or prompts available | Performance gap between zero-shot and supervised/fine-tuned models, especially for complex/nuanced tasks. Handling imbalanced datasets and ambiguous definitions in zero-shot settings. Need for methods applicable to longer texts and more complex reasoning. Understanding and optimising effects of batching (e.g., ordering). Addressing cost barriers for wider adoption. Research challenges due to proprietary, evolving models. | AI Accuracy and Reliability, AI Legal Reasoning Limitations, Data Availability and Quality, AI Scope and Functionality Limitations, Research and Evaluation Gaps, Computational Resource and Cost Issues | Designing effective prompts for diverse legal annotation tasks. Balancing performance vs. cost (especially regarding batch size). Handling model context length limitations. Achieving high accuracy for nuanced legal distinctions. Dealing with dataset imbalance. Reproducibility issues with closed models. | Prompt Engineering and Optimization, Financial Cost and Resource Constraints, LLM Context Window and Long Input Management, Accuracy and Reliability of LLM Output, LLM Reasoning Capabilities, Data Quality, Processing, and Preparation, Evaluation Challenges and Metrics, Transparency and Explainability of AI | Inaccuracy of annotations, potentially leading to incorrect analysis or decisions if used without verification. Cost can still be a barrier depending on scale and approach (batched vs. single). Dependence on proprietary, changing models. | Inaccurate or misleading AI output, Negative economic impact, Technical limitations of AI |
| Ey5B4UxN4Q8J.pdf | Google_Scholar | Bridging the Gap: Mapping Layperson Narratives to Legal Issues with Language Models | This paper proposes a system using language models to automatically map layperson factual descriptions of their problems to relevant legal issues, aiming to improve access to justice. Integrated into the JusticeBot tool, the system was evaluated on real-world user data and demonstrated high accuracy in suggesting appropriate legal pathways to users. | System Development, Language Model Application, Legal Issue Spotting from Layperson Descriptions, Access to Justice Enhancement, System Evaluation, Integration with Existing Tool (JusticeBot) | True | Idealistic | True | 1.0 | Positive | A system using a multilingual universal sentence encoder to create vector embeddings of layperson factual descriptions and pre-defined example situations. It employs an approximate nearest neighbor search (Annoy library) to match the user's description to the most similar example situations, thereby suggesting relevant legal issues and pathways, integrated within the JusticeBot. | Embedding-based Methods, Similarity Search, Multilingual Application, Legal Issue Spotting, Integration with Existing Systems, Information Retrieval / Search | The system was evaluated using real-world, anonymized user-submitted factual descriptions from the JusticeBot. Performance was measured by Precision@1 (P@1) and Precision@3 (P@3) against annotated ground truth pathways. Two main experimental setups were used: 1) training on seed examples and testing on user submissions, and 2) training on seed examples plus user submissions (excluding the test instance, in a leave-one-out manner) and testing on user submissions. A cold-start scenario comparing the language model approach to an SVM baseline was also conducted. | Custom Dataset Evaluation, Quantitative Metrics, Comparative Analysis | When trained with both seed examples and user-submitted data, the system achieved 93.5% P@3 (relevant legal issue suggested within the top 3 options) and 74.5% P@1 (relevant legal issue suggested as the top option) on user-submitted descriptions. | High performance, Moderate performance, Technique improves outcome | The 'gap' between layperson language (focusing on facts) and legal language (requiring identification of legal issues), causing laypeople to struggle in identifying their rights or relevant legal remedies. This hinders their ability to use self-help tools effectively. | Public Lack of Legal Knowledge/Awareness, Complexity of Legal Language/Documents, Difficulty in AI-Human Interaction | An 'augmented intelligence' system that analyzes layperson's factual descriptions to suggest potentially relevant legal issues and pathways. The system provides factual explanations for its suggestions, allowing users to verify the system's understanding before exploring a suggested legal pathway within tools like JusticeBot. | AI Tool Development, Human Oversight and Collaboration, Access to Legal Information and Advice, Transparency and Explainability in AI | Legal issue identification from layperson narratives, improving usability of legal self-help tools, bridging the language gap in legal information. | Access to Legal Advice, Support for Self-Represented Litigants, Language Access and Digital Divide, Access to Legal Information | Laypeople (individuals without legal training) facing legal disputes, particularly those who might self-represent or use online legal information tools. | Laypeople, Individuals lacking legal knowledge, Litigants, Self-represented litigants | Landlord-tenant disputes (primary focus of JusticeBot and evaluation), with potential applicability to other areas like consumer rights, debt, and employment law. | Landlord-Tenant Law, Consumer Law, Debt Collection, Employment Law | Quebec, Canada (based on the JusticeBot project and data source). | Canada | A combination of: 1) 'Seed example descriptions' (58 examples) created by the research team, formulating potential layperson descriptions for various legal issues. 2) 'User-submitted example descriptions' (3,250 annotated examples) from real JusticeBot users, representing genuine layperson narratives. Data is unstructured text. | Author-Created New Dataset, User-Generated Content, Legal Domain Data, Legal Q&A / Forum / User Query Data, Expert-Annotated / Human-Curated / Human-Generated Data, Unstructured Text Data | User-centered design (addressing observed user difficulties), augmented intelligence approach, use of pre-trained multilingual sentence encoders, approximate nearest neighbor search, iterative improvement based on user data (seed examples and real user feedback). | User-centered Design, Augmented Intelligence Design, Pre-trained Model Utilization, Information Retrieval Techniques, Iterative Design Process, User Feedback Integration | The proposed feature is integrated into the JusticeBot (https://justicebot.ca), an online legal decision support tool. Users can type a description of their situation, and the system suggests relevant pathways. | Integration into existing system/platform, Web-based access | True | False | The feature is described as part of the JusticeBot tool, which is accessible online at https://justicebot.ca. | Publicly accessible online tool or platform | Need to expand the dataset to cover more legal issues and domains. Further empirical evaluation with end-users is needed to assess real-world utility. Exploration of alternative embedding models (including newer LLMs like GPT-4) and classification approaches for potential performance improvements. | Data Availability and Quality, Research and Evaluation Gaps, User Interface and Usability Gaps | Handling the variability and ambiguity of layperson language compared to structured legal text. Overcoming the 'cold-start problem' when introducing new legal topics or tools. Ensuring suggestions are not misleading if a user's specific issue is not covered. | LLM Reasoning Capabilities, Domain-Specific Adaptation and Customization, Accuracy and Reliability of LLM Output | The system might provide irrelevant suggestions if a user's situation is not covered by the pre-defined pathways. Misinterpretation of the system's suggestions as legal advice rather than legal information, potentially leading to concerns about the unauthorized practice of law. | Inaccurate or misleading AI output, Technical limitations of AI, Unauthorized practice of law, Consumer harm |
| Zz495LiJ5oAJ.pdf | Google_Scholar | Exploring the Nexus of Large Language Models and Legal Systems: A Short Survey | This paper surveys the applications of Large Language Models (LLMs) in the legal domain, covering tasks like text comprehension, case retrieval, analysis, and legal education. It also discusses key challenges such as bias, hallucination, and ethical concerns, along with available datasets and fine-tuned models for various legal systems. | Survey of LLMs in Law, LLM Applications (Comprehension, Retrieval, Analysis, Education), Challenge Identification, Bias in AI, AI Hallucinations/Inaccuracy, Ethical Considerations, Legal Datasets Overview, Fine-tuned Legal Models Overview | True | Idealistic | True | 3.0 | Positive | Survey covering various techniques including fine-tuning LLMs (e.g., LawGPT, Lawyer LLaMA, LexiLaw, ChatLaw, DISC-LawLLM, LexGPT, LLaMandement), Prompt Engineering (LPE, CoT), Retrieval-Augmented Generation, and neuro-symbolic methods. | Literature Survey / Review, Fine-tuning, Prompt Engineering, Retrieval Augmented Generation (RAG), Neuro-Symbolic AI, Large Language Model | NaN | Not Applicable | NaN | NaN | Lack of true understanding (stochastic parrots), spurious correlations, biases (racial, gender, religious, LGBTQ+), hallucination, privacy encroachments, interpretability issues, challenges in distinguishing authentic AI-generated evidence, potential negative impacts on fairness and fundamental values. | AI Limitations in Legal Reasoning/Nuance, Bias in AI/Data, AI Unreliability/Inaccuracy, Data Privacy Concerns with AI, Lack of AI Transparency/Explainability, Difficulty Verifying AI-Generated Content, Ethical Concerns with AI in Law | Fine-tuning on diverse/representative data, adversarial prompts, retrieval augmentation, integration of external knowledge bases, development of methods to mitigate bias and ensure transparency, aligning models with human values ('Law Informs Code'), evolving legal frameworks, interdisciplinary collaboration. | Enhanced AI Capabilities, Data Curation and Management, Prompt Engineering and LLM Interaction Design, Bias Detection and Mitigation, Transparency and Explainability in AI, Regulation, Ethics, and Governance, Policy and Regulatory Reform, Open Source Initiatives and Collaboration | Legal text processing and understanding, legal case retrieval and analysis, legal education and examinations, legal practice assistance, dispute resolution, legal advice provision, enhancing accessibility to legal knowledge. | Improving Foundational AI Capabilities for Legal Applications, Access to Legal Information, LegalResearch Support, Legal Education for Professionals / Students, Dispute Resolution, Access to Legal Advice | NaN | NaN | General/Multiple (including criminal law, constitutional law, contract law, tort law, tax law, privacy law, parliamentary procedure) | General Law, Multiple Fields, Criminal Law, Constitutional Law, Contract Law, Tort Law, Tax Law, Data Privacy Law, Parliamentary Procedure | Multiple (including China, Taiwan, Palestine, France, US, UK, EU, CoE, Canada, India) | China, Taiwan, Palestine, France, USA, UK, EU, Council of Europe, Canada, India | Surveys works using various legal datasets (e.g., CAIL2018, LeCaRD, Pile of Law, LeXFiles, CaseHOLD, Cambridge Law Corpus, MultiLegalPile) from multiple jurisdictions and languages, including court cases, legislation, contracts, Q&A. | Data From Existing Public NLP/Legal Datasets/Benchmarks, Legal Domain Data, Case Law / Judgments, Legislation / Statutes / Regulations, Legal Contracts, Legal Q&A / Forum / User Query Data, Multilingual Data | NaN | NaN | NaN | Not applicable | True | True | The survey provides GitHub repository links for several specific fine-tuned LLMs it reviews (e.g., LawGPT, Lawyer LLaMA, LexiLaw, ChatLaw, DISC-LawLLM). The survey paper itself is available on arXiv. | Model available, Code available, Open access resource | Need for further mitigation of biases, enhanced interpretability, development of specialized data resources (especially multilingual), establishment of ethical guidelines, improved robustness and reliability for legal tasks, better handling of complex legal reasoning and causality, improved performance on benchmarks (e.g., LexGLUE), advanced multimodal capabilities. | Bias in AI, Transparency and Explainability, Data Availability and Quality, Multilingual and Low-Resource Language Gaps, Ethical Framework Deficiencies, AI Accuracy and Reliability, AI Legal Reasoning Limitations, Research and Evaluation Gaps, AI Scope and Functionality Limitations | Need for domain-specific data/training, preventing hallucination and ensuring factual accuracy (necessitating retrieval augmentation/human oversight), adapting general models to specialized legal tasks efficiently, addressing inherent biases in models and data, evaluating performance accurately in complex legal scenarios, ensuring transparency and interpretability. | Scarcity of High-Quality Legal Data, Domain-Specific Adaptation and Customization, LLM Hallucination and Factual Errors, Accuracy and Reliability of LLM Output, Need for Human Oversight and Intervention, Bias in AI Systems and Data, Evaluation Challenges and Metrics, Transparency and Explainability of AI | Privacy violations, perpetuation of societal biases (racial, gender, religious, etc.), generation of inaccurate or misleading information (hallucination), lack of genuine understanding leading to errors, potential for misuse in generating false evidence or overwhelming legal systems, undermining judicial integrity and fairness, threats to fundamental human values (autonomy, equality). | Data privacy and security breach, Bias and discrimination, Inaccurate or misleading AI output, Technical limitations of AI, Security vulnerabilities or malicious misuse, Undermining legal process or principles, Infringement on human rights |
| T4UCpfvU-usJ.pdf | Google_Scholar | laws clearly: large language models and plain language transformation | This paper investigates the capability of OpenAI's GPT-4 large language model to automatically transform complex Hungarian legal texts into plain language to improve access to legal information. The study manually evaluates the model's performance on specific linguistic simplification tasks, assessing both comprehensibility improvements and the preservation of legal meaning. | LLM Evaluation, Legal Text Simplification, Hungarian Law Focus, Access to Legal Information Enhancement, Manual Evaluation, Preservation of Legal Meaning | True | Idealistic | True | 2.0 | Neutral | Using GPT-4 with specifically crafted prompts to perform plain language transformations on legal text excerpts. | Large Language Model, Prompt Engineering, Legal Text Simplification | Manual analysis of GPT-4 outputs based on four specific linguistic features (avoiding long/interjected clauses, replacing light verb constructions, splitting long sentences, clarifying ambiguous conjunctions like 'illetve'). Evaluation focused on prompt adherence and preservation of normative legal content. | Qualitative Analysis | GPT-4 showed mixed performance. While promising for simplifying sentence structures (clause shortening, sentence splitting), it struggled to accurately replace light verb constructions (potentially due to internal translation issues altering meaning) and incorrectly interpreted the conjunction 'illetve', changing the legal meaning from 'or' to 'and'. Normative legal content was altered in almost all tested cases. | Mixed performance, Limitation: Operational or Technical | The complexity, specialized terminology, and convoluted sentence structures inherent in legal language (legalese) prevent citizens from understanding legal texts and representing themselves effectively. | Complexity of Legal Language/Documents, Challenges for Self-Represented Litigants | Leveraging Large Language Models (specifically GPT-4) to automatically simplify complex legal texts into more understandable plain language versions for laypeople. | Language Simplification and Multilingual Access, AI Tool Development | Access to legal information, Comprehensibility of legal texts, Plain language transformation | Access to Legal Information, Legal Text Simplification / Plain Language | Laypeople / citizens without legal expertise. | Laypeople, General public, Individuals lacking legal knowledge | Land Transaction Law (specifically Act CXXII of 2013 on Transactions in Agricultural and Forestry land) | Property Law, Agricultural Law | Hungary | Hungary | The study utilizes the pre-trained GPT-4 model from OpenAI; details of its training data are proprietary but known to be vast text corpora. | Pre-trained LLM's General Training Corpus, Proprietary Data, General Web Data / Broad Internet Text | Experimental approach using prompt engineering to guide GPT-4, followed by manual qualitative analysis of the generated text. | Experimental Design, Prompt Engineering, Qualitative Data Analysis | NaN | Not applicable | False | False | NaN | NaN | Current LLMs like GPT-4 are unsuitable for fully automatic plain language paraphrasing of legal texts due to the high risk of altering normative content. The task still requires significant human legal expertise and oversight. | AI Accuracy and Reliability, AI Legal Reasoning Limitations, Human Oversight and Professional Adaptation | Ensuring the preservation of normative legal content during simplification; potential misinterpretation of prompts or linguistic nuances by the LLM (e.g., function verbs, conjunctions); issues arising from the model's internal processing/translation for non-English languages. | Accuracy and Reliability of LLM Output, LLM Reasoning Capabilities, Prompt Engineering and Optimization, Multilingual and Low-Resource Language Support | The potential alteration or violation of the normative legal content during automatic simplification, leading to misinterpretations of the law by citizens relying on the simplified text. | Inaccurate or misleading AI output, Consumer harm |
| fyLCMIyr3Q4J.pdf | Google_Scholar | Generative AI, Cybersecurity And Cybercrime For Lawyers: Myths, Risks And Benefits | This paper discusses the historical context, risks (security, privacy, legal), and benefits of Generative AI for legal professionals, focusing on its implications for cybersecurity, cybercrime, and enhancing access to justice. It aims to debunk myths about AI replacing lawyers while highlighting its potential to improve efficiency, fairness, and reduce backlogs within the legal system if implemented responsibly. | Discussion of Generative AI in Law, Risk Identification, Benefit Identification, Cybersecurity Implications, Access to Justice Enhancement, Efficiency Improvement, Responsible AI Implementation | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | Suboptimal access to justice, especially for socially vulnerable groups, racial or ethnic minorities; Sluggish, expensive, and operationally inefficient legal and judicial systems leading to wrongful convictions and miscarriages of justice; Overloaded public defenders; Judicial system backlogs due to mounting cases and overcriminalization, impacting the quality and fairness of due process. | Unequal Access to Legal Services, Systemic Inequities in Justice System, Judicial/Legal System Inefficiencies, High Cost of Legal Services, Resource Constraints for Legal Aid Organizations, Overcriminalization | Properly implemented GenAI systems to streamline litigation and reduce judicial bottlenecks; AI tools for lawyers to summarize cases and assemble relevant information from diverse legal documents; AI assistance for lawyers, court clerks, and judges in prioritizing and summarizing case content; AI use by prosecutors to predict conviction chances (with safeguards against bias) for better resource allocation. | AI Tool Development, Judicial System Enhancement, Cost Reduction and Efficiency, Document Automation, Legal Research and Analysis Tools, Human Oversight and Collaboration, Bias Detection and Mitigation | Improving efficiency of legal and judicial systems; Reducing wrongful convictions and miscarriages of justice; Aiding overloaded public defenders; Streamlining litigation and case management for lawyers, judges, and prosecutors; Addressing judicial backlogs. | Improving Efficiency in Legal System / Profession, Judicial System Modernization / Efficiency, Protection of Rights, Legal Aid and Pro Bono Services | Socially vulnerable groups, racial or ethnic minorities, indigent defendants in criminal cases. | Vulnerable populations, Minority groups, Indigent criminal defendants | Criminal law, General legal practice, Cybersecurity law, Data protection law. | Criminal Law, General Legal Practice, Cyber Law, Data Privacy Law | US, UK, EU, Switzerland | USA, UK, EU, Switzerland | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Resolving AI hallucinations; Ensuring human oversight in AI-generated legal content; Developing effective guardrails for AI use in law firms; Addressing and mitigating AI bias, especially in criminal justice predictions to uphold principles like presumption of innocence; Technical limitations in managing personal data within AI models (e.g., deletion requests). | AI Accuracy and Reliability, Human Oversight and Professional Adaptation, Ethical Framework Deficiencies, Regulatory and Governance Gaps, Bias in AI, Security and Privacy of Data | Ensuring data security and confidentiality when using third-party AI tools or training proprietary models; Compliance with evolving data protection and AI regulations (e.g., EU AI Act, DSRs under privacy laws); Preventing copyright infringement when using data for AI training; Overcoming the technical difficulty of removing specific data from trained AI models; Dealing with AI-generated misinformation (hallucinations) and ensuring outputs are reviewed by legal professionals; Protecting against cyber-attacks targeting AI systems, such as data poisoning. | Data Privacy, Security, and Confidentiality, Regulatory Uncertainty and Compliance, Copyright and Intellectual Property Issues, LLM Hallucination and Factual Errors, Need for Human Oversight and Intervention, Safeguarding Against Misuse and Harm | Disclosure of confidential client information through AI systems; Legal liability and sanctions for lawyers relying on inaccurate AI-generated content (e.g., fake case law); Copyright infringement issues related to AI training data and outputs; Increased vulnerability to sophisticated cyber-attacks like deep fakes and data poisoning targeting AI; Perpetuation or amplification of biases through AI systems, particularly in criminal justice, leading to unfair outcomes or infringement of rights; Misuse of AI for malicious activities such as creating convincing phishing content or impersonation. | Data privacy and security breach, Lack of transparency, accountability, and redress, Ethical concerns, Inaccurate or misleading AI output, Copyright or intellectual property issues, Security vulnerabilities or malicious misuse, Bias and discrimination, Infringement on human rights |
| YimleaMoY5QJ.pdf | Google_Scholar | Towards Human-Centered Standards for Legal Help AI | This paper presents findings from interviews and design sessions with community members on their use of large language model-based AI tools (like Google Bard) for legal problems, specifically an eviction scenario. It highlights user preferences, trust factors, and concerns, advocating for participatory, human-centered approaches to design and policymaking for legal AI to enhance access to justice. | User Study of LLMs for Legal Problems, Eviction Scenario Focus, User Preferences, Trust in AI, Human-Centered Design Advocacy, Access to Justice Enhancement, Participatory Design | True | Idealistic | True | 2.0 | Positive | Users interacting with Google Bard (a large language model) for a fictional legal problem (eviction notice) as part of a research study. | User Study / Field Study, Large Language Model, Legal Problem Solving Simulation | Qualitative research study with 15 US adults involving: 1) background questions, 2) a scenario exercise using Google Bard for an eviction notice, 3) feedback/brainstorming. Data collected via online interviews with structured and open-ended questions. | User Study or Survey, Qualitative Analysis | Participants generally found Bard helpful (average rating 3.6/6), and trust in the AI tool increased after use (from an average of 2.7/6 to 4.2/6). Key desires included hyperlinks/citations for information, features like "People Also Ask," and simple responses with options for more detail; reactions to prominent warnings were mixed to negative. | Moderate performance, Benefit identified | General public's lack of awareness that life problems may have a legal dimension; inability to resolve problems via the formal justice system due to lack of capacity or limited help. For AI: risk of providing incorrect legal information, AI tools becoming a second-class service, and inequitable access due to digital divide or literacy barriers. | Public Lack of Legal Knowledge/Awareness, Limited Access to Legal Assistance, AI Unreliability/Inaccuracy, Risk of AI Exacerbating Inequality, Digital Divide, Lack of AI Literacy | Adopting human-centered design and participatory policy-making involving community members in AI development. Designing AI tools that are user-friendly, provide clear and actionable information, and incorporate safeguards. Specific suggestions include better referral systems, guardrails against case law hallucinations, jurisdiction-specific information, and prominent links to reliable human help. | User Interface and Accessibility Design, Policy and Regulatory Reform, AI Tool Development, Access to Legal Information and Advice, Regulation, Ethics, and Governance, Open Source Initiatives and Collaboration | Access to civil justice, specifically for issues like evictions. Use of AI for legal issue spotting, triage, guidance on options, finding free assistance, and understanding legal-procedural steps. | Democratizing Law / Closing Justice Gap / Rule of Law, Access to Legal Advice, Legal Aid and Pro Bono Services, Legal Literacy and Public Legal Education | General community members in America who have faced civil legal problems and might use AI for legal help. The study sample was a convenience sample with some demographic limitations. | General public, Population in USA, Individuals with civil legal problems | Civil justice, with a specific focus on landlord-tenant law (eviction). Also mentions debt collection, family law (divorce, custody), and employment law. | Civil Justice, Landlord-Tenant Law, Housing Law, Debt Collection, Family Law, Employment Law | United States (participants from California, New York, Maryland, New Jersey; scenario included elements like 'Alameda Eviction laws'). | USA | NaN | Not Applicable | Qualitative research methods derived from design research, participatory policymaking, and human-computer interaction. Scenario-based research protocol involving structured interviews, observation of AI tool use (Google Bard), and co-design discussions. | Qualitative Research Methods, Design Research Methodology, Participatory Design, Human-Computer Interaction (HCI) Methods, Scenario-based Research, User Observation, Co-design | NaN | Not applicable | True | False | The study used Google Bard, which is a publicly accessible web service provided by Google. | Publicly accessible online tool or platform | Need for more extensive and ongoing research with representative samples. Development of a comprehensive risk typology for legal AI. Creation of interface and technical solutions to mitigate specific harms like 'ersatz legal help' (correct-seeming but flawed information). Understanding how to design effective disclosures and warnings that users engage with meaningfully. | Research and Evaluation Gaps, Ethical Framework Deficiencies, User Interface and Usability Gaps, Consumer Protection Gaps, Public Understanding, Trust, and Adoption | For the study: limitations of a convenience sample (underrepresentation of certain demographics). For legal AI in general: ensuring accuracy and reliability of AI-generated legal information (avoiding hallucinations, providing context, jurisdictional accuracy); user over-reliance on AI; designing interfaces that meet diverse user needs and literacy levels; balancing simplicity with the complexity of legal matters and necessary warnings. | Research Methodology and Study Design Limitations, Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Outdated or Limited LLM Knowledge Base, User Adoption, Trust, and Acceptance, User Interface, Usability, and Accessibility, User Training, AI Literacy, and Skill Gaps | AI providing incorrect legal information (hallucinations, e.g., non-existent case law). Users misapplying information due to lack of context or jurisdictional errors. AI tools becoming a 'second-class' service. Inequitable access due to digital divide or varying tech literacy. Data privacy concerns (over-harvesting data). Users over-relying on AI without verification. 'Ersatz legal help' leading to poor outcomes (e.g., bad referrals, cherry-picking details). | Inaccurate or misleading AI output, Consumer harm, Risk of misapplication or misuse, Exacerbation of inequality or two-tiered system, Data privacy and security breach, Over-reliance on AI |
| I6Ful7p1yP0J.pdf | Google_Scholar | ARTIFICIAL INTELLIGENCE, ETHICS AND SPEED PROCESSING IN THE LAW SYSTEM | This paper reviews how generative AI can enhance the Brazilian Judiciary's efficiency by automating tasks and aiding sentence generation, exemplified by tools like VitorIA and Victor. It highlights the importance of embedding ethical considerations in AI to ensure fair, accessible, and non-discriminatory justice. | Review of Generative AI in Judiciary, Brazilian Judiciary Focus, Efficiency Improvement, Task Automation, Sentence Generation Aid, Ethical Considerations, Fairness in AI, Access to Justice Enhancement | True | Idealistic | True | 2.0 | Positive | Generative AI applications in the Brazilian Judiciary, specifically VitorIA (appeal profiling/binding) and Victor (appeal admissibility analysis). | Generative AI, AI Application in Judiciary, Legal Text Analysis, Named Tool / Platform | Qualitative review of secondary data and documentary evidence concerning the functionalities and operational impact of existing systems (VitorIA, Victor) in the Brazilian Judiciary. | Qualitative Analysis, References External Evaluation | Generative AI significantly expands judicial operational capacity by automating tasks and aiding sentence generation, leading to improved decision-making, effective legal strategies, and enhanced overall judicial efficiency. | Benefit identified, Descriptive or Conceptual finding | Risk of algorithmic bias leading to unfair/discriminatory outcomes; slowness and case overload in traditional judicial systems; high operational costs; complexity of ensuring ethical AI judgments, especially in heterogeneous societies. | Bias in AI/Data, Judicial/Legal System Inefficiencies, High Cost of Legal Services, Ethical Concerns with AI in Law, AI Limitations in Ethical Judgment | Use generative AI to automate tasks for speed and cost reduction; embed ethical standards in AI design for fairness; free human judges for complex ethical considerations; promote extrajudicial resolution for simpler cases identified by AI. | AI Tool Development, Cost Reduction and Efficiency, Regulation, Ethics, and Governance, Human Oversight and Collaboration, Judicial System Enhancement, Online Dispute Resolution (ODR) | Improving judicial efficiency (speed, cost); enhancing access to justice; ensuring fairness and reducing discrimination; supporting judicial decision-making and sentence generation; ethical application of AI in law. | Judicial System Modernization / Efficiency, Democratizing Law / Closing Justice Gap / Rule of Law, Ethical AI in Law and AI Governance | Society at large; specific challenges noted for heterogeneous societies (e.g., Brazil's indigenous populations) regarding ethical AI. | General public, Indigenous populations, Population in Brazil | General judicial processes and litigation. | General Law, Judicial Processes, Litigation | Brazil | Brazil | Not explicitly detailed, but implied to be case files, appeals, and jurisprudential databases from the Brazilian Federal Supreme Court for tools like VitorIA and Victor. | Legal Domain Data, Brazilian Legal Data, Case Law / Judgments, Other Legal Documents, Undisclosed Data Source/Availability | N/A (Paper discusses existing tools, does not detail their specific design methodologies beyond Victor being developed by STF's IT staff). | NaN | Deployed within the Brazilian Federal Supreme Court (STF) for internal use (e.g., VitorIA for appeal analysis, Victor for admissibility checks). | Government/Public institution deployment, Internal deployment/prototype | False | False | NaN | NaN | Teaching AI nuanced social values and ethical behaviors for sentencing; developing AI for ethical complexities in heterogeneous societies; current AI's inability to handle all circumstantial/mitigating factors like humans; need for AGI for more complex judicial tasks. | Ethical Framework Deficiencies, AI Legal Reasoning Limitations, AI Scope and Functionality Limitations | Ensuring ethical considerations, neutrality, and avoiding bias in AI for judicial tasks; defining and embedding ethical standard value criteria; adapting AI for culturally heterogeneous societies; balancing efficiency with human oversight. | Ethical Considerations, Bias in AI Systems and Data, Domain-Specific Adaptation and Customization, Need for Human Oversight and Intervention | Algorithmic bias leading to discriminatory or unfair sentences; doctrinal bias in AI-processed information; unjust punishment due to lack of nuanced human judgment; creation of legal uncertainty. | Bias and discrimination, Dehumanization of legal process, Undermining legal process or principles |
| BuN0HcT9T0sJ.pdf | Google_Scholar | Regenerating Justice: ChatGPT and the Legal Minefield of Generative AI | This paper critically examines Generative AI (GenAI), particularly systems like ChatGPT, and its profound implications for the legal field. Adopting an automation bias lens, it argues that unthinking reliance on GenAI risks undermining law's truth-seeking functions and core epistemic foundations through the propagation of inaccurate and sourceless information. | Critique of Generative AI in Law, Automation Bias, Risk to Legal Epistemology, AI Hallucinations/Inaccuracy | True | Idealistic | True | 2.0 | Negative | Generative AI / Large Language Models (specifically GPT models like ChatGPT) | Generative AI, Large Language Model | Theoretical analysis using an automation bias lens, literature review, and examination of real-world incidents and GenAI capabilities (e.g., hallucinations, performance claims). | Theoretical Analysis or Conceptual Proposal, Qualitative Analysis | GenAI fundamentally threatens legal truth-seeking and epistemic integrity due to inherent issues like hallucinations and sourceless information, compounded by human automation bias. | Risk or Ethical concern highlighted, Limitation: Hallucination or Factual inaccuracy | Misinformation and hallucinations from AI leading to incorrect legal guidance; lack of accountability for AI-provided advice; erosion of trust if AI is unreliable/biased; entrenchment of biases from training data; ethical issues (unlicensed practice of law, loss of solicitor-client privilege); over-reliance due to automation bias. | AI Unreliability/Inaccuracy, AI-driven Misinformation/Disinformation, Lack of AI Accountability, Lack of Trust in AI/Automated Systems, Bias in AI/Data, Ethical Concerns with AI in Law, Regulatory Hurdles, Erosion of Legal Professional Standards, Automation Bias | Enhanced critical thinking and awareness of AI limitations (automation bias, inherent nature of hallucinations); robust human oversight (while acknowledging its limits); caution in deploying AI, especially solutions that obviate human participation in legal reasoning and storytelling. | Education and AI Literacy, Human Oversight and Collaboration, Regulation, Ethics, and Governance | Automated legal advice for consumers/self-represented litigants; consumer protection in automated legal services; reliability and trustworthiness of AI tools for those unable to afford traditional legal services. | Access to Legal Advice, Support for Self-Represented Litigants, Protection of Rights, Ethical AI in Law and AI Governance, Affordability of Legal Services / Cost Reduction | Individuals unable to afford legal services; self-represented litigants (especially with low-value claims); consumers seeking rights protection. | Individuals unable to afford legal services, Self-represented litigants, Litigants with low-value claims, Consumers | Legal Practice, Legal Ethics, Consumer Law, Contract Law, Civil Procedure, Copyright Law. | General Legal Practice, Legal Ethics, Consumer Law, Contract Law, Civil Procedure, Copyright Law | International / Multiple (primarily US and Canada examples, but broadly applicable concerns) | International, USA, Canada | Vast quantities of text scraped from publicly accessible internet sites (e.g., websites, social media, digital books like BooksCorpus, Wikipedia), largely unlabelled and collected via webcrawling bots. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Publicly Available Data, Web Scraped Data | Machine learning (supervised, unsupervised, reinforcement learning), transformer architecture, pre-training on large unlabelled text datasets, fine-tuning for specific tasks like dialogue. | Machine Learning Model Development, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Transformer Architecture, Model Pre-training, Model Fine-tuning | Publicly accessible web interfaces (often with free tiers), APIs for developers, beta releases for public testing, integration into existing software products. | Evaluation of existing third-party tool, Web-based access, Freely accessible tool/service, API access, Research preview/Beta access, Integration into existing system/platform | True | True | Publicly accessible web interfaces (e.g., ChatGPT free tier) and APIs. Some models (e.g., Meta's Llama) are stated to be open-source and downloadable. | Publicly accessible online tool or platform, API access, Model available, Open-source | Technical: Inherent unreliability (hallucinations, factual inaccuracies, lack of true reasoning). Societal/Legal: Absence of robust legal/ethical frameworks for AI in law, accountability vacuum, risk of exacerbating inequalities, erosion of solicitor-client privilege, public over-trust and misunderstanding of AI capabilities. | AI Accuracy and Reliability, AI Legal Reasoning Limitations, Ethical Framework Deficiencies, Regulatory and Governance Gaps, Accountability and Redress Mechanisms, Access, Equity, and Digital Divide, Security and Privacy of Data, Public Understanding, Trust, and Adoption | Technical: Managing massive datasets, reducing hallucinations (though seen as inherent), addressing bias in training data, ensuring factual accuracy, resolving tokenization issues. Ethical/Societal: Preventing misuse (e.g., disinformation), managing copyrighted material in training, ensuring safety and avoiding harmful or biased outputs. | Data Quality, Processing, and Preparation, LLM Hallucination and Factual Errors, Bias in AI Systems and Data, Accuracy and Reliability of LLM Output, Safeguarding Against Misuse and Harm, Copyright and Intellectual Property Issues, Ethical Considerations | Undermining law’s truth-seeking functions with sourceless/incorrect information; automation bias leading to over-reliance on flawed AI; erosion of legal meaning and narrative; spread of misinformation; ethical violations by legal professionals; harm to individuals relying on faulty AI advice; entrenchment of societal biases; threats to privacy and solicitor-client privilege. | Inaccurate or misleading AI output, Over-reliance on AI, Dehumanization of legal process, Ethical concerns, Consumer harm, Bias and discrimination, Data privacy and security breach, Undermining legal process or principles |
| EA5UKSipTqkJ.pdf | Google_Scholar | Are Robot Lawyers the Future of Increasing Access to Justice? | The paper discusses the potential of AI-powered legal tools ("robot lawyers") to improve access to justice by providing affordable information and self-help options. It also highlights risks like exacerbating inequalities and excluding vulnerable populations if not developed responsibly. | AI Legal Tools, Access to Justice Enhancement, Affordable Legal Information, Self-Help Tools, Risk Identification, Exacerbating Inequalities, Exclusion of Vulnerable Populations, Responsible AI Development | True | Idealistic | True | 3.0 | Neutral | AI-powered legal information and self-help tools (e.g., AdviceNow, Farewill, Valla, Amicable) | AI Legal Tool, Legal Information Provision, Self-help Legal Tool, Named Tool / Platform | N/A (No specific evaluation performed by the author; cites tool provider claims) | No Evaluation by Author, Developer Claims Reported | N/A (No independent results reported; cites provider claims) | N/A, Developer or Vendor claim | Digital exclusion (affecting elderly, non-English speakers, digitally illiterate), varying digital/legal capabilities, lack of access to devices/digital literacy. | Digital Divide, Accessibility Barriers for Specific User Groups, Lack of AI Literacy | Responsible development, diverse training data, auditing/testing AI, Assisted Digital services, leveraging AI to free up human advisors for vulnerable clients. | Regulation, Ethics, and Governance, Data Curation and Management, Benchmarking and Evaluation Frameworks, Human Oversight and Collaboration, Support for Self-Represented Litigants | Access to legal information, self-representation tools, cost reduction in legal services, specific issues like benefits challenges, wills, employment claims, divorce. | Access to Legal Information, Support for Self-Represented Litigants, Affordability of Legal Services / Cost Reduction, Legal Document Creation / Automation | General public needing legal assistance, with specific concern for vulnerable groups (elderly, non-English speakers, digitally excluded, marginalized populations). | General public, Individuals with unmet legal needs, Vulnerable populations, Elderly people, Individuals with language barriers, Digitally excluded populations, Marginalized communities | Family law, Wills & Estates, Welfare Benefits, Employment Law, Civil Procedure. | Family Law, Wills and Estates, Social Security Law, Employment Law, Civil Procedure | UK, USA (mentioned briefly) | UK, USA | N/A (Mentions the need for diverse data but doesn't describe data used by specific tools). | Not Applicable | NaN | NaN | Online websites/platforms, integration into government digital justice services. | Evaluation of existing third-party tool, Web-based access, Government/Public institution deployment, Integration into existing system/platform | True | False | Online services (some free guidance/tools, some paid). | Publicly accessible online tool or platform, Commercial product or service, Freemium access | Ensuring equitable access, preventing digital exclusion, mitigating AI bias, ensuring tools accommodate varying needs and capabilities. | Access, Equity, and Digital Divide, Bias in AI, User Interface and Usability Gaps | Designing effective/accurate tools, addressing digital literacy/access issues, ensuring fairness/avoiding bias, integrating with existing legal systems. | Accuracy and Reliability of LLM Output, User Training, AI Literacy, and Skill Gaps, User Interface, Usability, and Accessibility, Bias in AI Systems and Data, Integration with Existing Systems and Workflows | Amplifying existing inequalities (racial, gender, socioeconomic, geographic bias), digital exclusion, inaccurate AI outputs. | Bias and discrimination, Exacerbation of inequality or two-tiered system, Inaccurate or misleading AI output |
| oZsXj4Vs990J.pdf | Google_Scholar | Prof Felix Steffek November 2024 PRESENTATIONS | This document is a list of academic presentations and convened conferences by Prof Felix Steffek, covering topics primarily in AI in law, corporate insolvency, dispute resolution, and access to justice. Several presentation titles highlight AI for legal tasks like court outcome prediction and the development of legal datasets such as the Cambridge Law Corpus. | Academic Output Summary (Presentations), AI in Law Topics, Access to Justice Topics, AI for Court Outcome Prediction, Legal Dataset Development | True | Idealistic | True | NaN | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | NaN | NaN | NaN | NaN | Application of AI to dispute resolution for access to justice, Online Dispute Resolution (ODR), consumer dispute resolution (ombuds proceedings, conciliation), people-centered justice services. | Dispute Resolution, Democratizing Law / Closing Justice Gap / Rule of Law, Protection of Rights | Consumers, Small and Medium-sized Enterprises (SMEs) | Consumers, Small businesses | Corporate Insolvency Law, Employment Law, Dispute Resolution, Civil Procedure Law, Consumer Law, Company Law, Private Law, Commercial Law | Bankruptcy Law, Employment Law, Dispute Resolution, Civil Procedure, Consumer Law, Corporate Law, Private Law, Commercial Law | UK, Singapore, Latvia, Germany, EU, Japan, US, Hong Kong, International | UK, Singapore, Latvia, Germany, EU, Japan, USA, Hong Kong, International | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| assyXFv39zkJ.pdf | Google_Scholar | The Cost of Justice at the Dawn of AI | This paper examines the historical and potential future impact of legal service costs, particularly in light of AI, on the legal system, including access to justice and trial rates. It analyzes whether law suffers from 'cost disease' and urges the legal system to proactively adapt its doctrines and procedures to either continued cost stagnation or an AI-driven productivity revolution. | Impact of Legal Costs on Justice System, AI's Role in Legal Costs, Access to Justice Enhancement, Productivity in Legal System, Call for System Adaptation | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | High cost of legal services, perceived stagnation in legal sector productivity (cost disease), leading to diminished access to justice, the 'vanishing trial' phenomenon, and difficulties for individuals to afford legal representation. | High Cost of Legal Services, Inefficiency in Legal Sector, Scale of Unmet Legal Need | Proactive adaptation of legal doctrines and procedures to explicitly incorporate and respond to changes in legal costs (e.g., in summary judgment, class actions, contracts of adhesion, arbitration, rules vs. standards). AI itself is presented as a potential solution to lower costs and thereby improve access to justice and potentially revive trials. | Policy and Regulatory Reform, Cost Reduction and Efficiency, AI Tool Development | Cost of legal services, access to legal representation (especially for those with limited means), efficiency of the civil and criminal justice systems, trial rates, plea bargaining, summary judgment, class actions, rule of law, impact of technology on the legal profession. | Affordability of Legal Services / Cost Reduction, Access to Legal Representation, Improving Efficiency in Legal System / Profession, Judicial System Modernization / Efficiency, Democratizing Law / Closing Justice Gap / Rule of Law | The general public, particularly individuals with limited financial means and underrepresented groups who face barriers to accessing legal services due to high costs. | General public, Low-income individuals, Underrepresented groups, Individuals unable to afford legal services | General civil litigation, criminal justice, contract law, administrative law, constitutional law (due process), intellectual property (as an example). | Civil Litigation, Criminal Justice, Contract Law, Administrative Law, Constitutional Law, Intellectual Property Law | United States (federal and state systems), with brief comparative mentions of the United Kingdom and Ontario (Canada) regarding trial rates. | USA, UK, Canada | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Persistent difficulty in accurately measuring legal productivity and service quality; technical limitations of current AI (e.g., reasoning depth, context limits, hallucination); potential exhaustion of high-quality AI training data; societal and professional inertia in adapting legal systems and practices to technological change and varying cost structures; uncertainty regarding the elasticity of demand for legal services and AI's impact on lawyer employment/wages. | Research and Evaluation Gaps, AI Legal Reasoning Limitations, AI Accuracy and Reliability, Data Availability and Quality, Human Oversight and Professional Adaptation, Computational Resource and Cost Issues | NaN | NaN | AI-driven efficiencies in criminal justice leading to harsher, unintended sentencing outcomes; continued cost stagnation exacerbating access to justice problems; lower legal costs due to AI causing undesirable overenforcement or frivolous litigation in some areas; potential for increased wage inequality among lawyers; ethical challenges and errors from AI use (e.g., hallucinations, lack of human judgment). | Undermining legal process or principles, Bias and discrimination, Negative economic impact, Exacerbation of inequality or two-tiered system, Ethical concerns, Inaccurate or misleading AI output, Dehumanization of legal process |
| informit.T2025011900000390025191863.pdf | Google_Scholar | Introduction: Law as Data, Data as Law | This paper introduces a symposium on "Law as Data, Data as Law," summarizing diverse contributions that analyze data-driven approaches and AI in law. It emphasizes the need for critical reflection, methodological rigor, and interdisciplinary engagement to navigate impacts on legal practice, education, and access to justice. | Symposium Introduction, Data-Driven Approaches in Law, AI in Law, Critical Reflection on AI, Interdisciplinary Engagement, Impact on Legal Practice, Impact on Legal Education, Access to Justice Enhancement | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | Unreliability and inaccuracy of current AI tools for complex legal tasks, potential for algorithmic bias and mismatches with legal reasoning principles in sensitive areas like asylum claims, and the risk of creating opaque systems that hinder rather than help justice. | AI Unreliability/Inaccuracy, Bias in AI/Data, AI Limitations in Legal Reasoning/Nuance, Lack of AI Transparency/Explainability | Enhancing AI reliability through further research, ensuring human oversight and expert legal involvement in AI system design and deployment, fostering interdisciplinary dialogue and critical interrogation of AI tools, and adopting human-centered design methodologies that incorporate stakeholder input. | Enhanced AI Capabilities, Human Oversight and Collaboration, Open Source Initiatives and Collaboration, User Interface and Accessibility Design, Regulation, Ethics, and Governance | Automated decision-making in refugee status determination and its fairness; reliability of LLMs for legal tasks crucial for accessing legal information or support; ethical integration of AI in legal education to prepare future professionals for promoting access to justice. | Ethical AI in Law and AI Governance, Protection of Rights, Support for Vulnerable Populations, Access to Legal Information, Legal Education for Professionals / Students | Asylum seekers/Refugees | Asylum seekers and refugees | Administrative Law (specifically refugee/asylum law), Legal Education, General Legal Practice (research, reasoning) | Administrative Law, Immigration Law, Legal Education, General Legal Practice | International | International | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Methodological gaps in legal research for evaluating data-driven law, lack of robust benchmarks for AI legal tools, need for deeper understanding of AI's societal impacts (bias, fairness, environmental costs), and insufficient interdisciplinary collaboration and expertise within the legal academy. | Research and Evaluation Gaps, Bias in AI, Environmental Impact Concerns, Need for Interdisciplinary Collaboration, Human Oversight and Professional Adaptation | Synthesizing diverse and technical contributions from various disciplines, evaluating research outside traditional legal expertise, and fostering a coherent, critical dialogue on the complex and rapidly evolving field of law and AI. | Interdisciplinary Collaboration Challenges, Evaluation Challenges and Metrics, Research Methodology and Study Design Limitations | Fossilization of law into opaque and difficult-to-challenge infrastructures, perpetuation of harmful bias and feedback loops through AI systems, negative social and environmental consequences of AI, uncritical adoption of AI tools by students and practitioners, and adverse transformations to legal processes if new technologies are not carefully vetted and implemented. | Lack of transparency, accountability, and redress, Bias and discrimination, Negative societal impact, Environmental impact, Over-reliance on AI, Risk of misapplication or misuse, Undermining legal process or principles |
| OpPoPkNx0W4J.pdf | Google_Scholar | Intelligent Legal Assistant: An Interactive Clarification System for Legal Question Answering | This paper introduces 'Intelligent Legal Assistant', an interactive legal question-answering system using large language models (LLMs). The system addresses incomplete user queries by asking for location, detecting missing information, generating clarifying questions with options, and then providing a detailed legal analysis. | System Development, AI Legal Assistant Development, Interactive Legal Question Answering, LLM Application, Clarifying Question Generation, Legal Analysis Provision | True | Idealistic | True | 1.0 | Positive | An interactive legal Q&A system ('Intelligent Legal Assistant') using LLMs (Llama-3.1-8B, GPT-4o) for information deficiency detection, Reinforcement Learning (DDPG) with GNNs for predicting missing information elements (nodes in a fact-rule graph), and LLMs/retrieval models for generating clarifying questions/options and final responses. | Legal Question Answering, AI Legal Assistant, Large Language Model, Reinforcement Learning, Graph Neural Network (GNN), Information Retrieval / Search, Interactive System, Fact-Rule Graph | Blind human evaluation with 100 users comparing the proposed system against GPT-4o, AI Lawyer, and Callidus AI. Users rated systems on accuracy, satisfaction (1-5 scale), and usage preference. | User Study or Survey, Human Evaluation, Comparative Analysis, Quantitative Metrics | The proposed system scored 4.8/5 for accuracy, 4.8/5 for satisfaction, and was preferred by 90% of users, significantly outperforming GPT-4o, AI Lawyer, and Callidus AI. | High performance, Outperforms others, Benefit identified | The general public often lacks professional legal knowledge, leading to incomplete questions that omit critical information, hindering traditional Q&A systems. The complexity and specialized nature of legal terminology and procedures act as barriers. | Public Lack of Legal Knowledge/Awareness, Difficulty in AI-Human Interaction, Complexity of Legal Language/Documents, Complexity of Legal System/Procedures | An interactive LLM-based system that: 1) asks for user location for jurisdiction-specific laws, 2) detects information deficiency in user questions, 3) generates clarifying questions and options to gather missing details, and 4) provides comprehensive legal analysis based on the completed information. | AI Tool Development, User Interface and Accessibility Design, Access to Legal Information and Advice, Prompt Engineering and LLM Interaction Design | Legal question answering, access to legal information/advice. | Access to Legal Information, Access to Legal Advice | The general public, non-specialists who lack financial resources or opportunity to consult lawyers directly. | General public, Laypeople, Individuals unable to afford legal services, Individuals facing access barriers | General (not specified) | General Law | NaN | NaN | Uses case law data processed by GPT-4o to generate questions for fine-tuning Llama-3.1-8B (deficiency detection). Constructs a fact-rule node graph from case law documents parsed via LLM into IRAC structure, used for RL training (missing node prediction). Utilizes a legal document database for retrieval. Sources (public/proprietary) not specified. | Fine-tuning Dataset, Legal Domain Data, Case Law / Judgments, Synthetic Data, Structured Data, RAG System Knowledge Corpus, Undisclosed Data Source/Availability | Information deficiency detection via prompt-based fine-tuning of Llama-3.1-8B. Missing node prediction via Deep Deterministic Policy Gradient (DDPG) reinforcement learning using Graph Neural Networks (GNNs) on a fact-rule graph. Clarifying question/option generation via LLMs using predicted missing nodes. Response generation via retrieval models (text-embedding-3-large, cosine similarity) and LLMs. | Model Fine-tuning, Prompt Engineering, Reinforcement Learning, Graph Neural Network Application, LLM-based Content Generation, Information Retrieval Techniques | A demonstration system is described. A GitHub link is provided for 'more materials'. | Dissemination via publication/presentation, Open source code release, Public dataset/benchmark release | False | False | A GitHub repository containing 'more materials' is mentioned: https://github.com/RujingYao/Intelligent-Legal-Assistant | Code available, Dataset available | NaN | NaN | Implicit challenges include generating varied quality training data (questions), constructing the detailed fact-rule graph, training the reinforcement learning agent effectively for the legal domain, ensuring relevant legal document retrieval, and integrating multiple complex AI components. | Scarcity of High-Quality Legal Data, Data Quality, Processing, and Preparation, Domain-Specific Adaptation and Customization, Accuracy and Reliability of LLM Output, Integration with Existing Systems and Workflows | NaN | NaN |
| 3NLzN6i5MaIJ.pdf | Google_Scholar | Artificial Intelligence & Criminal Justice: Cases and Commentary | This open-access casebook provides a comprehensive exploration of artificial intelligence's integration into the criminal justice system, featuring curated cases, commentary, and policy documents. It examines AI applications in areas like policing, lawyering, access to justice, and AI governance, while critically discussing associated benefits, risks, and ethical considerations. | Open Access Casebook, AI in Criminal Justice, AI Applications (Policing, Lawyering, A2J), AI Governance, Benefit Identification, Risk Identification, Ethical Considerations | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | AI potentially worsening access to justice; biased AI disadvantaging vulnerable groups like self-represented litigants and legal aid recipients; AI-generated misinformation; complexity of AI creating new barriers. | Risk of AI Exacerbating Inequality, Bias in AI/Data, AI-driven Misinformation/Disinformation, Complexity of AI as a Barrier | Promoting AI literacy for all stakeholders; ethical guidelines and professional standards for AI in legal services; human oversight and accountability in AI systems; leveraging AI to empower self-represented litigants; open-access educational resources. | Education and AI Literacy, Regulation, Ethics, and Governance, Human Oversight and Collaboration, Support for Self-Represented Litigants, Open Source Initiatives and Collaboration | Legal aid, self-represented litigants, judicial interim release, mental health disorders and AI. | Legal Aid and Pro Bono Services, Support for Self-Represented Litigants, Judicial System Modernization / Efficiency, Support for Vulnerable Populations | Self-represented litigants, individuals needing legal aid, persons with mental health disorders involved in the justice system, Indigenous communities. | Self-represented litigants, Clients of legal aid organizations, People with mental health conditions, Indigenous populations | Criminal Justice | Criminal Justice | Canada, United States, European Union | Canada, USA, EU | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | True | True | The casebook is available for free and open access via Allard Research Commons and the Canadian Legal Information Institute (CanLII) under a CC BY-NC-ND 4.0 license. | Open access resource | Lack of reliable, unbiased AI tools for access to justice; insufficient frameworks for ethical AI deployment in A2J; digital divide and literacy issues hindering equitable access; need for ongoing research on AI's A2J impact and development of safe tools. | AI Accuracy and Reliability, Bias in AI, Ethical Framework Deficiencies, Access, Equity, and Digital Divide, Public Understanding, Trust, and Adoption, Research and Evaluation Gaps | NaN | NaN | Generation of false/misleading information (hallucinations) by AI; perpetuation of societal biases leading to discriminatory outcomes; threats to privacy and data security; lack of transparency and accountability in AI decision-making; deepfakes and AI-generated misinformation undermining legal processes; AI exacerbating access to justice issues if not implemented equitably; misuse of AI for surveillance; potential for AI to be used for malicious purposes (adversarial AI). | Inaccurate or misleading AI output, Bias and discrimination, Data privacy and security breach, Lack of transparency, accountability, and redress, Security vulnerabilities or malicious misuse, Undermining legal process or principles, Exacerbation of inequality or two-tiered system |
| l4r5s2gwfukJ.pdf | Google_Scholar | Generative AI and Access to Justice in Canada: The Case of Self-Represented Litigants [SRLs] | This article examines the potential benefits and significant limitations of using Large Language Models (LLMs) like ChatGPT for self-represented litigants (SRLs) in the Canadian legal system. It argues that while LLMs can assist SRLs, their effectiveness is limited by factors like accuracy, cost, and the user's literacy, potentially causing more harm than good for those without legal knowledge. | LLMs for Self-Represented Litigants, Benefit Identification, Limitations Identified, Canadian Focus, Accuracy Issues, Cost Issues, User Literacy Impact, Risk of Harm to SRLs | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | High cost of legal representation leading to self-representation; SRLs finding law/litigation difficult; Lack of clear/practical legal information; Need for assistance with forms, drafting, court preparation; Ensuring a 'level playing field'; High cost of bespoke legal AI tools; Reliability/accuracy limitations of LLMs (hallucinations, jurisdictional errors); Lack of legal expertise for SRLs to verify AI output; Over-reliance on inaccurate AI; Basic language/digital literacy gaps; Lack of access to technology/internet. | High Cost of Legal Services, Challenges for Self-Represented Litigants, Difficulty Accessing/Interpreting Legal Information, High Cost of A2J Technology, AI Unreliability/Inaccuracy, Difficulty in AI-Human Interaction, Digital Divide, Accessibility Barriers for Specific User Groups | Using customized LLMs for SRLs; Developing AI tailored to SRL demographics (e.g., form completion); LLM interfaces directing users to verified resources; Calibrating AI reliance; Requiring disclosure of AI use in filings; Enhancing SRL AI literacy; Combining LLM use with existing free legal resources. Creating public AI models mentioned but feasibility questioned. | AI Tool Development, Support for Self-Represented Litigants, Document Automation, User Interface and Accessibility Design, Regulation, Ethics, and Governance, Education and AI Literacy, Open Source Initiatives and Collaboration | Access to legal information; Legal document drafting (pleadings, correspondence); Case preparation; Understanding legal rights and procedures; Facilitating settlement. | Access to Legal Information, Legal Document Creation / Automation, Dispute Resolution, Legal Literacy and Public Legal Education | Self-Represented Litigants (SRLs) in Canada (acknowledged as a diverse group). | Self-represented litigants, Population in Canada | General litigation, Family Law, Civil Procedure | Litigation, Family Law, Civil Procedure | Canada | Canada | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Affordability gap (cost of bespoke AI); Reliability/Accuracy gap (especially with generic LLMs); Literacy gap (digital, legal, AI); Lack of evaluation of everyday SRL use of LLMs; Funding gap between A2J tech and commercial legal tech; Uncertainty about market-driven development addressing SRL needs. | Computational Resource and Cost Issues, AI Accuracy and Reliability, Public Understanding, Trust, and Adoption, Research and Evaluation Gaps, Access, Equity, and Digital Divide | Accuracy/Hallucinations in LLMs; Bias from training data; Limited contextual understanding; Jurisdictional confusion; Cost/Affordability of bespoke tools; Need for user literacy (AI and legal). | Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Bias in AI Systems and Data, LLM Reasoning Capabilities, Outdated or Limited LLM Knowledge Base, Financial Cost and Resource Constraints, User Training, AI Literacy, and Skill Gaps | LLMs potentially harming SRLs without legal knowledge; Over-reliance on inaccurate/hallucinated information; Distortion of public understanding of law; SRLs submitting AI-generated false citations to courts; Widening justice gap due to AI cost disparities; Potential increase in frivolous litigation. | Consumer harm, Over-reliance on AI, Inaccurate or misleading AI output, Erosion of trust in legal system or AI, Exacerbation of inequality or two-tiered system, Undermining legal process or principles |
| W5ZX8VFbaeIJ.pdf | Google_Scholar | Evaluating AI for Law: Bridging the Gap with Open-Source Solutions | This study evaluates general-purpose AI like ChatGPT for legal question-answering, highlighting significant risks and performance issues such as lack of citations and verbosity. It proposes OpenJustice.ai, a domain-specific, open-source legal AI platform, advocating for collaborative development and improved benchmarks to enhance accuracy, transparency, and access to justice. | Evaluation of General-Purpose AI for Legal Q&A, Risk Identification, Performance Issues, Proposal for Domain-Specific LLM, Open Source AI, Collaborative Development Advocacy, Benchmark Improvement Advocacy, Accuracy Improvement, Transparency Improvement, Access to Justice Enhancement | True | Idealistic | True | 1.0 | Positive | Evaluation of LLMs (GPT-4, Mixtral-8x7B) on legal Q&A tasks using the curated LegalQA benchmark; proposal of OpenJustice.ai, an open-source legal AI platform and development framework. | AI System Evaluation, Large Language Model, Legal Question Answering, Benchmarking / Evaluation, Software / Platform Development, Open Source AI | GPT-4 and Mixtral-8x7B were evaluated on legal question-answering using two datasets: LegalQA (curated from Reddit, >2000 questions, answers by law students) and Law Stack Exchange (200 popular questions, top-voted answers). Evaluation involved automatic comparison (using GPT-4 via OpenAI Evals) of model-generated answers to expert answers based on factuality categories (subset, superset, same, disagree, incomparable), supplemented by qualitative review by law students. | Custom Dataset Evaluation, LLM as Judge, Comparative Analysis, Expert Evaluation, Qualitative Analysis | On the LegalQA task, GPT-4 had under 5% factually incorrect responses. Mixtral-8x7B performed significantly worse. Qualitative feedback indicated GPT-4's answers lacked citations and were often verbose compared to concise human expert answers. | High performance, Underperforms others, Limitation: Operational or Technical | Reliability issues of current AI (hallucinations, bias, lack of legal nuance, poor citation practices); limited accessibility and transparency of specialized legal AI tools (closed, proprietary systems benefiting mainly large firms); lack of diversity in AI-generated content and potential for creating AI echo chambers; inadequate regulatory frameworks and evaluation benchmarks for legal AI. | AI Unreliability/Inaccuracy, Bias in AI/Data, AI Limitations in Legal Reasoning/Nuance, Limited Access to A2J Technology, Proprietary Nature of AI as a Barrier, Lack of AI Transparency/Explainability, Lack of Diversity in AI Content, Inadequate Legal Frameworks for AI, Lack of Standardized Benchmarks for Legal AI | Develop domain-specific, open-source legal AI systems (e.g., OpenJustice.ai); revise benchmarks and protocols for evaluating legal AI in real-world settings, focusing on bias, fact-checking, legal reasoning, and narrative diversity; foster collaborative, crowdsourced development with expert feedback loops; emphasize high-quality data curation and advanced AI methodologies (DPO, world models, etc.). | AI Tool Development, Open Source Initiatives and Collaboration, Benchmarking and Evaluation Frameworks, Bias Detection and Mitigation, Enhanced AI Capabilities, Data Curation and Management | Legal question-answering for laypeople; improving accuracy, transparency, and narrative diversity in legal AI; addressing legal misinformation; assisting self-represented litigants; reducing legal fees and research costs. | Access to Legal Information, Ethical AI in Law and AI Governance, Support for Self-Represented Litigants, Affordability of Legal Services / Cost Reduction | Self-represented litigants, laypeople with legal questions, broader legal communities (beyond large firms), legal aid centers, law students, legal professionals. | Self-represented litigants, Laypeople, Individuals lacking legal knowledge, Legal professionals, Small law firms, Legal aid organizations, Law students | General law (covering various topics as found in public legal advice forums and general legal Q&A sites). | General Law | Canada (primary for LegalQA annotation context), US (source of some LegalQA questions, OpenJustice.ai data), France (OpenJustice.ai data), EU (OpenJustice.ai data, EU AI Act). | Canada, USA, France, EU | For the proposed OpenJustice.ai: A mix of curated open-source legal data (annotated question-answer pairs, case law from US, Canada, France, EU), crowdsourced human feedback, and proprietary partner data. For the LegalQA benchmark created: Publicly available questions from r/legaladvice with expert answers written by law students. | Author-Created New Dataset, Fine-tuning Dataset, Publicly Available Data, Legal Domain Data, US Legal Data, Canadian Legal Data, French Legal Data, European Legal Data, Case Law / Judgments, Structured Data, Expert-Annotated / Human-Curated / Human-Generated Data, User-Generated Content, Proprietary Data, Legal Q&A / Forum / User Query Data, Evaluation Dataset | For OpenJustice.ai: Open-source development, crowdsourcing human feedback from legal experts, iterative improvement, data curation, LLM fine-tuning, training Small Language Models (SLMs), and leveraging advanced AI techniques like Direct Preference Optimization (DPO), world models, Flash Attention 2, rejection sampling, reward modeling, supervised fine-tuning, and alignment research. | Open-source Development Approach, Crowdsourcing, Expert Feedback Integration, Iterative Design Process, Data Curation, Model Fine-tuning, Small Language Model (SLM) Training, Preference Alignment Techniques, Reward Modeling, Alignment Research | OpenJustice.ai launched in March 2023 by Conflict Analytics Lab, operating as a natural-language processing interface (www.OpenJustice.ai). The open version is intended for sophisticated users (legal background) to provide quality feedback. It aims to partner with law schools and aid centers. | Web-based access, Freely accessible tool/service, Research preview/Beta access, Partnership-based rollout, Proposed deployment (not implemented) | True | True | The OpenJustice.ai platform (www.OpenJustice.ai) is described as launched and operational. It has a core open-source component. Access to the 'open version' of the platform for feedback contribution is restricted to sophisticated users with a legal background. | Publicly accessible online tool or platform, Open-source, Restricted access | Existing legal benchmarks lack real-world complexity; insufficient empirical data on AI performance in diverse legal tasks; need for improved automatic evaluation methods for the legal domain; understanding the utility of unstructured legal databases for pretraining/domain-adaptation is unexplored; current AI struggles with nuanced legal reasoning, citation, and conciseness. | Research and Evaluation Gaps, Data Availability and Quality, AI Legal Reasoning Limitations, AI Accuracy and Reliability | Ensuring factual accuracy and avoiding hallucinations in legal AI; addressing and mitigating bias; achieving diversity in narrative representation; handling the dynamic nature of law with static training data; reliable source citation; modeling complex, non-algorithmic legal reasoning; high cost of developing purpose-built models; curating high-quality, representative, and unbiased legal datasets. | Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Bias in AI Systems and Data, Ethical Considerations, Outdated or Limited LLM Knowledge Base, LLM Reasoning Capabilities, Financial Cost and Resource Constraints, Scarcity of High-Quality Legal Data | Overreliance on unreliable general-purpose AI for legal tasks by both laypeople and professionals, leading to incorrect advice or actions; generation of 'hallucinated' or fictitious legal information (e.g., fake citations, case law); propagation of biases present in training data; misleading users with AI tools that appear specialized but are general-purpose; widening the access to justice gap if specialized tools remain closed and expensive; creation of AI echo chambers stifling diverse legal thought and democratic discourse; potential for ossification of law due to static models. | Over-reliance on AI, Inaccurate or misleading AI output, Bias and discrimination, Consumer harm, Exacerbation of inequality or two-tiered system, Undermining democratic processes, Technical limitations of AI, Undermining legal process or principles |
| Iv6wOJNR-lkJ.pdf | Google_Scholar | GoalAct: A Globally Adaptive Dynamic Legal Multi-agent Collaboration System | This paper proposes GoalAct, a multi-agent legal collaboration system using the GLM-4 language model, designed to provide legal services by accessing legal databases. GoalAct aims to improve accuracy and adaptability through integrated planning, reflection, and memory mechanisms at both global and local levels. | System Proposal, Multi-Agent System, LLM Application, Legal Services Provision, Legal Database Access, Accuracy Improvement, Adaptability Improvement | True | Idealistic | True | 1.0 | Positive | GoalAct, a globally adaptive dynamic legal multi-agent collaboration system composed of five agent types (Processor, Memorizer, Actor, Judge, Reflector) built on GLM-4, accessing legal databases through APIs. | Multi-Agent System, Large Language Model, Database Integration, Collaborative AI System, Named Tool / Platform | The paper mentions that "experimental results also demonstrate its superior performance for legal services" but provides no specific details on the testing procedure within the provided text. | No Evaluation by Author | The paper claims "superior performance for legal services" but does not provide specific metrics or quantitative results in the provided text. | Developer or Vendor claim | Limited availability and high cost of legal professionals, especially in regions with restricted access; complexity of user inquiries requiring AI systems to effectively filter information, generate logical plans, and self-correct. | Limited Availability/Access to Legal Professionals/Expertise, High Cost of Legal Services, Geographical Disparities in Legal Access, Technical Challenges in AI Development, Difficulty in AI-Human Interaction | Developing advanced AI-driven multi-agent systems like GoalAct, leveraging LLMs (GLM-4) with integrated planning, reflection, and memory to provide more efficient and adaptable legal services. | AI Tool Development, Enhanced AI Capabilities, Cost Reduction and Efficiency | Access to legal information and consultation services. | Access to Legal Information, Access to Legal Advice | Individuals in regions with limited access to legal professionals or those facing high costs for legal services. | Individuals in legal deserts, Individuals facing access barriers, Individuals unable to afford legal services | General legal services / Legal consultation | General Legal Practice | International | International | The system uses the pre-trained GLM-4 language model. It accesses unspecified external legal databases through API calls for information retrieval during operation, not explicitly for further training of the core model. | Pre-trained LLM's General Training Corpus, RAG System Knowledge Corpus, Legal Domain Data, Undisclosed Data Source/Availability | Multi-agent system design with specialized agents (Processor, Memorizer, Actor, Judge, Reflector); integration of planning, reflection (self-correction), and memory (short-term and long-term) mechanisms; emphasis on balancing local task accuracy with global objective consistency. | Multi-agent System Design, Planning Mechanism, Reflection/Self-correction Mechanism, Memory Mechanism Design | NaN | Not applicable | False | False | NaN | NaN | Ensuring robust filtering of user inputs, coherent logical planning, effective self-correction, and reliable memory formation in legal AI systems. Balancing local task accuracy with global objectives in multi-agent systems for complex legal problem-solving. | AI Accuracy and Reliability, AI Legal Reasoning Limitations, AI Scope and Functionality Limitations | Effectively filtering irrelevant or redundant information from user inputs; generating logical and coherent planning paths while avoiding local search loops; developing robust self-correction mechanisms; forming memory and accumulating experience to reduce repeated errors; balancing local task accuracy with global objective consistency in a multi-agent system. | LLM Reasoning Capabilities, Accuracy and Reliability of LLM Output | Risk of the system getting trapped in local search loops, leading to no responses; potential for degraded system performance if individual agents' tasks do not align with the overall global objective. | Technical limitations of AI, Inaccurate or misleading AI output |
| 4IGMF7HagUQJ.pdf | Google_Scholar | Lawyers Should Not Trust AI: A call for an Open-source Legal Language Model | The paper argues that general AI like ChatGPT is unsuitable for legal tasks due to significant risks such as misinformation and lack of transparency. It advocates for the development of domain-specific, open-source legal AI, like the proposed OpenJustice.ai, built through multi-layered fine-tuning and legal community feedback to improve legal research and access to justice. | Critique of General AI for Legal Tasks, Risk Identification, AI Hallucinations/Inaccuracy, Transparency Issues, Advocacy for Domain-Specific LLM, Open Source AI, Fine-tuning for Legal Domain, Legal Research Enhancement, Access to Justice Enhancement | True | Idealistic | True | 1.0 | Positive | Open-source and distributed legal AI (specifically OpenJustice.ai) developed through multi-layered fine-tuning: Raw Data Fine-tuning, Instruction Fine-tuning, Open-Source Feedback Fine-tuning from legal professionals, and Decentralised Fine-tuning combining open and closed datasets. | Open Source AI, Fine-tuning, Instruction Tuning, Human Feedback Integration, Distributed AI Development | The paper outlines the design and development process for OpenJustice.ai, involving supervised annotation and feedback from law students and legal professionals on real-world questions and generated legal scenarios. It does not present specific benchmark testing or quantitative evaluation results for OpenJustice.ai within this paper. | Iterative Design Feedback, Expert Evaluation, No Evaluation by Author | NaN | NaN | Limitations of general AI (legal misinformation/hallucinations, lack of transparency and precision, bias, inability to offer diverse narratives or perform contextual legal reasoning, unexplainability) hindering their safe use for legal tasks and access to justice. The risk of AI leading to ossification of law and undermining legal diversity. The current absence of reliable, open-source domain-specific legal AI. | AI Unreliability/Inaccuracy, Lack of AI Transparency/Explainability, Bias in AI/Data, AI Limitations in Legal Reasoning/Nuance, Risk of Ossification of Law by AI, Limited Access to A2J Technology | Development of OpenJustice.ai: an open-source, domain-specific legal LLM. This involves multi-layered fine-tuning (on raw legal data, instruction-response pairs) and reinforcement learning with human feedback from the legal community (law schools, legal professionals), with initial feedback restricted to experts to ensure data integrity. A decentralized approach allows incorporating proprietary data while keeping it localized. | AI Tool Development, Open Source Initiatives and Collaboration, Enhanced AI Capabilities, Human Oversight and Collaboration, Data Curation and Management | Improving legal research, enhancing legal reasoning tools, addressing shortcomings of general AI in legal problem-solving and dispute resolution, and ultimately providing access to justice for self-represented litigants through reliable legal information. | LegalResearch Support, Improving Foundational AI Capabilities for Legal Applications, Dispute Resolution, Support for Self-Represented Litigants, Access to Legal Information | The legal community (law schools, legal professionals, legal clinics, industry partners) for development, feedback, and initial use. Potentially self-represented litigants in the future, once the system is mature and reliable. | Legal professionals, Law schools, Legal aid organizations, Self-represented litigants | General Law, with applications in legal research, legal reasoning, and potentially specific areas like contract drafting. The focus is on foundational capabilities extendable to various legal domains. | General Law, Legal Research, Contract Law | International (as a general call and framework), with specific examples from Canada and the United States, implying the need for jurisdiction-specific adaptation for deployed systems. | International, Canada, USA | A combination of: 1) Unstructured legal data (case law, journals, other legal resources). 2) Structured data: question-response pairs from online forums (e.g., Reddit, Law Stack Exchange) annotated by law students and legal professionals. 3) Synthetic data: legal scenarios and contracts generated by other LLMs (e.g., Llama2) for further annotation. 4) Proprietary data from industry partners (used in a closed, decentralized fine-tuning manner). | Fine-tuning Dataset, Undisclosed Data Source/Availability, Legal Domain Data, Unstructured Text Data, Case Law / Judgments, Legal Scholarly Content / Textbooks, Structured Data, Legal Q&A / Forum / User Query Data, User-Generated Content, Expert-Annotated / Human-Curated / Human-Generated Data, Synthetic Data, Legal Contracts, Proprietary Data | Multi-layered fine-tuning of foundational language models (raw data fine-tuning, instruction fine-tuning, reinforcement learning from human feedback). A staged development process involving data collection from public and legal sources, annotation by law students under professional supervision, and iterative model refinement. Proposed use of decentralized learning to combine open and proprietary data sources. | Model Fine-tuning, Reinforcement Learning from Human/AI Feedback (RLHF/RLAIF), Staged Development Process, Data Collection, Supervised Annotation, Iterative Design Process, Decentralized Learning | Initially, a secured interface enabling law students and legal professionals to interact with and provide feedback to the model (OpenJustice.ai). The aim is a non-proprietary version openly accessible to the legal community, but not to the general public for feedback in early stages to maintain data quality. Decentralized learning architecture where industry partners can fine-tune on proprietary data locally. | Research preview/Beta access, Partnership-based rollout, Proposed deployment (not implemented), Freely accessible tool/service | False | False | NaN | NaN | The need for empirical performance evaluation of domain-specific legal LLMs using clear, industry-specific metrics (for hallucinations, reasoning, citation accuracy, narrative diversity). Research into effective human-AI collaboration, particularly the 'end-user prompt engineering' abilities of non-lawyers for legal AI. Persistent limitations in LLMs' legal citation retrieval capabilities. | Research and Evaluation Gaps, AI Accuracy and Reliability, AI Legal Reasoning Limitations, User Interface and Usability Gaps, Human Oversight and Professional Adaptation | Ensuring data integrity and quality during the open-source feedback process. Developing robust methods for legal citation retrieval within LLMs. Addressing the challenge of effective prompt engineering for users, especially non-experts, to extract useful information from legal AI. Scaling the annotation and expert feedback process. | Data Quality, Processing, and Preparation, Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Prompt Engineering and Optimization, User Training, AI Literacy, and Skill Gaps, Cost and Complexity of Data Annotation, Scalability of Solutions | Use of general AI for legal tasks: legal misinformation (hallucinations, fake citations), biased outputs, lack of transparency and explainability, creation of 'AI echo-chambers' narrowing perspectives, ossification of law. Premature public release of even domain-specific legal AI: providing incorrect legal information to self-represented litigants. Inaccurate feedback from non-experts if feedback mechanisms are opened too broadly too soon. | Risk of misapplication or misuse, Inaccurate or misleading AI output, Bias and discrimination, Lack of transparency, accountability, and redress, Technical limitations of AI, Undermining legal process or principles, Consumer harm |
| z68SfimV9U0J.pdf | Google_Scholar | LLMs & Legal Aid: Understanding Legal Needs Exhibited Through User Queries | The paper presents a preliminary analysis of 3,847 user queries submitted to a GPT-4 powered legal aid tool by Frank Bold in the Czech Republic. Using GPT-4o for zero-shot classification, it categorizes queries to understand user needs and interaction patterns when seeking legal help from LLMs. | Analysis of User Queries to Legal AI Tool, LLM Application, Legal Aid Focus, Czech Republic Focus, User Needs Identification, Interaction Pattern Analysis, Zero-Shot Classification | True | Idealistic | True | 1.0 | Neutral | A method for understanding user legal needs exhibited in queries to LLM-based legal aid tools, involving: 1) iterative development of a query categorization scheme (facts provided, information vs. advice, user control over answer) and 2) zero-shot classification of queries using GPT-4o based on this scheme. | User Needs Analysis, Query Categorization, Zero-shot Learning, Large Language Model, Methodology Development | The outcome of the zero-shot classification performed by GPT-4o was not formally evaluated for accuracy by the authors. | No Evaluation by Author | Classification of 3,847 queries: 29.95% provided facts, 64.93% sought information (vs. 35.07% advice), and 71.43% posed open-ended questions, granting control to the model. Only 3.35% of queries treated the LLM as a human expert, and 3.04% as a sophisticated search engine. | Descriptive or Conceptual finding | High cost of traditional legal services; users oversharing personal/sensitive information with LLMs; unfeasible user expectations of LLM capabilities; users granting excessive control to LLMs, increasing vulnerability; the blurry line between users seeking legal information versus actionable legal advice. | High Cost of Legal Services, Data Privacy Concerns with AI, Lack of Understanding of AI Capabilities/Limitations, Difficulty in AI-Human Interaction, Regulatory Uncertainty | Increase AI literacy among the public. Develop and implement technical and policy safeguards by LLM providers and legal aid organizations. Further research into augmenting LLMs with curated, reliable legal information (e.g., RAG). | Education and AI Literacy, Regulation, Ethics, and Governance, Policy and Regulatory Reform, Enhanced AI Capabilities, Data Curation and Management | Understanding user needs in legal aid; distinguishing between legal information seeking and legal advice seeking via LLMs; patterns of user interaction with LLM-based legal tools; user expectations of LLMs in legal contexts. | Legal Aid and Pro Bono Services, Access to Legal Information, Access to Legal Advice | General public in the Czech Republic seeking legal aid, laypeople, low-income and marginalized individuals. | General public, Population in Czech Republic, Clients of legal aid organizations, Laypeople, Low-income individuals, Marginalized communities | The underlying experiment covered environmental law, whistleblowing and corruption-related issues, civic rights, municipal laws, and civic engagement issues. The query analysis is broadly applicable to general legal queries. | Environmental Law, Anti-Corruption Law, Civil Rights Law, Administrative Law, General Law | Czech Republic | Czech Republic | The user query classification was performed by GPT-4o using zero-shot learning with prompts defining categories. The data classified was a corpus of 3,847 anonymized user queries in Czech collected from the Frank Bold experiment. The original Frank Bold RAG system (which users interacted with) used internal Frank Bold documents (guidelines, blog posts, articles) and selected Czech legal acts (proprietary, domain-specific, unstructured text). | Input Data for Task (Non-Training), User-Generated Content, Czech Legal Data, Legal Q&A / Forum / User Query Data, Pre-trained LLM's General Training Corpus, RAG System Knowledge Corpus, Proprietary Data, Legal Domain Data, Other Legal Documents, Legislation / Statutes / Regulations, Unstructured Text Data | For the query categorization approach: Iterative development of descriptive codes based on existing literature (Cheong et al.) and pilot analysis of 200 random queries. For the Frank Bold experiment (context): Experimental design with a web platform for query submission to GPT-4 with RAG, user registration, and single question-answer interaction. | Iterative Code Development, Literature Review as Design Input, Pilot Analysis, Experimental Design, Retrieval Augmented Generation (RAG), User Interface Development | The Frank Bold experiment tool was accessible via a public website (www.ai.frankbold.org, now defunct) from May 3, 2023, to July 25, 2023. It was publicized through Frank Bold’s internal mailing lists and several prominent online media outlets. | Web-based access, Freely accessible tool/service, Pilot program/Limited rollout, Dissemination via publication/presentation | False | False | NaN | NaN | Need for more rigorous experiments with controlled variables and demographic user data. Deeper understanding of user behaviors and query types that fall between the extremes of treating LLMs as search engines versus human experts. Lack of widespread AI literacy among lay-users. Insufficient safeguards in existing LLM-based legal aid tools. | Research and Evaluation Gaps, User Interface and Usability Gaps, Public Understanding, Trust, and Adoption, Consumer Protection Gaps | For the query analysis presented: Limitations of using unvalidated zero-shot classification. For the original Frank Bold experiment: Uncontrolled variables during the experiment (e.g., different GPT-4 model versions, RAG adjustments over time); lack of detailed demographic data about users. | Research Methodology and Study Design Limitations, Evaluation Challenges and Metrics | Oversharing of personal and sensitive information by users to LLMs. Users holding unfeasible expectations regarding LLMs' capabilities to provide personalized and actionable legal advice. Users ceding significant control over the response to LLMs, increasing their vulnerability to hallucinations and irrelevant information. Users developing a false sense of competence based on LLM-generated answers without proper verification. | Data privacy and security breach, Poor user experience, Over-reliance on AI, Negative impact on user agency or autonomy, Inaccurate or misleading AI output |
| Wgt3m-_XVr8J.pdf | Google_Scholar | Artificial Intelligence & the Future of Law Libraries: Mid-Atlantic Roundtable Report | This paper reports on a roundtable discussion among legal experts and information professionals about the impact of AI, particularly generative AI, on law libraries. It highlights opportunities for AI to improve library services, accessibility, and access to justice, while also discussing challenges such as rapid adoption pressures, the need for staff training, and budget constraints. | Roundtable Discussion Report, AI Impact on Law Libraries, Generative AI Application, Opportunity Identification, Accessibility Enhancement, Access to Justice Enhancement, Challenge Identification | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | Existing access to justice gaps, such as difficulties for self-represented litigants in navigating legal procedures and understanding legal information. | Scale of Unmet Legal Need, Challenges for Self-Represented Litigants, Difficulty Accessing/Interpreting Legal Information, Complexity of Legal System/Procedures | Development of AI-driven information retrieval and document automation systems for self-represented litigants; leveraging AI to adapt information for diverse needs and improve court processes. | AI Tool Development, Legal Research and Analysis Tools, Document Automation, Support for Self-Represented Litigants, Judicial System Enhancement | Assisting self-represented litigants (e.g., in child custody, landlord-tenant, criminal appeals); enhancing legal information accessibility and court processes. | Support for Self-Represented Litigants, Access to Legal Information, Judicial System Modernization / Efficiency | Self-represented litigants; individuals with diverse needs and limited resources. | Self-represented litigants, Vulnerable populations, Low-income individuals | Family law, Landlord-tenant law, Criminal law, General legal information services. | Family Law, Landlord-Tenant Law, Criminal Law, General Legal Practice | United States | USA | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Need for development of user-friendly AI tools tailored for access to justice; lack of open, machine-readable legal data for AI development; ensuring ethical AI deployment that promotes fairness and equity. | User Interface and Usability Gaps, Access, Equity, and Digital Divide, Data Availability and Quality, Ethical Framework Deficiencies, Bias in AI | Pressure for rapid AI adoption without strategic evaluation; staff skills gaps and need for training; high costs and complex procurement; privacy, security, and ethical concerns; advocating for the library's value and role; budget and resource limitations. | User Adoption, Trust, and Acceptance, User Training, AI Literacy, and Skill Gaps, Financial Cost and Resource Constraints, Data Privacy, Security, and Confidentiality, Ethical Considerations | Data privacy and security vulnerabilities with generative AI; potential for biased AI-driven collections or information; unethical AI use if vendor accountability is lacking; marginalization of librarians not adapting to AI. | Data privacy and security breach, Bias and discrimination, Ethical concerns, Lack of transparency, accountability, and redress, Job displacement |
| 9ocGP8hgKUoJ.pdf | Google_Scholar | Rapid Response Information Report Generative AI: Language models and multimodal foundation models | This Australian government-commissioned report analyzes the opportunities and risks of generative AI (LLMs and MFMs) across various sectors over the next decade. It also reviews international strategies to address the impacts of these technologies, aiming to inform national policy. | Government Report, Generative AI Analysis (LLMs, MFMs), Australian Focus, Opportunity Identification, Risk Identification, Review of International AI Strategies, National Policy Formation | True | Idealistic | True | 3.0 | Neutral | LLMs and MFMs (e.g., ChatGPT, GPT-3, GPT-4, LLaMa, Ernie Bot) | Large Language Model, Multimodal Foundation Models (MFMs) | NaN | Not Applicable | NaN | NaN | Bias in AI reproducing social inequalities (e.g., in law enforcement, social services); risks to human rights; lack of digital inclusion for communities like regional/older Australians, hindering access to AI-driven services; opacity and lack of accountability in AI systems. | Bias in AI/Data, Risk of AI Exacerbating Inequality, Risk to Human Rights from AI, Digital Divide, Accessibility Barriers for Specific User Groups, Lack of AI Transparency/Explainability, Lack of AI Accountability | Development of legal/regulatory frameworks (e.g., risk-based approaches, human rights due diligence); promoting transparency and accountability; multi-stakeholder collaboration; public investment in national AI capabilities and accessible infrastructure; measures to improve digital inclusion. | Policy and Regulatory Reform, Regulation, Ethics, and Governance, Transparency and Explainability in AI, Open Source Initiatives and Collaboration | Protecting human rights in AI deployment; mitigating bias and discrimination in AI impacting legal and social outcomes; ensuring equitable access to AI technologies and legal information; accountability for AI harms. | Protection of Rights, Ethical AI in Law and AI Governance, Access to Legal Information, Language Access and Digital Divide | Regional Australians, older Australians (digital inclusion); minority groups, over-policed populations (bias in AI); women (bias in data generally). | Rural populations, Population in Australia, Elderly people, Digitally excluded populations, Minority groups, Over-policed communities, Women | Law enforcement, contract law, privacy law, copyright law, anti-discrimination law, consumer law | Law Enforcement, Contract Law, Data Privacy Law, Copyright Law, Anti-Discrimination Law, Consumer Law | Australia, with references to international jurisdictions (US, EU, China, Canada, Singapore, Thailand). | Australia, International, USA, EU, China, Canada, Singapore, Thailand | Vast, diverse datasets (text, images, code) often scraped from the internet, including public-domain content (e.g., Wikipedia, books) and potentially personal or copyrighted material; specific datasets for models like GPT-3 are mentioned generally, but specifics for newer models like GPT-4 are often not disclosed by commercial entities. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Image Data, Publicly Available Data, Copyrighted Material (Source Mentioned), Undisclosed Data Source/Availability, Proprietary Data | Model pre-training, fine-tuning (supervised learning, reinforcement learning with human feedback), input/output filtering, red-teaming, fuzzing, staged release strategies, post-release monitoring and auditing. | Model Pre-training, Model Fine-tuning, Supervised Learning, Reinforcement Learning from Human/AI Feedback (RLHF/RLAIF), Input/Output Filtering, Red Teaming/Security Testing, Fuzzing, Staged Release Strategy, Post-release Monitoring | Controlled release via APIs and web interfaces (e.g., OpenAI's ChatGPT); open-sourcing of some models (e.g., Meta's LLaMa, Stanford's Alpaca) often aimed at researchers; integration into existing software products. | Evaluation of existing third-party tool, API access, Web-based access, Research preview/Beta access, Open source model release, Integration into existing system/platform | False | False | NaN | NaN | Lack of transparency in commercial LLM/MFM development (datasets, pre/post-processing); insufficient national capacity for AI development and oversight in some countries (e.g., Australia); persistent challenges in ensuring fairness, accuracy, and robustness of models; digital divide limiting equitable benefit; need for effective governance, standardized reporting, and redress mechanisms. | Transparency and Explainability, Data Availability and Quality, Regulatory and Governance Gaps, Access, Equity, and Digital Divide, Bias in AI, AI Accuracy and Reliability, Accountability and Redress Mechanisms | High resource requirements (monetary, computational, human); managing accuracy, bias, and safety of models; preventing misuse for harmful purposes (e.g., misinformation); ensuring data privacy, security, and sovereignty; addressing the environmental impact of large-scale computation; establishing robust ethical guidelines and governance. | High Computational and Resource Demands, Financial Cost and Resource Constraints, Accuracy and Reliability of LLM Output, Bias in AI Systems and Data, Safeguarding Against Misuse and Harm, Data Privacy, Security, and Confidentiality, Environmental Impact of AI, Ethical Considerations, Regulatory Uncertainty and Compliance | Generation of 'hallucinations' (erroneous/misleading information); perpetuation/amplification of biases leading to discrimination and social inequalities; misuse for misinformation, deepfakes, and malicious activities; privacy violations (data scraping, re-identification, unauthorized use of personal/copyrighted data); security vulnerabilities; lack of transparency and accountability ('black box' effect); negative impacts on democratic processes, labor markets, and the environment; erosion of trust; market concentration. | Inaccurate or misleading AI output, Bias and discrimination, Exacerbation of inequality or two-tiered system, Security vulnerabilities or malicious misuse, Data privacy and security breach, Lack of transparency, accountability, and redress, Undermining democratic processes, Job displacement, Environmental impact, Erosion of trust in legal system or AI, Negative economic impact, Copyright or intellectual property issues |
| OO-3hRkHauEJ.pdf | Google_Scholar | Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset | This paper introduces Pile of Law, a large-scale, open-source dataset of English legal and administrative text, intended for pretraining models and studying data filtering. It argues for grounding AI data filtering techniques in established legal norms for privacy and toxicity, demonstrating how such contextual rules can be learned from the dataset. | Dataset Creation, Open Source Dataset, Legal Text Corpus, AI Data Filtering, Privacy Norms in AI, Toxicity Norms in AI | True | Idealistic | True | 1.0 | Positive | Pile of Law dataset and the approach of learning context-aware data filtering rules (for privacy and toxicity) directly from legal text. | Dataset Creation / Curation, Data Filtering Technique, Privacy Preservation, Toxicity Detection / Mitigation, Rule Learning | Case studies involving training models (distill-BERT) to predict pseudonymity in Board of Immigration Appeals cases (~80% F1), comparing Masked Language Model (MLM) scores for pseudonym use in civil litigation, analyzing outputs of existing privacy (HIPAA tool) and toxicity filters (Perspective, Detoxify, etc.) on dataset subsets (BVA, DOL, Supreme Court opinions), and using causal lexicon induction. | Custom Dataset Evaluation, Quantitative Metrics, Qualitative Analysis, Comparative Analysis | Pseudonymity prediction model achieved ~80% F1 and aligned with legal rules; models pretrained on legal data better encoded pseudonymity norms. Existing toxicity filters showed low agreement, context/time sensitivity, and poor handling of nuance on legal text, highlighting limitations. | Moderate performance, Technique improves outcome, Limitation: Operational or Technical | Lack of responsible, legally-grounded, and context-aware data filtering practices for AI pretraining data, hindering development of trustworthy AI, including for legal applications potential applications. | Data Scarcity/Quality for AI, Technical Challenges in AI Development, Lack of Trust in AI/Automated Systems | Grounding AI data filtering practices in established legal norms; providing the large-scale, open-source Pile of Law dataset as a resource; proposing methods to learn contextual filtering rules directly from the dataset. | Data Curation and Management, Regulation, Ethics, and Governance, Open Source Initiatives and Collaboration, Enhanced AI Capabilities | General (as a potential application area for models trained on the dataset) | NaN | NaN | NaN | Court opinions, contracts, administrative law, legislation, constitutional law, immigration law, criminal law, civil litigation. | Case Law, Contract Law, Administrative Law, Statutory Law, Constitutional Law, Immigration Law, Criminal Law, Civil Litigation | Primarily U.S. federal, with comparative examples from Germany, China, Canada. | USA, Germany, China, Canada | Pile of Law dataset: ~256GB of publicly available, open-source, English-language, unstructured legal and administrative text (court opinions, contracts, administrative rules, legislation, etc.). | Data From Existing Public NLP/Legal Datasets/Benchmarks, Publicly Available Data, Legal Domain Data, Case Law / Judgments, Legal Contracts, Legislation / Statutes / Regulations, Other Legal Documents, Unstructured Text Data | Dataset curation by compiling public sources. Case studies using standard ML methods (classification, MLM scoring, causal inference) to analyze and learn patterns from the dataset. | Dataset Curation, Case Study as Design Methodology, Machine Learning Model Development, Classification Model Training, Self-supervised Learning, Causal Inference | Dataset released publicly on Hugging Face. | Public dataset/benchmark release | True | True | The Pile of Law dataset is available for download on Hugging Face. | Dataset available | Need for better text-based causal attribution methods for identifying drivers of filtering decisions; need for robust, value-aligned toxicity filters that handle legal context, domain shift, and long documents; further exploration of legal system differences (e.g., civil vs common law); challenge of reliably performing context-aware filtering at scale; limitations of model context windows for assessing toxicity. | Research and Evaluation Gaps, AI Scope and Functionality Limitations, Bias in AI, AI Legal Reasoning Limitations, Multilingual and Jurisdictional Specificity Gaps | Compiling a large-scale legal dataset from diverse public sources; handling the inherent context-dependency of legal text for filtering purposes; limitations of existing NLP tools (e.g., privacy, toxicity filters) when applied to the specialized legal domain; addressing potential sensitivity of information within publicly available legal data. | Scarcity of High-Quality Legal Data, Data Quality, Processing, and Preparation, Domain-Specific Adaptation and Customization, Data Privacy, Security, and Confidentiality | Biased/harmful model outputs due to pretraining data; filtering negatively impacting representation or utility; privacy violations via model memorization of sensitive information; release of sensitive information contained within the Pile of Law dataset despite its public sourcing; incorrect application of toxicity filters leading to censorship of important legal discussions (e.g., civil rights cases) or failure to flag genuinely harmful content. | Bias and discrimination, Harmful or unsafe AI output, Technical limitations of AI, Data privacy and security breach |
| d4pkaJu5lpAJ.pdf | Google_Scholar | Dallma: Semi-Structured Legal Reasoning and Drafting with Large Language Models | This paper introduces Dallma, a framework combining predefined templates, logical rules, user input, and Large Language Models (LLMs) for semi-structured legal tasks like drafting and reasoning. The framework aims to improve the safety and utility of LLMs in law, with potential applications in enhancing access to justice. | Framework Proposal, Hybrid AI Approach (Templates, Rules, LLMs), Semi-Structured Legal Tasks, Legal Document Drafting, Legal Reasoning, Safety in Legal AI, Utility of LLMs, Access to Justice Enhancement | True | Idealistic | True | 1.0 | Positive | The Dallma framework combines expert-defined templates (containing content, logic, variable specifications) with user interaction and calls to LLMs (e.g., GPT-4o) to perform semi-structured legal reasoning and document drafting tasks. | Framework Development, Template-based System, User Interaction, Large Language Model, Legal Reasoning Tool, Legal Document Generation / Automation, Hybrid AI System | Two illustrative examples are presented using GPT-4o: one for spotting legal issues based on user input and another for reasoning about a Quebec Civil Code article concerning tenant eviction. Formal evaluation across various tasks is planned for future work. | Demonstration or Illustrative Examples, No Evaluation by Author | The provided examples demonstrate plausible outputs where the system identifies relevant legal areas and applies legal criteria according to the template structure. No quantitative performance metrics are reported. | Descriptive or Conceptual finding, Moderate performance | Difficulty for laypeople in understanding legal issues and completing complex legal forms; inherent limitations of LLMs such as hallucinations and difficulties with logical reasoning. | Public Lack of Legal Knowledge/Awareness, Complexity of Legal Tasks for Laypersons, AI Unreliability/Inaccuracy, AI Limitations in Legal Reasoning/Nuance | The Dallma framework proposes using semi-structured templates created by legal experts to guide LLMs and users. This approach constrains LLM outputs, integrates deterministic logic, allows user verification, and breaks down complex tasks into smaller steps to improve accuracy and safety. | Conceptual Frameworks, AI Tool Development, Human Oversight and Collaboration, Enhanced AI Capabilities, Regulation, Ethics, and Governance | Legal issue spotting, legal form completion, applying for social aid/benefits, automating legal reasoning and drafting. | Access to Legal Advice, Legal Document Creation / Automation, Improving Foundational AI Capabilities for Legal Applications | Laypersons / Self-represented litigants. | Laypeople, Self-represented litigants | General (issue spotting), Landlord-tenant law (specific example). | General Law, Landlord-Tenant Law | Quebec (for one specific example); potentially general applicability. | Canada, International | N/A (Uses pre-trained LLMs like GPT-4o; templates contain expert-defined content and logic, not ML training data). | Not Applicable | NaN | NaN | Templates created by experts can be shared with target users, who can run the tool on their own computer. | Local deployment/Standalone application, Public dataset/benchmark release | False | False | NaN | NaN | Need to establish best practices for creating Dallma templates; requires formal evaluation of accuracy and performance; potential for extensions like Retrieval-Augmented Generation (RAG) and automatic template generation. | Research and Evaluation Gaps, AI Accuracy and Reliability, AI Scope and Functionality Limitations | Designing effective and comprehensive templates; ensuring LLM reliability and adherence to constraints within the framework; developing user-friendly interfaces for both template creators and end-users; mitigating general LLM limitations (hallucinations, reasoning errors). | Domain-Specific Adaptation and Customization, Accuracy and Reliability of LLM Output, Output Variability and Consistency, User Interface, Usability, and Accessibility, LLM Hallucination and Factual Errors, LLM Reasoning Capabilities | Potential for inaccurate LLM output despite the framework's constraints, although the design aims specifically to mitigate this risk common to LLM applications in law. | Inaccurate or misleading AI output |
| jVZShwYu2OUJ.pdf | Google_Scholar | Computational Law and AI Alignment in the Era of Large Language Models | This article examines the intersection of computational law, AI alignment, and risk mitigation concerning large language models (LLMs), discussing key concerns for various legal stakeholders. It explores AI alignment strategies, regulatory approaches, and concludes that a balanced approach between innovation and safety is essential to harness AI's transformative potential. | Computational Law, AI Alignment, LLM Risk Mitigation, AI Regulation, Balancing Innovation and Safety | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | Concerns about reliability, correctness, and ethical implications (fairness, accountability, transparency) of AI. For individuals, ensuring quality legal support from AI tools and addressing the digital divide are key hurdles. | AI Unreliability/Inaccuracy, Ethical Concerns with AI in Law, Lack of AI Accountability, Lack of AI Transparency/Explainability, Digital Divide | AI alignment strategies (explainability, transparency, fine-tuning, guardrails), comprehensive regulatory frameworks, development of benchmarks, and embedding legal principles into AI systems. | Enhanced AI Capabilities, Transparency and Explainability in AI, Regulation, Ethics, and Governance, Benchmarking and Evaluation Frameworks | Affordable and accessible legal help, quality of AI legal support for consumers, digital divide in AI access. | Affordability of Legal Services / Cost Reduction, Access to Legal Advice, Language Access and Digital Divide | General public/consumers, especially those needing affordable or accessible legal services. | General public, Consumers, Individuals unable to afford legal services, Individuals facing access barriers | General / Multiple | General Law, Multiple Fields | EU and US | EU, USA | The paper discusses techniques using various data. Examples include Anthropic's Constitutional AI (human principles, public input) and Pile of Law (open-source legal texts for filtering/research). | Pre-trained LLM's General Training Corpus, Fine-tuning Dataset, Expert-Annotated / Human-Curated / Human-Generated Data, User-Generated Content, Data From Existing Public NLP/Legal Datasets/Benchmarks, Legal Domain Data, Publicly Available Data | Techniques discussed include Constitutional AI (developed using supervised learning and reinforcement learning, including AI feedback and public input for drafting principles) and the Pile of Law (involves data gathering of legal texts, distillation of legal norms, and data-driven learning of filtering rules). | Constitutional AI, Supervised Learning, Reinforcement Learning from Human/AI Feedback (RLHF/RLAIF), Principle-based Design, Dataset Creation, Data Collection, Rule-based System Design | NaN | Not applicable | True | True | Mentions several available tools/datasets: e.g., Pile of Law (open-source dataset), Guardrails.ai (open-source), NeMo Guardrails (GitHub link provided), HELM (living benchmark), LegalBench (open for contribution). | Dataset available, Open-source, Code available | Defining/tracing AI harm, achieving LLM explainability/transparency, robust guardrails, integrating symbolic AI with LLMs, and broadening benchmark contributions. | Ethical Framework Deficiencies, Accountability and Redress Mechanisms, Transparency and Explainability, Regulatory and Governance Gaps, Integration and Interoperability Challenges, Research and Evaluation Gaps | NaN | NaN | Reliability issues, bias, lack of accountability, malpractice, job displacement, privacy infringement, societal division, unauthorized practice of law, and generation of illegal/harmful content. | Inaccurate or misleading AI output, Bias and discrimination, Lack of transparency, accountability, and redress, Ethical concerns, Job displacement, Data privacy and security breach, Negative societal impact, Unauthorized practice of law, Harmful or unsafe AI output |
| ys2Ue4MeS4MJ.pdf | Google_Scholar | Can GPT-4 Support Analysis of Textual Data in Tasks Requiring Highly Specialized Domain Expertise? | This paper evaluates GPT-4's ability to semantically analyze sentences from court opinions for interpreting legal concepts, a task requiring specialized legal expertise. It finds GPT-4 performs comparably to well-trained law students and explores prompt engineering, batch processing efficiency, and model sensitivity. | LLM Evaluation, Semantic Analysis of Court Opinions, Legal Concept Interpretation, Comparison with Human Performance, Prompt Engineering | True | Idealistic | True | 2.0 | Positive | GPT-4 with zero-shot prompting using detailed annotation guidelines, including batch processing and chain-of-thought variations. | Large Language Model, Zero-shot Learning, Prompt Engineering, Batch Processing | Comparison against gold-standard labels (consensus annotations by legal scholars) on a subset of the Statutory Interpretation Data Set, using Precision, Recall, F1-score, Accuracy, and Krippendorff's alpha. Compared performance against human (law student) annotators. | Benchmark Dataset Evaluation, Quantitative Metrics, Comparative Analysis, Expert Evaluation | Best configuration (updated guidelines, single sentence processing) achieved F1=0.57, Accuracy=0.57, and Krippendorff's alpha=0.48 (or alpha=0.53 for labels only setting). Performance was comparable to well-trained law students. Batch processing workable but slightly less accurate; CoT ineffective. | Moderate performance, Comparable to others, Technique improves outcome, Technique has limited or mixed impact | High cost and requirement for specialized domain expertise for annotating legal texts, acting as a bottleneck for research and development. | Resource Constraints for A2J Tech Development/Deployment, Data Scarcity/Quality for AI | Employing large language models (GPT-4) with detailed prompts derived from annotation guidelines for automated semantic analysis, potentially reducing cost and reliance on human experts. Iterative refinement of prompts/guidelines based on model output analysis. | AI Tool Development, Prompt Engineering and LLM Interaction Design, Document Automation, Cost Reduction and Efficiency, Human Oversight and Collaboration | Interpretation of legal concepts in statutory law; Legal understanding; Legal argumentation support. | Access to Legal Information, Legal Literacy and Public Legal Education, Improving Foundational AI Capabilities for Legal Applications | NaN | NaN | Statutory Interpretation (Task); Data from multiple fields including Intellectual Property, Criminal Law (Cybercrime). | Statutory Interpretation, Intellectual Property Law, Criminal Law, Cyber Law | United States | USA | The technique uses pre-trained GPT-4 (trained on broad web data). Evaluation uses the 'Statutory Interpretation Data Set' (publicly available on GitHub) containing sentences from US court opinions labeled by legal experts. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Evaluation Dataset, Publicly Available Data, US Legal Data, Legal Domain Data, Case Law / Judgments, Expert-Annotated / Human-Curated / Human-Generated Data, Structured Data | Prompt engineering (translating human guidelines, varying prompt structure for batching and CoT), comparative evaluation against human performance and gold standard, error analysis for iterative prompt refinement. | Prompt Engineering, Comparative Evaluation, Error Analysis, Iterative Design Process | NaN | Not applicable | True | False | Requires access to OpenAI's commercial GPT-4 API. The annotation guidelines used for prompting are available on GitHub. | API access, Commercial product or service, Configuration or prompts available | Need for broader testing across more tasks and larger datasets; Improving model robustness to prompt formatting; Addressing reproducibility challenges with proprietary models; Exploring few-shot/fine-tuning approaches. | Research and Evaluation Gaps, AI Accuracy and Reliability | Achieving high accuracy on complex legal tasks; Cost of API usage; Ineffectiveness of standard prompt techniques like CoT for this task; Model brittleness/sensitivity to prompt formatting. | Accuracy and Reliability of LLM Output, Financial Cost and Resource Constraints, Prompt Engineering and Optimization | Brittleness leading to unreliable predictions based on minor prompt changes; Potential for inaccurate analysis in complex legal tasks; General concerns about misuse of powerful LLMs (mentioned indirectly via OpenAI report). | Technical limitations of AI, Inaccurate or misleading AI output, Risk of misapplication or misuse |
| dofdWxvXYDgJ.pdf | Google_Scholar | Can AI make a case? AI vs. Lawyer in the Dutch Legal Context | This paper investigates the quality of AI-generated (GPT-4) legal argumentation compared to human-written arguments in the Dutch legal context using an experiment with 25 legal professionals. Results showed a strong preference (80%) for the AI-generated document, highlighting AI's potential for tasks like legal drafting and information retrieval. | Evaluation of AI-Generated Legal Arguments, LLM Application, Comparison with Human Performance, Dutch Law Focus, Legal Document Drafting, Legal Information Retrieval | True | Idealistic | True | 2.0 | Positive | GPT-4 was used to generate a legal letter. The input for GPT-4 was prepared using prompt engineering, which included manual co-reference resolution on 9 case documents, a 'Prompt Reducer' technique (using a Python script) to compress these documents into a summary fitting token limits, and a specific prompt instructing GPT-4 to rewrite an original lawyer's letter based on this summary and the original letter. | Large Language Model, Legal Document Generation / Automation, Prompt Engineering, Co-reference Resolution, Text Summarization / Compression | An online survey was conducted with 25 Dutch legal professionals (judges, lawyers, other legal professionals). Participants were given a case summary and two anonymized legal letters (one human-written, one AI-generated by GPT-4) arguing the same side. They rated both texts on persuasiveness, clarity/coherence, strength of arguments, and use of evidence (1-10 scale), and then chose the more effective text, providing justification. | User Study or Survey, Expert Evaluation, Quantitative Metrics, Comparative Analysis | 80% of participants chose the GPT-4 generated legal document (Text B) as more effective. GPT-4's text received higher average scores than the human-written text (Text A) across all four evaluated dimensions (persuasiveness, clarity & coherence, strength of arguments, use of evidence) and across nearly all demographic subgroups (age, profession, gender). | High performance, Outperforms others | Prohibitively expensive cost of traditional legal advice, slowness and poor quality of free legal aid services, language barriers excluding non-Dutch speakers from accessing free legal aid. | High Cost of Legal Services, Legal Aid System Inefficiencies, Accessibility Barriers for Specific User Groups | AI generating legal arguments and advice efficiently and in multiple languages to increase accessibility, timeliness, and equity. AI-driven tools for faster and more cost-efficient case preparation. AI-driven mediation processes. | AI Tool Development, Access to Legal Information and Advice, Language Simplification and Multilingual Access, Cost Reduction and Efficiency, Online Dispute Resolution (ODR) | Access to legal advice, cost of legal services, language barriers in legal services, efficiency in legal processes, quality of legal representation, legal drafting. | Access to Legal Advice, Affordability of Legal Services / Cost Reduction, Language Access and Digital Divide, Improving Efficiency in Legal System / Profession, Access to Legal Representation, Legal Document Creation / Automation | Economically disadvantaged individuals, expatriate population in the Netherlands, younger populations. | Low-income individuals, Migrants, Population in Netherlands, Youth | Employment Law | Employment Law | Netherlands (Dutch legal context) | The Netherlands | Input for the GPT-4 generation task consisted of a processed summary derived from 9 proprietary, unstructured legal documents (e.g., letters, emails, reports) from a real-world Dutch employment dispute case, and the original lawyer's letter. The processing involved manual co-reference resolution and a 'Prompt Reducer' technique for text compression. | Input Data for Task (Non-Training), Proprietary Data, Dutch Legal Data, Legal Domain Data, Other Legal Documents, Case Law / Judgments, Unstructured Text Data, Expert-Annotated / Human-Curated / Human-Generated Data | Experimental design comparing human-written vs. AI-generated text. Pre-processing of input case documents for GPT-4 involved manual co-reference resolution and a Python-scripted 'Prompt Reducer' technique. A specific instructional prompt was designed for GPT-4 to generate the alternative legal letter. | Experimental Design, Data Preprocessing, Manual Data Curation, Script-based Automation, Prompt Engineering | NaN | Not applicable | False | True | The Python script for the 'Prompt Reducer' technique and OpenAI API interaction is provided in Appendix 2 of the paper. | Code available, Research artifact published in paper | The study has limitations and requires replication in varied settings. Future research should explore client's unique circumstances as input, AI's impact on legal education, and client perspectives on AI-generated legal texts. Current AI, as used, may miss nuanced client context unless explicitly provided. | Research and Evaluation Gaps, AI Legal Reasoning Limitations, Human Oversight and Professional Adaptation, User Interface and Usability Gaps | Initial AI summarization tests had factual inaccuracies due to differing author perspectives and pronoun ambiguity. The primary challenge was the token limitation of GPT-4, necessitating text compression techniques (Prompt Reducer) for the case documents. | Accuracy and Reliability of LLM Output, LLM Reasoning Capabilities, LLM Context Window and Long Input Management | AI 'hallucinations' (generating incorrect output). AI lacking nuanced understanding of case-specific information or client's broader, unstated circumstances unless explicitly provided. Ambiguity in legal responsibility and accountability for AI-assisted services. Perpetuation of human biases by AI models. Potential job displacement in the legal field. Risk of AI creating an imbalance in legal disputes if one party has superior AI tools. Potential for the judicial system to be overwhelmed if AI-generated filings increase significantly before the system can adapt. | Inaccurate or misleading AI output, Technical limitations of AI, Lack of transparency, accountability, and redress, Bias and discrimination, Job displacement, Exacerbation of inequality or two-tiered system, Undermining legal process or principles |
| --chwZiMxA0J.pdf | Google_Scholar | Generative Contracts | This paper explores how consumers can use generative AI like GPT-4 to draft their own basic contracts, presenting this as an opportunity to improve access to justice for underserved populations. It demonstrates GPT-4's capabilities through generated contract examples and a case study, while also discussing the implications, limitations (like potential inaccuracies), and risks (technological, privacy, regulatory). | Generative AI for Contract Drafting, Consumer Focus, Access to Justice Enhancement, LLM Application, Capability Demonstration, Limitations Identified, Risk Identification | True | Idealistic | True | 2.0 | Positive | Using OpenAI's GPT-4 large language model via ChatGPT to generate various types of consumer contracts based on simple user prompts. | Large Language Model, Legal Document Generation / Automation, Prompt Engineering | Qualitative evaluation based on generating drafts of over a dozen different contracts (employment, lease, bill of sale, etc.) using GPT-4 with simple prompts, plus a proof-of-concept case study of hypothetical consumers using GPT-4 to draft and modify a car sale contract. | Qualitative Analysis, Demonstration or Illustrative Examples | GPT-4 generated contracts that were generally functional, enforceable, short, and simple, though susceptible to errors and inconsistencies. The case study highlighted ease of use, speed, low cost, flexibility, and modifiability. Quality was deemed lower than lawyer-drafted contracts but likely superior to undocumented 'handshake' deals. | Moderate performance, Limitation: Operational or Technical, Underperforms others, Outperforms others | High cost of legal services, shortage of lawyers (particularly in rural 'legal deserts'), and the difficulty consumers face in reading and understanding legal documents. | High Cost of Legal Services, Limited Availability/Access to Legal Professionals/Expertise, Geographical Disparities in Legal Access, Complexity of Legal Language/Documents, Public Lack of Legal Knowledge/Awareness | Leveraging generative AI (like GPT-4) to provide consumers with low-cost, easily accessible, and user-friendly tools ('generative contracts') to draft their own basic contracts. | AI Tool Development, Cost Reduction and Efficiency, User Interface and Accessibility Design, Document Automation, Support for Self-Represented Litigants | Drafting basic consumer contracts (e.g., contracts for sales, services, leases, employment, NDAs). | Legal Document Creation / Automation, Protection of Rights | Consumers underserved by the legal system, particularly low-income Americans and rural populations facing lawyer shortages. | Consumers, Individuals with unmet legal needs, Low-income individuals, Population in USA, Rural populations, Individuals in legal deserts | Contract Law, Consumer Law, Transactional Law | Contract Law, Consumer Law, Transactional Law | Primarily California, USA (used for examples and specific legal references), but the concept is presented with broader applicability. | USA, International | The study used OpenAI's GPT-4, which is generally known to be trained on massive, diverse datasets scraped from the internet. The paper does not specify further details or mention fine-tuning on legal data for this study. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Proprietary Data | NaN | NaN | NaN | Not applicable | True | False | The method relies on OpenAI's ChatGPT interface with the GPT-4 model, accessible via a paid subscription (ChatGPT Plus). | Publicly accessible online tool or platform, Commercial product or service | Current limitations in drafting long, complex business contracts using generative AI. Need for further research into fine-tuning LLMs for specific legal applications and prompt engineering in law. Implicitly, the need for consumer adoption and mitigation of technological/societal risks. | AI Scope and Functionality Limitations, AI Legal Reasoning Limitations, Research and Evaluation Gaps, Public Understanding, Trust, and Adoption, Ethical Framework Deficiencies | NaN | NaN | Technological risks (inscrutability, accuracy/hallucination, bias, adversarial attacks), privacy/data protection risks (violation of privacy laws like GDPR, breach of client confidentiality), intellectual property infringement risks (use of copyrighted training data), and regulatory risks (unauthorized practice of law, impact of emerging AI regulations). | Technical limitations of AI, Lack of transparency, accountability, and redress, Inaccurate or misleading AI output, Bias and discrimination, Security vulnerabilities or malicious misuse, Data privacy and security breach, Copyright or intellectual property issues, Regulatory challenges or gaps, Unauthorized practice of law |
| Wx_p4tUveXoJ.pdf | Google_Scholar | A Question-Answering Approach to Evaluating Legal Summaries | This paper proposes a novel method using GPT-4 to evaluate the quality of legal summaries by generating question-answer pairs based on argumentative structure (Issue, Reason, Conclusion). The approach involves using GPT-4 to answer these questions based on a generated summary and then grading the answers, showing reasonable correlation with human evaluations. | Methodology Proposal, Evaluation of Legal Summaries, LLM Application, Argumentative Structure Analysis, Question-Answer Pair Generation for Evaluation | True | Idealistic | True | 1.0 | Positive | QA-based evaluation framework for legal summaries using GPT-4. It involves: 1) Generating QA pairs from a reference summary based on argumentative structure (Issue, Reason, Conclusion). 2) Answering these questions based on the generated summary. 3) Grading the generated answers against the reference answers. | Evaluation Framework Development, Legal Text Summarization Evaluation, Large Language Model, Question Answering System, Argument Mining / Analysis | Compared GPT-4 evaluation grades (0-10 scale, binarized at thresholds 5 and 6) with human binary evaluations ('YES'/'NO') for answers derived from summaries created by BART, LED, and GPT-4. Evaluation used 10 Canadian case summaries (48 QA pairs) and Pearson/Spearman correlation metrics. | LLM as Judge, Human Evaluation, Custom Dataset Evaluation, Comparative Analysis, Quantitative Metrics | Correlations varied by summary generation model and argumentative component (Issue, Reason, Conclusion). The LED model showed the highest overall correlation (IRC Pearson 0.87/0.88, Spearman 0.84/0.85 at thresholds 5/6). Negative correlations were observed between GPT-4 grades and human evaluation for 'Reason' type questions on BART and GPT-4 generated summaries. | High performance, Mixed performance, Limitation: Operational or Technical | Difficulty in automatically evaluating the quality and argumentative structure of legal summaries, hindering the reliable assessment of tools meant to make legal text more accessible. | Technical Challenges in AI Development, Lack of Standardized Benchmarks for Legal AI | A QA-based evaluation framework using LLMs (GPT-4) to assess summary quality by focusing on argumentative structure (Issue, Reason, Conclusion), potentially improving summary generation and accessibility. | Benchmarking and Evaluation Frameworks, AI Tool Development, Document Automation, Access to Legal Information and Advice | Evaluation of legal text summarization quality | Improving Foundational AI Capabilities for Legal Applications, Legal Text Simplification / Plain Language | NaN | NaN | General Case Law | General Law, Case Law | Canada | Canada | Summarization models (BART, LED) fine-tuned on a dataset of 1,049 annotated Canadian legal case summaries (Issue, Reason, Conclusion annotations) paired with full texts from the Canadian Legal Information Institute. GPT-4 used zero-shot. The evaluation method itself (GPT-4 based QA) did not require specific training data beyond GPT-4's pre-training. | Fine-tuning Dataset, Author-Created New Dataset, Canadian Legal Data, Legal Domain Data, Case Law / Judgments, Structured Data, Expert-Annotated / Human-Curated / Human-Generated Data, Unstructured Text Data, Publicly Available Data, Pre-trained LLM's General Training Corpus | Experimental design involving prompt engineering for GPT-4 (QA generation, answer prediction, grading) and comparison with human evaluations using correlation analysis (Pearson, Spearman). | Experimental Design, Prompt Engineering, Automated Evaluation using LLM, Human Evaluation, Correlation Analysis | Code made available on GitHub. | Open source code release | True | False | Code available on GitHub (https://github.com/JoyceXu02/QA_evaluation), requires GPT-4 API access. | Code available | Sensitivity to prompt engineering, need for larger-scale evaluation, quality control for LLM generation (hallucinations, consistency, especially with longer input), potential exploration/calibration using open-source models. | User Interface and Usability Gaps, Research and Evaluation Gaps, AI Accuracy and Reliability | Cost of GPT-4 API and human evaluation limited test size; aligning automated scores with human perception; controlling for hallucination or out-of-context answers generated by the LLM; achieving consistent correlation across different argumentative components (Issue, Reason, Conclusion). | Financial Cost and Resource Constraints, Evaluation Challenges and Metrics, LLM Hallucination and Factual Errors, Accuracy and Reliability of LLM Output | LLM hallucination in generated answers. Potential divergence between automated evaluation scores and human judgments of quality (e.g., negative correlations found for 'Reason' component). | Inaccurate or misleading AI output, Technical limitations of AI |
| RtbC1q5BGA0J.pdf | Google_Scholar | How well do SOTA legal reasoning models support abductive reasoning? | This paper introduces L'ART, a new logic-augmented dataset, and a redefined task (𝛼𝑁𝐿𝐼*) to evaluate abductive reasoning in AI models, particularly in the legal domain. Experimental results show that current state-of-the-art legal models and large language models generally perform poorly, highlighting limitations in their abductive reasoning capabilities. | Dataset Creation, Task Definition for Abductive Reasoning, Evaluation of Abductive Reasoning, Legal Reasoning Assessment, Limitations of LLMs in Legal Reasoning | True | Idealistic | True | 1.0 | Neutral | L'ART dataset, a logic-augmented dataset for abductive reasoning, and the 𝛼𝑁𝐿𝐼* task, a redefined binary classification task for evaluating abduction. | Dataset Creation / Curation, Abductive Reasoning, Logical Reasoning, Evaluation Task Definition | The L'ART dataset and 𝛼𝑁𝐿𝐼* task were used to evaluate several SOTA transformer models (BERT Base/Large, BERT-PLI, Legal BERT, BERTLaw, NFSP ParaLaw Nets, GPT-3) on their abductive reasoning capabilities. Models were trained for binary classification (valid/invalid triple) and performance was measured by accuracy on a held-out test set. | Benchmark Dataset Evaluation, Quantitative Metrics, Comparative Analysis | BERT Base achieved the highest test accuracy (0.6162), outperforming specialized legal models (e.g., Legal BERT 0.5619, BERTLaw 0.5371) and even BERT Large (0.5000). GPT-3 (zero-shot) had the lowest accuracy (0.4959). | Moderate performance, Outperforms others, Underperforms others | Current AI models, including SOTA legal-specific LLMs, exhibit poor performance on abductive reasoning tasks, which are crucial for legal argumentation and interpretation. This deficiency limits their reliability for complex legal applications that could support access to justice. | AI Limitations in Legal Reasoning/Nuance, Technical Challenges in AI Development | Developing more robust datasets (like L'ART) and evaluation tasks (like 𝛼𝑁𝐿𝐼*) for abductive reasoning. Future research should explore alternative pretraining approaches, novel model architectures, and better integration of legal domain knowledge to improve AI's abductive reasoning capabilities. | Data Curation and Management, Benchmarking and Evaluation Frameworks, Enhanced AI Capabilities, Legal Knowledge Representation and Management | Improving foundational AI capabilities for legal reasoning (specifically abductive reasoning) to support the development of more reliable AI tools for legal services and access to justice. | Improving Foundational AI Capabilities for Legal Applications, Democratizing Law / Closing Justice Gap / Rule of Law | Underserved communities (mentioned generally). | Marginalized communities | General legal reasoning (examples include statute law retrieval, contract risk analysis, case law retrieval). | General Law, Statutory Law, Contract Law, Case Law | International | International | The L'ART dataset (498,697 samples) is built upon the ART dataset (crowdsourced commonsense narrative contexts). It includes: 1) high-plausibility positive samples from ART, 2) newly generated positive samples using a logic-based theorem generator on logically consistent inference chains, 3) augmented positive samples by interchanging the first observation (𝒪1) and hypothesis (ℋ), and 4) negative samples derived by logically negating the second observation (𝒪2) based on a truth table for the expression 𝒪1∧ℋ =⇒ 𝒪2. | Author-Modified Existing Dataset, Data From Existing Public NLP/Legal Datasets/Benchmarks, Non-Legal Domain Specific Data, Synthetic Data, Structured Data, User-Generated Content | Task redefinition (binary classification of (O1, H, O2) validity instead of choosing between two hypotheses), logic-based data generation and augmentation (observation-hypothesis interchangeability), and systematic negative sample creation using logical negation and truth tables, based on an initial crowdsourced dataset (ART). | Task Redefinition, Logic-based Data Generation, Data Augmentation, Negative Sampling Strategy, Crowdsourcing | NaN | Not applicable | False | False | NaN | NaN | Current SOTA models, including legal-specific ones, lack robust abductive reasoning. Technical gaps include: need for alternative pretraining approaches tailored to abductive reasoning, development of novel model architectures for legal reasoning, better understanding of model capacity vs. performance on such tasks, and effective integration of legal domain knowledge into pretraining. | AI Legal Reasoning Limitations, Data Availability and Quality, Research and Evaluation Gaps | Ensuring logical consistency and quality in initial crowdsourced data (ART). Defining abductive reasoning tasks precisely for evaluation. Generating meaningful and logically sound negative samples for abductive reasoning. Overcoming the bias in legal models trained primarily on legal reasoning rather than abductive reasoning. | Data Quality, Processing, and Preparation, Evaluation Challenges and Metrics, LLM Reasoning Capabilities, Bias in AI Systems and Data | Over-reliance on current SOTA LLMs for legal tasks that require significant abductive reasoning, given their demonstrated poor performance, potentially leading to flawed legal analyses or applications. Misdirection of development efforts if the limitations in abductive reasoning are not addressed. | Over-reliance on AI, Technical limitations of AI, Inaccurate or misleading AI output, Risk of misapplication or misuse |
| E7b4JLhQct8J.pdf | Google_Scholar | The Courtrooms Strikes Back: Generative AI’ s Force in Courts | This paper explores the increasing use of generative AI by judges in judicial decision-making, highlighting its potential to enhance court legitimacy and efficiency, which can support access to justice. However, it also details significant risks, such as bias, unreliability, and ethical concerns from AI systems like ChatGPT, which could undermine court legitimacy if not properly managed. | Generative AI in Judicial Decision-Making, Benefit Identification, Court Legitimacy Enhancement, Efficiency Improvement, Access to Justice Enhancement, Risk Identification, Bias in AI, Reliability Issues, Ethical Concerns | True | Idealistic | True | 3.0 | Neutral | Generative AI systems (e.g., ChatGPT) for judicial assistance tasks like legal drafting, case law summarization, and acting as a 'virtual sparring partner'. | Generative AI, Large Language Model, Judicial Assistance Tool, Legal Document Generation / Automation, Legal Text Summarization, Interactive Reasoning Support | NaN | Not Applicable | NaN | NaN | Bias and unreliability of AI systems due to opaque 'black box' nature and unrepresentative training data, risk of AI 'hallucinations', erosion of public trust and court legitimacy, and ethical concerns regarding AI's normative impact and the influence of private developers on judicial independence. | Bias in AI/Data, AI Unreliability/Inaccuracy, Lack of AI Transparency/Explainability, Data Scarcity/Quality for AI, Lack of Trust in Justice System, Threats to Justice System Legitimacy from AI, Ethical Concerns with AI in Law, Influence of Private Sector on Judiciary | Fostering AI literacy within the judiciary through training, and enhancing transparency and accountability in judicial decision-making, potentially by strengthening the judicial duty to state reasons when AI is used. | Education and AI Literacy, Judicial System Enhancement, Transparency and Explainability in AI, Regulation, Ethics, and Governance | Improving efficiency of judicial processes (leading to faster case resolution) and enhancing the quality of judicial decisions, which are pre-requisites for effective access to justice; maintaining court legitimacy. | Judicial System Modernization / Efficiency, Democratizing Law / Closing Justice Gap / Rule of Law | NaN | NaN | General (judicial decision-making across various fields) | General Law, Judicial Processes, Multiple Fields | Colombia, India, UK, Council of Europe (CEPEJ). Discussion is broadly applicable. | Colombia, India, UK, Council of Europe, International | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | True | False | The paper discusses the use of existing generative AI systems like ChatGPT, which are commercially available with free or paid access tiers. | Commercial product or service, Freemium access, Publicly accessible online tool or platform | Technical gaps include lack of transparency, bias, and unreliability in current generative AI. Societal gaps include insufficient AI literacy in the judiciary, need for enhanced transparency and accountability mechanisms (e.g., duty to state reasons), and concerns over democratic oversight and private sector influence on AI used in courts. | Transparency and Explainability, Bias in AI, AI Accuracy and Reliability, Human Oversight and Professional Adaptation, Accountability and Redress Mechanisms, Regulatory and Governance Gaps | NaN | NaN | Unreliable or biased outputs due to opaque models and unrepresentative training data, AI 'hallucinations' (generating false information), compromised judicial independence and impartiality from private developers' influence, privacy and data protection violations when handling sensitive data, judges' over-reliance due to automation bias, and overall erosion of court legitimacy and public trust. | Inaccurate or misleading AI output, Bias and discrimination, Lack of transparency, accountability, and redress, Undermining legal process or principles, Data privacy and security breach, Over-reliance on AI, Erosion of trust in legal system or AI |
| cZ5qYLunKBoJ.pdf | Google_Scholar | Is disclosure and certification of the use of generative AI really necessary? | This paper critiques the proliferation of individual judicial standing orders requiring disclosure and certification of generative AI (GenAI) use in legal filings, arguing they are often redundant, inconsistent, and may stifle innovation beneficial for access to justice. It proposes instead the adoption of consistent, court-wide rules developed through public consultation, or public notices, and emphasizes the applicability of existing legal and ethical rules. | Critique of GenAI Regulation in Courts, Disclosure Requirements, Impact on Innovation, Access to Justice Implications, Proposal for Consistent Court Rules | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | Inconsistent and burdensome individual judicial regulations regarding GenAI; the inherent unreliability of general GenAI (e.g., hallucinations, erroneous outputs) especially for pro se litigants; and potential discouragement of technology that could enhance access to justice. | Regulatory Inconsistency, AI Unreliability/Inaccuracy, Challenges for Self-Represented Litigants, Risk of Hindering A2J Innovation | Implement consistent, court-wide rules for GenAI use, developed after public notice and comment, instead of individual standing orders. Provide public guidance, especially for pro se litigants, on responsible GenAI use and verification obligations. Leverage existing rules of civil procedure and professional conduct, and encourage education by bar associations. | Regulation, Ethics, and Governance, Education and AI Literacy, Support for Self-Represented Litigants, Policy and Regulatory Reform | Judicial regulation of AI in legal practice; impact of AI governance on access to justice; enabling unrepresented parties (pro se litigants) to utilize legal tech; reducing legal costs and increasing efficiency through AI. | Regulatory Reform (Legal Services and AI), Ethical AI in Law and AI Governance, Support for Self-Represented Litigants, Affordability of Legal Services / Cost Reduction, Improving Efficiency in Legal System / Profession | Pro se litigants (unrepresented parties). | Self-represented litigants | Civil litigation | Civil Litigation | United States, Canada | USA, Canada | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | The need for a nuanced, consistent, and less burdensome regulatory approach to GenAI in the legal system that encourages beneficial uses for access to justice. The current unreliability of general-purpose GenAI for complex legal tasks and the limited access for pro se litigants to more specialized and verified legal AI tools. Lack of comprehensive institutional guidance from bodies like bar associations. | Regulatory and Governance Gaps, Access, Equity, and Digital Divide, AI Accuracy and Reliability, Human Oversight and Professional Adaptation | NaN | NaN | Generation of inaccurate legal information (hallucinations, fake citations) by GenAI; infringement on attorney work product due to overly broad disclosure orders; chilling innovation and use of technology beneficial for access to justice; inconsistent judicial orders creating confusion and increasing costs; disclosure of confidential client information when using public GenAI tools; difficulty in accurately detecting AI-generated content; lawyers violating ethical duties (competence, candor, confidentiality) through improper GenAI use. | Inaccurate or misleading AI output, Undermining legal process or principles, Stifling innovation, Regulatory challenges or gaps, Negative economic impact, Data privacy and security breach, Ethical concerns, Risk of misapplication or misuse |
| QWScjUiMBQwJ.pdf | Google_Scholar | Evolving Norms Governing AI Engagement in Legal Practice and the Prospective Alignment of Law School Curriculum | This paper investigates pioneering US professional standards for regulating generative AI in legal practice, emphasizing the need for lawyers to understand AI's benefits and risks. It argues for aligning law school curricula with AI advancements and regulatory norms to cultivate AI-empowered legal professionals and upholds the importance of access to AI. | Professional Standards for AI in Law, US Focus, Generative AI Regulation, Legal Education Reform, Lawyer Competence in AI, Access to AI | True | Idealistic | True | 3.0 | Positive | Generative AI (e.g., ChatGPT, LLMs) | Generative AI, Large Language Model | NaN | Not Applicable | NaN | NaN | The potential for an "AI divide" denying equitable access to AI's benefits in the legal field; risks of AI bias, lack of transparency, and generation of erroneous information undermining client rights and fair justice; breaches of client confidentiality. | Risk of AI Exacerbating Inequality, Digital Divide, Bias in AI/Data, Lack of AI Transparency/Explainability, AI Unreliability/Inaccuracy, Risk to Human Rights from AI, Data Privacy Concerns with AI | Comprehensive AI education for legal professionals starting in law school; development and enforcement of robust professional and judicial standards for AI use focusing on accountability and human oversight; ensuring equitable access to the benefits of AI for all. | Education and AI Literacy, Regulation, Ethics, and Governance, Human Oversight and Collaboration, Policy and Regulatory Reform | Equitable access to AI benefits in legal services; protection of client rights (confidentiality, competence, due diligence) in the context of AI use; mitigating AI bias in legal applications and the justice system. | Democratizing Law / Closing Justice Gap / Rule of Law, Protection of Rights, Ethical AI in Law and AI Governance | General public / All clients of legal services; implicitly, communities vulnerable to AI bias (e.g., racial or economic bias in criminal justice). | General public, Clients of legal services, Vulnerable populations, Minority groups, Low-income individuals | General legal practice, Professional responsibility, Legal education, Criminal justice (briefly mentioned in context of risk assessment tools). | General Legal Practice, Professional Responsibility, Legal Education, Criminal Justice | United States (primarily); relevance for other common law jurisdictions discussed. | USA, Common Law Jurisdictions | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | True | True | The paper discusses generally available generative AI tools like ChatGPT, which has publicly accessible free and paid versions. | Publicly accessible online tool or platform, Freemium access | The risk of an "AI divide" if benefits are not equitably distributed; inadequacy of current continuing legal education for comprehensive AI training, necessitating law school curriculum reform; persistent issues of AI bias, lack of transparency, and explainability in legal AI tools. | Access, Equity, and Digital Divide, Human Oversight and Professional Adaptation, Bias in AI, Transparency and Explainability | NaN | NaN | AI bias leading to unfair outcomes (e.g., racial, economic); lack of explainability and transparency in AI decision-making; generation of false information (hallucinations) by AI; breaches of client confidentiality; over-reliance on AI compromising lawyers' professional judgment; inadvertent creation of attorney-client relationships via AI; potential for an "AI divide" in society. | Bias and discrimination, Lack of transparency, accountability, and redress, Inaccurate or misleading AI output, Data privacy and security breach, Over-reliance on AI, Ethical concerns, Exacerbation of inequality or two-tiered system |
| l4PMeM8YyF0J.pdf | Google_Scholar | How can we manage the risks and liabilities associated with legal translation in the age of machine translation and generative AI? | This paper examines the legal and ethical challenges, particularly liability, copyright, and professional rules, associated with using NMT and generative AI for legal translation. It argues for a narrative shift to enhance the role of human translators and proposes due diligence standards and appropriate liability solutions to manage risks. | AI for Legal Translation, Neural Machine Translation, Generative AI Application, Legal Challenges, Ethical Challenges, Liability Issues, Copyright Issues, Role of Human Translators, Due Diligence Standards | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | Increased demand for legal translation unmet by human translators; difficulty ensuring availability of legal information in people's own languages; risks of bias, mis/disinformation, confidentiality breaches, and inaccuracies (e.g., omissions) from AI translation; inadequate liability frameworks for AI-generated translation errors; disruption of translators' professional standing and liability. | Resource Constraints, Accessibility Barriers for Specific User Groups, Bias in AI/Data, AI-driven Misinformation/Disinformation, Data Privacy Concerns with AI, AI Unreliability/Inaccuracy, Lack of AI Accountability, Impact on Legal Professionals | Short-term: Implementing due diligence standards for certifying legal translations generated with NMT or generative AI. Long-term: A narrative change to enhance and support the role of the human expert (legal translator), coupled with developing appropriate liability solutions. | Regulation, Ethics, and Governance, Human Oversight and Collaboration, Language Simplification and Multilingual Access, Policy and Regulatory Reform | Access to legal information in native languages; fair trial (translation of court documents); governmental transparency. | Language Access and Digital Divide, Protection of Rights, Access to Legal Information | Individuals who do not speak the language of the court; vulnerable individuals (e.g., in asylum adjudications); general public needing access to legal information in their own language. | Individuals with language barriers, Vulnerable populations, Asylum seekers and refugees, General public | General legal (transactional documents), Criminal law (court documents, fair trial), Civil procedure (court-related translation), Asylum law. | Transactional Law, Criminal Law, Civil Procedure, Immigration Law | International (mentions Canada and Europe as examples but discusses issues broadly). | International, Canada, Europe | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | The current 'human-in-the-loop' narrative is misleading and doesn't adequately value human expertise; lack of appropriate liability solutions that support human translators in AI-assisted workflows; need for better risk management for inaccuracies, bias, and confidentiality in AI legal translation. | Human Oversight and Professional Adaptation, Accountability and Redress Mechanisms, Regulatory and Governance Gaps, Ethical Framework Deficiencies, AI Accuracy and Reliability, Bias in AI, Security and Privacy of Data | NaN | NaN | Bias in translation; exacerbation of mis- and disinformation; breaches of confidentiality (e.g., lawyer-client relationship); increased vulnerability for individuals in sensitive contexts (e.g., asylum adjudications); inaccuracies and omissions in translations; misallocation of legal liability for translation errors. | Bias and discrimination, Inaccurate or misleading AI output, Security vulnerabilities or malicious misuse, Data privacy and security breach, Consumer harm, Lack of transparency, accountability, and redress |
| Z5qcyozSxVQJ.pdf | Google_Scholar | Artificial intelligence at the bench: Legal and ethical challenges of informing —or misinforming —judicial decision-making through generative AI | This paper examines the legal and ethical challenges of using Generative AI (GenAI) in judicial decision-making, highlighting risks like bias and misinformation. Through case studies and analysis of regulatory approaches, it proposes a comprehensive framework for the responsible and equitable deployment of GenAI in the judiciary to enhance access to justice and uphold the rule of law. | Generative AI in Judicial Decision-Making, Legal Challenges, Ethical Challenges, Risk Identification, Bias in AI, AI Hallucinations/Inaccuracy, Framework Proposal, Responsible AI Deployment, Equitable AI Deployment, Access to Justice Enhancement | True | Idealistic | True | 2.0 | Neutral | Generative AI (specifically, the use of Large Language Models like ChatGPT by judicial officers in decision-making processes). | Generative AI, Large Language Model, Judicial Decision-Making Support | Analysis of case studies from Colombia, Mexico, Peru, and India where judges used ChatGPT. Review of proactive regulatory approaches to AI/GenAI in other jurisdictions (UK, New Zealand, EU, Canada, Singapore, Estonia). | Qualitative Analysis, References External Evaluation | The analysis of case studies reveals unregulated, ad-hoc use of GenAI (like ChatGPT) by judges, leading to significant risks including bias, generation of misinformation ('hallucinations'), lack of transparency, accountability gaps, data privacy issues, and potential erosion of public trust and judicial independence. | Risk or Ethical concern highlighted, Limitation: Hallucination or Factual inaccuracy, Limitation: Bias, Limitation: Security or Privacy, Descriptive or Conceptual finding | Bias amplification, lack of transparency and explainability ('black box' problem), generation of fabricated information ('hallucinations'), undermining judicial independence and discretion, accountability and legal liability gaps, data protection risks, and potential for GenAI to widen access to justice disparities due to resource constraints and ad-hoc implementation. | Bias in AI/Data, Lack of AI Transparency/Explainability, AI Unreliability/Inaccuracy, Threats to Judicial Independence, Lack of AI Accountability, Data Privacy Concerns with AI, Risk of AI Exacerbating Inequality | A dual-prong framework for responsible GenAI integration: 1) Foundational standards for GenAI systems (capacity assessment, stakeholder engagement, licensing/verification, trusted datasets, explainability, clear responsibility allocation, prompt engineering). 2) Application principles for GenAI deployment (updating ethical standards, continuous legal education, case-based risk assessment, disclosure to parties, verification systems, specific procedural rights, ongoing audits). | Conceptual Frameworks, Regulation, Ethics, and Governance, Benchmarking and Evaluation Frameworks, Data Curation and Management, Transparency and Explainability in AI, Prompt Engineering and LLM Interaction Design, Education and AI Literacy | Access to justice, fairness in judicial decision-making, responsible use of AI in courts, ethical AI governance, rule of law. | Democratizing Law / Closing Justice Gap / Rule of Law, Judicial System Modernization / Efficiency, Ethical AI in Law and AI Governance | General public interacting with the judicial system, with specific mentions of implications for marginalized communities and individuals with disabilities (e.g., a case involving a child with autism). | General public, Litigants, Marginalized communities, People with disabilities, Children with disabilities | General judicial decision-making. Case studies cover health law/social security, civil procedure, family law (child support), electoral law, and criminal law (bail applications). | Judicial Processes, Health Law, Social Security Law, Civil Procedure, Family Law, Electoral Law, Criminal Law | Case studies from Colombia, Mexico, Peru, India. Comparative regulatory approaches from UK, New Zealand, EU, Canada, Singapore, Estonia. The proposed framework is intended for general applicability. | Colombia, Mexico, Peru, India, UK, New Zealand, EU, Canada, Singapore, Estonia, International | The paper discusses challenges with GenAI (like ChatGPT used in case studies) trained on vast, often unverified, non-legal, and potentially biased internet-scale data. It advocates for the use of 'trusted datasets', potentially closed-network and jurisdiction-specific, for judicial GenAI. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Data Bias Concerns Noted, Proprietary Data, Legal Domain Data | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Lack of consensus and comprehensive frameworks for GenAI in judiciaries; technical limitations of current GenAI (accuracy, bias, explainability, hallucinations); societal challenges (public trust, ensuring equitable access, resource disparities); unclear legal liability; need for standardized AI audits and refined prompt engineering for legal contexts; insufficient legal education on AI. | Regulatory and Governance Gaps, Ethical Framework Deficiencies, AI Accuracy and Reliability, Bias in AI, Transparency and Explainability, Public Understanding, Trust, and Adoption, Access, Equity, and Digital Divide, Accountability and Redress Mechanisms, Research and Evaluation Gaps, Human Oversight and Professional Adaptation | Key challenges identified in using GenAI in judiciaries include: ensuring transparency and interpretability of AI outputs, mitigating algorithmic bias, addressing poor data quality and AI 'hallucinations', establishing clear accountability and legal liability, protecting data privacy, preserving judicial independence, and ensuring GenAI equitably improves access to justice rather than exacerbating inequalities. | Transparency and Explainability of AI, Bias in AI Systems and Data, Data Quality, Processing, and Preparation, LLM Hallucination and Factual Errors, Accountability and Liability for AI Errors, Data Privacy, Security, and Confidentiality, Ethical Considerations, User Adoption, Trust, and Acceptance | Bias propagation leading to discriminatory outcomes, opacity in decision-making undermining due process, factual inaccuracies ('hallucinations') in AI-generated content, compromised judicial independence and discretion, erosion of public trust, unclear legal liability for AI-induced errors, data privacy breaches, increased justice disparities, and technological solutionism. | Bias and discrimination, Lack of transparency, accountability, and redress, Undermining legal process or principles, Inaccurate or misleading AI output, Erosion of trust in legal system or AI, Data privacy and security breach, Exacerbation of inequality or two-tiered system, Over-reliance on AI |
| _KwbPCDd_GwJ.pdf | Google_Scholar | Automatic Information Extraction From Employment Tribunal Judgements Using Large Language Models | This paper evaluates the performance of GPT-4 in automatically extracting eight key pieces of information (facts, claims, outcomes, statutes, precedents, reasons, etc.) from UK Employment Tribunal judgments. The study finds GPT-4 achieves high accuracy, suggesting its potential for legal information processing and facilitating downstream tasks like outcome prediction. | LLM Evaluation, Information Extraction from Legal Documents, UK Law Focus, Employment Law Focus, Accuracy Assessment, Legal Information Processing, Support for Legal Case Outcome Prediction | True | Idealistic | True | 2.0 | Positive | Using GPT-4 with engineered prompts for automatic information extraction of specific fields (facts, claims, statutes, precedents, outcomes, remedies, reasons) from legal judgments. | Large Language Model, Prompt Engineering, Information Extraction | Manual verification by a legal expert and senior legal expert on a stratified sample of 260 UKET judgments. Accuracy was scored (0 or 1) for each of the eight extracted fields. A second check assessed suitability for a downstream prediction task. | Expert Evaluation, Custom Dataset Evaluation, Quantitative Metrics | High accuracy across all extraction tasks (generally >0.9). Perfect accuracy (1.0) for references to legal statutes and precedents; near-perfect accuracy (0.996) for general outcomes, detailed outcomes, and reasons. Lowest accuracy for labelled outcomes (0.912) and facts (0.942 overall, 0.919 for prediction-suitable cases), still considered high. | High performance | Knowledge imbalance between employers and employees regarding access to legal knowledge and predictive tools derived from tribunal data. | Information Asymmetry, Unequal Access to A2J Technology | Develop accurate and open information extraction and predictive systems using AI, accessible to the general public (both employers and employees), to democratize access to legal knowledge and reduce imbalances. | AI Tool Development, Access to Legal Information and Advice, Open Source Initiatives and Collaboration, Enhanced AI Capabilities | Information extraction from court judgments, analysis of employment law disputes, case outcome prediction, access to legal information. | Legal Document Analysis / Review, Improving Foundational AI Capabilities for Legal Applications, Access to Legal Information | Employees involved in or considering UK Employment Tribunal claims, potentially lacking resources compared to employers; the general public. | Employees, Employment tribunal claimants, Population in UK, Low-income individuals, General public | Employment Law | Employment Law | United Kingdom (UK Employment Tribunal - England, Wales, Scotland) | UK | The study uses GPT-4, a large language model pre-trained by OpenAI on diverse text corpora. The input data for the extraction task consisted of 260 publicly available UK Employment Tribunal judgments from the Cambridge Law Corpus. | Pre-trained LLM's General Training Corpus, Proprietary Data, General Web Data / Broad Internet Text, Input Data for Task (Non-Training), Publicly Available Data, UK Legal Data, Legal Domain Data, Case Law / Judgments, Data From Existing Public NLP/Legal Datasets/Benchmarks | Iterative prompt engineering based on OpenAI guidelines (clear instructions, persona definition, delimiters, task specification, examples, systematic testing). Manual quality checks by legal experts to assess accuracy and suitability for prediction. | Iterative Design Process, Prompt Engineering, Guideline-based Design, Expert Validation | NaN | Not applicable | True | False | The technique uses the GPT-4 API (32k version), which is commercially available from OpenAI. The prompts are detailed in the paper. | API access, Commercial product or service, Research artifact published in paper | Need for improved prompting to consistently distinguish procedural vs. substantive facts. Potential information bias when using facts/claims extracted from judges' post-outcome decisions for prediction. Predictive models based solely on tribunal judgments lack context from original claim forms and out-of-court settlements. | User Interface and Usability Gaps, AI Legal Reasoning Limitations, Bias in AI, Data Availability and Quality | Designing prompts for accurate and consistent extraction across varied judgments (e.g., handling subsequent claims, rule types, outcome labelling nuances, multiple parties). Difficulty in making GPT-4 distinguish procedural/substantive facts contextually. Ensuring consistent labelling logic for ambiguous situations (e.g., withdrawals, preliminary rulings). | Prompt Engineering and Optimization, Accuracy and Reliability of LLM Output, Output Variability and Consistency, LLM Reasoning Capabilities | Potential inaccuracies or biases in LLM extractions. Information bias in prediction models trained on post-hoc judicial summaries. Incompleteness of models based solely on adjudicated cases (missing settlements, original filings). Exacerbation of inequality if powerful AI tools are only accessible to resourceful parties. Systems assisting judicial authorities may be classified as high-risk (EU AI Act context). | Inaccurate or misleading AI output, Bias and discrimination, Technical limitations of AI, Exacerbation of inequality or two-tiered system, Regulatory challenges or gaps |
| MSfmdl3ZpvMJ.pdf | Google_Scholar | Bridging Law and Data: Augmenting Reasoning via a Semi-Structured Dataset with IRAC methodology | This paper introduces LEGAL SEMI, a new benchmark dataset for legal reasoning based on the IRAC framework, focusing on Malaysian Contract Law. It includes 54 annotated scenarios and a structured knowledge graph (SKG), demonstrating through experiments with LLMs that integrating the SKG improves performance on IRAC tasks like issue identification and rule retrieval. | Benchmark Creation, Dataset Creation, Legal Reasoning Evaluation, IRAC Framework Application, Malaysian Law Focus, Contract Law Focus, Knowledge Graph Integration, LLM Performance Improvement | True | Idealistic | True | 1.0 | Positive | Creation and use of the LEGAL SEMI benchmark dataset, including a Structured Knowledge Graph (SKG), to augment Large Language Models (LLMs) for IRAC (Issue, Rule, Application, Conclusion) analysis of legal scenarios. | Dataset Creation / Curation, Benchmarking / Evaluation, Knowledge Graph Integration, Large Language Model Augmentation, Legal Analysis / Reasoning Tool | Experiments were conducted using four LLMs (GPT-3.5 Turbo, Llama 2, Mistral, Gemini) on the LEGAL SEMI dataset. Tasks included legal concept identification, issue identification, rule retrieval, application generation, and conclusion generation. Evaluation involved automatic metrics (e.g., F1 score for concepts, precision/recall/F1 for rules, GPT-3.5 Turbo as judge for generation tasks) and comparative human evaluation using legal rubrics and Spearman correlation. | Benchmark Dataset Evaluation, Quantitative Metrics, LLM as Judge, Expert Evaluation, Comparative Analysis | Integrating the SKG improved issue generation quality by over 21.4% across LLMs. Using the SKG (legal concepts + textbook interpretations) for rule retrieval achieved the best F1 score of 16.3% at top-5 results, significantly outperforming baseline retrieval. Providing identified issues and rules improved application generation (+18.9% for GPT-3.5 Turbo). Providing the application section improved conclusion generation (+71.4% for GPT-3.5 Turbo). | Technique improves outcome, Low performance, Outperforms others | Backlogs in courts, complexity of legal practice, scarcity of legal professionals, limitations of LLMs in accurate legal reasoning (wrong conclusions, incorrect rule citations, difficulty with legalese vs. everyday language). | Judicial/Legal System Inefficiencies, Complexity of Legal System/Procedures, Limited Availability/Access to Legal Professionals/Expertise, AI Limitations in Legal Reasoning/Nuance, AI Unreliability/Inaccuracy | Developing high-quality, structured legal datasets (like LEGAL SEMI with its SKG) to enhance LLM reasoning capabilities for legal tasks, specifically automating IRAC analysis to potentially assist legal professionals and improve efficiency. | Data Curation and Management, Legal Knowledge Representation and Management, Enhanced AI Capabilities, Document Automation, Cost Reduction and Efficiency | Automating IRAC analysis, Legal reasoning, Legal document analysis, Legal Information Retrieval. | Improving Foundational AI Capabilities for Legal Applications, Legal Document Analysis / Review, Access to Legal Information | NaN | NaN | Contract Law (specifically Formation of Contract) | Contract Law | Malaysia | Malaysia | The LEGAL SEMI dataset: 54 legal scenarios covering Malaysian Contract Law, annotated by law students/junior lawyers using the IRAC framework. Structured Knowledge Graph (SKG): automatically constructed via rule-based extraction from a Malaysian business law textbook ('Law for Business'), the Malaysian Contracts Act 1950, and 76 Malaysian court cases. The LLMs used (GPT-3.5, Llama 2, Mistral, Gemini) are pre-trained models. | Author-Created New Dataset, Malaysian Legal Data, Legal Domain Data, Legal Contracts, Expert-Annotated / Human-Curated / Human-Generated Data, Structured Data, Legal Scholarly Content / Textbooks, Legislation / Statutes / Regulations, Case Law / Judgments, Synthetic Data, Pre-trained LLM's General Training Corpus | Dataset construction involved scenario selection (human-written and LLM-generated/human-refined), expert review, and detailed human annotation according to IRAC guidelines using a custom-built annotation tool. SKG construction involved rule-based information extraction from structured legal texts (textbook index/content, legislation). Human evaluation rubrics based on legal education standards were used. | Dataset Creation, Scenario-based Design/Evaluation, Expert Review, Human Annotation, Framework-guided Design, Tool Development, Knowledge Graph Construction/Integration, Rule-based Information Extraction, Development of Evaluation Rubrics | The paper states the dataset (LEGAL SEMI) will be made publicly available upon acceptance. | Proposed deployment (not implemented), Public dataset/benchmark release | False | False | LEGAL SEMI will be publicly available upon acceptance of this paper. | Future public release | LLMs struggle with identifying lower-level legal concepts compared to high-level ones. Generating lay-language interpretations of legal rules using LLMs can suffer from hallucination. The dataset scope is limited to Malaysian Contract Law (formation). | AI Legal Reasoning Limitations, AI Accuracy and Reliability, Data Availability and Quality, Multilingual and Jurisdictional Specificity Gaps | High effort and expertise required for reliable legal annotation. Bridging the semantic gap between lay language in scenarios and legalese in rules. Ensuring factual accuracy and reasoning fidelity in LLM outputs for legal tasks. Evaluating complex generative tasks in the legal domain. Automating the construction of comprehensive and accurate legal knowledge graphs. | Cost and Complexity of Data Annotation, LLM Reasoning Capabilities, Accuracy and Reliability of LLM Output, Evaluation Challenges and Metrics, Data Quality, Processing, and Preparation | LLM limitations leading to incorrect legal conclusions, citation of wrong legal rules, and hallucination, which pose risks if used without expert oversight in real-world legal analysis. | Technical limitations of AI, Inaccurate or misleading AI output, Over-reliance on AI |
| sdjBd5vNE04J.pdf | Google_Scholar | Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learning | This paper proposes D3LM (Diagnostic Legal Large Language Model), a novel framework that uses adaptive lawyer-like diagnostic questions, driven by a Positive-Unlabeled Reinforcement Learning (PURL) algorithm, to gather comprehensive case information from users for improved legal consultations. The research also introduces a new English-language dataset for Court Views Generation (CVG) based on US criminal case law to support LLM research in the legal domain. | Framework Proposal, LLM Application Development, Adaptive Questioning for Legal Consultation, Reinforcement Learning Application, Case Information Gathering, Dataset Creation, US Criminal Law Focus | True | Idealistic | True | 1.0 | Positive | Diagnostic Legal Large Language Model (D3LM) incorporating a graph-based Positive-Unlabeled Reinforcement Learning (PURL) algorithm for adaptive question generation and Court Views Generation (CVG). | Large Language Model, Model Development, Reinforcement Learning, Graph-based Learning, Adaptive Question Generation, Content Generation | Evaluated using ROUGE and BLEU scores on the authors' newly created US-CVG dataset (derived from US criminal case law). Evaluation also involved human judgment (fluency, accuracy, adoptability) by legal professionals and usability testing (reliability, satisfaction, preference) comparing D3LM to GPT-4.0. | Custom Dataset Evaluation, Quantitative Metrics, Expert Evaluation, User Study or Survey, Comparative Analysis | D3LM achieved ROUGE-1 63.3%, ROUGE-2 53.1%, ROUGE-L 59.2%, BLEU-1 38.7%, BLEU-2 31.7%, and BLEU-N 26.9%. In human evaluation, D3LM scored 4.48 for accuracy and 4.19 for adoptability. 62.3% of users preferred D3LM over GPT-4.0 in usability tests. | High performance, Outperforms others, Benefit identified | Scarcity and high cost of legal resources, inequities in legal proceedings disadvantaging the underprivileged, and the difficulty for laypersons (users without legal backgrounds) to formulate effective queries and provide all critical factual details to LLMs. | High Cost of Legal Services, Limited Availability/Access to Legal Professionals/Expertise, Systemic Inequities in Justice System, Difficulty in AI-Human Interaction, Public Lack of Legal Knowledge/Awareness | Development of D3LM, an LLM-based system that actively engages users with diagnostic questions to elicit comprehensive case details, aiming to provide more accurate, tailored, and cost-effective legal guidance, especially for those lacking legal expertise. | AI Tool Development, Prompt Engineering and LLM Interaction Design, Access to Legal Information and Advice, Cost Reduction and Efficiency | Improving legal consultation for laypersons, interactive legal information gathering, court view generation, enhancing AI-driven legal assistants for better accuracy and user understanding. | Access to Legal Advice, Access to Legal Information, Judicial System Modernization / Efficiency, Improving Foundational AI Capabilities for Legal Applications | Individuals with modest means, economically disadvantaged individuals, and users lacking a legal background seeking legal assistance. | Low-income individuals, Moderate-income individuals, Laypeople, Individuals lacking legal knowledge, Individuals with unmet legal needs | Primarily US Criminal Law (based on the dataset, case study, and stated limitations on PURL algorithm effectiveness). | Criminal Law | USA | USA | A new English-language Court Views Generation dataset (US-CVG) created by the authors from US criminal legal documents (Caselaw Access Project). GPT-4.0 was used with the IRAC framework to summarize narratives into fact descriptions and court views, and to create fact-rule graphs for each case; dataset integrity ensured by review from legal professionals. | Author-Created New Dataset, US Legal Data, Legal Domain Data, Case Law / Judgments, Publicly Available Data, Synthetic Data, Expert-Annotated / Human-Curated / Human-Generated Data, Structured Data | LLM fine-tuning (Llama2-13B), Positive-Unlabeled Reinforcement Learning (PURL) with a bandit approach (NeuralUCB), graph-based knowledge representation (fact-rule graphs processed with DiGCN), IRAC framework for legal text summarization, and development of a new user-LLM interaction paradigm (LLM-navigated diagnostics). | Model Fine-tuning, Reinforcement Learning, Graph-based Knowledge Representation, Framework-guided Design, User Interaction Paradigm Development | The US-CVG dataset used for training and evaluation is made available on GitHub. The paper does not state other deployment strategies for the D3LM model itself. | Public dataset/benchmark release | False | False | NaN | NaN | The PURL algorithm's effectiveness is confined to the criminal cases domain; evaluation restricted to English language cases; the model demands significant computational and human annotation resources; operational speed lags behind existing large models. | AI Scope and Functionality Limitations, Multilingual and Low-Resource Language Gaps, Computational Resource and Cost Issues, Data Availability and Quality, AI Accuracy and Reliability | Creating domain-specific knowledge graphs (resource-intensive), handling narrative length and complexity of US legal cases within LLM token limits, ensuring LLM reading comprehension for question generation, integrating reinforcement learning for optimal fact selection, and conducting rigorous human expert evaluations. | Scarcity of High-Quality Legal Data, Cost and Complexity of Data Annotation, LLM Context Window and Long Input Management, LLM Reasoning Capabilities, Domain-Specific Adaptation and Customization, Evaluation Challenges and Metrics | NaN | NaN |
| 3614407.3643708.pdf | Google_Scholar | Rethinking Machine Learning Benchmarks in the Context of Professional Codes of Conduct | This paper argues that current machine learning benchmarks, often focused on accuracy mimicking professional exams, fail to capture essential skills mandated by professional codes of conduct. It proposes using these codes, particularly illustrated through a case study of legal machine translation, to guide the development of more comprehensive benchmarks that incorporate requirements like expressing uncertainty and adhering to specific professional rules. | Critique of ML Benchmarks, Professional Codes of Conduct in AI Evaluation, Proposal for Comprehensive Benchmarks, Legal Machine Translation Example, Benchmark Development Guidance | True | Idealistic | True | 1.0 | Neutral | Proposal to use Professional Codes of Conduct to guide ML benchmark creation, incorporating specific tests based on rules (e.g., preserving filler words, units, double negatives) and integrating 'Know-What-You-Know' (KWYK) checks for uncertainty quantification and abstention. | Benchmarking / Evaluation Framework Proposal, Machine Learning Evaluation, Rule-based Testing, Uncertainty Quantification, Ethical AI Guidelines Integration | Case study on legal machine translation with demonstrative experiments: 1) 'Unit tests' checking gpt-3.5-turbo's compliance with specific California court interpreter rules (unit/filler word/error/repetition/double negative preservation, idiom identification, clarification needed). 2) KWYK check experiments using NLLB/flan-t5-xl models on translation (Opus100/Flores/EAC-TM datasets) and bar exam tasks (MMLU), measuring abstain rate vs acceptability rate. | Qualitative Analysis, Benchmark Dataset Evaluation, Quantitative Metrics | For rule compliance tests with gpt-3.5-turbo, adherence varied significantly by rule (e.g., 100% unit preservation, 10% word repetition preservation, 61% double negative preservation). For KWYK checks on translation, a verifier KWYK check (xlm-roberta-base) achieved a target of 75% acceptability with an 18.9% abstain rate on a legal-adjacent translation task; KWYK checks failed to reach target accuracy on the bar exam task. | Mixed performance, Moderate performance, Limitation: Operational or Technical | Unreliability and lack of accountability of general-purpose AI tools (like MT) in high-stakes legal contexts; inaccuracies leading to severe negative consequences (asylum denial, rights violations); tendency for users to rely on tools without understanding limitations due to perceived general competence; shortage of qualified human professionals (e.g., translators) creating demand for potentially unsafe AI solutions. | AI Unreliability/Inaccuracy, Lack of AI Accountability, Risk to Human Rights from AI, Automation Bias, Lack of Understanding of AI Capabilities/Limitations, Limited Availability/Access to Legal Professionals/Expertise | Align ML benchmarks with professional codes of conduct; integrate specific tests based on professional rules into benchmarks; standardize evaluation of uncertainty quantification ('Know-What-You-Know' checks) allowing models to abstain; provide runtime transparency regarding model limitations and adherence to rules. | Benchmarking and Evaluation Frameworks, Regulation, Ethics, and Governance, Enhanced AI Capabilities, Transparency and Explainability in AI | Machine translation quality and reliability in legal contexts; AI safety and evaluation; Professional ethics in AI applications. | Language Access and Digital Divide, Ethical AI in Law and AI Governance | Individuals with Limited English Proficiency (LEP) interacting with the legal system, such as asylum seekers and individuals in police encounters. | Individuals with language barriers, Litigants, Asylum seekers and refugees, Individuals interacting with law enforcement | Immigration Law, Criminal Procedure, Evidence, Professional Responsibility (Interpreters, Lawyers) | Immigration Law, Criminal Procedure, Evidence Law, Professional Responsibility | United States (primarily California and federal context) | USA | The paper primarily evaluates existing models. Demonstrative experiments used public datasets: Opus100 (parallel corpora), Flores 200 (Wikipedia), EAC-TM (EU documents), MMLU Bar Exam (professional exam questions), Tang 2022 & Fadaee et al. 2018 (idiom datasets). KWYK checks used models pre-trained on large general corpora. | Evaluation Dataset, Data From Existing Public NLP/Legal Datasets/Benchmarks, Multilingual Data, European Legal Data, Legal Domain Data, Non-Legal Domain Specific Data, Pre-trained LLM's General Training Corpus | Comparative analysis (benchmarks vs. professional rules), Case study (legal machine translation), Conceptual proposal (rule-based benchmarks, KWYK checks), Demonstrative empirical evaluation. | Comparative Analysis of Approaches, Case Study as Design Methodology, Conceptual Proposal, Rule-based Benchmark Development, Empirical Evaluation | NaN | Not applicable | False | True | Code for demonstrative experiments stated to be available in Supplementary Material. | Code available | Technical: Need for improved KWYK check calibration and reliability, methods for robustly incorporating professional rules into models, handling conflicting rules. Societal: Need for broader adoption of professionally-grounded benchmarks, effective user communication of uncertainty, extension to other professional domains. | AI Accuracy and Reliability, AI Legal Reasoning Limitations, Research and Evaluation Gaps, User Interface and Usability Gaps | Current benchmarks focus narrowly on accuracy, neglecting professional standards; implementing robust KWYK checks is a research challenge; ensuring consistent rule adherence in stochastic models is difficult; quantitatively evaluating nuanced rule compliance; generalizing the proposed evaluation approach. | Evaluation Challenges and Metrics, Legal Professional Responsibility and Competence, Accuracy and Reliability of LLM Output, Output Variability and Consistency | Mistranslations leading to asylum denial, misunderstanding consent to search (Fourth Amendment violations), inadmissible evidence, or misinterpretation of law; over-reliance on seemingly capable AI leading to errors in critical situations; AI potentially removing crucial context (e.g., filler words indicating uncertainty); potential unauthorized practice of law; AI hallucinations in legal work. | Inaccurate or misleading AI output, Consumer harm, Undermining legal process or principles, Over-reliance on AI, Technical limitations of AI, Unauthorized practice of law |
| 6Yzvwm5r5_kJ.pdf | Google_Scholar | LawBench: Benchmarking Legal Knowledge of Large Language Models | This paper introduces LawBench, a comprehensive benchmark designed to evaluate the legal knowledge and capabilities of Large Language Models (LLMs) within the Chinese civil law system across three cognitive levels: memorization, understanding, and application. Based on evaluations of 51 LLMs, the study finds that while GPT-4 leads, all current models, including legally fine-tuned ones, have significant room for improvement in performing diverse and realistic legal tasks reliably. | Benchmark Creation, LLM Evaluation, Chinese Law Focus, Civil Law Focus, Assessment of Legal Knowledge and Capabilities, Identification of LLM Limitations | True | Idealistic | True | 2.0 | Neutral | LawBench: A benchmark suite comprising 20 diverse legal tasks for evaluating LLMs under the Chinese civil law system. | Benchmarking / Evaluation, Dataset Creation / Curation, Large Language Model Evaluation, Cross-jurisdictional Application | Evaluation of 51 LLMs (multilingual, Chinese-oriented, legal-specific) on LawBench (20 tasks across memorization, understanding, application levels; 5 task types). Tests performed in zero-shot and one-shot settings using task-specific metrics (Accuracy, F1, rc-F1, soft-F1, nLog-distance, F0.5, Rouge-L) and answer extraction rules. | Benchmark Dataset Evaluation, Quantitative Metrics, Comparative Analysis | GPT-4 performed best overall (average score 52.35 zero-shot, 53.85 one-shot), significantly outperforming other models. Legal-specific fine-tuning improved over base models but did not surpass top general models. Most models struggled to utilize provided legal article content effectively. | Moderate performance, Outperforms others, Technique has limited or mixed impact, Limitation: Operational or Technical | Current LLMs lack sufficient legal knowledge, understanding, and reasoning abilities for reliable performance on diverse legal tasks. Models struggle with instruction following, abstention on legal queries, and effectively integrating retrieved knowledge like legal articles. | AI Limitations in Legal Reasoning/Nuance, AI Unreliability/Inaccuracy, Technical Challenges in AI Development | Develop stronger foundation models; use high-quality legal-specific fine-tuning data and methods (potentially improving SFT and reconsidering RLHF impact); improve models' ability to utilize retrieved context; foster collaboration to overcome data confidentiality challenges. | AI Tool Development, Enhanced AI Capabilities, Data Curation and Management, Open Source Initiatives and Collaboration, Data Privacy and Security | Legal information provision, document analysis, case assessment, legal consultation simulation. | Access to Legal Information, Legal Document Analysis / Review, Access to Legal Advice | Non-professionals needing legal assistance | Laypeople, Individuals with unmet legal needs | Criminal Law, Civil Law (including Family Law), Procedural Law, General Legal Practice | Criminal Law, Civil Law, Family Law, Procedural Law, General Legal Practice | China | China | NaN | Not Applicable | Benchmark designed using a cognitive hierarchy (Bloom's taxonomy adapted for legal skills: Memorization, Understanding, Applying). Tasks selected and adapted from existing public legal NLP datasets (e.g., CAIL, LAIC, JEC-QA) and other sources, formatted for instruction-following LLMs. | Benchmark Development, Framework-guided Design, Task Design, Dataset Adaptation | Benchmark and evaluation code released via GitHub (OpenCompass platform). | Public dataset/benchmark release, Open source code release, Integration into existing system/platform | True | True | Benchmark data, model predictions, and evaluation code released on GitHub. | Dataset available, Model available, Code available | Technical gaps: Current LLMs lack robustness and reliability for complex legal reasoning, understanding, and application. They struggle to effectively integrate retrieved legal knowledge and can be hampered by safety alignments (RLHF). Societal gaps: Data confidentiality hinders the development of high-quality legal LLMs. | AI Accuracy and Reliability, AI Legal Reasoning Limitations, Ethical Framework Deficiencies, Security and Privacy of Data, Data Availability and Quality | Evaluation: Designing diverse tasks, reliable answer extraction, appropriate metrics (esp. for generation), preventing data contamination. LLM Development/Application: Effective scaling, domain-specific fine-tuning, balancing helpfulness and harmlessness (instruction following vs. abstention), enabling effective use of retrieved information. | Evaluation Challenges and Metrics, Scalability of Solutions, Domain-Specific Adaptation and Customization, Ethical Considerations, Accuracy and Reliability of LLM Output | Lack of reliability and accuracy in performing legal tasks; potential for models to refuse to answer relevant legal queries (abstention); risk of evaluation invalidity due to test set contamination. | Inaccurate or misleading AI output, Technical limitations of AI |
| ieWkuwRsfCYJ.pdf | Google_Scholar | EMPOWERING JUSTICE: BLOCKCHAIN AND LEGAL CHATBOTS AS CATALYSTS FOR ACCESS TO LEGAL AID | This paper explores how integrating blockchain technology and AI-powered legal chatbots can improve access to justice by addressing barriers like cost, complexity, and geographical distance. It reviews existing applications, discusses potential benefits, ethical challenges, regulatory needs, and proposes a roadmap for future development focusing on inclusivity and global cooperation. | Blockchain for Access to Justice, AI Chatbots for Access to Justice, Benefit Identification, Ethical Challenges, Need for AI Regulation, Roadmap for Future Development, Inclusivity in AI, Global Cooperation | True | Idealistic | True | 3.0 | Positive | Integration of blockchain technology and legal chatbots | Blockchain Technology, Chatbot / Conversational AI, System Integration | NaN | Not Applicable | NaN | NaN | Economic constraints (cost), lack of legal literacy/awareness, geographical barriers, systemic discrimination/bias, complexity/inefficiency of legal systems, digital divide. | High Cost of Legal Services, Public Lack of Legal Knowledge/Awareness, Geographical Disparities in Legal Access, Systemic Inequities in Justice System, Judicial/Legal System Inefficiencies, Digital Divide | Leveraging blockchain for secure document/evidence/identity management and smart contracts; using legal chatbots for accessible information, guidance, and document drafting automation; fostering interdisciplinary collaboration, ethical guidelines, regulatory frameworks, inclusive design, investment, transparency, education, and global cooperation. | AI Tool Development, Data Privacy and Security, Document Automation, Access to Legal Information and Advice, Open Source Initiatives and Collaboration, Regulation, Ethics, and Governance, User Interface and Accessibility Design, Policy and Regulatory Reform, Transparency and Explainability in AI, Education and AI Literacy | Access to legal information, advice, representation, document verification/management, identity protection, evidence management, dispute resolution. | Access to Legal Information, Access to Legal Advice, Access to Legal Representation, Legal Document Analysis / Review, Protection of Rights, Dispute Resolution | General public, economically disadvantaged individuals, people in rural/remote areas, refugees, stateless individuals, marginalized populations. | General public, Low-income individuals, Rural populations, Populations in remote areas, Asylum seekers and refugees, Stateless persons, Marginalized communities | General/Multiple | General Law, Multiple Fields | International | International | NaN | Not Applicable | Conceptual framework design, discussion of general AI/chatbot development. | Conceptual Framework Development | Discussion of existing case study deployments (e.g., government initiatives, commercial platforms), proposed pilot programs. | Evaluation of existing third-party tool, Government/Public institution deployment, Commercial product/service, Proposed deployment (not implemented) | True | False | Mentions several existing platforms (e.g., DoNotPay, Casetext, Kleros, LegalMation, Juro etc.) available as commercial services or platforms, some with free tiers or specific initial free uses. | Commercial product or service, Publicly accessible online tool or platform, Freemium access | Digital divide, need for ethical guidelines and robust regulatory frameworks, lack of global convergence/standards, technical limitations (scalability, interoperability), need for AI/tech literacy training, addressing AI bias. | Access, Equity, and Digital Divide, Ethical Framework Deficiencies, Regulatory and Governance Gaps, AI Scope and Functionality Limitations, Integration and Interoperability Challenges, Public Understanding, Trust, and Adoption, Human Oversight and Professional Adaptation, Bias in AI | Technical complexity of integration, scalability issues, interoperability challenges, designing for user accessibility (digital divide), legal and regulatory uncertainty/hurdles, data privacy and security concerns, ethical AI development (bias, fairness, accountability), cost of implementation, integration with traditional legal systems, ecological impact of certain blockchains. | Integration with Existing Systems and Workflows, Scalability of Solutions, User Interface, Usability, and Accessibility, Regulatory Uncertainty and Compliance, Data Privacy, Security, and Confidentiality, Ethical Considerations, Bias in AI Systems and Data, Accountability and Liability for AI Errors, Financial Cost and Resource Constraints, Environmental Impact of AI | Providing inaccurate or oversimplified legal information, errors in automated document drafting, perpetuating societal biases through AI, data privacy violations, potential for misuse (e.g., manipulation), lack of clear accountability for errors, non-compliance with regulations, exacerbating the digital divide. | Inaccurate or misleading AI output, Bias and discrimination, Data privacy and security breach, Security vulnerabilities or malicious misuse, Lack of transparency, accountability, and redress, Regulatory challenges or gaps, Exacerbation of inequality or two-tiered system |
| jVRZDuKmAFkJ.pdf | Google_Scholar | SwiLTra-Bench: The Swiss Legal Translation Benchmark | This paper introduces SwiLTra-Bench, a large multilingual benchmark for Swiss legal translation, and SwiLTra-Judge, an LLM-based evaluation system. It evaluates various LLMs, showing frontier models achieve the best performance, and while fine-tuning improves open SLMs, they still trail top zero-shot frontier models like Claude-3.5-Sonnet. | Benchmark Creation, Multilingual Legal Translation, Swiss Law Focus, LLM-Based Evaluation System, LLM Evaluation, Fine-tuning Evaluation | True | Idealistic | True | 1.0 | Positive | SwiLTra-Bench: a multilingual benchmark of over 180K aligned Swiss legal translation pairs. SwiLTra-Judge: an LLM-based evaluation system for legal translation quality assessment. | Benchmarking / Evaluation, Dataset Creation / Curation, Multilingual Application, Legal Translation, LLM-based Evaluation System | Various LLMs (translation-specific, frontier, reasoning, open, and fine-tuned SLMs) evaluated on SwiLTra-Bench using metrics like GEMBA-MQM, XCOMET, METEOR, ChrF. Human expert evaluations conducted on top models; SwiLTra-Judge's correlation with human scores was assessed. | Benchmark Dataset Evaluation, Quantitative Metrics, Expert Evaluation, LLM as Judge, Comparative Analysis | Frontier models like Claude-3.5-Sonnet and o1 demonstrated superior performance; fine-tuned open SLMs improved significantly but did not surpass zero-shot frontier models. SwiLTra-Judge, using GPT-4o-mini with a deduction prompt and diverse few-shot examples, achieved the highest correlation (Spearman 0.5 ± 0.07) with human expert judgments. | Outperforms others, Technique improves outcome, Moderate performance | Lack of specialized, high-quality multilingual legal translation data; inherent complexity (terminology, structure) of legal texts for NMT; translation bottlenecks hindering access to justice and governmental efficiency. | Data Scarcity/Quality for AI, Complexity of Legal Language/Documents, Resource Constraints | Creation of a large, high-quality multilingual Swiss legal translation benchmark (SwiLTra-Bench) for training and evaluation. Development of an LLM-based evaluation tool (SwiLTra-Judge) aligned with human legal expertise. Systematic evaluation and fine-tuning of LLMs to advance legal NMT capabilities. | Data Curation and Management, Benchmarking and Evaluation Frameworks, Language Simplification and Multilingual Access, AI Tool Development, Enhanced AI Capabilities | Legal machine translation, multilingual access to legal information, automated evaluation of translation quality, enhancing governmental efficiency and civic participation through NMT. | Language Access and Digital Divide, Access to Legal Information, Improving Efficiency in Legal System / Profession | Swiss citizens (especially speakers of official languages including the low-resource Romansh), legal professionals, and governmental bodies in Switzerland. | General public, Population in Switzerland, Speakers of low-resource languages, Legal professionals, Government agencies | Swiss law, including legislation (laws), court decisions (headnotes), and official communications (press releases). | General Law, Statutory Law, Case Law | Switzerland | Switzerland | SwiLTra-Bench comprises over 180K publicly available, officially translated Swiss legal document pairs (laws, headnotes, press releases) in German, French, Italian, and partially Romansh and English, aligned at various granularities (e.g., paragraph/text level). | Author-Created New Dataset, Publicly Available Data, Swiss Legal Data, Legal Domain Data, Legislation / Statutes / Regulations, Case Law / Judgments, Other Legal Documents, Multilingual Data, German Language Data, French Language Data, Italian Language Data, Structured Data | For SwiLTra-Bench: collection of official multilingual legal texts, segmentation, and strategic splitting into train/validation/test sets. For SwiLTra-Judge: ablation studies on LLM judge models, prompt engineering (testing basic, detailed, codebook styles), and few-shot example selection, validated against human expert judgments. | Dataset Creation, Data Segmentation, Ablation Study, Prompt Engineering, Few-shot Learning Application, Expert Validation | The SwiLTra-Bench datasets and associated code (including for SwiLTra-Judge evaluation) are made available on Hugging Face. | Public dataset/benchmark release, Open source code release | True | True | The SwiLTra-Bench datasets and code are available at https://huggingface.co/collections/joelniklaus/swiltra-bench-67c569a2ada47e4549733deb. | Dataset available, Code available | Fine-tuned open models still underperform large closed models. Need for further research into techniques like model merging to improve open models. Human expert evaluation scope was limited by resources (e.g., for Romansh, sample sizes). | AI Accuracy and Reliability, Research and Evaluation Gaps, Computational Resource and Cost Issues | Limited resources for comprehensive human expert evaluation, particularly for low-resource languages and large sample sizes. Some LLMs (Claude Sonnet/Haiku, o1/o1-mini) proving unsuitable as evaluators in SwiLTra-Judge development due to instruction-following failures or low correlation with human judgment. Token limits of certain automated evaluation metrics when processing longer texts (e.g., press releases). | Financial Cost and Resource Constraints, Evaluation Challenges and Metrics, Multilingual and Low-Resource Language Support, Accuracy and Reliability of LLM Output, LLM Context Window and Long Input Management | NaN | NaN |
| SYHocniYWCEJ.pdf | Google_Scholar | Literature Review: AI and the Law | This literature review examines AI's role, particularly LLMs, in the legal profession, covering applications in legal practice, judicial processes, and access to justice through tools like legal apps. It also discusses significant ethical concerns including professional competence, algorithmic bias, and data privacy. | Literature Review, AI in Legal Profession, LLM Application, AI in Judicial Processes, Access to Justice Enhancement, Legal Apps, Ethical Concerns, Professional Competence, Bias in AI, Data Privacy | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | High cost and scarcity of legal services leading to unmet legal needs for low-income and many middle-income individuals; disadvantages for self-represented litigants; the digital divide, including lack of access to technology and digital literacy. | High Cost of Legal Services, Limited Availability/Access to Legal Professionals/Expertise, Scale of Unmet Legal Need, Challenges for Self-Represented Litigants, Digital Divide | Employing AI-powered legal apps for information, advice, and document creation; leveraging AI to enhance lawyer efficiency and extend services; developing self-help resources powered by AI; implementing online dispute resolution platforms. | AI Tool Development, Access to Legal Information and Advice, Document Automation, Human Oversight and Collaboration, Cost Reduction and Efficiency, Support for Self-Represented Litigants, Online Dispute Resolution (ODR) | Affordability of legal services, access to legal information and advice, self-representation, online dispute resolution for small claims and specific civil matters. | Affordability of Legal Services / Cost Reduction, Access to Legal Information, Access to Legal Advice, Support for Self-Represented Litigants, Dispute Resolution | Low-income individuals, individuals below the poverty line, middle-income individuals, self-represented litigants. | Low-income individuals, Moderate-income individuals, Self-represented litigants | General civil law, contract law, dispute resolution (small claims, condominium, motor vehicle accidents), constitutional law, torts, legal ethics. | Civil Law, Contract Law, Dispute Resolution, Small Claims Law, Property Law, Tort Law, Constitutional Law, Legal Ethics | International (with examples from USA, Canada, China, Colombia, Italy, UK) | International, USA, Canada, China, Colombia, Italy, UK | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | True | True | The paper discusses tools like ChatGPT (free tier available) and publicly accessible services like Canada's Civil Resolution Tribunal, as well as commercial legal tech tools. | Publicly accessible online tool or platform, Freemium access, Commercial product or service | The digital divide (socio-economic, geographic, and literacy barriers to technology access); insufficient research on privacy and security of legal apps; outdated or lacking ethical guidelines and governance for AI tools in law. | Access, Equity, and Digital Divide, Public Understanding, Trust, and Adoption, Research and Evaluation Gaps, Security and Privacy of Data, Ethical Framework Deficiencies, Regulatory and Governance Gaps | Ensuring accuracy and completeness of AI-generated legal information; preventing user misinterpretation or over-reliance on AI outputs; addressing the digital divide for equitable access to AI-powered legal resources; managing data privacy and security risks; overcoming potential biases in AI systems and judicial applications; adapting legal education to incorporate AI ethically and effectively. | Accuracy and Reliability of LLM Output, User Adoption, Trust, and Acceptance, User Interface, Usability, and Accessibility, Data Privacy, Security, and Confidentiality, Bias in AI Systems and Data, User Training, AI Literacy, and Skill Gaps, Ethical Considerations | Breaches of lawyers' duty of competence from unverified AI use; dissemination of misleading or incomplete legal information by AI; perpetuation of systemic biases (e.g., racial, gender) by AI algorithms; privacy violations and data misuse from legal apps and AI systems; cybersecurity vulnerabilities (e.g., jailbreaking, prompt injection); negative impacts on judicial integrity, such as automation bias or perceived devaluation of human judgment. | Ethical concerns, Over-reliance on AI, Inaccurate or misleading AI output, Bias and discrimination, Data privacy and security breach, Security vulnerabilities or malicious misuse, Undermining legal process or principles, Dehumanization of legal process |
| PO2gt4t0fl4J.pdf | Google_Scholar | Decoding Legalese Without Borders: \nMultilingual Evaluation of Language Models on Long Legal Texts | This doctoral dissertation summarizes a body of research focused on advancing multilingual legal Natural Language Processing (NLP). It details the curation of extensive legal datasets and benchmarks for evaluating Large Language Models (LLMs) on long legal texts, and presents multidimensional analyses of model performance, explainability, fairness, and re-identification risks within the legal domain. | Dissertation Summary, Multilingual Legal NLP, Legal Dataset Curation, Legal Benchmark Curation, LLM Evaluation (Performance, Explainability, Fairness, Risk), Long Legal Text Processing | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | Lack of comprehensive multilingual legal datasets; suboptimal performance of models on low-resource languages and long legal texts; unique challenges of domain-specific legal tasks; difficulties in ensuring transparency and ethics in algorithmic jurisprudence. | Data Scarcity/Quality for AI, Technical Challenges in AI Development, Accessibility Barriers for Specific User Groups, Ethical Concerns with AI in Law, Lack of AI Transparency/Explainability | Curation and open release of extensive multilingual legal datasets (e.g., MultiLegalPile) and benchmarks (e.g., LEXTREME, LegalBench, SCALE); training and analysis of language models for legal text; proposing methods for anonymization, re-identification assessment, and explainability; advocating for dataset extension to unexplored legal tasks and underrepresented jurisdictions. | Data Curation and Management, Open Source Initiatives and Collaboration, Benchmarking and Evaluation Frameworks, AI Tool Development, Data Privacy and Security, Transparency and Explainability in AI | Multilingual legal NLP; evaluation of LLMs on legal texts; legal judgment prediction; anonymization and re-identification; legal reasoning; creation of open legal corpora and benchmarks for broader development and application, including for underrepresented languages and jurisdictions. | Language Access and Digital Divide, Improving Foundational AI Capabilities for Legal Applications, Ethical AI in Law and AI Governance | The broader legal NLP research community; users and developers in underrepresented jurisdictions and languages. | Researchers, Populations in underresourced jurisdictions, Speakers of low-resource languages | Multiple legal fields (e.g., public, penal, civil, social, insurance law, class actions, depending on the specific dataset/benchmark described within the summarized works). | Multiple Fields, Public Law, Criminal Law, Civil Law, Social Security Law, Insurance Law | International (covers multiple jurisdictions including Switzerland, US, India, EU, CoE, and aims for broader global coverage including underrepresented jurisdictions). | International, Switzerland, USA, India, EU, Council of Europe, Underrepresented Jurisdictions | The dissertation describes the creation and use of large multilingual legal corpora such as MultiLegalPile (689GB of diverse legal texts from public and other sources covering 24 languages/17 jurisdictions) and various specialized datasets for tasks like judgment prediction, sentence boundary detection, and negation scope resolution. | Author-Created New Dataset, Data From Existing Public NLP/Legal Datasets/Benchmarks, Publicly Available Data, Undisclosed Data Source/Availability, Legal Domain Data, Multilingual Data, Fine-tuning Dataset | NaN | NaN | Many of the described resources (datasets, models, code) are deployed via open platforms like Hugging Face, Zenodo, and GitHub, often under permissive licenses (e.g., CC-BY, CC BY-SA). | Evaluation of existing third-party tool, Public dataset/benchmark release, Open source model release, Open source code release | True | True | Numerous datasets, pretrained models, and codebases (detailed in Table 1 and individual publication summaries) are publicly available on platforms like Hugging Face, Zenodo, and GitHub, often under open licenses like CC-BY. | Dataset available, Model available, Code available, Open access resource, Open-source | Need for improved model performance on legal benchmarks (e.g., via domain adaptation, instruction tuning, advanced prompting); further analysis of dataset overlaps and model interpretability/explainability; extension of datasets to more legal tasks, languages, and jurisdictions, particularly with expert annotations. | AI Accuracy and Reliability, Research and Evaluation Gaps, Transparency and Explainability, Data Availability and Quality, Multilingual and Jurisdictional Specificity Gaps | NaN | NaN | Potential for re-identification of individuals in anonymized legal texts; lack of transparency and ethical concerns in algorithmic jurisprudence; inherent biases in models influencing predictions (e.g., from lower court decisions or training data). | Data privacy and security breach, Lack of transparency, accountability, and redress, Ethical concerns, Bias and discrimination |
| hCFGY1A7TzoJ.pdf | Google_Scholar | Ethical Foresight: Confronting Misinformation, Representation and Toxicity in Generative AI | This thesis investigates socio-technical harms such as misinformation, biased representation, and toxicity in Large Language Models (LLMs). It proposes a new comprehensive framework and taxonomy, developed through a systematic review of existing safety evaluations, to guide the ethical development and regulation of AI. | Thesis Summary, Socio-Technical Harms of LLMs, AI Hallucinations/Inaccuracy, Bias in AI, Toxicity in LLMs, Framework for Ethical AI Development, Taxonomy of AI Harms, AI Regulation Guidance | True | Idealistic | True | 1.0 | Positive | A comprehensive framework, taxonomy of harms (focusing on misinformation, representation, and toxicity), and evaluation guidelines for LLMs, developed through a systematic literature review. | Evaluation Framework Development, Literature Survey / Review, AI Ethics / Harms Taxonomy, AI System Evaluation | NaN | Not Applicable | NaN | NaN | Pervasiveness of misinformation, representational errors, and toxicity in LLMs; inherent biases in training data and algorithms; challenges in AI transparency and explainability ('black boxes'); difficulties in defining and operationalizing ethical principles like fairness; shortcomings in current AI regulations; underrepresentation of diverse perspectives in AI development; abstraction traps in applying technical solutions to social problems. | AI Unreliability/Inaccuracy, AI-driven Misinformation/Disinformation, Bias in AI/Data, Lack of AI Transparency/Explainability, Ethical Concerns with AI in Law, Inadequate Legal Frameworks for AI, Exclusion of Marginalized Communities in AI Governance/Development, Misalignment of Technical Solutions with Social Problems | Development of a comprehensive framework and taxonomy for AI harms; context-aware, interdisciplinary evaluation strategies with human oversight; clear definitions and criteria for AI assessment; improved AI governance and regulation (e.g., EU AI Act, regulatory sandboxes); fostering Human-Centred AI (HCAI) and multi-stakeholder collaboration; addressing abstraction traps by integrating social context into technical solutions; providing actionable guidelines for developers and policymakers. | Conceptual Frameworks, Benchmarking and Evaluation Frameworks, Human Oversight and Collaboration, Regulation, Ethics, and Governance, User Interface and Accessibility Design, Open Source Initiatives and Collaboration | Ethical AI development, AI governance, Bias detection and mitigation, Fairness and equity in AI systems, Misinformation, Representational harms, Toxicity in LLMs, Sociotechnical AI safety. | Ethical AI in Law and AI Governance, Protection of Rights | Marginalized and underrepresented communities generally (e.g., based on race, gender, language, socio-economic status, LGBTQ+ identity) who are disproportionately affected by AI biases and harms. | Marginalized communities, Underrepresented groups, Minority groups, Women, Individuals with language barriers, Low-income individuals, LGBTQ+ people | AI Law and Regulation, AI Ethics, Data Protection, Non-discrimination principles in AI. | AI Regulation, AI Ethics, Data Privacy Law, Anti-Discrimination Law | EU (focus on GDPR, DSA, AI Act), USA (examples cited). Principles and framework are intended for broad applicability (International). | EU, USA, International | Systematic review of over 170 academic papers on AI ethics, safety evaluations, misinformation, representation, and toxicity, primarily sourced from the DeepMind Sociotechnical Safety Evaluation repository and other academic databases. | Input Data for Task (Non-Training), Non-Legal Domain Specific Data, Publicly Available Data | Systematic literature review, qualitative analysis of academic papers, synthesis of findings to construct a new taxonomy of harms, and development of evaluation guidelines and recommendations. | Systematic Review, Qualitative Data Analysis, Taxonomy Development, Guideline Development | NaN | Not applicable | True | True | The proposed framework, taxonomy, and evaluation guidelines are detailed within this thesis, making the intellectual contribution accessible to readers. | Research artifact published in paper | Lack of comprehensive, context-aware AI safety evaluation frameworks; insufficient human oversight in AI development and evaluation; limited understanding and mitigation of multimodal risks; inadequacy of current regulations for rapidly evolving AI (e.g., general-purpose AI, misinformation definition); difficulty in translating ethical principles into concrete technical practices; underrepresentation of diverse (especially non-Western and marginalized) perspectives and languages in AI development and evaluation; challenges in achieving true algorithmic fairness beyond statistical metrics. | Research and Evaluation Gaps, Ethical Framework Deficiencies, Human Oversight and Professional Adaptation, AI Scope and Functionality Limitations, Regulatory and Governance Gaps, Bias in AI, Access, Equity, and Digital Divide, Multilingual and Low-Resource Language Gaps | Synthesizing a vast and evolving body of literature on AI ethics and safety; developing a comprehensive yet practical taxonomy of harms; ensuring proposed guidelines are actionable and adaptable across diverse AI systems and contexts; overcoming the limitations of existing evaluation methods (e.g., artificial setups, metric-related issues, generalizability). | Research Methodology and Study Design Limitations, Ethical Considerations, Safeguarding Against Misuse and Harm, Regulatory Uncertainty and Compliance, Evaluation Challenges and Metrics | Spread of AI-generated misinformation impacting public trust and democracy; perpetuation and amplification of societal biases and stereotypes leading to discrimination (representational harms); generation of toxic and harmful content (hate speech, harassment); infringements on privacy; challenges to human autonomy and decision-making; socio-economic disruption (e.g., job displacement); misuse of AI for malicious purposes (e.g., information warfare, astroturfing). | Inaccurate or misleading AI output, Erosion of trust in legal system or AI, Undermining democratic processes, Bias and discrimination, Harmful or unsafe AI output, Data privacy and security breach, Negative impact on user agency or autonomy, Job displacement, Security vulnerabilities or malicious misuse |
| 3700789.pdf | Google_Scholar | Integrating Content Moderation Systems with Large Language Models | This paper proposes integrating Large Language Models (LLMs) into content moderation systems to enable personalized moderation and improve user-platform communication. It evaluates the content moderation capabilities of GPT 3.5 and LLaMa 2 against commercial products, discussing the approach's benefits and limitations for creating fairer online environments. | LLM Application, Content Moderation Enhancement, Personalized Moderation, User Communication Improvement, LLM Evaluation, Fairness in Online Environments | True | Idealistic | True | 1.0 | Positive | An approach integrating LLMs (specifically GPT 3.5 and LLaMa 2) into content moderation pipelines. This uses prompting with user-defined or community-specific rules to enable personalized content evaluation (zero-shot) and generate explanations for moderation decisions. | Content Moderation, Large Language Model, Prompt Engineering, Zero-shot Learning, Explainable AI (XAI), Personalized System, Rule-based System | Quantitative analysis using F1 score to compare LLMs (GPT 3.5, LLaMa 2 7B) with commercial products (Perspective API, OpenAI Content Moderation) on two publicly available datasets: OpenAI's content moderation dataset and an English subset of the Reddit Multilingual Content Moderation dataset (r/judaism, r/feminism, r/naruto) with their respective community rules. | Benchmark Dataset Evaluation, Quantitative Metrics, Comparative Analysis | On the OpenAI dataset, GPT 3.5 (F1 score 0.7762) performed comparably to commercial solutions (e.g., OpenAI Content Moderation F1 0.7859). For the Reddit r/naruto dataset, whose rules often pertain to topics not inherently harmful, GPT 3.5 (F1 score 0.6410) outperformed traditional commercial solutions. | Moderate performance, Comparable to others, Outperforms others | Current content moderation systems exhibit unfairness towards historically marginalized individuals, fragile users, and minorities; policies are often hardcoded, hindering personalized moderation. There is a lack of effective communication between users and platforms, and platform policies can be difficult to understand and ambiguously defined, failing to address diverse cultural nuances. | Bias/Unfairness in Automated Systems, Lack of Personalization in Automated Systems, Ineffective Platform-User Communication, Complexity of Platform Policies, AI Limitations in Cultural/Linguistic Nuance | Integrating LLMs into content moderation systems to allow for personalized moderation based on customizable rules (user preferences or community norms). LLMs can also be used to generate explanations for moderation decisions, thereby improving transparency, user understanding, and communication between users and platforms. | AI Tool Development, User Interface and Accessibility Design, Prompt Engineering and LLM Interaction Design, Transparency and Explainability in AI | Fairness in content moderation, protection of marginalized groups online, reducing online harm, transparency and accountability of platform decisions, user empowerment in digital spaces, enabling personalized online experiences. | Ethical AI in Law and AI Governance, Protection of Rights, Support for Vulnerable Populations | Historically marginalized individuals, fragile users, minorities (including LGBTQ+ individuals, users from the Global South, Arab females, teenagers), and diverse online communities with specific norms. | Marginalized communities, Vulnerable populations, Minority groups, LGBTQ+ people, Global South populations, Women, Youth, Online communities | Platform governance, digital law, freedom of speech, human rights (related to non-discrimination, participation, and access to information). | Platform Governance, Internet Law, Constitutional Law, Human Rights Law, Anti-Discrimination Law, Access to Information Law | International | International | The LLMs studied (GPT 3.5, LLaMa 2) are pre-trained on large-scale, general web corpora. The paper's evaluation of these LLMs used: 1) A publicly available dataset from OpenAI (1680 text samples labeled for harmful content categories). 2) The Reddit Multilingual Content Moderation dataset (comments and subreddit-specific rules), available upon request from its original authors. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Evaluation Dataset, Publicly Available Data, Expert-Annotated / Human-Curated / Human-Generated Data, User-Generated Content, Multilingual Data | The proposed integration approach is based on a conceptual framework utilizing LLMs for rule-based content evaluation via prompting and dialogue for explanations. The evaluation of this approach employed a quantitative experimental methodology comparing LLM performance against benchmarks. | Conceptual Framework Development, Rule-based System Design, Prompt Engineering, Dialogue System Design, Quantitative Experimental Methodology, Benchmarking | N/A (The paper proposes an approach and evaluates models; no specific deployment of the integrated system by the authors is described). | Not applicable | True | True | The approach can be replicated using LLaMa 2 (7B), an open foundation model available for download, or GPT 3.5, accessible via OpenAI's commercial API. The prompting technique is described in the paper. | Model available, Open-source, API access, Commercial product or service, Research artifact published in paper | Technical gaps include LLMs' limited mathematical capabilities for confidence scoring, binary decision outputs needing more nuance, potential safety degradation from fine-tuning, high costs, and performance issues with low-resource languages, hallucinations, and knowledge recency. Societal and ethical gaps involve privacy concerns, mitigating biases, ensuring accountability, the need for multi-stakeholder collaboration for governance, and assessing efficacy across diverse global contexts. | AI Scope and Functionality Limitations, AI Accuracy and Reliability, Computational Resource and Cost Issues, Multilingual and Low-Resource Language Gaps, Knowledge Recency and Updatability, Security and Privacy of Data, Bias in AI, Accountability and Redress Mechanisms, Regulatory and Governance Gaps, Need for Interdisciplinary Collaboration, Research and Evaluation Gaps | Obtaining consistent, machine-parsable (e.g., Yes/No) responses from LLMs for classification. Effectively interpreting and applying ambiguous or context-dependent community rules. The financial and environmental costs associated with using large language models. Ensuring LLMs accurately understand and apply nuanced or lengthy rule sets. | Output Variability and Consistency, Accuracy and Reliability of LLM Output, LLM Reasoning Capabilities, Financial Cost and Resource Constraints, Environmental Impact of AI, High Computational and Resource Demands | Perpetuation of existing societal biases and harm against marginalized communities due to biased training data in LLMs. Exposure of users to harmful, false, or inappropriate content generated by LLMs (hallucinations or safety-compromised models). Privacy violations stemming from the handling of user data by LLM providers, especially closed-source models. Degradation of LLM safety alignment through fine-tuning processes. Negative impacts on freedom of speech and economic opportunities for content creators due to flawed or unfair moderation. | Bias and discrimination, Consumer harm, Harmful or unsafe AI output, Inaccurate or misleading AI output, Data privacy and security breach, Technical limitations of AI, Infringement on human rights, Negative economic impact |
| KsIty3_cK1AJ.pdf | Google_Scholar | Book Review—Shaping the Bar: The Future of Attorney Licensing | This paper reviews Joan Howarth's book advocating for reforms to attorney licensing, arguing the current bar exam fails public protection and equity, and needs alignment with actual practice competence. The reviewer supports these points, adding concerns about AI's impact on competence definitions and emphasizing the need to educate lawyers on their 'public citizen' role to address access to justice. | Book Review, Attorney Licensing Reform, AI Impact on Legal Competence, Legal Education Reform, Access to Justice Enhancement | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | Current bar exam inadequately measures competence, functions as an elitist filter hindering diversity (racial/ethnic disparities), fails public protection, high cost of legal education/licensing, institutional resistance to change, neglect of lawyer's 'public citizen' role contributing to access to justice crisis. | Ineffectiveness of Legal Licensing Systems, Hinders Diversity in Legal Profession, High Cost of Legal Education/Licensing, Institutional Resistance to Change (Legal Profession), Scale of Unmet Legal Need | Reform bar exam (e.g., NextGen) to focus on skills/application, evidence-based competency assessment, supervised practice/residencies, competence-based education (diploma privilege, portfolios), address disparities, reduce costs, portable licenses, reform character & fitness reviews, educate on 'public citizen' duties, adapt legal practice/education for AI. | Policy and Regulatory Reform, Education and AI Literacy, Cost Reduction and Efficiency | Attorney licensing reform, Bar examination reform, Lawyer competence assessment, Legal education reform, Equity and diversity in the legal profession, Access to Justice (linked to systemic reform and lawyer's public role). | Regulatory Reform (Legal Services and AI), Legal Education for Professionals / Students, Democratizing Law / Closing Justice Gap / Rule of Law | Historically excluded groups (immigrants, Jewish people, people of color), African American and Latinx students, individuals with criminal/mental health histories, the poor unable to afford legal assistance. | Marginalized communities, Migrants, Minority groups, Students, Minority students, Black individuals, Latinx individuals, Individuals with criminal records, People with mental health conditions, Low-income individuals, Individuals unable to afford legal services | General Legal Practice / Legal Education / Professional Regulation | General Legal Practice, Legal Education, Legal Profession Regulation | United States | USA | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Failure of current competency definitions/assessments, especially with AI; inadequate education on lawyers' 'public citizen'/stewardship role; insufficient integration of interdisciplinary perspectives; lack of focus on 'access to justice' as core lawyer responsibility; need for better transparency/review of licensing mechanisms. | Human Oversight and Professional Adaptation, Regulatory and Governance Gaps, Need for Interdisciplinary Collaboration, Access, Equity, and Digital Divide | NaN | NaN | AI automating tasks defining lawyer competence, causing economic disruption; licensing systems perpetuating racial/ethnic disparities; failure of legal profession/education to adapt to change; neglecting ethical/civic dimensions of lawyering; misuse of character/fitness reviews; inadequate public protection from incompetent lawyers. | Job displacement, Negative economic impact, Bias and discrimination, Regulatory challenges or gaps, Ethical concerns, Deskilling or erosion of human skills, Consumer harm |
| Sg6cPoNeAzoJ.pdf | Google_Scholar | A PROPOSAL FOR THE JOINT DEVELOPMENT OF GENERATIVE AI FOR THE DISPUTE RESOLUTION PROFESSION | This paper proposes the collaborative development of a specialized generative AI system, based on large language models, for the dispute resolution field. The goal is to create a fine-tuned, reliable tool to assist practitioners and parties, enhance access to justice, and mitigate risks associated with general-purpose AI. | Proposal for Specialized Generative AI, AI for Dispute Resolution, Collaborative AI Development, Fine-tuning for Legal Domain, Reliability Improvement, Access to Justice Enhancement, Mitigating Risks of General AI | True | Idealistic | True | 1.0 | Positive | Collaborative development of a fine-tuned generative AI system (based on LLMs like ChatGPT) specific to the dispute resolution field, involving shared data curation, guardrail setting, and privacy parameter definition. | Fine-tuning, Generative AI, Large Language Model, Dispute Resolution Support, Collaborative AI Development, Data Curation, AI Safety / Guardrails, Privacy Preservation | NaN | Not Applicable | NaN | NaN | Complexity and inaccessibility of dispute resolution information and processes, particularly for non-English speakers or individuals with impairments. | Complexity of Legal System/Procedures, Difficulty Accessing/Interpreting Legal Information, Accessibility Barriers for Specific User Groups | Develop a collaboratively built, fine-tuned generative AI tool for dispute resolution to provide accessible information (text/voice, multiple languages, 24x7) and assistance to parties and neutrals. | AI Tool Development, Open Source Initiatives and Collaboration, Online Dispute Resolution (ODR), Access to Legal Information and Advice, User Interface and Accessibility Design, Language Simplification and Multilingual Access | Providing information about dispute resolution processes (mediation, arbitration), facilitating negotiation, drafting agreements, addressing ethical questions for neutrals. | Access to Legal Information, Dispute Resolution, Legal Document Creation / Automation, Ethical AI in Law and AI Governance | Parties involved in disputes, particularly non-English speakers and individuals with hearing or visual impairments. | Litigants, Individuals with language barriers, People with disabilities | Dispute Resolution (Mediation, Arbitration) | Dispute Resolution, Mediation, Arbitration | International | International | Proposed: A collaboratively curated dataset using dispute resolution-specific supervised learning inputs, potentially including existing literature/materials from industry authors, to fine-tune a base LLM. | Author-Created New Dataset, Fine-tuning Dataset, Legal Domain Data, Expert-Annotated / Human-Curated / Human-Generated Data, Undisclosed Data Source/Availability | Collaborative development involving a centralized advisory board, expert curation of training data, supervised fine-tuning of LLMs, establishment of guardrails and privacy parameters, and ongoing feedback. | Collaborative Development, Expert Curation, Model Fine-tuning, Supervised Learning, Safety Feature Implementation, Privacy-Preserving Technique, Iterative Feedback Integration | Proposed: Access to the collaboratively developed system/dataset potentially via a fee, allowing individuals/providers to build applications. Neutrals could embed access on their websites. | Proposed deployment (not implemented), Commercial product/service, Collaborative development platform, Integration into existing system/platform, API access | False | False | NaN | NaN | Lack of a reliable, collaboratively developed AI tool specifically tailored for dispute resolution. Need for ongoing refinement and addressing concerns (privacy, accuracy) as the technology is used. | AI Accuracy and Reliability, Need for Interdisciplinary Collaboration, AI Scope and Functionality Limitations, Security and Privacy of Data | General LLM issues (accuracy, bias, IP, hallucinations); specific challenges for the proposal include organizing collaboration, securing data/cooperation, funding, defining/implementing guardrails and privacy standards. | Accuracy and Reliability of LLM Output, Bias in AI Systems and Data, Copyright and Intellectual Property Issues, LLM Hallucination and Factual Errors, Interdisciplinary Collaboration Challenges, Data Privacy, Security, and Confidentiality, Financial Cost and Resource Constraints, Safeguarding Against Misuse and Harm | Generation of biased/inappropriate content, factual inaccuracy/fabrications ('hallucinations'), intellectual property infringement related to training data, potentially misleading 'emotional' or 'sentient-like' responses. | Bias and discrimination, Harmful or unsafe AI output, Inaccurate or misleading AI output, Copyright or intellectual property issues, Poor user experience, Dehumanization of legal process |
| HsqxFTOulbAJ.pdf | Google_Scholar | InternLM-Law: An Open Source Chinese Legal Large Language Model | This paper introduces InternLM-Law, an open-source Large Language Model specialized for the Chinese legal domain, detailing its novel two-stage fine-tuning process and the construction of a comprehensive legal dataset. InternLM-Law demonstrates state-of-the-art performance on the LawBench benchmark, outperforming existing models including GPT-4 on many Chinese legal tasks, and is released to foster further research. | Legal Language Model Development, Open Source AI, Chinese Law Focus, Fine-tuning Methodology, Dataset Creation, System Evaluation, Benchmark Performance | True | Idealistic | True | 1.0 | Positive | InternLM-Law, a specialized LLM for Chinese legal queries, developed using a two-stage supervised fine-tuning (SFT) process on a new Chinese legal dataset. | Model Development, Large Language Model, Fine-tuning, Dataset Creation / Curation, Domain-Specific Model Adaptation, Named Tool / Platform | Evaluated on LawBench (20 legal subtasks covering memorization, understanding, and application), subjective evaluation (comparison with GPT-4 on legal consultation, case analysis, legal reasoning judged by GPT-4), and long-text evaluation (analyzing Chinese law judgments over 20k characters). | Benchmark Dataset Evaluation, LLM as Judge, Comparative Analysis, Qualitative Analysis | InternLM-Law-7B achieves the highest average performance on LawBench (67.71% zero-shot, 67.67% one-shot), outperforming GPT-4 on 13 out of 20 subtasks. In subjective evaluation, it achieved a 46.67% win-rate against GPT-4, and 87.5% on legal consultation. On long context evaluation, it achieved an 84.73% F1 score. | High performance, Outperforms others, Comparable to others | NaN | NaN | NaN | NaN | Legal consultation, consumer rights protection, criminal case analysis, financial remedy calculation, legal document understanding and information retrieval. | Access to Legal Advice, Protection of Rights, Legal Document Analysis / Review, Access to Legal Information | NaN | NaN | Chinese civil, criminal, and constitutional law, and other regulations. | Civil Law, Criminal Law, Constitutional Law, General Law | China | China | A dataset of over 1 million queries in the Chinese legal domain, sourced from public legal datasets (e.g., CAIL, LawBench), online legal consultation platforms (6 million anonymized Q&A records), and the Chinese National Legal Database (100K entries of laws & regulations). It also includes 1 million general SFT instruction instances from InternLM2-Chat training. | Author-Created New Dataset, Fine-tuning Dataset, Chinese Legal Data, Legal Domain Data, Data From Existing Public NLP/Legal Datasets/Benchmarks, Legal Q&A / Forum / User Query Data, User-Generated Content, Legislation / Statutes / Regulations, Publicly Available Data, Instruction-Tuning Formatted Data | Two-stage supervised fine-tuning (SFT) pipeline. Stage 1: fine-tuning on a mixture of legal and general-purpose tasks. Stage 2: refining the model on high-quality legal tasks. Data processing includes rule-based filtering, semantic filtering using LLMs (Qwen-1.5-72B), instruction generation using GPT-4, and data synthesis using GPT-4 with human feedback. | Model Fine-tuning, Supervised Learning, Pipeline Development, Rule-based Data Processing, LLM-aided Data Filtering, LLM-aided Data Generation, Synthetic Data Generation, Human Feedback Integration | The model, dataset, and code are made publicly available on GitHub. | Open source model release, Public dataset/benchmark release, Open source code release | True | True | Dataset, code, and models will be released on GitHub (https://github.com/InternLM/InternLM-Law). | Future public release | Model hallucinations and limitations in complex legal reasoning due to model size, which could hinder reliable application. | AI Accuracy and Reliability, AI Legal Reasoning Limitations, Computational Resource and Cost Issues | Collecting and cleaning a comprehensive SFT dataset; ensuring data quality and diversity; enabling the model to transfer general skills to legal tasks; designing an effective SFT strategy to learn crucial datasets and adjust response style; handling long legal texts. | Scarcity of High-Quality Legal Data, Data Quality, Processing, and Preparation, Domain-Specific Adaptation and Customization, LLM Context Window and Long Input Management | Model hallucinations and generation of inaccurate responses. | Inaccurate or misleading AI output |
| Y167Kf-vw-gJ.pdf | Google_Scholar | Large language models and their possible uses in law | This paper explores the workings of Large Language Models (LLMs) like ChatGPT and their potential applications in the legal field, particularly focusing on enhancing access to legal information and services. It discusses uses like text retrieval, generation, and analysis, and details an experiment building a law firm chatbot, while also acknowledging limitations and suggesting paths towards democratizing access to justice. | Exploration of LLMs in Law, ChatGPT Application, Access to Legal Information Enhancement, Legal Services Enhancement, Chatbot Development, Limitations Identified, Democratization of Access to Justice | True | Idealistic | True | 3.0 | Positive | A chatbot demo for a small law firm using the OpenAI GPT-3.5 API, customized with prompts and examples. | Chatbot / Conversational AI, Large Language Model, Prompt Engineering, Proof of Concept / Demo | An informal experiment conducted by one author to build and explore the capabilities and limitations of a demo chatbot. | Demonstration or Illustrative Examples, Qualitative Analysis | The demo chatbot could provide basic firm information entertainingly but was unsuitable for actual legal advice due to limitations (hallucinations, token limits, policy restrictions, lack of reality check/emotional intelligence). Customization required prompts and examples. | Low performance, Limitation: Hallucination or Factual inaccuracy, Limitation: Operational or Technical | LLM limitations: unreliability/hallucinations, inability to perform reality checks or understand deeper context/client needs, lack of emotional intelligence, token limits restricting input/customization, potential for misuse (e.g., unauthorized practice of law), non-transparency of models. | AI Unreliability/Inaccuracy, AI Limitations in Legal Reasoning/Nuance, Technical Challenges in AI Development, Risk of AI Misuse, Regulatory Hurdles, Lack of AI Transparency/Explainability | Using LLMs for specific tasks (retrieval, generation, analysis) within professional workflows, staged approaches (e.g., retrieval + ranking), connecting LLMs to curated knowledge bases, prompt engineering, fine-tuning, responsible API use, and domain-specific evaluation by legal experts. | AI Tool Development, Enhanced AI Capabilities, Legal Knowledge Representation and Management, Prompt Engineering and LLM Interaction Design, Regulation, Ethics, and Governance, Benchmarking and Evaluation Frameworks, Human Oversight and Collaboration | Providing legal information to the public, increasing efficiency of obtaining legal assistance. | Access to Legal Information, Improving Efficiency in Legal System / Profession | The broader public / laypeople. | General public, Laypeople | General / Multiple (including contract law, inheritance law). | General Law, Multiple Fields, Contract Law, Wills and Estates | International (with Hungarian context/examples). | International, Hungary | The underlying LLM (GPT-3.5) was pre-trained on a vast, general internet corpus. The specific chatbot demo was customized using hand-crafted prompts and question/answer examples specific to the law firm and ethical rules, provided via the API. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Input Data for Task (Non-Training), Author-Created New Dataset, Legal Domain Data, Expert-Annotated / Human-Curated / Human-Generated Data, Proprietary Data, Structured Data | Prompt engineering, few-shot learning (via examples provided in API calls). | Prompt Engineering, Few-shot Learning Application | A web interface for the demo chatbot was created and made publicly accessible (as stated in footnotes). | Web-based access, Freely accessible tool/service, Research preview/Beta access | True | False | Source code for demo front-end and examples available on GitHub; requires paid access to OpenAI API. | Code available | Need for domain-specific accuracy benchmarks and evaluation by legal experts; understanding LLM capabilities with higher-level legal concepts; determining reliability limits, especially for direct client use; need for more large-scale experimentation across jurisdictions. | Research and Evaluation Gaps, AI Legal Reasoning Limitations, AI Accuracy and Reliability, Consumer Protection Gaps | Adhering to deontological rules, preventing factual 'hallucinations', managing strict token limits (restricting customization and context length), ensuring accurate multilingual performance. | Ethical Considerations, LLM Hallucination and Factual Errors, Accuracy and Reliability of LLM Output, LLM Context Window and Long Input Management, Multilingual and Low-Resource Language Support | Providing incorrect legal information/advice (hallucinations); unauthorized practice of law; misrepresenting firm details; potential data confidentiality issues with API usage (mitigated by OpenAI policy); potential for misuse (e.g., generating misinformation). | Inaccurate or misleading AI output, Unauthorized practice of law, Data privacy and security breach, Security vulnerabilities or malicious misuse |
| lNX-6qdZr2wJ.pdf | Google_Scholar | How LLMs Can Help Address the Access to Justice Gap through the Courts | This paper explores how Large Language Models (LLMs) can improve access to justice for low-income individuals in the U.S. court system, focusing on externally-facing applications. It demonstrates five use cases using Arizona courts, including translation and GPT-powered chatbots for eviction and expungement, while also discussing potential risks and providing illustrative tools. | LLM Application, Access to Justice Enhancement, Low-Income Individual Assistance, US Focus (Arizona), Use Case Demonstration, Legal Translation, Chatbot Development, Eviction Law Focus, Expungement Focus, Risk Identification | True | Idealistic | True | 1.0 | Positive | Demonstration of five LLM use cases: 1) multi-language translation of court website text, 2) finding pro bono legal help, 3) building no-code AI chatbots for criminal expungement guidance, 4) building no-code AI chatbots for landlord/tenant disputes and eviction guidance, and 5) internal court brainstorming/strategic planning. Two GPT-powered chatbots for Arizona expungement and eviction were built using OpenAI's GPT builder. | Large Language Model Application, Multilingual Application, Legal Information Provision, No-Code AI Development, Chatbot / Conversational AI, Internal Organizational Tool, Software / Platform Development | Translation: Prompts given to ChatGPT 4.0, ChatGPT 3.5, Bard, Claude 1 & 2; reviewed by native speakers. Pro bono help finding: Tested with ChatGPT 4, Bard, Perplexity Pro; links/numbers checked. Chatbots (Expungement & Eviction): Built with OpenAI's GPT builder using Arizona court documents, tested with sample user queries for eligibility, form-filling guidance, and procedural explanations. Internal brainstorming: Prompts given to Claude 2 and ChatGPT 4. | Qualitative Analysis, Expert Evaluation, Demonstration or Illustrative Examples | For Spanish translation of legal text, ChatGPT 4 received a native speaker rating of 9/10. | High performance | Lack of adequate legal assistance for low-income individuals, difficulties for self-represented litigants in navigating the legal system (e.g., understanding rights, procedures, finding help, completing forms), and language barriers. | Limited Access to Legal Assistance, Challenges for Self-Represented Litigants, Public Lack of Legal Knowledge/Awareness, Accessibility Barriers for Specific User Groups | Utilizing LLMs for language translation of legal information, curating legal provider information, guiding users through self-help forms and procedures (e.g., for eviction and expungement via AI chatbots), and assisting courts with internal planning and improving IT infrastructure. | Language Simplification and Multilingual Access, Access to Legal Information and Advice, Document Automation, Support for Self-Represented Litigants, AI Tool Development, Judicial System Enhancement | Language access in courts, legal aid referrals, criminal record expungement, housing law (eviction, landlord-tenant disputes), support for self-represented litigants, and court administration. | Language Access and Digital Divide, Legal Aid and Pro Bono Services, Protection of Rights, Support for Self-Represented Litigants, Judicial System Modernization / Efficiency | Low-income Americans, self-represented litigants, individuals with limited English proficiency, and individuals with criminal records. | Low-income individuals, Population in USA, Self-represented litigants, Individuals with language barriers, Individuals with criminal records | Civil law, criminal law (specifically record clearing/expungement), housing law (landlord-tenant disputes, eviction), immigration law (for referral finding). | Civil Law, Criminal Law, Housing Law, Landlord-Tenant Law, Immigration Law | United States (with Arizona courts as a specific case study) | USA | Publicly available, unstructured textual information and forms (over 150 pages total for both bots) from the Arizona state courts' websites (specifically azcourts.gov, azcourthelp.org) regarding expungement, landlord-tenant disputes, and eviction. | RAG System Knowledge Corpus, Publicly Available Data, US Legal Data, Legal Domain Data, Other Legal Documents, Unstructured Text Data, Web Scraped Data | For chatbots: Utilization of OpenAI's GPT builder (a no-code approach), involving uploading relevant documents from Arizona court websites to create a knowledge base, and iterative testing with sample conversations. For other use cases: Prompt engineering with various LLMs (ChatGPT, Claude, Bard). | No-code/Low-code Platform Utilization, Knowledge Base Creation, Iterative Testing, Prompt Engineering | Two GPT-powered chatbots (AZExpungement and AZ-evictionbot) were made accessible via URLs. Prompts and instructions for implementing the five use cases are provided in an appendix. | Web-based access, Freely accessible tool/service, Public dataset/benchmark release | True | False | Two GPT-powered chatbots (Arizona Expungement Bot and Arizona Eviction Bot) accessible via provided URLs, requiring a ChatGPT 4 subscription. Prompts for all five use cases are in the appendix. | Publicly accessible online tool or platform, Commercial product or service, Research artifact published in paper, Configuration or prompts available | Current limitations of LLMs in accuracy and reliability (hallucinations), need for more sophisticated and reliable legal AI tools tailored for courts, and institutional/cultural challenges within courts for technology adoption. | AI Accuracy and Reliability, AI Scope and Functionality Limitations, Human Oversight and Professional Adaptation | Ensuring accuracy and reliability of LLM-generated information (hallucinations, e.g., incorrect legal deadlines), managing the risk of providing unauthorized legal advice, technical difficulties in translation (terms of art, less common languages), and the need for significant IT infrastructure upgrades and staff training for court adoption. | Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Unauthorized Practice of Law (UPL) Concerns, Multilingual and Low-Resource Language Support, Integration with Existing Systems and Workflows, Financial Cost and Resource Constraints, User Training, AI Literacy, and Skill Gaps | Generation of inaccurate or false information (hallucinations) by LLMs, perpetuation of bias from training data or model inferences, exacerbation of existing inequalities in the legal system (e.g., creating a two-tiered system of justice), potential for misuse (e.g., flooding courts with frivolous filings), and diversion of resources from other access to justice initiatives like right to civil counsel. | Inaccurate or misleading AI output, Bias and discrimination, Exacerbation of inequality or two-tiered system, Security vulnerabilities or malicious misuse, Negative economic impact |
| Hb7vkjE6mpUJ.pdf | Google_Scholar | Artificial Intelligence and the Sustainable Development Goals: An Exploratory Study in the Context of the Society Domain | This paper evaluates AI's potential contributions and risks concerning the United Nations' Sustainable Development Goals (SDGs) within the society domain by analyzing responses from the GPT-3 language model. The study highlights AI's capabilities for these goals while emphasizing the critical need for ethical guidelines and robust regulations. | LLM Evaluation, AI for Sustainable Development Goals, Benefit Identification, Risk Identification, Ethical Guidelines for AI, Need for AI Regulation | True | Idealistic | True | 2.0 | Neutral | The paper evaluates the capabilities and response patterns of GPT-3 (specifically the text-davinci-003 model) when queried about AI's role in achieving Sustainable Development Goals (SDGs). | AI System Evaluation, Large Language Model, AI for Social Good | GPT-3 (text-davinci-003 model) was prompted with queries regarding 9 societal SDGs and their 58 outcome targets, asking it to shorten target titles, maintain numbering, and provide 3-5 sentences on AI's benefits and risks. The generated outputs were then descriptively analyzed for content, structure, word counts, and patterns/errors. | Qualitative Analysis | GPT-3 (text-davinci-003) generated relevant responses discussing potential benefits and risks of AI for societal SDGs. However, the study identified inconsistencies in output format, varying sentence structures, and increased punctuation mistakes in longer texts, indicating it is not fully reliable or error-free. | Descriptive or Conceptual finding, Limitation: Operational or Technical | High-level obstacles/risks for AI in access to justice (derived from SDG 16 discussion) include: potential for targeting specific populations (e.g., minority groups), misinterpretation of data leading to false accusations, biased algorithms causing unfair discrimination, increased surveillance infringing on privacy, misuse by corrupt actors, racial biases in technologies like facial recognition for legal identity, and violation of fundamental freedoms through profiling. | Bias in AI/Data, AI Unreliability/Inaccuracy, Risk of AI Misuse, Data Privacy Concerns with AI, Risk to Human Rights from AI | The paper advocates for proper regulations and oversight for responsible, transparent, safe, and ethical AI use. It calls for a global debate leading to science-driven shared principles and legislation, careful monitoring of AI, building safeguards against discrimination into algorithms, and strict oversight for technologies like facial recognition. | Regulation, Ethics, and Governance, Transparency and Explainability in AI, Policy and Regulatory Reform, Bias Detection and Mitigation | Access to Justice for All (as part of SDG 16), promoting the rule of law, reducing illicit financial flows, reducing corruption and bribery, providing legal identity for all. | Democratizing Law / Closing Justice Gap / Rule of Law, Protection of Rights | Minority groups, disadvantaged groups, and vulnerable populations are mentioned as potentially at risk or in need. | Minority groups, Marginalized communities, Vulnerable populations | Public law, human rights, criminal justice (related to reducing violence, corruption), administrative law (effective institutions, rule of law). | Public Law, Human Rights Law, Criminal Justice, Anti-Corruption Law, Administrative Law | International | International | The GPT-3 model text-davinci-003, used in the study, was trained on general internet text data up to June 2021. This is large-scale, mostly unstructured text data. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Unstructured Text Data | The evaluation of GPT-3 involved AI model selection (comparing GPT-3 models), prompt engineering (designing specific queries), and descriptive analysis (analyzing GPT-3's output for patterns, word counts, etc.). | Model Selection, Prompt Engineering, Descriptive Analysis | NaN | Not applicable | True | False | The study used OpenAI's GPT-3 text-davinci-003 model, accessible via its platform (e.g., API, playground), which was available as a 'publicly available beta for research.' | API access, Publicly accessible online tool or platform, Commercial product or service | Technical gaps include the unreliability and error-proneness of GPT-3 for complex, evidence-based tasks, its potential for bias, and lack of robust interpretability. Societal and regulatory gaps include the need for frameworks for ethical AI deployment in justice, mechanisms to ensure AI enhances freedoms, and robust legislation for AI governance and accountability. | AI Accuracy and Reliability, Bias in AI, Transparency and Explainability, Ethical Framework Deficiencies, Regulatory and Governance Gaps, Accountability and Redress Mechanisms | Challenges faced by the authors in using GPT-3 included obtaining consistent output format, ensuring accuracy (avoiding 'hallucinations'), the AI's tendency to mimic human writing errors, and potential capacity issues with the free beta tier. | Output Variability and Consistency, Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Scalability of Solutions | For SDG 16 (Access to Justice): AI could enable targeting of specific populations; misinterpret data leading to false accusations; use biased algorithms for discrimination; increase surveillance infringing privacy; be misused by corrupt actors; exhibit racial bias in facial recognition for legal identity; and violate fundamental freedoms through profiling and targeted ads. | Security vulnerabilities or malicious misuse, Inaccurate or misleading AI output, Bias and discrimination, Data privacy and security breach, Infringement on human rights |
| Ra4RfztIjSYJ.pdf | Google_Scholar | Governing Data and AI to Protect Inner Freedoms Includes a Role for IP | This policy brief argues for comprehensive governance of data and AI, highlighting the crucial role of intellectual property, to protect fundamental human rights such as freedom of thought from the impacts of technologies like generative AI. It identifies current regulatory inadequacies and proposes solutions including enhanced international cooperation, technology pre-deployment testing, and increased corporate accountability. | Policy Brief, AI Governance, Data Governance, Intellectual Property in AI, Human Rights Protection, Generative AI Impact, Regulatory Reform Proposal | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | Lack of regulatory clarity and data governance hindering intellectual property protection for smaller entities, creating socio-economic and access-to-justice issues; pervasive data monetization without assured human rights protection; inadequate, siloed, and non-globalized AI/data regulations; manipulative AI practices (e.g., dark patterns, disinformation) threatening freedom of thought. | Regulatory Uncertainty, Inadequate Legal Frameworks for AI, Intellectual Property/Copyright Issues with AI, Risk to Human Rights from AI, Ethical Concerns with AI in Law, AI-driven Misinformation/Disinformation | Integrating intellectual property rights into AI governance frameworks to enhance transparency and algorithmic monitoring; establishing national personal data protection laws; fostering international regulatory cooperation (e.g., a Digital Stability Board); implementing technology testing (e.g., regulatory sandboxes) before deployment; promoting corporate responsibility through duty-of-care frameworks. | Regulation, Ethics, and Governance, Transparency and Explainability in AI, Policy and Regulatory Reform, Data Privacy and Security, Benchmarking and Evaluation Frameworks | Fairness in intellectual property protection, particularly for smaller entities; protection of fundamental human rights (especially freedom of thought) through AI and data governance. | Protection of Rights, Ethical AI in Law and AI Governance, Support for Vulnerable Populations | Smaller companies (regarding intellectual property rights and access to justice); the general public (regarding the protection of freedom of thought and other fundamental rights). | Small businesses, General public | Intellectual Property Law, Data Governance, AI Regulation, Human Rights Law, Competition Law | Intellectual Property Law, Data Governance, AI Regulation, Human Rights Law, Competition Law | International (with specific examples and discussions related to Canada, United States, European Union, United Kingdom, Australia, Japan, and bodies like G7, OECD). | International, Canada, USA, EU, UK, Australia, Japan | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Lack of coherent global guardrails, standards, and regulations for generative AI and data governance; insufficient mechanisms for multi-stakeholder international cooperation on AI regulation; unresolved international differences in IP treatment for AI-generated works and data used in AI training; inadequate protection for freedom of thought against technological encroachments. | Regulatory and Governance Gaps, Ethical Framework Deficiencies, Need for Interdisciplinary Collaboration | NaN | NaN | Monetization of nearly all human activity as data without upholding human rights (including freedom of thought, privacy, freedom of speech); covert tracking, surreptitious surveillance, and pervasive monitoring; opaque consent agreements; IP rights used by digital giants to impede competitors; uncertainty in IP law regarding AI-generated inventions and copyright for AI inputs/outputs; use of trade secrets to hinder transparency; web scraping of copyrighted data; AI-driven subtle influence on individuals and generation of social tensions (e.g., disinformation); weaponization of personal data through 'dark patterns'; AI 'hallucinations' and inaccuracies misrepresenting individuals. | Infringement on human rights, Data privacy and security breach, Regulatory challenges or gaps, Copyright or intellectual property issues, Lack of transparency, accountability, and redress, Security vulnerabilities or malicious misuse, Inaccurate or misleading AI output, Negative societal impact |
| ju14jCLQ_TMJ.pdf | Google_Scholar | Bekenbey AI: Innovative Solutions at the Intersection of Deep Learning and Law | This paper introduces Bekenbey AI, a system integrating generative artificial intelligence (including GANs, VAEs) and deep learning models like BERT for legal applications such as document analysis, generation, and predictive analytics. The model, tested on real-world legal data, demonstrates high performance on various metrics, aiming to enhance the efficiency, accuracy, and accessibility of legal services for professionals, organizations, and the public. | System Development, Generative AI Application, Deep Learning Application, Legal Document Analysis, Legal Document Generation, Predictive Analytics, Efficiency Improvement, Accuracy Improvement, Accessibility Enhancement | True | Idealistic | True | 1.0 | Positive | Bekenbey AI model: a hybrid system integrating Natural Language Processing (NLP) techniques, deep learning architectures (RNN, LSTM, BERT, CNN), and Generative AI technologies (GANs, VAEs) for legal text analysis, document generation, and predictive analytics. | Model Development, Hybrid AI System, Natural Language Processing (NLP), Deep Learning, Generative AI, Legal Text Analysis, Legal Document Generation / Automation, Predictive Analysis, Named Tool / Platform | The model was evaluated using metrics such as accuracy, precision, recall, F1-score, ROUGE (R-1, R-2, R-L), and BLEU scores on datasets of legal documents. Computation time and memory usage were also assessed across different dataset sizes. The datasets were compiled from legal databases, government/corporate websites, academic resources, and digital libraries, anonymized by Torun Law and Consulting. | Custom Dataset Evaluation, Quantitative Metrics | With 50 samples, the Bekenbey AI model achieved an accuracy of 88.73%, precision of 89.00%, recall of 88.00%, and F1-score of 88.50%. For text generation tasks with 50 samples, it achieved ROUGE-1: 97.50%, ROUGE-2: 93.80%, ROUGE-L: 96.50%, and BLEU: 93.00%. | High performance | High cost, time-consuming nature of traditional legal procedures; limited accessibility of legal services; complexity of legal texts and data management challenges in the legal sector. | High Cost of Legal Services, Judicial/Legal System Inefficiencies, Limited Access to Legal Assistance, Complexity of Legal Language/Documents, Data Management Challenges | The Bekenbey AI model is proposed to streamline complex legal processes, enhance legal document management and analysis, provide predictive analytics, and support decision-making. This is intended to reduce time and costs, and improve the accuracy and accessibility of legal services. | AI Tool Development, Document Automation, Legal Research and Analysis Tools, Enhanced AI Capabilities, Judicial System Enhancement, Cost Reduction and Efficiency, Access to Legal Information and Advice | Legal document generation, predictive legal analytics, legal text analysis, case outcome prediction, document management, enhancing accessibility of legal services. | Legal Document Creation / Automation, Improving Foundational AI Capabilities for Legal Applications, Legal Document Analysis / Review, Democratizing Law / Closing Justice Gap / Rule of Law | The public/citizens, legal professionals, and organizations. | General public, Legal professionals, Organizations | General legal domain (adaptable across various legal sectors and frameworks, not specified further). | General Law | International (not specified, model described as adaptable). | International | Proprietary datasets anonymized by Torun Law and Consulting, compiled from multiple sources including legal databases, government and corporate websites, academic resources, and digital libraries. The datasets include a mix of structured data (e.g., legal codes, statutes) and unstructured data (e.g., case law texts, legal opinions). | Proprietary Data, Legal Domain Data, Legislation / Statutes / Regulations, Case Law / Judgments, Legal Scholarly Content / Textbooks, Structured Data, Unstructured Text Data | The Bekenbey AI model uses a multi-layered architecture involving: data preprocessing (cleaning, tokenization, stemming, vectorization); embedding layers (Word2Vec, BERT); deep learning layers (CNN, RNN/LSTM, Transformer with attention mechanisms); classification layer (densely connected layers, softmax). The system integrates NLP techniques, generative AI (GANs, VAEs), and uses SQL/NoSQL databases (PostgreSQL, MongoDB) with a Python-based backend (Django, Flask) and FastAPI for APIs. | Multi-layered System Architecture, Data Preprocessing, Embedding Model Application, Deep Learning Model Development, Classification Model Training, Natural Language Processing (NLP) Techniques, Generative AI Techniques, Database Integration, API-based Development | The backend infrastructure uses Python with Django and Flask, and APIs are developed using FastAPI for integration. The paper mentions model deployment as part of its system architecture but does not detail broader public deployment or diffusion strategies. | API access, Internal deployment/prototype | False | False | NaN | NaN | The paper suggests future work to: analyze different generative models in legal contexts, conduct comparative analyses with other models, test the model on diverse datasets and application domains, and explore advanced techniques to enhance accuracy and overall performance. | Research and Evaluation Gaps, AI Accuracy and Reliability | Addressing data management challenges within legal processes; ensuring compliance with stringent security standards and privacy regulations (e.g., GDPR); meeting high demands for security and operational efficiency in legal applications; parsing and comprehending complex legal texts. | Data Quality, Processing, and Preparation, Data Privacy, Security, and Confidentiality, Regulatory Uncertainty and Compliance, LLM Reasoning Capabilities | The paper does not explicitly state concrete risks of the Bekenbey AI model itself, though it mentions the implementation of encryption and anonymization techniques for GDPR compliance, implicitly acknowledging data privacy as a concern to be managed. | Data privacy and security breach |
| UsGFKAW4Sj4J.pdf | Google_Scholar | AI, UPL, & A2J — GENERATIVE AI’S DISRUPTIONS IN THE DELIVERY OF LEGAL SERVICES TO LOW-INCOME INDIVIDUALS | This paper examines how generative AI (GenAI) is transforming legal services for low-income individuals, highlighting its potential for access to justice (A2J) alongside concerns about accuracy and unauthorized practice of law (UPL). It argues against restrictive regulations, advocating instead for integrating GenAI into guided legal assistance programs and relaxing UPL rules to foster innovation and expand access. | Generative AI for Legal Services, Low-Income Individual Assistance, Access to Justice Enhancement, Risk Identification, AI Hallucinations/Inaccuracy, Unauthorized Practice of Law, Advocacy for Relaxed Regulation, Innovation in Legal Tech | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | Cost of legal services; inaccessibility of attorneys; individuals not recognizing problems as legal; restrictive Unauthorized Practice of Law (UPL) doctrines hindering innovation; digital divide (access, tech literacy, reading literacy); limitations and risks of AI tools (accuracy, bias, hallucinations). | High Cost of Legal Services, Limited Availability/Access to Legal Professionals/Expertise, Public Lack of Legal Knowledge/Awareness, Regulatory Hurdles, Digital Divide, AI Unreliability/Inaccuracy, Bias in AI/Data | Relax UPL restrictions to permit nonlawyer and technology assistance; integrate GenAI with expert-guided systems (like document automation); foster collaboration between lawyers and AI developers; improve AI reliability (e.g., using RAG); focus on self-help resources beyond lawyer-centric models. | Policy and Regulatory Reform, Alternative Legal Service Delivery Models, Human Oversight and Collaboration, Open Source Initiatives and Collaboration, Enhanced AI Capabilities, Support for Self-Represented Litigants | Legal information provision; document automation; self-help legal resources; addressing common civil legal needs of low-income populations. | Access to Legal Information, Legal Document Creation / Automation, Support for Self-Represented Litigants, Support for Vulnerable Populations | Low-income individuals; disadvantaged persons; self-represented litigants. | Low-income individuals, Marginalized communities, Self-represented litigants | Civil Law; Estate Planning; Housing Law; Bankruptcy Law; Professional Responsibility (Unauthorized Practice of Law). | Civil Law, Wills and Estates, Housing Law, Bankruptcy Law, Professional Responsibility | United States (with specific examples and caselaw from Missouri, Colorado, North Carolina, Ohio, New York, Maryland, etc.) | USA | The paper discusses GenAI tools (like ChatGPT) trained on broad internet data and mentions Retrieval-Augmented Generation (RAG) using curated, authoritative sources, but does not specify datasets for any single tool studied. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, RAG System Knowledge Corpus, Legal Domain Data, Expert-Annotated / Human-Curated / Human-Generated Data | Mentions principles for guided interview systems like A2J Author (legal expertise, user-centered design, community engagement) and techniques for proposed integrated systems (RAG, enhanced prompting), but does not detail a methodology used by the author to develop a specific tool. | NaN | Discusses generally available online tools (search engines, document automation sites, chatbots) and court-deployed systems (guided interviews), but no specific deployment strategy for a novel tool proposed in the paper. | Evaluation of existing third-party tool, Web-based access, Government/Public institution deployment | False | False | NaN | NaN | Technical: AI reliability (hallucinations, accuracy), need for better integration of GenAI with expert systems, addressing AI bias. Societal: Digital divide, consumer trust issues (under/over-reliance), need for UPL reform, funding/resources for A2J tech development, ensuring tech serves low-income communities, UPL enforcement ambiguity with open-source AI. | AI Accuracy and Reliability, Integration and Interoperability Challenges, Bias in AI, Access, Equity, and Digital Divide, Public Understanding, Trust, and Adoption, Consumer Protection Gaps, Regulatory and Governance Gaps, Computational Resource and Cost Issues | NaN | NaN | Inaccurate/unreliable AI outputs (e.g., fake case law); violation of UPL rules; exacerbating inequality due to digital divide or poor AI design; creation of a two-tiered justice system; consumers over-trusting AI leading to poor decisions; potential for AI bias. | Inaccurate or misleading AI output, Unauthorized practice of law, Exacerbation of inequality or two-tiered system, Over-reliance on AI, Consumer harm, Bias and discrimination |
| W7292Ow-LfoJ.pdf | Google_Scholar | Empowering Air Travelers: A Chatbot for Canadian Air Passenger Rights | This paper presents a chatbot designed to help Canadian air travelers understand their rights by retrieving relevant information from legal documents. The system decomposes complex user queries and presents relevant passages directly to the user, aiming to avoid hallucinations common in generative models. | Chatbot Development, Canadian Focus, Air Traveler Rights, Legal Information Retrieval, Query Decomposition, Mitigating AI Hallucinations | True | Idealistic | True | 1.0 | Positive | A chatbot using LLM-based query decontextualization and decomposition (GPT-4 with in-context learning), followed by dense retrieval (OpenAI embeddings, cosine similarity) from a domain-specific knowledge base. Relevant passages are presented directly to the user, bypassing generative summarization. | Chatbot / Conversational AI, Large Language Model, Query Processing, In-context Learning, Information Retrieval / Search, Embedding-based Methods, Knowledge Base Integration, Direct Information Presentation | Comparative usability study (N=15) against Google Search using USE questionnaire on 4 air travel scenarios per participant. Hallucination analysis comparing the chatbot's retrieval-only output to a standard RAG approach on 40 examples. Evaluation of retrieval performance (P@5, R@5, F1@5, MAP@5) on 40 examples. | User Study or Survey, Comparative Analysis, Qualitative Analysis, Quantitative Metrics | User study: Chatbot rated significantly more useful and satisfying than Google Search, with comparable ease of use/learning. Hallucination analysis: Chatbot achieved 0% hallucinations versus 27.5% for the standard RAG approach. Retrieval achieved MAP@5 of 0.88. | High performance, Outperforms others, Comparable to others, Benefit identified, Limitation: Hallucination or Factual inaccuracy | Passengers' lack of knowledge about their rights, difficulty navigating complex regulations, deficient regulations and enforcement in Canada, high volume of inquiries overwhelming volunteer support systems. | Public Lack of Legal Knowledge/Awareness, Complexity of Legal System/Procedures, Inadequate Legal Frameworks, Resource Constraints for Legal Aid Organizations | An automated chatbot to provide quick, accurate information about passenger rights by understanding complex user narratives and retrieving relevant passages from reliable sources, thereby empowering users and reducing volunteer workload. | AI Tool Development, Access to Legal Information and Advice, Enhanced AI Capabilities, Cost Reduction and Efficiency | Access to information about air passenger rights. | Access to Legal Information, Protection of Rights | Canadian air travelers facing issues such as flight delays, cancellations, and baggage problems. | Consumers, Travelers, Population in Canada | Consumer protection law, Air passenger rights, Transportation law | Consumer Law, Transportation Law | Canada | Canada | A knowledge base constructed from 88 public web pages containing regulatory details, practical guides, and legal glossaries from the Air Passenger Rights (Canada) website and the Canadian Air Passenger Protection website. The system uses pre-trained LLMs (GPT-4, OpenAI embeddings) fine-tuned via in-context learning with provided prompts. | RAG System Knowledge Corpus, Publicly Available Data, Canadian Legal Data, Legal Domain Data, Legislation / Statutes / Regulations, Other Legal Documents, Web Scraped Data, Pre-trained LLM's General Training Corpus, Input Data for Task (Non-Training) | Retrieval-Augmented Generation (RAG) architecture modified to present retrieved passages directly instead of generating summaries. Use of LLMs (GPT-4, OpenAI Embeddings) via API. Development of a web application prototype (Python/FastAPI backend, Next.js frontend). User study for evaluation. | Retrieval Augmented Generation (RAG), API-based Development, Prototyping, User Interface Development, User Study | Implemented as a web application prototype. The code is made available on GitHub. | Web-based access, Internal deployment/prototype, Open source code release | False | True | Code is available on GitHub (link provided in footnote 1). | Code available | Knowledge base requires continuous updates to remain current. The chatbot lacks interactive dialogue capabilities to clarify ambiguous queries. Users may need help understanding and applying the presented legal information; simplified summaries are needed. | Knowledge Recency and Updatability, User Interface and Usability Gaps | Handling complex, multi-part user queries; ensuring high accuracy and avoiding hallucinations in a high-stakes domain; selecting and structuring the knowledge base; designing an intuitive user interface. | LLM Reasoning Capabilities, Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Scarcity of High-Quality Legal Data, User Interface, Usability, and Accessibility | Providing incorrect information (hallucinations) leading to financial loss or missed opportunities for passengers. Undermining user trust. Users potentially misinterpreting the retrieved legal passages. Privacy concerns regarding user inputs (partially addressed by using paid API). | Inaccurate or misleading AI output, Consumer harm, Erosion of trust in legal system or AI, Data privacy and security breach |
| YMpBXSigfgQJ.pdf | Google_Scholar | What Should ChatGPT Mean for Bioethics? | This paper discusses the implications of Large Language Models like ChatGPT for bioethics, comparing many issues to existing medical AI concerns. It also highlights new ethical dilemmas such as medical deepfakes, the need for AI interaction disclosure, and challenges posed by foundational models including equitable access and potential biases. | LLM Impact on Bioethics, Ethical Dilemmas of AI, Medical Deepfakes, AI Interaction Disclosure, Equitable Access to AI, Bias in AI | True | Idealistic | True | 3.0 | Positive | ChatGPT (a chatbot interface for OpenAI's GPT Large Language Models). | Chatbot / Conversational AI, Large Language Model, Named Tool / Platform | The paper cites other studies where ChatGPT was tested by its performance on: law school exams, the bar exam, United States Medical Licensing Exam (USMLE) steps, and a Stanford Medical School final exam in clinical reasoning. | References External Evaluation, Performance on Standardized Tests | According to cited studies, ChatGPT passed law school exams (GPT-3 just barely, GPT-4 scored above 90th percentile), passed the bar exam (earlier versions with fine-tuning, GPT-4 aced it scoring above 90th percentile), and performed at or near the passing threshold for all three USMLE exams without specialized training. GPT-3 also achieved a passing grade on a Stanford Medical School clinical reasoning exam. | High performance, Descriptive or Conceptual finding | For access to justice, the paper implies obstacles for "low-income people" and "pro se prisoners" in accessing legal help. Broader AI-specific obstacles pertinent to A2J include model unreliability (hallucinations), bias, and ensuring equitable access to such technologies. | Limited Access to Legal Assistance, Challenges for Self-Represented Litigants, AI Unreliability/Inaccuracy, Bias in AI/Data, Unequal Access to A2J Technology | The paper suggests chatbots, like ChatGPT, could enhance access to justice by providing direct legal services, such as helping low-income individuals get a head start in "lawyer for a day programs" or assisting pro se prisoners in bringing litigation. | AI Tool Development, Access to Legal Information and Advice, Support for Self-Represented Litigants | Direct legal services, assistance for pro se litigants, initial legal drafting and support. | Access to Legal Advice, Access to Legal Representation, Support for Self-Represented Litigants, Legal Document Creation / Automation | Low-income people, pro se prisoners. | Low-income individuals, Self-represented litigants, Prisoners | General legal services (e.g., drafting complaints, contracts, wills), litigation by pro se individuals. | General Legal Practice, Litigation, Document Drafting, Contract Law, Wills and Estates | US (based on examples like bar exams and legal services), with broader implications. | USA, International | GPT-3 was trained on 175 billion parameters from a large amount of internet text; GPT-4 on approximately 1 trillion parameters. Both models were further refined through reinforcement learning from human feedback (supervised reinforcement learning). | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Expert-Annotated / Human-Curated / Human-Generated Data | ChatGPT is a general LLM based on the GPT architecture, designed as an autoregressive model to predict subsequent text based on prior context. It is refined using Reinforcement Learning from Human Feedback (RLHF). | Autoregressive Model Design, Transformer Architecture, Reinforcement Learning from Human/AI Feedback (RLHF/RLAIF) | ChatGPT is deployed as a chatbot, accessible via a prompt-based interface. | Evaluation of existing third-party tool, Web-based access | True | True | ChatGPT is publicly available through OpenAI, offering both free and paid access tiers. | Publicly accessible online tool or platform, Freemium access | Unreliability and "hallucinations" in LLM outputs; data representativeness and algorithmic bias; privacy vulnerabilities; ethical need for users to know they are interacting with an AI; potential for generating misleading deepfakes (legal or medical); market concentration risks affecting access and ethical standards; significant environmental impact of large models; linguistic limitations (dominance of English); risk of model homogenization stifling diversity and ethical considerations. | AI Accuracy and Reliability, Data Availability and Quality, Bias in AI, Security and Privacy of Data, Ethical Framework Deficiencies, Transparency and Explainability, Access, Equity, and Digital Divide, Environmental Impact Concerns, Multilingual and Low-Resource Language Gaps | The paper discusses general challenges inherent to LLMs like ChatGPT: their immense size requiring substantial computational resources; their general-purpose nature often necessitating fine-tuning; their autoregressive functioning; their output unreliability including factual inaccuracies ("hallucinations"); and variability in responses to identical prompts (lack of consistent test-retest reliability). | High Computational and Resource Demands, Domain-Specific Adaptation and Customization, Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Output Variability and Consistency | Data ownership and consent issues for training data; perpetuation of biases from training data; privacy infringements via data breaches, user input leaks, or re-identification; deception if users are unaware they are interacting with AI; generation and dissemination of false information or deepfakes (e.g., in legal or medical contexts); market oligopolies by a few large AI developers affecting equitable access and ethical priorities; substantial environmental footprint; potential for misuse (e.g., patients relying on flawed AI medical advice, or flawed AI legal advice) leading to harm and liability issues. | Copyright or intellectual property issues, Data privacy and security breach, Bias and discrimination, Ethical concerns, Inaccurate or misleading AI output, Security vulnerabilities or malicious misuse, Negative economic impact, Exacerbation of inequality or two-tiered system, Environmental impact, Consumer harm, Lack of transparency, accountability, and redress |
| 2_gchPzjPIEJ.pdf | Google_Scholar | DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services | This paper introduces DISC-LawLLM, an intelligent legal system for Chinese legal services, developed by fine-tuning large language models using legal syllogism prompting and retrieval augmentation. The authors also present DISC-Law-Eval, a comprehensive benchmark to evaluate such systems, demonstrating DISC-LawLLM's effectiveness across various legal scenarios. | LLM Application Development, Chinese Law Focus, Legal Services Provision, Fine-tuning for Legal Domain, Legal Syllogism Prompting, Retrieval Augmented Generation, Benchmark Creation, System Evaluation | True | Idealistic | True | 1.0 | Positive | DISC-LawLLM: A fine-tuned LLM (Baichuan-13B-Base) using custom supervised fine-tuning datasets (DISC-Law-SFT) constructed with legal syllogism prompting strategies, and augmented with a retrieval module for external legal knowledge. | Model Development, Fine-tuning, Large Language Model, Dataset Creation / Curation, Prompt Engineering, Retrieval Augmented Generation (RAG), Named Tool / Platform | Evaluated using the custom DISC-Law-Eval benchmark. Objective evaluation involved multiple-choice questions from Chinese legal exams (NJE, PAE, CPA, UNGEE, PFE, LBK) across three difficulty levels, measuring accuracy. Subjective evaluation used 300 Q&A cases assessed by GPT-3.5 as a referee on accuracy, completeness, and clarity. | Custom Dataset Evaluation, Performance on Standardized Tests, Quantitative Metrics, LLM as Judge | DISC-LawLLM outperformed other LLMs, including GPT-3.5-turbo, on objective evaluation (e.g., 42.09% average accuracy on hard questions, improving over GPT-3.5-turbo by an average of 7%) and subjective evaluation (average score of 3.39 across accuracy, completeness, and clarity). | Moderate performance, Outperforms others | High demand for specialized legal reasoning capabilities and the need for reliable access to accurate, up-to-date external legal knowledge to avoid hallucinations and outdated information. | AI Limitations in Legal Reasoning/Nuance, Need for Reliable External Knowledge for AI, AI Unreliability/Inaccuracy | Fine-tuning LLMs with supervised datasets (DISC-Law-SFT) specifically constructed using legal syllogism prompting to enhance reasoning, and augmenting the LLM with a retrieval module to access external, current legal knowledge. | Enhanced AI Capabilities, Data Curation and Management, Prompt Engineering and LLM Interaction Design | Legal consultation for dispute resolution, statute interpretation, legal document summarization, legal question answering, and assistance with legal examinations. | Access to Legal Advice, Dispute Resolution, Access to Legal Information, Legal Text Simplification / Plain Language, Legal Education for Professionals / Students | General public / everyday individuals seeking legal advice, legal professionals, and law students. | General public, Laypeople, Individuals with unmet legal needs, Legal professionals, Law students | Chinese Judicial domain, covering areas such as Civil Law, Criminal Law, Administrative Procedure Law, Copyright Law, Patent Law, and Bidding Law. | Judicial Processes, Civil Law, Criminal Law, Administrative Procedure, Copyright Law, Patent Law, Commercial Law | China | China | DISC-Law-SFT dataset, constructed from: 1) Publicly available NLP legal task datasets (e.g., LEVEN, CAIL, JEC-QA, CJRC) for the Chinese justice domain. 2) Legal raw text (e.g., laws, judicial verdicts, consultation platform data). 3) Open-source instruction datasets. Data was processed using rule-based methods and LLM-assisted (GPT-3.5-turbo) refinement into supervised fine-tuning samples (pairs and triplets). | Author-Created New Dataset, Fine-tuning Dataset, Instruction-Tuning Formatted Data, Chinese Legal Data, Legal Domain Data, Data From Existing Public NLP/Legal Datasets/Benchmarks, Legislation / Statutes / Regulations, Case Law / Judgments, Legal Q&A / Forum / User Query Data, Synthetic Data, Structured Data | Supervised fine-tuning (SFT) of a base LLM (Baichuan-13B-Base). Dataset construction involved collecting data from diverse legal sources and processing it with rule-based methods and LLM-assisted refinement (behavior shaping with legal syllogism, knowledge expansion, law-specific chain of thought - LCoT). Retrieval augmentation with an external knowledge base was also implemented. | Model Fine-tuning, Supervised Learning, Dataset Creation, Data Collection, Rule-based Data Processing, LLM-aided Data Refinement, Prompt Engineering, Retrieval Augmentation | The paper states that detailed resources, including constructed datasets and model weights, are made available on GitHub. | Public dataset/benchmark release, Open source model release | True | True | Datasets and model weights are released on GitHub (https://github.com/FudanDISC/DISC-LawLLM). | Dataset available, Model available | The paper implies a continued need for comprehensive benchmarks for legal AI systems, as evidenced by their development of DISC-Law-Eval due to the lack of established alternatives. Other specific future gaps beyond improving model capabilities are not detailed. | Research and Evaluation Gaps | Ensuring intricate legal reasoning capabilities in LLMs, reliably integrating up-to-date and precise external legal knowledge to mitigate issues like outdated information and hallucinations, and constructing high-quality, diverse supervised fine-tuning datasets that effectively instill legal reasoning patterns like syllogism. | LLM Reasoning Capabilities, Accuracy and Reliability of LLM Output, Outdated or Limited LLM Knowledge Base, LLM Hallucination and Factual Errors, Scarcity of High-Quality Legal Data, Cost and Complexity of Data Annotation | The potential for LLMs to produce inaccurate responses due to hallucinations or reliance on outdated knowledge, which could lead to incorrect legal information or advice. | Inaccurate or misleading AI output, Technical limitations of AI |
| flw_DqScUF4J.pdf | Google_Scholar | Roles and challenges of ChatGPT and similar generative a rtificial intelligence for \nachiev ing the Sustainable Development Goals (SDGs) | This paper discusses the potential roles of generative AI like ChatGPT in achieving the UN's Sustainable Development Goals (SDGs), including for access to justice (SDG 16). It also outlines significant challenges, such as ethical concerns, data issues, security risks, and the need for robust governance to harness AI's benefits responsibly. | Generative AI for Sustainable Development Goals, Access to Justice Enhancement (SDG 16), Challenge Identification, Ethical Concerns, Data Issues in AI, Security Risks of AI, AI Governance | True | Idealistic | True | 3.0 | Positive | ChatGPT and similar generative artificial intelligence | Large Language Model, Generative AI | NaN | Not Applicable | NaN | NaN | Key obstacles include ethical concerns (data privacy, AI bias, accountability, misinformation), data quality and accessibility issues, language and cultural diversity barriers, security risks from misuse (e.g., deepfakes), environmental impact of AI, scalability challenges, the digital divide, and the need for robust regulatory frameworks. Specific to access to justice (SDG 16), challenges are ensuring legal accuracy, addressing AI bias in legal contexts, and overcoming unequal access to justice technologies. | Ethical Concerns with AI in Law, Data Privacy Concerns with AI, Bias in AI/Data, Lack of AI Accountability, AI-driven Misinformation/Disinformation, Data Scarcity/Quality for AI, Accessibility Barriers for Specific User Groups, Security Risks with AI, Environmental Impact of AI, Scalability Challenges for AI, Digital Divide, Inadequate Legal Frameworks for AI, AI Unreliability/Inaccuracy, Unequal Access to A2J Technology | Proposed solutions involve ethical, responsible, and inclusive AI development and deployment, global collaboration, developing robust policy and regulatory frameworks, fostering human-AI collaboration, enhancing digital literacy, and bridging the digital divide. For access to justice, AI can be used for disseminating legal information, promoting awareness of rights, and offering conflict resolution advice. | Regulation, Ethics, and Governance, Open Source Initiatives and Collaboration, Policy and Regulatory Reform, Human Oversight and Collaboration, Education and AI Literacy, Access to Legal Information and Advice, Online Dispute Resolution (ODR) | Legal information provision, access to justice promotion, legal rights awareness, conflict resolution, crime prediction, human rights awareness, strengthening legal institutions. | Access to Legal Information, Democratizing Law / Closing Justice Gap / Rule of Law, Legal Literacy and Public Legal Education, Dispute Resolution, Protection of Rights, Judicial System Modernization / Efficiency | Citizens, marginalized communities, vulnerable populations. | General public, Marginalized communities, Vulnerable populations | Public legal information, human rights law, criminal justice (related to crime prediction), dispute resolution, general access to justice. | General Legal Practice, Human Rights Law, Criminal Justice, Dispute Resolution, Access to Justice | International | International | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | True | True | The paper discusses ChatGPT and similar generative AI, which are existing technologies. ChatGPT offers publicly accessible versions, including free and paid tiers. | Publicly accessible online tool or platform, Freemium access | Technical gaps include developing AI that ensures legal accuracy and contextual understanding across diverse legal systems and languages, and creating robust, unbiased legal AI models. Societal gaps include establishing ethical guidelines and regulations for AI in law, bridging the digital divide for equitable access to legal AI, fostering public trust, and integrating AI effectively into existing legal institutions and workflows. | AI Accuracy and Reliability, AI Legal Reasoning Limitations, Multilingual and Jurisdictional Specificity Gaps, Bias in AI, Ethical Framework Deficiencies, Regulatory and Governance Gaps, Access, Equity, and Digital Divide, Public Understanding, Trust, and Adoption, Integration and Interoperability Challenges | Inherent challenges of generative AI like ChatGPT include managing ethical dimensions (data privacy, algorithmic bias, accountability), ensuring high-quality and accessible training data, adapting to language and cultural diversity, enabling effective human-AI collaboration, mitigating environmental impact, preventing security threats and misuse (e.g., generating harmful content, misinformation), achieving scalability and equitable resource allocation for widespread deployment, and navigating an evolving regulatory landscape. | Ethical Considerations, Data Privacy, Security, and Confidentiality, Bias in AI Systems and Data, Accountability and Liability for AI Errors, Scarcity of High-Quality Legal Data, Data Quality, Processing, and Preparation, Multilingual and Low-Resource Language Support, Need for Human Oversight and Intervention, Environmental Impact of AI, Safeguarding Against Misuse and Harm, Scalability of Solutions, Financial Cost and Resource Constraints, Regulatory Uncertainty and Compliance | Potential risks include perpetuation of societal biases, data privacy violations, spread of misinformation and deepfakes, security threats from malicious use, negative environmental impact from high energy consumption, and exacerbation of the digital divide and social disparities if not implemented equitably. | Bias and discrimination, Data privacy and security breach, Inaccurate or misleading AI output, Security vulnerabilities or malicious misuse, Environmental impact, Exacerbation of inequality or two-tiered system |
| vNG_5kHTgG0J.pdf | Google_Scholar | Evaluating the Use of Artificial Intelligence for \nan Effective Justice System in Sri Lanka | This paper evaluates the potential of Artificial Intelligence (AI), including chatbots and ChatGPT, to enhance Sri Lanka's legal system, particularly in improving access to justice. It discusses AI's applications, advantages, and challenges, recommending steps like robust data governance, ethical standard-setting, and capacity building for successful integration. | AI for Legal System Enhancement, Sri Lankan Focus, Access to Justice Enhancement, Chatbot Application, ChatGPT Application, Challenge Identification, Recommendations for AI Adoption, Data Governance, Ethical Standards | True | Idealistic | True | 3.0 | Positive | AI (general), Chatbots (e.g., NALA), ChatGPT, Robotics | Artificial Intelligence (General), Chatbot / Conversational AI, Large Language Model, Robotics, Named Tool / Platform | NaN | Not Applicable | NaN | NaN | Lack of complete legal data; slow adoption by legal professionals; potential for AI bias; infrastructure limitations (technology, language, digital literacy); high cost and accessibility issues for AI; large case backlogs and inefficient court processes; general resource constraints in the justice system; resistance to change from legal experts. | Data Scarcity/Quality for AI, Slow Technology Adoption by Legal Profession, Bias in AI/Data, Digital Divide, High Cost of A2J Technology, Limited Access to A2J Technology, Judicial/Legal System Inefficiencies, Resource Constraints, Institutional Resistance to Change (Legal Profession) | Implement robust data governance and security measures; establish clear ethical and legal standards for AI use; conduct thorough cost-benefit analyses of AI implementation; foster collaboration with international organizations; perform socio-economic impact assessments; invest in capacity building and training for legal professionals; create systems for collecting and distributing legal data for AI training; ensure AI systems are designed to be transparent and auditable. | Data Curation and Management, Data Privacy and Security, Regulation, Ethics, and Governance, Open Source Initiatives and Collaboration, Benchmarking and Evaluation Frameworks, Policy and Regulatory Reform, Education and AI Literacy, Transparency and Explainability in AI | Improving timely dispensation of justice; enhancing efficiency and reducing costs of legal services; supporting legal research, contract analysis, and case law analysis; enabling legal translation services; providing legal information and decision support for legal professionals; reducing court case backlogs. | Judicial System Modernization / Efficiency, Improving Efficiency in Legal System / Profession, Affordability of Legal Services / Cost Reduction, LegalResearch Support, Legal Document Analysis / Review, Language Access and Digital Divide, Access to Legal Information | Underserved communities, neglected populations, individuals unable to afford conventional legal services, and non-native speakers in Sri Lanka. | Marginalized communities, Vulnerable populations, Individuals unable to afford legal services, Individuals with language barriers, Population in Sri Lanka | General legal system | General Law | Sri Lanka | Sri Lanka | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | True | False | Discusses ChatGPT, a generally accessible AI model. Mentions 'NALA' chatbot developed by the Legal Aid Commission of Sri Lanka; its public accessibility is not detailed by the paper. | Publicly accessible online tool or platform | Absence of sufficient, comprehensive legal data for AI development; resistance to technological change among legal professionals; risk of AI systems reinforcing existing societal biases if not carefully designed; need for greater transparency and auditability in AI decision-making processes; loopholes and inadequacies in existing Sri Lankan law concerning AI liability and regulation; insufficient focused research and practical adoption of AI within Sri Lanka's legal sector. | Data Availability and Quality, Human Oversight and Professional Adaptation, Bias in AI, Transparency and Explainability, Accountability and Redress Mechanisms, Regulatory and Governance Gaps, Research and Evaluation Gaps, Public Understanding, Trust, and Adoption | Technological infrastructure limitations (e.g., reliable internet, data security); language barriers in a multilingual context; low digital literacy among potential users and some professionals; high cost and difficult accessibility of advanced AI technologies; scarcity of comprehensive and high-quality legal data for training AI; slow adoption rate of new technologies within the legal sector; ensuring fairness and mitigating algorithmic bias; lack of transparency in the operational mechanisms of some AI tools; addressing data privacy and security concerns related to sensitive legal information. | Integration with Existing Systems and Workflows, Data Privacy, Security, and Confidentiality, Multilingual and Low-Resource Language Support, User Training, AI Literacy, and Skill Gaps, Financial Cost and Resource Constraints, High Computational and Resource Demands, Scarcity of High-Quality Legal Data, User Adoption, Trust, and Acceptance, Bias in AI Systems and Data, Transparency and Explainability of AI | AI bias reinforcing or perpetuating systemic discrimination (e.g., the COMPAS example); lack of transparency in AI decision-making processes leading to 'black box' problems; over-reliance on AI potentially diminishing human judicial discretion and the human element in sensitive cases; AI systems generating inaccurate, fabricated, or misleading legal information (e.g., ChatGPT citing fake cases); breaches of data privacy and security of sensitive legal information; socio-economic disruption such as job displacement within the legal profession; inadequacy of existing legal and ethical frameworks to address harm or errors caused by AI; potential for AI to magnify social injustice if not implemented equitably. | Bias and discrimination, Lack of transparency, accountability, and redress, Over-reliance on AI, Dehumanization of legal process, Inaccurate or misleading AI output, Data privacy and security breach, Job displacement, Negative economic impact, Regulatory challenges or gaps, Exacerbation of inequality or two-tiered system |
| _Q1r5ohGDz8J.pdf | Google_Scholar | Intention and Context Elicitation with Large Language Models in the Legal Aid Intake Process | This paper proposes a proof-of-concept framework using Large Language Models (LLMs) to improve the legal aid intake process. The method uses conversational prompting to actively elicit clients' underlying intentions and relevant contextual details, aiming to generate more useful responses compared to direct one-shot LLM answers. | Framework Proposal, LLM Application, Legal Aid Intake Improvement, Conversational Prompting, Eliciting User Intent | True | Idealistic | True | 1.0 | Positive | An LLM-based conversational system designed to elicit client intentions and contextual information through guided dialogue prompts during the legal intake process. | Large Language Model, Chatbot / Conversational AI, Legal Intake System, Prompt Engineering, User Intent Elicitation | Qualitative comparison of the proposed method's output against a baseline one-shot LLM response using example scenarios (e.g., tenancy law). No formal benchmarks or extensive experimental evaluation. | Qualitative Analysis, Comparative Analysis, Demonstration or Illustrative Examples, No Evaluation by Author | The combined intention and context elicitation approach generated qualitatively more useful and tailored responses compared to generic one-shot LLM outputs, which were often too broad or non-specific. | Technique improves outcome, Outperforms others, Moderate performance | Clients often lack legal expertise, leading them to ask suboptimal questions that don't reveal their true intentions or necessary context. Limited capacity of legal aid organizations. | Public Lack of Legal Knowledge/Awareness, Difficulty in AI-Human Interaction, Resource Constraints for Legal Aid Organizations | Employing LLMs in a conversational manner to actively probe for and elicit underlying client intentions and specific contextual details before formulating a response, thereby improving the quality of information gathered and provided during intake. | AI Tool Development, Prompt Engineering and LLM Interaction Design, Access to Legal Information and Advice | Legal intake and triage for legal aid services. | Legal Aid and Pro Bono Services, Improving Efficiency in Legal System / Profession | Clients of legal aid organizations and court centers, particularly those with limited legal knowledge. | Clients of legal aid organizations, Individuals lacking legal knowledge | General legal aid intake, with examples drawn from Family Law, Immigration Law, Tenancy Law. | Legal Aid, Family Law, Immigration Law, Landlord-Tenant Law | United States context mentioned, but the technique appears generally applicable internationally. | USA, International | N/A (The proposed technique uses pre-trained LLMs like GPT-4 without specific fine-tuning on new datasets for the proof-of-concept. Proposes future generation of datasets for training). | Not Applicable | Proof-of-concept development based on prompt engineering and structuring conversational interactions with LLMs. | Proof-of-Concept Development, Prompt Engineering, Conversational Design | NaN | Not applicable | False | False | NaN | NaN | Lack of quantitative experimental evaluation and ablation studies with machine and human evaluators. Need for attorney review of LLM outputs in production settings. Need for verified conversational datasets for future training. | Research and Evaluation Gaps, Human Oversight and Professional Adaptation, Data Availability and Quality | LLM tendency to provide overconfident 'best guess' answers without probing. Difficulty in reliably prompting LLMs to assess information completeness due to overconfidence and lack of metrics. | Accuracy and Reliability of LLM Output, Ethical Considerations, Prompt Engineering and Optimization, Evaluation Challenges and Metrics | LLM inaccuracy (hallucination, incorrect information, inapplicable laws/organizations). Client over-reliance on potentially flawed AI output. Potential for unauthorized practice of law. Privacy concerns regarding client data. | Inaccurate or misleading AI output, Over-reliance on AI, Unauthorized practice of law, Data privacy and security breach, Consumer harm |
| JOrIKdo7d-kJ.pdf | Google_Scholar | Private ordering, generative AI and the ‘platformisation paradigm’: What can we learn from comparative analysis of models terms and conditions? | This paper analyzes the terms and conditions (T&C) and privacy policies of various generative AI providers from early 2023, focusing on copyright and data protection. It finds providers adopt a "platformisation paradigm," positioning themselves as neutral intermediaries by assigning output ownership and all liability to users, despite not fitting the legal definition of platforms, thus creating power imbalances and regulatory gaps. | Analysis of Generative AI Provider T&Cs, Copyright Issues, Data Protection Issues, Platform Liability, Regulatory Gaps | True | Idealistic | True | 2.0 | NaN | Comparative analysis of Terms & Conditions (T&Cs) and Privacy Policies of Generative AI providers (Private Ordering). | Legal Document Analysis, Regulatory Analysis / Policy Analysis, Generative AI Governance | Manual collection and qualitative comparative analysis of T&Cs, privacy policies, and related documents from a sample of 13 generative AI services (categorized by function: T2T, T2I, T2A/V) selected based on mode, size, jurisdiction, and open/proprietary nature. Initial data collected Jan-Mar 2023, with a follow-up review of privacy policies in Dec 2023. | Qualitative Analysis, Comparative Analysis | Providers consistently assign copyright ownership of outputs to users but grant extensive back-licenses to themselves and assign all liability for infringement or other harms to users. Initial privacy policies were often inadequate but improved over 2023. Providers implement platform-like content moderation (e.g., NTD) and position themselves as neutral intermediaries ('platformisation paradigm'), despite not fitting legal definitions. | Descriptive or Conceptual finding, Risk or Ethical concern highlighted, Limitation: Security or Privacy | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Contract Law, Terms and Conditions, Privacy Law, Data Protection Law (GDPR, CCPA), Copyright Law, Platform Regulation, Internet Law, Consumer Law, Comparative Law. | Contract Law, Data Privacy Law, Copyright Law, Platform Governance, Internet Law, Consumer Law, Comparative Law | International | International | NaN | Not Applicable | Qualitative comparative legal analysis based on manual collection of terms and conditions and privacy policies. | Qualitative Comparative Legal Analysis, Manual Data Collection | NaN | Not applicable | False | True | The paper is published as an Open Access article under a Creative Commons Attribution licence. | Open access resource | Regulatory gaps (e.g., EU DSA not clearly covering foundation models). Lack of transparency and due process in content moderation by providers. Need for fairer risk allocation between providers and users. Need for better operationalization and enforcement of data protection rights (rectification, erasure) for foundation models. Lack of insight into B2B T&Cs and market competition effects. | Regulatory and Governance Gaps, Transparency and Explainability, Accountability and Redress Mechanisms, Security and Privacy of Data, Access, Equity, and Digital Divide | Rapidly changing T&Cs requiring manual tracking. Difficulty obtaining B2B T&Cs due to commercial secrecy. Difficulty identifying the underlying models used by downstream applications. Complexity of the multi-dimensional comparative analysis within the given timeframe. | Data Quality, Processing, and Preparation, Scarcity of High-Quality Legal Data, Transparency and Explainability of AI, Research Methodology and Study Design Limitations | Risks from GenAI models: Bias, fake news, illegal/harmful content, hallucinations, copyright infringement, privacy violations. Risks from private ordering: Abuse of provider power via unfair/non-negotiable T&Cs, inadequate enforcement of user rights (privacy, due process), unfair shifting of liability to users, arbitrary content moderation and sanctions. Risk of regulatory gaps allowing platform-like entities to evade platform-specific obligations (e.g., under DSA). | Bias and discrimination, Inaccurate or misleading AI output, Harmful or unsafe AI output, Copyright or intellectual property issues, Data privacy and security breach, Ethical concerns, Lack of transparency, accountability, and redress, Infringement on human rights, Regulatory challenges or gaps |
| bByRJ9_vmPAJ.pdf | Google_Scholar | RE-REGULATING UPL IN AN AGE OF AI | This paper argues that US state Unauthorized Practice of Law (UPL) statutes should be re-evaluated to allow AI tools, particularly LLMs, to help address the access to justice gap. It proposes focusing consumer protection on transparency, liability, and private rights of action rather than broad prohibitions based on vague definitions of legal practice. | Unauthorized Practice of Law, Regulatory Reform Proposal, US Focus, LLM Application, Access to Justice Enhancement, Consumer Protection in AI | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | Vague, restrictive, and inconsistently enforced Unauthorized Practice of Law (UPL) statutes prevent potentially helpful AI tools from assisting consumers. A vast 'justice gap' exists where most people with civil legal problems cannot afford or access legal help. | Regulatory Hurdles, Scale of Unmet Legal Need, Limited Access to Legal Assistance | Re-evaluate and reform state UPL statutes to permit AI legal assistance tools. Shift consumer protection focus from broad UPL prohibitions to measures like transparency requirements (clear disclaimers that AI is not a lawyer, no attorney-client privilege), mandatory liability insurance for AI providers, and enabling private rights of action (e.g., for negligence, fraud, consumer protection violations) against incompetent or deceptive AI providers. | Policy and Regulatory Reform, Alternative Legal Service Delivery Models, Regulation, Ethics, and Governance, Transparency and Explainability in AI | Access to justice gap in civil legal matters, Self-represented litigants, Legal form completion (e.g., debt collection defense) | Democratizing Law / Closing Justice Gap / Rule of Law, Support for Self-Represented Litigants, Legal Document Creation / Automation | Low-income households, Individuals/families/small businesses unable to afford lawyers, Self-represented litigants | Low-income individuals, Individuals unable to afford legal services, Families, Small businesses, Self-represented litigants | General Civil Law (focusing on areas with high unmet need like debt collection, employment, housing, benefits, insurance), Unauthorized Practice of Law Regulation | Civil Law, Debt Collection, Employment Law, Housing Law, Social Security Law, Insurance Law, Professional Responsibility | United States (State-level UPL regulations, mentioning New York specifically) | USA | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Lack of clear, updated regulatory frameworks for AI legal tools that balance innovation and consumer protection. Need for effective consumer redress mechanisms beyond traditional UPL enforcement. Persistent societal gap in access to affordable legal assistance. | Regulatory and Governance Gaps, Consumer Protection Gaps, Accountability and Redress Mechanisms, Access, Equity, and Digital Divide | Ambiguity and restrictiveness of current UPL laws, which can chill innovation in legal AI for consumers. Technical limitations of current AI (e.g., hallucinations, ensuring confidentiality). | Regulatory Uncertainty and Compliance, Unauthorized Practice of Law (UPL) Concerns, LLM Hallucination and Factual Errors, Data Privacy, Security, and Confidentiality | AI providing incompetent, fraudulent, or negligent legal advice. AI 'hallucinations' leading to reliance on false information or non-existent case law. Breach of user confidentiality if prompts/data are used for model training or exposed. Consumers being deceived into believing AI software is a licensed lawyer or provides attorney-client privilege. | Inaccurate or misleading AI output, Ethical concerns, Consumer harm, Over-reliance on AI, Data privacy and security breach, Unauthorized practice of law |
| cpMmdLuTaPkJ.pdf | Google_Scholar | Who Wants a Robo-Lawyer Now?: On AI Chatbots in China’s Public Legal Services Sector | This essay discusses the potential for large language model (LLM) chatbots to be widely adopted within China's public legal services (PLS) sector to address the access to justice gap. It examines the political economy driving this adoption, potential benefits like reinforcing legality, and associated risks such as errors and confidentiality concerns. | LLM Chatbots for Public Legal Services, Chinese Focus, Access to Justice Enhancement, Political Economy of AI Adoption, Benefit Identification, Risk Identification, Confidentiality Concerns | True | Idealistic | True | 3.0 | Positive | AI chatbots (specifically mentioning Ernie LLM-powered ones) deployed within a government-run public legal services (PLS) system. | Chatbot / Conversational AI, Large Language Model, Public Legal Service Delivery, Deployed AI System | The paper mentions that chatbots deployed in Yunnan performed 620,000 consultations in the initial months, but provides no formal testing methodology or results for this specific deployment. It cites general LLM benchmark studies like LegalBench and LawBench. | Developer Claims Reported, References External Evaluation, No Evaluation by Author | NaN | NaN | Scarcity of legal professionals, particularly in rural/disadvantaged regions; geographic disparities in access to legal services; high demand for basic legal information unmet by existing resources. | Limited Availability/Access to Legal Professionals/Expertise, Geographical Disparities in Legal Access, Scale of Unmet Legal Need, Difficulty Accessing/Interpreting Legal Information | Leveraging AI chatbots within the government-funded Public Legal Services (PLS) system to provide automated, widely accessible basic legal information and advice, particularly for routine, statute-based questions. | AI Tool Development, Access to Legal Information and Advice, Policy and Regulatory Reform | Access to basic legal information and advice; Routine legal inquiries; Dispute resolution guidance (mediation, negotiation, litigation options). | Access to Legal Information, Access to Legal Advice, Dispute Resolution | General populace in China, particularly rural residents and those in disadvantaged regions (illustrated by the Yunnan case). | General public, Population in China, Rural populations, Marginalized communities | General civil law (e.g., labor law, family law, contract/property issues like landlord-tenant disputes mentioned implicitly via routine questions). | Civil Law, Employment Law, Family Law, Contract Law, Property Law, Landlord-Tenant Law | China (with specific examples from Yunnan province). | China | The paper mentions the use of Baidu's Ernie LLM for the Yunnan deployment. It discusses general LLM development approaches like fine-tuning on specific legal Q&A tasks, training specialized legal models with large legal datasets, and Retrieval Augmentation Generation (RAG) connecting models to external knowledge databases. | Pre-trained LLM's General Training Corpus, Chinese Legal Data, Fine-tuning Dataset, Legal Domain Data, Legal Q&A / Forum / User Query Data, RAG System Knowledge Corpus | The paper discusses general approaches like fine-tuning general LLMs, developing specialized legal LLMs, and using Retrieval Augmentation Generation (RAG). It mentions the specific chatbot in Yunnan is based on Baidu's Ernie LLM. | Model Fine-tuning, Specialized LLM Development, Retrieval Augmented Generation (RAG) | Deployment via government-run public legal services stations in rural villages (Yunnan example), accessible through devices stationed at local government offices. Government procurement of third-party (Legal Tech) services. | Government/Public institution deployment, Hardware deployment, Partnership-based rollout | False | False | NaN | NaN | Need for systemic methodologies for assessing LLM legal task performance; Optimal solutions for LLM hallucination are still developing; Technical limitations in ensuring confidentiality of user input; Need for regulatory frameworks and public oversight mechanisms for PLS chatbots; Potential for entrenching inequality if chatbot services remain inferior to human lawyers. | Research and Evaluation Gaps, AI Accuracy and Reliability, Security and Privacy of Data, Regulatory and Governance Gaps, Access, Equity, and Digital Divide | Overcoming regulatory barriers (e.g., unauthorized practice of law) for Legal Tech firms; Finding viable, large-scale use cases attractive to the Legal Tech industry; Ensuring user-friendly interfaces compared to previous technology generations; Managing risks associated with LLM limitations (hallucination, errors); Balancing confidentiality needs with the ability to use interaction data for model improvement. | Regulatory Uncertainty and Compliance, Unauthorized Practice of Law (UPL) Concerns, Domain-Specific Adaptation and Customization, User Interface, Usability, and Accessibility, LLM Hallucination and Factual Errors, Accuracy and Reliability of LLM Output, Data Privacy, Security, and Confidentiality | Loss of confidentiality for user information; Hallucination and errors in legal information provided; Potential for scams and malicious manipulation (e.g., fake platforms); Misuse by government officials (e.g., manipulating information, biasing advice); Exacerbating inequality by creating a two-tiered system or diverting resources from human legal aid. | Data privacy and security breach, Inaccurate or misleading AI output, Security vulnerabilities or malicious misuse, Exacerbation of inequality or two-tiered system, Negative economic impact |
| informit.T2025011900000101519001919.pdf | Google_Scholar | Using Generative AI to Identify Arguments in Judges’ Reasons: Accuracy and Benefits for Students | This paper evaluates the ability of Large Language Models (LLMs) like ChatGPT and Claude to identify and reconstruct legal arguments from Australian High Court judgments, finding significant performance variations. It concludes that while some LLMs show promise for legal education and potential A2J benefits through efficiency, critical human oversight remains essential due to varying accuracy and the risk of skill degradation. | LLM Evaluation, Legal Argument Identification, Legal Argument Reconstruction, Australian Law Focus, Performance Variation in LLMs, Legal Education Application, Access to Justice Enhancement, Need for Human Oversight, Risk of Skill Degradation | True | Idealistic | True | 2.0 | Neutral | Evaluation of Large Language Models (ChatGPT versions 4 and 4o; Claude versions 3.0 Opus and 3.5 Sonnet) for identifying and reconstructing legal arguments from judicial reasons in a modus ponens structure using single-shot prompting. | AI System Evaluation, Large Language Model, Argument Mining / Analysis, Logical Reasoning, Prompt Engineering | Two human assessors (a lawyer/legal academic and a philosopher) blind-marked LLM-generated argument reconstructions for five recent High Court of Australia decisions. Outputs were compared against pre-determined sample answers and assessed using a detailed rubric (20 marks total) covering identification of disposition, premises/conclusions, argument location (paragraph numbers), and modus ponens structure. | Expert Evaluation, Custom Dataset Evaluation, Quantitative Metrics, Comparative Analysis | Claude 3.5 Sonnet performed best, achieving average grades up to 18/20 (90%), with an overall system average of 16.2/20 for this version. In contrast, ChatGPT versions averaged around 8/20, with the lowest individual ChatGPT output scoring 4/20. | High performance, Outperforms others, Underperforms others | General barriers to access to justice include: financial cost, time, complexity of justice systems, lack of legal capability, and language skills. Specific to GAI, obstacles include inaccuracy, unreliability (e.g., hallucinations), and the current inability of GAI to perform all aspects of legal reasoning accurately. | High Cost of Legal Services, Resource Constraints, Complexity of Legal System/Procedures, Public Lack of Legal Knowledge/Awareness, Accessibility Barriers for Specific User Groups, AI Unreliability/Inaccuracy, AI Limitations in Legal Reasoning/Nuance | Accurate GAI could facilitate low- or no-cost legal advice and increase the speed and efficiency of legal processes, thereby potentially reducing costs associated with legal services and increasing individuals' access to justice. | Access to Legal Information and Advice, Cost Reduction and Efficiency, Enhanced AI Capabilities | Reducing costs of legal advice/services, increasing efficiency in legal analysis, improving legal education outcomes, potential for enhanced access to justice through technology. | Affordability of Legal Services / Cost Reduction, Improving Efficiency in Legal System / Profession, Legal Education for Professionals / Students, Democratizing Law / Closing Justice Gap / Rule of Law | Individuals who do not seek legal advice due to high costs or overworked judicial systems; law students and junior lawyers. | Individuals unable to afford legal services, Individuals facing access barriers, Law students, Junior legal professionals | The study used cases covering native title, criminal law, statutory interpretation, and immigration law. | Indigenous Law, Criminal Law, Statutory Interpretation, Immigration Law | Australia (High Court of Australia cases). | Australia | The LLMs (ChatGPT, Claude) were trained on 'enormous volumes of data – for example, text corpora scraped from vast swathes of the internet.' Input for the specific task was the PDF text of five High Court of Australia judgments. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Input Data for Task (Non-Training), Australian Legal Data, Legal Domain Data, Case Law / Judgments, Unstructured Text Data | N/A (The paper evaluates existing LLMs, it does not detail their internal design methodologies beyond general statements about deep learning. The paper's methodology is for evaluation, see 'testing'). | NaN | The LLMs (ChatGPT and Claude) are commercially deployed and accessible via web interfaces, which were used in the study. The study's specific prompting approach is described but not deployed as a standalone tool. | Evaluation of existing third-party tool, Commercial product/service, Web-based access, Dissemination via publication/presentation | True | False | ChatGPT and Claude models are commercially available through their respective platforms (OpenAI and Anthropic). The specific advanced versions tested (e.g., GPT-4, Claude 3.5 Sonnet) are typically part of paid subscription tiers. | Commercial product or service, Publicly accessible online tool or platform | Significant variance in accuracy across different LLMs and versions for legal argument identification. LLMs' tendency for 'hallucinations' and unreliability for certain legal tasks. The 'skill-gap' in users' ability to critically evaluate LLM outputs. Need for further research before LLMs can revolutionize the legal industry. | AI Accuracy and Reliability, User Interface and Usability Gaps, Human Oversight and Professional Adaptation, Research and Evaluation Gaps | For the study: The 'high cost of labour involved in the analysis of legal documents, which necessitates small numbers of annotators/assessors' was a limitation. For users (e.g. students): The varied accuracy of LLMs, the superficial plausibility of even poor answers, and the need to develop critical engagement skills to assess LLM outputs. | Research Methodology and Study Design Limitations, Cost and Complexity of Data Annotation, Accuracy and Reliability of LLM Output, Output Variability and Consistency, User Training, AI Literacy, and Skill Gaps | LLM 'hallucinations' (fabricating information, e.g., non-existent cases). Inaccurate legal advice leading to safety issues. Unsupervised creation of legal arguments by GAI. Over-reliance on LLMs leading to degradation of essential human legal skills, particularly argument analysis, if used uncritically by students. | Inaccurate or misleading AI output, Harmful or unsafe AI output, Consumer harm, Over-reliance on AI, Deskilling or erosion of human skills, Risk of misapplication or misuse |
| 8t--pP6kmsIJ.pdf | Google_Scholar | Generative Artificial Intelligence and the Practice of Law: Impact, Opportunities, and Risks | This article discusses the transformative impact of generative AI on the legal profession, including improving efficiency in tasks like drafting motions and enhancing legal education. It also explores the significant potential of generative AI to broaden access to legal services for underserved communities, while acknowledging associated challenges and risks like AI hallucinations and the need for regulatory adaptation. | Generative AI Impact on Legal Profession, Efficiency Improvement, Legal Document Drafting, Legal Education Enhancement, Access to Legal Services Expansion, Challenge Identification, AI Hallucinations/Inaccuracy, Need for Regulatory Adaptation | True | Idealistic | True | 3.0 | Positive | Generative AI / Large Language Models (LLMs) (e.g., ChatGPT, GPT-4) | Generative AI, Large Language Model | The paper cites studies by Choi, Monahan, and Schwarcz where GPT-4's impact on human legal analysis was assessed through a randomized controlled trial, and its performance was tested on law school exams. | References External Evaluation, Performance on Standardized Tests | Cited studies found that GPT-4 slightly and inconsistently improved the quality of legal analysis but induced large and consistent increases in speed. On law school exams, GPT-4 alone outperformed both students alone and students with AI assistance on simple multiple-choice questions; worst-performing students saw the largest gains from AI assistance, while best-performing students saw declines. | Mixed performance, Benefit identified, Outperforms others, Underperforms others | Need for AI systems of sufficient quality and reliability; concerns about privacy and privilege of personally identifiable information collected by AI systems; potential for unauthorized practice of law litigation against AI tool providers; and the time/effort required for software development, testing, navigating legal challenges, and updating regulatory frameworks. | AI Unreliability/Inaccuracy, Data Privacy Concerns with AI, Erosion of Legal Professional Standards, Regulatory Hurdles, Resource Constraints for A2J Tech Development/Deployment | Continued technological development to improve AI quality; careful system design ensuring user-friendliness, privacy, and verification features; navigating potential litigation; adapting regulatory frameworks over time to permit AI deployment while ensuring consumer protection; and learning from regulatory reforms in states like Utah and Arizona. | AI Tool Development, Enhanced AI Capabilities, User Interface and Accessibility Design, Data Privacy and Security, Policy and Regulatory Reform, Regulation, Ethics, and Governance | Support for pro se litigants, addressing tenant harassment, and mitigating common civil legal problems for low-income individuals (e.g., consumer issues, healthcare, housing, income maintenance). | Support for Self-Represented Litigants, Protection of Rights, Support for Vulnerable Populations | Low-income households, low-income tenants, tenants in rent-stabilized dwellings, and pro se litigants. | Low-income individuals, Tenants, Self-represented litigants | Civil litigation, Housing Law, Consumer Law, Healthcare Law, and general legal practice. | Civil Litigation, Housing Law, Consumer Law, Health Law, General Legal Practice | United States (citing federal rules, and state/local examples from Texas, Los Angeles, New York, Utah, Arizona). | USA | LLMs are described as being trained on 'massive datasets' of text and other content. The paper does not specify the exact composition or sources of these datasets for the general LLMs discussed, beyond noting they are trained on the data they have access to. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Undisclosed Data Source/Availability | NaN | NaN | Existing tools like ChatGPT were released publicly by companies (e.g., OpenAI), leading to rapid adoption. Commercial products (e.g., CoCounsel by Casetext, Lexis+AI by LexisNexis) are being adopted by law firms and made available to law students. Future access to justice tools might be deployed by public/private legal aid organizations or directly to pro se litigants. | Evaluation of existing third-party tool, Web-based access, Freely accessible tool/service, Commercial product/service, Internal deployment/prototype, Educational resource deployment, Proposed deployment (not implemented), Partnership-based rollout | True | False | ChatGPT is publicly accessible (with free and paid tiers). CoCounsel and Lexis+AI are commercially available products, with Lexis+AI also being made available to many law students. | Publicly accessible online tool or platform, Freemium access, Commercial product or service | Ensuring high quality, reliability, and accuracy (reducing 'hallucinations') of generative AI in legal contexts; developing robust data privacy and attorney-client privilege protection mechanisms for AI systems; establishing clear regulatory frameworks for AI-driven legal services, particularly concerning unauthorized practice of law and consumer protection; time needed for development, testing, and societal/professional adaptation. | AI Accuracy and Reliability, Security and Privacy of Data, Regulatory and Governance Gaps, Consumer Protection Gaps, Human Oversight and Professional Adaptation | For users/adopters: managing AI 'hallucinations' and ensuring factual/legal accuracy of AI outputs; cost and market volatility of AI tools; training legal professionals to use AI effectively and ethically; protecting client data confidentiality; adapting existing workflows and business models; and effectively integrating AI into legal education. | LLM Hallucination and Factual Errors, Accuracy and Reliability of LLM Output, Financial Cost and Resource Constraints, User Training, AI Literacy, and Skill Gaps, Legal Professional Responsibility and Competence, Data Privacy, Security, and Confidentiality, Integration with Existing Systems and Workflows | AI 'hallucinations' leading to incorrect legal information or filings; breaches of client confidentiality and data privacy; unauthorized practice of law claims against AI service providers; premature deployment of low-quality AI tools for access to justice, potentially harming users or discrediting the approach; over-reliance on AI without sufficient human oversight and critical judgment; challenges in maintaining academic integrity and ensuring effective student learning with AI in legal education. | Inaccurate or misleading AI output, Data privacy and security breach, Unauthorized practice of law, Risk of misapplication or misuse, Erosion of trust in legal system or AI, Consumer harm, Over-reliance on AI, Ethical concerns, Deskilling or erosion of human skills |
| CeA1rreEv_sJ.pdf | Google_Scholar | InLegalLLaMA: Indian Legal Knowledge Enhanced Large Language Model | This paper introduces InLegalLLaMA, a Large Language Model adapted for the Indian legal domain through continual pre-training on Indian legal texts and knowledge infusion from a legal knowledge graph. The paper also proposes a Retrieval Augmented Generation (RAG) based framework utilizing this model for petition drafting to improve access to legal processes. | Legal Language Model Development, India Focus, Continual Pre-training, Knowledge Graph Infusion, Retrieval Augmented Generation, Legal Document Drafting (Petitions), Access to Legal Processes | True | Idealistic | True | 1.0 | Positive | InLegalLLaMA: a LLaMA-2 model continually pretrained on Indian legal documents and instruction-tuned using legal knowledge graph triples and domain-specific tasks. A RAG-based framework for petition drafting is also proposed. | Model Development, Large Language Model, Pre-training Technique, Instruction Tuning, Knowledge Graph Integration, Retrieval Augmented Generation (RAG), Legal Document Generation / Automation, Domain-Specific Model Adaptation, Named Tool / Platform | InLegalLLaMA was evaluated on: 1) In-context masked triple prediction using data from Vasisht et al. (2023), with metrics Hits@1, BLEU, ROUGE-L. 2) Legal sentence rhetorical role classification using data from Bhattacharya et al. (2023), with metrics Precision, Recall, and F1-Score. | Custom Dataset Evaluation, Quantitative Metrics | For in-context triple prediction, InLegalLLaMA achieved Hits@1: 0.984, BLEU: 98.224, ROUGE-L: 99.191. For legal sentence rhetorical role prediction, InLegalLLaMA achieved F1-Score: 0.585. InLegalLLaMA outperformed LLaMA-2-7B on these Indian legal domain tasks. | High performance, Moderate performance, Outperforms others, Technique improves outcome | Complexity of legal procedures for individuals; poorly written petitions leading to information omission, incorrect filings, and dismissals, thereby increasing costs and hindering justice; significant backlog and volume of petitions in courts. | Complexity of Legal System/Procedures, Challenges for Self-Represented Litigants, Judicial/Legal System Inefficiencies | Development of domain-specific LLMs (InLegalLLaMA) enhanced with legal knowledge from Indian legal documents and knowledge graphs. Proposal of a RAG-based framework for petition drafting involving template selection, AI-assisted content generation, refinement, and evaluation, with human oversight. | AI Tool Development, Enhanced AI Capabilities, Legal Knowledge Representation and Management, Document Automation, Human Oversight and Collaboration | Petition drafting assistance; access to legal information and processes; understanding legal notices. | Legal Document Creation / Automation, Access to Legal Information, Legal Literacy and Public Legal Education | Citizens unfamiliar with legal processes; individuals seeking redressal of grievances; lawyers (for improving petition quality). | General public, Laypeople, Individuals lacking legal knowledge, Litigants, Legal professionals | General Indian law; court petitions. | General Law, Litigation | India | India | For continual pretraining: A new dataset of 10,000 Indian legal documents (Supreme Court judgments and legal statutes) and 5% replay data from RedPajama. For instruction-tuning: Triples from an Indian legal knowledge graph (derived from public Indian court/legal sources), datasets from Vasisht et al. (2023) for triple prediction, Bhattacharya et al. (2023) for rhetorical role classification, and LIMA instructions (Zhou et al., 2023). | Author-Created New Dataset, Fine-tuning Dataset, Indian Legal Data, Legal Domain Data, Case Law / Judgments, Legislation / Statutes / Regulations, Data From Existing Public NLP/Legal Datasets/Benchmarks, Structured Data, Instruction-Tuning Formatted Data, Publicly Available Data | Knowledge graph construction (building on prior work); continual pretraining of LLaMA-2 on domain-specific corpus using LoRA; instruction tuning with domain-specific tasks and general instructions; design of a RAG architecture for petition drafting. | Knowledge Graph Construction/Integration, Model Pre-training, Parameter-Efficient Fine-Tuning (PEFT), Model Fine-tuning, Retrieval Augmented Generation (RAG) | The InLegalLLaMA model (base and instruction-tuned versions) is publicly available on HuggingFace. The petition drafting framework is a proposal. | Open source model release, Proposed deployment (not implemented) | True | True | The base and instruction-tuned versions of InLegalLLaMA are publicly available on HuggingFace. | Model available | Need for more extensive instruction tuning for complex legal text analytics tasks; the current triple prediction task may not be sufficiently challenging, requiring a larger knowledge graph; further work needed to make LLMs useful for tasks requiring human expertise; need for code fine-tuned versions of InLegalLLaMA for RAG tools (e.g., Text-to-SQL). | Research and Evaluation Gaps, AI Legal Reasoning Limitations, Data Availability and Quality, AI Scope and Functionality Limitations | Resource constraints for large model training (addressed by LoRA); catastrophic forgetting during continual pretraining (addressed by replay data and learning rate strategies); designing LLMs to identify *missing* salient information in legal documents; ensuring legal soundness, completeness, and admissibility of AI-generated petitions (requiring human expert monitoring). | High Computational and Resource Demands, Domain-Specific Adaptation and Customization, LLM Reasoning Capabilities, Accuracy and Reliability of LLM Output, Need for Human Oversight and Intervention | Generation of poorly written or legally unsound petitions if AI assistance is inadequately supervised, potentially leading to negative legal outcomes for users. Regulatory challenges with LLMs trained on data with unestablished provenance (though this work aims to address this by using domain-specific Indian legal data). | Inaccurate or misleading AI output, Over-reliance on AI, Consumer harm, Regulatory challenges or gaps |
| Y81SvrL3LE0J.pdf | Google_Scholar | A Brief Report on LawGPT 1.0: A Virtual Legal Assistant Based on GPT-3 | This paper introduces LawGPT 1.0, a virtual legal assistant created by fine-tuning the GPT-3 language model on a large corpus of legal text. It briefly discusses the system's architecture, potential to improve legal service accessibility, and requirements like explainability for real-world application. | AI Legal Assistant Development, LLM Fine-tuning, Legal Service Accessibility, Explainable AI | True | Idealistic | True | 1.0 | Positive | LawGPT 1.0: GPT-3 fine-tuned on a large corpus of legal text. | Model Development, Large Language Model, Fine-tuning, Dataset Creation / Curation, Named Tool / Platform | Evaluated on a set of legal benchmark tasks including answering legal questions, generating legal documents, and providing legal advice. No specific benchmark names or detailed procedures provided. | No Evaluation by Author | The system is reported capable of providing high-quality legal assistance, with accuracy rates competitive with other virtual legal assistant systems. No specific metrics are provided. | High performance, Developer or Vendor claim, Comparable to others | The need for cost-effective, efficient, and accessible (e.g., 24/7) legal services. For AI deployment: ensuring explainability and establishing responsibility for AI-generated recommendations. | High Cost of Legal Services, Judicial/Legal System Inefficiencies, Limited Access to Legal Assistance, Lack of AI Transparency/Explainability, Lack of AI Accountability | Develop and deploy virtual legal assistants like LawGPT 1.0 to provide conversational legal assistance (answering questions, generating documents, advice) to improve efficiency and accessibility. | AI Tool Development, Access to Legal Information and Advice, Document Automation, Cost Reduction and Efficiency | Answering legal questions, generating legal documents, providing legal advice (general legal assistance). | Access to Legal Information, Legal Document Creation / Automation, Access to Legal Advice | Individuals and businesses, particularly those needing legal assistance outside normal business hours. | General public, Small businesses, Individuals facing access barriers | General legal domain. | General Law | NaN | NaN | A large corpus of legal text. Proprietary status implied by NDA, details not disclosed. | Fine-tuning Dataset, Legal Domain Data, Proprietary Data, Undisclosed Data Source/Availability | Fine-tuning of a pre-trained large language model (GPT-3) using standard deep learning techniques (stochastic gradient descent, backpropagation). | Model Fine-tuning, Deep Learning Model Development | NaN | Not applicable | False | False | NaN | NaN | Need for explainability in AI recommendations; need to establish responsibility frameworks for AI use in legal decisions; current version lacks RLHF; requires expansion to support multiple languages and legal systems. | Transparency and Explainability, Accountability and Redress Mechanisms, Regulatory and Governance Gaps, Research and Evaluation Gaps, Multilingual and Jurisdictional Specificity Gaps | Incorporating explainability; establishing responsibility for AI decisions; addressing legal and ethical considerations (data privacy, IP, confidential information); adapting the model for different languages and legal systems; current limitation of not supporting RLHF. | Transparency and Explainability of AI, Accountability and Liability for AI Errors, Ethical Considerations, Data Privacy, Security, and Confidentiality, Copyright and Intellectual Property Issues, Multilingual and Low-Resource Language Support, Domain-Specific Adaptation and Customization | Serious consequences from incorrect legal decisions made with AI assistance; lack of explainability hindering trust and accountability; potential issues with data privacy, intellectual property rights, and handling sensitive/confidential information. | Inaccurate or misleading AI output, Consumer harm, Lack of transparency, accountability, and redress, Erosion of trust in legal system or AI, Data privacy and security breach, Copyright or intellectual property issues |
| xJJvD-ECVPIJ.pdf | Google_Scholar | Access to Civil Justice in the Age of AI: Mindsets & Pathways to New Practices | This paper explores how Artificial Intelligence, particularly generative AI, can enhance access to civil justice by enabling new, scalable legal service models focused on legal information products. It argues that lawyers must adopt new mindsets and innovate beyond traditional practice to effectively leverage AI and address the justice gap in the PeopleLaw sector. | AI for Civil Justice, Generative AI Application, Access to Justice Enhancement, Scalable Legal Service Models, Legal Information Products, Innovation in Legal Practice | True | Idealistic | True | 3.0 | Positive | Using generative AI (e.g., ChatGPT, GPT-4) for creating, simplifying, organizing, and diversifying legal information products for consumers. | Generative AI, Large Language Model, Legal Information Provision, Content Generation, Legal Text Simplification, Information Organization | NaN | Not Applicable | NaN | NaN | High cost (affordability) of traditional legal services; Lack of scalability in the one-to-one lawyer service model; Limited funding and reach of legal aid; Neglect of the 'missing middle' income group; Difficulty navigating the complex legal system without help; Prevalence of poor quality online legal information ('sea of junk'). | High Cost of Legal Services, Inefficiency in Legal Sector, Limited Availability/Access to Legal Aid, Scale of Unmet Legal Need, Complexity of Legal System/Procedures, Reliance on Unreliable Information Sources | Lawyers adopting new mindsets (learning, adaptation, innovation); Leveraging generative AI for practice efficiency and cost reduction; Developing scalable legal information products (handouts, guides, videos) using AI; Implementing new business models (freemium, tiered pricing, subscription) centered around information products; Courts providing self-help resources and simplified procedures. | Education and AI Literacy, Human Oversight and Collaboration, Cost Reduction and Efficiency, Access to Legal Information and Advice, AI Tool Development, Alternative Legal Service Delivery Models, Judicial System Enhancement | Bridging the justice gap in civil law; Providing affordable legal help (information and services); Serving self-represented litigants; Innovating legal service delivery models in the PeopleLaw sector. | Democratizing Law / Closing Justice Gap / Rule of Law, Affordability of Legal Services / Cost Reduction, Support for Self-Represented Litigants, Regulatory Reform (Legal Services and AI) | Low-income individuals (including the ALICE population) and the 'missing middle' (middle-class individuals often priced out of legal services). | Low-income individuals, Moderate-income individuals, Individuals unable to afford legal services | Civil Justice (general), Family Law (divorce, custody, support mentioned as examples), Small Business Law (implied by PeopleLaw sector). | Civil Justice, Family Law, Commercial Law | United States | USA | NaN | Not Applicable | NaN | NaN | Discusses potential business models for lawyers (freemium, tiered pricing, subscription models) to deploy AI-assisted legal information services. | Proposed deployment (not implemented), Commercial product/service, Freely accessible tool/service, Dissemination via publication/presentation | False | False | NaN | NaN | Need for lawyers to overcome resistance to change and adopt new mindsets/business models; Ensuring quality and reliability of AI-generated legal information; Addressing regulatory frameworks around AI in legal services; Scaling legal advice, not just information; Need for continued focus on simplifying legal processes. | Human Oversight and Professional Adaptation, AI Accuracy and Reliability, Regulatory and Governance Gaps, AI Scope and Functionality Limitations | Understanding AI capabilities and limitations; Integrating AI ethically and competently into legal practice (duty of competence); Overcoming professional inertia and traditional practice models; Rethinking the value proposition beyond bespoke legal advice. | User Training, AI Literacy, and Skill Gaps, Legal Professional Responsibility and Competence, Ethical Considerations, User Adoption, Trust, and Acceptance, Domain-Specific Adaptation and Customization | AI 'hallucinations' leading to inaccurate legal information or citation of non-existent cases; Lawyers violating ethical duties (competence) through improper AI use; Potential for AI to exacerbate low-quality online information if not curated; Broader societal risks associated with advanced AI (job transformation, unforeseen consequences). | Inaccurate or misleading AI output, Ethical concerns, Risk of misapplication or misuse, Negative societal impact, Job displacement |
| 7dlcyVpmL_gJ.pdf | Google_Scholar | ІNFORMATION AND LEGAL SUPPORT FOR BALANCING THE INTERESTS OF JUSTICE AND HUMAN RIGHTS PROTECTION | The paper discusses balancing effective justice administration and human rights protection in Ukraine's criminal justice system. It proposes leveraging modern IT, including AI models like GPT-4 and associative rule mining, to analyze case data and support judicial decision-making in sentencing, while acknowledging potential risks. | AI in Criminal Justice, Ukrainian Focus, Judicial Decision-Making Support, Sentencing Support, Case Data Analysis, Risk Identification | True | Idealistic | True | 1.0 | Neutral | Using multimodal language models (like GPT-4) for text generation/analysis combined with associative rule models (mining) to extract relationships from unstructured case data for sentencing support (risk assessment, societal danger, analysis of similar cases). | Multimodal Language Model, Content Generation, Text Analysis, Hybrid AI System, Associative Rule Mining, Information Extraction, Sentencing Support Tool, Risk Assessment | NaN | Not Applicable | NaN | NaN | Balancing effective justice administration with human rights protection; Manual analysis of large volumes of unstructured text data in criminal proceedings is labor-intensive, inefficient, and subject to human bias; Potential risks of IT implementation like privacy violations, digital divide, and misuse for surveillance or rights violations. | Tension between Judicial Efficiency and Human Rights, Resource Constraints, Systemic Inequities in Justice System, Data Privacy Concerns with AI, Digital Divide, Risk of AI Misuse, Risk to Human Rights from AI | Leveraging modern IT (electronic document management, integrated databases, videoconferencing, etc.); Proposing the use of AI (GPT-4, associative rules) to automate analysis of unstructured text data for sentencing decisions; Emphasizing the need for legal frameworks, regulatory control, cybersecurity, training, and resources for IT implementation. | Judicial System Enhancement, AI Tool Development, Legal Research and Analysis Tools, Policy and Regulatory Reform, Data Privacy and Security, Education and AI Literacy | Sentencing support, Risk assessment (recidivism, danger to society), Analysis of similar cases, Balancing efficiency and human rights in criminal justice, Justice system transparency. | Judicial System Modernization / Efficiency, Ethical AI in Law and AI Governance, Protection of Rights | NaN | NaN | Criminal Law, Criminal Procedure | Criminal Law, Criminal Procedure | Ukraine | Ukraine | The paper mentions analyzing "large collections of unstructured text documents" from criminal proceedings. The specific source, availability, and nature (beyond unstructured text) are not detailed. | Legal Domain Data, Unstructured Text Data, Undisclosed Data Source/Availability | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Need for effective tools to analyze large amounts of unstructured legal text efficiently and objectively; Need for improved risk assessment tools for sentencing; Need for strategies to implement IT in justice effectively while mitigating risks (privacy, digital divide, misuse). | AI Scope and Functionality Limitations, Ethical Framework Deficiencies, Security and Privacy of Data, Access, Equity, and Digital Divide | Ensuring privacy and data protection; Bridging the digital divide; Preventing misuse of IT for surveillance or rights violations; Establishing proper regulatory control; Ensuring cybersecurity; Providing adequate staff training and resources; Overcoming inefficiency and bias in manual analysis of unstructured case data. | Data Privacy, Security, and Confidentiality, User Interface, Usability, and Accessibility, Safeguarding Against Misuse and Harm, Regulatory Uncertainty and Compliance, User Training, AI Literacy, and Skill Gaps, Financial Cost and Resource Constraints, Bias in AI Systems and Data, Data Quality, Processing, and Preparation | Privacy violations; Breach of personal data protection; Creation of a digital divide; Potential for systematic human rights violations; Potential for mass surveillance through IT systems. | Data privacy and security breach, Exacerbation of inequality or two-tiered system, Infringement on human rights, Security vulnerabilities or malicious misuse |
| HjuAmkWUb1QJ.pdf | Google_Scholar | Equitable Access to Justice: Logical LLMs Show Promise | This paper explores integrating Large Language Models (LLMs) with logic programming to enhance their reasoning capabilities for legal applications, aiming to improve access to justice. It demonstrates that OpenAI's o1-preview model significantly outperforms GPT-4o in translating a health insurance contract into logical Prolog code, suggesting potential for creating 'computable contracts'. | LLM Integration with Logic Programming, Legal Reasoning Enhancement, Access to Justice Enhancement, Contract Translation to Logic, Computable Contracts, LLM Evaluation | True | Idealistic | True | 1.0 | Positive | Using LLMs (GPT-4o and OpenAI o1-preview) to automatically generate logical representations (Prolog code) of a health insurance policy to create "computable contracts", enabling automated reasoning about policy coverage. | Large Language Model, Code Generation, Legal Knowledge Formalization, Computable Contracts, Automated Reasoning, Healthcare Application | GPT-4o and OpenAI o1-preview were prompted to translate a simplified health insurance policy into Prolog code. Then, both models were prompted to translate nine natural language yes/no questions about the policy into Prolog queries on their respective encodings. The number of correct answers from executing these queries was recorded over ten trials. | Qualitative Analysis, Quantitative Metrics, Comparative Analysis | OpenAI o1-preview averaged 7.5 out of 9 correct answers across ten trials when its generated Prolog code for the insurance policy was queried. GPT-4o averaged 2.4 correct answers. | High performance, Outperforms others, Underperforms others | High cost of legal services, complexity of the judicial system, widespread distrust of attorneys, large number of self-represented litigants, and consumer difficulty in understanding legal documents like insurance policies. | High Cost of Legal Services, Complexity of Legal System/Procedures, Lack of Trust in Legal Professionals, Challenges for Self-Represented Litigants, Complexity of Legal Language/Documents, Public Lack of Legal Knowledge/Awareness | Developing reliable and transparent technological solutions using AI (LLMs combined with logic programming) to create 'computable contracts' that simplify understanding and automate interpretation of legal documents like insurance policies, thereby scaling the encoding of legal text into logic programs. | AI Tool Development, Enhanced AI Capabilities, Transparency and Explainability in AI, Document Automation, Language Simplification and Multilingual Access | Understanding legal documents (insurance contracts), automated legal reasoning for contract interpretation, improving consumer access to information about their legal rights and obligations under contracts. | Legal Literacy and Public Legal Education, Legal Document Analysis / Review, Access to Legal Information, Protection of Rights | General public/consumers, especially those facing challenges in understanding complex legal documents like insurance policies, and self-represented litigants. | General public, Consumers, Individuals lacking legal knowledge, Self-represented litigants | Contract law, Insurance law (specifically health insurance). | Contract Law, Insurance Law, Health Law | USA (reference to American judicial system and California statistics; the example insurance policy specifies New York law). | USA | The LLMs (GPT-4o, OpenAI o1-preview) are pre-trained models; their specific training data is not detailed in the paper. The input for the experiment was a simplified version of the Chubb Hospital Cash Benefit insurance policy text, provided in the paper's appendix. | Pre-trained LLM's General Training Corpus, Undisclosed Data Source/Availability, Input Data for Task (Non-Training), Legal Domain Data, Legal Contracts, Unstructured Text Data, Paired Original-Simplified Text | Experimental comparison of LLM outputs (Prolog code) generated from a legal text (insurance policy). Evaluation involved qualitative analysis of the code's logical structure and interpretability, and quantitative empirical testing based on the accuracy of answers to nine natural language questions translated into Prolog queries, run over ten trials. | Experimental Comparison of Outputs, Qualitative Evaluation Methodology, Quantitative Evaluation Methodology, Logic Programming Application | NaN | Not applicable | False | False | NaN | NaN | Technical gaps include the accuracy and quality of LLM-generated logic (risk of misinterpretation, omission, inconsistency, overgeneralization), LLM struggles with legal nuances and temporal relationships, and potential biases in training data. Societal gaps include the need for consistent, transparent, reliable, and trustworthy AI solutions for legal applications. | AI Accuracy and Reliability, AI Legal Reasoning Limitations, Bias in AI, Transparency and Explainability, Ethical Framework Deficiencies | Ensuring the accuracy and quality of logical representations generated by LLMs from legal texts. LLMs may misinterpret terms, omit details, create logical inconsistencies, or overgeneralize. They also struggle with legal nuances, ambiguities, and conditional/temporal relationships. Potential biases in LLM training data can affect the validity of the generated logic. | Accuracy and Reliability of LLM Output, LLM Reasoning Capabilities, Bias in AI Systems and Data | LLMs may produce hallucinations and inconsistent answers. They can misinterpret legal terms, omit critical details, generate logical inconsistencies, or overgeneralize legal principles. Biases in their training data could compromise the validity of the generated legal logic, potentially leading to incorrect legal interpretations. | Inaccurate or misleading AI output, Technical limitations of AI, Bias and discrimination |
| VK3sHji0IeoJ.pdf | Google_Scholar | ARTIFICIAL INTELLIGENCE AND LAW — AN OVERVIEW OF RECENT TECHNOLOGICAL CHANGES : KEYNOTE ADDRESS AT THE 2024 IRA C. ROTHGERBER J R. & SILICON FLATIRONS CONFERENCE ON ARTIFICIAL INTELLIGENCE AND CONSTITUTIONAL LAW | This paper, a keynote address, offers an overview of artificial intelligence, detailing its historical evolution with a focus on recent advancements in large language models like GPT-4. It explores their applications and limitations within the legal field, particularly constitutional law, while stressing the importance of AI literacy for legal professionals and expressing cautious optimism about AI's potential to enhance the legal system. | Keynote Address, Overview of AI in Law, LLM Applications in Law, Constitutional Law Focus, Limitations of AI in Law, AI Literacy for Legal Professionals | True | Idealistic | True | 2.0 | Positive | Primary focus on Large Language Models (LLMs) such as OpenAI's GPT series (GPT-3, GPT-3.5, GPT-4, GPT-4o), Anthropic's Claude, and mentions of Google's Gemini Ultra and Meta's Llama 3. Also discusses specialized legal AI systems like Lexis+ AI and Westlaw CoCounsel, which utilize underlying LLM technology. | Large Language Model, Specialized Legal AI Systems, Review of Existing Technologies | The author evaluates LLMs through illustrative examples and personal experimentation. This includes posing commonsense questions (e.g., "How many legs does an apple have?"), legal queries (e.g., a Third Amendment scenario), requesting legal document drafting (e.g., merger agreement, motion for summary judgment), and analyzing legal texts (e.g., insurance contracts, torts fact patterns). Comparisons between models (e.g., GPT-4 vs. Claude) are also used. | Demonstration or Illustrative Examples, Qualitative Analysis, Comparative Analysis | GPT-4 is presented as significantly more capable than its predecessors, able to produce comprehensive legal document drafts, perform sensible legal analysis, and answer complex questions. However, it's noted that even advanced LLMs can 'hallucinate,' provide outdated information, make reasoning errors, and generate conflicting answers depending on the model or prompt. | High performance, Limitation: Hallucination or Factual inaccuracy, Limitation: Operational or Technical | Lack of AI literacy among legal professionals; inherent limitations of AI (e.g., hallucinations, potential for bias, lack of transparency, sensitivity to prompts); risk of over-reliance and misinterpretation of AI outputs; privacy and confidentiality concerns with certain AI usage models; the potential for AI to make non-transparent value judgments in legal interpretation. | Lack of AI Literacy, AI Unreliability/Inaccuracy, Bias in AI/Data, Lack of AI Transparency/Explainability, Difficulty in AI-Human Interaction, Automation Bias, Data Privacy Concerns with AI, Ethical Concerns with AI in Law | Enhancing AI literacy for legal professionals through education and hands-on experience; promoting careful, supervised use of AI tools with thorough verification of outputs; utilizing specialized legal AI systems for better reliability, security, and access to curated legal data; focusing on high-quality training data and improved AI architectures for future models; fostering a thoughtful societal adoption of AI to make the legal system more transparent, equitable, and accessible. | Education and AI Literacy, Human Oversight and Collaboration, AI Tool Development, Data Curation and Management, Enhanced AI Capabilities, Policy and Regulatory Reform, Regulation, Ethics, and Governance | Improving transparency, fairness, and general accessibility of the legal system; legal analysis; legal document generation; legal research; contract analysis; constitutional law interpretation. | Democratizing Law / Closing Justice Gap / Rule of Law, Ethical AI in Law and AI Governance, Legal Document Analysis / Review, Legal Document Creation / Automation, LegalResearch Support | General public / society at large, with the aim of a fairer and more accessible legal system for all. | General public | Constitutional law, contract law, torts law, general legal practice, legal research, and document drafting. | Constitutional Law, Contract Law, Tort Law, General Legal Practice, Legal Research, Document Drafting | United States (e.g., references to U.S. Constitution, Colorado Governor), though many principles discussed have international relevance. | USA, International | Large-scale, primarily unstructured text data from diverse sources such as unpublished books, public webpages, Wikipedia, and other internet content. The paper notes a trend towards using higher-quality, curated data like textbooks and research papers for training newer models. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Unstructured Text Data, Legal Scholarly Content / Textbooks | Machine learning, particularly deep learning utilizing neural networks and the transformer architecture. The development of models like ChatGPT also involves engineering improvements based on training with large datasets and techniques to enhance instruction following and problem-solving capabilities. | Machine Learning Model Development, Deep Learning Model Development, Neural Network Architecture, Transformer Architecture, Large-scale Data Training, Instruction Following Enhancement | General-purpose LLMs like ChatGPT are accessible via web-based interfaces and apps. Specialized legal AI systems (e.g., Lexis+ AI, Westlaw CoCounsel) are offered as commercial products to legal professionals. Some models (e.g., Llama 3) are available as open-weights. | Evaluation of existing third-party tool, Web-based access, Commercial product/service, Open source model release | True | False | General-purpose LLMs like ChatGPT (available via OpenAI with free and paid subscription tiers) and Claude (available from Anthropic) are accessible online. Specialized legal AI systems such as Lexis+ AI and Westlaw CoCounsel are commercially available through subscriptions. | Publicly accessible online tool or platform, Freemium access, Commercial product or service | Technical gaps include the need for improved reliability, reduction of hallucinations, enhanced transparency and interpretability, better bias mitigation, and reduced sensitivity to prompt variations in LLMs. Societal gaps involve fostering widespread AI literacy, ensuring equitable access to and fair application of AI in the legal domain, and addressing the challenge of AI making implicit value judgments without clear human oversight. | AI Accuracy and Reliability, Transparency and Explainability, Bias in AI, User Interface and Usability Gaps, Public Understanding, Trust, and Adoption, Access, Equity, and Digital Divide, Ethical Framework Deficiencies, Human Oversight and Professional Adaptation | Key challenges include ensuring the reliability and accuracy of LLM outputs (avoiding hallucinations and outdated information); managing and mitigating biases present in training data; overcoming the lack of transparency ('black box' nature) in how LLMs arrive at conclusions; addressing the sensitivity of LLMs to prompt phrasing; and managing the significant computational and hardware requirements for training and deploying advanced models. | Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Outdated or Limited LLM Knowledge Base, Bias in AI Systems and Data, Transparency and Explainability of AI, Prompt Engineering and Optimization, High Computational and Resource Demands | Inaccurate AI-generated outputs (hallucinations) leading to errors in legal work and potential professional sanctions; reliance on outdated information; propagation of biases embedded in training data; breaches of privacy and client confidentiality, especially with non-enterprise AI versions; over-reliance on AI by legal professionals without critical evaluation; lack of transparency in AI’s decision-making, particularly concerning for legal and constitutional interpretation where AI might make implicit, unscrutinized value judgments. | Inaccurate or misleading AI output, Ethical concerns, Bias and discrimination, Data privacy and security breach, Over-reliance on AI, Lack of transparency, accountability, and redress, Undermining legal process or principles |
| oiF84vWI26YJ.pdf | Google_Scholar | CERTIFYING LEGAL AI ASSISTANTS FOR UNREPRESENTED LITIGANTS: A GLOBAL SURVEY OF ACCESS TO CIVIL JUSTICE, UNAUTHORIZED PRACTICE OF LAW, AND AI | This paper surveys global approaches to AI, unauthorized practice of law (UPL), and access to civil justice for unrepresented litigants. It proposes a capability-based framework using public benchmarks to certify legal AI assistants, allowing their exemption from UPL rules to improve access to justice. | Survey of Global AI Approaches, Unauthorized Practice of Law, Access to Justice Enhancement, Self-Represented Litigant Assistance, Framework Proposal, AI Certification, Exemption from UPL Rules | True | Idealistic | True | 1.0 | Positive | A capability-based framework for certifying legal AI assistants based on testing accuracy against public benchmark datasets for specific legal tasks. | Certification Framework, AI Legal Assistant, Benchmarking / Evaluation, AI Governance | The paper proposes a framework that requires testing AI capabilities against public benchmark datasets (e.g., LegalBench, LawBench, JEC-QA, SARA) using metrics like f-measure, Bleu, MCC, or Task Success Rate to meet predefined accuracy thresholds. It does not test a specific tool itself. | Theoretical Analysis or Conceptual Proposal, No Evaluation by Author | NaN | NaN | The large number of unrepresented litigants lack access to affordable legal help; restrictive Unauthorized Practice of Law (UPL) rules prevent potentially helpful AI tools; risk of harm from inaccurate advice provided by unregulated AI; absence of a standardized certification framework for legal AI. | Challenges for Self-Represented Litigants, Limited Access to Legal Assistance, Regulatory Hurdles, AI Unreliability/Inaccuracy, Inadequate Legal Frameworks for AI | Amend UPL rules to explicitly exempt certified legal AI assistants; establish a capability-based certification framework evaluating AI accuracy on specific tasks using public benchmarks; create a third-party body to manage certification; foster collaboration and investment in developing necessary benchmark datasets. | Policy and Regulatory Reform, Alternative Legal Service Delivery Models, Regulation, Ethics, and Governance, Benchmarking and Evaluation Frameworks, Data Curation and Management, Open Source Initiatives and Collaboration | Access to legal information, guidance, advice; navigating legal procedures; drafting legal documents; case outcome prediction; dispute resolution for unrepresented litigants in civil justice. | Access to Legal Information, Access to Legal Advice, Legal Literacy and Public Legal Education, Legal Document Creation / Automation, Improving Foundational AI Capabilities for Legal Applications, Dispute Resolution, Support for Self-Represented Litigants | Unrepresented litigants (self-represented litigants, litigants in person, pro se litigants), particularly those with limited financial resources. | Self-represented litigants, Low-income individuals | Civil Justice (broadly, including examples from housing, consumer law, family law (protection orders), small claims). | Civil Justice, Housing Law, Consumer Law, Family Law, Small Claims Law | Global survey covering Argentina, Australia, Brazil, Canada (incl. provinces), China, European Union, Germany, India, New Zealand, Nigeria, Singapore, United Kingdom, United States (incl. 50-state/6-territory survey). Proposed framework intended for global adoption with local implementation. | Argentina, Australia, Brazil, Canada, China, EU, Germany, India, New Zealand, Nigeria, Singapore, UK, USA, International | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Need for more public benchmark datasets specifically designed for unrepresented litigant tasks; need for international coordination among legal regulators on AI; inconsistent definitions of 'practice of law' and UPL across jurisdictions; technical issues like data contamination potentially affecting benchmark evaluations. | Data Availability and Quality, Research and Evaluation Gaps, Regulatory and Governance Gaps | Defining the practice of law and UPL consistently; getting regulatory bodies to adopt UPL exemptions for AI; ensuring sufficient and representative benchmark datasets are created and maintained; addressing technical issues like data contamination in evaluating LLMs. | Regulatory Uncertainty and Compliance, Unauthorized Practice of Law (UPL) Concerns, Scarcity of High-Quality Legal Data, Evaluation Challenges and Metrics | Unrepresented litigants receiving inaccurate legal guidance from AI, leading to negative outcomes; potential for data contamination to lead to overly optimistic evaluations of AI accuracy; bias in AI systems (mentioned generally in cited sources/context). | Consumer harm, Inaccurate or misleading AI output, Technical limitations of AI, Bias and discrimination |
| eZOdC8SrDBkJ.pdf | Google_Scholar | Legal-Emotional BATNA: AI Chatbot Addressing Divorce Legalities and Emotional Complexities, and Research of Social Implementation in Japan | This paper introduces "Legal-Emotional BATNA," an AI chatbot designed to assist individuals in Japan undergoing divorce negotiations by integrating legal calculations (support, asset division) with emotional support, using GPT-4 for emotion analysis. User surveys indicate general satisfaction but highlight needs for improved privacy measures and handling of complex emotional and legal issues. | Chatbot Development, Japanese Focus, Divorce Negotiation Assistance, Legal Calculation Support, Emotional Support Provision, LLM Application, User Evaluation, Privacy Concerns | True | Idealistic | True | 1.0 | Positive | "Legal-Emotional BATNA" AI chatbot integrating legal calculations (child/spousal support, property/pension division, solatium based on Japanese legal standards/precedents) and emotional aspect analysis (using GPT-4) to guide divorce negotiations. | Chatbot / Conversational AI, Negotiation Support Tool, Legal Calculation, Emotional Analysis, Large Language Model, Domain-Specific Knowledge Integration, Decision Support System, Named Tool / Platform | Online user survey with 100 participants recruited via CloudWorks (Japanese crowdsourcing platform). Evaluation used demographics questions and a 5-point Likert scale assessing ease of use, trustworthiness, speed, clarity, accuracy, helpfulness, communication smoothness, privacy protection, and overall satisfaction, plus open-ended feedback. | User Study or Survey, Quantitative Metrics, Qualitative Analysis | Generally positive: >80% found it user-friendly (avg 1.65), trustworthy (avg 1.79), and were satisfied overall (avg 1.82). 77% expressed willingness to use it again. Areas needing improvement included privacy protection (avg 2.62) and desire for more personalized advice. | High performance, Benefit identified, Limitation: Security or Privacy, Limitation: Operational or Technical | The complexity of divorce negotiations involving both legal calculations and emotional factors, lack of tools integrating both aspects, and the potential cost of traditional professional support. | Complexity of Legal Domain (Emotional/Social Factors), Lack of Suitable A2J Tools, High Cost of Legal Services | An AI chatbot ("Legal-Emotional BATNA") that provides calculations based on legal standards and incorporates emotional considerations (using GPT-4 analysis) to offer early-stage guidance and bridge users to professional services. | AI Tool Development, Access to Legal Information and Advice, Enhanced AI Capabilities, Online Dispute Resolution (ODR) | Divorce negotiation support, Online Dispute Resolution (ODR) for divorce. | Dispute Resolution | Individuals undergoing divorce negotiations in Japan. | Individuals in family law disputes, Population in Japan | Family Law (specifically divorce, child support, spousal support, property division, pension splitting, solatium/compensation for emotional distress). | Family Law, Tort Law | Japan | Japan | Legal calculations use Japanese court standards ('Calculation Tables for Child Support / Expenses arising from Marriage') and legal precedents. Emotional analysis relies on GPT-4, implying its general training data. No specific proprietary dataset is mentioned. | Input Data for Task (Non-Training), Japanese Legal Data, Legal Domain Data, Other Legal Documents, Case Law / Judgments, Pre-trained LLM's General Training Corpus | A framework separating Legal-BATNA Calculation (using legal data, precedents, tables) and Emotional-BATNA Estimation (using GPT-4 emotion analysis). The system interacts with users, processes legal/emotional data, and provides integrated recommendations. | Conceptual Framework Development, Data-driven Analysis, LLM-based Emotion Analysis, User Interaction Design, Integrated System Design | The chatbot is accessible via a chatgpt.com link (likely requiring ChatGPT access). Evaluation involved deployment on a crowdsourcing platform (CloudWorks). The paper discusses potential future "societal implementation in Japan". | Web-based access, Integration into existing system/platform, Pilot program/Limited rollout, Proposed deployment (not implemented) | True | False | Available as a custom GPT accessible via a specific chatgpt.com URL provided in a footnote. | Publicly accessible online tool or platform, Commercial product or service | Need for improvement in handling complex legal scenarios (e.g., international divorces, mixed-income households), enhancing privacy protection clarity, increasing financial accuracy depth, providing more personalized advice vs. general calculations, and managing user expectations regarding preliminary vs. definitive legal advice. | AI Legal Reasoning Limitations, Security and Privacy of Data, AI Accuracy and Reliability, AI Scope and Functionality Limitations, User Interface and Usability Gaps, Public Understanding, Trust, and Adoption | Balancing legal accuracy with nuanced emotional support, ensuring user trust regarding privacy and data handling, addressing user desire for personalized advice while maintaining scalability, managing complexity in legal calculations for non-standard cases. | Accuracy and Reliability of LLM Output, LLM Reasoning Capabilities, Ethical Considerations, User Adoption, Trust, and Acceptance, Data Privacy, Security, and Confidentiality, Domain-Specific Adaptation and Customization, Scalability of Solutions | Potential for users to misunderstand preliminary guidance as definitive legal advice. Privacy concerns related to handling sensitive personal and financial data during divorce negotiations. | Consumer harm, Risk of misapplication or misuse, Data privacy and security breach |
| EABTT6lmmYYJ.pdf | Google_Scholar | Sociological Phenomenology: Understanding Neighborhood Development and Local Culture | This paper proposes an approach integrating sociological phenomenology with AI technologies (big data analysis, LLMs, generative AI) to understand neighborhood development and local culture. The research suggests this combined methodology can provide deeper insights for urban planning and policy-making, fostering more culturally sensitive and inclusive community growth. | Interdisciplinary Approach (Sociology and AI), AI for Urban Planning, Big Data Analysis, LLM Application, Generative AI Application, Cultural Sensitivity in Development | True | Idealistic | True | 1.0 | Positive | An integrated research approach combining sociological phenomenology with AI techniques, including big data analysis (with attention mechanisms), large language models (LLMs), generative AI for simulations, and prompt engineering. | Interdisciplinary Research Methodology, Big Data Analysis, Attention Mechanisms, Large Language Model, Generative AI, Prompt Engineering | A mixed-methods approach: qualitative ethnographic methods (in-depth interviews, participant observation, document analysis of local histories and community archives) to understand lived experiences, complemented by big data analysis of datasets (e.g., demographic, socioeconomic, real estate, social media data) using attention mechanisms and LLMs. Generative AI was used to simulate neighborhood development scenarios informed by resident and planner input. | Qualitative Analysis, Quantitative Metrics, Demonstration or Illustrative Examples | AI-driven tools and big data analysis identified critical factors (e.g., gentrification patterns, employment changes, social network shifts) influencing neighborhood transformation and predicted areas of significant cultural change. LLMs extracted evolving narratives of neighborhood identity from textual data (social media, news). Generative AI simulations visualized potential cultural shifts under different conditions, offering insights for culturally sensitive urban planning. | Descriptive or Conceptual finding, Benefit identified | Loss of cultural identity, community cohesion, and sense of belonging for residents due to neighborhood changes like gentrification if development is not sensitive to lived experiences and local culture. | Socio-cultural Impacts of Development/Gentrification | Employing sociological phenomenology integrated with AI (big data, LLMs, generative AI) to deeply understand residents' lived experiences, cultural narratives, and social dynamics. This understanding can inform urban planning and policy-making for more culturally sensitive, inclusive, and equitable neighborhood development that preserves community heritage and identity. | AI Tool Development, Legal Research and Analysis Tools, Policy and Regulatory Reform, Conceptual Frameworks | Equitable urban development, preservation of local culture and community identity, mitigating negative impacts of gentrification, inclusive policy-making for neighborhoods, understanding social dynamics of urban change. | NaN | Residents of urban and rural neighborhoods undergoing development or transformation, particularly those whose cultural identities, social cohesion, and sense of place might be threatened by such changes (e.g., long-term residents in gentrifying areas). | Urban populations, Rural populations, Residents in gentrifying areas, Vulnerable populations | NaN | NaN | International | International | The approach uses mixed data: Qualitative data from ethnographic methods (interviews, participant observation, local historical/cultural artifacts). Quantitative/textual data for AI analysis includes demographic information, socioeconomic indicators, real estate data, social media interactions, local news, and community forum discussions. | Survey/Interview Data, Non-Legal Domain Specific Data, User-Generated Content, Unstructured Text Data, Structured Data | Mixed-methods research design combining qualitative sociological phenomenology (ethnographic methods like participant observation, interviews, document analysis) with advanced data analytics techniques (big data analysis with attention mechanisms, large language models, generative AI, prompt engineering). | Mixed-Methods Research Design, Qualitative Research Methods, Ethnographic Methods, Big Data Analytics, Attention Mechanism Application, LLM Application, Generative AI Techniques, Prompt Engineering | NaN | Not applicable | False | False | NaN | NaN | The need to further refine methodologies to more effectively incorporate complex cultural nuances into AI-driven decision-making processes for neighborhood development. Ensuring AI tools are guided by ethical considerations to genuinely reflect and serve community values and lived experiences. | AI Legal Reasoning Limitations, Bias in AI, Ethical Framework Deficiencies, Research and Evaluation Gaps | Ensuring AI models accurately understand and incorporate complex cultural nuances for relevant, insightful, and ethically sound data generation and simulation, highlighted by the stated need for prompt engineering and culturally sensitive approaches. | LLM Reasoning Capabilities, Ethical Considerations, Bias in AI Systems and Data, Prompt Engineering and Optimization, Domain-Specific Adaptation and Customization | NaN | NaN |
| 4y_1wDzhPbUJ.pdf | Google_Scholar | A Comprehensive Framework for Reliable Legal AI: Combining Specialized Expert Systems and Adaptive Refinement | This paper proposes a framework combining a mixture of expert systems, knowledge graph-enhanced retrieval-augmented generation (RAG), and reinforcement learning from human feedback (RLHF) to improve AI accuracy and reliability in legal tasks, addressing issues like hallucinations. This approach utilizes specialized modules and human feedback, aiming to offer more precise, accessible, and affordable legal services. | Framework Proposal, Hybrid AI Approach, Expert System Application, Knowledge Graph Integration, Retrieval Augmented Generation, Reinforcement Learning from Human Feedback, Accuracy Improvement, Reliability Improvement, Mitigating AI Hallucinations, Accessible Legal Services, Affordable Legal Services | True | Idealistic | True | 1.0 | Positive | A framework integrating Mixture of Experts (MoE), Knowledge Graph (KG) enhanced Retrieval-Augmented Generation (RAG), and Reinforcement Learning from Human Feedback (RLHF). | Framework Development, Mixture of Experts (MoE), Knowledge Graph Integration, Retrieval Augmented Generation (RAG), Reinforcement Learning from Human Feedback (RLHF) | Comparative evaluation of the framework (using LLMs like GPT-4, LLaMA-3 enhanced with RAG/KG/RLHF) against baseline and fine-tuned models on nine legal tasks using multiple public datasets (LegalQA, CaseHold, LEDGAR, LEXTREME, COLIEE, SARA, LexGlue, Billsum, CUAD, Super-SCOTUS, EUR-LEX, ECHR). Metrics included Accuracy, Rouge-L, F1 Score, BLEU, and abstention rates. | Benchmark Dataset Evaluation, Quantitative Metrics, Comparative Analysis | The integrated framework, particularly using advanced models like GPT-4 and LLaMA-3 enhanced with RAG and RLHF, consistently outperformed baseline and solely fine-tuned models across various legal tasks. For example, GPT-4 with RLHF achieved approximately 10% higher accuracy than with KG integration on complex tasks, demonstrating enhanced reliability and reduced abstention. | High performance, Outperforms others, Technique improves outcome | High cost and time consumption of traditional legal support limiting access; unreliability and potential for inaccuracies (hallucinations) in existing AI models hindering their effective use in legal contexts. | High Cost of Legal Services, Resource Constraints, AI Unreliability/Inaccuracy | Utilize the proposed AI framework featuring MoE, KG-enhanced RAG, and RLHF to provide more reliable, accurate, scalable, and affordable legal assistance, thereby improving access to justice. | AI Tool Development, Enhanced AI Capabilities, Legal Knowledge Representation and Management, Cost Reduction and Efficiency, Access to Legal Information and Advice | Making general legal assistance tasks (e.g., document review, research, contract drafting, Q&A, case analysis, judgment prediction) more accessible and affordable through reliable AI. | Legal Document Analysis / Review, LegalResearch Support, Legal Document Creation / Automation, Access to Legal Information, Improving Foundational AI Capabilities for Legal Applications, Affordability of Legal Services / Cost Reduction, Democratizing Law / Closing Justice Gap / Rule of Law | General population needing affordable legal services. | General public, Individuals unable to afford legal services | General / Multiple (covers tasks related to contracts, legislation, case law, etc.) | General Law, Multiple Fields, Contract Law, Statutory Law, Case Law | Mixed (Uses datasets coveringUS, European, and potentially other/general legal contexts). | USA, Europe, International | The framework leverages multiple publicly available legal datasets for evaluation and potentially fine-tuning (LegalQA, CaseHold, LEDGAR, LEXTREME, COLIEE, SARA, LexGlue, Billsum, CUAD, Super-SCOTUS, EUR-LEX, ECHR), containing diverse structured and unstructured legal text. It relies heavily on retrieval from external knowledge sources (documents, KGs) rather than a single training dataset. | Data From Existing Public NLP/Legal Datasets/Benchmarks, Evaluation Dataset, Fine-tuning Dataset, Legal Domain Data, Structured Data, Unstructured Text Data, RAG System Knowledge Corpus | Modular integration of Retrieval-Augmented Generation (RAG), Knowledge Graphs (KG), Mixture of Experts (MoE), Reinforcement Learning from Human Feedback (RLHF), Supervised Fine-Tuning (SFT) with Low-Rank Adaptation (LoRA), and an 'Experts Collaboration Workflow' modeling human legal teams. | Modular System Architecture, Retrieval Augmented Generation (RAG), Knowledge Graph Construction/Integration, Mixture of Experts (MoE) Architecture, Reinforcement Learning from Human/AI Feedback (RLHF/RLAIF), Model Fine-tuning, Supervised Learning, Parameter-Efficient Fine-Tuning (PEFT), Workflow Modeling | NaN | Not applicable | False | False | NaN | NaN | Need for expansion to more legal domains, integration of real-time legal updates, enhanced explainability, and ongoing refinement through collaboration with legal professionals. Persistent difficulty with tasks requiring precise verbatim reproduction. | AI Scope and Functionality Limitations, Knowledge Recency and Updatability, Transparency and Explainability, Need for Interdisciplinary Collaboration, AI Accuracy and Reliability | Integrating multiple complex AI components (RAG, KG, MoE, RLHF); mitigating AI hallucinations and ensuring reliability in the legal domain; selecting and utilizing appropriate datasets; modeling complex legal reasoning; scaling human feedback processes (RLHF). | Integration with Existing Systems and Workflows, LLM Hallucination and Factual Errors, Accuracy and Reliability of LLM Output, Scarcity of High-Quality Legal Data, LLM Reasoning Capabilities, Cost and Complexity of Data Annotation, Scalability of Solutions | Generation of inaccurate or misleading information ('hallucinations') by AI, leading to serious legal consequences and undermining trust. Potential for toxic outputs if not properly managed (addressed via RLHF). | Inaccurate or misleading AI output, Consumer harm, Erosion of trust in legal system or AI, Harmful or unsafe AI output |
| Pnl5_5sEE-gJ.pdf | Google_Scholar | LawPal : A Retrieval Augmented Generation Based System for Enhanced Legal Accessibility in India | This paper proposes LawPal, a Retrieval-Augmented Generation (RAG) based chatbot using DeepSeek-R1:5B and FAISS, to improve legal accessibility and literacy in India by providing accurate legal information. The system demonstrated over 90% legal accuracy in evaluations, aiming to democratize legal knowledge and combat misinformation. | Chatbot Development, Retrieval Augmented Generation, India Focus, Legal Accessibility Enhancement, Legal Literacy Improvement, Accurate Legal Information Provision, System Evaluation, Democratization of Legal Knowledge | True | Idealistic | True | 1.0 | Positive | LawPal, a Retrieval-Augmented Generation (RAG)-based legal chatbot using DeepSeek-R1:5B for language understanding and FAISS for document retrieval. | Chatbot / Conversational AI, Retrieval Augmented Generation (RAG), Large Language Model, Document Retrieval, Named Tool / Platform | Retrieval accuracy (Precision@K, Mean Reciprocal Rank, Normalized Discounted Cumulative Gain), response quality (BLEU, ROUGE, Legal Consistency Score, human expert review), computational efficiency (query processing time analysis), robustness against adversarial inputs, and user feedback from lawyers, law students, and legal aid seekers. Comparative testing against rule-based chatbots and keyword-based search engines. | Quantitative Metrics, Expert Evaluation, User Study or Survey, Comparative Analysis | LawPal achieved over 90% legal accuracy. FAISS-based retrieval takes 10-50 milliseconds, and DeepSeek-R1:5B response generation ranges from 800 to 1500 milliseconds. User feedback indicated 85% satisfaction for accuracy and reliability. | High performance, Benefit identified, Developer or Vendor claim | Lack of awareness, misinformation, limited accessibility to judicial resources, difficulty for individuals in navigating complex legal frameworks, frequent misuse of laws, and inadequate legal protection. | Public Lack of Legal Knowledge/Awareness, Misinformation (General), Limited Access to Justice System Resources, Complexity of Legal System/Procedures, Misuse of Laws, Inadequate Legal Protections | Development of a Retrieval-Augmented Generation (RAG)-based legal chatbot (LawPal) to provide accurate, efficiently retrieved legal information, enhance legal literacy, and prevent the spread of misinformation. The platform also includes features like real-time legal news, blogs, and access to law-related books. | AI Tool Development, Enhanced AI Capabilities, Access to Legal Information and Advice, Education and AI Literacy | Legal information retrieval, enhancing legal literacy, combating legal misinformation, navigating complex legal frameworks, improving access to judicial resources. | Access to Legal Information, Legal Literacy and Public Legal Education, Ethical AI in Law and AI Governance, Judicial System Modernization / Efficiency | The general public in India, particularly individuals struggling with legal complexities, and those with limited access to legal resources. | General public, Population in India, Individuals lacking legal knowledge, Individuals facing access barriers | Indian Law, including the Indian Constitution, statutory laws, and case law. Specific examples like Criminal Law and Civil Law are mentioned for data categorization. | General Law, Constitutional Law, Statutory Law, Case Law, Criminal Law, Civil Law | India | India | Publicly available legal texts from authoritative sources such as government websites, Supreme Court archives, legal research papers, legal books, official documentation, and the Indian Constitution. The dataset includes structured and unstructured texts, preprocessed with OCR and segmentation. | Fine-tuning Dataset, Publicly Available Data, Indian Legal Data, Legal Domain Data, Legislation / Statutes / Regulations, Case Law / Judgments, Legal Scholarly Content / Textbooks, Other Legal Documents, Structured Data, Unstructured Text Data, OCR Processed Data | Retrieval-Augmented Generation (RAG) architecture, data collection from diverse legal sources, data preprocessing (cleaning, OCR correction, text normalization, chunking with LangChain’s RecursiveCharacterTextSplitter), vector embedding generation (DeepSeek-R1:5B), FAISS for efficient vector indexing and similarity search, prompt engineering, hierarchical indexing of legal topics, and a Streamlit-based user interface. | Retrieval Augmented Generation (RAG), Data Collection, Data Preprocessing, Embedding Model Application, Vector Database Implementation, Prompt Engineering, Hierarchical Indexing, User Interface Development | A Streamlit-based user interface was developed for user interaction. Broader deployment or diffusion strategies are not detailed. | Web-based access, Internal deployment/prototype | False | False | NaN | NaN | Need for multilingual support for regional Indian languages, improved handling of multi-jurisdictional queries, enhanced capability for processing long-context legal arguments, further fine-tuning for specialized legal domains (e.g., corporate and international law), and continuous bias mitigation. | Multilingual and Low-Resource Language Gaps, AI Scope and Functionality Limitations, AI Legal Reasoning Limitations, Bias in AI | Handling multi-jurisdictional legal queries, effectively processing long-context legal arguments, ensuring consistent up-to-date legal information, occasional errors in ambiguous legal queries, and the need for fine-tuning in specialized or niche legal fields. | Outdated or Limited LLM Knowledge Base, LLM Context Window and Long Input Management, Accuracy and Reliability of LLM Output, LLM Reasoning Capabilities, Domain-Specific Adaptation and Customization | Potential for legal misinformation due to occasional errors in ambiguous queries, and general ethical concerns in legal AI regarding bias (as noted in the literature review). | Inaccurate or misleading AI output, Ethical concerns, Bias and discrimination |
| aKgI4nl8ulwJ.pdf | Google_Scholar | Komodo: A Linguistic Expedition into Indonesia’s Regional Languages | This paper introduces Komodo-7B, a 7-billion-parameter Large Language Model family optimized for Indonesian, English, and 11 Indonesian regional languages. Komodo-7B-Instruct surpasses existing models on various benchmarks, aiming to improve linguistic inclusivity and access to education in Indonesia. | Legal Language Model Development, Indonesian Language Focus, Multilingual LLM, Linguistic Inclusivity, Access to Education Enhancement | True | Idealistic | True | 1.0 | Positive | Komodo-7B (Komodo-7B-Base and Komodo-7B-Instruct), a family of 7-billion-parameter Large Language Models based on Llama-2, with an expanded vocabulary for Indonesian and regional languages, trained with a bilingual next-token prediction strategy. | Model Development, Large Language Model, Vocabulary Expansion, Multilingual Model Training, Pre-training Technique, Named Tool / Platform | Evaluated on discriminative tasks (IndoMMLU, ID-EN Entailment, X-Copa-ID, Intent-Classification, Colloquial-Detection, NusaXSenti, ID-Hatespeech) and generative tasks (NusaX-MT, TydiQA-ID, IndoSum) using metrics like Accuracy, F1, CHRF++, Rouge-L-F1. Also tokenizer fertility, embedding position analysis, English capability regression (Perplexity, common benchmarks), and qualitative analysis. Compared against GPT-3.5, GPT-4, Llama-2, Mixtral, Gemma, Sealion, Aya, Bactrian-X, Qwen. | Benchmark Dataset Evaluation, Quantitative Metrics, Qualitative Analysis, Comparative Analysis | Komodo-7B-Instruct achieved state-of-the-art performance in several tasks, outperforming models like GPT-3.5 and Aya-101. For example, it scored 90.5% accuracy on ID-EN entailment, 79.3% accuracy on NusaX-Senti, 90.3% accuracy on TydiQA-ID, and a 43.0 Rouge-L-F1 score on IndoSum. Overall average score on the benchmark suite was 71.1%. | High performance, Moderate performance, Outperforms others | The primary obstacles identified are the significant gap in linguistic resources and high-performing LLMs for low-resource Indonesian regional languages. This digital language barrier hinders access to information and education for communities speaking these languages, contributing to educational disparities. | Data Scarcity/Quality for AI, Accessibility Barriers for Specific User Groups, Technical Challenges in AI Development, Risk of AI Exacerbating Inequality | The paper proposes Komodo-7B, a specialized LLM family, to address these obstacles. This involves creating comprehensive, high-quality datasets (including legal/jurisprudential corpora and textbooks), expanding tokenizer vocabularies for regional languages, and using advanced training techniques to enhance performance in these languages, thereby promoting informational and educational equity. | AI Tool Development, Data Curation and Management, Language Simplification and Multilingual Access, Enhanced AI Capabilities, Education and AI Literacy | Linguistic inclusivity in digital resources, Access to education in regional languages, Access to information in regional languages, Bridging language barriers with AI. Potential for access to legal information given training data and stated domains. | Language Access and Digital Divide, Access to Legal Information | Speakers of 11 Indonesian regional languages: Acehnese, Balinese, Banjarese, Buginese, Dayak Ngaju, Javanese, Lampungnese, Madurese, Minangkabau, Sundanese, and Toba Batak, particularly those in regions with lower educational quality compared to Java island. | Speakers of low-resource languages, Population in Indonesia, Individuals with low education levels | Legal Services, Jurisprudence (based on training data and mentioned application domains). | General Legal Practice, Jurisprudence | Indonesia | Indonesia | A combination of diverse open-source datasets, manually collected data for Indonesian regional languages, Indonesian textbooks (grades 1-12, covering subjects like local cultures, engineering, legal and jurisprudential corpus), colloquial data (subtitles, news, conversations), and English datasets with alternate parallel data for code-mixing. Preprocessing involved repetition removal, quality filtering, and deduplication. About 8.79 billion tokens were processed for pretraining. SFT data included open-source tasks, manually labeled data, and ChatGPT responses. | Fine-tuning Dataset, Data From Existing Public NLP/Legal Datasets/Benchmarks, Author-Created New Dataset, Indonesian Language Data, Indonesian Legal Data, Legal Domain Data, Legal Scholarly Content / Textbooks, General Web Data / Broad Internet Text, Multilingual Data, Expert-Annotated / Human-Curated / Human-Generated Data, Synthetic Data, Instruction-Tuning Formatted Data | Built on Llama-2-7B. Methodologies include vocabulary expansion for target languages, new embedding initialization by averaging existing ones, incremental pretraining and Supervised Fine-Tuning (SFT) using LORA, and a bilingual next-token prediction strategy. Data preprocessing techniques were also applied. | Vocabulary Expansion, Embedding Customization, Model Pre-training, Model Fine-tuning, Supervised Learning, Parameter-Efficient Fine-Tuning (PEFT), Bilingual Model Strategy, Data Preprocessing | NaN | Not applicable | False | False | NaN | NaN | The paper suggests that achieving optimal performance across all regional languages may require larger models (e.g., a future 13B parameter version). The current 7B model's English mathematical reasoning capability is also noted as an area with relative underperformance due to training data composition. The general challenge of subpar performance of models for low-resource languages persists, with Komodo-7B being a step towards addressing this. | Multilingual and Low-Resource Language Gaps, Computational Resource and Cost Issues, AI Scope and Functionality Limitations, AI Accuracy and Reliability, Data Availability and Quality | Effectively expanding tokenizer vocabularies for languages like Indonesian that share Latin script with English. Balancing vocabulary size with computational resources. Initializing new embeddings properly. Mitigating catastrophic forgetting during incremental pretraining. Managing hardware and cost requirements for large model training. Objectively evaluating generative outputs, sometimes requiring human or advanced AI (GPT-4) assistance. | Multilingual and Low-Resource Language Support, High Computational and Resource Demands, Domain-Specific Adaptation and Customization, Financial Cost and Resource Constraints, Evaluation Challenges and Metrics | NaN | NaN |
| -CZiMBSVZrgJ.pdf | Google_Scholar | Large Language Model Agent as Insurance Law Assistant | This thesis proposes and evaluates an LLM agent using Retrieval-Augmented Generation (RAG) to make Finnish traffic insurance law more accessible to ordinary individuals. Evaluation by a legal expert showed the agent could provide satisfactory answers and mitigate hallucinations, but highlighted the need for improved document retrieval. | LLM Agent Evaluation, Retrieval Augmented Generation, Finnish Law Focus, Traffic Insurance Law, Accessibility for Laypeople, Mitigating AI Hallucinations, Expert Evaluation, Document Retrieval Improvement Need | True | Idealistic | True | 1.0 | Positive | An intelligent agent employing Retrieval-Augmented Generation (RAG) with the GPT-4 Turbo Large Language Model, facilitated by the Embedchain library, to answer user questions based on custom-selected legal documents. | Intelligent Agent, Retrieval Augmented Generation (RAG), Large Language Model, Software Library / Tool Usage, Legal Question Answering, Custom Knowledge Base Integration | The agent was evaluated through human feedback from a legal expert who assessed the quality of responses to 10 predefined scenarios based on real-life situations. Quantitative metrics (context relevance, answer relevance) from Embedchain's evaluation method were also used. | Expert Evaluation, Custom Dataset Evaluation, Quantitative Metrics | The expert rated 9 out of 10 responses as satisfactory (score 10/10), demonstrating reduced hallucination compared to the base LLM. However, quantitative evaluation showed low context relevance scores (2-44%), indicating suboptimal document retrieval, while answer relevance scores were high (84-90%). | High performance, Low performance, Mixed performance, Technique improves outcome, Limitation: Operational or Technical | The complexity of traffic insurance law for laypeople, and the general lack of knowledge, networks, and financial resources needed to access legal support. | Complexity of Legal Information, Public Lack of Legal Knowledge/Awareness, Lack of Resources, Limited Access to Legal Assistance | Develop an accessible web-based LLM agent that utilizes RAG to retrieve information from specific legal documents (Finnish traffic insurance law, precedents) and answer user questions, thereby guiding them through the complexities of the legal domain. | AI Tool Development, User Interface and Accessibility Design, Enhanced AI Capabilities, Access to Legal Information and Advice, Data Curation and Management | Understanding rights to compensation under traffic insurance law after an accident, accessing relevant legal information. | Protection of Rights, Access to Legal Information | Ordinary individuals involved in traffic accidents in Finland who find it difficult to navigate insurance law. | General public, Laypeople, Individuals in traffic disputes, Population in Finland, Individuals lacking legal knowledge | Traffic Insurance Law | Insurance Law, Traffic Law | Finland | Finland | The RAG system uses publicly available Finnish legal documents (e.g., from Finlex, Liipo) and potentially undisclosed proprietary sources related to traffic insurance law. This unstructured text data (HTML, PDF) is chunked, embedded using OpenAI's 'text-embedding-ada-002', and stored in a ChromaDB vector database. The underlying LLM is OpenAI's GPT-4 Turbo. | RAG System Knowledge Corpus, Publicly Available Data, Finnish Legal Data, Legal Domain Data, Other Legal Documents, Unstructured Text Data, Proprietary Data, Undisclosed Data Source/Availability, Pre-trained LLM's General Training Corpus | Design Science Research Methodology (DSRM), including problem explication, requirements definition, iterative design/development (brainstorming, assessment, sketching, building, reflection), demonstration, and evaluation. Peer review via the Walk-Through method was also employed. | Design Science Research Methodology (DSRM), Iterative Design Process, Peer Review | The agent is accessed via a web application (built with Next.js, Nginx, Django API) requiring user registration/login. The system is containerized using Docker and hosted on Google Cloud. | Web-based access, Cloud platform deployment | False | False | NaN | NaN | Suboptimal document retrieval performance in the RAG system (low context relevance). Need for more extensive evaluation with more test cases and potentially more experts. Lack of sufficient data and expert resources for developing a planned Multi-Agent System (MAS). Need for self-hosted LLMs to improve privacy and reduce costs. | AI Accuracy and Reliability, AI Scope and Functionality Limitations, Research and Evaluation Gaps, Data Availability and Quality, Computational Resource and Cost Issues, Security and Privacy of Data | Selecting/developing LLMs with robust Finnish language understanding; optimizing RAG retrieval performance; effective prompt engineering; acquiring comprehensive real-world case data (esp. for MAS); limited expert availability for evaluation and multi-agent design; evaluating and fine-tuning open-source models for the task; managing costs associated with third-party APIs. | Multilingual and Low-Resource Language Support, Accuracy and Reliability of LLM Output, Prompt Engineering and Optimization, Scarcity of High-Quality Legal Data, Evaluation Challenges and Metrics, Cost and Complexity of Data Annotation, Domain-Specific Adaptation and Customization, Financial Cost and Resource Constraints | LLM hallucination leading to incorrect legal information. Failure to retrieve or include critical legal details in responses. Data privacy concerns associated with using third-party LLM APIs. Potential for sensitive information leakage between users (e.g., via improperly implemented caching). | Inaccurate or misleading AI output, Technical limitations of AI, Data privacy and security breach |
| aH2ZKJ7gUmYJ.pdf | Google_Scholar | Free LLMs Hallucinate and Rarely Signal Their Limitations in Solving Legal Problems | This study evaluates the ability of two free large language models (GPT-4o mini and Bielik-11B-v2) to answer simple Polish legal questions. The results show the models perform poorly on moderately complex issues, often hallucinate, fail to correct erroneous user assumptions, and rarely indicate their own limitations. | LLM Evaluation, Polish Law Focus, Legal Question Answering, Performance Issues, AI Hallucinations/Inaccuracy, Failure to Correct User Error, Lack of Self-Awareness in LLMs | True | Idealistic | True | 2.0 | Negative | Evaluation of GPT-4o mini and Bielik-11B-v2 | AI System Evaluation, Large Language Model | Models were prompted with 12 questions across 3 Polish legal fields (Constitutional, Criminal, Inheritance). Prompts included sensible/nonsensical assumptions and varied phrasing. 120 responses (5 per prompt/model) were evaluated by human experts for correctness and signalling of limitations. | Expert Evaluation, Custom Dataset Evaluation, Quantitative Metrics, Comparative Analysis | Models answered correctly only on the simplest constitutional law issue. They struggled significantly with criminal and inheritance law, especially when prompts contained nonsensical assumptions (Bielik: 0-20% correct, GPT: 20-100% correct depending on phrasing). Limitations were rarely signalled (17% overall). | Low performance, Mixed performance, Limitation: Operational or Technical | LLMs hallucinate and provide incorrect legal analysis, especially for non-trivial questions; they fail to correct user misconceptions posed in prompts; they rarely signal their own limitations (e.g., lack of access to real-time/accurate legal databases); opacity of commercial models. | AI Unreliability/Inaccuracy, AI Limitations in Legal Reasoning/Nuance, Difficulty in AI-Human Interaction, Lack of AI Transparency/Explainability, Proprietary Nature of AI as a Barrier | The paper suggests lawyers should use LLMs very carefully, be aware of their limitations, and calls for more research to scrutinize these limitations and raise awareness. | Human Oversight and Collaboration, Education and AI Literacy, Regulation, Ethics, and Governance | Legal analysis accuracy, hallucination in legal contexts, LLM limitations signalling, legal information retrieval. | Ethical AI in Law and AI Governance, Access to Legal Information | NaN | NaN | Constitutional Law, Criminal Law, Inheritance Law (Civil Law) | Constitutional Law, Criminal Law, Wills and Estates, Civil Law | Poland | Poland | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | True | True | The paper explicitly studies 'free LLMs'. GPT-4o mini is available via OpenAI. Bielik-11B-v2 is available on Hugging Face (as per reference [9]). | Publicly accessible online tool or platform, Model available | Unreliability of current free LLMs for legal analysis beyond simple cases; lack of precision and tendency to hallucinate; failure to signal limitations; need for more research on limitations of free models; issue of LLM transparency. | AI Accuracy and Reliability, AI Legal Reasoning Limitations, Research and Evaluation Gaps, Transparency and Explainability | Evaluating legal correctness of LLM outputs; designing realistic prompts; high variability and sensitivity of LLM responses to prompt phrasing. | Evaluation Challenges and Metrics, Accuracy and Reliability of LLM Output, Prompt Engineering and Optimization, Output Variability and Consistency | Users receiving incorrect legal information due to hallucinations; users being misled when LLMs confirm false premises; over-reliance on LLMs due to lack of signalled limitations; risks associated with the opacity (black-box nature) of LLMs in legal applications. | Inaccurate or misleading AI output, Consumer harm, Over-reliance on AI, Lack of transparency, accountability, and redress |
| blackham-2025-interrogating-new-methods-in-socio-legal-studies-content-analysis-case-law-and-artificial-intelligence.pdf | Google_Scholar | Interrogating new methods in socio-legal studies: Content analysis, case law and arti ficial intelligence | This article critically examines the use of artificial intelligence (AI) and large language models (LLMs) for empirical legal research, specifically focusing on content analysis of case law. It highlights significant risks such as inaccuracy, bias, hallucinations, and lack of reproducibility, concluding that researchers should be extremely cautious and implement stringent evaluation procedures when considering these tools. | Critique of AI for Empirical Legal Research, LLM Application, Content Analysis of Case Law, Risk Identification, AI Hallucinations/Inaccuracy, Bias in AI, Reproducibility Issues, Caution for Researchers | True | Idealistic | True | 3.0 | Negative | Using AI and LLMs for content analysis of case law | Artificial Intelligence (General), Large Language Model, Content Analysis, Legal Text Analysis | NaN | Not Applicable | NaN | NaN | Current unreliability and limitations of AI/LLMs (e.g., hallucinations, bias, inaccuracy) prevent their effective and trustworthy use in legal research tasks that could support access to justice, such as analysing case law to identify systemic enforcement gaps or practical legal issues. | AI Unreliability/Inaccuracy, Bias in AI/Data, Lack of Trust in AI/Automated Systems | Researchers should exercise significant caution when using AI/LLMs, implement stringent procedures for evaluating AI outputs (e.g., blind, independent testing), and develop a clear understanding of the tools' limitations before applying them in legal research, especially research with potential A2J implications. | Human Oversight and Collaboration, Regulation, Ethics, and Governance, Benchmarking and Evaluation Frameworks, Education and AI Literacy | Analysis of case law to understand legal operations in practice (e.g., who brings claims, claim resolution, identifying systemic patterns); identifying barriers to justice and gaps in legal enforcement. | LegalResearch Support, Democratizing Law / Closing Justice Gap / Rule of Law | Implicitly, communities that are underserved by the legal system whose issues might be illuminated by thorough case law analysis (example given in text: older women and young people in age discrimination cases). | Marginalized communities, Elderly people, Women, Youth, Individuals facing age discrimination | Equality law, employment law (used as primary examples for content analysis). | Anti-Discrimination Law, Employment Law | Australia, UK (examples and studies discussed are from these jurisdictions). | Australia, UK | General LLMs (e.g., GPT-4, Llama2-70B) are trained on large-scale, often uncurated internet-based data. Specific studies discussed use datasets like UK Employment Tribunal decisions for analysis. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Evaluation Dataset, UK Legal Data, Legal Domain Data, Case Law / Judgments | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Significant gap between the potential of AI/LLMs to assist in socio-legal research (including for A2J purposes) and their current capabilities, particularly regarding accuracy, reliability, reproducibility, and freedom from bias and hallucinations. | AI Accuracy and Reliability, Bias in AI, Research and Evaluation Gaps | Automation bias in human evaluation of AI outputs, AI 'hallucinations' (generating false or nonsensical information), inherent biases in LLMs derived from training data or design, and practical difficulties in ensuring reproducibility of results from third-party LLM services. | Evaluation Challenges and Metrics, Ethical Considerations, LLM Hallucination and Factual Errors, Bias in AI Systems and Data, Output Variability and Consistency | Lack of reproducibility, automation bias, inherent LLM bias, hallucinations leading to inaccurate or unfaithful outputs, general inaccuracy, production of poor or misleading research, potential for creating more work due to the need for extensive fact-checking and correction of AI outputs. | Technical limitations of AI, Over-reliance on AI, Bias and discrimination, Inaccurate or misleading AI output, Negative economic impact |
| Mc-1PNuCNIsJ.pdf | Google_Scholar | ChatGPT as an Artificial Lawyer? | This paper qualitatively evaluates ChatGPT's ability to provide legal information to laypeople using simulated landlord-tenant cases, comparing its performance against the expert system-based JusticeBot. While ChatGPT excels at user interaction and language comprehension, it suffers from significant inaccuracies and hallucinations, making it currently unsuitable for direct use, unlike the more reliable but less flexible JusticeBot. | ChatGPT Evaluation, Legal Information Provision for Laypeople, Landlord-Tenant Law Focus, Comparative Evaluation (ChatGPT vs Expert System), User Interaction Quality, AI Hallucinations/Inaccuracy, Unsuitability for Direct Use | True | Idealistic | True | 2.0 | Neutral | Evaluating ChatGPT's capability for providing legal information to laypeople compared to JusticeBot (an expert system). | AI System Evaluation, Large Language Model, Expert System, Comparative Analysis, Legal Information Provision | Qualitative evaluation using three simulated landlord-tenant cases (generated by ChatGPT) set in Quebec. Researchers interacted with ChatGPT and JusticeBot as layperson parties involved in these cases, assessing performance against criteria including language comprehension, accuracy, completeness, trustworthiness, harmlessness, and user-friendliness. | Qualitative Analysis, Comparative Analysis, Expert Evaluation | ChatGPT demonstrated good language comprehension and user-friendliness but lacked accuracy, completeness, and trustworthiness, often 'hallucinating' incorrect legal provisions and case law. JusticeBot provided accurate and trustworthy information within its defined scope but was less flexible and interactive. | Mixed performance, Limitation: Hallucination or Factual inaccuracy, Limitation: Operational or Technical, Benefit identified | Cost of legal services leading to 'legal advice deserts'; difficulty for laypeople in understanding their rights and legal procedures; information asymmetry and power imbalances in disputes (e.g., housing). | High Cost of Legal Services, Geographical Disparities in Legal Access, Public Lack of Legal Knowledge/Awareness, Complexity of Legal System/Procedures, Information Asymmetry, Power Imbalances | Using AI tools like ChatGPT and JusticeBot to provide legal information. The paper suggests combining the conversational strengths of LLMs (like ChatGPT) with the accuracy of verified knowledge bases or expert systems (like JusticeBot). | AI Tool Development, Access to Legal Information and Advice, Enhanced AI Capabilities | Provision of legal information; self-help tools for laypeople; everyday legal disputes. | Access to Legal Information, Support for Self-Represented Litigants, Dispute Resolution | Laypeople, self-represented litigants, individuals facing everyday legal problems without access to professional legal help. | Laypeople, Self-represented litigants, Individuals with unmet legal needs, Individuals facing access barriers | Landlord-Tenant Law (Housing Law) | Landlord-Tenant Law, Housing Law | Quebec, Canada | Canada | ChatGPT: Trained on 'enormous corpora of text data' (general, not specified as legal). JusticeBot: Based on content created by legal experts using an expert system methodology. Evaluation Data: Simulated landlord-tenant cases generated by ChatGPT. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Expert-Annotated / Human-Curated / Human-Generated Data, Rule-Based System (No Training Data), Evaluation Dataset, Synthetic Data, Legal Domain Data | Qualitative evaluation based on predefined criteria (Language comprehension, Accuracy, Completeness, Trustworthiness, Harmless, User-friendly) using simulated case interactions. | Qualitative Evaluation Methodology, Criteria-based Evaluation, Scenario-based Evaluation | JusticeBot is deployed online (justicebot.ca) and has been accessed by users. ChatGPT is available via OpenAI's web interface and API. | Evaluation of existing third-party tool, Web-based access, Freely accessible tool/service, API access | True | False | ChatGPT is available via OpenAI's interface/API. JusticeBot is available online at https://justicebot.ca. | Publicly accessible online tool or platform, API access | Accuracy, reliability, and trustworthiness of LLMs for legal information; ensuring information is up-to-date and properly sourced; difficulty in verifying AI-generated legal content for laypeople; potential for harmful reliance on incorrect information. | AI Accuracy and Reliability, Knowledge Recency and Updatability, Transparency and Explainability, User Interface and Usability Gaps, Public Understanding, Trust, and Adoption, Consumer Protection Gaps | ChatGPT's tendency to 'hallucinate' legal facts (false provisions, non-existent cases); ensuring accuracy and reliability for lay users who cannot easily verify information; the limited scope and inflexibility of expert systems like JusticeBot compared to LLMs; defining the boundary between providing legal information and unauthorized legal advice. | LLM Hallucination and Factual Errors, Accuracy and Reliability of LLM Output, User Training, AI Literacy, and Skill Gaps, Scalability of Solutions, Unauthorized Practice of Law (UPL) Concerns, Regulatory Uncertainty and Compliance | Laypeople making harmful decisions based on inaccurate or hallucinated information from AI; provision of misleading or incomplete information; privacy risks associated with user interaction data; potential for bias in AI responses. | Consumer harm, Inaccurate or misleading AI output, Data privacy and security breach, Bias and discrimination |
| XZX5nvn_88QJ.pdf | Google_Scholar | Summary of Young -OGEMID Symposium No. 13: “The Role of Artificial Intelligence in Shaping ADR Practices” (July 2023 ) | This paper summarizes a Young-OGEMID virtual symposium discussing the integration of Artificial Intelligence (AI) into Alternative Dispute Resolution (ADR), particularly arbitration. Experts explore AI's opportunities (efficiency, data analysis) and challenges (bias, ethics, transparency), its role in decision-making, and its potential future impact, including on access to justice. | Symposium Summary, AI in Alternative Dispute Resolution, AI in Arbitration, Opportunity Identification, Challenge Identification, Bias in AI, Ethical Concerns, Transparency Issues, Access to Justice Enhancement | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | Bias in AI algorithms; unequal access to AI tools leading to a 'digital divide' and tiered justice; lack of transparency ('black box' problem); potential increase in costs/inefficiencies due to verification needs; ethical issues of offloading underserved cases to potentially inferior AI; language barriers for less common dialects. | Bias in AI/Data, Unequal Access to A2J Technology, Digital Divide, Risk of AI Exacerbating Inequality, Lack of AI Transparency/Explainability, Resource Constraints, Ethical Concerns with AI in Law, Accessibility Barriers for Specific User Groups | Promote responsible AI use through education; develop unbiased and transparent AI; ensure broad access to AI tools; maintain human oversight and intervention (Human Experience + AI); focus AI on augmenting human capabilities rather than replacement; leverage AI to empower self-represented litigants. | Education and AI Literacy, Bias Detection and Mitigation, Transparency and Explainability in AI, Regulation, Ethics, and Governance, Human Oversight and Collaboration, Support for Self-Represented Litigants | Empowering self-represented litigants; reducing legal costs; increasing efficiency; overcoming language barriers; addressing resource inequality (digital divide); ensuring procedural fairness and due process. | Support for Self-Represented Litigants, Affordability of Legal Services / Cost Reduction, Improving Efficiency in Legal System / Profession, Language Access and Digital Divide, Protection of Rights | Self-represented litigants; parties with fewer resources (e.g., SMEs, individuals); parties from developing countries. | Self-represented litigants, Small businesses, Low-income individuals, Populations in developing countries | Alternative Dispute Resolution (ADR), International Arbitration (Commercial, Investment), Consumer Arbitration, Employment Arbitration. | Alternative Dispute Resolution, Arbitration, International Law, Commercial Law, Consumer Law, Employment Law | US, Canada, India, Colombia, Estonia, China, International | USA, Canada, India, Colombia, Estonia, China, International | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | True | True | Publicly available tools like ChatGPT (free tier) and free legal databases (worldlii.org, CLOUT) are discussed. Commercial tools (e.g., Lexis+ AI, Jus Mundi) and specific pilot projects (e.g., SUPACE) are also mentioned. | Publicly accessible online tool or platform, Freemium access, Dataset available, Commercial product or service | Technical: Improving AI accuracy, reducing bias, handling legal complexity/nuance, ensuring transparency, dealing with limited/confidential data, supporting more languages. Societal/Ethical: Addressing the digital divide, establishing clear ethical guidelines/regulations, building public trust, defining human oversight roles, preventing misuse (deepfakes), ensuring procedural justice. Need for more research on AI vs. human decision-making fairness/effectiveness. | AI Accuracy and Reliability, Bias in AI, AI Legal Reasoning Limitations, Transparency and Explainability, Data Availability and Quality, Security and Privacy of Data, Multilingual and Low-Resource Language Gaps, Access, Equity, and Digital Divide, Ethical Framework Deficiencies, Regulatory and Governance Gaps, Public Understanding, Trust, and Adoption, Human Oversight and Professional Adaptation, Research and Evaluation Gaps | Data limitations (availability, confidentiality, bias); ensuring fairness/mitigating bias; maintaining confidentiality/privacy; addressing the 'black box'/explainability issue; integrating AI with human judgment; overcoming user distrust; cost of sophisticated tools; rapid technological change; potential for misuse. | Scarcity of High-Quality Legal Data, Data Privacy, Security, and Confidentiality, Bias in AI Systems and Data, Transparency and Explainability of AI, Need for Human Oversight and Intervention, User Adoption, Trust, and Acceptance, Financial Cost and Resource Constraints, Safeguarding Against Misuse and Harm | Unfair/biased outcomes; confidentiality/privacy violations; erosion of public trust in ADR; creation of 'tiered justice'; inaccurate legal research/submissions; anchoring bias in human decision-making; use of deepfakes; challenges to award enforcement due to improper AI use; deskilling professionals. | Bias and discrimination, Data privacy and security breach, Erosion of trust in legal system or AI, Exacerbation of inequality or two-tiered system, Inaccurate or misleading AI output, Over-reliance on AI, Security vulnerabilities or malicious misuse, Undermining legal process or principles, Deskilling or erosion of human skills |
| GV6mowVAwRsJ.pdf | Google_Scholar | Legal AI: Enhancing Justice through Technology, Practical Considerations | This paper explores the applications, benefits, and limitations of AI, particularly LLMs, in the Indian legal field, aiming to improve efficiency and access to justice for legal professionals, government bodies, and citizens. It discusses various AI models and emphasizes the need for careful implementation, oversight, and customization while considering risks like hallucinations and the digital divide. | Exploration of AI in Indian Legal Field, LLM Application, Benefit Identification, Limitations Identified, Efficiency Improvement, Access to Justice Enhancement, Need for Oversight, Customization of AI, Risk Identification, AI Hallucinations/Inaccuracy, Digital Divide | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | AI hallucinations/errors, data privacy/security concerns, need for customization, digital divide, resistance to technology, cost, vendor dependence, need for professional training. | AI Unreliability/Inaccuracy, Data Privacy Concerns with AI, Security Risks with AI, Need for AI Customization, Digital Divide, Slow Technology Adoption by Legal Profession, High Cost of A2J Technology, Vendor Lock-in, Need for Professional Training | Human oversight, robust data protection, AI customization, developing inclusive use cases, training professionals, tailored implementation architecture, policy changes, prompt engineering, tuning models for local contexts. | Human Oversight and Collaboration, Data Privacy and Security, AI Tool Development, User Interface and Accessibility Design, Education and AI Literacy, Policy and Regulatory Reform, Prompt Engineering and LLM Interaction Design, Enhanced AI Capabilities | Access to legal information/resources, efficient case resolution, cost reduction in legal services, automation of legal/administrative tasks (e.g., filing applications, drafting), overcoming language barriers. | Access to Legal Information, Improving Efficiency in Legal System / Profession, Affordability of Legal Services / Cost Reduction, Legal Document Creation / Automation, Language Access and Digital Divide | General public/citizens, litigants, legal professionals, government departments (collectorate, police), particularly targeting issues relevant to developing regions (digital divide). | General public, Litigants, Legal professionals, Government agencies, Populations in developing countries, Digitally excluded populations | General Law | General Law | India | India | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Need for improved AI accuracy and robustness against hallucinations, better customization for specific legal/local contexts, addressing the digital divide, ensuring data privacy, effective integration into workflows, training for legal professionals. | AI Accuracy and Reliability, Multilingual and Jurisdictional Specificity Gaps, Access, Equity, and Digital Divide, Security and Privacy of Data, Integration and Interoperability Challenges, Human Oversight and Professional Adaptation | Prompt engineering, effective communication (especially local languages), contextual tuning, ensuring accuracy/avoiding hallucinations, data privacy, cost, vendor dependence, need for end-to-end architecture design. | Prompt Engineering and Optimization, Multilingual and Low-Resource Language Support, User Interface, Usability, and Accessibility, Domain-Specific Adaptation and Customization, Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Data Privacy, Security, and Confidentiality, Financial Cost and Resource Constraints, Integration with Existing Systems and Workflows | AI hallucinations leading to incorrect information, data privacy and security breaches, exacerbation of the digital divide, potential for erratic, divisive, or harmful outcomes due to lack of contextual understanding. | Inaccurate or misleading AI output, Data privacy and security breach, Exacerbation of inequality or two-tiered system, Harmful or unsafe AI output, Technical limitations of AI |
| q-DTIJ8ci6YJ.pdf | Google_Scholar | Bridging the Gap t o Every American: How a National Regulat ory Sandbo x Can Pr ompt Radical Collabor ation t o Adopt Legal Artificial Intelligence T ools | The paper highlights the significant access to justice gap in the United States, particularly for low-income individuals facing civil legal issues. It advocates for the creation of a national regulatory sandbox, overseen by the U.S. Supreme Court, to foster the development and responsible adoption of AI-powered legal tools to provide affordable legal services. | Access to Justice Gap, US Focus, Low-Income Individual Assistance, Regulatory Sandbox Proposal, AI Legal Tools Development, Affordable Legal Services, Responsible AI Adoption | True | Idealistic | True | 1.0 | Positive | Proposal for a National Regulatory Sandbox overseen by a National Office of Legal Services Innovation under the U.S. Supreme Court to regulate and foster AI-driven alternative legal service providers. | Regulatory Framework / Proposal, AI Governance | The proposed national regulatory sandbox is a policy recommendation and has not been tested. The paper references the operational data (consumer complaints, number of people served) from the existing Utah regulatory sandbox as evidence of the potential success of such an approach. | Theoretical Analysis or Conceptual Proposal, References External Evaluation | N/A (The proposed national sandbox has not been implemented or tested). Results cited for the analogous Utah sandbox include assisting over 2,500 people with a low rate of consumer harm complaints (approx. 1 per 6,851 services). | N/A, Benefit identified, Successful real-world application | High cost of legal services, insufficient legal aid for low-income populations leading to unresolved civil matters, negative life consequences (financial, health, housing) stemming from lack of legal help, complexity of the legal system, underfunding and overwork in traditional legal aid and public defender systems, unequal access based on income. | High Cost of Legal Services, Limited Availability/Access to Legal Aid, Scale of Unmet Legal Need, Complexity of Legal System/Procedures, Resource Constraints for Legal Aid Organizations, Unequal Access to Legal Services | Establishment of a National Regulatory Sandbox to allow controlled experimentation and deployment of AI-powered alternative legal service providers. Encouraging innovation in legal tech, particularly AI tools (like chatbots, document simplifiers, legal marketplaces), to offer low-cost, accessible legal information and services. | Policy and Regulatory Reform, Regulation, Ethics, and Governance, Alternative Legal Service Delivery Models, AI Tool Development, Cost Reduction and Efficiency, Access to Legal Information and Advice | Access to justice (civil), Alternative legal service providers, Legal technology regulation, Housing law, Immigration law, Domestic violence, Healthcare law, Discrimination law, Employment law, Consumer contracts (leases, mortgages, credit cards), Dispute resolution. | Democratizing Law / Closing Justice Gap / Rule of Law, Regulatory Reform (Legal Services and AI), Protection of Rights, Dispute Resolution | Low-income Americans, Economically vulnerable populations, Underserved communities facing civil legal issues. | Low-income individuals, Population in USA, Vulnerable populations, Marginalized communities, Individuals with civil legal problems | Civil Law (broadly, including family, housing, consumer, contract, immigration, employment law) | Civil Law, Family Law, Housing Law, Consumer Law, Contract Law, Immigration Law, Employment Law | United States | USA | NaN | Not Applicable | Policy design based on existing models (Utah regulatory sandbox, EU initiatives) and policy guidelines (CGAP's Practical Guide for Policy Makers), focusing on eligibility criteria, governance structure, experimentation timelines, evaluation metrics, and exit options. | Policy Development Methodology, Comparative Policy Analysis, Guideline-based Design | N/A (The paper proposes the creation and implementation of the sandbox, but it is not currently deployed). | Proposed deployment (not implemented) | False | False | NaN | NaN | Lack of affordable and accessible civil legal services, inadequacy of traditional service models, regulatory barriers to innovation in legal services, potential for AI to exacerbate inequality if not implemented equitably, digital divide limiting access to tech-based solutions. | Access, Equity, and Digital Divide, Regulatory and Governance Gaps | Distrust of AI among legal professionals and judiciary, data privacy concerns, potential job displacement in the legal sector, ensuring AI tools are effective and do not cause consumer harm, overcoming the digital divide, gaining stakeholder buy-in for regulatory innovation. | User Adoption, Trust, and Acceptance, Data Privacy, Security, and Confidentiality, Ethical Considerations, Accuracy and Reliability of LLM Output, Safeguarding Against Misuse and Harm, User Interface, Usability, and Accessibility, Regulatory Uncertainty and Compliance | Consumer harm (inaccurate advice/results, unnecessary services), Data privacy violations, Widening the access-to-justice gap if AI tools are costly or inaccessible, Creation of a two-tiered legal system (high-quality human lawyers vs. potentially inferior AI for the poor), Potential job displacement for legal professionals. | Consumer harm, Inaccurate or misleading AI output, Data privacy and security breach, Exacerbation of inequality or two-tiered system, Job displacement |
| 6Lu9Wgf2rMcJ.pdf | Google_Scholar | Public Consultation Response on “Copyright and AI” [Docket No. 2023-06] | This paper is a response to the U.S. Copyright Office regarding AI and copyright, arguing AI is a human-controlled tool that requires adapting existing laws, not new ones. It discusses fair use, authorship, transparency, and the need for a balanced approach to protect creators and foster innovation for societal benefit. | Policy Position (Copyright and AI), US Focus, AI as a Tool, Adaptation of Copyright Law, Fair Use, AI Authorship, Transparency in AI, Balancing Creator Rights and Innovation | True | Idealistic | True | 3.0 | Positive | Generative AI / Large Language Models (e.g. ChatGPT, DALLE-3) | Generative AI, Large Language Model, Text-to-Image Generation | NaN | Not Applicable | NaN | NaN | Misconceptions about AI's nature (e.g., anthropomorphism), hindering clear legal discourse; Lack of transparency in AI training data, impeding creators' ability to enforce rights; Difficulty in applying traditional legal concepts (e.g., authorship, fair use) to AI. | Lack of Understanding of AI Capabilities/Limitations, Lack of AI Transparency/Explainability, Intellectual Property/Copyright Issues with AI, Inadequate Legal Frameworks for AI | Promote an accurate understanding of AI as a human-controlled tool; Adapt existing legal frameworks, relying on courts for interpretation, rather than rushing new AI-specific laws; Increase transparency in AI systems (e.g., training data disclosure) and explore robust accountability mechanisms like 'networked responsibility'. | Education and AI Literacy, Policy and Regulatory Reform, Transparency and Explainability in AI, Regulation, Ethics, and Governance, Human Oversight and Collaboration | Ensuring fairness for creators (e.g., regarding use of their works in AI training, compensation); Upholding human authorship in AI-assisted creations; Enhancing transparency of AI systems for copyright enforcement. | Protection of Rights, Ethical AI in Law and AI Governance | Creators (e.g., artists, writers) and individuals whose data is used for AI training. | Creators, Data subjects | Copyright Law, Intellectual Property Law, Data Privacy | Copyright Law, Intellectual Property Law, Data Privacy Law | United States | USA | The paper discusses that these models are trained on vast amounts of data, including copyrighted works, web-scraped material, proprietary enterprise data, and user-generated content. It does not specify a single dataset for a technique it studies. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Copyrighted Material (Source Mentioned), Web Scraped Data, Proprietary Data, User-Generated Content | NaN | NaN | NaN | Not applicable | True | False | The paper mentions existing generative AI tools like ChatGPT and DALLE-3, some of which are publicly accessible for use (e.g., ChatGPT via OpenAI's platform). | Publicly accessible online tool or platform | Need for clear application of fair use to AI training and outputs; Development of effective, feasible opt-in/opt-out mechanisms and compensation models for creators; Establishing clear lines of authorship and responsibility for AI-assisted works. | Regulatory and Governance Gaps, Accountability and Redress Mechanisms, Ethical Framework Deficiencies | For enterprises adopting Generative AI: regulatory compliance (e.g., data use, privacy, security), ownership of proprietary data used for training, integration with existing workflows. For consumer data applications: gaining access to data, overcoming privacy/security concerns. | Regulatory Uncertainty and Compliance, Data Privacy, Security, and Confidentiality, Copyright and Intellectual Property Issues, Integration with Existing Systems and Workflows, Scarcity of High-Quality Legal Data | Hasty regulatory interventions based on misconceptions leading to stifled innovation or ineffective rules; Infringement of copyright through AI outputs if fair use and licensing are not clarified; Unfair exploitation of creators' works for AI training without consent or compensation; Generation of harmful or undesirable content if AI tools are not properly controlled or are misused. | Regulatory challenges or gaps, Stifling innovation, Copyright or intellectual property issues, Harmful or unsafe AI output, Risk of misapplication or misuse |
| AiNuo2C-gn4J.pdf | Google_Scholar | Interpretable Long-Form Legal Question Answering with\nRetrieval-Augmented Large Language Models | The paper proposes a retrieval-augmented LLM methodology for generating interpretable, long-form answers to French statutory law questions to improve access to legal information. It introduces the LLeQA dataset for this task and finds that while models generate fluent answers, they often suffer from factual inaccuracies. | Methodology Proposal, Retrieval Augmented Generation, LLM Application, Interpretable Legal Answers, French Law Focus, Statutory Law Question Answering, Access to Legal Information Enhancement, Dataset Creation, AI Hallucinations/Inaccuracy | True | Idealistic | True | 1.0 | Positive | A retrieve-then-read pipeline using a fine-tuned bi-encoder retriever (CamemBERT-based) and instruction-tuned Large Language Models (LLMs like Vicuna, WizardLM, TULU, Guanaco) adapted via in-context learning or QLoRA finetuning. Includes extractive rationale generation (paragraph IDs). | Information Retrieval / Search, Transformer Models, Fine-tuning, Instruction Tuning, Large Language Model, In-context Learning, Parameter-Efficient Fine-tuning, Rationale Generation | Retriever evaluated using Recall@k (k=5, 10) and MRR@10 on LLeQA dev set. Generator evaluated using METEOR for answer quality and F1 score for rationale extraction on LLeQA test set, supplemented by qualitative analysis. | Benchmark Dataset Evaluation, Quantitative Metrics, Qualitative Analysis | Fine-tuned CamemBERT retriever achieved R@5=48.6, R@10=60.6. Fine-tuned WizardLM-1.0 (7B) generator achieved the best METEOR score (20.4). Qualitative analysis revealed significant hallucination issues despite syntactic correctness. Rationale extraction F1 was very low (<3.5%). | Low performance, Moderate performance, Technique improves outcome, Limitation: Hallucination or Factual inaccuracy | Lack of legal understanding/literacy, prohibitive cost of legal assistance, difficulty navigating legal complexity, prevalence of unhelpful/commercial online legal advice. | Public Lack of Legal Knowledge/Awareness, High Cost of Legal Services, Complexity of Legal System/Procedures, Reliance on Unreliable Information Sources | Develop automated, interpretable long-form legal question answering systems using retrieval-augmented LLMs to provide affordable, accessible legal information. | AI Tool Development, Transparency and Explainability in AI, Enhanced AI Capabilities, Access to Legal Information and Advice, Cost Reduction and Efficiency | Access to legal information, automated legal aid, statutory law question answering (covering housing, healthcare, family, work, immigration, money, privacy, justice). | Access to Legal Information, Legal Aid and Pro Bono Services | Vulnerable individuals, laypersons, Belgian citizens, marginalized parties, people unable to afford legal assistance. | Vulnerable populations, Laypeople, Population in Belgium, Marginalized communities, Individuals unable to afford legal services | Statutory law (multiple domains) | Statutory Law, Multiple Fields | Belgium | Belgium | LLeQA dataset: 1,868 expert-annotated French legal questions with detailed answers and references to relevant Belgian statutory articles (27,942 article corpus). Paragraph-level rationales partly expert-annotated, partly synthetically generated (gpt-3.5-turbo). Sourced via partnership with Belgian non-profit Droits Quotidiens. | Author-Created New Dataset, Belgian Legal Data, Legal Domain Data, French Language Data, Legal Q&A / Forum / User Query Data, Expert-Annotated / Human-Curated / Human-Generated Data, Proprietary Data, Structured Data, Synthetic Data, Legislation / Statutes / Regulations | Retrieve-then-read pipeline; Bi-encoder retriever fine-tuned contrastively; LLM reader adapted via in-context learning and parameter-efficient fine-tuning (QLoRA); Dynamic NTK-aware scaling for context extension; Extractive rationale generation via prompting. | Pipeline Development, Information Retrieval Techniques, Contrastive Learning, Model Fine-tuning, In-context Learning, Parameter-Efficient Fine-Tuning (PEFT), Context Extension Techniques, Prompt Engineering | Public release of code, data, and models on GitHub. | Open source code release, Public dataset/benchmark release, Open source model release | True | True | Public release of code, dataset (LLeQA), and model checkpoints on GitHub. | Code available, Dataset available, Model available | Inadequacy of automatic metrics for evaluating long-form QA factuality; LLM propensity for hallucination; Need for improved retrieval performance; Scalability of reliable rationale generation for multi-document contexts. | Research and Evaluation Gaps, AI Accuracy and Reliability, AI Scope and Functionality Limitations, Transparency and Explainability | Handling long legal document context within LLM limits; Effective domain adaptation for retrieval; Ensuring factual accuracy and mitigating hallucinations in generation; Accurate evaluation of long-form answers; Generating faithful and interpretable rationales; Computational resource constraints. | LLM Context Window and Long Input Management, Domain-Specific Adaptation and Customization, Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Evaluation Challenges and Metrics, Transparency and Explainability of AI, High Computational and Resource Demands | Laypersons relying on potentially inaccurate or hallucinated AI-generated legal advice, leading to detrimental real-world consequences; Potential for misuse despite research purpose limitations. | Over-reliance on AI, Inaccurate or misleading AI output, Consumer harm, Risk of misapplication or misuse |
| 1hDJ716g7tIJ.pdf | Google_Scholar | TOWARDS THE EXPLOITATION OF LLM-BASED CHATBOT FOR PROVIDING LEGAL SUPPORT TO PALESTINIAN COOPERATIVES | This paper presents the development and evaluation of an LLM-based chatbot designed to provide legal support to Palestinian cooperatives by answering questions related to cooperative law. The chatbot, utilizing ChatGPT and LlamaIndex with curated legal documents and Q&A datasets, achieved an overall accuracy of 82% on expert-generated queries. | Chatbot Development, LLM Application, Legal Support Provision, Palestinian Law Focus, Cooperative Law Focus, System Evaluation, Curated Legal Data | True | Idealistic | True | 1.0 | Positive | LLM-based chatbot using ChatGPT API and LlamaIndex for vectorization and indexing of Palestinian cooperative law documents and Q&A datasets. | Chatbot / Conversational AI, Large Language Model, Software Library / Tool Usage, Vectorization / Indexing, Domain-Specific Knowledge Base, Legal Question Answering | Evaluation using 50 queries generated by a legal expert. Chatbot's answers were compared to the expert's answers, and metrics including overall accuracy, average satisfaction score (rated by legal counsel), precision, recall, and F1-score were calculated. | Custom Dataset Evaluation, Expert Evaluation, Quantitative Metrics | The chatbot achieved an overall accuracy of 82% (41 out of 50 questions answered correctly or relevantly). The F1 score was 79%, and the average satisfaction score was 78.3%. For distinguishing right/related answers, precision was 1.0, recall 0.79, and F1-score 0.88. | High performance, Moderate performance, Benefit identified | The urgent need for readily available legal answers for cooperative members due to new laws, the labor-intensive effort required for manual responses, and the large number of cooperative members needing timely assistance. | Scale of Unmet Legal Need, Resource Constraints, Difficulty Accessing/Interpreting Legal Information | Developing an LLM-based chatbot available 24/7 to provide legal information and answer inquiries about Palestinian cooperative law. | AI Tool Development, Access to Legal Information and Advice | Access to legal information and support regarding Palestinian cooperative law. | Access to Legal Information, Support for Vulnerable Populations | Palestinian cooperatives, cooperative societies, cooperative unions, and their members. | Cooperatives, Population in Palestine | Cooperative law | Cooperative Law | Palestine | Palestine | A dataset comprising: 1) Formal Legal Documents (Law No. 20 of 2017 on Cooperatives, Cooperatives Bylaws, Housing Cooperatives Bylaws) - text data. 2) Question and Answers Dataset consisting of a human-generated set (40 Q&A by a legal advisor) and a ChatGPT-generated set (350 Q&A based on Law No. 20 of 2017, prompted to format like a legal advisor). These documents were indexed by LlamaIndex for the chatbot. | RAG System Knowledge Corpus, Legal Domain Data, Legislation / Statutes / Regulations, Other Legal Documents, Unstructured Text Data, Author-Created New Dataset, Structured Data, Expert-Annotated / Human-Curated / Human-Generated Data, Synthetic Data, Legal Q&A / Forum / User Query Data, Undisclosed Data Source/Availability | The system uses LlamaIndex to index legal documents and Q&A datasets, creating vectors for document chunks (600 tokens, 50 token overlap) to overcome ChatGPT's token limits. A LlamaIndex query engine, leveraging ChatGPT, is used to answer legal queries. Prompt engineering was used to generate a portion of the Q&A dataset. | Third-party Library Utilization, Document Indexing, Vectorization, Dataset Creation, Query Engine Development, API-based Development, Prompt Engineering | NaN | Not applicable | True | True | The paper states a GitHub repository is available for more information and details, with a placeholder link: "Github". | Code available, Dataset available | Instances of incorrect chatbot answers, need for continuous development to improve accuracy and reliability, the necessity of transparency about chatbot limitations, insufficient Q&A data for long or complex legal articles, and the need for post-processing of chatbot answers. | AI Accuracy and Reliability, Transparency and Explainability, Data Availability and Quality | The primary challenge was handling the large volume of textual data, which exceeded ChatGPT GPT-4’s token processing limit, necessitating the use of LlamaIndex for document chunking and vectorization. Other challenges included ensuring sufficient Q&A data for comprehensive coverage of all legal articles, especially longer ones, and providing context for specific bylaws. | LLM Context Window and Long Input Management, Data Quality, Processing, and Preparation, Scarcity of High-Quality Legal Data | The chatbot providing incorrect answers, which could lead users to unintentionally violate legal regulations. | Inaccurate or misleading AI output, Consumer harm |
| tags21nqSE4J.pdf | Google_Scholar | AI, Justice, and the Ecosystem Approach – Notes from the OpenNyAI Mission | OpenNyAI is an Indian initiative leveraging AI to enhance access to justice by developing open-source public goods like AI models and APIs, supported by a collaborative ecosystem of legal and tech communities. The paper highlights projects like Jugalbandi, a conversational AI for legal information in local languages, and emphasizes transparent, inclusive practices to make justice more accessible. | AI Initiative for Access to Justice, Indian Focus, Open Source AI, Public Goods Development, Collaborative Ecosystem, Conversational AI, Local Language Support, Transparent AI Practices, Inclusive AI | True | Idealistic | True | 1.0 | Positive | OpenNyAI's initiatives including: NLP models for legal text analysis (Rhetorical Roles Model, Legal Named Entity Recognition Model, Judgment Summarizer); Jugalbandi Stack (LLM-based conversational AI for multilingual information access); Jugalbandi Studio (open-source chatbot development platform). | Software / Platform Development, Natural Language Processing (NLP), Legal Text Summarization, Chatbot / Conversational AI, Large Language Model, Multilingual Application, Open Source AI, Named Tool / Platform | User adoption (7000+ unique users for models) and an open-source testing environment provided by Jugalbandi Studio. No formal benchmarks or detailed evaluation procedures are mentioned. | No Evaluation by Author | The models (Rhetorical Roles, NER, Summarizer) are reported as generating value across law firms, government bodies, and other institutions. Jugalbandi enables multilingual access to information on government schemes and legal aid. Jugalbandi Studio allows organizations to rapidly iterate on chatbot development without extensive technical expertise or large capital investments. | Benefit identified, Successful real-world application, Descriptive or Conceptual finding | Initial landscape: significant divide between legal and tech professionals, lack of open-source reference solutions, and poor data quality. Broader A2J issues: information asymmetry, language barriers. Tech deployment challenges: understanding AI capabilities, resource constraints for SMEs/NGOs. | Divide between Legal and Tech Professionals, Limited Access to A2J Technology, Data Scarcity/Quality for AI, Information Asymmetry, Accessibility Barriers for Specific User Groups, Lack of Understanding of AI Capabilities/Limitations, Resource Constraints for A2J Tech Development/Deployment | Building a collaborative ecosystem (OpenNyAI mission); developing open-source AI public goods (models, Jugalbandi Stack, Jugalbandi Studio); creating data annotation pipelines; using AI for language access and information retrieval; providing tools to lower deployment barriers; ensuring data privacy. | Open Source Initiatives and Collaboration, AI Tool Development, Data Curation and Management, Language Simplification and Multilingual Access, Legal Research and Analysis Tools, Data Privacy and Security | Language access in legal information, access to government schemes/entitlements, legal aid, access to laws and court procedures, improving efficiency of legal processes, enhancing capacity of legal professionals and civil society. | Language Access and Digital Divide, Access to Legal Information, Legal Aid and Pro Bono Services, Improving Efficiency in Legal System / Profession | General public in India, particularly those facing information asymmetry and language barriers, including farmers, women, victims of domestic abuse, litigants, students, lawyers, judges, SMEs, and NGOs. | General public, Population in India, Individuals lacking legal knowledge, Individuals with language barriers, Farmers, Women, Victims of domestic violence, Litigants, Students, Legal professionals, Judges, Small businesses, Non-profit organizations | Administrative law (government schemes), family law (domestic abuse), criminal law (investigation guidelines), civil procedure (court processes), dispute resolution (ODR), general legal information access. | Administrative Law, Family Law, Domestic Violence Law, Criminal Law, Civil Procedure, Dispute Resolution, Online Dispute Resolution, General Legal Practice | India | India | For NLP models: Meticulously annotated datasets of Indian court judgments, created via a data annotation pipeline involving law students. For Jugalbandi: Verified knowledge bases curated by subject matter experts. These are domain-specific (legal) and structured (annotated) data. | Fine-tuning Dataset, Author-Created New Dataset, Indian Legal Data, Legal Domain Data, Case Law / Judgments, Expert-Annotated / Human-Curated / Human-Generated Data, Structured Data, RAG System Knowledge Corpus | Ecosystem approach, interdisciplinary collaboration (legal, tech, academia, civil society), community building (Maker Residency, learning circles), open-source development, development of a data annotation pipeline. | Ecosystem Development Approach, Interdisciplinary Collaboration, Community Building, Open-source Development Approach, Data Annotation Pipeline Development | Models used by over 7000 users. Jugalbandi Stack is a free and open tech stack. Jugalbandi Studio is an open-source platform running on an organization's own cloud server. General strategy is creating AI public goods and community empowerment. | Open source model release, Freely accessible tool/service, Open source code release, Local deployment/Standalone application, Dissemination via publication/presentation | True | True | Jugalbandi Stack is described as a 'free and open tech stack'. Jugalbandi Studio is an 'open-source platform'. GitHub links for OpenNyAI and Jugalbandi are provided in the references, indicating open accessibility of resources. | Code available, Open-source, Publicly accessible online tool or platform | A 'sheer knowledge gap that exists in accessing these technologies' among potential users and deployers of AI solutions. | Public Understanding, Trust, and Adoption | Understanding AI technology's capabilities by non-technical users/organizations, lack of resources (financial, technical) for SMEs/NGOs to deploy AI at scale, ensuring high-quality data for training models, bridging communication gaps between legal and tech communities. | User Training, AI Literacy, and Skill Gaps, Financial Cost and Resource Constraints, High Computational and Resource Demands, Scarcity of High-Quality Legal Data, Interdisciplinary Collaboration Challenges | Data privacy of users interacting with AI systems, particularly concerning sensitive personal information. Mitigation includes PII filtering and local/private cloud deployment. | Data privacy and security breach |
| 21TvEm4C_3QJ.pdf | Google_Scholar | Argumentative Segmentation Enhancement for Legal Summarization | This paper proposes a method to improve legal case summarization by first identifying argumentative segments within legal decisions using a novel classification task. These segments are then summarized by GPT-3.5, reportedly yielding higher quality summaries and overcoming token limits compared to baseline GPT models and non-GPT approaches. | Methodology Proposal, Legal Case Summarization, Argumentative Segment Identification, LLM Application, Quality Improvement, Overcoming Token Limits | True | Idealistic | True | 1.0 | Positive | An approach combining argumentative zoning principles (using IRC triples for legal arguments) with a C99 text segmentation algorithm to identify argumentative segments in legal decisions. These segments are then classified using LegalBERT and subsequently summarized using prompted GPT-3.5. | Hybrid AI System, Argument Mining / Analysis, Text Segmentation, Legal Text Classification, Transformer Models, Legal Text Summarization, Large Language Model, Prompt Engineering | Argumentative segment classification was evaluated using F1 score on a test set (LegalBERT vs. BERT). Summarization quality was evaluated using ROUGE-1, ROUGE-2, ROUGE-L, BLEU, METEOR, and BERTScore, comparing the proposed argumentative segmentation enhanced GPT-3.5 method against baseline GPT-3.5, GPT-4, and fine-tuned non-GPT models (LED, T5, BART) on a test set of CanLII decisions. | Custom Dataset Evaluation, Quantitative Metrics, Comparative Analysis | For argumentative segment classification, LegalBERT achieved an 80.14% F1 score. For summarization, the argumentative segmentation enhanced GPT-3.5 (temp 0, max_tokens 128) achieved Rouge-1: 49.42, Rouge-2: 23.98, Rouge-L: 46.07, BLEU: 17.54, METEOR: 0.32, and BERTScore: 87.30, outperforming baselines on several metrics. | High performance, Moderate performance, Technique improves outcome, Outperforms others | The difficulty in consuming and understanding long, complex legal documents, and the technical challenge of input token limitations in large language models when processing such documents. | Complexity of Legal Language/Documents, Difficulty Accessing/Interpreting Legal Information, Technical Challenges in AI Development | An AI-driven method for legal summarization that focuses on argumentative segments to make legal texts more digestible and to overcome token limitations of LLMs for processing long documents. | AI Tool Development, Document Automation, Enhanced AI Capabilities, Language Simplification and Multilingual Access | Improving understanding of legal documents (case decisions) through automated summarization to make legal information more accessible. | LegalText Simplification / Plain Language, Access to Legal Information | NaN | NaN | General case law (variety of legal claims). | General Law, Case Law, Multiple Fields | Canada | Canada | A corpus of 1,049 Canadian legal case decisions from CanLII. These decisions were sentence-split and annotated by researchers with Issue, Reason, Conclusion (IRC) triples. Text segments (derived using C99 algorithm) were then labeled as 'argumentative' or 'non-argumentative' based on the presence of IRC sentences. This dataset was used for training the argumentative segment classifier and fine-tuning baseline summarization models. | Author-Created New Dataset, Fine-tuning Dataset, Canadian Legal Data, Legal Domain Data, Case Law / Judgments, Publicly Available Data, Expert-Annotated / Human-Curated / Human-Generated Data, Structured Data, Unstructured Text Data | Linear text segmentation (C99), sentence embedding (Sentence-BERT), supervised classification (LegalBERT), prompt-based learning with LLMs (GPT-3.5, GPT-4), and principles of Argumentative Zoning and IRC triple annotation. | Data Segmentation, Embedding Model Application, Supervised Classification, Prompt Engineering, Framework-guided Design | NaN | Not applicable | False | False | NaN | NaN | Coherency issues in generated summaries; need for systematic human evaluation; reproducibility challenges with proprietary LLMs; need for reliable performance of proprietary models and alternative prompt engineering techniques. | AI Accuracy and Reliability, Research and Evaluation Gaps | Input token limitations of LLMs for long legal documents; ensuring summaries capture important argument-related information; cost of using advanced LLMs; potential coherency issues in generated summaries; reproducibility of results with proprietary models. | LLM Context Window and Long Input Management, Accuracy and Reliability of LLM Output, Financial Cost and Resource Constraints, Output Variability and Consistency, Transparency and Explainability of AI | Coherency issues in generated summaries; reproducibility challenges with proprietary LLMs and potential changes to these models by their providers. | Inaccurate or misleading AI output, Technical limitations of AI |
| XVXXHioJe_wJ.pdf | Google_Scholar | LEGAL PROCEDURE BOT | This paper proposes an AI-based chatbot, "LEGAL PROCEDURE BOT", designed to simplify access to information about legal procedures and required documents in India. The bot utilizes a generative deep learning architecture (RAG with an LLM) and features like speech-to-text, text-to-speech, geo-location, and multilingual support to address the complexities and inefficiencies of manual systems. | Chatbot Development, India Focus, Access to Legal Procedure Information, Retrieval Augmented Generation, LLM Application, Multilingual Support, Speech-to-Text, Text-to-Speech | True | Idealistic | True | 1.0 | Positive | An AI chatbot ("LEGAL PROCEDURE BOT") using a Retrieval-Augmented Generation (RAG) architecture with the Mistral-7B-Instruct-v0.2 LLM. It employs NLP (NLU/NLG), vector embeddings (Word2Vec/Doc2Vec) stored in a vector database, cosine similarity for pattern matching, Speech-to-Text (STT), Text-to-Speech (TTS), Geo Location services (Google Nearby Search API), and multilingual query processing. | Chatbot / Conversational AI, Retrieval Augmented Generation (RAG), Large Language Model, Natural Language Processing (NLP), Embedding-based Methods, Vector Database, Similarity Search, Speech-to-Text (STT), Text-to-Speech (TTS), Geo Location Integration, Multilingual Application, Named Tool / Platform | The paper includes screenshots of the user interface (login screen and chat window) as a demonstration. No quantitative evaluation or formal user testing results are reported. | Demonstration or Illustrative Examples, No Evaluation by Author | Results are presented visually via GUI screenshots (Figures 5 and 6), demonstrating the intended user interface. No performance metrics or evaluation outcomes are provided. | Descriptive or Conceptual finding | Complexity of government legal procedures, fragmented and inaccessible information, time-consuming and labor-intensive manual processes, potential for mistakes and misinterpretation in manual systems. | Complexity of Legal System/Procedures, Difficulty Accessing/Interpreting Legal Information, Resource Constraints, Risk of Errors in Manual Processes | An AI chatbot providing comprehensive guidance on legal procedures, lists of required documents, estimated fees, direct links to official websites/forms, and locations of nearby centres/courts/lawyers through a user-friendly interface. | AI Tool Development, Access to Legal Information and Advice, User Interface and Accessibility Design | Accessing information on government legal procedures (e.g., obtaining identity documents), required documentation, estimated costs, and relevant service locations. | Access to Legal Information | General public / citizens needing guidance on legal procedures. | General public, Laypeople, Individuals lacking legal knowledge | Administrative Law, Government procedures | Administrative Law | India | India | A CSV file containing legal procedures, acts, regulations, and case law. This unstructured text data is pre-processed into chunks and converted into vector embeddings for storage in a vector database. The source seems specific to the project, likely collected by the authors. | RAG System Knowledge Corpus, Author-Created New Dataset, Legal Domain Data, Legislation / Statutes / Regulations, Case Law / Judgments, Other Legal Documents, Unstructured Text Data, Structured Data | Generative Deep Learning architecture (RAG), semantic indexing, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Natural Language Generation (NLG), user-centered design principles. | Retrieval Augmented Generation (RAG), Semantic Indexing, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Natural Language Generation (NLG), User-centered Design, Principle-driven Design | NaN | Not applicable | False | False | NaN | NaN | Need for algorithm refinement, continuous updates of legal information, real-time updates, integration of user feedback, enhancements in privacy and data protection, expansion of multilingual capabilities, need for collaboration with legal experts for accuracy and reliability. | AI Accuracy and Reliability, Knowledge Recency and Updatability, User Interface and Usability Gaps, Security and Privacy of Data, Multilingual and Low-Resource Language Gaps, Need for Interdisciplinary Collaboration | Implicit challenges include ensuring the accuracy and reliability of legal information provided, handling the complexity of legal language and procedures, keeping the information up-to-date, and ensuring user trust and data privacy. | Accuracy and Reliability of LLM Output, LLM Reasoning Capabilities, Outdated or Limited LLM Knowledge Base, User Adoption, Trust, and Acceptance, Data Privacy, Security, and Confidentiality | Need for ethical considerations, ensuring user satisfaction, managing privacy and data protection. | Ethical concerns, Poor user experience, Data privacy and security breach |
| lRhwzB6FxjgJ.pdf | Google_Scholar | LexGPT 0.1: pre-trained GPT-J models with Pile of Law | This paper introduces LexGPT 0.1, a set of GPT-J models pre-trained on the Pile of Law dataset, aiming to provide a foundation model for the legal domain. It also explores a "No Code" approach for fine-tuning these models for classification tasks, finding this method less performant than state-of-the-art approaches. | Legal Language Model Development, Foundation Model for Law, Pre-training on Legal Data, No-Code Fine-tuning Evaluation | True | Idealistic | True | 1.0 | Positive | LexGPT 0.1: GPT-J models (6B, 1.6B, 456M parameters) pre-trained on Pile of Law using custom tokenizers. A 'No Code' fine-tuning method for classification using prompt format `(text) <|label|>(label)`. | Model Development, Large Language Model, Pre-training, Custom Tokenizer, No-Code AI Development, Fine-tuning, Prompt Engineering, Named Tool / Platform | Fine-tuning on LEDGAR (contract classification) and CaseHOLD (holding identification) datasets from the LexGLUE benchmark. Evaluation metrics: micro/macro-F1 (LEDGAR), accuracy (CaseHOLD). | Benchmark Dataset Evaluation, Quantitative Metrics | On LEDGAR (1.6B model): 83.9% micro-F1, 74.0% macro-F1. On CaseHOLD (456M model): 49.6% accuracy. These results were lower than reported state-of-the-art. | High performance, Moderate performance, Underperforms others | Technical skill barrier for legal professionals to utilize language models; potential for models to make factual mistakes and experience hallucinations; scarcity and expense of specialized legal datasets (though Pile of Law is used). | Lack of AI Literacy, AI Unreliability/Inaccuracy, Data Scarcity/Quality for AI, High Cost of A2J Technology | Pre-training domain-specific foundation models (LexGPT); proposing a "No Code" fine-tuning approach to lower technical barriers; public release of models and code; recommending initial use by legal professionals to filter errors. | AI Tool Development, Enhanced AI Capabilities, Open Source Initiatives and Collaboration, Human Oversight and Collaboration | Foundational model development; Legal text classification; Facilitating AI adoption by legal professionals. | Improving Foundational AI Capabilities for Legal Applications, Improving Efficiency in Legal System / Profession | Legal professionals | Legal professionals | General Legal (based on Pile of Law), Contract Law (LEDGAR), Case Law (CaseHOLD) | General Law, Contract Law, Case Law | US | USA | Pre-training: Pile of Law (~256GB public dataset of legal/administrative text). Fine-tuning: Publicly available LEDGAR and CaseHOLD datasets (subsets from LexGLUE). Unstructured text data. | Fine-tuning Dataset, Data From Existing Public NLP/Legal Datasets/Benchmarks, Publicly Available Data, Legal Domain Data, Unstructured Text Data | Domain-specific pre-training of Transformer models (GPT-J); Prompt-based fine-tuning for classification ('No Code'); Experimentation with model size, tokenizers, learning rates. | Model Pre-training, Domain Adaptation, Transformer Architecture, Prompt-based Fine-tuning, No-code/Low-code Platform Utilization, Experimental Design | Intention to release models, tokenizers, datasets, configurations, and source code publicly on GitHub upon publication. | Proposed deployment (not implemented), Open source model release, Public dataset/benchmark release, Open source code release | True | True | Models, tokenizers, datasets, config files, and code to be released on GitHub. | Future public release | Performance gap between 'No Code' fine-tuning and SOTA classification methods; effective 'No Code' approach for multi-label classification; improving 'No Code' performance (e.g., via CoT prompting); limited exploration of GPT models vs BERT in legal domain. | AI Accuracy and Reliability, AI Scope and Functionality Limitations, Research and Evaluation Gaps | Optimizing hyperparameters (e.g., learning rate) for pre-training; achieving competitive performance under the 'No Code' constraint; adapting generative models for classification; finding optimal data formats for fine-tuning. | Domain-Specific Adaptation and Customization, Accuracy and Reliability of LLM Output, Data Quality, Processing, and Preparation | Factual mistakes and hallucinations generated by the language models. | Inaccurate or misleading AI output |
| KS2K506sQ5sJ.pdf | Google_Scholar | CULTURAL FIDELITY IN LARGE-LANGUAGE MODELS: AN EVALUATION OF ONLINE LANGUAGE RESOURCES AS A DRIVER OFMODEL PERFORMANCE IN VALUE REPRESENTATION | This paper evaluates how well large language models (GPT-4o and GPT-4-turbo) represent societal values across different languages, finding their performance strongly correlates with the amount of online resources available in each language. The study highlights that models perform poorly for low-resource languages, potentially worsening digital divides and cultural homogenization, particularly in the Global South. | LLM Evaluation, Representation of Societal Values, Cross-Lingual Evaluation, Performance on Low-Resource Languages, Digital Divide Exacerbation, Cultural Homogenization Risk | True | Idealistic | True | 2.0 | Negative | Evaluation of GPT-4o and GPT-4-turbo's cultural value representation capabilities using World Values Survey (WVS) data and persona prompting. | AI System Evaluation, Large Language Model, Cultural Value Representation Assessment, Prompt Engineering, Dataset Usage | Compared LLM responses (prompted as a citizen of a specific country, answering WVS questions on the original scale) to average human responses from the WVS Wave 7 for 21 country-language pairs across 94 questions. An error was counted if the absolute difference between the LLM answer and WVS average was >= 50% of the WVS average. | Benchmark Dataset Evaluation, Quantitative Metrics, Comparative Analysis | For GPT-4o, 44% of the variance in the error rate correlated with the log of online websites available in the language (72% for GPT-4-turbo). Low-resource languages had significantly higher error rates (over 5 times higher for the lowest vs highest resource languages in GPT-4o). | Limitation: Operational or Technical, Low performance | The primary obstacle is the limited availability of digital resources (online content) for low-resource languages, leading to biased LLM training datasets derived predominantly from high-resource languages (mainly English). This results in poor AI performance in representing diverse societal values, exacerbates digital inequality, and potentially leads to cultural erosion, particularly impacting the Global South. Censorship further distorts the representativeness of available data in some regions. | Data Scarcity/Quality for AI, Accessibility Barriers for Specific User Groups, Bias in AI/Data, Risk of AI Exacerbating Inequality, Cultural Impacts of AI, Censorship Impacting Data | Proposed solutions include democratizing AI development (open-source initiatives, grassroots NLP communities), ethical regulation mandating transparency and diversity, collaborative data sharing with local communities, targeted digital inclusion programs (increasing internet access, digital literacy, speech synthesis for LRLs), developing inherently multilingual LLMs, and fine-tuning models on diverse, curated linguistic datasets (including audio/oral sources) rather than relying solely on web-scraping. | Open Source Initiatives and Collaboration, Regulation, Ethics, and Governance, Transparency and Explainability in AI, Data Curation and Management, Policy and Regulatory Reform, Education and AI Literacy, Language Simplification and Multilingual Access, Enhanced AI Capabilities, Bias Detection and Mitigation | Representation of societal values (political, social, ethical) by AI, linguistic diversity, digital inequality, cultural preservation, access to information, AI bias. | Ethical AI in Law and AI Governance, Language Access and Digital Divide, Access to Legal Information | Speakers of low-resource languages, particularly communities in the Global South. Specific languages studied include Swahili, Hindi, Burmese, Filipino, Amharic, Hausa, Shona, Tajik. | Speakers of low-resource languages, Global South populations | AI Ethics / Governance | AI Ethics, AI Governance | International | International | The paper evaluates models (GPT-4o, GPT-4-turbo) inferred to be trained primarily on large-scale web-scraped text (e.g., Common Crawl), supplemented by undisclosed proprietary data and potentially fine-tuning datasets. The study highlights the problematic nature and biases of this inferred training data, especially its underrepresentation of low-resource languages and potential pollution/censorship issues (e.g., Mandarin Chinese). | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Web Scraped Data, Proprietary Data, Undisclosed Data Source/Availability, Data Bias Concerns Noted, Multilingual Data, Chinese Language Data | The study employed an evaluation methodology involving: selecting country-language pairs and 94 questions from the World Values Survey (WVS), verifying question translations with native speakers, prompting LLMs (GPT-4o, GPT-4-turbo) to answer as citizens of specific countries on the WVS scale, calculating deviation from averaged WVS human responses, defining an error threshold (>=50% deviation), and correlating error rates with metrics of language resource availability (log of website count). | Evaluation Methodology, Dataset Selection, Prompt Engineering, Quantitative Analysis, Error Analysis, Correlation Analysis | NaN | Not applicable | True | False | The evaluated models (GPT-4o, GPT-4-turbo) are commercially available via API from OpenAI. | Model available, API access, Commercial product or service | Establishing causality between language resources and LLM performance; quantifying the data required for parity; controlling for confounders (e.g., GDP); developing better data collection methods for LRLs beyond web-scraping (curated, diverse, audio/oral sources); creating ethical frameworks for value conflicts and responsible deployment; addressing nuanced representation within high-resource languages; lack of transparency in commercial LLM training data. | Research and Evaluation Gaps, Data Availability and Quality, Multilingual and Low-Resource Language Gaps, Ethical Framework Deficiencies, Bias in AI, Transparency and Explainability | Evaluating LLM bias quantitatively (limitations of closed questions); data scarcity and quality for low-resource languages; inherent biases and limitations of web-scraped data (skewed demographics, spam, censorship); defining 'low-resource'; ensuring cultural fidelity without stereotyping during prompting. | Evaluation Challenges and Metrics, Bias in AI Systems and Data, Scarcity of High-Quality Legal Data, Data Quality, Processing, and Preparation, Multilingual and Low-Resource Language Support, Ethical Considerations, Prompt Engineering and Optimization | Exacerbation of digital divides; cultural homogenization/erosion; perpetuation of flawed information and stereotypes; biased resource allocation (recruiting, medicine); negative impacts on education (biased history/values); biased content generation (news, marketing); flawed censorship/moderation; potential for social/political discontent; risks for users in autocratic regimes; homogenization within high-resource languages; privacy risks related to sensitive information access. | Exacerbation of inequality or two-tiered system, Bias and discrimination, Inaccurate or misleading AI output, Technical limitations of AI, Negative societal impact, Infringement on human rights, Data privacy and security breach |
| m0OdIkZgr7MJ.pdf | Google_Scholar | Luck of the Draw III: Using AI to Examine Decision‐Making in Federal Court Stays of Removal | This paper uses a large language model (GPT-3) to extract and analyze data from Federal Court of Canada dockets concerning immigration law applications for stays of removal, revealing significant inconsistencies in stay grant rates among judges. It argues for measures to promote consistency in judicial decision-making and greater access to bulk legal data for research to enhance transparency and migrant rights. | LLM Application, Data Extraction from Court Dockets, Canadian Law Focus, Immigration Law Focus, Analysis of Judicial Inconsistency, Transparency in Judiciary, Migrant Rights | True | Idealistic | True | 1.0 | Positive | A multi-step computational legal research methodology: 1) Web-scraping Federal Court online dockets. 2) Docket and docket entry screening using Regex. 3) Fine-tuning GPT-3 models for specific data extraction and categorization tasks from docket entries (e.g., identifying stay motions, outcomes, judges). 4) Applying docket-level logic using Pandas to construct a final dataset for analysis. | Computational Legal Research Methodology, Web Scraping, Regular Expressions, Fine-tuning, Large Language Model, Information Extraction, Data Curation, Data Analysis Tool Usage | Data verification involved: 1) Comparing the automated process against one year's worth of manually reviewed stay of removal decisions from CanLII, where the automated process identified 98.0% (96 out of 98) of the manually identified decisions. 2) A research assistant manually verified 200 randomly selected, coded dockets, confirming 99% accuracy for key data points (judge, outcome, dates). | Custom Dataset Evaluation, Expert Evaluation, Quantitative Metrics | The automated data extraction technique achieved 98% coverage compared to a manual CanLII dataset and 99% accuracy on manually verified dockets. The substantive research findings revealed large unexplained variance in stay of removal grant rates depending on the deciding judge (e.g., some judges granting stays over 80% of the time, others less than 10%). | High performance, Technique improves outcome, Descriptive or Conceptual finding | Inconsistent and potentially arbitrary outcomes in high-stakes deportation proceedings due to judicial variance. Lack of transparency in legal decision-making processes. Restricted access to bulk legal data for non-commercial researchers, creating an asymmetry favouring commercial entities and the state. | Systemic Inequities in Justice System, Lack of Transparency in Justice System, Limited Access to Legal Data for Research, Information Asymmetry | The Federal Court should implement measures to encourage more consistency in stay decision-making (e.g., judicial discussions of hypotheticals). Facilitate fair and equal access to bulk legal data (e.g., via APIs) for non-commercial research to enhance transparency and rights. Utilize AI/LLM technology to scrutinize legal decision-making processes rather than solely for enhancing state power over marginalized groups. | Judicial System Enhancement, Policy and Regulatory Reform, Data Curation and Management, Open Source Initiatives and Collaboration, Transparency and Explainability in AI, Legal Research and Analysis Tools | Judicial decision-making consistency, access to justice in immigration and refugee law, stays of removal, deportation, transparency of legal systems, empirical legal studies. | Judicial System Modernization / Efficiency, Democratizing Law / Closing Justice Gap / Rule of Law, Support for Vulnerable Populations, Protection of Rights | Marginalized migrants and non-citizens facing deportation in Canada. | Migrants, Non-citizens, Individuals facing deportation, Population in Canada, Marginalized communities | Immigration Law, Refugee Law, Administrative Law (specifically judicial review, interlocutory orders). | Immigration Law, Administrative Law | Canada (Federal Court of Canada) | Canada | For fine-tuning GPT-3: A manually labelled dataset of hundreds of sample Federal Court docket entries (prompts) paired with desired completions (e.g., judge's name, outcome category like 'granted' or 'dismissed'). The raw data was scraped from 87,776 Federal Court online dockets (2012-2022), consisting of unstructured natural language text entries in English or French. | Fine-tuning Dataset, Author-Created New Dataset, Canadian Legal Data, Legal Domain Data, Other Legal Documents, Web Scraped Data, Publicly Available Data, Expert-Annotated / Human-Curated / Human-Generated Data, Structured Data, Unstructured Text Data, Multilingual Data | Iterative development of fine-tuned GPT-3 models: applying models to new docket entries, verifying outputs, providing additional labelled examples to correct errors or improve performance, re-fine-tuning, and re-testing until satisfactory accuracy was achieved for each extraction/classification task. | Iterative Design Process, Model Fine-tuning, Active Learning, System Testing | The Python code (Jupyter Notebook) and the dataset of scraped Federal Court dockets (with case names removed for privacy) are stated to be made available for non-commercial use by other researchers via a public GitHub repository upon the paper's publication in a law journal. | Proposed deployment (not implemented), Open source code release, Public dataset/benchmark release, Research preview/Beta access | False | False | The code and dataset are planned to be publicly available on GitHub for non-commercial research use after the paper is accepted for publication. | Future public release, Restricted access | Need for further research into the specific reasons for divergent stay grant rates across judges (e.g., different interpretations of legal tests). Investigation needed into causes of variance in stay grant rates across different cities (e.g., quality of counsel, access to legal aid). The primary systemic gap is the restricted access to bulk legal data for non-commercial researchers, hindering broader scrutiny and transparency. | Research and Evaluation Gaps, Data Availability and Quality, Access, Equity, and Digital Divide | Technical difficulty and resource intensiveness of systematically web-scraping and maintaining large, up-to-date databases of court dockets. Managing ethical concerns associated with LLMs, including inherent biases, potential for generating misinformation ('hallucinations'), copyright issues, and environmental impact. Ensuring high accuracy when processing unstructured, bilingual (English/French) legal text from dockets. | Data Quality, Processing, and Preparation, Scarcity of High-Quality Legal Data, Financial Cost and Resource Constraints, Ethical Considerations, Bias in AI Systems and Data, LLM Hallucination and Factual Errors, Copyright and Intellectual Property Issues, Environmental Impact of AI, Accuracy and Reliability of LLM Output, Multilingual and Low-Resource Language Support | LLMs may perpetuate biases present in their training data (e.g., racial, gender, religious biases). LLMs can 'hallucinate' or generate plausible but false information. Potential for misuse of LLMs for creating disinformation. Risk of automation bias due to the coherent-seeming text generated by LLMs. Significant environmental costs of training and running large language models. Copyright infringement concerns regarding data used for training commercial LLMs. Asymmetrical access to AI tools could exacerbate power imbalances if benefits primarily accrue to well-resourced actors. | Bias and discrimination, Inaccurate or misleading AI output, Security vulnerabilities or malicious misuse, Over-reliance on AI, Environmental impact, Copyright or intellectual property issues, Exacerbation of inequality or two-tiered system |
| kN0VpM62IsIJ.pdf | Google_Scholar | A Short Survey of Viewing Large Language Models in Legal Aspect | This paper surveys the applications of large language models (LLMs) in various legal tasks, such as judgment prediction and document analysis. It also discusses the associated legal challenges like bias and privacy, and the data resources required for specializing LLMs in the legal domain. | Survey of LLMs in Law, LLM Applications (Judgment Prediction, Document Analysis), Legal Challenges, Bias in AI, Privacy Concerns, Data Resources for Legal LLMs | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | Legal challenges including intellectual property ownership, data privacy (disclosure of sensitive information, GDPR compliance), bias and discrimination (e.g., anti-Muslim, anti-queer), and lack of explainability/transparency. | Intellectual Property/Copyright Issues with AI, Data Privacy Concerns with AI, Bias in AI/Data, Lack of AI Transparency/Explainability | Developing specialized legal data resources, methods to mitigate bias and ensure transparency, legal frameworks and guidelines for ethical use, privacy-preserving techniques, prompt engineering, and a legal informatics approach. | Data Curation and Management, Bias Detection and Mitigation, Transparency and Explainability in AI, Regulation, Ethics, and Governance, Data Privacy and Security, Prompt Engineering and LLM Interaction Design, Legal Knowledge Representation and Management | Legal judgment prediction, legal document analysis/writing, statutory reasoning, legal education, legal advice, access to justice. | Improving Foundational AI Capabilities for Legal Applications, Legal Document Analysis / Review, Legal Document Creation / Automation, Legal Education for Professionals / Students, Access to Legal Advice, Democratizing Law / Closing Justice Gap / Rule of Law | NaN | NaN | Criminal law, Constitutional law, Contract law, Tort law, Civil law, General legal practice. | Criminal Law, Constitutional Law, Contract Law, Tort Law, Civil Law, General Legal Practice | International (with specific examples/datasets from China, US, Japan, EU regulations mentioned) | International, China, USA, Japan, EU | Discusses publicly available legal datasets (e.g., CAIL2018 from China, LeCaRD from China, CaseHOLD derived from US law) and general large-scale web data used to train base LLMs. | Data From Existing Public NLP/Legal Datasets/Benchmarks, Chinese Legal Data, US Legal Data, Legal Domain Data, Publicly Available Data, Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Need for methods to mitigate bias and ensure transparency/interpretability; need for more specialized legal data; need for guidelines/standards for ethical use; need to address legal challenges (IP, privacy); need for better alignment with human/societal values. | Bias in AI, Transparency and Explainability, Data Availability and Quality, Ethical Framework Deficiencies, Regulatory and Governance Gaps, Security and Privacy of Data | Privacy concerns, bias perpetuation, lack of explainability, need for specialized domain data and adaptation, intellectual property issues, ensuring responsible and ethical deployment. | Data Privacy, Security, and Confidentiality, Bias in AI Systems and Data, Transparency and Explainability of AI, Scarcity of High-Quality Legal Data, Domain-Specific Adaptation and Customization, Copyright and Intellectual Property Issues, Ethical Considerations, Safeguarding Against Misuse and Harm | Copyright infringement, disclosure of private information, perpetuation of societal biases leading to discrimination, lack of transparency hindering accountability, potential misuse in legal education or practice. | Copyright or intellectual property issues, Data privacy and security breach, Bias and discrimination, Lack of transparency, accountability, and redress, Risk of misapplication or misuse, Ethical concerns |
| Qs9Hxl2Iir4J.pdf | Google_Scholar | ARTIFICIAL LAWYERING: A JEKYLL AND HYDE STORY | This paper examines the dual potential of artificial intelligence, particularly generative AI like ChatGPT, in the legal field. It argues that while AI can significantly improve access to justice for underserved communities, it also poses risks such as unauthorized practice of law, and thus proposes an amendment to the Model Rules of Professional Conduct to balance these aspects. | Generative AI in Legal Field, Access to Justice Enhancement, Risk Identification, Unauthorized Practice of Law, Proposal for Professional Conduct Rule Amendment | True | Idealistic | True | 3.0 | Positive | Proposed amendment to Rule 5.5 of the ABA Model Rules of Professional Conduct regarding 'practicing entities' (including AI) and UPL, allowing use by pro se litigants with informed consent. | Regulatory Framework / Proposal, Legal Ethics | NaN | Not Applicable | NaN | NaN | Inability of low-income individuals to afford legal counsel; lack of awareness among individuals about whether their problems are legal in nature; insufficient number of lawyers serving low-income populations; systemic inequalities. | High Cost of Legal Services, Public Lack of Legal Knowledge/Awareness, Limited Availability/Access to Legal Professionals/Expertise, Systemic Inequities in Justice System | Utilize AI (like ChatGPT) for legal education and information dissemination, especially for pro se litigants. Amend Rule 5.5 of the Model Rules of Professional Conduct with a new comment to address AI's potential for UPL, while allowing its use by pro se litigants under conditions of informed consent and disclosure to the court. | Education and AI Literacy, Access to Legal Information and Advice, Support for Self-Represented Litigants, Policy and Regulatory Reform, Regulation, Ethics, and Governance | Access to legal information, self-representation (pro se litigants), understanding legal rights, unauthorized practice of law (UPL) by AI, an LSC (Justice Gap) report. | Access to Legal Information, Support for Self-Represented Litigants, Legal Literacy and Public Legal Education, Regulatory Reform (Legal Services and AI), Democratizing Law / Closing Justice Gap / Rule of Law | Low-income Americans, veterans, persons with disabilities, parents of children under eighteen, survivors of domestic violence or sexual assault. | Low-income individuals, Population in USA, Veterans, People with disabilities, Parents, Survivors of domestic violence, Survivors of sexual assault | Civil law (specifically landlord-tenant disputes), Trademark law, General legal ethics (Unauthorized Practice of Law). | Civil Law, Landlord-Tenant Law, Trademark Law, Legal Ethics, Professional Responsibility | United States | USA | The paper discusses generative AI like ChatGPT which is trained on large language models (LLMs) using extensive text data to infer relationships between words and texts. Specific datasets for ChatGPT are not detailed by the paper beyond this general description. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Lack of clear legal and ethical rules addressing advanced AI (like ChatGPT) and the unauthorized practice of law; need for mechanisms to balance AI's benefits for access to justice with public protection; ethical rules (Model Rules) not sufficiently updated for AI advancements; issues of AI bias, language limitations, and lack of redressability for AI-inflicted harm if AI engages in law practice. | Regulatory and Governance Gaps, Ethical Framework Deficiencies, Consumer Protection Gaps, Access, Equity, and Digital Divide, Bias in AI, Multilingual and Low-Resource Language Gaps, Accountability and Redress Mechanisms | NaN | NaN | AI engaging in the unauthorized practice of law (UPL); public endangerment from incompetent or biased AI-generated legal advice/documents; AI producing non-existent legal precedents ('hallucinations'); generation of frivolous lawsuits; lack of legal redress for individuals harmed by AI's errors (AI malpractice); perpetuation or amplification of societal biases through AI; AI's limitations in understanding true context beyond language patterns. | Unauthorized practice of law, Consumer harm, Inaccurate or misleading AI output, Bias and discrimination, Undermining legal process or principles, Lack of transparency, accountability, and redress, Technical limitations of AI |
| wNT2cfBmGiQJ.pdf | Google_Scholar | From Bytes to Borsch: Fine-Tuning Gemma and Mistral for the Ukrainian Language Representation | This paper details the fine-tuning of open-source LLMs (Gemma and Mistral) for the Ukrainian language using existing and newly created datasets (UKID). It benchmarks the models, highlighting performance improvements and challenges like code-switching, arguing for the importance of developing language-specific models for low-resource languages. | LLM Fine-tuning, Open Source LLM Application, Ukrainian Language Focus, Dataset Creation, Benchmarking AI Models, Low-Resource Language Model Development, Challenge Identification (Code-Switching) | True | Idealistic | True | 1.0 | Positive | Fine-tuning open-source LLMs (Gemma-2b, Gemma-7b, Mistral-7b) using LoRA for the Ukrainian language, including the creation and use of a new instruction dataset (UKID). | Fine-tuning, Open Source AI, Large Language Model, Parameter-Efficient Fine-tuning, Multilingual Application, Language Adaptation, Dataset Creation / Curation | Benchmarking using two datasets: 1) Ukrainian External Independent Testing (EIT) Multiple Choice Questions (MCQ) dataset (3,063 questions on history, language, literature), automatically evaluated. 2) 100 Open Questions (OQ) for generative tasks, manually evaluated on language use, coherence, relevance, and grammar. Comparison against baselines and proprietary models. | Custom Dataset Evaluation, Performance on Standardized Tests, Quantitative Metrics, Human Evaluation, Comparative Analysis | The fine-tuned Mistral-7B (MistralFT) achieved 40.16% accuracy on History MCQs and 22.86% on Language & Literature MCQs. It achieved an average score of 40.75 out of 100 on Open Questions, though struggled with adhering to instructions (Relevance score was low). Proprietary models like GPT-4 performed significantly better. | Low performance, Technique improves outcome, Underperforms others, Limitation: Operational or Technical | Scarcity of suitable instruction datasets with authentic Ukrainian context; Language and cultural bias in existing LLMs; Uneven knowledge representation favouring dominant languages; Resource constraints for developing models for low-resource languages. | Data Scarcity/Quality for AI, Bias in AI/Data, Accessibility Barriers for Specific User Groups, Resource Constraints for A2J Tech Development/Deployment | Fine-tuning open-source LLMs with language-specific data; Creating and sharing new, culturally relevant datasets (e.g., UKID); Utilizing efficient fine-tuning techniques (LoRA); Advocating for investment and policy focus on LLM development for lower-resource languages; Creating language-specific benchmarks (e.g., ULIB). | Enhanced AI Capabilities, Open Source Initiatives and Collaboration, Data Curation and Management, Policy and Regulatory Reform, Language Simplification and Multilingual Access, Benchmarking and Evaluation Frameworks | Linguistic Inclusion, Cultural Preservation, Education, Countering Misinformation. | Language Access and Digital Divide, Legal Literacy and Public Legal Education, Ethical AI in Law and AI Governance | Ukrainian speakers, with potential applicability to other low-resource language communities. Specific examples mention Ukrainian refugees, rural Peruvian villagers (Quechua), and Navajo learners. | Speakers of low-resource languages, Ukrainian speakers, Refugees, Rural populations, Indigenous populations | NaN | NaN | Ukraine | Ukraine | Combined dataset including: 3,063 instruction rows from Ukrainian national exam (ZNO dataset); 10,000 rows from UAlpaca (translated general knowledge); Uk-Squad dataset (translated SQuAD); 962 question-answer-fact pairs from the newly created Ukrainian Knowledge and Instruction Dataset (UKID), derived from Ukrainian Wikipedia summaries via Gemini 1.0 API. Primarily unstructured text formatted as instructions. | Author-Created New Dataset, Fine-tuning Dataset, Ukrainian Language Data, Data From Existing Public NLP/Legal Datasets/Benchmarks, General Web Data / Broad Internet Text, Synthetic Data, Instruction-Tuning Formatted Data, Unstructured Text Data, Structured Data | LoRA (Low-Rank Adaptation) fine-tuning. Dataset creation (UKID) involved selecting high-traffic Ukrainian Wikipedia pages, filtering for relevance, and using Gemini 1.0 API with few-shot prompting to generate question-answer-fact triplets. | Parameter-Efficient Fine-Tuning (PEFT), Model Fine-tuning, Dataset Creation, Data Curation, API-based Development, Few-shot Learning Application, LLM-aided Data Generation | Fine-tuned model weights and the UKID dataset are shared via a GitHub repository. | Open source model release, Public dataset/benchmark release | True | True | Fine-tuned model weights and the UKID dataset are available on the associated "from-bytes-to-borsch" GitHub repository. | Model available, Dataset available | Need for larger, more comprehensive Ukrainian instruction datasets; Need for improved fine-tuning methods to avoid performance degradation and negative artifacts (e.g., code-switching); Significant performance gap between fine-tuned open-source and large proprietary models; Need for better evaluation benchmarks for Ukrainian (e.g., expanding ULIB); Lack of institutional support and resources for low-resource language model development. | Data Availability and Quality, Multilingual and Low-Resource Language Gaps, Research and Evaluation Gaps, AI Accuracy and Reliability, Computational Resource and Cost Issues | Reproducibility of fine-tuning setups; Scarcity of high-quality, culturally relevant training data; Models lacking foundational conceptual understanding in the target language; Compute and resource constraints; Adapting datasets to model-specific instruction formats; Negative side-effects of fine-tuning (impaired generation, poor instruction following, code-switching). | Evaluation Challenges and Metrics, Research Methodology and Study Design Limitations, Scarcity of High-Quality Legal Data, Multilingual and Low-Resource Language Support, LLM Reasoning Capabilities, High Computational and Resource Demands, Domain-Specific Adaptation and Customization, Accuracy and Reliability of LLM Output | Perpetuation of language/cultural bias; Uneven access to technology; Cultural erosion and loss of linguistic diversity; Negative impacts on education and linguistic identity; Increased vulnerability to targeted propaganda and misinformation; Emergence of a 'model divide' between languages; Digital extinction risk for threatened languages. | Bias and discrimination, Exacerbation of inequality or two-tiered system, Negative societal impact, Security vulnerabilities or malicious misuse, Technical limitations of AI |
| Vl9jvdYVpp4J.pdf | Google_Scholar | JudicialTech supporting Justice \nThe impact of AI and Emerging Technologies on the Judiciary, Courts and Justice | This paper defines JudicialTech as AI and emerging technologies for judges, courts, and dispute resolution, aiming to support the judiciary, enhance access to justice, and increase fairness. It reviews JudicialTech's future impact across the judicial process, highlighting benefits like efficiency and improved access, alongside risks such as the erosion of human-led legal decisions and the need for robust judicial oversight. | JudicialTech Definition, AI for Judiciary Support, Access to Justice Enhancement, Fairness in Justice, Benefit Identification, Risk Identification, Need for Judicial Oversight | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | Erosion of human-led legal decisions and judicial independence; lack of public confidence due to uncontrolled/untested AI; algorithmic bias, opacity, and inaccuracy (e.g., AI hallucinations, high error rates); insufficient or unsuitable legal data for training AI; negative impact on common law development from reduced trials due to predictive tools. | Threats to Judicial Independence, Lack of Trust in AI/Automated Systems, Bias in AI/Data, Lack of AI Transparency/Explainability, AI Unreliability/Inaccuracy, Data Scarcity/Quality for AI, Impact on Common Law Development | Strong judicial oversight, control, and robust testing regimes for AI; presumption against AI for judicial decision-making without thorough vetting; knowledge transfer, experimentation (sandboxes, tech sprints), and horizon scanning; development of "Open Justice" standards and JudicialTech Labs; human oversight and appeal mechanisms for AI-driven decisions. | Judicial System Enhancement, Human Oversight and Collaboration, Benchmarking and Evaluation Frameworks, Regulation, Ethics, and Governance, Open Source Initiatives and Collaboration, Policy and Regulatory Reform | Enhancing judicial efficiency and fairness; litigation advice and trial preparation (eDiscovery, document review); Online/Algorithmic Dispute Resolution (ODR/ADR); judicial guidance and decision support (including for sentencing); digital courts, managing court backlogs, supporting self-represented litigants. | Judicial System Modernization / Efficiency, Ethical AI in Law and AI Governance, Access to Legal Advice, Legal Document Analysis / Review, Dispute Resolution, Support for Self-Represented Litigants | Self-Representing Litigants (SRLs)/litigants-in-person (LIPs); general public affected by court backlogs. | Self-represented litigants, General public | Criminal law, Civil law, Commercial law, Family law, Regulatory law | Criminal Law, Civil Law, Commercial Law, Family Law, Regulatory Law | UK, US, India, Singapore, France, EU, Canada. Broadly applicable internationally. | UK, USA, India, Singapore, France, EU, Canada, International | Discusses training data for existing AI systems: LLMs (e.g., GPT-4) trained on vast quantities of often online data; legal predictive tools trained on past judicial rulings which can be limited or outdated in smaller jurisdictions or with evolving laws; legal analytics tools use docket entries and documents. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Legal Domain Data, Case Law / Judgments, Other Legal Documents, Data Bias Concerns Noted | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Lack of robust, judiciary-supervised appraisal and testing regimes for judicial AI; need for established standards for digital access to justice data and services ('Open Justice'); insufficient R&D capabilities within Justice Ministries; ensuring public confidence and addressing algorithmic bias, opacity, and errors; adapting AI to limited and evolving legal data. | Regulatory and Governance Gaps, Research and Evaluation Gaps, Data Availability and Quality, Access, Equity, and Digital Divide, Public Understanding, Trust, and Adoption, Bias in AI, Transparency and Explainability, AI Accuracy and Reliability, Knowledge Recency and Updatability | Data limitations for training legal AI (small datasets, evolving laws); ensuring AI systems are unbiased, transparent, and accurate; maintaining judicial control over technology; balancing innovation with Rule of Law and public confidence; complexity of automating legal reasoning; managing digital evidence and deepfakes. | Scarcity of High-Quality Legal Data, Outdated or Limited LLM Knowledge Base, Bias in AI Systems and Data, Transparency and Explainability of AI, Accuracy and Reliability of LLM Output, Need for Human Oversight and Intervention, Ethical Considerations, User Adoption, Trust, and Acceptance, LLM Reasoning Capabilities, Safeguarding Against Misuse and Harm | Erosion of human-centered legal decision-making; undermining public confidence in the Rule of Law; inaccurate or biased AI decisions leading to miscarriages of justice; misleading legal submissions from generative AI (hallucinations); reduction in trials impacting common law; deepfake evidence; lack of algorithmic accountability; exploitation of AI for cybercrime. | Dehumanization of legal process, Erosion of trust in legal system or AI, Undermining legal process or principles, Inaccurate or misleading AI output, Bias and discrimination, Security vulnerabilities or malicious misuse, Lack of transparency, accountability, and redress |
| -1ZohptBDsIJ.pdf | Google_Scholar | Good AI Legal Help, Bad AI Legal Help: Establishing quality standards for responses to people’s legal problem stories | This paper proposes 22 specific criteria to evaluate the quality of AI responses to legal problem stories from the public, particularly in civil justice. It then presents findings from a survey of 21 legal experts who ranked these criteria, aiming to establish robust standards for future AI benchmarking in access to justice. | Evaluation Criteria for AI Legal Responses, Civil Justice Focus, Expert Survey, AI Benchmarking Standards, Access to Justice Research | True | Idealistic | True | 1.0 | Positive | A set of 22 quality criteria, grouped into 6 categories (Presentation, Legal Content Coverage, Legal Content Quality, Content Sources, Warnings/Disclaimers, Equity), for evaluating AI responses to legal help questions. | Evaluation Framework Development, AI System Evaluation, Equity Assessment | Survey methodology: 21 legal domain experts (legal aid lawyers, court staff, etc.) reviewed and ranked the 22 criteria on a 0-6 importance scale in 30-minute one-to-one interviews. They also provided qualitative feedback and suggested additional criteria. | User Study or Survey, Expert Evaluation, Quantitative Metrics, Qualitative Analysis | Criteria such as 'Response is not toxic,' 'Response is in plain language,' 'Response does not misrepresent the substantive law,' and 'Response does not misrepresent any forms, paperwork, or tools' averaged highest importance (6/6). Experts prioritized usability, actionability, and accuracy, while de-emphasizing robustness, citations, and warnings to consult a lawyer. | Descriptive or Conceptual finding | Lack of well-defined, specific quality metrics for legal services and AI performance in the legal domain; current quality assessment is often subjective and ill-defined. | Lack of Standardized Quality Metrics (Legal Services/AI) | Proposing a specific, comprehensive list of 22 quality criteria, reviewed and ranked by legal domain experts, to serve as a basis for establishing actionable quality evaluation and benchmarking protocols for AI systems providing legal help. | Benchmarking and Evaluation Frameworks, Regulation, Ethics, and Governance | Evaluating the quality of AI-generated legal information for initial legal help requests; establishing benchmarks for AI in civil justice; improving public understanding of legal rights and procedures. | Ethical AI in Law and AI Governance, Access to Legal Information, Legal Literacy and Public Legal Education | General public needing legal help for civil justice problems such as housing, family, domestic violence, debt, and criminal records. | General public, Individuals with unmet legal needs, Individuals with housing disputes, Individuals in family law disputes, Victims of domestic violence, Individuals in debt or lending disputes, Individuals with criminal records | Civil justice (including housing, family, domestic violence, debt, criminal records, traffic). | Civil Justice, Housing Law, Family Law, Domestic Violence Law, Debt Collection, Criminal Law, Traffic Law | International (aims for broadly applicable standards, with expert outreach including US, Canada, UK, Australia, and other countries, though initial survey participants' specific locations are not detailed, some roles suggest a US context). | International, USA, Canada, UK, Australia | NaN | Not Applicable | Literature review of existing quality rubrics and AI benchmark standards; expert consultation via email inquiries; survey methodology involving semi-structured interviews with legal domain experts to rank and refine proposed criteria. | Literature Review as Design Input, Expert Consultation, Survey Methodology, Qualitative Research Methods, Criteria Development | NaN | Not applicable | False | False | NaN | NaN | The study is ongoing, and findings are provisional; further testing of criteria in benchmark efforts is needed; exploration of automated assessment of criteria; addressing language and disability access more comprehensively; ensuring AI is not trained on biased data. | Research and Evaluation Gaps, Multilingual and Low-Resource Language Gaps, User Interface and Usability Gaps, Access, Equity, and Digital Divide, Bias in AI, Data Availability and Quality | Defining and measuring 'quality' in the legal domain; creating specific yet broadly applicable evaluation criteria; balancing comprehensive legal information with user-friendly presentation; ensuring accuracy in a dynamic legal environment without setting unattainable standards. | Evaluation Challenges and Metrics, User Interface, Usability, and Accessibility, Accuracy and Reliability of LLM Output, Outdated or Limited LLM Knowledge Base | AI providing misleading or harmful legal information (e.g., hallucinations, over-simplifications, errors leading to missed deadlines or incorrect filings); AI exhibiting bias or creating disparate impacts; users being overwhelmed by information or paralyzed by disclaimers. | Inaccurate or misleading AI output, Harmful or unsafe AI output, Consumer harm, Bias and discrimination, Poor user experience |
| gHXfe3cys0IJ.pdf | Google_Scholar | Analyzing Images of Legal Documents: Toward Multi-Modal LLMs for Access to Justice | This paper investigates using a multi-modal large language model (GPT-4o) to extract structured information from images of handwritten legal forms, specifically an Ontario lease agreement. Initial results show the potential for such technology to aid access to justice by simplifying information gathering, but also reveal challenges related to image quality, handwriting variability, and potential biases. | Multi-Modal LLM Application, Information Extraction from Images, Handwritten Form Processing, Ontario Law Focus, Lease Agreement Analysis, Access to Justice Enhancement, Challenge Identification, Bias in AI | True | Idealistic | True | 2.0 | Positive | Using a multi-modal LLM (GPT-4o) via API to extract structured data (e.g., names, addresses) from images of filled-out legal forms. | Multimodal Language Model, Information Extraction, Image Data Processing | Evaluated GPT-4o on images of a filled-out standard Ontario lease form. Created a dataset with 3 scenarios (varying name/field complexity) and 5 image formats per scenario (typed PDF screenshot, neat handwritten HD, sloppy handwritten HD, neat handwritten SD, sloppy handwritten SD). Measured accuracy based on exact field value matches (case-insensitive) against ground truth across 14 fields. | Custom Dataset Evaluation, Quantitative Metrics | Overall accuracy was 73%. Typed PDF (HD) yielded 98% accuracy, while performance decreased with handwritten text, lower image quality, and messier handwriting (Sloppy SD: 60%). The model struggled most with handwritten street numbers and uncommon names (sometimes substituting common ones), but excelled with predictable fields like city/province. | Mixed performance, High performance, Moderate performance, Low performance, Limitation: Operational or Technical | Difficulty for laypeople and self-represented litigants in understanding legal requirements, finding relevant information scattered across documents, and correctly filling out forms; the burden of administrative processes ("administrative sludge"). | Public Lack of Legal Knowledge/Awareness, Challenges for Self-Represented Litigants, Difficulty Accessing/Interpreting Legal Information, Complexity of Legal System/Procedures | Leveraging multi-modal LLMs to automatically identify and extract needed information from images of paper documents (forms, certificates, contracts, letters), thereby assisting users in filling out other forms, understanding their rights, or drafting submissions. | AI Tool Development, Enhanced AI Capabilities, Document Automation, Access to Legal Information and Advice | Form filling automation, information extraction from legal documents, support for self-represented litigants, reducing administrative burden in legal processes. | Legal Document Creation / Automation, Legal Document Analysis / Review, Support for Self-Represented Litigants, Improving Efficiency in Legal System / Profession | Laypeople, self-represented litigants. | Laypeople, Self-represented litigants | Landlord-tenant law (residential leases), Administrative law (forms). | Landlord-Tenant Law, Administrative Law | Ontario, Canada | Canada | N/A (Paper evaluates a pre-trained model, GPT-4o. The described dataset is for testing.) | Not Applicable | Experimental design involving dataset creation (varying form scenarios, handwriting styles, image quality) and API-based LLM prompting for evaluation. | Experimental Design, Dataset Creation, API-based Development, Prompt Engineering | NaN | Not applicable | True | False | The technique uses the GPT-4o model via OpenAI's API, which is commercially available. The experimental code is available on GitHub. | API access, Commercial product or service, Code available | Need for larger-scale studies with more varied data; optimizing prompts and models; integrating the capability into user-facing systems; addressing performance issues with low-quality inputs and handwriting; mitigating model biases. | Research and Evaluation Gaps, User Interface and Usability Gaps, Integration and Interoperability Challenges, AI Accuracy and Reliability, Bias in AI | Achieving reliable extraction despite variations in image quality, form completeness, and handwriting (neatness, style); model tendencies to 'correct' uncommon names towards common ones. | Accuracy and Reliability of LLM Output, Data Quality, Processing, and Preparation, Bias in AI Systems and Data | Exacerbating the digital divide, as performance relies on good quality images (requiring modern devices and good lighting); potential for societal biases embedded in LLMs to affect outcomes (e.g., poorer recognition of less common names). | Exacerbation of inequality or two-tiered system, Technical limitations of AI, Bias and discrimination |
| iboDfGK_-oEJ.pdf | Google_Scholar | It Cannot Be Right If It Was Written by AI: On Lawyers’ Preferences of Documents Perceived as Authored by an LLM vs a Human | This paper investigates whether lawyers' and law students' perception of legal documents (acknowledgement of debt) varies based on the belief that they were AI-generated versus human-crafted. The study found a significant bias against documents labeled as AI-generated, which were rated lower in correctness and language quality, despite being identical to those labeled human-crafted. | Perception Study of AI-Generated Legal Documents, Bias Against AI-Generated Content, Legal Document Evaluation | True | Idealistic | True | 2.0 | Neutral | Experimental survey designed to measure perception bias. Participants evaluated identical human-written legal documents (acknowledgement of debt), where the only difference was a label indicating whether the document was supposedly 'AI-GENERATED' or 'HUMAN-CRAFTED'. | User Study / Experimental Survey, Perception Bias Measurement, Human-AI Interaction Study | 75 Czech lawyers and law students were randomly assigned to two groups. Each group evaluated two human-written 'acknowledgement of debt' documents (one Brief, one Verbose). Document labels ('AI-generated' vs 'human-crafted') were swapped between the groups. Participants rated documents on correctness and language quality (1-5 scale) via an online survey and provided qualitative explanations. Statistical analysis (Fisher exact test) and thematic analysis were performed. | User Study or Survey, Expert Evaluation, Quantitative Metrics, Qualitative Analysis, Comparative Analysis | Documents labeled 'human-crafted' were rated significantly higher than identical documents labeled 'AI-generated' on both correctness (mean 4.69 vs 4.21) and language quality (mean 4.55 vs 3.97). Thematic analysis revealed more negative comments regarding aspects like stylistics, structure, and formal correctness for documents perceived as AI-generated. Despite this bias, 93% of participants believe full automation of such documents is feasible. | Limitation: Bias, Descriptive or Conceptual finding | Negative perception and bias (algorithmic aversion) against AI-generated legal documents among legal professionals, even when the documents are objectively correct. This bias could disproportionately harm lower-income individuals who might rely on AI-powered legal aid or self-help tools, potentially undermining the goal of increasing access to justice. | Algorithmic Aversion/Bias against AI, Slow Technology Adoption by Legal Profession, Risk of AI Exacerbating Inequality | The paper highlights the need for awareness of this perception bias among legal practitioners, policymakers, and legislators. It suggests responsible implementation and adoption strategies for legal document generation technology and calls for discussions on updating legal processes. | Education and AI Literacy, Regulation, Ethics, and Governance, Policy and Regulatory Reform | Perception of AI-generated legal documents, Automated document drafting (specifically, acknowledgement of debt), Potential impact on access to justice for self-represented litigants or users of AI-powered legal aid. | Legal Document Creation / Automation, Support for Self-Represented Litigants, Legal Aid and Pro Bono Services, Ethical AI in Law and AI Governance | Lower-income groups (potentially relying on AI tools for legal aid or self-help). | Low-income individuals | Civil Law (specifically Contract Law / Obligations Law related to debt acknowledgement). | Civil Law, Contract Law, Debt Collection | Czechia | Czech Republic | NaN | Not Applicable | Experimental design involving manipulation of document labels (AI-generated vs. human-crafted) presented to two groups of participants (lawyers and law students). Data collection via online survey with Likert scale ratings and open-ended questions. Analysis using quantitative statistical tests (Fisher exact test) and qualitative thematic analysis. | Experimental Design, Survey Methodology, Quantitative Data Analysis, Qualitative Thematic Analysis | NaN | Not applicable | True | True | The documents and the survey used in the experiments are released in an accompanying online repository on GitHub. | Dataset available | Need for research involving other populations (e.g., judges, officials, general public), different types/complexity of legal documents, varying participant AI exposure/experience, and cross-jurisdictional/-linguistic validation. Societal gap: Addressing the identified perception bias to ensure AI fairly benefits access to justice. | Research and Evaluation Gaps, Bias in AI, Access, Equity, and Digital Divide | NaN | NaN | Over-reliance on or unfounded scepticism towards AI-generated documents influencing legal outcomes. Algorithmic aversion acting as a bias against users of AI tools, particularly affecting lower-income groups and potentially increasing social inequalities. Negative perceptions undermining the potential benefits of AI for access to justice. Potential conflict between transparency (disclosing AI use) and fairness due to perception bias. | Over-reliance on AI, Bias and discrimination, Exacerbation of inequality or two-tiered system, Erosion of trust in legal system or AI, Lack of transparency, accountability, and redress |
| WUp5XdawNLoJ.pdf | Google_Scholar | A(I)ccess to Justice: How AI and Ethics Opinions Approving Limited Scope Representation Support Legal Market Consolidation | This article argues that while general AI tools like ChatGPT pose risks due to misuse, legal-specific AI combined with ethically approved practices like limited scope representation and ghostwriting can enhance access to justice by lowering costs. This convergence, however, may also lead to the corporatization and consolidation of the legal market for low- and middle-income clients. | Legal-Specific AI, Access to Justice Enhancement, Limited Scope Representation, Ghostwriting, Cost Reduction in Legal Services, Risk of Market Corporatization | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | High cost of traditional legal representation bars access for low- and middle-income individuals. Unauthorized Practice of Law (UPL) rules prevent direct use of advanced AI by pro se litigants. Potential unsuitability of limited scope representation for complex cases or clients with limitations. | High Cost of Legal Services, Regulatory Hurdles, Challenges for Self-Represented Litigants, Limitations of Limited Scope Representation | Utilizing legal-specific Generative AI (like Westlaw Precision, Lexis+ AI) to improve efficiency and lower costs. Employing Limited Scope Representation (LSR) and ghostwriting, supervised by attorneys (potentially contract attorneys), to provide affordable, discrete legal tasks. Developing a 'TurboLaw' model combining AI tools and virtual attorney oversight. | AI Tool Development, Cost Reduction and Efficiency, Alternative Legal Service Delivery Models, Human Oversight and Collaboration | Affordability of legal services, Limited Scope Representation, Ghostwriting, Unauthorized Practice of Law, Legal technology adoption, Market structure of legal services. | Affordability of Legal Services / Cost Reduction, Access to Legal Representation, Regulatory Reform (Legal Services and AI) | Low- and middle-income individuals and families, pro se litigants (by enabling more affordable attorney assistance). | Low-income individuals, Moderate-income individuals, Families, Self-represented litigants | General legal practice | General Legal Practice | United States (references ABA Model Rules, federal courts, D.C., Texas, Maryland, New York, New Hampshire) | USA | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Need for reliable, legal-specific AI tools accessible at low cost. Clear frameworks to address Unauthorized Practice of Law issues with AI-assisted pro se litigants. Ensuring attorney competence and adequate supervision when using AI within LSR models. Addressing cybersecurity and confidentiality risks inherent in virtual practice and AI use. Potential negative socioeconomic impacts of market consolidation on solo and small firms. | AI Accuracy and Reliability, AI Scope and Functionality Limitations, Access, Equity, and Digital Divide, Computational Resource and Cost Issues, Regulatory and Governance Gaps, Human Oversight and Professional Adaptation, Security and Privacy of Data | NaN | NaN | Use of general GenAI (ChatGPT, Google Bard) leading to fabricated legal citations and court sanctions. AI providing legal advice constituting Unauthorized Practice of Law (UPL). Inadvertent disclosure of confidential client information through technology / virtual practice / outsourcing. Limited scope representation agreements being unreasonable or insufficient for a client's needs. Market consolidation driven by AI potentially harming smaller legal practices. | Inaccurate or misleading AI output, Ethical concerns, Unauthorized practice of law, Data privacy and security breach, Regulatory challenges or gaps, Negative economic impact |
| Z673F12s3tUJ.pdf | Google_Scholar | Advancing Legal Tech and Education - Developments in the United States and South Korea - | This paper compares the integration of artificial intelligence (AI) into legal practice and education in the United States and South Korea. It examines trends in AI tools, their adoption by law firms and law schools, regulatory challenges (particularly in Korea), and implications for preparing future lawyers. | Comparative Study (US and South Korea), AI in Legal Practice, AI in Legal Education, AI Tool Adoption Trends, Regulatory Challenges (Korea), Preparing Future Lawyers | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | Limited access to legal services for the general populace and small businesses, particularly in South Korea due to cost, lawyer concentration, and historical undersupply; Regulatory resistance from professional bodies (e.g., Korean Bar Association); Concerns about AI accuracy, privacy, ethics, and copyright. | Limited Access to Legal Assistance, High Cost of Legal Services, Geographical Disparities in Legal Access, Regulatory Hurdles, Protectionism by Legal Profession, AI Unreliability/Inaccuracy, Data Privacy Concerns with AI, Ethical Concerns with AI in Law, Intellectual Property/Copyright Issues with AI | Development and adoption of AI-driven legal tech tools for efficiency and service delivery; Creation of legal tech platforms to connect clients with lawyers and provide accessible legal information/consultation (e.g., LawTalk, AI DR & Aju); Integration of AI training, ethics, and practical application into law school curricula. | AI Tool Development, Cost Reduction and Efficiency, Access to Legal Information and Advice, Education and AI Literacy, Regulation, Ethics, and Governance | Access to legal services for underserved populations, Affordability of legal services, Lawyer-client matching platforms, Legal consultation accessibility, Legal education reform. | Support for Vulnerable Populations, Affordability of Legal Services / Cost Reduction, Access to Legal Advice, Legal Education for Professionals / Students, Democratizing Law / Closing Justice Gap / Rule of Law | General populace, individuals, small-business clients (especially in Korea), Consumers with everyday legal issues (US). | General public, Small businesses, Population in Korea, Consumers, Population in USA | General legal practice, Legal research, Document review, Contract analysis, E-discovery, Litigation analytics, Legal education. | General Legal Practice, Legal Research, Document Review, Contract Law, E-Discovery, Litigation Support, Legal Education | United States, South Korea | USA, South Korea | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Need for improved AI accuracy and reliability (reducing hallucinations); Addressing AI biases; Resolving data privacy and copyright concerns; Developing robust ethical and regulatory frameworks; Adapting legal education curricula and training faculty effectively; Overcoming resource limitations in educational institutions. | AI Accuracy and Reliability, Bias in AI, Security and Privacy of Data, Regulatory and Governance Gaps, Ethical Framework Deficiencies, Human Oversight and Professional Adaptation, Computational Resource and Cost Issues | Regulatory pushback and conflicts with traditional legal practice norms (e.g., LawTalk controversy in Korea); Resistance from established legal professionals; Keeping legal education curricula current with rapid technological advancements; Training law faculty in AI and legal tech; Market consolidation. | Regulatory Uncertainty and Compliance, User Adoption, Trust, and Acceptance, User Training, AI Literacy, and Skill Gaps, Financial Cost and Resource Constraints | AI inaccuracy leading to flawed legal analysis or advice ('hallucinations'); Data privacy breaches and misuse of sensitive legal information; Copyright infringement issues related to training data and AI outputs; Algorithmic bias perpetuating inequalities; Undermining professional ethics and standards; Commodification of legal services. | Inaccurate or misleading AI output, Data privacy and security breach, Copyright or intellectual property issues, Bias and discrimination, Exacerbation of inequality or two-tiered system, Ethical concerns, Negative economic impact |
| yytdIHOdBqkJ.pdf | Google_Scholar | Using deep learning to predict outcomes of legal appeals better than human experts: A study with data from Brazilian federal courts | This paper evaluates deep learning models (ULMFiT, BERT-LSTM, BigBird) for predicting the outcome of appeals in Brazilian Federal Small Claims Courts, using only the first-instance decision text. The best model outperformed human legal experts in prediction accuracy, suggesting AI's potential to enhance judicial efficiency and predictability. | Deep Learning Model Evaluation, Legal Case Outcome Prediction, Brazilian Law Focus, Small Claims Court Focus, Comparison with Human Experts, Judicial Efficiency Enhancement, Predictability in Law | True | Idealistic | True | 2.0 | Positive | Comparison of three deep learning architectures (ULMFiT, BERT-LSTM, BigBird) for binary classification of appeal outcomes (affirm vs. reverse) based on first-instance court decision text. The best performing was the bidirectional ULMFiT. | Deep Learning, Transformer Models, Recurrent Neural Network (RNN), Machine Learning Model Comparison, Predictive Legal Task, Legal Text Classification | Models trained and evaluated on the BrACJ-4 dataset (729,830 Brazilian Federal Small Claims Courts appeals, 2007-2020). Performance measured primarily by Matthews Correlation Coefficient (MCC) using a time-based split (train < Mar 2018, validation/test > Mar 2018). Compared against a baseline of 55 human legal experts (judges and clerks) who evaluated a subset of 810 cases. | Custom Dataset Evaluation, Quantitative Metrics, Comparative Analysis, Expert Evaluation | The bidirectional ULMFiT model achieved the highest MCC (0.3881 on the test set, 0.3647 on the human-evaluated subset), significantly outperforming the human expert baseline (MCC 0.1954). | Moderate performance, Outperforms others, Technique improves outcome | High volume of litigation and appeals in the Brazilian judiciary, leading to backlogs and slow processes. Significant economic impact of appeal costs, especially on poorer litigants in Small Claims Courts. High affirmance rate makes prediction challenging but necessary. Difficulty in achieving judicial consistency and predictability. | Judicial/Legal System Inefficiencies, High Cost of Legal Services, Lack of Judicial Consistency | Using AI (deep learning NLP models) to predict appeal outcomes can provide valuable information to litigants and lawyers considering appeals, potentially reducing unnecessary litigation. AI tools could also assist courts in managing caseloads, increasing efficiency, and promoting jurisprudential stability. | AI Tool Development, Legal Research and Analysis Tools, Access to Legal Information and Advice, Cost Reduction and Efficiency, Judicial System Enhancement | Judicial efficiency, case outcome prediction, reducing court backlogs, litigation costs, access to justice for low-income individuals, consistency in judicial decisions. | Judicial System Modernization / Efficiency, Improving Foundational AI Capabilities for Legal Applications, Affordability of Legal Services / Cost Reduction, Support for Vulnerable Populations | Litigants in Brazilian Federal Small Claims Courts (LSCIIs), particularly those in the 4th Region (TRF-4), often described as the 'poorest people' seeking social security or assistance benefits. | Litigants in small claims courts, Population in Brazil, Low-income individuals, Social security claimants | Social Security Law, Public Law (Administrative Law), Civil Procedure (specifically appeals in Small Claims Courts). | Social Security Law, Public Law, Administrative Law, Civil Procedure, Small Claims Law | Brazil (Federal Justice, 4th Regional Federal Court - TRF-4, and associated Federal Small Claims Courts - LSCIIs). | Brazil | A publicly derived dataset (BrACJ-4) containing text from 729,830 first-instance decisions and corresponding appeal outcomes from Brazilian Federal Small Claims Courts (4th Region) between 2007-2020. Pre-training also utilized general Portuguese corpora (Wikipedia, BrWaC). The data is unstructured text. | Author-Created New Dataset, Fine-tuning Dataset, Publicly Available Data, Brazilian Legal Data, Legal Domain Data, Case Law / Judgments, Unstructured Text Data, Portuguese Language Data, General Web Data / Broad Internet Text | Data collection from public court repositories, data cleaning and labeling, time-based data splitting for training/validation/testing, transfer learning (pre-training on general/domain corpora, fine-tuning on task), hyperparameter tuning using Bayesian Optimization, evaluation using Matthews Correlation Coefficient (MCC), baseline comparison against human experts. | Data Collection, Data Preprocessing, Data Labeling, Time-series Data Handling, Transfer Learning, Model Pre-training, Model Fine-tuning, Hyperparameter Optimization, Evaluation with Standard Metrics, Benchmarking | The dataset (BrACJ-4), code, and pre-trained models are made publicly available on Kaggle and GitHub to facilitate further research. | Public dataset/benchmark release, Open source code release, Open source model release | True | True | Dataset and pre-trained models available on Kaggle (https://www.kaggle.com/eliaskjacob/bracj4). Code available on GitHub (https://github.com/eliaskjacob/paper-bracj4). | Dataset available, Model available, Code available | Models currently only use first-instance decision text, ignoring potentially valuable information from appeal briefs and counter-arguments. The study is limited to one specific court region and type; generalizability needs testing. Lack of model explainability. | Data Availability and Quality, Research and Evaluation Gaps, Multilingual and Jurisdictional Specificity Gaps, Transparency and Explainability | Handling very long legal documents, managing large datasets, avoiding data leakage through time-sensitive splitting, establishing reliable ground truth labels from court data, creating a robust human baseline for comparison, mitigating potential biases in data, computational resource requirements for training large models. | LLM Context Window and Long Input Management, Data Quality, Processing, and Preparation, Evaluation Challenges and Metrics, Bias in AI Systems and Data, High Computational and Resource Demands | NaN | NaN |
| KRoJJ5fKn0IJ.pdf | Google_Scholar | Assessing ChatGPT as a Power Analysis Tool: An Empirical Investigation | This empirical study evaluates ChatGPT's (GPT-3.5-turbo and GPT-4) proficiency in conducting power analysis for sample size calculations, finding it capable, especially when GPT-4 generates R code. The paper suggests ChatGPT can serve as an accessible supporting tool for researchers, reducing barriers to performing power analysis. | ChatGPT Evaluation, Power Analysis for Sample Size Calculation, Support Tool for Researchers, Accessibility Enhancement | True | Idealistic | True | 2.0 | Positive | ChatGPT (GPT-3.5-turbo and GPT-4) for power analysis, specifically sample size calculation for t-tests, ANOVA, and chi-square tests. Evaluation included direct querying and code generation (R and Python) strategies. | Large Language Model, Statistical Analysis Support, Code Generation, AI System Evaluation | Two experiments: Exp1 assessed accuracy of sample size calculation using GPT-3.5-turbo and GPT-4 with three methods (direct, R code, Python code) for three test types (two-sample t-test, one-way ANOVA, χ² goodness-of-fit test), with 100 trials per condition, comparing results to G*Power. Exp2 assessed GPT-3.5-turbo's ability to identify missing input parameters for power analysis across these tests, with 100 trials per condition. | Quantitative Metrics, Comparative Analysis | In Experiment 1, the GPT-4 model generating R code achieved 100% accuracy in calculating the required sample size for two-sample t-tests, one-way ANOVA, and χ² goodness-of-fit tests. | High performance | For researchers conducting power analysis: need for specialized statistical expertise, cost and accessibility of specialized software, cognitive load of learning new statistical programs, and limitations of existing software for complex designs or all desired statistical tests. | Need for Specialized Knowledge for Tool Use, High Cost of Research Tools, Limited Access to Research Tools, Complexity of Advanced Analytical Methods | Proposes leveraging Large Language Models like ChatGPT as an interactive and accessible alternative or supplement for power analysis. ChatGPT can provide guidance, explain statistical parameters, and generate R or Python code for sample size calculations, thereby lowering cognitive and resource barriers for researchers. | AI Tool Development, Legal Research and Analysis Tools, Education and AI Literacy, User Interface and Accessibility Design | Access to statistical analysis tools; democratizing research methods; sample size calculation; power analysis support for researchers. | NaN | Researchers with limited access to specialized statistical software, funding for such software, technical training, or expert statistical consultation; graduate students; early-career researchers. | Researchers, Students, Early-career researchers, Resource-constrained researchers | NaN | NaN | International | International | The study utilized pre-trained OpenAI models (GPT-3.5-turbo and GPT-4). These models were trained by OpenAI on extensive, diverse datasets of text and code, including statistical information and programming code (e.g., R, Python) relevant to power analysis. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Proprietary Data, Non-Legal Domain Specific Data | Factorial experimental design (2 models × 3 methods × 3 test types), prompt engineering techniques (e.g., 'Take a deep breath', 'Use the existing code as it is', setting temperature to 0), quantitative accuracy assessment by comparing LLM-generated sample sizes to those from established statistical software (G*Power), and McNemar tests for statistical comparisons of performance. | Experimental Design, Prompt Engineering, Parameter Experimentation, Quantitative Evaluation Methodology, Benchmarking, Statistical Analysis | NaN | Not applicable | True | False | The approach involves using OpenAI's GPT-3.5-turbo (which has some free access tiers) and GPT-4 (a paid model) via their API. The prompting strategies are described and can thus be replicated by users with access to these models. | API access, Freemium access, Commercial product or service, Research artifact published in paper | Need for research on LLM performance for more complex statistical tests and simulation-based power analysis. Limited evaluation of other LLMs beyond GPT-3.5/GPT-4 and the impact of ongoing model updates on result consistency. Validation could be strengthened (e.g., multiple raters). Ensuring overall reliability, safety, and precision of LLMs in statistical applications remains an area for further investigation. | Research and Evaluation Gaps, AI Accuracy and Reliability | Authors faced challenges related to the probabilistic nature of LLMs (mitigated by temperature settings and prompt engineering), ChatGPT's known limitations in direct mathematical calculations (addressed by using code generation), and achieving consistent high accuracy. For users, challenges include ensuring research accountability when using AI-generated results and potential data security concerns if sensitive contextual information is inputted. | Output Variability and Consistency, Prompt Engineering and Optimization, LLM Reasoning Capabilities, Accuracy and Reliability of LLM Output, Ethical Considerations, Data Privacy, Security, and Confidentiality | Risk of incorrect sample size calculations leading to underpowered or inefficient research if AI outputs are not critically verified. Over-reliance on AI may lead to errors. Accountability issues for researchers using AI-generated results. Potential for data privacy breaches if users input sensitive study information, although power analysis parameters are typically not PII. | Inaccurate or misleading AI output, Risk of misapplication or misuse, Over-reliance on AI, Lack of transparency, accountability, and redress, Data privacy and security breach |
| cesta-2024-large-language-models-and-community-legal-centres-could-chatbots-help-reduce-australia-s-justice-gap.pdf | Google_Scholar | Large language models and community legal centres: Could chatbots help reduce Australia ’s justice gap? | This paper explores whether LLM-based chatbots can alleviate the unmet demand for services from Australian community legal centres (CLCs). It argues that while client-facing legal information chatbots hold potential to reduce Australia's justice gap, their realization is hindered by significant challenges including LLM accuracy, legal uncertainties, and implementation costs. | LLM Chatbots for Community Legal Centres, Australian Focus, Access to Justice Gap, Legal Information Chatbots, Challenge Identification, Accuracy Issues, Implementation Costs | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | Unmet demand for legal assistance due to under-resourced community legal centres (CLCs), limited government funding not based on needs assessment, and individuals not seeking necessary legal help. | Scale of Unmet Legal Need, Resource Constraints for Legal Aid Organizations, Public Lack of Legal Knowledge/Awareness | Proposing the careful development and deployment of client-facing legal information chatbots by CLCs to provide accessible legal information, while highlighting the need to overcome significant technical, legal, and practical challenges. | AI Tool Development, Access to Legal Information and Advice, Regulation, Ethics, and Governance | Provision of legal information and services by community legal centres (CLCs) to address the justice gap and unmet legal need for disadvantaged individuals. | Legal Aid and Pro Bono Services, Access to Legal Information, Democratizing Law / Closing Justice Gap / Rule of Law, Support for Vulnerable Populations | Individuals in Australia who cannot afford commercial legal services and rely on Community Legal Centres, including those facing digital exclusion or residing in regional areas with poor internet access. | Population in Australia, Individuals unable to afford legal services, Clients of legal aid organizations, Digitally excluded populations, Rural populations, Individuals facing access barriers | General legal issues typically handled by community legal centres in Australia. | General Law, Legal Aid | Australia | Australia | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | True | True | The paper refers to existing LLM tools; some are commercial (e.g., Ailira, CoCounsel), some have free access tiers (e.g., ChatGPT), and one specific AI model by Justice Connect is mentioned as available with a free license for Non-For-Profit organizations. | Commercial product or service, Freemium access, Model available, Restricted access | Technical gaps include LLM accuracy (hallucinations), explainability, cost-effective development for legal domains, and ensuring user prompts are effective. Societal/systemic gaps include legal/regulatory uncertainty regarding AI in law, the digital divide, ethical considerations for AI use by CLCs, and insufficient funding for CLCs to adopt such technology. | AI Accuracy and Reliability, Transparency and Explainability, Computational Resource and Cost Issues, User Interface and Usability Gaps, Regulatory and Governance Gaps, Access, Equity, and Digital Divide, Ethical Framework Deficiencies | NaN | NaN | Risk of LLM hallucinations misleading clients or lawyers; privacy and data protection breaches with sensitive information; systems being misconstrued as unauthorized legal advice or practice; inadvertent waiver of legal professional privilege; exacerbating digital exclusion and inequality; and potential professional negligence or ethical breaches from over-reliance on AI by CLCs. | Inaccurate or misleading AI output, Consumer harm, Data privacy and security breach, Unauthorized practice of law, Ethical concerns, Exacerbation of inequality or two-tiered system, Over-reliance on AI |
| 1PICXeaunP8J.pdf | Google_Scholar | THE GPTJUDGE: JUSTICE IN A GENERATIVE AI WORLD | This paper analyzes Generative AI's impact on the legal system, focusing on challenges to evidence authenticity, intellectual property, and litigation. It provides practical recommendations for courts and lawyers to manage GenAI-related evidentiary issues and discusses broader implications for justice and legal practice. | Generative AI Impact on Legal System, Evidence Authenticity Challenges, Intellectual Property Issues, Litigation Impact, Recommendations for Courts and Lawyers | True | Idealistic | True | 3.0 | Neutral | A proposed step-by-step procedural approach for courts and attorneys to handle evidentiary challenges posed by Generative AI, utilizing existing Federal Rules of Evidence, involving scheduling orders, disclosure, discovery, evidentiary hearings, and judicial rulings on admissibility. | Legal Procedural Framework Proposal, Evidence Law Application, Generative AI Governance | NaN | Not Applicable | NaN | NaN | Lack of legal representation for many citizens, particularly from marginalized communities; the risk of AI-generated vexatious lawsuits overwhelming courts; individuals' reliance on potentially faulty AI-generated legal advice; unpreparedness of courts for high-volume AI-generated filings. | Limited Access to Legal Assistance, Systemic Inequities in Justice System, Risk of AI Misuse, AI Unreliability/Inaccuracy, Judicial/Legal System Inefficiencies | The paper acknowledges GenAI's potential to assist unrepresented litigants by helping draft pleadings. However, it primarily focuses on managing the risks of AI misuse (e.g., vexatious lawsuits and faulty evidence) through judicial gatekeeping and procedural recommendations, rather than proposing specific high-level A2J solutions. | Support for Self-Represented Litigants, Document Automation, Regulation, Ethics, and Governance, Judicial System Enhancement | Assisting unrepresented litigants (pro se) in drafting legal documents; potential for generating vexatious or low-quality lawsuits; role of AI in providing legal information/advice to individuals. | Support for Self-Represented Litigants, Legal Document Creation / Automation, Ethical AI in Law and AI Governance, Access to Legal Information, Access to Legal Advice | Litigants who lack legal representation, often individuals from racialized or otherwise marginalized communities; ordinary people needing legal advice or facing debt collection. | Self-represented litigants, Minority groups, Marginalized communities, General public, Individuals with unmet legal needs, Individuals in debt or lending disputes | Evidence law, Intellectual Property (Copyright, Trademark), Civil Procedure, Criminal Procedure, Torts (defamation, liability for bad advice), Academic/University Law, Judicial Ethics. | Evidence Law, Intellectual Property Law, Copyright Law, Trademark Law, Civil Procedure, Criminal Procedure, Tort Law, Education Law, Legal Ethics | United States (primarily federal, but also state implications and examples). | USA | NaN | Not Applicable | The proposed procedural approach is based on an analysis of existing legal rules (Federal Rules of Evidence) and their flexible application to new technological challenges, informed by legal scholarship and judicial experience. | Legal Doctrinal Analysis as Design Input, Framework Adaptation | NaN | Not applicable | False | False | NaN | NaN | Courts are largely unprepared to differentiate beneficial uses of AI for A2J from misuse (e.g., vexatious litigation); current AI-detection capabilities are unreliable; lack of quality control and accountability for AI-generated legal advice for laypersons; underdeveloped ethical guidelines for AI in A2J. | Human Oversight and Professional Adaptation, AI Accuracy and Reliability, Transparency and Explainability, Consumer Protection Gaps, Accountability and Redress Mechanisms, Ethical Framework Deficiencies | The rapid pace of GenAI development versus the time-consuming process of revising formal rules of evidence, necessitating an approach that works within the existing legal framework while being adaptable to new technologies. Ensuring judicial gatekeeping is effective without unduly stifling GenAI's potential benefits. | Regulatory Uncertainty and Compliance, Ethical Considerations | Proliferation of deepfakes and difficulty in authenticating evidence; increased litigation costs due to need for experts; generation of misinformation and 'hallucinations' by AI; potential for AI to overwhelm courts with vexatious or low-quality lawsuits; undermining of intellectual property rights; misuse for scams and providing harmful advice; ethical breaches if judges or lawyers inappropriately use or rely on GenAI. | Security vulnerabilities or malicious misuse, Undermining legal process or principles, Negative economic impact, Inaccurate or misleading AI output, Copyright or intellectual property issues, Harmful or unsafe AI output, Ethical concerns, Risk of misapplication or misuse, Over-reliance on AI |
| FLt-qPRZA6YJ.pdf | Google_Scholar | Answering legal questions from laymen in German civil law system | This paper introduces GerLayQA, a new German dataset of 21k laymen's legal questions paired with lawyers' answers and grounded in law book paragraphs, to benchmark AI for legal question answering. Experiments with retrieval and generation models show moderate performance, highlighting challenges in understanding German legalese and the need for legally-trained models and expert evaluation. | Dataset Creation, German Law Focus, Legal Question Answering for Laypeople, Benchmark Creation, System Evaluation, Challenge Identification, Need for Legally-Trained Models | True | Idealistic | True | 1.0 | Neutral | A two-step QA pipeline involving document retrieval (embedding-based similarity) and answer generation (using GPT-3.5-turbo). Creation of a new dataset GerLayQA. | Legal Question Answering, Information Retrieval / Search, Embedding-based Methods, Large Language Model, Dataset Creation / Curation | Document retrieval: Precision, Recall, F1, MRR, MAP compared against random and oracle baselines on GerLayQA. Answer generation: ROUGE and BERTScore compared against lower and oracle baselines on GerLayQA. | Benchmark Dataset Evaluation, Quantitative Metrics, Comparative Analysis | For document retrieval, OpenAI's text-embedding-ada-002 performed best (F1=0.055, MRR=0.146, MAP=0.108). For answer generation, GPT-3.5-turbo with legal paragraphs achieved ROUGE-1=0.2910 and BERTScore=0.6550. | Low performance, Technique improves outcome | Laypeople avoid law books due to incomprehensibility; cost of lawyers creates a barrier favoring those with more financial resources; online resources often unhelpful. | Complexity of Legal Language/Documents, Public Lack of Legal Knowledge/Awareness, High Cost of Legal Services, Reliance on Unreliable Information Sources | Leveraging NLP tools for legal question answering, specifically by creating datasets and models that can understand laymen's questions and provide understandable answers grounded in legal text. | AI Tool Development, Access to Legal Information and Advice, Data Curation and Management, Language Simplification and Multilingual Access | Legal question answering, understanding legal texts, obtaining legal advice. | Access to Legal Information, Access to Legal Advice, Legal Literacy and Public Legal Education | Laypersons in Germany without legal expertise. | Laypeople, Population in Germany, Individuals lacking legal knowledge | German Civil Code (BGB), with mentions of German Criminal Code (StGB) and German Code of Civil Procedure (ZPO) for future work. Specific top categories mentioned are Tenancy law, condominium law; Labor law; Family law; Contract law; Inheritance law. | Civil Law, Criminal Law, Civil Procedure, Landlord-Tenant Law, Property Law, Employment Law, Family Law, Contract Law, Wills and Estates | Germany | Germany | GerLayQA dataset: 21,538 QA pairs scraped from a German legal online forum (frage-einen-anwalt.de), filtered for quality (lawyer references to paragraphs, user ratings). This is publicly available. | Author-Created New Dataset, Publicly Available Data, German Legal Data, Legal Domain Data, Legal Q&A / Forum / User Query Data, Web Scraped Data, User-Generated Content, Structured Data | Dataset creation involved web scraping, Regex-based extraction, and quality filtering. The QA pipeline involved standard NLP techniques: embedding generation for retrieval and prompting large language models for generation. | Dataset Creation, Data Collection, Information Extraction Techniques, Data Curation, Pipeline Development, NLP Technique Application, Embedding Model Application, Prompt Engineering | All datasets, source codes and models are publicly available at https://github.com/trusthlt/eacl24-german-legal-questions. | Public dataset/benchmark release, Open source code release, Open source model release | True | True | The GerLayQA dataset, source codes, and models are publicly available on GitHub. | Dataset available, Code available, Model available | Need for bespoke models trained on German legal text (both laymen and expert); inclusion of legal expertise in evaluation; expanding to more law books beyond BGB; manual filtering of dataset by legal experts; engaging secondary lawyers to verify gold standard answers. | Data Availability and Quality, Multilingual and Jurisdictional Specificity Gaps, Need for Interdisciplinary Collaboration, Research and Evaluation Gaps | Models' difficulty in understanding German legal texts (legalese); creating accurate semantic embeddings for both legalese and everyday language for effective retrieval; models struggling to grasp legal nuances and correlations; limited legal knowledge of the researchers for evaluation. | LLM Reasoning Capabilities, Multilingual and Low-Resource Language Support, Accuracy and Reliability of LLM Output, Evaluation Challenges and Metrics | Misguided legal counsel from NLP models leading to severe consequences; users not being aware they are interacting with an NLP model versus a certified lawyer; reliance on non-binding legal advice. | Inaccurate or misleading AI output, Consumer harm, Poor user experience, Over-reliance on AI |
| n1VAE2-uj0UJ.pdf | Google_Scholar | AI Law and Legal Training Interim Report | This interim report describes a UKRI-funded project developing open educational resources (OERs) to enhance understanding and responsible use of Generative AI in legal contexts. It outlines stakeholder engagement through workshops and details the planned course content targeting the public, free advice sector, students, and legal professionals. | Project Report, Open Educational Resources Development, Generative AI in Legal Contexts, Responsible AI Use Education, Stakeholder Engagement | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | Lack of knowledge/skills regarding GenAI use in legal contexts across different groups (public, advice sector, legal professionals); risk of societal harm from irresponsible AI use (e.g., misinformation, bias); potential exacerbation of inequalities; digital exclusion and resource limitations, particularly in the free advice sector; regulatory uncertainty. | Lack of AI Literacy, Risk of AI Misuse, AI-driven Misinformation/Disinformation, Bias in AI/Data, Risk of AI Exacerbating Inequality, Digital Divide, Resource Constraints for Legal Aid Organizations, Regulatory Uncertainty | Develop and provide free, open-access, engaging educational resources (OERs) co-produced with stakeholders to build knowledge, confidence, and skills for the ethical and responsible use of GenAI in legal contexts. Offer tailored learning pathways for different groups. | Education and AI Literacy, Open Source Initiatives and Collaboration, Regulation, Ethics, and Governance | Education and guidance on the ethical, responsible, and effective use of Generative AI (GenAI/LLMs) for accessing legal information and support; Mitigating risks associated with AI in legal contexts (misinformation, bias, digital exclusion). | Legal Literacy and Public Legal Education, Ethical AI in Law and AI Governance, Access to Legal Information, Language Access and Digital Divide | Public, free advice and voluntary sector organisations (advisors, volunteers, managers), small and medium-sized law firms, law students, legal academics. | General public, Legal aid organizations, Voluntary sector organizations, Small law firms, Law students, Academics | General Legal Field | General Law | United Kingdom (specifically England and Wales context) | UK | NaN | Not Applicable | Co-production through stakeholder engagement: three online learning design workshops were held with distinct groups (free advice sector, legal academics/students, legal practitioners) to gather insights and inform course development. | Stakeholder Engagement/Participatory Design, Workshop Methodology, Co-production | The courses are planned to launch in Summer 2025 as Open Educational Resources on The Open University’s OpenLearn platform. | Proposed deployment (not implemented), Educational resource deployment, Freely accessible tool/service, Web-based access | False | False | NaN | NaN | Significant gap in knowledge, awareness, and confidence regarding GenAI use in legal contexts among the public, advice sector, students, and practitioners. Lack of trustworthy, accessible educational resources, especially OERs. Regulatory and guidance gaps concerning AI use in legal services. | Public Understanding, Trust, and Adoption, Human Oversight and Professional Adaptation, Regulatory and Governance Gaps | Ensuring accuracy and reliability of GenAI; addressing ethical, privacy, and data security concerns; preventing skill degradation; overcoming digital exclusion and resource disparities; balancing AI augmentation with human oversight; managing stakeholder (e.g., funder, client) perceptions; developing effective training and governance strategies. | Accuracy and Reliability of LLM Output, Ethical Considerations, Data Privacy, Security, and Confidentiality, User Training, AI Literacy, and Skill Gaps, User Interface, Usability, and Accessibility, Financial Cost and Resource Constraints, Need for Human Oversight and Intervention, User Adoption, Trust, and Acceptance, Regulatory Uncertainty and Compliance | Inaccuracy and 'hallucinations' leading to misinformation; ethical issues (bias, interference with legal processes); privacy violations and data security breaches; degradation of legal skills; digital exclusion and exacerbation of inequalities; lack of regulatory clarity and liability issues; potential reduction in funding/support if AI is misconceived as a replacement for humans; referencing/plagiarism/fraud risks. | Inaccurate or misleading AI output, Ethical concerns, Bias and discrimination, Undermining legal process or principles, Data privacy and security breach, Deskilling or erosion of human skills, Exacerbation of inequality or two-tiered system, Regulatory challenges or gaps, Lack of transparency, accountability, and redress, Negative economic impact, Security vulnerabilities or malicious misuse |
| LHiwGkmrSvcJ.pdf | Google_Scholar | JUDICIAL ECONOMY IN THE AGE OF AI | This paper argues that AI, particularly LLMs, will dramatically lower access to justice barriers, creating a potential litigation boom that threatens judicial economy. It advocates for proactively integrating AI into the judicial process itself to enhance capacity and manage increased caseloads without curtailing substantive rights. | AI Impact on Access to Justice, Potential Litigation Boom, Threat to Judicial Economy, AI Integration in Judicial Process, Judicial Capacity Enhancement | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | High costs of legal services (lawyer and court fees); complexity of legal processes; sociolegal barriers including lack of legal consciousness (naming-blaming-claiming); potential for increased litigation volume overwhelming the judicial system. | High Cost of Legal Services, Complexity of Legal System/Procedures, Psychological/Cultural Barriers to Seeking Help/Engaging with Law, Public Lack of Legal Knowledge/Awareness, Risk of AI Exacerbating System Strain | Proactive integration of AI tools into the judicial process itself (e.g., for case management, summarization, document Q&A, drafting assistance, generative interpretation) to scale up the system's capacity, rather than reactive adjustments like raising fees or tightening procedural/substantive standards ('legal thermostats'). | Judicial System Enhancement, AI Tool Development, Document Automation, Policy and Regulatory Reform | Access to legal services; judicial economy; litigation volume; legal consciousness (naming-blaming-claiming); impact of technology on courts. | Democratizing Law / Closing Justice Gap / Rule of Law, Judicial System Modernization / Efficiency, Legal Literacy and Public Legal Education | Low-income individuals/Americans, ordinary people facing unresolved civil legal problems. | Low-income individuals, Population in USA, General public, Individuals with civil legal problems | General Civil Litigation, Administrative Law, Civil Procedure | Civil Litigation, Administrative Law, Civil Procedure | United States | USA | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Ensuring the judicial system can scale to handle increased access without compromising substantive justice; need for robust, reliable, ethical AI tools tailored for judicial use; bridging the gap between AI's potential for access and the system's current capacity and readiness. | Access, Equity, and Digital Divide, AI Accuracy and Reliability, Ethical Framework Deficiencies, Regulatory and Governance Gaps | AI unreliability (hallucinations, inaccuracy); ensuring confidentiality; costs and complexity of integrating AI into judicial systems; need for careful testing and validation; ethical concerns (human oversight, judicial authenticity); potential for AI misuse (e.g., facilitating frivolous claims); judicial skepticism and resistance. | Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Data Privacy, Security, and Confidentiality, Financial Cost and Resource Constraints, Integration with Existing Systems and Workflows, Evaluation Challenges and Metrics, Ethical Considerations, Need for Human Oversight and Intervention, Safeguarding Against Misuse and Harm, User Adoption, Trust, and Acceptance | Overwhelming judicial caseloads; erosion of substantive rights through reactive 'legal thermostat' adjustments; AI errors impacting case outcomes; potential bias in AI applications; confidentiality breaches; misuse of AI for vexatious litigation; loss of judicial authenticity and reasoned deliberation. | Undermining legal process or principles, Inaccurate or misleading AI output, Bias and discrimination, Data privacy and security breach, Security vulnerabilities or malicious misuse, Dehumanization of legal process |
| pf5cO5kK6_QJ.pdf | Google_Scholar | Chatlaw: A Multi-Agent Collaborative Legal Assistant with Knowledge Graph Enhanced Mixture-of-Experts Large Language Model | This paper introduces Chatlaw, an AI legal assistant using a Mixture-of-Experts (MoE) LLM and a multi-agent system mimicking law firm workflows to enhance reliability and reduce hallucination. Chatlaw integrates knowledge graphs and RAG, outperforming GPT-4 on Chinese legal benchmarks and expert evaluations. | AI Legal Assistant Development, Mixture-of-Experts LLM, Multi-Agent System, Reliability Improvement, Mitigating AI Hallucinations, Knowledge Graph Integration, Retrieval Augmented Generation, Chinese Law Focus, System Evaluation | True | Idealistic | True | 1.0 | Positive | Chatlaw system: Mixture-of-Experts (MoE) LLM combined with a multi-agent framework (Legal Assistant, Legal Researcher, Senior Lawyer, Legal Editor) using Standardized Operating Procedures (SOP), Knowledge Graphs, and Retrieval-Augmented Generation (RAG). | Software / Platform Development, Mixture of Experts (MoE), Large Language Model, Multi-Agent System, Standardized Operating Procedures (SOP), Knowledge Graph Integration, Retrieval Augmented Generation (RAG), Named Tool / Platform | Evaluation on LawBench (Chinese legal benchmark), China's Unified Qualification Exam for Legal Professionals (2018-2022), and real-world legal consultations assessed by legal experts based on Completeness, Correctness, Guidance, and Authority. | Benchmark Dataset Evaluation, Performance on Standardized Tests, Expert Evaluation, Quantitative Metrics | Outperformed GPT-4 on LawBench by 7.73% avg accuracy and on the Unified Qualification Exam by 11 points avg score. Achieved highest scores and win rates in real-case expert evaluations. | Outperforms others, High performance | Limited availability of legal professionals, high cost of legal services, gap between legal aid need and provision capacity, leading to restricted access and impacting justice/equity. | Limited Availability/Access to Legal Professionals/Expertise, High Cost of Legal Services, Limited Availability/Access to Legal Aid, Scale of Unmet Legal Need | Proposes Chatlaw, an automated legal assistant using an MoE LLM and multi-agent collaboration, integrating knowledge graphs and RAG to provide reliable, accurate, and accessible legal consulting services. | AI Tool Development, Enhanced AI Capabilities, Legal Knowledge Representation and Management, Access to Legal Information and Advice | General legal consultation, Divorce/family law (used as example). | Access to Legal Advice | General population in China lacking resources to navigate the legal system. | General public, Population in China, Low-income individuals, Individuals lacking legal knowledge | Multiple fields (based on benchmarks and dataset description), including Divorce Law (example). | Multiple Fields, Family Law | China | China | Proprietary 'Chatlaw legal dataset' (approx. 4 million samples) constructed from multi-source data, processed via deduplication, denoising, and human finetuning (students, experts). Covers 10 major/44 minor legal categories, includes knowledge graphs and agent task datasets. Formatted using LLaMA chat template. | Proprietary Data, Author-Created New Dataset, Fine-tuning Dataset, Legal Domain Data, Expert-Annotated / Human-Curated / Human-Generated Data, Structured Data, Instruction-Tuning Formatted Data, Undisclosed Data Source/Availability | Data Collection Pipeline, Mixture-of-Experts (MoE) Architecture Design, Multi-Agent System Design (Roles, SOPs), Knowledge Graph Integration, Retrieval-Augmented Generation (RAG), Human-in-the-loop Refinement. | Pipeline Development, Mixture of Experts (MoE) Architecture, Multi-agent System Design, Knowledge Graph Construction/Integration, Retrieval Augmented Generation (RAG), Human-in-the-loop System | An online trial phase was conducted, gathering user feedback. Plans mentioned to popularize the framework. | Pilot program/Limited rollout, Research preview/Beta access, Web-based access, Proposed deployment (not implemented), Dissemination via publication/presentation | True | True | Dataset, codes and deploy details are released in the GitHub repository: github.com/PKU-YuanGroup/ChatLaw. | Dataset available, Code available, Model available | Need for model compression (for on-device deployment), addressing user privacy concerns vs. record-keeping needs, managing computational resources at scale, ensuring robustness against adversarial inputs. | Computational Resource and Cost Issues, AI Scope and Functionality Limitations, Security and Privacy of Data, AI Accuracy and Reliability | Creating high-quality legal dataset, effective MoE model training, multi-agent coordination, hallucination mitigation (via RAG/SOPs), ensuring robustness, computational resource intensity, addressing privacy issues identified in trials. | Scarcity of High-Quality Legal Data, Domain-Specific Adaptation and Customization, Integration with Existing Systems and Workflows, LLM Hallucination and Factual Errors, Accuracy and Reliability of LLM Output, High Computational and Resource Demands, Data Privacy, Security, and Confidentiality | LLM hallucination (providing incorrect/fabricated legal information), privacy risks related to sensitive user data in consultations. | Inaccurate or misleading AI output, Data privacy and security breach |
| 99NYULRev451.pdf | HeinOnline | GENERATIVE INTERPRETATION | This paper introduces "generative interpretation," a novel approach using large language models (LLMs) to estimate contractual meaning, quantify ambiguity, fill gaps, and assess extrinsic evidence. It argues that this method can offer a cheaper, more accessible, and predictable way to interpret contracts, potentially bridging the gap between textualist and contextualist approaches and improving access to justice. | Methodology Proposal (Generative Interpretation), LLM Application, Contract Interpretation, Ambiguity Quantification, Gap Filling in Contracts, Access to Justice Enhancement | True | Idealistic | True | 1.0 | Positive | Generative interpretation using large language models (LLMs) for contractual interpretation, including querying models (GPT-4, Claude 2, Llama-2) with contract text and specific questions, analyzing embedding distances, and examining probabilistic outputs for meaning, ambiguity, and gap-filling. | Large Language Model, Contract Interpretation, Prompt Engineering, Embedding-based Methods, Probabilistic Output Analysis, Ambiguity Detection / Gap Filling | The approach was evaluated through grounded case studies using actual contracts from well-known contract law opinions (e.g., In re Katrina, C & J Fertilizer, Famiglio v. Famiglio, Trident Center, Ellington v. EMI, Haines v. City of New York, Stewart v. Newbury). This involved feeding contract text and specific queries to LLMs and analyzing their responses, sometimes with multiple prompts and temperature settings. | Qualitative Analysis, Custom Dataset Evaluation | LLMs demonstrated capabilities in ascertaining ordinary meaning in context (e.g., 'flood' in Katrina), quantifying ambiguity (e.g., prepayment clause in Trident), filling gaps (e.g., duration in Haines), and calculating the probative value of extrinsic evidence (e.g., phone call in Stewart). Model outputs were often plausible and offered nuanced perspectives, sometimes supporting and sometimes challenging judicial outcomes. | Descriptive or Conceptual finding, Moderate performance | The high cost and inaccessibility of current contract interpretation methods for ordinary parties and resource-constrained firms, leading to an access-to-justice problem. The uncertainty and potential biases in traditional methods like dictionary reliance and judicial intuition. | High Cost of Legal Services, Difficulty Accessing/Interpreting Legal Information, Uncertainty of Legal Outcomes, Bias in Traditional Legal Methods | Proposes generative interpretation as a cheaper, more accessible, transparent, and predictable methodology for contract interpretation. This can democratize access to sophisticated textual analysis, reduce litigation costs, and make outcomes more certain, thus improving access to justice for the "99%". | AI Tool Development, Document Automation, Cost Reduction and Efficiency, Access to Legal Information and Advice, Transparency and Explainability in AI, Legal Research and Analysis Tools | Contract interpretation, access to legal understanding for non-wealthy individuals and resource-constrained parties, reducing costs and uncertainties in contract litigation. | Legal Document Analysis / Review, Legal Literacy and Public Legal Education, Support for Vulnerable Populations, Affordability of Legal Services / Cost Reduction, Dispute Resolution | Non-wealthy individuals, ordinary parties, resource-constrained firms, and potentially judges in resource-deprived courts. | Low-income individuals, Moderate-income individuals, General public, Small businesses, Resource-constrained organizations, Judges | Contract Law, Insurance Law. | Contract Law, Insurance Law | United States (primarily, with case law examples from various US state and federal courts like New York, California, Iowa, Fifth Circuit, Florida, Alabama). | USA | The LLMs used (GPT-4, Claude 2, Llama-2) are trained on vast, general corpora of text ('torrents of existing texts'). The paper itself does not detail the specific datasets beyond what is generally known about these models' pre-training, but notes they are trained on 'trillions of words'. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Proprietary Data | The authors developed their "generative interpretation" approach by: 1) Obtaining and analyzing original contract texts from litigated cases. 2) Designing prompts and queries to elicit interpretations from LLMs. 3) Using techniques like embedding distance analysis. 4) Iterative querying with varied prompts and temperature settings to assess robustness. 5) Comparing LLM outputs to judicial reasoning and academic commentary. | Data Collection, Legal Document Analysis, Prompt Engineering, Embedding Analysis, Iterative Querying, Parameter Experimentation, Comparative Analysis of Outputs | The paper provides a GitHub link (https://github.com/yonathanarbel/generativeinterpretation/tree/main) for the code to replicate their results, suggesting the methods can be implemented using accessible LLMs. | Open source code release | True | True | The code for replicating results is available on GitHub. The LLMs discussed (e.g., Llama-2 is open source, GPT-4 and Claude 2 are accessible via APIs or chat interfaces) are generally available. | Code available, Model available, Open-source, API access, Publicly accessible online tool or platform | Technical gaps include model hallucinations, susceptibility to manipulation (adversarial attacks, prompt injection), majoritarian bias in outputs, sensitivity to linguistic drift over time, and the 'black box' nature of LLM reasoning (interpretability). Societal gaps include the need for a new 'language' or sociological framework for courts to justify and explain LLM-aided interpretations to ensure legitimacy. | AI Accuracy and Reliability, Bias in AI, Knowledge Recency and Updatability, Transparency and Explainability, Regulatory and Governance Gaps, Human Oversight and Professional Adaptation | Hallucinatory outputs, sensitivity of models to prompts ('leading prompts'), model biases towards majoritarian interpretations, adversarial attacks or prompt injections, models' insensitivity to the specific time of contract formation (linguistic drift), and the lack of full interpretability of model reasoning. | LLM Hallucination and Factual Errors, Prompt Engineering and Optimization, Bias in AI Systems and Data, Safeguarding Against Misuse and Harm, Outdated or Limited LLM Knowledge Base, Transparency and Explainability of AI | Generation of false or misleading information (hallucinations) by LLMs (e.g., citing fake cases). Manipulation of LLM outputs through carefully crafted prompts or adversarial attacks. Reinforcement of majoritarian biases, potentially silencing linguistic conventions of underrepresented communities. Difficulty in auditing or understanding the precise reasoning behind an LLM's interpretation ('black box' problem). Linguistic drift, where models trained on contemporary text misinterpret older contracts. E_DECREASE_IN_JUDICIAL_LEGITIMACY_IF_NOT_PROPERLY_INTEGRATED_AND_EXPLAINED. | Inaccurate or misleading AI output, Security vulnerabilities or malicious misuse, Bias and discrimination, Lack of transparency, accountability, and redress, Technical limitations of AI, Erosion of trust in legal system or AI |
| 28AALLSpectrum10.pdf | HeinOnline | Making the Justice Leap: Using Generative AI to Bridge the Literacy, Equity, Access, and Privilege Gaps for Self-Represented Litigants | This paper discusses the potential of generative AI (GenAI) to assist self-represented litigants (SRLs) in navigating the civil legal system, addressing literacy, equity, access, and privilege gaps. It proposes a conceptual GenAI tool named "Gideon" and calls for law librarians to advocate for SRLs and the ethical use of such technologies. | Generative AI for Self-Represented Litigants, Civil Legal System Navigation, Addressing Equity Gaps, Conceptual Tool Proposal, Role of Law Librarians, Ethical AI Use Advocacy | True | Idealistic | True | 1.0 | Positive | A conceptual GenAI tool named "Gideon" designed to assist self-represented litigants by leveraging a sophisticated language model trained on legal resources. Also, an advocacy strategy for law librarians to promote AI tools and SRL support. | Conceptual Tool Design, Generative AI, Large Language Model, Self-Represented Litigant Support, Advocacy Strategy | NaN | Not Applicable | NaN | NaN | Intimidating court procedures, confusing legal forms, unfamiliar legal jargon, complex judicial rules leading to case dismissals; insufficient legal aid resources and unaffordability of lawyers; SRLs' personal limitations in language fluency, digital proficiency, and social exclusion; restrictive Unauthorized Practice of Law (UPL) rules. | Complexity of Legal System/Procedures, Complexity of Legal Language/Documents, Resource Constraints for Legal Aid Organizations, High Cost of Legal Services, Challenges for Self-Represented Litigants, Accessibility Barriers for Specific User Groups, Digital Divide, Regulatory Hurdles | Develop a powerful GenAI tool (e.g., "Gideon") for pro se litigants, trained on extensive legal resources. Establish ethical AI guidelines through impact assessments and continuous outcome evaluations. Law librarians to advocate for SRLs and the use of AI by publishing articles in bar journals and promoting alternative legal service models. | AI Tool Development, Support for Self-Represented Litigants, Data Curation and Management, Regulation, Ethics, and Governance, Benchmarking and Evaluation Frameworks, Policy and Regulatory Reform, Alternative Legal Service Delivery Models | Access to justice for self-represented litigants; legal information navigation; legal document drafting; understanding court procedures; overcoming literacy and digital divides; role of law librarians in promoting legal tech; addressing Unauthorized Practice of Law (UPL) concerns. | Support for Self-Represented Litigants, Access to Legal Information, Left Document Creation / Automation, Legal Literacy and Public Legal Education, Language Access and Digital Divide, Regulatory Reform (Left Services and AI) | Self-represented litigants (SRLs), particularly those with modest or limited means, middle-income individuals who cannot afford an attorney, and those facing literacy, digital proficiency, or social exclusion challenges. | Self-represented litigants, Low-income individuals, Moderate-income individuals, Individuals unable to afford legal services, Individuals with low literacy, Individuals with low digital literacy, Marginalized communities | Civil law (explicitly mentions eviction, foreclosure, repossession, domestic violence, and child welfare cases). | Civil Law, Housing Law, Debt Collection, Domestic Violence Law, Family Law | United States (implied by discussion of U.S. legal aid, ULC, state bar magazines, and specific U.S. locations like Washington D.C. and Harris County, TX). | USA | For the conceptual tool "Gideon": An extensive range of legal resources, including legal aid websites, primary law (statutes, case law), legal practice guides for various jurisdictions, form books, and other secondary legal sources. | RAG System Knowledge Corpus, Legal Domain Data, Legislation / Statutes / Regulations, Case Law / Judgments, Legal Scholarly Content / Textbooks, Other Legal Documents, Publicly Available Data | N/A (Gideon is described as "purely conceptual in design"; no specific design methodologies for its creation are detailed). | NaN | N/A (Gideon is conceptual. The advocacy part suggests publishing articles in bar magazines as a diffusion strategy for ideas). | Proposed deployment (not implemented), Dissemination via publication/presentation | False | False | NaN | NaN | Limited empirical research on GenAI's impact, especially for SRLs; murky and divergent Unauthorized Practice of Law (UPL) definitions across jurisdictions hindering innovation; need for robust ethical guidelines for GenAI development and use by SRLs; societal gaps related to literacy, digital proficiency, access, and privilege affecting SRLs. | Research and Evaluation Gaps, Regulatory and Governance Gaps, Ethical Framework Deficiencies, Access, Equity, and Digital Divide, Public Understanding, Trust, and Adoption | Managing fears and navigating regulations concerning Unauthorized Practice of Law (UPL) violations; developing and implementing clear ethical guidelines for GenAI use by SRLs (covering fairness, transparency, non-discrimination); ensuring the proposed GenAI tool is accessible and effective for users with limited language fluency or digital proficiency. | User Adoption, Trust, and Acceptance, Regulatory Uncertainty and Compliance, Unauthorized Practice of Law (UPL) Concerns, Ethical Considerations, Bias in AI Systems and Data, Transparency and Explainability of AI, User Interface, Usability, and Accessibility, Multilingual and Low-Resource Language Support, User Training, AI Literacy, and Skill Gaps | GenAI tools producing fabricated or incorrect legal information (e.g., fake case citations); potential for AI to engage in the Unauthorized Practice of Law (UPL); negative consequences for SRLs if AI tools are not properly designed, ethically guided, or effectively used; model UPL legislation being used to curtail rather than expand access to justice programs. | Inaccurate or misleading AI output, Unauthorized practice of law, Consumer harm, Risk of misapplication or misuse, Regulatory challenges or gaps |
| 2024IntlJLEthicsTech186.pdf | HeinOnline | "Trustworthy AI" Cannot Be Trusted: A Virtue Jurisprudence-Based Approach to Analyse Who Is Responsible for AI Errors | This paper argues that humans, not AI, must be held responsible for AI errors because a genuine trust relationship with AI is impossible due to AI's lack of moral motivation and responsibility. It proposes that this human responsibility should be assigned to direct beneficiaries of AI products and vary according to the AI's risk level, advocating for technical authentication obligations for high-risk AI like deepfakes. | Human Responsibility for AI Errors, Trust in AI, Moral Agency of AI, Liability for AI, Risk-Based Responsibility, Authentication of High-Risk AI | True | Idealistic | True | 3.0 | Neutral | Virtue jurisprudence-based approach to analyse who is responsible for AI errors. | Ethical Framework / Philosophical Analysis, AI Accountability / Responsibility | NaN | Not Applicable | NaN | NaN | High cost and difficulty in authenticating AI-generated evidence (e.g., deepfakes), potential for misuse ("liar's dividend" leading to skepticism about genuine evidence), impacting affordability of justice. | High Cost of Verifying AI Content, Difficulty Verifying AI-Generated Content, Risk of AI Misuse, Lack of Trust in Evidence | Mandating technical authentication for high-risk AI evidence, requiring a good-faith basis for deepfake claims, ensuring developers facilitate access to detection tools for defense, and imposing obligations on responsible entities to provide reliable identification. | Regulation, Ethics, and Governance, Policy and Regulatory Reform, AI Tool Development | Authenticity and admissibility of AI-generated evidence (deepfakes), procedural fairness, equitable access to technical expertise in legal proceedings. | Ethical AI in Law and AI Governance, Protection of Rights, Judicial System Modernization / Efficiency | Litigants (especially those with limited resources), defence lawyers, and the justice system as a whole, impacted by challenges of AI-generated evidence. | Litigants, Low-income individuals, Legal professionals | AI Law, Product Liability, Evidence Law, Criminal Procedure, Ethics in Law. | AI Regulation, Product Liability Law, Evidence Law, Criminal Procedure, Legal Ethics | European Union (focus on EU AI Act), United Kingdom (mentions UK ETAF). Principles discussed have broader relevance. | EU, UK, International | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Technological gap between AI generation (e.g., deepfakes) and detection capabilities. Need for further research on differentiating human obligations based on AI risk levels across various fields. Ensuring affordable and accessible authentication methods for AI-generated evidence. | AI Accuracy and Reliability, Transparency and Explainability, Research and Evaluation Gaps, Access, Equity, and Digital Divide | Ensuring AI reliability, explainability, and trustworthiness; managing AI's autonomy and unpredictability; attributing moral and legal responsibility for AI errors. | Accuracy and Reliability of LLM Output, Transparency and Explainability of AI, User Adoption, Trust, and Acceptance, Output Variability and Consistency, Accountability and Liability for AI Errors, Ethical Considerations | Erroneous AI outputs causing harm; manipulation of individuals; undermining due process via deepfakes ('liar's dividend'); infringement on fundamental rights; erosion of cognitive trust in evidence ('seeing is believing'). | Inaccurate or misleading AI output, Consumer harm, Security vulnerabilities or malicious misuse, Undermining legal process or principles, Infringement on human rights, Erosion of trust in legal system or AI |
| 6LawTechHum88.pdf | HeinOnline | Prompts and Large Language Models: A New Tool for Drafting, Reviewing and Interpreting Contracts? | This paper examines the use of Large Language Models (LLMs) and prompt engineering for drafting, reviewing, and interpreting contracts, exploring both their potential to enhance efficiency and accessibility for lawyers and non-lawyers. It analyzes the significant challenges, including inaccuracies, biases, lack of transparency, and the crucial legal implications, particularly concerning the parol evidence rule and the admissibility of prompts in contractual disputes. | LLM Application, Prompt Engineering, Contract Drafting, Contract Review, Contract Interpretation, Efficiency Improvement, Accessibility Enhancement, Challenge Identification, AI Hallucinations/Inaccuracy, Bias in AI, Transparency Issues, Legal Implications (Parol Evidence Rule) | True | Idealistic | True | 3.0 | Neutral | Large Language Models (LLMs) and prompt engineering for contract drafting, review, and interpretation. | Large Language Model, Prompt Engineering, Contract Drafting / Review / Interpretation | Cites external studies such as the Allens AI Australian Law Benchmark (testing LLMs on Australian legal questions) and research on LLM performance in professional law tasks (Hendrycks et al.). | References External Evaluation | Cited studies indicate that even top LLMs are not consistently reliable for legal questions, may contain 'infection' from other jurisdictions' laws (Allens benchmark), and show low accuracy in professional law tasks (Hendrycks et al.). | Low performance, Limitation: Operational or Technical, Descriptive or Conceptual finding | Perpetuation of existing inequalities; generation of unfair, unconscionable, or market-distorting contracts; perpetuation of harmful stereotypes and discriminatory clauses; exacerbation of power asymmetries due to biased AI. | Risk of AI Exacerbating Inequality, Bias in AI/Data, Ethical Concerns with AI in Law, Power Imbalances | Careful curation of diverse and unbiased training data; transparency in algorithms; continuous monitoring and audits for bias; human oversight at critical junctures; responsible implementation focusing on fairness and ethical considerations; adapting legal doctrines like the parol evidence rule. | Data Curation and Management, Bias Detection and Mitigation, Transparency and Explainability in AI, Regulation, Ethics, and Governance, Human Oversight and Collaboration, Policy and Regulatory Reform | Improving accessibility and affordability of contract creation for non-legally trained individuals; ensuring fairness, equity, and non-discrimination in AI-generated legal documents; adapting legal doctrines to AI. | Legal Document Creation / Automation, Affordability of Legal Services / Cost Reduction, Ethical AI in Law and AI Governance, Regulatory Reform (Legal Services and AI) | Non-lawyers and individuals without legal expertise seeking to understand or create contracts, as well as the legal profession generally. | Laypeople, Individuals lacking legal knowledge, Legal professionals | Contract Law; Civil Procedure (specifically evidence and interpretation rules like the parol evidence rule). | Contract Law, Civil Procedure, Evidence Law, Statutory Interpretation | Australia (primary focus for legal analysis like parol evidence rule), with references to developments in the US, EU, Singapore, China, and UNCITRAL. | Australia, USA, EU, Singapore, China, International | The paper describes LLMs as being trained on large, generic text corpora, then fine-tuned on specialized datasets. For legal LLMs, this includes 'legalese' and potentially legal documents. Lexis+AI is mentioned as trained on 'Lexis authoritative primary and secondary materials'. Access to proprietary law firm data (client contracts, advice) for training professional law LLMs is noted as limited. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Fine-tuning Dataset, Legal Domain Data, Proprietary Data, Legal Scholarly Content / Textbooks, Other Legal Documents | NaN | NaN | Discusses commercial deployment by legal tech companies (e.g., Lexis+AI available in US and Australia) and adoption by law firms, including on-premises models for data privacy. | Evaluation of existing third-party tool, Commercial product/service, Internal deployment/prototype, Local deployment/Standalone application | True | False | Commercial products like Lexis+AI, Motionize, Robin.AI are mentioned as available from their respective vendors/companies. | Commercial product or service | Technical gaps include LLM inaccuracy, hallucinations, lack of nuanced legal reasoning, and 'black box' transparency issues. Societal/legal gaps include the unclear legal status of prompts, need for legal doctrine adaptation (e.g., parol evidence rule), ethical concerns (data collection, copyright, declaration of use), and ensuring effective human oversight to mitigate AI risks and biases. | AI Accuracy and Reliability, AI Legal Reasoning Limitations, Transparency and Explainability, Regulatory and Governance Gaps, Ethical Framework Deficiencies, Security and Privacy of Data, Human Oversight and Professional Adaptation | Ensuring accuracy and avoiding 'hallucinations' in LLM outputs; addressing the lack of transparency in LLM decision-making processes; mitigating inherent biases from training data and model design; defining the legal status of prompts and their interaction with existing legal rules (e.g., parol evidence rule); managing client confidentiality and data privacy with LLM use; effectively training legal professionals in prompt engineering and critical AI assessment. | Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Transparency and Explainability of AI, Bias in AI Systems and Data, Regulatory Uncertainty and Compliance, Prompt Engineering and Optimization, Data Privacy, Security, and Confidentiality, User Training, AI Literacy, and Skill Gaps, Legal Professional Responsibility and Competence | Generation of unfair, unconscionable, or market-distorting contracts; perpetuation of existing inequalities and harmful stereotypes; inclusion of discriminatory clauses; exacerbation of power asymmetries; 'hallucinations' (inaccurate outputs); 'stochastic parrots' (mindless repetition); biased interpretation; 'infection' by laws from irrelevant jurisdictions; manipulation of contract interpretation processes; lawyers citing LLM-fabricated non-existent cases. | Inaccurate or misleading AI output, Bias and discrimination, Exacerbation of inequality or two-tiered system, Technical limitations of AI, Security vulnerabilities or malicious misuse, Negative economic impact |
| 4JusCorpusLJ208.pdf | HeinOnline | Navigating Legal Advice through Al Chatbots | This paper discusses the growing trend of using AI chatbots like ChatGPT for legal advice, highlighting their current unreliability and the ethical and legal challenges involved. It compares chatbots to human lawyers, concluding that while AI holds future promise, professional legal counsel remains essential for accurate advice, especially given the lack of specific AI regulations in India. | AI Chatbots for Legal Advice, Unreliability of AI Legal Advice, Ethical Challenges, Legal Challenges, Comparison with Human Lawyers, Need for Professional Counsel, Lack of AI Regulation (India) | True | Idealistic | True | 3.0 | Neutral | AI Chatbots for legal advice (e.g., ChatGPT, Law Bot Pro) | Chatbot / Conversational AI, Legal Advisory System, Named Tool / Platform | Referenced studies indicating inaccuracy of AI chatbots like ChatGPT for legal advice, an anecdotal case study (Roberto Mata v Avianca Airline) of misuse demonstrating unreliability, user query to ChatGPT, and developer acknowledgment of limitations for Law Bot Pro. | References External Evaluation, Qualitative Analysis, Developer Claims Reported | AI chatbots like ChatGPT are reported as "INACCURATE and PROBLEMATIC" for legal advice, capable of creating fake cases, and self-admittedly not a substitute for professional legal advice. Law Bot Pro is acknowledged by its developers to have limitations, suitable for understanding basic laws/rights but not for proper legal advice. | Low performance, Limitation: Hallucination or Factual inaccuracy, Limitation: Operational or Technical, Developer or Vendor claim | Inaccuracy and unreliability of AI, ethical concerns like bias in AI outputs, lack of accountability for AI-generated advice, and absence of specific legal regulations governing AI. | AI Unreliability/Inaccuracy, Ethical Concerns with AI in Law, Bias in AI/Data, Lack of AI Accountability, Inadequate Legal Frameworks for AI | Emphasis on consulting human lawyers for reliable advice, development of clear accountability frameworks and specific AI regulations, and continued research and development for more accurate AI models. Pro-bono initiatives like 'Law Bot Pro' for basic legal information are also mentioned. | Human Oversight and Collaboration, Regulation, Ethics, and Governance, AI Tool Development, Access to Legal Information and Advice | Access to legal information and advice, reliability of AI in law. | Access to Legal Information, Access to Legal Advice, Ethical AI in Law and AI Governance | General public, particularly those seeking free or low-cost legal information and potentially underserved communities. | General public, Individuals unable to afford legal services, Marginalized communities | General | General Law | India (primary focus), with mentions of US and EU. | India, USA, EU | NaN | Not Applicable | NaN | NaN | Law Bot Pro deployed as a free legal AI app; ChatGPT accessible via web platform. | Evaluation of existing third-party tool, Freely accessible tool/service, Mobile app deployment, Web-based access | True | True | ChatGPT is accessible via its website with a free tier. Law Bot Pro is described as a 'free legal Al app'. | Publicly accessible online tool or platform, Freemium access | Lack of AI accuracy and reliability for complex legal advice, absence of robust legal and ethical frameworks for AI governance, and user over-reliance or misunderstanding of current AI capabilities. | AI Accuracy and Reliability, AI Legal Reasoning Limitations, Ethical Framework Deficiencies, Regulatory and Governance Gaps, Public Understanding, Trust, and Adoption | Ensuring accuracy and reliability of legal information provided by AI, mitigating AI bias, establishing accountability for AI-generated advice, and navigating the lack of specific AI regulations. | Accuracy and Reliability of LLM Output, Bias in AI Systems and Data, Accountability and Liability for AI Errors, Regulatory Uncertainty and Compliance | Provision of inaccurate or misleading legal advice by AI, professionals misusing AI leading to legal errors and reputational damage, propagation and reinforcement of societal biases by AI, and potential copyright infringement issues. | Inaccurate or misleading AI output, Risk of misapplication or misuse, Ethical concerns, Bias and discrimination, Copyright or intellectual property issues |
| 2023UIllLRevOnline165.pdf | HeinOnline | RAGE AGAINST THE MACHINE: WHO IS RESPONSIBLE FOR REGULATING GENERATIVE ARTIFICIAL INTELLIGENCE IN DOMESTIC AND CROSS-BORDER LITIGATION? | This paper analyzes which public and private bodies are best suited to regulate the use of generative AI in domestic and cross-border litigation, focusing on identifying who should act rather than proposing specific regulatory content. It suggests a multi-faceted approach involving courts, licensing authorities, legislatures, and international bodies to address generative AI's challenges to the justice system. | Regulation of Generative AI in Litigation, Identifying Regulatory Actors, Multi-Faceted Regulatory Approach, Cross-Border Litigation Challenges | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | Generation of false or misleading legal information by AI (hallucinations, misinterpretations); lack of reliability of AI-generated documents; erosion of public trust in the justice system; concerns regarding due process and procedural fairness; shifting of costs and burdens to other litigation participants; lack of transparency in AI-generated content. | AI Unreliability/Inaccuracy, AI-driven Misinformation/Disinformation, Lack of Trust in Justice System, Risk to Human Rights from AI, Increased Litigation Costs/Burdens, Lack of AI Transparency/Explainability | Establishment of clear, agile, and comprehensive regulatory frameworks in a phased manner (rules of court, rules of professional responsibility, legislation); involvement of various domestic (judicial, legislative, licensing authorities, research institutions) and international bodies (e.g., Hague Conference, UNIDROIT, IBA); conducting empirical and policy-oriented research to inform regulatory content; ensuring accountability for AI use. | Policy and Regulatory Reform, Regulation, Ethics, and Governance, Open Source Initiatives and Collaboration | Ensuring procedural fairness and due process; maintaining the integrity of legal information and court proceedings; upholding public confidence in the justice system; establishing accountability for AI-generated content in litigation. | Protection of Rights, Ethical AI in Law and AI Governance, Judicial System Modernization / Efficiency | Pro se litigants | Self-represented litigants | Civil litigation, Criminal litigation, Cross-border litigation, Procedural law | Civil Litigation, Criminal Litigation, International Law, Procedural Law | United States, Canada, UK, EU, China, International | USA, Canada, UK, EU, China, International | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Lack of comprehensive, proactive, and agile regulatory frameworks for generative AI in litigation; insufficient technical safeguards within AI tools to ensure accuracy and reliability for legal use; need for more empirical and policy research to define appropriate regulatory content; risk of regulatory inertia or uncoordinated piecemeal responses. | Regulatory and Governance Gaps, AI Accuracy and Reliability, Research and Evaluation Gaps | NaN | NaN | Erroneous legal outcomes due to AI hallucinations and misinterpretations; violations of due process and procedural fairness; erosion of public confidence in judicial systems; increased litigation costs and burden-shifting; unreliability of AI-generated legal documents; improper delegation of judicial authority; potential for misuse by pro se litigants leading to system burdens; incompetent or unethical use by legal professionals. | Inaccurate or misleading AI output, Undermining legal process or principles, Erosion of trust in legal system or AI, Negative economic impact, Risk of misapplication or misuse, Ethical concerns |
| 16JIntellPropInfoTechElec.pdf | HeinOnline | The Artificial Intelligence Act: Critical Overview | This article provides a critical overview of the European Union's Artificial Intelligence Act (Regulation (EU) 2024/1689), analyzing its structure, objectives, scope, key definitions, and risk-based approach. It discusses prohibited practices, high-risk AI systems, transparency obligations, general-purpose AI models, and concludes that the Act's complexity may undermine its goals of fostering responsible innovation and protecting public interests. | Review of EU AI Act, AI Regulation (EU), Risk-Based Approach to AI, High-Risk AI Systems, Transparency Obligations, General-Purpose AI Models, Critique of Regulation | True | Idealistic | True | 2.0 | Neutral | The EU Artificial Intelligence Act (Regulation (EU) 2024/1689) | Regulatory Framework / Legislation | The paper evaluates the AI Act through a dogmatic legal analysis, presenting a general descriptive legal analysis of the Regulation in the wider context of EU law. | Theoretical Analysis or Conceptual Proposal | The paper concludes that while the AI Act contains generally balanced and reasonable solutions, its complexity and poor legislative quality risk defeating its purpose, negatively affecting innovation, and potentially reducing the supply of advanced AI in the EU. | Risk or Ethical concern highlighted, Descriptive or Conceptual finding | AI-driven discrimination, lack of transparency in automated decisions affecting legal rights (para 3, 43, 45), and the potential for the AI Act's own complexity to hinder effective protection of fundamental rights relevant to access to justice (para 108). | Bias in AI/Data, Lack of AI Transparency/Explainability, Complexity of AI Regulation as a Barrier, Risk to Human Rights from AI | The paper suggests that the adoption of technical standards to reduce compliance costs and uncertainty, and increased involvement of legal experts to navigate and implement the complex AI Act, could help overcome the legislation's limitations (para 109). | Regulation, Ethics, and Governance, Cost Reduction and Efficiency, Human Oversight and Collaboration, Policy and Regulatory Reform | Protection of fundamental rights (non-discrimination, fairness, transparency, accountability) through AI regulation, particularly concerning AI in law enforcement, biometric identification, and judicial contexts, which indirectly relates to access to justice (para 4, Section F, Annex III). | Protection of Rights, Ethical AI in Law and AI Governance, Regulatory Reform (Legal Services and AI) | The AI Act, as analyzed in the paper, aims to protect vulnerable groups (e.g., based on age, disability, socio-economic status) and prevent discrimination based on protected characteristics (e.g., race, political opinions) from harmful AI practices (para 53, 65). | Vulnerable populations, Elderly people, Youth, People with disabilities, Low-income individuals, Minority groups | AI Law, EU Law, Product Safety Law, Fundamental Rights Law, Data Protection Law, Competition Law. | AI Regulation, EU Law, Product Safety Law, Human Rights Law, Data Privacy Law, Competition Law | European Union | EU | NaN | Not Applicable | The AI Act was developed through the EU legislative process, involving a proposal from the European Commission, intense negotiations between the Commission, Parliament, and Council, amendments, and a corrigendum (para 8). | Legislative Process | The AI Act (Regulation (EU) 2024/1689) was published in the Official Journal of the EU and is subject to a phased entry into force, with general application scheduled for August 2, 2026, and some parts applying earlier (para 8, 37). | Regulatory/Legal framework adoption | True | True | The EU AI Act (Regulation (EU) 2024/1689) is published in the Official Journal of the EU and is publicly accessible (para 8). | Open access resource | The paper highlights the AI Act's significant complexity and potential poor legislative quality as major gaps (para 108). It also cites critiques suggesting limitations, loopholes, and a potentially narrow scope for high-risk classification, which might leave some harmful AI applications inadequately regulated or enforced (para 71 footnote 106, para 140 footnote 140). | Regulatory and Governance Gaps | The EU legislators faced challenges in defining AI, establishing a risk classification system, regulating general-purpose AI models, addressing open-source AI, and balancing the promotion of innovation with the protection of fundamental rights and public safety during the Act's development (para 8, 13-14, 22, 36, 90-91). | Regulatory Uncertainty and Compliance, Ethical Considerations | The paper states that the AI Act itself, due to its complexity and legislative quality, risks negatively affecting innovation, hindering investment, reducing the supply of advanced AI in the EU, and potentially defeating its own purpose of promoting responsible innovation and protecting public interests (Abstract, para 108, footnote 137). | Regulatory challenges or gaps, Stifling innovation, Negative economic impact |
| 20OhioStTechLJ225.pdf | HeinOnline | PROMETHEUS' DIGITAL FIRE: THE CIVIC RESPONSIBILITIES OF ARTIFICIAL INTELLIGENCE | This article explores the civic responsibilities associated with AI, examining its benefits and risks, particularly regarding bias, privacy, and accuracy. It also discusses emerging regulatory frameworks in the EU and US and proposes industry actions to mitigate risks and maximize benefits. | Civic Responsibilities of AI, Benefit Identification, Risk Identification, Bias in AI, Privacy Concerns, Accuracy Issues, AI Regulation (EU and US), Industry Actions for AI | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | Bias in AI leading to digital redlining and discrimination in critical areas like housing, employment, credit, and law enforcement; lack of transparency in AI decision-making; privacy violations through extensive data collection; and the spread of AI-generated disinformation and harmful content. | Bias in AI/Data, Risk of AI Exacerbating Inequality, Lack of AI Transparency/Explainability, Data Privacy Concerns with AI, AI-driven Misinformation/Disinformation | Adherence to civil rights laws, employing debiasing strategies and explainable AI (XAI), developing robust regulatory frameworks and industry standards (e.g., NIST AI RMF), diligent fact-checking of AI outputs, and establishing strong contractual safeguards with AI vendors for data protection and system accountability. | Policy and Regulatory Reform, Bias Detection and Mitigation, Transparency and Explainability in AI, Regulation, Ethics, and Governance, Human Oversight and Collaboration, Data Privacy and Security | Preventing AI-driven discrimination and bias (digital redlining), upholding civil rights, ensuring access to accurate information by combating AI-generated disinformation and harmful content, protecting privacy rights, and promoting ethical use of AI in legal practice. | Ethical AI in Law and AI Governance, Protection of Rights, Access to Legal Information, Language Access and Digital Divide | Various vulnerable groups, including racial minorities (Black people, Asians, Latinos), women, and the elderly, who are disproportionately affected by biased AI systems. | Vulnerable populations, Minority groups, Black individuals, Asian individuals, Latinx individuals, Women, Elderly people | Civil Rights Law, Privacy Law, Defamation Law, Products Liability, First Amendment Law, Intellectual Property Law (minor mention), Criminal Law (re: AI-generated child pornography), Contract Law, and Legal Ethics. | Civil Rights Law, Data Privacy Law, Tort Law, Product Liability Law, Constitutional Law, Intellectual Property Law, Criminal Law, Contract Law, Legal Ethics | United States, European Union, and mentions China in the context of AI development and regulation. | USA, EU, China | The paper discusses AI systems trained on vast amounts of data scraped from the internet, including publicly available information, pirated and copyrighted materials (e.g., books), user-submitted data via prompts and APIs, and unstructured data. This data is often collected via web crawlers and third-party services. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Web Scraped Data, Publicly Available Data, Copyrighted Material (Source Mentioned), User-Generated Content, Unstructured Text Data | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Ongoing difficulties in mitigating AI bias and ensuring explainability (XAI); technical limitations in AI factuality and source citation; societal challenges in adapting education for critical thinking; and the need for further development and implementation of regulatory frameworks. | Bias in AI, Transparency and Explainability, AI Accuracy and Reliability, Human Oversight and Professional Adaptation, Regulatory and Governance Gaps | NaN | NaN | Bias and discrimination amplifying societal inequities; extensive privacy violations from data collection and misuse; spread of AI-generated disinformation, defamation, and deepfakes; provision of dangerous or inaccurate advice leading to harm; significant job displacement in creative and knowledge-based industries; and the erosion of critical thinking skills. | Bias and discrimination, Exacerbation of inequality or two-tiered system, Data privacy and security breach, Inaccurate or misleading AI output, Security vulnerabilities or malicious misuse, Harmful or unsafe AI output, Consumer harm, Job displacement, Deskilling or erosion of human skills |
| 8UPaJLPubAff129.pdf | HeinOnline | ADDRESSING THE EVOLVING CONCEPT OF GENDER AND INTERSECTIONAL STEREOTYPES IN INTERNATIONAL NORM CREATION: DIRECTIONS FOR A NEW CEDAW GENERAL RECOMMENDATION | This paper analyzes how gender and intersectional stereotypes are addressed by the CEDAW Committee and other human rights bodies, highlighting their evolving nature. It then discusses the emergent challenge of stereotypes embedded in Artificial Intelligence and proposes that a new CEDAW General Recommendation should address these digitized biases from a human rights perspective. | Gender Stereotypes in AI, Intersectional Bias in AI, Human Rights Perspective on AI Bias, CEDAW Application to AI, Proposal for CEDAW General Recommendation | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | Entrenched societal gender and intersectional stereotypes being codified and amplified by AI systems due to biased training data and lack of diversity in the AI workforce, leading to 'digitized bias' and 'algorithmic discrimination'. | Bias in AI/Data, Exclusion of Marginalized Communities in AI Governance/Development | Developing new international normative frameworks (e.g., a new CEDAW General Recommendation) grounded in human rights, substantive equality, and intersectionality; promoting education and diversity in AI development; establishing guidelines for AI data and design; and fostering multilateral collaboration to ensure AI governance addresses and mitigates bias. | Policy and Regulatory Reform, Conceptual Frameworks, Education and AI Literacy, Bias Detection and Mitigation, Regulation, Ethics, and Governance, Data Curation and Management, Open Source Initiatives and Collaboration | Elimination of gender and intersectional stereotypes in legal systems and AI, ensuring non-discrimination, and promoting substantive equality for women in all spheres, including protection from gender-based violence and fair judicial processes. | Ethical AI in Law and AI Governance, Protection of Rights, Support for Vulnerable Populations | Women, particularly those facing intersectional discrimination based on race, ethnicity, religion, age, disability, sexual orientation, migrant status, and other factors. | Women, Minority groups, Elderly people, People with disabilities, LGBTQ+ people, Migrants | International Human Rights Law, Anti-discrimination Law, Gender Law, Technology Law/AI Governance. | International Law, Human Rights Law, Anti-Discrimination Law, Gender Law, Technology Law, AI Governance | International; with examples from various national jurisdictions (e.g., MENA, South Asia, East Asia, Americas, European countries). | International, MENA, South Asia, East Asia, Americas, Europe | Discusses AI training data generally as often being unrepresentative of women and minority groups, leading to 'data bias' and the reproduction of societal gender biases in AI systems. It does not specify a particular dataset used for a proposed technique as the paper is a broad discussion. | Pre-trained LLM's General Training Corpus, Data Bias Concerns Noted | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Societal gaps in recognizing and addressing subtle and intersectional stereotypes. Technical and legal gaps in understanding and regulating AI-driven bias, including unrepresentative datasets, lack of diversity in AI development, the need for gender-sensitive AI design, and the application of human rights frameworks to AI governance. | Bias in AI, Ethical Framework Deficiencies, Data Availability and Quality, Regulatory and Governance Gaps, Access, Equity, and Digital Divide | NaN | NaN | Reproduction and amplification of societal gender and intersectional stereotypes through 'digitized bias' in AI systems. Stigmatization and marginalization of women, particularly those with intersectional identities, on a global scale. Normalization of gender-based violence and discrimination through biased AI outputs and interactions. Erosion of human rights if AI is not governed by inclusive, rights-based frameworks. | Bias and discrimination, Infringement on human rights, Harmful or unsafe AI output |
| 51FlaStULRev543.pdf | HeinOnline | WHAT'S A LAWYER FOR? ARTIFICIAL INTELLIGENCE AND THIRD-WAVE LAWYERING | The paper discusses the impact of AI and new technologies on the legal profession, framing it as a "third wave" of lawyering. It proposes a conceptual framework for assessing how different legal service delivery models, including technology-enhanced ones, can uphold the core values and functions of the legal profession, particularly concerning access to justice. | AI Impact on Legal Profession, Framework for Assessing Legal Service Models, Upholding Legal Values with AI, Access to Justice Enhancement | True | Idealistic | True | 1.0 | Positive | A conceptual framework for calibrating legal service delivery modes, assessing legal problems (complexity, agility, preventative/reactive, stakes) and clients (sophistication, capacity, ability to pay, access barriers) against professional values (adversarial role, democratic interests, rule of law, access to justice) and functions (instrumental, affective, political). | Conceptual Framework, Legal Service Delivery Model | The framework is illustrated through its application to two hypothetical real-world scenarios: the formation of a simple non-profit ('East Harlem All Stars') and a complex non-profit ('The Safe Center'). | Demonstration or Illustrative Examples, Qualitative Analysis | The framework application demonstrated that simpler legal needs with sophisticated clients (East Harlem All Stars) might be adequately served by technology-based solutions, while complex, high-stakes situations (The Safe Center) require traditional, full-service legal representation. | Descriptive or Conceptual finding | Cost of legal services; Lack of public awareness of legal problems or need for lawyers; Difficulty in accessing lawyers; The digital divide; Potential for technology to undermine core legal values if not thoughtfully deployed; Restrictive ethical rules and UPL regulations. | High Cost of Legal Services, Public Lack of Legal Knowledge/Awareness, Limited Availability/Access to Legal Professionals/Expertise, Digital Divide, Ethical Concerns with AI in Law, Regulatory Hurdles | Thoughtful deployment of technology, including AI, to enhance affordability and accessibility; Utilizing the proposed framework to determine appropriate service delivery models; Reforming ethical paradigms, including rules on non-lawyer investment and UPL, to support innovation in legal services. | AI Tool Development, Cost Reduction and Efficiency, Access to Legal Information and Advice, Conceptual Frameworks, Alternative Legal Service Delivery Models, Policy and Regulatory Reform, Regulation, Ethics, and Governance | Affordability and accessibility of legal services; Role of technology (AI) in bridging the justice gap; Models for legal service delivery to low- and moderate-income individuals and non-profits; Ethical considerations in legal tech. | Affordability of Legal Services / Cost Reduction, Democratizing Law / Closing Justice Gap / Rule of Law, Support for Vulnerable Populations, Ethical AI in Law and AI Governance | Low- and moderate-income individuals and non-profit organizations. | Low-income individuals, Moderate-income individuals, Non-profit organizations | General legal practice, Non-profit law, Legal ethics, Professional responsibility. | General Legal Practice, Non-Profit Law, Legal Ethics, Professional Responsibility | United States | USA | NaN | Not Applicable | Conceptual analysis, historical review of the legal profession, synthesis of legal ethics and theory, and application of business concepts (e.g., Christensen's 'jobs-to-be-done'). | Conceptual Analysis, Historical Analysis, Ethical Framework Application, Business Concept Application | NaN | Not applicable | True | False | The conceptual framework for assessing legal service delivery models is detailed within the paper and can be understood and applied by readers. | Research artifact published in paper | Need for technologies to accurately assess case complexity and client nuances; The persistent digital divide; Need for reform of ethical rules (UPL, non-lawyer ownership) to enable beneficial tech innovations; Ensuring new technologies uphold legal values and do not create a two-tiered justice system. | AI Legal Reasoning Limitations, AI Scope and Functionality Limitations, Access, Equity, and Digital Divide, Regulatory and Governance Gaps, Ethical Framework Deficiencies | Accurately assessing problem complexity and client capacity for technology use via automated or limited-service means; Ensuring new service delivery models preserve the instrumental, affective, and political functions of lawyering; Overcoming professional resistance to changes in legal service delivery; The high cost of developing and maintaining sophisticated legal technology tools. | User Training, AI Literacy, and Skill Gaps, User Interface, Usability, and Accessibility, Ethical Considerations, Legal Professional Responsibility and Competence, User Adoption, Trust, and Acceptance, Financial Cost and Resource Constraints | Undermining core values of the legal profession and democratic institutions; Displacing essential lawyer functions inappropriately; Loss of nuanced legal guidance through over-commoditization; Creation of a two-tiered justice system; Premature disruption by immature technologies; Malpractice from incorrect assessments or faulty tech-based advice; Exacerbation of inequality due to the digital divide hindering access to tech-based solutions. | Ethical concerns, Undermining democratic processes, Job displacement, Over-reliance on AI, Dehumanization of legal process, Negative economic impact, Exacerbation of inequality or two-tiered system, Risk of misapplication or misuse, Inaccurate or misleading AI output |
| 4LawTechHum109.pdf | HeinOnline | Framing the Future: The Foundation Series, Foundation Models and Framing AI | This paper critically examines how AI foundation models, particularly in NLP, risk embedding and amplifying dominant, often biased, neoliberal linguistic frames from law and economics. It argues that this uncritical adoption could entrench societal inequalities, hinder true progress, and make it harder to challenge existing power structures, drawing parallels with Asimov's Foundation series to highlight these dangers. | Critique of AI Foundation Models, Bias in NLP Models, Neoliberal Frames in AI, Risk of Entrenching Inequality, Challenge to Power Structures | True | Idealistic | True | 3.0 | Negative | NaN | NaN | NaN | Not Applicable | NaN | NaN | Entrenched hegemonic neoliberal frameworks and biases embedded in language, which are uncritically adopted into AI foundation models, leading to the perpetuation and amplification of societal inequalities and hindering access to justice. | Bias in AI/Data, Systemic Inequities in Justice System, Risk of AI Exacerbating Inequality | Promoting greater awareness of how linguistic framing shapes AI and society; developing a research agenda to identify and mitigate deep-seated biases in AI beyond explicit ones; actively reframing societal narratives to challenge dominant, inequitable ideologies; fostering conceptual tools that prioritize social well-being over purely economic or individualistic metrics. | Education and AI Literacy, Conceptual Frameworks, Bias Detection and Mitigation, Policy and Regulatory Reform | The risk of AI foundation models perpetuating socio-economic inequalities and unfair power dynamics by encoding and amplifying biased neoliberal frames; the impact of AI's linguistic framing on access to justice and the marginalization of non-dominant voices and values. | Ethical AI in Law and AI Governance, Democratizing Law / Closing Justice Gap / Rule of Law, Support for Vulnerable Populations | General population, particularly those marginalized or disadvantaged by dominant neoliberal socio-economic structures whose interests are not reflected in hegemonic frames. | General public, Marginalized communities | General law, Law and Economics, Law and Development | General Law, Law and Economics, Law and Development | International | International | Existing data created by (a subset of) humans, reflecting flawed, biased human preferences and assumptions, including text from the internet and other sources; data curated from interactions with the current generation of foundation models; unlabelled data for self-supervised learning tasks. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, User-Generated Content, Data Bias Concerns Noted, Fine-tuning Dataset | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Insufficient awareness and research into how deep-level linguistic framing (beyond explicit bias) encodes and perpetuates systemic inequalities within AI systems; lack of critical engagement with the hegemonic (neoliberal) conceptual tools being embedded in foundation models; the current focus of de-biasing on superficial aspects, neglecting foundational framing issues. | Bias in AI, Research and Evaluation Gaps, Ethical Framework Deficiencies | The complexity and monolithic nature of foundation models, making them difficult to adjust post-release; the tendency for AI systems to inherit and amplify biases from foundation models; the difficulty in identifying and remedying subtle, deeply embedded framing biases compared to explicit social biases. | Domain-Specific Adaptation and Customization, Bias in AI Systems and Data, Transparency and Explainability of AI | Preservation and amplification of hegemonic neoliberal frames in AI systems, entrenching existing inequalities; perpetuation of structural inequalities leading to tangible harms for sections of the population; limiting future interrogation and evolution of legal and economic concepts by 'preserving them in digital aspic'; shaping human users to conform to 'homines economici-juridici'; AI systems potentially allowing humanity to come to harm by entrenching socio-economic disadvantage. | Bias and discrimination, Exacerbation of inequality or two-tiered system, Consumer harm, Undermining legal process or principles, Deskilling or erosion of human skills, Technical limitations of AI |
| 5LegalIssuesDigitAge113.pdf | HeinOnline | Technologies Versus Justice: Challenges of Al Regulation in the Judicial System | This paper examines the integration of artificial intelligence into judicial systems, discussing current applications and the concept of "smart courts" in various countries. It argues that while AI can serve as a supportive tool, it fundamentally cannot replace human judges in delivering just decisions due to its lack of genuine understanding and ethical judgment, necessitating robust legal and ethical regulation. | AI in Judicial Systems, Smart Courts, AI as Support Tool, Limitations of AI Judges, Need for AI Regulation, Ethical Considerations | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | Inability of AI to deliver genuinely just outcomes due to its lack of human consciousness, understanding, interpretive capacity, and ethical judgment required for fair decision-making; threat to the rule of law and fair trial if AI oversteps its auxiliary role. | AI Limitations in Ethical Judgment, AI Limitations in Replicating Human Judgment, Ethical Concerns with AI in Law, Risk to Human Rights from AI, Concerns about Legal Sovereignty/Rule of Law | Strict legal and ethical regulation ensuring AI remains auxiliary to human judges, prohibiting automated judgments without human control, and enshrining in law that the authority to render justice cannot be delegated to AI. Establishing multi-tier regulatory systems, including ethical corporate standards and 'pilot court' regimes for testing, based on principles like security, legitimacy, fairness, transparency, and compliance with public order. | Regulation, Ethics, and Governance, Human Oversight and Collaboration, Policy and Regulatory Reform, Judicial System Enhancement, Benchmarking and Evaluation Frameworks, Transparency and Explainability in AI | Ensuring fairness and just outcomes in judicial decision-making; providing legal information and assistance (e.g., claim drafting, advice); improving court efficiency and accessibility while maintaining the human-centric nature of justice. | Judicial System Modernization / Efficiency, Ethical AI in Law and AI Governance, Access to Legal Information, Legal Document Creation / Automation, Access to Legal Advice | General public needing access to judicial protection and legal information. | General public, Individuals with unmet legal needs, Individuals lacking legal knowledge | General (judicial system), Civil law, Administrative law, Traffic law. | General Law, Judicial Processes, Civil Law, Administrative Law, Traffic Law | International (discusses China, India, Germany, Portugal, Singapore, Russia, and general principles). | International, China, India, Germany, Portugal, Singapore, Russia | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Technical gap: AI's inability to replicate human consciousness, cognition, understanding, and ethical judgment necessary for true justice. Societal gap: Lack of comprehensive and timely legal and ethical regulatory frameworks for AI in the judiciary; need for deeper understanding of AI's impact on judiciary institutions and the human nature of fair judgment. | AI Legal Reasoning Limitations, Ethical Framework Deficiencies, Regulatory and Governance Gaps, Research and Evaluation Gaps, Human Oversight and Professional Adaptation | NaN | NaN | Undermining the rule of law and fair trial; compromising the human nature of justice and the role of judges; debasement of judicial power; damage to fundamental values of the judicial system (e.g., fairness); potential harm to national security and legitimate interests of individuals/organizations if AI is misused or unregulated. | Undermining legal process or principles, Dehumanization of legal process, Security vulnerabilities or malicious misuse, Risk of misapplication or misuse, Ethical concerns |
| 103BULRev.pdf | HeinOnline | CHANGING ALL THE TIME: AI'S IMPACT ON HUMANITY'S ROLE IN COMMON LAW DEVELOPMENT AND INTERPRETATION | This paper examines the significant impact generative AI, such as ChatGPT, could have on the development and interpretation of common law, potentially severing humanity's connection to it. It argues for careful guidance and proposes amending professional conduct codes to ethically center the human role in law. | Generative AI Impact on Common Law, Risk to Human Connection with Law, Call for Careful Guidance, Professional Conduct Code Amendment, Centering Human Role in Law | True | Idealistic | True | 3.0 | Positive | Generative AI (e.g., ChatGPT) and hypothetical AI legal assistants (e.g., 'LegalBot') | Generative AI, Large Language Model, AI Legal Assistant | NaN | Not Applicable | NaN | NaN | Inability to afford legal representation, leading to individuals having to navigate the legal system pro se. | High Cost of Legal Services, Challenges for Self-Represented Litigants | AI-powered legal assistants (like the hypothetical LegalBot) to provide guidance and support to pro se litigants, potentially improving their ability to navigate legal processes and evening the playing field. | AI Tool Development, Access to Legal Information and Advice, Support for Self-Represented Litigants | Assistance for pro se litigants; Reducing public defender backlogs. | Support for Self-Represented Litigants, Legal Aid and Pro Bono Services, Improving Efficiency in Legal System / Profession | Pro se litigants (individuals unable to afford legal representation). | Self-represented litigants, Individuals unable to afford legal services | Common Law (general), Tort Law (example). | Common Law, Tort Law, General Law | United States (primarily American common law). | USA, Common Law Jurisdictions | Large datasets of legal texts (case law, motions, treatises), general textual data, and human-provided input, processed by machine learning algorithms. | Legal Domain Data, Case Law / Judgments, Legal Scholarly Content / Textbooks, Other Legal Documents, General Web Data / Broad Internet Text, User-Generated Content | NaN | NaN | Integration into legal practice through corporate adoption (e.g., PwC's Harvey) and potential court-sanctioned programs for pro se assistance (hypothetical LegalBot). | Evaluation of existing third-party tool, Internal deployment/prototype, Commercial product/service, Proposed deployment (not implemented), Government/Public institution deployment | True | True | ChatGPT, a key example discussed, is publicly accessible via OpenAI, with a free usage tier. | Publicly accessible online tool or platform, Freemium access | Lack of robust regulatory and ethical frameworks guiding AI development and deployment in the legal field, specifically concerning AI's role in substantive legal work and common law development. Need for a deeper understanding and preservation of the human-law relationship in the age of AI. | Regulatory and Governance Gaps, Ethical Framework Deficiencies, Human Oversight and Professional Adaptation | Ensuring factual accuracy and avoiding 'hallucinations' (e.g., fake caselaw); ethical concerns around plagiarism, attribution, and unlicensed practice of law; risk of overreliance and deskilling of human lawyers; preventing AI from unduly controlling legal development through feedback loops. | Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Ethical Considerations, Copyright and Intellectual Property Issues, Unauthorized Practice of Law (UPL) Concerns, User Adoption, Trust, and Acceptance, User Training, AI Literacy, and Skill Gaps, Bias in AI Systems and Data | Severing humanity's connection to the law; ceding control of common law development to AI algorithms and private companies; AI creating a feedback loop that dictates legal development based on past data; unlicensed practice of law; erosion of human legal reasoning skills; challenges to the integrity and fairness of the legal system. | Dehumanization of legal process, Undermining legal process or principles, Technical limitations of AI, Unauthorized practice of law, Deskilling or erosion of human skills, Ethical concerns |
| 16CaseWResJLTechInternet1.pdf | HeinOnline | Sam Altman, OpenAI, and the Importance of Corporate Governance | This paper examines the corporate governance crisis at OpenAI, focusing on the sudden firing of CEO Sam Altman and the subsequent turmoil, analyzing the company's unique structure and the influence of 'effective altruism'. It emphasizes the critical need for professional corporate governance in organizations developing powerful AI technologies with profound societal impacts. | Corporate Governance of AI Companies, OpenAI Case Study, Effective Altruism Influence, Societal Impact of AI Technologies | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | Reliability issues such as AI 'hallucinations' creating non-existent legal citations, and the general need for caution and humility in deploying AI for legal assistance. | AI Unreliability/Inaccuracy, Need for Cautious AI Deployment | Improved corporate governance of AI development companies to ensure responsible development and deployment of AI; exercising caution and humility when using AI for legal applications. | Regulation, Ethics, and Governance | Providing legal information and assistance for basic questions, document templates, and court form completion for those who cannot afford lawyers. | Access to Legal Information, Access to Legal Advice, Legal Document Creation / Automation, Affordability of Legal Services / Cost Reduction | Individuals who cannot afford a lawyer. | Individuals unable to afford legal services | General legal assistance, Contract law, Intellectual property law. | General Legal Practice, Contract Law, Intellectual Property Law | United States (focus on OpenAI, Delaware corporate law, US legal cases), European Union (AI Act), United Kingdom (CMA scrutiny). | USA, EU, UK | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Technical gaps in AI reliability (e.g., 'hallucinations'). Societal gaps include establishing effective AI regulation, combating bias, addressing job displacement, ensuring broadly distributed benefits of AI, and aligning powerful AI development with human values and safety through robust corporate governance. | AI Accuracy and Reliability, Regulatory and Governance Gaps, Bias in AI, Human Oversight and Professional Adaptation, Access, Equity, and Digital Divide, Ethical Framework Deficiencies | Balancing a non-profit mission with the capital requirements for AI research; managing internal disagreements on AI safety and development speed; establishing effective and experienced corporate governance for a company developing high-stakes AI technology; navigating the influence of philosophical movements like 'effective altruism' on corporate strategy and safety prioritization. | Financial Cost and Resource Constraints, Ethical Considerations, Safeguarding Against Misuse and Harm, Interdisciplinary Collaboration Challenges, Regulatory Uncertainty and Compliance | Misuse of AI, drastic accidents, societal disruption (including job displacement), spread of misinformation and deepfakes, national security threats (e.g., AI-powered espionage, intellectual property theft), existential risks from AGI, AI 'hallucinations' leading to false information, and economic disruptions. | Security vulnerabilities or malicious misuse, Harmful or unsafe AI output, Negative societal impact, Job displacement, Inaccurate or misleading AI output, Negative economic impact, Copyright or intellectual property issues |
| 2025AccesstoJustEEur241.pdf | HeinOnline | INNOVATIONS OF ARTIFICIAL INTELLIGENCE IN LIGHT OF THE APPLICABLE COPYRIGHT LAW: REALISTIC SOLUTIONS AND FUTURE PROSPECTS. A COMPARATIVE STUDY OF UAE, EGYPTIAN, AND FRENCH LAWS | This paper analyzes how current copyright laws in the UAE, Egypt, and France address AI-generated innovations, highlighting challenges in defining authorship. It argues for urgent legal reforms to create a framework that balances innovation promotion with the protection of rights, ensuring ethical and legally recognized AI development. | Copyright Law and AI-Generated Content, Authorship of AI Creations, Comparative Law (UAE, Egypt, France), Call for Legal Reform, Balancing Innovation and Rights, Ethical AI Development | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | Ambiguity in defining 'author' for AI-generated content under existing copyright laws, as AI lacks human personal characteristics; inadequacy of current legal frameworks to address the novel challenges posed by AI innovations; the lack of legal personality for AI systems, complicating attributions of rights. | Intellectual Property/Copyright Issues with AI, Lack of Legal Personality/Capacity for AI, Inadequate Legal Frameworks for AI | Reviewing and amending existing copyright laws to specifically address AI-generated innovations; developing a comprehensive legal framework that balances promoting AI innovation with protecting legal rights and ethical considerations; establishing a Code of Ethics for AI systems to guide their development and use responsibly. | Policy and Regulatory Reform, Regulation, Ethics, and Governance | Clarification of authorship and ownership rights for AI-generated creative works; establishment of fair and ethical legal frameworks for AI in the creative industries; ensuring legal certainty for creators, users, and developers in the context of AI and copyright. | NaN | NaN | NaN | Copyright Law; Intellectual Property Law | Copyright Law, Intellectual Property Law | UAE, Egyptian, and French laws | UAE, Egypt, France | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Absence of a specific legal framework tailored to AI-generated intellectual property; lack of clear provisions for attributing legal personality or a special legal status to AI; need for harmonized ethical guidelines and codes of conduct for AI development and use in creative sectors. | Regulatory and Governance Gaps, Ethical Framework Deficiencies | NaN | NaN | Legal uncertainty and increased litigation due to unadapted copyright laws; potential for infringement on existing copyrights by AI systems using protected works as training data or generating similar outputs; ethical concerns regarding AI replacing human creators or devaluing human creativity if not properly regulated. | Regulatory challenges or gaps, Undermining legal process or principles, Copyright or intellectual property issues, Ethical concerns, Dehumanization of legal process, Job displacement |
| 20NwJTechIntellProp309.pdf | HeinOnline | LAW INFORMS CODE: A LEGAL INFORMATICS APPROACH TO ALIGNING ARTIFICIAL INTELLIGENCE WITH HUMANS | This paper proposes a research agenda, "Law Informs Code," advocating for the use of legal processes, concepts, and data to improve the alignment of Artificial Intelligence (AI) with human goals and societal values. It argues that law offers a legitimate, scalable, and democratically endorsed framework for specifying objectives to AI, thereby enhancing AI safety and utility. | Research Agenda Proposal (Law Informs Code), AI Alignment with Human Values, Using Law to Guide AI, AI Safety, AI Utility | True | Idealistic | True | 1.0 | Positive | Law Informs Code: A legal informatics approach using legal theory, processes, data (e.g., contracts, standards, public law), and reasoning to align AI with human and societal values. | Legal Informatics Framework, AI Alignment / Value Alignment, Theoretical Approach | NaN | Not Applicable | NaN | NaN | The fundamental difficulty in specifying complex human goals and societal values (like those inherent in justice) to AI systems, leading to AI that may act unaligned with these values and lack legitimate grounding for its understanding of societal preferences. | Challenges in AI Alignment with Legal/Ethical Values, Technical Challenges in AI Development | Utilizing law (its theory, processes, data, and reasoning methods) as a legitimate and scalable framework to specify human intentions and democratically endorsed societal values to AI systems, thereby improving AI alignment. | Conceptual Frameworks, Regulation, Ethics, and Governance, Enhanced AI Capabilities | NaN | NaN | NaN | NaN | General (contracts, public law, fiduciary law, statutory interpretation, securities law, tax law, etc.) | General Law, Multiple Fields, Contract Law, Public Law, Fiduciary Law, Statutory Interpretation, Securities Law, Tax Law | U.S. law (with aspiration for global applicability) | USA, International | Proposed use of publicly available and potentially proprietary legal texts (constitutional, statutory, administrative, case law, contracts), legal training materials, rule-based systems, and expert feedback. Data is largely unstructured or semi-structured legal language. | Legal Domain Data, Publicly Available Data, Proprietary Data, Legislation / Statutes / Regulations, Case Law / Judgments, Legal Contracts, Other Legal Documents, Expert-Annotated / Human-Curated / Human-Generated Data, Unstructured Text Data, Rule-Based System (No Training Data) | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Remaining gaps include: determining how law can guide AI's proactive positive goals (not just prohibitions); systematically accounting for historical injustices and biases in legal data; scaling the approach globally; understanding AI's 'intention' for legal purposes; addressing issues of law's representativeness, AI truthfulness, and loophole exploitation; and improving NLP for long legal documents. | Ethical Framework Deficiencies, Regulatory and Governance Gaps, Bias in AI, Data Availability and Quality, Research and Evaluation Gaps, AI Legal Reasoning Limitations, AI Accuracy and Reliability, AI Scope and Functionality Limitations | Integrating complex and often ambiguous legal concepts and reasoning into computational AI models. Sourcing, curating, and processing vast amounts of diverse legal data while addressing biases. Developing robust methods for AI to generalize legal understanding to novel situations. Creating effective benchmarks to validate AI's legal comprehension and alignment. | LLM Reasoning Capabilities, Scarcity of High-Quality Legal Data, Data Quality, Processing, and Preparation, Bias in AI Systems and Data, Domain-Specific Adaptation and Customization, Evaluation Challenges and Metrics | Potential for AI to misinterpret complex legal directives or exploit loopholes. Risk of embedding historical biases or unjust aspects present in legal data into AI systems. Challenges in ensuring AI adapts to evolving legal norms and societal values, or that democratically produced law adequately reflects these values. | Technical limitations of AI, Security vulnerabilities or malicious misuse, Bias and discrimination, Regulatory challenges or gaps, Undermining legal process or principles |
| 30AIL561.pdf | HeinOnline | Thirty years of artificial intelligence and law: the third decade | This paper reviews eight significant papers from the Artificial Intelligence and Law journal's third decade (2012-2022), highlighting the field's major shift towards Machine Learning and Natural Language Processing techniques. It covers applications like document management, legal text analysis, outcome prediction, and detection of unfair contract clauses, discussing both advancements and challenges. | Review of AI and Law Research, Machine Learning in Law, NLP in Law, Legal Applications (Document Management, Text Analysis, Prediction), Unfair Contract Clause Detection | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | Key obstacles to A2J mentioned include: the difficulty for laypeople to understand legal texts and identify issues like unfair contract terms; the inherent complexity and volume of legal information hindering accessibility; the 'black box' nature of AI models which can impede trust and accountability; and the scarcity of high-quality, annotated legal data needed to train effective AI tools, especially for diverse legal areas and languages. | Public Lack of Legal Knowledge/Awareness, Complexity of Legal Language/Documents, Difficulty Accessing/Interpreting Legal Information, Lack of AI Transparency/Explainability, Lack of Trust in AI/Automated Systems, Lack of AI Accountability, Data Scarcity/Quality for AI | Proposed solutions to A2J obstacles include: developing AI tools for automatic analysis and retrieval of legal information (e.g., semantic parsing, unfair clause detection for consumers); employing explainable AI (XAI) methods to make system reasoning transparent and build trust; creating and sharing annotated legal datasets to foster research and tool development; and adapting advanced NLP models (like transformers) for specific legal tasks to improve information access and understanding. | AI Tool Development, Legal Research and Analysis Tools, Document Automation, Transparency and Explainability in AI, Data Curation and Management, Open Source Initiatives and Collaboration, Enhanced AI Capabilities, Access to Legal Information and Advice | Consumer protection (e.g., identifying unfair contract terms); access to and understanding of legal information (e.g., statutory provisions, ECHR case law); ensuring fairness and accountability in automated legal processes. | Protection of Rights, Legal Document Analysis / Review, Access to Legal Information, Ethical AI in Law and AI Governance | Consumers (e.g., understanding terms of service); general public/addressees of regulations; individuals interacting with human rights courts. | Consumers, General public, Litigants | Statutory Law, EU Consumer Protection Law, Patent Law, Japanese Pension and Civil Law, Human Rights Law (ECHR), WIPO Domain Name Dispute Resolution (Intellectual Property), Italian Civil Code, Contract Law, GDPR. | Statutory Law, EU Law, Consumer Law, Patent Law, Social Security Law, Civil Law, Human Rights Law, Intellectual Property Law, Dispute Resolution, Contract Law, Data Privacy Law | EU, Italy, Japan, ECHR (Council of Europe), WIPO (International arbitration). | EU, Italy, Japan, ECHR, WIPO | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Remaining gaps for A2J include: need for more robust and legally meaningful explainability in AI systems; challenges in ensuring AI models generalize well across diverse legal factual scenarios and evolving laws; the ongoing difficulty and cost of acquiring and annotating high-quality legal data for training A2J tools, particularly for multilingual contexts; and the risk of AI systems merely learning correlations instead of true legal reasoning, which could undermine fairness. | Transparency and Explainability, AI Accuracy and Reliability, AI Legal Reasoning Limitations, Knowledge Recency and Updatability, Data Availability and Quality, Computational Resource and Cost Issues, Multilingual and Low-Resource Language Gaps, Bias in AI | NaN | NaN | Potential risks include: AI systems making predictions or classifications based on spurious correlations rather than sound legal reasoning (e.g., using judge names for ECHR outcome prediction); lack of transparency in AI leading to difficulties in verifying legal soundness and accountability, particularly problematic for A2J applications; AI predictions degrading significantly when applied to new or evolving legal contexts without proper adaptation; and AI tools for A2J failing if they cannot be trusted or understood by their intended users (e.g., consumers). | Inaccurate or misleading AI output, Technical limitations of AI, Lack of transparency, accountability, and redress, Erosion of trust in legal system or AI, Consumer harm |
| 108Judicature42.pdf | HeinOnline | How to Harness AI for Justice | This paper explores how generative AI can enhance access to justice for self-represented litigants by automating legal tasks, democratizing information, and improving court processes. It also outlines significant risks, such as bias and inaccuracies, proposing careful implementation through best practices like diverse data, human oversight, and rigorous evaluation. | Generative AI for Access to Justice, Self-Represented Litigant Assistance, Task Automation, Information Democratization, Court Process Improvement, Risk Identification, Bias in AI, AI Hallucinations/Inaccuracy, Best Practices for AI Implementation | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | Complexity and impenetrability of the legal system; high cost of legal representation resulting in widespread self-representation; existing biases and discrimination within the justice system; barriers to technology adoption by self-represented litigants. | Complexity of Legal System/Procedures, High Cost of Legal Services, Challenges for Self-Represented Litigants, Systemic Inequities in Justice System, Slow Technology Adoption by Users | Leveraging generative AI to provide accessible legal information, automate routine legal tasks, facilitate online dispute resolution, and simplify legal procedures; implementing AI tools responsibly by adhering to best practices (diverse data, human oversight, impact assessments, transparency); adopting rigorous evaluation methodologies (e.g., RCTs, pilot programs) for AI innovations. | AI Tool Development, Access to Legal Information and Advice, Document Automation, Online Dispute Resolution (ODR), Regulation, Ethics, and Governance, Data Curation and Management, Human Oversight and Collaboration, Transparency and Explainability in AI, Benchmarking and Evaluation Frameworks | Assisting self-represented litigants; Online Dispute Resolution (ODR); legal information provision and document generation; litigation avoidance and conflict prevention; simplification of legal rules and procedures; improving court efficiency and user experience; reducing bias in legal decisions; procedural fairness including translation. | Support for Self-Represented Litigants, Dispute Resolution, Access to Legal Information, Legal Document Creation / Automation, Legal Text Simplification / Plain Language, Judicial System Modernization / Efficiency, Ethical AI in Law and AI Governance, Language Access and Digital Divide | Self-represented litigants, individuals unable to afford legal representation, racial and ethnic minorities, non-English speakers. | Self-represented litigants, Individuals unable to afford legal services, Minority groups, Individuals with language barriers | Civil Law (including family law, consumer debt, landlord-tenant/eviction), Administrative Law (unemployment benefits). | Civil Law, Family Law, Consumer Law, Debt Collection, Landlord-Tenant Law, Housing Law, Administrative Law, Social Security Law | United States | USA | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Overcoming training data limitations to ensure AI serves diverse populations equitably; completely eliminating AI 'hallucinations'; achieving full transparency in proprietary AI decision-making processes; insufficient evidence base for many legal practices and reluctance to adopt experimental evaluation methods. | Data Availability and Quality, Bias in AI, Access, Equity, and Digital Divide, AI Accuracy and Reliability, Transparency and Explainability, Research and Evaluation Gaps, Human Oversight and Professional Adaptation | Mitigating exposure bias from unrepresentative training data; managing AI 'hallucinations' (false information/citations); ensuring transparency for due process; high cost of advanced, less error-prone AI models for A2J applications; overcoming institutional inertia among legal professionals. | Bias in AI Systems and Data, LLM Hallucination and Factual Errors, Accuracy and Reliability of LLM Output, Transparency and Explainability of AI, Ethical Considerations, Financial Cost and Resource Constraints, User Adoption, Trust, and Acceptance | Incorrect AI guidance leading to adverse legal outcomes (e.g., default judgments); generation of harmful or inappropriate advice by AI; submission of fabricated information or false legal citations to courts; compromised due process rights due to opaque AI decision-making; exacerbation of societal inequities through biased AI tools. | Inaccurate or misleading AI output, Consumer harm, Harmful or unsafe AI output, Undermining legal process or principles, Lack of transparency, accountability, and redress, Exacerbation of inequality or two-tiered system, Bias and discrimination |
| 19OhioStTechLJ171.pdf | HeinOnline | THE SUBJECTS AND STAGES OF Al DATASET DEVELOPMENT: A FRAMEWORK FOR DATASET ACCOUNTABILITY | This paper examines the development process of large-scale AI datasets (LSLDs and LSCVDs), outlining the stages involved and the subjects affected, to identify pertinent legal issues such as copyright and privacy. It proposes a comprehensive framework, including a matrix of harms, to foster dataset accountability and mitigate adverse impacts from these datasets and the AI models trained on them. | AI Dataset Development, Legal Issues in AI Datasets (Copyright, Privacy), Framework for Dataset Accountability, Mitigation of AI Harms | True | Idealistic | True | 1.0 | Positive | A framework for dataset accountability, including taxonomies of dataset development stages (Problem Formulation, Data Collection, Data Cleaning, Data Annotation, Model Training and Evaluation, Model Implementation and Inference, Data and Representation Distribution) and dataset development subjects (data subjects, data annotators, copyright holders, model subjects), and a matrix mapping harms to these stages and subjects. | Dataset Accountability Framework, Data Governance, AI Ethics / Harms Taxonomy | NaN | Not Applicable | NaN | NaN | Opacity in dataset development processes; legal uncertainties regarding copyright and privacy for AI datasets; prevalence of biased, discriminatory, or otherwise harmful datasets impacting marginalized groups; difficulty in assigning responsibility for AI-driven harms; lack of meaningful consent and awareness from data subjects; perpetuation of harms through widely distributed datasets and pre-trained models. | Lack of Transparency in Data Practices, Regulatory Uncertainty, Intellectual Property/Copyright Issues with AI, Data Privacy Concerns with AI, Bias in AI/Data, Lack of AI Accountability, Ethical Concerns with Data Collection | Proposing a framework for dataset accountability (identifying stages, subjects, and potential harms); advocating for enhanced transparency and documentation in dataset development (e.g., datasheets); calling for recalibration of legal norms (copyright, privacy, due process) in the context of AI datasets; suggesting incorporation of accountability principles into legislative and regulatory measures. | Conceptual Frameworks, Data Curation and Management, Transparency and Explainability in AI, Policy and Regulatory Reform, Regulation, Ethics, and Governance | Algorithmic bias and discrimination; privacy violations in data collection and use; copyright infringement in AI datasets; accountability for AI-driven harms; due process in automated decision-making systems; systemic informational harms. | Ethical AI in Law and AI Governance, Protection of Rights | Marginalized social and economic groups; racial, ethnic, gender, and religious minorities; disabled individuals; refugees and migrants; individuals in the Global South. | Marginalized communities, Low-income individuals, Minority groups, Women, People with disabilities, Asylum seekers and refugees, Migrants, Global South populations | Copyright Law, Privacy Law, Constitutional Law (Due Process, Equal Protection), AI Law and Regulation. | Copyright Law, Data Privacy Law, Constitutional Law, AI Regulation | United States (primarily, with discussion of US legal doctrines like fair use, FTC, proposed US legislation), with references to international data sources and issues. | USA, International | NaN | Not Applicable | Literature review (law, computer science, social sciences); case study analysis of existing AI datasets (e.g., ImageNet, LFW, Common Crawl, The Pile); legal analysis; conceptual framework and matrix development. | Literature Review as Design Input, Case Study Analysis, Legal Doctrinal Analysis as Design Input, Conceptual Framework Development | Publication in an academic journal intended for adoption by researchers, policymakers, legal practitioners, and industry leaders to inform dataset governance and accountability practices. | Dissemination via publication/presentation | True | False | The conceptual framework and matrix are detailed within the published paper. Access to the paper itself (e.g., via HeinOnline) may require a subscription. | Research artifact published in paper | Lack of comprehensive legal and regulatory frameworks specifically addressing the lifecycle of AI dataset development; insufficient transparency and standardized documentation for datasets; challenges in applying existing legal doctrines (e.g., copyright, privacy) to novel harms engendered by AI datasets; need for effective individual and systemic accountability mechanisms and means of redress for data subjects and model subjects; limited understanding and conceptualization of novel informational harms. | Regulatory and Governance Gaps, Data Availability and Quality, Transparency and Explainability, Security and Privacy of Data, Accountability and Redress Mechanisms, Ethical Framework Deficiencies | The inherent complexity, opacity, and often poor documentation of current AI dataset development practices; the need to integrate multifaceted legal, ethical, and technical considerations; addressing the rapidly evolving nature of AI technologies and data practices when proposing a stable framework. | Data Quality, Processing, and Preparation, Transparency and Explainability of AI, Ethical Considerations, Regulatory Uncertainty and Compliance, Scarcity of High-Quality Legal Data | Wrongful accusations and arrests from biased AI systems (e.g., facial recognition); discrimination, stereotyping, and reinforcement of societal biases; significant privacy violations through data scraping, aggregation, and leakage of personally identifiable information; use of datasets for pervasive surveillance; reintroduction of security vulnerabilities via code generation models; copyright infringement and complex licensing conflicts; creation of offensive or harmful content by generative models; difficulty in retracting harmful datasets anAI models once distributed. | Bias and discrimination, Consumer harm, Data privacy and security breach, Security vulnerabilities or malicious misuse, Copyright or intellectual property issues, Harmful or unsafe AI output, Technical limitations of AI |
| 12ResolvedJAlternativeDis.pdf | HeinOnline | Ai: INCREASING ALTERNATIVES IN ALTERNATIVE DISPUTE RESOLUTION | This paper examines the application of Artificial Intelligence (AI) in Alternative Dispute Resolution (ADR), explaining AI mechanisms and their use in resolving disputes. It argues that AI can expand access to justice, lower costs, and increase efficiency in ADR, despite challenges such as bias and the need for human empathy. | AI in Alternative Dispute Resolution, Access to Justice Enhancement, Cost Reduction, Efficiency Improvement, Challenge Identification, Bias in AI, Need for Human Empathy | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | High cost of legal services, court congestion and delays, physical and geographical barriers to accessing courts, power imbalances in disputes (e.g., domestic violence), and the complexity of the legal system for self-represented litigants. | High Cost of Legal Services, Judicial/Legal System Inefficiencies, Geographical Disparities in Legal Access, Physical Access Barriers to Courts, Power Imbalances, Complexity of Legal System/Procedures, Challenges for Self-Represented Litigants | Utilizing AI in ADR (AIDR) to reduce costs, increase efficiency, alleviate court congestion, enable remote dispute resolution, lessen power imbalances, and support self-represented litigants through user-centric ODR platforms. The paper also suggests mandatory AIDR education in law schools and updating laws for new technologies. | Online Dispute Resolution (ODR), AI Tool Development, Cost Reduction and Efficiency, Judicial System Enhancement, Support for Self-Represented Litigants, User Interface and Accessibility Design, Education and AI Literacy, Policy and Regulatory Reform | Affordability of legal services, efficiency of the justice system (reducing backlogs/delays), accessibility for remote/constrained individuals, empowerment of vulnerable parties (e.g., domestic violence victims), support for self-represented litigants, Online Dispute Resolution. | Affordability of Legal Services / Cost Reduction, Improving Efficiency in Legal System / Profession, Judicial System Modernization / Efficiency, Support for Vulnerable Populations, Support for Self-Represented Litigants, Dispute Resolution | Low-income individuals, self-represented litigants, victims of domestic violence or assault, individuals facing geographic or physical barriers, and disputants with language barriers. | Low-income individuals, Self-represented litigants, Victims of domestic violence, Survivors of sexual assault, Geographically isolated populations, People with disabilities, Individuals with language barriers | Alternative Dispute Resolution (Arbitration, Mediation, Online Dispute Resolution), Family Law, Small Claims, Contract Law, Immigration Law, Tax Law, Civil Litigation. | Alternative Dispute Resolution, Arbitration, Mediation, Online Dispute Resolution, Family Law, Small Claims Law, Contract Law, Immigration Law, Tax Law, Civil Litigation | United States | USA | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | True | True | ChatGPT-4 mentioned as a subscription service; DoNotPay app described as an app providing free remedies; ROSS Intelligence, eBay's ODR, Lexis Nexis Legal Machina also mentioned as existing tools. | Commercial product or service, Publicly accessible online tool or platform | Technical gaps include AI's limited emotional intelligence/intuition and the 'black box' problem of AI decision-making. Societal and ethical gaps include algorithmic bias and accountability, data privacy concerns, the digital divide, lack of technological competence among legal professionals, and the need for updated legal and ethical frameworks for AIDR. | AI Legal Reasoning Limitations, Transparency and Explainability, Bias in AI, Accountability and Redress Mechanisms, Security and Privacy of Data, Access, Equity, and Digital Divide, Human Oversight and Professional Adaptation, Ethical Framework Deficiencies, Regulatory and Governance Gaps | The absence of human presence (empathy, intuition) in AI-driven ADR. The introduction and perpetuation of bias through AI algorithms and data. Increased risks of professional misconduct related to confidentiality, competence, and reliance on AI. Ensuring data privacy and security. Achieving public and professional trust and acceptance of AI in dispute resolution. | Ethical Considerations, LLM Reasoning Capabilities, Bias in AI Systems and Data, Legal Professional Responsibility and Competence, Data Privacy, Security, and Confidentiality, User Adoption, Trust, and Acceptance | Discriminatory outcomes from biased AI (e.g., mispredictions in criminal justice or immigration). Violations of client confidentiality and privacy through AI data processing. Professional misconduct by lawyers due to incompetent use or over-reliance on AI (e.g., citing non-existent cases). AI providing incorrect legal judgments or flawed advice. Data security breaches (e.g., hacking of LLMs). | Bias and discrimination, Data privacy and security breach, Ethical concerns, Over-reliance on AI, Inaccurate or misleading AI output, Security vulnerabilities or malicious misuse |
| 36SAcLJ307.pdf | HeinOnline | GENERATIVE ARTIFICIAL INTELLIGENCE The Protection of Personal Data and Countering False Narratives About the Person | This paper discusses the personal data protection and false information concerns arising from Generative AI (Gen AI), particularly in the Singaporean context. It examines current legal frameworks, policy responses, and proposes legal and non-legal measures to govern Gen AI and protect individuals. | Generative AI Risks, Personal Data Protection, False Information (Disinformation), Singaporean Focus, Legal Frameworks for AI, Policy Responses to AI, AI Governance | True | Idealistic | True | 3.0 | Neutral | Generative AI (Gen AI), including Large Language Models (LLMs) like ChatGPT | Generative AI, Large Language Model | NaN | Not Applicable | NaN | NaN | Threats to personal data privacy, accuracy of personal information, lack of transparency and accountability in Gen AI, and the generation of false narratives about individuals. | Data Privacy Concerns with AI, Data Accuracy Concerns, Lack of AI Transparency/Explainability, Lack of AI Accountability, AI-driven Misinformation/Disinformation | Purposive interpretation and adaptation of existing laws (data protection, false information), new governance measures (licensing, reporting, mandatory disclosures), emphasis on transparency (source citation), and education for users and professionals. | Policy and Regulatory Reform, Regulation, Ethics, and Governance, Transparency and Explainability in AI, Education and AI Literacy | Protection of personal data, Countering false narratives about individuals | Protection of Rights, Ethical AI in Law and AI Governance | Individuals generally (data subjects, persons subject to false narratives) | General public, Data subjects, Victims of misinformation | Data protection law, Privacy law, Laws against false information (e.g., defamation, POFMA, PHA), AI regulation/governance, Content regulation | Data Privacy Law, Anti-Misinformation Law, Defamation Law, AI Regulation, AI Governance, Content Regulation | Singapore (primary), with comparisons to EU, US, Canada, Australia, and international efforts | Singapore, EU, USA, Canada, Australia, International | General discussion: large datasets, potentially including publicly available and user-provided data; user interaction and feedback. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Publicly Available Data, User-Generated Content | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Need for more specific and harmonized AI/Gen AI regulations (nationally and internationally), enhanced transparency and accountability mechanisms for Gen AI, and more effective tools to combat AI-generated false narratives and protect personal data. | Regulatory and Governance Gaps, Transparency and Explainability, Accountability and Redress Mechanisms, AI Accuracy and Reliability, Security and Privacy of Data | NaN | NaN | Generation of false narratives about individuals, misuse and unauthorized collection, use, or disclosure (CUD) of personal data, lack of transparency and accountability in Gen AI systems, creation of deepfakes, and embedded bias leading to discrimination. | Inaccurate or misleading AI output, Data privacy and security breach, Lack of transparency, accountability, and redress, Security vulnerabilities or malicious misuse, Bias and discrimination |
| 92TennLRev87.pdf | HeinOnline | BEYOND CHATGPT: TRANSFORMING GOVERNMENT WITH AUGMENTED LLMS | This paper explores how generative AI, specifically augmented Large Language Models (LLMs), can enhance government efficiency and equitable access to services, particularly in legal administration like taxation. It discusses methods such as fine-tuning and Retrieval-Augmented Generation (RAG) to improve LLM performance and mitigate risks like bias and inaccuracy, advocating for a collaborative approach to responsible AI adoption in the public sector. | Generative AI in Government, LLM Application, Efficiency Enhancement, Equitable Access to Services, Legal Administration (Taxation), Fine-tuning, Retrieval Augmented Generation, Risk Mitigation (Bias, Inaccuracy), Responsible AI Adoption | True | Idealistic | True | 3.0 | Positive | Augmented LLMs, specifically fine-tuning (including Reinforcement Learning from Human Feedback - RLHF), Retrieval-Augmented Generation (RAG), and the use of local/open-source LLMs. | Large Language Model Augmentation, Fine-tuning, Reinforcement Learning from Human Feedback (RLHF), Retrieval Augmented Generation (RAG), Open Source AI, Local LLM Deployment | NaN | Not Applicable | NaN | NaN | Bias in AI, inaccuracy and hallucinations, lack of transparency (black box models), security and privacy vulnerabilities, "simplexity" (oversimplification of complex legal matters leading to misunderstanding), and the digital divide hindering universal accessibility to AI tools. | Bias in AI/Data, AI Unreliability/Inaccuracy, Lack of AI Transparency/Explainability, Security Risks with AI, Data Privacy Concerns with AI, Oversimplification by AI, Digital Divide | Proper design, careful application, and rigorous oversight of AI systems; using techniques like fine-tuning and RAG to improve accuracy and relevance; developing equity-focused AI tools (e.g., multilingual capabilities, tailored for specific communities/needs); creating tools to support intermediaries (e.g., legal aid, VITA sites) to bridge the digital divide; and improving transparency of automated legal guidance. | AI Tool Development, Human Oversight and Collaboration, Regulation, Ethics, and Governance, Enhanced AI Capabilities, Language Simplification and Multilingual Access, User Interface and Accessibility Design, Transparency and Explainability in AI, Access to Legal Information and Advice | Improving access to government services and legal information, facilitating understanding of legal obligations (e.g., tax compliance), enhancing access to benefits (like EITC), supporting pro se litigants in legal processes, addressing misinformation, and overcoming language and literacy barriers in government interactions. | Access to Legal Information, Legal Literacy and Public Legal Education, Support for Self-Represented Litigants, Ethical AI in Law and AI Governance, Language Access and Digital Divide | Marginalized communities, lower-income taxpayers, non-native English speakers, pro se litigants, the elderly, individuals in rural areas, persons with disabilities, and those with lower levels of education or digital literacy. | Marginalized communities, Low-income individuals, Taxpayers, Individuals with language barriers, Self-represented litigants, Elderly people, Rural populations, People with disabilities, Individuals with low education levels, Individuals with low digital literacy | Tax administration (primary case study), administrative law, and peripherally mentions immigration law, patent law, and securities law. | Tax Law, Administrative Law, Immigration Law, Patent Law, Securities Law | U.S. (focuses on federal agencies like the IRS and mentions state-level initiatives in California, Minnesota, etc.) | USA | General LLMs are trained on vast, diverse internet data, books, and articles. The paper advocates for augmenting these with curated, domain-specific datasets (e.g., legal texts, agency policies, anonymized case data), human feedback data for fine-tuning, and proprietary or public knowledge bases for RAG implementations. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Fine-tuning Dataset, Legal Domain Data, Expert-Annotated / Human-Curated / Human-Generated Data, User-Generated Content, RAG System Knowledge Corpus, Proprietary Data, Publicly Available Data | The paper discusses augmenting LLMs through fine-tuning (including Reinforcement Learning from Human Feedback - RLHF) and Retrieval-Augmented Generation (RAG). It also emphasizes a collaborative approach involving subject-matter experts, technical experts, government authorities, and community feedback. | Model Fine-tuning, Reinforcement Learning from Human/AI Feedback (RLHF/RLAIF), Retrieval Augmented Generation (RAG), Collaborative Development, Stakeholder Engagement/Participatory Design | Proposed deployment includes LLM-powered chatbots and voicebots for public interaction, internal tools for government employee training and support, systems for generating educational content (e.g., infographics, simplified explanations), tools for intermediaries assisting underserved communities, and applications to help individuals draft communications with government agencies. | Proposed deployment (not implemented), Web-based access, Government/Public institution deployment, Educational resource deployment, Partnership-based rollout | False | False | NaN | NaN | Technical gaps include mitigating hallucinations, reducing the cost and complexity of re-training and fine-tuning LLMs, and improving their transparency and interpretability. Societal gaps involve ensuring equitable technology access, building and maintaining public trust, bridging the digital divide, effectively reaching vulnerable populations, and fostering robust collaboration between legal, governmental, and technical experts for ethical AI deployment. | AI Accuracy and Reliability, Computational Resource and Cost Issues, Knowledge Recency and Updatability, Transparency and Explainability, Access, Equity, and Digital Divide, Public Understanding, Trust, and Adoption, Need for Interdisciplinary Collaboration, Ethical Framework Deficiencies | Key challenges include inherent LLM limitations (e.g., hallucinations, bias, reliability, security risks, opacity, cost of development and maintenance) and governmental hurdles such as budget constraints, knowledge deficits regarding AI, lower risk tolerance for new technologies, and the need for careful ethical and regulatory frameworks for public sector AI adoption and use. | LLM Hallucination and Factual Errors, Bias in AI Systems and Data, Accuracy and Reliability of LLM Output, Data Privacy, Security, and Confidentiality, Transparency and Explainability of AI, Financial Cost and Resource Constraints, High Computational and Resource Demands, User Training, AI Literacy, and Skill Gaps, User Adoption, Trust, and Acceptance, Ethical Considerations, Regulatory Uncertainty and Compliance | Bias perpetuation leading to discriminatory outcomes, dissemination of misinformation or inaccurate legal guidance, privacy breaches and misuse of sensitive data, malicious use for disinformation or fraud, over-reliance on imperfect technology leading to errors, "simplexity" causing misunderstandings of law, and the exacerbation of societal inequities through differential access to technology or flawed AI-driven services. | Bias and discrimination, Inaccurate or misleading AI output, Data privacy and security breach, Security vulnerabilities or malicious misuse, Over-reliance on AI, Consumer harm, Exacerbation of inequality or two-tiered system |
| 27SMUSciTechLRev11.pdf | HeinOnline | Algorithmic Adjudication and Constitutional AI - The Promise of a Better AI Decision Making Future? | This paper argues that algorithmic adjudication, where AI makes legal decisions without human intervention, is inevitable. It discusses the challenges of traditional AI perpetuating biases and lacking explainability, and proposes Anthropic's "Constitutional AI" framework as a potentially more explainable, fair, and societally-aligned approach for future AI decision-making systems in law. | Algorithmic Adjudication, Constitutional AI Framework, Explainable AI, Fairness in AI, AI in Legal Decision-Making | True | Idealistic | True | 2.0 | Positive | Constitutional AI (CAI), specifically Anthropic's methodology for training its LLM Claude, which involves using a predefined set of principles (a "constitution") to guide AI behavior during fine-tuning, particularly through Reinforcement Learning from AI Feedback (RLAIF). | Constitutional AI, AI Alignment / Governance, Fine-tuning Methodology, Reinforcement Learning from AI Feedback (RLAIF) | The paper describes Anthropic's methodology for Constitutional AI. This involves a supervised learning phase where an LLM critiques and revises its own responses based on a 'constitution,' followed by a reinforcement learning phase (RLAIF) where an AI model evaluates response pairs against the constitution to train a preference model. Evaluation is based on the model's adherence to principles of harmlessness, helpfulness, honesty, and the defined constitution, and its outputs are compared to those from RLHF models. | Theoretical Analysis or Conceptual Proposal, Qualitative Analysis, Comparative Analysis | Constitutional AI models, like Anthropic's Claude, are claimed to produce more explainable results that are better aligned with societal values and the defined 'constitution'. They are suggested to reduce the risk of introducing subjective human biases compared to RLHF, offer a more objective basis for training, and are potentially more efficient and scalable for fine-tuning. | Developer or Vendor claim, Technique improves outcome, Successful bias mitigation, Benefit identified | Traditional AI perpetuates existing biases and its decisions can be difficult to explain (opacity). AI systems may lack contextual understanding for nuanced legal cases and may not grasp cultural sensitivities. There's a 'human-AI fairness gap' where people perceive algorithmic decisions as less fair. The legal profession also shows resistance to understanding and adopting new technologies. | Bias in AI/Data, Lack of AI Transparency/Explainability, AI Limitations in Legal Reasoning/Nuance, Safety/Cultural Sensitivity Issues in AI, Lack of Trust in AI/Automated Systems, Slow Technology Adoption by Legal Profession | The paper proposes using "Constitutional AI" frameworks integrating legal and ethical standards into AI design. It advocates for legal professionals to gain greater understanding of AI, participate in the design, development, and monitoring of algorithmic adjudication systems, and collaborate to establish ethical guidelines. | Conceptual Frameworks, Regulation, Ethics, and Governance, Education and AI Literacy, Human Oversight and Collaboration, Open Source Initiatives and Collaboration, Judicial System Enhancement | Algorithmic adjudication, fairness and bias in AI legal decision-making, explainability and transparency of AI in law, accessibility of legal processes (e.g., for small claims, reducing backlogs), maintaining integrity of the legal system and public trust. | Judicial System Modernization / Efficiency, Ethical AI in Law and AI Governance, Democratizing Law / Closing Justice Gap / Rule of Law | General public needing access to dispute resolution, especially for smaller or routine cases, aiming to enhance accessibility and efficiency of the legal process. | General public, Individuals with unmet legal needs, Litigants in small claims courts | General (algorithmic adjudication), with examples and implications for administrative law, civil law (specifically small claims), criminal law (predictive aspects), and alternative dispute resolution (ADR). | General Law, Administrative Law, Civil Law, Small Claims Law, Criminal Law, Alternative Dispute Resolution | United States (primary focus regarding inevitability and implications), with international examples from Estonia, China, England and Wales, and Colombia. The Constitutional AI approach is discussed generally. | USA, Estonia, China, UK, Colombia, International | For Constitutional AI (Anthropic's Claude): The initial LLM is pre-trained on vast text corpora. The fine-tuning process uses AI-generated data: self-critiques, revisions, and preference labels generated by AI models, guided by a human-defined 'constitution' (inspired by sources like the UN Universal Declaration of Human Rights and ethical AI principles). | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Fine-tuning Dataset, Synthetic Data, Expert-Annotated / Human-Curated / Human-Generated Data | For Constitutional AI: Supervised Learning (SL) for initial alignment with the constitution, and Reinforcement Learning from AI Feedback (RLAIF) for further refinement based on AI-generated evaluations against the constitution. This embodies a principle-based design approach. | Constitutional AI, Supervised Learning, Reinforcement Learning from Human/AI Feedback (RLHF/RLAIF), Principle-based Design | NaN | Not applicable | True | False | Anthropic's LLM Claude 3, which embodies the Constitutional AI training methodology, is commercially available via API and web interface. | Model available, API access, Publicly accessible online tool or platform, Commercial product or service | The effectiveness of Constitutional AI depends on the quality and comprehensiveness of its guiding 'constitution.' The application of LLM technology in actual AI decision-making systems is still in its early stages. There's a need for greater involvement of legal professionals in the lifecycle of AI adjudication systems and for continued efforts to build and maintain public trust. | Ethical Framework Deficiencies, Research and Evaluation Gaps, Need for Interdisciplinary Collaboration, Human Oversight and Professional Adaptation, Public Understanding, Trust, and Adoption | For Constitutional AI: Ensuring the 'constitution' (set of principles) is well-defined, comprehensive, and effectively covers all ethical considerations. The technology is still nascent for complex decision-making systems. General LLM fine-tuning challenges like resource intensity and potential for bias (though CAI aims to mitigate these compared to RLHF) remain relevant contexts. | Ethical Considerations, Domain-Specific Adaptation and Customization, High Computational and Resource Demands, Bias in AI Systems and Data | Perpetuation of biases if the 'constitution' in CAI is not robust or if training data issues persist. Lack of explainability (though CAI aims for improvement). Potential for unfair or unjust outcomes if AI lacks nuanced understanding. Erosion of public trust. Algorithmic deference or automation bias leading to insufficient human oversight. Decisions being technically correct but failing to deliver broader justice. | Bias and discrimination, Lack of transparency, accountability, and redress, Undermining legal process or principles, Erosion of trust in legal system or AI, Over-reliance on AI, Technical limitations of AI |
| 14JChristianLegalThought1.pdf | HeinOnline | MORE THAN MACHINES: THE ETHICAL AND HUMAN IMPLICATIONS OF GENERATIVE Al ON LAWYERING | This paper examines the ethical challenges generative AI poses for lawyers, including issues of competence, confidentiality, and supervision. It further argues that AI's rise necessitates a renewed focus on uniquely human qualities such as advocacy, empathy, and wisdom, especially for Christian lawyers. | Ethical Challenges of Generative AI for Lawyers, Lawyer Competence, Confidentiality, Supervision, Importance of Human Qualities in Law, Christian Perspective | True | Idealistic | True | 3.0 | Neutral | Generative AI (e.g., ChatGPT, LLMs) | Generative AI, Large Language Model | NaN | Not Applicable | NaN | NaN | AI generating "hallucinations" (fabricated content presented as real authority); AI bias perpetuating societal biases; Risks to client confidentiality when inputting information into AI tools; Potential for AI to facilitate the unauthorized practice of law if not properly supervised; Over-reliance on AI diminishing human oversight and professional judgment. | AI Unreliability/Inaccuracy, Bias in AI/Data, Data Privacy Concerns with AI, Regulatory Hurdles, Automation Bias, Need for Human Oversight of AI | Lawyers should cultivate uniquely human qualities such as advocacy (especially for the vulnerable), empathy (understanding clients as fellow humans), and wisdom (moral and practical judgment, including biblical wisdom for Christian lawyers). Lawyers must adhere to ethical duties when using AI, including competence, diligence, confidentiality, proper supervision, and obtaining client consent where appropriate. | Human Oversight and Collaboration, Regulation, Ethics, and Governance, Education and AI Literacy | Ethical use of AI in legal practice; The role of human lawyers and their unique attributes (advocacy, empathy, wisdom) in an AI-driven legal landscape; The Christian lawyer's calling to advocacy for the poor, needy, and destitute. | Ethical AI in Law and AI Governance, Regulatory Reform (Legal Services and AI), Support for Vulnerable Populations | Poor, needy, destitute, those who cannot speak for themselves. | Low-income individuals, Vulnerable populations | General legal practice | General Legal Practice | United States | USA | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Current AI lacks true sentience, genuine empathy, and human-level wisdom; AI's unreliability due to issues like hallucinations and bias; The ongoing challenge of integrating AI into legal practice while upholding ethical responsibilities and preserving essential human elements. | AI Legal Reasoning Limitations, AI Accuracy and Reliability, Bias in AI, Integration and Interoperability Challenges, Ethical Framework Deficiencies, Human Oversight and Professional Adaptation | Ensuring lawyer competence and diligence with rapidly evolving AI technology; Maintaining client confidentiality when using AI platforms; Providing proper supervision for AI tools (akin to nonlawyer assistants); Avoiding the unauthorized practice of law through AI; Determining appropriate client communication regarding AI use; Establishing ethical billing practices when AI enhances efficiency without corresponding human effort. | Legal Professional Responsibility and Competence, User Training, AI Literacy, and Skill Gaps, Data Privacy, Security, and Confidentiality, Need for Human Oversight and Intervention, Unauthorized Practice of Law (UPL) Concerns, Ethical Considerations | Attorneys facing disciplinary action for submitting AI-generated misinformation (e.g., fabricated case law); Perpetuation of societal biases through biased AI outputs; Inadvertent disclosure of confidential client information via AI tools; Reliance on AI "hallucinations" leading to incorrect legal work; Diminished human oversight and independent professional judgment due to over-reliance on AI; AI tools enabling or assisting in the unauthorized practice of law if used improperly. | Ethical concerns, Inaccurate or misleading AI output, Bias and discrimination, Data privacy and security breach, Over-reliance on AI, Unauthorized practice of law |
| 6Issue3IntlJLMgmtHuman.pdf | HeinOnline | X-Raying the Legality of a Robot Lawyer in the Nigerian Courts | This paper analyzes the Nigerian legal system to determine if a robot lawyer could legally operate in its courts, concluding that current laws prevent this. It highlights the significant legal and professional reforms required for any future accommodation of AI in Nigerian legal practice. | AI in Nigerian Legal System, Legality of Robot Lawyers, Need for Legal Reform, Need for Professional Reform | True | Idealistic | True | 3.0 | Neutral | Robot lawyer / AI-powered legal assistance tools (e.g., chatbots, DoNotPay, ChatGPT) | AI Legal Tool, AI Legal Assistant, Chatbot / Conversational AI, Named Tool / Platform | NaN | Not Applicable | NaN | NaN | Existing Nigerian laws requiring lawyers to be human citizens, hold specific qualifications, be called to the bar, and be enrolled; lack of legal personality for robots; resistance from the legal profession; and Nigeria's technological/economic limitations. | Regulatory Hurdles, Lack of Legal Personality/Capacity for AI, Protectionism by Legal Profession, Resource Constraints for A2J Tech Development/Deployment | Comprehensive legislative reform to amend the Legal Practitioners Act, Rules of Professional Conduct, and other relevant laws to define and accommodate robot lawyers; development of a legal framework for their co-existence with human lawyers. | Policy and Regulatory Reform, Alternative Legal Service Delivery Models, Regulation, Ethics, and Governance | Affordable legal services, access to legal advice and representation in minor civil matters (e.g., traffic tickets, consumer rights disputes). | Affordability of Legal Services / Cost Reduction, Access to Legal Advice, Access to Legal Representation | General public needing cheaper legal services, particularly for everyday legal problems or small claims. | General public, Individuals unable to afford legal services, Litigants in small claims courts | General legal practice, Professional regulation, Civil litigation (minor disputes), Consumer rights, Evidence law | General Legal Practice, Legal Profession Regulation, Civil Litigation, Consumer Law, Evidence Law | Nigeria | Nigeria | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Lack of a specific legal framework for AI in legal practice; absence of provisions for non-human legal practitioners in existing statutes; insufficient adaptation of evidence laws for advanced AI; and a general lag in legal system modernization to accommodate technological advancements. | Regulatory and Governance Gaps | Satisfying legal requirements for being a lawyer (citizenship, education, bar admission, good character, practicing fees, continuous development, dress code); defining the legal status of a robot (juristic personality); overcoming resistance from the established legal profession; updating evidence laws; and the country's technological and economic readiness. | Regulatory Uncertainty and Compliance, User Adoption, Trust, and Acceptance, Financial Cost and Resource Constraints, Integration with Existing Systems and Workflows | Displacement of human lawyers; unauthorized practice of law by AI leading to legal sanctions; potential for AI to provide inadequate or incorrect legal advice; erosion of the integrity/dignity of the legal profession if unregulated AI participates in legal processes. | Job displacement, Unauthorized practice of law, Inaccurate or misleading AI output, Ethical concerns, Dehumanization of legal process |
| 99IndLJSupp37.pdf | HeinOnline | Framing Online Speech Governance as an Algorithmic Accountability Issue | The paper argues for a regulatory approach to online speech governance that focuses on the AI tools used for both content moderation and generation, framing it as an algorithmic accountability issue. It highlights the shortcomings of current legal frameworks and advocates for a systems-level approach to examine the development and deployment of these AI tools, considering their technical and normative features. | Regulation of Online Speech Governance by AI, Algorithmic Accountability, AI for Content Moderation, AI for Content Generation, Systems-Level Regulatory Approach | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | Error-prone AI tools, lack of transparency and accountability in AI development and deployment, biases in AI leading to unfair outcomes and censorship, inadequacy of current legal frameworks to govern AI, and power imbalances favoring platforms over users. | AI Unreliability/Inaccuracy, Lack of AI Transparency/Explainability, Lack of AI Accountability, Bias in AI/Data, Inadequate Legal Frameworks for AI, Power Imbalances | Adopting a systems-level regulatory approach centered on algorithmic accountability, including measures like mandatory documentation (datasheets), Algorithmic Impact Assessments (AIAs) for AI tools, increased transparency in development processes, and stronger legal frameworks for AI governance. | Policy and Regulatory Reform, Regulation, Ethics, and Governance, Data Curation and Management, Transparency and Explainability in AI | Algorithmic accountability in online speech governance, fairness in content moderation and generation, protection of freedom of expression, and mitigation of AI-driven harms like censorship and disinformation. | Ethical AI in Law and AI Governance, Protection of Rights | Users whose speech is erroneously moderated or censored, marginalized groups disproportionately affected by AI biases (e.g., ethnic/religious minorities, speakers of non-dominant languages), and populations in global south countries affected by platform failures (e.g., Myanmar). | Victims of censorship, Marginalized communities, Minority groups, Speakers of low-resource languages, Global South populations | Internet Law (including CDA Section 230, DMCA), Constitutional Law (Freedom of Speech, Due Process), Copyright Law, AI Law/Regulation, Human Rights Law. | Internet Law, Constitutional Law, Copyright Law, AI Regulation, Human Rights Law | Primarily United States (discussing CDA, DMCA, Gonzalez v. Google, proposed Algorithmic Accountability Act), with references to global impacts and international contexts (e.g., Myanmar, India, non-English content moderation). | USA, International, Myanmar, India | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Significant regulatory gaps in holding AI tools accountable, lack of transparency in AI development and deployment, insufficient understanding of AI's contextual and linguistic nuances (especially non-English), limitations in creating unbiased and representative datasets, and inadequate mechanisms for user recourse against AI-driven decisions. | Regulatory and Governance Gaps, Accountability and Redress Mechanisms, Transparency and Explainability, AI Legal Reasoning Limitations, Multilingual and Low-Resource Language Gaps, Data Availability and Quality, Bias in AI, Consumer Protection Gaps | NaN | NaN | Erroneous censorship of legitimate speech, amplification of misinformation and hate speech, generation of harmful content and disinformation by AI tools, perpetuation of societal biases, copyright infringement by generative AI, and potential misuse of AI or disclosed data by malicious actors including authoritarian regimes. | Technical limitations of AI, Infringement on human rights, Inaccurate or misleading AI output, Harmful or unsafe AI output, Bias and discrimination, Copyright or intellectual property issues, Security vulnerabilities or malicious misuse |
| 21NYUJLBus119.pdf | HeinOnline | Don't Kill the Baby! The Case for AI in Arbitration | This paper argues that Generative AI can and should be used as an arbitrator if parties contractually agree, consistent with the Federal Arbitration Act (FAA). It positions arbitration as an ideal starting point for AI adoption in law, emphasizing contractual autonomy and calling for empirical comparison between AI and human arbitration. | Generative AI as Arbitrator, Contractual Agreement for AI Use, Federal Arbitration Act (FAA), AI in Arbitration, Empirical Comparison (AI vs Human) | True | Idealistic | True | 3.0 | Positive | The use of AI (particularly Generative AI and Large Language Models) as the contractually chosen arbitrator in dispute resolution, leveraging the flexibility of the Federal Arbitration Act (FAA). | AI as Arbitrator, Generative AI, Large Language Model, Dispute Resolution Mechanism, Legal Framework Application | The paper does not conduct its own empirical testing of AI as arbitrators. It supports its arguments by referencing existing studies on general AI capabilities, such as a study on deceptive review detection where AI's performance was compared to humans and human-AI teams. | No Evaluation by Author, References External Evaluation | The paper does not present results from its own evaluation of AI arbitrators. It cites a study by Lai et al. where AI alone achieved 86.3% accuracy in deceptive review detection, compared to 54.6% for humans alone and 74% for a combined human-AI team, to illustrate AI's potential. | High performance, Outperforms others, Descriptive or Conceptual finding | Resistance to AI adoption in legal contexts due to concerns about bias, discrimination, lack of transparency, absence of human qualities like empathy, job displacement, and overly moralistic views that hinder experimentation and growth. | Slow Technology Adoption by Legal Profession, Bias in AI/Data, Lack of AI Transparency/Explainability, AI Limitations in Replicating Human Judgment, Fear of Job Displacement, Psychological/Cultural Barriers to AI Adoption | Upholding contractual autonomy under the Federal Arbitration Act to allow parties to choose AI-driven arbitration; utilizing arbitration as a flexible, contract-based environment for experimenting with AI in the legal field; fostering an open-minded approach and advocating for empirical studies comparing AI and human arbitration. | Online Dispute Resolution (ODR), Policy and Regulatory Reform, AI Tool Development, Benchmarking and Evaluation Frameworks | Dispute resolution (arbitration), cost reduction in legal processes, accessibility of legal services, enhancing subjective fairness in adjudication, contractual autonomy in choosing dispute resolution methods. | Dispute Resolution, Affordability of Legal Services / Cost Reduction, Democratizing Law / Closing Justice Gap / Rule of Law, Ethical AI in Law and AI Governance | Pro se litigants, individuals lacking legal expertise or strong writing skills, elderly and/or disabled individuals facing difficulties with traditional hearings, and generally those seeking more accessible, lower-cost, and faster dispute resolution. | Self-represented litigants, Individuals lacking legal knowledge, Individuals with low literacy, Elderly people, People with disabilities, Individuals facing access barriers, Individuals unable to afford legal services | Arbitration, Contract Law, Alternative Dispute Resolution. | Arbitration, Contract Law, Alternative Dispute Resolution | USA (due to the central focus on the Federal Arbitration Act - FAA). | USA | The paper discusses Generative AI and LLMs generally, which are trained on vast datasets (e.g., scraped from the internet). It specifically mentions SaulLM-7B, an LLM trained on an English legal corpus of over 30 billion tokens, as an example of relevant AI development. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Fine-tuning Dataset, Legal Domain Data | NaN | NaN | NaN | Not applicable | True | False | The paper argues that parties can, by contractual agreement under the FAA, use existing AI tools (like general-purpose LLMs or specialized legal AIs) as arbitrators. | NaN | The need for empirical research comparing the performance, fairness, and outcomes of AI arbitration versus human arbitration. Further development and fine-tuning of AI models are needed for specialized tasks in arbitration, ensuring confidentiality and building disputant trust. | Research and Evaluation Gaps, Bias in AI, AI Scope and Functionality Limitations, Security and Privacy of Data, Public Understanding, Trust, and Adoption | Overcoming skepticism and resistance to AI in legal decision-making; addressing ethical concerns such as bias, transparency, and accountability in AI systems; adapting general-purpose AI models for the nuanced and complex requirements of legal arbitration, including handling emotional and ethical subtleties. | User Adoption, Trust, and Acceptance, Ethical Considerations, Bias in AI Systems and Data, Transparency and Explainability of AI, Accountability and Liability for AI Errors, Domain-Specific Adaptation and Customization, LLM Reasoning Capabilities | Perpetuation of biases present in training data; lack of genuine empathy and emotional understanding; potential for job displacement; ethical concerns regarding consent, manipulation, and privacy; erosion of trust in the legal process due to opaque 'black-box' decision-making; potential for inaccuracies or inappropriate outputs from AI; undermining due process norms. | Bias and discrimination, Dehumanization of legal process, Job displacement, Ethical concerns, Data privacy and security breach, Erosion of trust in legal system or AI, Lack of transparency, accountability, and redress, Inaccurate or misleading AI output, Undermining legal process or principles |
| 4JusCorpusLJ601.pdf | HeinOnline | AI-powered Indian Courtroom: ChatGPT a boon or a bane? | This paper discusses the potential benefits and drawbacks of integrating AI, particularly tools like ChatGPT, into the Indian judicial system to improve efficiency and access to justice. It highlights existing AI initiatives in Indian courts, such as SUPACE and SUVAS, and emphasizes the need for a cautious, regulated approach to adoption while considering ethical implications and practical challenges. | AI in Indian Judiciary, ChatGPT Application, Benefit Identification, Drawback Identification, Efficiency Improvement, Access to Justice Enhancement, Existing AI Initiatives (India), Cautious AI Adoption, Regulated AI Approach, Ethical Implications | True | Idealistic | True | 3.0 | Neutral | ChatGPT, SUPACE (Supreme Court Portal for Assistance in Court's Efficiency), SUVAS (Supreme Court Vidhik Anuvaad Software), TERES (transcription tool), AI for administrative tasks, precedent analysis, and legal research. | Large Language Model, Named Tool / Platform, Judicial Assistance Tool, Legal Translation Tool, Transcription Tool | For SUVAS: Observation of initial high productivity in translation, followed by decline in speed and scope (focus on Hindi, criminal matters). For ChatGPT: Anecdotal use by a High Court judge to gauge bail jurisprudence. For TERES: Deployed for live transcription in Supreme Court. | Qualitative Analysis, References External Evaluation | SUVAS: Initially translated many judgments efficiently but later became sluggish and limited in scope (mostly Hindi, criminal matters). TERES: Successfully used for live transcription. ChatGPT: Used anecdotally by a judge to gauge bail jurisprudence, signifying potential for greater AI participation. | Mixed performance, Successful real-world application, Limitation: Operational or Technical | High case backlogs leading to delays, language barriers (Apex court judgments primarily in English making them inaccessible to many), physical distance from courts, and the general complexity of law for the layperson. | Judicial/Legal System Inefficiencies, Accessibility Barriers for Specific User Groups, Geographical Disparities in Legal Access, Complexity of Legal System/Procedures | Using AI for administrative tasks, legal research, and precedent analysis to reduce judicial workload and case backlogs. Implementing AI-powered translation services (like improved SUVAS) and virtual courtrooms to improve accessibility. Developing a legal framework to regulate AI use in courts and providing adequate training. | Judicial System Enhancement, Legal Research and Analysis Tools, Cost Reduction and Efficiency, Language Simplification and Multilingual Access, Policy and Regulatory Reform, Education and AI Literacy | Reducing case backlogs, enhancing judicial efficiency, language access to legal information through translation, physical access to courts via virtual proceedings, improving legal research, and transcription of court proceedings. | Judicial System Modernization / Efficiency, Language Access and Digital Divide, Access to Legal Information, LegalResearch Support | The general Indian populace, particularly the "middle and lower strata" not proficient in English and those facing challenges due to physical distance from courts. | General public, Population in India, Moderate-income individuals, Low-income individuals, Individuals with language barriers, Geographically isolated populations | General, with specific mention of criminal law (bail jurisprudence, translation of criminal matters) and contract law (drafting). | General Law, Criminal Law, Contract Law | India | India | For SUVAS: Supreme Court judgments and orders. For ChatGPT (implied by mention of GPT-4): Large, general text and code datasets from the internet. For SUPACE and TERES: Not specified in the paper. | Legal Domain Data, Case Law / Judgments, Other Legal Documents, Undisclosed Data Source/Availability, Indian Legal Data, Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text | NaN | NaN | SUPACE and SUVAS were launched as official Supreme Court initiatives. TERES was used in Supreme Court for live transcription. ChatGPT was used by a High Court judge via its public interface. | Government/Public institution deployment, Evaluation of existing third-party tool, Web-based access | True | True | ChatGPT is a publicly accessible LLM, with free usage tiers available online, as evidenced by its use by a judge mentioned in the paper. | Publicly accessible online tool or platform, Freemium access | Need for improved AI translation capabilities (broader language support, consistent performance for tools like SUVAS). Lack of adequate technological infrastructure and widespread technological literacy among legal professionals. Absence of a comprehensive legal and ethical framework to govern AI in the judiciary, including clear guidelines on bias mitigation, accountability, and data privacy. | Multilingual and Low-Resource Language Gaps, AI Accuracy and Reliability, Computational Resource and Cost Issues, Access, Equity, and Digital Divide, Public Understanding, Trust, and Adoption, Human Oversight and Professional Adaptation, Regulatory and Governance Gaps, Ethical Framework Deficiencies, Bias in AI, Accountability and Redress Mechanisms, Security and Privacy of Data | For specific tools like SUVAS: Maintaining translation quality, speed, and comprehensive coverage across languages and case types. For general AI adoption: Overcoming lack of technological literacy and resources among legal professionals, addressing fears of job displacement, mitigating algorithmic bias, defining accountability for AI-assisted decisions, and ensuring data privacy for sensitive court information. | Multilingual and Low-Resource Language Support, Accuracy and Reliability of LLM Output, User Training, AI Literacy, and Skill Gaps, Financial Cost and Resource Constraints, Ethical Considerations, Bias in AI Systems and Data, Accountability and Liability for AI Errors, Data Privacy, Security, and Confidentiality | Job displacement for court administrative staff, introduction of algorithmic bias leading to miscarriages of justice, erosion of judicial accountability if blame is shifted to AI, breaches of privacy and confidentiality of sensitive court data, and potential for cataclysmic outcomes from unregulated AI use in courtrooms. | Job displacement, Bias and discrimination, Undermining legal process or principles, Lack of transparency, accountability, and redress, Data privacy and security breach, Harmful or unsafe AI output, Regulatory challenges or gaps |
| 26JLegalEthicalRegulIsses.pdf | HeinOnline | ASPECTS OF ARTIFICIAL INTELLIGENCE ON E-JUSTICE AND PERSONAL DATA LIMITATIONS | This paper discusses the evolving applications of Artificial Intelligence (AI) within judicial systems, emphasizing the critical role of data availability and the necessity of robust personal data protection measures. It analyzes specific AI uses such as predictive justice and online dispute resolution, while also addressing key technoethical concerns, limitations, and the potential for algorithmic bias, particularly in criminal justice contexts. | AI in Judicial Systems, Data Availability for AI, Personal Data Protection, Predictive Justice, Online Dispute Resolution, Technoethical Concerns, Algorithmic Bias, Criminal Justice Focus | True | Idealistic | True | 3.0 | Neutral | Predictive justice systems, Online Dispute Resolution (ODR), AI tools in criminal justice (e.g., risk assessment, crime prevention), ChatGPT for legal tasks. | Predictive Justice, Online Dispute Resolution (ODR), AI in Criminal Justice, Risk Assessment, Large Language Model | Discusses evaluations of tools like COMPAS (showing racial bias from independent research) and ongoing testing of HART in the UK. Mentions a study on ChatGPT's legal drafting capabilities. | References External Evaluation | COMPAS algorithm showed discriminatory outcomes, with African-American individuals being assessed as twice as likely to reoffend compared to other groups. | Limitation: Bias, Low performance | Limited availability and quality of open data for training AI; technical difficulties in effective anonymization/pseudonymization to protect privacy; potential for algorithmic bias and discrimination; lack of transparency in proprietary algorithms. | Data Scarcity/Quality for AI, Technical Challenges in AI Development, Data Privacy Concerns with AI, Bias in AI/Data, Lack of AI Transparency/Explainability, Proprietary Nature of AI as a Barrier | Promoting open data policies for court decisions while ensuring robust anonymization/pseudonymization; upholding the right of individuals to contest automated decisions and to be informed about algorithmic reasoning (e.g., under GDPR); ensuring transparency, neutrality, and honesty in AI systems. | Policy and Regulatory Reform, Data Curation and Management, Data Privacy and Security, Regulation, Ethics, and Governance, Transparency and Explainability in AI | E-justice systems, online dispute resolution (ODR), predictive justice (including risk assessment in criminal cases), AI-assisted legal drafting, efficiency in judicial processes, personal data protection. | Judicial System Modernization / Efficiency, Dispute Resolution, Improving Foundational AI Capabilities for Legal Applications, Legal Document Creation / Automation, Protection of Rights | Individuals involved in the justice system, particularly those at risk of discriminatory treatment due to algorithmic bias (e.g., racial minorities in criminal justice). | Litigants, Vulnerable to AI bias, Minority groups, Criminal defendants | Civil law, commercial law, administrative law, criminal law. | Civil Law, Commercial Law, Administrative Law, Criminal Law | European Union, United Kingdom, United States, and mentions of specific AI adoption in China, Argentina, Colombia, Canada (Montreal). Discusses CEPEJ guidelines. | EU, UK, USA, China, Argentina, Colombia, Canada, Council of Europe | For HART: Durham police records from 2008 to 2012. For COMPAS: Information from accused individuals and their criminal records. For predictive justice generally: Court decisions and 'unrefined' data in structural computer databases. For ChatGPT: large volumes of data and documents. | Non-Legal Domain Specific Data, Proprietary Data, Legal Domain Data, Case Law / Judgments, Structured Data, Data Bias Concerns Noted, Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text | NaN | NaN | Discusses deployment of ODR systems in several European countries, COMPAS in US courts, ongoing testing of HART in the UK, and early adoption of AI tools (like ChatGPT) in courts in China, Argentina, and Colombia. | Evaluation of existing third-party tool, Government/Public institution deployment, Pilot program/Limited rollout | True | True | ChatGPT, developed by OpenAI, is mentioned as being publicly accessible and has been used in legal contexts, with a free tier available. | Publicly accessible online tool or platform, Freemium access | Limitations in the reliability of predictive justice systems; lack of fully effective automated anonymization techniques; insufficient transparency and accountability in algorithmic decision-making; potential for 'technological solutionism' where AI is misapplied to complex social problems. | AI Accuracy and Reliability, Security and Privacy of Data, Transparency and Explainability, Accountability and Redress Mechanisms, Ethical Framework Deficiencies | Ensuring data availability and quality for AI training; protecting personal data and privacy through effective anonymization/pseudonymization; mitigating algorithmic bias and ensuring fairness and non-discrimination; addressing lack of transparency in AI models; managing ethical implications and preventing over-reliance on AI. | Scarcity of High-Quality Legal Data, Data Quality, Processing, and Preparation, Data Privacy, Security, and Confidentiality, Bias in AI Systems and Data, Transparency and Explainability of AI, Ethical Considerations, User Adoption, Trust, and Acceptance | Algorithmic bias leading to discriminatory outcomes (e.g., racial bias); violation of privacy and human dignity through misuse of personal data; 'profiling' of individuals; lack of transparency and accountability in AI decision-making; over-reliance on AI leading to errors or deskilling; reinforcement of existing societal inequalities; spread of misinformation or flawed legal advice from AI tools like chatbots. | Bias and discrimination, Data privacy and security breach, Infringement on human rights, Security vulnerabilities or malicious misuse, Lack of transparency, accountability, and redress, Over-reliance on AI, Deskilling or erosion of human skills, Exacerbation of inequality or two-tiered system, Inaccurate or misleading AI output |
| 85UPittLRev331.pdf | HeinOnline | A PERFECT STORM FOR LEGAL EDUCATION: PRIVATIZATION, POLARIZATION, AND PEDAGOGY | The paper analyzes how emerging technologies, including AI like ChatGPT, and increasing political polarization are creating a 'perfect storm' for the legal profession and legal education. It argues these forces risk undermining lawyers' expertise and commitment to the public good, potentially leading to a stratified legal system with diminished access to justice and trust in law for ordinary people. | Impact of AI and Polarization on Legal Profession, Risk to Lawyer Expertise, Risk to Public Good Commitment, Stratified Legal System Risk, Diminished Access to Justice, Erosion of Trust in Law | True | Idealistic | True | 3.0 | Negative | Online Dispute Resolution (ODR) systems (including AI-supplemented and blockchain-based versions), AI-powered tools for legal tasks (e.g., chatbots like ChatGPT, DoNotPay's 'robot lawyer'). | Online Dispute Resolution (ODR), AI Supplemented ODR, Blockchain Technology, Chatbot / Conversational AI, Large Language Model, AI Legal Tool, Named Tool / Platform | ODR in courts: user experiences vary, some speedier. DoNotPay AI lawyer: plan withdrawn due to regulatory threats. AI in family law (e.g., Matterhorn): company-reported positive outcomes. ChatGPT: passed law school/bar exams, can draft briefs, but prone to factual errors. | References External Evaluation, Developer Claims Reported | Matterhorn (company-reported for its family law ODR): reduced hearings, improved child support collection. ChatGPT: passed bar exam with high scores (latest version). | Benefit identified, Developer or Vendor claim, High performance, Successful real-world application | Cost of legal services and lack of counsel for low-income individuals; technological divides and discomfort; potential for technology to entrench stratification in legal services (robust law for elites, automated processing for others); declining public trust in legal institutions due to polarization. | High Cost of Legal Services, Limited Access to Legal Assistance, Digital Divide, Psychological/Cultural Barriers to AI Adoption, Risk of AI Exacerbating Inequality, Lack of Trust in Justice System | Use of technology (e.g., ODR, AI tools) to improve efficiency and access to legal support for underserved populations; emphasis on legal proceduralism and ethical duties to counterbalance polarization; curricular reforms in law schools to foster skills for managing ideological conflict. | Online Dispute Resolution (ODR), AI Tool Development, Cost Reduction and Efficiency, Access to Legal Information and Advice, Regulation, Ethics, and Governance, Education and AI Literacy, Policy and Regulatory Reform | Access to legal representation for pro se litigants and people of modest means; use of technology in civil dispute resolution (e.g., family law, traffic courts); impact of technology and polarization on the perception and administration of justice. | Access to Legal Representation, Support for Self-Represented Litigants, Support for Vulnerable Populations, Dispute Resolution, Judicial System Modernization / Efficiency | People with limited means; pro se litigants; ordinary people interacting with the legal system. | Low-income individuals, Moderate-income individuals, Self-represented litigants, General public, Litigants | Civil litigation, Family law, Traffic court (briefly mentioned), Criminal law (noted as an area with less AI penetration). | Civil Litigation, Family Law, Traffic Law, Criminal Law | United States | USA | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | True | True | ChatGPT is publicly accessible (free/paid tiers). Some court-based ODR systems are operational. DoNotPay offers subscription services (though its AI court lawyer concept was halted). | Publicly accessible online tool or platform, Freemium access, Commercial product or service | Deepening stratification in legal services; erosion of public trust in legal institutions; difficulty in upholding social trusteeship of lawyers amid polarization; ensuring technology serves the 'greater good' rather than just market efficiency; lack of common understanding impacting how ordinary people access and perceive law. | Access, Equity, and Digital Divide, Public Understanding, Trust, and Adoption, Ethical Framework Deficiencies, Human Oversight and Professional Adaptation | Ensuring fairness, transparency, and ethical application of AI in legal contexts; addressing the unauthorized practice of law by AI tools; overcoming the digital divide and user difficulties with legal tech; resource limitations in courts for technology adoption; maintaining the legal profession's integrity and relevance amidst technological displacement and ideological pressures. | Bias in AI Systems and Data, Transparency and Explainability of AI, Ethical Considerations, Unauthorized Practice of Law (UPL) Concerns, User Interface, Usability, and Accessibility, Financial Cost and Resource Constraints, Legal Professional Responsibility and Competence | Displacement of lawyers in routine legal work by technology; AI systems making errors or lacking moral capacity; increased stratification of legal services; erosion of public trust and perception that law is only for elites; lawyers potentially misusing technology or succumbing to partisan pressures, undermining the administration of justice and democratic processes. | Job displacement, Inaccurate or misleading AI output, Ethical concerns, Dehumanization of legal process, Exacerbation of inequality or two-tiered system, Erosion of trust in legal system or AI, Risk of misapplication or misuse, Undermining democratic processes |
| 26NYUJLegisPubPoly625.pdf | HeinOnline | ANALOG PRIVILEGE | This paper introduces 'analog privilege' to describe how elites avoid AI systems and benefit from personalized human treatment, unlike the general populace. It argues this divide, explored through case studies in LegalTech and content moderation, exacerbates inequality and erodes social fabric, proposing multi-pronged solutions. | Analog Privilege Concept, AI Divide, Exacerbation of Inequality, LegalTech Case Study, Content Moderation Case Study | True | Idealistic | True | 3.0 | Negative | Analog privilege (conceptual framework) | Conceptual Framework, Legal Theory | NaN | Not Applicable | NaN | NaN | The ability of elites to access superior human legal services while less privileged individuals are relegated to potentially inadequate AI-driven LegalTech, exacerbating existing inequalities and creating a two-tiered justice system. | Risk of AI Exacerbating Inequality, Unequal Access to Legal Services | A multi-prong approach involving legal, technical, and governance interventions to reduce analog privilege, increase accountability and transparency, improve AI systems, and implement external checks and balances, including empowering affected individuals and whistleblowers. | Policy and Regulatory Reform, Regulation, Ethics, and Governance, Transparency and Explainability in AI, Enhanced AI Capabilities | Disparities in access to quality legal representation and services due to the differential impact of AI and LegalTech on various socio-economic groups. | Democratizing Law / Closing Justice Gap / Rule of Law, Support for Vulnerable Populations, Language Access and Digital Divide | Low-income individuals, middle-class families priced out of legal services, and racial minorities who face systemic barriers to accessing justice. | Low-income individuals, Moderate-income individuals, Individuals unable to afford legal services, Minority groups | Primarily civil law (e.g., housing, debt, family, torts, estate), with implications for access to justice broadly across legal fields. | Civil Law, Housing Law, Debt Collection, Family Law, Tort Law, Wills and Estates, Access to Justice | Primarily United States, with references to and implications for the European Union and international human rights law. The core concept ('analog privilege') is framed as broadly applicable. | USA, EU, International | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | True | False | The paper discusses publicly accessible (often commercial or freemium) tools like ChatGPT, DoNotPay, and LegalZoom. | Publicly accessible online tool or platform, Commercial product or service, Freemium access | Societal: The 'automation divide' and lack of understanding of its contours. Technical: Current AI limitations in complex reasoning, creativity, and handling nuances, particularly in legal applications. Governance: Inadequate legal and regulatory frameworks to address analog privilege and ensure equitable AI deployment; need for polycentric governance models. | Access, Equity, and Digital Divide, Public Understanding, Trust, and Adoption, AI Legal Reasoning Limitations, Regulatory and Governance Gaps | General challenges in deploying AI systems that lead to analog privilege include their inherent reductivism, determinism, and potential for voyeurism, as well as specific limitations in areas like legal reasoning (creativity, handling novel cases, emotional intelligence) and content moderation (context sensitivity, accuracy at scale). | Bias in AI Systems and Data, Ethical Considerations, LLM Reasoning Capabilities, Accuracy and Reliability of LLM Output, Safeguarding Against Misuse and Harm | Erosion of social fabric, increased social polarization and resentment due to perceived unfairness. In legal services, creation of a two-tiered justice system with lower quality for the non-elite, undermining fairness and judicial legitimacy. In content moderation, biased enforcement, disproportionate silencing of marginalized voices, or undue leniency for powerful actors, potentially enabling harm. | Negative societal impact, Exacerbation of inequality or two-tiered system, Undermining legal process or principles, Bias and discrimination, Harmful or unsafe AI output, Infringement on human rights |
| 9IJODR177.pdf | HeinOnline | Can ChatGPT-like AI Function as ODR Fourth Party for Handling School-Related Disputes in China? | The paper argues that ChatGPT-like AI, while not replacing human third-party ODR, can serve as a "fourth party" to assist in preventing and resolving school-related disputes in China, particularly those involving student mental health. It proposes customizing these AI models with specific legal and psychological knowledge to effectively fulfill this role. | AI as Fourth Party in ODR, School Dispute Resolution, Chinese Focus, Student Mental Health, Customization of AI Models, Legal Knowledge Integration, Psychological Knowledge Integration | True | Idealistic | True | 1.0 | Positive | Using ChatGPT-like AI as an "ODR fourth party" for handling school-related disputes, customized with legal and psychological knowledge for tasks like student mental health support. | Large Language Model, Online Dispute Resolution (ODR), Customized AI System, Mental Health Support Application, Domain-Specific Application | Illustrative examples of querying OpenAI ChatGPT and ChatSonic with scenarios related to student mental health. Reference to a Colombian judge's use of ChatGPT in a ruling. | Demonstration or Illustrative Examples, Qualitative Analysis, References External Evaluation | ChatGPT and ChatSonic provided generally relevant advice on psychological issues and risk assessment based on described symptoms, suggesting potential for the proposed role. The Colombian judge example illustrated AI as an assistant, not a replacement for human judgment. | Moderate performance, Descriptive or Conceptual finding | Limited access to and inconsistent quality of mental health support for students, especially out-of-hours and in remote areas; societal dismissal of youth psychological issues; lack of timely intervention for students with mental health struggles. | Limited Access to Support Services (Mental Health), Societal Stigma (Mental Health), System Inefficiencies (Mental Health Support) | Deploying customized ChatGPT-like AI as a 24/7 accessible "fourth party" for initial psychological support and dispute prevention guidance for students. Integrating AI with human professionals (psychologists, mediators) and training AI with relevant legal and psychological knowledge specific to school disputes in China. | AI Tool Development, Access to Legal Information and Advice, Online Dispute Resolution (ODR), Human Oversight and Collaboration, Data Curation and Management | Online Dispute Resolution (ODR), Online Dispute Prevention (ODP), student mental health support, resolution and prevention of school-related disputes (e.g., bullying, academic stress). | Dispute Resolution, Support for Vulnerable Populations | Students in China, particularly those in boarding schools or remote areas with limited access to mental health services. | Students, Population in China, Rural populations, Populations in remote areas, Individuals needing mental health support | Education law, mental health law/ethics, Online Dispute Resolution (ODR). | Education Law, Mental Health Law, Legal Ethics, Online Dispute Resolution | China (primary), Colombia (secondary example). | China, Colombia | For the proposed customized AI: Chinese legal and psychological data specific to school-related disputes for fine-tuning existing LLMs. Existing models (ChatGPT, Ernie bot) are noted as being trained on massive, general text datasets. | Author-Created New Dataset, Fine-tuning Dataset, Chinese Legal Data, Legal Domain Data, Non-Legal Domain Specific Data, Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text | Conceptual framework proposal. Suggests customization and fine-tuning of existing LLMs with domain-specific data (Chinese law and psychology for school disputes). | Conceptual Framework Development, Model Customization, Model Fine-tuning, Domain-specific Data Integration | Envisioned through ODR platforms or integrated into school support systems, potentially leveraging customized versions of AI from tech companies (e.g., Microsoft, Baidu, Alibaba, Tencent). | Proposed deployment (not implemented), Web-based access, Integration into existing system/platform, Educational resource deployment, Partnership-based rollout | False | False | NaN | NaN | Technical limitations of LLMs (accuracy, bias, outdated knowledge, language-specific performance); need for robust human oversight and integration with professional services; accessibility of some advanced AI models in China; lack of AI specifically designed and trained for ODR in school-related disputes. | AI Accuracy and Reliability, Bias in AI, Knowledge Recency and Updatability, Multilingual and Low-Resource Language Gaps, Human Oversight and Professional Adaptation, Access, Equity, and Digital Divide, AI Scope and Functionality Limitations | Ensuring accuracy, reliability, and lack of bias in AI-generated content; effectively customizing general LLMs for specialized legal and psychological domains relevant to Chinese school disputes; dealing with LLMs' existing knowledge limitations and regional accessibility hurdles. | Accuracy and Reliability of LLM Output, Bias in AI Systems and Data, Domain-Specific Adaptation and Customization, Outdated or Limited LLM Knowledge Base, Multilingual and Low-Resource Language Support | AI producing incorrect, harmful, or biased outputs; reliance on AI leading to diminished human critical thinking in dispute resolution and mental health support; privacy risks associated with handling sensitive student data (implied). | Inaccurate or misleading AI output, Harmful or unsafe AI output, Bias and discrimination, Over-reliance on AI, Deskilling or erosion of human skills, Data privacy and security breach |
| 53HofstraLRev391.pdf | HeinOnline | ARE A.I. LAWYERS A LEGAL PRODUCT OR LEGAL SERVICE?: WHY CURRENT UPL LAWS ARE NOT UP TO THE TASK OF REGULATING AUTONOMOUS A.I. ACTORS | The paper argues that current Unauthorized Practice of Law (UPL) regulations are inadequate for regulating autonomous AI actors in the legal field, exemplified by tools like Pactum AI. It proposes reforms to UPL laws to balance consumer protection and innovation, facilitate attorney-AI developer collaboration, and clearly define boundaries for AI in legal work. | Unauthorized Practice of Law, Regulation of Autonomous AI, UPL Reform Proposal, Consumer Protection, Balancing Innovation and Regulation, Attorney-AI Collaboration | True | Idealistic | True | 3.0 | Positive | Autonomous negotiation software (e.g., Pactum AI); Legal self-help platforms (e.g., DoNotPay, LegalZoom, Quicken Family Lawyer). | Autonomous Negotiation Software, Self-help Legal Tool, Legal Platform, Named Tool / Platform | Pactum AI: Pilot program with Walmart Canada involving 100 suppliers, evaluated on deal closure rate, turnaround time, cost savings, and supplier preference. Other tools (LegalZoom, QFL, DoNotPay): Evaluated through legal challenges and court cases assessing UPL compliance. | References External Evaluation, Quantitative Metrics, Qualitative Analysis | Pactum AI (Walmart pilot): 64% deal closure rate, 11-day average turnaround, 1.5% average savings (initial); later 68% closure, 3% savings. 75% of suppliers preferred AI negotiation. | Moderate performance, Benefit identified, Successful real-world application, Developer or Vendor claim | Inadequate and unclear Unauthorized Practice of Law (UPL) laws hindering the development and safe deployment of AI tools that could potentially improve access to justice, and risking consumer harm. | Regulatory Hurdles, Risk of Hindering A2J Innovation, Risk of Consumer Harm from AI | Reforming UPL laws to: 1) Allow attorney-AI developer collaboration, 2) Clearly define AI's permissible legal work, 3) Balance consumer protection, the sanctity of the bar, with promoting innovation. This framework would support the responsible development of AI tools that could enhance access to justice. Considers regulatory sandboxes like Utah's model. | Policy and Regulatory Reform, Alternative Legal Service Delivery Models, Open Source Initiatives and Collaboration, Regulation, Ethics, and Governance | Legal self-help tools, automated document preparation, consumer-facing legal services for common issues (e.g., small claims, contract disputes with corporations), regulation of AI in legal practice. | Support for Self-Represented Litigants, Legal Document Creation / Automation, Access to Legal Advice, Regulatory Reform (Legal Services and AI) | General consumers, particularly those needing assistance with common legal issues against corporations or for personal matters where hiring a lawyer is prohibitive. | Consumers, Individuals unable to afford legal services | Contracts, Estate Planning, Corporate Law, Small Claims, Traffic Law, Employment Law, Administrative Law (FOIA), Unauthorized Practice of Law (UPL) regulation. | Contract Law, Wills and Estates, Corporate Law, Small Claims Law, Traffic Law, Employment Law, Administrative Law, Access to Information Law, Professional Responsibility | United States (with examples from specific states like Texas, Missouri, California, Utah, and mentions of international application of tools like Pactum AI by Walmart). | USA, International | For AI negotiation tools like Pactum AI: Domain-specific data including negotiation project databases (e.g., Harvard's Negotiation Project), past negotiation experiences (via machine learning), and customer-specific contract data/forms. For other tools, generally legal forms, statutes, and related legal information. | Non-Legal Domain Specific Data, Legal Domain Data, Legal Contracts, Other Legal Documents, Legislation / Statutes / Regulations, Proprietary Data, Data From Existing Public NLP/Legal Datasets/Benchmarks | For AI negotiation tools like Pactum AI: Machine learning, natural language processing, game theory, value function algorithms, customer-specific onboarding and data integration (e.g., 'contract space'). | Machine Learning Model Development, Natural Language Processing (NLP) Techniques, Game-theoretic Algorithm Design, Value Function Algorithm Design, Customer-specific Data Integration | Commercial software-as-a-service for corporate clients (e.g., Pactum AI); Web-based services/apps for consumers, often subscription-based (e.g., DoNotPay, LegalZoom). | Evaluation of existing third-party tool, Commercial product/service, Web-based access | True | False | Pactum AI is commercially available as an autonomous negotiation suite for large corporations. DoNotPay, LegalZoom, and Quicken WillMaker & Trust (successor to QFL) offer web-based legal self-help services to consumers, typically via purchase or subscription. | Commercial product or service, Publicly accessible online tool or platform | Outdated and ambiguous UPL laws; lack of a national, uniform standard for AI in legal practice; need for clear ethical guidelines for AI-human lawyer collaboration; and frameworks for assessing AI competency and liability. | Regulatory and Governance Gaps, Ethical Framework Deficiencies, Accountability and Redress Mechanisms, Human Oversight and Professional Adaptation | For developers of advanced legal AI tools: Integrating complex AI components (LLMs, machine learning, NLP, game theory) effectively and ensuring ethical, accurate outputs. For early tools: Managing ambiguous content, personalized preferences, and complex goals. For all: Navigating unclear and inconsistent UPL regulations during development and deployment. | Integration with Existing Systems and Workflows, Ethical Considerations, Accuracy and Reliability of LLM Output, LLM Reasoning Capabilities, Domain-Specific Adaptation and Customization, Regulatory Uncertainty and Compliance, Unauthorized Practice of Law (UPL) Concerns | Unauthorized Practice of Law (UPL) by AI tools or those misusing them; Consumer harm from substandard, biased, or incorrect AI-generated legal advice/services; Stifling innovation due to unclear or overly restrictive regulations; Attorneys facing UPL liability or other disciplinary action for aiding AI developers improperly or for uncritical use of AI; Erosion of due process or democratic principles if AI is poorly implemented or regulated; Job displacement for legal professionals. | Unauthorized practice of law, Consumer harm, Inaccurate or misleading AI output, Bias and discrimination, Stifling innovation, Regulatory challenges or gaps, Ethical concerns, Undermining legal process or principles, Undermining democratic processes, Job displacement |
| 31AIL773.pdf | HeinOnline | Judicial knowledge-enhanced magnitude-aware reasoning for numerical legal judgment prediction | This paper introduces NumLJP, a novel architecture for numerical legal judgment prediction (imprisonment and penalty) in criminal cases. NumLJP enhances prediction by integrating judicial knowledge through a selection module, acquiring numerical commonsense via masked numeral prediction, and performing magnitude-aware reasoning using a specialized graph network, demonstrating significant improvements on Chinese legal datasets. | System Development (NumLJP), Numerical Legal Judgment Prediction, Criminal Case Focus, Judicial Knowledge Integration, Numerical Commonsense Learning, Magnitude-Aware Reasoning, Chinese Law Focus, System Evaluation | True | Idealistic | True | 1.0 | Positive | NumLJP: a judicial knowledge-enhanced magnitude-aware reasoning architecture using a contrastive learning-based judicial knowledge selector (JKS), a masked numeral prediction (MNP) task for legal numerical commonsense, and a magnitude-aware numerical reasoning network (MagNet) on a scale-based numerical graph. | Model Development, Numerical Reasoning Architecture, Contrastive Learning, Knowledge Integration, Masked Language Modeling Task, Graph-based Reasoning, Named Tool / Platform | Evaluation on three Chinese legal datasets (CAIL-small, CAIL-large, AIJudge) using accuracy, macro-precision, macro-recall, macro-F1, and ImpScore metrics, compared against several baselines. Includes ablation studies and robustness analysis on a manually constructed variant dataset (VarLJP100). | Benchmark Dataset Evaluation, Quantitative Metrics, Comparative Analysis, Ablation Study | Achieved state-of-the-art performance, with macro-F1 of NumLJP improving by at least 9.53% on penalty prediction and 11.57% on imprisonment prediction compared to competitive baselines. | High performance, Outperforms others, Technique improves outcome | Inaccurate numerical legal judgment prediction by existing AI systems due to ignoring numerical information, inability to perform numerical comparison and magnitude perception, limited training data, and sparse numerals in crime facts. | AI Limitations in Legal Reasoning/Nuance, Technical Challenges in AI Development, Data Scarcity/Quality for AI | Proposing NumLJP, which incorporates official judicial knowledge (numerical anchors) as reference points, uses a masked numeral prediction task for acquiring legal numerical commonsense, and employs a magnitude-aware numerical reasoning network (MagNet) on a scale-based graph to handle numerical comparison and magnitude. | AI Tool Development, Enhanced AI Capabilities, Legal Knowledge Representation and Management, Judicial System Enhancement | Numerical legal judgment prediction (imprisonment terms and penalty amounts in criminal cases) for enhanced legal information and understanding. | Improving Foundational AI Capabilities for Legal Applications, Access to Legal Information, Judicial System Modernization / Efficiency | Laypeople/general public without legal background. | Laypeople, General public, Individuals lacking legal knowledge | Criminal Law | Criminal Law | China | China | Publicly available Chinese legal case documents (fact descriptions, law articles, imprisonment terms, penalty terms) from CAIL2018 and AIJudge challenges, originally sourced from China Judgment Online. Judicial knowledge (containing numerical anchors) specific to criminal charges is also utilized. | Publicly Available Data, Chinese Legal Data, Legal Domain Data, Case Law / Judgments, Data From Existing Public NLP/Legal Datasets/Benchmarks, Structured Data, Expert-Annotated / Human-Curated / Human-Generated Data | Deep learning methodology including use of pre-trained language models (RoBERTa), contrastive learning, masked language modeling techniques (for numerals), and graph neural networks. Design involves modular architecture (JKS, MNP, MagNet) and task-specific loss functions. | Deep Learning Model Development, Pre-trained Model Utilization, Contrastive Learning, Self-supervised Learning, Graph Neural Network Application, Modular System Architecture, Task-specific Loss Function Design | NaN | Not applicable | False | False | NaN | NaN | Technical gaps include handling complex criminal cases (multiple defendants/facts, coreference), reasoning over diverse numeral types within a single judicial knowledge, addressing issues with duplicate/excessive/oversized numerals, and interpreting implicit numerals. | AI Legal Reasoning Limitations, AI Scope and Functionality Limitations, Data Availability and Quality | Designing a model capable of numerical comparison and magnitude perception, distinguishing confusing cases for correct judicial knowledge application, acquiring legal numerical commonsense from judicial knowledge, handling unseen numerals and few-shot scenarios, and managing training stability of graph-based models. | LLM Reasoning Capabilities, Outdated or Limited LLM Knowledge Base, Domain-Specific Adaptation and Customization, Accuracy and Reliability of LLM Output | Risk of incorrect predictions due to model errors (e.g., wrong judicial knowledge selection, misinterpretation of numerals). Potential for machine interference with judges' independent judgment if misused. Privacy risks if sensitive information in data is not properly handled. | Inaccurate or misleading AI output, Technical limitations of AI, Undermining legal process or principles, Risk of misapplication or misuse, Data privacy and security breach |
| 31AustlLLibr19.pdf | HeinOnline | ChatGPT – THE BLURST OF TIMES | This paper discusses OpenAI's ChatGPT, exploring its capabilities, market context, and potential applications in the legal field, including for access to justice via tools like DoNotPay. It also thoroughly outlines significant limitations such as inaccuracies, biases, ethical concerns, and the ongoing need for human judgment and oversight. | ChatGPT Overview, Legal Applications of ChatGPT, Access to Justice Enhancement, Limitations of ChatGPT, AI Hallucinations/Inaccuracy, Bias in AI, Ethical Concerns, Need for Human Oversight | True | Idealistic | True | 2.0 | Neutral | ChatGPT (a large language model by OpenAI) and its integration into legal tech applications like DoNotPay and Clausebase. | Large Language Model, Integration with Legal Tech Applications, Named Tool / Platform | The paper reports on various informal evaluations and observations: user experiences (e.g., Nick Cave lyrics generation), demonstrations (e.g., Google Bard's error), beta-testing feedback (e.g., Clausebase's module found 'useful, but imperfect'), and OpenAI's stated limitations. | Qualitative Analysis, Demonstration or Illustrative Examples, User Study or Survey, Developer Claims Reported | General capabilities include human-like dialogue and text generation. Specific application feedback is mixed: Clausebase's module was 'useful, but imperfect'; creative outputs can be poor. DoNotPay is described as functional for tasks like ticket disputes. Known limitations include factual inaccuracies, knowledge cutoff (post-2021), and potential for bias. | Mixed performance, Limitation: Hallucination or Factual inaccuracy, Limitation: Operational or Technical, Limitation: Bias, Developer or Vendor claim | High cost and insufficient availability of legal help for low-income individuals for a vast majority of their civil legal problems. | High Cost of Legal Services, Limited Availability/Access to Legal Aid, Scale of Unmet Legal Need | AI-powered chatbots like DoNotPay (using ChatGPT) to handle common legal issues (e.g., ticket disputes, consumer rights) and assist with government paperwork. AI tools for high-volume, less complex legal tasks like drafting wills and conveyancing. | AI Tool Development, Access to Legal Information and Advice, Document Automation | Access to basic legal assistance for common civil legal problems (ticket disputes, consumer rights, landlord issues, employee rights), government paperwork, and routine legal document drafting (wills, conveyancing). | Access to Legal Advice, Protection of Rights, Legal Document Creation / Automation | Low-income individuals and the general public facing common legal issues who lack access to traditional legal services. | Low-income individuals, General public, Individuals with unmet legal needs, Individuals facing access barriers | General civil law (consumer protection, housing, employment, administrative), contract law, wills and estates, property law. | Civil Law, Consumer Law, Housing Law, Employment Law, Administrative Law, Contract Law, Wills and Estates, Property Law | International (general discussion of ChatGPT), with specific examples/data from USA (LSC report, DoNotPay context) and Belgium (Clausebase). | International, USA, Belgium | ChatGPT was pre-trained on a large corpus of text and code ('large volumes of data gleaned from conversations between humans and the written word of humans') with a knowledge cut-off in 2021. The paper notes verification and truthfulness of training data as a concern. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, User-Generated Content, Data Bias Concerns Noted | NaN | NaN | ChatGPT (version 3.5) released publicly in November 2022, with a free tier and a paid subscription (ChatGPT Plus). An API is planned for broader integration. DoNotPay is an operational chatbot. Clausebase's ChatGPT-powered module was in beta-testing. | Evaluation of existing third-party tool, Web-based access, Freely accessible tool/service, Commercial product/service, API access, Research preview/Beta access | True | True | ChatGPT is accessible through a free tier online and a paid subscription; DoNotPay is an operational chatbot service. | Publicly accessible online tool or platform, Freemium access, Commercial product or service | Ensuring factual accuracy and truthfulness; overcoming knowledge limitations (post-2021 events); mitigating bias in outputs; incorporating human-like judgment, character, and contextual understanding; developing reliable methods for detecting AI-generated content; addressing ethical concerns regarding AI decision-making. | AI Accuracy and Reliability, Knowledge Recency and Updatability, Bias in AI, AI Legal Reasoning Limitations, Transparency and Explainability, Ethical Framework Deficiencies | NaN | NaN | Generation and spread of disinformation and falsely generated assertions; inbuilt bias in AI models leading to unfair outcomes; loss of human-centric decision-making; privacy violations due to data handling; security vulnerabilities; copyright infringement from using trained-on content; producing harmful or biased instructions. | Inaccurate or misleading AI output, Security vulnerabilities or malicious misuse, Bias and discrimination, Dehumanization of legal process, Data privacy and security breach, Copyright or intellectual property issues, Harmful or unsafe AI output |
| 15AmUIntellPropBrief23.pdf | HeinOnline | Al GENERATED ART AND THE GAP IN COPYRIGHT LAW | This paper examines the disruption caused by AI-generated art to artists, focusing on the inadequacy of current copyright law to protect them from unauthorized use of their work for AI training and style imitation. It argues that this creates a disincentive for human creativity, discusses the shortcomings of existing legal alternatives, and cautiously explores potential legislative solutions. | AI-Generated Art Impact, Copyright Law Inadequacy, Artist Protection, Disincentive for Human Creativity, Call for Legislative Solutions | True | Idealistic | True | 3.0 | Negative | Generative AI for art creation (e.g., Stable Diffusion, Midjourney) | Generative AI, Text-to-Image Generation, AI Art Generation | NaN | Not Applicable | NaN | NaN | Unauthorized use of artists' works for AI training; AI's ability to mimic uncopyrightable artistic styles, devaluing original work and threatening artists' income; existing copyright law not protecting artists from AI-generated imitations; uncertainty of fair use defense for AI developers using copyrighted training data. | Intellectual Property/Copyright Issues with AI, Economic Harm to Creators due to AI, Inadequate Legal Frameworks for AI, Regulatory Uncertainty | Potential legislative amendments to copyright law (clarifying infringement or fair use for AI training), exploring limited protection for artistic style (with caution), or creating new forms of intellectual property; overall, a cautious approach to immediate, drastic legal changes is advised, alongside improving artists' access to courts. | Policy and Regulatory Reform, Regulation, Ethics, and Governance | Copyright protection for artists; economic impact of AI on artists' livelihoods; unauthorized use of creative works for AI training; protection of artistic style against AI imitation; fair compensation for artists. | NaN | Artists, particularly independent (indie) artists and those relying on commission work. | Creators, Artists | Copyright Law, Intellectual Property Law | Copyright Law, Intellectual Property Law | United States | USA | Datasets of existing images paired with detailed text descriptions, including artists' publicly available works. Examples include the LAION database, which reportedly contains billions of images, some potentially used without regard to copyright ownership. Data is largely unstructured (images, text). | Image Data, Structured Data, Unstructured Text Data, Publicly Available Data, Copyrighted Material (Source Mentioned) | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Current copyright law's inability to protect uncopyrightable artistic styles from AI imitation; uncertainty regarding the applicability of fair use to AI training datasets; difficulty for artists in detecting and proving unauthorized use of their work for training AI; insufficiency of existing non-copyright legal alternatives to protect artists. | Regulatory and Governance Gaps, Accountability and Redress Mechanisms | NaN | NaN | Disincentive for human artists to create and share work; devaluation of art and artists' income due to mass-produced AI imitations; violation of artists' personal connection to their work (personhood); potential for new 'style protection' laws to be co-opted by corporations, harming individual artists; legislative changes may have unintended negative consequences or stifle technological development. | Stifling innovation, Negative economic impact, Dehumanization of legal process, Copyright or intellectual property issues, Regulatory challenges or gaps |
| 9IJODR147.pdf | HeinOnline | Comments on Artificial Intelligence | This paper compiles commentaries from experts on the integration of AI, exemplified by ChatGPT, into Online Dispute Resolution (ODR). The authors explore potential benefits for efficiency and access, alongside significant risks like bias, misinformation, and the need for human oversight and ethical frameworks. | Expert Commentaries, AI in Online Dispute Resolution, ChatGPT Application, Benefit Identification, Risk Identification, Bias in AI, AI Hallucinations/Inaccuracy, Need for Human Oversight, Ethical Frameworks | True | Idealistic | True | 3.0 | Neutral | ChatGPT and similar Large Language Models (LLMs) in the context of Online Dispute Resolution (ODR); the HUMANIS concept is also introduced. | Large Language Model, Online Dispute Resolution (ODR), Conceptual Framework | NaN | Not Applicable | NaN | NaN | Power imbalances, digital exclusion, pervasive AI bias reinforcing societal injustices, lack of AI transparency and accountability, misinformation risks, and over-reliance on imperfect AI systems. | Power Imbalances, Digital Divide, Bias in AI/Data, Risk of AI Exacerbating Inequality, Lack of AI Transparency/Explainability, Lack of AI Accountability, AI-driven Misinformation/Disinformation, Automation Bias | Developing ethical human-centered AI (e.g., HUMANIS initiative), robust human oversight, performance-based standards, redesigning neutral roles to audit AI, and interdisciplinary collaboration to combat AI bias. | AI Tool Development, Regulation, Ethics, and Governance, User Interface and Accessibility Design, Human Oversight and Collaboration, Benchmarking and Evaluation Frameworks, Open Source Initiatives and Collaboration, Bias Detection and Mitigation | Online Dispute Resolution (ODR), access to justice for online consumers and citizens, fair and impartial dispute resolution, ethical AI in legal decision-making, addressing digital power imbalances and bias. | Dispute Resolution, Democratizing Law / Closing Justice Gap / Rule of Law, Ethical AI in Law and AI Governance, Protection of Rights | Individual citizens, SMEs, digitally excluded/disadvantaged individuals, and marginalized communities vulnerable to AI bias. | General public, Small businesses, Digitally excluded populations, Marginalized communities, Vulnerable to AI bias | Dispute Resolution (specifically Online Dispute Resolution - ODR), ADR (Alternative Dispute Resolution), consumer law, civil procedure (small claims), ethics in legal practice. | Dispute Resolution, Online Dispute Resolution, Alternative Dispute Resolution, Consumer Law, Civil Procedure, Small Claims Law, Legal Ethics | International | International | ChatGPT: A large, general-purpose corpus of unverified internet text data (up to 2021), containing inherent biases and inaccuracies. HUMANIS (concept): Envisioned to use anonymized data voluntarily shared by users and entities. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Data Bias Concerns Noted, User-Generated Content, Proprietary Data | NaN | NaN | NaN | Not applicable | True | True | ChatGPT is available online through OpenAI, with free access tiers. | Publicly accessible online tool or platform, Freemium access | Technical limitations in AI's understanding of nuance, emotion, and truth; societal challenges in AI transparency, accountability, bias mitigation, equitable access, governance, and defining human-AI roles. | AI Legal Reasoning Limitations, AI Accuracy and Reliability, Transparency and Explainability, Accountability and Redress Mechanisms, Bias in AI, Access, Equity, and Digital Divide, Regulatory and Governance Gaps, Human Oversight and Professional Adaptation | For tools like ChatGPT in ODR: ensuring accuracy and truthfulness, mitigating pervasive biases, defining appropriate use-cases given cognitive limitations, establishing accountability, and preventing user over-reliance and misuse. | Accuracy and Reliability of LLM Output, Bias in AI Systems and Data, LLM Reasoning Capabilities, Ethical Considerations, Accountability and Liability for AI Errors, User Adoption, Trust, and Acceptance, Safeguarding Against Misuse and Harm | Spread of misinformation; perpetuation of biases leading to discrimination; lack of accountability for AI errors; erosion of critical thinking; reinforcement of past injustices; exacerbation of power imbalances; and misuse. | Inaccurate or misleading AI output, Bias and discrimination, Lack of transparency, accountability, and redress, Deskilling or erosion of human skills, Exacerbation of inequality or two-tiered system, Risk of misapplication or misuse |
| 43CardozoArtsEntLJ135.pdf | HeinOnline | The Doors of Janus: A Critical Analysis of the Socio-Technical Forces Eroding Trust in the Rule of Law | This paper critically analyzes how emerging data-driven technologies, particularly AI, contribute to eroding citizens' trust in the Rule of Law through systemic disinformation, algorithmic misgovernance, and the digitalization of the social contract. It proposes a framework to restore trust by better enforcement and reinterpretation of existing rights, and formulating new collective interest-based rights, emphasizing the mediating role of law and technology. | AI Impact on Rule of Law, Erosion of Trust, Systemic Disinformation, Algorithmic Misgovernance, Framework to Restore Trust, Collective Interest Rights | True | Idealistic | True | 3.0 | Negative | NaN | NaN | NaN | Not Applicable | NaN | NaN | Systemic disinformation (worsened by Generative AI) eroding epistemic justifications for trust; algorithmic misgovernance (e.g., lack of procedural justice, unfair social structuring, human rights violations) belying expectations of good governance; digitalization of the social contract disrupting temporal-spatial aspects of governance and citizen engagement. | AI-driven Misinformation/Disinformation, Lack of Trust in Information/Governance, Ethical Concerns with AI in Law, Risk to Human Rights from AI, Impact of Digitalization on Governance | Acknowledge the mediating relation of law and technology; better enforcement of existing rights (e.g., privacy as in SyRI case); reinterpretation of existing rights (e.g., horizontal application of fundamental rights against private corporations); formulation of new collective interest-based rights to counter systemic disinformation and algorithmic misgovernance. | Conceptual Frameworks, Policy and Regulatory Reform, Regulation, Ethics, and Governance | Erosion of trust in the Rule of Law; systemic disinformation and its impact on democratic processes and institutions; algorithmic misgovernance (including automated decision-making, procedural justice, legal certainty, social structuring, algorithmic bias, representation, and human rights); digitalization of the social contract; protection of fundamental rights in the digital age; need for collective rights and accountability for tech platforms. | Democratizing Law / Closing Justice Gap / Rule of Law, Ethical AI in Law and AI Governance, Protection of Rights | General citizenry in liberal democracies, with specific examples highlighting disproportionate impacts on vulnerable groups such as racial minorities (e.g., Dutch childcare scandal), economically disadvantaged students (e.g., UK A-level grading), and welfare recipients (e.g., Australian Robodebt). | General public, Vulnerable populations, Minority groups, Low-income individuals, Students, Welfare recipients | Constitutional Law, Administrative Law, Human Rights Law, Technology Law, Media Law | Constitutional Law, Administrative Law, Human Rights Law, Technology Law, Media Law | United States, European Union (and member states like the Netherlands), United Kingdom, Australia. The paper also refers to 'global techno-legal developments' and 'liberal democracies the world over'. | USA, EU, The Netherlands, UK, Australia, International | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Inadequacy of current legal frameworks to address collective harms from AI and digital platforms; insufficient accountability mechanisms for Big Tech corporations regarding their impact on democratic processes and fundamental rights; challenges in effective AI regulation due to factors like regulatory entrepreneurship and lobbying; the difficulty for citizens to distinguish truth from falsehood in an AI-influenced infosphere; the Rule of Law ceding governance space to the 'rule of code'. | Regulatory and Governance Gaps, Accountability and Redress Mechanisms, Ethical Framework Deficiencies, Public Understanding, Trust, and Adoption | NaN | NaN | Erosion of public trust in the Rule of Law and democratic institutions; AI-driven misinformation and disinformation threatening electoral processes and social cohesion; algorithmic misgovernance leading to biased, discriminatory, and unjust outcomes; lack of transparency and contestability in automated decision-making; invasion of privacy; dehumanization of the law; increased social and political polarization; failure to uphold fundamental rights in the digital environment; regulatory capture by tech companies. | Erosion of trust in legal system or AI, Undermining democratic processes, Inaccurate or misleading AI output, Bias and discrimination, Lack of transparency, accountability, and redress, Data privacy and security breach, Dehumanization of legal process, Infringement on human rights, Regulatory challenges or gaps |
| 24HousJHealthLPoly77.pdf | HeinOnline | Artificial Intelligence and the HIPAA Privacy Rule: A Primer | This paper examines how the HIPAA Privacy, Security, and Breach Notification Rules apply to various AI applications in healthcare, such as chatbots and diagnostic tools. It highlights significant regulatory gaps, data re-identification risks, and hurdles to data sharing, underscoring the need for updated guidance and rules to protect patient information in the age of AI. | AI in Healthcare, HIPAA Application to AI, Regulatory Gaps, Data Re-identification Risk, Data Sharing Hurdles, Patient Information Protection | True | Idealistic | True | 3.0 | Neutral | AI-driven symptom checkers, medical chatbots (e.g., Northwell Health Pregnancy Chats), AI-assisted medical image interpretation, AI-powered medical scribes (e.g., DAX Express with GPT-4), AI for health insurance claim review (e.g., nH Predict). | Healthcare AI Applications, Chatbot / Conversational AI, Medical Image Analysis, Medical Scribe AI, Large Language Model, Insurance Claim Processing AI, Named Tool / Platform | NaN | Not Applicable | NaN | NaN | Regulatory gaps in HIPAA making it difficult to apply to AI tools; risk of AI-powered re-identification of de-identified health data; lack of transparency for patients regarding AI's use of their data; potential for AI errors harming patients with unclear recourse; and a patchwork of laws offering inconsistent protection. | Inadequate Legal Frameworks for AI, Data Privacy Concerns with AI, Lack of AI Transparency/Explainability, AI Unreliability/Inaccuracy, Lack of Redress Mechanisms for AI Harms, Regulatory Inconsistency | The paper implicitly calls for regulatory reform, including HHS issuing clarifying guidance on HIPAA's application to AI (e.g., re-identification, synthetic data), and amending HIPAA for greater transparency (e.g., in Notices of Privacy Practices about AI use). | Policy and Regulatory Reform, Regulation, Ethics, and Governance, Data Privacy and Security, Transparency and Explainability in AI | Data privacy and security in AI-driven healthcare; patient rights (notice, access, amendment, restriction) with AI; regulation of AI tools; re-identification risks of health data; algorithmic bias and discrimination in AI healthcare decisions. | NaN | Patients generally, with specific examples including elderly beneficiaries of health insurance and individuals whose de-identified data is at risk of re-identification. | Patients, Elderly people, Data subjects | Health Law (HIPAA), Privacy Law, Data Security Law, Administrative Law. | Health Law, Data Privacy Law, Administrative Law | United States | USA | Discusses the use of large health datasets, including electronic medical records and claims data (both identifiable and purportedly de-identified), by healthcare entities and tech companies for AI development and deployment. | Non-Legal Domain Specific Data, Health Data, Proprietary Data, Publicly Available Data, Structured Data, Data Bias Concerns Noted | NaN | NaN | NaN | Not applicable | True | False | Several AI tools discussed, such as specific hospital-operated symptom checkers, commercial symptom checkers (e.g., Ubie), and AI scribes like DAX Express, are presented as existing and deployed services. | Publicly accessible online tool or platform, Commercial product or service | HIPAA's definitions inadequately cover all AI actors and data types; de-identification safe harbors may be insufficient against AI-re-identification; lack of specific regulation for synthetic data; inadequate patient notification about AI use; limited patient ability to restrict AI or amend AI-generated errors; inconsistent protection from patchwork laws. | Regulatory and Governance Gaps, Security and Privacy of Data, Transparency and Explainability, Consumer Protection Gaps, Accountability and Redress Mechanisms | Defining and regulating new AI actors outside traditional HIPAA-covered entities; balancing data sharing for AI innovation with patient privacy; ensuring accuracy and fairness of AI-generated health information and decisions; keeping regulations updated with rapid AI advancements; operationalizing patient rights with AI-generated content. | Regulatory Uncertainty and Compliance, Data Privacy, Security, and Confidentiality, Accuracy and Reliability of LLM Output, Bias in AI Systems and Data, Ethical Considerations | Increased privacy/security breaches; informational injuries; re-identification of de-identified data; incorrect AI-generated medical information or claim denials causing harm; potential for discrimination via AI tools; lack of patient control over AI's use of their data. | Data privacy and security breach, Consumer harm, Inaccurate or misleading AI output, Bias and discrimination, Negative impact on user agency or autonomy |
| 35FordhamIntellPropMediaE.pdf | HeinOnline | Al in the Courtroom: The Boundaries of RoboLawyers and RoboJudges | This paper examines the impact of AI, including LegalTech and JudicialTech, on the legal system, acknowledging its potential to enhance efficiency and access to justice. However, it argues for clear boundaries, asserting that AI should not fully replace human litigators and judges due to concerns about fundamental rights, legal legitimacy, and the nature of law. | AI Impact on Legal System, LegalTech, JudicialTech, Efficiency Enhancement, Access to Justice Enhancement, Limits of AI in Law, Preservation of Human Role, Fundamental Rights Protection, Legal Legitimacy | True | Idealistic | True | 3.0 | Neutral | Scoring algorithms (e.g., for risk assessment, outcome prediction) and Generative AI (e.g., for legal advice, document drafting), within broader categories of LegalTech and JudicialTech. | Algorithmic Scoring / Prediction, Risk Assessment, Generative AI, Legal Advisory System, Legal Document Generation / Automation, LegalTech, JudicialTech | NaN | Not Applicable | NaN | NaN | High cost of legal services, lack of sufficient legal help for low-income individuals, backlogs in courts, and legal uncertainty disproportionately affecting disadvantaged populations. | High Cost of Legal Services, Limited Availability/Access to Legal Aid, Judicial/Legal System Inefficiencies, Uncertainty of Legal Outcomes, Systemic Inequities in Justice System | AI legal tools (LegalTech and JudicialTech) can reduce costs, improve dissemination of legal information, provide services to underserved populations, enhance court efficiency, and reduce legal uncertainty. | AI Tool Development, Cost Reduction and Efficiency, Access to Legal Information and Advice, Judicial System Enhancement | Affordability and availability of legal services, court efficiency, reduction of legal uncertainty, ensuring fair trial and due process in the context of AI deployment. | Affordability of Legal Services / Cost Reduction, Judicial System Modernization / Efficiency, Protection of Rights | Low-income individuals, disadvantaged litigants, and those unable to afford traditional professional human legal services. | Low-income individuals, Marginalized communities, Litigants, Individuals unable to afford legal services | General / Multiple fields, including criminal law (sentencing, recidivism), civil litigation (e-commerce, product liability, patent, personal injury), family law (prenuptial agreements), and corporate law (due diligence, contract review). | General Law, Multiple Fields, Criminal Law, Civil Litigation, E-commerce Law, Product Liability Law, Patent Law, Tort Law, Family Law, Corporate Law | Multiple (USA, China, Estonia, England, Israel, EU extensively discussed as examples and for regulatory approaches). | USA, China, Estonia, UK, Israel, EU | The paper discusses various AI systems: scoring algorithms (e.g., COMPAS) using historical case data and personal information; generative AI (e.g., ChatGPT) trained on vast general text corpora; specific tools like Amazon's hiring algorithm trained on proprietary company data. | Non-Legal Domain Specific Data, Legal Domain Data, Case Law / Judgments, Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Proprietary Data | NaN | NaN | NaN | Not applicable | True | True | Tools like DoNotPay (initially mentioned as free for specific tasks), ChatGPT (publicly available with a free tier), and LegalZoom (commercial service) are discussed as operational. | Publicly accessible online tool or platform, Freemium access, Commercial product or service | The paper highlights significant gaps in ensuring AI's ethical and fair application in law, including underdeveloped legal/ethical frameworks, the challenge of balancing AI benefits with fundamental rights (fair trial, due process, explainability), mitigating bias, ensuring transparency and human control, protecting privacy, preventing AI from stunting legal development, maintaining legal system legitimacy, and addressing AI's limitations with moral/value judgments and cultural nuances. | Ethical Framework Deficiencies, Regulatory and Governance Gaps, Bias in AI, Transparency and Explainability, Human Oversight and Professional Adaptation, Security and Privacy of Data, AI Legal Reasoning Limitations | NaN | NaN | Inaccuracies and hallucinations in AI outputs; lack of accountability and liability for AI errors; opacity (black box effect) leading to lack of transparency and explainability; bias and discrimination; data privacy violations and cybersecurity threats; loss of human control and automation bias; infringement on fair trial, due process, and the rule of law; undermining legal system legitimacy; hindering dynamic legal development; AI's inability to handle nuanced values, morals, and cultural diversity; dehumanization and infringement on human dignity/autonomy; Unauthorized Practice of Law (UPL). | Inaccurate or misleading AI output, Lack of transparency, accountability, and redress, Bias and discrimination, Data privacy and security breach, Security vulnerabilities or malicious misuse, Over-reliance on AI, Negative impact on user agency or autonomy, Undermining legal process or principles, Erosion of trust in legal system or AI, Technical limitations of AI, Dehumanization of legal process, Infringement on human rights, Unauthorized practice of law |
| 37GeoJLegalEthics415.pdf | HeinOnline | Untangling Unreliable Citations | The paper argues that unreliable citation practices, exacerbated by new formats like "(cleaned up)" and the uncritical use of AI in legal research, threaten the integrity of the legal system and democratic stability. It advocates for a return to basic verification of sources to ensure accuracy in legal arguments and restore trust in the profession. | Unreliable Citation Practices, AI in Legal Research Risks, Threat to Legal System Integrity, Advocacy for Source Verification | True | Idealistic | True | 3.0 | Negative | The use of generative AI tools (e.g., ChatGPT, Google Bard) for legal research and brief preparation, and its propensity to 'hallucinate' or fabricate citations and information. | Generative AI, Large Language Model, Legal Research Tool, Legal Document Preparation, AI Hallucination / Reliability Issues | Case studies of lawyers misusing AI tools (e.g., ChatGPT in Mata v. Avianca, Google Bard in Michael Cohen incident) leading to sanctions and public embarrassment due to fabricated citations. | Qualitative Analysis, References External Evaluation | The unverified use of generative AI tools for legal research by lawyers led to the submission of briefs containing non-existent case citations, resulting in judicial sanctions (e.g., a $5,000 fine in the Mata case), professional embarrassment, and the undermining of the legal process. | Risk or Ethical concern highlighted, Limitation: Hallucination or Factual inaccuracy, Descriptive or Conceptual finding | Erosion of legal precedent and trust in the legal system due to unreliable citations, exacerbated by practices like unverified copy-pasting, misuse of citation formats (e.g., '(cleaned up)'), and uncritical adoption of AI tools that generate false information. Unequal access to information further complicates verification. | Threats to Legal Precedent/Integrity, Lack of Trust in Justice System, AI Unreliability/Inaccuracy, Poor Referencing Practices, Unequal Access to Information | A return to fundamental practices of thoroughly reading and verifying all cited sources, including those suggested by AI. Increased professional diligence, skepticism towards unverified information, and candor with courts are advocated. | Human Oversight and Collaboration, Regulation, Ethics, and Governance, Education and AI Literacy | Integrity of the legal process, reliability of legal precedent, professional ethics, and the impact of AI on these aspects, which are foundational to a just legal system. | Democratizing Law / Closing Justice Gap / Rule of Law, Ethical AI in Law and AI Governance, Regulatory Reform (Legal Services and AI) | NaN | NaN | Civil Procedure, Legal Ethics, Patent Law, General Legal Practice (research and writing). | Civil Procedure, Legal Ethics, Patent Law, General Legal Practice | United States (Federal and State, with specific examples from Kansas). | USA | The AI tools discussed (e.g., ChatGPT, Google Bard) are based on large, diverse datasets, including public internet text, but the specifics are proprietary to their developers. The paper highlights issues stemming from this training, like hallucinations. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Proprietary Data, Data Bias Concerns Noted | NaN | NaN | Commercial AI tools (e.g., ChatGPT, Google Bard) are deployed by tech companies via web interfaces and APIs, leading to widespread accessibility. | Evaluation of existing third-party tool, Commercial product/service, Web-based access, API access | True | True | Generative AI tools like ChatGPT and Google Bard are available online, often with free access tiers. The '(cleaned up)' citation is a practice that can be adopted by any legal writer. | Publicly accessible online tool or platform, Freemium access, Research artifact published in paper | Technical gaps in AI reliability (hallucinations) and verification. Societal/professional gaps include insufficient diligence in source checking by legal professionals, ethical challenges with AI use, and the need for updated rules and norms for technology in legal practice. | AI Accuracy and Reliability, Human Oversight and Professional Adaptation, Ethical Framework Deficiencies, Regulatory and Governance Gaps | General challenges for AI tools include ensuring factual accuracy, preventing 'hallucinations' of non-existent information, promoting critical use by legal professionals rather than blind reliance, and addressing the rapid pace of AI development that outstrips ethical guidelines and full understanding of its impact. | Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, User Training, AI Literacy, and Skill Gaps, User Adoption, Trust, and Acceptance, Ethical Considerations, Regulatory Uncertainty and Compliance | Submission of fabricated legal citations leading to professional sanctions (e.g., fines) and reputational damage for lawyers. Miscarriage of justice if decisions are based on false information. Broader risks include erosion of legal precedent, democratic instability, and diminished public trust in the legal system. | Inaccurate or misleading AI output, Ethical concerns, Undermining legal process or principles, Erosion of trust in legal system or AI, Undermining democratic processes |
| 92FordhamLRev (2).pdf | HeinOnline | The Legal Imitation Game: Generative AI's Incompatibility with Clinical Legal Education | This paper argues that Generative AI (GenAI) is largely incompatible with the core pedagogical goals of clinical legal education: practice readiness, justice readiness, and client-centered lawyering. It contends GenAI hinders genuine skill development and can exacerbate societal injustices and ethical issues, urging a critical approach to its integration. | Generative AI in Clinical Legal Education, Incompatibility with Pedagogical Goals, Hindrance to Skill Development, Exacerbation of Injustice, Ethical Issues, Critical Approach to AI Integration | True | Idealistic | True | 3.0 | Negative | NaN | NaN | NaN | Not Applicable | NaN | NaN | Worsening unequal access to legal information and services; Concentration of legal information and power in a few corporations, replicating information asymmetries; GenAI systems are trained on data reflecting human biases and historical discrimination, potentially exacerbating injustices; GenAI tends to reinforce the status quo. | Risk of AI Exacerbating Inequality, Unequal Access to Legal Information, Concentration of Power in Tech Companies, Information Asymmetry, Bias in AI/Data, Risk of Ossification of Law by AI | Clinicians should press students to critically interrogate how GenAI tools are built and operate, investigate their ethical implications for justice and society, and recognize the role lawyers using these tools may play in causing harm. The paper advocates for helping students make informed, value-based, and justice-ready decisions about technology, rather than uncritically adopting GenAI. | Education and AI Literacy, Regulation, Ethics, and Governance, Human Oversight and Collaboration | Access to legal information; Quality and ethics of legal services for underserved populations; Bias in legal technology; Impact of AI on justice systems and legal education. | Access to Legal Information, Ethical AI in Law and AI Governance, Support for Vulnerable Populations, Judicial System Modernization / Efficiency, Legal Education for Professionals / Students | Underserved communities generally, individuals without power, clients of public interest clinics, populations affected by systemic discrimination. | Marginalized communities, Vulnerable populations, Clients of legal aid organizations | NaN | NaN | United States | USA | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Societal: Lack of an agreed-upon framework for evaluating the risk or utility of GenAI in legal education; GenAI's tendency to reinforce existing economic/power structures and injustices due to its design and data. Technical: The 'black box' nature of GenAI, with no existing mechanisms for auditing or interrogating the logic behind responses; GenAI's inherent limitation to imitation rather than genuine understanding. | Research and Evaluation Gaps, Ethical Framework Deficiencies, Bias in AI, Access, Equity, and Digital Divide, Data Availability and Quality, Transparency and Explainability, AI Legal Reasoning Limitations | NaN | NaN | GenAI outputs may imitate competent lawyering but fall short, leading to substandard legal work; Automation bias can lead users to uncritically accept AI outputs, including inaccuracies; GenAI can produce 'hallucinated' or false information (e.g., fake citations); Over-reliance on GenAI may undermine students' development of core legal skills (analysis, reasoning, writing); Worsening of unequal access to legal information and services; Concentration of legal information and power in a few large technology corporations; Appropriation of human creativity and personal data without consent or compensation for training models; Perpetuation and amplification of societal biases and historical discrimination embedded in training data; Significant negative environmental impact (resource extraction, high energy and water consumption); Exploitation of precarious workers in the AI development and maintenance pipeline; Reinforcement of the status quo and existing injustices by design. | Inaccurate or misleading AI output, Over-reliance on AI, Deskilling or erosion of human skills, Exacerbation of inequality or two-tiered system, Negative economic impact, Copyright or intellectual property issues, Data privacy and security breach, Bias and discrimination, Environmental impact, Ethical concerns, Infringement on human rights |
| 15IJCA1.pdf | HeinOnline | Unboxing Generative AI for the Legal Professions: Functions, Impacts and Governance | This paper examines the integration of Generative AI (GenAI) into legal professions and the administration of justice, focusing on its functions, impacts, and initial attempts at governance. It discusses GenAI's capabilities, its use by lawyers and judges, and analyzes different regulatory approaches, highlighting the tension between user responsibility and system certification. | Generative AI in Legal Profession, Generative AI in Administration of Justice, AI Governance, Regulatory Approaches to AI, User Responsibility vs System Certification | True | Idealistic | True | 3.0 | Neutral | Generative AI (GenAI) / Large Language Models (LLMs) and their domain-specific applications (e.g., using Retrieval-Augmented Generation). Specific examples discussed include general chatbots (ChatGPT, Bard) and domain-specific tools like the Portuguese 'Practical Guide to Access to Justice (GPJ)'. | Generative AI, Large Language Model, Retrieval Augmented Generation (RAG), Chatbot / Conversational AI, Domain-Specific AI Application, Named Tool / Platform | References a Stanford University study (Magesh et al., 2024) that assessed hallucination rates in leading commercial legal AI research tools; the author also conducted an 'initial test' of the Portuguese GPJ system for consistency and accuracy of answers. | References External Evaluation, Qualitative Analysis, Quantitative Metrics | The cited Stanford study (Magesh et al., 2024) found that leading commercial legal AI research tools produced hallucinations in 17% to 33% of responses. The author's test of the Portuguese GPJ found it gave consistent answers to simple questions but could give misleading answers to complex ones, though it showed learning capability over time. | Limitation: Hallucination or Factual inaccuracy, Mixed performance | Reliance on end-user's ability to verify AI output, which is challenging for laypersons; risk of AI generating inaccurate or misleading legal information; potential costs of reliable, high-quality AI systems for access to justice initiatives. | Difficulty in AI-Human Interaction, Public Lack of Legal Knowledge/Awareness, AI Unreliability/Inaccuracy, High Cost of A2J Technology | Development of curated GenAI systems for delivering legal information (e.g., chatbots based on official, verified data); strong emphasis on human oversight, critical evaluation of AI-generated content, and user responsibility in a legal context. | AI Tool Development, Data Curation and Management, Access to Legal Information and Advice, Human Oversight and Collaboration, Regulation, Ethics, and Governance | Access to legal information for citizens; support for self-represented litigants; simplification of interaction with the justice system. | Access to Legal Information, Support for Self-Represented Litigants, Legal Text Simplification / Plain Language | General public / citizens seeking legal information or interacting with the justice system. | General public, Individuals lacking legal knowledge, Litigants | General (covers various fields including family law, company law, criminal law, contract law, and general legal research/drafting). | General Law, Multiple Fields, Family Law, Corporate Law, Criminal Law, Contract Law, Legal Research, Document Drafting | International | International | For general GenAI: extensive, sometimes non-contextualized datasets. For domain-specific legal AI: curated legal databases (judgments, doctrine, statutes), specific case files, law firm knowledge bases. For the Portuguese GPJ: content from the Ministry of Justice's Digital Justice platform. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Fine-tuning Dataset, Legal Domain Data, Case Law / Judgments, Legislation / Statutes / Regulations, Proprietary Data, Publicly Available Data, Portuguese Legal Data | Retrieval-Augmented Generation (RAG); semantic injection of domain-specific knowledge; prompt engineering; no-code/low-code development approaches using APIs/GPTs. | Retrieval Augmented Generation (RAG), Knowledge Infusion, Prompt Engineering, No-code/Low-code Platform Utilization, API-based Development | Integration into office applications (e.g., word processors, spreadsheets); standalone domain-specific applications; use of APIs for custom solutions; cloud platform deployment (e.g., Microsoft Azure for the Portuguese GPJ). | Evaluation of existing third-party tool, Integration into existing system/platform, Local deployment/Standalone application, API access, Cloud platform deployment, Government/Public institution deployment | True | True | The Portuguese 'Practical Guide to Access to Justice (GPJ)' is mentioned as being in beta stage and accessible via a public URL, implying free web-based access. Basic versions of general GenAI chatbots (e.g., ChatGPT) are also noted as available online for free. | Publicly accessible online tool or platform, Open access resource | Need for robust, independent validation of AI tools' reliability and claims made by providers; the difficulty for laypersons to adequately verify AI outputs in legal contexts; current regulatory frameworks and guidelines struggle to keep pace with rapid technological advancements; weak accountability mechanisms for AI use. | Research and Evaluation Gaps, AI Accuracy and Reliability, User Interface and Usability Gaps, Public Understanding, Trust, and Adoption, Regulatory and Governance Gaps, Accountability and Redress Mechanisms | Ensuring factual accuracy and avoiding 'hallucinations' in AI outputs; maintaining data confidentiality and privacy, especially with sensitive legal information; the necessity for users to possess sufficient expertise to verify AI-generated content; managing the 'black box' nature and potential biases of LLMs. | Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Data Privacy, Security, and Confidentiality, User Training, AI Literacy, and Skill Gaps, Need for Human Oversight and Intervention, Transparency and Explainability of AI, Bias in AI Systems and Data | Generation of 'hallucinations' (false or misleading information, e.g., fake case citations); breaches of privacy and data protection; potential deskilling of legal professionals; over-reliance on AI leading to unchecked errors; economic shifts concentrating resources with tech providers; undermining public trust in the justice system if AI is misused; adverse impacts on due process if AI outputs are not rigorously verified. | Inaccurate or misleading AI output, Data privacy and security breach, Deskilling or erosion of human skills, Over-reliance on AI, Negative economic impact, Erosion of trust in legal system or AI, Undermining legal process or principles, Risk of misapplication or misuse |
| 25TransactionsTennJBusL25.pdf | HeinOnline | ESTABLISHING A FUTURE-PROOF FRAMEWORK FOR Al REGULATION: BALANCING ETHICS, TRANSPARENCY, AND INNOVATION | This paper examines the multifaceted applications and societal impacts of artificial intelligence, particularly generative AI, covering its benefits in areas like healthcare and access to justice, alongside significant risks such as bias, job displacement, and misinformation. It advocates for a comprehensive, future-proof regulatory framework by analyzing global legislative efforts, aiming to balance innovation with ethics, transparency, and human rights. | Societal Impact of AI, Generative AI Applications, Benefit Identification (Healthcare, A2J), Risk Identification (Bias, Job Displacement, Misinformation), Regulatory Framework Proposal, Balancing Innovation and Ethics, Global AI Legislation Analysis | True | Idealistic | True | 3.0 | Neutral | Generative AI (e.g., ChatGPT, Sora), Large Language Models, facial recognition technology, algorithmic decision-making systems (e.g., COMPAS), legal chatbots (e.g., DoNotPay), predictive policing tools. | Generative AI, Large Language Model, Facial Recognition Technology, Algorithmic Decision-Making System, Chatbot / Conversational AI, Predictive Policing, Named Tool / Platform | The paper cites evaluations by others (e.g., institutional reports like FTC, ProPublica; academic studies) which involve analyzing AI outputs for accuracy, bias (e.g., racial, gender), and real-world impact (e.g., false identifications, discriminatory loan/rental decisions). | References External Evaluation | Reports findings from cited studies: facial recognition shows higher error rates for minorities and women; predictive policing tools (e.g., COMPAS) demonstrate racial bias by disproportionately flagging minorities as high-risk; some healthcare algorithms underdiagnose underserved populations or assign lower risk scores to Black patients with similar needs as white patients. | Limitation: Bias, Low performance, Descriptive or Conceptual finding | Incorrect/outdated/misleading AI legal information, embedded bias, liability gaps for AI advice, user difficulty in assessing AI advice quality, need for constant AI system updates for legal accuracy, cross-jurisdictional compliance issues, AI's limited nuanced understanding for complex legal matters, risk of widening the digital divide, confidentiality and attorney-client privilege concerns. | AI Unreliability/Inaccuracy, Bias in AI/Data, Lack of AI Accountability, Difficulty in AI-Human Interaction, Need for Continuous AI Updates, Jurisdictional Complexity, AI Limitations in Legal Reasoning/Nuance, Risk of AI Exacerbating Inequality, Data Privacy Concerns with AI, Erosion of Legal Professional Standards | Develop robust AI data protection mechanisms (confidentiality, privilege), ensure regular AI system updates for legal accuracy, provide clear disclosures about AI capabilities and limitations, adopt a balanced regulatory approach promoting innovation while upholding ethics and compliance, mandate audits for bias, establish ethical guidelines for AI in legal services, train legal professionals on responsible AI use and verification of AI outputs. | Data Privacy and Security, Enhanced AI Capabilities, Transparency and Explainability in AI, Regulation, Ethics, and Governance, Policy and Regulatory Reform, Bias Detection and Mitigation, Education and AI Literacy | Providing basic legal information, assistance with simple legal matters, enhancing understanding of legal proceedings, use of legal chatbots for initial guidance, potential for AI-assisted counsel for indigent defendants and readily accessible AI legal support for ordinary citizens. | Access to Legal Information, Access to Legal Advice, Legal Literacy and Public Legal Education, Access to Legal Representation, Support for Vulnerable Populations | Low-income individuals, marginalized communities, indigent criminal defendants, ordinary citizens needing legal assistance. | Low-income individuals, Marginalized communities, Indigent criminal defendants, General public, Individuals with unmet legal needs | Civil law (general), Criminal law, Family law, Housing law, Employment law, Intellectual Property law, Privacy law. | Civil Law, Criminal Law, Family Law, Housing Law, Employment Law, Intellectual Property Law, Data Privacy Law | International | International | The paper discusses various AI systems trained on diverse large-scale datasets, including public internet text and image data, copyrighted materials (news articles, artworks, music), official records (crime reports, arrest records), and consumer data (PII, credit history, behavioral data). It highlights issues with unverified, biased information within these datasets. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Image Data, Copyrighted Material (Source Mentioned), Official Documents / Government Data, Non-Legal Domain Specific Data, Data Bias Concerns Noted | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Ensuring reliability and legal accuracy of AI tools; establishing clear liability frameworks for AI-generated legal advice; developing AI with nuanced understanding for complex legal cases; addressing the digital divide for equitable AI access; lack of robust confidentiality/privilege mechanisms in current AI; insufficient legal professional training on AI; need for a comprehensive AI regulatory framework in legal services. | AI Accuracy and Reliability, AI Legal Reasoning Limitations, Regulatory and Governance Gaps, Accountability and Redress Mechanisms, Access, Equity, and Digital Divide, Security and Privacy of Data, Human Oversight and Professional Adaptation | NaN | NaN | Deepfakes and misinformation eroding trust and manipulating democratic processes; algorithmic bias leading to discrimination in justice, housing, employment, and healthcare; privacy violations through enhanced surveillance and data misuse; AI-powered cybersecurity threats; significant white-collar job displacement; widespread intellectual property infringement; safety, control, and accountability issues with advanced AI; negative impacts on mental health and societal cohesion; high environmental costs of AI development. | Security vulnerabilities or malicious misuse, Erosion of trust in legal system or AI, Undermining democratic processes, Bias and discrimination, Data privacy and security breach, Job displacement, Copyright or intellectual property issues, Technical limitations of AI, Lack of transparency, accountability, and redress, Negative societal impact, Environmental impact |
| 10RevBrasileiradeDireitoP.pdf | HeinOnline | Towards a Digitalised Criminal Justice System: Lessons from Poland | This paper examines technological advancements in the Polish criminal justice system, accelerated by COVID-19, focusing on remote hearings, case file digitization, and automated translation. It analyzes their impact on efficiency and fair trial rights, highlighting existing limitations and proposing solutions like expanded remote access and hybrid translation models. | Technology in Polish Criminal Justice, Remote Hearings, Case File Digitization, Automated Legal Translation, Efficiency Improvement, Fair Trial Rights, Hybrid Translation Models | True | Idealistic | True | 2.0 | Neutral | Digitalization approaches in the Polish criminal justice system: remote hearings (including for detention), digitization of criminal proceeding files (e.g., PROK-SYS), and automated translation services. | Digitalization in Justice System, Remote Court Hearings, Digitization of Legal Records, Automated Translation Services | Legal analysis against Polish law, ECHR standards, EU directives, and assessment of practical implications for fair trial rights, defendant guarantees, and judicial efficiency. | Theoretical Analysis or Conceptual Proposal | Remote hearings improve efficiency but risk defendant rights (e.g., confidentiality, counsel access) without proper safeguards; digitization offers significant benefits (accessibility, efficiency, security) but implementation is slow and faces challenges like digital exclusion; automated translation is currently insufficient alone for legal contexts and requires human oversight to ensure fairness and accuracy. | Benefit identified, Risk or Ethical concern highlighted, Low performance, Limitation: Operational or Technical, Descriptive or Conceptual finding | Infringement on the right to defense in remote hearings (e.g. lack of confidentiality, limited counsel access); digital exclusion and security risks with digitization; inaccuracy of automated translation for complex legal texts; institutional resistance, costs, and concerns about procedural guarantees. | Risk to Human Rights from AI, Digital Divide, Security Risks with AI, AI Unreliability/Inaccuracy, Institutional Resistance to Change (Legal Profession), High Cost of A2J Technology | Expand remote hearings with robust safeguards for confidential counsel-client communication; implement unified, secure digital case file systems with alternatives for digitally excluded persons; adopt a hybrid human-machine translation model with rights to human verification and intervention. | Judicial System Enhancement, Data Privacy and Security, Language Simplification and Multilingual Access, Human Oversight and Collaboration, Regulation, Ethics, and Governance | Remote hearings, digitization of case files, automated translation, right to a fair trial, right to defense, access to case files, right to an interpreter, efficiency of criminal proceedings, pre-trial detention hearings. | Judicial System Modernization / Efficiency, Language Access and Digital Divide, Protection of Rights, Improving Efficiency in Legal System / Profession | Defendants in criminal proceedings, particularly those deprived of liberty, non-native language speakers requiring translation/interpretation, and digitally excluded individuals. | Criminal defendants, Prisoners, Individuals with language barriers, Digitally excluded populations | Criminal Law, Criminal Procedure | Criminal Law, Criminal Procedure | Poland (with reference to EU law and ECHR) | Poland, EU, ECHR | The paper discusses LLM-based automated translation, noting these models are pre-trained on massive and diverse textual datasets; specific datasets for the particular LLMs are not detailed. | General Web Data / Broad Internet Text, Multilingual Data | NaN | NaN | Remote hearings were legislatively adopted and expanded, particularly post-COVID-19, through amendments to the Polish Code of Criminal Procedure. The PROK-SYS digitization system is under gradual implementation by the National Prosecutor's Office. | Regulatory/Legal framework adoption, Government/Public institution deployment, Pilot program/Limited rollout | True | False | Remote hearings are established in Polish law and used in courts for specific criminal proceedings. | NaN | Need for unified, interoperable, and secure digital infrastructure; improving AI translation accuracy for legal texts; addressing digital exclusion; ensuring full confidentiality and effective defense rights in digitalized procedures; overcoming institutional resistance to technological adoption. | Integration and Interoperability Challenges, Computational Resource and Cost Issues, Multilingual and Low-Resource Language Gaps, AI Accuracy and Reliability, Access, Equity, and Digital Divide, Security and Privacy of Data, Human Oversight and Professional Adaptation | Costs and security considerations for new technologies; ensuring protection of procedural guarantees and attorney-client privilege; overcoming technical limitations and ensuring system reliability; addressing institutional resistance and change management; balancing efficiency gains with the protection of fundamental rights. | Financial Cost and Resource Constraints, Data Privacy, Security, and Confidentiality, Ethical Considerations, Accuracy and Reliability of LLM Output, User Adoption, Trust, and Acceptance | Infringement of the right to defense (e.g., confidential communication with counsel) in remote hearings; digital exclusion hindering access to justice; cybersecurity threats to digitized case files (hacking, data leakage); inaccurate automated translations leading to miscarriages of justice; erosion of fair trial principles if technology is improperly implemented. | Undermining legal process or principles, Infringement on human rights, Exacerbation of inequality or two-tiered system, Data privacy and security breach, Security vulnerabilities or malicious misuse, Inaccurate or misleading AI output |
| 54TexTechLRev255.pdf | HeinOnline | Limits of Using Artificial Intelligence and GPT-3 in Patent Prosecution | This paper discusses the potential applications and limitations of large language models like GPT-3 in patent prosecution, particularly for claim drafting and translating legal text. It also explores the legal (enablement, utility, inventorship), ethical (attorney supervision, bias), and social justice (access to innovation) consequences of using such AI tools in patent law. | LLM in Patent Prosecution, Patent Claim Drafting, Legal Text Translation, Legal Consequences, Ethical Consequences, Social Justice Consequences (Access to Innovation), Patent Law Focus | True | Idealistic | True | 2.0 | Neutral | Application of GPT-3 (a large language model) for patent prosecution tasks like claim generation, specification drafting, and legal text simplification. | Large Language Model, Patent Law Application, Legal Document Generation / Automation, Legal Text Simplification | The paper cites existing evaluations: GPT-2 was evaluated for patent claim generation using a dataset of 55,890 patent claims. GPT-3's general writing capabilities were assessed by various users and researchers (e.g., Branwen, Elkins & Chun) through qualitative analysis, and its ability to translate legalese was demonstrated by a beta tester. | References External Evaluation | GPT-2 produced patent claims of 'reasonable quality'. GPT-3 demonstrated strong general writing capabilities, 'shockingly good' and creative, but with weaknesses in long-term coherence, consistency, commonsense reasoning, and exhibited bias. It could also 'impressively translate legalese into plain English' with few prompts. | Moderate performance, High performance, Limitation: Operational or Technical, Limitation: Bias | If advanced AI tools like GPT-3 are only available to large, wealthy firms, it can widen the innovation inequality gap, making it harder for new entrants, small entities, and innovators from underrepresented groups to patent their inventions and compete. | Risk of AI Exacerbating Inequality, Unequal Access to A2J Technology | Provide equal access to AI tools for all inventors, possibly through USPTO regional offices or Patent and Trademark Resource Centers (PTRCs). Make USPTO's AI-powered search systems available to small-entity inventors. | Policy and Regulatory Reform, Access to Legal Information and Advice, AI Tool Development, Open Source Initiatives and Collaboration | Innovation inequality, access to legal technology for patenting, social mobility for new innovators, fair competition in innovation. | NaN | New entrants to the market, small entities, innovators facing gender and racial inequalities. | Small businesses, Entrepreneurs, Women, Minority groups | Patent Law (specifically Patent Prosecution) | Patent Law | United States | USA | GPT-3: Trained on a general dataset of 175 billion parameters from diverse internet text. The paper suggests potential for fine-tuning on millions of patents for domain-specific tasks. A cited study on GPT-2 for patent claims used a dataset of 55,890 patent claims. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Fine-tuning Dataset, Legal Domain Data, Other Legal Documents | For GPT-3 (as described in the paper): Autoregressive language model using deep learning; few-shot learning capabilities. Fine-tuning is mentioned as a potential customization technique. | Autoregressive Model Design, Deep Learning Model Development, Few-shot Learning Application, Model Fine-tuning | GPT-3 was initially available via an API to select beta testers and was later exclusively licensed to Microsoft for commercial use. | Evaluation of existing third-party tool, API access, Research preview/Beta access, Commercial product/service, Partnership-based rollout | True | False | GPT-3 is commercially available through an API, as it was licensed by OpenAI to Microsoft and is used in commercial projects. | API access, Commercial product or service | Unequal access to powerful AI tools for patent prosecution, which can exacerbate existing disparities in innovation. Need for more sophisticated analysis and mitigation of biases in AI models like GPT-3. | Access, Equity, and Digital Divide, Computational Resource and Cost Issues, Bias in AI, Research and Evaluation Gaps | Ensuring adequate attorney supervision of AI-generated content, managing AI's limitations (e.g., coherence, factual accuracy, bias), addressing patentability issues (enablement, utility, definiteness) for AI-assisted claims, and ethical concerns regarding competence and bias. | Need for Human Oversight and Intervention, Legal Professional Responsibility and Competence, Accuracy and Reliability of LLM Output, Bias in AI Systems and Data, Copyright and Intellectual Property Issues, Ethical Considerations | AI generating overly broad patent claims beyond an inventor's actual conception; exacerbation of the access to justice gap in innovation; AI reflecting and amplifying societal biases (e.g., racial, gender); attorneys violating ethical duties through inadequate supervision of AI; creation of denser patent thickets hindering competition; difficulty distinguishing AI-generated prophetic examples from actual working examples. | Inaccurate or misleading AI output, Technical limitations of AI, Exacerbation of inequality or two-tiered system, Bias and discrimination, Ethical concerns, Over-reliance on AI, Negative economic impact, Copyright or intellectual property issues |
| 90UCinLRev.pdf | HeinOnline | Prospects for Legal Analytics: Some Approaches to Extracting More Meaning from Legal Texts | This paper surveys recent research in legal text analytics focused on extracting more semantic meaning from legal texts, such as case decisions, contracts, and statutes. It discusses various AI approaches, including machine learning, deep learning (e.g., BERT, GPT-3), and knowledge representation, to improve tasks like outcome prediction, factor identification, argument mining, and providing explanations, with prospects for enhancing both legal practice and access to justice. | Survey of Legal Text Analytics, Semantic Meaning Extraction, AI Approaches (ML, DL, KR), Legal Applications (Prediction, Factor ID, Argument Mining), Explainable AI, Access to Justice Enhancement | True | Idealistic | True | 3.0 | Positive | Advanced NLP and ML (including transformers like BERT, GPT-3, and deep learning) combined with knowledge representation for extracting deeper semantic meaning from legal texts (e.g., identifying factors, argument structures, explaining statutory terms). | Natural Language Processing (NLP), Machine Learning, Transformer Models, Deep Learning, Knowledge Representation, Semantic Analysis, Argument Mining / Analysis, Statutory Interpretation Support | NaN | Not Applicable | NaN | NaN | Current AI's inability to fully understand and interpret legal texts as humans do (e.g., implicit meanings, common sense); lack of robust explainability in AI predictions; difficulty in extracting and reasoning with implicit information from texts. From an A2J perspective: general unfairness and lack of access to legal resources for laypersons. | AI Limitations in Legal Reasoning/Nuance, Lack of AI Transparency/Explainability, Systemic Inequities in Justice System, Limited Access to Legal Assistance | Developing AI techniques to extract more semantic meaning from legal texts by combining machine learning (especially deep learning and transformers) with knowledge representation. Specifically, identifying factors, argument structures (issues, reasons, conclusions), and sentences explaining statutory terms. For A2J, deploying advanced AI tools through accessible platforms like Legal Information Institutes (LIIs) to provide free access to legal sources for the public. | AI Tool Development, Enhanced AI Capabilities, Legal Knowledge Representation and Management, Access to Legal Information and Advice, Open Source Initiatives and Collaboration | Access to legal information; Understanding legal texts (statutes, case law); Legal reasoning and argumentation support. | Access to Legal Information, Legal Literacy and Public Legal Education, Improving Foundational AI Capabilities for Legal Applications | Lay persons (as a target for an NSF project discussed); legal professionals. | Laypeople, Legal professionals | General / Multiple (examples include human rights law, domain name disputes, trade secret law, contract law, copyright law, Fourth Amendment issues, veterans' benefits claims). | General Law, Multiple Fields, Human Rights Law, Intellectual Property Law, Trade Secret Law, Contract Law, Copyright Law, Constitutional Law, Veterans Law | International / Multiple (includes specific examples or datasets from US, European Court of Human Rights, WIPO, Singapore, Japan). | International, USA, ECHR, WIPO, Singapore, Japan | Various legal text corpora, including case decisions (e.g., ECHR, WIPO, US caselaw from Harvard Caselaw Corpus, BVA), statutes, and contracts. Data includes publicly available sources and manually annotated corpora created for specific research tasks (e.g., WIPO cases for SCALE, trade secret cases for VJAP factors, sentences for statutory term explanation, case summaries for argument triples). Primarily unstructured text, domain-specific. | Legal Domain Data, Case Law / Judgments, European Legal Data, US Legal Data, Legislation / Statutes / Regulations, Legal Contracts, Publicly Available Data, Expert-Annotated / Human-Curated / Human-Generated Data, Author-Created New Dataset, Unstructured Text Data, Structured Data | Manual annotation of legal texts to create labeled training datasets; application of machine learning algorithms (including deep learning NNs, LSTMs, transformer models like BERT); development and use of knowledge representation schemes (e.g., tag systems for WIPO cases, domain models for trade secret law); iterative development and evaluation, including active learning in some instances. | Manual Annotation, Dataset Creation, Machine Learning Model Development, Deep Learning Model Development, Knowledge Representation Design, Iterative Design Process, Active Learning | For the author's A2J project: planned deployment through Legal Information Institutes (LIIs) for free public access. Other mentioned tools have commercial deployments or are research prototypes. | Proposed deployment (not implemented), Partnership-based rollout, Freely accessible tool/service, Web-based access, Evaluation of existing third-party tool | False | False | NaN | NaN | Technical: AI's limited ability to understand implicit meaning and common-sense knowledge, lack of robust and legally intelligible explainability, challenges in integrating structured legal knowledge with deep learning models effectively. Societal: Insufficient access to and understanding of legal information for laypersons; a need for better education of legal professionals on AI's capabilities and limitations. | AI Legal Reasoning Limitations, Transparency and Explainability, Integration and Interoperability Challenges, Access, Equity, and Digital Divide, Public Understanding, Trust, and Adoption, Human Oversight and Professional Adaptation | The knowledge representation bottleneck requiring significant manual effort for annotation and model creation; the need for large, high-quality, domain-specific annotated datasets for training ML models; high computational costs associated with training and fine-tuning large language models; difficulty in conducting extrinsic evaluations to assess real-world utility and impact on users; handling the inherent ambiguity, complexity, and stylistic variability of legal language; ensuring fairness and mitigating biases in AI models. | Cost and Complexity of Data Annotation, Scarcity of High-Quality Legal Data, High Computational and Resource Demands, Evaluation Challenges and Metrics, LLM Reasoning Capabilities, Bias in AI Systems and Data | Over-reliance on AI-generated predictions or answers without critical assessment of their limitations and potential for error; AI models (e.g., GPT-3) producing plausible-sounding but incorrect or misleading information; models making predictions or classifications without true legal understanding, leading to flawed outputs if underlying data or logic is misinterpreted. | Over-reliance on AI, Inaccurate or misleading AI output, Technical limitations of AI |
| 73DePaulLRev301.pdf | HeinOnline | AI Malpractice | This paper explores whether AI modelers should be held to a professional malpractice standard of care, similar to doctors or lawyers, by comparing AI work to conventional software development and analyzing the applicability of malpractice doctrine. It suggests that for the immediate term, strict liability might be more appropriate for AI, with a potential transition to malpractice or ordinary reasonable care as AI technology and its societal integration mature. | Malpractice Standard for AI Modelers, Liability for AI, Comparison with Software Development, Strict Liability for AI | True | Idealistic | True | 1.0 | NaN | Application of professional malpractice law (and other liability frameworks like strict liability or ordinary negligence) to AI modelers, based on an analysis of AI work considering factors like subjective judgments, risk of bad outcomes, and essential societal function. | Legal Liability Framework for AI, Professional Malpractice Law Application, AI Governance | NaN | Not Applicable | NaN | NaN | Biased, incorrect, or insufficient training data leading to unfair or inaccurate AI systems that perpetuate societal harms and discrimination (e.g., in policing, employment), impacting fairness and due process. | Bias in AI/Data, AI Unreliability/Inaccuracy, Data Scarcity/Quality for AI, Risk of AI Exacerbating Inequality, Risk to Human Rights from AI | Establishing clear liability frameworks (e.g., professional malpractice, strict liability, ordinary negligence) for AI modelers to incentivize the development of safer, fairer, and more accountable AI systems, thereby mitigating access to justice-relevant harms. | Regulation, Ethics, and Governance, Policy and Regulatory Reform | Algorithmic bias, discrimination, fairness, and accountability in AI systems, particularly in contexts like criminal justice and employment, which have significant implications for individual rights and due process. | Ethical AI in Law and AI Governance, Protection of Rights | Protected classes (e.g., based on race, gender) and other individuals disproportionately harmed by biased or flawed AI systems in critical decision-making processes such as law enforcement, hiring, and credit scoring. | Minority groups, Women, Vulnerable to AI bias | Tort Law (malpractice, negligence, strict liability), with illustrative examples touching upon criminal law, employment law, and intellectual property. | Tort Law, Criminal Law, Employment Law, Intellectual Property Law | United States | USA | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Persistent scientific uncertainties in aspects of AI development (e.g., optimal model configurations, hyperparameter tuning); challenges in ensuring training data is comprehensive, unbiased, and legitimate; limitations in current AI testing methodologies for guaranteeing robustness, fairness, and generalizability; and a lack of consensus on how to effectively translate AI ethics principles into enforceable legal duties for AI modelers. | Research and Evaluation Gaps, Data Availability and Quality, Bias in AI, AI Accuracy and Reliability, Ethical Framework Deficiencies, Regulatory and Governance Gaps | Defining appropriate and adaptable liability standards for AI modelers given the unique characteristics of AI development (e.g., opacity of some models, data-driven nature, rapid evolution, and the difficulty in foreseeing all potential harms) and distinguishing AI work from traditional software or product liability for doctrinal purposes. | Accountability and Liability for AI Errors, Regulatory Uncertainty and Compliance, Transparency and Explainability of AI | Accidental harms from AI errors (e.g., autonomous vehicle crashes, misidentification); intentional misuse for malicious purposes (e.g., deepfakes, disinformation, fraud); perpetuation and amplification of societal biases leading to discrimination; systemic risks such as erosion of trust in institutions or market instability; data privacy violations; and significant labor displacement. | Inaccurate or misleading AI output, Harmful or unsafe AI output, Security vulnerabilities or malicious misuse, Bias and discrimination, Erosion of trust in legal system or AI, Negative economic impact, Data privacy and security breach, Job displacement |
| 25DukeLTechRev116.pdf | HeinOnline | FINE-TUNING LLMS: STRUCTURAL FLUENCY AND AUGMENTATION FOR THE GREAT AND POWERFUL WIZARD OF Al | The paper argues that LLMs, despite their potential, can perpetuate existing biases in the civil legal system rooted in structural injustice. It proposes "structural fluency" through fine-tuning and prompt augmentation, informed by social justice principles, as a method for legal professionals to mitigate these risks and enhance fairness in LLM outputs. | Bias Perpetuation by LLMs, Structural Injustice in Legal System, Bias Mitigation Strategy (Structural Fluency), Fine-tuning for Fairness, Prompt Augmentation for Fairness, Social Justice Principles in AI | True | Idealistic | True | 1.0 | Positive | "Structural fluency" achieved through fine-tuning prompts and prompt augmentation for LLMs, guided by social justice principles and structural competency frameworks. | Prompt Engineering, Large Language Model, AI for Social Justice, Structural Competency Framework, Bias Mitigation | NaN | Not Applicable | NaN | NaN | LLMs replicating ineffective patterns and biases of the past rooted in racism and power imbalances; the civil legal system's inherent assumptions and biases; "color-evasive" policies and LLM deployment perpetuating racism; lack of access to justice and procedural unfairness; LLMs being developed by homogeneous groups. | Bias in AI/Data, Systemic Inequities in Justice System, Risk of AI Exacerbating Inequality, Exclusion of Marginalized Communities in AI Governance/Development | Engaging in machine learning frameworks informed by social justice principles; fine-tuning LLMs and using prompt augmentation to enhance their fluency in structural injustice; prompting LLMs to consider macro structures, systemic forces, historical legacies of injustice, and social identity; incorporating critical lenses like cultural competency and racial literacy into LLM interaction; developing "structural fluency" in LLM interactions. | Conceptual Frameworks, Enhanced AI Capabilities, Prompt Engineering and LLM Interaction Design, Bias Detection and Mitigation, Education and AI Literacy | Mitigating bias in AI/LLMs; ensuring fairness and equal justice in AI-assisted legal processes; addressing systemic and structural injustice within the legal system through AI; the role of social context and identity in legal AI; ethical use of AI by legal professionals. | Ethical AI in Law and AI Governance, Democratizing Law / Closing Justice Gap / Rule of Law | Subordinated individuals/groups, people of color, women and trans people, people in lower socioeconomic classes. | Marginalized communities, Minority groups, Women, LGBTQ+ people, Low-income individuals | Civil law, Civil procedure | Civil Law, Civil Procedure | United States | USA | NaN | Not Applicable | Conceptual framework development drawing from critical legal theories (e.g., LatCrit, Critical Race Theory), social justice principles, legal pedagogy (Socratic method, scaffolded learning), and analogies from other fields (e.g., structural competency in medicine). | Conceptual Framework Development, Critical Theory Application, Principle-driven Design, Pedagogical Method Application | NaN | Not applicable | False | False | NaN | NaN | Lack of a method for prompting machines to "fine-tune" them for social justice; need for AI tools to move beyond replicating past injustices and incorporate social context and identity-consciousness; the legal system's "structural incompetence" and procedural unfairness; current LLM training and deployment often reflecting "color-evasiveness." | Bias in AI, Ethical Framework Deficiencies, Research and Evaluation Gaps, Access, Equity, and Digital Divide, AI Legal Reasoning Limitations | NaN | NaN | LLMs proposing outcomes based on ineffective past patterns, perpetuating a "civil legal system twilight zone"; replication of bias, prejudice, and discrimination; LLM "hallucinations" or fabricated information; misuse by legal professionals without proper verification; entrenchment of systemic injustice if LLMs are not intentionally guided; potential to worsen disparities in legal services; AI tools reflecting biases of their homogeneous developers; "color-evasive" LLM deployment. | Bias and discrimination, Inaccurate or misleading AI output, Risk of misapplication or misuse, Exacerbation of inequality or two-tiered system, Undermining legal process or principles, Technical limitations of AI |
| 56ArizStLJ545.pdf | HeinOnline | Systemic Regulation of Artificial Intelligence | The paper argues for systemic regulation of AI as a technology, beyond specific applications, due to broad societal risks (present and future, including bias, fraud, unemployment, geopolitical instability, and existential threats) and the AI alignment problem. It proposes principles for domestic and international AI regulation, emphasizing a precautionary approach and ex-ante oversight. | Systemic AI Regulation, Societal Risks of AI, AI Alignment Problem, Principles for AI Regulation, Precautionary Approach, Ex-Ante Oversight | True | Idealistic | True | 3.0 | NaN | NaN | NaN | NaN | Not Applicable | NaN | NaN | Bias and discrimination by AI systems against vulnerable groups; projection of historical inequity into the future. | Bias in AI/Data, Risk of AI Exacerbating Inequality | Systemic regulation of AI as a technology, including ex-ante oversight, to mitigate AI risks such as bias and discrimination. | Policy and Regulatory Reform, Regulation, Ethics, and Governance, Bias Detection and Mitigation | Algorithmic bias and discrimination; preventing AI-driven harms to vulnerable communities. | Ethical AI in Law and AI Governance, Support for Vulnerable Populations | Vulnerable groups, people of color, women, minorities, groups with a history of discrimination or disadvantage. | Vulnerable populations, Minority groups, Women, Marginalized communities | General Law / AI Regulation | General Law, AI Regulation | US, China, EU, International | USA, China, EU, International | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Lack of effective systemic regulation for AI; limitations of technical tools to address algorithmic discrimination; the unresolved AI alignment problem making it difficult to ensure AI systems consistently uphold human values and avoid discriminatory outcomes. | Regulatory and Governance Gaps, Bias in AI, Ethical Framework Deficiencies, AI Accuracy and Reliability | The AI alignment problem (including goal specification, instrumental convergence, orthogonality thesis); complexity and poor auditability of AI systems; the rapid and unexpected rate of AI capability growth. | Ethical Considerations, Safeguarding Against Misuse and Harm, Transparency and Explainability of AI, Accuracy and Reliability of LLM Output | Bias and discrimination, fraud, privacy violations, unemployment, inequality, dangerous military applications (autonomous weapons), geopolitical imperialism, terrorism, totalitarianism, threats to democracy (misinformation, deepfakes), harms from misaligned AI (including deception and power-seeking), existential risks, misuse of AI for nefarious purposes (e.g., bioweapons). | Bias and discrimination, Security vulnerabilities or malicious misuse, Data privacy and security breach, Job displacement, Exacerbation of inequality or two-tiered system, Harmful or unsafe AI output, Undermining democratic processes, Infringement on human rights, Negative societal impact |
| 4ModLRsch32.pdf | HeinOnline | Research on Generative Artificial Intelligence Legal Profession Substitution | This paper empirically examines the application of generative AI in the legal field, analyzing its potential to enhance efficiency and promote social justice. It also discusses the risks, limitations, and ethical considerations, proposing that AI will complement rather than fully replace legal professionals and advocating for regulation through AI and legal professional ethics. | Empirical Study of Generative AI in Law, Efficiency Enhancement, Social Justice Promotion, Risk Identification, Limitations Identified, Ethical Considerations, AI as Complement to Professionals, Regulation through AI Ethics, Regulation through Legal Ethics | True | Idealistic | True | 3.0 | Neutral | Generative Artificial Intelligence (e.g., ChatGPT, GPT-4, Harvey, various Chinese large models like Iflytek Spark, Baidu Wen Xin Yi Yan) | Generative AI, Large Language Model, Named Tool / Platform | NaN | Not Applicable | NaN | NaN | Data security and privacy leakage; risk of unethical use; lack of controllability of legal decisions by AI; AI generating incorrect or fabricated information ('hallucinations'); algorithmic discrimination and bias; lack of trustworthiness and social acceptability of AI in law; non-interpretability of AI decisions; and lack of human sensitivity/empathy in AI-assisted legal processes. | Security Risks with AI, Data Privacy Concerns with AI, Ethical Concerns with AI in Law, Lack of AI Controllability, AI Unreliability/Inaccuracy, Bias in AI/Data, Lack of Trust in AI/Automated Systems, Lack of AI Transparency/Explainability, AI Limitations in Replicating Human Judgment | Regulation of generative AI applications in the legal field from the dimensions of AI ethics and legal professional ethics. Emphasizing people-centered humanism, fairness and justice over utilitarianism, and constructing a robust regulatory framework and assessment mechanism for legal technology ethics. | Regulation, Ethics, and Governance, Conceptual Frameworks, Benchmarking and Evaluation Frameworks | Enhancing efficiency and quality of legal services, promoting (social) justice, reducing cost of legal services, professional substitution in the legal field. | Improving Efficiency in Legal System / Profession, Democratizing Law / Closing Justice Gap / Rule of Law, Affordability of Legal Services / Cost Reduction, Regulatory Reform (Legal Services and AI) | General public / 'the people' | General public | General legal profession (lawyers, judges, judicial support staff), judicial applications, legal services market, legal advice, legal content generation. | Legal Profession Regulation, Judicial Processes, General Legal Practice | International (with specific examples and regulatory discussions concerning China, USA, UK, and EU) | International, China, USA, UK, EU | Public information from the judicial domain (e.g., judicial decision documents network, social media, judiciary websites, lawyer databases) for user profiling and language model training; copyrighted works (as highlighted by lawsuits against OpenAI). | Publicly Available Data, Legal Domain Data, User-Generated Content, Copyrighted Material (Source Mentioned), Fine-tuning Dataset | NaN | NaN | NaN | Not applicable | True | True | The paper lists several AI platforms (e.g., Iflytek Spark, Baidu Wen Xin Yi Yan, Chatlaw) with URLs and notes on client app availability, and discusses widely accessible tools like ChatGPT which have free tiers. Commercial tools like Harvey are mentioned as used by specific firms. | Publicly accessible online tool or platform, Freemium access, Commercial product or service | Ensuring accuracy and reliability of generated content; overcoming model 'hallucinations'; addressing algorithmic discrimination and bias; building trustworthiness and social acceptability of AI in law; improving interpretability of AI decisions; maintaining human sensitivity and empathy in legal processes; and establishing comprehensive ethical and regulatory frameworks for legal AI. | AI Accuracy and Reliability, Bias in AI, Public Understanding, Trust, and Adoption, Transparency and Explainability, Human Oversight and Professional Adaptation, Ethical Framework Deficiencies, Regulatory and Governance Gaps | Managing security risks (data safety, privacy); preventing unethical use; addressing intellectual property infringement; ensuring controllability of AI in legal decision-making; mitigating 'hallucinations' and fictitious outputs; combating algorithmic discrimination and bias; building trust in AI systems; dealing with the 'black box' nature (non-interpretability) of some AI; and preserving humanistic elements in the legal profession. | Data Privacy, Security, and Confidentiality, Safeguarding Against Misuse and Harm, Ethical Considerations, Copyright and Intellectual Property Issues, Output Variability and Consistency, LLM Hallucination and Factual Errors, Bias in AI Systems and Data, User Adoption, Trust, and Acceptance, Transparency and Explainability of AI | Data and personal privacy leakage from training on judicial data; unethical use of AI; intellectual property infringement by AI models trained on copyrighted works; lack of controllability of legal decisions made or assisted by AI; generation of incorrect or wholly fabricated information ('hallucinations'); algorithmic discrimination and bias leading to unfair outcomes; security threats; model illusion; environmental/social and regulatory risks; third-party risks. | Data privacy and security breach, Ethical concerns, Copyright or intellectual property issues, Lack of transparency, accountability, and redress, Technical limitations of AI, Inaccurate or misleading AI output, Bias and discrimination, Security vulnerabilities or malicious misuse, Environmental impact, Regulatory challenges or gaps |
| 34AlbLJSciTech1.pdf | HeinOnline | THE NEW KID ON THE BLOCK -THE USE OF ARTIFICIAL INTELLIGENCE IN ALTERNATIVE DISPUTE RESOLUTION | This paper discusses the growing role of Artificial Intelligence (AI) in Alternative Dispute Resolution (ADR), particularly in mediation and online dispute resolution (ODR). It explores AI's benefits, such as increased efficiency and data-driven insights, alongside cautions like ethical concerns, potential biases, and the limitations of AI in tasks requiring human emotional intelligence. | AI in Alternative Dispute Resolution, AI in Mediation, AI in Online Dispute Resolution, Benefit Identification, Ethical Concerns, Bias in AI, Limitations of AI (Emotional Intelligence) | True | Idealistic | True | 3.0 | Neutral | AI in Alternative Dispute Resolution (ADR), particularly AI-assisted mediation and Online Dispute Resolution (ODR), including tools like ChatGPT, Modria, Smartsettle, Cybersettle, Kleros, and the adjusted winner procedure. | AI in Alternative Dispute Resolution (ADR), AI-assisted Mediation, Online Dispute Resolution (ODR), Named Tool / Platform, Algorithmic Procedure | NaN | Not Applicable | NaN | NaN | High cost and delays in traditional litigation and court systems; potential impersonality of online dispute resolution; risk of algorithmic bias in AI perpetuating societal inequities; privacy and data security vulnerabilities in AI systems. | High Cost of Legal Services, Judicial/Legal System Inefficiencies, Ethical Concerns with AI in Law, Bias in AI/Data, Risk of AI Exacerbating Inequality, Data Privacy Concerns with AI, Security Risks with AI | Wider adoption of Alternative Dispute Resolution (ADR) and Online Dispute Resolution (ODR) to improve efficiency and accessibility; leveraging AI to enhance ADR/ODR processes; emphasizing ethical AI development, robust data governance, and maintaining human oversight, particularly for tasks requiring emotional intelligence and complex judgment. | Online Dispute Resolution (ODR), Alternative Legal Service Delivery Models, Cost Reduction and Efficiency, AI Tool Development, Regulation, Ethics, and Governance, Data Curation and Management, Human Oversight and Collaboration | Reducing court backlogs; making dispute resolution more affordable and faster; resolving family law disputes (e.g., parenting plans, asset division); handling small claims cases. | Judicial System Modernization / Efficiency, Affordability of Legal Services / Cost Reduction, Dispute Resolution | General public involved in disputes, particularly in family law and small claims, and potentially those who find traditional litigation costly or slow. | General public, Litigants, Individuals in family law disputes, Litigants in small claims courts, Individuals unable to afford legal services | Alternative Dispute Resolution (Mediation, Arbitration), Family Law, Contract Law, Small Claims, Tort Law (contextually), Criminal Law (AI examples mentioned). | Alternative Dispute Resolution, Mediation, Arbitration, Family Law, Contract Law, Small Claims Law, Tort Law, Criminal Law | United States (including specific states like Idaho and California, and federal bodies), with some international examples (England, Estonia, China, eBay global operations). | USA, UK, Estonia, China, International | For ChatGPT: Books, journals, articles, and general web content. For other AI/ADR systems: Prior case data, user-submitted dispute information, legal documents, parties' preferences and submitted evidence. Some systems also employ rule-based logic. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Legal Domain Data, Case Law / Judgments, User-Generated Content, Other Legal Documents, Rule-Based System (No Training Data) | NaN | NaN | Platform adoption by legal institutions (courts, arbitration associations), mandatory ODR programs in some jurisdictions, commercial offerings by tech companies, and public availability of some tools (e.g., ChatGPT). | Evaluation of existing third-party tool, Government/Public institution deployment, Regulatory/Legal framework adoption, Commercial product/service, Freely accessible tool/service | True | False | Several AI-ADR tools and platforms (e.g., ChatGPT, Modria, Cybersettle, Smartsettle, Kleros, Tyler's ODR) are described as operational and in use, offered commercially, by institutions, or publicly (like ChatGPT's free tier). | Publicly accessible online tool or platform, Commercial product or service, Freemium access | Need for AI systems with improved emotional intelligence emulation or effective human-AI teaming models; ensuring fairness, unbiasedness, and robust privacy/security in AI-ADR systems; enhancing trust and acceptance of AI tools within the legal profession; training for legal professionals to use AI effectively. | AI Legal Reasoning Limitations, AI Scope and Functionality Limitations, Bias in AI, Security and Privacy of Data, Public Understanding, Trust, and Adoption, Human Oversight and Professional Adaptation | NaN | NaN | AI systems perpetuating errors from flawed training data; lack of emotional intelligence in AI leading to inappropriate responses in sensitive situations; violation of privacy and disclosure of confidential information through AI data handling; propagation of discriminatory practices due to algorithmic bias; over-reliance on AI potentially diminishing critical human skills in mediation. | Inaccurate or misleading AI output, Technical limitations of AI, Dehumanization of legal process, Data privacy and security breach, Bias and discrimination, Over-reliance on AI, Deskilling or erosion of human skills |
| 90GeoWashLRev83.pdf | HeinOnline | Contracts in the Age of Smart Readers | This paper explores "smart readers," AI tools based on language models like GPT-3, which can simplify, personalize, interpret, and benchmark contracts, potentially improving consumer understanding and market competition. It also analyzes significant risks including errors, adversarial attacks by firms, discrimination, and the need for legal and doctrinal adaptations to this emerging technology. | AI Tools for Contract Analysis (Smart Readers), LLM Application, Contract Simplification, Personalized Contract Interpretation, Consumer Understanding Enhancement, Risk Identification (Errors, Adversarial Attacks, Discrimination), Need for Legal Adaptation | True | Idealistic | True | 3.0 | Neutral | Smart readers (AI language models for contract analysis, e.g., GPT-3 for simplification/personalization/construction, and tools like PrivacyCheck for benchmarking). | AI Contract Analysis Tool, Large Language Model, Contract Simplification / Personalization, Benchmarking / Evaluation, Named Tool / Platform | Illustrative examples generated by GPT-3, acknowledged by authors as 'cherry-picked'. The paper also describes the functionality of PrivacyCheck, an existing tool for ranking privacy policies, as an example. | Demonstration or Illustrative Examples, Qualitative Analysis | GPT-3 examples demonstrated capabilities such as simplification of complex legal language, personalization of contractual presentation, and construction of term meaning. PrivacyCheck was cited as a tool that scores privacy policies and compares them to competitors. | Descriptive or Conceptual finding, Benefit identified | Information barriers (complexity and length of contracts, cognitive load), lack of consumer understanding of contractual terms, high cost of legal services, potential for contractual bias and discrimination, digital divide limiting access to technology. | Difficulty Accessing/Interpreting Legal Information, Complexity of Legal Language/Documents, Public Lack of Legal Knowledge/Awareness, High Cost of Legal Services, Bias in Contracts, Digital Divide | Employing "smart readers" to simplify, personalize, interpret, and benchmark contracts; increasing term transparency to empower consumers and potentially foster market competition; providing on-demand "know-your-rights" services to improve access to legal understanding. | AI Tool Development, Document Automation, Language Simplification and Multilingual Access, User Interface and Accessibility Design, Transparency and Explainability in AI, Access to Legal Information and Advice, Benchmarking and Evaluation Frameworks | Understanding contract terms, identifying unfair or one-sided clauses, comparing contracts, enhancing consumer comprehension of legal agreements, addressing information asymmetry in consumer contracting. | Legal Literacy and Public Legal Education, Legal Document Analysis / Review, Protection of Rights | Consumers in general, with a particular focus on vulnerable consumers such as low-income individuals, recent immigrants, and young people who may struggle with complex legal texts. | Consumers, Vulnerable populations, Low-income individuals, Recent immigrants, Youth, Individuals lacking legal knowledge | Contract law, Consumer law, Privacy law. | Contract Law, Consumer Law, Data Privacy Law | United States (primary examples and legal framework discussed, e.g., US cases, FTC, Draft Restatement of Consumer Contracts), with general applicability often implied for consumer contracts. | USA, International | For GPT-3 (a key example model): Trained on a large corpus of text including Wikipedia ("45TB of compressed plaintext"). For PrivacyCheck: Built on machine learning algorithms; specific training data not detailed in this paper. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Non-Legal Domain Specific Data, Undisclosed Data Source/Availability | NaN | NaN | For PrivacyCheck: Described as a browser extension. For GPT-3: Accessible via API or publicly available interfaces (e.g., AI Dungeon for some exmaples). | Evaluation of existing third-party tool, Integration into existing system/platform, Web-based access, API access | True | True | PrivacyCheck is available as a free browser extension. GPT-3 examples were generated via publicly accessible interfaces or API. | Publicly accessible online tool or platform, API access | Digital inclusion disparities limiting access to smart readers, difficulty in detecting and proving adversarial attacks and algorithmic bias, defining relevant comparison groups for benchmarking increasingly personalized contracts, ensuring smart reader accuracy and reliability, potential for regressive cross-subsidies if firms discriminate based on smart reader use, effective regulation for emerging risks. | Access, Equity, and Digital Divide, AI Accuracy and Reliability, Bias in AI, Research and Evaluation Gaps, Consumer Protection Gaps, Regulatory and Governance Gaps | Ensuring accuracy and reliability of smart readers, managing errors (isolated, correlated), preventing and detecting sophisticated adversarial attacks by firms, addressing potential for bias and discrimination in smart reader outputs or arising from their usage patterns, achieving widespread and equitable consumer uptake, developing appropriate legal and regulatory frameworks. | Accuracy and Reliability of LLM Output, Safeguarding Against Misuse and Harm, Bias in AI Systems and Data, User Adoption, Trust, and Acceptance, Regulatory Uncertainty and Compliance | Exploitation by sophisticated parties through adversarial attacks, inscrutability of black-box models leading to unaccountable errors, exacerbation of contractual bias and discrimination (e.g., firms offering worse terms to non-users of smart readers), premature relaxation of consumer protection measures by policymakers, consumer overcompliance with unenforceable terms due to simplified explanations, harms from misinterpretation of contract terms. | Security vulnerabilities or malicious misuse, Lack of transparency, accountability, and redress, Bias and discrimination, Exacerbation of inequality or two-tiered system, Regulatory challenges or gaps, Consumer harm, Over-reliance on AI, Inaccurate or misleading AI output |
| 14JChristianLegalThought8.pdf | HeinOnline | The Multifaceted Impact of Generative AI on Lawyers and Legal Services | This paper explores the transformative potential of Generative AI (Gen AI) on the legal sector, detailing its effects on law firm business models, the redefinition of lawyer roles, and the acceleration of lawyer professional development. It also examines Gen AI's capacity to enhance access to justice and discusses the associated spiritual and ethical implications from a Christian perspective. | Generative AI Impact on Legal Sector, Law Firm Business Model Transformation, Redefinition of Lawyer Roles, Lawyer Professional Development, Access to Justice Enhancement, Spiritual Implications, Ethical Implications, Christian Perspective | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | High cost and general inaccessibility of legal services for most people; decaying legal system infrastructure and public lack of legal knowledge; legal profession's historical aversion to scaling services through technology; current Gen AI's insufficient dependability for reliable legal use. | High Cost of Legal Services, Limited Access to Legal Assistance, Judicial/Legal System Inefficiencies, Public Lack of Legal Knowledge/Awareness, Slow Technology Adoption by Legal Profession, AI Unreliability/Inaccuracy | Leveraging Gen AI to create scalable, accessible (affordable and convenient), and dependable legal information and solutions; ensuring Gen AI legal systems are developed ethically, particularly for disadvantaged groups, with informed consent and appropriate compensation; advocating for efforts to ensure AI-driven justice solutions remain widely and equitably available. | AI Tool Development, Access to Legal Information and Advice, Cost Reduction and Efficiency, Regulation, Ethics, and Governance, Policy and Regulatory Reform | Affordability and availability of legal services; scalable provision of legal information and guidance; overcoming systemic barriers to justice. | Affordability of Legal Services / Cost Reduction, Access to Legal Information, Access to Legal Advice, Democratizing Law / Closing Justice Gap / Rule of Law | The general public unable to afford traditional legal services, particularly disadvantaged and marginalized populations. | General public, Individuals unable to afford legal services, Marginalized communities | NaN | NaN | NaN | NaN | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Current deficiency in Gen AI's dependability as a legal resource; the need to ensure Gen AI legal systems are developed to be suitable for all, especially vulnerable populations, rather than as experiments; challenge of ensuring widespread and equitable ongoing access to effective AI-driven justice solutions; absence of a fully developed Christian ethical framework for addressing issues like biased or unethically sourced AI training data. | AI Accuracy and Reliability, Access, Equity, and Digital Divide, Ethical Framework Deficiencies, Bias in AI, Data Availability and Quality | NaN | NaN | Gen AI models being trained on data acquired without consent or through illegal means, such as copyright infringement or data theft; the potential for Gen AI legal solutions to be developed through unethical 'experimentation' on disadvantaged or marginalized individuals without their fully informed consent or due compensation; the possibility that advanced AI justice tools might become exclusive to a select few rather than remaining broadly accessible. | Copyright or intellectual property issues, Data privacy and security breach, Ethical concerns, Infringement on human rights, Consumer harm, Exacerbation of inequality or two-tiered system |
| 16IntlInHouseCounselJ (1).pdf | HeinOnline | Generative Artificial Intelligence: Legal Profession Disrupted? | This paper discusses the disruptive potential of generative AI in the legal profession, stressing that technology adoption should prioritize client needs and the administration of justice over mere efficiency. It highlights the high cost of AI tools, the importance of specialized legal AI, and showcases judiciary-led innovations as positive examples for harnessing technology responsibly. | Disruptive Potential of Generative AI, Client-Centric AI Adoption, Prioritizing Justice over Efficiency, Cost of AI Tools, Importance of Specialized Legal AI, Judiciary-Led Innovation | True | Idealistic | True | 3.0 | Neutral | Generative AI, Large Language Models (e.g., ChatGPT), and specialized legal AI platforms (e.g., Casetext, Spellbook, Luminance Autopilot, LawGeex). | Generative AI, Large Language Model, Specialized Legal AI Platforms, Named Tool / Platform | The paper cites evaluations of LLMs passing professional exams (e.g., Bar exam by ChatGPT) and a comparative study of LawGeex AI vs. human lawyers for NDA review (5 NDAs reviewed). | References External Evaluation, Performance on Standardized Tests | The paper cites a LawGeex demonstration where AI reviewed five Non-Disclosure Agreements in 26 seconds, compared to an average of 92 minutes for lawyer participants. | High performance, Outperforms others, Benefit identified, Developer or Vendor claim | High cost of AI tools; technology adoption driven by 'solutionism' or commercial interests rather than client/public needs; risk of over-reliance on AI diminishing human judgment; challenges in creating AI that genuinely meets user needs leading to low adoption (e.g., ODR); lack of trust in AI outputs without verification. | High Cost of A2J Technology, Misalignment of Research/Innovation with Practical Needs, Automation Bias, Technical Challenges in AI Development, Lack of Trust in AI/Automated Systems | Prioritizing client and public interests (paramountcy of consumer needs) in technology adoption; judiciary-led innovations focusing on proportionate justice, therapeutic justice, and safety for vulnerable parties; favoring specialized and verified legal AI tools over generic ones; critical evaluation of whether AI is the best or most cost-effective solution. | Policy and Regulatory Reform, Judicial System Enhancement, AI Tool Development, Regulation, Ethics, and Governance, User Interface and Accessibility Design | Online Dispute Resolution (ODR), proportionate justice, therapeutic justice in family law, safety of vulnerable parties in family law (child abuse/family violence), contract review, legal research. | Dispute Resolution, Judicial System Modernization / Efficiency, Support for Vulnerable Populations, Protection of Rights, Legal Document Analysis / Review, LegalResearch Support | Litigants in general, consumers of legal services, families undergoing dissolution, children and vulnerable parties affected by abuse and family violence. | Litigants, Consumers, Families, Individuals in family law disputes, Children, Vulnerable populations, Victims of domestic violence | General legal practice, contract law, family law, dispute resolution, litigation. | General Legal Practice, Contract Law, Family Law, Dispute Resolution, Litigation | International, with specific examples and discussions related to Singapore, Australia, USA, UK, Canada, France, and Europe. | International, Singapore, Australia, USA, UK, Canada, France, Europe | The paper implies that generic LLMs (like ChatGPT) are trained on vast internet data. Specialized legal AIs are mentioned as using more limited, curated data sources such as 'the White Book, the National Archives case law database, BAILLI, Westlaw, and Lexis Nexis.' | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Fine-tuning Dataset, Legal Domain Data, Legal Scholarly Content / Textbooks, Case Law / Judgments, Proprietary Data, Publicly Available Data | NaN | NaN | Widespread public availability for tools like ChatGPT; commercial licensing for specialized legal software (e.g., Microsoft 365 Copilot, LexisNexis tools); implementation within court systems for judiciary-led innovations. | Evaluation of existing third-party tool, Web-based access, Freely accessible tool/service, Commercial product/service, Integration into existing system/platform, Government/Public institution deployment | True | True | Public availability of tools like ChatGPT (including a free tier) and commercial availability of specialized legal AI tools and platforms (e.g., LexisNexis AI, Microsoft Copilot, Casetext, Spellbook). | Publicly accessible online tool or platform, Freemium access, Commercial product or service | Ensuring trustworthiness and verifiability of LLM outputs for legal work; need for specialized legal AI that is more reliable than generic models; aligning AI development with genuine needs of justice and clients rather than just 'solutionism' or commercial interests; addressing low adoption rates of ODR by better understanding user needs. | AI Accuracy and Reliability, AI Scope and Functionality Limitations, Ethical Framework Deficiencies, Access, Equity, and Digital Divide, Public Understanding, Trust, and Adoption, Research and Evaluation Gaps | High cost of generative AI-enabled tools; avoiding 'solutionism' (adopting technology for its own sake); ensuring security of technology; training and support required for new technologies; differentiating law firm services when all use similar AI tools. | Financial Cost and Resource Constraints, Ethical Considerations, Data Privacy, Security, and Confidentiality, User Training, AI Literacy, and Skill Gaps, Domain-Specific Adaptation and Customization | Over-reliance on AI leading to diminished human judgment and critical thinking; inaccuracy of AI outputs, particularly from unspecialized models; technology dominating rather than assisting justice; potential for system failures to jeopardize dignity and due process; loss of human contact in legal processes. | Over-reliance on AI, Deskilling or erosion of human skills, Inaccurate or misleading AI output, Undermining legal process or principles, Dehumanization of legal process |
| 6Issue6IntlJLMgmtHuman312.pdf | HeinOnline | From Data to Verdict: Navigating AI's Growth & Blemish in the Legal System | This paper discusses the increasing adoption of artificial intelligence, including large language models like ChatGPT, within the legal sector for tasks such as document analysis, contract drafting, and predicting case outcomes. It explores the potential benefits for efficiency and access to justice, while also highlighting significant ethical concerns, risks of bias, the need for human oversight, and regulatory challenges. | AI Adoption in Legal Sector, LLM Application, Legal Document Analysis, Contract Drafting, Case Outcome Prediction, Benefit Identification, Access to Justice Enhancement, Ethical Concerns, Bias in AI, Need for Human Oversight, Regulatory Challenges | True | Idealistic | True | 3.0 | Neutral | Kira Systems (Machine learning program for contract review) | Machine Learning, Contract Review Tool, Named Tool / Platform | Reported by Kira Systems' clients based on their use of the program. | Developer Claims Reported | Reduction in lawyer time required for contract review between 20% to 60%. | Benefit identified, High performance | Limited public access to court systems; Overwhelming case backlogs; Potential for algorithmic bias and lack of transparency in AI tools used in the justice system; Ethical dilemmas related to AI in legal decision-making. | Limited Access to Justice System Resources, Judicial/Legal System Inefficiencies, Bias in AI/Data, Lack of AI Transparency/Explainability, Ethical Concerns with AI in Law | Digitalization of judicial proceedings (e.g., online courts); Use of AI for court efficiency, transcription (e.g., Teres), and translation (e.g., SUVAS); Development of comprehensive legal, regulatory, and ethical frameworks for AI; Ensuring transparency, explainability, and human oversight of AI in legal applications. | Judicial System Enhancement, AI Tool Development, Cost Reduction and Efficiency, Language Simplification and Multilingual Access, Policy and Regulatory Reform, Regulation, Ethics, and Governance, Transparency and Explainability in AI, Human Oversight and Collaboration | Improving access to court systems; Reducing judicial backlogs; Enhancing transparency of judicial proceedings; AI-assisted legal document analysis and decision support in the justice system. | Judicial System Modernization / Efficiency, Access to Legal Information, Legal Document Analysis / Review | General public, particularly those with limited access to legal assistance or facing overwhelmed court systems. | General public, Individuals facing access barriers | Contracts, Litigation, Criminal Law (bail decisions), Patent Law, General Court Procedures. | Contract Law, Litigation, Criminal Law, Patent Law, Judicial Processes | India, USA, Canada, Europe (GDPR reference). General discussion with specific examples from these jurisdictions. | India, USA, Canada, EU | For Kira Systems: Proprietary legal documents (contracts), with the software being trained and refined by human legal experts. | Proprietary Data, Legal Domain Data, Legal Contracts, Expert-Annotated / Human-Curated / Human-Generated Data, Fine-tuning Dataset | For Kira Systems: Iterative refinement of standard machine learning algorithms based on human expert feedback over an extended period. | Iterative Design Process, Machine Learning Model Development, Expert Feedback Integration | For Kira Systems: Commercial licensing to law firms. | Evaluation of existing third-party tool, Commercial product/service, Internal deployment/prototype | True | False | ChatGPT is available as an online service. Tools like Kira Systems, Lex Machina, and Ravel Law are commercially available. AI tools like Teres, SUPACE, and SUVAS are deployed within the Indian judicial system. | Publicly accessible online tool or platform, Commercial product or service | AI's inability to replicate human judgment, resourcefulness, empathy, and creativity in complex legal scenarios; Uncertainty in how much better AI contract writers can become due to lack of domain experience and linguistic accuracy for autonomous operation; Need for AI systems to be fully transparent and explainable; Lack of comprehensive legal, regulatory, and ethical frameworks for AI in the justice system. | AI Legal Reasoning Limitations, AI Accuracy and Reliability, Transparency and Explainability, Ethical Framework Deficiencies, Regulatory and Governance Gaps | For Kira Systems (as reported for its development): Significant time and effort required to refine the software to accurately identify specific legal concepts within documents (took 2.5 years instead of an expected 4 months). | Domain-Specific Adaptation and Customization, Accuracy and Reliability of LLM Output, Financial Cost and Resource Constraints, LLM Reasoning Capabilities | Job displacement for legal professionals; Embedded bias in AI leading to unfair or discriminatory outcomes; Lack of transparency and explainability in AI decisions, undermining due process; Amplification of errors if AI relies on flawed legal data; Over-reliance on AI (automation bias); Breaches of data security and privacy for sensitive legal information; Ethical concerns about machines making decisions on personal liberty. | Job displacement, Bias and discrimination, Lack of transparency, accountability, and redress, Undermining legal process or principles, Inaccurate or misleading AI output, Over-reliance on AI, Data privacy and security breach, Ethical concerns, Dehumanization of legal process |
| 96PhilLJ793.pdf | HeinOnline | Will Artificial Intelligence Replace Lawyers in the Philippines? | This paper examines how AI, including LLMs, might transform the legal profession in the Philippines by reviewing economic theories on automation and current AI capabilities. It argues that while routine legal tasks are automatable, lawyers' roles requiring creative/social intelligence will remain crucial, and suggests policy recommendations for AI integration and improving access to justice. | AI Impact on Legal Profession (Philippines), LLM Application, Automation of Routine Legal Tasks, Importance of Human Skills, Policy Recommendations for AI Integration, Access to Justice Enhancement | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | Oligopoly of information, high cost of legal services for indigent clients, bureaucratic processes for legal aid. | Information Asymmetry, Concentration of Power in Information Providers, High Cost of Legal Services, Legal Aid System Inefficiencies | Automation of legal services with AI to reduce legal fees and increase access to legal information; upskilling of lawyers and legal staff; reform of legal education to include technology. | AI Tool Development, Cost Reduction and Efficiency, Access to Legal Information and Advice, Education and AI Literacy, Policy and Regulatory Reform | Access to legal information, Affordability of legal services, Legal aid. | Access to Legal Information, Affordability of Legal Services / Cost Reduction, Legal Aid and Pro Bono Services | Indigent clients. | Low-income individuals, Clients of legal aid organizations | General practice of law | General Legal Practice | Philippines (primarily), with references to US and Europe. | Philippines, USA, Europe | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | True | False | The paper discusses several AI tools, some of which are commercially available (e.g., DoNotPay, LexMachina) or have free/freemium access (e.g., ChatGPT, Bard). | Commercial product or service, Freemium access, Publicly accessible online tool or platform | Ethical guidelines for AI use in law are not yet established; risk of errors in AI outputs and need for human oversight; technological gap including lack of digitized and OCR-enabled government documents in the Philippines; AI's current inability to fully replicate human creative and social intelligence required for some legal tasks. | Ethical Framework Deficiencies, AI Accuracy and Reliability, Human Oversight and Professional Adaptation, Data Availability and Quality, AI Legal Reasoning Limitations | Ensuring competent and ethical use of AI by lawyers; addressing the technological gap and lack of digitized/OCR-enabled documents in the Philippines; difficulty in automating tasks requiring high degrees of creative and social intelligence; need for upskilling lawyers and legal staff. | Legal Professional Responsibility and Competence, Ethical Considerations, Integration with Existing Systems and Workflows, Data Quality, Processing, and Preparation, LLM Reasoning Capabilities, User Training, AI Literacy, and Skill Gaps | Job displacement for legal support staff performing routine tasks; errors in AI-generated legal research (e.g., citing non-existent cases); breach of attorney-client confidentiality through third-party AI tools; unauthorized practice of law by non-lawyers using AI; over-reliance by lawyers on AI tools. | Job displacement, Inaccurate or misleading AI output, Data privacy and security breach, Unauthorized practice of law, Over-reliance on AI |
| 2024JComIntellPropL249.pdf | HeinOnline | CYBERSYMBIOSIS OF HUMAN JUDGES AND ARTIFICIAL INTELLIGENCE: PROBLEMS AND POTENTIAL SOLUTIONS FOR INTEGRATION AND FOR THE SUCCESSFUL MODERNIZATION OF THE JUDICIAL SYSTEMS OF THE BRICS COUNTRIES | This paper explores the concept of 'cybersymbiosis' between human judges and artificial intelligence to modernize judicial systems in BRICS countries, aiming to improve efficiency and access to justice. It proposes a model for integrating AI tools with human oversight, outlining an AI assistant architecture and discussing necessary legal and ethical frameworks to support this integration. | Human-AI Cybersymbiosis in Judiciary, Modernization of Judicial Systems (BRICS), Efficiency Improvement, Access to Justice Enhancement, AI Integration Model, Need for Human Oversight, Legal and Ethical Frameworks | True | Idealistic | True | 1.0 | Positive | Cybersymbiosis model for human-AI judicial work, and an AI assistant architecture featuring NLP, information extraction (neuro-symbolic programming, machine learning), Natural Language Generation (NLG), explainability, and security modules. | Conceptual Model, Human-AI Collaboration Framework, AI Assistant Architecture, Natural Language Processing (NLP), Information Extraction, Neuro-Symbolic AI, Machine Learning, Natural Language Generation (NLG), Explainable AI (XAI), AI Security | NaN | Not Applicable | NaN | NaN | Case backlogs, lack of timely access to justice for vulnerable populations, inconsistent judicial decisions, difficulties processing large data volumes, and systemic inefficiencies including potential corruption. | Judicial/Legal System Inefficiencies, Systemic Inequities in Justice System, Lack of Judicial Consistency, Data Management Challenges, Corruption in Legal System | A human-AI 'cybersymbiosis' model with clear task distribution, AI-powered tools for legal analysis and support, and new legal/ethical frameworks including transparency, audits, and redress mechanisms. | Conceptual Frameworks, Human Oversight and Collaboration, AI Tool Development, Legal Research and Analysis Tools, Regulation, Ethics, and Governance, Transparency and Explainability in AI | Reducing judicial backlogs, improving access for vulnerable populations, enhancing decision consistency and fairness, increasing court efficiency and transparency. | Judicial System Modernization / Efficiency, Support for Vulnerable Populations, Ethical AI in Law and AI Governance | Socially vulnerable populations, linguistic minorities. | Vulnerable populations, Individuals with language barriers, Minority groups | General judicial processes, court administration, rule-making. | Judicial Processes, Court Administration, Regulatory Law | BRICS Countries (Brazil, Russia, India, China, South Africa) | BRICS Countries, Brazil, Russia, India, China, South Africa | Proposed use of structured/unstructured court decisions, human rights documents, and other legal texts from BRICS countries; notes data protection challenges. | Legal Domain Data, Case Law / Judgments, Other Legal Documents, Structured Data, Unstructured Text Data, Multilingual Data, Data Bias Concerns Noted | Conceptual model developed via literature review, comparative analysis of BRICS approaches, and multi-criteria analysis; system proposes neuro-symbolic programming and machine learning. | Conceptual Framework Development, Literature Review as Design Input, Comparative Analysis of Approaches, Multi-criteria Analysis, Neuro-symbolic Programming, Machine Learning Model Development | NaN | Not applicable | False | False | NaN | NaN | Incomplete court digitalization, varying data standards, data protection restrictions, human-AI communication challenges, legal system diversity, budgetary differences, need for updated ethical/legal frameworks, AI explainability, and skilled personnel. | Data Availability and Quality, Security and Privacy of Data, User Interface and Usability Gaps, Multilingual and Jurisdictional Specificity Gaps, Computational Resource and Cost Issues, Ethical Framework Deficiencies, Regulatory and Governance Gaps, Transparency and Explainability, Human Oversight and Professional Adaptation | Incomplete digitalization and data protection issues hindering AI training, AI system imperfections, adapting to diverse legal systems and budgets within BRICS, developing multilingual and legally-aware AI, ensuring security, and creating user-friendly interfaces. | Data Quality, Processing, and Preparation, Data Privacy, Security, and Confidentiality, Accuracy and Reliability of LLM Output, Domain-Specific Adaptation and Customization, Financial Cost and Resource Constraints, Multilingual and Low-Resource Language Support, User Interface, Usability, and Accessibility | Ethical issues, AI-driven discrimination, inaccuracies, compromised fair sentencing, inappropriate AI use, harm from AI errors, and inherent AI biases. | Ethical concerns, Bias and discrimination, Inaccurate or misleading AI output, Undermining legal process or principles, Risk of misapplication or misuse, Consumer harm |
| 98TulLRev363.pdf | HeinOnline | Why Can't I Have a Robot Lawyer? Limits on the Right to Appear Pro Se | This article analyzes the historical limitations imposed by courts on the right to self-representation (pro se) and considers how these limits will impact litigants using new artificial intelligence technology. It then proposes a framework for how courts should address AI-assisted pro se litigants, suggesting an initial bar on AI use until its utility is proven, followed by mandatory disclosure of its use. | Right to Self-Representation and AI, AI-Assisted Pro Se Litigants, Framework for Courts on AI Use, Mandatory Disclosure of AI Use | True | Idealistic | True | 1.0 | Neutral | A framework for courts to manage AI use by pro se litigants, involving an initial prohibition until AI utility is assured, followed by permission with mandatory disclosure. | Regulatory Framework / Proposal, AI Governance in Courts, Access to Justice Consideration | NaN | Not Applicable | NaN | NaN | Established judicial limitations on the right to self-representation (e.g., restrictions on who can appear pro se, rules against unauthorized assistance like ghostwriting); current unreliability of AI (e.g., inaccuracy, fabrication of sources). | Regulatory Hurdles, Challenges for Self-Represented Litigants, AI Unreliability/Inaccuracy | Courts should initially bar self-represented litigants from using AI until its utility is assured. Subsequently, courts should allow its use only if a litigant discloses their use of an AI product to the court, enabling judges to properly assess litigant sophistication and provide appropriate leniency. | Judicial System Enhancement, Support for Self-Represented Litigants, Regulation, Ethics, and Governance, Policy and Regulatory Reform, Transparency and Explainability in AI | Right to self-representation (pro se), AI assistance for litigants, court procedure and administration, access to justice. | Support for Self-Represented Litigants, Protection of Rights, Judicial System Modernization / Efficiency, Democratizing Law / Closing Justice Gap / Rule of Law | Pro se litigants (often low and middle-income individuals). | Self-represented litigants, Low-income individuals, Moderate-income individuals | General (civil and criminal procedure), with examples from various specific fields including family law, bankruptcy, and criminal law. | General Law, Civil Procedure, Criminal Procedure, Family Law, Bankruptcy Law, Criminal Law | United States (federal and state courts). | USA | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Technical: AI's current lack of robustness and truthfulness, including tendencies to fabricate sources or 'hallucinate' facts. Societal/Legal: How to ensure that AI assistance enhances, rather than undermines, the fairness and integrity of the judicial process for pro se litigants; determining when an AI product's benefits outweigh its risks of harm. | AI Accuracy and Reliability, Ethical Framework Deficiencies, Access, Equity, and Digital Divide, Consumer Protection Gaps | NaN | NaN | AI providing inaccurate or misleading legal information or advice; AI fabricating legal citations or facts ('hallucinations'); pro se litigants lacking understanding of AI-generated content and strategy; courts being misled about a litigant's actual sophistication if AI use is undisclosed; potential for AI use to constitute the unauthorized practice of law; violation of court rules (e.g., prohibitions on recording court proceedings if the AI requires it). | Inaccurate or misleading AI output, Consumer harm, Risk of misapplication or misuse, Undermining legal process or principles, Unauthorized practice of law, Regulatory challenges or gaps |
| 7Issue5IntlJLMgmtHuman651.pdf | HeinOnline | Justice Is Mechanized: Ethical Implications of AI in Law | This paper explores the ethical implications of using artificial intelligence in the legal field, focusing on equality, accountability, and accuracy. It argues that AI, while offering benefits in efficiency and accessibility for tasks like legal research and contract review, should serve as a supplementary tool to human judgment to ensure justice is served effectively and ethically. | Ethical Implications of AI in Law, Equality in AI, Accountability in AI, Accuracy in AI, AI as Supplementary Tool, Efficiency Improvement, Accessibility Enhancement | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | Cost and complexity of traditional legal representation; for AI-driven A2J solutions: data privacy concerns, and unreliability/inaccuracy of AI-generated legal advice (e.g., hallucinations); systemic issues a_ffecting overall justice delivery such as massive case backlogs and shortage of judges. | High Cost of Legal Services, Complexity of Legal System/Procedures, Data Privacy Concerns with AI, AI Unreliability/Inaccuracy, Judicial/Legal System Inefficiencies, Limited Availability/Access to Legal Professionals/Expertise | Utilizing AI-powered legal self-help applications for accessible legal information and assistance; integrating AI into the legal system to enhance efficiency and expedite case resolution; developing robust ethical rules and regulations for AI use, ensuring lawyer accountability and AI's supplementary role to human judgment. | AI Tool Development, Support for Self-Represented Litigants, Access to Legal Information and Advice, Judicial System Enhancement, Cost Reduction and Efficiency, Regulation, Ethics, and Governance, Human Oversight and Collaboration | Legal information and self-help; Court efficiency and case processing; Ethical use of AI in law. | Access to Legal Information, Support for Self-Represented Litigants, Judicial System Modernization / Efficiency, Ethical AI in Law and AI Governance | General public, particularly those facing minor legal issues or lacking access to traditional legal representation; people in countries with overburdened legal systems (e.g., India). | General public, Individuals facing access barriers, Populations in developing countries, Population in India | General Law, Contract Law, Administrative Law (specifically mentions parking tickets). | General Law, Contract Law, Administrative Law, Traffic Law | India (primary focus for regulatory reform), USA (examples like ROSS, DoNotPay, NYC chatbot), UK (ethics codes mentioned). | India, USA, UK | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | True | True | DoNotPay is described as a publicly available app, often free, for legal self-help tasks like challenging parking tickets. | Publicly accessible online tool or platform | Lack of adequate regulatory frameworks for AI in law in jurisdictions like India; persistent issues with the reliability and accuracy of AI-generated legal advice (e.g., hallucinations); data privacy concerns associated with AI systems. | Regulatory and Governance Gaps, AI Accuracy and Reliability, Security and Privacy of Data | NaN | NaN | Inaccuracies and potential professional misconduct from reliance on unverified AI output; decline in critical thinking and analytical skills among legal practitioners; data privacy violations and security breaches due to AI's handling of sensitive information; generation of incorrect or deceptive legal advice by AI (hallucinations), potentially leading to adverse legal consequences; inherent data bias in AI systems leading to skewed and discriminatory outcomes; AI's inability to replicate human qualities essential for legal practice such as honesty, courage, judgment, and fellowship. | Inaccurate or misleading AI output, Ethical concerns, Over-reliance on AI, Deskilling or erosion of human skills, Data privacy and security breach, Consumer harm, Bias and discrimination, Dehumanization of legal process |
| 49QueensLJ73.pdf | HeinOnline | Luck of the Draw III: Using Al to Extract Data About Decision-Making in Federal Court Stays of Removal | This article uses GPT-3 to extract data from Canadian Federal Court dockets on immigration stay of removal applications, revealing significant inconsistencies in grant rates among justices. It advocates for increased judicial consistency and greater access to legal data for research, demonstrating AI's potential for scrutinizing legal decision-making to enhance migrant rights. | LLM Application, Data Extraction from Court Dockets, Canadian Law Focus, Immigration Law Focus, Analysis of Judicial Inconsistency, Transparency in Judiciary, Migrant Rights | True | Idealistic | True | 1.0 | Positive | A multi-step computational legal research methodology involving: 1) Web-scraping of Federal Court dockets; 2) Regex-based screening of dockets and entries; 3) Fine-tuning and application of GPT-3 for categorizing docket entries (e.g., identifying motions for stays, orders) and extracting specific data (e.g., deciding justice, outcome); 4) Pandas for docket-level data aggregation and analysis. | Computational Legal Research Methodology, Web Scraping, Regular Expressions, Fine-tuning, Large Language Model, Legal Text Classification, Information Extraction, Data Aggregation / Analysis | The data extraction technique was evaluated by: 1) Comparing its identification of stay of removal cases against a manually reviewed dataset for one year, achieving 98.0% (96/98) coverage. 2) Manually verifying 200 coded dockets for accuracy of extracted datapoints, resulting in 99% accuracy. | Custom Dataset Evaluation, Expert Evaluation, Quantitative Metrics | The automated data extraction technique identified 98.0% of manually identified stay of removal cases in a comparison sample and achieved 99% accuracy in extracting specific datapoints from dockets based on manual verification of 200 cases. | High performance, Technique improves outcome | Limited access to bulk legal data for research and analysis due to restrictive terms of service and lack of court-provided bulk access mechanisms, hindering transparency and oversight. Inconsistent and potentially arbitrary judicial decision-making in high-stakes deportation cases (the 'luck of the draw' phenomenon), impacting fairness for migrants. | Limited Access to Legal Data for Research, Systemic Inequities in Justice System, Lack of Transparency in Justice System | Making legal data (court dockets, decisions) accessible in bulk via APIs for non-commercial research by courts and tribunals. The Federal Court taking measures to encourage more consistency in stay decision-making, possibly through internal discussions, guideline development, or legislative intervention. Researchers sharing code and data, as done in this project. | Data Curation and Management, Open Source Initiatives and Collaboration, Judicial System Enhancement, Policy and Regulatory Reform | Fairness and consistency in judicial decision-making in deportation/removal proceedings; transparency in the legal system; access to legal information for research; procedural justice in immigration and refugee law. | Judicial System Modernization / Efficiency, Ethical AI in Law and AI Governance, Access to Legal Information, Support for Vulnerable Populations, Protection of Rights | Marginalized migrants, non-citizens facing deportation in Canada, particularly those at risk of irreparable harm. | Migrants, Non-citizens, Individuals facing deportation, Population in Canada, Marginalized communities, Vulnerable populations | Immigration and refugee law, Administrative law (specifically judicial review). | Immigration Law, Administrative Law | Canada (Federal Court of Canada). | Canada | For GPT-3 fine-tuning: A human-coded dataset of hundreds of sample Federal Court docket entries (prompts) paired with desired completions (e.g., outcome categories like 'granted', 'dismissed', 'other'; extracted judge names). This training data was derived from publicly available, unstructured, bilingual (English/French) online Federal Court dockets web-scraped by the author. | Fine-tuning Dataset, Author-Created New Dataset, Canadian Legal Data, Legal Domain Data, Other Legal Documents, Publicly Available Data, Web Scraped Data, Expert-Annotated / Human-Curated / Human-Generated Data, Structured Data, Unstructured Text Data, Multilingual Data | An iterative process for machine learning model development: applying the GPT-3 model to new docket entries, verifying performance, providing additional labeled examples for fine-tuning if errors were found, re-training, and re-testing until satisfactory performance was achieved. The overall research methodology involved sequential data processing steps (scraping, regex, ML extraction, logical aggregation). | Iterative Design Process, Machine Learning Model Development, Active Learning, Model Fine-tuning, Data Processing Pipeline | The Python code (excluding the web-scraping program), human-coded training datasets for GPT-3 fine-tuning, and the full dataset of scraped Federal Court dockets (87,776 dockets) were made available on GitHub for non-commercial research use. | Open source code release, Public dataset/benchmark release, Research preview/Beta access | True | False | The code (excluding web-scraping), human-coded training datasets for fine-tuning GPT-3, and the dataset of Federal Court dockets are available on GitHub for non-commercial research use. Execution of the GPT-3 component of the technique requires a paid OpenAI API key. | Code available, Dataset available, Restricted access | Need for systemic solutions for bulk access to legal data beyond individual researcher efforts (e.g., court-provided APIs and permissive terms of service). Further empirical research on reasons for judicial inconsistencies (e.g., role of counsel quality, interpretation of legal tests, country of origin). Addressing limitations in court decision publishing practices that hinder bilingual access and large-scale computational research. | Data Availability and Quality, Access, Equity, and Digital Divide, Research and Evaluation Gaps, Multilingual and Low-Resource Language Gaps | Technical complexity and resource intensiveness of systematically web-scraping and maintaining an up-to-date large-scale database of court dockets. Difficulty of accurately extracting structured information from unstructured, natural language, and often bilingual court docket entries which may lack standardized phrasing. Requirement for manual data labeling to create training sets for fine-tuning machine learning models. | Data Quality, Processing, and Preparation, Scarcity of High-Quality Legal Data, High Computational and Resource Demands, Multilingual and Low-Resource Language Support, Cost and Complexity of Data Annotation | Inherent limitations of LLMs like GPT-3, including potential for bias amplification from training data, generation of 'hallucinated' or incorrect information, and susceptibility to misuse for disinformation. Risk of exacerbating power imbalances if advanced AI legal tools are asymmetrically available, primarily benefiting well-resourced entities (e.g., government) over marginalized individuals and their advocates. The identified inconsistencies in human judicial decision-making themselves pose a risk to justice, particularly when these decisions have high stakes like deportation and are relied upon for constitutional safeguards. | Technical limitations of AI, Bias and discrimination, Inaccurate or misleading AI output, Security vulnerabilities or malicious misuse, Exacerbation of inequality or two-tiered system, Undermining legal process or principles |
| 32AustlLLibr68.pdf | HeinOnline | HOW TECHNOLOGY CAN SUPPORT OPEN JUSTICE AND TRANSPARENCY: A UK PERSPECTIVE | This paper surveys various technological advancements, from historical innovations like writing and printing to modern developments such as the Internet and AI, illustrating their role in enhancing open justice and transparency within the UK legal system. It highlights how these technologies, including AI-driven tools for case summarization, improve public access to and understanding of legal information and judicial processes. | Survey of Technology for Open Justice, AI for Open Justice, UK Law Focus, Transparency Enhancement, Public Access to Legal Information, AI for Case Summarization | True | Idealistic | True | 3.0 | Positive | AI-generated case summaries (using GPT-4 via Jurisage) for unreported judgments, integrated into ICLR's Case Genie brief analysis tool and general case search on the ICLR.4 platform. | Legal Text Summarization, Large Language Model, Integration with Existing Platforms, Named Tool / Platform | The AI summaries are generated by GPT-4 based entirely on the judgment text to avoid hallucination. The paper mentions trying various prototypes before settling on the Jurisage system. No specific benchmark or formal user testing results for the AI summaries are detailed. | Demonstration or Illustrative Examples, No Evaluation by Author | The AI system generates 100-word summaries and three bullet points identifying the top three issues for unreported cases, aiming to make case law clearer and more accessible to users searching on the ICLR.4 platform. | Benefit identified, Descriptive or Conceptual finding | Physical barriers in courtrooms (sightlines, acoustics); low public legal literacy; cost of accessing court documents; incomplete digitization of court processes; potential for critical errors in online legal systems. | Physical Access Barriers to Courts, Public Lack of Legal Knowledge/Awareness, High Cost of Accessing Legal Information, Incomplete Digitization of Legal System, Risk of Errors in Online Legal Systems | Improved design of court spaces (physical and virtual); creation of easy-read legal guides; online publication of judgments and legislation; use of legal blogs, podcasts, and social media for public education; Online Dispute Resolution systems; AI tools for legal research and information accessibility. | Judicial System Enhancement, Access to Legal Information and Advice, Language Simplification and Multilingual Access, Education and AI Literacy, Online Dispute Resolution (ODR), AI Tool Development, Legal Research and Analysis Tools | Open justice, legal transparency, public legal education, access to primary legal information, accessibility of court proceedings, online dispute resolution for unrepresented litigants. | Democratizing Law / Closing Justice Gap / Rule of Law, Judicial System Modernization / Efficiency, Legal Literacy and Public Legal Education, Access to Legal Information, Dispute Resolution, Support for Self-Represented Litigants | General public, unrepresented litigants. | General public, Self-represented litigants | General (common law, statute law, family law, criminal justice). | General Law, Common Law, Statutory Law, Family Law, Criminal Justice | United Kingdom (primarily England & Wales). | UK | For AI summaries: GPT-4 is used, with summaries reportedly 'entirely based on what’s in the judgment' text itself. For Case Genie: A corpus of primary legal sources, including unreported judgments. | Input Data for Task (Non-Training), Legal Domain Data, Case Law / Judgments, RAG System Knowledge Corpus, Undisclosed Data Source/Availability | Iterative prototyping ('tried various prototypes') and collaboration with a technology developer (Jurisage) for the AI summary feature. | Iterative Design Process, Prototyping, Collaborative Development | The AI-generated summaries are integrated into the ICLR.4 online platform, accessible via subscription, enhancing Case Genie and general case search functionalities. | Integration into existing system/platform, Web-based access, Commercial product/service | True | False | The AI summaries are available as part of the ICLR.4 platform, which is a subscription-based service. More information is available on the ICLR website. | Commercial product or service, Publicly accessible online tool or platform | Cost as a barrier to accessing some digitized court documents (e.g., CE-file); the HMCTS Reform programme for digitisation is still incomplete; the AI in Case Genie does not explain *why* it recommends certain cases, only *what* the recommended cases are about via summaries. | Access, Equity, and Digital Divide, Computational Resource and Cost Issues, Data Availability and Quality, Transparency and Explainability | Initial 'teething problems' with new digital systems (e.g., TNA judgment feed); ensuring AI-generated content is accurate and free of hallucinations (addressed by grounding summaries in source judgment text); the 'closed box' nature of AI recommendation reasoning for Case Genie, which necessitated the development of AI summaries for explication. | Integration with Existing Systems and Workflows, Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors, Transparency and Explainability of AI | Potential for severe, irreversible errors in online legal processes (e.g., accidental online divorce); reputational damage to legal professionals from misuse of social media; the gradually decreasing outlandishness of 'cyber judges' as AI capabilities advance. | Inaccurate or misleading AI output, Consumer harm, Ethical concerns, Dehumanization of legal process, Erosion of trust in legal system or AI |
| 92GeoWashLRev.pdf | HeinOnline | Artificial Authorship and Judicial Opinions | This essay predicts how generative AI will transform judicial opinions, making them cheaper and more widespread but also potentially less deliberative and more rhetorical. It explores paradoxes such as AI-enhanced persuasion leading to the obsolescence of legal reasoning and courts resisting AI despite its utility due to threats to judicial authority. | Generative AI Impact on Judicial Opinions, Transformation of Legal Reasoning, Threat to Judicial Authority | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | Cost and limited availability of legal opinions and persuasive resources; Inegalitarian distribution of judicial attention and legal representation due to wealth disparities; Complexity and inaccessibility of legal language and judicial reasoning for laypersons. | High Cost of Legal Services, Limited Availability/Access to Legal Professionals/Expertise, Systemic Inequities in Justice System, Complexity of Legal Language/Documents, Difficulty Accessing/Interpreting Legal Information | AI making judicial opinions cheaper, more widely available, and customizable for different audiences, including legally unsophisticated individuals; Potential for court-appointed AI tools ("AI Gideon") to assist underresourced parties; AI-facilitated deliberation leading to more equitable distribution of judicial attention. | Judicial System Enhancement, Cost Reduction and Efficiency, Access to Legal Information and Advice, Language Simplification and Multilingual Access, Support for Self-Represented Litigants, AI Tool Development | Access to legal information and understanding of judicial decisions; Fairness and equity in adversarial proceedings; Equitable distribution of judicial resources and attention. | Access to Legal Information, Judicial System Modernization / Efficiency, Ethical AI in Law and AI Governance | Legally unsophisticated individuals, underresourced litigants, and the general public. | Laypeople, Individuals lacking legal knowledge, Low-income individuals, Litigants, General public | General/Multiple | General Law, Multiple Fields | International | International | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Ensuring AI fairness and mitigating bias amplification from training data; Maintaining authenticity in AI-generated legal explanations and respecting human dignity; Preventing AI from enabling deceptive rhetoric that undermines truth and justice; Addressing the potential for AI to create an 'artificially balkanized readership,' thereby fracturing shared legal understanding; Establishing clear regulatory frameworks for AI use in the judiciary that ensure accountability and preserve judicial independence. | Bias in AI, Ethical Framework Deficiencies, Transparency and Explainability, Public Understanding, Trust, and Adoption, Regulatory and Governance Gaps, Accountability and Redress Mechanisms | NaN | NaN | Erosion of judicial authority and public cynicism towards courts; Obsolescence of legal reasoning due to a surfeit of AI-generated rhetoric; Reduced deliberation in judicial opinion writing; Perpetuation and amplification of societal biases by AI tools; An 'arms race' of rhetoric between AI-equipped courts and a skeptical public; AI 'hallucinations' and factual errors in legal contexts; Increased difficulty in discerning truth from sophisticated, AI-generated sophistry; Deepening of partisan divides through AI-tailored, balkanizing content; Loss of human authenticity and accountability in judicial expression; AI being used to conceal improper bases for decisions; Over-reliance on AI diminishing human critical thinking and judgment; Threats to judicial independence from potential regulation of AI tools used by courts. | Erosion of trust in legal system or AI, Undermining legal process or principles, Deskilling or erosion of human skills, Dehumanization of legal process, Bias and discrimination, Security vulnerabilities or malicious misuse, Inaccurate or misleading AI output, Undermining democratic processes, Lack of transparency, accountability, and redress, Over-reliance on AI, Regulatory challenges or gaps |
| Justice AI Legal Case Retrieval Using Dense Passage Retrieval.pdf | IEEE_Xplore | Justice AI: Legal Case Retrieval Using Dense Passage Retrieval | This paper introduces Justice AI, a system developed for Korean legal case retrieval using Dense Passage Retrieval (DPR) with KoBERT and LCube models. It aims to make legal information more accessible to the general public and demonstrates its efficacy through performance metrics like an F1 score of 0.5915 for the LCube model. | System Development, Korean Law Focus, Legal Case Retrieval, Dense Passage Retrieval, Access to Legal Information for Public, System Evaluation | True | Idealistic | True | 1.0 | Positive | Justice AI: A legal case retrieval system using Dense Passage Retrieval (DPR) with BERT-based KoBERT and GPT-based LCube models. | Information Retrieval / Search, Software / Platform Development, Dense Passage Retrieval (DPR), Transformer Models, Named Tool / Platform | The system was evaluated using cosine similarity for relevance, and performance metrics including Precision, Recall, and F1 Score. The evaluation used a dataset of Korean legal documents, with user queries to retrieve relevant cases. | Custom Dataset Evaluation, Quantitative Metrics | The LCube model achieved a Precision of 0.42, Recall of 1.0, and an F1 Score of 0.5915. A high cosine similarity score of 0.9002 was achieved for a highly relevant document. | Moderate performance | Accessing and utilizing legal information is challenging for many, and it is difficult for the general public to acquire and use precise legal knowledge. | Difficulty Accessing/Interpreting Legal Information, Public Lack of Legal Knowledge/Awareness | Justice AI uses Dense Passage Retrieval (DPR) to match user keywords with relevant legal cases, providing reliable legal information and enabling personalized legal services. | AI Tool Development, Enhanced AI Capabilities, Access to Legal Information and Advice, Legal Research and Analysis Tools | Legal information retrieval, access to legal information, personalized legal services, legal case understanding. | Access to Legal Information, Legal Literacy and Public Legal Education | General public, individuals with limited legal knowledge. | General public, Individuals lacking legal knowledge, Laypeople | General Korean legal documents including case law, statutes, regulations, administrative orders. Examples mentioned cover criminal law (murder, drunk driving, theft) and civil law (wrongful termination). | General Law, Case Law, Statutory Law, Administrative Law, Criminal Law, Civil Law, Employment Law | South Korea | South Korea | An enhanced and tailored version of the Open Law Data from The Korean Ministry of Government Legislation, consisting of 87,160 Korean legal case documents. The 'reason' field was extracted for analysis. The data includes case law, statutes, regulations, and administrative orders. | Author-Modified Existing Dataset, Data From Existing Public NLP/Legal Datasets/Benchmarks, Korean Legal Data, Legal Domain Data, Case Law / Judgments, Legislation / Statutes / Regulations, Other Legal Documents, Publicly Available Data | Dense Passage Retrieval (DPR). Documents and queries were vectorized using pre-trained language models (KoBERT, LCube). Mean of text vectors was used for embeddings. Cosine similarity was used to retrieve top documents. | Information Retrieval Techniques, Pre-trained Model Utilization, Vectorization, Embedding Model Application | NaN | Not applicable | False | False | NaN | NaN | Lack of extensive, annotated datasets for Korean legal texts limits model generalization. The agglutinative nature of the Korean language poses challenges. Need for developing diverse, comprehensive datasets tailored to Korean legal language and adapting models accordingly. | Data Availability and Quality, Multilingual and Low-Resource Language Gaps, Research and Evaluation Gaps | The dataset was not originally structured as query-document pairs. Limited availability of extensive annotated Korean legal datasets compared to other languages (e.g., Chinese). The agglutinative nature of the Korean language causes complexity in tokenization and contextual understanding. | Data Quality, Processing, and Preparation, Scarcity of High-Quality Legal Data, Multilingual and Low-Resource Language Support, LLM Reasoning Capabilities | NaN | NaN |
| Too Legal- Didn-t Read -TLDR- Summarization of Court Opinions.pdf | IEEE_Xplore | Too Legal; Didn’t Read (TLDR): Summarization of Court Opinions | This paper proposes NLP-based methods for summarizing court opinions, exploring both extractive classifiers (with LSTM performing best for relevance tagging) and a domain-adapted abstractive model, PEGASUS CourtOp, fine-tuned from PEGASUS LARGE. The aim is to assist legal professionals by reducing the time and effort for document review, potentially lowering legal costs and thereby improving access to justice. | Methodology Proposal, Legal Text Summarization (Court Opinions), NLP Application, Extractive Summarization, Abstractive Summarization, Legal Professional Assistance, Cost Reduction, Access to Justice Enhancement | True | Idealistic | True | 1.0 | Positive | PEGASUS CourtOp (fine-tuned PEGASUS LARGE for abstractive summarization) and various classifiers (Naive Bayes, Decision Tree, Random Forest, LSTM NN) for extractive summarization by identifying relevant text segments. | Transformer Models, Fine-tuning, Abstractive Summarization, Machine Learning Classifiers, Extractive Summarization, Hybrid Summarization Approach, Named Tool / Platform | Extractive models (including LSTM) evaluated using 5-fold cross-validation for classification performance (Recall, F1-score) on automatically labeled opinion segments, and ROUGE scores for generated summaries. Abstractive models (including PEGASUS CourtOp) evaluated using ROUGE scores against human-written summaries on a held-out test set comprising 25% of the dataset. | Custom Dataset Evaluation, Quantitative Metrics, Comparative Analysis | For abstractive summarization, PEGASUS CourtOp achieved a ROUGE-1 F1 score of 0.53 and ROUGE-1 Recall of 0.66, outperforming PEGASUS LARGE and Legal PEGASUS. For extractive sentence/paragraph classification, LSTM NN performed best (e.g., paragraph level Recall 0.85, F1-Score 0.73; ROUGE-1 F1 0.34 for summary from LSTM parts). | Moderate performance, Outperforms others, Technique improves outcome | High cost of legal services partly due to the time-consuming and labor-intensive process of parsing very long and complex legal texts (court opinions), which requires specialized training and skills. | High Cost of Legal Services, Resource Constraints, Complexity of Legal Language/Documents, Need for Specialized Legal Skills | Developing NLP-based automatic text summarization tools (both extractive and abstractive) to help legal professionals create summaries more quickly or to automate the process, aiming to reduce costs and thereby increase access to the legal system for people of lower-income brackets. | AI Tool Development, Document Automation, Cost Reduction and Efficiency, Human Oversight and Collaboration, Access to Legal Information and Advice | Improving accessibility and understanding of lengthy legal documents (court opinions) by automatic summarization, aiding legal professionals, and potentially reducing legal service costs. | LegalText Simplification / Plain Language, Access to Legal Information, Improving Efficiency in Legal System / Profession, Affordability of Legal Services / Cost Reduction | People of lower-income brackets. | Low-income individuals | Case Law / Court Opinions | Case Law | United States (Utah, Idaho, Arizona, New Mexico, Nevada, Colorado state supreme courts) | USA | A proprietary dataset of court opinions from six US State supreme courts (Utah, Idaho, Arizona, New Mexico, Nevada, Colorado) and corresponding human-written summaries provided by Justia under a data-sharing agreement. 3661 pairs were used for fine-tuning PEGASUS CourtOp. The base PEGASUS model was pre-trained on general web data, news, social media, and the BillSum dataset. | Fine-tuning Dataset, Proprietary Data, US Legal Data, Legal Domain Data, Case Law / Judgments, Expert-Annotated / Human-Curated / Human-Generated Data, Structured Data, Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text | For extractive summarization: automatic labeling of sentences/paragraphs in court opinions based on similarity (N-Grams, LCS, Semantic Similarity, ROUGE score) to human summaries, followed by training binary classifiers. For abstractive summarization: fine-tuning the pre-trained PEGASUS LARGE model on court opinions and their summaries by freezing encoder weights and training decoder layers (creating PEGASUS CourtOp). | Automated Data Labeling, Classification Model Training, Model Fine-tuning, Transfer Learning, Extractive Summarization, Abstractive Summarization | NaN | Not applicable | False | False | NaN | NaN | Need for improved legal-text-specific Named Entity Recognition for court opinions. Potential for better results by fine-tuning newer, more powerful (though potentially not open-source) language models. Further work to enhance the generation of novel language in abstractive summaries that is not explicitly present in the source opinions. | AI Scope and Functionality Limitations, Research and Evaluation Gaps, AI Legal Reasoning Limitations | For extractive summarization: dataset imbalance between relevant and irrelevant text segments when labeling data for classifier training. For abstractive summarization: effective domain adaptation of general-purpose pre-trained language models to the specific characteristics and vocabulary of legal court opinions. | Data Quality, Processing, and Preparation, Domain-Specific Adaptation and Customization | NaN | NaN |
| Interactive Legal Assistance System using Large Language Models.pdf | IEEE_Xplore | Interactive Legal Assistance System using Large \nLanguage Models | This paper presents a Retrieval Augmented Generation (RAG) chatbot designed to help laypersons in India understand complex Food Safety Regulations, operating in both English and Tamil. The system utilizes LLMs like GPT-4 and Llama2, an embedding model, and a translation model to provide query-based assistance and document section summarization. | Chatbot Development, Retrieval Augmented Generation, India Focus, Food Safety Regulation Navigation, Legal Information Access for Laypeople, Multilingual System (English/Tamil), Document Summarization | True | Idealistic | True | 1.0 | Positive | A RAG-based chatbot using LLMs (GPT-4, Llama2, GPT-4 Vision), IndicTrans2 for translation, and 'Snowflake-arctic-embed' for embeddings. It includes Q&A and summarization components for legal documents. | Chatbot / Conversational AI, Retrieval Augmented Generation (RAG), Large Language Model, Multimodal Language Model, Machine Translation, Embedding-based Methods, Legal Question Answering, Legal Text Summarization | Qualitative comparison of summaries generated by the proposed system and ChatGPT for a specific topic within the Food Safety Regulations. The comparison focused on precision and reflection of original content. | Qualitative Analysis, Comparative Analysis | The system's summaries were found to be significantly more precise and better reflected the original content of the Food Safety Regulations when compared to summaries generated by ChatGPT, which exhibited inaccuracies. | High performance, Outperforms others, Limitation: Hallucination or Factual inaccuracy | Complexity of legal documents for non-experts, leading to misunderstanding and unintentional violations; language barriers for regional language speakers in India where regulations are often in English. | Complexity of Legal Language/Documents, Public Lack of Legal Knowledge/Awareness, Accessibility Barriers for Specific User Groups | Development of a user-friendly RAG chatbot that provides clarifications (Q&A) and summaries of legal documents in both English and Tamil, incorporating translation models to overcome language barriers. | AI Tool Development, User Interface and Accessibility Design, Enhanced AI Capabilities, Access to Legal Information and Advice, Document Automation, Language Simplification and Multilingual Access | Understanding legal documents (specifically Food Safety Regulations), language accessibility in legal information, simplification of legal text. | Legal Literacy and Public Legal Education, Language Access and Digital Divide, LegalText Simplification / Plain Language | Common people in India, particularly Tamil speakers, needing to understand Food Safety Regulations. | General public, Population in India, Individuals with language barriers, Consumers | Food Safety Regulations | Food Safety Law, Regulatory Law | India | India | Publicly available PDF documents of India's Food Safety Regulations from the Food Safety and Standards Authority of India (FSSAI). These documents are processed (converted to HTML, chunked) to create embeddings for the RAG system using 'Snowflake-arctic-embed'. The system utilizes pre-trained LLMs (GPT-4, Llama2, GPT-4 Vision) and a pre-trained translation model (IndicTrans2). | RAG System Knowledge Corpus, Publicly Available Data, Indian Legal Data, Legal Domain Data, Legislation / Statutes / Regulations, OCR Processed Data, Unstructured Text Data, Pre-trained LLM's General Training Corpus | System architecture involving PDF processing (conversion to HTML using GPT-4 Vision), text chunking, embedding generation (Snowflake-arctic-embed), vector storage (ChromaDB), query processing with language identification, translation (IndicTrans2), RAG with LLMs (GPT-4, Llama2) for Q&A, and content extraction with LLM-based summarization. | System Architecture Design, Data Preprocessing, Data Segmentation, Embedding Model Application, Vector Database Implementation, Multilingual Processing, Translation Model Application, Retrieval Augmented Generation (RAG), Information Extraction Techniques, LLM-based Summarization | Local models are pulled from an Ollama server. Embeddings and HTML data are stored locally. No broader public deployment strategy is mentioned. | Local deployment/Standalone application, Internal deployment/prototype | False | False | NaN | NaN | The system currently does not allow users to request or download specific forms related to the legal documents. | AI Scope and Functionality Limitations, User Interface and Usability Gaps | Ensuring relevance and accuracy of retrieved documents, as improper preprocessing or embedding can lead to irrelevant or noisy information; maintaining efficient performance at scale (challenges in optimizing index structures, caching, retrieval latency); validating correctness and relevance of generated answers in real-time. | Accuracy and Reliability of LLM Output, Data Quality, Processing, and Preparation, Scalability of Solutions, Evaluation Challenges and Metrics | Risk of generating inaccurate or misleading information if the RAG system retrieves irrelevant or noisy content, potentially leading to misinterpretation of legal regulations. | Inaccurate or misleading AI output, Technical limitations of AI, Consumer harm |
| EMPOWER-KARE Deep Prompt Learning for Knowledge-aware Response Generation in Clinical Counseling and Legal Support Conversations (1).pdf | IEEE_Xplore | EMPOWER-KARE: Deep Prompt Learning for Knowledge-aware Response Generation in Clinical Counseling and Legal Support Conversations | This paper introduces KARE, a novel dataset of knowledge-grounded conversations for clinical counseling and legal support for crime victims. It also proposes EMPOWER, a dual-tier deep prompt learning framework that uses KARE to generate knowledge-aware responses, demonstrating improved performance over existing methods. | Dataset Creation, Knowledge-Grounded Conversations, Legal Support for Crime Victims, Framework Proposal, Deep Prompt Learning, System Evaluation | True | Idealistic | True | 1.0 | Positive | EMPOWER, a dual-tier deep prompt learning framework for knowledge-aware response generation. It includes Knowledge-attributed Deep Prompt Learning (KDPL), Response-attributed Deep Prompt Learning (RDPL), and a Dynamic Dialogue-Knowledge Module (DDKM). | Framework Development, Prompt Learning, Knowledge-aware AI, Dialogue System Component, Response Generation, Named Tool / Platform | Evaluated on the KARE dataset using automatic metrics (PPL, BLEU-4, Avg. BLEU, F1, Knowledge-F1, BERTScore F1, EA, VE, GM) and human evaluation (Fluency, Adequacy, Contextual Relevance, Knowledge Existence, Correctness, Relevance, Helpfulness, Safety). | Benchmark Dataset Evaluation, Quantitative Metrics, Human Evaluation | EMPOWER achieved improvements of 11.50% in BLEU-4, 28.5% in Knowledge-F1, and 11.6% in BERTScore compared to the best baseline on the KARE dataset. It attained a Perplexity of 7.11 and BERTScore F1 of 0.86. | High performance, Outperforms others, Technique improves outcome | Societal stigmatization, lack of adequate and accessible support services for crime victims, and the multifaceted challenges victims face, including mental trauma and navigating complex legal processes. | Psychological/Cultural Barriers to Seeking Help/Engaging with Law, Limited Access to Support Services, Complexity of Legal System/Procedures | Development of AI-powered knowledge-grounded dialogue systems (like EMPOWER-KARE) to provide 24/7 clinical counseling and legal support, thereby improving access to assistance for crime victims. | AI Tool Development, Legal Knowledge Representation and Management, Access to Legal Information and Advice | Clinical counseling and legal support for crime victims, specifically mental health support and guidance on legal processes related to various crimes. | Access to Legal Advice, Support for Vulnerable Populations, Protection of Rights | Crime victims, with a specific focus on women and children who have experienced violence. | Victims of crime, Women, Children, Victims of violence | Criminal law, cybercrime law (specifically related to cyberstalking), victim support services, and legal aid procedures. | Criminal Law, Cyber Law, Victim Support Services, Legal Aid | India | India | The KARE dataset, built upon the synthetically created POEM dialogue dataset (5,000 English dialogues for crime victims). KARE augments POEM with external domain-specific knowledge collected via web scraping (using Google Search API, content extracted from URLs, segmented using Spacy) and processed into knowledge triplets using OpenIE and Sentence-BERT for relevance. | Author-Modified Existing Dataset, Synthetic Data, Non-Legal Domain Specific Data, Web Scraped Data, Structured Data, Data From Existing Public NLP/Legal Datasets/Benchmarks | Dual-tier deep prompt learning (prefix-tuning) with Knowledge-attributed and Response-attributed prompts, Knowledge Triplets Construction (using Stanford OpenIE, filtering rules, Sentence-BERT for relevance, and GPT-J for verbalization), and a Dynamic Dialogue-Knowledge Module (using multi-head attention and a re-parameterization technique). | Prompt Engineering, Parameter-Efficient Fine-Tuning (PEFT), Knowledge Extraction, Knowledge Representation Design, Dialogue System Design, Attention Mechanism Application | The code and dataset are made available via GitHub and an institutional webpage; no specific user deployment strategies are mentioned beyond research access. | Open source code release, Public dataset/benchmark release, Research preview/Beta access | True | True | Code and dataset are available on GitHub (https://github.com/priyanshu528priya/EMPOWER-KARE/) and an institutional resources page (https://www.iitp.ac.in/~ai-nlp-ml/resources.html). | Code available, Dataset available | Need for incorporating commonsense knowledge to induce commonsense reasoning ability and empathy. Reliance on the quality of source data and knowledge extraction methods can introduce inaccuracies or biases. | AI Legal Reasoning Limitations, Data Availability and Quality, Bias in AI, AI Accuracy and Reliability | Limited computational resources for experimenting with larger language models. Ensuring the quality of source data and the accuracy of knowledge extraction. Effectively integrating external knowledge into response generation (addressed by the dual-tier prompt learning). | High Computational and Resource Demands, Data Quality, Processing, and Preparation, Accuracy and Reliability of LLM Output, Outdated or Limited LLM Knowledge Base, Prompt Engineering and Optimization | Generation of wrong or inaccurate information by the model. Potential for responses to contain repetitions, be inconsistent with context, or exhibit semantic variance from ideal answers. | Inaccurate or misleading AI output, Technical limitations of AI |
| Bettercall AI based legal assistant.pdf | IEEE_Xplore | Bettercall: AI based legal assistant | This paper introduces "Bettercall," an AI-based chatbot designed to improve access to legal and judicial information in India using advanced natural language processing and semantic search capabilities. The system aims to provide primary legal aid and promote legal awareness, with the paper detailing its methodology, challenges, and performance. | Chatbot Development, Access to Legal Information Enhancement, India Focus, Primary Legal Aid, Legal Awareness Promotion, NLP Application, Semantic Search | True | Idealistic | True | 1.0 | Positive | An AI chatbot ('Bettercall') utilizing semantic search (NLP, vector embeddings from legal texts, cosine similarity for query matching) and an LLM (OpenAI's GPT-3.5) combined with a legal ontology database for generating responses to user queries. | Chatbot / Conversational AI, Semantic Search, Natural Language Processing (NLP), Embedding-based Methods, Similarity Search, Large Language Model, Ontology Integration, Knowledge Base Integration, Hybrid AI System, Named Tool / Platform | The system was evaluated using precision and recall metrics on a diverse set of legal queries compared against a manually created "gold standard". User satisfaction and usability were assessed through user feedback surveys. | Custom Dataset Evaluation, Quantitative Metrics, User Study or Survey, Comparative Analysis | The system demonstrated high precision and respectable recall scores across various legal domains (e.g., Criminal Law: Precision ~0.9, Recall ~0.85). User satisfaction scores were notably high, with an overall average satisfaction around 4.5 out of 5. | High performance, Benefit identified | Lack of accessible legal information and understanding, especially for marginalised communities and those with low legal literacy in India; linguistic barriers. | Difficulty Accessing/Interpreting Legal Information, Public Lack of Legal Knowledge/Awareness, Systemic Inequities in Justice System, Accessibility Barriers for Specific User Groups | Development of a multilingual, user-friendly AI-powered digital assistant (Bettercall) that uses semantic search to provide clear legal information, answer common legal queries, and guide users on legal procedures and rights. | AI Tool Development, Language Simplification and Multilingual Access, User Interface and Accessibility Design, Enhanced AI Capabilities, Access to Legal Information and Advice | Access to legal information, legal query answering, guidance on legal procedures (e.g., complaint filing), understanding legal rights, promoting legal literacy. | Access to Legal Information, Access to Legal Advice, Legal Literacy and Public Legal Education | Indian populace, especially marginalised communities and individuals lacking legal literacy. | General public, Population in India, Marginalized communities, Individuals with low literacy, Individuals lacking legal knowledge | General Indian Law, including Criminal Law, Family Law, Property Law, Labour Law, Constitutional Law, Corporate Law, Environmental Law, Intellectual Property Law. | General Law, Criminal Law, Family Law, Property Law, Employment Law, Constitutional Law, Corporate Law, Environmental Law, Intellectual Property Law | India | India | Publicly available Indian legislation (acts and sections with metadata like act name, section number, etc.) web-scraped from indiacode.nic.in. The data is textual and domain-specific. | RAG System Knowledge Corpus, Publicly Available Data, Indian Legal Data, Legal Domain Data, Legislation / Statutes / Regulations, Web Scraped Data, Unstructured Text Data, Structured Data | Web scraping for data collection, data cleaning and formatting, chunking and tokenization, vectorization of text into embeddings, storage in a vector database (Supabase) and a non-relational database (MongoDB) for ontology, cosine similarity for query-document matching, and LLM (GPT-3.5) for response generation. | Data Collection, Data Preprocessing, Data Segmentation, Vectorization, Embedding Model Application, Vector Database Implementation, Ontology Development, Information Retrieval Techniques, LLM-based Content Generation | NaN | Not applicable | False | False | NaN | NaN | Existing gaps in scalability, multilingual support, and domain coverage of legal assistance tools. Future work includes continuous improvement of chatbot capabilities, expansion of legal ontology, and refinement of multilingual functions. | AI Scope and Functionality Limitations, Multilingual and Low-Resource Language Gaps, Data Availability and Quality | Constructing a comprehensive legal database due to lack of pre-existing structured data; inefficiencies in PDF scraping leading to a pivot to web scraping; accurately storing metadata for chunked data; managing and integrating legal ontology effectively without causing data duplication, reduced embedding accuracy, or increased costs. | Scarcity of High-Quality Legal Data, Data Quality, Processing, and Preparation, Financial Cost and Resource Constraints | Inaccurate interpretation of keywords in legal texts could lead to disparate or incorrect outcomes from the chatbot. | Inaccurate or misleading AI output, Bias and discrimination |
| Classifying European Court of Human Rights Cases Using Transformer-Based Techniques.pdf | IEEE_Xplore | Classifying European Court of Human Rights Cases Using Transformer-Based Techniques | This paper proposes and evaluates transformer-based models, using a sliding window approach and data scraping for balancing, to classify European Court of Human Rights (ECHR) case documents. Experimental results show RoBERTa excels at binary classification and BigBird at multi-class classification, indicating AI's potential to enhance legal aid efficiency. | Transformer Model Evaluation, ECHR Case Classification, Legal Aid Efficiency Enhancement, Binary Classification, Multi-Class Classification | True | Idealistic | True | 1.0 | Positive | A legal document classification framework using various transformer-based models (BERT, Legal-BERT, RoBERTa, BigBird, ELECTRA, XLNet) enhanced with a sliding window approach to handle long text sequences and data scraping from the ECHR portal for dataset balancing. | Framework Development, Legal Text Classification, Transformer Models, Long Text Handling Technique, Data Scraping, Dataset Balancing | The models were evaluated on the ECHR dataset (split 70% training, 30% evaluation) using 5-fold cross-validation. Performance was measured by precision, recall, and F1-score, comparing transformer models against conventional machine learning techniques (SVM, DT, NB, AdaBoost) and previous benchmarks. Both binary and multi-class classification tasks were performed. | Benchmark Dataset Evaluation, Quantitative Metrics, Comparative Analysis | For binary classification, RoBERTa achieved the best performance with precision, recall, and F1-score of 89.1%, 86.2%, and 86.7%, respectively. For multi-class classification (after data scraping), BigBird performed best with a weighted F1-score of 78.1%. | High performance, Moderate performance, Outperforms others | High cost of legal representation, limited eligibility for public legal aid programs due to restrictive means tests (considering income, assets, and home value), leading to many individuals being unable to afford legal assistance or being excluded from aid. | High Cost of Legal Services, Limited Availability/Access to Legal Aid, Restrictive Eligibility Criteria for Legal Aid | Automating the classification of legal cases to improve the efficiency of legal assistance provision. This can potentially reduce the cost of legal aid and increase the number of cases that can be assisted within publicly funded budgets. | AI Tool Development, Legal Research and Analysis Tools, Cost Reduction and Efficiency | Improving efficiency of legal aid provision, reducing costs of legal services, automating legal document classification. | Legal Aid and Pro Bono Services, Improving Efficiency in Legal System / Profession, Affordability of Legal Services / Cost Reduction, Legal Document Creation / Automation | Individuals who cannot afford high-quality legal representation and those who may be excluded from or inadequately served by public legal aid programs. | Individuals unable to afford legal services, Individuals lacking access to legal aid | Human Rights Law | Human Rights Law | European Court of Human Rights (ECHR) | ECHR | A publicly available ECHR (European Court of Human Rights) dataset (Chalkidis et al., 2019) consisting of unstructured text (case facts). This dataset was augmented by scraping additional case articles from the ECHR public database to balance class distribution, particularly for the multi-class task. | Author-Modified Existing Dataset, Data From Existing Public NLP/Legal Datasets/Benchmarks, Publicly Available Data, European Legal Data, Legal Domain Data, Case Law / Judgments, Unstructured Text Data, Web Scraped Data | Application of various transformer-based neural networks and conventional machine learning models. A sliding window technique was used for handling long text sequences in transformer models. Data scraping and regular expressions were used for additional data collection and pre-processing. Dataset balancing was a key consideration. | Transformer Architecture, Neural Network Architecture, Machine Learning Model Development, Sliding Window Technique, Data Collection, Data Preprocessing, Dataset Balancing | NaN | Not applicable | False | False | NaN | NaN | Technical: Need for improvement in multi-class classification performance; potential overfitting from sliding window overlaps; transformer models not fully leveraging additional meta-data features. Dataset-related: Need for more high-quality, potentially domain-specific pre-trained models (e.g., combining Legal-BERT's domain specificity with BigBird's long sequence handling) and further dataset augmentation/quality improvements. | AI Accuracy and Reliability, Data Availability and Quality, Research and Evaluation Gaps | Handling long sequences of text data from legal documents with transformer models that have input length limitations (addressed via sliding window). Managing highly imbalanced datasets (addressed via data scraping). High computational load associated with training transformer models, especially with the sliding window approach generating multiple sub-sequences. Effectively incorporating additional meta-data features (like case importance or court branch) into text-centric transformer models. | LLM Context Window and Long Input Management, Data Quality, Processing, and Preparation, High Computational and Resource Demands, Domain-Specific Adaptation and Customization | Potential for overfitting due to the overlapping windows in the sliding window technique. Biases in algorithms were acknowledged as an area not focused on but are a general risk with AI in law. | Technical limitations of AI, Bias and discrimination |
| Unlocking the Potential of Large Language Models in Legal Discourse Challenges- Solutions- and Future Directions.pdf | IEEE_Xplore | Unlocking the Potential of Large Language Models in Legal Discourse: Challenges, Solutions, and Future Directions | This paper evaluates the performance of various state-of-the-art Large Language Models (LLMs) on Canadian tax law queries, identifying issues like hallucinations. It then proposes and experiments with fine-tuning smaller LLMs (Gemma and Mistral) using domain-specific legal texts and vocabulary enhancement as a potential solution, though initial fine-tuning results showed limitations. | LLM Evaluation, Canadian Tax Law Focus, Legal Question Answering, AI Hallucinations/Inaccuracy, Fine-tuning for Legal Domain, Vocabulary Enhancement, Limitations Identified | True | Idealistic | True | 1.0 | Neutral | Fine-tuning of LLMs (Gemma-2b and Mistral-7B-Instruct-v0.2) using semantic chunking of Canadian legal documents and domain-specific vocabulary updates. | Fine-tuning, Large Language Model, Semantic Chunking, Domain-Specific Vocabulary Adaptation, Legal Text Processing | Initial evaluation of six general LLMs (Gemini, Mistral Large, Gemma 7B, Falcon 180B, Llama2 70B, GPT-3.5) on 40 Canadian tax law questions rated by a tax expert. The fine-tuned Gemma and Mistral models were qualitatively evaluated with a sample legal question. | Expert Evaluation, Custom Dataset Evaluation, Quantitative Metrics, Qualitative Analysis, Comparative Analysis | For the initial evaluation of general LLMs, Gemini achieved the highest accuracy (77.5% correct answers on 40 tax law questions). The fine-tuned Gemma-2B model (using an unsupervised dataset) repeatedly generated the input question, while the fine-tuned Mistral-7B model provided a tax-related but incorrect answer to a sample question. | Moderate performance, Outperforms others, Technique has limited or mixed impact, Low performance | Inaccuracy and unreliability of LLMs (e.g., hallucinations, biases), lack of interpretability, the complexity of legal language and reasoning for AI models, and scarcity of high-quality, labeled legal data suitable for training effective access to justice tools. | AI Unreliability/Inaccuracy, Bias in AI/Data, Lack of AI Transparency/Explainability, AI Limitations in Legal Reasoning/Nuance, Complexity of Legal Language/Documents, Data Scarcity/Quality for AI | Development of domain-specific LLMs through fine-tuning with curated domain-specific datasets and vocabulary. Methodologies include semantic chunking for text preparation and iterative refinement based on expert feedback. Emphasis on creating instructional datasets for better fine-tuning. | AI Tool Development, Enhanced AI Capabilities, Data Curation and Management, Human Oversight and Collaboration | Democratizing access to legal advice, providing legal guidance to non-expert users, improving legal information retrieval and question answering. | Access to Legal Advice, Democratizing Law / Closing Justice Gap / Rule of Law, Access to Legal Information | Non-expert users, general citizens requiring legal information (e.g., on taxation), and individuals who struggle with navigating legal processes. | Laypeople, General public, Individuals lacking legal knowledge, Taxpayers | Canadian tax law; more broadly, the legal domain. | Tax Law, General Law | Canada | Canada | For fine-tuning: A dataset of 10,000 unlabeled Canadian legal documents (federal and provincial laws, statutes, regulations), processed using semantic chunking. Domain-specific legal terminology was also integrated. | Fine-tuning Dataset, Canadian Legal Data, Legal Domain Data, Legislation / Statutes / Regulations, Other Legal Documents, Undisclosed Data Source/Availability, Unstructured Text Data | Semantic chunking of legal documents, domain-specific vocabulary expansion, and fine-tuning of pre-trained language models (Gemma-2b, Mistral-7B-Instruct-v0.2) on an unlabeled legal corpus. | Data Segmentation, Vocabulary Expansion, Domain Adaptation, Model Fine-tuning, Unsupervised Learning | NaN | Not applicable | False | False | NaN | NaN | Scarcity of extensively labeled legal documents for supervised fine-tuning, significant computational resources (especially memory) required for fine-tuning LLMs, need for high-quality and representative training data (addressing bias, privacy, timeliness, scalability), and the need for more explainable and transparent AI models to ensure trustworthiness and mitigate bias. | Data Availability and Quality, Computational Resource and Cost Issues, Bias in AI, Security and Privacy of Data, Knowledge Recency and Updatability, Transparency and Explainability | Detecting and mitigating LLM hallucinations in legal contexts, adapting general LLMs to domain-specific nuances like legal terminology and reasoning, achieving satisfactory results when fine-tuning with unlabeled legal corpora (e.g., models repeating questions or providing incorrect/inaccurate answers), managing high computational costs, and curating comprehensive domain-specific vocabularies. | LLM Hallucination and Factual Errors, Domain-Specific Adaptation and Customization, LLM Reasoning Capabilities, Accuracy and Reliability of LLM Output, High Computational and Resource Demands, Data Quality, Processing, and Preparation | Dissemination of inaccurate legal information or advice (legal hallucinations), perpetuation of biases embedded in training data leading to unfair outcomes, security vulnerabilities in AI systems handling sensitive legal information, and the potential for AI systems to mislead or harm human interests if not properly developed and governed. | Inaccurate or misleading AI output, Bias and discrimination, Security vulnerabilities or malicious misuse, Data privacy and security breach, Consumer harm, Risk of misapplication or misuse |
| Fine-tuning a Large Language Model for the Indian Legal System.pdf | IEEE_Xplore | Fine-tuning a Large Language Model for the Indian Legal System | This paper details the development and fine-tuning of a Llama 3.1 8B large language model specifically for the Indian legal system, employing techniques such as LoRA, QLoRA, RAG, and pruning. The resulting AI-driven chat application aims to provide accurate legal information and assistance, showing improved performance and reduced hallucinations on benchmarks like HaluEval. | Legal Language Model Development, Fine-tuning Methodology (LoRA, QLoRA), Indian Law Focus, Retrieval Augmented Generation, Pruning, Chatbot Development, Accurate Legal Information Provision, Reduced Hallucinations, System Evaluation | True | Idealistic | True | 1.0 | Positive | A fine-tuned LLM (Llama 3.1 8B) for the Indian legal system, enhanced with Low-Rank Adaptation (LoRA), Quantized Low-Rank Adaptation (QLoRA), Retrieval Augmented Generation (RAG), and pruning, delivered via a chat application. | Fine-tuning, Large Language Model, Domain-Specific Model Adaptation, Parameter-Efficient Fine-tuning, Retrieval Augmented Generation (RAG), Model Pruning, Chatbot / Conversational AI | The system was evaluated using accuracy, precision, recall, F1-score, ROUGE scores for summarization, and the HaluEval benchmark for factual reliability and hallucination rates. Comparisons were made between base, pre-trained, fine-tuned (LoRA, QLoRA), and compressed model versions. | Benchmark Dataset Evaluation, Custom Dataset Evaluation, Quantitative Metrics, Comparative Analysis | The fine-tuned model showed substantial improvements: on HaluEval for Question Answering, the score increased from 49.6 (base) to 58.1. The hallucination rate decreased from 5.10% to 3.10% with fine-tuning. | Technique improves outcome, Moderate performance, Limitation: Hallucination or Factual inaccuracy | The exponential growth, volume, and complexity of legal documentation in intricate legal systems, and the reliance on extensive, time-consuming manual review and human judgment in traditional legal research. | Volume and Complexity of Legal Information, Resource Constraints, Judicial/Legal System Inefficiencies | Developing a specialized LLM tailored to the Indian legal system to simplify legal advisory services and decision-support, making legal knowledge more accessible. This involves fine-tuning on Indian legal data and using techniques like RAG for contextually accurate responses. | AI Tool Development, Enhanced AI Capabilities, Access to Legal Information and Advice, Data Curation and Management | Legal information retrieval, answering complex legal queries, legal advisory services, decision-support systems within the judiciary. | Access to Legal Information, Access to Legal Advice, Judicial System Modernization / Efficiency | Law students, legal practitioners, and individuals seeking legal assistance in India. | Law students, Legal professionals, Individuals with unmet legal needs, Population in India | Criminal law, civil law, constitutional law, corporate law, consumer law, real estate law. | Criminal Law, Civil Law, Constitutional Law, Corporate Law, Consumer Law, Real Estate Law | India | India | A diverse corpus of Indian legal texts from official government and court websites (Ministry of Law and Justice, Supreme Court, High Courts) including legal documents, statutes, case laws, and commentaries. This included approximately 4,000 question-answer pairs (CSV) and a JSON dataset of case file data. | Author-Created New Dataset, Indian Legal Data, Legal Domain Data, Legislation / Statutes / Regulations, Case Law / Judgments, Legal Scholarly Content / Textbooks, Other Legal Documents, Structured Data, Web Scraped Data, Publicly Available Data | Data collection and preprocessing, pre-training of the base model, fine-tuning using LoRA and QLoRA, Retrieval Augmented Generation (RAG) implementation for personalized queries, and model compression using structured pruning. | Data Collection, Data Preprocessing, Model Pre-training, Model Fine-tuning, Parameter-Efficient Fine-Tuning (PEFT), Retrieval Augmented Generation (RAG), Model Compression | The system was developed as a chat application with a Flask backend and HTML/CSS/JavaScript frontend. LM Studio was used for local model client setup during development. No broad public deployment strategy is detailed. | Web-based access, Internal deployment/prototype | False | False | NaN | NaN | Technical: Need for advanced RAG architectures, alternative parameter-efficient fine-tuning methods, dynamic pruning, knowledge distillation, multilingual support for regional Indian languages, specialized evaluation metrics for Indian legal tasks, and temporal awareness for legal updates. Societal: Further enhancing the accessibility, actionability, and impact of legal knowledge. | AI Scope and Functionality Limitations, Research and Evaluation Gaps, Multilingual and Low-Resource Language Gaps, Knowledge Recency and Updatability, Access, Equity, and Digital Divide, User Interface and Usability Gaps | Substantial computational and memory requirements of LLMs; inherent ambiguity and context-dependency of legal terminology; balancing model performance with resource efficiency; ensuring factual reliability and minimizing hallucinations in legal responses. | High Computational and Resource Demands, LLM Reasoning Capabilities, Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors | Generation of fabricated legal meanings (hallucinations) by the LLM, potentially leading to misinterpretations if accuracy is not sufficiently high. | Inaccurate or misleading AI output, Consumer harm |
| Iraqi Legal GPT.pdf | IEEE_Xplore | Iraqi Legal GPT | This paper proposes 'Iraqi Legal GPT,' an AI chatbot using the h2ogpt framework and Iraqi legal documents to provide accessible legal information within Iraqi jurisprudence, aiming to be locally deployable and overcome limitations of large models. The system demonstrates promising results with 80% accuracy and 1-minute response times, intending to enhance access to justice for individuals in Iraq. | Chatbot Development, Iraqi Law Focus, Accessible Legal Information, Locally Deployable AI, Access to Justice Enhancement, System Evaluation | True | Idealistic | True | 1.0 | Positive | A legal chatbot system named 'Iraqi Legal GPT' built using the open-source h2ogpt framework, trained on curated Iraqi legal documents, employing the 'instructor' embedding algorithm and Chroma db vector store for local deployment and offline use. | Chatbot / Conversational AI, Open Source AI Framework Usage, Domain-Specific Training Data, Embedding-based Methods, Vector Database, Local LLM Deployment, Offline AI System, Named Tool / Platform | The proposed system, Iraqi Legal GPT (using h2ogpt with llama2-7b-chat), was evaluated for accuracy and response time. This involved comparative testing against other LLMs (Mistral, Mixtral variants) and different embedding algorithms (instructor-large vs. others) on Iraqi legal document processing tasks. | Custom Dataset Evaluation, Quantitative Metrics, Comparative Analysis | The Iraqi Legal GPT system, specifically using the h2oai/h2ogpt-4096-llama2-7b-chat model, achieved an accuracy of 70-80% (reported as 80% in abstract) and a 1-minute response time. The 'hkulp/instructor-large' embedding algorithm demonstrated 98% accuracy in document conversion. | Moderate performance, High performance, Benefit identified | Lack of a comprehensive legal framework for free or reduced-cost legal aid in Iraq. Challenges in finding affordable and specialized lawyers, and understanding legal rights. Time-consuming and costly processes for existing legal aid where available, particularly for underserved communities. | Inadequate Legal Frameworks for Legal Aid, Limited Availability/Access to Legal Professionals/Expertise, High Cost of Legal Services, Public Lack of Legal Knowledge/Awareness, Legal Aid System Inefficiencies | Development of a locally deployable AI chatbot ('Iraqi Legal GPT') using curated local legal documents and an open-source LLM (h2ogpt) to provide free, accessible legal information and guidance, operable offline on standard computers. | AI Tool Development, Open Source Initiatives and Collaboration, Data Curation and Management, Access to Legal Information and Advice, User Interface and Accessibility Design | Access to legal information, Legal aid, Understanding legal rights, Navigating the legal system | Access to Legal Information, Legal Aid and Pro Bono Services, Legal Literacy and Public Legal Education | Citizens and non-citizens in Iraq (including permanent residents, migrants, asylum seekers, refugees, victims of human trafficking, foreign students, temporary visitors, and stateless persons) with limited economic resources or facing difficulties accessing legal services. | General public, Non-citizens, Population in Iraq, Migrants, Asylum seekers and refugees, Victims of human trafficking, Students, Stateless persons, Low-income individuals, Individuals facing access barriers | General Iraqi law | General Law | Iraq | Iraq | Publicly available, unstructured Iraqi legal documents (laws, constitution, etc.) collected from governmental and legal information websites such as Yasaii.info, Legislation.krd. These documents were curated and split into text chunks. | RAG System Knowledge Corpus, Publicly Available Data, Iraqi Legal Data, Legal Domain Data, Legislation / Statutes / Regulations, Other Legal Documents, Web Scraped Data, Unstructured Text Data, Expert-Annotated / Human-Curated / Human-Generated Data | The system was designed using a block diagram approach, involving data collection and curation, document splitting, embedding using the 'instructor' algorithm, storage in a Chroma db vector store, and integration with the h2ogpt LLM. Comparative analysis of different LLMs and embedding algorithms was conducted. | System Architecture Design, Data Collection, Data Curation, Data Segmentation, Embedding Model Application, Vector Database Implementation, LLM Integration, Comparative Analysis of Models | The system is designed for local deployment on a personal computer, capable of running without an internet connection. A website interface (GUI) was developed for user interaction. | Local deployment/Standalone application, Web-based access | False | False | NaN | NaN | Hardware limitations for running advanced LLM models locally. The need for larger volumes of legal data and improved support for Arabic/Kurdish languages in local chatbot generators. Ongoing need for more efficient algorithms. | Computational Resource and Cost Issues, Data Availability and Quality, Multilingual and Low-Resource Language Gaps, AI Accuracy and Reliability | Hardware resource constraints for running large models. Obtaining and processing sufficient legal data, including translation to English due to tool limitations. Selecting and integrating optimal LLMs, embedding algorithms, and vector stores for accuracy and speed. | High Computational and Resource Demands, Scarcity of High-Quality Legal Data, Data Quality, Processing, and Preparation, Multilingual and Low-Resource Language Support, Domain-Specific Adaptation and Customization, Accuracy and Reliability of LLM Output | Potential for inaccuracies in legal information provided if the model hallucinates or if the underlying data is incomplete/incorrect, a known issue with language models in legal contexts (e.g., ChatGPT generating false legal provisions). | Inaccurate or misleading AI output, Technical limitations of AI |
| Transforming Legal Workflows A Deep Dive into NLP Solutions for Legal Challenges.pdf | IEEE_Xplore | Transforming Legal Workflows: A Deep Dive into NLP Solutions for Legal Challenges | This paper proposes a novel framework using a modified BERT-based model for legal document summarization and a Doc2Vec approach for case similarity analysis. The system, evaluated on legal datasets, demonstrates its potential to streamline legal processes, enhance legal reasoning, and improve access to legal services. | Framework Proposal, BERT Application, Legal Document Summarization, Case Similarity Analysis, Streamlining Legal Processes, Legal Reasoning Enhancement, Access to Legal Services | True | Idealistic | True | 1.0 | Positive | A modified BERT-based model for legal document summarization and a Doc2Vec-based approach (using UMAP and HDBSCAN) for legal case similarity, with visualization using LeetTopic. | Transformer Models, Legal Document Summarization, Embedding-based Methods, Dimensionality Reduction, Clustering Algorithm, Legal Case Similarity, Visualization Tool | The summarization model was evaluated using precision, recall, F1 score, accuracy, and ROUGE metrics (ROUGE-1, ROUGE-2, ROUGE-L) on a split dataset (train, test, validation from BillSum and Australian legal cases). | Benchmark Dataset Evaluation, Custom Dataset Evaluation, Quantitative Metrics | The modified BERT summarization model achieved a validation accuracy of 0.7327 (training accuracy 0.7179, loss 0.5562). For summarization quality, it scored ROUGE-1: 0.79, ROUGE-2: 0.81, and ROUGE-L: 0.80. | Moderate performance, Technique improves outcome | Manual legal research is time-consuming, error-prone, and struggles with the volume and dynamic nature of legal information. Processing and clustering lengthy documents manually is inefficient, overworking legal practitioners. | Resource Constraints, Judicial/Legal System Inefficiencies, Volume and Complexity of Legal Information, Risk of Errors in Manual Processes | Employing NLP (modified BERT, LLMs, RAG) for legal document summarization, case similarity analysis, and other tasks to automate research, improve efficiency, and make legal information more accessible, thereby democratizing legal assistance. | AI Tool Development, Enhanced AI Capabilities, Document Automation, Legal Research and Analysis Tools, Cost Reduction and Efficiency, Access to Legal Information and Advice | Legal document summarization, legal case similarity analysis, improving access to legal services, democratizing legal assistance, enhancing legal reasoning. | LegalText Simplification / Plain Language, Improving Foundational AI Capabilities for Legal Applications, Democratizing Law / Closing Justice Gap / Rule of Law | Marginalized communities | Marginalized communities | General Law | General Law | United States, Canada, Australia | USA, Canada, Australia | Publicly available legal summaries from the BillSum dataset (US and Canadian legislation) and Australian legal cases (2006-2009) from the Federal Court of Australia sourced from AustLII via Kaggle. | Evaluation Dataset, Data From Existing Public NLP/Legal Datasets/Benchmarks, Publicly Available Data, US Legal Data, Canadian Legal Data, Australian Legal Data, Legal Domain Data, Legislation / Statutes / Regulations, Case Law / Judgments | Transfer learning with a modified BERT architecture (additional custom dense layers with ReLU activation and dropout layers) for summarization. For case similarity: Doc2Vec for text embeddings, UMAP for dimensionality reduction, and HDBSCAN for clustering. Standard NLP preprocessing techniques were applied. | Transfer Learning, Model Architecture Design, Deep Learning Model Development, Document Embedding, Dimensionality Reduction, Clustering Algorithms, Data Preprocessing | NaN | Not applicable | False | False | NaN | NaN | Need for comprehensive, context-aware NLP systems integrating various legal functions. Robustness and accuracy of LLMs for specific legal answers. Need for improved model design, broader applicability across diverse legal systems and languages, and integration of advanced AI techniques for better performance and security. | AI Scope and Functionality Limitations, Integration and Interoperability Challenges, AIAccuracy and Reliability, Multilingual and Jurisdictional Specificity Gaps, Security and Privacy of Data | Addressing the lack of comprehensive existing solutions for legal NLP tasks. For the conversational aspect of their work, insufficient context to optimize performance and user experience. General need for continued research, addressing limitations, and ethical considerations in legal AI. | Domain-Specific Adaptation and Customization, User Interface, Usability, and Accessibility, Research Methodology and Study Design Limitations, Ethical Considerations | Accuracy and reliability concerns with LLMs providing comprehensive legal answers tailored to specific inquiries. | Inaccurate or misleading AI output, Technical limitations of AI |
| AI Legal Assistant for IPC.pdf | IEEE_Xplore | AI Legal Assistant for IPC | This paper introduces an NLP-based chatbot, 'AILA', designed to improve access to legal information regarding the Indian Penal Code (IPC) using LLMs (mistral-7b-instruct) and RAG techniques. The system, featuring a Streamlit interface and evaluated with high accuracy, aims to simplify complex legal language for individuals and small businesses in India. | Chatbot Development, NLP Application, Access to Legal Information Enhancement, Indian Penal Code Focus, LLM Application, Retrieval Augmented Generation, Legal Text Simplification, System Evaluation | True | Idealistic | True | 1.0 | Positive | An NLP-based chatbot (AILA) using Retrieval-Augmented Generation (RAG). It employs FAISS for vector database management, the mistral-7b-instruct LLM for generation, and a Streamlit user interface, focusing on the Indian Penal Code. | Chatbot / Conversational AI, Natural Language Processing (NLP), Retrieval Augmented Generation (RAG), Vector Database, Large Language Model, User Interface Development, Domain-Specific Application, Named Tool / Platform | Evaluated on a custom test dataset of legal queries using accuracy, precision, recall, F1-score, AUC-ROC, and confusion matrix analysis. Performance was compared against individual models (BERT, GPT-3, RoBERTa). | Custom Dataset Evaluation, Quantitative Metrics, Comparative Analysis | AILA achieved 94% accuracy, 0.92 precision, 0.93 recall, 0.92 F1-score, and 0.97 AUC-ROC. It outperformed individual models (BERT, GPT-3, RoBERTa) on these metrics. | High performance, Outperforms others, Technique improves outcome | High complexity and density of legal information leading to public lack of awareness; prohibitive cost and inaccessibility of traditional legal consultation. | Complexity of Legal Information, Public Lack of Legal Knowledge/Awareness, High Cost of Legal Services, Limited Access to Legal Assistance | An NLP-based chatbot (AILA) that simplifies legal text, provides user-friendly access to legal information on the Indian Penal Code, and improves efficiency for individuals and small businesses. | AI Tool Development, Language Simplification and Multilingual Access, User Interface and Accessibility Design, Access to Legal Information and Advice, Cost Reduction and Efficiency | Access to legal information, understanding legal rights and obligations, legal self-help, legal awareness. | Access to Legal Information, Legal Literacy and Public Legal Education, Support for Self-Represented Litigants | General public, individuals, and small businesses in India. | General public, Small businesses, Population in India | Indian Penal Code (IPC) | Criminal Law | India | India | A legal corpus derived from 'official legal sources' including the Indian Penal Code (IPC), related statutes, and judicial interpretations. Fine-tuning data consisted of 'pairs of legal questions and corresponding answers extracted from legal documents and expert annotations.' | Fine-tuning Dataset, Indian Legal Data, Legal Domain Data, Legislation / Statutes / Regulations, Case Law / Judgments, Expert-Annotated / Human-Curated / Human-Generated Data, Structured Data, Publicly Available Data | System architecture design involving data preprocessing, NLP module, LLM integration (mistral-7b-instruct), RAG module (with FAISS), and UI development (Streamlit). Iterative refinement based on performance monitoring and user feedback is implied. | System Architecture Design, Data Preprocessing, NLP Module Development, LLM Integration, Retrieval Augmented Generation (RAG), User Interface Development, Iterative Design Process | The system is designed for deployment on a cloud platform for scalability and accessibility, involving cloud infrastructure setup and security measures. | Cloud platform deployment | False | False | NaN | NaN | Need for expanding the system’s knowledge base, improving NLP algorithm adaptability, and incorporating multilingual support. | Data Availability and Quality, Knowledge Recency and Updatability, AI Accuracy and Reliability, Multilingual and Low-Resource Language Gaps | Ensuring accuracy and contextual relevance of legal advice, efficient retrieval of pertinent legal information from a vast legal corpus, interpreting complex legal language, creating an engaging and user-friendly interface, and maintaining an up-to-date legal knowledge base. | Accuracy and Reliability of LLM Output, LLM Reasoning Capabilities, User Interface, Usability, and Accessibility, Outdated or Limited LLM Knowledge Base | NaN | NaN |
| Generative Artificial Intelligence in Legal Drafting.pdf | IEEE_Xplore | Generative Artificial Intelligence in Legal Drafting | This paper introduces "Lexi," a generative AI tool designed to simplify legal document drafting by translating complex legal jargon into understandable language. Lexi aims to enhance accessibility and efficiency in legal documentation for both legal professionals and the general public, particularly for individuals and small businesses. | Generative AI Tool Development, Legal Document Drafting Simplification, Legal Jargon Translation, Accessibility Enhancement, Efficiency Improvement | True | Idealistic | True | 1.0 | Positive | Lexi, an AI tool for legal document drafting and jargon simplification, based on a fine-tuned Llama 2 7B model with a chat interface. | AI Legal Tool, Legal Document Generation / Automation, Legal Text Simplification, Fine-tuning, Large Language Model, Chatbot / Conversational AI, Named Tool / Platform | Lexi (fine-tuned Llama 2 7B) was compared to a base Llama 2 7b chat model and GPT-3.5. Evaluation metrics included domain specificity, legal jargon level, token count, and an ease of understanding score. Training and validation loss curves for the fine-tuning process were also presented. | Quantitative Metrics, Qualitative Analysis, Comparative Analysis | The fine-tuned model (Lexi) demonstrated domain-specific capabilities, produced text with 'basic' legal jargon, an average token count of approximately 512, and achieved the highest ease of understanding score of 9.5 out of 10. | High performance, Technique improves outcome, Benefit identified | The main hurdles identified are the complexity, time-intensiveness, and high cost of traditional legal document drafting. Additionally, the use of intricate legal jargon makes documents inaccessible and difficult for non-specialists to understand, creating a barrier to legal knowledge. | Complexity of Legal Tasks for Laypersons, Resource Constraints, High Cost of Legal Services, Complexity of Legal Language/Documents, Difficulty Accessing/Interpreting Legal Information | The paper proposes Lexi, an AI-powered tool, to streamline the legal drafting process and simplify complex legal terminology into understandable language. This is intended to democratize legal paperwork and enhance accessibility through a user-friendly interface, especially for individuals and small businesses. | AI Tool Development, Document Automation, Language Simplification and Multilingual Access, User Interface and Accessibility Design, Access to Legal Information and Advice | Legal document drafting, simplification of legal language/jargon, improving access to legal information and services for laypersons. | Legal Document Creation / Automation, LegalText Simplification / Plain Language, Access to Legal Information | Individuals and small businesses lacking legal expertise or resources, the general public, non-specialists, and non-lawyers. | Individuals lacking legal knowledge, Small businesses, Low-income individuals, General public, Laypeople | Rental law (specifically Indian rental rules mentioned as an example), with plans for expansion to a broader range of legal areas. | Landlord-Tenant Law, Housing Law | India (explicitly mentioned for rental rule examples), though the general problem and tool are framed more broadly. | India, International | An extensive collection of current legal papers, formatted into JSON objects with 'inputs' and 'responses' keys for fine-tuning the Llama 2 7B model. The specific source or public/proprietary nature of the dataset is not detailed. | Fine-tuning Dataset, Legal Domain Data, Legal Scholarly Content / Textbooks, Instruction-Tuning Formatted Data, Structured Data, Undisclosed Data Source/Availability | The system architecture includes user interaction, chat interface, iterative questioning, data handling, and AI/ML components. Methodologies include fine-tuning a pre-trained LLM (Llama 2 7B), prompt engineering, and UI/UX design principles for the web interface. | System Architecture Design, User Interface Development, Conversational Design, Iterative Questioning, Model Fine-tuning, Prompt Engineering, User Experience Focus | Lexi is deployed as a web application with user authentication (Firebase Auth), chat data storage (MongoDB), and model hosting on the Replicate platform. | Web-based access, Cloud platform deployment | False | False | NaN | NaN | The paper mentions the need to expand the AI's knowledge beyond rental rules, enhance usability (e.g., document export features), and crucially, ensure the preservation of legal accuracy and significance when simplifying language. | Data Availability and Quality, AI Scope and Functionality Limitations, User Interface and Usability Gaps, AI Accuracy and Reliability, AI Legal Reasoning Limitations | Challenges included acquiring and formatting a large, domain-specific legal dataset for fine-tuning, meeting hardware requirements for LLMs, effectively fine-tuning the model for legal language, and designing a user-friendly interface for complex legal drafting tasks. | Scarcity of High-Quality Legal Data, Data Quality, Processing, and Preparation, High Computational and Resource Demands, Domain-Specific Adaptation and Customization, User Interface, Usability, and Accessibility | A key risk highlighted is the potential loss of accuracy and significance of legal content if simplification is not handled carefully, ensuring the integrity of legal information is paramount. | Inaccurate or misleading AI output, Undermining legal process or principles |
| An Analysis on Integrating Advanced Conversational AI in Legal Summarization and Information Retrieval.pdf | IEEE_Xplore | An Analysis on Integrating Advanced \nConversational AI in Legal Summarization and \nInformation Retrieval | This paper introduces LawGPT, a conversational AI specialized for the Indian Penal Code, which utilizes a Retrieval-Augmented Generation (RAG) architecture for accurate legal summarization and information retrieval. The study affirms LawGPT's efficacy through validation, aiming to democratize access to legal knowledge for both professionals and laypersons. | Conversational AI Development, Indian Penal Code Focus, Retrieval Augmented Generation, Legal Summarization, Legal Information Retrieval, System Evaluation, Democratization of Legal Knowledge | True | Idealistic | True | 1.0 | Positive | LawGPT, a conversational AI chatbot using Retrieval-Augmented Generation (RAG) architecture, Dense Passage Retriever (DPR), and BART architecture for generation, specialized for the Indian Penal Code. | Chatbot / Conversational AI, Retrieval Augmented Generation (RAG), Dense Passage Retrieval (DPR), Transformer Models, Domain-Specific Application, Named Tool / Platform | Validation against human-generated responses using metrics like ROUGE score. ROUGE F1 scores for LLAMA 2, MISTRAL, and PHI2 were also reported for summarization context. | Quantitative Metrics, Comparative Analysis | LawGPT's efficacy and accuracy were affirmed through validation against human-generated responses, demonstrating accurate retrieval and summarization of legal information. For summarization context, ROUGE F1 scores for other models were: LLAMA 2 (0.48), MISTRAL (0.46), PHI2 (0.40). | High performance, Comparable to others, Low performance | Limited effectiveness of general-purpose AI in understanding complex legal terminology and navigating intricate legal frameworks, hindering access to legal information. | AI Limitations in Legal Reasoning/Nuance, Complexity of Legal Language/Documents, Difficulty Accessing/Interpreting Legal Information | Development of specialized conversational AI solutions like LawGPT, trained on specific legal corpora (e.g., Indian Penal Code) and employing advanced AI architectures (e.g., RAG), to provide tailored, efficient, and accurate access to legal knowledge. | AI Tool Development, Enhanced AI Capabilities, Data Curation and Management, Access to Legal Information and Advice, Cost Reduction and Efficiency | Access to legal information, legal research, legal text summarization, interpretation of legal statutes (Indian Penal Code). | Access to Legal Information, LegalResearch Support, LegalText Simplification / Plain Language | Laypersons and legal professionals. | Laypeople, Legal professionals | Criminal Law (specifically Indian Penal Code). | Criminal Law | India | India | Indian Penal Code (IPC) data and additional data from the OpenAI API. The nature of the IPC data (e.g., public, proprietary) is not specified beyond being the text of the code. | Fine-tuning Dataset, Indian Legal Data, Legal Domain Data, Legislation / Statutes / Regulations, Undisclosed Data Source/Availability, Synthetic Data | Integration of Retrieval-Augmented Generation (RAG) architecture, Dense Passage Retriever (DPR) for retrieval, BART model for generation, Streamlit for user interface development, LangChain for text processing, and the TogetherAI API for the Legal Language Model (LLM). | Retrieval Augmented Generation (RAG), Information Retrieval Techniques, Generative Model Application, User Interface Development, Third-party Library Utilization, API-based Development | NaN | Not applicable | False | False | NaN | NaN | NaN | NaN | Developing a system capable of accurately interpreting complex legal terminology, performing efficient and relevant information retrieval from legal texts (Indian Penal Code), and generating contextually appropriate and accurate legal responses. | LLM Reasoning Capabilities, Accuracy and Reliability of LLM Output | The paper's related works section cites general risks associated with AI in law, such as biases, ethical considerations, lack of transparency, accountability, and fairness, as well as potential negative impacts on justice, democratic governance, legal responsibility, and liability. | Bias and discrimination, Ethical concerns, Lack of transparency, accountability, and redress, Undermining legal process or principles, Undermining democratic processes |
| LAWBOTS Utilization of AI Chatbots for Legal Advising in the Philippines.pdf | IEEE_Xplore | LAWBOTS : Utilization of AI Chatbots for Legal \nAdvising in the Philippines | This paper explores the potential use of AI chatbots (Lawbots) for legal advising in the Philippines, examining existing chatbots and public perception through a survey. The study analyzes Filipinos' views on Lawbots' benefits, challenges, and impact, aiming to inform their acceptance and implementation. | AI Chatbots for Legal Advising, Philippine Focus, Public Perception Study, Benefit Identification, Challenge Identification, Implementation Considerations | True | Idealistic | True | 3.0 | Neutral | AI Chatbots for legal advising (Lawbots) | Chatbot / Conversational AI, Legal Advisory System | A survey of 60 Filipino respondents was conducted via Google Forms, using multiple choice, Likert scale, and linear scale questions to gather perceptions on familiarity with AI, awareness of legal chatbots, and views on the benefits and challenges of Lawbots. | User Study or Survey | Survey (N=60) indicated nuanced public perception: while 88.3% had used chatbots, 60% were unaware of their use for legal advising. On implementing Lawbots, 31.7% were neutral, 28.3% agreed, and 26.7% disagreed. Key perceived benefits included 24/7 availability and efficiency; key perceived challenges were limited scope/inadequacy of advice and lack of personalization. | Descriptive or Conceptual finding, Benefit identified, Limitation: Operational or Technical | General A2J obstacles in the Philippines: insufficient funds, distance/traffic issues, prolonged cases, lack of contact with lawyers. For Lawbots contributing to A2J: lack of public trust and acceptance, concerns about advice adequacy and personalization, ethical considerations, and the complexity of legal issues. | Resource Constraints, Geographical Disparities in Legal Access, Judicial/Legal System Inefficiencies, Lack of Trust in AI/Automated Systems, AI Unreliability/Inaccuracy, Ethical Concerns with AI in Law, Complexity of Legal System/Procedures | Improving access through AI chatbots like Tisya Hustisya. For broader Lawbot adoption: continuous system development for privacy and regulation, improved legal frameworks for Lawbots, standardization, and public education to build trust and address concerns about advice quality. | AI Tool Development, Access to Legal Information and Advice, Data Privacy and Security, Policy and Regulatory Reform, Regulation, Ethics, and Governance, Education and AI Literacy | Access to legal aid, legal information, human rights, domestic violence, labor issues, general legal advising. | Legal Aid and Pro Bono Services, Access to Legal Information, Protection of Rights, Access to Legal Advice | Marginalized communities in the Philippines, general public, victims of specific issues (e.g., sexual violence, human rights violations). | Marginalized communities, Population in Philippines, General public, Survivors of sexual assault, Victims of human rights violations | General legal advising, human rights law, labor law, criminal law (related to sexual violence), immigration law (in discussed examples). | General Legal Practice, Human Rights Law, Employment Law, Criminal Law, Immigration Law | Philippines (primary focus); Canada (mentioned for an example chatbot). | Philippines, Canada | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Need for clearer public communication and education about Lawbots (benefits, challenges, implications). Gaps in public trust and acceptance. Technical gaps include AI's understanding of human emotions and ensuring completeness of responses. | Public Understanding, Trust, and Adoption, AI Legal Reasoning Limitations, AI Accuracy and Reliability | Limited scope/inadequacy of advice, lack of personalization, handling legal complexity, addressing ethical and moral considerations, ensuring data privacy and security, adapting to dynamic and evolving laws, fostering user trust and acceptance, overcoming communication challenges, potential for bias, integrating technology into the legal sector, and establishing clear legal regulations and responsibility. | Accuracy and Reliability of LLM Output, Domain-Specific Adaptation and Customization, LLM Reasoning Capabilities, Ethical Considerations, Data Privacy, Security, and Confidentiality, Outdated or Limited LLM Knowledge Base, User Adoption, Trust, and Acceptance, User Interface, Usability, and Accessibility, Bias in AI Systems and Data, Integration with Existing Systems and Workflows, Regulatory Uncertainty and Compliance, Accountability and Liability for AI Errors | Concerns regarding data sharing, privacy, and security. Potential for incomplete responses and unauthorized practice of law. Societal threats like manipulation of beliefs and emotions. Lack of legal responsibility for erroneous advice. Potential for algorithmic bias. | Data privacy and security breach, Inaccurate or misleading AI output, Unauthorized practice of law, Security vulnerabilities or malicious misuse, Lack of transparency, accountability, and redress, Bias and discrimination |
| LDAA Legal Documents Automation and Assistance.pdf | IEEE_Xplore | LDAA: Legal Documents Automation and Assistance | This paper proposes "Legal Documents Automation and Assistance (LDAA)," a system utilizing fine-tuned open-source Large Language Models (like Llama3 or Gemma) to automate legal document creation and provide assistance, specifically targeting illiterate and underprivileged rural populations in India. LDAA aims to offer a user-friendly, efficient solution by integrating AI with legal expertise for personalized guidance, document generation via LaTeX, and an AI chatbot for legal queries. | System Development, Fine-tuned LLM Application, Open Source LLM, Legal Document Automation, Legal Assistance Provision, India Focus, Rural Population Assistance, User-Friendly Interface, Personalized Guidance, Chatbot Development | True | Idealistic | True | 1.0 | Positive | Legal Documents Automation and Assistance (LDAA) system: fine-tuned LLMs (Llama3/Gemma), Retrieval Augmented Generation (RAG), LaTeX for document generation, vector databases (Chroma, FAISS) with TF-IDF for similarity search, and a chatbot for legal assistance. | Software / Platform Development, Fine-tuning, Large Language Model, Retrieval Augmented Generation (RAG), Document Formatting Tool, Vector Database, Information Retrieval / Search, Chatbot / Conversational AI, Legal Document Generation / Automation, Legal AI Assistant, Named Tool / Platform | LLM performance evaluated using BLEU (0.95), Perplexity (1.5), and Word Error Rate (0.01). A demonstrative use case of revising a 'deed of hypothecation' document based on user input is presented. | Quantitative Metrics, Demonstration or Illustrative Examples | The LLM component achieved a BLEU score of 0.95, a Perplexity of 1.5, and a Word Error Rate of 0.01. The system demonstrated successful document modification based on user prompts in a hypothecation deed example. | High performance | Complexity and high cost of traditional legal documentation, limited access to legal experts for many, rudimentary nature and lack of personalization in existing automated systems, and misalignment of current tools with specific legal practice needs. | Complexity of Legal Language/Documents, High Cost of Legal Services, Limited Availability/Access to Legal Professionals/Expertise, Lack of Personalization in Automated Systems, Misalignment of Research/Innovation with Practical Needs | Automating legal document creation, review, and assistance using fine-tuned LLMs and a user-friendly interface. Providing personalized guidance, an AI-powered chatbot for legal queries, ensuring document security, and enabling customization to user needs. | AI Tool Development, Document Automation, User Interface and Accessibility Design, Access to Legal Information and Advice, Data Privacy and Security | Automation of legal document creation, legal assistance via chatbot, simplifying legal processes for accessibility. | Legal Document Creation / Automation, Access to Legal Advice, LegalText Simplification / Plain Language | Illiterate, underprivileged rural people in India. | Individuals with low literacy, Low-income individuals, Rural populations, Population in India | General legal document drafting (e.g., contracts, agreements, legal notifications, deeds). | Document Drafting, Contract Law, Property Law | India | India | Fine-tuning of pre-trained open-source LLMs (Llama3 or Gemma series) using legal documents and articles. A proprietary knowledge base of codified laws and legal principles developed by the team for the chatbot. | Fine-tuning Dataset, Legal Domain Data, Other Legal Documents, Legal Scholarly Content / Textbooks, RAG System Knowledge Corpus, Proprietary Data, Legislation / Statutes / Regulations | System architecture involving a user interface, LaTeX for document compilation, fine-tuned LLMs for text generation and understanding, Retrieval Augmented Generation (RAG) for document modification, vector databases (Chroma, FAISS) with TF-IDF for semantic search, and an iterative user feedback loop for document refinement. | System Architecture Design, User Interface Development, Document Compilation Automation, Model Fine-tuning, LLM-based Content Generation, Retrieval Augmented Generation (RAG), Vector Database Implementation, Information Retrieval Techniques, User Feedback Integration, Iterative Design Process | NaN | Not applicable | False | False | NaN | NaN | Need for broader legal document type coverage, development of more advanced Large Legal Language Models (LLLMs), mobile application accessibility, voice and multilingual support, integration with blockchain for secure document management, and incorporation of e-signature approvals and legal document verification features. | AI Scope and Functionality Limitations, User Interface and Usability Gaps, Multilingual and Low-Resource Language Gaps, Integration and Interoperability Challenges, Security and Privacy of Data | Overcoming the labor-intensive, error-prone, and expensive nature of manual legal drafting. Addressing the limitations (rudimentary, lack of sophistication and personalization) of existing automated legal tools. Ensuring security, accuracy, and adaptability of the automated system to diverse legal requirements and user needs. | Financial Cost and Resource Constraints, Accuracy and Reliability of LLM Output, Domain-Specific Adaptation and Customization, Data Privacy, Security, and Confidentiality | NaN | NaN |
| Generative vs Intent-based Chatbot for Judicial Advice.pdf | IEEE_Xplore | Generative vs Intent-based Chatbot for Judicial Advice | This paper presents and compares two AI chatbot approaches, a generative model using OpenAI API and an intent-based model using Google's Dialogflow, designed to provide judicial advice on Indian laws. The generative chatbot demonstrated higher accuracy and more contextually rich responses, while the intent-based chatbot excelled in precision for predefined queries. | Comparative AI Chatbot Approaches, Generative AI Chatbot, Intent-Based Chatbot, Judicial Advice Provision, Indian Law Focus, System Evaluation | True | Idealistic | True | 1.0 | Positive | Comparative development and evaluation of a generative chatbot (using OpenAI API, GPT-3.5 turbo, fine-tuned on custom Indian legal conversations) and an intent-based chatbot (using Google's Dialogflow with custom intents for Indian law). | Chatbot / Conversational AI, Generative AI, Large Language Model, Fine-tuning, Intent-based Chatbot, Comparative Analysis, AI System Evaluation, Domain-Specific Application | Both chatbots were tested against 100 test conversations. Performance was measured by calculating true positives, true negatives, false positives, and false negatives, from which accuracy, precision, recall, and F1-score were derived. Qualitative comparison of response nature, quality, handling changing scenarios, data requirements, and user experience was also conducted. | Custom Dataset Evaluation, Quantitative Metrics, Qualitative Analysis, Comparative Analysis | The generative chatbot achieved an accuracy of 96.00%, precision of 96.67%, recall of 98.86%, and F1-score of 97.75%. The intent-based chatbot achieved an accuracy of 80.00%, precision of 90.47%, recall of 97.43%, and F1-score of 93.82%. | High performance | Traditional legal advice is often lengthy and expensive. Key challenges in AI for legal advice include ensuring legal accuracy and reliability of responses, handling ambiguity and uncertainty in legal queries, and difficulties in obtaining diverse and extensive datasets due to privacy and legal restrictions. | High Cost of Legal Services, AI Unreliability/Inaccuracy, AI Limitations in Legal Reasoning/Nuance, Difficulty in AI-Human Interaction, Data Scarcity/Quality for AI, Data Privacy Concerns with AI | The paper proposes the development and deployment of AI-powered chatbots (both generative and intent-based) to provide accessible, immediate, and 24/7 judicial advice on Indian legal matters, thereby addressing the cost and time barriers of traditional legal consultations. | AI Tool Development, Access to Legal Information and Advice, Cost Reduction and Efficiency, Judicial System Enhancement | Providing judicial advice, guidance on legal issues, procedures, and relevant laws. | Access to Legal Advice, Judicial System Modernization / Efficiency | Indians seeking judicial advice, particularly those with limited knowledge of Indian civil and criminal laws. | Population in India, Individuals with unmet legal needs, Individuals lacking legal knowledge | Indian civil and criminal laws. | Civil Law, Criminal Law, General Law | India | India | Generative chatbot: A custom-made dataset of 100 conversations (100-150 words each), simulating user queries and lawyer-like responses on Indian civil and criminal law, informed by the National Judicial Data Grid, used to fine-tune GPT-3.5 turbo. Intent-based chatbot: 34 intents (abstract mentions 36, methodology details 34 created plus default ones) with training phrases and predefined responses based on Indian civil and criminal laws, developed within Google's Dialogflow. | Fine-tuning Dataset, Author-Created New Dataset, Indian Legal Data, Legal Domain Data, Synthetic Data, Legal Q&A / Forum / User Query Data, Expert-Annotated / Human-Curated / Human-Generated Data, Structured Data, Rule-Based System (No Training Data) | Generative chatbot: Developed using Python, OpenAI API (GPT-3.5 turbo model), 'llama-index' and 'langchain' packages for indexing and interaction. Fine-tuning GPT-3.5 turbo on the custom legal conversation dataset. User interface built with Streamlit. \nIntent-based chatbot: Developed using Google's Dialogflow. Conversational flow designed using intents, entities, and follow-up intents. Training phrases and responses created for each intent. Support for English and Hindi, and text-to-speech functionality. | Chatbot Development, API-based Development, Third-party Library Utilization, Model Fine-tuning, Dataset Creation, User Interface Development, Intent-based Design, Conversational Design, Multilingual Support | Generative chatbot: Deployed as a Streamlit application made accessible to users via a public URL using Ngrok. \nIntent-based chatbot: Integrated into a custom website (built with HTML, CSS, JavaScript) using Dialogflow's Web Demo (for English, with text-to-speech) and Dialogflow Messenger (for Hindi). | Web-based access, Freely accessible tool/service, Integration into existing system/platform | True | False | The generative chatbot was deployed via Ngrok to a public URL. The intent-based chatbot was integrated into a website using Dialogflow's Web Demo and Messenger. | Publicly accessible online tool or platform | Ensuring legal accuracy and reliability of chatbot responses, especially for generative models. Improving the ability of chatbots to handle ambiguous and uncertain legal queries. Overcoming challenges in obtaining diverse and extensive legal datasets due to privacy and legal restrictions. | AI Accuracy and Reliability, AI Legal Reasoning Limitations, Data Availability and Quality, Security and Privacy of Data | Generative chatbot: Some responses required post-processing to improve clarity, despite being contextually rich and fluent. \nIntent-based chatbot: Difficulty handling user input outside predefined categories; initial poor performance necessitated detailed training phrases, meticulous entity definition, and a sufficient number of intents. | Accuracy and Reliability of LLM Output, Data Quality, Processing, and Preparation, Domain-Specific Adaptation and Customization | Generative AI chatbot responses can sometimes be inaccurate or provide partial guidance due to being derived from patterns in data. Validating the accuracy of legal information generated by AI is challenging, especially given the complexity of legal matters. | Inaccurate or misleading AI output, Technical limitations of AI |
| Artificial_intelligence_AI_or_augmented_intelligen.pdf | Scopus | Artificial intelligence (AI) or augmented intelligence? \nHow big data and AI are transforming healthcare: \nChallenges and opportunities | This paper discusses how big data and AI are transforming healthcare, highlighting both innovative opportunities and significant ethical, legal, and social challenges. It emphasizes the critical need for robust governance frameworks, particularly in low- and middle-income countries, to address issues like the digital divide, data bias, and potential exacerbation of health inequities. | Big Data and AI in Healthcare, Opportunity Identification, Ethical Challenges, Legal Challenges, Social Challenges, Governance Frameworks for AI, LMICs Focus, Digital Divide, Data Bias, Health Inequities | True | Idealistic | True | 3.0 | Neutral | NaN | NaN | NaN | Not Applicable | NaN | NaN | Digital divide; exacerbation of health inequities; data and algorithmic bias; low data literacy in LMICs; commercial exploitation of data from LMICs; lack of robust, context-specific governance and legislation in LMICs. | Digital Divide, Risk of AI Exacerbating Inequality, Bias in AI/Data, Lack of AI Literacy, Ethical Concerns with Data Collection, Inadequate Legal Frameworks for AI | Developing context-specific ethical and legal frameworks for AI in LMICs; ensuring transparency, accountability, and human oversight; improving data literacy; promoting equitable benefit-sharing and sustainable AI practices; and adopting a hybrid human-AI approach to healthcare. | Regulation, Ethics, and Governance, Conceptual Frameworks, Transparency and Explainability in AI, Human Oversight and Collaboration, Education and AI Literacy, Policy and Regulatory Reform | Health equity; digital divide in healthcare; ethical AI governance in LMICs; data privacy and security; algorithmic bias in medicine; regulation of AI in healthcare. | NaN | Populations in Low- and Middle-Income Countries (LMICs); resource-depleted settings; historically underrepresented groups in medical data (e.g., women, children, ethnic minorities, people with disabilities). | Populations in developing countries, Underrepresented groups, Women, Children, Minority groups, People with disabilities | Medical ethics and law; data protection and privacy law; AI-specific legislation and regulation; liability and medical malpractice law; constitutional rights; consumer protection law; intellectual property law. | Health Law, Legal Ethics, Data Privacy Law, AI Regulation, Tort Law, Medical Malpractice Law, Constitutional Law, Consumer Law, Intellectual Property Law | South Africa; International (with specific mentions of WHO, EU, USA, China); Low- and Middle-Income Countries (LMICs) generally. | South Africa, International, EU, USA, China, LMICs | Discusses LLMs trained on massive internet texts and medical AI using varied datasets (EHRs, images, mobile data); highlights concerns over use of identifiable patient data and inherent biases in historical medical data. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, Non-Legal Domain Specific Data, Health Data, Image Data, Data Bias Concerns Noted, Proprietary Data | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Absence of AI-specific and context-relevant governance, ethical guidelines, and legislation in many LMICs (including South Africa); lack of harmonisation in international AI regulations; unaddressed ethical and technical debt in rapid AI deployment. | Regulatory and Governance Gaps, Ethical Framework Deficiencies | NaN | NaN | Propagation of inaccurate/hallucinated information; amplification of societal biases leading to discriminatory outcomes and health disparities; erosion of clinical skills; severe privacy violations and data misuse; psychosocial harm from human-like AI; exploitative data commercialisation disadvantaging LMICs; significant environmental impact; and complex medicolegal liability. | Inaccurate or misleading AI output, Bias and discrimination, Deskilling or erosion of human skills, Data privacy and security breach, Dehumanization of legal process, Poor user experience, Negative economic impact, Exacerbation of inequality or two-tiered system, Environmental impact, Lack of transparency, accountability, and redress |
| Clopton-Huq-76-Stan.-L.-Rev.-893.pdf | Scopus | The Necessary and Proper Stewardship of Judicial Data | This paper argues federal judicial data is a vital, underused public asset that Congress should regulate for improved collection, management, and accessibility to advance public good and access to justice, countering its current imperfect availability and potential for private monopolization. It offers a descriptive analysis of current data practices, a doctrinal examination of regulatory power, and a normative vision for reform, including the use of LLMs. | Federal Judicial Data Management, Regulation of Legal Data, Public Access to Legal Data, Access to Justice Enhancement, LLM Application for Data Analysis, US Focus | True | Idealistic | True | 3.0 | Positive | NaN | NaN | NaN | Not Applicable | NaN | NaN | Imperfect availability and high cost of judicial data (e.g., PACER fees, clunky interface); significant data loss and inconsistency in collection (e.g., "dark data", lack of standardization); monopolization of data by commercial firms for private profit; lack of comprehensive congressional regulation and some judicial resistance to open data; information asymmetry favoring well-resourced litigants. | Limited Access to Legal Data for Research, High Cost of Accessing Legal Information, Data Scarcity/Quality for AI, Concentration of Power in Tech Companies, Inadequate Legal Frameworks for Data Access, Information Asymmetry | Enact congressional legislation to treat judicial data as a public asset, ensuring its systematic production and broad public availability with narrow exceptions; improve data accuracy, consistency, and searchability through standardization (e.g., for NOS codes) and better capture methods, possibly involving court staff or public-regarding privatization; reform public disclosure by increasing transparency, reducing access barriers like fees (e.g., "Free PACER"), and improving data formats; leverage technologies like LLMs for public good analyses. | Policy and Regulatory Reform, Data Curation and Management, Open Source Initiatives and Collaboration, Transparency and Explainability in AI, Judicial System Enhancement | Access to court records and dockets; Improving judicial processes (e.g., IFP status, case management, sentencing); Reducing information asymmetry for litigants; Supporting legal research and policy-making for judicial reform; Enhancing judicial accountability and transparency. | Access to Legal Information, Judicial System Modernization / Efficiency, LegalResearch Support | Litigants with limited resources (e.g., pro se, in forma pauperis); the general public; academics and researchers; legal services providers (e.g., public defenders). | Litigants, Low-income individuals, Self-represented litigants, General public, Academics, Researchers, Legal aid professionals | Civil Procedure, Criminal Procedure, Constitutional Law, Administrative Law, and general federal litigation. | Civil Procedure, Criminal Procedure, Constitutional Law, Administrative Law, Federal Law, Litigation | United States (federal judiciary) | USA | NaN | Not Applicable | NaN | NaN | NaN | Not applicable | False | False | NaN | NaN | Technical gaps in data capture (accuracy, consistency, searchability), data formats, and public access interfaces. Societal/legal gaps include the lack of a comprehensive legislative framework for judicial data, judicial resistance to open data, and the need to balance transparency with privacy and judicial integrity. The full potential of LLMs for analyzing judicial data remains unmapped. | Data Availability and Quality, User Interface and Usability Gaps, Regulatory and Governance Gaps, Human Oversight and Professional Adaptation, Transparency and Explainability, Security and Privacy of Data, Research and Evaluation Gaps | NaN | NaN | Private monopolization of public data for profit; exacerbation of inequality due to costly access systems; misinterpretation of disclosed data leading to unwarranted criticism or distorted judicial behavior; privacy violations from improper handling of sensitive information; compromising essential judicial deliberations or safety; incentivizing judges to "teach to the test" at the expense of accuracy; potential for errors in LLM-generated analyses of judicial data. | Negative economic impact, Exacerbation of inequality or two-tiered system, Risk of misapplication or misuse, Erosion of trust in legal system or AI, Data privacy and security breach, Undermining legal process or principles, Inaccurate or misleading AI output |
| s10506-023-09367-6 (1).pdf | Scopus | Bringing legal knowledge to the public by constructing a legal question bank using large‑scale pre‑trained language model | This paper presents a three-step approach to make legal information more accessible to laypersons by improving navigability and comprehensibility. It focuses on using large language models (GPT-3) with novel prompting strategies to construct a Legal Question Bank (LQB) from simplified legal texts, and a recommender system (CRec) to guide users to relevant information. | Methodology Proposal, Legal Information Access for Laypeople, LLM Application, Prompting Strategies, Legal Question Bank Creation, Recommender System for Legal Info | True | Idealistic | True | 1.0 | Positive | A three-step approach: 1) CLIC-pages (plain language legal summaries), 2) a Legal Question Bank (LQB) constructed using GPT-3 with a 'Hybrid' partitioning prompting strategy, and 3) a CLIC Recommender (CRec) to match user queries to the LQB. | Multi-step System, Plain Language Summaries, Dataset Creation / Curation, Large Language Model, Prompt Engineering, Recommender System, Legal Question Answering | The LQB generation method was evaluated by comparing GPT-3 (using three prompting/partitioning strategies: section-based, paragraph-based, Hybrid) generated questions (MGQs) with human-composed questions (HCQs) for 100 CLIC-pages. Metrics included quantity, precision (verified by legal experts), coverage, and diversity. | Custom Dataset Evaluation, Expert Evaluation, Quantitative Metrics, Comparative Analysis | The 'Hybrid' GPT-3 partitioning strategy yielded the best MGQs: 3,400 correct questions (vs. 2,686 HCQs), 68% precision, 93% coverage, greater diversity, and generation of 'augmenting questions' for content improvement. | High performance, Moderate performance, Technique improves outcome, Outperforms others | The primary obstacle is the 'legal knowledge gap' for the general public, stemming from difficulties in: 1) Navigability: finding relevant legal rules for their situation. 2) Comprehensibility: understanding technical legal language and concepts. | Public Lack of Legal Knowledge/Awareness, Difficulty Accessing/Interpreting Legal Information, Complexity of Legal Language/Documents | A three-step approach: 1) Creating 'CLIC-pages' with legal information in layperson's terms to enhance comprehensibility. 2) Constructing a 'Legal Question Bank' (LQB) using GPT-3 to provide model questions, improving navigability and comprehensibility. 3) Designing an AI-powered 'CLIC Recommender' (CRec) to guide users from their problem descriptions to relevant LQB questions and CLIC-pages, further aiding navigability. | AI Tool Development, Access to Legal Information and Advice, Language Simplification and Multilingual Access, Legal Knowledge Representation and Management, User Interface and Accessibility Design | Improving navigability and comprehensibility of legal information for the general public, legal knowledge dissemination. | Access to Legal Information, Legal Literacy and Public Legal Education | Laypersons, general public, individuals without legal education or formal legal training. | Laypeople, General public, Individuals lacking legal knowledge | Various fields relevant to daily life. The evaluation sample included: Landlord and Tenant, Defamation, Insurance, Personal Data Privacy, Intellectual Property. The CLIC platform covers 32 legal topics. | Multiple Fields, Landlord-Tenant Law, Tort Law, Insurance Law, Data Privacy Law, Intellectual Property Law | Hong Kong | Hong Kong | For question generation: CLIC-pages, which are human-written plain language summaries of Hong Kong law hosted on the CLIC platform. GPT-3 (the LLM used) was pre-trained on diverse, large-scale text and code datasets (e.g., Common Crawl, WebText2, books, Wikipedia). | Synthetic Data, Hong Kong Legal Data, Legal Domain Data, Expert-Annotated / Human-Curated / Human-Generated Data, Publicly Available Data, Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text | For LQB creation: prompt engineering for GPT-3 (including section-based, paragraph-based, and a novel 'Hybrid' partitioning strategy), sentence embedding (DistilBERT), and single-link clustering for question deduplication. For CRec: text embedding (all-mpnet-base-v2) of user input and LQB questions/answers, cosine similarity for matching, and a redundancy removal strategy. | Prompt Engineering, Data Segmentation, Embedding Model Application, Clustering Algorithms, Data Deduplication, Information Retrieval Techniques | The CLIC platform (clic.org.hk) is an operational online platform. The CRec is presented as a prototype component being developed for and integrated into this platform, using the LQB generated by the described methods. | Integration into existing system/platform, Web-based access, Internal deployment/prototype, Public dataset/benchmark release | True | False | The CLIC platform (clic.org.hk), which incorporates the CLIC-pages and the CRec recommender prototype using the described LQB, is an online public resource. | Publicly accessible online tool or platform, Open access resource | The paper notes that 'augmenting questions' generated by the AI reveal omissions in current CLIC-page content, suggesting a need for continuous content enrichment. The sub-100% precision of AI-generated questions (Hybrid at 68%) implies a remaining need for human verification and curation. | Data Availability and Quality, Knowledge Recency and Updatability, AI Accuracy and Reliability, Human Oversight and Professional Adaptation | Designing effective GPT-3 prompts (partitioning strategies) to optimize question quantity, precision, coverage, and diversity. Managing the probabilistic nature of LLM outputs leading to variability in question quality. The significant human effort and cost required for verifying machine-generated questions. Effectively deduplicating semantically similar questions. | Prompt Engineering and Optimization, Output Variability and Consistency, Cost and Complexity of Data Annotation, Evaluation Challenges and Metrics, Data Quality, Processing, and Preparation | Imperfect precision of machine-generated questions (e.g., the best strategy achieved 68% precision) could lead to users being presented with irrelevant or unhelpful legal information if not properly curated before deployment in the LQB. | Inaccurate or misleading AI output, Technical limitations of AI, Consumer harm |
| Automatic-Text-Simplification-fortheLegal-Domain-inBrazilian-Portuguese_2025_Springer-Science-and-Business-Media-Deutschland-GmbH.pdf | Scopus | Automatic Text Simplification for the Legal Domain in Brazilian Portuguese | This paper investigates automatic text simplification for legal documents in Brazilian Portuguese, aiming to improve access to justice for laypeople. It evaluates five different LLM-based approaches, including fine-tuned models and prompted generative models, using both quantitative metrics and qualitative expert assessment. | Legal Text Simplification, Brazilian Portuguese Focus, Access to Justice Enhancement, LLM Application Evaluation, Fine-tuned Model Evaluation, Prompted Model Evaluation, Quantitative Evaluation, Qualitative Evaluation | True | Idealistic | True | 2.0 | Positive | Evaluation of five LLM-based approaches for text simplification: fine-tuned PTT5 (FT-PTT5), FT-PTT5 with Reinforcement Learning (FT-PTT5 + RL), GPT-3.5-Turbo, GPT-4o, and Flan-T5-Large. | AI System Evaluation, Large Language Model, Text Simplification, Fine-tuning, Reinforcement Learning | Quantitative evaluation using SARI, BLEU, BERTScore, and ROUGE metrics on a test set of 91 hand-picked legal sentences. Qualitative evaluation by a judicial analyst assessing correctness, simplicity, and overall quality of simplifications for the same 91 instances. | Custom Dataset Evaluation, Quantitative Metrics, Expert Evaluation, Qualitative Analysis | Qualitatively, GPT-3.5-Turbo was judged best by a human expert (e.g., 98% of its simplifications were deemed simpler and 84% of 'Good' quality). Quantitatively, GPT-4o achieved the highest SARI score (0.43). | High performance, Outperforms others | Difficulty for laypeople to understand legal documents due to domain-specific jargon and complex sentence structures; lack of parallel datasets of complex-simple legal sentences in Brazilian Portuguese; the slow process of manual simplification adoption by courts. | Public Lack of Legal Knowledge/Awareness, Complexity of Legal Language/Documents, Data Scarcity/Quality for AI, Slow Technology Adoption by Legal System | Employing automatic text simplification (ATS) using Large Language Models to make legal texts more accessible. This includes fine-tuning existing models and using prompting strategies with generative models, supported by assembling relevant datasets. | Language Simplification and Multilingual Access, AI Tool Development, Enhanced AI Capabilities, Prompt Engineering and LLM Interaction Design, Data Curation and Management | Understandability of legal documents, plain language in the legal domain, access to justice through improved legal text accessibility. | LegalText Simplification / Plain Language, Legal Literacy and Public Legal Education, Democratizing Law / Closing Justice Gap / Rule of Law | Laypeople without legal domain expertise, individuals with reading issues, or those with a low education level. | Laypeople, Individuals lacking legal knowledge, Individuals with low literacy, Individuals with low education levels | General legal documents, including rulings, laws, agreements, contracts, judicial decisions, warrants, notifications, and legal case status updates. | General Law, Case Law, Statutory Law, Contract Law | Brazil | Brazil | A merged dataset of parallel complex-simple sentence pairs in Brazilian Portuguese, comprising: 1) 8,120 pairs from news articles (PorSimples), 2) 1,424 filtered legal case status updates simplified by an OpenAI model (JusBrasil), and 3) 149 hand-picked examples from court materials. Used for fine-tuning PTT5. | Fine-tuning Dataset, Author-Created New Dataset, Author-Modified Existing Dataset, Brazilian Legal Data, Legal Domain Data, Portuguese Language Data, Paired Original-Simplified Text, Structured Data, Data From Existing Public NLP/Legal Datasets/Benchmarks, Synthetic Data, Expert-Annotated / Human-Curated / Human-Generated Data, Case Law / Judgments, Other Legal Documents | For the evaluated models: pre-training on large corpora (PTT5, Flan-T5, GPTs). For adapted models studied (FT-PTT5, FT-PTT5+RL): fine-tuning of a pre-trained model (PTT5) on the custom-assembled Portuguese text simplification dataset and application of reinforcement learning using FKGL, SAMSA, and Levenshtein Distance as reward components. For generative models (GPTs, Flan-T5): Prompt engineering with few-shot in-context learning. | Model Pre-training, Model Fine-tuning, Dataset Creation, Reinforcement Learning, Reward Modeling, Prompt Engineering, Few-shot Learning Application, In-context Learning | NaN | Not applicable | False | False | NaN | NaN | Lack of high-quality, domain-specific parallel datasets for Portuguese legal text simplification; need for more robust and comprehensive evaluation metrics for TS; limited generalizability of models to the specific nuances of the legal domain and its sub-fields without sufficient in-domain training data; high cost associated with fine-tuning very large models. | Data Availability and Quality, Multilingual and Low-Resource Language Gaps, Research and Evaluation Gaps, AI Legal Reasoning Limitations, Computational Resource and Cost Issues | Assembling a suitable parallel dataset for Portuguese legal text simplification, particularly with in-domain legal examples; effectively fine-tuning models with limited in-domain data leading to generalization issues; achieving good performance with instruction-only prompting for certain models (e.g., PTT5); selecting appropriate and effective reward metrics for reinforcement learning in text simplification; infrastructure limitations for training large models. | Scarcity of High-Quality Legal Data, Multilingual and Low-Resource Language Support, Domain-Specific Adaptation and Customization, Accuracy and Reliability of LLM Output, Prompt Engineering and Optimization, Evaluation Challenges and Metrics, High Computational and Resource Demands | Generation of legally inaccurate simplifications that alter meaning (e.g., FT-PTT5+RL had 53% 'No' for correctness), introduce grammatical errors, add extraneous information, or fail to simplify adequately, potentially leading to misinterpretation of legal documents by laypeople. | Inaccurate or misleading AI output, Consumer harm |
| GPT4-passes-the-bar-exam_2024_Royal-Society-Publishing.pdf | Scopus | GPT-4 passes the bar exam | This paper experimentally evaluates GPT-4's zero-shot performance on the full Uniform Bar Examination (UBE), including multiple-choice, essay, and performance test components. The results show GPT-4 significantly outperforms prior models and human test-takers, passing the UBE by a considerable margin, indicating its potential to support legal service delivery. | LLM Evaluation (GPT-4), Performance on Bar Examination (UBE), Zero-Shot Learning, Comparison with Human Performance, Potential for Legal Service Delivery Support | True | Idealistic | True | 2.0 | Positive | GPT-4 (Generative Pre-trained Transformer 4) | Large Language Model | Zero-shot evaluation on the full Uniform Bar Examination (UBE), including the Multistate Bar Examination (MBE) multiple-choice questions, the Multistate Essay Exam (MEE), and the Multistate Performance Test (MPT). MBE questions were official NCBE questions; MEE and MPT questions were from the July 2022 Bar Examination. MEE/MPT answers were graded by two author-experts against representative 'good' answers. | Performance on Standardized Tests, Expert Evaluation, Quantitative Metrics | GPT-4 scored approximately 297 points on the UBE, significantly exceeding the passing threshold for all UBE jurisdictions. On the MBE, GPT-4 achieved 75.7% accuracy, outperforming average human test-takers. On the MEE and MPT, GPT-4 scored an average of 4.2/6.0. | High performance, Outperforms others, Moderate performance | The complexity of legal language and the legal system; the high cost and unmet demand for legal services. | Complexity of Legal Language/Documents, Complexity of Legal System/Procedures, High Cost of Legal Services, Scale of Unmet Legal Need | Proposes Large Language Models like GPT-4 as a 'technology-based force multiplier' to support the delivery of legal services and address cost and accessibility issues. | AI Tool Development, Cost Reduction and Efficiency, Access to Legal Information and Advice | Accessibility of legal services, Cost of legal services, Evaluation of AI in professional licensing. | Democratizing Law / Closing Justice Gap / Rule of Law, Affordability of Legal Services / Cost Reduction, Regulatory Reform (Legal Services and AI) | General public / Individuals and organizations facing challenges with the quantity, quality, and accessibility of legal services due to cost and complexity. | General public, Organizations, Individuals facing access barriers, Individuals unable to afford legal services | Civil Procedure, Constitutional Law, Contracts, Criminal Law and Procedure, Evidence, Real Property, Torts, Corporations, Trusts & Estates, Family Law, Legal Ethics (as covered by the Uniform Bar Exam). | Civil Procedure, Constitutional Law, Contract Law, Criminal Law, Criminal Procedure, Evidence Law, Property Law, Tort Law, Corporate Law, Wills and Estates, Family Law, Legal Ethics | USA (Uniform Bar Exam applicable in multiple states) | USA | GPT-4 was pre-trained on publicly available data (such as internet data) and data licensed from third-party providers, then fine-tuned using Reinforcement Learning from Human Feedback (RLHF). Test data contamination checks were performed. | Pre-trained LLM's General Training Corpus, Publicly Available Data, General Web Data / Broad Internet Text, Proprietary Data, Expert-Annotated / Human-Curated / Human-Generated Data | GPT-4 is a transformer-style model pre-trained to predict the next token in a document, using both publicly available data (such as internet data) and data licensed from third-party providers. The model was then fine-tuned using reinforcement learning from human feedback (RLHF). | Transformer Architecture, Model Pre-training, Data Collection, Reinforcement Learning from Human/AI Feedback (RLHF/RLAIF), Model Fine-tuning | Access to a same or significantly similar version of the GPT-4 model is generally available under commercial terms from OpenAI. | Evaluation of existing third-party tool, Commercial product/service, API access, Web-based access | True | False | GPT-4 is generally available under commercial terms from OpenAI. | Commercial product or service, API access, Publicly accessible online tool or platform | Translating LLM capabilities like GPT-4 into safe and efficient real-world public and private legal applications; addressing LLM issues like hallucinations, factual incorrectness, and ethical compliance failures; comparative performance of other foundational models (e.g., open-source vs. closed-source, domain-specific vs. general) on legal tasks; advancing LLM performance through techniques such as prompt engineering, few-shot learning, retrieval augmented generation, and other systematic engineering methods. | Research and Evaluation Gaps, AI Accuracy and Reliability, Ethical Framework Deficiencies, User Interface and Usability Gaps | Ensuring test data was not part of the model's training set (contamination checks with OpenAI); handling long documents for MPT tasks (requiring an '8K' version of ChatGPT with a wider context window); inherent variability and subjectivity in qualitative assessment/grading of open-ended MEE and MPT responses; the MPT's requirement for models to work within the four corners of provided exam material, potentially suspending broader knowledge. | Evaluation Challenges and Metrics, LLM Context Window and Long Input Management, LLM Reasoning Capabilities | GPT-4 may hallucinate sources, incorrectly interpret facts, or fail to follow ethical requirements; GPT-4 has various biases in its outputs. | Inaccurate or misleading AI output, Technical limitations of AI, Ethical concerns, Bias and discrimination |
| JOIA2023022.pdf | Scopus | A New Era of Maritime Arbitration: Ex Machina Determinations | This paper explores the potential of Large Language Models, specifically ChatGPT 3.5, to act as arbitrators in maritime disputes. Through four hypothetical test cases, it evaluates ChatGPT's capabilities and limitations in this role, discussing benefits like speed and cost-reduction alongside challenges such as accuracy and legal reasoning. | LLM as Arbitrator, ChatGPT Evaluation, Maritime Dispute Resolution, Capability Assessment, Limitation Identification, Benefit Identification, Challenge Identification | True | Idealistic | True | 2.0 | Positive | Using ChatGPT version 3.5 as an AI arbitrator to make determinations in hypothetical maritime disputes based on structured prompts detailing facts and party submissions. | Large Language Model, AI as Arbitrator, Dispute Resolution Mechanism, Prompt Engineering, Domain-Specific Application | Four hypothetical charterparty disputes were presented to ChatGPT 3.5. The prompts included agreed facts, party submissions, and specific questions for determination. ChatGPT's responses (determinations and reasoning) were then analyzed. | Qualitative Analysis, Custom Dataset Evaluation | ChatGPT 3.5 made determinations rapidly and showed some understanding of legal/trade terms. However, it struggled with nuanced legal reasoning, failed to cite relevant or correct case law (exhibiting 'hallucinations'), and its decisions sometimes differed from human arbitrator outcomes in similar published cases. | Benefit identified, Moderate performance, Limitation: Operational or Technical, Limitation: Hallucination or Factual inaccuracy, Underperforms others | The high cost of traditional litigation and arbitration, which acts as a significant barrier to accessing justice, especially for small value claims. | High Cost of Legal Services | The paper proposes using AI LLMs like ChatGPT as arbitrators to provide almost instantaneous, low-cost dispute resolution, particularly for small claims, thereby enhancing access to justice. | Online Dispute Resolution (ODR), AI Tool Development, Cost Reduction and Efficiency, Access to Legal Information and Advice | Access to justice for small value disputes in maritime arbitration. | Dispute Resolution, Affordability of Legal Services / Cost Reduction | Individuals or small businesses in the maritime industry with small value claims. | Individuals, Small businesses, Litigants with low-value claims | Maritime law, Arbitration | Maritime Law, Arbitration | Maritime law, primarily with reference to English law and international arbitration practices (LMAA, SMA, SCMA). | Maritime Law, UK, USA, Singapore, International | ChatGPT 3.5 was trained on 'vast amounts of data from the internet written by humans' up to September 2021. This is general, unstructured internet data. | Pre-trained LLM's General Training Corpus, General Web Data / Broad Internet Text, User-Generated Content, Unstructured Text Data | NaN | NaN | NaN | Not applicable | True | True | The publicly available version of ChatGPT 3.5, used for the experiments, is accessible, including a free tier. | Publicly accessible online tool or platform, Freemium access | Technical gaps include data limitations, hallucinations, inability to manage arbitration procedures, lack of real-time legal updates, and difficulty assessing witness credibility. Societal/legal gaps include the need for legal frameworks for AI arbitrators, ensuring enforceability of AI awards (e.g., revising the New York Convention), maintaining confidentiality, developing appeal mechanisms, and addressing potential biases or manipulation. | Data Availability and Quality, AI Accuracy and Reliability, AI Scope and Functionality Limitations, Knowledge Recency and Updatability, AI Legal Reasoning Limitations, Regulatory and Governance Gaps, Accountability and Redress Mechanisms, Security and Privacy of Data, Bias in AI | Authors faced challenges in initially prompting ChatGPT to make legal determinations and observed its limitations in legal reasoning, accuracy (including hallucinations and incorrect case citations), and applying deep subject matter expertise during the tests. | Prompt Engineering and Optimization, LLM Reasoning Capabilities, Accuracy and Reliability of LLM Output, LLM Hallucination and Factual Errors | Risks include AI generating factually incorrect or misleading determinations ('hallucinations'), lack of transparency in AI decision-making undermining natural justice, awards being unenforceable under current legal frameworks (e.g., New York Convention), potential for AI responses to be manipulated by developers or users through prompt engineering, and decisions being influenced by online falsehoods if AI has unfiltered real-time internet access. | Inaccurate or misleading AI output, Lack of transparency, accountability, and redress, Undermining legal process or principles, Regulatory challenges or gaps, Security vulnerabilities or malicious misuse |
Occurrence Statistics and Wordclouds
This section displays the values occurence counts of columns with few unique values and wordclouds for columns with many unique values.
Prevalence for column 'source': Google_Scholar: 204 (70.6%) HeinOnline: 63 (21.8%) IEEE_Xplore: 16 (5.5%) Scopus: 6 (2.1%)
Prevalence for column 'paper_type': 3.0: 118 (40.8%) 1.0: 117 (40.5%) 2.0: 53 (18.3%)
Prevalence for column 'sentiment': Positive: 190 (65.7%) Neutral: 76 (26.3%) Negative: 20 (6.9%)
Prevalence for column 'claimed_availability': False: 158 (54.7%) True: 131 (45.3%)
Prevalence for column 'claimed_open_availability': False: 215 (74.4%) True: 74 (25.6%)
wordcloud_challenges
wordcloud_community
wordcloud_deployment
wordcloud_design_methodologies
wordcloud_filename
wordcloud_gaps
wordcloud_jurisdiction
wordcloud_legal_field
wordcloud_obstacles
wordcloud_results
wordcloud_risks
wordcloud_solutions
wordcloud_summary
wordcloud_technique
wordcloud_testing
wordcloud_title
wordcloud_topics
wordcloud_training_data
wordcloud_which_claimed_availability
Type 1 Statistics and Wordclouds
This section shows the values occurence counts of columns with few unique values and wordclouds for columns with many unique values after filtering for type 1 papers (papers that propose a new specific technique, tool, or approach).
Prevalence for column 'source' (paper_type = 1.0): Google_Scholar: 89 (76.1%) IEEE_Xplore: 15 (12.8%) HeinOnline: 12 (10.3%) Scopus: 1 (0.9%)
Prevalence for column 'sentiment' (paper_type = 1.0): Positive: 106 (90.6%) Neutral: 9 (7.7%) Negative: 1 (0.9%)
Prevalence for column 'claimed_availability' (paper_type = 1.0): False: 75 (64.1%) True: 42 (35.9%)
Prevalence for column 'claimed_open_availability' (paper_type = 1.0): False: 82 (70.1%) True: 35 (29.9%)
wordcloud_challenges_type1
wordcloud_community_type1
wordcloud_deployment_type1
wordcloud_design_methodologies_type1
wordcloud_filename_type1
wordcloud_gaps_type1
wordcloud_jurisdiction_type1
wordcloud_legal_field_type1
wordcloud_obstacles_type1
wordcloud_results_type1
wordcloud_risks_type1
wordcloud_solutions_type1
wordcloud_summary_type1
wordcloud_technique_type1
wordcloud_testing_type1
wordcloud_title_type1
wordcloud_topics_type1
wordcloud_training_data_type1
wordcloud_which_claimed_availability_type1
Label Statistics
This section allows you to view statistics for the LLM-generated labels for each free-text column.
Type 1 Label Statistics
This section allows you to view statistics for the LLM-generated labels for each free-text column after filtering for type 1 papers (papers that propose a new specific technique, tool, or approach).